diff --git a/preprint/preprint__058b96ab087d835fa63b04a30fdaedd9181f846e45e28f9d3a407df85e5d2a78/images_list.json b/preprint/preprint__058b96ab087d835fa63b04a30fdaedd9181f846e45e28f9d3a407df85e5d2a78/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..2e31e1beabff2d00ba886fa232546aebe17c2b12 --- /dev/null +++ b/preprint/preprint__058b96ab087d835fa63b04a30fdaedd9181f846e45e28f9d3a407df85e5d2a78/images_list.json @@ -0,0 +1,77 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1: Ring attractor network model [10,31,36,37]. (A) Schematic representation of the model. The central ring forms the main body of the model based on its recurrent connections (labeled with ①). Its reciprocal offset connections with the rotation CW and CCW rings (labeled with ② and ③) creates a push-pull mechanism that modulates the intrinsically controlled neural activity of the central ring based on external inputs from the CW and CCW velocity neurons (labeled with ④). An additional external input is provided to the central ring from the visual ring (labeled with ⑤), corresponding to a set of sensory neurons that are tuned to visual landmarks. (B) Synaptic weight function \\(W_{c c}:S^{1}\\to \\mathbb{R}\\) that describes the recurrent connections within the central ring according to the well-known local excitation and global inhibition pattern. (C) Numerical demonstration of how recurrent connectivity within the central ring can autonomously maintain a persistent activity bump. Simulation of the central ring neurons was started with initial conditions that are assigned pseudo-randomly (light green line labeled with ①). Within \"100 milliseconds, a bump of activity emerges (medium green line labeled with ①). Eventually, the firing rates converge to an equilibrium, forming a persistent bump of activity (dark green line labeled with ①). (D) Tuning curves of CCW and CW velocity neurons shown with blue-dashed and red-dashed lines, respectively.", + "footnote": [], + "bbox": [ + [ + 155, + 32, + 840, + 168 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2: Models of the ring attractor's position representation. (A) The position representation \\(\\theta\\) , decoded from the peak location of an example ansatz solution \\(r_{\\mathrm{c}}^{*}\\) to the central ring's firing rates. (B) Model of path integration. Top left: Circular track with uniformly spaced points. Top right: internal representation of this track with a spatially inhomogenous path-integration (PI) gain that ranges from 0.6 at \\(\\theta = \\pi\\) to 1.4 at \\(\\theta = 0\\) . The position representation \\(\\theta\\) is visualized here by the pale brown rat. As the rat moves through physical space at velocity \\(v\\) , the representation moves through neural space at \\(k(\\theta)v\\) . Bottom: Firing rate of uniformly distributed cells in the neural space as a function of the animal's position in physical space. Left shows a 'traditional' network model, including a single, global PI gain of 1. Right shows our unconstrained network model with the spatially inhomogenous PI gain in the top row. (C) Stabilizing visual feedback. Top: The central ring's activity bump \\(r_{\\mathrm{c}}^{*}\\) (green) and the bump-shaped synaptic input \\(I_{\\mathrm{vis}}\\) to the central ring from the visual ring (pink). The activities of both rings are aligned with respect to the same neural space, so in this example, the visual ring bump is \"ahead of\" the central ring bump. Bottom: The function \\(\\beta\\) captures the stabilizing feedback from visual landmarks. Note that \\(\\beta\\) operates on the difference, \\(\\theta^{*} - \\theta\\) , so here, the \\(x\\) axis is a dummy variable. (D) Model of path integration with visual feedback. The pale brown rat symbolizes the internal representation of the animal's position as in (B), while the medium brown rat symbolizes the animal's actual location as represented by the visual drive. Left: the temporal change in the position representation is visualized by two arrows acting on the pale brown rat, one corresponding to updating by the path integration term \\(k(\\theta)v\\) and the other corresponding to updating by the visual feedback term \\(\\beta (\\theta^{*} - \\theta)\\) . Note that in this position, the PI gain is \"low\" and thus PI underestimates position relative to the landmarks. Right: Same as Left but PI overestimates position relative to the landmarks due to \"high\" PI gain in this position.", + "footnote": [], + "bbox": [ + [ + 117, + 32, + 884, + 177 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3: Numerical simulation of the two example gain update rules. For both simulations, we chose the initial condition \\(k_{0}(0) = 1\\) and the parameters \\(\\beta (\\tilde{\\theta}) = 0.66\\times \\sin (\\tilde{\\theta})\\) , \\(k^{*} = 1.5\\) , \\(\\mu = 0.02\\) . The gain choices imply that the initial value of gain error is \\(\\tilde{k} (0) = 0.5\\) . Additionally, we chose \\(\\eta = 0.12\\) for the second example. (A) Temporal progression of the smoothed animal's velocity from an experiment in [35]. (B) Simulated error trajectories under example gain update rule 1. As soon as the animal begins its movement at \\(t = 0\\) , the positional error \\(\\tilde{\\theta}\\) (black line relative to the left y axis) quickly increases because of the nonzero gain error \\(\\tilde{k}\\) (purple line relative to the right y axis). As the animal recalibrates its gain, the gain error gradually converges to zero (i.e., \\(\\tilde{k}\\to 0\\) ), accompanied by positional error gradually converging to zero also (i.e., \\(\\tilde{\\theta}\\to 0\\) ). In addition to these gradual convergent trends, the error trajectories include many fast, transitory changes. As can be seen from the black line, the instantaneous value of positional error \\(\\tilde{\\theta}\\) is correlated with the animal's velocity \\(v\\) also. For example, when animal slows down, the positional error decreases, becoming zero when the velocity is zero. This is a reflection of the relatively increased landmark stabilization \\(\\beta (\\tilde{\\theta})\\) when path integration inputs \\(kv\\) are decreased. On the other hand, the temporal changes in the gain are correlated with the multiplication of the positional error \\(\\tilde{\\theta}\\) and the animal's velocity \\(v\\) as determined by the gain update rule \\(g_{0}\\) . When the animal pauses temporarily around minutes 5 and 20 (i.e., \\(v = 0\\) ), the positional error \\(\\tilde{\\theta}\\) is completely corrected by landmarks (i.e., \\(\\tilde{\\theta} = 0\\) ), causing the gain updates to pause also (i.e., \\(\\frac{d\\tilde{k}}{dt} = 0\\) ). As the animal continues moving, the positional error and the velocity fine-tune the gain until the gain error converges to zero, demonstrating that the system can achieve complete gain recalibration. (C) Simulated error trajectories under example gain update rule 2. The convention is the same as panel B. The error trajectories exhibit similar trends to the panel B except that their final values do not converge to zero, demonstrating that the system can only achieve partial gain recalibration.", + "footnote": [], + "bbox": [ + [ + 150, + 263, + 852, + 393 + ] + ], + "page_idx": 14 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4: Visualization of mechanistic constraints for a numerical simulation based on a hypothetical gain update rule \\(g_{0}(k_{0},\\hat{\\theta},v) = \\mu \\hat{\\theta} v\\) Except for the animal's velocity profile, we chose the parameters and initial conditions the same as Fig. 3B. For color coding, we use Fig. 1A as the reference, where red and blue denote the CW and CCW rotation rings, and green denotes the central ring. (A) Top graph shows the simulated velocity of the animal (purple line) on the right y axis and the temporal progression of the positional error (black line) on the left y axis. Notice the synchronous fluctuations in the positional error and the animal's velocity. As explained in Fig. 3B, these synchronous fluctuations occur because the positional error is correlated with the animal's velocity. Bottom graph shows the PI and visual gains with solid and dashed purple lines, respectively, on the right y axis and the time-integral of the positional error with the black line on the left y axis. Notice that as the PI gain gradually converges to the visual gain, the temporal progression of the time-integral of the positional error follows a very similar trajectory. This similarity indicates that the integration gain reflects the past accumulation of positional representation errors, thus opening up the possibility for the network to track the time-integral of the error as a proxy signal to encode the integration gain. (B) The mechanistic constraint for recalibration through plasticity of the velocity-to-rotation ring connections. Top graph shows the mean firing rates of the CCW and CW rotation rings over time with blue and red lines. Notice that they are similar to the trajectory of the positional error in panel A, except that the changes in the CW rotation ring's mean firing rate (red line) is the negative of those in the CCW rotation ring's mean firing rate. Bottom graph shows the direct relationship between mean firing rates and positional error in the attractor's representation. (C) The mechanistic constraint for recalibration through plasticity of the rotation-to-central ring connections. Top graph shows the mean firing rates of either the rotation rings or the central ring over time with the orange line. Notice that the changes in these firing rates follow a similar trend as the temporal progression of the positional error. Bottom graph shows this relationship directly (the positive correlation is chosen arbitrarily as our analysis does not provide a conclusive insight into the required direction). (D) The mechanistic constraint for recalibration through changes in the velocity neurons' slopes. Top graph shows the mean firing rate of the CCW and CW rotation rings, the same quantities as panel B. However, unlike panel B where the mean firing rates were similar to the instantaneous positional error, the mean firing rates in this panel are similar to the time-integral of the error. Bottom graph shows this relationship between the mean firing rates and the time-integral of the error directly. (E) The mechanistic constraint for recalibration through changes in the rotation rings' activity bumps. Top graph shows the bump width of both rotation rings over time. Similar to how the mean firing rates of the rotation rings encode the time-integral of the positional error in panel D, the bump widths encode the time-integral of the error in this panel. Bottom graph shows this relationship directly. (F) The mechanistic constraint for recalibration through changes in the central ring's activity bump. Top graph shows the temporal progression of the mean firing rate of the central ring, which is tightly but negatively correlated with the temporal progression of the time-integral of the positional error. Bottom graph shows this relationship directly.", + "footnote": [], + "bbox": [ + [ + 155, + 32, + 852, + 179 + ] + ], + "page_idx": 17 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5: A modified ring attractor network model. (A) A proposed connectivity pattern that varies firing rate of population X3 as a monotonic function of the difference in the activity-bump locations of populations X1 and X2. Arrow and circle terminals denote excitatory and inhibitory connections, respectively. (B) Schematic diagrams depicting the computation within the proposed connectivity in panel (A). The first row shows the activity of the X2 population (yellow) relative to the activity of the X1 population (green) for different conditions: CW difference (left column), no difference (middle column), and CCW difference (right column). The second row shows the synaptic inputs to the X3 population from the excitatory X1 (green line) and inhibitory X2 (yellow line) populations. The fourth row shows the resulting firing rates of the X3 population. (C) Schematic representation of the model. Solid and dashed lines denote hardwired and plastic connections, respectively. The labels M1, M2, M3, correspond to the three modifications made to the classical ring attractor: M1 is the association ring, M2 refers to the plasticity of the velocity-to-rotation-ring connections, and M3 refers to the hardwired, offset association-to-rotation connections. (D) Numerical simulation demonstrating the association of the visual ring's activity with the central ring's activity through Hebbian plasticity. Top and bottom rows show the initial and final values of the simulated variables. The left column shows the firing rates of the central (green) and visual (pink) rings. The middle column visualizes the weight matrix describing the visual-to-association ring connections. The right column shows the firing rates of the association ring (yellow) and the synaptic inputs from the visual ring (pink). (E) Tuning curves depicting the relationships between the rotation rings' mean and peak firing rates vs. the error (left and right graph, respectively). The color coding is the same as panel C. (F) Numerical simulations of the gain recalibration within the proposed model. The top shows the the recalibration of PI gain (green) toward the visual gain (pink) in a selected simulation. The middle shows the progression of the weights of the velocity-to-rotation ring connections (four samples normalized to initial condition of the weights with the opacity changes from the lightest ( \\(t = 0\\) min) and to the darkest green ( \\(t = 30\\) min) corresponding to chronological order of the samples.). The bottom shows the final values of the PI gain for various visual gains (green line) and the hypothetical perfect recalibration (dashed black line). (G) Numerical simulation demonstrating how visual landmarks correct positional error. The top panel shows the progression of the bump locations of the visual (pink) and central rings (green). Bottom panel shows the mean firing rate of CW (red) and CCW (blue) rotation rings over time.", + "footnote": [], + "bbox": [ + [ + 115, + 110, + 880, + 515 + ] + ], + "page_idx": 20 + } +] \ No newline at end of file diff --git a/preprint/preprint__058b96ab087d835fa63b04a30fdaedd9181f846e45e28f9d3a407df85e5d2a78/preprint__058b96ab087d835fa63b04a30fdaedd9181f846e45e28f9d3a407df85e5d2a78.mmd b/preprint/preprint__058b96ab087d835fa63b04a30fdaedd9181f846e45e28f9d3a407df85e5d2a78/preprint__058b96ab087d835fa63b04a30fdaedd9181f846e45e28f9d3a407df85e5d2a78.mmd new file mode 100644 index 0000000000000000000000000000000000000000..3ab480f66d8abee5868afcbc03a2bfaf0bd21d60 --- /dev/null +++ b/preprint/preprint__058b96ab087d835fa63b04a30fdaedd9181f846e45e28f9d3a407df85e5d2a78/preprint__058b96ab087d835fa63b04a30fdaedd9181f846e45e28f9d3a407df85e5d2a78.mmd @@ -0,0 +1,614 @@ + +# Continuous Bump Attractor Networks Require Explicit Error Coding for Gain Recalibration + +Gorkem Secer gsecer@gmail.com + +Johns Hopkins University https://orcid.org/0000- 0001- 6612- 7782 James Knierim Johns Hopkins University https://orcid.org/0000- 0002- 1796- 2930 Noah Cowan Johns Hopkins University https://orcid.org/0000- 0003- 2502- 3770 + +## Article + +# Keywords: + +Posted Date: April 15th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4209280/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on October 17th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 63817- 0. + +<--- Page Split ---> + +# Continuous Bump Attractor Networks Require Explicit Error + +# Coding for Gain Recalibration + +Gorkem Secer \(^{1,2}\) , James J. Knierim \(^{2,3,4,*}\) , and Noah J. Cowan \(^{1,5,*}\) + +\(^{1}\) Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD 21218, USA. + +\(^{2}\) Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA. + +\(^{3}\) Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. + +\(^{4}\) Kavli Neuroscience Discovery Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA + +\(^{5}\) Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA. + +\*These authors jointly supervised the work. + +April 2, 2024 + +## Abstract + +Representations of continuous variables are crucial to create internal models of the external world. A prevailing model of how the brain maintains these representations is given by continuous bump attractor networks (CBANs) in a broad range of brain functions across different areas, such as spatial navigation in hippocampal/entorhinal circuits and working memory in prefrontal cortex. Through recurrent connections, a CBAN maintains a persistent activity bump, whose peak location can vary along a neural space, corresponding to different values of a continuous variable. To track the value of a continuous variable changing over time, a CBAN updates the location of its activity bump based on inputs that encode the changes in the continuous variable (e.g., movement velocity in the case of spatial navigation)—a process akin to mathematical integration. This integration process is not perfect and accumulates error over time. For error correction, CBANs can use additional inputs providing ground- truth information about the continuous variable's correct value (e.g., visual landmarks for spatial navigation). These inputs enable the network dynamics to automatically correct any representation error. Recent experimental work on hippocampal place cells has shown that, beyond correcting errors, ground- truth inputs also fine- tune the gain of the integration process, a crucial factor that links the change in the continuous variable to the updating of the activity bump's location. However, existing CBAN models lack this plasticity, offering no insights into the neural mechanisms and representations involved in the recalibration of the + +<--- Page Split ---> + +integration gain. In this paper, we explore this gap by using a ring attractor network, a specific type of CBAN, to model the experimental conditions that demonstrated gain recalibration in hippocampal place cells. Our analysis reveals the necessary conditions for neural mechanisms behind gain recalibration within a CBAN. Unlike error correction, which occurs through network dynamics based on ground- truth inputs, gain recalibration requires an additional neural signal that explicitly encodes the error in the network's representation via a rate code. Finally, we propose a modified ring attractor network as an example CBAN model that verifies our theoretical findings. Combining an error- rate code with Hebbian synaptic plasticity, this model achieves recalibration of integration gain in a CBAN, ensuring accurate representation for continuous variables. + +<--- Page Split ---> + +## 1 Introduction + +The brain's ability to represent and process continuous variables, such as location, time, and sensory information, is fundamental to our understanding and interaction with the external world. A compelling theoretical framework for how the brain constructs these representations is provided by continuous bump attractor networks (CBANs) in a diverse range of brain functions, such as orientation tuning in visual cortex [1], working memory [2,3], evidence accumulation and decision- making [4- 7], and spatial navigation [8- 11]. + +The CBAN is a class of recurrent neural network, which maintains persistent patterns of population activity through interactions among its neurons. This persistent activity typically forms the shape of a bell curve like a 'bump', when visualized on an appropriate topological arrangement of neurons (known as a low- dimensional manifold), such as a plane, circle, or torus [12]. Although the shape of the activity bump is constrained by network dynamics, its center location can vary along this low- dimensional manifold, corresponding to different values of the encoded continuous variable. Neural activity consistent with these key properties of CBANs, namely, the activity bump and the low- dimensional manifold, have been observed in recordings from various regions of the mammalian brain that encode continuous variables [13- 15]. More conclusive and direct evidence for CBANs has been found in the central complex of fly brain, where a biological CBAN encoding the fly's heading angle, a continuous variable, has been identified based on the connectome and a combination of techniques, such as calcium imaging and optogenetics [16- 18]. While these experimental findings support the idea of brain circuits employing CBANs to represent continuous variables, the neural mechanisms that enable CBANs to accurately update their representations in response to changes in continuous variables remain incompletely understood. + +CBANs update their representations of a continuous variable based on one or both of two distinct types of inputs. The first type provides 'absolute' information, namely, the true value of a continuous variable, such as spatial location relative to visual landmarks or the item to be held in the working memory. When this absolute information is available, it provides localized input on the CBAN's low- dimensional manifold to a location that is associated with the true value to the continuous variable. In response to this localized input, internal dynamics of the CBAN creates a 'basin' on its low- dimensional manifold toward which the activity bump gravitates, resulting in nearly perfect encoding of the continuous variable [19- 21]. Strong experimental evidence for this phenomenon has been observed in fly brain where optogenetic excitation of the central complex, a seemingly biological CBAN, at a specific anatomical location mimicking an absolute information resulted in the CBAN's activity bump, normally changing its location to encode the fly's heading, being pulled to the excitation location [16]. As more indirect evidence, neural recordings from the hippocampus and entorhinal cortex, two regions modeled as CBANs in mammalian brain, showed that their representations for the animal's location are anchored to the visual landmarks even when the landmarks are rotated around an open arena [22- 26]. + +In contrast to the first type of inputs providing absolute information to the CBAN, the second type provides 'differential' information, namely, the changes in the continuous variable. Sources of such inputs may be, for instance, self- generated movements providing velocity information to be integrated in the context of spatial navigation or sensory cues serving as pieces of evidence to be accumulated in the context of decision- making. In response to these inputs, the internal dynamics of the CBAN shifts the activity bump along the low- dimensional manifold—in a process akin to mathematical integration—such that the bump's location reflects the value of the continuous variable. However, compared to the absolute information, the encoding accuracy of this integration process depends critically on an additional factor, + +<--- Page Split ---> + +namely, the integration gain of the network that relates the cumulative change in the continuous variable to the updating of the bump location in a proportional manner [27,28]. If this gain factor is miscalibrated, the result of the CBAN's integration begins drifting away from the true value of the continuous variable; that is, it accumulates error. In the presence of absolute information sources such as visual landmarks for spatial localization, the aforementioned 'basin' mechanism continuously corrects error, preventing it from accumulating. However, without such absolute information, error accumulation continues, which may cause, for example, a CBAN integrating evidence to reach a decision threshold too soon or too late or a CBAN integrating an animal's angular head velocity to over- or underestimate the correct head direction. Thus, a finely tuned integration gain is crucial for a CBAN to accurately encode a continuous variable based on inputs with only differential information. + +Present CBAN models of path integration treat the integration gain as a constant that is perfectly set via carefully chosen, hard- wired model parameters (e.g., synaptic weights) [11,29- 31] (but see [32]). However, recent data from time cells and place cells of the rodent hippocampal formation, hypothesized to rely on CBANs [33], showed that the integration gain is actually a plastic variable whose value is adjusted based on the feedback from absolute information sources [34,35]. In the first study that demonstrated this phenomenon on place cells [35], the virtual visual landmarks, which provided the absolute information, were moved as a function of the animal's movement. This movement created persistent error between the encoded location based on path integration and the actual location relative to the landmarks. Prolonged exposure to this conflict led to recalibration of the system's integration gain, altering it in a direction and by an amount that reduced the positional encoding error. This recalibration was most evident after the landmarks were extinguished (i.e., when the absolute information to the putative CBAN was abolished); the space encoded by hippocampal cells during pure path integration either expanded or contracted, depending on the direction of the preceding landmark manipulation. Therefore, an open question is how CBANs adjust their integration gain based on error feedback from absolute information sources. + +In the present paper, we aim to address this open question and generate testable physiological predictions about the neural mechanisms of gain recalibration in brain circuits that encode continuous variables. As a representative problem, we focus on hippocampal place coding and theoretically examine how visual landmarks, being the absolute information source, recalibrate the integration gain of a CBAN that encodes the animal's position on a circular track, as demonstrated experimentally in [35]. To this end, we first derive analytical expressions of the integration gain, along with a simple dynamical model of the activity bump's location in the ring attractor network, a specific type of CBAN for circular position encoding. In contrast to the previous work implicitly assuming the network's integration gain as a constant, global parameter independent of the bump location in the low- dimensional manifold, our analysis reveals that the integrator gain of a CBAN is a spatially distributed, possibly inhomogeneous, parameter. We then employed control theory techniques to dissect the necessary algorithmic conditions for accurate recalibration of this spatially distributed integration gain via feedback from the absolute information sources. Mapping these conditions from the algorithmic level to the mechanistic level uncovered a key mechanistic requirement for gain recalibration. We found that, unlike correction of encoding errors that happens automatically and implicitly through network dynamics when feedback from an absolute information source is available, the process of learning/recalibrating the integration gain through Hebbian plasticity requires an additional neural signal that explicitly encodes the error in the network's representation. In other words, all prior work demonstrating error correction in a CBAN does not require explicit error encoding, but our work shows that correcting the integration process itself (i.e., recalibration) requires an explicit representation of error. This error signal must be provided by changes in the + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1: Ring attractor network model [10,31,36,37]. (A) Schematic representation of the model. The central ring forms the main body of the model based on its recurrent connections (labeled with ①). Its reciprocal offset connections with the rotation CW and CCW rings (labeled with ② and ③) creates a push-pull mechanism that modulates the intrinsically controlled neural activity of the central ring based on external inputs from the CW and CCW velocity neurons (labeled with ④). An additional external input is provided to the central ring from the visual ring (labeled with ⑤), corresponding to a set of sensory neurons that are tuned to visual landmarks. (B) Synaptic weight function \(W_{c c}:S^{1}\to \mathbb{R}\) that describes the recurrent connections within the central ring according to the well-known local excitation and global inhibition pattern. (C) Numerical demonstration of how recurrent connectivity within the central ring can autonomously maintain a persistent activity bump. Simulation of the central ring neurons was started with initial conditions that are assigned pseudo-randomly (light green line labeled with ①). Within "100 milliseconds, a bump of activity emerges (medium green line labeled with ①). Eventually, the firing rates converge to an equilibrium, forming a persistent bump of activity (dark green line labeled with ①). (D) Tuning curves of CCW and CW velocity neurons shown with blue-dashed and red-dashed lines, respectively.
+ +firing rate of some neurons with one of two signals—the instantaneous error or the time- integral of the error—for recalibration of the integration gain. Finally, we propose a modified ring attractor network as an example CBAN model that instantiates our theoretical findings. Combining an error- rate code with Hebbian plasticity, this model achieves recalibration of integration gain in a CBAN, ensuring accurate representation for continuous variables. + +## 2 Model Setup: Ring Attractor Network + +A continuous bump attractor network is a recurrently connected neural network in which neighboring neurons excite one another and inhibit distant neurons according to a connectivity pattern known as local excitation and global inhibition [1,38]. This connectivity gives rise to a persistent bump of activity as a stable, equilibrium state of the system. Invariance of the connectivity across the network leads to a continuum of such equilibrium states, called attractor states. Arrangement of neurons and the exact pattern of the recurrent connectivity determine the topology of this attractor. In the case of a ring attractor, neurons are arranged conceptually as a topological ring [11]. By sustaining an activity bump whose location can be shifted along the ring based on external inputs (i.e., relative and absolute information sources), a ring attractor network is well- suited to represent a variable on a closed curve (e.g., angular location of an animal on a circular track). Augmenting the arrangement of neurons to a two dimensional plane results in a plane attractor whose activity bump is well- suited to represent two variables, for example, the x and y coordinates of location in a room [37]. + +The activities of place and grid cells in 2D environments have been traditionally modeled using a plane attractor [12,31,37]. However, in the present investigation of gain recalibration based on location encoding, originally demonstrated in place cells from rats running laps on a 1D circular track, we chose the ring attractor as the basis of our model because of its analytical tractability. + +The ring attractor that we analyzed is a network model consisting of three groups of neurons ordered in a ring arrangement: a central ring, a clockwise (CW) rotation ring, and a counter- clockwise (CCW) rotation ring [8,11], as depicted in Fig. 1A. Neurons in this network receive synaptic input from other neurons in the network via intrinsic connections and from upstream neurons carrying velocity information + +<--- Page Split ---> + +(i.e., a 'differential' type of input) and the positional feedback from visual landmarks (i.e., an 'absolute' type of inputs) via extrinsic connections. + +We can model the dynamics of the ring attractor network using a set of equations that model the firing rate of a continuum of neurons in response to their synaptic inputs. If we parameterize a neuron based on its angle \(\psi \in S^{1}\) in the circular neural space, the model of the central ring neurons takes the form + +\[\tau_{c}\frac{\partial r_{c}(t,\psi)}{\partial t} = -r_{c}(t,\psi) + \sigma [W_{c - c}(\psi)\circledast r_{c}(t,\psi) + I_{\mathrm{ext}}(t,\psi)], \quad (1)\] + +where \(r_{c}(t,\psi)\) denotes the firing rate of the central ring neuron \(\psi\) at time \(t\) , \(\tau_{c}\) denotes the synaptic time constant of central ring neurons, \(\circledast\) denotes the circular convolution operation, \(\sigma\) denotes an activation function (chosen as rectified linear unit (RELU) in our current study), \(I_{\mathrm{ext}}(t,\psi)\) denotes external synaptic inputs to the central ring, and \(W_{\mathrm{c - c}}:S^{1}\to \mathbb{R}\) denotes a rotationally invariant synaptic weight function that describes the recurrent connections ( \(\textcircled{1}\) in Fig. 1A) according to the pattern known as local excitation and global inhibition (Fig. 1B). Being a hallmark of the CBANs, this recurrent connectivity pattern leads to stabilization of a persistent "bump" of activity within the network [38- 40] (Fig. 1C). While the shape of the emergent activity bump is determined by the shape of the recurrent connectivity pattern, the location of the bump can be controlled by external synaptic inputs to the central ring. + +These external inputs are provided by neurons of the rotation rings and the visual ring. The rotation rings conjoin the self- movement velocity information with the positional information by receiving inputs from two different afferent neurons: By changing their firing rates in different directions (Fig. 1D), CW and CCW 'velocity' neurons signal the animal's velocity information to their respective rotation rings through synaptic weight functions \(W_{\mathrm{v - cw}}\) , \(W_{\mathrm{v - ccw}}:S^{1}\to \mathbb{R}\) ( \(\textcircled{4}\) in Fig. 1A), whereas the central ring provides the positional information, represented by the bump location, to both rotation rings through synaptic weight functions \(W_{\mathrm{c - cw}}\) , \(W_{\mathrm{c - ccw}}:S^{1}\to \mathbb{R}\) ( \(\textcircled{2}\) in Fig. 1A). The resulting conjunctive codes of velocity and position within the rotation rings are then transmitted back to the central ring through offset synaptic weight functions \(W_{\mathrm{cw - c}}\) , \(W_{\mathrm{cw - cc}}:S^{1}\to \mathbb{R}\) ( \(\textcircled{3}\) in Fig. 1A), leading to a shift of the persistent activity bump within the central ring proportional to the animal's velocity, a process known as path integration (PI). In contrast to the rotation rings, the visual ring does not explicitly receive any inputs; instead, its neurons are presumed to autonomously fire at specific locations of the animal relative to landmarks, capturing the absolute positional information received from visual landmarks available at each position (modeling how egocentric visual processes can calculate position from landmarks is beyond the scope of this paper). Through synaptic weight function \(W_{\mathrm{vis - c}}:S^{1}\to \mathbb{R}\) ( \(\textcircled{5}\) , in Fig. 1A), this firing of the visual rings provides to the central ring a bump- like synaptic input encoding the animal's "true" position relative to landmarks. This bump- like input pulls the activity bump of the central ring, hence correcting positional errors in the ring attractor's representation. + +Before concluding this section, we clarify an important distinction between traditional models of the ring attractor network and our model in the present paper. In traditional models, the synaptic weights \(W_{\mathrm{v - cw}}\) , \(W_{\mathrm{v - ccw}}\) and \(W_{\mathrm{cw - c}}\) , \(W_{\mathrm{cw - cc}}\) are treated as constants, each constrained to take a uniform value across the entire neural space. However, in our model, we intentionally relax this constraint. Instead, we treat these weights as functions that can vary throughout the neural space, possibly taking nonuniform values. While this approach may be less mathematically convenient, it becomes necessary when we later explore the possibility of gain recalibration through plasticity of spatially distributed synapses in the ring attractor network. As will be evident in the subsequent sections, our approach has broader implications, especially for the spatial metric of the ring attractor network. The complete mathematical model of the + +<--- Page Split ---> + +ring attractor, including the functional synaptic weights and the dynamics of the rotation and visual rings, is given in Appendix 6.1. + +## 3 Algorithmic and Mechanistic Requirements for Gain Recalibration + +In this section, we analyze the complex dynamics of our unconstrained ring attractor model to garner insight into how the network's integration gain, hereafter referred to as the PI gain, can be recalibrated by visual landmarks. Our analysis begins with reduction of complex ring- attractor dynamics into a simple, one- dimensional differential equation model, including an analytical expression of the PI gain, in Section 3.1. Leveraging the analytical tractability of this simple model, we then identify algorithmic conditions for gain recalibration in Section 3.2. Finally, we use the analytical expression of the PI gain to map the algorithmic conditions within the simple model to mechanistic prerequisites for gain recalibration within the high- dimensional, complex model of the ring attractor network, in Section 3.3. + +### 3.1 Dimensionality reduction reveals computational principles of the network + +To derive a simple model for how the location of the attractor's activity bump is controlled by external velocity and visual inputs, we follow the dimensionality reduction protocol described in [41]. Briefly, the protocol exploits the fact that ring- attractor dynamics constrain the population activity to form a bump whose overall shape stays invariant, but its center location can vary across the central ring. Although we do not know the exact solution to the network dynamics that can describe this activity pattern, we can 'guess' a solution form that describes its general properties without relying on a specific function. This guess, termed an ansatz solution, makes the analysis mathematically tractable by reducing the complex network dynamics to a one- dimensional differential equation that tracks the temporal change in the location of the activity bump as a function of external inputs. + +Using this protocol, prior work derived the simple models for the traditional ring attractors, constraining the synaptic weights \(W_{\mathrm{v - cw}}\) , \(W_{\mathrm{v - ccw}}\) and \(W_{\mathrm{cw - c}}\) , \(W_{\mathrm{cw - c}}\) to be constant [41,42]. Here, we extend this approach to our unconstrained ring attractor. We develop the differential equation models progressively, starting from the simplest case that the animal is stationary in the absence of landmarks. Next, we add movement inputs and show that the network employs a spatially distributed PI gain. Finally, we extend the model to include landmark- based correction, and show that the network combines the spatially distributed integration with landmarks in a computation that resembles a Kalman filter [43]. The resulting model forms the basis for our search for the algorithmic conditions of the gain recalibration. + +#### 3.1.1 Ansatz solution to network dynamics + +When the central ring's recurrent weight function \(W_{\mathrm{c - c}}\) is symmetric with local excitation and global inhibition, firing rate of its neurons converges to a symmetric, persistent activity bump, taking nonzero values in a limited range and featuring a single peak corresponding to the internal representation of the animal's position (Fig. 1C). Although specific functions such as thresholded Gaussians or sinusoids possess these characteristics and are often employed to explain neurophysiological data [11,19,44- 47], we do not restrict ourselves to such a specific structure in the present analysis of the ring attractor. Instead, we assume that the firing rates \(r_{\mathrm{c}}(t, \psi)\) of the central ring neurons can be represented by a general ansatz solution + +\[r_{\mathrm{c}}(t,\psi) = r_{\mathrm{c}}^{*}(\psi -\theta (t)), \quad (2)\] + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2: Models of the ring attractor's position representation. (A) The position representation \(\theta\) , decoded from the peak location of an example ansatz solution \(r_{\mathrm{c}}^{*}\) to the central ring's firing rates. (B) Model of path integration. Top left: Circular track with uniformly spaced points. Top right: internal representation of this track with a spatially inhomogenous path-integration (PI) gain that ranges from 0.6 at \(\theta = \pi\) to 1.4 at \(\theta = 0\) . The position representation \(\theta\) is visualized here by the pale brown rat. As the rat moves through physical space at velocity \(v\) , the representation moves through neural space at \(k(\theta)v\) . Bottom: Firing rate of uniformly distributed cells in the neural space as a function of the animal's position in physical space. Left shows a 'traditional' network model, including a single, global PI gain of 1. Right shows our unconstrained network model with the spatially inhomogenous PI gain in the top row. (C) Stabilizing visual feedback. Top: The central ring's activity bump \(r_{\mathrm{c}}^{*}\) (green) and the bump-shaped synaptic input \(I_{\mathrm{vis}}\) to the central ring from the visual ring (pink). The activities of both rings are aligned with respect to the same neural space, so in this example, the visual ring bump is "ahead of" the central ring bump. Bottom: The function \(\beta\) captures the stabilizing feedback from visual landmarks. Note that \(\beta\) operates on the difference, \(\theta^{*} - \theta\) , so here, the \(x\) axis is a dummy variable. (D) Model of path integration with visual feedback. The pale brown rat symbolizes the internal representation of the animal's position as in (B), while the medium brown rat symbolizes the animal's actual location as represented by the visual drive. Left: the temporal change in the position representation is visualized by two arrows acting on the pale brown rat, one corresponding to updating by the path integration term \(k(\theta)v\) and the other corresponding to updating by the visual feedback term \(\beta (\theta^{*} - \theta)\) . Note that in this position, the PI gain is "low" and thus PI underestimates position relative to the landmarks. Right: Same as Left but PI overestimates position relative to the landmarks due to "high" PI gain in this position.
+ +where \(r_{\mathrm{c}}^{*}(\psi - \theta (t))\) is a function that described the aforementioned persistent activity with a single peak at \(\psi = \theta (t)\) . This moderate generality allows our analysis to be valid for a broad range of ring attractor models with various particular ansatzes (including, but not limited to, the commonly used ones mentioned above). See Appendix 6.2.1 for a formal description of the properties of the ansatz function \(r_{\mathrm{c}}^{*}\) . + +The persistent activity in the central ring also spreads to the CW and CCW rotation rings through synaptic connections, resulting in the following ansatz solutions to their firing rates \(r_{\mathrm{cw}}\) , \(r_{\mathrm{ccw}}\) : + +\[\begin{array}{r l} & {r_{\mathrm{cw}}(t,\psi)\triangleq r_{\mathrm{cw}}^{*}(\psi ,\theta (t),v(t)) = \sigma [W_{\mathrm{c - cw}}(\psi)r_{\mathrm{c}}^{*}(\psi -\theta (t)) + W_{\mathrm{v - cw}}(\psi)(u_{\mathrm{cw}}^{0} - \alpha_{\mathrm{cw}}v(t)) - \bar{I} ]}\\ & {r_{\mathrm{ccw}}(t,\psi)\triangleq r_{\mathrm{ccw}}^{*}(\psi ,\theta (t),v(t)) = \sigma [W_{\mathrm{c - ccw}}(\psi)r_{\mathrm{c}}^{*}(\psi -\theta (t)) + W_{\mathrm{v - ccw}}(\psi)(u_{\mathrm{ccw}}^{0} + \alpha_{\mathrm{ccw}}v(t)) - \bar{I} ],} \end{array} \quad (3)\] + +where \(r_{\mathrm{cw}}^{*},r_{\mathrm{ccw}}^{*}\) denote the assumed form of the ansatz solutions, \(u_{\mathrm{cw}}^{0}\) , \(u_{\mathrm{ccw}}^{0}\) denote the baseline firing rates of the CW, CCW velocity neurons (during the animal's immobility), \(\alpha_{\mathrm{cw}}\) , \(\alpha_{\mathrm{ccw}}\) denote the absolute value of the slopes of the velocity neurons' tuning curves (i.e., the absolute change in their firing rates per unit velocity of the animal), and \(\bar{I}\) denotes global inhibition. With a properly set inhibition \(\bar{I}\) , the solutions \(r_{\mathrm{cw}}^{*},r_{\mathrm{ccw}}^{*}\) take the shape of a bump, similar to the ansatz \(r_{\mathrm{c}}^{*}\) for the central ring (see Appendix 6.2 for further details). + +Collectively, Eqs. (2) and (3) constitute a solution to the entire ring attractor network. That is, for a given velocity \(v\) of the animal, the firing rates of all neurons in a given network can be computed using the ansatz \(r_{\mathrm{c}}^{*}\) that describes the shape of the persistent activity bump within the central ring and \(\theta\) that denotes the location of the bump. While the exact form of \(r_{\mathrm{c}}^{*}\) is determined by the profile of the recurrent weight function \(W_{\mathrm{c - c}}\) , the bump location \(\theta\) is controlled by external inputs \(I_{\mathrm{ext}}\) to the central ring. Therefore, assuming that the ansatz \(r_{\mathrm{c}}^{*}\) persists at all times like the previous work [41, 42], we can reduce the high dimensional ring- attractor dynamics to a one dimensional differential equation that + +<--- Page Split ---> + +models the position representation \(\theta\) as a function of the external inputs. As derived in Appendix 6.2, if the central ring receives balanced (i.e., symmetric) inputs from the rotation rings during the animal's immobility in the absence of landmarks (a classical assumption in CBAN models [3, 11, 31, 48, 49]), the position representation \(\theta\) remains invariant. This invariance can be simply modeled as + +\[\frac{d}{dt}\theta = 0. \quad (4)\] + +## 3.1.2 Reduced-order path integration model reveals spatially distributed gain + +In the present section, we examine how the position representation \(\theta\) , decoded from the bump location, varies as the animal moves on a circular track in the absence of visual landmarks. By triggering differential changes in the firing of CW, CCW velocity neurons (Fig. 1D), such movement modulates the persistent activity bumps of the CW, CCW rotation rings also differentially. Synaptic connections from the rotation rings to the central ring then translate these changes in the activities of rotation rings to a change in the synaptic inputs to the central ring. As a result, the central ring no longer receives balanced excitation about its activity bump during the animal's movement. With its magnitude proportional to the animal's speed, this movement- based imbalance shifts the activity bump across the central ring, instantiating PI. + +To obtain a model for the temporal dynamics of the position representation \(\theta\) during this process, we apply the dimensionality reduction protocol described in [41]. Under mild assumptions detailed in Appendix 6.2.4, this reduction protocol leads to an ordinary differential equation + +\[\frac{d\theta}{dt} = k(\theta)v, \quad (5)\] + +showing a linear relationship between the temporal change in \(\theta\) and the animal's velocity \(v\) via a factor \(k(\theta)\) . This factor quantifies the ring attractor network's PI gain, and its analytical expression takes the form + +\[k(\theta) = \frac{-b}{\tau_{c}\| \frac{\partial r_{c}^{*}}{\partial\psi}\|^{2}}\int_{0}^{2\pi}\frac{\partial^{2}r_{c}^{*}(\psi - \theta)}{\partial\psi^{2}}\sum_{i\in \{\mathrm{cw},\mathrm{ccw}\}}\alpha_{i}W_{i - c}(\psi)W_{\mathrm{v - }i}(\psi)\mathrm{sign}[r_{i}^{*}(\psi ,\theta ,0)]d\psi , \quad (6)\] + +where \(i\) denotes the index of the summation, representing either the CW or CCW rotation ring, \(\alpha\) denotes the absolute value of the slope of the velocity neurons' tuning curves, and \(b\) denotes the value of the offset in the connections between rotation rings and the central ring. As shown by this equation, the PI gain \(k(\theta)\) is a parameter determined by the network's functional properties (i.e., profiles of the activity bumps and the tuning slope of the velocity neurons) and structural properties (i.e., time constant of the neurons and synaptic weights of the velocity- to- rotation ring and rotation- to- central ring connections), thereby capturing the complex interaction between the network's internally generated persistent activity and the external inputs from the velocity neurons. A comprehensive examination of how these network properties relate to the PI gain is provided later in Section 3.3 when we explore the mechanisms of gain recalibration. + +As mentioned previously, in our model, we do not adopt the assumption of the traditional models that constrains the synaptic weights of the velocity- to- rotation ring and rotation- to- central ring connections \((W_{\mathrm{v - cw}},W_{\mathrm{v - ccw}}\) and \(W_{\mathrm{cw - c}},W_{\mathrm{ccw - c}}\) , respectively) to be constants, each taking a uniform value in the entire neural space. Rather, we relax this constraint and treat these weights as functions that can vary along the neural space of the attractor network model, a heterogeneity likely to exist in biological networks. This functional treatment of the synaptic weights in our unconstrained model reveals an important characteristic of integration in CBANs: Unlike traditional treatments that implicitly assume a single, + +<--- Page Split ---> + +spatially global integration gain, CBANs employ a distributed, possibly inhomogenous, gain factor that can vary as the position representation varies (Equation 6 and top row in Fig. 2B). This implies for the ring attractor network that, by employing an inhomogenous PI gain, the network can adjust its spatial resolution locally, which would result in 'overrepresentation' or 'underrepresentation' of certain locations (bottom row in Fig. 2B) as is seen under various conditions of hippocampal place cell recordings [50,51]. + +#### 3.1.3 Landmark correction to path integration resembles a Kalman filter + +Lastly, we examine how the position representation \(\theta\) varies during the most general case that the animal moves on the circular track in the presence of landmarks. To derive this one- dimensional model of \(\theta\) , we employ the same dimensionality reduction protocol [41] but this time taking into account the visual neurons that provide feedback from landmarks. The resulting model will be crucial later when searching for the algorithmic conditions of the gain recalibration. + +It is known that feedback from landmarks anchors the internal representation of position in a stable manner [22- 25]. In classical ring attractor models, this stabilizing feedback is achieved with an allocentrically anchored, bump- like synaptic current applied onto the central ring from the visual ring, encoding the animal's location relative to the landmarks [52,53]. We adopt the same approach in our unconstrained ring attractor model and incorporate the following synaptic current from the visual ring onto the central ring: + +\[I_{vis}(t,\psi) = \rho_{vis}(\psi -\theta^{\star}), \quad (7)\] + +where \(\rho_{\mathrm{vis}}:S^{1}\to \mathbb{R}\) denotes a function describing the bump shape of this current, and \(\theta^{\star}\) denotes the location of the peak current, corresponding to the animal's current position relative to landmarks. Note that, throughout the paper, we use the superscript \(\star\) to distinguish a true value of a quantity (measured relative to the external world) from its internal value (e.g., \(\theta^{\star}\) vs \(\theta\) ). + +With Equation (7) in mind, applying the dimensionality reduction protocol [41] leads to a differential equation model for how the position representation \(\theta\) is controlled by the PI and the landmarks as follows: + +\[\frac{d\theta}{dt} = \beta (\theta^{\star} - \theta) + k(\theta)v. \quad (8)\] + +Here, \(\beta :S^{1}\to \mathbb{R}^{1}\) is a function that, locally, takes the same sign as its argument (Fig. 2C). This sign property, a form of negative feedback, is crucial for stable control of the position representation \(\theta\) ; for example, in the absence of movement, the sign property of \(\beta\) ensures \(\theta \to \theta^{\star}\) . In the more general case, when the animal's velocity is nonzero, the position representation \(\theta\) is changed by a combination of the (stabilizing) visual feedback \(\beta :S^{1}\to \mathbb{R}\) and the feedforward PI- related term \(k(\theta)v\) , as given in Equation (8). While the PI- related term, \(k(\theta)v\) , shifts the position representation proportionally with the animal's velocity, the function \(\beta\) acts in an additive manner to bring \(\theta\) toward \(\theta^{\star}\) , the animal's true position relative to visual landmarks. This fusion between the feedforward PI- related term and feedback correction from landmarks is illustrated in Fig. 2D. A properly balanced combination would stabilize \(\theta\) around \(\theta^{\star}\) , with only minor deviations due to PI error. As a result, the ring attractor effectively "estimates" the position of the animal, a computation that bares striking similarity to a Kalman filter in engineering. Here, the state being estimated by the network is the animal's true position, \(\theta^{\star}\) , and the estimate is the bump location, \(\theta\) . + +<--- Page Split ---> + +Experiments showed that the PI gain is a plastic variable that can be recalibrated accurately by visual landmarks [35]. In this section, we seek necessary conditions for this recalibration using Equation (8), the simple model presented in the previous section. + +We begin by recalling the experimental conditions that brought about the recalibration of the PI gain [35]: An animal moved on a circular track while an array of visual landmarks was rotated around the track as a function of the animal's velocity and an experimentally controlled, visual gain factor, \(k^{\star}\) . When \(k^{\star} < 1\) , the landmarks moved in the same direction as the animal, decreasing the animal's speed relative to the landmarks; when \(k^{\star} > 1\) , the landmarks moved in the opposite direction as the animal, increasing the animal's speed relative to the landmarks; when \(k^{\star} = 1\) (veridical condition), the landmarks were stationary. Neural recordings showed that persistent exposure to these visual conditions recalibrated the animal's PI gain such that a tight correlation was observed between the average value of the PI gain measured over many laps after the landmarks were extinguished and the final value of the visual gain \(k^{\star}\) before the landmarks were extinguished. Here, we analyze this recalibration using our simplified ring attractor model. + +The experimental conditions leading to the gain recalibration can be simulated in a ring attractor model with a visual synaptic drive \(I_{\mathrm{vis}}\) revolving around the central ring at a rate equal to the animal's velocity \(v\) times the visual gain \(k^{\star}\) . Extending (8), the simple model for the position representation \(\theta\) , with an additional equation for modeling the changes in the peak location \(\theta^{\star}\) of such a visual drive, we obtain + +\[\begin{array}{l}\frac{d\theta^{\star}}{dt} = k^{\star}v,\\ \displaystyle \frac{d\theta}{dt} = \beta \big(\theta^{\star} - \theta \big) + k(\theta)v, \end{array} \quad (9)\] + +where the first equation models the relation between the visual gain \(k^{\star}\) , the animal's velocity \(v\) , and the resulting temporal change in the visual drive's bump location \(\theta^{\star}\) , and the second equation models the change in the attractor's bump location \(\theta\) based on the external velocity and visual inputs. Recall that, as we previously showed, the PI gain \(k\) of a ring attractor network is a spatially distributed parameter that may potentially take different values as the network's position representation \(\theta\) varies during the animal's movement in the environment. However, in recalibration experiments [35], average neural activity over many laps was used to estimate the average value of the PI gain, providing no information about whether the PI gain took different values across the environment as predicted by the model. Therefore, from a theoretical perspective, the experimental result that the PI gain was recalibrated to the visual gain [35] only suggests that its spatial average \(k_{0}\) converges to the visual gain \(k^{\star}\) (i.e., \(\lim_{t \to \infty} k_{0}(t) = k^{\star}\) ), where + +\[k_{0} \triangleq \frac{1}{2\pi} \int_{0}^{2\pi} k(\theta) d\theta . \quad (10)\] + +This convergence of \(k_{0}\) toward \(k^{\star}\) necessitates \(k_{0}\) to be updated over time during recalibration. The firing rate of neurons in the network is a biologically plausible candidate for controlling these updates. Therefore, to garner mathematical insight into this process, we searched for a general equation that could represent the updating of \(k_{0}\) based on firing rates within the ring attractor, assuming an environment with a spatially homogenous feedback from visual landmarks. Under mild assumptions described in Appendix 6.3.1, this search led to a surprisingly simple equation + +\[\frac{dk_{0}}{dt} = g_{0}(k_{0}, \theta^{\star} - \theta , v), \quad (11)\] + +<--- Page Split ---> + +where \(g_{0}:\mathbb{R}\times S^{1}\times \mathbb{R}\to \mathbb{R}\) denotes an analytic function that instantiates the instantaneous change in \(k_{0}\) based on three variables: the current gain \(k_{0}\) , the animal's velocity \(v\) and the difference between the visual drive's position representation \(\theta^{\star}\) and the ring attractor's position representation \(\theta\) , without directly depending on either \(\theta\) or \(\theta^{\star}\) (since the visual feedback is assumed to be uniform across the environment). + +#### 3.2.1 Necessity of matching the sign in gain change with the product of error and velocity + +The update rule \(g_{0}\) could be any function fitting the form in Equation (11); however, some such functions may fail to result in gain recalibration, i.e., the PI gain's spatial average \(k_{0}\) would not converge to the visual gain \(k^{\star}\) . What are the necessary properties of the gain update rule \(g_{0}\) for convergence of \(k_{0}\) to \(k^{\star}\) ? + +To seek these properties, we revisit Equation (9), the simple model of the central ring's and visual ring's position representations \(\theta\) and \(\theta^{\star}\) , now taking into account that the PI gain's spatial average \(k_{0}\) is time varying according to the gain update rule in Equation (11). Perfect convergence of \(k_{0}\) to \(k^{\star}\) through this update rule would imply that the error between these two gains, namely \(\tilde{k} \triangleq k^{\star} - k_{0}\) , approaches zero. When this gain error becomes zero, it is intuitively expected that the error in the attractor's position representation relative to the visual drive, namely \(\tilde{\theta} \triangleq \theta^{\star} - \theta\) , also approaches zero. Hence, analyzing the temporal progression of these error terms—namely the gain error \(\tilde{k}\) and the positional error \(\tilde{\theta}\) —provides an opportunity to garner insight into the algorithmic underpinnings of the gain recalibration process. + +To this end, let us assume a constant visual gain, implying \(dk^{\star} / dt = 0\) . To track how the PI gain's spatial average \(k_{0}\) recalibrates to this constant value, we subtract the second row of Equation (9) from its first row and Equation (11) from \(dk^{\star} / dt = 0\) . This yields the so- called error dynamics + +\[\begin{array}{l}\frac{d\tilde{\theta}}{dt} = -\beta (\tilde{\theta}) + \tilde{k}v - k_{ac}(t,\theta)v,\\ \displaystyle \frac{d\tilde{k}}{dt} = -g_{0}(k_{0},\tilde{\theta},v), \end{array} \quad (12)\] + +where \(k_{ac}(\theta) \triangleq k(\theta) - k_{0}\) captures the spatial variation of PI gain \(k(\theta)\) from its spatial average \(k_{0}\) . Analyzing these error dynamics with tools from feedback control theory, we then identify the necessary conditions for complete recalibration of \(k_{0}\) to \(k^{\star}\) . + +First, we find that, if the ring attractor achieves and maintains zero gain error \(\tilde{k} = 0\) , as required for complete recalibration, then it also maintains zero positional error \(\tilde{\theta}\) , rendering the origin \((\tilde{\theta}, \tilde{k}) = 0\) an equilibrium point of the error dynamics. Convergence to this equilibrium point, however, requires the animal must be moving, else there exists no gain update rule that can achieve it. This is an unsurprising result, because, were the animal stationary, the visual landmarks would correct all the positional error \(\tilde{\theta}\) , making the gain error \(\tilde{k}\) imperceptible to the animal. Assuming accordingly that the animal is always moving, our analysis then reveals a sign requirement that must be satisfied by any gain update rule \(g_{0}\) . For stable convergence of the gain and positional error to the equilibrium point at the origin \((\tilde{\theta}, \tilde{k}) = 0\) , the gain's spatial average \(k_{0}\) must be updated in the same direction as the product of the animal's velocity \(v\) and the ring attractor's positional error \(\tilde{\theta}\) in some neighborhood of \(\tilde{\theta} = 0\) : + +\[\mathrm{sign}[g_{0}(k_{0},\tilde{\theta},v)] = \mathrm{sign}[\tilde{\theta} v]. \quad (13)\] + +See Appendix 6.3.2 for formal statements and proofs of these findings. + +What if the biological system can be recalibrated only partially? That is, at the steady state, the + +<--- Page Split ---> + +PI gain's spatial average \(k_{0}\) converges to a value biased towards, but not necessarily the same as, the visual gain \(k^{*}\) . Under a simplifying assumption that the animal's velocity is constant, we can generalize our findings for the complete recalibration to a case that covers both complete and partial recalibration. To this end, we re- analyze the error dynamics (Equation 12). First, we find that convergence of the gain error \(\tilde{k}\) to some value, say \(\tilde{k}^{\infty} \triangleq \lim_{t \to \infty} \tilde{k} (t)\) , after recalibration results in convergence of the ring attractor's positional error \(\tilde{\theta}\) also, say to \(\tilde{\theta}^{\infty} \triangleq \lim_{t \to \infty} \tilde{\theta} (t)\) . If the gain recalibration is complete (i.e., \(\tilde{k}^{\infty} = 0\) ), then this steady- state positional error \(\tilde{\theta}^{\infty}\) is zero; if the recalibration is partial (i.e., \(\tilde{k}^{\infty} \neq 0\) ), however, \(\tilde{\theta}^{\infty}\) is nonzero, proportional to the product of the steady- state gain error \(\tilde{k}^{\infty}\) and the animal's velocity \(v\) . Our analysis then reveals a generalized sign requirement that must be satisfied by any gain update rule: For gain recalibration, the gain's spatial average \(k_{0}\) must be updated in the same direction as the product of the animal's velocity \(v\) and the deviation of the current positional representation error \(\tilde{\theta} (t)\) relative to its steady- state value \(\tilde{\theta}^{\infty}\) in some neighborhood of \(\tilde{\theta} = \tilde{\theta}^{\infty}\) : + +\[\mathrm{sign}\big[g_{0}(k_{0},\tilde{\theta},v)\big] = \mathrm{sign}\big[(\tilde{\theta} -\tilde{\theta}^{\infty})v\big]. \quad (14)\] + +This generalized sign requirement captures both complete and partial recalibration; it, for example, reduces to Equation (13), the requirement for the complete gain recalibration, if the steady- state positional error \(\tilde{\theta}^{\infty}\) is zero. See Appendix 6.3.3 for formal statements and proofs of these findings. + +#### 3.2.2 Sufficiency of positive gain change with respect to the product of error and velocity + +We next analyze whether a gain update rule satisfying Equations (13) and (14), the necessary algorithmic conditions for recalibration, achieves gain recalibration, i.e., is it sufficient? + +To address this question, we further analyzed the error dynamics in Equation (12). This analysis reveals a sufficient condition for gain recalibration. According to this condition, a gain update rule is guaranteed to achieve gain recalibration (may be partial or complete) for any visual gain \(k^{*}\) and a given velocity \(v\) of the animal if it has a positive slope with respect to the product of velocity \(v\) and positional error \(\tilde{\theta}\) at its zero value (i.e., \(g_{0}(\tilde{\theta}, v) = 0\) ), namely, + +\[\frac{\partial g_{0}(k_{0},\tilde{\theta},v)}{\partial(\tilde{\theta}v)}\bigg|_{v = v_{0}} = \frac{\partial g_{0}(k_{0},\tilde{\theta},v_{0})}{\partial\tilde{\theta}}\frac{1}{v_{0}} >0. \quad (15)\] + +This sufficient condition is a small extension to the generalized necessary condition for recalibration such that it guarantees recalibration if a gain update rule satisfies the generalized sign condition in Equation (14) together with the mild additional requirement that the update rule also has a nonzero derivative with respect to the product of \(\tilde{\theta}\) and \(v\) . For example, a linear update rule \(g_{0}(k_{0},\tilde{\theta},v) = \mu \tilde{\theta} v\) with \(\mu > 0\) satisfies the sufficient condition but a cubic rule \(g_{0}(k_{0},\tilde{\theta},v) = \mu (\tilde{\theta} v)^{3}\) only satisfies the necessary condition. We now would like to demonstrate how this sufficiency leads to gain recalibration via two example update rules. Although both rules satisfy the sufficient condition, Example 1 achieves complete recalibration, while example 2 achieves only partial recalibration. + +Example 1. The simplest gain update rule that satisfies Equation (15), the slope condition guarantee for gain recalibration, takes the form + +\[g_{0}(\tilde{\theta},v) = \mu \tilde{\theta} v,\] + +where \(\mu\) denotes a positive learning rate. Furthermore, the update rule takes the same sign as the product \(\tilde{\theta} v\) , thus also satisfying the necessary condition for complete recalibration (Fig. 3B). + +<--- Page Split ---> + +Example 2. This example is inspired from the modified ring attractor network that we propose in Section 4 as a model that achieves gain recalibration. Let \(\mu\) denote a positive learning rate as before and \(\eta\) denote a constant. Consider the gain update rule + +\[g_{0}(k_{0},\tilde{\theta},v) = \mu (\eta k_{0}v^{2} + \tilde{\theta} v).\] + +Since its partial derivative is positive (i.e., \(\frac{\partial g_{0}(k_{0},\tilde{\theta},v)}{\partial(\tilde{\theta}v)} = \mu >0\) ), this update rule results in gain recalibration like Example 1. However, unlike Example 1, the recalibration can be complete or partial, depending on the value of \(\eta\) . If \(\eta = 0\) , the update rule takes the same sign as the product \(\tilde{\theta} v\) , thus satisfying the necessary condition for complete recalibration. Otherwise, it only satisfies the necessary condition for partial recalibration by taking the same sign as \(v(\tilde{\theta} - \tilde{\theta}^{\infty})\) for \(\tilde{\theta}^{\infty} = \eta v\) (Fig. 3C). + +![](images/Figure_3.jpg) + +
Figure 3: Numerical simulation of the two example gain update rules. For both simulations, we chose the initial condition \(k_{0}(0) = 1\) and the parameters \(\beta (\tilde{\theta}) = 0.66\times \sin (\tilde{\theta})\) , \(k^{*} = 1.5\) , \(\mu = 0.02\) . The gain choices imply that the initial value of gain error is \(\tilde{k} (0) = 0.5\) . Additionally, we chose \(\eta = 0.12\) for the second example. (A) Temporal progression of the smoothed animal's velocity from an experiment in [35]. (B) Simulated error trajectories under example gain update rule 1. As soon as the animal begins its movement at \(t = 0\) , the positional error \(\tilde{\theta}\) (black line relative to the left y axis) quickly increases because of the nonzero gain error \(\tilde{k}\) (purple line relative to the right y axis). As the animal recalibrates its gain, the gain error gradually converges to zero (i.e., \(\tilde{k}\to 0\) ), accompanied by positional error gradually converging to zero also (i.e., \(\tilde{\theta}\to 0\) ). In addition to these gradual convergent trends, the error trajectories include many fast, transitory changes. As can be seen from the black line, the instantaneous value of positional error \(\tilde{\theta}\) is correlated with the animal's velocity \(v\) also. For example, when animal slows down, the positional error decreases, becoming zero when the velocity is zero. This is a reflection of the relatively increased landmark stabilization \(\beta (\tilde{\theta})\) when path integration inputs \(kv\) are decreased. On the other hand, the temporal changes in the gain are correlated with the multiplication of the positional error \(\tilde{\theta}\) and the animal's velocity \(v\) as determined by the gain update rule \(g_{0}\) . When the animal pauses temporarily around minutes 5 and 20 (i.e., \(v = 0\) ), the positional error \(\tilde{\theta}\) is completely corrected by landmarks (i.e., \(\tilde{\theta} = 0\) ), causing the gain updates to pause also (i.e., \(\frac{d\tilde{k}}{dt} = 0\) ). As the animal continues moving, the positional error and the velocity fine-tune the gain until the gain error converges to zero, demonstrating that the system can achieve complete gain recalibration. (C) Simulated error trajectories under example gain update rule 2. The convention is the same as panel B. The error trajectories exhibit similar trends to the panel B except that their final values do not converge to zero, demonstrating that the system can only achieve partial gain recalibration.
+ +### 3.3 Mechanistic Constraints Reveal Instrumental Role of Positional Error Codes + +What are the mechanistic prerequisites for meeting the necessary algorithmic condition derived in the previous section for gain recalibration? To address this question, we investigate an analytical expression of the PI gain's spatial average \(k_{0}\) which can be simply obtained by averaging the PI gain \(k(\theta)\) in Equation (6) over \(\theta\) as follows: + +\[k_{0}\triangleq \frac{-b}{2\pi\tau_{c}\| \frac{\partial r_{c}^{*}(\psi)}{\partial\psi}\|^{2}}\int_{0}^{2\pi}\int_{0}^{2\pi}\frac{\partial^{2}r_{c}^{*}(\psi)}{\partial\psi^{2}}\sum_{i\in \{\mathrm{cw},\mathrm{ccw}\}}\alpha_{i}W_{i - c}(\psi)W_{\mathrm{v - }i}(\psi)\mathrm{sign}[r_{i}^{*}(\psi ,\theta ,0)]d\psi d\theta \quad (16)\] + +The terms in this expression identifies a number of possible loci or mechanisms for updating \(k_{0}\) that satisfy the algorithmic requirement for gain recalibration in Equation (13). These terms include: (i) the offset \(b\) in the central- to- rotation ring connections, (ii) the synaptic time constant \(\tau_{c}\) , (iii) the slope parameters + +<--- Page Split ---> + +\(\alpha_{\mathrm{cw}},\alpha_{\mathrm{ccw}}\) quantifying the absolute value of the tuning slopes of velocity neurons, (iv) the synaptic weight functions \(W_{\mathrm{v - cw}},W_{\mathrm{v - ccw}}\) of the velocity- to- rotation ring connections, (v) the synaptic weight functions \(W_{\mathrm{cw - c}},W_{\mathrm{cw - c}}\) of the rotation- to- central ring connections, (vi) the function \(r_{\mathrm{c}}^{*}\) describing the central ring's persistent activity bump, and (vii) the functions \(r_{\mathrm{cw}}^{*}\) , \(r_{\mathrm{cw}}^{*}\) describing solutions to the rotation ring's persistent activity bump. Note that we treat the CW and CCW components of the same term as an inseparable pair. Out of the seven terms, we consider the last five terms (iii- vii) as candidates driving the gain recalibration within the ring attractor model via temporal changes, implicitly assuming that the first two terms, the offset \(b\) and the synaptic time constant \(\tau_{c}\) , are "hardwired" (i.e., time- invariant). + +The rationale behind excluding the first two terms arises, in part, from limitations of our modeling framework. First, the rate- based approach, upon which we describe the network dynamics, does not include any cellular and receptor details to capture possible temporal changes in the synaptic time constant \(\tau_{c}\) . Instead, our model includes \(\tau_{c}\) as a "lumped parameter" reduction of complex phenomena that governs the changes in membrane potential with ion flux through receptors; future work could use chemical kinetics modeling to investigate how changes in \(\tau_{c}\) could contribute to gain recalibration, but that is beyond the scope of the present study. Second, our model employs a simplified one- to- one connectivity between the rotation rings and the central ring such that one neuron in a rotation ring connects to only one neuron in the central ring with a fixed offset \(b\) , rather than a one- to- all connectivity required to capture plasticity in \(b\) through gradual modulation of weights along the neural space. Therefore, excluding \(\tau_{c}\) and \(b\) from further consideration, we focused our analysis on the remaining five terms as the driver of gain recalibration. + +By analyzing the relation between the temporal change in each of these candidate terms and the resulting temporal change in \(k_{0}\) , we find that rate- based encoding of the positional error \(\tilde{\theta}\) is required to satisfy the necessary algorithmic condition for the gain recalibration regardless of which term drives the changes in PI gain. However, the specific nature of the error code depends on the driver term (Fig. 4A) as demonstrated in the next subsections. + +#### 3.3.1 Plasticity in the velocity pathway requires a rate code of the instantaneous error + +We first consider the scenario that the temporal change in the PI gain is driven by temporal changes in either set of the synaptic weights along the pathway from velocity neurons to the central ring. These sets include the pair \(W_{\mathrm{v - cw}},W_{\mathrm{v - ccw}}\) , describing the strength of velocity- to- rotation ring connections (4) in Fig. 1A), and the pair \(W_{\mathrm{cw - c}},W_{\mathrm{cw - c}}\) , describing the strength of rotation- to- central ring connections (3) in Fig. 1A). + +According to Equation (16), the CW and CCW components of these weight pairs additively influence the PI gain \(k_{0}\) . This additive influence suggests a possibility where CW and CCW components vary independently of one another during recalibration to tune the PI gain's spatial average \(k_{0}\) . However, this possibility is limited by the requirement that the central ring must receive balanced (i.e., symmetric) inputs from the CW and CCW rotation rings to keep the activity bump stationary during the animal's immobility as we show in Appendix 6.4. Thus, we assume hereafter that the CW and CCW components vary in a coordinated fashion to ensure that their individual contribution to \(k_{0}\) is symmetric. This symmetry assumption implies that if the overall value of \(k_{0}\) changes as per the necessary algorithmic condition in (13), then the individual contribution to this change from both CW and CCW components must be in the direction of the product of the animal's velocity \(v\) and the positional error \(\tilde{\theta}\) . To identify the mechanistic underpinnings of such symmetric gain recalibration, we revisit Equation (16). By + +<--- Page Split ---> + +differentiating this equation with respect to time and considering Hebbian plasticity as the mechanism underlying the changes in the weight pairs \(W_{\mathrm{v - cw}},W_{\mathrm{v - ccw}}\) or \(W_{\mathrm{cw - c}}\) , \(W_{\mathrm{ccw - c}}\) , we find that the algorithmic condition translates into a mechanistic constraint as follows: + +Hebbian plasticity of the velocity- to- rotation ring connections \((W_{\mathrm{v - cw}},W_{\mathrm{v - ccw}})\) : A change in the weights \(W_{\mathrm{v - cw}},W_{\mathrm{v - ccw}}\) leads to a commensurate change in the speed at which the network's activity bump is shifted along the ring for a given speed of the animal. These commensurate changes suggest a positively correlated relationship between the weights \(W_{\mathrm{v - cw}},W_{\mathrm{v - ccw}}\) and the PI gain \(k_{0}\) . Assuming the symmetry between CW and CCW components, we indeed show in Appendix 6.4.1 that Equation (16), relating the weights \(W_{\mathrm{v - cw}},W_{\mathrm{v - ccw}}\) to the PI gain \(k_{0}\) via positively- weighted integrals, can be reformulated as + +\[k_{0}\propto \frac{1}{2\pi}\int_{0}^{2\pi}W_{\mathrm{v - cw}}(\psi)d\psi ,\] \[k_{0}\propto \frac{1}{2\pi}\int_{0}^{2\pi}W_{\mathrm{v - ccw}}(\psi)d\psi ,\] + +where the \(\propto\) symbol denotes the existence of a positively- sloped, proportional relationship. Because of these positive correlations, satisfying the algorithmic condition for recalibration implies that the average strength of both CW and CCW velocity- to- rotation ring synapses is modified in the direction of the product of the animal's velocity \((v)\) and the network's positional error \((\tilde{\theta})\) , namely, + +\[\mathrm{sign}\left[\frac{1}{2\pi}\int_{0}^{2\pi}\dot{W}_{\mathrm{v - cw}}(\psi)\right]d\psi = \mathrm{sign}\left[\frac{1}{2\pi}\int_{0}^{2\pi}\dot{W}_{\mathrm{v - ccw}}(\psi)\right] = \mathrm{sign}[\tilde{\theta} v]. \quad (17)\] + +Here, the dots appearing over the weights denote the temporal change in the weight. Recall that Hebbian plasticity of a synapse is driven by the joint activity of pre- and post- synaptic neurons. In the specific case of velocity- to- rotation ring synapses, the pre- synaptic side is composed of the velocity neurons, designated to solely encode the animal's velocity \(v\) with tuning curves that have a negative slope for the CW velocity neuron and a positive slope for the CCW velocity neuron (previously shown in Fig. 1D). Therefore, in a manner matching these differential signs of \(v\) - encoding on the presynaptic side, the CW and CCW rotation rings on the post- synaptic side must monotonically decrease and increase their average firing rates with the instantaneous positional error \(\tilde{\theta}\) to satisfy the equality in Equation (17) (Fig. 4B). Mathematical details are provided in Appendix 6.4.1. + +Hebbian plasticity of the rotation- to- central ring connections \((W_{\mathrm{cw - c}},W_{\mathrm{ccw - c}})\) : Like the velocity- to- rotation ring connections discussed above, the rotation- to- central ring synaptic weight functions \(W_{\mathrm{cw - c}},W_{\mathrm{ccw - c}}\) enter linearly in the calculation of the PI gain in Equation (16). Therefore, like the previous case, the algorithmic condition for recalibration via \(W_{\mathrm{cw - c}},W_{\mathrm{ccw - c}}\) requires Hebbian plasticity to modify their average strength in the direction of the product of the animal's velocity \((v)\) and the network's positional error \((\tilde{\theta})\) , namely, + +\[\mathrm{sign}\left[\frac{1}{2\pi}\int_{0}^{2\pi}\dot{W}_{\mathrm{cw - c}}(\psi)\right]d\psi = \mathrm{sign}\left[\frac{1}{2\pi}\int_{0}^{2\pi}\dot{W}_{\mathrm{ccw - c}}(\psi)\right] = \mathrm{sign}[\tilde{\theta} v]. \quad (18)\] + +In the previous case, the mechanistic prerequisite for meeting a similar sign requirement was error- encoding on the postsynaptic side since the pre- synaptic neurons were assumed to solely encode the velocity. In the present case, however, it is feasible to encode the error in either the pre- or post- synaptic side since neither side is subject to such a limitation. Therefore, when the animal is traveling in one direction (as was the case in the experiments that originally demonstrated the gain recalibration [35]), satisfying the equality in Equation (18) requires mean firing rate of either the rotation rings or the central + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4: Visualization of mechanistic constraints for a numerical simulation based on a hypothetical gain update rule \(g_{0}(k_{0},\hat{\theta},v) = \mu \hat{\theta} v\) Except for the animal's velocity profile, we chose the parameters and initial conditions the same as Fig. 3B. For color coding, we use Fig. 1A as the reference, where red and blue denote the CW and CCW rotation rings, and green denotes the central ring. (A) Top graph shows the simulated velocity of the animal (purple line) on the right y axis and the temporal progression of the positional error (black line) on the left y axis. Notice the synchronous fluctuations in the positional error and the animal's velocity. As explained in Fig. 3B, these synchronous fluctuations occur because the positional error is correlated with the animal's velocity. Bottom graph shows the PI and visual gains with solid and dashed purple lines, respectively, on the right y axis and the time-integral of the positional error with the black line on the left y axis. Notice that as the PI gain gradually converges to the visual gain, the temporal progression of the time-integral of the positional error follows a very similar trajectory. This similarity indicates that the integration gain reflects the past accumulation of positional representation errors, thus opening up the possibility for the network to track the time-integral of the error as a proxy signal to encode the integration gain. (B) The mechanistic constraint for recalibration through plasticity of the velocity-to-rotation ring connections. Top graph shows the mean firing rates of the CCW and CW rotation rings over time with blue and red lines. Notice that they are similar to the trajectory of the positional error in panel A, except that the changes in the CW rotation ring's mean firing rate (red line) is the negative of those in the CCW rotation ring's mean firing rate. Bottom graph shows the direct relationship between mean firing rates and positional error in the attractor's representation. (C) The mechanistic constraint for recalibration through plasticity of the rotation-to-central ring connections. Top graph shows the mean firing rates of either the rotation rings or the central ring over time with the orange line. Notice that the changes in these firing rates follow a similar trend as the temporal progression of the positional error. Bottom graph shows this relationship directly (the positive correlation is chosen arbitrarily as our analysis does not provide a conclusive insight into the required direction). (D) The mechanistic constraint for recalibration through changes in the velocity neurons' slopes. Top graph shows the mean firing rate of the CCW and CW rotation rings, the same quantities as panel B. However, unlike panel B where the mean firing rates were similar to the instantaneous positional error, the mean firing rates in this panel are similar to the time-integral of the error. Bottom graph shows this relationship between the mean firing rates and the time-integral of the error directly. (E) The mechanistic constraint for recalibration through changes in the rotation rings' activity bumps. Top graph shows the bump width of both rotation rings over time. Similar to how the mean firing rates of the rotation rings encode the time-integral of the positional error in panel D, the bump widths encode the time-integral of the error in this panel. Bottom graph shows this relationship directly. (F) The mechanistic constraint for recalibration through changes in the central ring's activity bump. Top graph shows the temporal progression of the mean firing rate of the central ring, which is tightly but negatively correlated with the temporal progression of the time-integral of the positional error. Bottom graph shows this relationship directly.
+ +ring vary monotonically with the network's instantaneous positional error (Fig. 4C). However, unlike the previous case, our analysis of the present case does not provide conclusive information about the direction of these monotonic relations. Mathematical details are provided in Appendix 6.4.2. + +Collectively, these findings show that Hebbian plasticity in the pathway carrying the external velocity information to the central ring requires a rate code of the network's instantaneous positional error to update the synaptic weights in the direction of the product of the animal's velocity and the error as per the algorithmic condition for gain recalibration in Equation (13). + +#### 3.3.2 Plasticity elsewhere requires a rate code of the time-integral of the error + +We next consider the scenario that the synaptic weights along the pathway from velocity neurons to the central ring are hardwired (i.e., constant). This scenario implies that the gain recalibration is instead driven by temporal changes in one of the three firing- rate related terms, including the slope parameters \(\alpha_{cw}\) , \(\alpha_{ccw}\) quantifying the absolute value of the slopes of the CW, CCW velocity neurons' tuning curves, the ansatz functions describing the persistent activity bumps \(r_{cw}^*\) , \(r_{ccw}^*\) of the rotation rings, or the ansatz + +<--- Page Split ---> + +of the persistent activity bump \(r_{c}^{*}\) of the central ring. Independent of which of these terms undergoes temporal changes, the algorithmic condition for gain recalibration translates into a mechanistic constraint that, while still a rate code of error, differs in its fundamental characteristics as detailed below: + +Changes in the slopes of velocity neurons' tuning curves ( \(\alpha_{cw}\) , \(\alpha_{ccw}\) ): As shown previously in Fig. 1D, the CW and CCW velocity neurons are tuned to the animal's velocity with slopes having different signs. Assuming these differential signs to be hard- wired (i.e., constant), we examine in the present case that absolute values of the slopes, denoted by parameters \(\alpha_{cw}\) and \(\alpha_{ccw}\) , undergo temporal changes. A change in these parameters leads to a commensurate change in the speed at which the network's activity bump is shifted along the central ring for a given speed of the animal. As in the previous section, this implies a positively correlated relationship between the slope parameters \(\alpha_{cw}\) and \(\alpha_{ccw}\) and the PI gain \(k_{0}\) . Indeed, this relationship is explicitly seen in Equation (16). Thus, as in the previous section, satisfying the algorithmic condition for recalibration (Equation (13)) requires \(\alpha_{cw}\) and \(\alpha_{ccw}\) to change in the direction of the product of the animal's velocity \((v)\) and the network's positional error \((\tilde{\theta})\) , namely, + +\[\mathrm{sign}[\dot{\alpha}_{cw}] = \mathrm{sign}[\dot{\alpha}_{ccw}] = \mathrm{sign}[\tilde{\theta} v] \quad (19)\] + +An implication of this requirement is that, when the animal is traveling in one direction, the change in the slope parameters is monotonically related to the positional error, reflecting its value on a moment- to- moment basis with a sign additionally depending on the sign of the velocity. When these changes are integrated over time, the current value of the slope parameters reflects the accumulation of positional errors through the past, depending monotonically on the time- integral of the positional error in a direction that depends on the animal's velocity. Through connections between the velocity neurons and the rotation rings, these monotonic relationships are also translated to the mean firing rate of rotation rings. The direction of the monotonic relationships between the time- integral of the error and the mean firing rate of the rotation rings, however, depends additionally on the sign of the velocity neurons' tuning slopes. For example, when the animal is traveling in one direction (say CCW) like the original recalibration experiments [35], the mean firing rate of the CCW rotation ring increases monotonically with the time- integral of the error due to the positive slope of the CCW velocity neuron. Conversely, the mean firing rate of the CW rotation ring decreases monotonically with the time- integral of the error due to the negative slope of the CW velocity neuron (Fig. 4D). If the animal travels in the other direction, the direction of these monotonic relationships is also reversed. Mathematical details are provided in Appendix 6.4.3. + +Changes in the persistent activity bump of the rotation rings \((r_{cw}^{*},r_{ccw}^{*})\) : Velocity information is transmitted to the central ring by the rotation rings whose firing rates are modulated by the animal's velocity. In this transmission, an increase in the number of actively firing rotation ring neurons (i.e., larger width of the bumps \(r_{cw}^{*}\) , \(r_{ccw}^{*}\) ) would result in a commensurate increase in the number of central ring neurons that receive the velocity information. As a result, the network's activity bump shifts faster along the central ring even when the animal's velocity is unchanged. This implies a positively correlated relationship between the widths of the rotation rings' activity bumps and the PI gain \(k_{0}\) , reminiscent of the relationship in the previously investigated case of \(\alpha_{cw}, \alpha_{ccw}\) . Thus, like the previous case, satisfying the algorithmic condition requires the rotation rings' activity widths to change monotonically with the product of the animal's velocity and the positional error. Consequently, when the animal is traveling in one direction (say positive), the widths of the rotation rings' activity bumps must increase monotonically with the time- integral of the error (Fig. 4E). If the animal's travel direction is negative, this relationship turns into a monotonically decreasing function. Mathematical details are provided in Appendix 6.4.4. + +<--- Page Split ---> + +Changes in the persistent activity bump of the central ring ( \(r_{c}^{*}\) ): Consider as an example that there are two networks with the same Gaussian bump profile but one has a higher peak firing rate. In this case, if all else is equal, the network with the higher firing requires higher velocity inputs to shift its activity bump from point A to point B in the same time as the other network. This need of higher velocity inputs indicates an inversely correlated relationship between the magnitude of the central ring's activity bump and the PI gain \(k_{0}\) , which can also be verified from Equation (16) wherein the denominator includes a term proportional to the bump magnitude: the squared norm of the activity bump's gradient. Thus, satisfying the algorithmic condition for gain recalibration is subject to a mechanistic constraint that is similar to the previous case in spirit but slightly different due to the inverse effect: When the animal is traveling in one direction (say positive), the mean firing rate of the central ring must decrease monotonically with the time- integral of the error (Fig. 4F). If the animal's travel direction is negative, the direction of this monotonic relationship is reversed. Note that, for deriving this result, we assume the general shape of the central ring's activity bump to be invariant, unlike its magnitude. Mathematical details are provided in Appendix 6.4.5. + +Collectively, these findings show that gain recalibration might be possible without synaptic plasticity in the pathway carrying the external velocity information to the central ring, provided that there is a rate code of the time- integral of the positional error as opposed to the error itself. Our analysis does not provide any insights into the mechanisms necessary for such a rate code and the temporal changes in the terms associated with it (e.g., slopes of velocity neurons). However, it may be the case that plasticity elsewhere than the velocity pathway is required. Independent of the underlying mechanism, however, an absence of plasticity in the velocity pathway would lead to the conclusion that the PI gain is no longer encoded in the synaptic weights: instead, it is encoded in the firing rates that track the time- integral of the error, a proxy of the PI gain (bottom row in Fig. 4A). + +## 4 Implementing Gain Recalibration in a Ring Attractor + +In this section, we propose a modified ring attractor model that can achieve gain recalibration through a mechanism devised based on the theoretical insights we have garnered so far. Briefly, the model relies on synaptic plasticity in the velocity- to- rotation ring connections and its mechanistic prerequisite (a rate code for the positional error instantiated in the rotation rings as shown in Fig. 4B) to achieve gain recalibration. We chose this mechanism, instead of other theoretical candidate mechanisms examined in the previous section, partly because we found it relatively easy to implement compared to others, but we conjecture that some of the other candidate mechanisms also have biologically feasible implementations. Thus, our model should be viewed as an example that demonstrates the effectiveness of our theoretical analysis rather than the only network model that can achieve gain recalibration. In addition to gain recalibration, the model can also reproduce two other important aspects of biological path integration: (1) flexible association of visual landmarks to different positions in the neural space and (2) correction of accumulated PI errors by visual landmarks. In the next subsections, we describe the model in detail and explain the mechanisms by which it achieves these aspects. + +### 4.1 A connectivity pattern yielding a rate code for error + +We begin by proposing a connectivity pattern that causes a neuron population to vary its firing rate monotonically with the difference in the bump locations of two other populations. As will be evident, this connectivity pattern plays a crucial role by providing the means to achieve a rate code for the + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Figure 5: A modified ring attractor network model. (A) A proposed connectivity pattern that varies firing rate of population X3 as a monotonic function of the difference in the activity-bump locations of populations X1 and X2. Arrow and circle terminals denote excitatory and inhibitory connections, respectively. (B) Schematic diagrams depicting the computation within the proposed connectivity in panel (A). The first row shows the activity of the X2 population (yellow) relative to the activity of the X1 population (green) for different conditions: CW difference (left column), no difference (middle column), and CCW difference (right column). The second row shows the synaptic inputs to the X3 population from the excitatory X1 (green line) and inhibitory X2 (yellow line) populations. The fourth row shows the resulting firing rates of the X3 population. (C) Schematic representation of the model. Solid and dashed lines denote hardwired and plastic connections, respectively. The labels M1, M2, M3, correspond to the three modifications made to the classical ring attractor: M1 is the association ring, M2 refers to the plasticity of the velocity-to-rotation-ring connections, and M3 refers to the hardwired, offset association-to-rotation connections. (D) Numerical simulation demonstrating the association of the visual ring's activity with the central ring's activity through Hebbian plasticity. Top and bottom rows show the initial and final values of the simulated variables. The left column shows the firing rates of the central (green) and visual (pink) rings. The middle column visualizes the weight matrix describing the visual-to-association ring connections. The right column shows the firing rates of the association ring (yellow) and the synaptic inputs from the visual ring (pink). (E) Tuning curves depicting the relationships between the rotation rings' mean and peak firing rates vs. the error (left and right graph, respectively). The color coding is the same as panel C. (F) Numerical simulations of the gain recalibration within the proposed model. The top shows the the recalibration of PI gain (green) toward the visual gain (pink) in a selected simulation. The middle shows the progression of the weights of the velocity-to-rotation ring connections (four samples normalized to initial condition of the weights with the opacity changes from the lightest ( \(t = 0\) min) and to the darkest green ( \(t = 30\) min) corresponding to chronological order of the samples.). The bottom shows the final values of the PI gain for various visual gains (green line) and the hypothetical perfect recalibration (dashed black line). (G) Numerical simulation demonstrating how visual landmarks correct positional error. The top panel shows the progression of the bump locations of the visual (pink) and central rings (green). Bottom panel shows the mean firing rate of CW (red) and CCW (blue) rotation rings over time.
+ +<--- Page Split ---> + +positional error, with the error being the difference in the bump locations of the visual drive and the attractor activity. + +The connectivity pattern can be described by considering three distinct neuron populations (X1- 3 in Fig. 5A), each arranged on a circle like the populations in the ring attractor network. Suppose that populations X1 and X2 consist of excitatory and inhibitory neurons, respectively, and maintain their own activity bumps. The population X3 derives its activity based on the inputs from X1 and X2. The excitatory inputs from X1 to X3 are routed through topographic connections that wire together the neurons at the same angular location in the neural space. The inhibitory inputs from X2 to X3, however, are routed through CCW offset connections that wire together the neurons at different angular locations. To understand how this connectivity causes X3 to vary its firing rate as a monotonic function of the difference in the bump locations of X1 and X2, we can track the flow of neural activity as follows: + +Let \(x_{1}\) and \(x_{2}\) denote the location of X1 and X2's activity bumps on the circular neural space, respectively. When \(x_{2} - x_{1} = 0\) , these activities are aligned, but the synaptic inputs to the population X3 from X1 and X2 are misaligned due to the CCW offset in X2- to- X3 connections (the second column of Fig. 5B). When \(x_{2} - x_{1} \neq 0\) , however, X1 and X2's activity bumps are misaligned with a CW \((x_{2} - x_{1} < 0)\) or CCW \((x_{2} - x_{1} > 0)\) difference in their locations. Consider first the CW- difference case (the first column of Fig. 5B). In this case, the bump location of X2 is shifted in the CW direction compared to that of X1. Because of the CCW offset in the X2- to- X3 connections, this misalignment between the activity bumps decreases at the level of synaptic inputs, bringing the inhibition from X1 closer to active neurons of X3, thereby decreasing the firing rate of X3 compared to the no- difference case. Consider next the CCW- difference case (the third column of Fig. 5C). In this case, the CCW offset in the X2- to- X3 connections redirects the inhibition from X2 further away from the active neurons of X3, thus increasing the firing rate of X3. Through this mechanism, the proposed connectivity pattern causes population X3 to vary its firing rate as a monotonically increasing function of the difference in the bump locations of X1 and X2. The direction of this monotonic relationship can be easily reversed if the offset in the X2- to- X3 connections is reversed from CCW to CW. + +How can we make use of this connectivity pattern in our modified ring attractor network that will rely on plastic velocity- to- rotation ring connections for gain recalibration? The connectivity lends itself naturally to this recalibration mechanism, as it requires the rotation rings (X3) to vary their firing rates monotonically with the positional error, a quantity equal to the difference in the bump locations of the central ring (X1) and the visual drive (X2). Despite this suitability, however, employing the connectivity pattern in the ring attractor network requires an additional modification for a reason which will be clear in the next section. + +### 4.2 Flexible Association of Landmarks to Positions through Hebbian Plasticity + +In traditional ring attractor models, feedback from landmarks is incorporated into the network via direct synaptic connections from the visual ring [20, 46, 54]. In these connections, synaptic weights between coactive neuron pairs encoding the same position of the animal are potentiated through Hebbian plasticity, resulting in a flexible associative mapping between the visual ring and the attractor network [20, 55]. + +However, this approach, relying on direct plastic connections from the visual ring onto the attractor network, is incompatible with the connectivity pattern proposed in the previous section for the error- rate code. That is, the error code's connectivity pattern requires the wiring of neurons representing different positions by an offset as opposed to Hebbian plasticity wiring together the neurons representing the + +<--- Page Split ---> + +same position. To resolve this incompatibility, our modified ring attractor network model makes a small change to the approach in traditional models by placing the plastic associative mapping problem outside the network, which makes it possible to include the error code's connectivity pattern inside the network. + +The modification is as follows: We first remove the visual- to- central ring connections (5 in Fig. 1A) and introduce an intermediate ring of neurons, which we call an association ring, that associates the activity in the visual ring with that in the central ring by receiving inputs from both (see M1 in Fig. 5C). The afferent connections to this ring from the central ring are hardwired and weak, while those from the visual ring are plastic, hence capable of becoming strong. In a novel environment, where the plastic visual connections are initially untuned and random, the spatial selectivity of the visual ring's activity is not conveyed in its synaptic inputs to the association ring. In contrast, inputs from the central ring to the association ring always preserve the spatial selectivity of the central ring's activity because of the hard- wired, topographic connections. This combination of malleable inputs from the visual ring and weak but hardwired inputs from the central ring biases the visual- to- association ring connections near the central ring's activity bump to be selectively potentiated through Hebbian plasticity (top row in Fig. 5D). Eventually, synaptic inputs from the visual ring become sufficiently strong and aligned with the inputs from the central ring, making the association ring's activity strongly visually driven such that it implicitly represents a flexible associative mapping between the representations in the visual ring and the attractor network (bottom row Fig. 5D). + +Thus, by serving as an intermediary, the association ring promises to act in our modified ring attractor network model as a proxy visual drive that can circumvent the previously noted incompatibility issues with the error- rate code's connectivity pattern. In the next section, we describe how the association ring can be combined with this connectivity pattern to implement gain recalibration. + +### 4.3 Gain Recalibration by Landmarks through Hebbian Plasticity + +As noted earlier, our ring attractor network model relies on plasticity in the velocity- to- rotation ring connections for gain recalibration (M2 in Fig. 5C). However, this recalibration mechanism requires as its mechanistic prerequisite (described in Section 3.3) that CCW and CW rotation rings respectively increase and decrease their firing rates with the positional error in the central ring's representation relative to the visual landmarks. To obtain these error- rate codes, we make use of the visual information in the association ring by connecting it to the CCW and CW rotation rings with CCW and CW offset, respectively (M3 in Fig. 5C). Combined with the topographic central- to- rotation ring connections, these offset connections from the association ring implement the connectivity pattern described in Section 4.1, thereby achieving the required error- rate codes (Fig. 5E). With these error- rate codes in the rotation rings and the plasticity in the velocity- to- rotation ring connection, our ring attractor model now includes all the necessary ingredients for gain recalibration. + +To test if these ingredients are also sufficient to achieve gain recalibration, we first take an analytical approach based on Equation (15), which states a sufficient condition for gain recalibration. According to this condition, gain recalibration is guaranteed if the temporal change in the gain's spatial average \(k_0\) has a positive slope with respect to the product of the animal's velocity \(v\) and the attractor's positional error \(\tilde{\theta}\) . As discussed in Section 3.3.1, the gain's spatial average \(k_0\) is positively correlated with the average strength of velocity- to- rotation ring connections. Therefore, the sufficient condition can be rephrased as follows: the gain recalibration is guaranteed if the temporal change in the average strength of velocity- to- rotation ring connections has a positive slope with respect to the product of \(v\) and \(\tilde{\theta}\) . The presynaptic side of these plastic connections has velocity neurons encoding the animal's velocity \(v\) , while the postsynaptic + +<--- Page Split ---> + +side has the rotation ring neurons encoding the positional error \(\tilde{\theta}\) . Since both of these rate codes have the same slopes on the CW and CCW parts of the network (negative on the CW part, positive on the CCW part), their correlated activity, as the driver of Hebbian plasticity, modifies the strength of velocity- to- rotation ring connections with a positive slope relative to the product of \(v\) and \(\tilde{\theta}\) , satisfying the sufficient condition in (15). However, as in Example 2 in Section 3.2.2, gain recalibration is expected to be imperfect due to imperfect encoding of the positional error \(\tilde{\theta}\) in the rotation rings whose firing rates are additionally modulated by the animal's velocity \(v\) . As a result, our model is expected to recalibrate its average PI gain \(k_{0}\) to steady- state values that are close to, but not the same as, the visual gain, \(k^{\star}\) . During this recalibration, the model is expected to go through a transitory stage with a spatially non- homogenous PI gain \(k(\theta)\) as Hebbian plasticity modifies the spatially distributed velocity- to- rotation ring connections non- uniformly because of non- uniform firing rate of rotation rings' neurons across the neural space at any moment in time. However, as the animal runs in the environment, these non- uniform effects will be washed out, resulting in a spatially homogeneous PI gain \(k(\theta)\) . Mathematical details of these analytical findings are given in Appendix 6.5. + +We next verify these analytical findings by performing numerical simulations of our modified ring attractor model for a simulated rat running on a circular track while visual landmarks were moved as per the visual gain \(k^{\star}\) . The model demonstrated imperfect yet stable gain recalibration for a range of \(k^{\star}\) values (Fig. 5F). + +### 4.4 Correction of Positional Errors by Landmarks through a Rate Code of Error + +Next, we test if visual landmarks can correct PI errors in our model. The PI error manifests itself as a misalignment between the peak locations of the path- integration driven activity bump in the central ring and the strongly visually driven activity bump in the association ring. Traditional models correct for this misalignment by providing visual drive directly onto the central ring (like in Fig. 1A), toward which its activity bump is gravitated by means of attractor dynamics. In our model, however, we have removed such direct connections in Section 4.2 because of their incompatibility with the connectivity pattern needed for the error- rate codes. Instead, we have connected the association ring to the rotation rings with some offset. The question then arises: are these offset connections sufficient for PI error correction or do we need to reinstate direct connections onto the central ring from the visual drive? + +As explained in the previous section, the offset connections causes the CCW and CW rotation rings to increase and decrease their firing rates monotonically with the positional error, respectively. This differential modulation of the rotation rings' firing rates by the error is similar to their differential modulation by the velocity; when the animal is moving, the firing rate of one rotation ring increases and that of the the other decreases, which in turn shifts the activity bump along the central ring. Therefore, by employing rate codes of the positional error, our model effectively transforms the positional error into a virtual velocity signal, shifting the activity bump along the central ring in a manner decreasing this error—a manifestation of error correction provided by visual landmarks. We verified this error correction mechanism in numerical simulation of our model. Following a positional error introduced abruptly between the activity bumps of the ring attractor and the association ring, the differential changes in the rotation rings' firing rates eliminated the error by re- aligning the central ring's activity bump with that of the association ring (Fig. 5E). + +<--- Page Split ---> + +## 5 Discussion + +Fine- tuning of a neural integration computation is crucial to maintain accurate representations of continuous variables since the relationship between the sensing of the relative change in a continuous variable and its actual value can fluctuate on both developmental (e.g., changes in body size that can affect location coding) and behavioral timescales (e.g., swimming versus walking in the case of location coding) and even due to dynamic biological processes, such as circadian rhythm, that can alter synaptic transmission and intrinsic electrical properties of neurons. Building upon previous behavioral work on perceptual plasticity of human locomotion [56], physiological evidence for such fine- tuning was first observed in hippocampal place cells [35], where persistent conflict between self- motion and external visual cues recalibrated the integrator gain. In the present paper, we give the first theoretical examination of this phenomenon in continuous bump attractor networks (CBANs), a prevailing model for representations of continuous variables. + +Our examination unveiled the algorithmic and mechanistic requirements for gain recalibration in a ring attractor network, a representative CBAN model used for circular continuous variables. In CBAN models, when the integration gain is inaccurate, an internal representation of a continuous variable slightly drifts relative to its actual value, resulting in encoding errors. Absolute 'ground- truth' information, such as feedback from visual landmarks for location coding, correct these errors through internal dynamics of the network, without the need for an explicit rate- based representation of the error. In contrast to this automatic error correction through network dynamics, we found that fine- tuning the integration gain based on errors requires an explicit error signal, i.e., firing rate of some neurons to encode the error in the CBAN's representation of a continuous variable relative to its actual value. Building upon this insight, we also proposed a ring attractor network model that shows how a CBAN can recalibrate its integration gain through biologically known plasticity mechanisms. Although the ring attractor is specialized for integration of 1D circular continuous variables (e.g., an animal's location on a circular track), our findings can be readily extended to higher dimensions and other types of continuous variables. Overall, our findings suggest that a rate code for the error in the internal representation of a continuous variable is a core component of the bump attractor- type neural integrators, and that such a rate code plays an essential role in their gain recalibration. + +### 5.1 The Bump Attractor Network as an Adaptive Kalman Filter + +To identify algorithmic requirements for recalibration of the integration gain, we simplified dynamics of the ring attractor through a dimensionality reduction protocol described in [41]. Similar approaches have been successfully applied in recent years to explore the neural dynamics capturing how high- dimensional neural data evolves within low- dimensional topological structures [6, 14, 57]. In our specific case, the dimensionality reduction led to a simplified 1D model of the ring attractor, capturing the dynamics of its representation as a function of external inputs that provide differential (e.g., animal's velocity) and absolute (e.g., positional feedback from visual landmarks) information [41, 42, 58]. + +Previous research showed that, when two external cues are presented as inputs, the ring attractor network fuses them optimally in the Bayesian sense [20, 59- 61]. Furthermore, if one of the cues provides only differential information like the animal's velocity that is integrated over time to compute the overall change in the continuous variable, the ring attractor network performs the Bayesian fusion recursively for each step of the integration [41]. This recursive computation is known as Kalman filtering and has been proposed as a model of cue integration in the entorhinal cortex of the mammalian brain [21]. Consistent + +<--- Page Split ---> + +with this prior work, we found that the ring attractor network operates as a Kalman filter updating the representation by a combination of integrated relative information (the internal model component of the Kalman filter) and the instantaneous feedback from absolute information (the measurement model component). + +In engineered systems, the accuracy of a Kalman filter relies on precise knowledge of its internal model parameters; to address this issue, adaptive Kalman filters that fine- tune their own parameters have been proposed [62, 63]. Like adaptive Kalman filters, we showed that a ring attractor network can fine- tune itself through gain recalibration. We also elucidated the algorithmic requirement for this recalibration, showing that the integration gain must change in the same direction as the product of the animal's velocity and the error in the attractor's representation relative to the absolute 'ground- truth' information. Interestingly, this requirement resembles characteristics of a classical algorithm known as the MIT rule in adaptive control systems and Kalman filtering [64]. Thus, a ring attractor with gain recalibration effectively operates as an adaptive Kalman filter, updating its representation accurately through a finely tuned integration gain. + +### 5.2 Necessity of a Rate-Based Explicit Error Signal in the Bump Attractor Networks + +Satisfying the algorithmic requirement for gain recalibration is subject to certain mechanistic constraints, which we discovered analyzing the network dynamics. In essence, gain recalibration requires that some neurons vary their firing rates monotonically with the instantaneous value or the time- integral of the error in the representation of the continuous variable relative to its true value. Without such error- rate codes, the network does not have a teaching signal that can guide tuning of its gain for recalibration. This shows that, for CBAN networks, learning from representational errors to recalibrate the integration gain is a very different neural process than correcting the errors. In the case of error correction, input signals from absolute 'ground- truth' information sources, such as visual landmarks for a CBAN encoding location, are sufficient to trigger an error correction response from network dynamics. In contrast, recalibrating the integration gain based on representational error additionally requires a neural signal that explicitly encodes this error via a rate code. + +This hypothesized error signal resembles reward and sensory prediction error signals within the mammalian brain. Dopamine neurons in the midbrain of mammals encode error in the internal predictions of reward via monotonic changes in their firing rates [65, 66]; they exhibit elevated activity with more reward than predicted, remain at baseline activity for fully predicted rewards, and exhibit depressed activity with less reward than predicted. Climbing fiber inputs to Purkinje cells of the mammalian cerebellum encode errors in the predicted sensory consequences of motor commands relative to the actual sensory feedback via changes in the rate and duration of complex spikes [67, 68]. Both of these prediction error codes are thought to act as a teaching signal that fine- tunes the internal models, mapping the stimulus to reward prediction in the dopamine system and the motor commands to sensory prediction in the cerebellum, through plasticity, just like how error coding can act as a teaching signal that recalibrates the integration gain of a CBAN. + +To instantiate this idea computationally, we presented a modified ring attractor model that can recalibrate its integrator gain based on a rate code of the instantaneous error via Hebbian plasticity. Relevant to this model, a previous study hypothesized a currently unknown plasticity rule as a mechanism for gain recalibration within the ring attractor network [32]; the hypothesized plasticity rule modifies synaptic weights of each neuron according to an implicit positional error signal, computed locally within each neu + +<--- Page Split ---> + +ron through comparison of the synaptic inputs at its basal and apical dendrites receiving, respectively, the absolute 'ground- truth' information and the network's current representation information. While it is unclear whether such a plasticity rule exists in the brain, our model demonstrates a biologically plausible alternative: Hebbian plasticity, combined with an explicit rate- based representation of the error, is sufficient to achieve gain recalibration. It remains as a future work to experimentally test if such an error signal exists in the brain circuits that are thought to employ CBANs for encoding continuous variables. + +### 5.3 Implications of a Distributed, Inhomogenous Integration Gain + +Prior CBAN models implicitly assumed the integration gain to be a single, global parameter of the network, independent of the value of the encoded continuous variable [11, 31]. Although the idea of different, hard- wired integration gains has previously been suggested in the context of location coding to explain the changes in the spatial scale of place coding along the dorsal- ventral axis of the hippocampus [27], it is assumed that the integration gains are constant at all locations within an environment. In contrast, we showed that the integration gain within a single CBAN, specifically the ring- attractor network, is a distributed parameter instantiated in the network's array of synaptic weights, implying that the network can adopt unique integration gains for different values of the encoded continuous variable. This would be, for example, a CBAN changing its integration gain depending on the location of the animal in the context of spatial navigation or depending on the amount of accumulated evidence in the context of decision- making. In the latter case, the CBAN may look like it unevenly weights early or late evidence, which is a well- established phenomenon known as primacy and recency effects in the decision- making literature [69- 71]. Compared to a network with a single, global integration gain, a network with a distributed, possibly inhomogeneous, gain can adjust its representation metric for the continuous variable locally, hence providing greater flexibility in representing different values of the continuous variable with uneven resolutions, depending on, for instance, their behavioral significance [72]. + +How might gain inhomogeneity arise? Theoretically, it can be a product of the recalibration process if the teaching signal (e.g., feedback from absolute 'ground- truth' information sources) is available unevenly across the values of the continuous variable. In the context of location coding, for example, such differences may occur between when the animal is nearby the boundaries of the environment, which is a relatively rich area in terms of external ground- truth information, versus when it is near the center of the arena. We speculate that such spatially distributed recalibration of the integration gain may offer a mechanistic explanation to some of the experimental findings about local distortions and deformations in the activity patterns of entorhinal grid cells and hippocampal place cells, encoding the animal's location, during environmental manipulations [73- 80]. According to this speculation, grid patterns might get distorted nearby environmental boundaries through changes in the local integration gain of the network; through the same mechanism, place cells might represent locations nearby landmarks and boundaries with a greater spatial resolution (also known as overrepresentation). Overall, inhomogenous integration gain of CBANs offer a potential explanation to an array of seemingly complex responses in spatial navigation as well as other brain functions. + +<--- Page Split ---> + +[1] R. Ben- Yishai, R. L. Bar- Or, and H. 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Jeffery, "Experience- dependent rescaling of entorhinal grids," Nature neuroscience, vol. 10, no. 6, pp. 682- 684, 2007. + +[76] C. Barry, L. L. Ginzberg, J. O'Keefe, and N. Burgess, "Grid cell firing patterns signal environmental novelty by expansion," Proceedings of the National Academy of Sciences, vol. 109, no. 43, pp. 17687- 17692, 2012. + +[77] A. T. Keinath, R. A. Epstein, and V. Balasubramanian, "Environmental deformations dynamically shift the grid cell spatial metric," Elife, vol. 7, p. e38169, 2018. + +[78] P. A. Hetherington and M. L. Shapiro, "Hippocampal place fields are altered by the removal of single visual cues in a distance- dependent manner.," Behavioral neuroscience, vol. 111, no. 1, p. 20, 1997. + +[79] H. Eichenbaum, S. I. Wiener, M. Shapiro, and N. Cohen, "The organization of spatial coding in the hippocampus: a study of neural ensemble activity," Journal of Neuroscience, vol. 9, no. 8, pp. 2764- 2775, 1989. + +[80] M. Sato, K. Mizuta, T. Islam, M. Kawano, Y. Sekine, T. Takekawa, D. Gomez- Dominguez, A. Schmidt, F. Wolf, K. Kim, et al., "Distinct mechanisms of over- representation of landmarks and rewards in the hippocampus," Cell reports, vol. 32, no. 1, 2020. + +[81] M. Noorman, B. K. Hulse, V. Jayaraman, S. Romani, and A. M. Hermundstad, "Accurate angular integration with only a handful of neurons," bioRxiv, pp. 2022- 05, 2022. + +[82] P. Dayan and L. F. Abbott, Theoretical neuroscience: computational and mathematical modeling of neural systems. MIT press, 2005. + +[83] H. Khalil, Nonlinear Systems. Pearson Education, Prentice Hall, 2002. + +[84] J. Guckenheimer and P. Holmes, Nonlinear oscillations, dynamical systems, and bifurcations of vector fields, vol. 42. Springer Science & Business Media, 2013. + +<--- Page Split ---> + +## Acknowledgments + +This work was supported by National Institutes of Health grants R01 NS102537 (N.J.C., J.J.K.), U01 NS131438 (N.J.C.), and R01 MH118926 (J.J.K., N.J.C.). We thank Ravikrishnan Jayakumar, Kathryn Hedrick, Bharath Krishnan, Manu Madhav, Francesco Savelli, and Kechen Zhang. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- appendix.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__058b96ab087d835fa63b04a30fdaedd9181f846e45e28f9d3a407df85e5d2a78/preprint__058b96ab087d835fa63b04a30fdaedd9181f846e45e28f9d3a407df85e5d2a78_det.mmd b/preprint/preprint__058b96ab087d835fa63b04a30fdaedd9181f846e45e28f9d3a407df85e5d2a78/preprint__058b96ab087d835fa63b04a30fdaedd9181f846e45e28f9d3a407df85e5d2a78_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..895567b925fe333f3169308f848bda404bc52809 --- /dev/null +++ b/preprint/preprint__058b96ab087d835fa63b04a30fdaedd9181f846e45e28f9d3a407df85e5d2a78/preprint__058b96ab087d835fa63b04a30fdaedd9181f846e45e28f9d3a407df85e5d2a78_det.mmd @@ -0,0 +1,849 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 840, 177]]<|/det|> +# Continuous Bump Attractor Networks Require Explicit Error Coding for Gain Recalibration + +<|ref|>text<|/ref|><|det|>[[44, 196, 234, 242]]<|/det|> +Gorkem Secer gsecer@gmail.com + +<|ref|>text<|/ref|><|det|>[[44, 268, 640, 383]]<|/det|> +Johns Hopkins University https://orcid.org/0000- 0001- 6612- 7782 James Knierim Johns Hopkins University https://orcid.org/0000- 0002- 1796- 2930 Noah Cowan Johns Hopkins University https://orcid.org/0000- 0003- 2502- 3770 + +<|ref|>sub_title<|/ref|><|det|>[[44, 422, 104, 440]]<|/det|> +## Article + +<|ref|>title<|/ref|><|det|>[[44, 460, 136, 479]]<|/det|> +# Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 498, 300, 517]]<|/det|> +Posted Date: April 15th, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 536, 473, 556]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4209280/v1 + +<|ref|>text<|/ref|><|det|>[[42, 573, 916, 616]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 634, 535, 654]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 690, 944, 733]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on October 17th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 63817- 0. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[148, 93, 848, 119]]<|/det|> +# Continuous Bump Attractor Networks Require Explicit Error + +<|ref|>title<|/ref|><|det|>[[325, 135, 671, 161]]<|/det|> +# Coding for Gain Recalibration + +<|ref|>text<|/ref|><|det|>[[225, 184, 770, 205]]<|/det|> +Gorkem Secer \(^{1,2}\) , James J. Knierim \(^{2,3,4,*}\) , and Noah J. Cowan \(^{1,5,*}\) + +<|ref|>text<|/ref|><|det|>[[201, 225, 840, 270]]<|/det|> +\(^{1}\) Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD 21218, USA. + +<|ref|>text<|/ref|><|det|>[[120, 278, 870, 323]]<|/det|> +\(^{2}\) Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD 21218, USA. + +<|ref|>text<|/ref|><|det|>[[130, 330, 866, 376]]<|/det|> +\(^{3}\) Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA. + +<|ref|>text<|/ref|><|det|>[[120, 383, 866, 428]]<|/det|> +\(^{4}\) Kavli Neuroscience Discovery Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA + +<|ref|>text<|/ref|><|det|>[[120, 435, 877, 480]]<|/det|> +\(^{5}\) Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA. + +<|ref|>text<|/ref|><|det|>[[314, 488, 682, 508]]<|/det|> +\*These authors jointly supervised the work. + +<|ref|>text<|/ref|><|det|>[[444, 538, 555, 556]]<|/det|> +April 2, 2024 + +<|ref|>sub_title<|/ref|><|det|>[[465, 591, 532, 606]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[152, 618, 845, 948]]<|/det|> +Representations of continuous variables are crucial to create internal models of the external world. A prevailing model of how the brain maintains these representations is given by continuous bump attractor networks (CBANs) in a broad range of brain functions across different areas, such as spatial navigation in hippocampal/entorhinal circuits and working memory in prefrontal cortex. Through recurrent connections, a CBAN maintains a persistent activity bump, whose peak location can vary along a neural space, corresponding to different values of a continuous variable. To track the value of a continuous variable changing over time, a CBAN updates the location of its activity bump based on inputs that encode the changes in the continuous variable (e.g., movement velocity in the case of spatial navigation)—a process akin to mathematical integration. This integration process is not perfect and accumulates error over time. For error correction, CBANs can use additional inputs providing ground- truth information about the continuous variable's correct value (e.g., visual landmarks for spatial navigation). These inputs enable the network dynamics to automatically correct any representation error. Recent experimental work on hippocampal place cells has shown that, beyond correcting errors, ground- truth inputs also fine- tune the gain of the integration process, a crucial factor that links the change in the continuous variable to the updating of the activity bump's location. However, existing CBAN models lack this plasticity, offering no insights into the neural mechanisms and representations involved in the recalibration of the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[152, 36, 845, 219]]<|/det|> +integration gain. In this paper, we explore this gap by using a ring attractor network, a specific type of CBAN, to model the experimental conditions that demonstrated gain recalibration in hippocampal place cells. Our analysis reveals the necessary conditions for neural mechanisms behind gain recalibration within a CBAN. Unlike error correction, which occurs through network dynamics based on ground- truth inputs, gain recalibration requires an additional neural signal that explicitly encodes the error in the network's representation via a rate code. Finally, we propose a modified ring attractor network as an example CBAN model that verifies our theoretical findings. Combining an error- rate code with Hebbian synaptic plasticity, this model achieves recalibration of integration gain in a CBAN, ensuring accurate representation for continuous variables. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[156, 36, 302, 52]]<|/det|> +## 1 Introduction + +<|ref|>text<|/ref|><|det|>[[155, 70, 844, 191]]<|/det|> +The brain's ability to represent and process continuous variables, such as location, time, and sensory information, is fundamental to our understanding and interaction with the external world. A compelling theoretical framework for how the brain constructs these representations is provided by continuous bump attractor networks (CBANs) in a diverse range of brain functions, such as orientation tuning in visual cortex [1], working memory [2,3], evidence accumulation and decision- making [4- 7], and spatial navigation [8- 11]. + +<|ref|>text<|/ref|><|det|>[[154, 195, 844, 480]]<|/det|> +The CBAN is a class of recurrent neural network, which maintains persistent patterns of population activity through interactions among its neurons. This persistent activity typically forms the shape of a bell curve like a 'bump', when visualized on an appropriate topological arrangement of neurons (known as a low- dimensional manifold), such as a plane, circle, or torus [12]. Although the shape of the activity bump is constrained by network dynamics, its center location can vary along this low- dimensional manifold, corresponding to different values of the encoded continuous variable. Neural activity consistent with these key properties of CBANs, namely, the activity bump and the low- dimensional manifold, have been observed in recordings from various regions of the mammalian brain that encode continuous variables [13- 15]. More conclusive and direct evidence for CBANs has been found in the central complex of fly brain, where a biological CBAN encoding the fly's heading angle, a continuous variable, has been identified based on the connectome and a combination of techniques, such as calcium imaging and optogenetics [16- 18]. While these experimental findings support the idea of brain circuits employing CBANs to represent continuous variables, the neural mechanisms that enable CBANs to accurately update their representations in response to changes in continuous variables remain incompletely understood. + +<|ref|>text<|/ref|><|det|>[[154, 485, 844, 772]]<|/det|> +CBANs update their representations of a continuous variable based on one or both of two distinct types of inputs. The first type provides 'absolute' information, namely, the true value of a continuous variable, such as spatial location relative to visual landmarks or the item to be held in the working memory. When this absolute information is available, it provides localized input on the CBAN's low- dimensional manifold to a location that is associated with the true value to the continuous variable. In response to this localized input, internal dynamics of the CBAN creates a 'basin' on its low- dimensional manifold toward which the activity bump gravitates, resulting in nearly perfect encoding of the continuous variable [19- 21]. Strong experimental evidence for this phenomenon has been observed in fly brain where optogenetic excitation of the central complex, a seemingly biological CBAN, at a specific anatomical location mimicking an absolute information resulted in the CBAN's activity bump, normally changing its location to encode the fly's heading, being pulled to the excitation location [16]. As more indirect evidence, neural recordings from the hippocampus and entorhinal cortex, two regions modeled as CBANs in mammalian brain, showed that their representations for the animal's location are anchored to the visual landmarks even when the landmarks are rotated around an open arena [22- 26]. + +<|ref|>text<|/ref|><|det|>[[154, 777, 844, 938]]<|/det|> +In contrast to the first type of inputs providing absolute information to the CBAN, the second type provides 'differential' information, namely, the changes in the continuous variable. Sources of such inputs may be, for instance, self- generated movements providing velocity information to be integrated in the context of spatial navigation or sensory cues serving as pieces of evidence to be accumulated in the context of decision- making. In response to these inputs, the internal dynamics of the CBAN shifts the activity bump along the low- dimensional manifold—in a process akin to mathematical integration—such that the bump's location reflects the value of the continuous variable. However, compared to the absolute information, the encoding accuracy of this integration process depends critically on an additional factor, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[153, 35, 844, 238]]<|/det|> +namely, the integration gain of the network that relates the cumulative change in the continuous variable to the updating of the bump location in a proportional manner [27,28]. If this gain factor is miscalibrated, the result of the CBAN's integration begins drifting away from the true value of the continuous variable; that is, it accumulates error. In the presence of absolute information sources such as visual landmarks for spatial localization, the aforementioned 'basin' mechanism continuously corrects error, preventing it from accumulating. However, without such absolute information, error accumulation continues, which may cause, for example, a CBAN integrating evidence to reach a decision threshold too soon or too late or a CBAN integrating an animal's angular head velocity to over- or underestimate the correct head direction. Thus, a finely tuned integration gain is crucial for a CBAN to accurately encode a continuous variable based on inputs with only differential information. + +<|ref|>text<|/ref|><|det|>[[153, 242, 844, 529]]<|/det|> +Present CBAN models of path integration treat the integration gain as a constant that is perfectly set via carefully chosen, hard- wired model parameters (e.g., synaptic weights) [11,29- 31] (but see [32]). However, recent data from time cells and place cells of the rodent hippocampal formation, hypothesized to rely on CBANs [33], showed that the integration gain is actually a plastic variable whose value is adjusted based on the feedback from absolute information sources [34,35]. In the first study that demonstrated this phenomenon on place cells [35], the virtual visual landmarks, which provided the absolute information, were moved as a function of the animal's movement. This movement created persistent error between the encoded location based on path integration and the actual location relative to the landmarks. Prolonged exposure to this conflict led to recalibration of the system's integration gain, altering it in a direction and by an amount that reduced the positional encoding error. This recalibration was most evident after the landmarks were extinguished (i.e., when the absolute information to the putative CBAN was abolished); the space encoded by hippocampal cells during pure path integration either expanded or contracted, depending on the direction of the preceding landmark manipulation. Therefore, an open question is how CBANs adjust their integration gain based on error feedback from absolute information sources. + +<|ref|>text<|/ref|><|det|>[[153, 534, 844, 944]]<|/det|> +In the present paper, we aim to address this open question and generate testable physiological predictions about the neural mechanisms of gain recalibration in brain circuits that encode continuous variables. As a representative problem, we focus on hippocampal place coding and theoretically examine how visual landmarks, being the absolute information source, recalibrate the integration gain of a CBAN that encodes the animal's position on a circular track, as demonstrated experimentally in [35]. To this end, we first derive analytical expressions of the integration gain, along with a simple dynamical model of the activity bump's location in the ring attractor network, a specific type of CBAN for circular position encoding. In contrast to the previous work implicitly assuming the network's integration gain as a constant, global parameter independent of the bump location in the low- dimensional manifold, our analysis reveals that the integrator gain of a CBAN is a spatially distributed, possibly inhomogeneous, parameter. We then employed control theory techniques to dissect the necessary algorithmic conditions for accurate recalibration of this spatially distributed integration gain via feedback from the absolute information sources. Mapping these conditions from the algorithmic level to the mechanistic level uncovered a key mechanistic requirement for gain recalibration. We found that, unlike correction of encoding errors that happens automatically and implicitly through network dynamics when feedback from an absolute information source is available, the process of learning/recalibrating the integration gain through Hebbian plasticity requires an additional neural signal that explicitly encodes the error in the network's representation. In other words, all prior work demonstrating error correction in a CBAN does not require explicit error encoding, but our work shows that correcting the integration process itself (i.e., recalibration) requires an explicit representation of error. This error signal must be provided by changes in the + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[155, 32, 840, 168]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 177, 883, 345]]<|/det|> +
Figure 1: Ring attractor network model [10,31,36,37]. (A) Schematic representation of the model. The central ring forms the main body of the model based on its recurrent connections (labeled with ①). Its reciprocal offset connections with the rotation CW and CCW rings (labeled with ② and ③) creates a push-pull mechanism that modulates the intrinsically controlled neural activity of the central ring based on external inputs from the CW and CCW velocity neurons (labeled with ④). An additional external input is provided to the central ring from the visual ring (labeled with ⑤), corresponding to a set of sensory neurons that are tuned to visual landmarks. (B) Synaptic weight function \(W_{c c}:S^{1}\to \mathbb{R}\) that describes the recurrent connections within the central ring according to the well-known local excitation and global inhibition pattern. (C) Numerical demonstration of how recurrent connectivity within the central ring can autonomously maintain a persistent activity bump. Simulation of the central ring neurons was started with initial conditions that are assigned pseudo-randomly (light green line labeled with ①). Within "100 milliseconds, a bump of activity emerges (medium green line labeled with ①). Eventually, the firing rates converge to an equilibrium, forming a persistent bump of activity (dark green line labeled with ①). (D) Tuning curves of CCW and CW velocity neurons shown with blue-dashed and red-dashed lines, respectively.
+ +<|ref|>text<|/ref|><|det|>[[154, 370, 844, 469]]<|/det|> +firing rate of some neurons with one of two signals—the instantaneous error or the time- integral of the error—for recalibration of the integration gain. Finally, we propose a modified ring attractor network as an example CBAN model that instantiates our theoretical findings. Combining an error- rate code with Hebbian plasticity, this model achieves recalibration of integration gain in a CBAN, ensuring accurate representation for continuous variables. + +<|ref|>sub_title<|/ref|><|det|>[[155, 492, 536, 510]]<|/det|> +## 2 Model Setup: Ring Attractor Network + +<|ref|>text<|/ref|><|det|>[[154, 526, 844, 771]]<|/det|> +A continuous bump attractor network is a recurrently connected neural network in which neighboring neurons excite one another and inhibit distant neurons according to a connectivity pattern known as local excitation and global inhibition [1,38]. This connectivity gives rise to a persistent bump of activity as a stable, equilibrium state of the system. Invariance of the connectivity across the network leads to a continuum of such equilibrium states, called attractor states. Arrangement of neurons and the exact pattern of the recurrent connectivity determine the topology of this attractor. In the case of a ring attractor, neurons are arranged conceptually as a topological ring [11]. By sustaining an activity bump whose location can be shifted along the ring based on external inputs (i.e., relative and absolute information sources), a ring attractor network is well- suited to represent a variable on a closed curve (e.g., angular location of an animal on a circular track). Augmenting the arrangement of neurons to a two dimensional plane results in a plane attractor whose activity bump is well- suited to represent two variables, for example, the x and y coordinates of location in a room [37]. + +<|ref|>text<|/ref|><|det|>[[155, 776, 843, 852]]<|/det|> +The activities of place and grid cells in 2D environments have been traditionally modeled using a plane attractor [12,31,37]. However, in the present investigation of gain recalibration based on location encoding, originally demonstrated in place cells from rats running laps on a 1D circular track, we chose the ring attractor as the basis of our model because of its analytical tractability. + +<|ref|>text<|/ref|><|det|>[[155, 857, 843, 936]]<|/det|> +The ring attractor that we analyzed is a network model consisting of three groups of neurons ordered in a ring arrangement: a central ring, a clockwise (CW) rotation ring, and a counter- clockwise (CCW) rotation ring [8,11], as depicted in Fig. 1A. Neurons in this network receive synaptic input from other neurons in the network via intrinsic connections and from upstream neurons carrying velocity information + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[155, 35, 842, 72]]<|/det|> +(i.e., a 'differential' type of input) and the positional feedback from visual landmarks (i.e., an 'absolute' type of inputs) via extrinsic connections. + +<|ref|>text<|/ref|><|det|>[[155, 76, 844, 154]]<|/det|> +We can model the dynamics of the ring attractor network using a set of equations that model the firing rate of a continuum of neurons in response to their synaptic inputs. If we parameterize a neuron based on its angle \(\psi \in S^{1}\) in the circular neural space, the model of the central ring neurons takes the form + +<|ref|>equation<|/ref|><|det|>[[300, 153, 840, 183]]<|/det|> +\[\tau_{c}\frac{\partial r_{c}(t,\psi)}{\partial t} = -r_{c}(t,\psi) + \sigma [W_{c - c}(\psi)\circledast r_{c}(t,\psi) + I_{\mathrm{ext}}(t,\psi)], \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[155, 188, 844, 370]]<|/det|> +where \(r_{c}(t,\psi)\) denotes the firing rate of the central ring neuron \(\psi\) at time \(t\) , \(\tau_{c}\) denotes the synaptic time constant of central ring neurons, \(\circledast\) denotes the circular convolution operation, \(\sigma\) denotes an activation function (chosen as rectified linear unit (RELU) in our current study), \(I_{\mathrm{ext}}(t,\psi)\) denotes external synaptic inputs to the central ring, and \(W_{\mathrm{c - c}}:S^{1}\to \mathbb{R}\) denotes a rotationally invariant synaptic weight function that describes the recurrent connections ( \(\textcircled{1}\) in Fig. 1A) according to the pattern known as local excitation and global inhibition (Fig. 1B). Being a hallmark of the CBANs, this recurrent connectivity pattern leads to stabilization of a persistent "bump" of activity within the network [38- 40] (Fig. 1C). While the shape of the emergent activity bump is determined by the shape of the recurrent connectivity pattern, the location of the bump can be controlled by external synaptic inputs to the central ring. + +<|ref|>text<|/ref|><|det|>[[154, 374, 844, 744]]<|/det|> +These external inputs are provided by neurons of the rotation rings and the visual ring. The rotation rings conjoin the self- movement velocity information with the positional information by receiving inputs from two different afferent neurons: By changing their firing rates in different directions (Fig. 1D), CW and CCW 'velocity' neurons signal the animal's velocity information to their respective rotation rings through synaptic weight functions \(W_{\mathrm{v - cw}}\) , \(W_{\mathrm{v - ccw}}:S^{1}\to \mathbb{R}\) ( \(\textcircled{4}\) in Fig. 1A), whereas the central ring provides the positional information, represented by the bump location, to both rotation rings through synaptic weight functions \(W_{\mathrm{c - cw}}\) , \(W_{\mathrm{c - ccw}}:S^{1}\to \mathbb{R}\) ( \(\textcircled{2}\) in Fig. 1A). The resulting conjunctive codes of velocity and position within the rotation rings are then transmitted back to the central ring through offset synaptic weight functions \(W_{\mathrm{cw - c}}\) , \(W_{\mathrm{cw - cc}}:S^{1}\to \mathbb{R}\) ( \(\textcircled{3}\) in Fig. 1A), leading to a shift of the persistent activity bump within the central ring proportional to the animal's velocity, a process known as path integration (PI). In contrast to the rotation rings, the visual ring does not explicitly receive any inputs; instead, its neurons are presumed to autonomously fire at specific locations of the animal relative to landmarks, capturing the absolute positional information received from visual landmarks available at each position (modeling how egocentric visual processes can calculate position from landmarks is beyond the scope of this paper). Through synaptic weight function \(W_{\mathrm{vis - c}}:S^{1}\to \mathbb{R}\) ( \(\textcircled{5}\) , in Fig. 1A), this firing of the visual rings provides to the central ring a bump- like synaptic input encoding the animal's "true" position relative to landmarks. This bump- like input pulls the activity bump of the central ring, hence correcting positional errors in the ring attractor's representation. + +<|ref|>text<|/ref|><|det|>[[155, 748, 844, 930]]<|/det|> +Before concluding this section, we clarify an important distinction between traditional models of the ring attractor network and our model in the present paper. In traditional models, the synaptic weights \(W_{\mathrm{v - cw}}\) , \(W_{\mathrm{v - ccw}}\) and \(W_{\mathrm{cw - c}}\) , \(W_{\mathrm{cw - cc}}\) are treated as constants, each constrained to take a uniform value across the entire neural space. However, in our model, we intentionally relax this constraint. Instead, we treat these weights as functions that can vary throughout the neural space, possibly taking nonuniform values. While this approach may be less mathematically convenient, it becomes necessary when we later explore the possibility of gain recalibration through plasticity of spatially distributed synapses in the ring attractor network. As will be evident in the subsequent sections, our approach has broader implications, especially for the spatial metric of the ring attractor network. The complete mathematical model of the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[155, 35, 842, 72]]<|/det|> +ring attractor, including the functional synaptic weights and the dynamics of the rotation and visual rings, is given in Appendix 6.1. + +<|ref|>sub_title<|/ref|><|det|>[[155, 94, 789, 112]]<|/det|> +## 3 Algorithmic and Mechanistic Requirements for Gain Recalibration + +<|ref|>text<|/ref|><|det|>[[155, 129, 844, 291]]<|/det|> +In this section, we analyze the complex dynamics of our unconstrained ring attractor model to garner insight into how the network's integration gain, hereafter referred to as the PI gain, can be recalibrated by visual landmarks. Our analysis begins with reduction of complex ring- attractor dynamics into a simple, one- dimensional differential equation model, including an analytical expression of the PI gain, in Section 3.1. Leveraging the analytical tractability of this simple model, we then identify algorithmic conditions for gain recalibration in Section 3.2. Finally, we use the analytical expression of the PI gain to map the algorithmic conditions within the simple model to mechanistic prerequisites for gain recalibration within the high- dimensional, complex model of the ring attractor network, in Section 3.3. + +<|ref|>sub_title<|/ref|><|det|>[[155, 310, 803, 327]]<|/det|> +### 3.1 Dimensionality reduction reveals computational principles of the network + +<|ref|>text<|/ref|><|det|>[[155, 339, 844, 522]]<|/det|> +To derive a simple model for how the location of the attractor's activity bump is controlled by external velocity and visual inputs, we follow the dimensionality reduction protocol described in [41]. Briefly, the protocol exploits the fact that ring- attractor dynamics constrain the population activity to form a bump whose overall shape stays invariant, but its center location can vary across the central ring. Although we do not know the exact solution to the network dynamics that can describe this activity pattern, we can 'guess' a solution form that describes its general properties without relying on a specific function. This guess, termed an ansatz solution, makes the analysis mathematically tractable by reducing the complex network dynamics to a one- dimensional differential equation that tracks the temporal change in the location of the activity bump as a function of external inputs. + +<|ref|>text<|/ref|><|det|>[[155, 526, 844, 687]]<|/det|> +Using this protocol, prior work derived the simple models for the traditional ring attractors, constraining the synaptic weights \(W_{\mathrm{v - cw}}\) , \(W_{\mathrm{v - ccw}}\) and \(W_{\mathrm{cw - c}}\) , \(W_{\mathrm{cw - c}}\) to be constant [41,42]. Here, we extend this approach to our unconstrained ring attractor. We develop the differential equation models progressively, starting from the simplest case that the animal is stationary in the absence of landmarks. Next, we add movement inputs and show that the network employs a spatially distributed PI gain. Finally, we extend the model to include landmark- based correction, and show that the network combines the spatially distributed integration with landmarks in a computation that resembles a Kalman filter [43]. The resulting model forms the basis for our search for the algorithmic conditions of the gain recalibration. + +<|ref|>title<|/ref|><|det|>[[155, 708, 520, 724]]<|/det|> +#### 3.1.1 Ansatz solution to network dynamics + +<|ref|>text<|/ref|><|det|>[[155, 737, 844, 899]]<|/det|> +When the central ring's recurrent weight function \(W_{\mathrm{c - c}}\) is symmetric with local excitation and global inhibition, firing rate of its neurons converges to a symmetric, persistent activity bump, taking nonzero values in a limited range and featuring a single peak corresponding to the internal representation of the animal's position (Fig. 1C). Although specific functions such as thresholded Gaussians or sinusoids possess these characteristics and are often employed to explain neurophysiological data [11,19,44- 47], we do not restrict ourselves to such a specific structure in the present analysis of the ring attractor. Instead, we assume that the firing rates \(r_{\mathrm{c}}(t, \psi)\) of the central ring neurons can be represented by a general ansatz solution + +<|ref|>equation<|/ref|><|det|>[[421, 904, 840, 920]]<|/det|> +\[r_{\mathrm{c}}(t,\psi) = r_{\mathrm{c}}^{*}(\psi -\theta (t)), \quad (2)\] + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[117, 32, 884, 177]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 188, 883, 437]]<|/det|> +
Figure 2: Models of the ring attractor's position representation. (A) The position representation \(\theta\) , decoded from the peak location of an example ansatz solution \(r_{\mathrm{c}}^{*}\) to the central ring's firing rates. (B) Model of path integration. Top left: Circular track with uniformly spaced points. Top right: internal representation of this track with a spatially inhomogenous path-integration (PI) gain that ranges from 0.6 at \(\theta = \pi\) to 1.4 at \(\theta = 0\) . The position representation \(\theta\) is visualized here by the pale brown rat. As the rat moves through physical space at velocity \(v\) , the representation moves through neural space at \(k(\theta)v\) . Bottom: Firing rate of uniformly distributed cells in the neural space as a function of the animal's position in physical space. Left shows a 'traditional' network model, including a single, global PI gain of 1. Right shows our unconstrained network model with the spatially inhomogenous PI gain in the top row. (C) Stabilizing visual feedback. Top: The central ring's activity bump \(r_{\mathrm{c}}^{*}\) (green) and the bump-shaped synaptic input \(I_{\mathrm{vis}}\) to the central ring from the visual ring (pink). The activities of both rings are aligned with respect to the same neural space, so in this example, the visual ring bump is "ahead of" the central ring bump. Bottom: The function \(\beta\) captures the stabilizing feedback from visual landmarks. Note that \(\beta\) operates on the difference, \(\theta^{*} - \theta\) , so here, the \(x\) axis is a dummy variable. (D) Model of path integration with visual feedback. The pale brown rat symbolizes the internal representation of the animal's position as in (B), while the medium brown rat symbolizes the animal's actual location as represented by the visual drive. Left: the temporal change in the position representation is visualized by two arrows acting on the pale brown rat, one corresponding to updating by the path integration term \(k(\theta)v\) and the other corresponding to updating by the visual feedback term \(\beta (\theta^{*} - \theta)\) . Note that in this position, the PI gain is "low" and thus PI underestimates position relative to the landmarks. Right: Same as Left but PI overestimates position relative to the landmarks due to "high" PI gain in this position.
+ +<|ref|>text<|/ref|><|det|>[[114, 462, 844, 562]]<|/det|> +where \(r_{\mathrm{c}}^{*}(\psi - \theta (t))\) is a function that described the aforementioned persistent activity with a single peak at \(\psi = \theta (t)\) . This moderate generality allows our analysis to be valid for a broad range of ring attractor models with various particular ansatzes (including, but not limited to, the commonly used ones mentioned above). See Appendix 6.2.1 for a formal description of the properties of the ansatz function \(r_{\mathrm{c}}^{*}\) . + +<|ref|>text<|/ref|><|det|>[[115, 566, 842, 603]]<|/det|> +The persistent activity in the central ring also spreads to the CW and CCW rotation rings through synaptic connections, resulting in the following ansatz solutions to their firing rates \(r_{\mathrm{cw}}\) , \(r_{\mathrm{ccw}}\) : + +<|ref|>equation<|/ref|><|det|>[[185, 617, 840, 666]]<|/det|> +\[\begin{array}{r l} & {r_{\mathrm{cw}}(t,\psi)\triangleq r_{\mathrm{cw}}^{*}(\psi ,\theta (t),v(t)) = \sigma [W_{\mathrm{c - cw}}(\psi)r_{\mathrm{c}}^{*}(\psi -\theta (t)) + W_{\mathrm{v - cw}}(\psi)(u_{\mathrm{cw}}^{0} - \alpha_{\mathrm{cw}}v(t)) - \bar{I} ]}\\ & {r_{\mathrm{ccw}}(t,\psi)\triangleq r_{\mathrm{ccw}}^{*}(\psi ,\theta (t),v(t)) = \sigma [W_{\mathrm{c - ccw}}(\psi)r_{\mathrm{c}}^{*}(\psi -\theta (t)) + W_{\mathrm{v - ccw}}(\psi)(u_{\mathrm{ccw}}^{0} + \alpha_{\mathrm{ccw}}v(t)) - \bar{I} ],} \end{array} \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[154, 679, 842, 799]]<|/det|> +where \(r_{\mathrm{cw}}^{*},r_{\mathrm{ccw}}^{*}\) denote the assumed form of the ansatz solutions, \(u_{\mathrm{cw}}^{0}\) , \(u_{\mathrm{ccw}}^{0}\) denote the baseline firing rates of the CW, CCW velocity neurons (during the animal's immobility), \(\alpha_{\mathrm{cw}}\) , \(\alpha_{\mathrm{ccw}}\) denote the absolute value of the slopes of the velocity neurons' tuning curves (i.e., the absolute change in their firing rates per unit velocity of the animal), and \(\bar{I}\) denotes global inhibition. With a properly set inhibition \(\bar{I}\) , the solutions \(r_{\mathrm{cw}}^{*},r_{\mathrm{ccw}}^{*}\) take the shape of a bump, similar to the ansatz \(r_{\mathrm{c}}^{*}\) for the central ring (see Appendix 6.2 for further details). + +<|ref|>text<|/ref|><|det|>[[154, 804, 844, 945]]<|/det|> +Collectively, Eqs. (2) and (3) constitute a solution to the entire ring attractor network. That is, for a given velocity \(v\) of the animal, the firing rates of all neurons in a given network can be computed using the ansatz \(r_{\mathrm{c}}^{*}\) that describes the shape of the persistent activity bump within the central ring and \(\theta\) that denotes the location of the bump. While the exact form of \(r_{\mathrm{c}}^{*}\) is determined by the profile of the recurrent weight function \(W_{\mathrm{c - c}}\) , the bump location \(\theta\) is controlled by external inputs \(I_{\mathrm{ext}}\) to the central ring. Therefore, assuming that the ansatz \(r_{\mathrm{c}}^{*}\) persists at all times like the previous work [41, 42], we can reduce the high dimensional ring- attractor dynamics to a one dimensional differential equation that + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[154, 35, 844, 114]]<|/det|> +models the position representation \(\theta\) as a function of the external inputs. As derived in Appendix 6.2, if the central ring receives balanced (i.e., symmetric) inputs from the rotation rings during the animal's immobility in the absence of landmarks (a classical assumption in CBAN models [3, 11, 31, 48, 49]), the position representation \(\theta\) remains invariant. This invariance can be simply modeled as + +<|ref|>equation<|/ref|><|det|>[[468, 127, 840, 155]]<|/det|> +\[\frac{d}{dt}\theta = 0. \quad (4)\] + +<|ref|>sub_title<|/ref|><|det|>[[155, 173, 805, 191]]<|/det|> +## 3.1.2 Reduced-order path integration model reveals spatially distributed gain + +<|ref|>text<|/ref|><|det|>[[154, 202, 844, 367]]<|/det|> +In the present section, we examine how the position representation \(\theta\) , decoded from the bump location, varies as the animal moves on a circular track in the absence of visual landmarks. By triggering differential changes in the firing of CW, CCW velocity neurons (Fig. 1D), such movement modulates the persistent activity bumps of the CW, CCW rotation rings also differentially. Synaptic connections from the rotation rings to the central ring then translate these changes in the activities of rotation rings to a change in the synaptic inputs to the central ring. As a result, the central ring no longer receives balanced excitation about its activity bump during the animal's movement. With its magnitude proportional to the animal's speed, this movement- based imbalance shifts the activity bump across the central ring, instantiating PI. + +<|ref|>text<|/ref|><|det|>[[155, 369, 843, 427]]<|/det|> +To obtain a model for the temporal dynamics of the position representation \(\theta\) during this process, we apply the dimensionality reduction protocol described in [41]. Under mild assumptions detailed in Appendix 6.2.4, this reduction protocol leads to an ordinary differential equation + +<|ref|>equation<|/ref|><|det|>[[457, 440, 840, 468]]<|/det|> +\[\frac{d\theta}{dt} = k(\theta)v, \quad (5)\] + +<|ref|>text<|/ref|><|det|>[[154, 483, 843, 541]]<|/det|> +showing a linear relationship between the temporal change in \(\theta\) and the animal's velocity \(v\) via a factor \(k(\theta)\) . This factor quantifies the ring attractor network's PI gain, and its analytical expression takes the form + +<|ref|>equation<|/ref|><|det|>[[252, 539, 840, 581]]<|/det|> +\[k(\theta) = \frac{-b}{\tau_{c}\| \frac{\partial r_{c}^{*}}{\partial\psi}\|^{2}}\int_{0}^{2\pi}\frac{\partial^{2}r_{c}^{*}(\psi - \theta)}{\partial\psi^{2}}\sum_{i\in \{\mathrm{cw},\mathrm{ccw}\}}\alpha_{i}W_{i - c}(\psi)W_{\mathrm{v - }i}(\psi)\mathrm{sign}[r_{i}^{*}(\psi ,\theta ,0)]d\psi , \quad (6)\] + +<|ref|>text<|/ref|><|det|>[[153, 585, 844, 787]]<|/det|> +where \(i\) denotes the index of the summation, representing either the CW or CCW rotation ring, \(\alpha\) denotes the absolute value of the slope of the velocity neurons' tuning curves, and \(b\) denotes the value of the offset in the connections between rotation rings and the central ring. As shown by this equation, the PI gain \(k(\theta)\) is a parameter determined by the network's functional properties (i.e., profiles of the activity bumps and the tuning slope of the velocity neurons) and structural properties (i.e., time constant of the neurons and synaptic weights of the velocity- to- rotation ring and rotation- to- central ring connections), thereby capturing the complex interaction between the network's internally generated persistent activity and the external inputs from the velocity neurons. A comprehensive examination of how these network properties relate to the PI gain is provided later in Section 3.3 when we explore the mechanisms of gain recalibration. + +<|ref|>text<|/ref|><|det|>[[154, 792, 844, 935]]<|/det|> +As mentioned previously, in our model, we do not adopt the assumption of the traditional models that constrains the synaptic weights of the velocity- to- rotation ring and rotation- to- central ring connections \((W_{\mathrm{v - cw}},W_{\mathrm{v - ccw}}\) and \(W_{\mathrm{cw - c}},W_{\mathrm{ccw - c}}\) , respectively) to be constants, each taking a uniform value in the entire neural space. Rather, we relax this constraint and treat these weights as functions that can vary along the neural space of the attractor network model, a heterogeneity likely to exist in biological networks. This functional treatment of the synaptic weights in our unconstrained model reveals an important characteristic of integration in CBANs: Unlike traditional treatments that implicitly assume a single, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[154, 36, 844, 134]]<|/det|> +spatially global integration gain, CBANs employ a distributed, possibly inhomogenous, gain factor that can vary as the position representation varies (Equation 6 and top row in Fig. 2B). This implies for the ring attractor network that, by employing an inhomogenous PI gain, the network can adjust its spatial resolution locally, which would result in 'overrepresentation' or 'underrepresentation' of certain locations (bottom row in Fig. 2B) as is seen under various conditions of hippocampal place cell recordings [50,51]. + +<|ref|>title<|/ref|><|det|>[[155, 154, 765, 171]]<|/det|> +#### 3.1.3 Landmark correction to path integration resembles a Kalman filter + +<|ref|>text<|/ref|><|det|>[[155, 183, 844, 283]]<|/det|> +Lastly, we examine how the position representation \(\theta\) varies during the most general case that the animal moves on the circular track in the presence of landmarks. To derive this one- dimensional model of \(\theta\) , we employ the same dimensionality reduction protocol [41] but this time taking into account the visual neurons that provide feedback from landmarks. The resulting model will be crucial later when searching for the algorithmic conditions of the gain recalibration. + +<|ref|>text<|/ref|><|det|>[[155, 288, 844, 406]]<|/det|> +It is known that feedback from landmarks anchors the internal representation of position in a stable manner [22- 25]. In classical ring attractor models, this stabilizing feedback is achieved with an allocentrically anchored, bump- like synaptic current applied onto the central ring from the visual ring, encoding the animal's location relative to the landmarks [52,53]. We adopt the same approach in our unconstrained ring attractor model and incorporate the following synaptic current from the visual ring onto the central ring: + +<|ref|>equation<|/ref|><|det|>[[416, 411, 840, 428]]<|/det|> +\[I_{vis}(t,\psi) = \rho_{vis}(\psi -\theta^{\star}), \quad (7)\] + +<|ref|>text<|/ref|><|det|>[[155, 439, 844, 519]]<|/det|> +where \(\rho_{\mathrm{vis}}:S^{1}\to \mathbb{R}\) denotes a function describing the bump shape of this current, and \(\theta^{\star}\) denotes the location of the peak current, corresponding to the animal's current position relative to landmarks. Note that, throughout the paper, we use the superscript \(\star\) to distinguish a true value of a quantity (measured relative to the external world) from its internal value (e.g., \(\theta^{\star}\) vs \(\theta\) ). + +<|ref|>text<|/ref|><|det|>[[155, 524, 843, 561]]<|/det|> +With Equation (7) in mind, applying the dimensionality reduction protocol [41] leads to a differential equation model for how the position representation \(\theta\) is controlled by the PI and the landmarks as follows: + +<|ref|>equation<|/ref|><|det|>[[415, 575, 840, 601]]<|/det|> +\[\frac{d\theta}{dt} = \beta (\theta^{\star} - \theta) + k(\theta)v. \quad (8)\] + +<|ref|>text<|/ref|><|det|>[[154, 617, 844, 884]]<|/det|> +Here, \(\beta :S^{1}\to \mathbb{R}^{1}\) is a function that, locally, takes the same sign as its argument (Fig. 2C). This sign property, a form of negative feedback, is crucial for stable control of the position representation \(\theta\) ; for example, in the absence of movement, the sign property of \(\beta\) ensures \(\theta \to \theta^{\star}\) . In the more general case, when the animal's velocity is nonzero, the position representation \(\theta\) is changed by a combination of the (stabilizing) visual feedback \(\beta :S^{1}\to \mathbb{R}\) and the feedforward PI- related term \(k(\theta)v\) , as given in Equation (8). While the PI- related term, \(k(\theta)v\) , shifts the position representation proportionally with the animal's velocity, the function \(\beta\) acts in an additive manner to bring \(\theta\) toward \(\theta^{\star}\) , the animal's true position relative to visual landmarks. This fusion between the feedforward PI- related term and feedback correction from landmarks is illustrated in Fig. 2D. A properly balanced combination would stabilize \(\theta\) around \(\theta^{\star}\) , with only minor deviations due to PI error. As a result, the ring attractor effectively "estimates" the position of the animal, a computation that bares striking similarity to a Kalman filter in engineering. Here, the state being estimated by the network is the animal's true position, \(\theta^{\star}\) , and the estimate is the bump location, \(\theta\) . + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[155, 65, 842, 123]]<|/det|> +Experiments showed that the PI gain is a plastic variable that can be recalibrated accurately by visual landmarks [35]. In this section, we seek necessary conditions for this recalibration using Equation (8), the simple model presented in the previous section. + +<|ref|>text<|/ref|><|det|>[[154, 128, 844, 351]]<|/det|> +We begin by recalling the experimental conditions that brought about the recalibration of the PI gain [35]: An animal moved on a circular track while an array of visual landmarks was rotated around the track as a function of the animal's velocity and an experimentally controlled, visual gain factor, \(k^{\star}\) . When \(k^{\star} < 1\) , the landmarks moved in the same direction as the animal, decreasing the animal's speed relative to the landmarks; when \(k^{\star} > 1\) , the landmarks moved in the opposite direction as the animal, increasing the animal's speed relative to the landmarks; when \(k^{\star} = 1\) (veridical condition), the landmarks were stationary. Neural recordings showed that persistent exposure to these visual conditions recalibrated the animal's PI gain such that a tight correlation was observed between the average value of the PI gain measured over many laps after the landmarks were extinguished and the final value of the visual gain \(k^{\star}\) before the landmarks were extinguished. Here, we analyze this recalibration using our simplified ring attractor model. + +<|ref|>text<|/ref|><|det|>[[155, 356, 844, 453]]<|/det|> +The experimental conditions leading to the gain recalibration can be simulated in a ring attractor model with a visual synaptic drive \(I_{\mathrm{vis}}\) revolving around the central ring at a rate equal to the animal's velocity \(v\) times the visual gain \(k^{\star}\) . Extending (8), the simple model for the position representation \(\theta\) , with an additional equation for modeling the changes in the peak location \(\theta^{\star}\) of such a visual drive, we obtain + +<|ref|>equation<|/ref|><|det|>[[413, 450, 840, 504]]<|/det|> +\[\begin{array}{l}\frac{d\theta^{\star}}{dt} = k^{\star}v,\\ \displaystyle \frac{d\theta}{dt} = \beta \big(\theta^{\star} - \theta \big) + k(\theta)v, \end{array} \quad (9)\] + +<|ref|>text<|/ref|><|det|>[[154, 508, 844, 713]]<|/det|> +where the first equation models the relation between the visual gain \(k^{\star}\) , the animal's velocity \(v\) , and the resulting temporal change in the visual drive's bump location \(\theta^{\star}\) , and the second equation models the change in the attractor's bump location \(\theta\) based on the external velocity and visual inputs. Recall that, as we previously showed, the PI gain \(k\) of a ring attractor network is a spatially distributed parameter that may potentially take different values as the network's position representation \(\theta\) varies during the animal's movement in the environment. However, in recalibration experiments [35], average neural activity over many laps was used to estimate the average value of the PI gain, providing no information about whether the PI gain took different values across the environment as predicted by the model. Therefore, from a theoretical perspective, the experimental result that the PI gain was recalibrated to the visual gain [35] only suggests that its spatial average \(k_{0}\) converges to the visual gain \(k^{\star}\) (i.e., \(\lim_{t \to \infty} k_{0}(t) = k^{\star}\) ), where + +<|ref|>equation<|/ref|><|det|>[[435, 724, 840, 770]]<|/det|> +\[k_{0} \triangleq \frac{1}{2\pi} \int_{0}^{2\pi} k(\theta) d\theta . \quad (10)\] + +<|ref|>text<|/ref|><|det|>[[154, 782, 844, 904]]<|/det|> +This convergence of \(k_{0}\) toward \(k^{\star}\) necessitates \(k_{0}\) to be updated over time during recalibration. The firing rate of neurons in the network is a biologically plausible candidate for controlling these updates. Therefore, to garner mathematical insight into this process, we searched for a general equation that could represent the updating of \(k_{0}\) based on firing rates within the ring attractor, assuming an environment with a spatially homogenous feedback from visual landmarks. Under mild assumptions described in Appendix 6.3.1, this search led to a surprisingly simple equation + +<|ref|>equation<|/ref|><|det|>[[421, 918, 840, 942]]<|/det|> +\[\frac{dk_{0}}{dt} = g_{0}(k_{0}, \theta^{\star} - \theta , v), \quad (11)\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[154, 35, 844, 134]]<|/det|> +where \(g_{0}:\mathbb{R}\times S^{1}\times \mathbb{R}\to \mathbb{R}\) denotes an analytic function that instantiates the instantaneous change in \(k_{0}\) based on three variables: the current gain \(k_{0}\) , the animal's velocity \(v\) and the difference between the visual drive's position representation \(\theta^{\star}\) and the ring attractor's position representation \(\theta\) , without directly depending on either \(\theta\) or \(\theta^{\star}\) (since the visual feedback is assumed to be uniform across the environment). + +<|ref|>title<|/ref|><|det|>[[154, 155, 842, 190]]<|/det|> +#### 3.2.1 Necessity of matching the sign in gain change with the product of error and velocity + +<|ref|>text<|/ref|><|det|>[[154, 202, 844, 260]]<|/det|> +The update rule \(g_{0}\) could be any function fitting the form in Equation (11); however, some such functions may fail to result in gain recalibration, i.e., the PI gain's spatial average \(k_{0}\) would not converge to the visual gain \(k^{\star}\) . What are the necessary properties of the gain update rule \(g_{0}\) for convergence of \(k_{0}\) to \(k^{\star}\) ? + +<|ref|>text<|/ref|><|det|>[[154, 265, 844, 426]]<|/det|> +To seek these properties, we revisit Equation (9), the simple model of the central ring's and visual ring's position representations \(\theta\) and \(\theta^{\star}\) , now taking into account that the PI gain's spatial average \(k_{0}\) is time varying according to the gain update rule in Equation (11). Perfect convergence of \(k_{0}\) to \(k^{\star}\) through this update rule would imply that the error between these two gains, namely \(\tilde{k} \triangleq k^{\star} - k_{0}\) , approaches zero. When this gain error becomes zero, it is intuitively expected that the error in the attractor's position representation relative to the visual drive, namely \(\tilde{\theta} \triangleq \theta^{\star} - \theta\) , also approaches zero. Hence, analyzing the temporal progression of these error terms—namely the gain error \(\tilde{k}\) and the positional error \(\tilde{\theta}\) —provides an opportunity to garner insight into the algorithmic underpinnings of the gain recalibration process. + +<|ref|>text<|/ref|><|det|>[[154, 430, 842, 488]]<|/det|> +To this end, let us assume a constant visual gain, implying \(dk^{\star} / dt = 0\) . To track how the PI gain's spatial average \(k_{0}\) recalibrates to this constant value, we subtract the second row of Equation (9) from its first row and Equation (11) from \(dk^{\star} / dt = 0\) . This yields the so- called error dynamics + +<|ref|>equation<|/ref|><|det|>[[395, 500, 840, 562]]<|/det|> +\[\begin{array}{l}\frac{d\tilde{\theta}}{dt} = -\beta (\tilde{\theta}) + \tilde{k}v - k_{ac}(t,\theta)v,\\ \displaystyle \frac{d\tilde{k}}{dt} = -g_{0}(k_{0},\tilde{\theta},v), \end{array} \quad (12)\] + +<|ref|>text<|/ref|><|det|>[[154, 575, 842, 633]]<|/det|> +where \(k_{ac}(\theta) \triangleq k(\theta) - k_{0}\) captures the spatial variation of PI gain \(k(\theta)\) from its spatial average \(k_{0}\) . Analyzing these error dynamics with tools from feedback control theory, we then identify the necessary conditions for complete recalibration of \(k_{0}\) to \(k^{\star}\) . + +<|ref|>text<|/ref|><|det|>[[154, 638, 844, 842]]<|/det|> +First, we find that, if the ring attractor achieves and maintains zero gain error \(\tilde{k} = 0\) , as required for complete recalibration, then it also maintains zero positional error \(\tilde{\theta}\) , rendering the origin \((\tilde{\theta}, \tilde{k}) = 0\) an equilibrium point of the error dynamics. Convergence to this equilibrium point, however, requires the animal must be moving, else there exists no gain update rule that can achieve it. This is an unsurprising result, because, were the animal stationary, the visual landmarks would correct all the positional error \(\tilde{\theta}\) , making the gain error \(\tilde{k}\) imperceptible to the animal. Assuming accordingly that the animal is always moving, our analysis then reveals a sign requirement that must be satisfied by any gain update rule \(g_{0}\) . For stable convergence of the gain and positional error to the equilibrium point at the origin \((\tilde{\theta}, \tilde{k}) = 0\) , the gain's spatial average \(k_{0}\) must be updated in the same direction as the product of the animal's velocity \(v\) and the ring attractor's positional error \(\tilde{\theta}\) in some neighborhood of \(\tilde{\theta} = 0\) : + +<|ref|>equation<|/ref|><|det|>[[406, 860, 840, 877]]<|/det|> +\[\mathrm{sign}[g_{0}(k_{0},\tilde{\theta},v)] = \mathrm{sign}[\tilde{\theta} v]. \quad (13)\] + +<|ref|>text<|/ref|><|det|>[[154, 900, 621, 915]]<|/det|> +See Appendix 6.3.2 for formal statements and proofs of these findings. + +<|ref|>text<|/ref|><|det|>[[175, 920, 842, 936]]<|/det|> +What if the biological system can be recalibrated only partially? That is, at the steady state, the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[152, 35, 844, 280]]<|/det|> +PI gain's spatial average \(k_{0}\) converges to a value biased towards, but not necessarily the same as, the visual gain \(k^{*}\) . Under a simplifying assumption that the animal's velocity is constant, we can generalize our findings for the complete recalibration to a case that covers both complete and partial recalibration. To this end, we re- analyze the error dynamics (Equation 12). First, we find that convergence of the gain error \(\tilde{k}\) to some value, say \(\tilde{k}^{\infty} \triangleq \lim_{t \to \infty} \tilde{k} (t)\) , after recalibration results in convergence of the ring attractor's positional error \(\tilde{\theta}\) also, say to \(\tilde{\theta}^{\infty} \triangleq \lim_{t \to \infty} \tilde{\theta} (t)\) . If the gain recalibration is complete (i.e., \(\tilde{k}^{\infty} = 0\) ), then this steady- state positional error \(\tilde{\theta}^{\infty}\) is zero; if the recalibration is partial (i.e., \(\tilde{k}^{\infty} \neq 0\) ), however, \(\tilde{\theta}^{\infty}\) is nonzero, proportional to the product of the steady- state gain error \(\tilde{k}^{\infty}\) and the animal's velocity \(v\) . Our analysis then reveals a generalized sign requirement that must be satisfied by any gain update rule: For gain recalibration, the gain's spatial average \(k_{0}\) must be updated in the same direction as the product of the animal's velocity \(v\) and the deviation of the current positional representation error \(\tilde{\theta} (t)\) relative to its steady- state value \(\tilde{\theta}^{\infty}\) in some neighborhood of \(\tilde{\theta} = \tilde{\theta}^{\infty}\) : + +<|ref|>equation<|/ref|><|det|>[[380, 299, 840, 319]]<|/det|> +\[\mathrm{sign}\big[g_{0}(k_{0},\tilde{\theta},v)\big] = \mathrm{sign}\big[(\tilde{\theta} -\tilde{\theta}^{\infty})v\big]. \quad (14)\] + +<|ref|>text<|/ref|><|det|>[[155, 338, 842, 395]]<|/det|> +This generalized sign requirement captures both complete and partial recalibration; it, for example, reduces to Equation (13), the requirement for the complete gain recalibration, if the steady- state positional error \(\tilde{\theta}^{\infty}\) is zero. See Appendix 6.3.3 for formal statements and proofs of these findings. + +<|ref|>title<|/ref|><|det|>[[155, 414, 842, 450]]<|/det|> +#### 3.2.2 Sufficiency of positive gain change with respect to the product of error and velocity + +<|ref|>text<|/ref|><|det|>[[155, 462, 842, 500]]<|/det|> +We next analyze whether a gain update rule satisfying Equations (13) and (14), the necessary algorithmic conditions for recalibration, achieves gain recalibration, i.e., is it sufficient? + +<|ref|>text<|/ref|><|det|>[[155, 504, 843, 604]]<|/det|> +To address this question, we further analyzed the error dynamics in Equation (12). This analysis reveals a sufficient condition for gain recalibration. According to this condition, a gain update rule is guaranteed to achieve gain recalibration (may be partial or complete) for any visual gain \(k^{*}\) and a given velocity \(v\) of the animal if it has a positive slope with respect to the product of velocity \(v\) and positional error \(\tilde{\theta}\) at its zero value (i.e., \(g_{0}(\tilde{\theta}, v) = 0\) ), namely, + +<|ref|>equation<|/ref|><|det|>[[360, 614, 840, 648]]<|/det|> +\[\frac{\partial g_{0}(k_{0},\tilde{\theta},v)}{\partial(\tilde{\theta}v)}\bigg|_{v = v_{0}} = \frac{\partial g_{0}(k_{0},\tilde{\theta},v_{0})}{\partial\tilde{\theta}}\frac{1}{v_{0}} >0. \quad (15)\] + +<|ref|>text<|/ref|><|det|>[[155, 660, 844, 821]]<|/det|> +This sufficient condition is a small extension to the generalized necessary condition for recalibration such that it guarantees recalibration if a gain update rule satisfies the generalized sign condition in Equation (14) together with the mild additional requirement that the update rule also has a nonzero derivative with respect to the product of \(\tilde{\theta}\) and \(v\) . For example, a linear update rule \(g_{0}(k_{0},\tilde{\theta},v) = \mu \tilde{\theta} v\) with \(\mu > 0\) satisfies the sufficient condition but a cubic rule \(g_{0}(k_{0},\tilde{\theta},v) = \mu (\tilde{\theta} v)^{3}\) only satisfies the necessary condition. We now would like to demonstrate how this sufficiency leads to gain recalibration via two example update rules. Although both rules satisfy the sufficient condition, Example 1 achieves complete recalibration, while example 2 achieves only partial recalibration. + +<|ref|>text<|/ref|><|det|>[[155, 832, 843, 868]]<|/det|> +Example 1. The simplest gain update rule that satisfies Equation (15), the slope condition guarantee for gain recalibration, takes the form + +<|ref|>equation<|/ref|><|det|>[[446, 873, 550, 889]]<|/det|> +\[g_{0}(\tilde{\theta},v) = \mu \tilde{\theta} v,\] + +<|ref|>text<|/ref|><|det|>[[155, 901, 844, 939]]<|/det|> +where \(\mu\) denotes a positive learning rate. Furthermore, the update rule takes the same sign as the product \(\tilde{\theta} v\) , thus also satisfying the necessary condition for complete recalibration (Fig. 3B). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[155, 35, 842, 91]]<|/det|> +Example 2. This example is inspired from the modified ring attractor network that we propose in Section 4 as a model that achieves gain recalibration. Let \(\mu\) denote a positive learning rate as before and \(\eta\) denote a constant. Consider the gain update rule + +<|ref|>equation<|/ref|><|det|>[[402, 112, 594, 129]]<|/det|> +\[g_{0}(k_{0},\tilde{\theta},v) = \mu (\eta k_{0}v^{2} + \tilde{\theta} v).\] + +<|ref|>text<|/ref|><|det|>[[155, 145, 844, 250]]<|/det|> +Since its partial derivative is positive (i.e., \(\frac{\partial g_{0}(k_{0},\tilde{\theta},v)}{\partial(\tilde{\theta}v)} = \mu >0\) ), this update rule results in gain recalibration like Example 1. However, unlike Example 1, the recalibration can be complete or partial, depending on the value of \(\eta\) . If \(\eta = 0\) , the update rule takes the same sign as the product \(\tilde{\theta} v\) , thus satisfying the necessary condition for complete recalibration. Otherwise, it only satisfies the necessary condition for partial recalibration by taking the same sign as \(v(\tilde{\theta} - \tilde{\theta}^{\infty})\) for \(\tilde{\theta}^{\infty} = \eta v\) (Fig. 3C). + +<|ref|>image<|/ref|><|det|>[[150, 263, 852, 393]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 401, 884, 667]]<|/det|> +
Figure 3: Numerical simulation of the two example gain update rules. For both simulations, we chose the initial condition \(k_{0}(0) = 1\) and the parameters \(\beta (\tilde{\theta}) = 0.66\times \sin (\tilde{\theta})\) , \(k^{*} = 1.5\) , \(\mu = 0.02\) . The gain choices imply that the initial value of gain error is \(\tilde{k} (0) = 0.5\) . Additionally, we chose \(\eta = 0.12\) for the second example. (A) Temporal progression of the smoothed animal's velocity from an experiment in [35]. (B) Simulated error trajectories under example gain update rule 1. As soon as the animal begins its movement at \(t = 0\) , the positional error \(\tilde{\theta}\) (black line relative to the left y axis) quickly increases because of the nonzero gain error \(\tilde{k}\) (purple line relative to the right y axis). As the animal recalibrates its gain, the gain error gradually converges to zero (i.e., \(\tilde{k}\to 0\) ), accompanied by positional error gradually converging to zero also (i.e., \(\tilde{\theta}\to 0\) ). In addition to these gradual convergent trends, the error trajectories include many fast, transitory changes. As can be seen from the black line, the instantaneous value of positional error \(\tilde{\theta}\) is correlated with the animal's velocity \(v\) also. For example, when animal slows down, the positional error decreases, becoming zero when the velocity is zero. This is a reflection of the relatively increased landmark stabilization \(\beta (\tilde{\theta})\) when path integration inputs \(kv\) are decreased. On the other hand, the temporal changes in the gain are correlated with the multiplication of the positional error \(\tilde{\theta}\) and the animal's velocity \(v\) as determined by the gain update rule \(g_{0}\) . When the animal pauses temporarily around minutes 5 and 20 (i.e., \(v = 0\) ), the positional error \(\tilde{\theta}\) is completely corrected by landmarks (i.e., \(\tilde{\theta} = 0\) ), causing the gain updates to pause also (i.e., \(\frac{d\tilde{k}}{dt} = 0\) ). As the animal continues moving, the positional error and the velocity fine-tune the gain until the gain error converges to zero, demonstrating that the system can achieve complete gain recalibration. (C) Simulated error trajectories under example gain update rule 2. The convention is the same as panel B. The error trajectories exhibit similar trends to the panel B except that their final values do not converge to zero, demonstrating that the system can only achieve partial gain recalibration.
+ +<|ref|>sub_title<|/ref|><|det|>[[155, 703, 830, 721]]<|/det|> +### 3.3 Mechanistic Constraints Reveal Instrumental Role of Positional Error Codes + +<|ref|>text<|/ref|><|det|>[[155, 732, 842, 811]]<|/det|> +What are the mechanistic prerequisites for meeting the necessary algorithmic condition derived in the previous section for gain recalibration? To address this question, we investigate an analytical expression of the PI gain's spatial average \(k_{0}\) which can be simply obtained by averaging the PI gain \(k(\theta)\) in Equation (6) over \(\theta\) as follows: + +<|ref|>equation<|/ref|><|det|>[[238, 824, 839, 870]]<|/det|> +\[k_{0}\triangleq \frac{-b}{2\pi\tau_{c}\| \frac{\partial r_{c}^{*}(\psi)}{\partial\psi}\|^{2}}\int_{0}^{2\pi}\int_{0}^{2\pi}\frac{\partial^{2}r_{c}^{*}(\psi)}{\partial\psi^{2}}\sum_{i\in \{\mathrm{cw},\mathrm{ccw}\}}\alpha_{i}W_{i - c}(\psi)W_{\mathrm{v - }i}(\psi)\mathrm{sign}[r_{i}^{*}(\psi ,\theta ,0)]d\psi d\theta \quad (16)\] + +<|ref|>text<|/ref|><|det|>[[155, 882, 842, 940]]<|/det|> +The terms in this expression identifies a number of possible loci or mechanisms for updating \(k_{0}\) that satisfy the algorithmic requirement for gain recalibration in Equation (13). These terms include: (i) the offset \(b\) in the central- to- rotation ring connections, (ii) the synaptic time constant \(\tau_{c}\) , (iii) the slope parameters + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[153, 37, 844, 198]]<|/det|> +\(\alpha_{\mathrm{cw}},\alpha_{\mathrm{ccw}}\) quantifying the absolute value of the tuning slopes of velocity neurons, (iv) the synaptic weight functions \(W_{\mathrm{v - cw}},W_{\mathrm{v - ccw}}\) of the velocity- to- rotation ring connections, (v) the synaptic weight functions \(W_{\mathrm{cw - c}},W_{\mathrm{cw - c}}\) of the rotation- to- central ring connections, (vi) the function \(r_{\mathrm{c}}^{*}\) describing the central ring's persistent activity bump, and (vii) the functions \(r_{\mathrm{cw}}^{*}\) , \(r_{\mathrm{cw}}^{*}\) describing solutions to the rotation ring's persistent activity bump. Note that we treat the CW and CCW components of the same term as an inseparable pair. Out of the seven terms, we consider the last five terms (iii- vii) as candidates driving the gain recalibration within the ring attractor model via temporal changes, implicitly assuming that the first two terms, the offset \(b\) and the synaptic time constant \(\tau_{c}\) , are "hardwired" (i.e., time- invariant). + +<|ref|>text<|/ref|><|det|>[[153, 203, 844, 446]]<|/det|> +The rationale behind excluding the first two terms arises, in part, from limitations of our modeling framework. First, the rate- based approach, upon which we describe the network dynamics, does not include any cellular and receptor details to capture possible temporal changes in the synaptic time constant \(\tau_{c}\) . Instead, our model includes \(\tau_{c}\) as a "lumped parameter" reduction of complex phenomena that governs the changes in membrane potential with ion flux through receptors; future work could use chemical kinetics modeling to investigate how changes in \(\tau_{c}\) could contribute to gain recalibration, but that is beyond the scope of the present study. Second, our model employs a simplified one- to- one connectivity between the rotation rings and the central ring such that one neuron in a rotation ring connects to only one neuron in the central ring with a fixed offset \(b\) , rather than a one- to- all connectivity required to capture plasticity in \(b\) through gradual modulation of weights along the neural space. Therefore, excluding \(\tau_{c}\) and \(b\) from further consideration, we focused our analysis on the remaining five terms as the driver of gain recalibration. + +<|ref|>text<|/ref|><|det|>[[154, 451, 844, 550]]<|/det|> +By analyzing the relation between the temporal change in each of these candidate terms and the resulting temporal change in \(k_{0}\) , we find that rate- based encoding of the positional error \(\tilde{\theta}\) is required to satisfy the necessary algorithmic condition for the gain recalibration regardless of which term drives the changes in PI gain. However, the specific nature of the error code depends on the driver term (Fig. 4A) as demonstrated in the next subsections. + +<|ref|>title<|/ref|><|det|>[[154, 570, 842, 604]]<|/det|> +#### 3.3.1 Plasticity in the velocity pathway requires a rate code of the instantaneous error + +<|ref|>text<|/ref|><|det|>[[154, 617, 844, 718]]<|/det|> +We first consider the scenario that the temporal change in the PI gain is driven by temporal changes in either set of the synaptic weights along the pathway from velocity neurons to the central ring. These sets include the pair \(W_{\mathrm{v - cw}},W_{\mathrm{v - ccw}}\) , describing the strength of velocity- to- rotation ring connections (4) in Fig. 1A), and the pair \(W_{\mathrm{cw - c}},W_{\mathrm{cw - c}}\) , describing the strength of rotation- to- central ring connections (3) in Fig. 1A). + +<|ref|>text<|/ref|><|det|>[[154, 722, 844, 946]]<|/det|> +According to Equation (16), the CW and CCW components of these weight pairs additively influence the PI gain \(k_{0}\) . This additive influence suggests a possibility where CW and CCW components vary independently of one another during recalibration to tune the PI gain's spatial average \(k_{0}\) . However, this possibility is limited by the requirement that the central ring must receive balanced (i.e., symmetric) inputs from the CW and CCW rotation rings to keep the activity bump stationary during the animal's immobility as we show in Appendix 6.4. Thus, we assume hereafter that the CW and CCW components vary in a coordinated fashion to ensure that their individual contribution to \(k_{0}\) is symmetric. This symmetry assumption implies that if the overall value of \(k_{0}\) changes as per the necessary algorithmic condition in (13), then the individual contribution to this change from both CW and CCW components must be in the direction of the product of the animal's velocity \(v\) and the positional error \(\tilde{\theta}\) . To identify the mechanistic underpinnings of such symmetric gain recalibration, we revisit Equation (16). By + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[155, 35, 842, 92]]<|/det|> +differentiating this equation with respect to time and considering Hebbian plasticity as the mechanism underlying the changes in the weight pairs \(W_{\mathrm{v - cw}},W_{\mathrm{v - ccw}}\) or \(W_{\mathrm{cw - c}}\) , \(W_{\mathrm{ccw - c}}\) , we find that the algorithmic condition translates into a mechanistic constraint as follows: + +<|ref|>text<|/ref|><|det|>[[155, 97, 844, 219]]<|/det|> +Hebbian plasticity of the velocity- to- rotation ring connections \((W_{\mathrm{v - cw}},W_{\mathrm{v - ccw}})\) : A change in the weights \(W_{\mathrm{v - cw}},W_{\mathrm{v - ccw}}\) leads to a commensurate change in the speed at which the network's activity bump is shifted along the ring for a given speed of the animal. These commensurate changes suggest a positively correlated relationship between the weights \(W_{\mathrm{v - cw}},W_{\mathrm{v - ccw}}\) and the PI gain \(k_{0}\) . Assuming the symmetry between CW and CCW components, we indeed show in Appendix 6.4.1 that Equation (16), relating the weights \(W_{\mathrm{v - cw}},W_{\mathrm{v - ccw}}\) to the PI gain \(k_{0}\) via positively- weighted integrals, can be reformulated as + +<|ref|>equation<|/ref|><|det|>[[406, 230, 589, 297]]<|/det|> +\[k_{0}\propto \frac{1}{2\pi}\int_{0}^{2\pi}W_{\mathrm{v - cw}}(\psi)d\psi ,\] \[k_{0}\propto \frac{1}{2\pi}\int_{0}^{2\pi}W_{\mathrm{v - ccw}}(\psi)d\psi ,\] + +<|ref|>text<|/ref|><|det|>[[155, 305, 844, 385]]<|/det|> +where the \(\propto\) symbol denotes the existence of a positively- sloped, proportional relationship. Because of these positive correlations, satisfying the algorithmic condition for recalibration implies that the average strength of both CW and CCW velocity- to- rotation ring synapses is modified in the direction of the product of the animal's velocity \((v)\) and the network's positional error \((\tilde{\theta})\) , namely, + +<|ref|>equation<|/ref|><|det|>[[275, 396, 840, 434]]<|/det|> +\[\mathrm{sign}\left[\frac{1}{2\pi}\int_{0}^{2\pi}\dot{W}_{\mathrm{v - cw}}(\psi)\right]d\psi = \mathrm{sign}\left[\frac{1}{2\pi}\int_{0}^{2\pi}\dot{W}_{\mathrm{v - ccw}}(\psi)\right] = \mathrm{sign}[\tilde{\theta} v]. \quad (17)\] + +<|ref|>text<|/ref|><|det|>[[154, 446, 844, 630]]<|/det|> +Here, the dots appearing over the weights denote the temporal change in the weight. Recall that Hebbian plasticity of a synapse is driven by the joint activity of pre- and post- synaptic neurons. In the specific case of velocity- to- rotation ring synapses, the pre- synaptic side is composed of the velocity neurons, designated to solely encode the animal's velocity \(v\) with tuning curves that have a negative slope for the CW velocity neuron and a positive slope for the CCW velocity neuron (previously shown in Fig. 1D). Therefore, in a manner matching these differential signs of \(v\) - encoding on the presynaptic side, the CW and CCW rotation rings on the post- synaptic side must monotonically decrease and increase their average firing rates with the instantaneous positional error \(\tilde{\theta}\) to satisfy the equality in Equation (17) (Fig. 4B). Mathematical details are provided in Appendix 6.4.1. + +<|ref|>text<|/ref|><|det|>[[155, 635, 863, 755]]<|/det|> +Hebbian plasticity of the rotation- to- central ring connections \((W_{\mathrm{cw - c}},W_{\mathrm{ccw - c}})\) : Like the velocity- to- rotation ring connections discussed above, the rotation- to- central ring synaptic weight functions \(W_{\mathrm{cw - c}},W_{\mathrm{ccw - c}}\) enter linearly in the calculation of the PI gain in Equation (16). Therefore, like the previous case, the algorithmic condition for recalibration via \(W_{\mathrm{cw - c}},W_{\mathrm{ccw - c}}\) requires Hebbian plasticity to modify their average strength in the direction of the product of the animal's velocity \((v)\) and the network's positional error \((\tilde{\theta})\) , namely, + +<|ref|>equation<|/ref|><|det|>[[275, 765, 840, 804]]<|/det|> +\[\mathrm{sign}\left[\frac{1}{2\pi}\int_{0}^{2\pi}\dot{W}_{\mathrm{cw - c}}(\psi)\right]d\psi = \mathrm{sign}\left[\frac{1}{2\pi}\int_{0}^{2\pi}\dot{W}_{\mathrm{ccw - c}}(\psi)\right] = \mathrm{sign}[\tilde{\theta} v]. \quad (18)\] + +<|ref|>text<|/ref|><|det|>[[155, 816, 844, 939]]<|/det|> +In the previous case, the mechanistic prerequisite for meeting a similar sign requirement was error- encoding on the postsynaptic side since the pre- synaptic neurons were assumed to solely encode the velocity. In the present case, however, it is feasible to encode the error in either the pre- or post- synaptic side since neither side is subject to such a limitation. Therefore, when the animal is traveling in one direction (as was the case in the experiments that originally demonstrated the gain recalibration [35]), satisfying the equality in Equation (18) requires mean firing rate of either the rotation rings or the central + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[155, 32, 852, 179]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 190, 883, 620]]<|/det|> +
Figure 4: Visualization of mechanistic constraints for a numerical simulation based on a hypothetical gain update rule \(g_{0}(k_{0},\hat{\theta},v) = \mu \hat{\theta} v\) Except for the animal's velocity profile, we chose the parameters and initial conditions the same as Fig. 3B. For color coding, we use Fig. 1A as the reference, where red and blue denote the CW and CCW rotation rings, and green denotes the central ring. (A) Top graph shows the simulated velocity of the animal (purple line) on the right y axis and the temporal progression of the positional error (black line) on the left y axis. Notice the synchronous fluctuations in the positional error and the animal's velocity. As explained in Fig. 3B, these synchronous fluctuations occur because the positional error is correlated with the animal's velocity. Bottom graph shows the PI and visual gains with solid and dashed purple lines, respectively, on the right y axis and the time-integral of the positional error with the black line on the left y axis. Notice that as the PI gain gradually converges to the visual gain, the temporal progression of the time-integral of the positional error follows a very similar trajectory. This similarity indicates that the integration gain reflects the past accumulation of positional representation errors, thus opening up the possibility for the network to track the time-integral of the error as a proxy signal to encode the integration gain. (B) The mechanistic constraint for recalibration through plasticity of the velocity-to-rotation ring connections. Top graph shows the mean firing rates of the CCW and CW rotation rings over time with blue and red lines. Notice that they are similar to the trajectory of the positional error in panel A, except that the changes in the CW rotation ring's mean firing rate (red line) is the negative of those in the CCW rotation ring's mean firing rate. Bottom graph shows the direct relationship between mean firing rates and positional error in the attractor's representation. (C) The mechanistic constraint for recalibration through plasticity of the rotation-to-central ring connections. Top graph shows the mean firing rates of either the rotation rings or the central ring over time with the orange line. Notice that the changes in these firing rates follow a similar trend as the temporal progression of the positional error. Bottom graph shows this relationship directly (the positive correlation is chosen arbitrarily as our analysis does not provide a conclusive insight into the required direction). (D) The mechanistic constraint for recalibration through changes in the velocity neurons' slopes. Top graph shows the mean firing rate of the CCW and CW rotation rings, the same quantities as panel B. However, unlike panel B where the mean firing rates were similar to the instantaneous positional error, the mean firing rates in this panel are similar to the time-integral of the error. Bottom graph shows this relationship between the mean firing rates and the time-integral of the error directly. (E) The mechanistic constraint for recalibration through changes in the rotation rings' activity bumps. Top graph shows the bump width of both rotation rings over time. Similar to how the mean firing rates of the rotation rings encode the time-integral of the positional error in panel D, the bump widths encode the time-integral of the error in this panel. Bottom graph shows this relationship directly. (F) The mechanistic constraint for recalibration through changes in the central ring's activity bump. Top graph shows the temporal progression of the mean firing rate of the central ring, which is tightly but negatively correlated with the temporal progression of the time-integral of the positional error. Bottom graph shows this relationship directly.
+ +<|ref|>text<|/ref|><|det|>[[155, 645, 842, 701]]<|/det|> +ring vary monotonically with the network's instantaneous positional error (Fig. 4C). However, unlike the previous case, our analysis of the present case does not provide conclusive information about the direction of these monotonic relations. Mathematical details are provided in Appendix 6.4.2. + +<|ref|>text<|/ref|><|det|>[[155, 707, 842, 787]]<|/det|> +Collectively, these findings show that Hebbian plasticity in the pathway carrying the external velocity information to the central ring requires a rate code of the network's instantaneous positional error to update the synaptic weights in the direction of the product of the animal's velocity and the error as per the algorithmic condition for gain recalibration in Equation (13). + +<|ref|>title<|/ref|><|det|>[[155, 807, 812, 824]]<|/det|> +#### 3.3.2 Plasticity elsewhere requires a rate code of the time-integral of the error + +<|ref|>text<|/ref|><|det|>[[155, 837, 842, 935]]<|/det|> +We next consider the scenario that the synaptic weights along the pathway from velocity neurons to the central ring are hardwired (i.e., constant). This scenario implies that the gain recalibration is instead driven by temporal changes in one of the three firing- rate related terms, including the slope parameters \(\alpha_{cw}\) , \(\alpha_{ccw}\) quantifying the absolute value of the slopes of the CW, CCW velocity neurons' tuning curves, the ansatz functions describing the persistent activity bumps \(r_{cw}^*\) , \(r_{ccw}^*\) of the rotation rings, or the ansatz + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[154, 35, 843, 92]]<|/det|> +of the persistent activity bump \(r_{c}^{*}\) of the central ring. Independent of which of these terms undergoes temporal changes, the algorithmic condition for gain recalibration translates into a mechanistic constraint that, while still a rate code of error, differs in its fundamental characteristics as detailed below: + +<|ref|>text<|/ref|><|det|>[[154, 97, 844, 302]]<|/det|> +Changes in the slopes of velocity neurons' tuning curves ( \(\alpha_{cw}\) , \(\alpha_{ccw}\) ): As shown previously in Fig. 1D, the CW and CCW velocity neurons are tuned to the animal's velocity with slopes having different signs. Assuming these differential signs to be hard- wired (i.e., constant), we examine in the present case that absolute values of the slopes, denoted by parameters \(\alpha_{cw}\) and \(\alpha_{ccw}\) , undergo temporal changes. A change in these parameters leads to a commensurate change in the speed at which the network's activity bump is shifted along the central ring for a given speed of the animal. As in the previous section, this implies a positively correlated relationship between the slope parameters \(\alpha_{cw}\) and \(\alpha_{ccw}\) and the PI gain \(k_{0}\) . Indeed, this relationship is explicitly seen in Equation (16). Thus, as in the previous section, satisfying the algorithmic condition for recalibration (Equation (13)) requires \(\alpha_{cw}\) and \(\alpha_{ccw}\) to change in the direction of the product of the animal's velocity \((v)\) and the network's positional error \((\tilde{\theta})\) , namely, + +<|ref|>equation<|/ref|><|det|>[[388, 320, 840, 338]]<|/det|> +\[\mathrm{sign}[\dot{\alpha}_{cw}] = \mathrm{sign}[\dot{\alpha}_{ccw}] = \mathrm{sign}[\tilde{\theta} v] \quad (19)\] + +<|ref|>text<|/ref|><|det|>[[154, 357, 844, 666]]<|/det|> +An implication of this requirement is that, when the animal is traveling in one direction, the change in the slope parameters is monotonically related to the positional error, reflecting its value on a moment- to- moment basis with a sign additionally depending on the sign of the velocity. When these changes are integrated over time, the current value of the slope parameters reflects the accumulation of positional errors through the past, depending monotonically on the time- integral of the positional error in a direction that depends on the animal's velocity. Through connections between the velocity neurons and the rotation rings, these monotonic relationships are also translated to the mean firing rate of rotation rings. The direction of the monotonic relationships between the time- integral of the error and the mean firing rate of the rotation rings, however, depends additionally on the sign of the velocity neurons' tuning slopes. For example, when the animal is traveling in one direction (say CCW) like the original recalibration experiments [35], the mean firing rate of the CCW rotation ring increases monotonically with the time- integral of the error due to the positive slope of the CCW velocity neuron. Conversely, the mean firing rate of the CW rotation ring decreases monotonically with the time- integral of the error due to the negative slope of the CW velocity neuron (Fig. 4D). If the animal travels in the other direction, the direction of these monotonic relationships is also reversed. Mathematical details are provided in Appendix 6.4.3. + +<|ref|>text<|/ref|><|det|>[[154, 670, 844, 935]]<|/det|> +Changes in the persistent activity bump of the rotation rings \((r_{cw}^{*},r_{ccw}^{*})\) : Velocity information is transmitted to the central ring by the rotation rings whose firing rates are modulated by the animal's velocity. In this transmission, an increase in the number of actively firing rotation ring neurons (i.e., larger width of the bumps \(r_{cw}^{*}\) , \(r_{ccw}^{*}\) ) would result in a commensurate increase in the number of central ring neurons that receive the velocity information. As a result, the network's activity bump shifts faster along the central ring even when the animal's velocity is unchanged. This implies a positively correlated relationship between the widths of the rotation rings' activity bumps and the PI gain \(k_{0}\) , reminiscent of the relationship in the previously investigated case of \(\alpha_{cw}, \alpha_{ccw}\) . Thus, like the previous case, satisfying the algorithmic condition requires the rotation rings' activity widths to change monotonically with the product of the animal's velocity and the positional error. Consequently, when the animal is traveling in one direction (say positive), the widths of the rotation rings' activity bumps must increase monotonically with the time- integral of the error (Fig. 4E). If the animal's travel direction is negative, this relationship turns into a monotonically decreasing function. Mathematical details are provided in Appendix 6.4.4. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[154, 36, 844, 321]]<|/det|> +Changes in the persistent activity bump of the central ring ( \(r_{c}^{*}\) ): Consider as an example that there are two networks with the same Gaussian bump profile but one has a higher peak firing rate. In this case, if all else is equal, the network with the higher firing requires higher velocity inputs to shift its activity bump from point A to point B in the same time as the other network. This need of higher velocity inputs indicates an inversely correlated relationship between the magnitude of the central ring's activity bump and the PI gain \(k_{0}\) , which can also be verified from Equation (16) wherein the denominator includes a term proportional to the bump magnitude: the squared norm of the activity bump's gradient. Thus, satisfying the algorithmic condition for gain recalibration is subject to a mechanistic constraint that is similar to the previous case in spirit but slightly different due to the inverse effect: When the animal is traveling in one direction (say positive), the mean firing rate of the central ring must decrease monotonically with the time- integral of the error (Fig. 4F). If the animal's travel direction is negative, the direction of this monotonic relationship is reversed. Note that, for deriving this result, we assume the general shape of the central ring's activity bump to be invariant, unlike its magnitude. Mathematical details are provided in Appendix 6.4.5. + +<|ref|>text<|/ref|><|det|>[[155, 326, 844, 508]]<|/det|> +Collectively, these findings show that gain recalibration might be possible without synaptic plasticity in the pathway carrying the external velocity information to the central ring, provided that there is a rate code of the time- integral of the positional error as opposed to the error itself. Our analysis does not provide any insights into the mechanisms necessary for such a rate code and the temporal changes in the terms associated with it (e.g., slopes of velocity neurons). However, it may be the case that plasticity elsewhere than the velocity pathway is required. Independent of the underlying mechanism, however, an absence of plasticity in the velocity pathway would lead to the conclusion that the PI gain is no longer encoded in the synaptic weights: instead, it is encoded in the firing rates that track the time- integral of the error, a proxy of the PI gain (bottom row in Fig. 4A). + +<|ref|>sub_title<|/ref|><|det|>[[155, 530, 671, 549]]<|/det|> +## 4 Implementing Gain Recalibration in a Ring Attractor + +<|ref|>text<|/ref|><|det|>[[155, 564, 844, 830]]<|/det|> +In this section, we propose a modified ring attractor model that can achieve gain recalibration through a mechanism devised based on the theoretical insights we have garnered so far. Briefly, the model relies on synaptic plasticity in the velocity- to- rotation ring connections and its mechanistic prerequisite (a rate code for the positional error instantiated in the rotation rings as shown in Fig. 4B) to achieve gain recalibration. We chose this mechanism, instead of other theoretical candidate mechanisms examined in the previous section, partly because we found it relatively easy to implement compared to others, but we conjecture that some of the other candidate mechanisms also have biologically feasible implementations. Thus, our model should be viewed as an example that demonstrates the effectiveness of our theoretical analysis rather than the only network model that can achieve gain recalibration. In addition to gain recalibration, the model can also reproduce two other important aspects of biological path integration: (1) flexible association of visual landmarks to different positions in the neural space and (2) correction of accumulated PI errors by visual landmarks. In the next subsections, we describe the model in detail and explain the mechanisms by which it achieves these aspects. + +<|ref|>sub_title<|/ref|><|det|>[[155, 850, 631, 867]]<|/det|> +### 4.1 A connectivity pattern yielding a rate code for error + +<|ref|>text<|/ref|><|det|>[[155, 880, 843, 937]]<|/det|> +We begin by proposing a connectivity pattern that causes a neuron population to vary its firing rate monotonically with the difference in the bump locations of two other populations. As will be evident, this connectivity pattern plays a crucial role by providing the means to achieve a rate code for the + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 110, 880, 515]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 532, 883, 865]]<|/det|> +
Figure 5: A modified ring attractor network model. (A) A proposed connectivity pattern that varies firing rate of population X3 as a monotonic function of the difference in the activity-bump locations of populations X1 and X2. Arrow and circle terminals denote excitatory and inhibitory connections, respectively. (B) Schematic diagrams depicting the computation within the proposed connectivity in panel (A). The first row shows the activity of the X2 population (yellow) relative to the activity of the X1 population (green) for different conditions: CW difference (left column), no difference (middle column), and CCW difference (right column). The second row shows the synaptic inputs to the X3 population from the excitatory X1 (green line) and inhibitory X2 (yellow line) populations. The fourth row shows the resulting firing rates of the X3 population. (C) Schematic representation of the model. Solid and dashed lines denote hardwired and plastic connections, respectively. The labels M1, M2, M3, correspond to the three modifications made to the classical ring attractor: M1 is the association ring, M2 refers to the plasticity of the velocity-to-rotation-ring connections, and M3 refers to the hardwired, offset association-to-rotation connections. (D) Numerical simulation demonstrating the association of the visual ring's activity with the central ring's activity through Hebbian plasticity. Top and bottom rows show the initial and final values of the simulated variables. The left column shows the firing rates of the central (green) and visual (pink) rings. The middle column visualizes the weight matrix describing the visual-to-association ring connections. The right column shows the firing rates of the association ring (yellow) and the synaptic inputs from the visual ring (pink). (E) Tuning curves depicting the relationships between the rotation rings' mean and peak firing rates vs. the error (left and right graph, respectively). The color coding is the same as panel C. (F) Numerical simulations of the gain recalibration within the proposed model. The top shows the the recalibration of PI gain (green) toward the visual gain (pink) in a selected simulation. The middle shows the progression of the weights of the velocity-to-rotation ring connections (four samples normalized to initial condition of the weights with the opacity changes from the lightest ( \(t = 0\) min) and to the darkest green ( \(t = 30\) min) corresponding to chronological order of the samples.). The bottom shows the final values of the PI gain for various visual gains (green line) and the hypothetical perfect recalibration (dashed black line). (G) Numerical simulation demonstrating how visual landmarks correct positional error. The top panel shows the progression of the bump locations of the visual (pink) and central rings (green). Bottom panel shows the mean firing rate of CW (red) and CCW (blue) rotation rings over time.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[155, 36, 842, 71]]<|/det|> +positional error, with the error being the difference in the bump locations of the visual drive and the attractor activity. + +<|ref|>text<|/ref|><|det|>[[155, 77, 844, 260]]<|/det|> +The connectivity pattern can be described by considering three distinct neuron populations (X1- 3 in Fig. 5A), each arranged on a circle like the populations in the ring attractor network. Suppose that populations X1 and X2 consist of excitatory and inhibitory neurons, respectively, and maintain their own activity bumps. The population X3 derives its activity based on the inputs from X1 and X2. The excitatory inputs from X1 to X3 are routed through topographic connections that wire together the neurons at the same angular location in the neural space. The inhibitory inputs from X2 to X3, however, are routed through CCW offset connections that wire together the neurons at different angular locations. To understand how this connectivity causes X3 to vary its firing rate as a monotonic function of the difference in the bump locations of X1 and X2, we can track the flow of neural activity as follows: + +<|ref|>text<|/ref|><|det|>[[154, 264, 844, 570]]<|/det|> +Let \(x_{1}\) and \(x_{2}\) denote the location of X1 and X2's activity bumps on the circular neural space, respectively. When \(x_{2} - x_{1} = 0\) , these activities are aligned, but the synaptic inputs to the population X3 from X1 and X2 are misaligned due to the CCW offset in X2- to- X3 connections (the second column of Fig. 5B). When \(x_{2} - x_{1} \neq 0\) , however, X1 and X2's activity bumps are misaligned with a CW \((x_{2} - x_{1} < 0)\) or CCW \((x_{2} - x_{1} > 0)\) difference in their locations. Consider first the CW- difference case (the first column of Fig. 5B). In this case, the bump location of X2 is shifted in the CW direction compared to that of X1. Because of the CCW offset in the X2- to- X3 connections, this misalignment between the activity bumps decreases at the level of synaptic inputs, bringing the inhibition from X1 closer to active neurons of X3, thereby decreasing the firing rate of X3 compared to the no- difference case. Consider next the CCW- difference case (the third column of Fig. 5C). In this case, the CCW offset in the X2- to- X3 connections redirects the inhibition from X2 further away from the active neurons of X3, thus increasing the firing rate of X3. Through this mechanism, the proposed connectivity pattern causes population X3 to vary its firing rate as a monotonically increasing function of the difference in the bump locations of X1 and X2. The direction of this monotonic relationship can be easily reversed if the offset in the X2- to- X3 connections is reversed from CCW to CW. + +<|ref|>text<|/ref|><|det|>[[155, 575, 844, 715]]<|/det|> +How can we make use of this connectivity pattern in our modified ring attractor network that will rely on plastic velocity- to- rotation ring connections for gain recalibration? The connectivity lends itself naturally to this recalibration mechanism, as it requires the rotation rings (X3) to vary their firing rates monotonically with the positional error, a quantity equal to the difference in the bump locations of the central ring (X1) and the visual drive (X2). Despite this suitability, however, employing the connectivity pattern in the ring attractor network requires an additional modification for a reason which will be clear in the next section. + +<|ref|>sub_title<|/ref|><|det|>[[155, 734, 816, 752]]<|/det|> +### 4.2 Flexible Association of Landmarks to Positions through Hebbian Plasticity + +<|ref|>text<|/ref|><|det|>[[155, 764, 844, 864]]<|/det|> +In traditional ring attractor models, feedback from landmarks is incorporated into the network via direct synaptic connections from the visual ring [20, 46, 54]. In these connections, synaptic weights between coactive neuron pairs encoding the same position of the animal are potentiated through Hebbian plasticity, resulting in a flexible associative mapping between the visual ring and the attractor network [20, 55]. + +<|ref|>text<|/ref|><|det|>[[155, 869, 844, 947]]<|/det|> +However, this approach, relying on direct plastic connections from the visual ring onto the attractor network, is incompatible with the connectivity pattern proposed in the previous section for the error- rate code. That is, the error code's connectivity pattern requires the wiring of neurons representing different positions by an offset as opposed to Hebbian plasticity wiring together the neurons representing the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[154, 36, 844, 92]]<|/det|> +same position. To resolve this incompatibility, our modified ring attractor network model makes a small change to the approach in traditional models by placing the plastic associative mapping problem outside the network, which makes it possible to include the error code's connectivity pattern inside the network. + +<|ref|>text<|/ref|><|det|>[[153, 97, 845, 404]]<|/det|> +The modification is as follows: We first remove the visual- to- central ring connections (5 in Fig. 1A) and introduce an intermediate ring of neurons, which we call an association ring, that associates the activity in the visual ring with that in the central ring by receiving inputs from both (see M1 in Fig. 5C). The afferent connections to this ring from the central ring are hardwired and weak, while those from the visual ring are plastic, hence capable of becoming strong. In a novel environment, where the plastic visual connections are initially untuned and random, the spatial selectivity of the visual ring's activity is not conveyed in its synaptic inputs to the association ring. In contrast, inputs from the central ring to the association ring always preserve the spatial selectivity of the central ring's activity because of the hard- wired, topographic connections. This combination of malleable inputs from the visual ring and weak but hardwired inputs from the central ring biases the visual- to- association ring connections near the central ring's activity bump to be selectively potentiated through Hebbian plasticity (top row in Fig. 5D). Eventually, synaptic inputs from the visual ring become sufficiently strong and aligned with the inputs from the central ring, making the association ring's activity strongly visually driven such that it implicitly represents a flexible associative mapping between the representations in the visual ring and the attractor network (bottom row Fig. 5D). + +<|ref|>text<|/ref|><|det|>[[155, 409, 844, 488]]<|/det|> +Thus, by serving as an intermediary, the association ring promises to act in our modified ring attractor network model as a proxy visual drive that can circumvent the previously noted incompatibility issues with the error- rate code's connectivity pattern. In the next section, we describe how the association ring can be combined with this connectivity pattern to implement gain recalibration. + +<|ref|>sub_title<|/ref|><|det|>[[155, 507, 708, 524]]<|/det|> +### 4.3 Gain Recalibration by Landmarks through Hebbian Plasticity + +<|ref|>text<|/ref|><|det|>[[154, 536, 845, 760]]<|/det|> +As noted earlier, our ring attractor network model relies on plasticity in the velocity- to- rotation ring connections for gain recalibration (M2 in Fig. 5C). However, this recalibration mechanism requires as its mechanistic prerequisite (described in Section 3.3) that CCW and CW rotation rings respectively increase and decrease their firing rates with the positional error in the central ring's representation relative to the visual landmarks. To obtain these error- rate codes, we make use of the visual information in the association ring by connecting it to the CCW and CW rotation rings with CCW and CW offset, respectively (M3 in Fig. 5C). Combined with the topographic central- to- rotation ring connections, these offset connections from the association ring implement the connectivity pattern described in Section 4.1, thereby achieving the required error- rate codes (Fig. 5E). With these error- rate codes in the rotation rings and the plasticity in the velocity- to- rotation ring connection, our ring attractor model now includes all the necessary ingredients for gain recalibration. + +<|ref|>text<|/ref|><|det|>[[154, 765, 845, 947]]<|/det|> +To test if these ingredients are also sufficient to achieve gain recalibration, we first take an analytical approach based on Equation (15), which states a sufficient condition for gain recalibration. According to this condition, gain recalibration is guaranteed if the temporal change in the gain's spatial average \(k_0\) has a positive slope with respect to the product of the animal's velocity \(v\) and the attractor's positional error \(\tilde{\theta}\) . As discussed in Section 3.3.1, the gain's spatial average \(k_0\) is positively correlated with the average strength of velocity- to- rotation ring connections. Therefore, the sufficient condition can be rephrased as follows: the gain recalibration is guaranteed if the temporal change in the average strength of velocity- to- rotation ring connections has a positive slope with respect to the product of \(v\) and \(\tilde{\theta}\) . The presynaptic side of these plastic connections has velocity neurons encoding the animal's velocity \(v\) , while the postsynaptic + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[152, 35, 845, 321]]<|/det|> +side has the rotation ring neurons encoding the positional error \(\tilde{\theta}\) . Since both of these rate codes have the same slopes on the CW and CCW parts of the network (negative on the CW part, positive on the CCW part), their correlated activity, as the driver of Hebbian plasticity, modifies the strength of velocity- to- rotation ring connections with a positive slope relative to the product of \(v\) and \(\tilde{\theta}\) , satisfying the sufficient condition in (15). However, as in Example 2 in Section 3.2.2, gain recalibration is expected to be imperfect due to imperfect encoding of the positional error \(\tilde{\theta}\) in the rotation rings whose firing rates are additionally modulated by the animal's velocity \(v\) . As a result, our model is expected to recalibrate its average PI gain \(k_{0}\) to steady- state values that are close to, but not the same as, the visual gain, \(k^{\star}\) . During this recalibration, the model is expected to go through a transitory stage with a spatially non- homogenous PI gain \(k(\theta)\) as Hebbian plasticity modifies the spatially distributed velocity- to- rotation ring connections non- uniformly because of non- uniform firing rate of rotation rings' neurons across the neural space at any moment in time. However, as the animal runs in the environment, these non- uniform effects will be washed out, resulting in a spatially homogeneous PI gain \(k(\theta)\) . Mathematical details of these analytical findings are given in Appendix 6.5. + +<|ref|>text<|/ref|><|det|>[[155, 326, 844, 404]]<|/det|> +We next verify these analytical findings by performing numerical simulations of our modified ring attractor model for a simulated rat running on a circular track while visual landmarks were moved as per the visual gain \(k^{\star}\) . The model demonstrated imperfect yet stable gain recalibration for a range of \(k^{\star}\) values (Fig. 5F). + +<|ref|>sub_title<|/ref|><|det|>[[155, 424, 830, 441]]<|/det|> +### 4.4 Correction of Positional Errors by Landmarks through a Rate Code of Error + +<|ref|>text<|/ref|><|det|>[[154, 453, 844, 635]]<|/det|> +Next, we test if visual landmarks can correct PI errors in our model. The PI error manifests itself as a misalignment between the peak locations of the path- integration driven activity bump in the central ring and the strongly visually driven activity bump in the association ring. Traditional models correct for this misalignment by providing visual drive directly onto the central ring (like in Fig. 1A), toward which its activity bump is gravitated by means of attractor dynamics. In our model, however, we have removed such direct connections in Section 4.2 because of their incompatibility with the connectivity pattern needed for the error- rate codes. Instead, we have connected the association ring to the rotation rings with some offset. The question then arises: are these offset connections sufficient for PI error correction or do we need to reinstate direct connections onto the central ring from the visual drive? + +<|ref|>text<|/ref|><|det|>[[154, 640, 844, 886]]<|/det|> +As explained in the previous section, the offset connections causes the CCW and CW rotation rings to increase and decrease their firing rates monotonically with the positional error, respectively. This differential modulation of the rotation rings' firing rates by the error is similar to their differential modulation by the velocity; when the animal is moving, the firing rate of one rotation ring increases and that of the the other decreases, which in turn shifts the activity bump along the central ring. Therefore, by employing rate codes of the positional error, our model effectively transforms the positional error into a virtual velocity signal, shifting the activity bump along the central ring in a manner decreasing this error—a manifestation of error correction provided by visual landmarks. We verified this error correction mechanism in numerical simulation of our model. Following a positional error introduced abruptly between the activity bumps of the ring attractor and the association ring, the differential changes in the rotation rings' firing rates eliminated the error by re- aligning the central ring's activity bump with that of the association ring (Fig. 5E). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[155, 36, 283, 52]]<|/det|> +## 5 Discussion + +<|ref|>text<|/ref|><|det|>[[155, 71, 844, 293]]<|/det|> +Fine- tuning of a neural integration computation is crucial to maintain accurate representations of continuous variables since the relationship between the sensing of the relative change in a continuous variable and its actual value can fluctuate on both developmental (e.g., changes in body size that can affect location coding) and behavioral timescales (e.g., swimming versus walking in the case of location coding) and even due to dynamic biological processes, such as circadian rhythm, that can alter synaptic transmission and intrinsic electrical properties of neurons. Building upon previous behavioral work on perceptual plasticity of human locomotion [56], physiological evidence for such fine- tuning was first observed in hippocampal place cells [35], where persistent conflict between self- motion and external visual cues recalibrated the integrator gain. In the present paper, we give the first theoretical examination of this phenomenon in continuous bump attractor networks (CBANs), a prevailing model for representations of continuous variables. + +<|ref|>text<|/ref|><|det|>[[155, 299, 844, 626]]<|/det|> +Our examination unveiled the algorithmic and mechanistic requirements for gain recalibration in a ring attractor network, a representative CBAN model used for circular continuous variables. In CBAN models, when the integration gain is inaccurate, an internal representation of a continuous variable slightly drifts relative to its actual value, resulting in encoding errors. Absolute 'ground- truth' information, such as feedback from visual landmarks for location coding, correct these errors through internal dynamics of the network, without the need for an explicit rate- based representation of the error. In contrast to this automatic error correction through network dynamics, we found that fine- tuning the integration gain based on errors requires an explicit error signal, i.e., firing rate of some neurons to encode the error in the CBAN's representation of a continuous variable relative to its actual value. Building upon this insight, we also proposed a ring attractor network model that shows how a CBAN can recalibrate its integration gain through biologically known plasticity mechanisms. Although the ring attractor is specialized for integration of 1D circular continuous variables (e.g., an animal's location on a circular track), our findings can be readily extended to higher dimensions and other types of continuous variables. Overall, our findings suggest that a rate code for the error in the internal representation of a continuous variable is a core component of the bump attractor- type neural integrators, and that such a rate code plays an essential role in their gain recalibration. + +<|ref|>sub_title<|/ref|><|det|>[[155, 646, 700, 663]]<|/det|> +### 5.1 The Bump Attractor Network as an Adaptive Kalman Filter + +<|ref|>text<|/ref|><|det|>[[155, 675, 844, 817]]<|/det|> +To identify algorithmic requirements for recalibration of the integration gain, we simplified dynamics of the ring attractor through a dimensionality reduction protocol described in [41]. Similar approaches have been successfully applied in recent years to explore the neural dynamics capturing how high- dimensional neural data evolves within low- dimensional topological structures [6, 14, 57]. In our specific case, the dimensionality reduction led to a simplified 1D model of the ring attractor, capturing the dynamics of its representation as a function of external inputs that provide differential (e.g., animal's velocity) and absolute (e.g., positional feedback from visual landmarks) information [41, 42, 58]. + +<|ref|>text<|/ref|><|det|>[[155, 822, 844, 940]]<|/det|> +Previous research showed that, when two external cues are presented as inputs, the ring attractor network fuses them optimally in the Bayesian sense [20, 59- 61]. Furthermore, if one of the cues provides only differential information like the animal's velocity that is integrated over time to compute the overall change in the continuous variable, the ring attractor network performs the Bayesian fusion recursively for each step of the integration [41]. This recursive computation is known as Kalman filtering and has been proposed as a model of cue integration in the entorhinal cortex of the mammalian brain [21]. Consistent + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[155, 35, 844, 113]]<|/det|> +with this prior work, we found that the ring attractor network operates as a Kalman filter updating the representation by a combination of integrated relative information (the internal model component of the Kalman filter) and the instantaneous feedback from absolute information (the measurement model component). + +<|ref|>text<|/ref|><|det|>[[155, 118, 844, 321]]<|/det|> +In engineered systems, the accuracy of a Kalman filter relies on precise knowledge of its internal model parameters; to address this issue, adaptive Kalman filters that fine- tune their own parameters have been proposed [62, 63]. Like adaptive Kalman filters, we showed that a ring attractor network can fine- tune itself through gain recalibration. We also elucidated the algorithmic requirement for this recalibration, showing that the integration gain must change in the same direction as the product of the animal's velocity and the error in the attractor's representation relative to the absolute 'ground- truth' information. Interestingly, this requirement resembles characteristics of a classical algorithm known as the MIT rule in adaptive control systems and Kalman filtering [64]. Thus, a ring attractor with gain recalibration effectively operates as an adaptive Kalman filter, updating its representation accurately through a finely tuned integration gain. + +<|ref|>sub_title<|/ref|><|det|>[[155, 341, 841, 376]]<|/det|> +### 5.2 Necessity of a Rate-Based Explicit Error Signal in the Bump Attractor Networks + +<|ref|>text<|/ref|><|det|>[[155, 388, 844, 612]]<|/det|> +Satisfying the algorithmic requirement for gain recalibration is subject to certain mechanistic constraints, which we discovered analyzing the network dynamics. In essence, gain recalibration requires that some neurons vary their firing rates monotonically with the instantaneous value or the time- integral of the error in the representation of the continuous variable relative to its true value. Without such error- rate codes, the network does not have a teaching signal that can guide tuning of its gain for recalibration. This shows that, for CBAN networks, learning from representational errors to recalibrate the integration gain is a very different neural process than correcting the errors. In the case of error correction, input signals from absolute 'ground- truth' information sources, such as visual landmarks for a CBAN encoding location, are sufficient to trigger an error correction response from network dynamics. In contrast, recalibrating the integration gain based on representational error additionally requires a neural signal that explicitly encodes this error via a rate code. + +<|ref|>text<|/ref|><|det|>[[155, 617, 844, 841]]<|/det|> +This hypothesized error signal resembles reward and sensory prediction error signals within the mammalian brain. Dopamine neurons in the midbrain of mammals encode error in the internal predictions of reward via monotonic changes in their firing rates [65, 66]; they exhibit elevated activity with more reward than predicted, remain at baseline activity for fully predicted rewards, and exhibit depressed activity with less reward than predicted. Climbing fiber inputs to Purkinje cells of the mammalian cerebellum encode errors in the predicted sensory consequences of motor commands relative to the actual sensory feedback via changes in the rate and duration of complex spikes [67, 68]. Both of these prediction error codes are thought to act as a teaching signal that fine- tunes the internal models, mapping the stimulus to reward prediction in the dopamine system and the motor commands to sensory prediction in the cerebellum, through plasticity, just like how error coding can act as a teaching signal that recalibrates the integration gain of a CBAN. + +<|ref|>text<|/ref|><|det|>[[155, 846, 843, 944]]<|/det|> +To instantiate this idea computationally, we presented a modified ring attractor model that can recalibrate its integrator gain based on a rate code of the instantaneous error via Hebbian plasticity. Relevant to this model, a previous study hypothesized a currently unknown plasticity rule as a mechanism for gain recalibration within the ring attractor network [32]; the hypothesized plasticity rule modifies synaptic weights of each neuron according to an implicit positional error signal, computed locally within each neu + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[154, 35, 844, 156]]<|/det|> +ron through comparison of the synaptic inputs at its basal and apical dendrites receiving, respectively, the absolute 'ground- truth' information and the network's current representation information. While it is unclear whether such a plasticity rule exists in the brain, our model demonstrates a biologically plausible alternative: Hebbian plasticity, combined with an explicit rate- based representation of the error, is sufficient to achieve gain recalibration. It remains as a future work to experimentally test if such an error signal exists in the brain circuits that are thought to employ CBANs for encoding continuous variables. + +<|ref|>sub_title<|/ref|><|det|>[[155, 175, 711, 193]]<|/det|> +### 5.3 Implications of a Distributed, Inhomogenous Integration Gain + +<|ref|>text<|/ref|><|det|>[[154, 203, 845, 533]]<|/det|> +Prior CBAN models implicitly assumed the integration gain to be a single, global parameter of the network, independent of the value of the encoded continuous variable [11, 31]. Although the idea of different, hard- wired integration gains has previously been suggested in the context of location coding to explain the changes in the spatial scale of place coding along the dorsal- ventral axis of the hippocampus [27], it is assumed that the integration gains are constant at all locations within an environment. In contrast, we showed that the integration gain within a single CBAN, specifically the ring- attractor network, is a distributed parameter instantiated in the network's array of synaptic weights, implying that the network can adopt unique integration gains for different values of the encoded continuous variable. This would be, for example, a CBAN changing its integration gain depending on the location of the animal in the context of spatial navigation or depending on the amount of accumulated evidence in the context of decision- making. In the latter case, the CBAN may look like it unevenly weights early or late evidence, which is a well- established phenomenon known as primacy and recency effects in the decision- making literature [69- 71]. Compared to a network with a single, global integration gain, a network with a distributed, possibly inhomogeneous, gain can adjust its representation metric for the continuous variable locally, hence providing greater flexibility in representing different values of the continuous variable with uneven resolutions, depending on, for instance, their behavioral significance [72]. + +<|ref|>text<|/ref|><|det|>[[154, 536, 845, 824]]<|/det|> +How might gain inhomogeneity arise? Theoretically, it can be a product of the recalibration process if the teaching signal (e.g., feedback from absolute 'ground- truth' information sources) is available unevenly across the values of the continuous variable. In the context of location coding, for example, such differences may occur between when the animal is nearby the boundaries of the environment, which is a relatively rich area in terms of external ground- truth information, versus when it is near the center of the arena. We speculate that such spatially distributed recalibration of the integration gain may offer a mechanistic explanation to some of the experimental findings about local distortions and deformations in the activity patterns of entorhinal grid cells and hippocampal place cells, encoding the animal's location, during environmental manipulations [73- 80]. According to this speculation, grid patterns might get distorted nearby environmental boundaries through changes in the local integration gain of the network; through the same mechanism, place cells might represent locations nearby landmarks and boundaries with a greater spatial resolution (also known as overrepresentation). Overall, inhomogenous integration gain of CBANs offer a potential explanation to an array of seemingly complex responses in spatial navigation as well as other brain functions. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[152, 70, 844, 108]]<|/det|> +[1] R. Ben- Yishai, R. L. Bar- Or, and H. 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Shapiro, "Hippocampal place fields are altered by the removal of single visual cues in a distance- dependent manner.," Behavioral neuroscience, vol. 111, no. 1, p. 20, 1997. + +<|ref|>text<|/ref|><|det|>[[152, 652, 844, 708]]<|/det|> +[79] H. Eichenbaum, S. I. Wiener, M. Shapiro, and N. Cohen, "The organization of spatial coding in the hippocampus: a study of neural ensemble activity," Journal of Neuroscience, vol. 9, no. 8, pp. 2764- 2775, 1989. + +<|ref|>text<|/ref|><|det|>[[152, 719, 844, 775]]<|/det|> +[80] M. Sato, K. Mizuta, T. Islam, M. Kawano, Y. Sekine, T. Takekawa, D. Gomez- Dominguez, A. Schmidt, F. Wolf, K. Kim, et al., "Distinct mechanisms of over- representation of landmarks and rewards in the hippocampus," Cell reports, vol. 32, no. 1, 2020. + +<|ref|>text<|/ref|><|det|>[[152, 786, 844, 822]]<|/det|> +[81] M. Noorman, B. K. Hulse, V. Jayaraman, S. Romani, and A. M. Hermundstad, "Accurate angular integration with only a handful of neurons," bioRxiv, pp. 2022- 05, 2022. + +<|ref|>text<|/ref|><|det|>[[152, 833, 844, 869]]<|/det|> +[82] P. Dayan and L. F. Abbott, Theoretical neuroscience: computational and mathematical modeling of neural systems. MIT press, 2005. + +<|ref|>text<|/ref|><|det|>[[152, 879, 656, 895]]<|/det|> +[83] H. Khalil, Nonlinear Systems. Pearson Education, Prentice Hall, 2002. + +<|ref|>text<|/ref|><|det|>[[152, 905, 844, 941]]<|/det|> +[84] J. Guckenheimer and P. Holmes, Nonlinear oscillations, dynamical systems, and bifurcations of vector fields, vol. 42. Springer Science & Business Media, 2013. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[156, 37, 319, 54]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[155, 71, 842, 129]]<|/det|> +This work was supported by National Institutes of Health grants R01 NS102537 (N.J.C., J.J.K.), U01 NS131438 (N.J.C.), and R01 MH118926 (J.J.K., N.J.C.). We thank Ravikrishnan Jayakumar, Kathryn Hedrick, Bharath Krishnan, Manu Madhav, Francesco Savelli, and Kechen Zhang. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 92, 768, 112]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 200, 150]]<|/det|> +- appendix.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__05a18c267c6d7dedb4a7e980e586fa6f60b4fb0553596df82b5ab1b095ebd821/images_list.json b/preprint/preprint__05a18c267c6d7dedb4a7e980e586fa6f60b4fb0553596df82b5ab1b095ebd821/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..74b1f6487ddda3187d4af5c6081a77cd1d3bac09 --- /dev/null +++ b/preprint/preprint__05a18c267c6d7dedb4a7e980e586fa6f60b4fb0553596df82b5ab1b095ebd821/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [ + [ + 66, + 50, + 940, + 490 + ] + ], + "page_idx": 13 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 48, + 45, + 944, + 475 + ] + ], + "page_idx": 14 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [ + [ + 50, + 49, + 940, + 587 + ] + ], + "page_idx": 15 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4", + "footnote": [], + "bbox": [ + [ + 45, + 52, + 944, + 435 + ] + ], + "page_idx": 16 + } +] \ No newline at end of file diff --git a/preprint/preprint__05a18c267c6d7dedb4a7e980e586fa6f60b4fb0553596df82b5ab1b095ebd821/preprint__05a18c267c6d7dedb4a7e980e586fa6f60b4fb0553596df82b5ab1b095ebd821.mmd b/preprint/preprint__05a18c267c6d7dedb4a7e980e586fa6f60b4fb0553596df82b5ab1b095ebd821/preprint__05a18c267c6d7dedb4a7e980e586fa6f60b4fb0553596df82b5ab1b095ebd821.mmd new file mode 100644 index 0000000000000000000000000000000000000000..89a1420ba3a4e6852fc276d323986a512e59e8b9 --- /dev/null +++ b/preprint/preprint__05a18c267c6d7dedb4a7e980e586fa6f60b4fb0553596df82b5ab1b095ebd821/preprint__05a18c267c6d7dedb4a7e980e586fa6f60b4fb0553596df82b5ab1b095ebd821.mmd @@ -0,0 +1,293 @@ + +# Climate action and gender equality matter most for China's sustainable development + +Chaoyang Wu + +wucyeigsnrr.ac.cn + +Institute of Geographic Sciences and Natural Resources Research https://orcid.org/0000- 0001- 6163- 8209 + +Qiang Xing Aerospace Information Research Institute, Chinese Academy of Sciences + +Fang Chen International Research Center of Big Data for Sustainable Development Goals + +Jianguo Liu Michigan State University https://orcid.org/0000- 0001- 6344- 0087 + +Prajal Pradhan Potsdam Institute for Climate Impact Research https://orcid.org/0000- 0003- 0491- 5489 + +Brett Bryan Deakin University https://orcid.org/0000- 0003- 4834- 5641 + +Thomas Schaubroeck Luxembourg Institute of Science and Technology + +Luis Roman Carrasco National University of Singapore https://orcid.org/0000- 0002- 2894- 1473 + +Alemu Gonsamo McMaster University https://orcid.org/0000- 0002- 2461- 618X + +Yunkai Li College of Water Resource & Civil Engineering, China Agriculture University + +Xiuzhi Chen China Agricultural University https://orcid.org/0000- 0002- 9371- 4648 + +Xiangzheng Deng Chinese Academy of Sciences https://orcid.org/0000- 0002- 7993- 5540 + +Andrea Albanese Luxembourg Institute of Socio- Economic Research + +Yingjie Li Stanford University + +Zhenci Xu + +<--- Page Split ---> + +## Article + +## Keywords: + +Posted Date: June 29th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 3053894/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on March 13th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 46491- 6. + +<--- Page Split ---> + +## Abstract + +Rescuing Sustainable Development Goals (SDGs) from failing requires understanding their interactions networks, i.e., synergies and trade- offs, at national and especially sub- national levels, where SDGs were delivered. This understanding will help identifying the key hurdles and opportunities to prioritize the 17 SDGs in a indivisible manner for a country. However, current research on SDG priorities at sub- national levels remains limited mainly due to difficulty in data collection. Here, we collect a unified annual dataset of 102 indicators covering national and 31 provinces in China over 2000–2020. We analyze the importance of the 17 SDGs at national, provincial and regional levels through synergy and trade- off networks. The key SDGs in trade- off (provincial: 12/31, regional: 1/6) differ more than synergy (provincial: 7/31, regional: 0). Nevertheless, combating climate change (SDG13) and improving gender equality (SDG5) are overall key hurdles for China to achieving 2030 agenda. Focusing on poverty eradication (SDG1) and increasing clean water and sanitation (SDG6) have highly compound positive effect. Our findings provide essential knowledge and insight on adopting common but diffrentiaed SDGs priorities and balance mattering China's sustainable development. + +## Introduction + +The 2030 Agenda for Sustainable Development, consisting of the 17 Sustainable Development Goals (SDGs) and 169 targets, is a global agenda for people, the planet, and prosperity to transform the world onto a sustainable and resilient path1. However, SDGs have had a limited transformative impact2, 3. A reason for this failure of SDGs is their selective implementation without considering their complex interactions4. As a system of interacting components, SDGs have complex interconnections with synergies (a pair of SDGs improve or deteriorate together) and trade- offs (one SDG improves while the other deteriorates), which play essential roles in achieving or inhibiting their effectiveness4–6. These complex interactions largely depend on strategies to achieve an SDG. For example, infrastructure like roads are necessary for poverty alleviation (SDG1) and economic development (SDG8) but may be detrimental for coast (SDG14) and land ecosystem (SDG15)7. Thus, to rescue SDGs from failing, it is essential to understand their synergies and trade- offs for determining priorities and improving the balance and integrity of policies towards achieving the 2030 Agenda holistically4,8. + +Systems thinking and analysis to assess the complex interactions among all 17 SDGs is at the forefront of sustainability research9. Existing SDG studies qualitatively evaluate SDG interactions by literature review10,23, expert rating11–13, text mining14. The model- based analysis focuses more on environmental SDGs, less on social and economic dimensions15. With public database, some research used network analysis to quantitatively analyze the differences in SDG interaction networks at global and national levels16–18. However, due to economic, social and environmental heterogeneity, SDG interactions may vary at a local or sub- national level within a country, where SDGs were actually implemented3–4,16. Understanding variations in SDG interaction networks at different spatial levels, especially at the sub + +<--- Page Split ---> + +national level, remain a fundamental research gap, which in fact are essential for identifying context- and location- specific strategies for an integrated SDG implementation, especially for big countries. + +As the largest developing country in geographic area and population, China has experienced rapid economic development over the past few decades. However, it has also faced social problems and environmental challenges while striving a rapid economic development \(^{19 - 20}\) . Still, earlier studies suggested the uneven progress among the 17 SDGs at the sub- national level as a significant challenge for China's sustainable development \(^{21 - 22}\) . We fill the above- highlighted research gaps by addressing the following two questions from synergy and trade- off perspectives. (1) What are the common SDG priorities among provincial, regional and national levels? (2) How much are the differentiated priorities for synergy and trade- off? + +To this end, we collected as much data as possible to cover all 17 SDGs at national and sub- national levels simultaneously on a yearly basis from 2000 to 2020. In total, a unified dataset of 102 indicators were used in our analysis, consisting of 31 provinces in China (see Methods). We proposed synergy and trade- off networks at national and sub- national levels, respectively. The synergy and trade- off intensity was set to be the weighted edge and the hub score of the 17 goals were set to be the nodes in the networks (see Methods). We analyzed the hub score to determine which goal served as the central hub in the synergy and trade- off networks. The larger the hub score represented the more important the node as the central hub in the networks. We analyzed these variations at national, provincial and regional levels among 17 goals. Our findings can provide essential knowledge and insights on the priority of the SDGs to accelerate implementing the SDGs in a holistic manner at different spatial levels in China. + +## Results + +## The SDGs priority at the national level + +At the national level, 1023 out of 5100 indicators pairs showed synergy, and 374 pairs showed trade- off with the average ABS(R) (absolute value of Spearman Correlation coefficient R) of 0.95 and 0.94 for both indicators and goals (Bonferroni corrected \(p < 0.05\) and ABS(R) \(> 0.6\) ) (Fig. 1(a) and 1(b) for indicators, see SI for more details, Fig. 2(a) and 2(d) for goals). The average Ratio (ratio of the number of the selected indicator pairs out of the total number of all possible combinations among goals) was 0.69 and 0.31 for synergy and trade- off (Fig. 2(b) and 2(e)). Overall, we found that China faced challenges on SDG13 (Climate Change Action), SDG5 (Gender Equality), SDG17 (Partnerships for the Goals), and SDG16 (Peace, Justice and Strong Institutions), which showed highest hub scores in the trade- off network (0.96, 1, 0.86 and 0.81) and lowest in synergy (0.16, 0.46, 0.52 and 0.65) (Fig. 2(c) and 2(f)). SDG12 (Responsible Consumption and Production) showed a comparable score between synergy (0.69) and trade- off (0.71). China achieved great co- benefits on the other 12 goals with the score in synergy higher than trade- off (Fig. 2(c) and 2(f), see SI for more details). + +<--- Page Split ---> + +## The similarity and differences of SDGs priority among provinces + +Among the 158,100 indicator pairs from the 31 provinces, 19,046 pairs showed synergy and 6,067 pairs showed trade- off with the averaged ABS(R) of 0.94 and 0.92, respectively (Bonferroni corrected \(\mathsf{p}< 0.05\) and \(\mathsf{ABS(R)} > 0.6\) ) (see SI Tab. S1 and the data file for more details). Among goals, we found that SDG13 (Climate Action) and SDG5 (Gender Equality) had the lower hub scores in synergy on average (0.19 and 0.34) (Fig. 3(a), 3(c) and 3(e)) and higher in trade- off (0.76 for both) for 19 provinces (Fig. 3(b), 3(d) and 3(f), see SI for more details). The highest goal in trade- off differed among the other 12 provinces (Fig. 3(b) and 3(d), see SI for more details). We also found that most of the SDGs had the higher scores in synergy than trade- off. Among them, SDG1 (No Poverty), SDG6 (Clean Water and Sanitation), showed the high scores in synergy (0.98 and 0.97) for 24 provinces (Fig. 3(a), 3(c) and 3(e)) and lower in trade- off (0.35 and 0.42) (Fig. 3(b), 3(d) and 3(f), see SI for more details). The highest goal in synergy differed among the other 7 provinces (Fig. 3(a) and 3(c), see SI for more details). + +## The comparison of SDGs priority among regionals + +For the 6 geographical regions in China, we found that SDG13 (Climate Action) and SDG5 (Gender Equality) had the highest average hub scores in trade- off (0.73 and 0.72) of all regions and the lowest in synergy (0.19 and 0.37). SDG14 (Life below Water) had a comparable score between synergy (0.30) and trade- off (0.35). The other 14 goals had higher synergy than trade- off. Among them, SDG1 (No Poverty) and SDG6 (Clean Water and Sanitation) had the highest synergy (Fig. 4(a) and 4(d)). At the regional level, we found that SDG1 and SDG6 dominated all the 6 regions in synergy (Fig. 4(b) and 4(c)). SDG13 and SDG5 dominated all the regions except for Northeast China, where SDG4 (Quality Education) had the highest trade- off (Fig. 4(e) and 4(f)). The trade- off in SDG5 in southern regions were also higher than the northern regions (Fig. 4(e)). The trade- off in SDG13 presented an opposite pattern between north and south, i.e. it increased from east to west in the north, while it decreased in the south (Fig. 4 (f)). + +## Discussion and policy implication + +We used social network analysis to quantitatively and systematically identify the priorities of the 17 SDGs through SDGs interaction networks using a unified dataset of 102 indicators at the sub- national and national levels in China. This understanding helps prioritize goals to implement the SDGs in an integrated and holistic way, so synergies can be reinforced and trade- offs can be mitigated. + +Combatting climate change (SDG13) can reinforce all 17 SDGs \(^{23}\) . Gender equality (SDG5) is an enabler and accelerator for all the SDGs \(^{24}\) . Nevertheless, China became the world's largest emitter of carbon dioxide (CO2) in 2006 \(^{25}\) . China slipped from 63rd position in 2006 to 106th in the global gender gap rankings among 153 countries in 2019 \(^{26 - 27}\) . The gender gap in labor force participation between men and women rised from 9% to almost 15% between 1990s and 2020 \(^{26 - 27}\) . These brought serious trade- offs along with the rapid economic development. The most important is that China overall needs to take + +<--- Page Split ---> + +decisive actions to mitigate the negative impact from SDG13 (Climate Action) and SDG5 (Gender Equality) (see SI for more discussions). + +Poverty alleviation (SDG1) can have compound positive effects on all SDGs17. Water (SDG6) is a vital and irreplaceable resource for life and therefore underpins all SDGs28. By the end of 2020, Chinese government declared to the world that the poverty alleviation targets have been fulfilled as scheduled, lifting all 98.99 million rural people out of absolute poverty under the current standard29. The popularity rate of water supply reached 99.0% in urban area30. The centralized water supply and tap water reached 88% and 83% in rural areas30. The popularity rate of sanitary toilets in rural areas increased from 35.3% to more than 68% between 2017 and 202030. China needs continuing their great efforts to maintain the highly compound positive impact from SDG1 (No Poverty) and SDG6 (Clean Water and Sanitation) on the whole. + +Besides the common priorities, the key SDGs also differ at provincial and regional levels, especially in the trade- off network. Differentiated policies should be considered based on their own key SDGs. At the provincial level, Beijing and Chongqing needs to reduce the dominated trade- off in their rapid economic development (SDG8)31- 32. Xinjiang faces great trade- off from social dimension and the key priority is to improve the inequalities (SDG10)33. Tibet has poor health condition and needs to mitigate the highly negative impact from good health and well- being (SDG3)34. Heilongjiang and Jilin face high trade- off from their traditional industry35, the most important is to reduce the negative impact from industry (SDG9), and the consumption and production sectors (SDG12), respectively. At the regional level, Northeast China ought to mitigate the great trade- off from quality education (SDG4) through cooperation with other provinces outside the region36. East and Northwest China need to reduce the negative impact from high carbon emission per capita (SDG13)37- 38 and the southern part of China should focus on mitigating the great trade- off from gender equality (SDG5) along with their rapid economic development, including East, Central South and Southwest China20,26. + +To make the results meaningful in statistics, we used Bonferroni correction to avoid many possible spurious positives when several statistical tests being performed simultaneously39. We knew that the 17 goals, as the basic needs for humanity's survival and development on the planet, were related with each other and each indicator reflected one aspect of the goal it belonged to1- 3. We further used expert knowledge to explain the association between indicators for all the selected indicator pairs (see the thematic excel file in SI for more details). SDG synergies and trade- offs may be also affected by cross- boundary interactions through flows of energy, people, technology, financial capital, etc. Future research and policy on SDGs interaction can further account for cross- boundary issues and their complex mechanisms behind them40. + +With over two decades of data over China, we provide new insights into the common but differentiated SDGs priorities at provincial, regional, and national levels through interaction networks. Although the key SDGs in trade- off differed more than synergy at both provincial and regional levels, SDG13 (Climate + +<--- Page Split ---> + +Action) and SDG5 (Gender Equality) are the key hurdles for China to achieving 2030 agenda. SDG1 (No Poverty) and SDG6 (Clean Water and Sanitation) can have compound positive impact. Our study also provides China's example for determining priorities and improving the balance and integrity of measures towards achieving the SDGs to the other countries in the world. + +## Declarations + +## Data availability + +All data are available in the supplementary information. + +## Code availability + +All R scripts used to process the data are available from the corresponding authors upon request. + +Acknowledgments: We thank Prof. Mark Stafford Smith from Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia for providing comments on improving the quality of the paper. This study was supported by Strategic Priority Research Program of the Chinese Academy of Sciences grant (XDA19040103), National Natural Science Foundation of China grant (42125101) and CAS Interdisciplinary Innovation Team grant (JCTD- 2020- 05). + +## Author contributions: + +Conceptualization: CYW, FC Methodology: QX, PPYKL, XZC, AA Investigation: QX, CYW Visualization: QX, CYW Funding acquisition: CYW, FC Project administration: CYW, FC Supervision: CYW, JGL, PP Writing – original draft: QX, CYW, JGL Writing – review & editing: PP, BB, TS, LRC, YKL, XZC, AG, XZD, YJL, ZCX + +Competing interests: Authors declare that they have no competing interests. + +## References + +<--- Page Split ---> + +1. "Transforming our World: the 2030 Agenda for Sustainable Development," UN A/RES/70/1 (2015). + +2. UN. The sustainable development goals report 2022. United Nations (2022). + +3. Biermann, F., Hickmann, T., Sénit, C. A., Beisheim, M., Bernstein, S., Chasek, P., ... & Wicke, B.. 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The rapid growth of these industries: behind Beijing's rise to become China's first \(4 trillion city, https://new.qq.com/rain/a/20220110A040JB00, accessed on Feb. 10, 2023 (In Chinese).32. How fast is Chongqing's economy growing? 13 times in 22 years, https://baijiahao.baidu.com/s? id=1636782431316611699&wfr=spider&for=pc, accessed on Jan. 18, 2023 (In Chinese).33. Qing P., The particularity, influence and causes of the imbalanced socio-economic development in Xinjiang, Journal of Xinjiang University (Philosophy, Humanities & Social Science), 42(02):1-6 (2014) + +<--- Page Split ---> + +(In Chinese). + +34. Adhere to the people-centered development philosophy and strive to solve the difficulties in building a healthy Tibet, https://www.12371.cn/2019/07/19/ART11563527888637660. shtml, accessed on March, 2023 [in Chinese]. + +35. Shuai Z., Shuomin Z., Tianshuai X. & Wanhong L., The Structural Transformation of Manufacturing Industry in Northeast China and the Solution to Its Economic Growth, Advances in Social Science, Education and Humanities Research (ASSEHR), volume 199 (2018). + +36. Yang, Y., Ma, Z., & Xue, X. (2023). Why are University Teachers in Northeastern China Lost? An Analysis based on the Survey of Teachers' Intention to Leave. African and Asian Studies, 1(aop), 1-28. + +37. Liang, H., Dong, L., Luo, X., Ren, J., Zhang, N., Gao, Z., Dou, Y., 2016. Balancing regional industrial development: analysis on regional disparity of China's industrial emissions and policy implications. J. Clean. Prod. 126, 223e235. + +38. Dong F., Long R., Chen H., Li X. & Yang Q.. Factors Affecting Regional Per-Capita Carbon Emissions in China Based on an LMDI Factor Decomposition Model. PLoS ONE 8(12): e80888 (2013). + +39. Weisstein, E. W. (2004). Bonferroni correction. https://mathworld.wolfram.com/. + +40. Xu, Z., Li, Y., Chau, S.N. et al. Impacts of international trade on global sustainable development. Nat Sustain 3, 964-971. https://doi.org/10.1038/s41893-020-0572-z (2020). + +41. Inter-agency and Expert Group on SDG Indicators, (IAEG-SDGs), Tier Classification for Global SDG Indicators, https://unstats.un.org/sdgs/iaeg-sdgs/tier-classification/ (2021). + +42. Sachs, J., Lafortune, G., Kroll, C., Fuller, G., Woelm, F.. From Crisis to Sustainable Development: the SDGs as Roadmap to 2030 and Beyond. Sustainable Development Report 2022, Cambridge: Cambridge University Press, doi.org/10.1017/9781009210058 (2022). + +43. China Statistics Press, "China Statistical Yearbook" (National Bureau of Statistics of the People's Republic of China, 2001-2021) [in Chinese]. + +44. China Financial & Economic Publishing House, "Finance Yearbook of China" (Ministry of Finance of the People's Republic of China, 2001-2021) [in Chinese]. + +45. China Statistics Press, "China Statistical Yearbook on Environment" (National Bureau of Statistics & State Environmental Protection Administration of the People's Republic of China, 2001-2021) [in Chinese]. + +46. People's Education Press, "Educational Statistics Yearbook of China" (Ministry of Education of the People's Republic of China, 2001-2021) [in Chinese]. + +47. China Statistics Press, "China Health and Family Planning Statistical Yearbook" (National Bureau of Statistics of the People's Republic of China, 2001-2021) [in Chinese]. + +48. China Statistics Press, "China Energy Statistical Yearbook" (National Bureau of Statistics of the People's Republic of China, 2001-2021) [in Chinese]. + +<--- Page Split ---> + +49. Hauke, J., & Kossowski, T. Comparison of values of Pearson's and Spearman's correlation coefficients on the same sets of data. Quaestiones Geographicae, 30(2), 87 (2011). +50. Warchold, A., Pradhan, P. & Kropp, J. P. Variations in sustainable development goal interactions: population, regional, and income disaggregation. Sustain. Dev. 29, 285-299 (2020). +51. Borgatti, Stephen P., et al. "Network analysis in the social sciences", Science 323.5916, 892-895 (2009). +52. Luke, Douglas A., and Jenine K. Harris. Network analysis in public health: history, methods, and applications, Annu. Rev. Public Health 28, 69 (2007). +53. Felipe-Lucia M R, Soliveres S, Penone C, et al. Land-use intensity alters networks between biodiversity, ecosystem functions, and services. Proc. Natl. Acad. Sci. U.S.A. 117(45): 28140-28149 (2020). +54. Horvath, Steve. Weighted network analysis: applications in genomics and systems biology. Springer Science & Business Media (2011). +55. Csardi, G. & Nepusz, T. Te igraph software package for complex network research. InterJournal https://igraph.org (2006). +56. J. Kleinberg. Authoritative sources in a hyperlinked environment. Proc. 9th ACM-SIAM Symposium on Discrete Algorithms, 1998. Extended version in Journal of the ACM 46(1999). Also appears as IBM Research Report RJ 10076, May 1997. + +## Methods + +## Data collection and pre-processing + +We selected indicators based on the definitions of goals, targets, and indicators in the UN official SDGs documents41, the 2022 SDG Index and Dashboards Report from the Sustainable Development Solutions Network42, and some recent studies20, 22. For each SDG, we chose as many SDG indicators as feasible from the list of recommended indicators based on available data at the sub- national and national levels simultaneously and the availability of the indicators across the temporal scale. + +Data for the selected indicators in this study were obtained from the following official sources: the National Bureau of Statistics of the People's Republic of China, the China Statistical Yearbook43, the Finance Yearbook of China44, the China Statistical Yearbook on the Environment45, the Educational Statistics Yearbook of China46, the China Health Statistics Yearbook47, the China Energy Statistical Yearbook48, and other 9 Yearbooks from various ministries, such as insurance, urban construction, tourism, transportation & communications, industry, civil affairs, marine, forestry and population. See SI Tab. S2 for a list of SDGs and their corresponding indicators, data sources, and the period used in this paper. + +If the indicator had different elements, the average value of all the elements was calculated for the analysis. For example, the proportion of the population covered by insurance (endowment, + +<--- Page Split ---> + +unemployment, and medicare) (SDG 1, Indicator 1.3.1) was calculated from the average of that covered by endowment, unemployment, and medicare insurance. The averaged SDGs' indicators included the following: 1.3.1, 1.4.1, 2.3.1, 4. a.L, 4. c.1, 8.4.2, 9.1.2 and 12.2.2 (SI Tab. S2). We originally collected data for 118 indicators at national and sub- national levels annually. Then, the data were narrowed down to 102 indicators after the averaged calculation of various elements within one indicator. These data for 102 indicators are related to 81 targets and 17 goals. + +## Synergy and trade-off calculation at the indicator level + +The longitudinal Spearman correlation analyses covering non- linear relations were conducted between all 102 indicators at the 31 sub- national units one by one. The missing indicators data at certain years were dropped individually for each pairwise correlation by using the 'pairwise.complete.observation' mode. A Bonferroni correction was conducted to correct the P value when undertaking this many correlation tests39. The absolute value of the correlation coefficient \(|\mathbb{R}|\) more than 0.6 were applied further to select the indicator pairs5,22,49- 50. Since a higher value of an indicator did not necessarily mean a positive impact on sustainable development, we made a specific judgment based on the meaning of each indicator. For example, for the malnutrition rate of children under the age of 5 (SDG 2, Indicator 2.2.2), the lower value indicated a positive outcome. In contrast, for the proportion of GDP used to protect the biodiversity and ecosystem (SDG 15, Indicator 15. a.1), a lower value indicated a negative contribution to sustainable development. The detailed judgment table was listed in Supplementary Information, with "+1" indicating the better for sustainable development and "- 1" indicating worse (see SI Table S1). We used expert knowledge to explain the association between indicators for all the selected indicator pairs (see the excel files for more details). + +## Synergy and trade-off calculation at the goal level + +Based on the affiliation between indicator, target and goal41, the synergy intensity was calculated as follows: + +\[I n t e n s i t y_{s y n e r g y} = \frac{E N_{s y n e r g y}}{T N_{s y n e r g y}}\times \frac{\sum_{i = 1}^{E N_{s y n e r g y}}|R_{s y n e r g y}|}{E N_{s y n e r g y}} \quad (1)\] + +Where \(I n t e n s i t y_{s y n e r g y}\) was the synergy intensity, \(E N_{s y n e r g y}\) was the number of effective indicator pairs belonging to synergy, \(T N_{s y n e r g y}\) was the total number of indicator pairs between goals, \(R_{s y n e r g y}\) was the Spearman correlation cofficient of the effective indicator pair. + +The trade- off intensity was calculated as follows: + +<--- Page Split ---> + +\[Intensity_{trade - off} = \frac{EN_{trade - off}}{TN_{trade - off}} \times \frac{\sum_{i = 1}^{EN_{trade - off}}|R_{trade - off}|}{EN_{trade - off}} \quad (2)\] + +Where \(Intensity_{trade - off}\) was the trade- off intensity, \(EN_{trade - off}\) was the number of effective indicator pairs belonging to trade- off, \(TN_{trade - off}\) was the total number of indicator pairs between goals, \(R_{trade - off}\) was the Spearman correlation coefficient of the effective indicator pair. If we calculated the synergy and trade- off intensity directly at the goal level, we will ignore the fact that there were both synergies and trade- offs between different SDGs. + +## Network analysis + +Network analysis, which has been applied in social science \(^{51}\) , public heath \(^{52}\) , ecology \(^{53}\) and biology \(^{54}\) to study complex systems, is a holistic approach to studying the complexity of SDG interactions to identify the importance of goals or targets. The synergy and trade- off networks were built separately for the national and 31 provinces using iGraph package in R Studio, respectively \(^{55}\) . Kleinberg's hub centrality score is defined as the principal eigenvector of A\*(A), where A is the adjacency matrix of the graph. Similarly, Kleinberg's authority centrality score is defined as the principal eigenvector of t(A)\*A, where A is the adjacency matrix of the graph. For undirected matrices the adjacency matrix is symmetric and the hub scores are the same as authority scores \(^{56}\) . The hub scores of the 17 SDGs were set as nodes, and the synergy or trade- off intensity among SDGs was set as the weighted edge in the network. These hub scores were used to calculate and assess the importance of the SDGs in the synergy and trade- off networks accounting for the direct and the indirect interactions. The larger the hub score was, the more important the node as a central hub was in the synergy or trade- off networks. The priority of the SDGs was identified based on the hub score in the networks from synergy and trade- off perspectives. + +## The importance of the SDGs at different spatial levels + +At the national level, we combined the 102 indicators in pairs, resulting in 5100 pairs in total. At the provincial level, we combined the 102 indicators in pairs for all the 31 provinces. The number of indicator pairs reached 158, 100 (5100 pairs 31 provinces) pairs in total. From indicator to goal, the importance of the SDGs was analyzed following the procedures above at national and all the provincial levels. For the detailed statistics of the number of the selected indicator pairs and Spearman correlation coefficients of the 31 provinces, please refer to SI Tab. S2. The results at the regional level were aggregated from those at provincial levels following the geographic regions divisions in China (See SI Tab. S3 for more details). + +## Figures + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1
+ +The indicator pairs of synergy and trade- off at the national level (a): the distribution of the indicator pairs with numbering of the indicators showing the affiliation between the 102 indicators and 17 goals. The selected criteria are that Bonferroni corrected p value less than 0.05 and the absolute value of the Spearman correlation coefficient R more than 0.6. Each indicator is judged to have a positive or negative impact on sustainable development based on its own meaning. The indicator pairs are divided by 5 groups, including synergy, trade- off, weak synergy, weak trade- off and invalid indicator pairs. Different colors indicate different SDGs following the official UN color palette. (b): The averaged R and number of indicator pairs for each group. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2
+ +The synergy and trade- off networks at the goal level (a) and (d): the absolute value of Spearman correlation coefficient R (short for ABS(R)) among goals at the national level. (a) is for synergy and (d) is for trade- off. Set Fig.2(a) as an example, the width of the colored line indicates the arithmetic mean of ABS(R) among goals calculated from the indicator pairs in Fig 1(a). The width of the arc represents the cumulative value of each line width for that goal. The number outside the circle is the scale of each goal. (b) and (e): the ratio of the number of the selected indicator pairs out of the total number of all possible combinations among goals (short for Ratio). (b) is for synergy and (e) is for trade- off. (c) and (f): the networks built upon ABS(R) and Ratio. The thickness of the edge in the network indicates the synergy or trade- off intensity among goals. The thicker the edge is the stronger the intensity is. The size of the circle suggests its importance as a central hub in the network. The larger the circle is, the more important the node as a central hub is. (c) is for synergy (hub score: 0.16- 1) and (f) is for trade- off (hub score: 0.14- 1). In the synergy network the edge is shown in blue and in red in the trade- off networks. Different colors indicate different SDGs following the official UN color palette. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3
+ +The hubs scores of the 17 SDGs in synergy and trade- off at the provincial level (a) and (b): the hub scores in the synergy (a) and trade- off (b) networks. (c) and (d): the ranking of the 17 SDGs in the synergy (c) and trade- off (d) networks in order of its hub score. (e) and (f): the statistics of the hub score of the 17 SDGs for synergy (e) and trade- off (f). The black line in each box shows the minimum value, lower quartile, median, upper quartile and maximum value from left to right for each SDG. The solid black circle indicates the arithmetic mean value. Different colors indicate different SDGs following the official UN color palette. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4
+ +The hub score of the 17 SDGs at the regional level (a) and (d): the hub score in the synergy (a) and trade- off (d) networks. Different colors indicate different SDGs following the official UN color palette. (b) and (e): the spatial pattern of the hub score of SDG5 (Gender equality) in the synergy (b) and trade- off (e) networks. (c) and (f): the spatial pattern of the hub score of SDG13 (Climate action) in the synergy (c) and trade- off (f) networks. + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- Explanationsontheassociationbetweenindicators.xlsx- Dataforthesynergyandtradeoffamongindicatorsatnationalandprovinciallevels.xlsx- SupplementaryInformation.docx + +<--- Page Split ---> diff --git a/preprint/preprint__05a18c267c6d7dedb4a7e980e586fa6f60b4fb0553596df82b5ab1b095ebd821/preprint__05a18c267c6d7dedb4a7e980e586fa6f60b4fb0553596df82b5ab1b095ebd821_det.mmd b/preprint/preprint__05a18c267c6d7dedb4a7e980e586fa6f60b4fb0553596df82b5ab1b095ebd821/preprint__05a18c267c6d7dedb4a7e980e586fa6f60b4fb0553596df82b5ab1b095ebd821_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..27de0b31c341dd9a49a32d0ac2ede7b0e7518fad --- /dev/null +++ b/preprint/preprint__05a18c267c6d7dedb4a7e980e586fa6f60b4fb0553596df82b5ab1b095ebd821/preprint__05a18c267c6d7dedb4a7e980e586fa6f60b4fb0553596df82b5ab1b095ebd821_det.mmd @@ -0,0 +1,400 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 933, 175]]<|/det|> +# Climate action and gender equality matter most for China's sustainable development + +<|ref|>text<|/ref|><|det|>[[44, 196, 196, 215]]<|/det|> +Chaoyang Wu + +<|ref|>text<|/ref|><|det|>[[52, 224, 245, 240]]<|/det|> +wucyeigsnrr.ac.cn + +<|ref|>text<|/ref|><|det|>[[44, 268, 936, 310]]<|/det|> +Institute of Geographic Sciences and Natural Resources Research https://orcid.org/0000- 0001- 6163- 8209 + +<|ref|>text<|/ref|><|det|>[[44, 317, 684, 356]]<|/det|> +Qiang Xing Aerospace Information Research Institute, Chinese Academy of Sciences + +<|ref|>text<|/ref|><|det|>[[44, 362, 728, 404]]<|/det|> +Fang Chen International Research Center of Big Data for Sustainable Development Goals + +<|ref|>text<|/ref|><|det|>[[44, 409, 636, 450]]<|/det|> +Jianguo Liu Michigan State University https://orcid.org/0000- 0001- 6344- 0087 + +<|ref|>text<|/ref|><|det|>[[44, 455, 820, 496]]<|/det|> +Prajal Pradhan Potsdam Institute for Climate Impact Research https://orcid.org/0000- 0003- 0491- 5489 + +<|ref|>text<|/ref|><|det|>[[44, 502, 564, 542]]<|/det|> +Brett Bryan Deakin University https://orcid.org/0000- 0003- 4834- 5641 + +<|ref|>text<|/ref|><|det|>[[44, 548, 481, 589]]<|/det|> +Thomas Schaubroeck Luxembourg Institute of Science and Technology + +<|ref|>text<|/ref|><|det|>[[44, 595, 696, 636]]<|/det|> +Luis Roman Carrasco National University of Singapore https://orcid.org/0000- 0002- 2894- 1473 + +<|ref|>text<|/ref|><|det|>[[44, 641, 595, 681]]<|/det|> +Alemu Gonsamo McMaster University https://orcid.org/0000- 0002- 2461- 618X + +<|ref|>text<|/ref|><|det|>[[44, 687, 697, 728]]<|/det|> +Yunkai Li College of Water Resource & Civil Engineering, China Agriculture University + +<|ref|>text<|/ref|><|det|>[[44, 733, 661, 774]]<|/det|> +Xiuzhi Chen China Agricultural University https://orcid.org/0000- 0002- 9371- 4648 + +<|ref|>text<|/ref|><|det|>[[44, 780, 675, 820]]<|/det|> +Xiangzheng Deng Chinese Academy of Sciences https://orcid.org/0000- 0002- 7993- 5540 + +<|ref|>text<|/ref|><|det|>[[44, 826, 496, 866]]<|/det|> +Andrea Albanese Luxembourg Institute of Socio- Economic Research + +<|ref|>text<|/ref|><|det|>[[44, 872, 223, 912]]<|/det|> +Yingjie Li Stanford University + +<|ref|>text<|/ref|><|det|>[[44, 918, 133, 936]]<|/det|> +Zhenci Xu + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 106, 102, 124]]<|/det|> +## Article + +<|ref|>sub_title<|/ref|><|det|>[[44, 144, 136, 163]]<|/det|> +## Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 181, 302, 201]]<|/det|> +Posted Date: June 29th, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 220, 474, 239]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3053894/v1 + +<|ref|>text<|/ref|><|det|>[[44, 257, 910, 300]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 317, 531, 337]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 372, 925, 417]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on March 13th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 46491- 6. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 159, 68]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[40, 82, 950, 377]]<|/det|> +Rescuing Sustainable Development Goals (SDGs) from failing requires understanding their interactions networks, i.e., synergies and trade- offs, at national and especially sub- national levels, where SDGs were delivered. This understanding will help identifying the key hurdles and opportunities to prioritize the 17 SDGs in a indivisible manner for a country. However, current research on SDG priorities at sub- national levels remains limited mainly due to difficulty in data collection. Here, we collect a unified annual dataset of 102 indicators covering national and 31 provinces in China over 2000–2020. We analyze the importance of the 17 SDGs at national, provincial and regional levels through synergy and trade- off networks. The key SDGs in trade- off (provincial: 12/31, regional: 1/6) differ more than synergy (provincial: 7/31, regional: 0). Nevertheless, combating climate change (SDG13) and improving gender equality (SDG5) are overall key hurdles for China to achieving 2030 agenda. Focusing on poverty eradication (SDG1) and increasing clean water and sanitation (SDG6) have highly compound positive effect. Our findings provide essential knowledge and insight on adopting common but diffrentiaed SDGs priorities and balance mattering China's sustainable development. + +<|ref|>sub_title<|/ref|><|det|>[[44, 400, 207, 426]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[41, 439, 930, 721]]<|/det|> +The 2030 Agenda for Sustainable Development, consisting of the 17 Sustainable Development Goals (SDGs) and 169 targets, is a global agenda for people, the planet, and prosperity to transform the world onto a sustainable and resilient path1. However, SDGs have had a limited transformative impact2, 3. A reason for this failure of SDGs is their selective implementation without considering their complex interactions4. As a system of interacting components, SDGs have complex interconnections with synergies (a pair of SDGs improve or deteriorate together) and trade- offs (one SDG improves while the other deteriorates), which play essential roles in achieving or inhibiting their effectiveness4–6. These complex interactions largely depend on strategies to achieve an SDG. For example, infrastructure like roads are necessary for poverty alleviation (SDG1) and economic development (SDG8) but may be detrimental for coast (SDG14) and land ecosystem (SDG15)7. Thus, to rescue SDGs from failing, it is essential to understand their synergies and trade- offs for determining priorities and improving the balance and integrity of policies towards achieving the 2030 Agenda holistically4,8. + +<|ref|>text<|/ref|><|det|>[[41, 736, 940, 927]]<|/det|> +Systems thinking and analysis to assess the complex interactions among all 17 SDGs is at the forefront of sustainability research9. Existing SDG studies qualitatively evaluate SDG interactions by literature review10,23, expert rating11–13, text mining14. The model- based analysis focuses more on environmental SDGs, less on social and economic dimensions15. With public database, some research used network analysis to quantitatively analyze the differences in SDG interaction networks at global and national levels16–18. However, due to economic, social and environmental heterogeneity, SDG interactions may vary at a local or sub- national level within a country, where SDGs were actually implemented3–4,16. Understanding variations in SDG interaction networks at different spatial levels, especially at the sub + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 952, 90]]<|/det|> +national level, remain a fundamental research gap, which in fact are essential for identifying context- and location- specific strategies for an integrated SDG implementation, especially for big countries. + +<|ref|>text<|/ref|><|det|>[[41, 105, 955, 289]]<|/det|> +As the largest developing country in geographic area and population, China has experienced rapid economic development over the past few decades. However, it has also faced social problems and environmental challenges while striving a rapid economic development \(^{19 - 20}\) . Still, earlier studies suggested the uneven progress among the 17 SDGs at the sub- national level as a significant challenge for China's sustainable development \(^{21 - 22}\) . We fill the above- highlighted research gaps by addressing the following two questions from synergy and trade- off perspectives. (1) What are the common SDG priorities among provincial, regional and national levels? (2) How much are the differentiated priorities for synergy and trade- off? + +<|ref|>text<|/ref|><|det|>[[41, 305, 955, 532]]<|/det|> +To this end, we collected as much data as possible to cover all 17 SDGs at national and sub- national levels simultaneously on a yearly basis from 2000 to 2020. In total, a unified dataset of 102 indicators were used in our analysis, consisting of 31 provinces in China (see Methods). We proposed synergy and trade- off networks at national and sub- national levels, respectively. The synergy and trade- off intensity was set to be the weighted edge and the hub score of the 17 goals were set to be the nodes in the networks (see Methods). We analyzed the hub score to determine which goal served as the central hub in the synergy and trade- off networks. The larger the hub score represented the more important the node as the central hub in the networks. We analyzed these variations at national, provincial and regional levels among 17 goals. Our findings can provide essential knowledge and insights on the priority of the SDGs to accelerate implementing the SDGs in a holistic manner at different spatial levels in China. + +<|ref|>sub_title<|/ref|><|det|>[[44, 555, 144, 580]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[44, 592, 614, 625]]<|/det|> +## The SDGs priority at the national level + +<|ref|>text<|/ref|><|det|>[[40, 638, 950, 911]]<|/det|> +At the national level, 1023 out of 5100 indicators pairs showed synergy, and 374 pairs showed trade- off with the average ABS(R) (absolute value of Spearman Correlation coefficient R) of 0.95 and 0.94 for both indicators and goals (Bonferroni corrected \(p < 0.05\) and ABS(R) \(> 0.6\) ) (Fig. 1(a) and 1(b) for indicators, see SI for more details, Fig. 2(a) and 2(d) for goals). The average Ratio (ratio of the number of the selected indicator pairs out of the total number of all possible combinations among goals) was 0.69 and 0.31 for synergy and trade- off (Fig. 2(b) and 2(e)). Overall, we found that China faced challenges on SDG13 (Climate Change Action), SDG5 (Gender Equality), SDG17 (Partnerships for the Goals), and SDG16 (Peace, Justice and Strong Institutions), which showed highest hub scores in the trade- off network (0.96, 1, 0.86 and 0.81) and lowest in synergy (0.16, 0.46, 0.52 and 0.65) (Fig. 2(c) and 2(f)). SDG12 (Responsible Consumption and Production) showed a comparable score between synergy (0.69) and trade- off (0.71). China achieved great co- benefits on the other 12 goals with the score in synergy higher than trade- off (Fig. 2(c) and 2(f), see SI for more details). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 41, 861, 104]]<|/det|> +## The similarity and differences of SDGs priority among provinces + +<|ref|>text<|/ref|><|det|>[[40, 118, 955, 368]]<|/det|> +Among the 158,100 indicator pairs from the 31 provinces, 19,046 pairs showed synergy and 6,067 pairs showed trade- off with the averaged ABS(R) of 0.94 and 0.92, respectively (Bonferroni corrected \(\mathsf{p}< 0.05\) and \(\mathsf{ABS(R)} > 0.6\) ) (see SI Tab. S1 and the data file for more details). Among goals, we found that SDG13 (Climate Action) and SDG5 (Gender Equality) had the lower hub scores in synergy on average (0.19 and 0.34) (Fig. 3(a), 3(c) and 3(e)) and higher in trade- off (0.76 for both) for 19 provinces (Fig. 3(b), 3(d) and 3(f), see SI for more details). The highest goal in trade- off differed among the other 12 provinces (Fig. 3(b) and 3(d), see SI for more details). We also found that most of the SDGs had the higher scores in synergy than trade- off. Among them, SDG1 (No Poverty), SDG6 (Clean Water and Sanitation), showed the high scores in synergy (0.98 and 0.97) for 24 provinces (Fig. 3(a), 3(c) and 3(e)) and lower in trade- off (0.35 and 0.42) (Fig. 3(b), 3(d) and 3(f), see SI for more details). The highest goal in synergy differed among the other 7 provinces (Fig. 3(a) and 3(c), see SI for more details). + +<|ref|>sub_title<|/ref|><|det|>[[45, 368, 806, 401]]<|/det|> +## The comparison of SDGs priority among regionals + +<|ref|>text<|/ref|><|det|>[[41, 413, 952, 640]]<|/det|> +For the 6 geographical regions in China, we found that SDG13 (Climate Action) and SDG5 (Gender Equality) had the highest average hub scores in trade- off (0.73 and 0.72) of all regions and the lowest in synergy (0.19 and 0.37). SDG14 (Life below Water) had a comparable score between synergy (0.30) and trade- off (0.35). The other 14 goals had higher synergy than trade- off. Among them, SDG1 (No Poverty) and SDG6 (Clean Water and Sanitation) had the highest synergy (Fig. 4(a) and 4(d)). At the regional level, we found that SDG1 and SDG6 dominated all the 6 regions in synergy (Fig. 4(b) and 4(c)). SDG13 and SDG5 dominated all the regions except for Northeast China, where SDG4 (Quality Education) had the highest trade- off (Fig. 4(e) and 4(f)). The trade- off in SDG5 in southern regions were also higher than the northern regions (Fig. 4(e)). The trade- off in SDG13 presented an opposite pattern between north and south, i.e. it increased from east to west in the north, while it decreased in the south (Fig. 4 (f)). + +<|ref|>sub_title<|/ref|><|det|>[[44, 661, 485, 690]]<|/det|> +## Discussion and policy implication + +<|ref|>text<|/ref|><|det|>[[43, 703, 925, 793]]<|/det|> +We used social network analysis to quantitatively and systematically identify the priorities of the 17 SDGs through SDGs interaction networks using a unified dataset of 102 indicators at the sub- national and national levels in China. This understanding helps prioritize goals to implement the SDGs in an integrated and holistic way, so synergies can be reinforced and trade- offs can be mitigated. + +<|ref|>text<|/ref|><|det|>[[42, 810, 936, 953]]<|/det|> +Combatting climate change (SDG13) can reinforce all 17 SDGs \(^{23}\) . Gender equality (SDG5) is an enabler and accelerator for all the SDGs \(^{24}\) . Nevertheless, China became the world's largest emitter of carbon dioxide (CO2) in 2006 \(^{25}\) . China slipped from 63rd position in 2006 to 106th in the global gender gap rankings among 153 countries in 2019 \(^{26 - 27}\) . The gender gap in labor force participation between men and women rised from 9% to almost 15% between 1990s and 2020 \(^{26 - 27}\) . These brought serious trade- offs along with the rapid economic development. The most important is that China overall needs to take + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 883, 88]]<|/det|> +decisive actions to mitigate the negative impact from SDG13 (Climate Action) and SDG5 (Gender Equality) (see SI for more discussions). + +<|ref|>text<|/ref|><|det|>[[40, 106, 955, 319]]<|/det|> +Poverty alleviation (SDG1) can have compound positive effects on all SDGs17. Water (SDG6) is a vital and irreplaceable resource for life and therefore underpins all SDGs28. By the end of 2020, Chinese government declared to the world that the poverty alleviation targets have been fulfilled as scheduled, lifting all 98.99 million rural people out of absolute poverty under the current standard29. The popularity rate of water supply reached 99.0% in urban area30. The centralized water supply and tap water reached 88% and 83% in rural areas30. The popularity rate of sanitary toilets in rural areas increased from 35.3% to more than 68% between 2017 and 202030. China needs continuing their great efforts to maintain the highly compound positive impact from SDG1 (No Poverty) and SDG6 (Clean Water and Sanitation) on the whole. + +<|ref|>text<|/ref|><|det|>[[40, 336, 950, 642]]<|/det|> +Besides the common priorities, the key SDGs also differ at provincial and regional levels, especially in the trade- off network. Differentiated policies should be considered based on their own key SDGs. At the provincial level, Beijing and Chongqing needs to reduce the dominated trade- off in their rapid economic development (SDG8)31- 32. Xinjiang faces great trade- off from social dimension and the key priority is to improve the inequalities (SDG10)33. Tibet has poor health condition and needs to mitigate the highly negative impact from good health and well- being (SDG3)34. Heilongjiang and Jilin face high trade- off from their traditional industry35, the most important is to reduce the negative impact from industry (SDG9), and the consumption and production sectors (SDG12), respectively. At the regional level, Northeast China ought to mitigate the great trade- off from quality education (SDG4) through cooperation with other provinces outside the region36. East and Northwest China need to reduce the negative impact from high carbon emission per capita (SDG13)37- 38 and the southern part of China should focus on mitigating the great trade- off from gender equality (SDG5) along with their rapid economic development, including East, Central South and Southwest China20,26. + +<|ref|>text<|/ref|><|det|>[[41, 659, 944, 867]]<|/det|> +To make the results meaningful in statistics, we used Bonferroni correction to avoid many possible spurious positives when several statistical tests being performed simultaneously39. We knew that the 17 goals, as the basic needs for humanity's survival and development on the planet, were related with each other and each indicator reflected one aspect of the goal it belonged to1- 3. We further used expert knowledge to explain the association between indicators for all the selected indicator pairs (see the thematic excel file in SI for more details). SDG synergies and trade- offs may be also affected by cross- boundary interactions through flows of energy, people, technology, financial capital, etc. Future research and policy on SDGs interaction can further account for cross- boundary issues and their complex mechanisms behind them40. + +<|ref|>text<|/ref|><|det|>[[42, 886, 944, 952]]<|/det|> +With over two decades of data over China, we provide new insights into the common but differentiated SDGs priorities at provincial, regional, and national levels through interaction networks. Although the key SDGs in trade- off differed more than synergy at both provincial and regional levels, SDG13 (Climate + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 944, 133]]<|/det|> +Action) and SDG5 (Gender Equality) are the key hurdles for China to achieving 2030 agenda. SDG1 (No Poverty) and SDG6 (Clean Water and Sanitation) can have compound positive impact. Our study also provides China's example for determining priorities and improving the balance and integrity of measures towards achieving the SDGs to the other countries in the world. + +<|ref|>sub_title<|/ref|><|det|>[[45, 156, 212, 182]]<|/det|> +## Declarations + +<|ref|>sub_title<|/ref|><|det|>[[44, 198, 187, 217]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[44, 235, 525, 255]]<|/det|> +All data are available in the supplementary information. + +<|ref|>sub_title<|/ref|><|det|>[[44, 273, 189, 293]]<|/det|> +## Code availability + +<|ref|>text<|/ref|><|det|>[[44, 310, 880, 332]]<|/det|> +All R scripts used to process the data are available from the corresponding authors upon request. + +<|ref|>text<|/ref|><|det|>[[42, 348, 922, 461]]<|/det|> +Acknowledgments: We thank Prof. Mark Stafford Smith from Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia for providing comments on improving the quality of the paper. This study was supported by Strategic Priority Research Program of the Chinese Academy of Sciences grant (XDA19040103), National Natural Science Foundation of China grant (42125101) and CAS Interdisciplinary Innovation Team grant (JCTD- 2020- 05). + +<|ref|>sub_title<|/ref|><|det|>[[44, 478, 227, 497]]<|/det|> +## Author contributions: + +<|ref|>text<|/ref|><|det|>[[42, 515, 680, 860]]<|/det|> +Conceptualization: CYW, FC Methodology: QX, PPYKL, XZC, AA Investigation: QX, CYW Visualization: QX, CYW Funding acquisition: CYW, FC Project administration: CYW, FC Supervision: CYW, JGL, PP Writing – original draft: QX, CYW, JGL Writing – review & editing: PP, BB, TS, LRC, YKL, XZC, AG, XZD, YJL, ZCX + +<|ref|>text<|/ref|><|det|>[[44, 857, 697, 878]]<|/det|> +Competing interests: Authors declare that they have no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[45, 900, 195, 925]]<|/det|> +## References + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[58, 45, 933, 66]]<|/det|> +1. 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Te igraph software package for complex network research. InterJournal https://igraph.org (2006). +56. J. Kleinberg. Authoritative sources in a hyperlinked environment. Proc. 9th ACM-SIAM Symposium on Discrete Algorithms, 1998. Extended version in Journal of the ACM 46(1999). Also appears as IBM Research Report RJ 10076, May 1997. + +<|ref|>sub_title<|/ref|><|det|>[[45, 501, 164, 526]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[45, 543, 346, 563]]<|/det|> +## Data collection and pre-processing + +<|ref|>text<|/ref|><|det|>[[42, 580, 945, 696]]<|/det|> +We selected indicators based on the definitions of goals, targets, and indicators in the UN official SDGs documents41, the 2022 SDG Index and Dashboards Report from the Sustainable Development Solutions Network42, and some recent studies20, 22. For each SDG, we chose as many SDG indicators as feasible from the list of recommended indicators based on available data at the sub- national and national levels simultaneously and the availability of the indicators across the temporal scale. + +<|ref|>text<|/ref|><|det|>[[41, 712, 940, 900]]<|/det|> +Data for the selected indicators in this study were obtained from the following official sources: the National Bureau of Statistics of the People's Republic of China, the China Statistical Yearbook43, the Finance Yearbook of China44, the China Statistical Yearbook on the Environment45, the Educational Statistics Yearbook of China46, the China Health Statistics Yearbook47, the China Energy Statistical Yearbook48, and other 9 Yearbooks from various ministries, such as insurance, urban construction, tourism, transportation & communications, industry, civil affairs, marine, forestry and population. See SI Tab. S2 for a list of SDGs and their corresponding indicators, data sources, and the period used in this paper. + +<|ref|>text<|/ref|><|det|>[[42, 917, 895, 960]]<|/det|> +If the indicator had different elements, the average value of all the elements was calculated for the analysis. For example, the proportion of the population covered by insurance (endowment, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 44, 955, 180]]<|/det|> +unemployment, and medicare) (SDG 1, Indicator 1.3.1) was calculated from the average of that covered by endowment, unemployment, and medicare insurance. The averaged SDGs' indicators included the following: 1.3.1, 1.4.1, 2.3.1, 4. a.L, 4. c.1, 8.4.2, 9.1.2 and 12.2.2 (SI Tab. S2). We originally collected data for 118 indicators at national and sub- national levels annually. Then, the data were narrowed down to 102 indicators after the averaged calculation of various elements within one indicator. These data for 102 indicators are related to 81 targets and 17 goals. + +<|ref|>sub_title<|/ref|><|det|>[[42, 196, 514, 217]]<|/det|> +## Synergy and trade-off calculation at the indicator level + +<|ref|>text<|/ref|><|det|>[[41, 234, 953, 555]]<|/det|> +The longitudinal Spearman correlation analyses covering non- linear relations were conducted between all 102 indicators at the 31 sub- national units one by one. The missing indicators data at certain years were dropped individually for each pairwise correlation by using the 'pairwise.complete.observation' mode. A Bonferroni correction was conducted to correct the P value when undertaking this many correlation tests39. The absolute value of the correlation coefficient \(|\mathbb{R}|\) more than 0.6 were applied further to select the indicator pairs5,22,49- 50. Since a higher value of an indicator did not necessarily mean a positive impact on sustainable development, we made a specific judgment based on the meaning of each indicator. For example, for the malnutrition rate of children under the age of 5 (SDG 2, Indicator 2.2.2), the lower value indicated a positive outcome. In contrast, for the proportion of GDP used to protect the biodiversity and ecosystem (SDG 15, Indicator 15. a.1), a lower value indicated a negative contribution to sustainable development. The detailed judgment table was listed in Supplementary Information, with "+1" indicating the better for sustainable development and "- 1" indicating worse (see SI Table S1). We used expert knowledge to explain the association between indicators for all the selected indicator pairs (see the excel files for more details). + +<|ref|>sub_title<|/ref|><|det|>[[42, 571, 476, 592]]<|/det|> +## Synergy and trade-off calculation at the goal level + +<|ref|>text<|/ref|><|det|>[[42, 610, 911, 653]]<|/det|> +Based on the affiliation between indicator, target and goal41, the synergy intensity was calculated as follows: + +<|ref|>equation<|/ref|><|det|>[[204, 671, 825, 715]]<|/det|> +\[I n t e n s i t y_{s y n e r g y} = \frac{E N_{s y n e r g y}}{T N_{s y n e r g y}}\times \frac{\sum_{i = 1}^{E N_{s y n e r g y}}|R_{s y n e r g y}|}{E N_{s y n e r g y}} \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[52, 730, 825, 866]]<|/det|> +Where \(I n t e n s i t y_{s y n e r g y}\) was the synergy intensity, \(E N_{s y n e r g y}\) was the number of effective indicator pairs belonging to synergy, \(T N_{s y n e r g y}\) was the total number of indicator pairs between goals, \(R_{s y n e r g y}\) was the Spearman correlation cofficient of the effective indicator pair. + +<|ref|>text<|/ref|><|det|>[[42, 892, 472, 912]]<|/det|> +The trade- off intensity was calculated as follows: + +<--- Page Split ---> +<|ref|>equation<|/ref|><|det|>[[190, 52, 830, 98]]<|/det|> +\[Intensity_{trade - off} = \frac{EN_{trade - off}}{TN_{trade - off}} \times \frac{\sum_{i = 1}^{EN_{trade - off}}|R_{trade - off}|}{EN_{trade - off}} \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[58, 109, 816, 279]]<|/det|> +Where \(Intensity_{trade - off}\) was the trade- off intensity, \(EN_{trade - off}\) was the number of effective indicator pairs belonging to trade- off, \(TN_{trade - off}\) was the total number of indicator pairs between goals, \(R_{trade - off}\) was the Spearman correlation coefficient of the effective indicator pair. If we calculated the synergy and trade- off intensity directly at the goal level, we will ignore the fact that there were both synergies and trade- offs between different SDGs. + +<|ref|>sub_title<|/ref|><|det|>[[44, 310, 196, 330]]<|/det|> +## Network analysis + +<|ref|>text<|/ref|><|det|>[[40, 348, 955, 650]]<|/det|> +Network analysis, which has been applied in social science \(^{51}\) , public heath \(^{52}\) , ecology \(^{53}\) and biology \(^{54}\) to study complex systems, is a holistic approach to studying the complexity of SDG interactions to identify the importance of goals or targets. The synergy and trade- off networks were built separately for the national and 31 provinces using iGraph package in R Studio, respectively \(^{55}\) . Kleinberg's hub centrality score is defined as the principal eigenvector of A\*(A), where A is the adjacency matrix of the graph. Similarly, Kleinberg's authority centrality score is defined as the principal eigenvector of t(A)\*A, where A is the adjacency matrix of the graph. For undirected matrices the adjacency matrix is symmetric and the hub scores are the same as authority scores \(^{56}\) . The hub scores of the 17 SDGs were set as nodes, and the synergy or trade- off intensity among SDGs was set as the weighted edge in the network. These hub scores were used to calculate and assess the importance of the SDGs in the synergy and trade- off networks accounting for the direct and the indirect interactions. The larger the hub score was, the more important the node as a central hub was in the synergy or trade- off networks. The priority of the SDGs was identified based on the hub score in the networks from synergy and trade- off perspectives. + +<|ref|>sub_title<|/ref|><|det|>[[44, 664, 512, 686]]<|/det|> +## The importance of the SDGs at different spatial levels + +<|ref|>text<|/ref|><|det|>[[41, 701, 950, 860]]<|/det|> +At the national level, we combined the 102 indicators in pairs, resulting in 5100 pairs in total. At the provincial level, we combined the 102 indicators in pairs for all the 31 provinces. The number of indicator pairs reached 158, 100 (5100 pairs 31 provinces) pairs in total. From indicator to goal, the importance of the SDGs was analyzed following the procedures above at national and all the provincial levels. For the detailed statistics of the number of the selected indicator pairs and Spearman correlation coefficients of the 31 provinces, please refer to SI Tab. S2. The results at the regional level were aggregated from those at provincial levels following the geographic regions divisions in China (See SI Tab. S3 for more details). + +<|ref|>sub_title<|/ref|><|det|>[[44, 883, 142, 910]]<|/det|> +## Figures + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[66, 50, 940, 490]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 520, 115, 539]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[41, 561, 949, 743]]<|/det|> +The indicator pairs of synergy and trade- off at the national level (a): the distribution of the indicator pairs with numbering of the indicators showing the affiliation between the 102 indicators and 17 goals. The selected criteria are that Bonferroni corrected p value less than 0.05 and the absolute value of the Spearman correlation coefficient R more than 0.6. Each indicator is judged to have a positive or negative impact on sustainable development based on its own meaning. The indicator pairs are divided by 5 groups, including synergy, trade- off, weak synergy, weak trade- off and invalid indicator pairs. Different colors indicate different SDGs following the official UN color palette. (b): The averaged R and number of indicator pairs for each group. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[48, 45, 944, 475]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 494, 118, 514]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[39, 535, 949, 830]]<|/det|> +The synergy and trade- off networks at the goal level (a) and (d): the absolute value of Spearman correlation coefficient R (short for ABS(R)) among goals at the national level. (a) is for synergy and (d) is for trade- off. Set Fig.2(a) as an example, the width of the colored line indicates the arithmetic mean of ABS(R) among goals calculated from the indicator pairs in Fig 1(a). The width of the arc represents the cumulative value of each line width for that goal. The number outside the circle is the scale of each goal. (b) and (e): the ratio of the number of the selected indicator pairs out of the total number of all possible combinations among goals (short for Ratio). (b) is for synergy and (e) is for trade- off. (c) and (f): the networks built upon ABS(R) and Ratio. The thickness of the edge in the network indicates the synergy or trade- off intensity among goals. The thicker the edge is the stronger the intensity is. The size of the circle suggests its importance as a central hub in the network. The larger the circle is, the more important the node as a central hub is. (c) is for synergy (hub score: 0.16- 1) and (f) is for trade- off (hub score: 0.14- 1). In the synergy network the edge is shown in blue and in red in the trade- off networks. Different colors indicate different SDGs following the official UN color palette. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[50, 49, 940, 587]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[43, 612, 116, 631]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[41, 653, 955, 812]]<|/det|> +The hubs scores of the 17 SDGs in synergy and trade- off at the provincial level (a) and (b): the hub scores in the synergy (a) and trade- off (b) networks. (c) and (d): the ranking of the 17 SDGs in the synergy (c) and trade- off (d) networks in order of its hub score. (e) and (f): the statistics of the hub score of the 17 SDGs for synergy (e) and trade- off (f). The black line in each box shows the minimum value, lower quartile, median, upper quartile and maximum value from left to right for each SDG. The solid black circle indicates the arithmetic mean value. Different colors indicate different SDGs following the official UN color palette. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[45, 52, 944, 435]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 464, 118, 483]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[42, 504, 945, 618]]<|/det|> +The hub score of the 17 SDGs at the regional level (a) and (d): the hub score in the synergy (a) and trade- off (d) networks. Different colors indicate different SDGs following the official UN color palette. (b) and (e): the spatial pattern of the hub score of SDG5 (Gender equality) in the synergy (b) and trade- off (e) networks. (c) and (f): the spatial pattern of the hub score of SDG13 (Climate action) in the synergy (c) and trade- off (f) networks. + +<|ref|>sub_title<|/ref|><|det|>[[44, 641, 312, 668]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 691, 765, 711]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[59, 729, 792, 802]]<|/det|> +- Explanationsontheassociationbetweenindicators.xlsx- Dataforthesynergyandtradeoffamongindicatorsatnationalandprovinciallevels.xlsx- SupplementaryInformation.docx + +<--- Page Split ---> diff --git a/preprint/preprint__05a8899c72e9d6e9f51dc6ee54f196890bbf834aca0909920ef6acb684782069/preprint__05a8899c72e9d6e9f51dc6ee54f196890bbf834aca0909920ef6acb684782069.mmd b/preprint/preprint__05a8899c72e9d6e9f51dc6ee54f196890bbf834aca0909920ef6acb684782069/preprint__05a8899c72e9d6e9f51dc6ee54f196890bbf834aca0909920ef6acb684782069.mmd new file mode 100644 index 0000000000000000000000000000000000000000..b149897fdddfe4e05f043b459d29de16157549ce --- /dev/null +++ b/preprint/preprint__05a8899c72e9d6e9f51dc6ee54f196890bbf834aca0909920ef6acb684782069/preprint__05a8899c72e9d6e9f51dc6ee54f196890bbf834aca0909920ef6acb684782069.mmd @@ -0,0 +1,391 @@ + +# Effects of introducing the WHO Labour Care Guide on Caesarean section: a pragmatic, stepped-wedge, cluster randomized trial in India + +Joshua Vogel ( \(\boxed{ \begin{array}{r l} \end{array} }\) joshua.vogel@burnet.edu.au) Burnet Institute https://orcid.org/0000- 0002- 3214- 7096 + +Yeshita Pujar KLE Academy of Higher Education and Research + +Sunil Vemekar KLE Academy of Higher Education and Research + +Elizabeth Armarit Burnet Institute + +Veronica Pingray Instituto de Efectividad Clínica y Sanitaria (IECS- CONICET), Buenos Aires, Argentina + +Fernando Althabe Instituto de Efectividad Clínica y Sanitaria (IECS- CONICET), Buenos Aires, Argentina + +Luz Gibbons IECS https://orcid.org/0000- 0002- 0235- 1635 + +Mabel Berrueta Instituto de Efectividad Clínica y Sanitaria (IECS- CONICET), Buenos Aires, Argentina + +Manjunath Somannavar KLE Academy of Higher Education and Research + +Alvaro Ciganda Instituto de Efectividad Clínica y Sanitaria (IECS- CONICET), Buenos Aires, Argentina + +Rocio Rodriguez Instituto de Efectividad Clínica y Sanitaria (IECS- CONICET), Buenos Aires, Argentina + +Savitri Bendigeri KLE Academy of Higher Education and Research + +Jayashree Ashok Kumar Gadag Institute of Medical Sciences + +Shruti Bhavi Patil Gadag Institute of Medical Sciences + +Aravind Karinagannanavar Gadag Institute of Medical Sciences + +Raveendra Anteen + +<--- Page Split ---> + +Gokak General Hospital + +Pavithra M. R. Gokak General Hospital + +Shukla Shetty JJM Medical College + +Latha B JJM Medical College + +Megha H. M. JJM Medical College + +Suman Gaddi Vijayanagar Institute of Medical Sciences + +Shaila Chikkagowdra Vijayanagar Institute of Medical Sciences + +Bellara Raghavendra Vijayanagar Institute of Medical Sciences + +Caroline Homer Burnet Institute https://orcid.org/0000- 0002- 7454- 3011 + +Tina Lavender Liverpool School of Tropical Medicine + +Pralhad Kushtagi Manipal Academy of Higher Education + +Justus Hofmeyr Department of Health, Universities of the Witwatersrand, Walter Sisulu and Fort Hare, East London, South Africa https://orcid.org/0000- 0002- 3080- 1007 + +Richard Derman Thomas Jefferson University + +Shivaprasad Goudar Jawaharlal Nehru Medical College, India + +## Article + +Keywords: + +Posted Date: July 28th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 3175470/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> + +Version of Record: A version of this preprint was published at Nature Medicine on January 30th, 2024. See the published version at https://doi.org/10.1038/s41591-023-02751-4. + +<--- Page Split ---> + +# Effects of introducing the WHO Labour Care Guide on Caesarean section: a pragmatic, stepped-wedge, cluster randomized trial in India + +Joshua P. Vogel \(^{1*}\) , Yeshita Pujar \(^{2}\) , Sunil S Vernekar \(^{2}\) , Elizabeth Armar \(^{1}\) , Veronica Pingray \(^{3}\) , Fernando Althabe \(^{3}\) , Luz Gibbons \(^{3}\) , Mabel Berrueta \(^{3}\) , Manjunath Somannavar \(^{2}\) , Alvaro Ciganda \(^{3}\) , Rocio Rodriguez \(^{3}\) , Savitri Bendigeri \(^{2}\) , Jayashree Ashok Kumar \(^{4}\) , Shruti Bhavi Patil \(^{4}\) , Aravind Karinagannanavar \(^{4}\) , Raveendra R Anteen \(^{5}\) , Pavithra M. R. \(^{5}\) , Shukla Shetty \(^{6}\) , Latha B. \(^{6}\) , Megha H. M. \(^{6}\) , Suman S. Gaddi \(^{7}\) , Shaila Chikkagowdra \(^{7}\) , Bellara Raghavendra \(^{7}\) , Caroline SE Homer \(^{1}\) , Tina Lavender \(^{8}\) , Pralhad Kushtagi \(^{9}\) , G. Justus Hofmeyr \(^{10,11}\) , Richard Derman \(^{12}\) , Shivaprasad Goudar \(^{3}\) + +\* corresponding author – Joshua.vogel@burnet.edu.au \(^{1}\) Maternal, Child and Adolescent Health Program, Burnet Institute, Melbourne, Victoria, Australia \(^{2}\) Women's and Children's Health Research Unit, Jawaharlal Nehru Medical College, KLE Academy of Higher Education and Research, Belgaum, Karnataka, India \(^{3}\) Instituto de Efectividad Clínica y Sanitaria (IECS- CONICET), Buenos Aires, Argentina \(^{4}\) Gadag Institute of Medical Sciences, Gadag, Karnataka, India \(^{5}\) General Hospital, Gokak, Belgaum, Karnataka, India \(^{6}\) JJM Medical College, Davangere, Karnataka, India \(^{7}\) Vijayanagar Institute of Medical Sciences (VIMS), Ballari, Karnataka, India \(^{8}\) Department of International Health, Liverpool School of Tropical Medicine, Liverpool, United Kingdom \(^{9}\) Manipal Academy of Higher Education, Karnataka, India \(^{10}\) Department of Obstetrics and Gynaecology, University of Botswana, Gaborone, Botswana \(^{11}\) University of the Witwatersrand and Walter Sisulu University, East London, South Africa \(^{12}\) Thomas Jefferson University, Philadelphia, United States of America + +<--- Page Split ---> + +## ABSTRACT + +The World Health Organization's Labour Care Guide (LCG) is the "next generation" partograph, designed to improve the quality of intrapartum care and enhance women's experiences. However, the effects of the LCG on maternal and newborn outcomes have not been evaluated. We developed a novel strategy to introduce the LCG into routine intrapartum care, comprising a co- designed training program for labour ward clinicians, alongside monthly audit and feedback. We implemented the strategy and measured its effects using a stepped- wedge, randomised trial in four hospitals in India. We captured data from 26,331 women who gave birth at \(> = 20\) weeks' gestation, over a 54- week period. Following implementation, a \(5.5\%\) crude absolute reduction in the Caesarean section rate amongst women in Robson Group 1 was observed (45.2% vs 39.7%; relative risk 0.85, 95% confidence interval 0.54- 1.33). Maternal process- of- care measures were not significantly different, though labour augmentation with oxytocin was 18.0% lower with the LCG strategy. No differences were observed for maternal, fetal or newborn health outcomes, or women's birth experiences. This "proof of concept" study provides important evidence on the effects of introducing LCG into routine practice, suggesting a 15% relative risk reduction in Caesarean section use amongst women in Robson Group 1. Larger trials are warranted, particularly in settings where urgent reversal of the Caesarean section epidemic is needed. + +<--- Page Split ---> + +## MAIN + +An estimated 287,000 maternal deaths, 2.4 million neonatal deaths and 1.9 million stillbirths occur each year, the vast majority of which occur in low- and middle- income countries (LMICs).(1- 3). As many as \(45\%\) of these maternal deaths, stillbirths, and neonatal deaths occur during labour, birth, and the first 24 hours postpartum.(4) Ensuring good- quality care is available to all women during the intrapartum period is thus critical to any efforts to reduce global maternal and neonatal morbidity and mortality.(5) Caesarean section is an essential component of good- quality intrapartum care - when used appropriately, it is a life- saving intervention for women and babies. However, Caesarean section rates globally more than doubled between 2000 and 2015, driven in large part by those performed without a clear medical indication.(6)(7) While unnecessary Caesarean section causes avoidable harms to women and newborns,(8, 9) it By 2030, 38 million women each year (28.5% of births) will experience a Caesarean.(10) + +The World Health Organization (WHO) has long recommended that a woman in labour should be monitored by a skilled healthcare provider using a partograph to document clinical assessments and help make decisions.(11) When completed prospectively, the partograph can help determine whether and when an intervention - such as labour augmentation, Caesarean section or episiotomy - is warranted. A WHO- led 1994 trial showed that prospective partograph use combined with intensive provider training optimized the use of intrapartum interventions, and improved maternal and newborn outcomes.(12) Consequently, the WHO simplified partograph was widely disseminated and adopted as a component of routine intrapartum care internationally.(13) While more women than ever are giving birth in health facilities,(14) partographs are often used poorly, or not at all. Inadequate provider training and skills, heavy staff workloads, a lack of clinical equipment and supplies, and restrictive hospital policies are known barriers to partograph use.(15- 17) + +In 2018, WHO published 56 updated recommendations to improve quality of intrapartum care and enhance women's childbirth experiences.(8) Key recommendations included changing the definition of active first stage of labour from the widely used 3cm or 4cm to starting from 5cm of cervical dilation, and removal of the 'alert' and 'action' lines. These changes reflected a growing body of evidence that the historical '1cm per hour' rule for active labour progress is unrealistic for most women, and that slower dilation rates are not associated with adverse birth outcomes. In response to these recommendations, a "next generation" partograph known as the WHO Labour Care Guide (LCG) was developed in 2020 through expert consultations, primary research with maternity healthcare providers, and a multi- country usability study.(18- 20) + +<--- Page Split ---> + +WHO states that the LCG should be globally disseminated and implemented as a component of routine clinical care.(21) However, introducing the LCG into routine care requires a strategy that can improve healthcare provider's clinical practice, thereby enhancing the quality of intrapartum care, reducing use of unnecessary interventions, and improving support to women during labour. However, as the LCG is a novel tool, no such strategy has been tested in a randomised trial. This knowledge gap was highlighted in WHO's recent global LCG research prioritization exercise, in which identifying optimal implementation strategies, as well as understanding LCG's effect on maternal and perinatal outcomes, were top research priorities.(22) We therefore conducted a multi- centre trial to evaluate the effects of implementing a strategy to promote LCG use in labour wards. + +## METHODS + +## Overview of study design + +We designed and conducted a pragmatic, stepped- wedge, cluster- randomized trial which was conducted between \(1^{\text{st}}\) July 2021 and \(15^{\text{th}}\) July 2022. It was intended as a "proof- of- concept" study. We used an evidence- based, theory- informed approach to developing the intervention, and conducted the trial to determine whether it might have an effect on overuse of Caesarean section, or other important maternal and perinatal outcomes. The trial was preceded by a six- month formative phase, which was guided by the COM- B model of behaviour change, which recognises that individuals must have Capability, Motivation, and both physical and social Opportunity to perform a behaviour.(23) We used co- design principles and primary data collection to develop and refine the 'LCG strategy' intervention. The intervention was then introduced in a stepwise manner in four public hospitals in Karnataka State, India, in accordance with a randomisation schedule. Given the risk of cross- contamination, individual randomization was not possible. We used a stepped- wedge approach as the LCG reflects WHO's current advice regarding standard of care,(24) and it was thus not ethically feasible to use a parallel- group design. + +## Trial approvals and oversight + +This trial was designed and conducted in accordance with the ethical principles of the World Medical Association's Declaration of Helsinki, the Ottawa Statement for the Ethical Design and Conduct of Cluster Randomised Trials, and Good Clinical Practice (GCP) standards.(25- 27) We developed the trial protocol and reported findings in accordance with SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) guidance for randomised trials, and the CONSORT (Consolidated Standards of Reporting Trials) statement for stepped- wedge cluster- randomised trials + +<--- Page Split ---> + +(CONSORT Checklist in Supplementary File S1).(28, 29) The trial protocol was pre- registered (CTRI/2021/01/030695), with the protocol and statistical analysis plan published prior to trial closure; there were no major deviations or changes.(30) + +We sought permission from the head of study hospitals (gatekeepers) and individual providers before commencing the trial. The study protocol specified a waiver of individual consent for data collected on women giving birth – these data were non- identifiable, routinely- collected clinical variables in medical records and labour ward registries. For women invited to complete a postpartum survey, an informed consent was conducted. The trial was approved by the Alfred Hospital Human Ethics Committee (737/20), and the institutional ethics committees of the KLE Academy of Higher Education and Research (D- 281120003), J J M Medical College, Davanagere (IEC- 136/2020); Vijayanagar Institute of Medical Sciences (SVN IEC/20/2020- 2021) and the Gadag Institute of Medical Sciences, (IEC/01/2020- 21), as well as the State Ethics Committee, Department of Health and Family Welfare, Government of Karnataka (DD(MH)/71/2020- 21); and the Health Ministry's Screening Committee, Indian Council of Medical Research (2020- 10127). An independent, three- member Data and Safety Monitoring Committee oversaw the trial. + +## Setting and Participants + +We purposively selected four public maternity hospitals in Karnataka State to participate, based on their capacity to provide comprehensive emergency obstetric care (including access to caesarean section). All four hospitals attend to more than 4,000 women giving birth each year, and have an overall caesarean section rate of \(30\%\) or more. In three hospitals labour monitoring and partograph completion is primarily performed by postgraduate resident doctors, while in the remaining hospital it was performed by nurses. All hospitals had either completed or were undergoing accreditation under the Government of India's national Labour Room Quality Initiative ("LaQshya") which closely aligned with WHO intrapartum care recommendations.(31) + +Each hospital was treated as a cluster (H1, H2, H3 and H4). Two senior obstetricians working at each hospital were appointed as facility investigators and were responsible for trial activities at each hospital. The target of the intervention were labour ward staff, including obstetricians, postgraduate doctors and nurses. These staff routinely use a WHO simplified partograph to make decisions about labour interventions. We hypothesized that the intervention would promote correct LCG use by these providers, changing their labour monitoring and management practices to align with WHO's + +<--- Page Split ---> + +intrapartum recommendations. In turn, this could reduce overuse of Caesarean section, improve maternal and newborn outcomes, and enhance women's care experiences. + +## Randomisation and blinding + +Prior to trial commencement, the four clusters (hospitals) were randomly assigned to one of four sequences (H1, H2, H3, or H4, see Figure 1) using a computer- generated list of random numbers that was managed by the study statistician. The allocation sequence was concealed from the investigators and study teams and only revealed by the statistician one month prior to cross over to allow time for planning LCG implementation activities. Once the hospital had commenced the intervention, blinding of hospital staff, research staff and individual women was not possible. The intervention was commenced in hospitals according to the randomly assigned sequence, with one hospital transitioning to intervention at 2- month intervals (i.e., a step occurred every 2 months). A two- week transition period was used to allow for the intervention to be fully adopted. + +## Control and intervention + +The control condition for the trial was current labour monitoring and management practices ('usual clinical care'). While the WHO simplified partograph is widely used in India, the formative phase showed that its use was inconsistent and oftentimes retrospective. Training seminars were conducted at all hospitals on using the WHO simplified partograph to standardize the control condition. The WHO intrapartum care recommendations were also disseminated at all hospitals at the start of the trial. + +The LCG strategy intervention included a co- designed LCG training program for doctors and nurses working on labour ward, and a monthly audit and feedback process using hospital Caesarean section data. For training, we developed and ran two- day workshops for all labour ward staff, co- ordinated by facility investigators who had undergone a "training of trainers" workshop. After this, all providers working on labour ward underwent an 8- week "low- dose, high- frequency" training program,(32) comprising of clinical cases and bedside teaching using LCG with senior clinical staff. The 8- week training was delivered in cycles to accommodate postgraduate resident rotations every 3 months. Refresher training was used if new staff arrived during the intervention period. At time of randomization, all simplified WHO partographs in the labour ward were replaced with the LCG. Senior labour ward staff were encouraged to monitor and promote consistent, accurate LCG use through supportive supervision. + +<--- Page Split ---> + +The intervention also included monthly audit and feedback meetings on Caesarean rates, using the Robson Classification. Audit and feedback is widely used to promote evidence- based clinical practice, and is recommended by WHO for avoiding unnecessary Caesarean sections.(33, 34) WHO also recommends that countries use the Robson Classification for assessing, monitoring and comparing their Caesarean rates over time.(6) The Robson Classification organises all births in a facility into one of 10 mutually exclusive, all- inclusive groups, on the basis of parity, previous Caesarean, onset of labour, fetal presentation and lie, number of neonates and gestational age (term or preterm).(35) Providers at randomized hospitals underwent a brief training on how to interpret Robson Classification data, and how audit and feedback can help optimize Caesarean section use. Robson Classification tables were prepared using trial data, and shared directly with the study hospital on a monthly basis. These data were presented by senior clinical staff at monthly meetings, with structured discussions amongst the attendees on how to improve hospital performance. Hospitals and staff were instructed that all other aspects of clinical care during the trial should be in accordance with relevant local guidelines and protocols. In addition, facility leads were encouraged to identify and address anticipated barriers to the LCG strategy in their hospital. This included revision of hospital policies, standardisation of clinical protocols, rearrangements to the physical labour ward environment, and addressing some supply and equipment constraints. We used logbooks, tracking sheets and site visits to confirm that all eligible staff underwent LCG training activities, were using the LCG routinely, and attended monthly Caesarean audit meetings as planned. + +## Primary and secondary outcomes + +Trained research staff collected non- identifiable, individual- level data on all women giving birth from 20 weeks' gestation onwards and their babies. Data were collected from the time of admission for childbirth until the time of discharge, transfer, death or until 7 days after admission (whichever came first). The primary trial outcome was the use of Caesarean section amongst women in Robson Group 1. That is, women who were nulliparous, gave birth to a singleton, term pregnancy in cephalic presentation, and were in spontaneous labour. While Robson Group 1 is a subset of all women giving birth (usually around \(30\%\) of the obstetric population), it is a group of largely low- risk women in whom Caesarean is often overused.(35) Should the LCG strategy have any effect, we anticipated that it would be more easily detected amongst these women. Secondary outcomes included use of intrapartum interventions, and maternal, fetal and neonatal health outcomes. The denominator varied depending on the outcome of interest (see Supplementary Table S1 for outcome definitions). + +<--- Page Split ---> + +We also measured women's experiences of care using a pre- tested, interviewer- administered survey, conducted in a local language (Kannada, Hindi or Marathi), that was completed by postnatal day 7 or discharge (whichever came first) in a sample of postpartum women. This sample comprised women in Robson Group 1 or 3 who gave birth in the last 15 days of each 2- month period, had a liveborn baby, were 18 years or older, and who provided informed consent. In each hospital, trained interviewers approached and invited all eligible women to complete the survey. + +All data were collected into pre- designed study forms and managed using REDCap electronic data capture via tablets. Each hospital team had access to their own hospital data only, and facility investigators were responsible for checking completeness and accuracy of all collected data. To minimize errors, data validation processes were implemented in the data collection system. Statistical methods and data cleaning algorithms were utilized to identify potential errors and outliers for further investigation and correction. Regular data and trial progress review meetings and audits were conducted to identify and rectify any inconsistencies or outliers. Data monitors periodically visit the study sites to verify the accuracy and completeness of the collected data. They also provided training and guidance to study personnel, addressing any issues or concerns that might arise during the study. The trial concluded when \(15^{\text{th}}\) July 2022 was reached, as planned. + +## Sample size + +No previous trial using LCG has been conducted, meaning the effect size of our strategy was difficult to estimate. For the year 2020 (prior to the trial) these four hospitals collectively averaged 24,000 births per year, and their overall Caesarean rate was \(44\%\) . The Caesarean rate in women in Robson Group 1 (i.e., the primary outcome) for all four hospitals was at least \(40\%\) . The trial was designed to provide \(92\%\) power to detect a \(25\%\) reduction in the Robson Group 1 Caesarean rate from \(40\%\) to \(30\%\) , assuming an intraclass correlation coefficient (ICC) equal to 0.02, a cluster auto correlation equal to 0.90, and an average of 300 women per cluster per step with a coefficient of variation of cluster size equal to 0.60. (36) + +## Statistical methods and analysis + +Analyses were performed according to the intention- to- treat principle. Maternal baseline characteristics were summarized by trial arm as means and standard deviations, or numbers and percentages, as appropriate. For the primary and secondary outcomes, a generalized estimating equation (GEE) to estimate the effect of the intervention with respect to the population- average was used. A bias correction method and degree of freedom approximation due to the small number of + +<--- Page Split ---> + +clusters was applied in the GEE models to maintain the validity of the estimations. Manck and DeRouen correction method with N- 2 degrees of freedom was selected due to being the most conservative option.(37) An exchangeable correlation structure was assumed and the modified Poisson distribution with a log link function was considered. The model was constructed considering two variables: a binary indicator for treatment - indicating whether the observation was made during the control or the intervention period - and a categorical variable indicating the step. The relative risk and the \(95\%\) confidence interval were reported as the size effect. For the secondary outcomes in which duration was measured in days, the effect size was calculated as the difference between the mean of days in the intervention group and the mean of days in the control group. The ICC was estimated under the control period using the GEE model. As no adjustment for multiplicity testing of secondary outcomes was considered, their results are reported as point estimates with \(95\%\) confidence intervals and p- values. + +## RESULTS + +## Characteristics of study population + +Between 1 July 2021 and 15 July 2022, 26,331 women gave birth to 26,595 babies in the four hospitals during the control and intervention periods and were included for analysis (Figure 1). The total number of women giving birth differed between hospitals, ranging from 5,295 to 8,772 women per hospital. The analysis population comprised 11,517 women (11,624 babies) who gave birth in the control period and 14,814 women (14,971 babies) who gave birth in the intervention period. The main analysis did not include the 1,080 women (1,089 babies) who gave birth in the transition period. + +While there were more women in intervention than control, the characteristics of women were similar (Table 1). Nearly half of included women were nulliparous (46.7% of the control group and 47.5% of the intervention group), while more than half of multiparous women had no prior Caesarean section (56.7% vs 55.0%) The distribution of women across the 10 Robson Classification groups was also similar (Supplementary Table S1). Robson Group 1 accounted for 30.8% (3,543/11,517) of women in the control group and 29.0% (4,302/14,814) of women in the intervention group. The intervention group had a slightly higher proportion of women in Group 2 and a slightly lower proportion of women in Group 3. + +<--- Page Split ---> + +## Primary and secondary outcomes + +Table 2 reports the intervention effect sizes for the primary outcome and secondary maternal process- of- care outcomes. The Caesarean section rate in Robson Group 1 for the control group was \(45.2\%\) , while in the intervention group it was \(39.7\%\) with a crude absolute difference of \(- 5.5\%\) (relative risk [RR] 0.85, \(95\%\) confidence interval [CI] 0.54- 1.33, p value 0.1088). The estimated ICC for the primary outcome during the control period was \(0.015 (0; 0.043)\) . + +The Caesarean section rate in Robson Groups 1 and 3 was \(30.9\%\) for the control group, and \(26.9\%\) for the intervention group - a crude absolute difference of \(- 4.0\%\) (RR 0.81, \(95\%\) CI 0.59 - 1.11). For the outcome augmentation with oxytocin during spontaneous labour, the prevalence in control group was \(27.3\%\) and in the intervention group it was \(9.3\%\) (crude absolute difference - \(18.0\%\) ). However, the estimate of effect was not significant (RR 0.34, \(95\%\) CI 0.01 - 15.04) - the wide confidence interval was attributable to the high variability in outcome prevalence between hospitals and steps. Table 3 reports the intervention effects on maternal, fetal and newborn health outcomes. The prevalence of these outcomes was low in both intervention and control groups, and there were no clear differences. + +A total of 1,438 women in the control group and 1,277 women in the intervention group consented ( \(100\%\) and \(99.9\%\) consent rate, respectively) and completed postpartum surveys. Table 4 reports the effects on women's experiences at birth, for which there were no differences between groups. In terms of adverse events, there were 5 maternal deaths, 196 neonatal deaths and 367 stillbirths in the control period, and 13 maternal deaths, 200 neonatal deaths and 449 stillbirths in the intervention period (Supplementary Tables S3 and S4). None of these deaths were assessed as being related to the intervention. + +## DISCUSSION + +In this stepped- wedge, cluster- randomised trial in India, we implemented a novel strategy to introduce the LCG into routine care, as well as initiating monthly audit and feedback meetings on Caesarean section data using Robson Classification. We observed a \(5.5\%\) crude absolute reduction in Caesarean rates amongst women in Robson Group 1 following introduction of the intervention, however this difference was not statistically significant. Maternal process- of- care measures were not significantly different, though the crude absolute difference for labour augmentation using oxytocin was \(- 18.0\%\) . We did not observe any clear differences in maternal, fetal or newborn health + +<--- Page Split ---> + +outcomes, or women's experiences at birth. The findings do not preclude the possibility that the LCG strategy may reduce Caesarean section and augmentation of labour in larger trials. + +Reversing the worldwide trend in rising Caesarean section rates, driven in large part by medically unnecessary Caesarean use, has proven to be a challenging problem - a 2018 WHO guideline identified few effective interventions to address this.(34, 38) The LCG promotes several supportive care measures which have been shown in trials to prevent Caesarean section, such as labour companionship, mobilisation during labour, and adequate pain relief.(39- 41) Also, the use of 5cm dilatation to define active first stage, as well as removal of the "1cm per hour rule" would, assumedly, lead to fewer intrapartum interventions. As the LCG is a novel clinical tool, there are few effectiveness studies available for comparison, though more trials using LCG are planned.(42, 43) In 2022, Pandey et al published findings of an individually- randomized trial of 271 low- risk women in a single hospital in India, comparing the effects of using LCG versus modified partograph.(44) They reported a dramatic reduction in Caesarean section - 1.5% in the LCG group compared with 17.8% in the control group (p- value 0.0001) - as well as significantly lower oxytocin use and shorter duration of active phase of labour with LCG. Our trial was powered to detect a 25% risk reduction for Caesarean section rate in Robson Group 1, equating to an absolute reduction of 10% (from 40% to 30%). Though we lacked power to detect a smaller magnitude of effect, our findings suggest that an effect does exist, and is probably closer to a 15% risk reduction. While we lacked power to test a superiority hypothesis for rarer adverse outcomes (such as mortality and severe morbidity of women and babies), reassuringly there was no evidence of short- term harms associated with the LCG strategy. Data on these outcomes should be monitored in future, larger- scale research. + +We did not detect any differences for outcomes on women's experiences. However, these data showed women had high levels of satisfaction with the amount of time health workers spent with them, the way they were communicated with, and with their overall birth experience. It also showed that some supportive care practices, such as being offered a labour companion, were reasonably common, though other women- centred interventions were not well- implemented. For example, being offered pain relief (5.2% and 15.3%), and being asked which birth position they preferred (0.7% and 2.1%) were poorly used. This highlights that substantive gaps persist in the provision of supportive care around the time of birth - additional strategies are needed to address these gaps. + +This trial was conducted in large, busy, public tertiary hospitals in India with high Caesarean use. In three hospitals, partograph completion was the responsibility of postgraduate residents only. In + +<--- Page Split ---> + +India, the LaQshya national initiative and hospital accreditation process (31) has a strong emphasis on respectful maternity care, which is well- aligned with WHO's recommendations and the LCG's foundational principles. These factors mean the trial findings may not necessarily generalize to other settings that are naïve to respectful maternity care principles and policies. For example, it may be more challenging to generate provider behaviour change in settings without a national policy framework. Contextual differences around how frequently obstetric interventions are used, as well as differences in the risk profile of obstetric populations, may mean the LCG strategy has variable effects. + +This study was designed as a "proof of concept" study of a novel, complex intervention. Strengths include the use of a theory- based, evidence- informed, co- design approach to developing the LCG strategy, which aimed to address factors known to impair partograph use.(17) We also used a robust, cluster- randomised design, and recruited a large number of participants in a real- world clinical setting. The stepped- wedge design means that all hospitals were implementing the LCG strategy at trial conclusion. This trial nonetheless has some limitations. The intervention did not have a specific component aimed at the antenatal period, though in retrospect it would be helpful to better prepare women for the introduction of new supportive care options. Also, women arriving at hospital in advanced labour had only a short period of time in which they could benefit from LCG, thereby diminishing any possible effects. The stepped- wedge design meant that other, secular trends – such as changes in COVID case numbers over time - could have affected the findings. However, COVID data shows that infections in these hospitals were quite infrequent. The use of the same clusters over a 54- week period means we cannot exclude the possibility that some women may have given birth twice during the study. We measured women's experiences using a survey instrument in their language of choice, however their responses may have been affected by social or courtesy biases. + +## CONCLUSION + +Findings from this multi- centred, stepped- wedge, cluster- randomized trial suggest that the LCG strategy is a promising intervention that can improve quality of labour and childbirth care, reducing overuse of intrapartum interventions. This study provides important evidence on the debate around introduction of LCG into routine clinical practice internationally. Further evaluation in larger scale, multi- country trials in hospital with high rates of Caesarean section are warranted. + +<--- Page Split ---> + +## FUNDING + +This study was supported by a Grand Challenges grant from the Bill & Melinda Gates Foundation (GNT INV- 023273). We received additional funding support from the Burnet Institute, via the Alastair Lucas Award. JPV and CSEH are supported by Investigator Grants from the Australian National Health and Medical Research Council (NHMRC). EA is supported by a NHMRC Postgraduate Student Award. The study funder had no role in study design, data collection, analysis, interpretation, or writing of the report. The corresponding author had full access to all the study data, and takes final responsibility for the decision to submit for publication. + +## ACKNOWLEDGEMENTS + +We gratefully acknowledge Ana Pilar Betran (Chair), Dr Dennis Wallace and Shuchita Mundle for their role as Data and Safety Monitoring Board members, and Olufemi T. Oladapo and Mercedes Bonet for their role as Observers to the study. + +## INCLUSION AND ETHICS STATEMENT + +Our study team support the principles of the Cape Town Statement, in particular the commitment to equitable international collaborations. The study was designed in partnership between three research groups (India, Argentina, Australia), building on multiple years of research collaborations and co- authored publications between several co- authors. + +This study was funded by a Global Grand Challenges grant – the submission was jointly prepared by JPV, SG, YP, SV, VP, FA and LG. This grant funding went to all three of our research organisations, with the largest amount of this funding received by the JNMC- India research team. The study protocol had 14 named investigators – 12 from India, 1 from Argentina, and 1 from Australia. JPV and SG were named as co- Principal Investigators. During the study, decisions were taken by consensus amongst the steering group, during fortnightly teleconferences. + +The authorship group (29 individuals) comprised 17 women and 12 men, and included late- , mid- and early- career individuals. Members of the authorship group include researchers in India (YP, SSV, MS, SB, JAK, SBP, AK, RRA, PMR, SS, LB, MHM, SSG, SC, BR), Argentina (VP, FA, LG, MB, AV, RR) and Australia (JPV, EA, CSEH). The lead author (JPV) is in Australia and the senior author (SG) is in India. Our Technical Advisory Group (TL, PK, GJH, RD) included senior researchers from India, UK, South + +<--- Page Split ---> + +Africa and USA, and our Data and Safety Monitoring Committee included individuals from India, Switzerland and the USA. + +## DATA AND CODE AVAILABILITY STATEMENT + +In keeping with the Bill & Melinda Gates Foundation Open Access Policy, the trial dataset generated during this study, the data dictionary and syntax used for analyses are hosted at the Gates Open Research- approved repository Zenodo at time of paper publication under DOI: https://doi.org/10.5281/zenodo.8140454 + +## REFERENCES + +1. WHO, UNICEF, UNFPA, World Bank, UNDP. Trends in maternal mortality 2000 to 2020. Geneva: World Health Organization; 2023. +2. United Nations Inter-Agency Group for Child Mortality Estimation. Levels and trends in child mortality. New York: UNICEF; 2022. +3. Hug L, You D, Blencowe H, Mishra A, Wang Z, Fix MJ, et al. 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Laisser R, Actis Danna V, Bonet M, Oladapo O, Lavender T. An exploration of midwives' views of the latest World Health Organization labour care guide. Afr J Midwifery Womens Health. 2021;15(4).19. Vogel JP, Comrie-Thomson L, Pingray V, Gadama L, Galadanci H, Goudar S, et al. Usability, acceptability, and feasibility of the World Health Organization Labour Care Guide: A mixed-methods, multicountry evaluation. Birth. 2020.20. Pingray V, Bonet M, Berrueta M, Mazzoni A, Belizan M, Keil N, et al. The development of the WHO Labour Care Guide: an international survey of maternity care providers. Reprod Health. 2021;18(1):66.21. World Health Organization. WHO Labour Care Guide: User's Manual. https://appswohint/iris/rest/bitstreams/1322094/retrieve. 2020.22. World Health Organization Labour Care Guide Research Prioritization Group. Global research priorities related to the World Health Organization Labour Care Guide: results of a global consultation. Reprod Health. 2023;20(1):57.23. Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6:42.24. World Health Organization. WHO recommendations: Intrapartum care for a positive childbirth experience Geneva: World Health Organisation; 2018.25. Taljaard M, Weijer C, Grimshaw JM, Eccles MP, Ottawa Ethics of Cluster Randomised Trials Consensus G. The Ottawa Statement on the ethical design and conduct of cluster randomised trials: precis for researchers and research ethics committees. BMJ. 2013;346:f2838.26. World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191-4.27. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. ICH E6 Good Clinical Practice (GCP) Guideline. https://www.ich.org/page/efficacy-guidelines#6-2; 2016.28. Chan AW, Tetzlaff JM, Altman DG, Laupacis A, Gotzsche PC, Krle A-Jeric K, et al. SPIRIT 2013 Statement: defining standard protocol items for clinical trials. Rev Panam Salud Publica. 2015;38(6):506-14. + +<--- Page Split ---> + +29. Hemming K, Taljaard M, McKenzie JE, Hooper R, Copas A, Thompson JA, et al. Reporting of stepped wedge cluster randomised trials: extension of the CONSORT 2010 statement with explanation and elaboration. BMJ. 2018;363:k1614. +30. Vogel JP, Pingray V, Althabe F, Gibbons L, Berrueta M, Pujar Y, et al. Implementing the WHO Labour Care Guide to reduce the use of Caesarean section in four hospitals in India: protocol and statistical analysis plan for a pragmatic, stepped-wedge, cluster-randomized pilot trial. Reprod Health. 2023;20(1):18. +31. Government of India. Labour Room Quality Improvement Initiative. https://nhm.gov.in/index1.php?lang=1&level=3&sublinkid=1307&lid=6902017. +32. Bluestone J, Johnson P, Fullerton J, Carr C, Alderman J, BonTempo J. Effective in-service training design and delivery: evidence from an integrative literature review. Hum Resour Health. 2013;11:51. +33. Ivers N, Jamtvedt G, Flottorp S, Young JM, Odgaard-Jensen J, French SD, et al. Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2012(6):CD000259. +34. World Health Organization. WHO recommendations: non-clinical interventions to reduce unnecessary caesarean sections. Geneva: World Health Organization; 2018. +35. World Health Organization. Robson Classification: Implementation Manual. Geneva: World Health Organization; 2017. +36. Hemming K, Kasza J, Hooper R, Forbes A, Taljaard M. A tutorial on sample size calculation for multiple-period cluster randomized parallel, cross-over and stepped-wedge trials using the Shiny CRT Calculator. Int J Epidemiol. 2020;49(3):979-95. +37. Ford WP, Westgate PM. Maintaining the validity of inference in small-sample stepped wedge cluster randomized trials with binary outcomes when using generalized estimating equations. Stat Med. 2020;39(21):2779-92. +38. The Lancet. Stemming the global caesarean section epidemic. Lancet. 2018;392(10155):1279. +39. Bohren MA, Hofmeyr GJ, Sakala C, Fukuzawa RK, Cuthbert A. Continuous support for women during childbirth. Cochrane Database Syst Rev. 2017;7:CD003766. +40. Lawrence A, Lewis L, Hofmeyr GJ, Styles C. Maternal positions and mobility during first stage labour. Cochrane Database Syst Rev. 2013(10):CD003934. +41. Anim-Somuah M, Smyth RM, Cyna AM, Cuthbert A. Epidural versus non-epidural or no analgesia for pain management in labour. Cochrane Database Syst Rev. 2018;5(5):CD000331. +42. Bernitz S. The Norwegian World Health Organisation Labour Care Guide Trial (NORWEL): study protocol (NCT05791630) clinicaltrials.gov2023 [ +43. Blomberg M. Can the Use of a Next Generation Partograph Improve Neonatal Outcomes? (PICRINO): study protocol (NCT05560802) clinicaltrials.gov [ +44. Pandey D, Bharti R, Dabral A, Khanam Z. Impact of WHO Labor Care Guide on reducing cesarean sections at a tertiary center: an open-label randomized controlled trial. AJOG Glob Rep. 2022;2(3):100075. + +<--- Page Split ---> + +
CharacteristicIntervention period
(N = 14,814 women)		Control period
(N = 11,517 women)
n (%)n (%)
Maternal age (years)*23.9 (3.6)23.4 (3.6)
Maternal age
Less than 201,020 (6.9%)1,010 (8.8%)
20-3413,572 (91.6%)10,357 (89.9%)
35 or more222 (1.5%)150 (1.3%)
Previous Caesarean Section**
04,282 (55.0%)3,484 (56.7%)
12,819 (36.2%)2,133 (34.7%)
2 or more682 (8.8%)525 (8.5%)
Gravida
16,394 (43.2%)4,940 (42.9%)
2-48,160 (55.1%)6,369 (55.3%)
5 or more260 (1.8%)208 (1.8%)
Parity
07,031 (47.5%)5,375 (46.7%)
1-37,674 (51.8%)6,022 (52.3%)
4 or more109 (0.7%)120 (1.0%)
Women receive antenatal care during pregnancy14,745 (99.5%)11,438 (99.3%)
Covid status at admission
Positive32 (0.2%)5 (0.0%)
Negative8,208 (55.4%)9,168 (79.6%)
Pending or not done6,574 (44.4%)2,344 (20.4%)
Transferred from another health facility during labour2,102 (14.2%)1,881 (16.3%)
Gestational age at time of birth*38.3 (2.5)38.3 (2.6)
+ +* Mean and (Standard deviation) is reported + +** Multiparous women only were considered + +Table 1. Characteristics of study population + +<--- Page Split ---> + + +Table 2. Effect of the intervention on primary outcome, and maternal process of care outcomes + +
Intervention period
(N = 14,814 women)		Control period
(N = 11,517 women)		Relative Risk
(95% CI)α
n/N(%)n/N(%)
Primary outcome
Cesarean section in Robson Group 11709/4302(39.7%)1602/3543(45.2%)0.85 (0.54; 1.33)
Maternal process of care outcomes
Cesarean section in women in Robson Groups 1 and 32012/7485(26.9%)1919/6204(30.9%)0.81 (0.59; 1.11)
Cesarean section in women in Robson Groups 1, 2, 3, 4 and 56529/12735(51.3%)5028/9808(51.3%)0.92 (0.78; 1.10)
Caesarean section (all women)7505/14814(50.7%)5817/11517(50.5%)0.91 (0.71; 1.15)
Augmentation with oxytocin during labourβ912/9764(9.3%)2273/8318(27.3%)0.34 (0.01; 15.04)
Artificial rupture of the membranes*β553/9764(5.7%)559/8318(6.7%)-
Episiotomyε4820/7309(65.9%)3137/5700(55.0%)0.99 (0.73; 1.35)
Operative vaginal birthε192/7309(2.63%)112/5700(1.96%)1.12 (0.13; 9.36)
Days from admission to childbirth**0.34(0.73)0.30(0.68)0.05 (-0.31; 0.41)
Days from childbirth to discharge**3.29(1.75)3.52(1.88)0.23 (-0.84; 1.30)
+ +\(\beta\) Women in spontaneous labour were considered \(\epsilon\) Women with vaginal deliveries were considered \\*\\*The mean of the days and (S.D.) is reported. The effect size was calculated as the difference between the mean of days in the intervention group and the mean of days in the control group. \(\ast \ast\) RR was not estimated since convergence of the model was not achieved \(\Omega\) The relative risk and \(95\%\) confidence interval \((95\% CI)\) was estimated with the Generalized Estimating Equation method employing the "Manck and DeRouen" bias correction method and a degree of freedom approximation. + +<--- Page Split ---> + + +Table 3. Effect of the intervention on maternal, perinatal and neonatal health outcomes + +
Intervention period
(N = 14,814 women)		Control period
(N = 11,517 women)		Relative Risk
(95% CI)Ω
n/N(%)n/N(%)
Maternal Secondary Outcomes
3rd or 4th degree tears18/14814(0.12%)25/11517(0.22%)0.51 (0.01; 29.16)
PPH requiring uterine balloon tamponade or surgical intervention28/14814(0.19%)46/11517(0.40%)0.38 (0.00; 84.07)
Suspected or confirmed maternal infection requiring therapeutic antibiotics114/14814(0.77%)53/11517(0.46%)2.12 (0.06; 70.96)
Fetal/Neonatal Secondary Outcomes
Stillbirth449/14971(3.00%)367/11624(3.16%)0.97 (0.43; 2.19)
Antepartum stillbirth279/14971(1.86%)286/11624(2.46%)0.91 (0.34; 2.47)
Intrapartum stillbirth163/14971(1.09%)79/11624(0.68%)0.90 (0.49; 1.65)
Apgar score &lt;7 at 5 minutes670/14522(4.61%)567/11257(5.04%)1.17 (0.86; 1.59)
Bag and mask ventilation of newborn424/14522(2.92%)256/11257(2.27%)1.21 (0.08; 18.75)
Mechanical ventilation of newborn293/14522(2.02%)260/11257(2.31%)1.29 (0.36; 4.66)
Prolonged (&gt;48 hour) admission in NICU1843/14522(12.7%)1014/11257(9.0%)1.14 (0.47; 2.79)
Newborns requiring NICU admission for hypoxic ischaemic encephalopathy34/14522(0.23%)152/11257(1.35%)0.40 (0.04; 3.74)
Composite neonatal morbidity outcome*376/14522(2.59%)377/11257(3.35%)1.11 (0.32; 3.79)
Neonatal death200/14522(1.38%)196/11257(1.74%)1.31 (0.37; 4.71)
Perinatal death (stillbirth or neonatal death)649/14971(4.34%)563/11624(4.84%)1.06 (0.41; 2.73)
+ +\(\Omega\) The relative risk and \(95\%\) confidence interval \((95\% CI)\) was estimated with the Generalized Estimating Equation method employing the "Manc and DeRouen" bias correction method and a degree of freedom approximation. \\* The composite neonatal outcome was defined as one or more of the following: Mechanical ventilation of the newborn or requirement of NICU admission for hypoxic ischaemic encephalopathy of the newborn or neonatal death + +<--- Page Split ---> + + +Table 4. Effect of the intervention on women's experience outcomes (Women in Robson Group 1 or 3) + +
Intervention period
(N=1277 women)		Control period
(N=1438 women)		Relative Risk
(95% CI) a
n/N(%)n/N(%)
Women reporting labour companion982/1277(76.9%)1206/1438(83.9%)1.19 (0.89; 1.59)
Women reporting being offered pain relief196/1277(15.3%)75/1438(5.2%)2.30 (0.00; 1281.82)
Women reporting being very satisfied or somewhat satisfied with how their pain was managed827/1277(64.8%)957/1437(66.6%)0.94 (0.06; 16.14)
Women reporting being encouraged to drink water863/1277(67.6%)1123/1438(78.1%)0.98 (0.34; 2.86)
Women reporting being encouraged to eat food657/1277(51.4%)823/1438(57.2%)0.99 (0.13; 7.37)
Women reporting being encouraged to walk827/1277(64.8%)863/1437(60.1%)1.10 (0.34; 3.58)
Women reporting being asked which birth position they preferred27/1277(2.11%)10/1438(0.70%)1.96 (0.00; 1384.48)
Women reporting being very or somewhat satisfied with the amount of time health provider spent with them1260/1277(98.7%)1424/1437(99.1%)0.99 (0.93; 1.05)
Women reporting being very or somewhat satisfied with the way health provider communicated with them1262/1277(98.8%)1424/1438(99.0%)0.99 (0.91; 1.07)
Women who strongly agreed or agreed that their privacy was respected1234/1277(96.6%)1315/1438(91.4%)0.99 (0.56; 1.75)
Women who reported being asked permission before examinations596/1277(46.7%)992/1438(69.0%)0.84 (0.07; 10.34)
Women who reported being asked permission before treatments588/1277(46.0%)996/1438(69.3%)0.85 (0.07; 10.37)
Women who strongly agreed or agreed that they were satisfied with their labour and birth experience1268/1277(99.3%)1404/1438(97.6%)1.01 (0.95; 1.07)
+ +\(\Omega\) The relative risk and \(95\%\) confidence interval (95% CI) was estimated with the Generalized Estimating Equation method employing the "Manc and DeRouen" bias correction method and a degree of freedom approximation. + +<--- Page Split ---> + +# FIGURES + +Figure 1. Trial diagram showing number of women with a gestational age >20 weeks by hospital and steps + +
STEP (2 months periods)Total
(Transition
period)**
1234\(5^{**}\)
HOSPITAL1946915
(240)*		112787723746239
(240)
2965983708
(267)*		71919205295
(267)
31398167715291015
(302)*		31538772
(302)
4950106010879222006
(271)*		6025
(271)
Total
(Transition
period)		42594635
(240)		4451
(267)		3533
(302)		9453
(271)		26331
(1080)
+ +![PLACEHOLDER_23_0] + +Control Study period Intervention study period + +* Number of women recruited during the two weeks-transition period + +** The sample size was larger for step 4 because this step included 4 months of data, compared with 2 months for preceding steps and baseline period. + +<--- Page Split ---> + +## SUPPLEMENTARY APPENDIX + +### Table S1. Primary and secondary outcomes + +#### Primary Outcome + +CS rate amongst women in Robson Group 1 (i.e. women who are nulliparous, singleton, cephalic, ≥37 weeks' gestation, in spontaneous labour). The numerator are the women in Robson Group 1 who had a CS and the denominator the number of women in Robson group 1. + +#### Maternal Secondary Outcomes + +
OutcomeOutcome definition
CS rate in women in Robson Groups 1 and 3Numerator: Number of women undergoing CS
Denominator: Number of women in Robson Groups 1 and 3
CS rate in women in Robson Groups 1 to 5Numerator: Number of women undergoing CS
Denominator: Number of women in Robson Groups 1 to 5
Overall CS rateNumerator: Number of women undergoing CS
Denominator: Number of women giving birth
Augmentation with oxytocin during labour rateNumerator: Number of women given oxytocin for augmentation during labour
Denominator: Number of women who experienced spontaneous labour
Artificial rupture of the membranes rateNumerator: Number of women who had artificial rupture of membranes
Denominator: Number of women who experienced spontaneous labour
Episiotomy rateNumerator: Number of women who had episiotomy
Denominator: Number of women with vaginal birth
Operative vaginal birth rateNumerator: Number of women who had operative vaginal birth (forceps or vacuum)
Denominator: Number of women with vaginal birth
Days between admission to childbirthMean of the days between admission to childbirth
Days between childbirth to dischargeMean of the days between childbirth to discharge
3rd or 4th degree tearsNumerator: Number of women experiencing 3rd or 4th degree tears
Denominator: Number of women giving birth
PPH requiring uterine balloon tamponade or surgical interventionNumerator: Number of women requiring uterine balloon tamponade OR surgical intervention for PPH
Denominator: Number of women giving birth
+ +<--- Page Split ---> + + +
Suspected or confirmed maternal
infection requiring therapeutic
antibiotics		Numerator: Number of women with clinical signs or symptoms
of maternal infection AND therapeutic antibiotics were required
Denominator: Number of women giving birth
+ +Fetal/Neonatal Secondary Outcomes + +
OutcomeOutcome definition
StillbirthNumerator: Fetal death
Denominator: All born babies
Antepartum stillbirthNumerator: Fetal death prior to admission
Denominator: All born babies
Intrapartum stillbirthNumerator: Fetal death after admission
Denominator: All born babies
Apgar score <7 at 5 minutesNumerator: Liveborn babies with Apgar <7 at 5 minutes
Denominator: Liveborn babies
Bag and mask ventilation of newbornNumerator: Use of continuous bag and mask ventilation of
newborn for >1 minute
Denominator: Liveborn babies
Mechanical ventilation of newbornNumerator: Use of mechanical ventilation of newborn
Denominator: Liveborn babies
Composite neonatal outcomeNumerator: Use of mechanical ventilation of newborn or
admission to NICU for suspected or confirmed or neonatal death
Denominator: Liveborn babies
Prolonged (>48 hour) admission in NICUNumerator: Admission to NICU for >48 hours
Denominator: Liveborn babies
Newborns requiring NICU admission for
hypoxic ischaemic encephalopathy		Numerator: Admission to NICU for suspected or confirmed
Denominator: Liveborn babies
Composite neonatal outcomeNumerator: Use of mechanical ventilation of newborn or
admission to NICU for suspected or confirmed or neonatal death
Denominator: Liveborn babies
Neonatal deathNumerator: Neonatal death in a liveborn infant by day 7 or
discharge (whichever came first)
Denominator: All liveborn babies
Perinatal deathNumerator: Fetal death or neonatal death in a liveborn infant by
day 7 or discharge (whichever came first)
Denominator: All born babies
+ +<--- Page Split ---> + + +Women's experience outcomes + +
OutcomeOutcome definition
Woman's experience with labour companionNumerator: Women who reported a labour companion was present during labour or birth
Denominator: Women in Robson Group 1 or 3 who completed the survey
Woman's experience of being offered pain reliefNumerator: Women who reported that they were asked whether they would like any pain relief
Denominator: Women in Robson Group 1 or 3 who completed the survey
Women's satisfaction with their pain management during labour and birthNumerator: Women who reported being very satisfied or somewhat satisfied with how their pain was managed during labour and birth
Denominator: Women in Robson Group 1 or 3 who completed the survey
Woman's experience of being encouraged to drink oral fluidsNumerator: Women who reported that a health worker encouraged them to drink water
Denominator: Women in Robson Group 1 or 3 who completed the survey
Woman's experience of being encouraged to eat foodNumerator: Women who reported that a health worker encouraged them to eat food
Denominator: Women in Robson Group 1 or 3 who completed the survey
Woman's experience of mobilising during labourNumerator: Women who reported that a health worker encouraged them to walk around during labour
Denominator: Women in Robson Group 1 or 3 who completed the survey
Woman's experience of birth position of choiceNumerator: Women who reported that a health worker asked them which birth position they preferred
Denominator: Women in Robson Group 1 or 3 who completed the survey
Woman's experience of time health worker spent with themNumerator: Women who reported being very satisfied or somewhat satisfied with amount of time health worker spent with them during labour
Denominator: Women in Robson Group 1 or 3 who completed the survey
+ +<--- Page Split ---> + + +Women's experience outcomes (cont.) + +
OutcomeOutcome definition
Women's satisfaction with the way health providers communicated with themNumerator: Women who reported being very satisfied or somewhat satisfied with the way health workers communicated with them during labour and birth
Denominator: Women in Robson Group 1 or 3 who completed the survey
Woman's experience of privacyNumerator: Number of women who strongly agreed or agreed that their privacy was respected during examinations and treatments
Denominator: Women in Robson Group 1 or 3 who completed the survey
Women's experience of being asked permissionNumerator: Number of women who said their health worker always asked permission before examinations and treatments
Denominator: Women in Robson Group 1 or 3 who completed the survey
Woman's overall experience of careNumerator: Number of women who strongly agreed or agreed that they felt satisfied with their labour and birth experience
Denominator: Women in Robson Group 1 or 3 who completed the survey
+ +<--- Page Split ---> + +Table S2. Application of Robson Classification to intervention and control groups + +
Robson Classification GroupIntervention
period
(N=14,814
women)		Control period
(N=11,517
women)
Group 1: Nulliparous, singleton, cephalic, term, spontaneous labour4,302 (29.0%)3,543 (30.8%)
Group 2: Nulliparous, singleton, cephalic, term, induced/prelabour Caesarean1,729 (11.7%)1,022 (8.9%)
· Group 2a: Nulliparous, singleton, cephalic, term, induced848 (5.7%)471 (4.1%)
· Group 2b: Nulliparous, singleton, cephalic, term, prelabour
Caesarean		881 (5.9%)551 (4.8%)
Group 3: Multiparous (no previous Caesarean), singleton, cephalic, term,
spontaneous labour		3,183 (21.5%)2,661 (23.1%)
Group 4: Multiparous (no previous Caesarean), singleton, cephalic, term,
induced/prelabour Caesarean		450 (3.0%)282 (2.4%)
· Group 4a: Multiparous (no previous Caesarean), singleton, cephalic,
term, induced		292 (2.0%)212 (1.8%)
· Group 4a: Multiparous (no previous Caesarean), singleton, cephalic,
term, prelabour Caesarean		158 (1.0%)70 (0.6%)
Group 5: Previous Caesarean, singleton, cephalic, term, (spontaneous labour,
induced labour or prelabour Caesarean)		3,071 (20.7%)2,300 (20.0%)
Group 6: Nulliparous with a singleton breech235 (1.6%)182 (1.6%)
Group 7: Multiparous with a singleton breech (including previous Caesarean)224 (1.5%)153 (1.3%)
Group 8: Multiple pregnancies (including previous Caesarean)155 (1.0%)107 (0.9%)
Group 9: Single pregnancy, transverse or oblique lie (including previous
Caesarean)		21 (0.1%)36 (0.3%)
Group 10: Singleton, cephalic, preterm (including previous Caesarean)1,444 (9.7%)1,231 (10.7%)
+ +<--- Page Split ---> + +Table S3. Serious adverse events by period + +
Intervention Period
(N of women = 14,814)
(N of liveborns=14,522)
(N of newborns=14,971)		Transition period
(N of women=1,080)
(N of liveborns=1,060)
(N of newborns: 1,089)		Control Period
(N of women= 11,517)
(N of liveborns= 11,257)
(N of newborns= 11,624)
n (%)n (%)n (%)
Maternal death13 (0.09)1 (0.09)5 (0.04)
Neonatal death200 (1.38)11 (1.04)196 (1.74)
Neonatal death (less than 28 weeks)18 (0.12)1 (0.09)16 (0.14)
Neonatal death (28 weeks or more)182 (1.25)10 (0.94)180 (1.60)
Stillbirth449 (3.00)29 (2.66)367 (3.16)
Stillbirth (less than 28 weeks)175 (1.17)10 (0.92)139 (1.20)
Stillbirth (28 weeks or more)274 (1.83)19 (1.74)228 (1.96)
+ +Table S4. Causes of maternal deaths, by period + +
Intervention Period
(N = 13)		Transition Period
(N = 1)		Control Period
(N = 5)
Pre-eclampsia/eclampsia504
Obstructed labour000
Haemorrhage100
Infection210
Other*501
+ +*The case classified as "Other" in the control period was a postpartum cardiomyopathy. The five cases classified as "Other"in the intervention period were: (1) Immediate cause: a) Hepatic encephalopathy with MODS Antecedent cause: b) Acute fatty liver of pregnancy, (2) Amniotic fluid embolism, (3) Disseminated intravascular coagulation secondary to acute fatty liver of pregnancy, (4) Cerebrovascular Accident, (5) Pulmonary embolism. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- 2SupplementaryFileS1.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__05a8899c72e9d6e9f51dc6ee54f196890bbf834aca0909920ef6acb684782069/preprint__05a8899c72e9d6e9f51dc6ee54f196890bbf834aca0909920ef6acb684782069_det.mmd b/preprint/preprint__05a8899c72e9d6e9f51dc6ee54f196890bbf834aca0909920ef6acb684782069/preprint__05a8899c72e9d6e9f51dc6ee54f196890bbf834aca0909920ef6acb684782069_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..defbd030c9bdcb1697c9244dcce0ec7f69fb64e3 --- /dev/null +++ b/preprint/preprint__05a8899c72e9d6e9f51dc6ee54f196890bbf834aca0909920ef6acb684782069/preprint__05a8899c72e9d6e9f51dc6ee54f196890bbf834aca0909920ef6acb684782069_det.mmd @@ -0,0 +1,508 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 944, 207]]<|/det|> +# Effects of introducing the WHO Labour Care Guide on Caesarean section: a pragmatic, stepped-wedge, cluster randomized trial in India + +<|ref|>text<|/ref|><|det|>[[44, 228, 545, 270]]<|/det|> +Joshua Vogel ( \(\boxed{ \begin{array}{r l} \end{array} }\) joshua.vogel@burnet.edu.au) Burnet Institute https://orcid.org/0000- 0002- 3214- 7096 + +<|ref|>text<|/ref|><|det|>[[44, 275, 480, 316]]<|/det|> +Yeshita Pujar KLE Academy of Higher Education and Research + +<|ref|>text<|/ref|><|det|>[[44, 322, 480, 363]]<|/det|> +Sunil Vemekar KLE Academy of Higher Education and Research + +<|ref|>text<|/ref|><|det|>[[44, 368, 193, 408]]<|/det|> +Elizabeth Armarit Burnet Institute + +<|ref|>text<|/ref|><|det|>[[44, 414, 777, 456]]<|/det|> +Veronica Pingray Instituto de Efectividad Clínica y Sanitaria (IECS- CONICET), Buenos Aires, Argentina + +<|ref|>text<|/ref|><|det|>[[44, 460, 777, 503]]<|/det|> +Fernando Althabe Instituto de Efectividad Clínica y Sanitaria (IECS- CONICET), Buenos Aires, Argentina + +<|ref|>text<|/ref|><|det|>[[44, 507, 457, 548]]<|/det|> +Luz Gibbons IECS https://orcid.org/0000- 0002- 0235- 1635 + +<|ref|>text<|/ref|><|det|>[[44, 554, 777, 596]]<|/det|> +Mabel Berrueta Instituto de Efectividad Clínica y Sanitaria (IECS- CONICET), Buenos Aires, Argentina + +<|ref|>text<|/ref|><|det|>[[44, 600, 480, 641]]<|/det|> +Manjunath Somannavar KLE Academy of Higher Education and Research + +<|ref|>text<|/ref|><|det|>[[44, 647, 777, 689]]<|/det|> +Alvaro Ciganda Instituto de Efectividad Clínica y Sanitaria (IECS- CONICET), Buenos Aires, Argentina + +<|ref|>text<|/ref|><|det|>[[44, 693, 777, 735]]<|/det|> +Rocio Rodriguez Instituto de Efectividad Clínica y Sanitaria (IECS- CONICET), Buenos Aires, Argentina + +<|ref|>text<|/ref|><|det|>[[44, 740, 480, 781]]<|/det|> +Savitri Bendigeri KLE Academy of Higher Education and Research + +<|ref|>text<|/ref|><|det|>[[44, 787, 371, 828]]<|/det|> +Jayashree Ashok Kumar Gadag Institute of Medical Sciences + +<|ref|>text<|/ref|><|det|>[[44, 833, 371, 874]]<|/det|> +Shruti Bhavi Patil Gadag Institute of Medical Sciences + +<|ref|>text<|/ref|><|det|>[[44, 880, 371, 920]]<|/det|> +Aravind Karinagannanavar Gadag Institute of Medical Sciences + +<|ref|>text<|/ref|><|det|>[[44, 926, 204, 944]]<|/det|> +Raveendra Anteen + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[52, 45, 262, 64]]<|/det|> +Gokak General Hospital + +<|ref|>text<|/ref|><|det|>[[44, 70, 262, 110]]<|/det|> +Pavithra M. R. Gokak General Hospital + +<|ref|>text<|/ref|><|det|>[[44, 116, 242, 156]]<|/det|> +Shukla Shetty JJM Medical College + +<|ref|>text<|/ref|><|det|>[[44, 163, 242, 202]]<|/det|> +Latha B JJM Medical College + +<|ref|>text<|/ref|><|det|>[[44, 209, 242, 248]]<|/det|> +Megha H. M. JJM Medical College + +<|ref|>text<|/ref|><|det|>[[44, 255, 420, 295]]<|/det|> +Suman Gaddi Vijayanagar Institute of Medical Sciences + +<|ref|>text<|/ref|><|det|>[[44, 300, 420, 341]]<|/det|> +Shaila Chikkagowdra Vijayanagar Institute of Medical Sciences + +<|ref|>text<|/ref|><|det|>[[44, 347, 420, 387]]<|/det|> +Bellara Raghavendra Vijayanagar Institute of Medical Sciences + +<|ref|>text<|/ref|><|det|>[[44, 393, 545, 434]]<|/det|> +Caroline Homer Burnet Institute https://orcid.org/0000- 0002- 7454- 3011 + +<|ref|>text<|/ref|><|det|>[[44, 440, 387, 480]]<|/det|> +Tina Lavender Liverpool School of Tropical Medicine + +<|ref|>text<|/ref|><|det|>[[44, 486, 393, 527]]<|/det|> +Pralhad Kushtagi Manipal Academy of Higher Education + +<|ref|>text<|/ref|><|det|>[[44, 533, 905, 595]]<|/det|> +Justus Hofmeyr Department of Health, Universities of the Witwatersrand, Walter Sisulu and Fort Hare, East London, South Africa https://orcid.org/0000- 0002- 3080- 1007 + +<|ref|>text<|/ref|><|det|>[[44, 601, 310, 641]]<|/det|> +Richard Derman Thomas Jefferson University + +<|ref|>text<|/ref|><|det|>[[44, 648, 405, 689]]<|/det|> +Shivaprasad Goudar Jawaharlal Nehru Medical College, India + +<|ref|>sub_title<|/ref|><|det|>[[44, 732, 102, 750]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 769, 137, 788]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 806, 296, 826]]<|/det|> +Posted Date: July 28th, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 845, 475, 864]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3175470/v1 + +<|ref|>text<|/ref|><|det|>[[44, 882, 909, 925]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 100, 923, 142]]<|/det|> +Version of Record: A version of this preprint was published at Nature Medicine on January 30th, 2024. See the published version at https://doi.org/10.1038/s41591-023-02751-4. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[118, 84, 852, 125]]<|/det|> +# Effects of introducing the WHO Labour Care Guide on Caesarean section: a pragmatic, stepped-wedge, cluster randomized trial in India + +<|ref|>text<|/ref|><|det|>[[117, 154, 864, 294]]<|/det|> +Joshua P. Vogel \(^{1*}\) , Yeshita Pujar \(^{2}\) , Sunil S Vernekar \(^{2}\) , Elizabeth Armar \(^{1}\) , Veronica Pingray \(^{3}\) , Fernando Althabe \(^{3}\) , Luz Gibbons \(^{3}\) , Mabel Berrueta \(^{3}\) , Manjunath Somannavar \(^{2}\) , Alvaro Ciganda \(^{3}\) , Rocio Rodriguez \(^{3}\) , Savitri Bendigeri \(^{2}\) , Jayashree Ashok Kumar \(^{4}\) , Shruti Bhavi Patil \(^{4}\) , Aravind Karinagannanavar \(^{4}\) , Raveendra R Anteen \(^{5}\) , Pavithra M. R. \(^{5}\) , Shukla Shetty \(^{6}\) , Latha B. \(^{6}\) , Megha H. M. \(^{6}\) , Suman S. Gaddi \(^{7}\) , Shaila Chikkagowdra \(^{7}\) , Bellara Raghavendra \(^{7}\) , Caroline SE Homer \(^{1}\) , Tina Lavender \(^{8}\) , Pralhad Kushtagi \(^{9}\) , G. Justus Hofmeyr \(^{10,11}\) , Richard Derman \(^{12}\) , Shivaprasad Goudar \(^{3}\) + +<|ref|>text<|/ref|><|det|>[[117, 323, 870, 675]]<|/det|> +\* corresponding author – Joshua.vogel@burnet.edu.au \(^{1}\) Maternal, Child and Adolescent Health Program, Burnet Institute, Melbourne, Victoria, Australia \(^{2}\) Women's and Children's Health Research Unit, Jawaharlal Nehru Medical College, KLE Academy of Higher Education and Research, Belgaum, Karnataka, India \(^{3}\) Instituto de Efectividad Clínica y Sanitaria (IECS- CONICET), Buenos Aires, Argentina \(^{4}\) Gadag Institute of Medical Sciences, Gadag, Karnataka, India \(^{5}\) General Hospital, Gokak, Belgaum, Karnataka, India \(^{6}\) JJM Medical College, Davangere, Karnataka, India \(^{7}\) Vijayanagar Institute of Medical Sciences (VIMS), Ballari, Karnataka, India \(^{8}\) Department of International Health, Liverpool School of Tropical Medicine, Liverpool, United Kingdom \(^{9}\) Manipal Academy of Higher Education, Karnataka, India \(^{10}\) Department of Obstetrics and Gynaecology, University of Botswana, Gaborone, Botswana \(^{11}\) University of the Witwatersrand and Walter Sisulu University, East London, South Africa \(^{12}\) Thomas Jefferson University, Philadelphia, United States of America + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 85, 202, 99]]<|/det|> +## ABSTRACT + +<|ref|>text<|/ref|><|det|>[[115, 107, 880, 487]]<|/det|> +The World Health Organization's Labour Care Guide (LCG) is the "next generation" partograph, designed to improve the quality of intrapartum care and enhance women's experiences. However, the effects of the LCG on maternal and newborn outcomes have not been evaluated. We developed a novel strategy to introduce the LCG into routine intrapartum care, comprising a co- designed training program for labour ward clinicians, alongside monthly audit and feedback. We implemented the strategy and measured its effects using a stepped- wedge, randomised trial in four hospitals in India. We captured data from 26,331 women who gave birth at \(> = 20\) weeks' gestation, over a 54- week period. Following implementation, a \(5.5\%\) crude absolute reduction in the Caesarean section rate amongst women in Robson Group 1 was observed (45.2% vs 39.7%; relative risk 0.85, 95% confidence interval 0.54- 1.33). Maternal process- of- care measures were not significantly different, though labour augmentation with oxytocin was 18.0% lower with the LCG strategy. No differences were observed for maternal, fetal or newborn health outcomes, or women's birth experiences. This "proof of concept" study provides important evidence on the effects of introducing LCG into routine practice, suggesting a 15% relative risk reduction in Caesarean section use amongst women in Robson Group 1. Larger trials are warranted, particularly in settings where urgent reversal of the Caesarean section epidemic is needed. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 86, 166, 100]]<|/det|> +## MAIN + +<|ref|>text<|/ref|><|det|>[[117, 108, 879, 365]]<|/det|> +An estimated 287,000 maternal deaths, 2.4 million neonatal deaths and 1.9 million stillbirths occur each year, the vast majority of which occur in low- and middle- income countries (LMICs).(1- 3). As many as \(45\%\) of these maternal deaths, stillbirths, and neonatal deaths occur during labour, birth, and the first 24 hours postpartum.(4) Ensuring good- quality care is available to all women during the intrapartum period is thus critical to any efforts to reduce global maternal and neonatal morbidity and mortality.(5) Caesarean section is an essential component of good- quality intrapartum care - when used appropriately, it is a life- saving intervention for women and babies. However, Caesarean section rates globally more than doubled between 2000 and 2015, driven in large part by those performed without a clear medical indication.(6)(7) While unnecessary Caesarean section causes avoidable harms to women and newborns,(8, 9) it By 2030, 38 million women each year (28.5% of births) will experience a Caesarean.(10) + +<|ref|>text<|/ref|><|det|>[[117, 394, 880, 653]]<|/det|> +The World Health Organization (WHO) has long recommended that a woman in labour should be monitored by a skilled healthcare provider using a partograph to document clinical assessments and help make decisions.(11) When completed prospectively, the partograph can help determine whether and when an intervention - such as labour augmentation, Caesarean section or episiotomy - is warranted. A WHO- led 1994 trial showed that prospective partograph use combined with intensive provider training optimized the use of intrapartum interventions, and improved maternal and newborn outcomes.(12) Consequently, the WHO simplified partograph was widely disseminated and adopted as a component of routine intrapartum care internationally.(13) While more women than ever are giving birth in health facilities,(14) partographs are often used poorly, or not at all. Inadequate provider training and skills, heavy staff workloads, a lack of clinical equipment and supplies, and restrictive hospital policies are known barriers to partograph use.(15- 17) + +<|ref|>text<|/ref|><|det|>[[117, 681, 875, 891]]<|/det|> +In 2018, WHO published 56 updated recommendations to improve quality of intrapartum care and enhance women's childbirth experiences.(8) Key recommendations included changing the definition of active first stage of labour from the widely used 3cm or 4cm to starting from 5cm of cervical dilation, and removal of the 'alert' and 'action' lines. These changes reflected a growing body of evidence that the historical '1cm per hour' rule for active labour progress is unrealistic for most women, and that slower dilation rates are not associated with adverse birth outcomes. In response to these recommendations, a "next generation" partograph known as the WHO Labour Care Guide (LCG) was developed in 2020 through expert consultations, primary research with maternity healthcare providers, and a multi- country usability study.(18- 20) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 107, 879, 317]]<|/det|> +WHO states that the LCG should be globally disseminated and implemented as a component of routine clinical care.(21) However, introducing the LCG into routine care requires a strategy that can improve healthcare provider's clinical practice, thereby enhancing the quality of intrapartum care, reducing use of unnecessary interventions, and improving support to women during labour. However, as the LCG is a novel tool, no such strategy has been tested in a randomised trial. This knowledge gap was highlighted in WHO's recent global LCG research prioritization exercise, in which identifying optimal implementation strategies, as well as understanding LCG's effect on maternal and perinatal outcomes, were top research priorities.(22) We therefore conducted a multi- centre trial to evaluate the effects of implementing a strategy to promote LCG use in labour wards. + +<|ref|>sub_title<|/ref|><|det|>[[118, 352, 201, 367]]<|/det|> +## METHODS + +<|ref|>sub_title<|/ref|><|det|>[[118, 376, 315, 392]]<|/det|> +## Overview of study design + +<|ref|>text<|/ref|><|det|>[[115, 400, 880, 705]]<|/det|> +We designed and conducted a pragmatic, stepped- wedge, cluster- randomized trial which was conducted between \(1^{\text{st}}\) July 2021 and \(15^{\text{th}}\) July 2022. It was intended as a "proof- of- concept" study. We used an evidence- based, theory- informed approach to developing the intervention, and conducted the trial to determine whether it might have an effect on overuse of Caesarean section, or other important maternal and perinatal outcomes. The trial was preceded by a six- month formative phase, which was guided by the COM- B model of behaviour change, which recognises that individuals must have Capability, Motivation, and both physical and social Opportunity to perform a behaviour.(23) We used co- design principles and primary data collection to develop and refine the 'LCG strategy' intervention. The intervention was then introduced in a stepwise manner in four public hospitals in Karnataka State, India, in accordance with a randomisation schedule. Given the risk of cross- contamination, individual randomization was not possible. We used a stepped- wedge approach as the LCG reflects WHO's current advice regarding standard of care,(24) and it was thus not ethically feasible to use a parallel- group design. + +<|ref|>sub_title<|/ref|><|det|>[[118, 735, 344, 751]]<|/det|> +## Trial approvals and oversight + +<|ref|>text<|/ref|><|det|>[[115, 758, 880, 895]]<|/det|> +This trial was designed and conducted in accordance with the ethical principles of the World Medical Association's Declaration of Helsinki, the Ottawa Statement for the Ethical Design and Conduct of Cluster Randomised Trials, and Good Clinical Practice (GCP) standards.(25- 27) We developed the trial protocol and reported findings in accordance with SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) guidance for randomised trials, and the CONSORT (Consolidated Standards of Reporting Trials) statement for stepped- wedge cluster- randomised trials + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 83, 820, 150]]<|/det|> +(CONSORT Checklist in Supplementary File S1).(28, 29) The trial protocol was pre- registered (CTRI/2021/01/030695), with the protocol and statistical analysis plan published prior to trial closure; there were no major deviations or changes.(30) + +<|ref|>text<|/ref|><|det|>[[116, 179, 875, 461]]<|/det|> +We sought permission from the head of study hospitals (gatekeepers) and individual providers before commencing the trial. The study protocol specified a waiver of individual consent for data collected on women giving birth – these data were non- identifiable, routinely- collected clinical variables in medical records and labour ward registries. For women invited to complete a postpartum survey, an informed consent was conducted. The trial was approved by the Alfred Hospital Human Ethics Committee (737/20), and the institutional ethics committees of the KLE Academy of Higher Education and Research (D- 281120003), J J M Medical College, Davanagere (IEC- 136/2020); Vijayanagar Institute of Medical Sciences (SVN IEC/20/2020- 2021) and the Gadag Institute of Medical Sciences, (IEC/01/2020- 21), as well as the State Ethics Committee, Department of Health and Family Welfare, Government of Karnataka (DD(MH)/71/2020- 21); and the Health Ministry's Screening Committee, Indian Council of Medical Research (2020- 10127). An independent, three- member Data and Safety Monitoring Committee oversaw the trial. + +<|ref|>sub_title<|/ref|><|det|>[[118, 491, 304, 507]]<|/det|> +## Setting and Participants + +<|ref|>text<|/ref|><|det|>[[116, 513, 877, 699]]<|/det|> +We purposively selected four public maternity hospitals in Karnataka State to participate, based on their capacity to provide comprehensive emergency obstetric care (including access to caesarean section). All four hospitals attend to more than 4,000 women giving birth each year, and have an overall caesarean section rate of \(30\%\) or more. In three hospitals labour monitoring and partograph completion is primarily performed by postgraduate resident doctors, while in the remaining hospital it was performed by nurses. All hospitals had either completed or were undergoing accreditation under the Government of India's national Labour Room Quality Initiative ("LaQshya") which closely aligned with WHO intrapartum care recommendations.(31) + +<|ref|>text<|/ref|><|det|>[[117, 728, 880, 867]]<|/det|> +Each hospital was treated as a cluster (H1, H2, H3 and H4). Two senior obstetricians working at each hospital were appointed as facility investigators and were responsible for trial activities at each hospital. The target of the intervention were labour ward staff, including obstetricians, postgraduate doctors and nurses. These staff routinely use a WHO simplified partograph to make decisions about labour interventions. We hypothesized that the intervention would promote correct LCG use by these providers, changing their labour monitoring and management practices to align with WHO's + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 84, 848, 125]]<|/det|> +intrapartum recommendations. In turn, this could reduce overuse of Caesarean section, improve maternal and newborn outcomes, and enhance women's care experiences. + +<|ref|>sub_title<|/ref|><|det|>[[118, 156, 338, 172]]<|/det|> +## Randomisation and blinding + +<|ref|>text<|/ref|><|det|>[[116, 179, 878, 390]]<|/det|> +Prior to trial commencement, the four clusters (hospitals) were randomly assigned to one of four sequences (H1, H2, H3, or H4, see Figure 1) using a computer- generated list of random numbers that was managed by the study statistician. The allocation sequence was concealed from the investigators and study teams and only revealed by the statistician one month prior to cross over to allow time for planning LCG implementation activities. Once the hospital had commenced the intervention, blinding of hospital staff, research staff and individual women was not possible. The intervention was commenced in hospitals according to the randomly assigned sequence, with one hospital transitioning to intervention at 2- month intervals (i.e., a step occurred every 2 months). A two- week transition period was used to allow for the intervention to be fully adopted. + +<|ref|>sub_title<|/ref|><|det|>[[118, 419, 312, 435]]<|/det|> +## Control and intervention + +<|ref|>text<|/ref|><|det|>[[117, 442, 870, 579]]<|/det|> +The control condition for the trial was current labour monitoring and management practices ('usual clinical care'). While the WHO simplified partograph is widely used in India, the formative phase showed that its use was inconsistent and oftentimes retrospective. Training seminars were conducted at all hospitals on using the WHO simplified partograph to standardize the control condition. The WHO intrapartum care recommendations were also disseminated at all hospitals at the start of the trial. + +<|ref|>text<|/ref|><|det|>[[116, 608, 876, 867]]<|/det|> +The LCG strategy intervention included a co- designed LCG training program for doctors and nurses working on labour ward, and a monthly audit and feedback process using hospital Caesarean section data. For training, we developed and ran two- day workshops for all labour ward staff, co- ordinated by facility investigators who had undergone a "training of trainers" workshop. After this, all providers working on labour ward underwent an 8- week "low- dose, high- frequency" training program,(32) comprising of clinical cases and bedside teaching using LCG with senior clinical staff. The 8- week training was delivered in cycles to accommodate postgraduate resident rotations every 3 months. Refresher training was used if new staff arrived during the intervention period. At time of randomization, all simplified WHO partographs in the labour ward were replaced with the LCG. Senior labour ward staff were encouraged to monitor and promote consistent, accurate LCG use through supportive supervision. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 80, 880, 535]]<|/det|> +The intervention also included monthly audit and feedback meetings on Caesarean rates, using the Robson Classification. Audit and feedback is widely used to promote evidence- based clinical practice, and is recommended by WHO for avoiding unnecessary Caesarean sections.(33, 34) WHO also recommends that countries use the Robson Classification for assessing, monitoring and comparing their Caesarean rates over time.(6) The Robson Classification organises all births in a facility into one of 10 mutually exclusive, all- inclusive groups, on the basis of parity, previous Caesarean, onset of labour, fetal presentation and lie, number of neonates and gestational age (term or preterm).(35) Providers at randomized hospitals underwent a brief training on how to interpret Robson Classification data, and how audit and feedback can help optimize Caesarean section use. Robson Classification tables were prepared using trial data, and shared directly with the study hospital on a monthly basis. These data were presented by senior clinical staff at monthly meetings, with structured discussions amongst the attendees on how to improve hospital performance. Hospitals and staff were instructed that all other aspects of clinical care during the trial should be in accordance with relevant local guidelines and protocols. In addition, facility leads were encouraged to identify and address anticipated barriers to the LCG strategy in their hospital. This included revision of hospital policies, standardisation of clinical protocols, rearrangements to the physical labour ward environment, and addressing some supply and equipment constraints. We used logbooks, tracking sheets and site visits to confirm that all eligible staff underwent LCG training activities, were using the LCG routinely, and attended monthly Caesarean audit meetings as planned. + +<|ref|>sub_title<|/ref|><|det|>[[119, 574, 377, 590]]<|/det|> +## Primary and secondary outcomes + +<|ref|>text<|/ref|><|det|>[[116, 597, 881, 856]]<|/det|> +Trained research staff collected non- identifiable, individual- level data on all women giving birth from 20 weeks' gestation onwards and their babies. Data were collected from the time of admission for childbirth until the time of discharge, transfer, death or until 7 days after admission (whichever came first). The primary trial outcome was the use of Caesarean section amongst women in Robson Group 1. That is, women who were nulliparous, gave birth to a singleton, term pregnancy in cephalic presentation, and were in spontaneous labour. While Robson Group 1 is a subset of all women giving birth (usually around \(30\%\) of the obstetric population), it is a group of largely low- risk women in whom Caesarean is often overused.(35) Should the LCG strategy have any effect, we anticipated that it would be more easily detected amongst these women. Secondary outcomes included use of intrapartum interventions, and maternal, fetal and neonatal health outcomes. The denominator varied depending on the outcome of interest (see Supplementary Table S1 for outcome definitions). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 83, 878, 222]]<|/det|> +We also measured women's experiences of care using a pre- tested, interviewer- administered survey, conducted in a local language (Kannada, Hindi or Marathi), that was completed by postnatal day 7 or discharge (whichever came first) in a sample of postpartum women. This sample comprised women in Robson Group 1 or 3 who gave birth in the last 15 days of each 2- month period, had a liveborn baby, were 18 years or older, and who provided informed consent. In each hospital, trained interviewers approached and invited all eligible women to complete the survey. + +<|ref|>text<|/ref|><|det|>[[117, 250, 880, 485]]<|/det|> +All data were collected into pre- designed study forms and managed using REDCap electronic data capture via tablets. Each hospital team had access to their own hospital data only, and facility investigators were responsible for checking completeness and accuracy of all collected data. To minimize errors, data validation processes were implemented in the data collection system. Statistical methods and data cleaning algorithms were utilized to identify potential errors and outliers for further investigation and correction. Regular data and trial progress review meetings and audits were conducted to identify and rectify any inconsistencies or outliers. Data monitors periodically visit the study sites to verify the accuracy and completeness of the collected data. They also provided training and guidance to study personnel, addressing any issues or concerns that might arise during the study. The trial concluded when \(15^{\text{th}}\) July 2022 was reached, as planned. + +<|ref|>sub_title<|/ref|><|det|>[[118, 515, 211, 530]]<|/det|> +## Sample size + +<|ref|>text<|/ref|><|det|>[[117, 538, 875, 723]]<|/det|> +No previous trial using LCG has been conducted, meaning the effect size of our strategy was difficult to estimate. For the year 2020 (prior to the trial) these four hospitals collectively averaged 24,000 births per year, and their overall Caesarean rate was \(44\%\) . The Caesarean rate in women in Robson Group 1 (i.e., the primary outcome) for all four hospitals was at least \(40\%\) . The trial was designed to provide \(92\%\) power to detect a \(25\%\) reduction in the Robson Group 1 Caesarean rate from \(40\%\) to \(30\%\) , assuming an intraclass correlation coefficient (ICC) equal to 0.02, a cluster auto correlation equal to 0.90, and an average of 300 women per cluster per step with a coefficient of variation of cluster size equal to 0.60. (36) + +<|ref|>sub_title<|/ref|><|det|>[[119, 754, 366, 769]]<|/det|> +## Statistical methods and analysis + +<|ref|>text<|/ref|><|det|>[[117, 777, 881, 890]]<|/det|> +Analyses were performed according to the intention- to- treat principle. Maternal baseline characteristics were summarized by trial arm as means and standard deviations, or numbers and percentages, as appropriate. For the primary and secondary outcomes, a generalized estimating equation (GEE) to estimate the effect of the intervention with respect to the population- average was used. A bias correction method and degree of freedom approximation due to the small number of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[116, 82, 877, 365]]<|/det|> +clusters was applied in the GEE models to maintain the validity of the estimations. Manck and DeRouen correction method with N- 2 degrees of freedom was selected due to being the most conservative option.(37) An exchangeable correlation structure was assumed and the modified Poisson distribution with a log link function was considered. The model was constructed considering two variables: a binary indicator for treatment - indicating whether the observation was made during the control or the intervention period - and a categorical variable indicating the step. The relative risk and the \(95\%\) confidence interval were reported as the size effect. For the secondary outcomes in which duration was measured in days, the effect size was calculated as the difference between the mean of days in the intervention group and the mean of days in the control group. The ICC was estimated under the control period using the GEE model. As no adjustment for multiplicity testing of secondary outcomes was considered, their results are reported as point estimates with \(95\%\) confidence intervals and p- values. + +<|ref|>sub_title<|/ref|><|det|>[[118, 400, 188, 415]]<|/det|> +## RESULTS + +<|ref|>sub_title<|/ref|><|det|>[[119, 424, 388, 440]]<|/det|> +## Characteristics of study population + +<|ref|>text<|/ref|><|det|>[[116, 446, 881, 608]]<|/det|> +Between 1 July 2021 and 15 July 2022, 26,331 women gave birth to 26,595 babies in the four hospitals during the control and intervention periods and were included for analysis (Figure 1). The total number of women giving birth differed between hospitals, ranging from 5,295 to 8,772 women per hospital. The analysis population comprised 11,517 women (11,624 babies) who gave birth in the control period and 14,814 women (14,971 babies) who gave birth in the intervention period. The main analysis did not include the 1,080 women (1,089 babies) who gave birth in the transition period. + +<|ref|>text<|/ref|><|det|>[[116, 637, 863, 824]]<|/det|> +While there were more women in intervention than control, the characteristics of women were similar (Table 1). Nearly half of included women were nulliparous (46.7% of the control group and 47.5% of the intervention group), while more than half of multiparous women had no prior Caesarean section (56.7% vs 55.0%) The distribution of women across the 10 Robson Classification groups was also similar (Supplementary Table S1). Robson Group 1 accounted for 30.8% (3,543/11,517) of women in the control group and 29.0% (4,302/14,814) of women in the intervention group. The intervention group had a slightly higher proportion of women in Group 2 and a slightly lower proportion of women in Group 3. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[119, 85, 378, 100]]<|/det|> +## Primary and secondary outcomes + +<|ref|>text<|/ref|><|det|>[[117, 108, 877, 222]]<|/det|> +Table 2 reports the intervention effect sizes for the primary outcome and secondary maternal process- of- care outcomes. The Caesarean section rate in Robson Group 1 for the control group was \(45.2\%\) , while in the intervention group it was \(39.7\%\) with a crude absolute difference of \(- 5.5\%\) (relative risk [RR] 0.85, \(95\%\) confidence interval [CI] 0.54- 1.33, p value 0.1088). The estimated ICC for the primary outcome during the control period was \(0.015 (0; 0.043)\) . + +<|ref|>text<|/ref|><|det|>[[116, 250, 880, 460]]<|/det|> +The Caesarean section rate in Robson Groups 1 and 3 was \(30.9\%\) for the control group, and \(26.9\%\) for the intervention group - a crude absolute difference of \(- 4.0\%\) (RR 0.81, \(95\%\) CI 0.59 - 1.11). For the outcome augmentation with oxytocin during spontaneous labour, the prevalence in control group was \(27.3\%\) and in the intervention group it was \(9.3\%\) (crude absolute difference - \(18.0\%\) ). However, the estimate of effect was not significant (RR 0.34, \(95\%\) CI 0.01 - 15.04) - the wide confidence interval was attributable to the high variability in outcome prevalence between hospitals and steps. Table 3 reports the intervention effects on maternal, fetal and newborn health outcomes. The prevalence of these outcomes was low in both intervention and control groups, and there were no clear differences. + +<|ref|>text<|/ref|><|det|>[[116, 490, 880, 653]]<|/det|> +A total of 1,438 women in the control group and 1,277 women in the intervention group consented ( \(100\%\) and \(99.9\%\) consent rate, respectively) and completed postpartum surveys. Table 4 reports the effects on women's experiences at birth, for which there were no differences between groups. In terms of adverse events, there were 5 maternal deaths, 196 neonatal deaths and 367 stillbirths in the control period, and 13 maternal deaths, 200 neonatal deaths and 449 stillbirths in the intervention period (Supplementary Tables S3 and S4). None of these deaths were assessed as being related to the intervention. + +<|ref|>sub_title<|/ref|><|det|>[[118, 688, 216, 702]]<|/det|> +## DISCUSSION + +<|ref|>text<|/ref|><|det|>[[117, 710, 878, 871]]<|/det|> +In this stepped- wedge, cluster- randomised trial in India, we implemented a novel strategy to introduce the LCG into routine care, as well as initiating monthly audit and feedback meetings on Caesarean section data using Robson Classification. We observed a \(5.5\%\) crude absolute reduction in Caesarean rates amongst women in Robson Group 1 following introduction of the intervention, however this difference was not statistically significant. Maternal process- of- care measures were not significantly different, though the crude absolute difference for labour augmentation using oxytocin was \(- 18.0\%\) . We did not observe any clear differences in maternal, fetal or newborn health + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 84, 879, 126]]<|/det|> +outcomes, or women's experiences at birth. The findings do not preclude the possibility that the LCG strategy may reduce Caesarean section and augmentation of labour in larger trials. + +<|ref|>text<|/ref|><|det|>[[115, 154, 880, 605]]<|/det|> +Reversing the worldwide trend in rising Caesarean section rates, driven in large part by medically unnecessary Caesarean use, has proven to be a challenging problem - a 2018 WHO guideline identified few effective interventions to address this.(34, 38) The LCG promotes several supportive care measures which have been shown in trials to prevent Caesarean section, such as labour companionship, mobilisation during labour, and adequate pain relief.(39- 41) Also, the use of 5cm dilatation to define active first stage, as well as removal of the "1cm per hour rule" would, assumedly, lead to fewer intrapartum interventions. As the LCG is a novel clinical tool, there are few effectiveness studies available for comparison, though more trials using LCG are planned.(42, 43) In 2022, Pandey et al published findings of an individually- randomized trial of 271 low- risk women in a single hospital in India, comparing the effects of using LCG versus modified partograph.(44) They reported a dramatic reduction in Caesarean section - 1.5% in the LCG group compared with 17.8% in the control group (p- value 0.0001) - as well as significantly lower oxytocin use and shorter duration of active phase of labour with LCG. Our trial was powered to detect a 25% risk reduction for Caesarean section rate in Robson Group 1, equating to an absolute reduction of 10% (from 40% to 30%). Though we lacked power to detect a smaller magnitude of effect, our findings suggest that an effect does exist, and is probably closer to a 15% risk reduction. While we lacked power to test a superiority hypothesis for rarer adverse outcomes (such as mortality and severe morbidity of women and babies), reassuringly there was no evidence of short- term harms associated with the LCG strategy. Data on these outcomes should be monitored in future, larger- scale research. + +<|ref|>text<|/ref|><|det|>[[117, 633, 881, 820]]<|/det|> +We did not detect any differences for outcomes on women's experiences. However, these data showed women had high levels of satisfaction with the amount of time health workers spent with them, the way they were communicated with, and with their overall birth experience. It also showed that some supportive care practices, such as being offered a labour companion, were reasonably common, though other women- centred interventions were not well- implemented. For example, being offered pain relief (5.2% and 15.3%), and being asked which birth position they preferred (0.7% and 2.1%) were poorly used. This highlights that substantive gaps persist in the provision of supportive care around the time of birth - additional strategies are needed to address these gaps. + +<|ref|>text<|/ref|><|det|>[[118, 848, 864, 891]]<|/det|> +This trial was conducted in large, busy, public tertiary hospitals in India with high Caesarean use. In three hospitals, partograph completion was the responsibility of postgraduate residents only. In + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 83, 878, 270]]<|/det|> +India, the LaQshya national initiative and hospital accreditation process (31) has a strong emphasis on respectful maternity care, which is well- aligned with WHO's recommendations and the LCG's foundational principles. These factors mean the trial findings may not necessarily generalize to other settings that are naïve to respectful maternity care principles and policies. For example, it may be more challenging to generate provider behaviour change in settings without a national policy framework. Contextual differences around how frequently obstetric interventions are used, as well as differences in the risk profile of obstetric populations, may mean the LCG strategy has variable effects. + +<|ref|>text<|/ref|><|det|>[[116, 298, 881, 675]]<|/det|> +This study was designed as a "proof of concept" study of a novel, complex intervention. Strengths include the use of a theory- based, evidence- informed, co- design approach to developing the LCG strategy, which aimed to address factors known to impair partograph use.(17) We also used a robust, cluster- randomised design, and recruited a large number of participants in a real- world clinical setting. The stepped- wedge design means that all hospitals were implementing the LCG strategy at trial conclusion. This trial nonetheless has some limitations. The intervention did not have a specific component aimed at the antenatal period, though in retrospect it would be helpful to better prepare women for the introduction of new supportive care options. Also, women arriving at hospital in advanced labour had only a short period of time in which they could benefit from LCG, thereby diminishing any possible effects. The stepped- wedge design meant that other, secular trends – such as changes in COVID case numbers over time - could have affected the findings. However, COVID data shows that infections in these hospitals were quite infrequent. The use of the same clusters over a 54- week period means we cannot exclude the possibility that some women may have given birth twice during the study. We measured women's experiences using a survey instrument in their language of choice, however their responses may have been affected by social or courtesy biases. + +<|ref|>sub_title<|/ref|><|det|>[[118, 707, 224, 721]]<|/det|> +## CONCLUSION + +<|ref|>text<|/ref|><|det|>[[117, 729, 877, 842]]<|/det|> +Findings from this multi- centred, stepped- wedge, cluster- randomized trial suggest that the LCG strategy is a promising intervention that can improve quality of labour and childbirth care, reducing overuse of intrapartum interventions. This study provides important evidence on the debate around introduction of LCG into routine clinical practice internationally. Further evaluation in larger scale, multi- country trials in hospital with high rates of Caesarean section are warranted. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 133, 196, 148]]<|/det|> +## FUNDING + +<|ref|>text<|/ref|><|det|>[[117, 155, 875, 317]]<|/det|> +This study was supported by a Grand Challenges grant from the Bill & Melinda Gates Foundation (GNT INV- 023273). We received additional funding support from the Burnet Institute, via the Alastair Lucas Award. JPV and CSEH are supported by Investigator Grants from the Australian National Health and Medical Research Council (NHMRC). EA is supported by a NHMRC Postgraduate Student Award. The study funder had no role in study design, data collection, analysis, interpretation, or writing of the report. The corresponding author had full access to all the study data, and takes final responsibility for the decision to submit for publication. + +<|ref|>sub_title<|/ref|><|det|>[[118, 348, 299, 364]]<|/det|> +## ACKNOWLEDGEMENTS + +<|ref|>text<|/ref|><|det|>[[118, 371, 848, 436]]<|/det|> +We gratefully acknowledge Ana Pilar Betran (Chair), Dr Dennis Wallace and Shuchita Mundle for their role as Data and Safety Monitoring Board members, and Olufemi T. Oladapo and Mercedes Bonet for their role as Observers to the study. + +<|ref|>sub_title<|/ref|><|det|>[[118, 467, 401, 483]]<|/det|> +## INCLUSION AND ETHICS STATEMENT + +<|ref|>text<|/ref|><|det|>[[118, 490, 878, 579]]<|/det|> +Our study team support the principles of the Cape Town Statement, in particular the commitment to equitable international collaborations. The study was designed in partnership between three research groups (India, Argentina, Australia), building on multiple years of research collaborations and co- authored publications between several co- authors. + +<|ref|>text<|/ref|><|det|>[[117, 608, 870, 747]]<|/det|> +This study was funded by a Global Grand Challenges grant – the submission was jointly prepared by JPV, SG, YP, SV, VP, FA and LG. This grant funding went to all three of our research organisations, with the largest amount of this funding received by the JNMC- India research team. The study protocol had 14 named investigators – 12 from India, 1 from Argentina, and 1 from Australia. JPV and SG were named as co- Principal Investigators. During the study, decisions were taken by consensus amongst the steering group, during fortnightly teleconferences. + +<|ref|>text<|/ref|><|det|>[[117, 776, 875, 890]]<|/det|> +The authorship group (29 individuals) comprised 17 women and 12 men, and included late- , mid- and early- career individuals. Members of the authorship group include researchers in India (YP, SSV, MS, SB, JAK, SBP, AK, RRA, PMR, SS, LB, MHM, SSG, SC, BR), Argentina (VP, FA, LG, MB, AV, RR) and Australia (JPV, EA, CSEH). The lead author (JPV) is in Australia and the senior author (SG) is in India. Our Technical Advisory Group (TL, PK, GJH, RD) included senior researchers from India, UK, South + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 85, 840, 125]]<|/det|> +Africa and USA, and our Data and Safety Monitoring Committee included individuals from India, Switzerland and the USA. + +<|ref|>sub_title<|/ref|><|det|>[[118, 133, 457, 149]]<|/det|> +## DATA AND CODE AVAILABILITY STATEMENT + +<|ref|>text<|/ref|><|det|>[[117, 156, 872, 245]]<|/det|> +In keeping with the Bill & Melinda Gates Foundation Open Access Policy, the trial dataset generated during this study, the data dictionary and syntax used for analyses are hosted at the Gates Open Research- approved repository Zenodo at time of paper publication under DOI: https://doi.org/10.5281/zenodo.8140454 + +<|ref|>sub_title<|/ref|><|det|>[[118, 304, 217, 319]]<|/det|> +## REFERENCES + +<|ref|>text<|/ref|><|det|>[[115, 343, 875, 886]]<|/det|> +1. WHO, UNICEF, UNFPA, World Bank, UNDP. Trends in maternal mortality 2000 to 2020. Geneva: World Health Organization; 2023. +2. United Nations Inter-Agency Group for Child Mortality Estimation. Levels and trends in child mortality. New York: UNICEF; 2022. +3. Hug L, You D, Blencowe H, Mishra A, Wang Z, Fix MJ, et al. Global, regional, and national estimates and trends in stillbirths from 2000 to 2019: a systematic assessment. Lancet. 2021;398(10302):772-85. +4. Alliance for Maternal Newborn Health Improvement Mortality Study Group. Population-based rates, timing, and causes of maternal deaths, stillbirths, and neonatal deaths in south Asia and sub-Saharan Africa: a multi-country prospective cohort study. Lancet Glob Health. 2018;6(12):e1297-e308. +5. World Health Organization. The Global Strategy for Women's, Children's and Adolescents' Health Geneva: World Health Organization; 2017 [Available from: https://www.who.int/data/maternal-newborn-child-adolescent-ageing/global-strategy-data. +6. Betran A, Torloni M, Zhang J, Gülmezoglu A, WHO Working Group on Caesarean Section. WHO Statement on Caesarean Section Rates. BJOG. 2016;123(5):667-70. +7. Boerma T, Ronsmans C, Melesse DY, Barros AJD, Barros FC, Juan L, et al. Global epidemiology of use of and disparities in caesarean sections. Lancet. 2018;392(10155):1341-8. +8. Sobhy S, Arroyo-Manzano D, Murugesu N, Karthikeyan G, Kumar V, Kaur I, et al. Maternal and perinatal mortality and complications associated with caesarean section in low-income and middle-income countries: a systematic review and meta-analysis. Lancet. 2019;393(10184):1973-82. +9. Sandall J, Tribe RM, Avery L, Mola G, Visser GH, Homer CS, et al. Short-term and long-term effects of caesarean section on the health of women and children. Lancet. 2018;392(10155):1349-57. +10. Betran AP, Ye J, Moller AB, Souza JP, Zhang J. Trends and projections of caesarean section rates: global and regional estimates. BMJ Glob Health. 2021;6(6). +11. World Health Organization. Preventing prolonged labour: a practical guide. Geneva: World Health Organization; 1994. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 85, 875, 901]]<|/det|> +12. World Health Organization. The Partograph: the application of the WHO partograph in the management of labour, report of a WHO multicentre study, 1990-1991. Geneva: World Health Organization; 1994.13. World Health Organization, UNICEF, United Nations Population Fund. Managing complications in pregnancy and childbirth: a guide for midwives and doctors. Geneva; 2017.14. World Health Organization, UNICEF. Protect the promise: 2022 progress report on the Every Woman Every Child Global Strategy for Women's, Children's and Adolescents' Health (2016-2030). 2022.15. Ayenew AA, Zewdu BF. Partograph utilization as a decision-making tool and associated factors among obstetric care providers in Ethiopia: a systematic review and meta-analysis. Syst Rev. 2020;9(1):251.16. Ollerhead E, Osin D. Barriers to and incentives for achieving partograph use in obstetric practice in low- and middle-income countries: a systematic review. BMC Pregnancy Childbirth. 2014;14:281.17. Bedwell C, Levin K, Pett C, Lavender DT. A realist review of the partograph: when and how does it work for labour monitoring? BMC Pregnancy Childbirth. 2017;17(1):31.18. Laisser R, Actis Danna V, Bonet M, Oladapo O, Lavender T. An exploration of midwives' views of the latest World Health Organization labour care guide. Afr J Midwifery Womens Health. 2021;15(4).19. Vogel JP, Comrie-Thomson L, Pingray V, Gadama L, Galadanci H, Goudar S, et al. Usability, acceptability, and feasibility of the World Health Organization Labour Care Guide: A mixed-methods, multicountry evaluation. Birth. 2020.20. Pingray V, Bonet M, Berrueta M, Mazzoni A, Belizan M, Keil N, et al. The development of the WHO Labour Care Guide: an international survey of maternity care providers. Reprod Health. 2021;18(1):66.21. World Health Organization. WHO Labour Care Guide: User's Manual. https://appswohint/iris/rest/bitstreams/1322094/retrieve. 2020.22. World Health Organization Labour Care Guide Research Prioritization Group. Global research priorities related to the World Health Organization Labour Care Guide: results of a global consultation. Reprod Health. 2023;20(1):57.23. Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6:42.24. World Health Organization. WHO recommendations: Intrapartum care for a positive childbirth experience Geneva: World Health Organisation; 2018.25. Taljaard M, Weijer C, Grimshaw JM, Eccles MP, Ottawa Ethics of Cluster Randomised Trials Consensus G. The Ottawa Statement on the ethical design and conduct of cluster randomised trials: precis for researchers and research ethics committees. BMJ. 2013;346:f2838.26. World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA. 2013;310(20):2191-4.27. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. ICH E6 Good Clinical Practice (GCP) Guideline. https://www.ich.org/page/efficacy-guidelines#6-2; 2016.28. Chan AW, Tetzlaff JM, Altman DG, Laupacis A, Gotzsche PC, Krle A-Jeric K, et al. SPIRIT 2013 Statement: defining standard protocol items for clinical trials. Rev Panam Salud Publica. 2015;38(6):506-14. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 85, 864, 800]]<|/det|> +29. Hemming K, Taljaard M, McKenzie JE, Hooper R, Copas A, Thompson JA, et al. Reporting of stepped wedge cluster randomised trials: extension of the CONSORT 2010 statement with explanation and elaboration. BMJ. 2018;363:k1614. +30. Vogel JP, Pingray V, Althabe F, Gibbons L, Berrueta M, Pujar Y, et al. Implementing the WHO Labour Care Guide to reduce the use of Caesarean section in four hospitals in India: protocol and statistical analysis plan for a pragmatic, stepped-wedge, cluster-randomized pilot trial. Reprod Health. 2023;20(1):18. +31. Government of India. Labour Room Quality Improvement Initiative. https://nhm.gov.in/index1.php?lang=1&level=3&sublinkid=1307&lid=6902017. +32. Bluestone J, Johnson P, Fullerton J, Carr C, Alderman J, BonTempo J. Effective in-service training design and delivery: evidence from an integrative literature review. Hum Resour Health. 2013;11:51. +33. Ivers N, Jamtvedt G, Flottorp S, Young JM, Odgaard-Jensen J, French SD, et al. Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 2012(6):CD000259. +34. World Health Organization. WHO recommendations: non-clinical interventions to reduce unnecessary caesarean sections. Geneva: World Health Organization; 2018. +35. World Health Organization. Robson Classification: Implementation Manual. Geneva: World Health Organization; 2017. +36. Hemming K, Kasza J, Hooper R, Forbes A, Taljaard M. A tutorial on sample size calculation for multiple-period cluster randomized parallel, cross-over and stepped-wedge trials using the Shiny CRT Calculator. Int J Epidemiol. 2020;49(3):979-95. +37. Ford WP, Westgate PM. Maintaining the validity of inference in small-sample stepped wedge cluster randomized trials with binary outcomes when using generalized estimating equations. Stat Med. 2020;39(21):2779-92. +38. The Lancet. Stemming the global caesarean section epidemic. Lancet. 2018;392(10155):1279. +39. Bohren MA, Hofmeyr GJ, Sakala C, Fukuzawa RK, Cuthbert A. Continuous support for women during childbirth. Cochrane Database Syst Rev. 2017;7:CD003766. +40. Lawrence A, Lewis L, Hofmeyr GJ, Styles C. Maternal positions and mobility during first stage labour. Cochrane Database Syst Rev. 2013(10):CD003934. +41. Anim-Somuah M, Smyth RM, Cyna AM, Cuthbert A. Epidural versus non-epidural or no analgesia for pain management in labour. Cochrane Database Syst Rev. 2018;5(5):CD000331. +42. Bernitz S. The Norwegian World Health Organisation Labour Care Guide Trial (NORWEL): study protocol (NCT05791630) clinicaltrials.gov2023 [ +43. Blomberg M. Can the Use of a Next Generation Partograph Improve Neonatal Outcomes? (PICRINO): study protocol (NCT05560802) clinicaltrials.gov [ +44. Pandey D, Bharti R, Dabral A, Khanam Z. Impact of WHO Labor Care Guide on reducing cesarean sections at a tertiary center: an open-label randomized controlled trial. AJOG Glob Rep. 2022;2(3):100075. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[117, 149, 874, 716]]<|/det|> +
CharacteristicIntervention period
(N = 14,814 women)		Control period
(N = 11,517 women)
n (%)n (%)
Maternal age (years)*23.9 (3.6)23.4 (3.6)
Maternal age
Less than 201,020 (6.9%)1,010 (8.8%)
20-3413,572 (91.6%)10,357 (89.9%)
35 or more222 (1.5%)150 (1.3%)
Previous Caesarean Section**
04,282 (55.0%)3,484 (56.7%)
12,819 (36.2%)2,133 (34.7%)
2 or more682 (8.8%)525 (8.5%)
Gravida
16,394 (43.2%)4,940 (42.9%)
2-48,160 (55.1%)6,369 (55.3%)
5 or more260 (1.8%)208 (1.8%)
Parity
07,031 (47.5%)5,375 (46.7%)
1-37,674 (51.8%)6,022 (52.3%)
4 or more109 (0.7%)120 (1.0%)
Women receive antenatal care during pregnancy14,745 (99.5%)11,438 (99.3%)
Covid status at admission
Positive32 (0.2%)5 (0.0%)
Negative8,208 (55.4%)9,168 (79.6%)
Pending or not done6,574 (44.4%)2,344 (20.4%)
Transferred from another health facility during labour2,102 (14.2%)1,881 (16.3%)
Gestational age at time of birth*38.3 (2.5)38.3 (2.6)
+ +<|ref|>text<|/ref|><|det|>[[119, 710, 374, 721]]<|/det|> +* Mean and (Standard deviation) is reported + +<|ref|>text<|/ref|><|det|>[[119, 721, 373, 731]]<|/det|> +** Multiparous women only were considered + +<|ref|>table_caption<|/ref|><|det|>[[119, 734, 456, 748]]<|/det|> +Table 1. Characteristics of study population + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[80, 155, 777, 680]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[83, 120, 602, 143]]<|/det|> +Table 2. Effect of the intervention on primary outcome, and maternal process of care outcomes + +
Intervention period
(N = 14,814 women)		Control period
(N = 11,517 women)		Relative Risk
(95% CI)α
n/N(%)n/N(%)
Primary outcome
Cesarean section in Robson Group 11709/4302(39.7%)1602/3543(45.2%)0.85 (0.54; 1.33)
Maternal process of care outcomes
Cesarean section in women in Robson Groups 1 and 32012/7485(26.9%)1919/6204(30.9%)0.81 (0.59; 1.11)
Cesarean section in women in Robson Groups 1, 2, 3, 4 and 56529/12735(51.3%)5028/9808(51.3%)0.92 (0.78; 1.10)
Caesarean section (all women)7505/14814(50.7%)5817/11517(50.5%)0.91 (0.71; 1.15)
Augmentation with oxytocin during labourβ912/9764(9.3%)2273/8318(27.3%)0.34 (0.01; 15.04)
Artificial rupture of the membranes*β553/9764(5.7%)559/8318(6.7%)-
Episiotomyε4820/7309(65.9%)3137/5700(55.0%)0.99 (0.73; 1.35)
Operative vaginal birthε192/7309(2.63%)112/5700(1.96%)1.12 (0.13; 9.36)
Days from admission to childbirth**0.34(0.73)0.30(0.68)0.05 (-0.31; 0.41)
Days from childbirth to discharge**3.29(1.75)3.52(1.88)0.23 (-0.84; 1.30)
+ +<|ref|>table_footnote<|/ref|><|det|>[[82, 690, 914, 803]]<|/det|> +\(\beta\) Women in spontaneous labour were considered \(\epsilon\) Women with vaginal deliveries were considered \\*\\*The mean of the days and (S.D.) is reported. The effect size was calculated as the difference between the mean of days in the intervention group and the mean of days in the control group. \(\ast \ast\) RR was not estimated since convergence of the model was not achieved \(\Omega\) The relative risk and \(95\%\) confidence interval \((95\% CI)\) was estimated with the Generalized Estimating Equation method employing the "Manck and DeRouen" bias correction method and a degree of freedom approximation. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[90, 152, 907, 750]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[84, 121, 562, 143]]<|/det|> +Table 3. Effect of the intervention on maternal, perinatal and neonatal health outcomes + +
Intervention period
(N = 14,814 women)		Control period
(N = 11,517 women)		Relative Risk
(95% CI)Ω
n/N(%)n/N(%)
Maternal Secondary Outcomes
3rd or 4th degree tears18/14814(0.12%)25/11517(0.22%)0.51 (0.01; 29.16)
PPH requiring uterine balloon tamponade or surgical intervention28/14814(0.19%)46/11517(0.40%)0.38 (0.00; 84.07)
Suspected or confirmed maternal infection requiring therapeutic antibiotics114/14814(0.77%)53/11517(0.46%)2.12 (0.06; 70.96)
Fetal/Neonatal Secondary Outcomes
Stillbirth449/14971(3.00%)367/11624(3.16%)0.97 (0.43; 2.19)
Antepartum stillbirth279/14971(1.86%)286/11624(2.46%)0.91 (0.34; 2.47)
Intrapartum stillbirth163/14971(1.09%)79/11624(0.68%)0.90 (0.49; 1.65)
Apgar score &lt;7 at 5 minutes670/14522(4.61%)567/11257(5.04%)1.17 (0.86; 1.59)
Bag and mask ventilation of newborn424/14522(2.92%)256/11257(2.27%)1.21 (0.08; 18.75)
Mechanical ventilation of newborn293/14522(2.02%)260/11257(2.31%)1.29 (0.36; 4.66)
Prolonged (&gt;48 hour) admission in NICU1843/14522(12.7%)1014/11257(9.0%)1.14 (0.47; 2.79)
Newborns requiring NICU admission for hypoxic ischaemic encephalopathy34/14522(0.23%)152/11257(1.35%)0.40 (0.04; 3.74)
Composite neonatal morbidity outcome*376/14522(2.59%)377/11257(3.35%)1.11 (0.32; 3.79)
Neonatal death200/14522(1.38%)196/11257(1.74%)1.31 (0.37; 4.71)
Perinatal death (stillbirth or neonatal death)649/14971(4.34%)563/11624(4.84%)1.06 (0.41; 2.73)
+ +<|ref|>table_footnote<|/ref|><|det|>[[84, 747, 910, 822]]<|/det|> +\(\Omega\) The relative risk and \(95\%\) confidence interval \((95\% CI)\) was estimated with the Generalized Estimating Equation method employing the "Manc and DeRouen" bias correction method and a degree of freedom approximation. \\* The composite neonatal outcome was defined as one or more of the following: Mechanical ventilation of the newborn or requirement of NICU admission for hypoxic ischaemic encephalopathy of the newborn or neonatal death + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[58, 150, 921, 787]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[82, 120, 590, 140]]<|/det|> +Table 4. Effect of the intervention on women's experience outcomes (Women in Robson Group 1 or 3) + +
Intervention period
(N=1277 women)		Control period
(N=1438 women)		Relative Risk
(95% CI) a
n/N(%)n/N(%)
Women reporting labour companion982/1277(76.9%)1206/1438(83.9%)1.19 (0.89; 1.59)
Women reporting being offered pain relief196/1277(15.3%)75/1438(5.2%)2.30 (0.00; 1281.82)
Women reporting being very satisfied or somewhat satisfied with how their pain was managed827/1277(64.8%)957/1437(66.6%)0.94 (0.06; 16.14)
Women reporting being encouraged to drink water863/1277(67.6%)1123/1438(78.1%)0.98 (0.34; 2.86)
Women reporting being encouraged to eat food657/1277(51.4%)823/1438(57.2%)0.99 (0.13; 7.37)
Women reporting being encouraged to walk827/1277(64.8%)863/1437(60.1%)1.10 (0.34; 3.58)
Women reporting being asked which birth position they preferred27/1277(2.11%)10/1438(0.70%)1.96 (0.00; 1384.48)
Women reporting being very or somewhat satisfied with the amount of time health provider spent with them1260/1277(98.7%)1424/1437(99.1%)0.99 (0.93; 1.05)
Women reporting being very or somewhat satisfied with the way health provider communicated with them1262/1277(98.8%)1424/1438(99.0%)0.99 (0.91; 1.07)
Women who strongly agreed or agreed that their privacy was respected1234/1277(96.6%)1315/1438(91.4%)0.99 (0.56; 1.75)
Women who reported being asked permission before examinations596/1277(46.7%)992/1438(69.0%)0.84 (0.07; 10.34)
Women who reported being asked permission before treatments588/1277(46.0%)996/1438(69.3%)0.85 (0.07; 10.37)
Women who strongly agreed or agreed that they were satisfied with their labour and birth experience1268/1277(99.3%)1404/1438(97.6%)1.01 (0.95; 1.07)
+ +<|ref|>table_footnote<|/ref|><|det|>[[80, 784, 911, 831]]<|/det|> +\(\Omega\) The relative risk and \(95\%\) confidence interval (95% CI) was estimated with the Generalized Estimating Equation method employing the "Manc and DeRouen" bias correction method and a degree of freedom approximation. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[118, 87, 189, 98]]<|/det|> +# FIGURES + +<|ref|>table_caption<|/ref|><|det|>[[118, 111, 856, 147]]<|/det|> +Figure 1. Trial diagram showing number of women with a gestational age >20 weeks by hospital and steps + +<|ref|>table<|/ref|><|det|>[[118, 185, 879, 567]]<|/det|> + +
STEP (2 months periods)Total
(Transition
period)**
1234\(5^{**}\)
HOSPITAL1946915
(240)*		112787723746239
(240)
2965983708
(267)*		71919205295
(267)
31398167715291015
(302)*		31538772
(302)
4950106010879222006
(271)*		6025
(271)
Total
(Transition
period)		42594635
(240)		4451
(267)		3533
(302)		9453
(271)		26331
(1080)
+ +<|ref|>image<|/ref|><|det|>[[118, 579, 152, 598]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[180, 583, 647, 594]]<|/det|> +Control Study period Intervention study period + +<|ref|>text<|/ref|><|det|>[[118, 612, 508, 621]]<|/det|> +* Number of women recruited during the two weeks-transition period + +<|ref|>text<|/ref|><|det|>[[118, 624, 861, 648]]<|/det|> +** The sample size was larger for step 4 because this step included 4 months of data, compared with 2 months for preceding steps and baseline period. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 85, 340, 99]]<|/det|> +## SUPPLEMENTARY APPENDIX + +<|ref|>sub_title<|/ref|><|det|>[[118, 110, 452, 124]]<|/det|> +### Table S1. Primary and secondary outcomes + +<|ref|>sub_title<|/ref|><|det|>[[118, 158, 258, 171]]<|/det|> +#### Primary Outcome + +<|ref|>text<|/ref|><|det|>[[118, 182, 848, 244]]<|/det|> +CS rate amongst women in Robson Group 1 (i.e. women who are nulliparous, singleton, cephalic, ≥37 weeks' gestation, in spontaneous labour). The numerator are the women in Robson Group 1 who had a CS and the denominator the number of women in Robson group 1. + +<|ref|>sub_title<|/ref|><|det|>[[118, 276, 339, 288]]<|/det|> +#### Maternal Secondary Outcomes + +<|ref|>table<|/ref|><|det|>[[118, 287, 880, 906]]<|/det|> +
OutcomeOutcome definition
CS rate in women in Robson Groups 1 and 3Numerator: Number of women undergoing CS
Denominator: Number of women in Robson Groups 1 and 3
CS rate in women in Robson Groups 1 to 5Numerator: Number of women undergoing CS
Denominator: Number of women in Robson Groups 1 to 5
Overall CS rateNumerator: Number of women undergoing CS
Denominator: Number of women giving birth
Augmentation with oxytocin during labour rateNumerator: Number of women given oxytocin for augmentation during labour
Denominator: Number of women who experienced spontaneous labour
Artificial rupture of the membranes rateNumerator: Number of women who had artificial rupture of membranes
Denominator: Number of women who experienced spontaneous labour
Episiotomy rateNumerator: Number of women who had episiotomy
Denominator: Number of women with vaginal birth
Operative vaginal birth rateNumerator: Number of women who had operative vaginal birth (forceps or vacuum)
Denominator: Number of women with vaginal birth
Days between admission to childbirthMean of the days between admission to childbirth
Days between childbirth to dischargeMean of the days between childbirth to discharge
3rd or 4th degree tearsNumerator: Number of women experiencing 3rd or 4th degree tears
Denominator: Number of women giving birth
PPH requiring uterine balloon tamponade or surgical interventionNumerator: Number of women requiring uterine balloon tamponade OR surgical intervention for PPH
Denominator: Number of women giving birth
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[118, 83, 877, 150]]<|/det|> + +
Suspected or confirmed maternal
infection requiring therapeutic
antibiotics		Numerator: Number of women with clinical signs or symptoms
of maternal infection AND therapeutic antibiotics were required
Denominator: Number of women giving birth
+ +<|ref|>table_caption<|/ref|><|det|>[[118, 179, 381, 190]]<|/det|> +Fetal/Neonatal Secondary Outcomes + +<|ref|>table<|/ref|><|det|>[[118, 197, 886, 861]]<|/det|> + +
OutcomeOutcome definition
StillbirthNumerator: Fetal death
Denominator: All born babies
Antepartum stillbirthNumerator: Fetal death prior to admission
Denominator: All born babies
Intrapartum stillbirthNumerator: Fetal death after admission
Denominator: All born babies
Apgar score <7 at 5 minutesNumerator: Liveborn babies with Apgar <7 at 5 minutes
Denominator: Liveborn babies
Bag and mask ventilation of newbornNumerator: Use of continuous bag and mask ventilation of
newborn for >1 minute
Denominator: Liveborn babies
Mechanical ventilation of newbornNumerator: Use of mechanical ventilation of newborn
Denominator: Liveborn babies
Composite neonatal outcomeNumerator: Use of mechanical ventilation of newborn or
admission to NICU for suspected or confirmed or neonatal death
Denominator: Liveborn babies
Prolonged (>48 hour) admission in NICUNumerator: Admission to NICU for >48 hours
Denominator: Liveborn babies
Newborns requiring NICU admission for
hypoxic ischaemic encephalopathy		Numerator: Admission to NICU for suspected or confirmed
Denominator: Liveborn babies
Composite neonatal outcomeNumerator: Use of mechanical ventilation of newborn or
admission to NICU for suspected or confirmed or neonatal death
Denominator: Liveborn babies
Neonatal deathNumerator: Neonatal death in a liveborn infant by day 7 or
discharge (whichever came first)
Denominator: All liveborn babies
Perinatal deathNumerator: Fetal death or neonatal death in a liveborn infant by
day 7 or discharge (whichever came first)
Denominator: All born babies
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[117, 102, 886, 875]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[119, 87, 342, 99]]<|/det|> +Women's experience outcomes + +
OutcomeOutcome definition
Woman's experience with labour companionNumerator: Women who reported a labour companion was present during labour or birth
Denominator: Women in Robson Group 1 or 3 who completed the survey
Woman's experience of being offered pain reliefNumerator: Women who reported that they were asked whether they would like any pain relief
Denominator: Women in Robson Group 1 or 3 who completed the survey
Women's satisfaction with their pain management during labour and birthNumerator: Women who reported being very satisfied or somewhat satisfied with how their pain was managed during labour and birth
Denominator: Women in Robson Group 1 or 3 who completed the survey
Woman's experience of being encouraged to drink oral fluidsNumerator: Women who reported that a health worker encouraged them to drink water
Denominator: Women in Robson Group 1 or 3 who completed the survey
Woman's experience of being encouraged to eat foodNumerator: Women who reported that a health worker encouraged them to eat food
Denominator: Women in Robson Group 1 or 3 who completed the survey
Woman's experience of mobilising during labourNumerator: Women who reported that a health worker encouraged them to walk around during labour
Denominator: Women in Robson Group 1 or 3 who completed the survey
Woman's experience of birth position of choiceNumerator: Women who reported that a health worker asked them which birth position they preferred
Denominator: Women in Robson Group 1 or 3 who completed the survey
Woman's experience of time health worker spent with themNumerator: Women who reported being very satisfied or somewhat satisfied with amount of time health worker spent with them during labour
Denominator: Women in Robson Group 1 or 3 who completed the survey
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[117, 103, 886, 525]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[119, 86, 390, 99]]<|/det|> +Women's experience outcomes (cont.) + +
OutcomeOutcome definition
Women's satisfaction with the way health providers communicated with themNumerator: Women who reported being very satisfied or somewhat satisfied with the way health workers communicated with them during labour and birth
Denominator: Women in Robson Group 1 or 3 who completed the survey
Woman's experience of privacyNumerator: Number of women who strongly agreed or agreed that their privacy was respected during examinations and treatments
Denominator: Women in Robson Group 1 or 3 who completed the survey
Women's experience of being asked permissionNumerator: Number of women who said their health worker always asked permission before examinations and treatments
Denominator: Women in Robson Group 1 or 3 who completed the survey
Woman's overall experience of careNumerator: Number of women who strongly agreed or agreed that they felt satisfied with their labour and birth experience
Denominator: Women in Robson Group 1 or 3 who completed the survey
+ +<--- Page Split ---> +<|ref|>table_caption<|/ref|><|det|>[[117, 85, 738, 99]]<|/det|> +Table S2. Application of Robson Classification to intervention and control groups + +<|ref|>table<|/ref|><|det|>[[118, 108, 878, 602]]<|/det|> + +
Robson Classification GroupIntervention
period
(N=14,814
women)		Control period
(N=11,517
women)
Group 1: Nulliparous, singleton, cephalic, term, spontaneous labour4,302 (29.0%)3,543 (30.8%)
Group 2: Nulliparous, singleton, cephalic, term, induced/prelabour Caesarean1,729 (11.7%)1,022 (8.9%)
· Group 2a: Nulliparous, singleton, cephalic, term, induced848 (5.7%)471 (4.1%)
· Group 2b: Nulliparous, singleton, cephalic, term, prelabour
Caesarean		881 (5.9%)551 (4.8%)
Group 3: Multiparous (no previous Caesarean), singleton, cephalic, term,
spontaneous labour		3,183 (21.5%)2,661 (23.1%)
Group 4: Multiparous (no previous Caesarean), singleton, cephalic, term,
induced/prelabour Caesarean		450 (3.0%)282 (2.4%)
· Group 4a: Multiparous (no previous Caesarean), singleton, cephalic,
term, induced		292 (2.0%)212 (1.8%)
· Group 4a: Multiparous (no previous Caesarean), singleton, cephalic,
term, prelabour Caesarean		158 (1.0%)70 (0.6%)
Group 5: Previous Caesarean, singleton, cephalic, term, (spontaneous labour,
induced labour or prelabour Caesarean)		3,071 (20.7%)2,300 (20.0%)
Group 6: Nulliparous with a singleton breech235 (1.6%)182 (1.6%)
Group 7: Multiparous with a singleton breech (including previous Caesarean)224 (1.5%)153 (1.3%)
Group 8: Multiple pregnancies (including previous Caesarean)155 (1.0%)107 (0.9%)
Group 9: Single pregnancy, transverse or oblique lie (including previous
Caesarean)		21 (0.1%)36 (0.3%)
Group 10: Singleton, cephalic, preterm (including previous Caesarean)1,444 (9.7%)1,231 (10.7%)
+ +<--- Page Split ---> +<|ref|>table_caption<|/ref|><|det|>[[117, 86, 450, 99]]<|/det|> +Table S3. Serious adverse events by period + +<|ref|>table<|/ref|><|det|>[[70, 108, 930, 383]]<|/det|> + +
Intervention Period
(N of women = 14,814)
(N of liveborns=14,522)
(N of newborns=14,971)		Transition period
(N of women=1,080)
(N of liveborns=1,060)
(N of newborns: 1,089)		Control Period
(N of women= 11,517)
(N of liveborns= 11,257)
(N of newborns= 11,624)
n (%)n (%)n (%)
Maternal death13 (0.09)1 (0.09)5 (0.04)
Neonatal death200 (1.38)11 (1.04)196 (1.74)
Neonatal death (less than 28 weeks)18 (0.12)1 (0.09)16 (0.14)
Neonatal death (28 weeks or more)182 (1.25)10 (0.94)180 (1.60)
Stillbirth449 (3.00)29 (2.66)367 (3.16)
Stillbirth (less than 28 weeks)175 (1.17)10 (0.92)139 (1.20)
Stillbirth (28 weeks or more)274 (1.83)19 (1.74)228 (1.96)
+ +<|ref|>table_caption<|/ref|><|det|>[[117, 424, 483, 436]]<|/det|> +Table S4. Causes of maternal deaths, by period + +<|ref|>table<|/ref|><|det|>[[125, 444, 872, 597]]<|/det|> + +
Intervention Period
(N = 13)		Transition Period
(N = 1)		Control Period
(N = 5)
Pre-eclampsia/eclampsia504
Obstructed labour000
Haemorrhage100
Infection210
Other*501
+ +<|ref|>text<|/ref|><|det|>[[117, 608, 876, 658]]<|/det|> +*The case classified as "Other" in the control period was a postpartum cardiomyopathy. The five cases classified as "Other"in the intervention period were: (1) Immediate cause: a) Hepatic encephalopathy with MODS Antecedent cause: b) Acute fatty liver of pregnancy, (2) Amniotic fluid embolism, (3) Disseminated intravascular coagulation secondary to acute fatty liver of pregnancy, (4) Cerebrovascular Accident, (5) Pulmonary embolism. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 130, 317, 150]]<|/det|> +- 2SupplementaryFileS1.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__05aa0c2fc7100b1500f07e08d2628280742c5c4e1a4bf250c2c7ba23e4cc9a7a/preprint__05aa0c2fc7100b1500f07e08d2628280742c5c4e1a4bf250c2c7ba23e4cc9a7a.mmd b/preprint/preprint__05aa0c2fc7100b1500f07e08d2628280742c5c4e1a4bf250c2c7ba23e4cc9a7a/preprint__05aa0c2fc7100b1500f07e08d2628280742c5c4e1a4bf250c2c7ba23e4cc9a7a.mmd new file mode 100644 index 0000000000000000000000000000000000000000..73b3a1d072efc74ed7fa16c5b74522ad17cb31d4 --- /dev/null +++ b/preprint/preprint__05aa0c2fc7100b1500f07e08d2628280742c5c4e1a4bf250c2c7ba23e4cc9a7a/preprint__05aa0c2fc7100b1500f07e08d2628280742c5c4e1a4bf250c2c7ba23e4cc9a7a.mmd @@ -0,0 +1,281 @@ + +# TMEM16 scramblases thin the membrane to enable lipid scrambling + +Alessio Accardi ( \(\boxed{ \begin{array}{r l} \end{array} }\) ala2022@med.cornell.edu) Weill Cornell Medical College https://orcid.org/0000- 0002- 6584- 0102 + +Maria Falzone Weill Cornell Medical College + +Zhang Feng Weill Cornell Medical College + +Omar Alvarenga Weill Cornell Medical College + +Yangang Pang Weill Cornell Medical College + +Byoung Lee Cornell University + +Xiaolu Cheng Cornell University https://orcid.org/0000- 0002- 2785- 6488 + +Eva Fortea Weill Cornell Medical College + +Simon Scheuring Weill Cornell Medicine https://orcid.org/0000- 0003- 3534- 069X + +## Article + +Keywords: TMEM16 scramblases, lipid scrambling, membrane thinning + +Posted Date: October 8th, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 955726/v1 + +License: \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on May 11th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 30300- z. + +<--- Page Split ---> + +# TMEM16 scramblases thin the membrane to enable lipid scrambling + +Maria E. Falzone \(^{1,2}\) , Zhang Feng \(^{1}\) , Omar E. Alvarenga \(^{1,3}\) , Yangang Pang \(^{1}\) , ByoungCheol Lee \(^{1,\wedge}\) , Xiaolu Cheng \(^{4}\) , Eva Fortea \(^{1,3}\) , Simon Scheuring \(^{1}\) , Alessio Accardi \(^{1,2,4*}\) + +1 Department of Anesthesiology, Weill Cornell Medical College; 2 Department of Biochemistry, Weill Cornell Medical College; 3 Physiology, Biophysics and Systems Biology Graduate Program, Weill Cornell Medical College; 4 Department of Physiology and Biophysics, Weill Cornell Medical College + +\* correspondence to: ala2022@med.cornell.edu + +\(^{\wedge}\) ByoungCheol Lee's present address: Neurovascular Unit Research Group, Korea Brain Research Institute (KBRI), Daegu 41062, Republic of Korea. + +<--- Page Split ---> + +## Abstract + +AbstractTMEM16 scramblases dissipate the plasma membrane lipid asymmetry to activate multiple eukaryotic cellular pathways. It was proposed that lipid headgroups move between leaflets through a membrane- spanning hydrophilic groove. Direct information on lipid- groove interactions is lacking. We report the 2.3 Å resolution cryoEM structure of the \(\mathrm{Ca^{2 + }}\) - bound afTMEM16 scramblase in nanodiscs showing how rearrangement of individual lipids at the open pathway results in pronounced membrane thinning. Only the groove’s intracellular vestibule contacts lipids, and mutagenesis suggests scrambling does not entail specific protein- lipid interactions with the extracellular vestibule. Further, we find scrambling can occur outside a closed groove in thinner membranes and is inhibited in thicker membranes despite an open pathway. Our results show how afTMEM16 thins the membrane to enable scrambling and that an open hydrophilic pathway is not a structural requirement to allow rapid transbilayer movement of lipids. This mechanism could be extended to other scramblases lacking a hydrophilic groove. + +<--- Page Split ---> + +## Introduction + +Biological membranes play a fundamental role in many cellular signaling pathways as they define the physical boundaries of cellular compartments and actively modulate the function of integral and membrane- associated proteins. In eukaryotic cells, the composition and distribution of the phospholipid constituents of the membrane is tightly regulated by the activity of a variety of dedicated enzymes, lipases, lipases and scramblases \(^{1}\) . The headgroup asymmetry of the plasma membrane is established by the action of ATP- driven pumps which distribute phosphatidylethanolamine (PE) and phosphatidylserine (PS) to the inner leaflet and phosphatidylcholine (PC) to the outer leaflet \(^{1}\) . Activated phospholipid scramblases dissipate this asymmetry and expose PS on the extracellular leaflet. This is critical for multiple signaling pathways, ranging from apoptosis to blood coagulation and cell- cell fusion \(^{1,2}\) . There are two known families of scramblases, the \(\mathrm{Ca}^{2 + }\) - activated TMEM16 \(^{3 - 5}\) and the caspase- activated Xk- related (Xkr) proteins \(^{6}\) . Lipid scrambling by the TMEM16's is of critical importance for a myriad of physiological processes, including blood coagulation, bone mineralization, membrane fusion and viral entry \(^{2,4,7}\) . Dysregulation of TMEM16 scramblase activity can have disastrous consequences, as both gain- and loss- of function mutations have been associated with disorders of blood, brain, bone and muscle \(^{3,8 - 11}\) . The TMEM16 superfamily is comprised of CI channels and dual function scramblases/non- selective ion channels \(^{4}\) . Both subtypes share a common homodimeric architecture where each monomer is comprised of 10 transmembrane (TM) helices \(^{12 - 18}\) (Fig. 1A- B). In each protomer, the TM3- TM7 helices form a hydrophilic permeation pathway, or groove, that can adopt multiple conformations to allow passage of ions, lipids or to prevent movement of both substrates \(^{15 - 19}\) . + +<--- Page Split ---> + +Upon activation, the TMEM16 scramblases mediate rapid lipid movement between leaflets causing the membrane asymmetry to collapse and thus initiating signaling cascades. The mechanism underlying scrambling has been investigated at the functional, computational and structural levels \(^{3,8,15 - 32}\) . The consensus proposal is a 'credit- card' like mechanism \(^{33}\) , where the lipid headgroups penetrate and traverse the open hydrophilic groove while their tails remain embedded within the hydrocarbon core of the membrane \(^{20,25,28}\) . Within this framework, lipid scrambling is enabled by specific interactions of the permeating lipids with charged and polar groove- lining residues \(^{20,25,28}\) . However, TMEM16 scramblases do not discriminate among lipids such as PS, PE, PG, PC and DOTAP with headgroups of different charge, structure and size \(^{13,21,23,26}\) . Further, PE lipids conjugated to 5 kDa cargoes are also efficiently scrambled \(^{24}\) . These observations suggest that specific interactions between the groove and the scrambled lipids are not necessary. Notably, this lack of headgroup selectivity is also shared by other scramblases that lack an explicit hydrophilic groove, such as the GPCR opsin \(^{24,34}\) and XKR8 and 9 \(^{6,35,36}\) . + +Moderate resolution structures of the fungal afTMEM16 and nhTMEM16 in nanodiscs showed these scramblases than the membrane near the groove \(^{15,17}\) , suggesting that membrane thinning at an open pathway might be important for lipid scrambling \(^{15}\) . Membrane thinning was also observed near the closed pathway of mTMEM16F, leading to the proposal that scrambling can also occur outside a closed groove \(^{16}\) . Thus, it is not clear whether an open hydrophilic groove is required for scrambling. Direct structural information on how TMEM16 scramblases interact with lipids is essential to elucidate the molecular mechanisms of lipid permeation. + +Here we use cryogenic electron microscopy (cryoEM) to determine the 2.3 Å resolution structure of the afTMEM16 scramblase in lipid nanodiscs. Our structure allows the direct visualization of lipids associated with the protein at the open groove and reveals that afTMEM16 + +<--- Page Split ---> + +thins the membrane at the open pathway by \(\sim 50\%\) . The closest point of approach of the two membrane leaflets occurs near the wide intracellular vestibule of the groove, and no lipids could be resolved inside or interacting with the extracellular portion of the pathway. Mutagenesis of groove- lining residues does not perturb function, suggesting that specific interactions of permeating lipids with groove- lining residues are not essential for scrambling. We show that in thicker membranes scrambling is inhibited, while the groove remains in an open conformation. Conversely, in thinner membranes scrambling is enhanced although the groove is closed. Thus, lipid permeation is not always enabled by an open groove or prevented by a closed pathway. Based on these findings we propose that when the groove is open, the thinned membrane and the hydrophilic nature of the pathway synergistically lower the energy barrier for lipid scrambling. When the groove is closed, scrambling can occur, but at reduced rates in bilayers with plasma- membrane like thickness. In thinner membranes, closed- groove scrambling is enhanced allowing for lipid transport in the absence of \(\mathrm{Ca^{2 + }}\) . + +## Results + +## Structural basis of lipid reorganization by the afTMEM16 scramblase + +To gain insight into how the afTMEM16 scramblase alters the organization of the membrane and interacts with the surrounding lipids we used cryo- EM to determine its structure in the \(\mathrm{Ca^{2 + }}\) - bound conformation in nanodiscs at 2.3 Å (Fig. 1, Supp. Fig. 1). Nanodiscs were comprised of a mixture of \(70\%\) 1,2- Dioleoyl- sn- glycero- 3- phosphocholine (DOPC, or 18:1 PC) and \(30\%\) 1,2- Dioleoyl- sn- Glycero- 3- Phosphatidylglycerol (DOPG, 18:1 PG), which we will refer to as C18 lipids. In these conditions, referred to as \(\mathrm{C18 / Ca^{2 + }}\) , afTMEM16 is maximally active \(^{15}\) , therefore we hypothesize this represents the active state of the scramblase. The present structure is nearly superimposable to + +<--- Page Split ---> + +the previously determined \(\mathrm{Ca^{2 + }}\) - bound structure of afTMEM16 in 3 1- palmitoyl- 2- oleoyl- sn- glycero- 3- phosphoethanolamine (POPE): 1 1- palmitoyl- 2- oleoyl- sn- glycero- 3- phospho- (1'- racglycerol) POPG nanodiscs \(^{15}\) , \(\mathrm{Ca}\) rmsd \(\sim 0.8 \mathrm{\AA}\) , indicating that headgroup choice and acyl- chain saturation do not influence the conformation of the protein. The significantly improved resolution of the C18/Ca \(^{2 + }\) map allowed us to resolve 4 water molecules in the \(\mathrm{Ca^{2 + }}\) binding sites which coordinate two bound ions (Supp. Fig. 2B). The presence of these water molecules brings the coordination number of bound \(\mathrm{Ca^{2 + }}\) ions to 7 and 8, consistent with the high affinity of these sites \(^{26}\) (Supp. Fig. 2B). The map also contains non- protein densities that could be modeled as lipids associated with the protein (Fig. 1C- H, Supp. Fig. 2). To improve the quality of the density of the lipids near the pathway, we carried out symmetry expansion and additional rounds of 3D classification, which yielded one class with an additional four resolved lipids (Supp. Fig. 1E), for a total of 32 resolved lipids, 16 in each monomer (Fig. 1F- H, Supp. Fig. 2). The observed lipids define nearly continuous interfaces of the scramblase with the inner and outer membrane leaflets near the dimer interface (lipids D1- D9) and illustrate how the poses adopted by individual lipids result in the profound remodeling of the membrane induced by afTMEM16 near the lipid pathway (lipids P1- P7) (Fig. 1F- H). + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1 Lipid-protein interactions in \(\mathbf{Ca^{2 + }}\) -bound afTMEM16. A: Structural model of afTMEM16 in \(0.5\mathrm{mM}\mathrm{Ca}^{2 + }\) in C18 lipid nanodiscs. B: View of the open permeation pathway. C-E: Unsharpened maps of the protein (grey) and associated lipids (red) viewed from the membrane
+ +<--- Page Split ---> + +plane (C), extracellular side (D) and close- up of the open groove (E). The map showing the density of the nanodisc membrane low- pass filtered to \(10 \mathrm{\AA}\) and shown in transparent red (C- D). F- H: Views of the afTMEM16 dimer from the plane of the membrane (F), extra- (G) and intra- cellular (H) sides with modeled lipids shown in stick representation. Lipids at the dimer interface are labeled D1- 9 and those at the permeation pathway are labeled P1- 7. Lipids from the inner and outer leaflets are colored in yellow and blue, respectively. The cytosolic domain of afTMEM16 was omitted for clarity. I: Close up view of the density map at the dimer interface showing the two afTMEM16 monomers (gray and cyan) and intercalated lipid tails (red). \* denotes the symmetry axis. J: The dimer interface salt bridge between TM9 and 10 (in cartoon representation) is formed by E618 and H619 (in stick representation) and is shielded from the intra- and extra- cellular solutions by lipids D3, D4, D6, and D7 (in spheres and colored as in F- H). + +## Lipids form a cap around the transmembrane dimer interface + +The transmembrane dimer interface of afTMEM16 is formed by the extracellular half of TM10 from each monomer (Fig. 1I- J, Supp. Fig. 2D- E). This minimal interface contains several hydrophobic residues and two membrane- embedded salt bridges formed by E618 and H619 of opposite subunits positioned \(\sim 1 / 3\) of the way through the membrane from the extracellular leaflet (Fig. 1I- J). In the \(\mathrm{C18 / Ca^{2 + }}\) structure, these salt bridges appear to be isolated from the intra- and extra- cellular solutions by eight well- defined lipids (D3, D4, D6 and D7 from each subunit), four above and four below the interacting residues (Fig. 1J). On the extracellular side, the D3 lipids from opposite subunits straddle the N terminal region of TM10 with their heads positioned by the side chains of C607 and W608 to make direct contact above the symmetry axis (Supp. Fig. 2D). A second lipid, D4, is wedged between TM9 and TM10 with its head coordinated by polar and charged residues in the TM9- 10 linker (N593, P598, T604 and R606; Supp. Fig. 2D). On the intracellular side, the heads of D6 from each subunit make contact across the symmetry axis and are wedged between the C- termini of the TM10s (Supp. Fig. 2E). They are coordinated by D571, G574 and W578 on the TM9 from one subunit and by R625, Y626 and R629 from TM10 on the other (Supp. Fig. 2E). Additionally, the head of D7 is coordinated by Y626, S630 and K634 from TM10 of one subunit and by Q364 on TM5 and D571 on TM9 from the opposite subunit (Supp. + +<--- Page Split ---> + +Fig. 2E). The tails of these 8 lipids are accommodated in hydrophobic grooves between TM2, 9, 10 from both subunits (Fig. 1I, Supp. Fig. 2D- E). The intercalated organization of the lipid tails and helices gives rise to densely packed hydrophobic regions that shield the interacting E618 and H619 residues from water access, possibly strengthening their electrostatic interaction (Fig. 1J). These observations, together with the evolutionary conservation of the E618/H619 pair (Supp. Fig. 2C) and of the TMEM16 fold suggests these lipids might play a structural role in stabilizing the dimeric architecture of all TMEM16s. + +## Structural basis of membrane thinning at the scrambling pathway + +The C18/Ca \(^{2 + }\) structure reveals how the scramblase reorients the lipids that approach the open scrambling pathway (Fig. 1F, 2A). Near the dimer interface, the planes of the outer (OL) and inner (IL) leaflets are respectively defined by lipids D1- 4 and D8- 9, in good agreement with the outline visualized in the low pass filtered nanodisc map (Fig. 1C). The downward slope of the OL starts at D5, a well- defined PG lipid (Fig. 1F, 2B, Fig. Supp. 2A), and progresses towards the open groove as P1 and P2 adopt distorted poses with their headgroups becoming increasingly tilted (Fig. 2A- B). The IL bends upwards and P5- P7 become increasingly tilted as their heads climb around the intracellular portions of TM3 and TM4, coordinated by the hydrophilic side chains of T341, K345 and T334 (Fig. 2A,C). Within the pathway, P3 is sandwiched between TM4 and TM6 near the constriction formed by T325 and Y432 and its headgroup points towards the extracellular side such that it is contiguous to other OL lipids (Fig. 2A). The distance between the phosphate atoms of the headgroups of P3 and P4 in the OL and IL is \(< 22 \text{Å}\) (Fig. 2A), showing that the hydrocarbon core of the membrane is thinned by \(\sim 50\%\) at the open pathway. A similar thinning is seen in the low- pass filtered nanodiscs map near the pathway (Fig. 1C- D). + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2. Coordination of lipids outside the permeation pathway. A: View of the seven pathway lipids (in sticks, colored as in Fig. 1F). T325 and Y423 are shown as green sticks. Dashed arrow indicates the distance between the phosphate atoms of the last lipid from the inner (P4) and outer (P3) leaflets. B-C: Coordination of P1-P2 (B) and of P4-P7 (C). Side chains are shown in green sticks. D-E: forward \((\alpha)\) and reverse \((\beta)\) scrambling rate constants for indicated quadruple mutants of residues coordinating lipids outside the pathway (P1-2 and P4-7). Bars are average values for \(\alpha\) (black) and \(\beta\) (grey), error bars are S. Dev., and red circles are values from individual repeats.
+ +## Lipids outside the open pathway define the distorted membrane interface + +The identification of sites where lipids bind at or near the open groove raises the possibility that scrambling could occur via a 'conveyor belt' mechanism, where lipids translocate between leaflets by moving from site to site. Alternatively, the observed lipids could define the protein- membrane boundary but not necessarily be translocated, with the possible exception of P3 within the pathway + +<--- Page Split ---> + +(Fig. 2A). To distinguish between these hypotheses, we investigated how mutating residues coordinating the headgroups of P1- 2 and P4- 6 impacts scrambling. We found that mutations aimed at disrupting the headgroup interactions of P1- P2 (W202A/R427A/I431A/W529A), P4- P5- P6 (R279A/T334A/K345A/Y349A) or P2- P5- P6 (R279A/K345A/R427A/K428A) have minimal functional effects (Fig. 2D- E, Supp. Fig. 3). This suggests that these lipid association sites are not obligatory on the path taken by scrambled lipids. Rather, other factors, such as tail interactions with interhelical grooves, contribute to their association with afTMEM16 (Supp. Fig 2F- G) and stabilize the distorted membrane- protein interface that results in thinning at the pathway. + +Scrambling does not require specific interactions with extracellular groove- lining residuesOne unexpected feature of our structure is that the extracellular vestibule of the groove does not directly interact with the membrane and no lipids could be resolved (Fig. 1C- D), as they traverse the groove below this region (Fig. 2A). We mutated side chains lining the extracellular vestibule or the central constriction of the groove and assessed their impact on scrambling. Single or multiple simultaneous alanine substitutions of I298, F302, E305 and E310 on TM3, of K317, Y319, F322, T325 and I332 on TM4, of T373 and S374 on TM5 and of R425, K428, Q429, Y432 and F433 on TM6 have no effects on lipid scrambling (Fig. 3, Supp. Fig. 3). Thus, scrambling does not entail specific interactions of lipids with residues lining the extracellular vestibule or the central constriction of the groove. + +In contrast, the wide intracellular vestibule is embedded in the nanodisc membrane, and the resolved P3 and P4 lipids at the open pathway have opposite orientations (Fig. 2A). This suggests lipid headgroups only need to traverse the wide intracellular vestibule of the pathway, below the constriction formed by T325 and Y432 (Fig. 2A). The pronounced membrane thinning + +<--- Page Split ---> + +at the pathway lowers the energy barrier for transbilayer lipid movement and the hydrophilic environment of the open groove allows water access to this thinned membrane region, synergistically lower the energy barrier for scrambling. Scrambling by afTMEM16 and hTMEM16K is modulated by lipid acyl chain length \(^{15,23}\) , supporting the idea that membrane thinning is critical for scrambling. Our proposal predicts that this modulation should reflect whether the scramblase can sufficiently thin these membranes, rather than arise from lipid-dependent changes in the conformation of the groove. + +![](images/Figure_3.jpg) + +
Figure 3. Functional role of groove-lining residues in lipid scrambling. A-C: Residues lining the extracellular vestibule (A), coordinating P3 (B) and lining the central constriction (C) are shown as green sticks. D-E: forward \((\alpha)\) and reverse \((\beta)\) scrambling rate constants of single and multiple alanine substitutions at the indicated positions. Bars are average values for \(\alpha\) (black) and \(\beta\) (grey), error bars are S. Dev., and red circles are values from individual repeats.
+ +<--- Page Split ---> + +## Regulation of lipid scrambling by membrane thickness + +We measured how systematic variation of lipid acyl chain length affects lipid scrambling by afTMEM16. We kept the lipid headgroup composition at a constant ratio of 7 PC: 3 PG and used acyl chains with a single unsaturation and 16- 22 carbons, C16- C22 lipids (Table 1). Liposomes formed from this mix of 14:1 lipids were not stable in our scrambling assay (Supp Fig. 4C), therefore we generated thinner membranes using a mixture comprised of \(50\%\) 1,2- dimyristoyl- sn- glycero- 3- phosphocholine (DMPC) and 1,2- dimyristoyl- sn- glycero- 3- phospho- (1'-rac- glycerol) (DMPG) in a 7:3 ratio and \(50\%\) of POPC and POPG in a 7:3 ratio \(^{23}\) ; we will refer to this mix as C14 (Table 1). Atomic force microscopy (AFM) measurements show that membrane thickness varies between \(\sim 3.2 \mathrm{nm}\) and \(\sim 4.1 \mathrm{nm}\) , with near- linear scaling with acyl chain length (Table 1, Supp. Fig. 4A- B). + +In the presence of saturating \(0.5 \mathrm{mM Ca^{2 + }}\) the scrambling rate constants do not depend on membrane thickness between \(\sim 3.2 \mathrm{nm}\) (C14 lipids) and \(\sim 3.9 \mathrm{nm}\) (C20 lipids) (Fig. 4A). In contrast, scrambling is nearly completely inhibited in C22 lipids (Fig. 4A) \(^{15}\) . Thus, in saturating \(\mathrm{Ca^{2 + }}\) there is chain length selectivity with a threshold for activity below membrane thickness of \(\sim 4.1 \mathrm{nm}\) . In contrast, in \(0 \mathrm{Ca^{2 + }}\) scrambling displays a nearly exponential inverse dependence on membrane thickness (Fig. 4B). Remarkably, in C14 lipid membranes scrambling by afTMEM16 is nearly \(\mathrm{Ca^{2 + }}\) - independent, with rate constants only \(\sim 3\) - fold lower in \(0 \mathrm{Ca^{2 + }}\) compared to the \(\sim 20\) - fold reduction seen in C18 membranes (Fig. 4A- B, Supp. Fig. 4). To test whether the long C22 acyl chains inhibit scrambling in saturating \(\mathrm{Ca^{2 + }}\) by occluding the pathway \(^{37}\) we measured scrambling in membranes formed by \(70\%\) C22 lipids and \(30\%\) C18 lipids, which are \(\sim 4 \mathrm{nm}\) thick (Table 1). In saturating \(\mathrm{Ca^{2 + }}\) scrambling activity is similar to that seen in \(100\%\) C18 lipids (Fig. 4A), while in \(0 \mathrm{Ca^{2 + }}\) there is a \(\sim 17\) - fold reduction, consistent with the reduction expected for membranes of + +<--- Page Split ---> + +this thickness (Fig. 4B). This behavior does not depend on whether the mixed chain lengths were segregated by headgroup. Thus, the tails of C22 lipids are not ‘blockers’ of the afTMEM16 permeation pathway. These results suggest that in \(0 \mathrm{Ca}^{2 + }\) scrambling rates are proportional to the energetic cost of lipid headgroups crossing the hydrophobic core of the membrane, while in the presence of \(\mathrm{Ca}^{2 + }\) other factors contribute to scrambling. + +## \(\mathrm{Ca}^{2 + }\) -bound afTMEM16 has an open groove in C22 membranes + +To determine whether the C22 lipids inhibit scrambling by inducing groove closure we determined the cryo- EM structure of nanodisc- reconstituted afTMEM16 in the presence of saturating \(\mathrm{Ca}^{2 + }\) to 2.7 Å (Supp. Fig. 5A- G). Despite a \(\sim 500\) - fold reduction in scrambling activity the groove remains open in a conformation nearly identical to that seen in C18 lipids, \(\mathrm{Ca}\) r.m.s.d. \(\sim 0.35 \mathrm{\AA}\) (Fig. 4D). Importantly, neither the \(\mathrm{C18 / Ca}^{2 + }\) nor the \(\mathrm{C22 / Ca}^{2 + }\) datasets display structural heterogeneity as no additional classes could be identified using multiple rounds of iterative 3D classifications on afTMEM16 dimers and monomers using different classification parameters and software (see Methods, Supp. Fig. 1,5, 7, 8, 10). Further, The \(\mathrm{C22 / Ca}^{2 + }\) structure of afTMEM16 in the larger MSP2N2 nanodiscs at 3.5 Å resolution (Supp. Fig. 5H- N) shows an open permeation pathway in all 3D reconstructions, with \(\mathrm{Ca}\) r.m.s.d. \(\sim 0.5 \mathrm{\AA}\) to \(\mathrm{C18 / Ca}^{2 + }\) and \(\sim 0.4 \mathrm{\AA}\) to \(\mathrm{C22 / Ca}^{2 + }\) MSP1E3 (Fig. 4D). Thus, in afTMEM16 an open groove is not sufficient to enable lipid scrambling and nanodisc size does not influence the conformation. + +In the \(\mathrm{C22 / Ca}^{2 + }\) maps we resolved several of the lipids near the dimer interface corresponding to D2, D3, D6, and D7 (in MSP1E3 map) and to D2 and D6 (in MSP2N2 map) seen in the \(\mathrm{C18 / Ca}^{2 + }\) map (Supp. Fig. 6). In the MSP1E3 map we detected strong density for P6 and P7, located near the intracellular loop connecting TM3 and TM4 (Supp. Fig. 6). However, despite + +<--- Page Split ---> + +the high resolution of the \(\mathrm{C22 / Ca^{2 + }}\) MSP1E3 map, we detect only weak signals for lipids associated with the pathway- delimiting helices TM4 and TM6. This suggests that the interactions of C22 lipids with the pathway helices are weaker than those of C18 lipids, likely reflecting the higher energy cost associated with distorting these longer acyl chain lipids. + +![](images/Figure_4.jpg) + +
Figure 4. Functional and structural regulation of lipid scrambling by membrane thickness. A-B: Forward \((\alpha ,\) black circles) and reverse \((\beta ,\) red circles) scrambling rate constants as a function of membrane thickness in the presence of \(0.5\mathrm{mM}\) (A) or \(0\mathrm{Ca}^{2 + }\) (B). Values are the mean and error bars represent standard deviation. Corresponding lipid compositions are noted above. C-E: Alignment of the permeation pathway of afTMEM16 in (C) C18 nanodiscs in \(0.5\mathrm{mM}\) (grey) or 0
+ +<--- Page Split ---> + +\(\mathrm{Ca}^{2 + }\) (pink), (D) in \(0.5\mathrm{mM}\mathrm{Ca}^{2 + }\) and C18 (grey) or C22 MSP1E3 nanodiscs (light pink) or C22 MSP2N2 nanodiscs (orange), (E) in \(0\mathrm{Ca}^{2 + }\) in C18 (red) and C14 nanodiscs (cyan). + +## Scrambling in \(0\mathrm{Ca}^{2 + }\) does not require groove opening + +These finding that an open groove is not sufficient to allow lipid movement raises the question of whether a closed groove prevents lipid scrambling entirely. Many proteins that scramble lipids lack explicit membrane- exposed hydrophilic grooves \(^{34 - 36,38}\) and most purified TMEM16's scramble lipids in \(0\mathrm{Ca}^{2 + }\) when the groove is predominantly closed (Fig. 4B) \(^{13,23,26}\) . This basal activity could reflect transient openings of the pathway, however an open groove \(\mathrm{Ca}^{2 + }\) - free conformation has not been observed in a membrane environment \(^{15 - 18}\) . Alternatively, these scramblases could thin the membrane enough to enable slow lipid scrambling outside of a closed groove, as proposed for the mammalian TMEM16F \(^{16}\) . + +To elucidate the structural bases of scrambling in the absence of \(\mathrm{Ca}^{2 + }\) , we determined the 3.1 Å resolution structure of afTMEM16 in C18 lipids in \(0\mathrm{Ca}^{2 + }\) (Supp. Fig. 7). Extensive classification of afTMEM16 dimers and of symmetry- expanded monomers (see Methods) revealed only reconstructions corresponding to a closed groove conformation (Fig 4C, Supp. Fig. 7). However, in C18 lipids the basal scrambling activity of afTMEM16 is modest, \(\sim 4.5\%\) of that in saturating \(\mathrm{Ca}^{2 + }\) (Fig. 4A- B, Supp. Fig. 4), suggesting that the fraction of particles that could adopt a \(\mathrm{Ca}^{2 + }\) - free open groove conformation could be too small to be detected. In contrast, in C14 lipids scrambling in \(0\mathrm{Ca}^{2 + }\) is only \(\sim 3\) - fold slower than in saturating \(\mathrm{Ca}^{2 + }\) (Fig. 4A- B, Supp. Fig 4) so that a significant portion of the particles should adopt a \(\mathrm{Ca}^{2 + }\) - free open- groove conformation. Analysis of a C14/0 \(\mathrm{Ca}^{2 + }\) afTMEM16 dataset yields only classes with a closed groove (Fig. 4E), \(\mathrm{Ca}\) r.m.s.d. \(\sim 0.9\mathrm{\AA}\) to C18/0 \(\mathrm{Ca}^{2 + }\) , the highest of which reached 3.3 Å average resolution (Supp. Fig. 8). Thus, in \(0\mathrm{Ca}^{2 + }\) there is a \(\sim 30\) - fold increase in scrambling between C14 and C18 lipid + +<--- Page Split ---> + +membranes that is not accompanied by an increase in the probability of opening of the groove. This suggests that the basal, \(\mathrm{Ca}^{2 + }\) independent activity is due to closed- groove scrambling. + +This hypothesis is further supported by the analysis of the D511A/E514A mutant of afTMEM16 that disrupts the \(\mathrm{Ca}^{2 + }\) - binding site. This mutation impairs TMEM16 activity by preventing opening of the pathway \(^{8,26,39,40}\) and scrambles lipids in a \(\mathrm{Ca}^{2 + }\) - independent manner at rates comparable to those of the WT protein in \(0 \mathrm{Ca}^{2 + }\) . Scrambling by D511A/E514A afTMEM16 is modulated by membrane thickness like the WT protein in \(0 \mathrm{Ca}^{2 + }\) (Fig. 5A, Supp. Fig. 9), so that in C14 membranes its activity is \(\sim 30\%\) of that of the WT protein in C18 lipids and saturating \(\mathrm{Ca}^{2 + }\) . To test whether the D511A/E514A afTMEM16 adopts an open- pathway conformation in conditions of high activity, we determined its structure in C14 nanodiscs with 0.5 mM \(\mathrm{Ca}^{2 + }\) to 3.1 Å resolution (Supp. Fig. 10). As expected, despite the presence of 0.5 mM \(\mathrm{Ca}^{2 + }\) , the protein adopts the same conformation as in the WT apo structure and there is no density in the \(\mathrm{Ca}^{2 + }\) binding site. In all reconstructions the permeation pathway is closed, with \(\mathrm{Ca}\) r.m.s.d. \(\sim 1.1 \mathrm{\AA}\) to C18/0 \(\mathrm{Ca}^{2 + }\) and \(\sim 0.4 \mathrm{\AA}\) to C14/0 \(\mathrm{Ca}^{2 + }\) (Supp. Fig. 10), indicating that increased scrambling is not accompanied by higher open probability of the groove. Together, our results suggest that scrambling of afTMEM16 in \(0 \mathrm{Ca}^{2 + }\) occurs outside of a closed groove. Calcium- independent openings of the lipid permeation pathway, if they occur, are transient and cannot account for the observed increase in activity. Thus, an open groove is not necessary for lipid scrambling. + +In the three datasets for apo afTMEM16 (C18/0 \(\mathrm{Ca}^{2 + }\) , C14/0 \(\mathrm{Ca}^{2 + }\) and DA/EA in 0.5 mM \(\mathrm{Ca}^{2 + }\) ) we could resolve 4- 9 lipids per monomer, all localized near the dimer interface in positions closely resembling those seen in C18/ \(\mathrm{Ca}^{2 + }\) structure (Supp. Fig. 11), supporting the notion that these lipids interact strongly with the protein. No lipids could be resolved near the closed pathway + +<--- Page Split ---> + +in these structures. The average resolution of these datasets is lower than that of the two \(\mathrm{Ca^{2 + }}\) - bound structures, preventing us from drawing mechanistic inferences from this observation. + +![](images/Figure_5.jpg) + +
Figure 5. Functional and structural characterization of afTMEM16 D511A/E514A. A-B: Forward \((\alpha ,\) black circles) and reverse \((\beta ,\) red circles) scrambling rate constants of D511A/E514A afTMEM16 in \(0.5\mathrm{mM}\) (filled symbols) or \(0\mathrm{Ca}^{2 + }\) (empty symbols). Values are the mean and error bars represent standard deviation. Corresponding lipid compositions are noted above. B: Alignment of afTMME16 D511A/E514A in the presence of \(\mathrm{Ca^{2 + }}\) (green) in C14 lipids with wildtype afTMEM16 in \(0\mathrm{Ca}^{2 + }\) in C18 lipids (grey) with close up view of the permeation pathway.
+ +## Scrambling activity correlates with membrane thinning at the pathway + +Our proposal that afTMEM16 enables scrambling by thinning the membrane at the permeation pathway predicts there should be a correlation between thinning and function. Although a quantitative evaluation of thinning is precluded by the different resolutions of the maps, a qualitative analysis of the nanodisc density maps supports this notion (Fig. 6). Far from the protein, membrane thickness of C14, C18 and C22 nanodiscs is comparable to that determined by AFM (Table 1). Near the open groove, in the \(\mathrm{C18 / Ca^{2 + }}\) map the membrane appears significantly thinned (Fig. 6B), closely tracking the position of individual lipids (Fig. 2A). Thinning is reduced near the open pathway of the \(\mathrm{C22 / Ca^{2 + }}\) map (Fig. 6A) and near the closed pathway of the \(\mathrm{C18 / 0 Ca^{2 + }}\) map (Fig. 6C), consistent with the reduced scrambling activity (Fig. 4A-B). In the \(\mathrm{C14 / 0 Ca^{2 + }}\) map, thickness at the closed pathway qualitatively approaches that at the open pathway of the \(\mathrm{C18 / Ca^{2 + }}\) + +<--- Page Split ---> + +map (Fig. 6D), consistent with enhanced scrambling (Fig. 4B). These qualitative observations suggest there is a direct correlation between the thickness of the membrane near the pathway and scrambling activity. This supports the idea that in C22 membranes scrambling could be inhibited because of the reduced thinning despite an open groove, and that the closed groove conformation of afTMEM16 is scrambling competent because it thins the membrane enough to enable lipid flipping. + +![](images/Figure_6.jpg) + +
Figure 6. Membrane thinning at the afTMEM16 pathway as a function of acyl chain length. A-D: Views of the density maps for afTMEM16 in C22/Ca \(^{2 + }\) (A), C18/Ca \(^{2 + }\) (B), C18/0 Ca \(^{2 + }\) (C) and C14/0 Ca \(^{2 + }\) (D) from the extracellular (top panels) and intracellular (bottom panel) side. The C1 final unsharpened maps containing nanodisc densities were aligned, resampled on the same grid, and colored according to the Z coordinate using UCSF Chimera. The density corresponding to the protein is segmented and shown in gray. Nanodisc densities are colored by displacement along the Z axis and the 0 Å reference height is the same for all structures in each view. Negative values represent membrane thinning relative to the overall nanodisc. The position of the permeation pathway is denoted with arrows.
+ +## Discussion + +Activation of scramblases catalyzes the rapid movement of phospholipids between membrane leaflets and results in the externalization of charged and polar lipids that trigger a variety of + +<--- Page Split ---> + +fundamental physiological processes \(^{1,2,4}\) . The current consensus is that TMEM16 scramblases mediate lipid transport via a credit- card like mechanism \(^{33}\) , with the headgroups forming specific interactions with polar and charged residues lining the full length of the hydrophilic groove \(^{20,23,25,28}\) . This predicts that scramblases should discriminate among lipids based on their headgroups but not their tails, and that mutations of groove- lining residues should affect lipid scrambling. Notably, neither scramblases like the TMEM16's \(^{13,18,21,24,26}\) or the Xkr's \(^{6,41}\) nor GPCR's moonlighting as scramblases \(^{34}\) , show selectivity among lipids with different headgroups. Further, both the Xkr's and GPCR's lack explicit hydrophilic grooves \(^{34- 36}\) , bringing the structural requisites of the credit card mechanism into question. However, the modes of lipid- protein interactions of TMEM16 scramblases had not been structurally resolved \(^{15- 18}\) . + +Here, we combined structural and functional experiments to investigate the mechanism of lipid scrambling by the TMEM16's. The 2.3 Å structure of afTMEM16 reconstituted in C18 nanodiscs shows how individual lipids interact with the scramblase to define the thinned and distorted profile of the membrane near the open pathway (Fig. 1C- H). Lipids mainly localize to the periphery of the groove, interacting with the intracellular portions of TM3- 4 and with the extracellular portions of TM6- 7. The position of the last lipids from the intra- and extra- cellular leaflets suggests that headgroup flipping occurs in the intracellular vestibule. No density for lipids was visible near the extracellular vestibule (Fig. 1, 2A) and mutations of residues lining this narrow constriction or the groove interior have no functional effects (Fig. 2, 3). Reconstituting afTMEM16 in membranes formed from lipids with longer acyl chains dramatically inhibits scrambling although the groove remains open (Fig. 4). Conversely, reconstitution in thinner membranes facilitates scrambling even when the groove is closed (Fig. 4- 5). + +<--- Page Split ---> + +Together, these results have three important implications; first, lipid scrambling does not entail specific interactions with the groove's hydrophilic interior or its extracellular vestibule. Second, acyl chains rather than headgroups are key determinants of scrambling activity (Fig. 4A- B). Third, an open groove is neither sufficient nor necessary for scrambling (Fig. 4, 5). These findings are inconsistent with a credit- card mechanism. Rather, we propose that lipid scrambling is primarily determined by the ability of afTMEM16 to thin the membrane near the pathway, so that lipids only interact with the surface of the groove without penetrating deep within its narrow and hydrophilic interior (Fig. 7). The membrane- thinning mechanism readily explains evolutionarily conserved properties of TMEM16 scramblases that are difficult to reconcile with the credit card mechanism, such as the lack of discrimination based on headgroup size, chemistry or charge \(^{13,18,21,23,26,42}\) and scrambling of lipids conjugated to large cargoes \(^{24}\) . Thus, we propose this mechanism applies to other TMEM16's. + +Two lines of evidence support the credit card hypothesis: mutating groove- lining residues impairs lipid scrambling by nhTMEM16 and TMEM16F \(^{25,28}\) and MD simulations show lipid headgroups penetrating and traversing the whole length of the groove \(^{20,23,25,28,43,44}\) . Strikingly we find that mutating similar residues in afTMEM16 does not impair scrambling (Fig. 2,3). This contradiction could be explained if the mutants impair scrambling by favoring groove closure rather than by impairing lipid movement through an open groove. In afTMEM16 only the \(\mathrm{Ca^{2 + }}\) - bound open conformation has been observed \(^{15}\) (Fig. 1, 4). In contrast, \(\mathrm{Ca^{2 + }}\) - bound nhTMEM16 adopts both open and closed groove conformations \(^{17}\) and in mTMEM16F only the \(\mathrm{Ca^{2 + }}\) - bound closed groove conformation has been observed \(^{16,18}\) , suggesting in these homologues the \(\mathrm{Ca^{2 + }}\) - bound open conformation is less stable than in afTMEM16. Further, several scrambling- incompetent nhTMEM16 mutants retain measurable channel activity \(^{25}\) , suggesting stabilization + +<--- Page Split ---> + +of an ion channel- like groove conformation \(^{19}\) . Discrepancies with molecular dynamics simulations could be due to incomplete relaxation of the membrane during the equilibration, especially if the chosen initial conditions for the protein- lipid arrangements are far from equilibrium. Indeed, recent work suggests extended equilibration protocols are needed to capture the full extent of membrane deformations induced by some proteins \(^{45}\) . Further, our structures show several lipids with tails tightly intercalated with TM helices at the dimer cavity (Fig. 1) that might affect the dynamic rearrangements of afTMEM16 in MD simulations. It will be interesting to see how incorporating new information on the modes of lipid interactions with the afTMEM16 scramblase affects these results. + +The implications of our proposed membrane- thinning mechanism for scrambling (Fig. 7) can be appreciated if we make the simplifying assumptions that (i) the energy barrier for lipid scrambling is due to the polar head and (ii) that the head can be modeled with a sphere of radius \(r\) and charge \(q\) , then the energy barrier for scrambling, \(\Delta G_{scramb}\) would be given by \(^{46}\) + +\[\Delta G_{scramb} = \frac{q^{2}}{2\epsilon_{m}r} -\frac{q^{2}}{\epsilon_{m}L} ln\left(\frac{2\epsilon_{w}}{\epsilon_{m} + \epsilon_{w}}\right)\] + +Where \(\epsilon_{\mathrm{m,w}}\) are the dielectric constants of the membrane and of water and L is the thickness of the membrane. Membrane thinning decreases \(\Delta G_{scramb}\) as L is reduced and the dielectric constant \(\epsilon_{\mathrm{m}}\) is increased because of higher water access to the hydrocarbon core of the membrane \(^{47}\) . When the pathway is open scrambling is fast because thinning is pronounced, and the hydrophilic interior of the open groove further decreases \(\epsilon_{\mathrm{m}}\) (Fig. 7A). In thicker or more rigid membranes (Fig. 4) \(^{15}\) , scrambling is impaired because their deformation cost is higher thus preventing lipids to reach positions conducive to scrambling (Fig. 7B). When the groove is closed membrane thinning is diminished, but not absent, which allows for slow scrambling activity (Fig. 7C), that is enhanced in membranes formed from shorter chain length lipids (Fig. 7D). Notably, the proposed membrane + +<--- Page Split ---> + +thinning mechanism could naturally explain how proteins lacking hydrophilic grooves, such as GPCR's and Xkr's, can scramble lipids and share functional properties with the structurally unrelated TMEM16's \(^{24,34 - 36,38,48}\) . + +![](images/Figure_7.jpg) + +
Figure 7. TMEM16 scramblases enable scrambling by thinning the membrane. A-D: Schematic representation of the open (A-B, colored in green) and closed pathways (C-D, colored in red) in membranes of different thickness. Cyan denotes regions accessible to water. Arrows denote high (solid line), low (dashed line) or no (no line) scrambling.
+ +In sum, our results support a mechanism where during scrambling, lipids interact with the surface of the groove without having to penetrate and interact with its narrow and hydrophilic interior. Scrambling by the TMEM16's is modulated by two signals, binding of \(\mathrm{Ca}^{2 + }\) facilitates opening of the groove while the properties of the membrane determine whether the scramblase can thin the membrane enough to enable lipid flipping. This dual control of scrambling activity, by ligand binding and membrane properties, could constitute a secondary layer of regulation that + +<--- Page Split ---> + +prevents undesired lipid flipping by the TMEM16's during fluctuations in cellular cytosolic \(\mathrm{Ca}^{2 + }\) levels or when family members that reside in intracellular membranes are transiently localized to the plasma membrane. Similarly, rigidifying or thickening bilayer constituents, such as cholesterol, could silence the scrambling activity of other scramblases such as GPCR's in cellular membranes. + +<--- Page Split ---> + +## Data Availability + +Data AvailabilityAll constructs are available on request. All models and associated cryoEM maps have been deposited into the Electron Microscopy Data Bank (EMDB) and the Protein Data Bank (PDB). The depositions include final maps, unsharpened maps, and associated FSC curves. + +Table 5: Data Availability + +
StructurePDBEMDB
C18/Ca2+ dimer7RXHEMD-24730
C18/Ca2+ monomer7RXGEMD-24731
C22/Ca2+ MSP1E37RX2EMD-24722
C22/Ca2+ MSP2N27RWJEMD-24717
C18/0Ca2+7RXBEMD-24727
C14/0Ca2+7RX3EMD-24723
D511A/E514A C14/Ca2+7RXAEMD-24726
+ +## Acknowledgements + +AcknowledgementsThe authors thank members of the Accardi lab and Richard Hite for helpful discussions. The work was supported by National Institutes of Health (NIH) Grant R01GM106717 (to A.A.), by a Margaret and Herman Sokol Fellowship from Weill Cornell Medicine (M.E.F.), by the KBRI Basic Research Program through Korea Brain Research Institute funded by Ministry of Science and ICT (21- BR- 01- 08 to B.- C. L.) and National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019R1C1C1002699 to B.- C. L.). Some of this work was performed at the Simons Electron Microscopy Center and National Resource for Automated Molecular Microscopy located at the New York Structural Biology Center, supported by grants from the Simons Foundation (SF349247), NYSTAR, and the NIH National Institute of General Medical Sciences (GM103310). Part of the work was performed at the Cryo- EM Core Facility at University of Massachusetts Chan Medical School with the help of Dr. Kangkang Song and Dr. Chen Xu. Initial screening was performed at NYU Langone Health's Cryo- Electron Microscopy Laboratory (RRID: SCR_019202). + +<--- Page Split ---> + +## Author contributions + +M.F., Z.F. and A.A. designed the experiments; M.F., Z.F., O.E.A., Y.P. and B.- C.L. performed experiments; M.F., Z.F., O.E.A., Y.P., B.- C.L., X.C., E.F., S.S. and A.A. analyzed the data; M.F., S.S. and A.A. wrote the paper. All authors edited the manuscript. + +## Competing Interests statement + +The authors declare no competing interests. + +<--- Page Split ---> + +## References + +<--- Page Split ---> + +19 Khelashvili, G. et al. Dynamic modulation of the lipid translocation groove generates a conductive ion channel in \(\mathrm{Ca2 + }\) - bound nhTMEM16. Nature Communications 10, 4972, doi:10.1038/s41467- 019- 12865- 4 (2019). 20 Bethel, N. P. & Grabe, M. 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NameLipid Component Chain Length (70% PC:30%PG)Height from AFM (nm)Height estimated from EM maps (nm)
C1450% 14:0, 50% 16:0-18:13.2 ± 0.293.0 ± 1.5
C16100% 16:13.2 ± 0.22N.d.
C18100% 18:13.4 ± 0.19(0 Ca2+) 3.1 ± 3.5
(0.5 mM Ca2+) 3.3 ± 0.95
C20100% 20:13.7 ± 0.22N.d.
70% C2270% 22:1, 30% 18:14.0 ± 0.26N.d.
70% C2270% 22:1 PC, 30% 18:1 PG4.0 ± 0.19N.d.
C22100% 22:14.1 ± 0.193.9 ± 0.85
+ +Table 1: AFM and cryoEM determination of membrane thickness for considered lipid compositions. Heights were estimated using AFM tomography, reported values indicate the peak FWHH ± of the value distribution (see Supp. Fig. 4A-B and Methods). For cryoEM membrane height was determined from C1 unsharpened EM maps using the difference in z coordinate for the inner and outer leaflet at (x,y) far from the protein. Reported values are the average ± S.Dev of 3 different points. N.d. indicates compositions for which no cryoEM data was determined. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- FalzoneetalSI.pdf- FalzoneetalMethods.pdf- Falzonenrreportingsummary.pdf- nreditorialpolicychecklist.pdf- FalzoneetalValidationReports.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__05aa0c2fc7100b1500f07e08d2628280742c5c4e1a4bf250c2c7ba23e4cc9a7a/preprint__05aa0c2fc7100b1500f07e08d2628280742c5c4e1a4bf250c2c7ba23e4cc9a7a_det.mmd b/preprint/preprint__05aa0c2fc7100b1500f07e08d2628280742c5c4e1a4bf250c2c7ba23e4cc9a7a/preprint__05aa0c2fc7100b1500f07e08d2628280742c5c4e1a4bf250c2c7ba23e4cc9a7a_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..1a64ae4871d91599aacb25dd8c7336539ffbf810 --- /dev/null +++ b/preprint/preprint__05aa0c2fc7100b1500f07e08d2628280742c5c4e1a4bf250c2c7ba23e4cc9a7a/preprint__05aa0c2fc7100b1500f07e08d2628280742c5c4e1a4bf250c2c7ba23e4cc9a7a_det.mmd @@ -0,0 +1,346 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 950, 177]]<|/det|> +# TMEM16 scramblases thin the membrane to enable lipid scrambling + +<|ref|>text<|/ref|><|det|>[[44, 195, 666, 238]]<|/det|> +Alessio Accardi ( \(\boxed{ \begin{array}{r l} \end{array} }\) ala2022@med.cornell.edu) Weill Cornell Medical College https://orcid.org/0000- 0002- 6584- 0102 + +<|ref|>text<|/ref|><|det|>[[44, 243, 310, 283]]<|/det|> +Maria Falzone Weill Cornell Medical College + +<|ref|>text<|/ref|><|det|>[[44, 290, 310, 330]]<|/det|> +Zhang Feng Weill Cornell Medical College + +<|ref|>text<|/ref|><|det|>[[44, 336, 310, 376]]<|/det|> +Omar Alvarenga Weill Cornell Medical College + +<|ref|>text<|/ref|><|det|>[[44, 382, 310, 422]]<|/det|> +Yangang Pang Weill Cornell Medical College + +<|ref|>text<|/ref|><|det|>[[44, 428, 208, 468]]<|/det|> +Byoung Lee Cornell University + +<|ref|>text<|/ref|><|det|>[[44, 474, 566, 515]]<|/det|> +Xiaolu Cheng Cornell University https://orcid.org/0000- 0002- 2785- 6488 + +<|ref|>text<|/ref|><|det|>[[44, 521, 310, 561]]<|/det|> +Eva Fortea Weill Cornell Medical College + +<|ref|>text<|/ref|><|det|>[[44, 567, 608, 608]]<|/det|> +Simon Scheuring Weill Cornell Medicine https://orcid.org/0000- 0003- 3534- 069X + +<|ref|>sub_title<|/ref|><|det|>[[44, 650, 102, 667]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 687, 660, 707]]<|/det|> +Keywords: TMEM16 scramblases, lipid scrambling, membrane thinning + +<|ref|>text<|/ref|><|det|>[[44, 725, 313, 744]]<|/det|> +Posted Date: October 8th, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 763, 463, 782]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 955726/v1 + +<|ref|>text<|/ref|><|det|>[[44, 801, 910, 844]]<|/det|> +License: \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 879, 909, 922]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on May 11th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 30300- z. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[207, 194, 789, 213]]<|/det|> +# TMEM16 scramblases thin the membrane to enable lipid scrambling + +<|ref|>text<|/ref|><|det|>[[120, 296, 875, 353]]<|/det|> +Maria E. Falzone \(^{1,2}\) , Zhang Feng \(^{1}\) , Omar E. Alvarenga \(^{1,3}\) , Yangang Pang \(^{1}\) , ByoungCheol Lee \(^{1,\wedge}\) , Xiaolu Cheng \(^{4}\) , Eva Fortea \(^{1,3}\) , Simon Scheuring \(^{1}\) , Alessio Accardi \(^{1,2,4*}\) + +<|ref|>text<|/ref|><|det|>[[113, 540, 885, 667]]<|/det|> +1 Department of Anesthesiology, Weill Cornell Medical College; 2 Department of Biochemistry, Weill Cornell Medical College; 3 Physiology, Biophysics and Systems Biology Graduate Program, Weill Cornell Medical College; 4 Department of Physiology and Biophysics, Weill Cornell Medical College + +<|ref|>text<|/ref|><|det|>[[113, 750, 493, 769]]<|/det|> +\* correspondence to: ala2022@med.cornell.edu + +<|ref|>text<|/ref|><|det|>[[113, 820, 819, 875]]<|/det|> +\(^{\wedge}\) ByoungCheol Lee's present address: Neurovascular Unit Research Group, Korea Brain Research Institute (KBRI), Daegu 41062, Republic of Korea. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 91, 191, 108]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[112, 123, 886, 530]]<|/det|> +AbstractTMEM16 scramblases dissipate the plasma membrane lipid asymmetry to activate multiple eukaryotic cellular pathways. It was proposed that lipid headgroups move between leaflets through a membrane- spanning hydrophilic groove. Direct information on lipid- groove interactions is lacking. We report the 2.3 Å resolution cryoEM structure of the \(\mathrm{Ca^{2 + }}\) - bound afTMEM16 scramblase in nanodiscs showing how rearrangement of individual lipids at the open pathway results in pronounced membrane thinning. Only the groove’s intracellular vestibule contacts lipids, and mutagenesis suggests scrambling does not entail specific protein- lipid interactions with the extracellular vestibule. Further, we find scrambling can occur outside a closed groove in thinner membranes and is inhibited in thicker membranes despite an open pathway. Our results show how afTMEM16 thins the membrane to enable scrambling and that an open hydrophilic pathway is not a structural requirement to allow rapid transbilayer movement of lipids. This mechanism could be extended to other scramblases lacking a hydrophilic groove. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[114, 90, 224, 108]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[112, 120, 886, 844]]<|/det|> +Biological membranes play a fundamental role in many cellular signaling pathways as they define the physical boundaries of cellular compartments and actively modulate the function of integral and membrane- associated proteins. In eukaryotic cells, the composition and distribution of the phospholipid constituents of the membrane is tightly regulated by the activity of a variety of dedicated enzymes, lipases, lipases and scramblases \(^{1}\) . The headgroup asymmetry of the plasma membrane is established by the action of ATP- driven pumps which distribute phosphatidylethanolamine (PE) and phosphatidylserine (PS) to the inner leaflet and phosphatidylcholine (PC) to the outer leaflet \(^{1}\) . Activated phospholipid scramblases dissipate this asymmetry and expose PS on the extracellular leaflet. This is critical for multiple signaling pathways, ranging from apoptosis to blood coagulation and cell- cell fusion \(^{1,2}\) . There are two known families of scramblases, the \(\mathrm{Ca}^{2 + }\) - activated TMEM16 \(^{3 - 5}\) and the caspase- activated Xk- related (Xkr) proteins \(^{6}\) . Lipid scrambling by the TMEM16's is of critical importance for a myriad of physiological processes, including blood coagulation, bone mineralization, membrane fusion and viral entry \(^{2,4,7}\) . Dysregulation of TMEM16 scramblase activity can have disastrous consequences, as both gain- and loss- of function mutations have been associated with disorders of blood, brain, bone and muscle \(^{3,8 - 11}\) . The TMEM16 superfamily is comprised of CI channels and dual function scramblases/non- selective ion channels \(^{4}\) . Both subtypes share a common homodimeric architecture where each monomer is comprised of 10 transmembrane (TM) helices \(^{12 - 18}\) (Fig. 1A- B). In each protomer, the TM3- TM7 helices form a hydrophilic permeation pathway, or groove, that can adopt multiple conformations to allow passage of ions, lipids or to prevent movement of both substrates \(^{15 - 19}\) . + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 886, 528]]<|/det|> +Upon activation, the TMEM16 scramblases mediate rapid lipid movement between leaflets causing the membrane asymmetry to collapse and thus initiating signaling cascades. The mechanism underlying scrambling has been investigated at the functional, computational and structural levels \(^{3,8,15 - 32}\) . The consensus proposal is a 'credit- card' like mechanism \(^{33}\) , where the lipid headgroups penetrate and traverse the open hydrophilic groove while their tails remain embedded within the hydrocarbon core of the membrane \(^{20,25,28}\) . Within this framework, lipid scrambling is enabled by specific interactions of the permeating lipids with charged and polar groove- lining residues \(^{20,25,28}\) . However, TMEM16 scramblases do not discriminate among lipids such as PS, PE, PG, PC and DOTAP with headgroups of different charge, structure and size \(^{13,21,23,26}\) . Further, PE lipids conjugated to 5 kDa cargoes are also efficiently scrambled \(^{24}\) . These observations suggest that specific interactions between the groove and the scrambled lipids are not necessary. Notably, this lack of headgroup selectivity is also shared by other scramblases that lack an explicit hydrophilic groove, such as the GPCR opsin \(^{24,34}\) and XKR8 and 9 \(^{6,35,36}\) . + +<|ref|>text<|/ref|><|det|>[[113, 541, 886, 771]]<|/det|> +Moderate resolution structures of the fungal afTMEM16 and nhTMEM16 in nanodiscs showed these scramblases than the membrane near the groove \(^{15,17}\) , suggesting that membrane thinning at an open pathway might be important for lipid scrambling \(^{15}\) . Membrane thinning was also observed near the closed pathway of mTMEM16F, leading to the proposal that scrambling can also occur outside a closed groove \(^{16}\) . Thus, it is not clear whether an open hydrophilic groove is required for scrambling. Direct structural information on how TMEM16 scramblases interact with lipids is essential to elucidate the molecular mechanisms of lipid permeation. + +<|ref|>text<|/ref|><|det|>[[113, 784, 885, 876]]<|/det|> +Here we use cryogenic electron microscopy (cryoEM) to determine the 2.3 Å resolution structure of the afTMEM16 scramblase in lipid nanodiscs. Our structure allows the direct visualization of lipids associated with the protein at the open groove and reveals that afTMEM16 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 530]]<|/det|> +thins the membrane at the open pathway by \(\sim 50\%\) . The closest point of approach of the two membrane leaflets occurs near the wide intracellular vestibule of the groove, and no lipids could be resolved inside or interacting with the extracellular portion of the pathway. Mutagenesis of groove- lining residues does not perturb function, suggesting that specific interactions of permeating lipids with groove- lining residues are not essential for scrambling. We show that in thicker membranes scrambling is inhibited, while the groove remains in an open conformation. Conversely, in thinner membranes scrambling is enhanced although the groove is closed. Thus, lipid permeation is not always enabled by an open groove or prevented by a closed pathway. Based on these findings we propose that when the groove is open, the thinned membrane and the hydrophilic nature of the pathway synergistically lower the energy barrier for lipid scrambling. When the groove is closed, scrambling can occur, but at reduced rates in bilayers with plasma- membrane like thickness. In thinner membranes, closed- groove scrambling is enhanced allowing for lipid transport in the absence of \(\mathrm{Ca^{2 + }}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 578, 179, 595]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[115, 611, 703, 632]]<|/det|> +## Structural basis of lipid reorganization by the afTMEM16 scramblase + +<|ref|>text<|/ref|><|det|>[[113, 646, 886, 876]]<|/det|> +To gain insight into how the afTMEM16 scramblase alters the organization of the membrane and interacts with the surrounding lipids we used cryo- EM to determine its structure in the \(\mathrm{Ca^{2 + }}\) - bound conformation in nanodiscs at 2.3 Å (Fig. 1, Supp. Fig. 1). Nanodiscs were comprised of a mixture of \(70\%\) 1,2- Dioleoyl- sn- glycero- 3- phosphocholine (DOPC, or 18:1 PC) and \(30\%\) 1,2- Dioleoyl- sn- Glycero- 3- Phosphatidylglycerol (DOPG, 18:1 PG), which we will refer to as C18 lipids. In these conditions, referred to as \(\mathrm{C18 / Ca^{2 + }}\) , afTMEM16 is maximally active \(^{15}\) , therefore we hypothesize this represents the active state of the scramblase. The present structure is nearly superimposable to + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 81, 886, 636]]<|/det|> +the previously determined \(\mathrm{Ca^{2 + }}\) - bound structure of afTMEM16 in 3 1- palmitoyl- 2- oleoyl- sn- glycero- 3- phosphoethanolamine (POPE): 1 1- palmitoyl- 2- oleoyl- sn- glycero- 3- phospho- (1'- racglycerol) POPG nanodiscs \(^{15}\) , \(\mathrm{Ca}\) rmsd \(\sim 0.8 \mathrm{\AA}\) , indicating that headgroup choice and acyl- chain saturation do not influence the conformation of the protein. The significantly improved resolution of the C18/Ca \(^{2 + }\) map allowed us to resolve 4 water molecules in the \(\mathrm{Ca^{2 + }}\) binding sites which coordinate two bound ions (Supp. Fig. 2B). The presence of these water molecules brings the coordination number of bound \(\mathrm{Ca^{2 + }}\) ions to 7 and 8, consistent with the high affinity of these sites \(^{26}\) (Supp. Fig. 2B). The map also contains non- protein densities that could be modeled as lipids associated with the protein (Fig. 1C- H, Supp. Fig. 2). To improve the quality of the density of the lipids near the pathway, we carried out symmetry expansion and additional rounds of 3D classification, which yielded one class with an additional four resolved lipids (Supp. Fig. 1E), for a total of 32 resolved lipids, 16 in each monomer (Fig. 1F- H, Supp. Fig. 2). The observed lipids define nearly continuous interfaces of the scramblase with the inner and outer membrane leaflets near the dimer interface (lipids D1- D9) and illustrate how the poses adopted by individual lipids result in the profound remodeling of the membrane induced by afTMEM16 near the lipid pathway (lipids P1- P7) (Fig. 1F- H). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[202, 80, 789, 840]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 850, 884, 905]]<|/det|> +
Figure 1 Lipid-protein interactions in \(\mathbf{Ca^{2 + }}\) -bound afTMEM16. A: Structural model of afTMEM16 in \(0.5\mathrm{mM}\mathrm{Ca}^{2 + }\) in C18 lipid nanodiscs. B: View of the open permeation pathway. C-E: Unsharpened maps of the protein (grey) and associated lipids (red) viewed from the membrane
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 282]]<|/det|> +plane (C), extracellular side (D) and close- up of the open groove (E). The map showing the density of the nanodisc membrane low- pass filtered to \(10 \mathrm{\AA}\) and shown in transparent red (C- D). F- H: Views of the afTMEM16 dimer from the plane of the membrane (F), extra- (G) and intra- cellular (H) sides with modeled lipids shown in stick representation. Lipids at the dimer interface are labeled D1- 9 and those at the permeation pathway are labeled P1- 7. Lipids from the inner and outer leaflets are colored in yellow and blue, respectively. The cytosolic domain of afTMEM16 was omitted for clarity. I: Close up view of the density map at the dimer interface showing the two afTMEM16 monomers (gray and cyan) and intercalated lipid tails (red). \* denotes the symmetry axis. J: The dimer interface salt bridge between TM9 and 10 (in cartoon representation) is formed by E618 and H619 (in stick representation) and is shielded from the intra- and extra- cellular solutions by lipids D3, D4, D6, and D7 (in spheres and colored as in F- H). + +<|ref|>sub_title<|/ref|><|det|>[[115, 316, 635, 334]]<|/det|> +## Lipids form a cap around the transmembrane dimer interface + +<|ref|>text<|/ref|><|det|>[[112, 345, 886, 895]]<|/det|> +The transmembrane dimer interface of afTMEM16 is formed by the extracellular half of TM10 from each monomer (Fig. 1I- J, Supp. Fig. 2D- E). This minimal interface contains several hydrophobic residues and two membrane- embedded salt bridges formed by E618 and H619 of opposite subunits positioned \(\sim 1 / 3\) of the way through the membrane from the extracellular leaflet (Fig. 1I- J). In the \(\mathrm{C18 / Ca^{2 + }}\) structure, these salt bridges appear to be isolated from the intra- and extra- cellular solutions by eight well- defined lipids (D3, D4, D6 and D7 from each subunit), four above and four below the interacting residues (Fig. 1J). On the extracellular side, the D3 lipids from opposite subunits straddle the N terminal region of TM10 with their heads positioned by the side chains of C607 and W608 to make direct contact above the symmetry axis (Supp. Fig. 2D). A second lipid, D4, is wedged between TM9 and TM10 with its head coordinated by polar and charged residues in the TM9- 10 linker (N593, P598, T604 and R606; Supp. Fig. 2D). On the intracellular side, the heads of D6 from each subunit make contact across the symmetry axis and are wedged between the C- termini of the TM10s (Supp. Fig. 2E). They are coordinated by D571, G574 and W578 on the TM9 from one subunit and by R625, Y626 and R629 from TM10 on the other (Supp. Fig. 2E). Additionally, the head of D7 is coordinated by Y626, S630 and K634 from TM10 of one subunit and by Q364 on TM5 and D571 on TM9 from the opposite subunit (Supp. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 885, 319]]<|/det|> +Fig. 2E). The tails of these 8 lipids are accommodated in hydrophobic grooves between TM2, 9, 10 from both subunits (Fig. 1I, Supp. Fig. 2D- E). The intercalated organization of the lipid tails and helices gives rise to densely packed hydrophobic regions that shield the interacting E618 and H619 residues from water access, possibly strengthening their electrostatic interaction (Fig. 1J). These observations, together with the evolutionary conservation of the E618/H619 pair (Supp. Fig. 2C) and of the TMEM16 fold suggests these lipids might play a structural role in stabilizing the dimeric architecture of all TMEM16s. + +<|ref|>sub_title<|/ref|><|det|>[[115, 367, 671, 388]]<|/det|> +## Structural basis of membrane thinning at the scrambling pathway + +<|ref|>text<|/ref|><|det|>[[112, 400, 886, 878]]<|/det|> +The C18/Ca \(^{2 + }\) structure reveals how the scramblase reorients the lipids that approach the open scrambling pathway (Fig. 1F, 2A). Near the dimer interface, the planes of the outer (OL) and inner (IL) leaflets are respectively defined by lipids D1- 4 and D8- 9, in good agreement with the outline visualized in the low pass filtered nanodisc map (Fig. 1C). The downward slope of the OL starts at D5, a well- defined PG lipid (Fig. 1F, 2B, Fig. Supp. 2A), and progresses towards the open groove as P1 and P2 adopt distorted poses with their headgroups becoming increasingly tilted (Fig. 2A- B). The IL bends upwards and P5- P7 become increasingly tilted as their heads climb around the intracellular portions of TM3 and TM4, coordinated by the hydrophilic side chains of T341, K345 and T334 (Fig. 2A,C). Within the pathway, P3 is sandwiched between TM4 and TM6 near the constriction formed by T325 and Y432 and its headgroup points towards the extracellular side such that it is contiguous to other OL lipids (Fig. 2A). The distance between the phosphate atoms of the headgroups of P3 and P4 in the OL and IL is \(< 22 \text{Å}\) (Fig. 2A), showing that the hydrocarbon core of the membrane is thinned by \(\sim 50\%\) at the open pathway. A similar thinning is seen in the low- pass filtered nanodiscs map near the pathway (Fig. 1C- D). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[113, 120, 872, 540]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 556, 883, 700]]<|/det|> +
Figure 2. Coordination of lipids outside the permeation pathway. A: View of the seven pathway lipids (in sticks, colored as in Fig. 1F). T325 and Y423 are shown as green sticks. Dashed arrow indicates the distance between the phosphate atoms of the last lipid from the inner (P4) and outer (P3) leaflets. B-C: Coordination of P1-P2 (B) and of P4-P7 (C). Side chains are shown in green sticks. D-E: forward \((\alpha)\) and reverse \((\beta)\) scrambling rate constants for indicated quadruple mutants of residues coordinating lipids outside the pathway (P1-2 and P4-7). Bars are average values for \(\alpha\) (black) and \(\beta\) (grey), error bars are S. Dev., and red circles are values from individual repeats.
+ +<|ref|>sub_title<|/ref|><|det|>[[115, 732, 730, 752]]<|/det|> +## Lipids outside the open pathway define the distorted membrane interface + +<|ref|>text<|/ref|><|det|>[[113, 767, 884, 893]]<|/det|> +The identification of sites where lipids bind at or near the open groove raises the possibility that scrambling could occur via a 'conveyor belt' mechanism, where lipids translocate between leaflets by moving from site to site. Alternatively, the observed lipids could define the protein- membrane boundary but not necessarily be translocated, with the possible exception of P3 within the pathway + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 354]]<|/det|> +(Fig. 2A). To distinguish between these hypotheses, we investigated how mutating residues coordinating the headgroups of P1- 2 and P4- 6 impacts scrambling. We found that mutations aimed at disrupting the headgroup interactions of P1- P2 (W202A/R427A/I431A/W529A), P4- P5- P6 (R279A/T334A/K345A/Y349A) or P2- P5- P6 (R279A/K345A/R427A/K428A) have minimal functional effects (Fig. 2D- E, Supp. Fig. 3). This suggests that these lipid association sites are not obligatory on the path taken by scrambled lipids. Rather, other factors, such as tail interactions with interhelical grooves, contribute to their association with afTMEM16 (Supp. Fig 2F- G) and stabilize the distorted membrane- protein interface that results in thinning at the pathway. + +<|ref|>text<|/ref|><|det|>[[112, 401, 886, 736]]<|/det|> +Scrambling does not require specific interactions with extracellular groove- lining residuesOne unexpected feature of our structure is that the extracellular vestibule of the groove does not directly interact with the membrane and no lipids could be resolved (Fig. 1C- D), as they traverse the groove below this region (Fig. 2A). We mutated side chains lining the extracellular vestibule or the central constriction of the groove and assessed their impact on scrambling. Single or multiple simultaneous alanine substitutions of I298, F302, E305 and E310 on TM3, of K317, Y319, F322, T325 and I332 on TM4, of T373 and S374 on TM5 and of R425, K428, Q429, Y432 and F433 on TM6 have no effects on lipid scrambling (Fig. 3, Supp. Fig. 3). Thus, scrambling does not entail specific interactions of lipids with residues lining the extracellular vestibule or the central constriction of the groove. + +<|ref|>text<|/ref|><|det|>[[113, 750, 885, 876]]<|/det|> +In contrast, the wide intracellular vestibule is embedded in the nanodisc membrane, and the resolved P3 and P4 lipids at the open pathway have opposite orientations (Fig. 2A). This suggests lipid headgroups only need to traverse the wide intracellular vestibule of the pathway, below the constriction formed by T325 and Y432 (Fig. 2A). The pronounced membrane thinning + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 319]]<|/det|> +at the pathway lowers the energy barrier for transbilayer lipid movement and the hydrophilic environment of the open groove allows water access to this thinned membrane region, synergistically lower the energy barrier for scrambling. Scrambling by afTMEM16 and hTMEM16K is modulated by lipid acyl chain length \(^{15,23}\) , supporting the idea that membrane thinning is critical for scrambling. Our proposal predicts that this modulation should reflect whether the scramblase can sufficiently thin these membranes, rather than arise from lipid-dependent changes in the conformation of the groove. + +<|ref|>image<|/ref|><|det|>[[118, 330, 875, 775]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 790, 883, 884]]<|/det|> +
Figure 3. Functional role of groove-lining residues in lipid scrambling. A-C: Residues lining the extracellular vestibule (A), coordinating P3 (B) and lining the central constriction (C) are shown as green sticks. D-E: forward \((\alpha)\) and reverse \((\beta)\) scrambling rate constants of single and multiple alanine substitutions at the indicated positions. Bars are average values for \(\alpha\) (black) and \(\beta\) (grey), error bars are S. Dev., and red circles are values from individual repeats.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[114, 90, 575, 110]]<|/det|> +## Regulation of lipid scrambling by membrane thickness + +<|ref|>text<|/ref|><|det|>[[113, 123, 886, 459]]<|/det|> +We measured how systematic variation of lipid acyl chain length affects lipid scrambling by afTMEM16. We kept the lipid headgroup composition at a constant ratio of 7 PC: 3 PG and used acyl chains with a single unsaturation and 16- 22 carbons, C16- C22 lipids (Table 1). Liposomes formed from this mix of 14:1 lipids were not stable in our scrambling assay (Supp Fig. 4C), therefore we generated thinner membranes using a mixture comprised of \(50\%\) 1,2- dimyristoyl- sn- glycero- 3- phosphocholine (DMPC) and 1,2- dimyristoyl- sn- glycero- 3- phospho- (1'-rac- glycerol) (DMPG) in a 7:3 ratio and \(50\%\) of POPC and POPG in a 7:3 ratio \(^{23}\) ; we will refer to this mix as C14 (Table 1). Atomic force microscopy (AFM) measurements show that membrane thickness varies between \(\sim 3.2 \mathrm{nm}\) and \(\sim 4.1 \mathrm{nm}\) , with near- linear scaling with acyl chain length (Table 1, Supp. Fig. 4A- B). + +<|ref|>text<|/ref|><|det|>[[112, 472, 886, 877]]<|/det|> +In the presence of saturating \(0.5 \mathrm{mM Ca^{2 + }}\) the scrambling rate constants do not depend on membrane thickness between \(\sim 3.2 \mathrm{nm}\) (C14 lipids) and \(\sim 3.9 \mathrm{nm}\) (C20 lipids) (Fig. 4A). In contrast, scrambling is nearly completely inhibited in C22 lipids (Fig. 4A) \(^{15}\) . Thus, in saturating \(\mathrm{Ca^{2 + }}\) there is chain length selectivity with a threshold for activity below membrane thickness of \(\sim 4.1 \mathrm{nm}\) . In contrast, in \(0 \mathrm{Ca^{2 + }}\) scrambling displays a nearly exponential inverse dependence on membrane thickness (Fig. 4B). Remarkably, in C14 lipid membranes scrambling by afTMEM16 is nearly \(\mathrm{Ca^{2 + }}\) - independent, with rate constants only \(\sim 3\) - fold lower in \(0 \mathrm{Ca^{2 + }}\) compared to the \(\sim 20\) - fold reduction seen in C18 membranes (Fig. 4A- B, Supp. Fig. 4). To test whether the long C22 acyl chains inhibit scrambling in saturating \(\mathrm{Ca^{2 + }}\) by occluding the pathway \(^{37}\) we measured scrambling in membranes formed by \(70\%\) C22 lipids and \(30\%\) C18 lipids, which are \(\sim 4 \mathrm{nm}\) thick (Table 1). In saturating \(\mathrm{Ca^{2 + }}\) scrambling activity is similar to that seen in \(100\%\) C18 lipids (Fig. 4A), while in \(0 \mathrm{Ca^{2 + }}\) there is a \(\sim 17\) - fold reduction, consistent with the reduction expected for membranes of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 885, 249]]<|/det|> +this thickness (Fig. 4B). This behavior does not depend on whether the mixed chain lengths were segregated by headgroup. Thus, the tails of C22 lipids are not ‘blockers’ of the afTMEM16 permeation pathway. These results suggest that in \(0 \mathrm{Ca}^{2 + }\) scrambling rates are proportional to the energetic cost of lipid headgroups crossing the hydrophobic core of the membrane, while in the presence of \(\mathrm{Ca}^{2 + }\) other factors contribute to scrambling. + +<|ref|>sub_title<|/ref|><|det|>[[115, 298, 648, 318]]<|/det|> +## \(\mathrm{Ca}^{2 + }\) -bound afTMEM16 has an open groove in C22 membranes + +<|ref|>text<|/ref|><|det|>[[112, 330, 886, 736]]<|/det|> +To determine whether the C22 lipids inhibit scrambling by inducing groove closure we determined the cryo- EM structure of nanodisc- reconstituted afTMEM16 in the presence of saturating \(\mathrm{Ca}^{2 + }\) to 2.7 Å (Supp. Fig. 5A- G). Despite a \(\sim 500\) - fold reduction in scrambling activity the groove remains open in a conformation nearly identical to that seen in C18 lipids, \(\mathrm{Ca}\) r.m.s.d. \(\sim 0.35 \mathrm{\AA}\) (Fig. 4D). Importantly, neither the \(\mathrm{C18 / Ca}^{2 + }\) nor the \(\mathrm{C22 / Ca}^{2 + }\) datasets display structural heterogeneity as no additional classes could be identified using multiple rounds of iterative 3D classifications on afTMEM16 dimers and monomers using different classification parameters and software (see Methods, Supp. Fig. 1,5, 7, 8, 10). Further, The \(\mathrm{C22 / Ca}^{2 + }\) structure of afTMEM16 in the larger MSP2N2 nanodiscs at 3.5 Å resolution (Supp. Fig. 5H- N) shows an open permeation pathway in all 3D reconstructions, with \(\mathrm{Ca}\) r.m.s.d. \(\sim 0.5 \mathrm{\AA}\) to \(\mathrm{C18 / Ca}^{2 + }\) and \(\sim 0.4 \mathrm{\AA}\) to \(\mathrm{C22 / Ca}^{2 + }\) MSP1E3 (Fig. 4D). Thus, in afTMEM16 an open groove is not sufficient to enable lipid scrambling and nanodisc size does not influence the conformation. + +<|ref|>text<|/ref|><|det|>[[113, 749, 885, 876]]<|/det|> +In the \(\mathrm{C22 / Ca}^{2 + }\) maps we resolved several of the lipids near the dimer interface corresponding to D2, D3, D6, and D7 (in MSP1E3 map) and to D2 and D6 (in MSP2N2 map) seen in the \(\mathrm{C18 / Ca}^{2 + }\) map (Supp. Fig. 6). In the MSP1E3 map we detected strong density for P6 and P7, located near the intracellular loop connecting TM3 and TM4 (Supp. Fig. 6). However, despite + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 884, 214]]<|/det|> +the high resolution of the \(\mathrm{C22 / Ca^{2 + }}\) MSP1E3 map, we detect only weak signals for lipids associated with the pathway- delimiting helices TM4 and TM6. This suggests that the interactions of C22 lipids with the pathway helices are weaker than those of C18 lipids, likely reflecting the higher energy cost associated with distorting these longer acyl chain lipids. + +<|ref|>image<|/ref|><|det|>[[113, 230, 850, 770]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 787, 883, 878]]<|/det|> +
Figure 4. Functional and structural regulation of lipid scrambling by membrane thickness. A-B: Forward \((\alpha ,\) black circles) and reverse \((\beta ,\) red circles) scrambling rate constants as a function of membrane thickness in the presence of \(0.5\mathrm{mM}\) (A) or \(0\mathrm{Ca}^{2 + }\) (B). Values are the mean and error bars represent standard deviation. Corresponding lipid compositions are noted above. C-E: Alignment of the permeation pathway of afTMEM16 in (C) C18 nanodiscs in \(0.5\mathrm{mM}\) (grey) or 0
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 127]]<|/det|> +\(\mathrm{Ca}^{2 + }\) (pink), (D) in \(0.5\mathrm{mM}\mathrm{Ca}^{2 + }\) and C18 (grey) or C22 MSP1E3 nanodiscs (light pink) or C22 MSP2N2 nanodiscs (orange), (E) in \(0\mathrm{Ca}^{2 + }\) in C18 (red) and C14 nanodiscs (cyan). + +<|ref|>sub_title<|/ref|><|det|>[[114, 160, 568, 179]]<|/det|> +## Scrambling in \(0\mathrm{Ca}^{2 + }\) does not require groove opening + +<|ref|>text<|/ref|><|det|>[[113, 193, 886, 457]]<|/det|> +These finding that an open groove is not sufficient to allow lipid movement raises the question of whether a closed groove prevents lipid scrambling entirely. Many proteins that scramble lipids lack explicit membrane- exposed hydrophilic grooves \(^{34 - 36,38}\) and most purified TMEM16's scramble lipids in \(0\mathrm{Ca}^{2 + }\) when the groove is predominantly closed (Fig. 4B) \(^{13,23,26}\) . This basal activity could reflect transient openings of the pathway, however an open groove \(\mathrm{Ca}^{2 + }\) - free conformation has not been observed in a membrane environment \(^{15 - 18}\) . Alternatively, these scramblases could thin the membrane enough to enable slow lipid scrambling outside of a closed groove, as proposed for the mammalian TMEM16F \(^{16}\) . + +<|ref|>text<|/ref|><|det|>[[112, 472, 886, 878]]<|/det|> +To elucidate the structural bases of scrambling in the absence of \(\mathrm{Ca}^{2 + }\) , we determined the 3.1 Å resolution structure of afTMEM16 in C18 lipids in \(0\mathrm{Ca}^{2 + }\) (Supp. Fig. 7). Extensive classification of afTMEM16 dimers and of symmetry- expanded monomers (see Methods) revealed only reconstructions corresponding to a closed groove conformation (Fig 4C, Supp. Fig. 7). However, in C18 lipids the basal scrambling activity of afTMEM16 is modest, \(\sim 4.5\%\) of that in saturating \(\mathrm{Ca}^{2 + }\) (Fig. 4A- B, Supp. Fig. 4), suggesting that the fraction of particles that could adopt a \(\mathrm{Ca}^{2 + }\) - free open groove conformation could be too small to be detected. In contrast, in C14 lipids scrambling in \(0\mathrm{Ca}^{2 + }\) is only \(\sim 3\) - fold slower than in saturating \(\mathrm{Ca}^{2 + }\) (Fig. 4A- B, Supp. Fig 4) so that a significant portion of the particles should adopt a \(\mathrm{Ca}^{2 + }\) - free open- groove conformation. Analysis of a C14/0 \(\mathrm{Ca}^{2 + }\) afTMEM16 dataset yields only classes with a closed groove (Fig. 4E), \(\mathrm{Ca}\) r.m.s.d. \(\sim 0.9\mathrm{\AA}\) to C18/0 \(\mathrm{Ca}^{2 + }\) , the highest of which reached 3.3 Å average resolution (Supp. Fig. 8). Thus, in \(0\mathrm{Ca}^{2 + }\) there is a \(\sim 30\) - fold increase in scrambling between C14 and C18 lipid + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 144]]<|/det|> +membranes that is not accompanied by an increase in the probability of opening of the groove. This suggests that the basal, \(\mathrm{Ca}^{2 + }\) independent activity is due to closed- groove scrambling. + +<|ref|>text<|/ref|><|det|>[[112, 156, 886, 704]]<|/det|> +This hypothesis is further supported by the analysis of the D511A/E514A mutant of afTMEM16 that disrupts the \(\mathrm{Ca}^{2 + }\) - binding site. This mutation impairs TMEM16 activity by preventing opening of the pathway \(^{8,26,39,40}\) and scrambles lipids in a \(\mathrm{Ca}^{2 + }\) - independent manner at rates comparable to those of the WT protein in \(0 \mathrm{Ca}^{2 + }\) . Scrambling by D511A/E514A afTMEM16 is modulated by membrane thickness like the WT protein in \(0 \mathrm{Ca}^{2 + }\) (Fig. 5A, Supp. Fig. 9), so that in C14 membranes its activity is \(\sim 30\%\) of that of the WT protein in C18 lipids and saturating \(\mathrm{Ca}^{2 + }\) . To test whether the D511A/E514A afTMEM16 adopts an open- pathway conformation in conditions of high activity, we determined its structure in C14 nanodiscs with 0.5 mM \(\mathrm{Ca}^{2 + }\) to 3.1 Å resolution (Supp. Fig. 10). As expected, despite the presence of 0.5 mM \(\mathrm{Ca}^{2 + }\) , the protein adopts the same conformation as in the WT apo structure and there is no density in the \(\mathrm{Ca}^{2 + }\) binding site. In all reconstructions the permeation pathway is closed, with \(\mathrm{Ca}\) r.m.s.d. \(\sim 1.1 \mathrm{\AA}\) to C18/0 \(\mathrm{Ca}^{2 + }\) and \(\sim 0.4 \mathrm{\AA}\) to C14/0 \(\mathrm{Ca}^{2 + }\) (Supp. Fig. 10), indicating that increased scrambling is not accompanied by higher open probability of the groove. Together, our results suggest that scrambling of afTMEM16 in \(0 \mathrm{Ca}^{2 + }\) occurs outside of a closed groove. Calcium- independent openings of the lipid permeation pathway, if they occur, are transient and cannot account for the observed increase in activity. Thus, an open groove is not necessary for lipid scrambling. + +<|ref|>text<|/ref|><|det|>[[113, 715, 885, 842]]<|/det|> +In the three datasets for apo afTMEM16 (C18/0 \(\mathrm{Ca}^{2 + }\) , C14/0 \(\mathrm{Ca}^{2 + }\) and DA/EA in 0.5 mM \(\mathrm{Ca}^{2 + }\) ) we could resolve 4- 9 lipids per monomer, all localized near the dimer interface in positions closely resembling those seen in C18/ \(\mathrm{Ca}^{2 + }\) structure (Supp. Fig. 11), supporting the notion that these lipids interact strongly with the protein. No lipids could be resolved near the closed pathway + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 883, 144]]<|/det|> +in these structures. The average resolution of these datasets is lower than that of the two \(\mathrm{Ca^{2 + }}\) - bound structures, preventing us from drawing mechanistic inferences from this observation. + +<|ref|>image<|/ref|><|det|>[[115, 160, 884, 348]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 367, 883, 476]]<|/det|> +
Figure 5. Functional and structural characterization of afTMEM16 D511A/E514A. A-B: Forward \((\alpha ,\) black circles) and reverse \((\beta ,\) red circles) scrambling rate constants of D511A/E514A afTMEM16 in \(0.5\mathrm{mM}\) (filled symbols) or \(0\mathrm{Ca}^{2 + }\) (empty symbols). Values are the mean and error bars represent standard deviation. Corresponding lipid compositions are noted above. B: Alignment of afTMME16 D511A/E514A in the presence of \(\mathrm{Ca^{2 + }}\) (green) in C14 lipids with wildtype afTMEM16 in \(0\mathrm{Ca}^{2 + }\) in C18 lipids (grey) with close up view of the permeation pathway.
+ +<|ref|>sub_title<|/ref|><|det|>[[115, 508, 710, 528]]<|/det|> +## Scrambling activity correlates with membrane thinning at the pathway + +<|ref|>text<|/ref|><|det|>[[113, 541, 884, 878]]<|/det|> +Our proposal that afTMEM16 enables scrambling by thinning the membrane at the permeation pathway predicts there should be a correlation between thinning and function. Although a quantitative evaluation of thinning is precluded by the different resolutions of the maps, a qualitative analysis of the nanodisc density maps supports this notion (Fig. 6). Far from the protein, membrane thickness of C14, C18 and C22 nanodiscs is comparable to that determined by AFM (Table 1). Near the open groove, in the \(\mathrm{C18 / Ca^{2 + }}\) map the membrane appears significantly thinned (Fig. 6B), closely tracking the position of individual lipids (Fig. 2A). Thinning is reduced near the open pathway of the \(\mathrm{C22 / Ca^{2 + }}\) map (Fig. 6A) and near the closed pathway of the \(\mathrm{C18 / 0 Ca^{2 + }}\) map (Fig. 6C), consistent with the reduced scrambling activity (Fig. 4A-B). In the \(\mathrm{C14 / 0 Ca^{2 + }}\) map, thickness at the closed pathway qualitatively approaches that at the open pathway of the \(\mathrm{C18 / Ca^{2 + }}\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 283]]<|/det|> +map (Fig. 6D), consistent with enhanced scrambling (Fig. 4B). These qualitative observations suggest there is a direct correlation between the thickness of the membrane near the pathway and scrambling activity. This supports the idea that in C22 membranes scrambling could be inhibited because of the reduced thinning despite an open groove, and that the closed groove conformation of afTMEM16 is scrambling competent because it thins the membrane enough to enable lipid flipping. + +<|ref|>image<|/ref|><|det|>[[115, 295, 884, 595]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 610, 883, 770]]<|/det|> +
Figure 6. Membrane thinning at the afTMEM16 pathway as a function of acyl chain length. A-D: Views of the density maps for afTMEM16 in C22/Ca \(^{2 + }\) (A), C18/Ca \(^{2 + }\) (B), C18/0 Ca \(^{2 + }\) (C) and C14/0 Ca \(^{2 + }\) (D) from the extracellular (top panels) and intracellular (bottom panel) side. The C1 final unsharpened maps containing nanodisc densities were aligned, resampled on the same grid, and colored according to the Z coordinate using UCSF Chimera. The density corresponding to the protein is segmented and shown in gray. Nanodisc densities are colored by displacement along the Z axis and the 0 Å reference height is the same for all structures in each view. Negative values represent membrane thinning relative to the overall nanodisc. The position of the permeation pathway is denoted with arrows.
+ +<|ref|>sub_title<|/ref|><|det|>[[115, 803, 205, 820]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[115, 836, 884, 891]]<|/det|> +Activation of scramblases catalyzes the rapid movement of phospholipids between membrane leaflets and results in the externalization of charged and polar lipids that trigger a variety of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 86, 886, 423]]<|/det|> +fundamental physiological processes \(^{1,2,4}\) . The current consensus is that TMEM16 scramblases mediate lipid transport via a credit- card like mechanism \(^{33}\) , with the headgroups forming specific interactions with polar and charged residues lining the full length of the hydrophilic groove \(^{20,23,25,28}\) . This predicts that scramblases should discriminate among lipids based on their headgroups but not their tails, and that mutations of groove- lining residues should affect lipid scrambling. Notably, neither scramblases like the TMEM16's \(^{13,18,21,24,26}\) or the Xkr's \(^{6,41}\) nor GPCR's moonlighting as scramblases \(^{34}\) , show selectivity among lipids with different headgroups. Further, both the Xkr's and GPCR's lack explicit hydrophilic grooves \(^{34- 36}\) , bringing the structural requisites of the credit card mechanism into question. However, the modes of lipid- protein interactions of TMEM16 scramblases had not been structurally resolved \(^{15- 18}\) . + +<|ref|>text<|/ref|><|det|>[[112, 436, 886, 842]]<|/det|> +Here, we combined structural and functional experiments to investigate the mechanism of lipid scrambling by the TMEM16's. The 2.3 Å structure of afTMEM16 reconstituted in C18 nanodiscs shows how individual lipids interact with the scramblase to define the thinned and distorted profile of the membrane near the open pathway (Fig. 1C- H). Lipids mainly localize to the periphery of the groove, interacting with the intracellular portions of TM3- 4 and with the extracellular portions of TM6- 7. The position of the last lipids from the intra- and extra- cellular leaflets suggests that headgroup flipping occurs in the intracellular vestibule. No density for lipids was visible near the extracellular vestibule (Fig. 1, 2A) and mutations of residues lining this narrow constriction or the groove interior have no functional effects (Fig. 2, 3). Reconstituting afTMEM16 in membranes formed from lipids with longer acyl chains dramatically inhibits scrambling although the groove remains open (Fig. 4). Conversely, reconstitution in thinner membranes facilitates scrambling even when the groove is closed (Fig. 4- 5). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 886, 494]]<|/det|> +Together, these results have three important implications; first, lipid scrambling does not entail specific interactions with the groove's hydrophilic interior or its extracellular vestibule. Second, acyl chains rather than headgroups are key determinants of scrambling activity (Fig. 4A- B). Third, an open groove is neither sufficient nor necessary for scrambling (Fig. 4, 5). These findings are inconsistent with a credit- card mechanism. Rather, we propose that lipid scrambling is primarily determined by the ability of afTMEM16 to thin the membrane near the pathway, so that lipids only interact with the surface of the groove without penetrating deep within its narrow and hydrophilic interior (Fig. 7). The membrane- thinning mechanism readily explains evolutionarily conserved properties of TMEM16 scramblases that are difficult to reconcile with the credit card mechanism, such as the lack of discrimination based on headgroup size, chemistry or charge \(^{13,18,21,23,26,42}\) and scrambling of lipids conjugated to large cargoes \(^{24}\) . Thus, we propose this mechanism applies to other TMEM16's. + +<|ref|>text<|/ref|><|det|>[[112, 506, 886, 878]]<|/det|> +Two lines of evidence support the credit card hypothesis: mutating groove- lining residues impairs lipid scrambling by nhTMEM16 and TMEM16F \(^{25,28}\) and MD simulations show lipid headgroups penetrating and traversing the whole length of the groove \(^{20,23,25,28,43,44}\) . Strikingly we find that mutating similar residues in afTMEM16 does not impair scrambling (Fig. 2,3). This contradiction could be explained if the mutants impair scrambling by favoring groove closure rather than by impairing lipid movement through an open groove. In afTMEM16 only the \(\mathrm{Ca^{2 + }}\) - bound open conformation has been observed \(^{15}\) (Fig. 1, 4). In contrast, \(\mathrm{Ca^{2 + }}\) - bound nhTMEM16 adopts both open and closed groove conformations \(^{17}\) and in mTMEM16F only the \(\mathrm{Ca^{2 + }}\) - bound closed groove conformation has been observed \(^{16,18}\) , suggesting in these homologues the \(\mathrm{Ca^{2 + }}\) - bound open conformation is less stable than in afTMEM16. Further, several scrambling- incompetent nhTMEM16 mutants retain measurable channel activity \(^{25}\) , suggesting stabilization + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 86, 885, 388]]<|/det|> +of an ion channel- like groove conformation \(^{19}\) . Discrepancies with molecular dynamics simulations could be due to incomplete relaxation of the membrane during the equilibration, especially if the chosen initial conditions for the protein- lipid arrangements are far from equilibrium. Indeed, recent work suggests extended equilibration protocols are needed to capture the full extent of membrane deformations induced by some proteins \(^{45}\) . Further, our structures show several lipids with tails tightly intercalated with TM helices at the dimer cavity (Fig. 1) that might affect the dynamic rearrangements of afTMEM16 in MD simulations. It will be interesting to see how incorporating new information on the modes of lipid interactions with the afTMEM16 scramblase affects these results. + +<|ref|>text<|/ref|><|det|>[[113, 402, 885, 527]]<|/det|> +The implications of our proposed membrane- thinning mechanism for scrambling (Fig. 7) can be appreciated if we make the simplifying assumptions that (i) the energy barrier for lipid scrambling is due to the polar head and (ii) that the head can be modeled with a sphere of radius \(r\) and charge \(q\) , then the energy barrier for scrambling, \(\Delta G_{scramb}\) would be given by \(^{46}\) + +<|ref|>equation<|/ref|><|det|>[[338, 541, 658, 583]]<|/det|> +\[\Delta G_{scramb} = \frac{q^{2}}{2\epsilon_{m}r} -\frac{q^{2}}{\epsilon_{m}L} ln\left(\frac{2\epsilon_{w}}{\epsilon_{m} + \epsilon_{w}}\right)\] + +<|ref|>text<|/ref|><|det|>[[112, 596, 886, 899]]<|/det|> +Where \(\epsilon_{\mathrm{m,w}}\) are the dielectric constants of the membrane and of water and L is the thickness of the membrane. Membrane thinning decreases \(\Delta G_{scramb}\) as L is reduced and the dielectric constant \(\epsilon_{\mathrm{m}}\) is increased because of higher water access to the hydrocarbon core of the membrane \(^{47}\) . When the pathway is open scrambling is fast because thinning is pronounced, and the hydrophilic interior of the open groove further decreases \(\epsilon_{\mathrm{m}}\) (Fig. 7A). In thicker or more rigid membranes (Fig. 4) \(^{15}\) , scrambling is impaired because their deformation cost is higher thus preventing lipids to reach positions conducive to scrambling (Fig. 7B). When the groove is closed membrane thinning is diminished, but not absent, which allows for slow scrambling activity (Fig. 7C), that is enhanced in membranes formed from shorter chain length lipids (Fig. 7D). Notably, the proposed membrane + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 883, 176]]<|/det|> +thinning mechanism could naturally explain how proteins lacking hydrophilic grooves, such as GPCR's and Xkr's, can scramble lipids and share functional properties with the structurally unrelated TMEM16's \(^{24,34 - 36,38,48}\) . + +<|ref|>image<|/ref|><|det|>[[113, 193, 881, 582]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 597, 883, 670]]<|/det|> +
Figure 7. TMEM16 scramblases enable scrambling by thinning the membrane. A-D: Schematic representation of the open (A-B, colored in green) and closed pathways (C-D, colored in red) in membranes of different thickness. Cyan denotes regions accessible to water. Arrows denote high (solid line), low (dashed line) or no (no line) scrambling.
+ +<|ref|>text<|/ref|><|det|>[[113, 702, 884, 898]]<|/det|> +In sum, our results support a mechanism where during scrambling, lipids interact with the surface of the groove without having to penetrate and interact with its narrow and hydrophilic interior. Scrambling by the TMEM16's is modulated by two signals, binding of \(\mathrm{Ca}^{2 + }\) facilitates opening of the groove while the properties of the membrane determine whether the scramblase can thin the membrane enough to enable lipid flipping. This dual control of scrambling activity, by ligand binding and membrane properties, could constitute a secondary layer of regulation that + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 884, 214]]<|/det|> +prevents undesired lipid flipping by the TMEM16's during fluctuations in cellular cytosolic \(\mathrm{Ca}^{2 + }\) levels or when family members that reside in intracellular membranes are transiently localized to the plasma membrane. Similarly, rigidifying or thickening bilayer constituents, such as cholesterol, could silence the scrambling activity of other scramblases such as GPCR's in cellular membranes. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 261, 108]]<|/det|> +## Data Availability + +<|ref|>text<|/ref|><|det|>[[114, 124, 884, 214]]<|/det|> +Data AvailabilityAll constructs are available on request. All models and associated cryoEM maps have been deposited into the Electron Microscopy Data Bank (EMDB) and the Protein Data Bank (PDB). The depositions include final maps, unsharpened maps, and associated FSC curves. + +<|ref|>table<|/ref|><|det|>[[115, 226, 501, 364]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[115, 364, 323, 380]]<|/det|> +Table 5: Data Availability + +
StructurePDBEMDB
C18/Ca2+ dimer7RXHEMD-24730
C18/Ca2+ monomer7RXGEMD-24731
C22/Ca2+ MSP1E37RX2EMD-24722
C22/Ca2+ MSP2N27RWJEMD-24717
C18/0Ca2+7RXBEMD-24727
C14/0Ca2+7RX3EMD-24723
D511A/E514A C14/Ca2+7RXAEMD-24726
+ +<|ref|>sub_title<|/ref|><|det|>[[115, 414, 280, 432]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[113, 447, 886, 888]]<|/det|> +AcknowledgementsThe authors thank members of the Accardi lab and Richard Hite for helpful discussions. The work was supported by National Institutes of Health (NIH) Grant R01GM106717 (to A.A.), by a Margaret and Herman Sokol Fellowship from Weill Cornell Medicine (M.E.F.), by the KBRI Basic Research Program through Korea Brain Research Institute funded by Ministry of Science and ICT (21- BR- 01- 08 to B.- C. L.) and National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2019R1C1C1002699 to B.- C. L.). Some of this work was performed at the Simons Electron Microscopy Center and National Resource for Automated Molecular Microscopy located at the New York Structural Biology Center, supported by grants from the Simons Foundation (SF349247), NYSTAR, and the NIH National Institute of General Medical Sciences (GM103310). Part of the work was performed at the Cryo- EM Core Facility at University of Massachusetts Chan Medical School with the help of Dr. Kangkang Song and Dr. Chen Xu. Initial screening was performed at NYU Langone Health's Cryo- Electron Microscopy Laboratory (RRID: SCR_019202). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 125, 296, 143]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[114, 158, 884, 250]]<|/det|> +M.F., Z.F. and A.A. designed the experiments; M.F., Z.F., O.E.A., Y.P. and B.- C.L. performed experiments; M.F., Z.F., O.E.A., Y.P., B.- C.L., X.C., E.F., S.S. and A.A. analyzed the data; M.F., S.S. and A.A. wrote the paper. All authors edited the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[115, 299, 375, 317]]<|/det|> +## Competing Interests statement + +<|ref|>text<|/ref|><|det|>[[115, 333, 460, 352]]<|/det|> +The authors declare no competing interests. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 209, 106]]<|/det|> +## References + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 90, 886, 895]]<|/det|> +19 Khelashvili, G. et al. Dynamic modulation of the lipid translocation groove generates a conductive ion channel in \(\mathrm{Ca2 + }\) - bound nhTMEM16. Nature Communications 10, 4972, doi:10.1038/s41467- 019- 12865- 4 (2019). 20 Bethel, N. P. & Grabe, M. Atomistic insight into lipid translocation by a TMEM16 scramblase. Proc Natl Acad Sci USA 113, 14049- 14054 (2016). 21 Suzuki, J. et al. Calcium- dependent phospholipid scramblase activity of TMEM16 protein family members. J Biol Chem 288, 13305- 13316, doi:10.1074/jbc.M113.457937 (2013). 22 Segawa, K., Suzuki, J. & Nagata, S. Constitutive exposure of phosphatidylserine on viable cells. PNAS 108, 19246- 19251 (2011). 23 Bushell, S. R. et al. The structural basis of lipid scrambling and inactivation in the endoplasmic reticulum scramblase TMEM16K. Nature Communications 10, 3956, doi:10.1038/s41467- 019- 11753- 1 (2019). 24 Malvezzi, M. et al. Out- of- the- groove transport of lipids by TMEM16 and GPCR scramblases. Proc Natl Acad Sci U S A 115, E7033- E7042, doi:10.1073/pnas.1806721115 (2018). 25 Lee, B.- C. et al. Gating mechanism of the extracellular entry to the lipid pathway in a TMEM16 scramblase. Nature Communications 9, 3251, doi:10.1038/s41467- 018- 05724- 1 (2018). 26 Malvezzi, M. et al. \(\mathrm{Ca2 + }\) - dependent phospholipid scrambling by a reconstituted TMEM16 ion channel. Nature Communications 4, 2367, doi:10.1038/ncomms3367 https://www.nature.com/articles/ncomms3367#supplementary- information (2013). 27 Brunner, J. D., Schenck, S. & Dutzler, R. Structural basis for phospholipid scrambling in the TMEM16 family. Curr Opin Struct Biol 39, 61- 70, doi:10.1016/j.sbi.2016.05.020 (2016). 28 Jiang, T., Yu, K., Hartzell, H. C. & Tajkhorshid, E. Lipids and ions traverse the membrane by the same physical pathway in the nhTMEM16 scramblase. Elife 6, doi:10.7554/eLife.28671 (2017). 29 Griffin, D. A. et al. Defective membrane fusion and repair in Anoctamin5- deficient muscular dystrophy. Hum Mol Genet 25, 1900- 1911, doi:10.1093/hmg/ddw063 (2016). 30 Yu, K. et al. Identification of a lipid scrambling domain in ANO6/TMEM16F. eLife 4, 1- 23, doi:10.7554/eLife.06901 (2015). 31 Peters, C. J. et al. The Sixth Transmembrane Segment Is a Major Gating Component of the TMEM16A Calcium- Activated Chloride Channel. Neuron 97, 1063- 1077. e1064, doi:https://doi.org/10.1016/j.neuron.2018.01.048 (2018). 32 Le, T. et al. An inner activation gate controls TMEM16F phospholipid scrambling. Nature Communications 10, 1846, doi:10.1038/s41467- 019- 09778- 7 (2019). 33 Pomorski, T. & Menon, A. K. Lipid flippases and their biological functions. Cell Mol Life Sci 63, 2908- 2921 (2006). 34 Menon, I. et al. Opsin is a phospholipid flippase. Current Biology 21, 149- 153, doi:10.1016/j.cub.2010.12.031 (2011). 35 Straub, M. S., Alvadia, C., Sawicka, M. & Dutzler, R. Cryo- EM structures of the caspase- activated protein XKR9 involved in apoptotic lipid scrambling. eLife 10, e69800, doi:10.7554/eLife.69800 (2021). 36 Sakuragi, T. et al. An intramolecular scrambling path controlled by a gatekeeper in Xkr8 phospholipid scramblase. bioRxiv, 2021.2005.2006.442885, doi:10.1101/2021.05.06.442885 (2021). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 886, 669]]<|/det|> +37 Khelashvili, G. et al. Membrane lipids are both the substrates and a mechanistically responsive environment of TMEM16 scramblase proteins. Journal of computational chemistry 41, 538- 551, doi:10.1002/jcc.26105 (2020).38 Goren, M. A. et al. Constitutive phospholipid scramblase activity of a G protein- coupled receptor. Nat Commun 5, 5115, doi:10.1038/ncomms6115 (2014).39 Yu, K., Duran, C., Qu, Z., Cui, Y.- Y. & Hartzell, H. C. Explaining Calcium- Dependent Gating of Anoctamin- 1 Chloride Channels Requires a Revised Topology. Circulation Research 110, 990- 999, doi:10.1161/CIRCRESAHA.112.264440 (2012).40 Tien, J. et al. A comprehensive search for calcium binding sites critical for TMEM16A calcium- activated chloride channel activity. Elife, e02772, doi:10.7554/eLife.02772 (2014).41 Suzuki, J., Imanishi, E. & Nagata, S. Xkr8 phospholipid scrambling complex in apoptotic phosphatidylserine exposure. Proc Natl Acad Sci USA 113, 9509- 9514 (2016).42 Watanabe, R., Sakuragi, T., Noji, H. & Nagata, S. Single- molecule analysis of phospholipid scrambling by TMEM16F. Proceedings of the National Academy of Sciences 115, 3066- 3071, doi:10.1073/pnas.1717956115 (2018).43 Stansfeld, P. J. et al. MemProtMD: automated insertion of membrane protein structures into explicit lipid membranes. Structure 23, 1350- 1361, doi:10.1016/j.str.2015.05.006 (2015).44 Kostritskii, A. Y. & Machtens, J.- P. Molecular mechanisms of ion conduction and ion selectivity in TMEM16 lipid scramblases. Nature Communications 12, 2826, doi:10.1038/s41467- 021- 22724- w (2021).45 Stix, R., Song, J., Banerjee, A. & Faraldo- Gómez, J. D. DHHC20 Palmitoyl- Transferase Reshapes the Membrane to Foster Catalysis. Biophys J 118, 980- 988, doi:10.1016/j.bpj.2019.11.003 (2020).46 Parsegian, A. Energy of an ion crossing a low dielectric membrane: solutions to four relevant electrostatic problems. Nature 221, 844- 846, doi:10.1038/221844a0 (1969).47 Bennett, W. F. D. & Tieleman, D. P. The Importance of Membrane Defects—Lessons from Simulations. Accounts of Chemical Research 47, 2244- 2251, doi:10.1021/ar4002729 (2014).48 Wang, L. et al. Scrambling of natural and fluorescently tagged phosphatidylinositol by reconstituted G protein- coupled receptor and TMEM16 scramblases. Journal of Biological Chemistry 293, 18318- 18327, doi:10.1074/jbc.RA118.004213 (2018). + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[114, 88, 699, 375]]<|/det|> + +
NameLipid Component Chain Length (70% PC:30%PG)Height from AFM (nm)Height estimated from EM maps (nm)
C1450% 14:0, 50% 16:0-18:13.2 ± 0.293.0 ± 1.5
C16100% 16:13.2 ± 0.22N.d.
C18100% 18:13.4 ± 0.19(0 Ca2+) 3.1 ± 3.5
(0.5 mM Ca2+) 3.3 ± 0.95
C20100% 20:13.7 ± 0.22N.d.
70% C2270% 22:1, 30% 18:14.0 ± 0.26N.d.
70% C2270% 22:1 PC, 30% 18:1 PG4.0 ± 0.19N.d.
C22100% 22:14.1 ± 0.193.9 ± 0.85
+ +<|ref|>table_caption<|/ref|><|det|>[[114, 376, 883, 480]]<|/det|> +Table 1: AFM and cryoEM determination of membrane thickness for considered lipid compositions. Heights were estimated using AFM tomography, reported values indicate the peak FWHH ± of the value distribution (see Supp. Fig. 4A-B and Methods). For cryoEM membrane height was determined from C1 unsharpened EM maps using the difference in z coordinate for the inner and outer leaflet at (x,y) far from the protein. Reported values are the average ± S.Dev of 3 different points. N.d. indicates compositions for which no cryoEM data was determined. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 311, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 375, 257]]<|/det|> +- FalzoneetalSI.pdf- FalzoneetalMethods.pdf- Falzonenrreportingsummary.pdf- nreditorialpolicychecklist.pdf- FalzoneetalValidationReports.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__05b5b4aca808df2aaf8b5007ff8ad0b8986241d7c3a5ae8a42254ed176560413/images_list.json b/preprint/preprint__05b5b4aca808df2aaf8b5007ff8ad0b8986241d7c3a5ae8a42254ed176560413/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/preprint/preprint__05b5b4aca808df2aaf8b5007ff8ad0b8986241d7c3a5ae8a42254ed176560413/images_list.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/preprint/preprint__05b5b4aca808df2aaf8b5007ff8ad0b8986241d7c3a5ae8a42254ed176560413/preprint__05b5b4aca808df2aaf8b5007ff8ad0b8986241d7c3a5ae8a42254ed176560413.mmd b/preprint/preprint__05b5b4aca808df2aaf8b5007ff8ad0b8986241d7c3a5ae8a42254ed176560413/preprint__05b5b4aca808df2aaf8b5007ff8ad0b8986241d7c3a5ae8a42254ed176560413.mmd new file mode 100644 index 0000000000000000000000000000000000000000..ca9a90f3f5c985f932e24e7684ca71613ba2630d --- /dev/null +++ b/preprint/preprint__05b5b4aca808df2aaf8b5007ff8ad0b8986241d7c3a5ae8a42254ed176560413/preprint__05b5b4aca808df2aaf8b5007ff8ad0b8986241d7c3a5ae8a42254ed176560413.mmd @@ -0,0 +1,307 @@ + +# LARP3, LARP7, and MePCE are Involved in the Early Stage of Human Telomerase RNA Biogenesis + +Chi- Kang Tseng ( ckt0513@ntu.edu.tw) Department of Microbiology, College of Medicine, National Taiwan University Tsai- Ling Kao ( tsailing0905@gmail.com) College of Medicine, National Taiwan University, Yi- Hsuan Chen ( yihsuan1chen@gmail.com) College of Medicine, National Taiwan University, https://orcid.org/0000- 0002- 1029- 7632 Yu- Cheng Huang ( lilyhuang901102@gmail.com) College of Medicine, National Taiwan University, Peter Baumann ( peter@baumannlab.org) Gutenberg University https://orcid.org/0000- 0003- 4892- 1485 + +## Article + +# Keywords: + +DOI: https://doi.org/ + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> + +# LARP3, LARP7, and MePCE are Involved in the Early Stage of Human Telomerase RNA Biogenesis + +Tsai- Ling Kao1, Yi- Hsuan Chen1, Yu- Cheng Huang1, Peter Baumann2,3, and Chi- Kang Tseng1\* + +1Department of Microbiology, College of Medicine, National Taiwan University, Taipei 100, Taiwan + +2Institute of Developmental Biology and Neurobiology, Johannes Gutenberg University, 55099 Mainz, Germany + +3Institute of Molecular Biology, 55128 Mainz, Germany + +\* Corresponding authors + +<--- Page Split ---> + +## Abstract + +Human telomerase assembly is a highly dynamic process. Using biochemical approaches, we found that LARP3 and LARP7/MePCE are involved in the early stage and that their binding to hTR is destabilized when the mature hTR is produced. LARP7 and MePCE knockdown inhibits the conversion of the 3'- extended short (exS) form into mature hTR and the cytoplasmic accumulation of hTR, resulting in telomere shortening. LARP3 plays a negative role in preventing the processing of the 3'- extended long (exL) form and binding of LARP7 and MePCE. Interestingly, the tertiary structure of the exL form prevents LARP3 binding and facilitates hTR biogenesis. Supporting this process, LARP3 at low levels promotes hTR maturation, increases telomerase activity, and elongates telomeres. Our data suggest that LARP3 and LARP7/MePCE mediate the processing of hTR precursors and thus control the production of functional telomerase. + +<--- Page Split ---> + +Telomerase is a ribonucleoprotein complex that contains two highly conserved components in its catalytic core. In humans, one core component is a noncoding RNA called human telomerase RNA (hTR); the other is a protein enzyme called human telomerase reverse transcriptase (hTERT). hTERT copies the template region within hTR to replenish telomeric DNA sequences. Thus, the ends of chromosomes are protected, and the lengths of telomeres are maintained \(^{1}\) . hTR is transcribed by RNA polymerase II and accumulates in a cell as a 451- nt- long RNA \(^{2}\) . Longer forms of this transcript have been reported \(^{3,4}\) . Accumulating evidence suggests that the longer transcripts are predominantly degraded by RNA exosomes in a process mediated by CBC and NEXT \(^{5}\) . However, a fraction of the long transcripts may be processed into the mature form \(^{5}\) . During hTR maturation, 3'- end processing is mediated in concert with multiple 3'- to- 5' exonucleases \(^{3,6,7}\) . A 3'- extended long form (exL, \(\geq 460\) - nt hTR) of hTR is first trimmed by the exosomal component RRP6 to produce the 3'- extended short form (exS, \(\geq 452\) - nt and \(\leq 460\) - nt hTR) \(^{3}\) . The exS of hTR is then processed by two other exonucleases (PARN and TOE1) that function in parallel and/or sequentially to produce mature 451- nt hTR \(^{5 - 8}\) . Given that PARN is detected mainly in the nucleolus and that TOE1 is located in Cajal bodies \(^{7}\) , the formation of mature hTR has been suggested to couple to 3'- end processing with RNA trafficking. + +Human telomerase assembly proceeds via precise stepwise binding of protein components to hTR during 3'- end maturation \(^{3,5,9}\) . The structure of hTR is highly organized and plays a role in mediating its stability, trafficking, and maturation \(^{10}\) . The 3'- domain of hTR folds into a box H/ACA- like domain \(^{11}\) , which is bound by the box H/ACA complex \(^{12}\) . The assembly of the pre- H/ACA complex on hTR via cotranscription is thought to be critical for protecting longer transcripts from rapid degradation \(^{5,9}\) . A biochemical study showed that the exL form of hTR folds into a triple- helix structure \(^{3}\) . Although how the triple helix conformation transiently protects the + +<--- Page Split ---> + +exL form of hTR from rapid degradation remains unclear, it creates an opportunity for the H/ACA complex to bind3. The binding of the H/ACA complex is not only critical for hTR stability but also attenuates processing by PARN at position \(451^{3}\) . Mutations in most well- characterized components of human telomerase and telomeres, as well as their accessory factors, have been reported in premature- ageing diseases, such as dyskeratosis congenita, Hoyeral- Hreidarsson syndrome, aplastic anaemia, and idiopathic pulmonary fibrosis13- 16. + +At the molecular level, patients with these telomere biology disorders (TBDs) have characteristic accelerated telomere shortening. In addition to canonical TBDs, mutations in several other genes are associated with dysregulation of telomere maintenance in human diseases17. One of these dysregulated gene products is in the La- related protein (LARP) family, which is an important RNA- binding protein family that emerged early in eukaryote evolution and engaging in a large range of crucial functions in a cell involving both coding and noncoding RNAs18,19. Seven distinct LARP- encoding genes have been identified in humans. Among human LARPs, LARP3 (a genuine La protein) and LARP7 have previously been implicated in human telomere maintenance20,21. Aberrantly expression of LARP3 has been found in various cancer types, including chronic myelogenous leukaemia (CML)22. LARP3 has been shown to interact directly with hTR and cause telomere shortening when exogenous LARP3 is overexpressed in certain cell lines20. Whether LARP3 is involved in hTR biogenesis remains unclear. + +A total of 52 pathogenic variants in the LARP7 gene have been identified in patients with Alazami syndrome23. Patients with Alazami syndrome exhibit very short telomeres, and LARP7 knockdown in cancer cells causes a reduction in telomerase activity and telomere shortening21. In ciliated protozoa and fission yeast, LARP family protein p65 in Tetrahymena thermophila24, p43 in Euplotes aediculatus25, and Pof8 in S. pombe26- 28 are constitutive components of active + +<--- Page Split ---> + +telomerase. In addition to LARP7, MePCE, a LARP7- interacting protein, has been implicated in neurodevelopment29. The heterozygous MePCE nonsense variant c.1552C>T/p. (Arg518\*) has been identified29. Patients with MePCE mutations exhibit a neurodevelopmental disorder phenotype similar to that of patients with loss- of- function mutations in LARP7. Bmc1, a human MePCE homologue, is a fission yeast telomerase holoenzyme30,31. Bmc1 cooperates with Pof8 to recognize correctly folded TER130. Whether human LARP7 and MePCE are involved in hTR and biological functions in a manner analogous to that in other species remains unclear. + +In this study, we established in vitro systems that allowed us to monitor hTR 3'- end maturation and protein component assembly. Telomerase assembled in vitro was functional. Using these systems, we found that LARP3, LARP7, and MePCE participate in the early stage of human telomerase biogenesis. LARP3 binds to the exL form of hTR before H/ACA complex binding and prevents 3'- end processing. The triple- helix structure of the exL form of hTR prevents binding by LARP3 and facilitates 3'- end processing. Supporting these observations, LARP3 knockdown facilitated the maturation of hTR and caused increases in telomerase activity. Consistent with the expression levels of LARP3 increasing during CML progression32 and CML patients normally exhibiting short telomeres33, our data showed that reducing LARP3 expression caused an increase in telomerase activity and telomere elongation in K562 cells. LARP7 and MePCE binding then increases as LARP3 decreases during the conversion of the exS form into the mature form. LARP7 and MePCE knockdown caused telomere shortening by affecting both hTR maturation and localization. Our data suggest that human telomerase assembly is a highly dynamic process that involves compositional and conformational rearrangement, which leads to the production of a functional telomerase. + +<--- Page Split ---> + +## Results + +## The establishment of in vitro systems to examine the biogenesis of human telomerase + +Establishing research to study the fates of human hTR precursors is challenging due to the extremely low abundance of endogenous pre- telomerase complexes in a cell. To overcome these limitations and to dissect the molecular mechanisms involved in hTR maturation, in vitro systems of human telomerase biogenesis are established. The in vitro systems we established to study hTR can be classified into 3 major parts: the examination of \(3^{\prime}\) - end maturation of hTR, telomerase component assembly, and telomerase activity (Supplementary Fig. 1a). To analyse the \(3^{\prime}\) - end processing of hTR, we synthesized the H/ACA domain of hTR (starting with nucleotide 206) of the exL form in vitro with oligo A tails in the presence of \(\alpha\) - \(^{32}\mathrm{P}\) - UTP. The oligoadenylated exL form of hTR was incubated in whole- 293T cell extract. During the incubation period, deadenylation neared completion within 10 min (Fig. 1a, lane 2). The exS and mature forms of hTR were produced after a 30\~60- min and 2- hour incubation, respectively (Fig. 1a, lanes 4 and 5). Consistent with previous observations indicating that more than \(80\%\) of the exL form is degraded in vivo \(^5\) , only \(20\%\) of the exL form was converted into the mature form of hTR in our assay (Fig. 1b and Supplementary Fig. 1b). These data indicate that the \(3^{\prime}\) - end processing of hTR was successful in vitro. + +To examine the assembly of telomerase components on hTR, telomerase complexes assembled on the biotinylated exL form of hTR (nucleotides from 1 to 461 with an oligo A tail) with a monoguanosine cap (MMG) were purified at different time points by pulling RNP's down with streptavidin beads (Supplementary Fig. 1a). The purified telomerase complexes were subjected to western blotting (Fig. 1c). DHX36, which has been shown to interact with the \(5^{\prime}\) - + +<--- Page Split ---> + +guanosine tracts of hTR \(^{34}\) , was pulled down. The binding of DHX36 to hTR exerted a minor effect on the maturation of the hTR 3' end (Fig. 1c, lanes 8- 12). All H/ACA complex components (DKC1, NHP2, NOP10, NAF1, and GAR1) were detected. This finding supports in vivo observations \(^{9,35}\) suggesting that NAF1 in the pre- H/ACA complex (NAF1- DKC1- NOP10- NHP2) binds to hTR and is subsequently replaced with GAR1 to produce a functional H/ACA complex. NAF1 was associated mainly with the exL form of hTR in the early maturation stage (Fig. 1c, lanes 9 and 10). In contrast, GAR1 was associated with hTR in the late stage, while NAF1 disassociated from the exL form of hTR (lanes 10- 12). TCAB1 appeared to associate with hTR after NAF1 disassociated along with GAR1 binding (lanes 10- 12). + +To measure the catalytic effect of the in vitro assembled telomerase with either the mature or exL forms of hTR, a direct primer extension assay was performed to measure telomerase activity (Fig. 1d). The telomerase assembled with the exL and mature forms showed enzymatic activity (Fig. 1d). However, the enzymatic activity of the purified telomerase with mature hTR (lanes 5- 8) was higher than that of the exL- containing telomerase (lanes 1- 4). We further examined the processivity of the purified telomerase and plotted the normalized intensity of each telomere- extended species against the repeat number (Fig. 1e). Quantification analysis showed that the telomerase with the mature hTR showed higher processivity than that with the exL form (Fig. 1e). These data suggest that telomerase assembly undergoes compositional rearrangement during 3'- end maturation and that the removal of 3'- extended sequences from hTR may increase telomerase activity and processivity. + +LARP3, LARP7, and MePCE are involved in the early stage of telomerase assemblyBiochemical and structural studies of telomerases from ciliated protozoa and fission yeast revealed + +<--- Page Split ---> + +that an La- related protein and its interacting partners are the constitutive components of a telomerase holoenzyme and are critical for the assembly and activity of this telomerase26,30,36. Therefore, we examined the associations of all human La- related proteins with hTR. LARP3, LARP7 and MePCE were found to be significantly associated with hTR (Fig. 2a). A time course analysis revealed that LARP3 associated with the exL form of hTR before the binding of LARP7, MePCE, and the pre- H/ACA complex (Fig. 2a, lane 8). LARP7 and MePCE appeared to bind to hTR after LARP3 association and concurrently with binding of the pre- H/ACA complex (Fig. 2a, lane 9). All component binding in the telomerase complexes was destabilized when the mature hTR forms were produced (Fig. 2a, lanes 11- 12). To examine how the 3'- extended sequence affects component binding. The different forms of hTR species were individually generated, including the exL, exS, and mature forms, and the pseudoknot plus the 5' stem loop (3' ST del) and the pseudoknot domain of hTR (Fig. 2b). Western blotting of telomerase complexes assembled with the different hTR species revealed that DHX36 bound to all the forms of hTR. Consistent with previous studies showing that the 3' stem loop is critical for H/ACA complex assembly37, deletion of the 3' stem loop abolished the binding of DKC1 (Fig. 2b, lane 11). LARP7 and MePCE preferentially associated with the 3'- extended forms (exL and exS forms) of hTR (Fig. 2b, lanes 8- 10). The extension of the 451- nt hTR- end by 5 nucleotides (nucleotides 1- 455 in the exS form) and 10 nucleotides (nucleotides 1- 461 in the exL form) stabilized the binding of LARP7/MePCE and LARP3, respectively (Fig. 2b, lanes 8 and 9), suggesting that a 3'- extended sequence is required for the stable binding of LARP3, LARP7, and MePCE to hTR. Taken together, these data suggest that LARP3, LARP7, and MePCE are involved in the early stage of telomerase assembly and disassociate from each other when the functional telomerase is produced. Supporting these observations, purified LARP3-, LARP7-, and MePCE- associated endogenous telomerases were + +<--- Page Split ---> + +produced and showed only low levels of telomerase activity (Fig. 2c). + +## The LARP3 binding competes with tertiary structure formation + +LARP3 preferentially associated with the exL form of hTR (Fig. 2b). The exL form of hTR is a highly organized structure that contains two stem- loop conformations and a 3'- terminal UUU stretch. The 3'- terminal UUU stretch in the exL form has been proposed to form a triple helix structure in concert with box H and the UCU sequence between the P4.2 and P5 stems \(^3\) (Fig. 3a). In addition, the 3'- terminal U stretch, which is commonly in the 3'- end of RNA polymerase III transcripts, is the binding target of LARP3 \(^{38}\) . This prompted us to speculate that the preferential binding of LARP3 to the exL form is due to either the 3'- terminal UUU stretch or the structure of the triple helix (Fig. 3a). First, we investigated the effect of the 3'- terminal UUU sequence on LARP3 binding. First, we generated U460C mutant hTR. The U460C mutant lacked the 3'- terminal UUU stretch but showed a strengthened triple- base interaction \(^3\) . If the exL form is normally bound by LARP3 via the triple helix structure, then the U460C mutant would recruit more LARP3 than if LARP3 binds the UUU stretch. In contrast, if the 3'- terminal UUU stretch of exL is the binding site of LARP3, then the U460C mutant will destabilize the association of LARP3 with the exL form of hTR. The results showed that the U460C hTR mutant pulled down less recombinant LARP3 than wild- type hTR, suggesting that LARP3 binding to hTR relied on the terminal UUU stretch in the exL form of hTR, while LARP3 binding to the exL form appeared to be attenuated by the triple helix structure (Fig. 3b). + +The triple helix structure of the exL form plays a role in transiently protecting it from rapid degradation and creates an opportunity for the exL form to enter the biogenesis process that yields the mature form \(^3\) . Consistent with previous observations \(^3\) , the U460C hTR mutant, which + +<--- Page Split ---> + +stabilized the tertiary structure in our study, affected the dynamics of 3'- end processing (Fig. 3c and Supplementary Fig. 2a). Incubation of the U460C mutant for 60 min led to the production of large quantities of the mature form (lane 9) compared to the amount of mature form obtained from wild- type hTR (lane 4). Analysis of in vitro telomerase assembly with the U460C mutant showed a decrease in the binding of LARP3 compared to its binding on wild- type hTR (Fig. 3d). Taken together, these data suggest that LARP3 binding to the exL form of hTR competes with the formation of tertiary structures in the exL form, determining the efficiency of mature hTR production. + +## LARP3 plays a negative role in telomerase biogenesis + +Our results suggest a role for tertiary RNA interactions in the exL form of hTR which promotes 3'- end processing via a mechanism that leads to competition between 3'- end processing and LARP3 binding (Fig. 3). We wondered whether LARP3 plays a negative role in hTR biogenesis. To examine the role of LARP3 in the processing of the exL form, LARP3 was either knocked down or overexpressed in 293T cells (Fig. 4a). There was a minor effect on the steady- state levels of hTR in LARP3- knockdown cells (Fig. 4b). LARP3 knockdown, however, did not substantially affect the assembly of DKC1 with hTR in vitro (Fig. 4c, lanes 1- 12). However, the telomerase activity was increased by 20% when the telomerase was purified by immunoprecipitation based on DKC1 pulldown (Fig. 4d, lane 4). Subjecting LARP3 knockdown- containing extracts to in vitro 3' processing facilitated the biogenesis of hTR (Fig. 4e, lanes 7- 12 and Supplementary Fig. 3a) in contrast to the outcome with control extracts (Fig. 4e, lanes 1- 6). These observations mimicked the effects of the U460C mutant (Fig. 3) and suggested that LARP3 plays a role in preventing 3'- end maturation. + +<--- Page Split ---> + +To confirm the negative role of LARP3 in hTR 3'- end processing, we overexpressed LARP3 in 293T cells (Fig. 4a, lanes 3\~4). LARP3 overexpression caused a 1.2-fold and 3.1-fold increase in all forms and the 3'- exL form of hTR, respectively (Fig. 4b). In addition, a 40% decrease in telomerase activity was observed (Fig. 4d, lanes 5\~8). An in vitro telomerase assembly assay revealed that although it exerted minor effects on DKC1 binding, LARP3 overexpression markedly blocked the binding of LARP7 and MePCE to hTR (Fig. 4c, lanes 20\~24). Supporting the observations that LARP3 overexpression caused an increase in the fraction of the exL form, the 3'- end processing of exL was profoundly blocked (Fig. 4f and Supplementary Fig. 3b). These data indicated that the binding of LARP3 to hTR prevented the 3'- end processing of the exL form and may lead to exL targeted for degradation. In addition, the binding of LARP7/MePCE to hTR was blocked by LARP3 binding, suggesting that a switch from LARP3 binding to LARP7/MePCE binding is required for telomerase biogenesis. + +## Reducing the expression level of LARP3 increases telomerase function and causes telomere elongation + +Aberrant expression of LARP3 has been found in various cancers, including CML22. CML patients normally exhibit short telomeres33. Since LARP3 overexpression blocked the 3'- end processing of the exL form hTR (Fig. 4f), we speculated that reducing LARP3 expression levels would rescue the defects caused by LARP3 and may lead to increased telomere length. To evaluate this possibility, LARP3 was depleted in K562 cells (Fig. 5a and Supplementary Fig. 4a). The levels of both the mature and 3'- extended forms of hTR were increased in LARP3- knockdown cells (Fig. 5b). Consistent with this observation, an in vitro 3'- end processing assay indicated that relatively more of the exL from was converted into mature hTR (Fig. 5c and Supplementary Fig. 4b). In + +<--- Page Split ---> + +addition, more telomerase was formed in vitro, as determined by more DKC1 molecules binding to hTR (Fig. 5d, lanes 8\~10). Supporting these findings, increased levels of telomerase activity were observed (Fig. 5e and Supplementary Fig. 4c). We measured telomere length by a telomere restriction fragment (TRF) assay (Fig. 5f). Telomeres were elongated in LARP3- knockdown cells (Fig. 5f, lanes 4\~6). + +In aggregate, our data suggest that LARP3 plays a negative role in controlling telomere length by affecting telomerase biogenesis. LARP3 binds to the exL form of hTR, which prevents not only the binding of LARP7 and MePCE but also the processing of the exL form. As a result, telomerase assembly was impaired, which led to telomere shortening. + +## LARP7 and MePCE knockdown impairs the processing of the exS form and causes cytoplasmic localization of hTR + +LARP7 and MePCE were associated mainly with the exS form (Fig. 2). How LARP7 and MePCE affect exS processing or degradation remains unclear. To evaluate the requirements of LARP7 and MePCE for telomerase biogenesis, we prepared extracts from cells that had been subjected to LARP7 or MePCE knockdown (Fig. 6a and Supplementary Fig. 5a). LARP7 and MePCE knockdown did not affect the steady- state levels of hTR (Fig. 6b). An in vitro telomerase assembly assay showed that MePCE bound the exS form of hTR in the absence of LARP7 (Fig. 6c, lanes 5\~8) and vice versa. In the absence of LARP7 or MePCE, LARP3 bound to hTR relatively longer than it did in the shRNA- treated control cell extracts (Fig. 6c). We investigated the effect of LARP7 and MePCE knockdown on the processing of the exS form. Although exS was processed into mature hTR in the extracts from cells with LARP7 and MePCE knocked down (Fig. 6d, lanes 5, 10, and 15 and Supplementary Fig. 5b), the conversion rate of the exS form into the mature form + +<--- Page Split ---> + +was reduced in extracts from cells with both LARP7 and MePCE knocked down (Fig. 6d, lanes 9 and 14) compared to that in the control extracts (Fig. 6d, lane 4). + +LARP7 and MePCE knockdown mimicked the effect of inhibited PARN activity on the exS form processing3, which substantially impaired the conversion of exS into the mature form in vitro (Fig. 6d). Cytoplasmic localization of hTR has been previously observed in PARN- knockdown cells39. Induced pluripotent stem (iPS) cells from patients with PARN mutations produced short telomeres8. Supporting these observations, PARN- knockdown cells produced shorter telomeres (Fig. 6e, lanes 7 and 8), and 46% of hTR localized to the cytoplasm in PARN- knockdown cells, which was higher than that in control cells (32%) (Fig. 6f and g). We measured telomere length in LARP7- and MePCE- knockdown cells (Fig. 6e and Supplementary Fig. 5d). Telomeres in the LARP7- and MePCE- knockdown cells (Fig. 6e, lanes 3- 6) were shorter than those in the control cells (Fig. 6e, lanes 1- 2). In addition, the fraction of cytoplasmic hTR was increased (Fig. 6f and g). Taken together, these results indicated that LARP7 and MePCE were involved in the conversion of the exS form into the mature form. Defects in LARP7 and MePCE proteins caused the accumulation of hTR in the cytoplasm and telomere shortening. + +## Discussion + +Human telomerase biogenesis is a highly dynamic process that is initiated by the precise stepwise binding of protein components to the RNA subunit hTR/TERC. Each step serves as a checkpoint for quality control and plays a decisive role in the production of a mature telomerase versus the elimination of improper products3,5. Deficiency in telomerase components, including proteins and RNA, leads to degenerative human disease. Therefore, understanding telomerase biogenesis is critical for determining its medical relevance and elucidates the cause of telomere disorder + +<--- Page Split ---> + +syndrome. Unfortunately, the low abundance of endogenous telomerase pre- complexes in cells makes it difficult to characterize the molecular mechanisms involved in vivo. To overcome these limitations, we established in vitro cell- free systems that allowed us to investigate telomerase assembly and 3'- end processing of hTR. Using these systems, we uncovered LARP3, LARP7, and MePCE as previously unknown players that are sequentially involved in the early stage of hTR biogenesis (Fig. 7). + +LARP3 has been shown to bind with high affinity to 3' uridylate residues of RNA polymerase III transcripts immediately upon transcription termination; these precursors include 5S rRNA \(^{40}\) , tRNA \(^{40}\) and 7SK RNA \(^{41,42}\) . Similarly, our data indicated that LARP3 preferentially bound to the hTR precursor form exL prior to the binding of LARP7, MePCE, and the H/ACA complex (Fig. 2). The association of LARP3 with the exL form was stabilized by a terminal U stretch (Fig. 3). LARP3 knockdown facilitated the maturation of hTR and telomerase activity (Fig. 4). In contrast, LARP3 overexpression clearly prevented both the processing and degradation of the exL form in vitro and caused a reduction in telomerase activity (Fig. 4). Our data suggest that LARP3 negatively regulates telomerase activity at the level of telomerase biogenesis. Interestingly, the terminal U stretch is also essential for the tertiary structure conformations in the exL form, protecting it from rapid degradation and creating an opportunity for hTR maturation \(^{3}\) . The biogenesis pathway was highly activated when the tertiary structure of the exL form is stabilized after the introduction of a U460C mutation that attenuated LARP3 binding (Fig. 3). These data suggest that the amount of mature hTR is determined by kinetic competition between LARP3 binding to the exL form and the formation of the tertiary exL structure (Fig. 7). Excessive LARP3 blocked the maturation of hTR, and the disassociation of LARP3 was essential for the assembly of functional RNPs with hTR. LARP3 acts as an RNA chaperone to prevent pre- tRNA + +<--- Page Split ---> + +misfolding43,44. However, whether LARP3 specifically recognizes the exL form hTR that fails to fold into the triple helix and targets this faulty hTR for degradation is unclear. + +The average telomere length is normally shorter in CML patients than in healthy individuals33. Additionally, CML patients with long telomeres have been suggested to have a lower clinical risk profile than patients with short telomeres45. The study of the correlation between telomere length and CML progression suggests that patients in later phases (the accelerated phase and blast phase) present with considerably shorter telomeres than patients in the early phase (chronic phase)33. Notably, LARP3 expression correlates with poor clinical prognosis of CML and increases during CML progression32. Our studies established a link between LARP3 expression and human telomerase biogenesis. We showed that reducing the expression level of LARP3 in a CML cell line increased telomere length by promoting telomerase biogenesis and activity (Fig. 5). Together with the observations that the tertiary structure of the exL form attenuates LARP3 binding and facilitates telomerase biogenesis, our data suggest a novel drug intervention point. The development of a small molecule that either directly inhibits LARP3 binding to hTR or specifically targets hTR structure to prevent LARP3 binding may be an important strategy to increase telomerase function and improve the prognosis of CML. + +Previous study suggested that RRP6 processes the terminal U tract and irreversibly disrupts the triple helix and generates the exS form and subsequently dyskerin precisely establishes the 451- nt end by attenuating the 3'- end processing of the exS form via PARN, suggesting that structural rearrangements of hTR is required for efficient maturation3. Our data indicated that the compositional exchange of protein components occurs in this process (Fig. 7). NAF1 is replaced with GAR1. LARP3 appears to disassociate from the exL form of hTR. The mutually exclusive interaction of 7SK RNP with LARP3 or LARP7 has been suggested, and LARP3 needs to be + +<--- Page Split ---> + +replaced by LARP7 for the maturation of 7SK RNPs41. This may be the case with hTR. The binding of LARP7 and MePCE to hTR was attenuated by LARP3 overexpression (Fig. 4c). Notably, LARP3 maintained the association with the exS form longer in the absence of LARP7 and MePCE (Fig. 6c), suggesting that LARP7 and MePCE bind instead of LARP3. We found that LARP7 and MePCE knockdown impaired the PARN-mediated processing of the exS form into the mature form and, like PARN knockdown cells, caused cytoplasmic accumulation of hTR. Together with these observations, these data support a model in which LARP7 and MePCE promote the transition of a LARP3- associated pre- telomerase to a H/ACA complex- associated telomerase that promotes the conversion of the exS form into the mature form. Once this conversion is impaired, hTR would be exported to the cytoplasm and degraded by DCP2- XRN1. Loss of function in LARP7 and MePCE has been shown to cause Alazami syndrome21 and neurodevelopmental disorders29, respectively. Reduced expression of LARP7 has been shown to cause a reduction in telomerase activity and result in progressively shorter telomeres in human cancer cell lines21. Previous works with S. pombe demonstrated that Pof8 plays a key role in S. pombe telomerase RNA folding quality control and forms a complex with Bmc1, the orthologue of MePCE, and Telomerase Holoenzyme Component 1 (Thc1), which promotes the assembly of a functional telomerase26- 28,30,31,46. Similar to that associated with S. pombe Bmc1, telomere shortening has been observed in MePCE- deficient human cells. Interestingly, Thc1 shares structural similarity with the nuclear cap- binding complex and PARN30. LARP7 and MePCE knockdown impaired the conversion of the exS form into the mature form. LARP7 bound hTR in a MePCE- independent manner and was required to stabilize the interaction of MePCE with hTR (Fig. 6c). Although LARP7 and MePCE contribute to the conversion of the exS form into the mature form, they do not remain stably associated with the active telomerase (Fig. 7). Compared + +<--- Page Split ---> + +to S. pombe telomerase RNA, an additional 3'- end processing step is required for the 3' end maturation of telomerase RNA after spliceosomal cleavage in other species47,48. An investigation into the requirement of Pof8 for 3'- end processing in these species may yield interesting results. Our data not only suggest an evolutionary link in the biogenesis of telomerase among distant organisms but also provide new insights into the mechanisms underlying the pathogenesis of LARP3 and LARP7/MePCE deficiencies. + +## Online methods + +## Preparation of hTR RNA substrates + +In vitro transcription reactions were carried out in 1X transcription buffer (Promega), \(0.5\mathrm{mM}\) ATP (CROYEZ), CTP (CROYEZ), and GTP (CROYEZ); \(0.1\mathrm{mM}\) UTP (CROYEZ); \(\alpha\) - 32P- UTP (3000 Ci mmol- 1, \(10\mathrm{mCi}\mathrm{ml}^{- 1}\) , PerkinElmer), \(0.2\mu \mathrm{g}\) of DNA template, \(40\mathrm{U}\) of RNasin (TOOLs), and 1 unit \(\mu \mathrm{l}^{- 1}\) T7 RNA polymerase (Promega). The reaction mixtures ( \(10\mu \mathrm{l}\) ) were incubated at \(37^{\circ}\mathrm{C}\) for 30 min followed by the addition of an equal volume of formamide dye. The RNA products were purified on a \(6\%\) polyacrylamide (19:1) gel containing \(8\mathrm{M}\) urea. The primers used to generate the DNA templates are listed in Supplementary Table 1. The loading control actin- 1 RNA was expressed via the Sp6 RNA polymerase (Promega). + +## In vitro hTR 3'-end processing assay + +In vitro hTR processing reactions ( \(10\mu \mathrm{l}\) ) were carried out at \(37^{\circ}\mathrm{C}\) in a buffer containing \(20\mathrm{mM}\) Tris- HCl (pH 7.5), \(50\mathrm{mM}\) KCl, \(2.5\mathrm{mM}\) \(\mathrm{MgCl}_2\) , \(40\mathrm{U}\) RNasin, \(2\mathrm{nM}\) 32P- labelled hTR RNA, and \(40\mu \mathrm{g}\) of whole- cell extracts. Reactions were stopped by the addition of stop buffer ( \(10\mathrm{mg}\mathrm{ml}^{- 1}\) + +<--- Page Split ---> + +proteinase K in \(0.5\%\) SDS; \(40\mathrm{mM}\) EDTA; \(20\mathrm{mM}\) Tris- HCl, pH 7.5; and \(1000\mathrm{c.p.m.}\mu \mathrm{l}^{- 1}32\mathrm{P}\) - labelled actin mRNA) and incubation at \(37^{\circ}\mathrm{C}\) for \(30\mathrm{min}\) followed by extraction with phenol/chloroform preequilibrated with \(50\mathrm{mM}\) NaOAc (pH 5.0) and ethanol precipitation. The RNA was dissolved in \(80\%\) formamide dye and analysed on a \(6\%\) polyacrylamide (19:1) gel containing \(8\mathrm{M}\) urea. + +## Preparation of capped hTR RNA substrates + +In vitro transcription reactions were performed in 1X transcription buffer (Promega), \(0.5\mathrm{mM}\) nucleoside 5- triphosphates (NTPs), \(25\mu \mathrm{M}\) Bio11 UTP, \(\alpha - 32\mathrm{P}\) - UTP (3000 Ci mmol \(^{- 1}\) , \(10\mathrm{mCi}\mathrm{ml}^{- 1}\) , PerkinElmer), \(0.1\mu \mathrm{g}\) of DNA template, \(40\mathrm{U}\) of RNasin, and \(1\mathrm{U}\) of SP6 RNA polymerase (RiboMAX™, Promega). The reaction mixtures ( \(100\mu \mathrm{l}\) ) were incubated at \(37^{\circ}\mathrm{C}\) for 4 hours and then treated with DNase I (New England Biolabs) at \(37^{\circ}\mathrm{C}\) for 1 hour, followed by extraction with phenol/chloroform preequilibrated with \(50\mathrm{mM}\) NaOAc (pH 5.0) and ethanol precipitation. RNA was dissolved in \(80\%\) formamide dye and purified on a \(4\%\) polyacrylamide (29:1) gel containing \(8\mathrm{M}\) urea. The capping reactions were carried out in 1X capping buffer (New England Biolabs), \(0.5\mathrm{mM}\) GTP, \(0.1\mathrm{mM}\) SAM, and \(1\mathrm{U}\) of vaccinia virus capping enzyme (New England Biolabs). Reaction mixtures were incubated at \(37^{\circ}\mathrm{C}\) for 2 hours, followed by extraction with phenol/chloroform preequilibrated with \(50\mathrm{mM}\) NaOAc (pH 5.0) and ethanol precipitation. The RNA was dissolved in \(\mathrm{ddH_2O}\) . + +## Telomerase pulldown + +Telomerase was assembled in a buffer containing \(20\mathrm{mM}\) Tris- HCl (pH 7.5), \(50\mathrm{mM}\) KCl, \(2.5\mathrm{mM}\) \(\mathrm{MgCl}_2\) , \(40\mathrm{U}\) RNasin, \(50\mathrm{mM}\) capped hTR RNA, and \(10\mu \mathrm{g}\) of whole- cell extracts. The reaction + +<--- Page Split ---> + +mixture (25 μl) was incubated at \(37^{\circ}\mathrm{C}\) for the desired times, followed by centrifugation at 15,000 r.p.m. at \(4^{\circ}\mathrm{C}\) for 2 min. The supernatant was incubated with streptavidin beads at \(4^{\circ}\mathrm{C}\) for 1 hour. The precipitants were washed with NET- 2 buffer (50 mM Tris- HCl pH 7.5, 150 mM NaCl, and \(0.05\%\) NP- 40) and then subjected to Western blotting. + +## Immunoblotting + +The human cell pellets were lysed in CHAPS lysis buffer containing \(0.5\%\) CHAPS, \(50\mathrm{mM}\) Tris- HCl (pH 8), \(50\mathrm{mM}\) KCl, \(1\mathrm{mM}\) MgCl2, \(1\mathrm{mM}\) EGTA, \(10\%\) glycerol, \(5\mathrm{mM}\) DTT, and \(1\mathrm{mM}\) PMSF. Cell extracts were diluted in \(2\times \mathrm{LDS}\) sample buffer. Proteins in cell lysate were loaded onto a 4- \(20\%\) Tris- glycine protein gel (mPAGE™ \(4 - 20\%\) Bis- Tris, Millipore) and transferred to a PVDF blot membrane (Bio- Rad). Low- fat milk (5%) in wash buffer ( \(10\mathrm{mM}\) Tris- HCl, pH 8.0; \(150\mathrm{mM}\) NaCl; \(1\mathrm{mM}\) EDTA; and \(10\%\) Triton X- 100) was used as a blocking reagent. The antibodies used in this study are listed in Supplementary Table 3. + +## Cell culture and transduction + +293T cells (ATCC® CRL- 3216TM) were maintained in DMEM (Gibco) supplemented with \(10\%\) heat- inactivated foetal bovine serum (Corning) and \(2\mathrm{mM}\) L- glutamine (Gibco) at \(37^{\circ}\mathrm{C}\) in a humidified atmosphere containing \(5\%\) CO2. HeLa cells (ATCC® CCL- 2TM) were maintained in DMEM (Gibco) supplemented with \(10\%\) heat- inactivated foetal bovine serum. K562 cells (horizon, HD PAR- 131) were maintained in IMEM medium (HyClone™) supplemented with \(10\%\) heat- inactivated foetal bovine serum. The cells were subcultured when the confluency reached \(80\%\) . The cells were transfected with \(15\mu \mathrm{g}\) of plasmid DNA using TransIT® LT1 (Mirus) for \(24\mathrm{hr}\) . The plasmids used for transfection are listed in Supplementary Table 2. Cells were transduced with + +<--- Page Split ---> + +shRNAs for 24 hr. Medium containing \(2 \mu \mathrm{g} \mathrm{ml}^{- 1}\) puromycin was used to select the knockdown cells. Information on the shRNAs used for transduction is presented in Supplementary Table 4. + +## Genomic DNA extraction + +Genomic DNA was prepared from pellets (5×106 cells) with a GenEluteTM Mammalian Genomic DNA Miniprep Kit (Sigma–Aldrich, Cat. No: G1N350- 1KT) according to the manufacturer's instructions. + +## Terminal restriction fragment (TRF) analysis + +Genomic DNA (1 \(\mu \mathrm{g}\) ) from 293T cells was digested with Hinf I (New England Biolabs) and Rsa I (New England Biolabs) restriction enzymes in 10X CutSmart® Buffer (NEB) at 37°C overnight. The digested gDNA fragments were separated on a 1% SeaKem® LE agarose gel (Lonza) by electrophoresis at 120 V for 12 hours, followed by capillary transfer to a Hybond- N+ nylon transfer membrane (GE Healthcare) in 10X saline sodium citrate (SSC) for 14 hours. DNA was subsequently crosslinked twice to the membrane at 120 mJ in a UV Stratalinker 1800 (Stratagene, 254 nm, 120 mJ). The blot was prehybridized in Church buffer at 65°C for 1 hour and then hybridized with 32P- dCTP- labelled (TTAGGG)3 overnight. The blot was exposed to a phosphor imaging screen (Fujifilm) at room temperature overnight. Phosphor images were scanned by an Amersham Typhoon 5 scanner (Cytiva). The telomere length images were quantified and analysed by ImageQuantTL software (Cytiva). + +## Telomerase activity assay + +<--- Page Split ---> + +Telomerase activity reactions were performed in a \(10 - \mu \mathrm{l}\) reaction volume consisting of \(50~\mathrm{mM}\) Tris- HCl, \(\mathrm{pH}8.0\) ; \(50~\mathrm{mM}\) KCl; \(1\mathrm{mM}\) \(\mathrm{MgCl}_2\) ; \(1\mathrm{mM}\) spermidine; \(5\mathrm{mM}\) DTT; \(1\mathrm{mM}\) dATP; \(1\mathrm{mM}\) dTTP; \(10\mu \mathrm{M}\) dGTP; \(0.75\mu \mathrm{M}^{32}\mathrm{P}\) - \(\alpha\) - dGTP (3000 Ci mmol \(^{- 1}\) ); \(1\mu \mathrm{M}\) telomeric primer (TTAGGG) \(_3\) and \(2\mu \mathrm{g}\) of cell extract at \(37^{\circ}\mathrm{C}\) for 2 hours. Reactions were stopped with \(10~\mu \mathrm{l}\) of \(1\mathrm{mgml}^{- 1}\) proteinase K. DNA was extracted with phenol/chloroform equilibrated with \(50\mathrm{mM}\) NaOAc (pH 7.0) and ethanol precipitated with \(2.5\mathrm{M}\) ammonium acetate and \(10\mu \mathrm{g}\) of glycogen at \(- 80^{\circ}\mathrm{C}\) overnight. Reactions were then centrifuged for \(20\mathrm{min}\) at 14,000 r.p.m., and the pellets were washed with \(1\mathrm{ml}\) of \(70\%\) ethanol. The dried pellets were then resuspended in \(5\mu \mathrm{l}\) of \(80\%\) formamide loading buffer. Reaction products were analysed on a \(10\%\) polyacrylamide (19:1) gel containing \(8\mathrm{M}\) urea. All blots were prepared with products obtained from the same experiment and processed in parallel. + +## qRT-PCR + +Quantitative reverse transcription(qRT)- PCR was performed via the SYBR Green method. The 50- fold diluted random hexamer priming cDNA was amplified with the primers shown in Supplementary Table 5 and was performed with a CFX384TM Real- Time PCR System in a C1000 Touch™ Thermal Cycler (Bio- Rad) using iQ™ SYBR® Green Supermix (Bio- Rad, Cat. No. 1708882). The results were normalized to the GAPDH, ATP5β, and HPRT reference gene levels and measured by CFX Maestro software (Bio- Rad). Graphing and statistical analysis of the qRT- PCR results were performed using Prism 9 (GraphPad). + +In situ hybridization (FISH) and immunofluorescence (IF) + +<--- Page Split ---> + +Cells were fixed on coverslips with \(4\%\) paraformaldehyde (Thermo Scientific, Cat. No. 047317) and permeabilized with \(0.1\%\) Triton- X- 100. The cells were hybridized with hybridization buffer (2X SSC, \(10\%\) formamide, \(0.2\mathrm{mgml}^{- 1}\) , \(10\%\) dextran sulfate, \(0.4\mathrm{U}\) RNase inhibitor, \(1\mathrm{mgml}^{- 1}E\) coli tRNA). The cells were incubated with 7 Cy3- conjugated hTR oligos (Supplementary Table 6) at \(37^{\circ}\mathrm{C}\) overnight. For immunofluorescence experiments, cells were incubated with anti- Coilin primary antibody (Abcam, Cat. No. ab11822, \(0.5\mu \mathrm{gml}^{- 1}\) ) in \(1\%\) bovine serum albumin (BSA) for 2 hours, followed by FITC- conjugated AffiniPure goat anti- mouse IgG (H+L) (Jackson ImmunoResearch, Cat. No. 115- 095- 003, 1:100 dilution) secondary antibody for 1 hour. Cells were stained with Hoechst 33258 (Sigma- Aldrich, Cat. No. B2883- 1g, 1:1000 dilution) in \(1\%\) BSA for 10 min. Coverslips were mounted with Fluoromount™ Aqueous Mounting Medium (Sigma- Aldrich, Cat. No. F4680- 25ML). The images were photographed with a Carl Zeiss LSM880 microscope. + +<--- Page Split ---> + +## References + +1. Nandakumar, J. & Cech, T.R. Finding the end: recruitment of telomerase to telomeres. Nat. Rev. Mol. Cell Biol. 14, 69–82 (2013). +2. Feng, J. et al. 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Telomere length at diagnosis of chronic phase chronic myeloid leukemia (CML-CP) identifies a subgroup with favourable prognostic parameters and molecular response according to the ELN criteria after 12 months of treatment with nilotinib. Leukemia 29, 2402-4 (2015).46. Hu, X. et al. Quality-control mechanism for telomerase RNA folding in the cell. Cell Rep. 33, 108568 (2020).47. Qi, X. et al. Prevalent and distinct spliceosomal 3'-end processing mechanisms for fungal telomerase RNA. Nat. Commun. 6, 6105 (2015).48. Kannan, R., Helston, R.M., Dannebaum, R.O. & Baumann, P. Diverse mechanisms for spliceosome-mediated 3' end processing of telomerase RNA. Nat. Commun. 6, 6104 (2015). + +## Acknowledgements + +We thank all members of the Tseng laboratory for helpful discussions. This work was supported by MOST 111- 2636- B- 002- 026 - and NTU- 112V1403- 5. + +## Figure legends + +Fig. 1 The establishment of in vitro systems to examine the biogenesis of human telomerase. (a) The in vitro 3' end processing assay with \(^{32}\mathrm{P}\) - labeled hTR fragments (nucleotides 206 to 461 with oligo A tails) were carried out in 293T cell extracts at \(37^{\circ}\mathrm{C}\) for the indicated times. RNA was purified and resolved by a \(6\%\) polyacrylamide gel containing \(8\mathrm{M}\) urea. Actin acted as the loading control. (b) The exL and mature forms of hTR signals were quantified by ImageQuantTL and normalized to \(0\mathrm{min}\) , respectively. (c) Western blotting analysis of telomerase assembled on biotin + +<--- Page Split ---> + +labelled hTR pulled down with streptavidin beads for the indicated times. (d) Telomerase activity of the in vitro purified telomerase assembled on exL and mature forms of hTR. (e) The intensity of each major band \((+4, + 10, + 16, + 22, + 28\) , and so on) from the telomerase activity assay in d was quantitated by phosphorimager analysis. + +Fig. 2 LARP3, LARP7, and MePCE are involved in the early stage of telomerase assembly. (a) Western blotting analysis of in vitro assembled telomerase purified from the indicated times. (b) Western blotting analysis of in vitro assembled telomerase assembled on the different hTR species as shown in the schematic (exL, exS, mature, \(3^{\prime}\) stem loop- deleted, and pseudoknot). (c) Endogenous LARP3, LARP7, MePCE, and DKC1 were immunoprecipitated and subjected to telomerase activity assay. + +Fig. 3 The LARP3 binding competes with tertiary structure formation. (a) Schematic showing the proposed mechanism of how the tertiary structure of exL affects the binding of LARP3. (b) Western blotting analysis of recombinant LARP3 pulled down with biotinylated wild type or the U460C mutant hTR. (c) The in vitro \(3^{\prime}\) end processing assay with \(^{32}\mathrm{P}\) - labeled wild type or U460C mutant hTR fragments (nucleotides 206 to 461 with oligo A tails) were carried out in 293T cell extracts at \(37^{\circ}\mathrm{C}\) for the indicated times. RNA was purified and resolved by a \(6\%\) polyacrylamide gel containing 8 M urea. Actin acted as the loading control. (d) Western blotting analysis of telomerase assembled on biotin- labeled wild type or U460C mutant hTR pulled down with streptavidin beads for the indicated times. + +Fig. 4 LARP3 plays a negative role in telomerase biogenesis. (a) Western blots of cell extracts prepared from 293T cells treated with either shRNA targeting LARP3 or transfected with an LARP3 plasmid. Endogenous TUBULIN served as a loading control. (b) Total RNA prepared from 293T cells treated with either shRNA targeting LARP3 or transfected with an LARP3 + +<--- Page Split ---> + +plasmid was subjected to qRT- PCR for total hTR, \(3^{\prime}\) - extended hTR, GAPDH, ATP5β, and HPRT. Bar graph of mean fold change for hTR relative to the control samples and normalized to GAPDH, ATP5β, and HPRT. Mean values were calculated from triplicate qRT- PCR experiments of three biological replicates with bars representing SE. (c) Western blotting analysis of telomerase assembled on biotin- labelled hTR in the indicated extracts, followed by pulldown with streptavidin beads for the indicated times. (d) LARP3 was immunoprecipitated from cell extracts prepared from 293T cells either treated with either shRNA targeting LARP3 or transfected with an LARP3 plasmid and subjected to telomerase activity assay. (e and f) The in vitro \(3^{\prime}\) end processing assay with \(^{32}\mathrm{P}\) - labeled hTR fragments (nucleotides 206 to 461 with oligo A tails) were carried out in cell extracts prepared from 293T cells either treated with either shRNA targeting LARP3 (e) or transfected with an LARP3 plasmid (f) at \(37^{\circ}\mathrm{C}\) for the indicated times. RNA was purified and resolved by a \(6\%\) polyacrylamide gel containing \(8\mathrm{M}\) urea. Actin acted as the loading control. + +Fig. 5 Reducing the expression level of LARP3 increases telomerase function and causes telomere elongation. (a) Western blots of cell extracts prepared from K562 cells treated with shRNA targeting Luciferase or LARP3. Endogenous TUBULIN served as a loading control. (b) Total RNA from LARP3- knockdown K562 cells was subjected to qRT- PCR for the measurement of the levels of total hTR, \(3^{\prime}\) - extended hTR, GAPDH, ATP5β, and HPRT. Bar graph of mean fold change for \(3^{\prime}\) - extended hTR relative to the control samples and normalized to GAPDH, ATP5β, and HPRT. Mean values were calculated from triplicate qRT- PCR experiments of three biological replicates with bars representing SE. (c) The in vitro \(3^{\prime}\) end processing assay with \(^{32}\mathrm{P}\) - labeled hTR fragments (nucleotides 206 to 461 with oligo A tails) were carried out in the indicated cell extracts. RNA was purified and resolved by a \(6\%\) polyacrylamide gel containing \(8\mathrm{M}\) urea. Actin acted as the loading control. (d) Western blotting analysis of telomerase assembled on biotin- labeled hTR in the + +<--- Page Split ---> + +indicated extracts, followed by pulldown with streptavidin beads for the indicated times. (e) Endogenous DKC1 was immunoprecipitated and subjected to telomerase activity assay. (f) Telomere lengths determined by TRF analysis of gDNA prepared from K562 cells treated with the shRNA targeting Luciferase or LARP3. + +Fig. 6 LARP7 and MePCE knockdown impairs the processing of the exS form and causes cytoplasmic localization of hTR. (a) Western blots of cell extracts prepared from 293T cells treated with shRNAs targeting LARP7 or MePCE. (b) Total RNA prepared from 293T cells treated with the shRNA targeting Luciferase, LARP7, or MePCE was subjected to qRT- PCR for the total hTR, 3'- extended hTR, GAPDH, ATP5β, and HPRT. Bar graph of mean fold change for 3'- extended hTR relative to the control samples and normalized to GAPDH, ATP5β, and HPRT. Mean values were calculated from triplicate qRT- PCR experiments of three biological replicates with bars representing SE. (c) Western blots of telomerase assembled on biotin- labeled hTR in the indicated extracts, followed by pulldown with streptavidin beads for the indicated times. (d) The in vitro 3' end processing assay with \(^{32}\mathrm{P}\) - labeled hTR fragments (nucleotides 206 to 461 with oligo A tails) were carried out in the indicated cell extracts. RNA was purified and resolved by a 6% polyacrylamide gel containing 8 M urea. Actin acted as the loading control. (e) Telomere lengths determined by TRF analysis of gDNA prepared from 293T cells treated with the shRNA targeting Luciferase, LARP7, MePCE, or PARN. (f) In situ hybridization and immunofluorescence data after sh- Luc, sh- PARN, sh- LARP7, and sh- MePCE treatment in HeLa cells. Coilin served as a Cajal body marker. The scale bar represents 5 μm. (g) Bar graph illustrating the distribution of hTR in the cytosolic and nuclear fractions. + +Fig. 7 Schematic showing the working model. + +<--- Page Split ---> + +## Figures + +图 + +Figure 1 + +Figure 1- 7 + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +Supplementary materials.pdf.pdfSupplementary materials.pdf.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__05f1891193c1720f597ee19876aa1d2b3d061937d2a7931dcff717edf288996f/images_list.json b/preprint/preprint__05f1891193c1720f597ee19876aa1d2b3d061937d2a7931dcff717edf288996f/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..1addfcefe59ea386bdf9414f349fef56cb49a6ca --- /dev/null +++ b/preprint/preprint__05f1891193c1720f597ee19876aa1d2b3d061937d2a7931dcff717edf288996f/images_list.json @@ -0,0 +1,122 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1. Behavioural task and model explanation. (a) Example trials from a representative participant, showing the true probability of high (H) and low (L) stimuli given current stimuli, trial stimulation given, and participant rated probabilities. Arrows pointing to jump points of true probabilities, where a large change happens. (b) Participant rating screens during the task, where they were asked to estimate the identity of the upcoming stimulus given the current one. For example, after a low stimulus participants would be asked to rate the probability of the upcoming stimulus being low (L -> L) or high (L -> H). (c) Markovian generative process of the sequence of low and high intensity stimuli, depicted in a. The transition probability matrix was resampled at change points, determined by a fixed probability of a jump.", + "footnote": [], + "bbox": [ + [ + 234, + 189, + 900, + 420 + ] + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2. Behavioural results. (a) True vs rated probabilities for \\(\\mathrm{p(H|H)}\\) and \\(\\mathrm{p(H|L)}\\) from an example participant, a positive correlation suggests the participant correctly learned the stimuli probability, (b) Pearson's r for true vs rated probabilities for \\(\\mathrm{p(H|H)}\\) and \\(\\mathrm{p(H|L)}\\) within individual participants.", + "footnote": [], + "bbox": [ + [ + 228, + 616, + 910, + 836 + ] + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3. Model comparison results. (a) Bayesian model comparison based on model fitting evidence, in fMRI sessions. Subjects' predictive ratings of next trial's pain intensity were fitted with posterior means from Bayesian models, values from Rescorla-Wagner (reinforcement learning) model, and random fixed probabilities. Bayesian jump frequency model (assuming jumps in sequence and inference with stimuli frequency) was the winning model in both cases. (b) Individual subject model evidence.", + "footnote": [], + "bbox": [ + [ + 237, + 90, + 900, + 365 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4. Brain responses to noxious stimuli (high \\(>\\) low pain stimuli) in (a) sagittal, (b) axial and (c) coronal views (colorbar shows Z scores thresholded at \\(z > 3.3\\) , FWE corrected \\(p< 0.05\\) ).", + "footnote": [], + "bbox": [ + [ + 228, + 480, + 907, + 865 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5. Posterior probability mean of high pain in Bayesian jump frequency model showed activations in the bilateral primary and secondary somatosensory cortex, primary motor cortex and right caudate (FDR corrected p<0.001, colorbar shows Z scores >3.3). (a) sagittal (b) axial and (c) coronal view.", + "footnote": [], + "bbox": [ + [ + 328, + 395, + 808, + 840 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6. Uncertainty (SD) of the posterior probability of high pain in Bayesian jump frequency model was associated with activations in the right superior parietal cortex (FDR corrected \\(\\mathrm{p}< 0.001\\) , colorbar shows Z scores \\(>3.3\\) ). (a) sagittal (b) axial and (c) coronal view.", + "footnote": [], + "bbox": [ + [ + 396, + 353, + 740, + 756 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_7.jpg", + "caption": "Figure 7. Neural activity associated with the model update, i.e. the KL divergence between posteriors from successive trials (positive contrast), in (a) sagittal, (b) coronal, and (c) axial views (FDR corrected \\(p< 0.001\\) , colorbar shows Z scores \\(>3.3\\) ).", + "footnote": [], + "bbox": [ + [ + 225, + 313, + 909, + 699 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_8.jpg", + "caption": "Figure 8. Overlaying the temporal prediction of high pain (mean posterior probability, red-yellow), its uncertainty (SD posterior probability, blue) and the model update (KL divergence between successive posterior distributions, green); (FDR corrected \\(p< 0.001\\) , colorbar shows Z scores \\(>3.3\\) )", + "footnote": [], + "bbox": [ + [ + 350, + 473, + 799, + 844 + ] + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/preprint/preprint__05f1891193c1720f597ee19876aa1d2b3d061937d2a7931dcff717edf288996f/preprint__05f1891193c1720f597ee19876aa1d2b3d061937d2a7931dcff717edf288996f.mmd b/preprint/preprint__05f1891193c1720f597ee19876aa1d2b3d061937d2a7931dcff717edf288996f/preprint__05f1891193c1720f597ee19876aa1d2b3d061937d2a7931dcff717edf288996f.mmd new file mode 100644 index 0000000000000000000000000000000000000000..6919462930e94d6897484bc6c69566d6a2415809 --- /dev/null +++ b/preprint/preprint__05f1891193c1720f597ee19876aa1d2b3d061937d2a7931dcff717edf288996f/preprint__05f1891193c1720f597ee19876aa1d2b3d061937d2a7931dcff717edf288996f.mmd @@ -0,0 +1,332 @@ + +# Learning the statistics of pain: computational and neural mechanisms + +Flavia Mancini ( fm456@cam.ac.uk) University of Cambridge https://orcid.org/0000- 0001- 8441- 9236 + +Suyi Zhang University of Oxford + +Ben Seymour University of Oxford https://orcid.org/0000- 0003- 1724- 5832 + +## Article + +Keywords: pain, Bayesian models, sensory pain pathways + +Posted Date: November 8th, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 1003293/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on November 3rd, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 34283- 9. + +<--- Page Split ---> + +## Learning the statistics of pain: computational and neural mechanisms + +Flavia Mancini \(^{1}\) , Suyi Zhang \(^{2}\) , and Ben Seymour \(^{2}\) + +\(^{1}\) Department of Engineering, University of Cambridge, Trumpington Street, Cambridge \(^{2}\) CB2 1PZ, United Kingdom \(^{2}\) Wellcome Centre for Integrative Neuroimaging, John Radcliffe Hospital, Headington, Oxford OX3 9DU, United Kingdom + +\(^{8}\) Corresponding author: \(^{9}\) Flavia Mancini \(^{1}\) \(^{10}\) Email address: flavia.mancini@eng.cam.ac.uk + +## ABSTRACT + +\(^{12}\) Pain invariably changes over time, and these temporal fluctuations are riddled with uncertainty about body safety. In theory, statistical regularities of pain through time contain useful information that can be learned, allowing the brain to generate expectations and inform behaviour. To investigate this, we exposed healthy participants to probabilistic sequences of low and high- intensity electrical stimuli to the left hand, containing sudden changes in stimulus frequencies. We demonstrate that humans can learn to extract these regularities, and explicitly predict the likelihood of forthcoming pain intensities in a manner consistent with optimal Bayesian models with dynamic update of beliefs. We studied brain activity using functional MRI whilst subjects performed the task, which allowed us to dissect the underlying neural correlates of these statistical inferences from their uncertainty and update. We found that the inferred frequency (posterior probability) of high intensity pain correlated with activity in bilateral sensorimotor cortex, secondary somatosensory cortex and right caudate. The uncertainty of statistical inferences of pain was encoded in the right superior parietal cortex. An intrinsic part of this hierarchical Bayesian model is the way that unexpected changes in frequency lead to shift beliefs and update the internal model. This is reflected by the KL divergence between consecutive posterior distributions and associated with brain responses in the premotor cortex, dorsolateral prefrontal cortex, and posterior parietal cortex. In conclusion, this study extends what is conventionally considered a sensory pain pathway dedicated to process pain intensity, to include the generation of Bayesian internal models of temporal statistics of pain intensity levels in sensorimotor regions, which are updated dynamically through the engagement of premotor, prefrontal and parietal regions. + +## INTRODUCTION + +\(^{32}\) In recent years, our understanding of pain has shifted from viewing it as a simple responsive system to a complex predictive system, that interprets incoming inputs based on past experience and future goals (Fields, 2018). Indeed, all types of pain response, including perception, judgement and decision- making, are invariably and often strongly shaped by what pain is being predicted, and the nature of this influence gives clues regarding the fundamental architecture of the pain system in the brain (Buchel et al., 2014; Seymour and Mancini, 2020; Roy et al., 2014; Wiech, 2016). To date, most experimental strategies to study prediction have come from explicit cue- based paradigms, in which a learned or given cue, such as visual image, contains the relevant information about an upcoming pain stimulus. (Atlas et al., 2010; Buchel et al., 2014; Fazeli and Buchel, 2018; Geuter et al., 2017; Zhang et al., 2016). However, a much more general route to generate predictions relates to the background statistics of pain over time - the underlying base- rate of getting pain, and of different pain intensities, at any one moment. In principle, the pain system should be able to generate predictions based on how pain changes over time, in absence of external cues. This possibility is suggested by research in other sensory domains, showing that the temporal statistics of sequences of inputs are learned and inferred through experience - a process termed temporal statistical learning (Dehaene et al., 2015; Frost et al., 2015; Fiser and Aslin, 2002; Kourtzi and Welchman, 2019; Turk- Browne et al., 2005; Wang et al., 2017). We hypothesise that temporal statistical + +<--- Page Split ---> + +learning also occurs in the pain system, allowing the brain to infer the prospective likelihood of pain by keeping track of ongoing temporal statistics and patterns. In this way, pain should effectively act as the cue for itself, instead of utilising a cue from a different sensory modality. This may be especially important in clinical contexts, in which pain typically comes in streams of inputs changing over time (Kajander and Bennett, 1992). + +Here, we tested this hypothesis by designing a frequency learning paradigm involving long, probabilistic sequences of noxious stimuli of two intensities (low and high) that could suddenly change. We tested people's ability to generate explicit predictions about the probability of forthcoming pain, and probed the underlying neural mechanisms. In particular, following evidence in other sensory domains (Meyniel et al., 2016), we proposed that the brain uses a optimal Bayesian strategy to infer the background temporal statistics of pain. Importantly, this approach may allow us to map core regions of the pain system to specific functional information processing operations: the temporal prediction of pain, its uncertainty and update. Our hypothesis predicts that the predictive inference of pain stimuli should be encoded largely within pain processing brain regions (Conway and Christiansen, 2005). The uncertainty of the prediction is expected to implicate multisensory, intraparietal regions, as shown previously using visual and auditory stimuli (Meyniel and Dehaene, 2017). + +## RESULTS + +Thirty- five participants (17 females; mean age 27.4 years old; age range 18- 45 years) completed an experiment with concurrent brain fMRI scanning. They received continuous sequences of low and high intensity painful electrical stimuli, wherein they were required to intermittently judge the likelihood that the next stimulus was of high versus low intensity (figure 2 a). We designed the task such that the statistics of the sequence could occasionally and suddenly change, which meant that the the sequences effectively incorporated sub- sequences of stimuli. The statistics themselves incorporated two types of information. First, they varied in terms of the relative frequency of high and low intensity stimuli, to test the primary hypothesis that frequency statistics can be learned. Second, sequences also contained an additional aspect of predictability, in which the conditional probability of a stimulus depended on the identity of the previous stimulus (i.e. its transition probability). By having different transition probabilities between high and low stimuli within subsequences, it is possible to make a more accurate prediction of a forthcoming stimulus intensity over- and- above simply learning the general background statistics. For instance, if low pain tends to predict low pain, and high predicts high, then one tends to get 'clumping' patterns of pain (runs of high or low stimuli); or conversely if high predicts low and vice versa, one tends to get alternating patterns. Both might have the same overall frequency of high and low pain, but better predictions can be made by learning the temporal patterns within. Thus we were able to test the supplementary hypothesis that humans can learn the specific transition probabilities between different intensities, as shown previously with visual stimuli (Meyniel et al., 2016). + +At the beginning of the experiment, participants were informed that the sequence was set by the computer and could occasionally change at any point in time. This design mirrored a well- studied task used to probe statistical learning with visual stimuli (Meyniel and Dehaene, 2017); participants were explicitly and occasionally asked to estimate the probability of forthcoming stimuli (figure 2 b). The sequence was thus defined by a set of transition probabilities: the probability of high or low pain following a high pain stimulus; and the probability of high or low pain following a low pain stimulus (i.e. a Markovian transition matrix; see example in figure 2 c). Occasionally, these probabilities were suddenly resampled, such that in fact the total task length of 1300 stimuli (split into 5 blocks) comprised typically about 50 subsequences (mean \(25 \pm 4\) stimuli per subsequence). Participants were not explicitly informed when these changes happened. Within these subsequences, the frequency of high (versus low) stimuli varied from 15% to 85%, and figure 2 a illustrates an example of a snapshot of a typical sequence, showing a couple of 'jump' points where the probabilities change. Figure 2 b shows the rating screen, with ratings being required on 4.8% of stimuli. Before the main experimental scanning session, subjects practiced the task for an average of roughly 1200 trials before the MRI sessions. + +## Behavioural results + +Participants were able to successfully learn to predict the intensity (high versus low) of the upcoming painful stimulus within the sequence. Fig 2a shows the positive correlation between stimulus rated and true probabilities for low and high pain respectively for an example individual (Pearson correlation for + +<--- Page Split ---> + +this participant \(\mathrm{p(H|H)}\) \(\mathrm{r = 0.567}\) \(\mathrm{p = 4.61e - 4}\) \(\mathrm{p(H|L)}\) \(\mathrm{r = 0.348}\) \(\mathrm{p = 0.075}\) ; see supplementary figs 1- 2 for plots from all subjects). Across subjects, the within- individual Pearson's r between true and rated probabilities was significantly above zero. (Fig 2b, 26 out of 35 subjects had \(\mathrm{r > 0}\) : \(\mathrm{p(H|H)}\) \(\mathrm{r = 0.138\pm 0.225}\) \(\mathrm{t(34) = 3.65}\) \(\mathrm{p = 0.00088}\) , Cohen's \(\mathrm{d = 0.871}\) ; \(\mathrm{p(H|L)}\) \(\mathrm{r = 0.117\pm 0.220}\) \(\mathrm{t(34) = 3.15}\) \(\mathrm{p = 0.0034}\) , Cohen's \(\mathrm{d = 0.752}\) ; see also supplementary figures 1- 2; note that \(\mathrm{p(H|L)}\) and \(\mathrm{p(L|L)}\) are reciprocal, as well as \(\mathrm{p(H|L)}\) and \(\mathrm{p(L|L)}\) ). + +![](images/Figure_1.jpg) + +
Figure 1. Behavioural task and model explanation. (a) Example trials from a representative participant, showing the true probability of high (H) and low (L) stimuli given current stimuli, trial stimulation given, and participant rated probabilities. Arrows pointing to jump points of true probabilities, where a large change happens. (b) Participant rating screens during the task, where they were asked to estimate the identity of the upcoming stimulus given the current one. For example, after a low stimulus participants would be asked to rate the probability of the upcoming stimulus being low (L -> L) or high (L -> H). (c) Markovian generative process of the sequence of low and high intensity stimuli, depicted in a. The transition probability matrix was resampled at change points, determined by a fixed probability of a jump.
+ +![](images/Figure_2.jpg) + +
Figure 2. Behavioural results. (a) True vs rated probabilities for \(\mathrm{p(H|H)}\) and \(\mathrm{p(H|L)}\) from an example participant, a positive correlation suggests the participant correctly learned the stimuli probability, (b) Pearson's r for true vs rated probabilities for \(\mathrm{p(H|H)}\) and \(\mathrm{p(H|L)}\) within individual participants.
+ +<--- Page Split ---> + +## Behavioural data modelling + +## Model choice + +Based on previous evidence in other sensory domains, we hypothesised that subjects use an optimal Bayesian strategy to infer the statistics over time (Meyniel, 2020; Meyniel et al., 2016). We fit subjects' ratings to four variations of a Bayesian model, according to two factors: first, sequence inference through stimulus frequency (by assuming the sequence as generated by a Bernoulli process, where subjects track how often they encountered previous stimuli), versus inference through transition probability (by assuming the sequence follows a Markov transition probability between successive stimuli, where the subject tracks such transition of previous stimuli). This distinguishes between whether participants learn simple statistics (our primary hypothesis), or are able to learn the full transition probabilities (supplementary hypothesis). The second factor was to whether the model incorporates the possibility of sudden changes (jumps) in stimuli probability, as occurs in the task paradigm, or ignores such possibilities (fixed). To compare against alternative models, we also fit a basic reinforcement learning model (Rescorla- Wagner with fixed learning rate, which is an established model of Pavlovian conditioning; (Rescorla et al., 1972)) and a baseline random model that assumes constant probabilities throughout the experiment for high and low pain respectively. + +## Model fitting + +The selected models estimate the probability of a pain stimulus' identity in each trial. The values predicted by the model can be fitted to actual subject predictive ratings gathered during the experiment. A model is considered a good fit to the data if the total difference between the model predicted values and the subjects' predictions is small. Within each model, free parameters were allowed to differ for individual subjects in order to minimise prediction differences. For Bayesian 'jump' models, the free parameter is the prior probability of sequence jump occurrence. For Bayesian fixed models, the free parameters are the window length for stimuli history tracking, and an exponential decay parameter that discounts increasingly distant previous stimuli. The RL model's free parameter is the initial learning rate, and random model assumes a fixed high pain probability that varies across subjects. The model fitting procedure minimises each subject's negative log likelihood for each model, based on residuals from a linear model that predicts subject's ratings using learning model predictors. The smaller the sum residual, the better fit a model's predictions are to the subject's ratings. + +## Model comparison + +We compared the different models using the likelihood calculated during fitting as model evidence. Fig 3a showed model frequency, model exceedance probability, and protected exceedance probability for each model, fitted for fMRI sessions of the experiment. Both comparisons showed the winning model was the 'Bayesian jump frequency' model inferring both the frequency of pain states and their volatility, producing predictions significantly better than alternative models (Bayesian jump frequency model frequency=0.563, exceedance probability=0.923, protected exceedance=0.924). Fig 3b reports the model evidence for each subject; it shows that, although the majority (n=23) of participants were best fit by the model that infers the background frequency, some participants (n=12) were better fit by the more sophisticated model that infers specific transition probabilities. + +## Neuroimaging results + +We used the winning computational model to generate trial- by- trial regressors for the neuroimaging analyses. The rationale of this approach is that neural correlation of core computational components of a specific model provides evidence that and how the model is implemented in the brain (Cohen et al., 2017). + +First, a simple high>low pain contrast identified BOLD responses in the right thalamus, sensorimotor, premotor and supplementary motor cortex, insula, anterior cingulate cortex and left cerebellum (with peaks in laminae V- VI), consistent with the known neuroanatomy of pain responses (fig 4, table 1). The opposite contrast (low>high pain) is reported in Supplementary Figure 3 and Supplementary Table 1. + +Next, we looked at BOLD correlations with the modelled posterior probability of high pain. For any pain stimulus, this reflects the newly calculated probability that the next stimulus will be high, i.e. the dynamic and probabilistic inference of high pain. This analysis identified BOLD responses in the bilateral primary and secondary somatosensory cortex, primary motor cortex and right caudate (fig 5, table 2). We report the opposite contrasts (posterior probability of low pain) in Supplementary Figure 3 and Supplementary Table 2. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3. Model comparison results. (a) Bayesian model comparison based on model fitting evidence, in fMRI sessions. Subjects' predictive ratings of next trial's pain intensity were fitted with posterior means from Bayesian models, values from Rescorla-Wagner (reinforcement learning) model, and random fixed probabilities. Bayesian jump frequency model (assuming jumps in sequence and inference with stimuli frequency) was the winning model in both cases. (b) Individual subject model evidence.
+ +![](images/Figure_4.jpg) + +
Figure 4. Brain responses to noxious stimuli (high \(>\) low pain stimuli) in (a) sagittal, (b) axial and (c) coronal views (colorbar shows Z scores thresholded at \(z > 3.3\) , FWE corrected \(p< 0.05\) ).
+ +<--- Page Split ---> + +
Cluster IDXYZPeak StatCluster Size (mm3)
0113-1756.8862587
11a13-22-34.957
2233-22586.5245247
32a30-19495.953
42b16-17685.524
5337-17155.7421186
646-7495.1283486
74a0-2434.967
84b4-19494.333
94c11-14494.289
105-17-60-194.8511707
115a-10-55-164.651
125b-7-62-224.217
+ +Table 1. High pain > low pain stimuli activation clusters (FWE p<0.05). + +![](images/Figure_5.jpg) + +
Figure 5. Posterior probability mean of high pain in Bayesian jump frequency model showed activations in the bilateral primary and secondary somatosensory cortex, primary motor cortex and right caudate (FDR corrected p<0.001, colorbar shows Z scores >3.3). (a) sagittal (b) axial and (c) coronal view.
+ +<--- Page Split ---> + + +Table 2. Activation clusters associated with the posterior mean \(\mathrm{p(H)}\) of the Bayesian jump frequency model. + +
Cluster IDXYZPeak StatCluster Size (mm3)
0166-7276.477
11a52-7335.787
22-62-7335.924
32a-46-12434.002
4321-12244.885
53a11-2154.197
63b13-7214.140
+ +In contrast, a right superior parietal region, bordering with the supramarginal gyrus, was implicated in the computation of the uncertainty (SD) of the posterior probability of high pain, a measure that reflects the uncertainty of pain predictions (figure 6 and table 3). The negative contrast of the posterior SD did not yield any significant cluster. + +![](images/Figure_6.jpg) + +
Figure 6. Uncertainty (SD) of the posterior probability of high pain in Bayesian jump frequency model was associated with activations in the right superior parietal cortex (FDR corrected \(\mathrm{p}< 0.001\) , colorbar shows Z scores \(>3.3\) ). (a) sagittal (b) axial and (c) coronal view.
+ +A key aspect of the Bayesian model is that it provides a metric of the model update, quantified as the KL divergence between successive trial's posterior distribution. The KL divergence increases when the two successive posteriors are more different from each other, and the opposite when the posteriors are similar. We found that the KL divergence was associated with BOLD responses in left premotor cortex, bilateral dorsolateral prefrontal cortex, superior parietal lobe, supramarginal gyrus, and left somatosensory cortex (fig 7, table 4). For completeness, we report the negative contrast in Supplementary Figure 5 and + +<--- Page Split ---> + + +Table 3. Activation clusters associated with the uncertainty of the Bayesian jump frequency model. + +
Cluster IDXYZPeak StatCluster Size (mm3)
0140-48584.311
11a47-38584.168
21b33-41433.745
3228-58494.084
+ +Supplementary Table 3. Figure 8 overlays the posterior probability with its uncertainty and update (KL divergence). This shows that the temporal prediction of high pain and its update activate distinct, although neighbouring regions in the sensorimotor and premotor cortex, bilaterally. In contrast, the uncertainty of pain predictions activates a right superior parietal region that partially overlaps with the neural correlates of model update. + +![](images/Figure_7.jpg) + +
Figure 7. Neural activity associated with the model update, i.e. the KL divergence between posteriors from successive trials (positive contrast), in (a) sagittal, (b) coronal, and (c) axial views (FDR corrected \(p< 0.001\) , colorbar shows Z scores \(>3.3\) ).
+ +## DISCUSSION + +Pain is typically uncertain, and this is most often true when pain persists after injury. When pain persists, the brain needs to be able to track changes in intensity and patterns over time, in order to predict what will happen next and what to do about it. Here we investigated whether, in absence of external cues, the human brain can generate explicit (conscious) predictions about the likelihood of forthcoming pain, as these are central to the generation of internal models of pain and can be formally compared to normative models of statistical learning (Dehaene et al., 2015; Meyniel et al., 2016). This study provides evidence that humans can learn and predict the background temporal statistics of pain using optimal + +<--- Page Split ---> + + +Table 4. Activation clusters positively associated with the update (KL divergence) of the Bayesian jump frequency model. + +
Cluster IDXYZPeak StatCluster Size (mm3)
01-586366.191
11a-26-2495.945
21b-604214.935
31c-430554.516
42-46-41406.098
52a-36-50525.438
62b-50-41553.789
735911245.308
8447-41585.295
94a37-50524.972
104b37-58614.460
114c30-65614.255
125-62-17334.814
135a-50-24334.584
145b-46-29273.849
+ +![](images/Figure_8.jpg) + +
Figure 8. Overlaying the temporal prediction of high pain (mean posterior probability, red-yellow), its uncertainty (SD posterior probability, blue) and the model update (KL divergence between successive posterior distributions, green); (FDR corrected \(p< 0.001\) , colorbar shows Z scores \(>3.3\) )
+ +<--- Page Split ---> + +Bayesian inference with dynamic update of beliefs, allowing explicit prediction of the probability of forthcoming pain at any moment in time. Using neuroimaging, we reveal the neural correlates of the internal models of pain predictions. We found distinct neural correlates for the probabilistic, predictive inference of pain and its update. Pain predictions (i.e. mean posterior probability) are encoded in the bilateral, primary somatosensory and motor regions, secondary somatosensory cortex and right caudate, whereas the signal representing the update of the probabilistic model localises in adjacent premotor and superior parietal cortex. The superior parietal cortex is also implicated in the computation of the uncertainty of the probabilistic inference of pain. Overall, the results show that cortical regions typically associated with the sensory processing of pain (primary and secondary somatosensory cortices) encode how likely different pain intensities are to occur at any moment in time, in the absence of any other cues or information; the uncertainty of this inference is encoded in superior parietal cortex and used by a network of parietal- prefrontal regions to update the temporal statistical representation of pain intensity. + +The ability of the brain to extract regularities from temporal sequences is well- documented in other sensory domains such as vision and audition (Kourtzi and Welchman, 2019; Dehaene et al., 2015), but pain is a fundamentally different system with intrinsic motivational value and direct impact on the state of the body (Baliki and Apkarian, 2015; Fields, 2018; Seymour, 2019). Despite this fundamental difference, we show that temporal inferences of pain are generated using optimal Bayesian inference - tracking the frequency of low and high intensity pain states and their volatility (i.e. how likely they are to change) based on past experience. A more complex strategy involves trying to infer higher level statistical patterns within these sequences, namely representing all the transition probabilities between different states (Meyniel et al., 2016). Although this model fits 1/3 of our subjects best, overall it was not favoured over the simpler frequency learning model, which best describes the behaviour of approximately 2/3 of our sample (figure 3). At this stage it is not clear whether this is because of stable inter- individual differences, or whether given more time, more participants would be able to learn specific transition probabilities. However, it is worth noting that stable, individual differences in learning strategy have been previously reported in visual statistical learning (Karlaftis et al., 2019; Wang et al., 2017). + +The Bayesian frequency model is consistent with many other tasks that involve cognitive model learning or acquisition of explicit contingency knowledge across modalities, including pain (Yoshida et al., 2013; Jepma et al., 2018; Hoskin et al., 2019). This reflects a fundamentally different process to pain response learning - either in Pavlovian conditioning where simple autonomic, physiological or motor responses are acquired, or basic stimulus- response (instrumental / operant) avoidance or escape response learning. These behaviours are usually best captured by reinforcement learning models such as temporal difference learning (Seymour, 2019), and reflect a computationally different process (Carter et al., 2006). Having said that, such error- driven learning models have been applied to statistical learning paradigms in other domains before (Orpella et al., 2021), and so here we were able to directly demonstrate that it provided a less accurate model than Bayesian models (figure 3). In contrast to simple reinforcement learning models, Bayesian models allow building an internal, hierarchical model of the temporal statistics of the environment that can support a range of cognitive functions (Honey et al., 2012; Meyniel et al., 2016; Weiss et al., 2021). + +A key benefit of the computational approach is that it allows us to accurately map underlying operations of pain information processing to their neural substrates. Our study shows that the probabilistic inference of high pain frequency is encoded in the bilateral sensorimotor cortex, secondary somatosensory cortex, and right caudate (figure 5). The neural correlates of pain predictions arising from predictive Bayesian inference seem to contrast to a certain extent with those arising from value- based learning, which is typically characterised by non- probabilistic model- free learning and involves insula, anterior cingulate and ventromedial prefrontal cortices (Seymour and Mancini, 2020). An exception to this is the observation that the caudate nucleus correlates well with the posterior probability of high pain (i.e. its temporal inference). Although it is difficult to interpret this without an accompanying experimentally- matched value learning task, and without measuring conditioned responses such as autonomic responses, it may represent the parallel or integrative role of caudate in multiple divergent learning processes. + +A specific facet of the Bayesian model is the representation of an uncertainty signal, i.e. the posterior SD, and a model update signal, defined as the statistical KL divergence between consecutive posterior distributions. This captures the extent to which a model is updated when an incoming pain stimulus deviates from that expected, taking into account the uncertainty inherent in the original prediction. In our task, the uncertainty of the prediction was encoded in a right superior parietal region, which partially + +<--- Page Split ---> + +overlapped with a wider parietal region associated with the encoding of the model update (figures 6, 8). This emphasises the close relationship between uncertainty and learning in Bayesian inference (Koblinger et al., 2021). A previous study on statistical learning in other sensory domains reported that a more posterior, intraparietal region, was associated with the precision of the temporal inference (Meyniel and Dehaene, 2017). The role of the superior parietal cortex in uncertainty representation is also evident in other memory- based decision- making tasks, as the superior parietal cortex is more active for low vs. high confidence judgements (Hutchinson et al., 2014; Moritz et al., 2006; Sestieri et al., 2010). In addition to the parietal cortex, the model update signal was encoded in the left premotor cortex and bilateral dorsolateral prefrontal cortex (figure 7), neighbouring regions activated by pain statistical inferences (figure 8). This is particularly interesting, as the premotor cortex sits along a hierarchy of reciprocally and highly interconnected regions within the sensorimotor cortex. The premotor cortex has also been implicated in the computation of an update signal in visual and auditory statistical learning tasks (Meyniel and Dehaene, 2017). + +In conclusion, our study demonstrates that the pain system generates probabilistic predictions about the background temporal statistics of pain states, in absence of external cues and using Bayesian- like inference strategy. This extends both current anatomical and functional concepts of what is conventionally considered a 'sensory pain pathway', to include encoding not just stimulus intensity (Segerdahl et al., 2015; Wager et al., 2013) and location (Mancini et al., 2012), but the generation of more sophisticated and dynamic internal models of temporal statistics of pain intensity levels. Future studies will need to determine whether temporal statistical predictions modulate pain perception, similarly to other kinds of pain expectations (Büchel et al., 2014; Wiech, 2016; Wager et al., 2004). More broadly, temporal statistical learning is likely to be most important after injury, when continuous streams of fluctuating pain signals ascend nociceptive afferents to the brain, and their underlying pattern may hold important clues as to the nature of the injury, its future evolution, and its broader semantic meaning in terms of the survival and prospects of the individual. It is therefore possible that the underlying computational process might go awry in certain instances of chronic pain, especially when instrumental actions can be performed that might influence the pattern of pain intensity (Jepma et al., 2018; Jung et al., 2017). Thus, future studies could explore both how temporal statistical learning interacts with pain perception and controllability, as well as its application to clinical pain. + +## METHODS + +## Code and data availability + +Raw functional imaging data is deposited at OpenNEURO https://openneuro.org/datasets/ds003836 and derived statistical maps are available at NeuroVault (upon acceptance]). Sequence generation, task instructions and behavioural data can be found at https://github.com/NoxLab- cam/pain_statistics_3tfrmri. Analysis code can be found at https://github.com/syzhang/tsl_paper. + +## Participants + +Thirty- five healthy participants (17 females; mean age 27.4 years old; age range 18- 45 years) took part in two experimental sessions, 2- 3 days apart: a pain- tuning and training session and an MRI session. Each participant gave informed consent according to procedures approved by University of Cambridge ethics committee (PRE.2018.046). + +## Protocol + +The electrical stimuli were generated using a DS5 isolated bipolar current stimulator (Digitimer), delivered to surface electrodes placed on the index and middle fingers of the left hand. All participants underwent a standardised intensity work- up procedure at the start of each testing day, in order to match subjective pain levels across sessions to a low- intensity level (just above pain detection threshold) and a high- intensity level that was reported to be painful but bearable (>4 out of 10 on a VAS ranging from 0 ['no pain'] to 10 ['worst imaginable pain']). The pain delivery setup was identical for lab- based and MR sessions. After identifying appropriate intensity levels, we checked that discrimination accuracy was >95% in a short sequence of 20 randomised stimuli. This was done to ensure that uncertainty in the sequence task would derive from the temporal order of the stimuli rather than their current intensity level or discriminability. If needed, we tweaked the stimulus intensities to achieve our target discriminability. Next, we gave the task instructions to each participants (openly available https://github.com/NoxLab- cam/pain_statistics_3tfrmri). + +<--- Page Split ---> + +After receiving a shock on trial t, subjects were asked to predict the probability of receiving a stimulus of the same or different intensity on the upcoming trial (trial t+1). We informed participants that in the task they "would receive two kinds of stimuli, a low intensity shock and a high intensity shock. The L and H stimuli would be presented in a sequence, in an order set by the computer. After each stimulus, the following stimulus could be either the same or different than the previous one. The computer sets the probability that after a given stimulus (for example L) there would be either L or H" (we showed a visual representation of this example). We asked participants to "always try to guess the probability that after each stimulus there will the same or a different one" and we informed them that "the computer sometimes changes its settings and sets new probabilities", so to pay attention all the time. We also told them the sequence would be paused occasionally in order to collect probability estimates from participants using the scale depicted in Fig 1. A white fixation cross was displayed on a dark screen throughout the trial, except when a response was requested every 12- 18 trials. The interstimulus interval was 2.8- 3 seconds. There were 300 stimuli in each block, lasting approx. 8 minutes. Average intensity ratings for each stimulus level were collected after each block during a short break. Low intensity stimuli were felt by participants as barely painful, rated on average 1.39 (SD 0.77) on a scale ranging from 0 (no pain) to 10 (worst pain imaginable). In contrast, high intensity stimuli were rated as more than 4 times higher than low intensity stimuli (mean 5.74, SD 4.85). Participants were given 4 blocks of practice, 2- 3 days prior the imaging sessions, and 5 blocks (1500 stimuli in total) during task fMRI. + +The sequence of stimuli was unique and generated as in (Meynie et al., 2016). L and H stimuli were drawn randomly from a 2x2 transition probability matrix, which remained constant for a number of trials (chunks). The probability of a change was 0.014. Chunks had to be \(>5\) and \(< 200\) trials long. In each chunk, transition probabilities were sampled independently and uniformly in the 0.15- 0.85 range (in steps of 0.05), with the constraint that at least one of the two transition probabilities must be \(> / < 0.2\) than in the previous chunk. Participants were not informed when the matrix was resampled, and a new chunk started. + +Behavioural data analysis were conducted with Python packages pandas (pypi version 1.1.3) and scipy (pypi version 1.5.3). Effect size was calculated as Cohen's d for t- tests. + +## Computational modelling of temporal statistical learning + +Learning models The models used in comparison are listed as followed: + +Random (baseline model) Probabilities are assumed fixed and reciprocal for high and low stimuli, where \(p_{h} = 1 - p_{l}\) ( \(p_{l}\) as free parameter). Uncertainty are also assumed fixed for high/low pain. + +Rescorla- Wagner (RW model) Rated probabilities are assumed to be state values, which were updated as + +\[V_{t + 1}\gets V_{t} + \alpha (R_{t} - V_{t})\] + +, where \(R_{t} = 1\) if stimulus was low, and O otherwise. \(\alpha\) was fitted as free parameter (see (Rescorla et al., 1972)). + +Bayesian models Bayesian models update each trial with stimulus identity information to obtain upcoming trial probability from posterior distribution (Meynie et al., 2016). Using Bayes' rule, the model parameters \(\theta_{t}\) is estimated at each trial \(t\) provided previous observations \(y_{1:t}\) (sequence of high or low pain), given a model \(M\) . + +\[p(\theta_t|y_{1:t},M)\sim p(y_{1:t}|\theta_t,M)p(\theta_t,M)\] + +Stimulus information can either be frequency or transition of the binary sequence. There are 'fixed' models that assume no sudden jump in stimuli probabilities, and 'jump' models that assume the opposite. The four combinations were fitted and compared. + +1. Fixed frequency model For fixed models, the likelihood of parameters \(\theta\) follows a Beta distribution with parameters \(N_{h} + 1\) and \(N_{l} + 1\) , where \(N_{h}\) and \(N_{l}\) are the numbers of high and low pain in the sequence \(y_{1:t}\) . Given that the prior is also a flat Beta distribution with parameters [1,1], the posterior can be analytically obtained with: + +\[p(\theta |y_{1:t}) = Beta(\theta |N_{h} + 1,N_{l} + 1)\] + +<--- Page Split ---> + +The likelihood of a sequence \(y_{1:t}\) given model parameters \(\theta\) can be calculated as: + +\[p(y_{1:t}|\theta) = p(y_{1}|\theta)\prod_{t = 2}^{t}p(y_{i}|\theta ,y_{i - 1})\] + +Finally, the posterior probability of a stimulus occurring in the next trial can be estimated with Bayes' rule: + +\[p(y_{t + 1}|y_{1:t}) = \int p(y_{t + 1}|\theta ,y_{t})p(\theta |y_{1:t})d\theta\] + +Priors window and decay were fitted as free parameters, where window is the previous \(n\) trials where frequency of stimuli were estimated, and decay is the previous \(n\) trials where the frequency of stimuli further from current trial were discounted following an exponential decay. + +When window \(= w\) is applied, then \(N_{h}\) and \(N_{l}\) are counted within the window of \(w\) trials \(y_{t - w,t}\) . When decay \(= d\) is applied, an exponential decay factor \(e^{(-\frac{k}{d})}\) is applied to the \(k\) trials before their sum is calculated. Both window and decay were used simultaneously. + +2. Fixed transition model Priors window and decay were fitted as free parameters as Fixed frequency model above, however, the transition probability was estimated instead of frequency. The likelihood of a stimuli now depends on the estimated transition probability vector \(\theta \sim [\theta_{h|l},\theta_{l|h}]\) and the previous stimulus pairs \(N\sim [N_{h|l},N_{l|h}]\) . Given that both likelihood and prior can be represented using Beta distributions as before, the posterior result can be analytically obtained as: + +\[p(\theta |y_{1:t}) = Beta(\theta_{h|l}|N_{h|l} + 1,N_{l|l} + 1)Beta(\theta_{l|h}|N_{l|h} + 1,N_{h|h} + 1)\] + +3. Jump frequency model In jump models, parameter \(\theta\) is no longer fixed, instead it can change from one trial to another with a probability of \(p_{jump}\) . Prior \(p_{jump}\) was fitted as a free parameter, representing the subject's assumed probability of a jump occurring during the sequence of stimuli (e.g. a high \(p_{jump}\) assumes the sequence can reverse quickly from a low pain majority to a high pain majority). The model can be approximated as a Hidden Markov Model (HMM) in order to compute the joint distribution of \(\theta\) and observed stimuli iteratively, + +\[p(\theta_{t + 1},y_{1:t + 1}) = p(y_{t + 1}|\theta_{t + 1},y_{t})\int p(\theta_{t},y_{1:t})p(\theta_{t + 1}|\theta_{t})d\theta_{t}\] + +where the integral term captures the change in \(\theta\) from one observation \(t\) to the next \(t + 1\) , with probability \((1 - p_{jump})\) of staying the same and probability \(p_{jump}\) of changing. This integral can be calculated numerically within a discretised grid. The posterior probability of a stimulus occurring in the next trial can then be calculated using Bayes' rule as + +\[p(y_{t + 1}|y_{1:t}) = \int p(y_{t + 1}|\theta_{t + 1})p(\theta_{t + 1}|y_{1:t}\theta_{t + 1})\] \[\qquad = \int p(y_{t + 1}|\theta_{t + 1})\left[\int p(\theta_{t}|y_{1:t})p(\theta_{t + 1}|\theta_{t})d\theta_{t}\right]d\theta_{t + 1}\] \[\qquad = \int p(y_{t + 1}|\theta_{t + 1})\left[(1 - p_{jump})p(\theta_{t + 1} = \theta_{t}|y_{1:t}) + p_{jump}p(\theta_{0})\right]d\theta_{t + 1}\] + +4. Jump transition model Similar to jump frequency model above, prior \(p_{jump}\) was fitted as a free parameter, but estimating transition instead of frequency. The difference is the stimulus at trial \(y_{t + 1}\) now dependent of stimulus at the previous trial, hence the addition of the term \(y_{t}\) in the joint distribution term, shown below. + +\[p(y_{t + 1}|y_{1:t}) = \int p(y_{t + 1}|\theta_{t + 1},y_{t})\left[(1 - p_{jump})p(\theta_{t + 1} = \theta_{t}|y_{1:t}) + p_{jump}p(\theta_{0})\right]d\theta_{t + 1}\] + +<--- Page Split ---> + +KL divergence Kullback- Leibler (KL) divergence quantifies the distance between two probability distributions. In the current context, it measures the difference between the posterior probability distributions of successive trials. It is calculated as + +\[D_{K L}(P\parallel Q) = \sum_{x\in \mathcal{X}}P(x)l o g\left(\frac{P(x)}{Q(x)}\right)\] + +330 , where \(P\) and \(Q\) represents the two discrete posterior probability distributions calculated in discretised 331 grids \(\mathcal{X}\) . KL divergence can be used to represent information gains when updating after successive trials 332 (Meyniel and Dehaene, 2017). + +Subject rated probability For each individual subject, model predicted probabilities \(p_{k}\) from the trial \(k\) was used as predictors in the regression: + +\[y_{k}\sim \beta_{0} + \beta_{1}\cdot p_{k}(M_{i},\theta_{i}) + \beta_{2}\cdot N_{s} + \epsilon\] + +333 where \(y_{k}\) is the subject rated probabilities, \(M_{i}\) is the \(i\) th candidate model, \(N_{s}\) is the session number within subject, \(\beta_{0}\) , \(\beta_{1}\) , \(\beta_{2}\) and \(\theta_{i}\) are free parameters to be fitted, and \(\epsilon\) is normally distributed noise added 335 to avoid fitting errors (Maheu et al., 2019). + +## Model fitting + +337 To estimate the model free parameters from data, Bayesian information criteria (BIC) values were 338 calculated as: + +\[\mathrm{BIC} = n\cdot \log \hat{\sigma}_{\epsilon}^{2} + k\cdot \log n\] + +\[\hat{\sigma}_{\epsilon}^{2} = \min_{n}\frac{1}{n}\sum_{k = 1}^{n}\left(y_{k} - \hat{y}_{k}\right)\] + +339 where \(\hat{\sigma}^{2}\) is the squared residual from the linear model above that relates subject ratings to model predicted 340 probabilities, and \(n\) is the number of free parameters fitted. + +341 We use fmincon in MATLAB to minimise the BIC (as approximate for negative log likelihood, Maheu 342 et al. (2019)) for each subject/model. The procedure was repeated 100 times with different parameter 343 initialisation, and the mean results of these repetitions were taken as the fitted parameters and minimised 344 log likelihoods. + +## Model comparison + +346 In general, the best fit model was defined as the candidate model with the lowest averaged BIC. We 347 conducted a random effect analysis with VBA toolbox (Daunizeau et al., 2014), where fitted log likelihoods 348 from each subject/model pair was used as model evidence. With this approach, model was treated as 349 random effects that could differ between individuals. This comparison produces model frequency (how 350 often a given model is used by individuals), model exceedance probability (how likely it is that any given 351 model is more frequent than all other models in the comparison set), and protected exceedance probability 352 (corrected exceedance probability for observations due to chance) (Stephan et al., 2009; Rigoux et al., 353 2014). These values are correlated and would be considered together when selecting the best fit model. + +## Neuroimaging data + +## Data acquisition + +356 First, we collected a T1- weighted MPRAGE structural scan (voxel size 1 mm isotropic) on a 3T Siemens 357 Magnetom Skyra (Siemens Healthcare), equipped with a 32- channel head coil (Wolfson Brain Imaging 358 Centre, Cambridge). Then we collected 5 task fMRI sessions of 246 volumes using a gradient echo 359 planar imaging (EPI) sequence (TR = 2000 ms, TE = 23 ms, flip angle = 78°, slices per volume = 31, 360 Grappa 2, voxel size 2.4 mm isotropic, A>P phase- encoding; this included four dummy volumes, in 361 addition to those pre- discarded by the scanner). In order to correct for inhomogeneities in the static 362 magnetic field, we imaged 4 volumes using an EPI sequence identical to that used in task fMRI, inverted 363 in the posterior- to- anterior phase encoding direction. Full sequence metadata are available at OpenNeuro 364 (https://openneuro.org/datasets/ds003836). + +<--- Page Split ---> + +## Preprocessing + +Imaging data were preprocessed using fmriprep (pypi version: 20.1.1, RRID:SCR_016216) with Freesurfer option disabled, within its Docker container. Processed functional images had first four dummy scans removed, and then smoothed in an 8mm Gaussian filter in SPM12. + +## GLM analysis + +Nipype (pypi version: 1.5.1) was used for all fMRI processing and analysis within its published Docker container. Nipype is a python package that wraps around fMRI analysis tools including SPM12 and FLS in a Debian environment. + +First and second level GLM analyses were conducted using SPM12 through nipype. In all first level analyses, 25 regressors of no interest were included from fmriprep confounds output: CSF, white matter, global signal, dvars, std_dvars, framewise displacement, rmsd, 6 a_comp_cor with corresponding cosine components, translation in 3 axis and rotation in 3 axis. Sessions within subject are not concatenated. + +In second level analyses, all first level contrasts were entered into a one- sample T- test, with group subject mask applied. The default FDR threshold used was 0.001 (set in Nipype threshold node height_threshold=0.001). + +For visualisation and cluster statistics extraction, nilearn (pypi version: 1.6.1) was used. A cluster extent of 10 voxels was applied. Visualised slice coordinates were chosen based on cluster peaks identified. Activation clusters were overlayed on top of a subject averaged anatomical scan normalised to MNI152 space as output by fmriprep. + +## GLM design + +All imaging results were obtained from a single GLM model. We investigated neural correlates using the winning Bayesian jump frequency model. All model predictors were generated with the group mean fitted parameters in order to minimise noise. First level regressors include the onset times for all trials, high pain trials, and low pain trials (duration=0). The all trial regressor was parametrically modulated by model- predicted posterior mean of high pain, the KL divergence between successive posterior distributions on jump probability, and the posterior SD of high pain. + +For second level analysis, both positive and negative T- contrasts were obtained for posterior mean, KL divergence and uncertainty parametric modulators, across all the first level contrast images from all subjects. A group mean brain mask was applied to exclude activations outside the brain. Given that high and low pain are reciprocal in probabilities, a negative contrast of posterior mean of low pain would be equivalent to the posterior mean of high pain. In addition, high and low pain comparisons were done using a subtracting T- contrast between high and low pain trial regressors. We corrected for multiple comparisons with a cluster- wise FDR threshold of \(p< 0.001\) for both parametric modulator analyses, reporting only clusters that survived this. + +## ACKNOWLEDGEMENTS + +The study was funded by a Medical Research Council Career Development Award to Flavia Mancini (MR/T010614/1) and Wellcome Trust grants to Ben Seymour (097490). We are grateful to Professor Zoe Kourtzi and Dr Michael Lee for helpful discussions about the concept of the study, and to the staff of the Wolfson Brain Imaging Centre for their support during data collection. The authors declare no competing interest. + +## AUTHOR CONTRIBUTIONS + +FM and BS designed the study. FM collected the data and SZ analysed the data. All authors wrote the paper. + +## REFERENCES + +Atlas, L. Y., Bolger, N., Lindquist, M. A., and Wager, T. D. (2010). Brain mediators of predictive cue effects on perceived pain. Journal of Neuroscience, 30(39):12964- 12977. Baliki, M. N. and Apkarian, A. V. (2015). 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(2013). Uncertainty increases pain: evidence for a novel mechanism of pain modulation involving the periaqueductal gray. Journal of Neuroscience, 33(13):5638- 5646. Zhang, S., Mano, H., Ganesh, G., Robbins, T., and Seymour, B. (2016). Dissociable learning processes underlie human pain conditioning. Current Biology, 26(1):52- 58. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- tslfrmisupplementary.pdf- rs.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__05f28e060c3bc16824dbd8aa76dba38dd15d721d8f4e14b66cd59ec29caee55f/preprint__05f28e060c3bc16824dbd8aa76dba38dd15d721d8f4e14b66cd59ec29caee55f.mmd b/preprint/preprint__05f28e060c3bc16824dbd8aa76dba38dd15d721d8f4e14b66cd59ec29caee55f/preprint__05f28e060c3bc16824dbd8aa76dba38dd15d721d8f4e14b66cd59ec29caee55f.mmd new file mode 100644 index 0000000000000000000000000000000000000000..1277488dff85b438140fdb49a1a2fd3a07d1831f --- /dev/null +++ b/preprint/preprint__05f28e060c3bc16824dbd8aa76dba38dd15d721d8f4e14b66cd59ec29caee55f/preprint__05f28e060c3bc16824dbd8aa76dba38dd15d721d8f4e14b66cd59ec29caee55f.mmd @@ -0,0 +1,873 @@ + +# Role of the hippocampus in decision making under uncertainty + +Bahaaeddin Attaallah atallah.bahaa@gmail.com + +University of Oxford https://orcid.org/0000- 0002- 7842- 7974 Pierre Petitet University of Oxford https://orcid.org/0000- 0003- 1422- 5326 Rhea Zambellas University of Oxford Sofia Toniolo University of Oxford Maria Maio University of Oxford Akke Ganse- Dumrath University of Oxford https://orcid.org/0000- 0002- 4828- 8117 Sarosh Irani University of Oxford Sanjay Manohar University of Oxford https://orcid.org/0000- 0003- 0735- 4349 Masud Husain University of Oxford https://orcid.org/0000- 0002- 6850- 9255 + +## Article + +Keywords: + +Posted Date: August 11th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 3227833/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> + +Version of Record: A version of this preprint was published at Nature Human Behaviour on April 29th, 2024. See the published version at https://doi.org/10.1038/s41562-024-01855-2. + +<--- Page Split ---> + +# Role of the hippocampus in decision making under uncertainty + +Bahaaeeddin Attaallah \(^{1*}\) , Pierre Petitet \(^{2}\) , Rhea Zambellas \(^{1}\) , Sofia Toniolo \(^{1}\) , Maria Raquel Maio \(^{1}\) , Akke Ganse- Dumrath \(^{1,2}\) , Sarosh R. Irani \(^{1}\) , Sanjay G. Manohar \(^{1,2}\) , Masud Husain \(^{1,2}\) + +\(^{1}\) Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU \(^{2}\) Department of Experimental Psychology, University of Oxford, Oxford OX1 3PH + +\*To whom correspondence should be addressed; E- mail: Bahaaeeddin.Attaallah@nhs.net + +The role of the hippocampus in decision making is poorly understood. Because of its prospective and inferential functions, we hypothesized that it might be required specifically when decisions involve evaluation of uncertain values. Here, a group of individuals with autoimmune limbic encephalitis (ALE) – a condition known to locally affect the hippocampus – was tested on how they evaluate reward against uncertainty compared to evaluation of reward against another key attribute – physical effort. Across four experiments requiring participants to make trade- offs between reward, uncertainty and effort, ALE patients demonstrated blunted sensitivity to reward and effort whenever uncertainty was considered, despite demonstrating intact uncertainty sensitivity. By contrast, valuation of these two attributes (reward and effort) was intact on uncertainty- free tasks. Reduced sensitivity to changes in reward under uncertainty correlated with severity of hippocampal damage. Together, these findings provide evidence for a context- sensitive role of the hippocampus in value- based decision making, apparent specifically under conditions of uncertainty. + +<--- Page Split ---> + +## Introduction + +Humans often face situations where they have to decide if the reward they might obtain from their actions is worth the cost required for it - e.g., when having to allocate effort to accomplish something. Whether it is buying an item in a grocery store or making life- changing resolutions, such trade- offs can influence our decisions and behaviour in our daily lives. Emerging evidence from animal studies suggests that the hippocampus might contribute to reward processing and valuation, with reports indicating several forms of reward representation in the hippocampal formation and its extended networks (for a review, see Sosa and Giocomo, 2021). Theories and empirical reports investigating such a possible hippocampal role in humans have tied it to its well- known functions in memory, associative inference and imagination (Biderman et al., 2020; Palombo et al., 2015a). Mechanistically, these investigations implicate the hippocampus in several processes including spreading of values between different contexts (Gerraty et al., 2014; Hassabis et al., 2007; Wimmer and Shohamy, 2012), constructing values from prior experiences (Barron et al., 2013), updating (Gupta et al., 2009; Gutbrod et al., 2006) and stabilisation of preferences (Enkavi et al., 2017). + +One evolving concept connecting these unique properties of the hippocampus proposes that it provides context against which reward is evaluated to support value- based decisions and preferences (Bornstein and Norman, 2017; Gershman and Daw, 2017). This process might be mediated by hippocampus- dependent mental time travel both into the past (sampling from memory) and the future (sampling from projected possible futures) to allocate these contexts. For example, ‘preplay’ signals – corresponding to reward delivery in yet- to- be- explored environments – and ‘look ahead’ signals – representing future trajectories leading to goals – have both been recorded in rodent hippocampus (Freyja Ólafsdóttir et al., 2015; Johnson and Redish, 2007; Pfeiffer and Foster, 2013). Similarly, in humans, fMRI hippocampal activity was observed when people make decisions that involve reward anticipation and future considerations (Iigaya + +<--- Page Split ---> + +1 et al., 2020; Palombo et al., 2019, 2015b). + +2 + +3 With this perspective, the hippocampus might be implicated in evaluation of reward when episodic thinking is critically involved (e.g., to process values of projected possible futures) (Hunter and Daw, 2021). Such scenarios involve probabilistic consideration of future value states (i.e., making decisions under uncertainty) (Schacter et al., 2015; Tversky and Kahneman, 1974). By contrast, this conceptualisation of the hippocampal role in motivated behaviour suggests that contexts of deterministic nature (e.g., when evaluating reward against known physical effort costs) should be less influenced by hippocampus- related prospective computations. + +10 In a recent report, we demonstrated that the hippocampus might be implicated in active information gathering prior to committing to decisions under uncertainty in people with subjective cognitive impairment (Attaallah et al., 2022). Markers of increased reactivity to uncertainty (e.g., rapid collection of information) were found to be associated with heightened hippocampal- insular connectivity. Such a finding aligns in part with previous studies highlighting hippocampal contribution to uncertainty processing and related forms of decision making such inter- temporal choices and visual information search (Harrison et al., 2006; Lucas et al., 2019; Rigoli et al., 2019; Strange et al., 2005; Tobia et al., 2012). However, it remains unclear whether such a proposed role of the hippocampus in valuation and decision making is contextually specific to uncertainty (i.e., implicated only when agents have to consider uncertainty) or reflects a general hippocampal processing of reward and value regardless of contexts. + +21 + +22 To answer this question and to directly investigate hippocampal involvement in goal- directed decision making and reward valuation, we recruited 19 people with autoimmune limbic encephalitis (ALE) – a rare neurological condition known to affect the hippocampus. Patients in the chronic phase of ALE characteristically have highly focal hippocampal atrophy (Argy + +<--- Page Split ---> + +1 ropoulos et al., 2019; Finke et al., 2017; Hanert et al., 2019; Irani et al., 2013; Kotsenas et al., 2 2014; Malter et al., 2014; Miller et al., 2017; Szots et al., 2014), making them an ideal model of 3 selective hippocampal dysfunction that is well suited to making inferences based on structure- 4 function correlations (Argyropoulos et al., 2019; Miller et al., 2020; Spanò et al., 2020a,b). This 5 is especially feasible in such an experimental group as the extent of hippocampal damage varies 6 between ALE patients depending on the course of the illness and the interval between disease 7 onset and treatment initiation (Finke et al., 2017; Malter et al., 2014). + +9 Participants (patients and healthy matched controls) were tested in four experiments examining how people make decisions considering reward, uncertainty and/or physical effort attributes. In the first two experiments, we used the Circle Quest behavioural paradigm which has been previously tested and validated in healthy people and patient with subjective cognitive impairment (Attaallah et al., 2022; Petitet et al., 2021). The original paradigm has two versions: active and passive (Exps. 1 & 2). The active version of the task examines how people give up reward to obtain information and reduce uncertainty before committing to decisions. The passive version allows limited agency over uncertainty to examine how people make passive decisions on whether to accept or reject offers based on predetermined levels of reward and uncertainty. In Exp. 3, effort-based decision making (EBDM) was examined using a modified version of an extensively validated behavioural paradigm used in previous studies in healthy people and individuals with neurological disorders (Le Heron et al., 2018a; Saleh et al., 2021). This paradigm has a similar design to the passive version of Circle Quest and examines how people make passive decision weighing rewards against physical effort. In Exp. 4, a third novel version of Circle Quest was introduced to investigate how people make passive decisions considering the three attributes of interest (reward, uncertainty and physical effort). In other words, participants in Exp. 4 were required to make decisions weighing the reward on offer given both physical effort + +<--- Page Split ---> + +1 cost and uncertainty in the environment. + +2 + +3 The results from these experiments converged to indicate that in ALE patients, despite intact 4 sensitivity to uncertainty, the presence of uncertainty is associated with blunted sensitivity to 5 other value attributes (reward and effort cost). In the active version of the Circle Quest task 6 (Exp. 1), patients were less sensitive to cost of sampling when gathering information to support 7 decisions under uncertain conditions, resulting in faster, extensive and wasteful sampling when 8 cost of sampling and reward on offer increased. In the passive version of Circle Quest (Exp. 2), 9 ALE patients were significantly less sensitive to changes in reward, and this effect correlated 10 with lower sensitivity to sampling cost changes observed in active sampling (Exp. 1) as well 11 as with severity of hippocampal atrophy. When assessed on the effort- based decision making 12 task (Exp. 3), no significant difference was found between patients and controls when they 13 made EBDM decisions without uncertainty, indicating intact valuation of reward and effort un- 14 der conditions that do not feature uncertainty. By contrast, on the third version of Circle Quest 15 (Exp. 4), patients were less sensitive to changes in effort and reward compared to controls, 16 while their sensitivity to uncertainty was intact. Intact sensitivity to uncertainty was observed 17 across all versions of Circle Quest paradigm (Exps. 1, 2 & 4). + +18 + +19 Taken together, the results indicate an uncertainty- sensitive role of the hippocampus in 20 value- based decision making. The findings might represent an important step forward in un- 21 derstanding selective hippocampal contributions to goal- directed and motivated behaviour. + +<--- Page Split ---> + +## Results + +## Experimental paradigms + +Exp. 1 – Active information gathering prior to committing to decisions under uncertaintyIn the first experiment (Exp. 1), participants completed a shorter version of the \*Circle Quest\* paradigm, a recently developed behavioural task investigating active information sampling before committing to decisions under uncertainty (Attaallah et al., 2022; Petitet et al., 2021). Participants were asked to maximise their reward by localising a fixed-size hidden circle as precisely as possible. Uncertainty about the precise location of the hidden circle could be reduced by gathering information through touching the screen at different locations. If the location where they touched was situated inside the hidden screen, a purple dot appeared and if the location was outside that hidden circle, a white dot appeared. Participants started each trial with an initial credit reserve \((R_0)\) from which the cost to obtain a new sample \((\eta_s)\) was subtracted with each additional sample. After the active sampling phase, during which participants could sample the screen for information without restriction to speed or location, a blue disc matching the size of the hidden circle appeared. Participants were then required to move this blue disc to where they thought the hidden circle was located. Depending on localisation error (how far the blue disc centre is from the true location of the hidden circle) and cost of sampling, the score for each trial was calculated and provided as feedback at the end of the trial (equation in Figure 1). The task thus imposed an economic trade-off between the benefit and cost of obtaining information. There were two levels of sampling cost (low and high) and two levels of initial credit reserve (low and high). Uncertainty – indexed by circle localisation expected error \((EE)\) – was quantified as the probability-weighted average of all the possible errors that could occur upon placing the localisation disc (Figure 2d.; see \*Methods\*). In order to expose participants to the task environment and its scoring, the testing session began with a training task in which they practised circle localisation for various levels of uncertainty and reward. This also helped + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1: Task paradigm (Exp. 1) – Active information gathering. At the beginning of each trial, a purple dot is displayed to give an initial clue about the location of the hidden circle. Participants were informed that touching the screen could provide further clues (either purple or white dots) to narrow the solution space of the location of the hidden circle. If touching the screen at a certain location yielded a purple dot, this location was situated inside the hidden circle. By contrast, if a white dot appeared, then the location was outside the hidden circle. Participants were instructed that there were no constraints on where and when to touch the screen within the allocated 18 seconds per trial, and that they could stop whenever they wanted to. Touching the screen to gather information, however, came at a cost which was subtracted from the initial reward reserve participants start the trial with ( \(R_{0}\) ; shown inside the two purple circles on the side of the search space). In the depicted trial for example, the participant started with 95 credits and lost one credit per additional sample acquired. After the active sampling phase (18 seconds), a blue disc appeared automatically in the centre of the screen, and participants were instructed to move this disc to where they thought the hidden circle was. Each trial was scored by removing a localisation error penalty ( \(e.\eta_{e}\) where \(e\) is the localisation error in Pixels, and \(\eta_{e}\) is the error cost in credits per pixel) from the remaining reward reserve ( \(R_{0} - s.\eta_{s}\) where \(s\) is the number of extra sampled acquired and \(\eta_{s}\) is the sampling cost). Error cost ( \(\eta_{e}\) ) was constant and equal to 1.2 credits per pixel
+ +<--- Page Split ---> + +1 establish the effect of visuospatial demand on localisation performance. + +## 2 Exp. 2 - Passive decision making under uncertainty + +3 In the second experiment (Exp. 2), participants performed a second version of the Circle Quest paradigm examining how they weigh potential rewards against uncertainty when making decisions. Eight dots (four purple and four white) were always presented on the screen in each trial. The spatial configurations of these dots was manipulated experimentally to produce different levels of uncertainty, e.g., when purple dots were spaced widely apart, the location of the hidden circle was less uncertain than when they were clumped closer together because the former configuration imposes more limitations on possible circle placements. To limit memory load, a circle of the same size as the hidden circle was always present on each side of the screen to provide a continuous reminder of its size. + +On each trial of the passive task, participants were asked to report uncertainty estimates (confidence rating on a scale from 0- 100) reflecting how well they thought they might be able to locate the hidden circle, given the configuration of dots on the screen. Next, they were presented with the reward on offer, and were asked "Do you want to play this trial for this potential reward?" to which they could respond either "Yes" or "No" (by pressing on the corresponding answer on the touchscreen). Participants were told that 10 of their "Yes" responses would be randomly selected at the end of the experiment, and that they would have to place a blue disc (of the same size as the hidden circle) where they thought the hidden circle was located for each of these trials. Their monetary reward would be based on their localisation performance on these 10 trials. If they located the hidden circle perfectly (i.e., they placed the blue disc exactly on top of the hidden circle), they won all credits on offer. If not, they lost credits proportionally to the magnitude of their localisation error. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2: Task paradigm (Exp. 2) – Passive decision making under uncertainty. The task examines how people weigh potential rewards against uncertainty when making decisions. a. The purple and white dots on the screen provide clues about the location of a hidden circle of fixed size. Purple dots fall within the area of the hidden circle, while white dots are located outside it. The two purple circles on the side are of the same size as the hidden circle. The two numbers on the side display the number of credits (reward) on offer. b. Different spatial configurations are associated with different levels of uncertainty. Uncertainty is quantified as the expected error upon circle localisation \((EE)\) . This is equal to the probability-weighted average of all the possible errors that could be resulting when placing the localisation disc (blue disc in d) at the best possible location. The best possible location is the centroid of the solution space, which is the set of the centres of all the possible circles that could be a solution for the spatial configuration displayed. d. Participants first reported their subjective estimation of uncertainty (i.e., how confident they are about the location of the hidden circle given the information displayed on the screen). After this, the credits on offer appeared (displayed inside the two purple circles on the side). Participants could decide to accept or reject the offer to locate the hidden circle. d. At the end of the experiment, participants had to play 10 of the accepted offers by placing a blue disc on top of where they thought the hidden circle was located. These trials determined the scores in the task. The score was equal to the reward on offer minus a penalty reflecting their localisation error (i.e., how far the blue disc centre was from the centre of the hidden circle).
+ +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3: Task paradigm (Exp. 3) – Effort-based decision making task. The task was similar in design to Circle Quest passive choices task. First, calibration of the hand-held dynamometer was done based on each participant's maximum voluntary contraction. Participants were then familiarised with the effort levels they would encounter in the task by asking them to squeeze the handle up to the effort level indicated by the yellow line: the higher the line the more effort they needed to exert. They did this twice for each effort level. Next, participants made decisions indicating whether the reward (apples) on offer is worth the effort assigned to it. They were told that at the end of the experiment, 10 of their decisions would be selected randomly and that they would have to play them in order to obtain the apples.
+ +## Exp. 3 – Effort-based decision making + +To investigate effort- based decision making we used a modified version of a well- validated effort- based decision making task that has been extensively used in healthy people and different patient groups (Aridan et al., 2019; Bonnelle et al., 2016, 2015; Chong et al., 2016; Le Heron et al., 2018b; Saleh et al., 2021). This task had a similar design as the passive choices task used in Exp. 2 to investigate reward valuation against uncertainty (Figure 2a). Reward was represented as apples on trees that participants were asked to weigh against physical effort levels that they needed to exert in order to obtain the apples (Figure 7). Physical effort in the task pertains to squeezing a hand- held dynamometer up to various force levels. There were five effort levels corresponding to \(16, 32, 48, 64\) and \(80\%\) of participant's maximal voluntary contraction (MVC) measured at the beginning of the task. After a familiarisation period with these effort levels, participants made Accept- Reject decisions for various reward- effort combinations. Similar to the passive version of Circle Quest task, individuals were instructed + +<--- Page Split ---> + +1 that some of the offers they accepted would be randomly selected at the end of the experiment 2 to be carried out, and that they would receive monetary rewards based on the number of apples 3 they collect. + +## Exp. 4 - Effort-based decision making under uncertainty + +5 In the fourth experiment (Exp. 4), participants were re- invited to perform a third version of 6 the Circle Quest paradigm but this time designed to investigate effort- based decision- making 7 under uncertainty. The task was similar to those in Exps. 2 and 3. However, instead of making 8 decisions (accept/reject) based on two attributes (reward vs. uncertainty as in Exp. 2 and reward 9 vs. effort in Exp. 3), participants now had to make decisions under the three attributes together 10 (reward, uncertainty and effort simultaneously) (Figure 4a.). + +11 Reward was presented as credits that could be won if participants completed two steps. 12 First, they had to achieve the required level of effort (as in Exp. 3). Second, they had to find 13 the location of the hidden circle without errors (as in Exp. 1 & 2). But the blue disc used to 14 localise the hidden circle appeared only when the required level of effort was achieved. If the 15 required effort level was met, participants could then win credits depending on how accurate 16 their localisation using the blue disc was from the true location of the hidden purple circle. + +17 Thus, similar to Exp. 2, on each trial, participants were presented with a number of credits 18 on offer (same levels as in Exp. 2) that they could achieve if they managed to perfectly localise 19 the hidden circle given the configuration of dots on display. However, in order to make the 20 localisation disc appear, they now had to achieve the effort level assigned to the trial (same levels 21 as in Exp. 3). Importantly, uncertainty was omitted on half of the trials by showing the true 22 location of the hidden circle. On the other half where uncertainty was present, it corresponded 23 to the mid- range of expected error (EE) used in Exp. 2 (EE: 31.8- 73.95 pixels). Participants 24 also reported their subjective estimates of uncertainty separately. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4: Task paradigm (Exp. 4) – Effort-based decision making under uncertainty. a. Once training was complete, participants made accept/reject decisions (200 trials) during the decision making phase weighing reward (credits) on offer against different effort levels required. They also had to take into consideration whether decisions were made under uncertainty or not, i.e., they had to decide whether the reward on offer, given the level of uncertainty, was worth making the effort. Uncertainty pertains to the location of the hidden circle which participants estimate from the configuration of dots on the screen (see Methods). Absence of uncertainty was indicated simply by displaying the purple circle at its precise location. b. At the end of the experiment, participants played 24 trials that were randomly selected from the decision phase. They had to make the physical effort in order to be given the opportunity to place the blue circle where they thought the purple circle was hidden (in 12 of the trials, the location of the purple circle was shown; in the other 12 it was not). Performance accuracy determined the credits participants eventually won. Participants were familiarised with the task environment and scoring function in three stages: i) following an interactive tutorial explaining Circle Quest task as in Exps. 1 & 2, they were required to place the blue disc for fixed levels of uncertainty with credits displayed on the side. This exposed them to the scoring function and served as a control task measuring localisation accuracy (Figure 10a.). ii) Participants reported their confidence about the location of the hidden circle using a rating scale ranging between zero and 100. The configuration of dots used were the ones they would face in the decision making phase as well as in catch trials, which were added to widen the uncertainty range used to characterise subjective estimations. Subjective uncertainty estimates were obtained by simply sign-flipping and z-scoring these confidence ratings (Figure 10b.). iii) Effort calibration and familiarisation were the same as in Exp. 3. Maximum voluntary contraction (MVC) was first obtained by asking participants to squeeze the effort handle as hard as they could and then effort levels were calibrated based on MVC (five levels).
+ +<--- Page Split ---> + +## 1 Demographics + +2 Demographics and group characteristics are summarised in Tables S1 and S2. ALE patients had lower cognitive scores on Addenbrooke's cognitive examination (controls: \(\mu = 97.52, SD =\) \(\pm = 2.03\) , ALE: \(\mu = 93.42\) , \(SD = \pm 5.64\) , \(t_{36} = 2.99\) , \(95\% CI = [1.31, 6.89]\) , \(p < 0.01\) ). These cognitive differences were seen mainly in two domains of ACE- III: memory (controls: \(\mu = 24.79, = \pm = 1.36\) , ALE: \(\mu = 23.16\) , \(SD = \pm 2.54\) , \(z = 2.19\) , \(p = 0.02\) ) and fluency (controls: \(\mu = 13.32\) , \(SD = \pm = 1.36\) , ALE: \(\mu = 11.21\) , \(SD = \pm 2.69\) , \(z = 2.81\) , \(p < 0.01\) ). The other three domains including language, visuospatial abilities and attention were not significantly different between the two groups. There was no significant difference in measures of executive function (DS), apathy (AMI), fatigue (FSS), depression (BDI- II) or hedonic experience (SHAPS). + +## 2 Reduced sensitivity to changes in information cost in ALE patients. + +Participants in both groups acquired samples to reduce uncertainty. Similar to previous reports (Attaallah et al., 2022; Petitet et al., 2021), reduction in uncertainty followed an exponential decay as a function of the number of samples acquired, indicating purposeful sampling abiding with task rules (Figure 5a.). Both patients and healthy controls behaved rationally, sampling less when acquiring samples was more expensive (Main effect of \(\eta_{s}\) on the number of samples acquired: \(\beta = - 0.11\) , \(t_{2272} = - 6.25\) , \(p < 0.0001\) , Table S3), and not responding to changes in initial reward reserve (Main effect of \(R_{0}\) on the number of samples acquired: \(\beta = 0.023\) , \(t_{2272} = 1.29\) , \(p = 0.20\) ). Furthermore, there was a significant interaction between the effect of sampling cost and the effect of initial reward reserve ( \(\eta_{s} \times R_{0}\) : \(\beta = - 0.030\) , \(t_{2272} = - 2.07\) , \(p = 0.038\) , Figure 5b., Table S3), indicating that higher initial reward reserves blunted the aversive effect of the sampling cost on the number of samples acquired. This interaction effect was significantly greater + +<--- Page Split ---> + +1 in patients than controls \((A L E\times \eta_{s}\times R_{0};\beta = 0.054,t_{2272} = 2.68,p< 0.01\) , Table S3). In other words, patients were less sensitive to changes in sampling cost when initial reward reserve was high and thus kept acquiring a lot of samples when there was a conflict between large reward reserve but expensive samples (Group difference in the number of samples acquired in the high \(R_{0}\) high \(\eta_{s}\) condition: \(z = - 2.2484\) , \(p = 0.024\) ). + +Next, patients' and controls' performance in the active task was evaluated with regard to optimal sampling behaviour to determine whether they tended to under- or over- sample (Figure 5c- d.). Optimal sampling refers to the number of samples, \(s^{\star}\) , that maximises expected return, given the current cost- benefit structure \((R_{0}, \eta_{s}, \eta_{e})\) and search efficiency (i.e., the rate at which participants reduce uncertainty from one sample to the next, parameterised as the information extraction rate, \(\alpha\) , see Methods). In our task, uncertainty decreases exponentially over successive samples, and for each participant, sampling efficiency captures how steep this decline is (Figure 5a.). + +The optimal number of samples – which takes into consideration such differences in sample utility – thus provides a more parsimonious account of how sampling behaviour is influenced by economic changes. It provides a useful measure against which to benchmark the willingness to give up rewards in exchange for information. For samples acquired beyond \(s^{\star}\) , the cost of acquiring a new sample is objectively larger than its benefit. Conversely, for samples acquired before \(s^{\star}\) , the benefit of acquiring a new sample is objectively larger than its cost (Figure 5c.). + +Both ALE patients and healthy controls over- sampled when sampling cost was high (Deviation from optimal; ALE: \(\beta = 3.93\) , \(t_{1138} = 4.32\) , \(p < 0.0001\) ; Controls: \(\beta = 1.93\) , \(t_{1138} = 3.485\) , \(p < 0.001\) , see also Table S4), but patients over- sampled to a greater extent than controls when the initial reward reserve was high in these conditions ( \(z = 2.267\) , \(p = 0.023\) , Figure 5d.). When sampling cost was low, performance of ALE patients approached optimal behaviour ( \(\beta = - 0.94\) , \(t_{1138} = - 0.76\) , \(p = 0.44\) ) while controls under- sampled ( \(\beta =\) + +<--- Page Split ---> + +\(- 1.78, t_{1138} = - 2.59, p < 0.01\) . However, there was no significant difference between the two groups \((t_{36} = 0.579, p = 0.56)\) . + +These findings suggest that ALE patients' sampling behaviour demonstrates, at least partially, blunted sensitivity to sampling cost leading to over- sampling (i.e., giving up more reward than needed in exchange for information) (see Supplementary materials for a computational model characterising this effect, Figure S1). This had consequences in terms of total reward received as patients' scores suffered to a greater extent than controls' when the sampling cost increased \((ALE \times \eta_s : \beta = - 3.66, t_{t2272} = - 2.00, p = 0.046, \text{Figure 5e., Table S3})\) . + +## ALE patients are less sensitive to changes in reward against uncertainty. + +The passive task paradigm offers a reliable mechanistic delineation between response to reward and uncertainty when making value- based decisions. This helps to answer whether oversampling in ALE patients is indeed related to lower sensitivity to changes in reward, rather than increased sensitivity to uncertainty. + +A generalised logistic mixed- effects model \((\mathrm{L}_g\mathrm{MM})\) with maximal randomness was used to analyse the accept- reject choice data (Table S7). As expected, participants (patients and controls) adjusted their decisions rationally according to offer attributes: accepting more offers with higher rewards and lower uncertainty (Main effect of reward on offer acceptance: \(\beta = 1.41, t_{3748} = 7.16, p < 0.001\) ; Main effect of uncertainty on offer acceptance: \(\beta = - 2.73, t_{3748} = - 8.72, p < 0.001\) , Figure 6c.). There was no significant interaction between the effect of reward and uncertainty \((\mathrm{R} \times \mathrm{EE}: \beta = 0.065, t_{3748} = 0.53, p = 0.60)\) . Patients and controls did not significantly differ in the time they took to accept \((z = 3.79, p = 0.70)\) or reject offers \((t_{36} = - 0.34, p = 0.73)\) . However, they differed with regards to the influence reward exerted on the decision to accept the offer. Compared to controls, ALE patients were overall less influenced by reward on offer \((\mathrm{ALE} \times \mathrm{Reward}: \beta = - 0.983, t_{3748} = - 3.58, p < 0.001)\) . + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Figure 5: Exp. 1 – Reduced sensitivity to changes in information cost in ALE patients. a. Uncertainty (indexed as expected error, \(EE\) ) decreases with sampling and follows an exponential decay slope on average in both patients and controls. b. ALE patients, compared to controls, sampled more when initial reward reserve and sampling cost were both high. c-d. The optimal number of samples \((s^{\star})\) is the number of samples (s) at which the maximum return (Expected Value, \(EV\) ) could be achieved. Obtaining more samples before \(s^{\star}\) results in increases in \(EV\) , while acquiring samples beyond \(s^{\star}\) results in lower \(EVs\) . ALE patients and healthy controls over-sampled when sampling cost was high. Patients, however, over-sampled to a greater extent than controls, mainly when initial reward reserve and sampling cost both increased. There was no significant difference between the two groups at low-cost conditions. Lines in c. show the individual EV timecourses centred on their peaks (optimal number of samples to acquire). Ellipses contain \(90\% CI\) for each participant. \(\eta_{s}\) : Sampling cost. \(R_{0}\) : Initial reward reserve. e. Patients achieved lower scores than controls, especially at higher sampling cost, where patients deviated more than controls from optimal sampling. Error bars show \(\pm \mathrm{SEM}\) . \*:p <0.05. See Tables S3 & S4 for full statistical details.
+ +<--- Page Split ---> + +1 By contrast, their sensitivity to uncertainty was not significantly different from controls' (ALE \(\times EE: \beta = 0.336, t_{3748} = 0.76, p = 0.45\) ). Taken together, this means that patients accepted fewer of the high- value offers (high- reward and low- uncertainty) \((z = - 2.14, p = 0.03 \& z = - 2.93, p < 0.01\) , for the two offers of the highest value, Figure 2b- c.). + +Next, we investigated whether sensitivity to changes in reward and uncertainty was associated with differences observed in sampling behaviour in Exp. 1. Sensitivity to reward and uncertainty were extracted from a \(\mathrm{L}_{g}\mathrm{MM}\) that included the two attributes and their interaction as predictors of offer acceptance (i.e., the same model used above but with no group effect). For each participant, reward and uncertainty sensitivity correspond to model- derived parameter estimates that capture how decisions are influenced by changes in these two offer attributes. A robust regression model showed that reward, but not uncertainty sensitivity, correlated significantly with the sensitivity to sampling cost at high initial reward reserve in the active task, i.e., the effect differentiating patients from controls in Exp. 1 (indexed by difference in number of samples between low and high sampling cost conditions) \((R^{2} = 0.23, t_{17} = 2.26 p = 0.03\) ; Figure 6d.). + +In brief, the findings from Exps. 1 & 2 converge to indicate that ALE patients are less responsive to changes in reward under conditions of uncertainty in active and passive contexts. This is evidenced by less flexible sampling in response to changes in sampling cost in the active task, and reduced sensitivity to changes in reward on offer in the passive task. + +## 20 Intact effort-based decision making in ALE patients + +In Exp. 3, we asked whether blunted reward sensitivity observed in ALE patients for decisions involving a trade- off with uncertainty was also evident for a different discounting attribute – physical effort. In other words, is this a generalised phenomenon? A novel version of a well- validated paradigm measuring effort and reward sensitivity was used to examine effort- based + +<--- Page Split ---> +![](images/Figure_6.jpg) + +
Figure 6: Exp. 2 – ALE patients are less sensitive to changes in reward under uncertainty. q. Patients and healthy controls adjusted their decisions according to the reward and uncertainty on offer. The influence of reward on offer acceptance was blunted in hippocampal patients when compared to controls (shallower reward slope). Level 1 indicates lowest reward/uncertainty level on offer. b. Controls accepted more of the high-value offers (blue region) when compared to hippocampal patients. c. Investigation of the group effect using logistic regression model with mixed effects ( \(\mathrm{L}_{\theta}\mathrm{MM}\) ) revealed that patients had significantly lower sensitivity to reward than controls but did not significantly differ in their sensitivity to uncertainty. Additionally, it showed that the impact of uncertainty on decision making was more significant than the impact of reward (histogram on the corner). d. Lower sensitivity to reward, but not uncertainty, in the passive task is associated with lower sensitivity to sampling cost in the active sampling task (Exp. 1) driving group differences in number of samples collected. Colour bar indicates the contribution of each data point to the model. Blue dots represent controls and are added for visual comparison. Error bars in a. show \(\pm\) SEM. ***: \(\mathrm{p}< 0.001\) . Shaded area in d. show \(95\%\) CI. See Table S7 for full statistical details.
+ +<--- Page Split ---> + +1 decision making (see Methods). + +2 A \(\mathrm{L}_{\theta}\mathrm{MM}\) with full randomness was used to analyse the choice data (Table S9). The results showed that, as expected, participants from both groups accepted more offers (showed more willingness to allocate effort) when reward on offer increased and required effort decreased (Main effect of reward on offer acceptance: \(\beta = 2.97\) , \(t_{4739} = 11.12\) , \(p < 0.001\) , Main effect of physical effort on offer acceptance: \(\beta = - 2.82\) , \(t_{4739} = - 8.45\) , \(p < 0.001\) ). However, neither reward nor effort sensitivity differed significantly between patients and controls (ALE \(\times\) Reward and ALE \(\times\) Effort both \(p > 0.05\) ). To quantify the evidence in favour of this null result, the same analysis was run using Bayesian mixed modelling. Once again, this analysis did not suggest differences between the two groups in reward (or effort) valuation ( \(BF = 3.78\) for null effect of ALE \(\times\) reward, \(BF = 5.28\) for null effect ALE \(\times\) effort, Figure S3, Table S10). Note that there was also no significant difference in total acceptance of offers (Effect of disease (ALE) on offer acceptance: \(\beta = - 0.40\) , \(t_{3748} = - 0.88\) , \(p = 0.45\) ) as well as in decision times (Accept decisions: \(t_{36} = 0.87\) , \(p = 0.38\) , Reject decision: \(t_{36} = 0.99\) , \(p = 0.32\) ). Finally, the group effect on reward sensitivity across Exps. 2 and 3 was examined using a generalised mixed model after combining choices from both tasks (Tables S11 & S12). As expected, reward sensitivity in ALE patients compared to controls was significantly blunted in Exp. 2 compared to Exp. 3 (ALE \(\times\) Reward \(\times\) Task \(= \beta = 0.467\) , \(t_{8495} = 4.15\) , \(p < 0.0001\) , Table S11). + +These results are consistent with a sparing of reward sensitivity in ALE patients when reward had to be weighed against effort, without uncertainty being considered. + +## Blunted reward and effort sensitivity under uncertainty in ALE patients + +One might argue that the comparison between reward sensitivity in Exps. 2 & 3 is not fully matched due to the difference in task cues and environment (e.g., reward as credits vs. virtual apples). These concerns were addressed using a novel version of Circle Quest that was designed + +<--- Page Split ---> +![](images/Figure_7.jpg) + +
Figure 7: Exp. 3 – Intact reward valuation against effort in ALE patients. a. There was no significant difference in offer acceptance between patients and controls. b. The estimates of these acceptance slopes indicate how sensitive participants are to reward/effort changes of offers. These sensitivity estimates were extracted from a generalised mixed model with full randomness and plotted for visualisation. Error bars show \(\pm \mathrm{SEM}\) . \(^{*}\mathrm{p}< 0.05\) . See Tables S9 & S10 for full statistical details.
+ +1 to examine effort- based decision making under uncertainty. The task had the same reward levels and cues used in Exp. 2 and the same effort levels and cues used in Exp. 3. Participants also made the decisions either with or without uncertainty. Thus, the task overall examined how participants adjusted their decisions taking into consideration these three attributes (reward, effort and uncertainty). + +Choice data were analysed using the same approach as in Exp. 2 & 3 with generalised mixed- effects models (Table S13). The results showed that participants from both groups generally made decisions reflecting changes in offer attributes, i.e., accepting more offers with increasing reward, decreasing effort and absent uncertainty (Main effect of reward on choice: \(\beta = +1.73\) , \(t_{4184} = 5.21\) , \(p < 0.0001\) ; Main effect of effort: \(\beta = - 2.45\) , \(t_{4184} = - 5.38\) , \(p < 0.0001\) ; Main effect of uncertainty: \(\beta = - 1.9\) , \(t_{4184} = - 3.49\) , \(p < 0.001\) ). However, ALE patients were significantly less sensitive to changes in reward and effort compared to healthy controls (ALE \(\times\) reward: \(\beta = - 1.41\) , \(t_{4184} = - 2.83\) , \(p < 0.01\) ; ALE \(\times\) effort: \(\beta = +1.24\) , \(t_{4184} =\) + +<--- Page Split ---> +![](images/Figure_8.jpg) + +
Figure 8: Exp. 4 – Blunted reward and effort sensitivity under uncertainty in ALE patients. In Exp. 4, participants were required to accept/reject offers taking into consideration three attributes: reward, physical effort, and uncertainty. The results showed that ALE patients, compared to controls, were less sensitive to changes in reward and effort while having intact sensitivity to uncertainty. Such results are consistent with findings from Exps. 1 & 2, highlighting disrupted reward and cost valuation in ALE patients under uncertainty. Error bars show \(\pm \mathrm{SEM}\) . \*: \(p< 0.05\) . See Table S13 for full statistical details.
+ +1. \(2.99 p < 0.01\) ; Figure 8). On the other hand, patients and controls did not differ in their sensitivity to uncertainty \((p = 0.21)\) . The two-way interaction between uncertainty and reward was also significant, indicating that the presence of uncertainty resulted in blunted sensitivity to changes in rewards (Reward \(\times\) uncertainty: \(\beta = -0.67\) , \(t_{4184} = -3.61\) , \(p < 0.001\) ). This effect was significantly more prominent in ALE patients than matched controls (Reward \(\times\) uncertainty \(\times\) ALE: \(\beta = +0.568\) , \(t_{4184} = 2.09\) , \(p = 0.036\) ). These findings are consistent with the results from Exps. 1 & 2 showing intact sensitivity to uncertainty and reduced sensitivity to reward in presence of uncertainty in ALE patients. They also suggest that effort sensitivity can also be blunted in ALE patients under conditions requiring uncertainty consideration. Thus overall, while healthy controls managed to flexibly adjust their decision taking into consideration the three attributes of the offers, ALE patients were generally more responsive to the uncertainty component of the offers, with less emphasis on other economic attributes such as reward and effort. + +<--- Page Split ---> + +## 1 Severity of hippocampal atrophy correlates with decreased reward sensitivity under uncertainty + +3 A whole- brain voxel- based morphometry (VBM) analysis was performed. Compared to healthy controls, ALE patients had lower grey matter intensity in three clusters involving limbic, thalamic and temporal regions (Table 1). As expected, the largest cluster (limbic region) included mainly the hippocampal and para- hippocampal regions (Figure 9a.). Hippocampal atrophy was also demonstrated by comparing extracted total hippocampal volume using Freesurfer analysis pipeline, showing reduced right whole hippocampal volumes in ALE patients (Table S1). Note that a few participants had also severely atrophied left hippocampal (with and without right hippocampal atrophy, see Table S2). + +Next, robust regression analysis was performed to examine the relationship between hippocampal atrophy and reduced reward sensitivity observed in ALE patients. For this purpose, reward and uncertainty sensitivities from Exp. 2 were used as key behavioural markers characterising performance of hippocampal patients when compared to controls. The results showed that sensitivity to changes in reward was associated with total average hippocampal volumes in patients (Model \(R^{2} = 0.41\) , \(t_{13} = 3.05\) , \(p < 0.01\) ; Figure 9b.). This correlation remained significant after controlling for age and gender (Model \(R^{2} = 0.38\) , \(t_{11} = 2.28\) , \(p = 0.043\) ). + +Repeating the same analysis for reward sensitivity from Exp. 3 revealed no significant correlation between total hippocampal volume and reward sensitivity against effort ( \(p = 0.88\) ). Due to limitation of sample size, this analysis was not performed for Exp. 4. + +To assess the anatomical specificity of this result, the volume of the amygdala – another limbic brain region that was highlighted in VBM analysis – was also extracted and the same analysis was run on this metric. This showed no significant correlation between the amygdala volume and sensitivity to reward or uncertainty (Figure S4). + +<--- Page Split ---> + + +
ClusterMain regions involvedVoxelsMAX X (mm)MAX Y (mm)MAX Z (mm)
1Right Limbic (Hippocampus, Para-hippocampus, Amygdala)19922-32-14
2Thalamus (Bilateral)1202-14-2
3Right Temporal Lobe (STG, MTG, AG)6568-406
+ +Table 1: VBM analysis controls vs. patients. ALE patients had lower grey matter volumes compared to controls in three main clusters (1 -3). The largest difference was seen in the limbic region which includes mainly hippocampal regions (Figure 9a.) STG: Superior Temporal Gyrus. MTG: Middle Temporal Gyrus. AG: Angular Gyrus. + +![](images/Figure_9.jpg) + +
Figure 9: Severity of hippocampal atrophy correlates with decreased reward sensitivity against uncertainty. a. VBM analysis shows that ALE patients have significantly reduced grey matter intensity in right hippocampal region (cluster 1, Table 1). b. Patients with more severely atrophied hippocampi were less sensitive to reward when traded against uncertainty. By contrast, hippocampal volumes were not significantly correlated with sensitivity to uncertainty which was preserved in ALE patients.
+ +<--- Page Split ---> + +## Intact localisation and uncertainty estimation in ALE patients + +Two control analyses were performed to examine possible factors that might influence response to uncertainty in the Circle Quest task. First, participants might differ in their motor performance and accuracy of placing the blue disc for given uncertainty levels, which might bias their estimation and valuation. To examine this, we analysed performance in the training task where participants were required to move the blue disc for fixed levels of uncertainty. Distance to the hidden circle was compared between the two groups. There was no significant difference between ALE patients and controls in this metric, indicating similar placement performance on the task (Difference between ALE and controls in Exps. 1 & 2: \(z = 1.10\) , \(p = 0.26\) ; Exp. 4: \(z = 0.10\) , \(p = 0.91\) ; Figure 10a.). To compare localisation performance on trials that did not feature the same levels of uncertainty, the distance to the optimal placement point (the centroid of the posterior belief of all the possible locations of the hidden circle) was calculated. This measure across all versions of Circle Quest was not significantly different between the two groups (Figure S5). + +Second, different participants might have different estimations of uncertainty for the same visual displays, ultimately affecting their performance on the task as they gather information and make decisions. To examine inter- individual differences in uncertainty estimation, participants provided these estimates as confidence ratings before making decisions in Exp. 2 and during training in Exp. 4 (see Methods). A generalised mixed- effects model indicated that in both experiments there was no significant difference in subjective uncertainty estimation between patients and controls (Exp. 2; \(\mathrm{ALE}\times \mathrm{EE}\) : \(\beta = - 0.054\) , \(t_{3752} = - 0.77\) , \(p = 0.44\) ; Exp. 4; \(\mathrm{ALE}\times \mathrm{EE}\) : \(\beta = - 0.022\) , \(t_{2306} = - 0.16\) , \(p = 0.87\) , Figure 10b., Table S14). The results indicate that subjective estimates of uncertainty mapped well onto experimentally defined uncertainty across study participants. As a result, choice performance results did not change when these subjective estimates were used to analyse performance instead of objective uncertainty ( \(EE\) ) + +<--- Page Split ---> + +1 (Figure S6, Table S16). + +<--- Page Split ---> +![](images/Figure_10.jpg) + +
Figure 10: Exps. 1, 2 & 4 – Intact localisation and subjective uncertainty estimation in ALE. a. ALE patients and controls did not differ in their localisation error (distance between the centre of the blue disc and hidden circle) for fixed levels of uncertainty, indicating similar motor and localisation performance. b. Subjective uncertainty is z-scored signed-flipped confidence ratings that participants reported before seeing the reward on offer (Exp. 2) or during training (Exp. 4). There was no significant difference between ALE patients and controls in this measure, indicating intact uncertainty estimation. Error bars in a. show \(\pm\) SEM. Shaded area show \(95\%\) CI. See Table S14 for full statistical details.
+ +<--- Page Split ---> + +## Discussion + +The studies presented here assessed acute limbic encephalitis (ALE), which is associated with damage to the hippocampus. In four experiments, patients were assessed on how they evaluate reward, uncertainty, and effort when making value- based decisions. The results converged to indicate that ALE patients had intact uncertainty processing across different contexts such as passive reward valuation (Figures 6 & 8) and active information gathering prior to decisions (Figure 5). On the other hand, whenever uncertainty was a factor that needed to be considered, sensitivity to other attributes (reward and physical effort) was blunted (Figure 6 & 8), despite intact valuation of these attributes in a context that does not feature uncertainty (Figure 7). Reduced sensitivity to changes in reward in the context of uncertainty correlated with severity of hippocampal atrophy in ALE patients (Figure 9). Thus, the results indicate a specific role of the hippocampus in processing uncertain rewards and costs. + +13 + +Previous decision- making studies demonstrated a potential role of the hippocampus in different forms of goal- directed behaviour. For example, several reports indicated that the hippocampus might be critical for inter- temporal decision making, especially when participants are required to imagine future outcomes (Benoit et al., 2011; Lebreton et al., 2013; Peters and Bui, 2011). The results of other investigations demonstrated that the hippocampus might be needed for deliberation prior to value- based decisions but not sensory discrimination (Bakkour et al., 2018), suggesting that it supports valuation by sampling from memories and previous experiences. Similarly, some researchers demonstrated hippocampal involvement in inferring values of novel stimuli from previously encountered cues and stimuli (Barron et al., 2013; Hassabis et al., 2007; Wimmer and Shohamy, 2012). These functions are thought to be related to the established role of the hippocampus in episodic thinking and prospection (Buckner and Carroll, 2007; Gilbert and Wilson, 2007; Schacter et al., 2007, 2012, 2017). + +<--- Page Split ---> + +A common theme of such decision making scenarios is that they critically involve uncertainty regarding future rewards that agents consider to guide their decisions. For example, several previous reports highlighted similarities between delay and probabilistic discounting (i.e., decisions under uncertainty), suggesting that they might feature a common cognitive process (Green and Myerson, 1996; Myerson et al., 2003; Prelec and Loewenstein, 1991; Rachlin et al., 1991; Stevenson, 1986). These similarities are of special relevance when considering hippocampal contribution to inter-temporal decision making as its role seems to be specific to temporal discounting that requires simulated future trajectories (Palombo et al., 2015b; Peters and Buchel, 2010; Ye et al., 2021). According to this perspective, exaggerated delay discounting observed with hippocampal damage in previous studies might reflect intolerance to uncertainty of future reward. + +Such reports highlighting a potential role of the hippocampus in uncertainty processing are supported by functional neuroimaging studies which have demonstrated hippocampal activation that correlates with the degree of uncertainty (entropy) of sensory stimuli when making decisions (Harrison et al., 2006; Rigoli et al., 2019; Strange et al., 2005; Tobia et al., 2012). These investigations align with the view that regards uncertainty as a threatening stimulus (i.e., carries risk signals) processed by hippocampus- centred behavioural inhibition system (BIS) (Gray and McNaughton, 2003). The hippocampus, according to this view, is considered to work as a mismatch detection system comparing expectation with perceived stimuli and triggering behavioural avoidance when confronted by uncertainty (or other anxiety- inducing stimuli) (Gray and McNaughton, 2003). + +These features of uncertainty as probabilistically discounted risk signal offer a key distinction from other costs such as physical effort, which is more deterministic in nature. The results presented in this study provide evidence that the hippocampus is critically involved in valuation processes under the first context (uncertainty) but not the second (physical effort), + +<--- Page Split ---> + +providing specific insights into the role of the hippocampus in value- based decision making. Counter- intuitively, hippocampal damage was associated with preservation of uncertainty estimation and valuation, but blunted sensitivity to other decision attributes (e.g., reward and effort) if simultaneously considered with uncertainty. + +These findings suggest that hippocampal damage might be related to a specific deficit in the integration of relatively intact uncertainty signal with other attributes that contribute to the value space. Note that this is unlikely to reflect a general cognitive deficit in decision- making or value computation, as participants demonstrated intact effort- based decision making in Exp. 3 (Figure 7). One possible explanation for this might be that people with hippocampal atrophy possess limited computational resources that prioritise uncertainty processing (and perhaps other risk- related signals) over other value determinants when computing subjective values to guide behaviour and decisions. In fact, the results from the active search experiment (Exp. 1) could also be interpreted as evidence of uncertainty prioritisation, as ALE patients sampled faster and more extensively than controls to abolish uncertainty before committing to their decisions (Figure S2), disregarding changes in sampling cost. The hippocampal role therefore might be to infer decision values by multiplying probability of outcomes (i.e., uncertainty) with other economic attributes such as reward and effort. These signals might then be related to other regions involved in subjective value estimation such ACC and OFC (Bakkour et al., 2019; Chen, 2021; Gluth et al., 2015; Ito et al., 2008; LeGates et al., 2018; Maller et al., 2019; Mizrak et al., 2021; Pessiglione et al., 2018; Rudebeck et al., 2006; Wimmer and Shohamy, 2012). + +It is however difficult to fully determine whether the prioritisation of uncertainty in this process reflects a specific computational property of the hippocampus versus general disruption of cognitive processing that might be observed with other brain lesions. Two factors make this possibility unlikely: i) the correlation between behaviour and severity of hippocampal atrophy, + +<--- Page Split ---> + +rather than other closely related regions such as the amygdala, which might have been affected by the disease process as well (see Figures 9 & S4) and ii) the results do not change when controlling for overall cognitive dysfunction indexed by ACE- III scores or meta- cognitive deficits in uncertainty estimation (Table S17). In a similar vein, it could be argued that the results might merely reflect a difficulty effect imposed by uncertainty cues in the task which biases participants to focus on uncertainty when evaluating it again other attributes. If this was the case, one would expect that patients would take longer than controls to make their decisions, which was not the case. More importantly, these effects persisted in the uncertainty- free condition in Exp. 4 where participants were shown the true location of the hidden circle and they did not need to process uncertainty when making effort- based decisions. + +These findings resonate with previous reports that highlighted the hippocampus as part of a wider brain valuation network, which includes regions that have established roles in processing reward and costs such as dopaminergic brain regions (e.g., ventral striatum), ventromedial prefrontal cortex and anterior cingulate cortex (Haber, 2017). The hippocampus shares functional and structural connections with these regions and is thought to provide contextual information that support the valuation process (Bakkour et al., 2019; Gluth et al., 2015; Ito et al., 2008; LeGates et al., 2018; Maller et al., 2019; Mizrak et al., 2021; Wimmer and Shohamy, 2012). The behavioural results in this chapter indicate that hippocampal processing of uncertainty signals might be a key determinant of how different brain regions evaluate rewards and costs when computing subjective values. + +The neural mechanism underlying how hippocampal signals support reward- related behaviours is not yet fully established and will require further research. However, previous studies demonstrated that the hippocampus might share future- related signals (e.g., preplay and look- ahead signals) with dopaminergic regions involved in reward evaluation and processing (Freyja Ólafsdóttir et al., 2015; Johnson and Redish, 2007; Pfeiffer and Foster, 2013). Similarly, + +<--- Page Split ---> + +several forms of reward representation in the hippocampal formation and its extended networks have been reported, correlating with various reward- related behaviours and cognitive processes such as appetitive responses to rewards (Jarrard, 1973; Tracy et al., 2001), approach- avoid decisions (Ito and Lee, 2016), and reward- guided exploration (Sosa and Giocomo, 2021). While hippocampal- lesioned animals might show behavioural adjustments in responses to changes in reward in the environment (Flaherty et al., 1998; Kelley and Mittleman, 1999), these findings might differ between contexts and depend on the way reward is manipulated (e.g., whether animals move from high reward environment to low, or the opposite) (Tracy et al., 2001). Consumatory responses of lesioned animals seem to be unaffected despite the changes in behaviour, challenging the notion that the role of the hippocampus in reward processing is of a hedonic nature (Kelley and Mittleman, 1999), and further substantiating its goal- related and context- dependent contribution. + +This uncertainty- sensitive hippocampal contribution might serve to support not only passive valuation but also active goal- directed behaviours such as information seeking to resolve uncertainty. While results from a previous investigation have shown that people with hippocampal damage exhibit a less structured and stochastic visual information exploration compared to controls (Lucas et al., 2019), it was unclear how economic factors come into play. In this study, less optimal decision making when uncertainty- related value computations had to occur was evident in hippocampal patients when they actively gathered information and sequentially updated the expected values of their decisions, as well as when they made passive decisions under uncertainty. The source of this disruption was related to less flexible decision adjustments based on reward changes in the environment while maintaining sensitivity to uncertainty. Thus, the results provide insights into how the hippocampus contributes to the economics of information gathering, in addition to its potential exploratory role (Johnson et al., 2012), which was not strongly affected in ALE patients as they managed to reduce uncertainty as efficiently as + +<--- Page Split ---> + +1 healthy controls. + +2 + +A positive correlation was observed between total hippocampal volumes and reward sensitivity under uncertainty. Future investigations might build on this to investigate the drivers of this relationship. For example, such an association might be related to specific hippocampal subfields and sub- circuits. In rats, reward cells have been described in the subiculum and CA1 subfields (Gauthier and Tank, 2018), which might suggest that atrophy or disruption of these regions might have a more specific role in the process. Moreover, distinct hippocampal subfields and regions might have differential functional connectivity profiles with other cortical and subcortical regions that might be contributing to motivation and decision making (Attaallah et al., 2022; Dalton et al., 2019; de Flores et al., 2017; Vos De Wael et al., 2018; Zeidman et al., 2015). Hippocampal damage observed in ALE is likely to be associated with more widespread disturbances of such networks contributing to reward valuation under uncertainty (Argyropoulos et al., 2019; Heine et al., 2018; Navarro et al., 2016; Qiao et al., 2020). With advanced neuroimaging acquisition and analysis techniques, it would be crucial to try to answer these questions at the subfield level and provide a more comprehensive account of hippocampal contribution to motivation. + +18 + +The present study has a few limitations. First, it is possible that the dissociation between effort and uncertainty in Exp. 2 and Exp. 3 might be not well- delineated. For example, in the Circle Quest paradigm one might potentially need to take into account effort costs such as moving the blue localisation disc on the screen or additional cognitive effort required to translate the configuration of dots into uncertainty estimates, etc. Such costs can also have an impact on active sampling behaviour influencing the speed and efficiency of gathering information (Petitet et al., 2021) (see also supplementary materials for a computational model characterising + +<--- Page Split ---> + +these effects). Similarly, the effort task (Exp. 3) might theoretically involve some elements of uncertainty (e.g., aligning visualised effort levels to subjective cost estimates). Second, while Exp. 4 was designed to address such limitations, only a subset of the participants performed it which might not be strictly representative of the larger group. This task also introduced a more complex decision structure that might require more planning (two steps to reach goals) and additional cognitive load on memory and attention. It is important to discuss and consider the results from one experiment in the context of the other experiments performed to obtain a bigger picture that paints how the hippocampus might be contributing to value processing. Finally, it might also be beneficial for future studies to obtain more objective measures of such processes, e.g., pupillometry for reward sensitivity (Le Heron et al., 2018a; Muhammed et al., 2016), which could help to bridge the gap with conclusions based on subjective estimates obtained from decision making and goal- directed behaviour. + +13 + +In conclusion, the results presented here provide evidence from human participants that the hippocampus plays a crucial role in decision making. This contribution appears to be specific to contexts that involve uncertainty, influencing how people evaluate other economic attributes such as reward and effort. + +<--- Page Split ---> + +## Materials and Methods + +## Participants + +In Exps. 1- 3, 19 individuals with a previously established diagnosis of ALE (age: \(\mu =\) \(60.00,SD = \pm 11.36,\) 13 males) were tested along with 19 healthy age- and gender- matched controls (age: \(\mu = 61.16,SD = \pm 11.71,\) 13 males). In Exp. 4, eight ALE patients and 12 controls completed an additional follow- up task. Sample size was determined based on previous comparable work in ALE patients (Hanert et al., 2019; Spano et al., 2020a,b) as well as previous research using the behavioural paradigms used in the study (Attaallah et al., 2022; Le Heron et al., 2018a,b; Petitet et al., 2021). All participants gave written consent to take part in the study and were offered monetary compensation for their time. The study was approved by the University of Oxford ethics committee (RAS ID: 248379, Ethics Approval Reference: 18/SC/0448). Tables S1 & S2 show the demographics and characteristics of the study groups. Due to a technical error during testing, two blocks (out of five) were missing from the passive choices task for one patient (code 14 in Table S2). Analyses were conducted with and without this patient's data and there was no difference in the results or conclusions. After completing a practice session, participants had to answer correctly all the questions of a task comprehension quiz in order to be eligible to do the task and continue the experiment. + +## External measures + +All participants underwent a cognitive assessment using Addenbrooke's Cognitive Examination III (ACE III) (Hsieh et al., 2013) and executive function assessment with digit span (DS). In addition, they completed self- report questionnaires of apathy (Apathy Motivation Index, AMI (Ang et al., 2017)), depression (Beck Depression Inventory- II, BDI- II (Beck et al., 1996)), fatigue (Fatigue Severity Scale, FSS (Krupp et al., 1989)), and hedonic experience (Snaith- Hamilton Pleasure Scale, SHAPS (Snaith et al., 1995)). + +<--- Page Split ---> + +## 1 Procedure + +2 The tasks were presented on a 17- inch touchscreen PC using MATLAB (The MathWorks inc., version 2018b) and Psychtoolbox(Brainard, 1997; Kleiner et al., 2007) version 3. Testing was done in a quiet room with an experimenter present at all times. Participants sat within reaching distance of the screen ( \(\sim 50 \mathrm{cm}\) ) and were instructed to use the index finger of their dominant hand to respond. The task environment was adjusted according to handedness (e.g., uncertainty rating on the side of the dominant hand in Exps. 2 & 3). + +## 8 Experimental paradigm - Exps. 1 & 2 + +9 A modified, shorter, version of "Circle Quest" task was used (described in detail in Petitet et al., 2021). In Exp. 1, participants performed the active sampling version of the task designed to test active information gathering prior to committing to decisions. In Exp. 2, participants performed the passive choices/decisions version designed to test decision making under fixed, experimentally defined levels of uncertainty and reward. Participants were told that their goal in the two parts of the task was to maximise reward. All participants performed a training task followed by the active task and then the passive task. The purpose of this order was to ensure that by the time participants performed the passive choices version of the task, they had extensive training and exposure to the task environment and scoring function through their interaction with the task during the active version. The average total duration of the testing session was approximately one hour (Average duration in minutes \(\pm \mathrm{SD}\) ; training: \(7.38 \pm 2.96\) , active sampling: \(30.11 \pm 5.20\) , passive choices: \(22.86 \pm 1.48\) ). + +Training and exposure. The task was explained to participants using an interactive tutorial in the presence of the experimenter. In this interactive tutorial, participants were simply required to localise a hidden purple circle on the screen. This circle had a fixed size on all trials (radius: 130 Px; area: \(5.80\%\) of the search space). Two purple circles of the same size were always + +<--- Page Split ---> + +1 present on the right and left sides of the screen as a visual reminder of the circle on quest. This 2 served to limit the memory demands of the task. Clues about the location of the hidden circle 3 could be obtained by touching the screen. If a purple dot appeared where they touched (radius: 4 Px), this indicated that the location was located inside the hidden circle. Alternatively, if 5 a white dot appeared where they touched, the location was situated outside the hidden circle. 6 After completing the search, they were asked to move a blue disc of the same size as the hidden 7 circle on top of where they thought the hidden circle was located, based on the information 8 they gathered. During this tutorial, participants performed five trials with no constraints on 9 the number of samples they could acquire and without any sampling penalty. They were also 10 encouraged to ask the experimenter in case they had any questions. + +Following this short introduction to the game, participants performed a training task that included 20 trials. The goal of this task was to: (1) practice localising the hidden circle for various levels of uncertainty, and (2) expose participants to the scoring function. On each trial of this training task, a configuration of eight dots (four purple and four white) was displayed on the screen, and participants were required to move the blue disc on top of where they thought the hidden circle was located. Different configurations mapped onto different levels of uncertainty. For example, displays with spaced-out purple dots represented lower levels of uncertainty than displays in which the purple dots were clustered more closely. The former configuration had a lower number of possible circle placements that were compatible with the dots displayed on the screen (purple dots should be inside the hidden circle), and consequently, the expected localisation error ( \(EE\) , a quantitative measure of uncertainty) for such a configuration was smaller. \(EE\) was calculated as the probability-weighted average of all possible errors a participant could incur by placing the blue disc at the best possible location (centroid of posterior belief) (Figure 2b., for more details see supplementary materials). The two circles on the right and left side of the search space contained the reward at stake which participants could obtain if they managed + +<--- Page Split ---> + +1 to perfectly localise the hidden circle. After placing the blue disc, the location of the hidden circle was revealed and the score they obtained for this localisation appeared. The score was calculated as the reward at stake minus the localisation error penalty upon placing the blue disc \((\eta_{e}.e)\) , where \(e\) is the distance between the centre of the blue disc and the centre of the hidden circle in pixels. \(\eta_{e}\) is the spatial error cost per pixel which was constant and equal to 1.2 credits/pixel. + +7 Active sampling task (Exp. 1). In the active version of the task (Figure 1), participants could reduce their uncertainty about the location of the hidden circle by actively sampling the search space, i.e., touching the screen to obtain information. Similar to the training tutorial, this provided them with binary information about the location of that sample in relation to the hidden circle. If where they touched was inside the hidden circle, the sample was purple. Otherwise, the sample was white. At the beginning of every trial, participants had 18 seconds to sample whenever, wherever, and how much they wanted. After this, they were required to move the blue disc to where they thought the hidden circle was located in order to collect monetary credits. Participants started each trial with an initial reward reserve, \(R_{0}\) , from which they lost credits each time they acquired a new sample depending on the cost of sampling, \(\eta_{s}\) , on that trial. There were two levels of initial reward ( \(R_{0}\) : low = 95 credits and high = 130 credits) and two levels of sampling cost ( \(\eta_{s}\) : low = - 1 credit/sample and high = - 5 credits/sample) giving rise to four conditions in four blocks that were counterbalanced between participants. Each condition included 15 trials. The score (in credits) participants obtained on each trial was calculated as follows: + +\[Score = R_{0} - s.\eta_{s} - e.\eta_{e} \quad (1)\] + +Where \(R_{0}\) is the initial reward reserve, \(s\) is the number of samples obtained, \(\eta_{s}\) is the cost of acquiring a new sample, \(e\) is the placement error, \(\eta_{e}\) is the error cost per pixel which was fixed + +<--- Page Split ---> + +1 and equal to 1.2 credit/pixel. + +2 Quantifying deviations from optimal behaviour. The expected value, \(EV\) , changed dynamically with each sample, \(s\) , as follows: + +\[EV(s) = R_{0} - s.\eta_{s} - EE(s).\eta_{e} \quad (2)\] + +5 Where \(EV(s)\) is the expected value at the \(s\) - th sample, which is calculated as what is left 6 of the initial reward reserve \(R_{0}\) after subtracting the credits lost during sampling \((s.\eta_{s})\) and 7 the expected localisation error penalty \((EE(s).\eta_{e})\) . The optimal number of samples to acquire 8 corresponds to the number of samples, \(s^{\star}\) , that maximises the expected value. Deviation from 9 optimal sampling (either over- or under- sampling) is the difference between the number of 10 samples obtained and \(s^{\star}\) . Note that in the equation above, the rate at which \(EE\) decays from 11 one sample to the next depends on the participant's choices of sampling locations (Figure 5a.). 12 Therefore, the dynamics of \(EE\) over successive samples (i.e., the efficiency of the search) may 13 vary across trials and participants. Sampling efficiency was parameterised as the information 14 extraction rate, \(\alpha\) , which was computed for each trial as follows: + +\[\begin{array}{l}{{\hat{E}\hat{E}_{(n,1)}=\frac{\sum_{i=1}^{60}E E_{(i,1)}}{60}}}\\ {{\hat{E}\hat{E}_{(n,s)}=(\hat{E}\hat{E}_{(n,1)}-\hat{E}\hat{E}_{\infty}).(1-\alpha_{n})^{s-1}+\hat{E}\hat{E}_{\infty}}}\\ {{0<\alpha<1\& \hat{E}\hat{E}_{\infty}>0}}\end{array} \quad (3)\] + +16 Where \(n\) is the trial number, \(s\) is the sample number within the trial (the \(0^{th}\) sample is the 17 sample displayed on the screen at the beginning of the trial, before participants touched the 18 screen, Figure 1), and \(\hat{E}\hat{E}_{\infty}\) is the asymptotic \(EE\) level reflecting limitations in uncertainty 19 reduction. This model was fitted using fmincon in MATLAB (The MathWorks inc., version 20 2019a) with a minimum mean square error method. + +<--- Page Split ---> + +1 Passive choices task (Exp. 2). Each trial of the passive version of the task had two parts 2 (Figure 2c- d.). First, participants saw a configuration of dots on the screen (four purple dots and 3 four white dots) that mapped onto an experimentally defined level of uncertainty, \(EE\) . Based 4 on this configuration, participants were required to indicate how confident they were about the 5 location of the hidden circle. They could do this by touching on a rating scale on the side of 6 the screen ranging between zero and 100. A zero on this scale meant that the participant had no 7 idea where the hidden circle was, and 100 meant that the participant knew exactly where it was. 8 Metacognitive accuracy, a measure of how objective uncertainty is translated into subjective 9 estimates, was defined as the slope of the relationship between expected error and sign- flipped 10 confidence ratings. + +Next, once participants reported their confidence, the reward on offer appeared. They then were required to make "Yes/No" decisions based on whether they wished to place the blue disc given the reward on offer and the uncertainty they have just rated. There were four reward levels ( \(R\) : 40, 65, 90, 115 credits) and five uncertainty levels ( \(EE\) : 16.3- 24.4, 27.1- 38.9, 57.5- 58.9, 91.9- 93.3 pixels). Participants were told that ten of the offers they accepted would be randomly selected at the end of the experiment, and they would be required to play them (i.e., localise the hidden circle using the blue disc). The credits they collected on these ten trials determined the monetary rewards they won in this version of the task. The score was calculated in the same way as the training task. Participants were rewarded at a rate of £1 per 150 credits (Table S18). + +## Experimental paradigm - Exp. 3 + +A modified version of a well- validated effort- based decision making task was used (Aridan et al., 2019; Bonnelle et al., 2016, 2015; Chong et al., 2016; Le Heron et al., 2018b; Saleh et al., 2021). This task had a similar design as Circle Quest passive choices task, however, instead + +<--- Page Split ---> + +1 of uncertainty as the discounting attribute, participants evaluated reward against physical effort (Figure 7a.). Reward in the task was represented as apples on trees and effort levels were indicated by bars on the trunk of the trees. The higher the effort bar, the more effort participants needed to exert in order to obtain the apples. Effort in the task was exerted by squeezing a handheld dynamometer. Participants were first asked to squeeze the handle as hard as they could in order to measure their maximal voluntary contraction (MVC). Crucially, the effort handle was calibrated based on MVC for each participant. They were then familiarised with the different effort levels that they would encounter when making decisions. These effort levels corresponded to 16, 32, 48, 64 and \(0.8\%\) of MVC. Participants experienced each effort level twice before performing the decisions phase of the task where they had to evaluate the worthiness of reward (apples) against these effort levels. There were five reward levels corresponding to different numbers of apples (1, 4, 7, 10, and 13). Participants could indicate whether they would like to accept or reject the offer on the screen by pressing either the left or right arrow on the keyboard to select either "Yes" or "No", which were displayed on the sides of the screen. The positions of "Yes" and "No" changed randomly between trials. They were told that 10 of their decisions would be randomly selected at the end of the experiment and that they would have to play them (i.e., squeeze the effort handle to obtain reward). They were rewarded based on perfromance on these trials at a rate £1 per 10 apples. Before making their decisions, participants had the chance to perform five practice decisions. + +## Experimental paradigm - Exp. 4 + +In Exp. 4, a novel version of Circle Quest (Exps. 1 & 2) was designed to investigate effort- based decision making under uncertainty (Figure 4). Participants were familiarised with uncertainty, reward and effort cues using the same training as in Exps. 1–3. After training, they were required to respond to (accept or reject) offers that had three attributes: reward, uncertainty + +<--- Page Split ---> + +1 and effort (Figure 4 a.). Reward was presented as credits that participants could win if they managed to complete two steps: i) achieve the required level of effort (as in Exp. 3) and ii) find the location of the hidden circle without errors (as in Exps. 1 & 2). The blue disc used to localise the hidden circle appeared only when the required level of effort was achieved. If effort level was met, participants then could win credits depending on how far their localisation using the blue disc was from the true location of the hidden purple circle. There were four levels of reward (R: 40, 65, 90, 115 credits; same as in Exp. 2), five levels of effort (16, 32, 48, 64 and \(0.8\%\) of MVC; same as in Exp. 3) and two levels of uncertainty (present and absent). Absence of uncertainty was indicated by showing the true location of the hidden circle on the screen when offers were presented. On trials in which uncertainty was present, it corresponded to the midrange of expected error (EE) used in Exp. 2 ( \(EE\) : 31.8- 73.95 pixels). Additional catch trails (20 in total) were added to increase the range of uncertainty during confidence rating ( \(EE\) : 13.2 - 93.3) to fully capture subjective estimation of uncertainty as in Exp. 2. Unlike Exp. 2, confidence ratings were blocked and reported during the training phase rather than prior to each decision. This was done to ensure that offers with and without uncertainty were experimentally matched during the decision phase. The decision phase involved 40 different trial types (four reward levels \(\times\) five effort levels \(\times\) two uncertainty levels), and each was repeated five times over ten blocks (200 trials in total). Participants were told that at the end of the decision phase, 24 trials (12 with uncertainty) will be randomly selected for them to play. Performance on these trials decided the reward that participants eventually won. Similar to Exp. 2, they were rewarded at a rate of £1 per 150 credits (Table S18). + +## Statistical analyses of behavioural data + +Statistical analyses and modelling were done in MATLAB R2019a or R version 4.0.2. Generalised mixed- effects models and robust regression models were fitted using fitglm and fitlm func + +<--- Page Split ---> + +1 tions in MATLAB, respectively. Baysian mixed- effects models was performed using Stan computational framework (http://mc- stan.org/) accessed using brms in R (Bürkner, 2017). Bayes factors (BF) were calculated using brms hypothesis function. For group comparisons, student t- test was used if parametric assumptions were fulfilled and Wilcoxon rank sum test if not. All statistical tests were two- tailed with a testing level (alpha) 0.05. Full description of mixed effects models used and statistical results are reported in Supplementary Materials. + +## 7 Magnetic Resonance data acquisition + +8 MRI scans were obtained at Acute Vascular Imaging Centre (AVIC) at John Radcliff Hospital (Oxford) using SIEMENS Verio 3T scanner. High- resolution T1- weighted structural MR images (MPRAGE; 208 sagittal slices of 1 mm thickness, voxel size = 1 mm isotropic, TR/TE = 2000/1.94 ms; flip angle = 8°, FOV read = 256, iPAT =2, prescan- normalise) and T2 weighted fluid- attenuated inversion recovery (FLAIR) images (192 sagittal slices of 1.05 mm thickness, voxel size = \(1\times 1\times 1.1\) , TR/TE = 5000/397 ms; FOV read = 256; iPAT = 2, partial Fourier = 7/8, fat saturation, prescan- normalise) were acquired. Four patients and two controls were not MRI compatible or did not consent to be scanned, therefore, imaging data was acquired for 15/19 patients and 17/19 controls. + +## 7 Magnetic Resonance data analysis + +18 Whole hippocampal volumes were extracted using T1 and T2 imaging in Freesurfer version 7.1 (http://surfer.nmr.mgh.harvard.edu/). The automated standard segmentation protocol was used. Hippocampal volumes were adjusted for intracranial volume ( \(ICV\) ) using the following equation (Jack et al., 1998; Voevodskaya, 2014): + +\[V_{adj} = V - \beta \times (ICV - \overline{ICV}) \quad (4)\] + +<--- Page Split ---> + +1 where \(V_{adj}\) and \(V\) are the adjusted and observed volumes respectively, \(\beta\) is the slope of the relationship between \(ICV\) and \(V\) in a larger sample of healthy controls with similar demographics ( \(\mathrm{N} = 31\) , including the study sample and participants recruited for a different study), and \(\overline{ICV}\) is the mean intracranial volume in this control sample. Amygdala volumes were also extracted and used as a control comparison region (Figure S4). + +## 6 Data and code availability + +7 Anonymised data and code for replicating the main results in the manuscript have been deposited on the Open Science Framework platform: https://osf.io/u4n2a/. + +<--- Page Split ---> + +## 2 References + +3 Acerbi L, Ma WJ. 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Investigating the functions of subregions within anterior hippocampus. Cortex. 2015 12; 73:240- 256. + +<--- Page Split ---> + +## 1 Acknowledgments + +2 We thank Dr Kinan Muhammed for helping in recruiting some of the patients for the study. This work was supported by a Wellcome Trust grant to M.H. (206330/Z/17/Z) and a Rhodes scholarship awarded to B.A. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. + +6 + +7 Author contributions. B.A., P.P and M.H. designed the study. B.A, R.Z., S.I and M.H recruited the patients. B.A, S.T., M.R.M., A.G-D., and R.Z collected the data. B.A, P.P. and S.M. analysed the data. B.A. and M.H. wrote the paper. + +<--- Page Split ---> + +## 1 Supplementary materials + +## 2 Expected error and uncertainty + +3 Objective uncertainty in Circle Quest task was quantified as the expected error of localisation (EE). This is equal to the error the ideal agent would obtain on average by placing the blue disc at the best possible location given the information on the screen. For each location \(\lambda\) on the screen the probability that this location is the centre of the hidden circle given the observation \(o\) at location \(\sigma\) can be calculated using Bayes' rule as follows: + +\[\begin{array}{l}{{p_{s}(\lambda|o,\sigma)=\frac{p_{s}(\lambda).p_{s}(o,\sigma|\lambda)}{p_{s}(o,\sigma)}}}\\ {{p_{s+1}(\lambda)=p_{s}(\lambda|o,\sigma)}}\end{array} \quad (1)\] + +With successive sampling, this rule is applied sequentially. Therefore, the posterior probability \(p_{s}(\lambda |o,\sigma)\) becomes the prior probability \(p_{s + 1}(\lambda)\) from one sample to the next. Given the rules of the task and that there is no uncertainty regarding the radius of the hidden circle, the likelihood of observing a purple dot \((o^{+})\) at a location \(\sigma\) is 1 for locations within one radius distance of \(\sigma\) , and zero otherwise. The opposite is true for the likelihood of observing a white dot \((o^{- })\) . Thus, the likelihood function can be expressed mathematically as: + +\[\left\{ \begin{array}{l l}{p_{s}(o^{+},\sigma |\lambda) = 1\mathrm{~if~}|\lambda -\sigma |\leq r}\\ {p_{s}(o^{+},\sigma |\lambda) = 0\mathrm{~if~}|\lambda -\sigma | > r}\\ {p_{s}(o^{-},\sigma |\lambda) = 1 - p_{s}(o^{+},\sigma |\lambda)} \end{array} \right. \quad (2)\] + +Where \(r\) is the radius of the hidden circle which is fixed. + +The probability of the observation \(o\) at the sampling location \(\sigma\) is the sum over all possible hidden circle centres \(\lambda\) of the probability of the observation given \(\lambda\) , weighted by the probability of \(\lambda\) to be the hidden circle centre: + +\[p_{s}(o,\sigma) = \sum_{\lambda}p_{s}(o,\sigma |\lambda).p_{s}(\lambda) \quad (3)\] + +<--- Page Split ---> + +Thus, for every possible circle placement, an expected error can be computed as: + +\[E E_{s}(\lambda) = \sum_{i}p_{s}(\lambda_{i}).|\lambda -\lambda_{i}| \quad (4)\] + +## Computational modelling of active information gathering + +To further characterise active information sampling performance in Exp. 1, we analysed the behaviour using a well- validated computational model previously implemented in healthy and patient groups (Attaallah et al., 2022; Petitet et al., 2021). + +The model calculates the expected utility of a sample \((E U_{s})\) accounting for economic and hidden cognitive effort costs to return five parameter estimates per participant. The first two parameters represent the weights participants assign to sample costs \((w_{s})\) and benefits \((w_{e})\) . Two parameters describe the cognitive cost function \(\eta_{c}(ISI,\alpha)\) in terms of a penalty for sampling speed \((w_{speed})\) and efficiency \((w_{\alpha})\) . The fifth parameter represents an intercept per participant describing their baseline valuation of samples \((w_{0})\) . + +This was formalised quantitatively as follows: + +\[\begin{array}{r l} & {E U_{s}(I S I,\alpha ,t_{m a x}) = E U_{s - 1} + p(s|I S I,t_{m a x}).[w_{e}.\eta_{e}.(1 - \alpha).(E E_{s - 1} - E\hat{E}_{\infty})}\\ & {~ - w_{s}.\eta_{s}^{1 + \gamma .s} - \eta_{c}(I S I,\alpha)]} \end{array} \quad (5)\] + +Previous EU + Probability of acquiring the sample given the current time. [Expected information benefit - Sampling cost - Cognitive effort cost] where \(\eta_{e}\) is the placement error penalty (1.2 credits/pixel) and \(t_{max}\) is the allowed search time per trial (18 seconds). \(E\hat{E}_{\infty}\) is the per- individual information sampling asymptotic limit estimated beforehand to take into consideration inter- participant variations in asymptotic information sampling performance. + +where \(\eta_{e}\) is the placement error penalty (1.2 credits/pixel) and \(t_{max}\) is the allowed search time per trial (18 seconds). \(E\hat{E}_{\infty}\) is the per- individual information sampling asymptotic limit estimated beforehand to take into consideration inter- participant variations in asymptotic information sampling performance. + +Based on previous work (Attaallah et al., 2022; Petitet et al., 2021), we used quadratic + +<--- Page Split ---> + +1 cognitive cost function as follows: + +\[\eta_{c}(ISI,\alpha) = w_{0} + w_{speed}\times \frac{1}{ISI^{2}} +w_{\alpha}\times \alpha^{2} \quad (6)\] + +3 To obtain the likelihood function, softmax function was applied over the 3- dimensional 4 space of \(EU\) ( \(EU\) depends on \(ISI,\alpha ,s)\) for a given task condition as follows: + +\[p_{s}(stop|ISI,\alpha ,t_{max}) = \frac{\exp(EU_{s}(ISI,\alpha,t_{max}))}{\sum_{i}\sum_{a}\sum_{t}^{t_{max}}\exp(EU_{s}(i,a,t))} \quad (7)\] + +6 For each individual, model fitting involved findings the parameters that achieved the low- 7 est negative log- likelihood of observing the multivariate distribution of the number of samples + +8 acquired \((s)\) , inter- sampling interval (ISI) and sampling efficiency \((\alpha)\) + +9 Optimisation of parameters was performed in MATLAB (The MathWorks inc., version 2019a) using Bayesian Adaptive Direct Search (BADS (Acerbi and Ma, 2017)). Further information about this modelling framework is provided in Attaallah et al. (2022); Petitet et al. (2021). + +13 After the exclusion of potential outliers (1 patient with values \(>3SD\) ), comparing parameter estimates between two groups showed that ALE patients had lower weights assigned to sampling cost compared to controls ( \(t_{35} = - 2.24\) , \(p = 0.031\) ; Figure S1). There was no significant difference between the two groups in any of the other parameters. These results thus represent a computational formalisation of the findings from Exp. 1 suggesting that ALE patients have lower sensitivity to the cost of sampling. + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + +
Figure S1: Computational modelling of active information sampling (Exp. 1). Compared to healthy matched controls, ALE patients assigned lower economic costs \((w_{s})\) to sample acquisition. All other model parameters including weights assigned to sample benefit \((w_{e})\) , efficiency \(w_{\alpha}\) , and speed \((w_{speed})\) were not significantly different between patients and controls. \(w_{0}\) captures a subjective fixed cost of sampling that is not explicitly specified in the task (e.g., cost of the motor action). This was not significantly different between the two groups. \(*:p< 0.05\)
+ +<--- Page Split ---> + +## Supplementary Figures + +2 Figs. S2 to S6 + +<--- Page Split ---> +![](images/Figure_unknown_1.jpg) + +
Figure S2: Active sampling (Exp. 1) – Hippocampal patients commit to decisions at similar uncertainty levels as controls. a. Final uncertainty is the expected error (EE) in pixels (Px) that a participant is likely to obtain at the end of their search. In the experimental condition where hippocampal patients over-sampled more than controls, there was no significant difference between hippocampal patients and controls in this measure. b. Similarly, the actual error that participants obtained upon localising the circle (distance to hidden circle in pixels) was not significantly different between patients and controls. These two results indicate that hippocampal patients wasted monetary resources on samples with limited utility (i.e., over-sampled). c. In the same condition, hippocampal patients gathered information at a significantly faster rate than controls. d. Sampling behaviour in hippocampal patients and controls was characterised by a speed-efficiency trade-off whereby faster sampling rates (shorter \(ISI\) ) were associated with lower sampling efficiency (smaller \(\alpha\) ). The figure shows this trade-off for the same condition in which patients over-sampled more than controls, demonstrating that hippocampal patients were also both faster and less efficient than controls. Error bars show \(\pm\) SEM. \*:p<0.05. See Tables S3, S5 & S6 for additional statistical details.
+ +<--- Page Split ---> +![](images/Figure_unknown_2.jpg) + +
Figure S3: Baysian mixed-effects model. The purple dots show the median of the posterior distributed with \(95\%\) credible intervals (thin green line) and \(50\%\) posterior interval (thick dark red lines). Model was specified as follows:choice \(\sim 1 + \mathrm{group}^{*}\mathrm{Reward} + \mathrm{group}^{*}\mathrm{Effort} + \mathrm{Re}-\) ward\*Effort + group:Reward:Effort + (1 + reward\*Effort |participant).
+ +<--- Page Split ---> +![](images/Figure_unknown_3.jpg) + +
Figure S4: Amygdala as control region. No significant correlation was detected between amygdala volume and sensitivity to reward or uncertainty. Note also there was no significant difference in amygdala volumes between patients and controls.
+ +![](images/Figure_unknown_4.jpg) + +
Figure S5: Intact localisation performance. Distance to optimal placement is the distance between the centre of the blue disc and the best localisation given the configuration of the dots on display. Across the three versions of Circle Quest (Exps. 1, 2 & 3), there was no significant difference between ALE patients and controls in this measure, indicating intact localisation performance. Error bars show \(\pm\) SEM.
+ +<--- Page Split ---> +![](images/Figure_unknown_5.jpg) + +
Figure S6: Passive choices as a function of reward and subjective uncertainty estimates. There is no change in choice performance results when subjective estimates of uncertainty are used instead of expected error \((EE)\) in the analysis. ALE patients demonstrate lower sensitivity to reward and intact sensitivity to uncertainty when compared to healthy controls. Reward levels 1-4 correspond to the number of credits on display \((R: 40, 65, 90, 115\) credits). Subjective uncertainty levels were calculated by binning sign-flipped z-scored confidence ratings into five bins. level five describes the lowest level of subjective uncertainty estimate. For statistical details see Table S16.
+ +<--- Page Split ---> + +## Supplementary Tables + +2 Tables S1 to S18 + +<--- Page Split ---> + + +
Count (M/F) VariableControlsPatients
MeanSDMeanSDp-value
Age61.1611.7160.0011.360.76
ACE-III97.522.0393.425.640.005
DS18.053.4919.795.000.21
AMI1.180.401.250.490.65
FSS3.061.123.361.860.59
BDI-II5.95.2610.9510.150.06
SHAPS18.844.5621.584.850.08
Lt. Hipp Volume*3495.06199.413357.71722.710.457
Rt. Hipp Volume*3662.99167.063288.50675.580.034
+ +Table S1: Demographics. ACE-III: Addenbrooke's Cognitive Examination. DS: Digit Span.AMI: Apathy Motivation Index. FSS: Fatigue Severity Scale. BDI-II: Beck Depression Inven-tory. SHAPS: Snaith-Hamilton Pleasure Scale. Hipp: Adjusted Hippocampal Volumes.* 15patients and 17 controls. + +
CodeAgeGenderAbsLt. Hipp.Rt. Hipp.Years Since
Diagnosis
VolumePercentileVolumePercentile
153FLGI13559.64293547.33213.38
247FLGI13157.676c2717.64\(<2.5^{c}\)3.44
359FLGI12332.83\(<2.5\)2255.53\(<2.5\)3.49
463FLGI12860.65\(<2.5\)3744.13432.59
572FLGI13170.72\(17^{c}\)2495.43\(<2.5^{c}\)7.67
664MLGI1----4.66
755MLGI14835.20974125.12462.56
853MLGI13663.60193754.24172.36
966MLGI14109.11724341.71801.18
1065MLGI13488.01183379.2093.27
1172MLGI12973.8352905.0431.82
1226MLGI1----0.96
1368MCASPR23050.2743364.09101.03
1477MCASPR2----10.52
1565MCASPR23455.68163411.77105.29
1667MCASPR24118.84743942.82484.30
1758FLGI1/CASPR23670.25413073.4143.26
1858MLGI1/CASPR2----7.16
1952MSeronegative2370.64\(<2.5^{c}\)2752.98\(<2.5^{c}\)1.99
+ +Table S2: Patients Characteristics. Abs: Autoantibodies. Lt. Hipp.: Left Hippocampus. Rt.Hipp.: Right Hippocampus. Percentile is determined by plotting raw hippocampal volumes against normative brain volumes from UK biobank data (Nobis et al., 2019). c: Describes percentiles outside the age range of the UK biobank nomograms. Percentiles according to the closest age value within the UK biobank range was used instead. + +<--- Page Split ---> + +
SscoreEEErrorISI
(Intercept)β = +2.18β = +65.7β = +20.3β = +2.06β = +1.5
SE = 0.0817SE = 1.39SE = 2.12SE = 0.0873SE = 0.0902
t2272 = +26.72t2272 = +47.32t2272 = +9.57t2272 = +23.64t2272 = +16.67
p<0.0001p<0.0001p<0.0001p<0.0001
ALEβ = +0.153β = -7.93β = -0.683β = +0.162β = -0.211
SE = 0.116SE = 1.96SE = 2.99SE = 0.123SE = 0.128
t2272 = +1.32t2272 = -4.04t2272 = -0.23t2272 = +1.32t2272 = -1.65
p = 0.19p<0.0001p = 0.82p = 0.19p = 0.10
ALE:R0β = +0.0247β = -1.66β = -0.952β = -0.0265β = -0.0393
SE = 0.0251SE = 1.06SE = 0.819SE = 0.0376SE = 0.027
t2272 = +0.98t2272 = -1.56t2272 = -1.16t2272 = -0.70t2272 = -1.45
p = 0.33p = 0.12p = 0.25p = 0.48p = 0.15
ALE:ηsβ = +0.0318β = -3.66β = -0.924β = -0.0277β = -0.0719
SE = 0.0247SE = 1.84SE = 0.665SE = 0.0318SE = 0.0337
t2272 = +1.29t2272 = -2.00t2272 = -1.39t2272 = -0.87t2272 = -2.14
p = 0.20p = 0.046p = 0.16p = 0.38p = 0.033
ALE:ηs:R0β = +0.0542β = -0.403β = -0.881β = -0.0463β = -0.0273
SE = 0.0202SE = 1.04SE = 0.602SE = 0.0302SE = 0.0231
t2272 = +2.68t2272 = -0.39t2272 = -1.46t2272 = -1.53t2272 = -1.18
p = 0.0074p = 0.70p = 0.14p = 0.13p = 0.24
R0β = +0.0231β = +17.6β = -0.169β = +0.0104β = +0.00487
SE = 0.018SE = 0.751SE = 0.579SE = 0.0266SE = 0.0191
t2272 = +1.29t2272 = +23.45t2272 = -0.29t2272 = +0.39t2272 = +0.25
p = 0.20p<0.0001p = 0.77p = 0.70p = 0.80
ηsβ = -0.111β = -18.7β = +2.07β = +0.0919β = +0.12
SE = 0.0177SE = 1.3SE = 0.47SE = 0.0225SE = 0.0238
t2272 = -6.25t2272 = -14.41t2272 = +4.40t2272 = +4.09t2272 = +5.04
p<0.0001p<0.0001p<0.0001p<0.0001
ηs:R0β = -0.0301β = -0.654β = +0.477β = +0.0211β = +0.0135
SE = 0.0145SE = 0.732SE = 0.426SE = 0.0213SE = 0.0164
t2272 = -2.07t2272 = -0.89t2272 = +1.12t2272 = +0.99t2272 = +0.83
p = 0.038p = 0.37p = 0.26p = 0.32p = 0.41
adj - R20.790.710.580.270.71
Nobs22802280228022802280
AIC295.8419913.9016521.034656.471163.69
+ +Table S3: Active Search (Exp. 1) – Generalised mixed-effects models of the effect of the group (ALE) on performance. Models were specified as follows. Predicted variable ~ 1 + group*ηs + group*R0 + ηs*R0 + group:ηs:R0 + (1 + ηs*R0 |participant). S: Raw number of samples. EE: Expected Error (Uncertainty). Error: Distance to hidden circle. ISI: Inter-Sampling Interval. ηs : Sampling Cost. R0: Initial Reward Reserve. + +<--- Page Split ---> + + +
Condition (R/hs)ControlsALE
Low/Low\(\beta =-2.24\)\(\beta =-1.6\)
\(SE=0.715\)\(SE=1.23\)
\(t_{1136}=-3.14\)\(t_{1136}=-1.30\)
\(p=0.0018\)\(p=0.19\)
Low/High\(\beta =+1.97\)\(\beta =+3.14\)
\(SE=0.543\)\(SE=0.81\)
\(t_{1136}=+3.63\)\(t_{1136}=+3.88\)
\(p=0.00029\)\(p=0.00011\)
High/Low\(\beta =-1.32\)\(\beta =-0.277\)
\(SE=0.689\)\(SE=1.34\)
\(t_{1136}=-1.92\)\(t_{1136}=-0.21\)
\(p=0.06\)\(p=0.84\)
High/High\(\beta =+1.9\)\(\beta =+4.74\)
\(SE=0.625\)\(SE=1.05\)
\(t_{1136}=+3.04\)\(t_{1136}=+4.52\)
\(p=0.0024\)\(p<0.0001\)
adj-R2
\(N_{obs}\) AIC		0.860.84
11401140
4273.505545.49
+ +Table S4: Active Search (Exp. 1) - Deviation from optimal number of samples.. Models were specified as follows: Deviation $\sim $ Condition + (Condition |participant). $R$ : Initial reward reserve. $\eta _{s}:$ Sampling cost. + +
ControlsALE
(Intercept)\(\beta =-1.67\)\(\beta =-1.9\)
\(SE=0.0721\)\(SE=0.0881\)
\(t_{1138}=-23.12\)\(t_{1138}=-21.57\)
\(p<0.0001\)\(p<0.0001\)
ISI\(\beta =+0.137\)\(\beta =+0.26\)
\(SE=0.0463\)\(SE=0.054\)
\(t_{1138}=+2.96\)\(t_{1138}=+4.82\)
\(p=0.0032\)\(p<0.0001\)
adj-R2
\(N_{obs}\) BIC		0.220.27
11401140
570.28858.72
+ +Table S5: Active Search (Exp. 1) - Relationship between inter-sampling interval and in-formation extraction rate. Model was specified as follows: $\alpha \sim 1+ISI+(1|trial)+(1+ISI$ condition) $+(1+ISI|participant)$ + +<--- Page Split ---> + + +
Exp. 1
(Intercept)\(\beta =+0.244\) \(SE=0.00934\) \(t_{566}=+26.11\) \(p<0.0001\)
ALE\(\beta =-0.026\) \(SE=0.0131\) \(t_{566}=-1.98\) \(p=0.048\)
ALE:ISI\(\beta =-0.00165\) \(SE=0.0128\) \(t_{566}=-0.13\) \(p=0.90\)
ISI\(\beta =+0.0122\) \(SE=0.00905\) \(t_{566}=+1.35\) \(p=0.18\)
\(adj-R^{2}\)0.25
\(N_{obs}\)570
AIC-1256.45
+ +Table S6: Active Search (Exp. 1) - Generalised mixed-effects model investigating the effect of ALE on efficiency (α) in the condition with high sampling cost and high initial reward reserve, i.e., the condition where ALE patients over-sampled more than controls. Model was specified as follows.: \(\alpha \sim 1+\mathrm {group}^{*}\mathrm {ISI}+(1|\mathrm {trial})+(1+\mathrm {ISI}|\mathrm {participant}).\) + +<--- Page Split ---> + + +
Exp. 2
(Intercept)\(\beta =-0.423\)
\(SE=0.323\) \(t_{3748}=-1.31\) \(p=0.19\)
ALE\(\beta =-0.402\)
\(SE=0.457\) \(t_{3748}=-0.88\) \(p=0.38\)
ALE:EE\(\beta =+0.336\)
\(SE=0.442\) \(t_{3748}=+0.76\) \(p=0.45\)
ALE:R\(\beta =-0.983\)
\(SE=0.275\) \(t_{3748}=-3.58\) \(p=0.00035\)
ALE:R:EE\(\beta =+0.162\)
\(SE=0.17\) \(t_{3748}=+0.95\) \(p=0.34\)
EE\(\beta =-2.73\)
\(SE=0.313\) \(t_{3748}=-8.72\) \(p<0.0001\)
R\(\beta =+1.41\)
\(SE=0.198\) \(t_{3748}=+7.16\) \(p<0.0001\)
R:EE\(\beta =+0.0659\)
\(SE=0.125\) \(t_{3748}=+0.53\) \(p=0.60\)
adj-R20.93
\(N_{obs}\)3756
AIC2938.44
+ +Table S7: Reward against uncertainty (Exp. 2) - Generalised mixed-effects model examin-ing effect of reward and uncertainty on choices as well as differences between ALE group and controls. Model was specified as follows: choice $\sim 1+\mathrm {grou}\mathrm {p}^{*}R+\mathrm {grou}\mathrm {p}^{*}EE+R^{*}EE$ +group: $R:EE+(1+R^{*}EE$ |participant). Control group was set as the reference group. EE: Expected Error. R: Reward. ALE: Autoimmune Limbic Encephalitis. + +<--- Page Split ---> + +
Exp. 2 with outlier removed
(Intercept)β = -0.59
SE = 0.315
t3648 = -1.87
p = 0.06
LEβ = -0.226
SE = 0.439
t3648 = -0.52
p = 0.61
LE:EEβ = +0.529
SE = 0.412
t3648 = +1.28
p = 0.20
LE:Rβ = -0.868
SE = 0.259
t3648 = -3.35
p = 0.00081
LE:R:EEβ = +0.18
SE = 0.173
t3648 = +1.04
p = 0.30
EEβ = -2.9
SE = 0.298
t3648 = -9.73
p <0.0001
Rβ = +1.31
SE = 0.189
t3648 = +6.97
p <0.0001
R:EEβ = +0.843
SE = 0.128
t3648 = +0.66
p = 0.51
adj - R²
Nobs
AIC		0.91
3656
2879.44
+ +Table S8: Generalised mixed-effects model of the effect of the group (ALE vs. Controls) on choices with one outlier removed from the control group. Model was specified as follows: choice ~ 1 + group*R + group*EE + R*EE + group:R:EE + (1 + R*EE |participant). EE : Expected Error. R : Reward. + +<--- Page Split ---> + + +
(Exp. 3)
(Intercept)\(\beta =+1.96\)
\(SE=0.442\) \(t_{4739}=+4.43\) \(p<0.0001\)
ALE\(\beta =-0.137\)
\(SE=0.622\) \(t_{4739}=-0.22\) \(p=0.83\)
ALE:Effort\(\beta =+0.306\)
\(SE=0.467\) \(t_{4739}=+0.66\) \(p=0.51\)
ALE:Reward\(\beta =-0.48\)
\(SE=0.37\) \(t_{4739}=-1.30\) \(p=0.19\)
ALE:Reward:Effort\(\beta =-0.22\)
\(SE=0.408\) \(t_{4739}=-0.54\) \(p=0.59\)
Effort\(\beta =-2.82\)
\(SE=0.334\) \(t_{4739}=-8.45\) \(p<0.0001\)
Reward\(\beta =+2.97\)
\(SE=0.267\) \(t_{4739}=+11.12\) \(p<0.0001\)
Reward:Effort\(\beta =-0.281\)
\(SE=0.292\) \(t_{4739}=-0.96\) \(p=0.34\)
adj-R20.97
\(N_{obs}\)4747
AIC2775.57
+ +Table S9: Effort-based decision making (Exp. 3)- Generalised mixed-effects models exam-ining effect of reward and effort on choices as well as differences between ALE group and controls. Models were specified as follows. Effort-based choices: choice $\sim 1+group*Reward$ +group*Effort + Reward*Effort + group:Reward:Effort + (1 + Reward*Effort |participant). Controls group was set as the reference group. ALE: ALE: Autoimmune Limbic Encephalitis group. + +<--- Page Split ---> + + +
ParameterRhatn. effmeansd2.5%50%97.5%
Intercept1.029061.60.50.61.62.5
Reward1.067862.80.32.22.83.5
Effort1.05174-2.70.4-3.5-2.7-2.0
Reward:Effort1.07592-0.20.3-0.8-0.20.4
ALE1.030970.00.6-1.3-0.01.3
Reward:ALE1.06647-0.30.4-1.1-0.30.5
Effort:ALE1.049450.20.5-0.80.21.1
Reward:Effort:ALE1.07563-0.20.4-1.0-0.20.5
+ +Table S10: Baysian mixed- effects modelling of effort- based choices data (Exp. 3) – Posterior summary statistics. Model was specified as follows: choice \(\sim 1 + \mathrm{group}^{*}R + \mathrm{group}^{*}EE + R^{*}EE + \mathrm{group}:R:EE + (1 + R^{*}EE | \mathrm{participant})\) . To improve convergence and guard against over- fitting, mildly informative conservative priors were specified. + +<--- Page Split ---> + + +
Exps. 2 &amp; 3
(Intercept)\(\beta =+0.0983\)
\(SE=0.141\)
\(t_{8495}=+0.70\)
\(p=0.49\)
Group\(\beta =-0.235\)
\(SE=0.202\)
\(t_{8495}=-1.17\)
\(p=0.24\)
Group:Task\(\beta =+0.0471\)
\(SE=0.102\)
\(t_{8495}=+0.46\)
\(p=0.65\)
Group:Task:Reward\(\beta =+0.467\)
\(SE=0.113\)
\(t_{8495}=+4.15\)
\(p<0.0001\)
Group:Reward\(\beta =-0.569\)
\(SE=0.159\)
\(t_{8495}=-3.58\)
\(p=0.00035\)
Task\(\beta =+0.534\)
\(SE=0.0737\)
\(t_{8495}=+7.25\)
\(p<0.0001\)
Task:Reward\(\beta =+0.544\)
\(SE=0.0815\)
\(t_{8495}=+6.68\)
\(p<0.0001\)
Reward\(\beta =+0.665\)
\(SE=0.112\)
\(t_{8495}=+5.94\)
\(p<0.0001\)
adj-R20.39
\(N_{obs}\)8503
AIC9824.54
+ +Table S11: Generalised mixed-effects model examining the effect of Group (ALE) and Task on reward sensitivity in Exps. 2 & 3. Models were specified as follows. Exps. 2 3: choice $\sim 1+Group*Task+Group*Reward+Task*Reward+Group:Task:Reward+(1+$Reward $|Participant)+(1+Reward|Task).$ + +3: choice \(\sim 1+Group*Task+Group*Reward+Task*Reward+Group:Task:Reward+(1+\) Reward |Participant) + (1 + Reward |Task). + +<--- Page Split ---> + + +
Exp.s. 2 &amp; 3 (ALE only)
(Intercept)\(\beta =-0.137\) \(SE=0.144\) \(t_{4224}=-0.95\) \(p=0.34\)
Task\(\beta =+0.582\) \(SE=0.0709\) \(t_{4224}=+8.20\) \(p<0.0001\)
Task:Reward\(\beta =+1.01\) \(SE=0.0778\) \(t_{4224}=+13.03\) \(p<0.0001\)
Reward\(\beta =+0.0975\) \(SE=0.12\) \(t_{4224}=+0.81\) \(p=0.42\)
\(adj-R^{2}\)0.37
\(N_{obs}\)4228
AIC4973.27
+ +Table S12: Generalised mixed-effects model examining the effect of task on reward sensi-tivity in ALE patients. Models were specified as follows: choice $\sim 1+Task\ast Reward+(1+$ Reward |Participant) $+(1+Reward|Task).$ + +<--- Page Split ---> + +
Exp. 4
(Intercept)β = +1.93
SE = 0.579
t4184 = +3.34
p = 0.00084
LEβ = -1.03
SE = 0.927
t4184 = -1.12
p = 0.26
ALE:Uncertaintyβ = +1.09
SE = 0.875
t4184 = +1.25
p = 0.21
ALE:Uncertainty:Effortβ = -0.257
SE = 0.239
t4184 = -1.08
p = 0.28
ALE:Uncertainty:Rewardβ = +0.568
SE = 0.272
t4184 = +2.09
p = 0.036
ALE:Uncertainty:Reward:Effortβ = -0.123
SE = 0.24
t4184 = -0.51
p = 0.61
ALE:Effortβ = +1.24
SE = 0.415
t4184 = +2.99
p = 0.0028
ALE:Rewardβ = -1.41
SE = 0.499
t4184 = -2.83
p = 0.0046
ALE:Reward:Effortβ = -0.146
SE = 0.209
t4184 = -0.70
p = 0.48
Uncertaintyβ = -1.9
SE = 0.545
t4184 = -3.49
p = 0.0005
Uncertainty:Effortβ = +0.251
SE = 0.164
t4184 = +1.53
p = 0.13
Uncertainty:Rewardβ = -0.671
SE = 0.186
t4184 = -3.61
p = 0.0003
Uncertainty:Reward:Effortβ = +0.0537
SE = 0.164
t4184 = +0.33
p = 0.74
Effortβ = -1.45
SE = 0.269
t4184 = -5.38
p < 0.0001
Rewardβ = +1.73
SE = 0.327
t4184 = +5.31
p < 0.0001
Reward:Effortβ = +0.207
SE = 0.141
t4184 = +1.47
p = 0.14
adj - R²0.99
Nobs4200
AC3352.10
+ +Table S13: Generalised mixed-effects model of the effect of the group (ALE vs. controls) on effort-based decisions under uncertainty (Exp. 4). Models were specified as follows: choice ~ 1 + group*Uncertainty + group*Reward + Uncertainty*Reward + group*Effort + Uncertainty*Effort + Reward*Effort + group:Uncertainty:Reward + group:Uncertainty:Effort + group:Reward:Effort + Uncertainty:Reward:Effort + group:Uncertainty:Reward:Effort + (1 + Uncertainty*Reward + Uncertainty*Effort + Reward*Effort + Uncertainty:Reward:Effort |participant). + +<--- Page Split ---> + + +
Exp.s. 1 &amp; 2Exp. 4
(Intercept)\(\beta =-0.00398\)\(\beta =-0.0106\)
\(SE=0.0903\)\(SE=0.174\)
\(t_{3752}=-0.04\)\(t_{2306}=-0.06\)
\(p=0.96\)\(p=0.95\)
EE\(\beta =+0.647\)\(\beta =+0.476\)
\(SE=0.0502\)\(SE=0.0873\)
\(t_{3752}=+12.88\)\(t_{2306}=+5.45\)
\(p<0.0001\)\(p<0.0001\)
ALE\(\beta =-0.009\)\(\beta =+0.028\)
\(SE=0.127\)\(SE=0.281\)
\(t_{3752}=-0.07\)\(t_{2306}=+0.10\)
\(p=0.94\)\(p=0.92\)
ALE:EE\(\beta =-0.0546\)\(\beta =-0.0226\)
\(SE=0.0711\)\(SE=0.141\)
\(t_{3752}=-0.77\)\(t_{2306}=-0.16\)
\(p=0.44\)\(p=0.87\)
\(adj-R^{2}\)0.580.71
\(N_{obs}\)37562310
AIC7645.653940.63
+ +Table S14: Generalised mixed-effects model of the effect of the group (ALE vs. controls)on flexibility of uncertainty estimation. Models were specified as follows: Uncertainty Score~1+group*EE+(1+EE|participant)+(1|trial). + +<--- Page Split ---> + + +
Subjective Uncertainty non-z-scored
(Intercept)\(\beta =-0.407\) \(SE=0.022\) \(t_{3752}=-18.54\) \(p<0.0001\)
EE\(\beta =+0.157\) \(SE=0.0122\) \(t_{3752}=+12.88\) \(p<0.0001\)
Group\(\beta =-0.00219\) \(SE=0.031\) \(t_{3752}=-0.07\) \(p=0.94\)
Group:EE\(\beta =-0.0133\) \(SE=0.0173\) \(t_{3752}=-0.77\) \(p=0.44\)
adj-R20.58
\(N_{obs}\)3756
AIC-2972.29
+ +Table S15: Generalised mixed-effects model examining the effect Group (ALE vs. Con-trols) on non-z-scored values of subjective uncertainty. Models were specified as follows.Subjective Uncertainty non-z-scored: Uncertainty (non-z-scored) $\sim 1+group*EE+(1+EE$ participant) $+(1|trial_{a}ll).$ + +<--- Page Split ---> + + +
Choices with Confidence (Exp. 2)
(Intercept)\(\beta =+0.361\)
\(SE=0.367\)
\(t_{3748}=+0.98\)
\(p=0.33\)
Group\(\beta =-0.157\)
\(SE=0.518\)
\(t_{3748}=-0.30\)
\(p=0.76\)
Group:Reward\(\beta =-1.13\)
\(SE=0.279\)
\(t_{3748}=-4.06\)
\(p<0.0001\)
Group:Reward:Confidence\(\beta =-0.305\)
\(SE=0.22\)
\(t_{3748}=-1.38\)
\(p=0.17\)
Group:Confidence\(\beta =-0.573\)
\(SE=0.574\)
\(t_{3748}=-1.00\)
\(p=0.32\)
Reward\(\beta =+1.49\)
\(SE=0.2\)
\(t_{3748}=+7.47\)
\(p<0.0001\)
Reward:Confidence\(\beta =+0.228\)
\(SE=0.165\)
\(t_{3748}=+1.38\)
\(p=0.17\)
Confidence\(\beta =+3.94\)
\(SE=0.41\)
\(t_{3748}=+9.62\)
\(p<0.0001\)
adj-R²0.98
\(N_{obs}\)3756
AIC2436.28
+ +Table S16: Generalised mixed-effects model examining the effect of Group (ALE), Reward and Subjective Confidence on choices (Exp. 2). Models were specified as follows. Choices with Confidence: choice $\sim 1+Group\ast Reward+Group\ast Confidence+Reward\ast Confidence+$ Group:Reward:Confidence $+(1+Reward\ast Confidence$ Participant). + +<--- Page Split ---> + + +
Choice Model Controlled for Metacognitive Differences
(Intercept)\(\beta =-0.457\)
\(SE=0.330\)
\(t_{3747}=-1.34\)
\(p=0.18\)
Group\(\beta =-0.351\)
\(SE=0.481\)
\(t_{3747}=-0.73\)
\(p=0.47\)
Group:EE\(\beta =+0.31\)
\(SE=0.430\)
\(t_{3747}=+0.70\)
\(p=0.48\)
Group:Reward\(\beta =-0.976\)
\(SE=0.273\)
\(t_{3747}=-3.57\)
\(p=0.00036\)
Group:Reward:EE\(\beta =+0.165\)
\(SE=0.172\)
\(t_{3747}=+0.96\)
\(p=0.34\)
EE\(\beta =-2.72\)
\(SE=0.311\)
\(t_{3747}=-8.73\)
\(p<0.0001\)
Metacognitive differences\(\beta =+0.276\)
\(SE=0.153\)
\(t_{3747}=+1.81\)
\(p=0.07\)
Reward\(\beta =+1.4\)
\(SE=0.196\)
\(t_{3747}=+7.15\)
\(p<0.0001\)
Reward:EE\(\beta =+0.0658\)
\(SE=0.125\)
\(t_{3747}=+0.53\)
\(p=0.60\)
\(a_{dj}-R^{2}\) \(N_{obs}\) AIC0.93
3756
2938.62
+ +Table S17: Generalised mixed-effects model examining choice behaviour in Exp. 2 while controlling for differences in uncertainty estimation. Models were specified as follows. Choice Model Controlled for Metacognitive Differences: Choice $\sim 1+Metacognitive$ differences + Group*Reward + Group*EE + Reward*EE + Group:Reward:EE + (1 + Reward*EE |participant). Metacognitive differences represent the slope of the correlation between subjec-tive and objective uncertainty estimates. + +
GroupExp. 1 (cds)Exp. 2 (cds)Exp. 3 (apples)Exp. 4 (cds)
ControlsALEControlsALEControlsALEControlsALE
Mean Total Score3941.873466.06595.11555.953.5651.11932.64693.68
SD211.14483.42106.33141.729.029.26155.97387.51
p-value\(<0.001**\)0.550.080.42
Reward in £*£1 per 150 cds£1 per 10 apples£1 per 150 cds
+ +Table S18: Scores in Exps. 1-4. * While participants were told that this is the reward structure of the tasks, most of them were paid a maximum of £5 per experiment. Similar to Exp. 2,participants were paid for 1/10 of the trials in Exp. 1. ** Please refer to Table S3 for more details about the effect of different conditions on scores in Exp. 1. + +<--- Page Split ---> diff --git a/preprint/preprint__05f28e060c3bc16824dbd8aa76dba38dd15d721d8f4e14b66cd59ec29caee55f/preprint__05f28e060c3bc16824dbd8aa76dba38dd15d721d8f4e14b66cd59ec29caee55f_det.mmd b/preprint/preprint__05f28e060c3bc16824dbd8aa76dba38dd15d721d8f4e14b66cd59ec29caee55f/preprint__05f28e060c3bc16824dbd8aa76dba38dd15d721d8f4e14b66cd59ec29caee55f_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..9f98605e9a0ad20f37d64ee07c5fc4949f70b48b --- /dev/null +++ b/preprint/preprint__05f28e060c3bc16824dbd8aa76dba38dd15d721d8f4e14b66cd59ec29caee55f/preprint__05f28e060c3bc16824dbd8aa76dba38dd15d721d8f4e14b66cd59ec29caee55f_det.mmd @@ -0,0 +1,1130 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 933, 177]]<|/det|> +# Role of the hippocampus in decision making under uncertainty + +<|ref|>text<|/ref|><|det|>[[44, 196, 323, 242]]<|/det|> +Bahaaeddin Attaallah atallah.bahaa@gmail.com + +<|ref|>text<|/ref|><|det|>[[44, 268, 590, 666]]<|/det|> +University of Oxford https://orcid.org/0000- 0002- 7842- 7974 Pierre Petitet University of Oxford https://orcid.org/0000- 0003- 1422- 5326 Rhea Zambellas University of Oxford Sofia Toniolo University of Oxford Maria Maio University of Oxford Akke Ganse- Dumrath University of Oxford https://orcid.org/0000- 0002- 4828- 8117 Sarosh Irani University of Oxford Sanjay Manohar University of Oxford https://orcid.org/0000- 0003- 0735- 4349 Masud Husain University of Oxford https://orcid.org/0000- 0002- 6850- 9255 + +<|ref|>sub_title<|/ref|><|det|>[[44, 701, 102, 718]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 738, 137, 756]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 776, 320, 795]]<|/det|> +Posted Date: August 11th, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 814, 474, 833]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3227833/v1 + +<|ref|>text<|/ref|><|det|>[[44, 852, 909, 894]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 912, 530, 932]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 916, 88]]<|/det|> +Version of Record: A version of this preprint was published at Nature Human Behaviour on April 29th, 2024. See the published version at https://doi.org/10.1038/s41562-024-01855-2. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[102, 190, 843, 250]]<|/det|> +# Role of the hippocampus in decision making under uncertainty + +<|ref|>text<|/ref|><|det|>[[131, 271, 951, 339]]<|/det|> +Bahaaeeddin Attaallah \(^{1*}\) , Pierre Petitet \(^{2}\) , Rhea Zambellas \(^{1}\) , Sofia Toniolo \(^{1}\) , Maria Raquel Maio \(^{1}\) , Akke Ganse- Dumrath \(^{1,2}\) , Sarosh R. Irani \(^{1}\) , Sanjay G. Manohar \(^{1,2}\) , Masud Husain \(^{1,2}\) + +<|ref|>text<|/ref|><|det|>[[192, 343, 900, 385]]<|/det|> +\(^{1}\) Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU \(^{2}\) Department of Experimental Psychology, University of Oxford, Oxford OX1 3PH + +<|ref|>text<|/ref|><|det|>[[198, 411, 892, 430]]<|/det|> +\*To whom correspondence should be addressed; E- mail: Bahaaeeddin.Attaallah@nhs.net + +<|ref|>text<|/ref|><|det|>[[168, 460, 825, 917]]<|/det|> +The role of the hippocampus in decision making is poorly understood. Because of its prospective and inferential functions, we hypothesized that it might be required specifically when decisions involve evaluation of uncertain values. Here, a group of individuals with autoimmune limbic encephalitis (ALE) – a condition known to locally affect the hippocampus – was tested on how they evaluate reward against uncertainty compared to evaluation of reward against another key attribute – physical effort. Across four experiments requiring participants to make trade- offs between reward, uncertainty and effort, ALE patients demonstrated blunted sensitivity to reward and effort whenever uncertainty was considered, despite demonstrating intact uncertainty sensitivity. By contrast, valuation of these two attributes (reward and effort) was intact on uncertainty- free tasks. Reduced sensitivity to changes in reward under uncertainty correlated with severity of hippocampal damage. Together, these findings provide evidence for a context- sensitive role of the hippocampus in value- based decision making, apparent specifically under conditions of uncertainty. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[102, 133, 280, 156]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[100, 180, 872, 614]]<|/det|> +Humans often face situations where they have to decide if the reward they might obtain from their actions is worth the cost required for it - e.g., when having to allocate effort to accomplish something. Whether it is buying an item in a grocery store or making life- changing resolutions, such trade- offs can influence our decisions and behaviour in our daily lives. Emerging evidence from animal studies suggests that the hippocampus might contribute to reward processing and valuation, with reports indicating several forms of reward representation in the hippocampal formation and its extended networks (for a review, see Sosa and Giocomo, 2021). Theories and empirical reports investigating such a possible hippocampal role in humans have tied it to its well- known functions in memory, associative inference and imagination (Biderman et al., 2020; Palombo et al., 2015a). Mechanistically, these investigations implicate the hippocampus in several processes including spreading of values between different contexts (Gerraty et al., 2014; Hassabis et al., 2007; Wimmer and Shohamy, 2012), constructing values from prior experiences (Barron et al., 2013), updating (Gupta et al., 2009; Gutbrod et al., 2006) and stabilisation of preferences (Enkavi et al., 2017). + +<|ref|>text<|/ref|><|det|>[[98, 624, 872, 927]]<|/det|> +One evolving concept connecting these unique properties of the hippocampus proposes that it provides context against which reward is evaluated to support value- based decisions and preferences (Bornstein and Norman, 2017; Gershman and Daw, 2017). This process might be mediated by hippocampus- dependent mental time travel both into the past (sampling from memory) and the future (sampling from projected possible futures) to allocate these contexts. For example, ‘preplay’ signals – corresponding to reward delivery in yet- to- be- explored environments – and ‘look ahead’ signals – representing future trajectories leading to goals – have both been recorded in rodent hippocampus (Freyja Ólafsdóttir et al., 2015; Johnson and Redish, 2007; Pfeiffer and Foster, 2013). Similarly, in humans, fMRI hippocampal activity was observed when people make decisions that involve reward anticipation and future considerations (Iigaya + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[100, 135, 456, 155]]<|/det|> +1 et al., 2020; Palombo et al., 2019, 2015b). + +<|ref|>text<|/ref|><|det|>[[100, 170, 115, 181]]<|/det|> +2 + +<|ref|>text<|/ref|><|det|>[[100, 198, 872, 408]]<|/det|> +3 With this perspective, the hippocampus might be implicated in evaluation of reward when episodic thinking is critically involved (e.g., to process values of projected possible futures) (Hunter and Daw, 2021). Such scenarios involve probabilistic consideration of future value states (i.e., making decisions under uncertainty) (Schacter et al., 2015; Tversky and Kahneman, 1974). By contrast, this conceptualisation of the hippocampal role in motivated behaviour suggests that contexts of deterministic nature (e.g., when evaluating reward against known physical effort costs) should be less influenced by hippocampus- related prospective computations. + +<|ref|>text<|/ref|><|det|>[[100, 419, 872, 754]]<|/det|> +10 In a recent report, we demonstrated that the hippocampus might be implicated in active information gathering prior to committing to decisions under uncertainty in people with subjective cognitive impairment (Attaallah et al., 2022). Markers of increased reactivity to uncertainty (e.g., rapid collection of information) were found to be associated with heightened hippocampal- insular connectivity. Such a finding aligns in part with previous studies highlighting hippocampal contribution to uncertainty processing and related forms of decision making such inter- temporal choices and visual information search (Harrison et al., 2006; Lucas et al., 2019; Rigoli et al., 2019; Strange et al., 2005; Tobia et al., 2012). However, it remains unclear whether such a proposed role of the hippocampus in valuation and decision making is contextually specific to uncertainty (i.e., implicated only when agents have to consider uncertainty) or reflects a general hippocampal processing of reward and value regardless of contexts. + +<|ref|>text<|/ref|><|det|>[[100, 770, 112, 781]]<|/det|> +21 + +<|ref|>text<|/ref|><|det|>[[100, 797, 870, 911]]<|/det|> +22 To answer this question and to directly investigate hippocampal involvement in goal- directed decision making and reward valuation, we recruited 19 people with autoimmune limbic encephalitis (ALE) – a rare neurological condition known to affect the hippocampus. Patients in the chronic phase of ALE characteristically have highly focal hippocampal atrophy (Argy + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[98, 135, 872, 345]]<|/det|> +1 ropoulos et al., 2019; Finke et al., 2017; Hanert et al., 2019; Irani et al., 2013; Kotsenas et al., 2 2014; Malter et al., 2014; Miller et al., 2017; Szots et al., 2014), making them an ideal model of 3 selective hippocampal dysfunction that is well suited to making inferences based on structure- 4 function correlations (Argyropoulos et al., 2019; Miller et al., 2020; Spanò et al., 2020a,b). This 5 is especially feasible in such an experimental group as the extent of hippocampal damage varies 6 between ALE patients depending on the course of the illness and the interval between disease 7 onset and treatment initiation (Finke et al., 2017; Malter et al., 2014). + +<|ref|>text<|/ref|><|det|>[[98, 386, 872, 914]]<|/det|> +9 Participants (patients and healthy matched controls) were tested in four experiments examining how people make decisions considering reward, uncertainty and/or physical effort attributes. In the first two experiments, we used the Circle Quest behavioural paradigm which has been previously tested and validated in healthy people and patient with subjective cognitive impairment (Attaallah et al., 2022; Petitet et al., 2021). The original paradigm has two versions: active and passive (Exps. 1 & 2). The active version of the task examines how people give up reward to obtain information and reduce uncertainty before committing to decisions. The passive version allows limited agency over uncertainty to examine how people make passive decisions on whether to accept or reject offers based on predetermined levels of reward and uncertainty. In Exp. 3, effort-based decision making (EBDM) was examined using a modified version of an extensively validated behavioural paradigm used in previous studies in healthy people and individuals with neurological disorders (Le Heron et al., 2018a; Saleh et al., 2021). This paradigm has a similar design to the passive version of Circle Quest and examines how people make passive decision weighing rewards against physical effort. In Exp. 4, a third novel version of Circle Quest was introduced to investigate how people make passive decisions considering the three attributes of interest (reward, uncertainty and physical effort). In other words, participants in Exp. 4 were required to make decisions weighing the reward on offer given both physical effort + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[100, 135, 444, 155]]<|/det|> +1 cost and uncertainty in the environment. + +<|ref|>text<|/ref|><|det|>[[98, 170, 115, 181]]<|/det|> +2 + +<|ref|>text<|/ref|><|det|>[[98, 198, 872, 666]]<|/det|> +3 The results from these experiments converged to indicate that in ALE patients, despite intact 4 sensitivity to uncertainty, the presence of uncertainty is associated with blunted sensitivity to 5 other value attributes (reward and effort cost). In the active version of the Circle Quest task 6 (Exp. 1), patients were less sensitive to cost of sampling when gathering information to support 7 decisions under uncertain conditions, resulting in faster, extensive and wasteful sampling when 8 cost of sampling and reward on offer increased. In the passive version of Circle Quest (Exp. 2), 9 ALE patients were significantly less sensitive to changes in reward, and this effect correlated 10 with lower sensitivity to sampling cost changes observed in active sampling (Exp. 1) as well 11 as with severity of hippocampal atrophy. When assessed on the effort- based decision making 12 task (Exp. 3), no significant difference was found between patients and controls when they 13 made EBDM decisions without uncertainty, indicating intact valuation of reward and effort un- 14 der conditions that do not feature uncertainty. By contrast, on the third version of Circle Quest 15 (Exp. 4), patients were less sensitive to changes in effort and reward compared to controls, 16 while their sensitivity to uncertainty was intact. Intact sensitivity to uncertainty was observed 17 across all versions of Circle Quest paradigm (Exps. 1, 2 & 4). + +<|ref|>text<|/ref|><|det|>[[98, 680, 115, 690]]<|/det|> +18 + +<|ref|>text<|/ref|><|det|>[[98, 702, 870, 787]]<|/det|> +19 Taken together, the results indicate an uncertainty- sensitive role of the hippocampus in 20 value- based decision making. The findings might represent an important step forward in un- 21 derstanding selective hippocampal contributions to goal- directed and motivated behaviour. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[102, 133, 215, 156]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[102, 173, 377, 195]]<|/det|> +## Experimental paradigms + +<|ref|>text<|/ref|><|det|>[[101, 201, 872, 925]]<|/det|> +Exp. 1 – Active information gathering prior to committing to decisions under uncertaintyIn the first experiment (Exp. 1), participants completed a shorter version of the \*Circle Quest\* paradigm, a recently developed behavioural task investigating active information sampling before committing to decisions under uncertainty (Attaallah et al., 2022; Petitet et al., 2021). Participants were asked to maximise their reward by localising a fixed-size hidden circle as precisely as possible. Uncertainty about the precise location of the hidden circle could be reduced by gathering information through touching the screen at different locations. If the location where they touched was situated inside the hidden screen, a purple dot appeared and if the location was outside that hidden circle, a white dot appeared. Participants started each trial with an initial credit reserve \((R_0)\) from which the cost to obtain a new sample \((\eta_s)\) was subtracted with each additional sample. After the active sampling phase, during which participants could sample the screen for information without restriction to speed or location, a blue disc matching the size of the hidden circle appeared. Participants were then required to move this blue disc to where they thought the hidden circle was located. Depending on localisation error (how far the blue disc centre is from the true location of the hidden circle) and cost of sampling, the score for each trial was calculated and provided as feedback at the end of the trial (equation in Figure 1). The task thus imposed an economic trade-off between the benefit and cost of obtaining information. There were two levels of sampling cost (low and high) and two levels of initial credit reserve (low and high). Uncertainty – indexed by circle localisation expected error \((EE)\) – was quantified as the probability-weighted average of all the possible errors that could occur upon placing the localisation disc (Figure 2d.; see \*Methods\*). In order to expose participants to the task environment and its scoring, the testing session began with a training task in which they practised circle localisation for various levels of uncertainty and reward. This also helped + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[190, 256, 864, 470]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 490, 870, 821]]<|/det|> +
Figure 1: Task paradigm (Exp. 1) – Active information gathering. At the beginning of each trial, a purple dot is displayed to give an initial clue about the location of the hidden circle. Participants were informed that touching the screen could provide further clues (either purple or white dots) to narrow the solution space of the location of the hidden circle. If touching the screen at a certain location yielded a purple dot, this location was situated inside the hidden circle. By contrast, if a white dot appeared, then the location was outside the hidden circle. Participants were instructed that there were no constraints on where and when to touch the screen within the allocated 18 seconds per trial, and that they could stop whenever they wanted to. Touching the screen to gather information, however, came at a cost which was subtracted from the initial reward reserve participants start the trial with ( \(R_{0}\) ; shown inside the two purple circles on the side of the search space). In the depicted trial for example, the participant started with 95 credits and lost one credit per additional sample acquired. After the active sampling phase (18 seconds), a blue disc appeared automatically in the centre of the screen, and participants were instructed to move this disc to where they thought the hidden circle was. Each trial was scored by removing a localisation error penalty ( \(e.\eta_{e}\) where \(e\) is the localisation error in Pixels, and \(\eta_{e}\) is the error cost in credits per pixel) from the remaining reward reserve ( \(R_{0} - s.\eta_{s}\) where \(s\) is the number of extra sampled acquired and \(\eta_{s}\) is the sampling cost). Error cost ( \(\eta_{e}\) ) was constant and equal to 1.2 credits per pixel
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[101, 137, 686, 156]]<|/det|> +1 establish the effect of visuospatial demand on localisation performance. + +<|ref|>sub_title<|/ref|><|det|>[[101, 177, 558, 197]]<|/det|> +## 2 Exp. 2 - Passive decision making under uncertainty + +<|ref|>text<|/ref|><|det|>[[98, 216, 872, 490]]<|/det|> +3 In the second experiment (Exp. 2), participants performed a second version of the Circle Quest paradigm examining how they weigh potential rewards against uncertainty when making decisions. Eight dots (four purple and four white) were always presented on the screen in each trial. The spatial configurations of these dots was manipulated experimentally to produce different levels of uncertainty, e.g., when purple dots were spaced widely apart, the location of the hidden circle was less uncertain than when they were clumped closer together because the former configuration imposes more limitations on possible circle placements. To limit memory load, a circle of the same size as the hidden circle was always present on each side of the screen to provide a continuous reminder of its size. + +<|ref|>text<|/ref|><|det|>[[98, 500, 872, 867]]<|/det|> +On each trial of the passive task, participants were asked to report uncertainty estimates (confidence rating on a scale from 0- 100) reflecting how well they thought they might be able to locate the hidden circle, given the configuration of dots on the screen. Next, they were presented with the reward on offer, and were asked "Do you want to play this trial for this potential reward?" to which they could respond either "Yes" or "No" (by pressing on the corresponding answer on the touchscreen). Participants were told that 10 of their "Yes" responses would be randomly selected at the end of the experiment, and that they would have to place a blue disc (of the same size as the hidden circle) where they thought the hidden circle was located for each of these trials. Their monetary reward would be based on their localisation performance on these 10 trials. If they located the hidden circle perfectly (i.e., they placed the blue disc exactly on top of the hidden circle), they won all credits on offer. If not, they lost credits proportionally to the magnitude of their localisation error. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[131, 193, 852, 480]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 502, 870, 867]]<|/det|> +
Figure 2: Task paradigm (Exp. 2) – Passive decision making under uncertainty. The task examines how people weigh potential rewards against uncertainty when making decisions. a. The purple and white dots on the screen provide clues about the location of a hidden circle of fixed size. Purple dots fall within the area of the hidden circle, while white dots are located outside it. The two purple circles on the side are of the same size as the hidden circle. The two numbers on the side display the number of credits (reward) on offer. b. Different spatial configurations are associated with different levels of uncertainty. Uncertainty is quantified as the expected error upon circle localisation \((EE)\) . This is equal to the probability-weighted average of all the possible errors that could be resulting when placing the localisation disc (blue disc in d) at the best possible location. The best possible location is the centroid of the solution space, which is the set of the centres of all the possible circles that could be a solution for the spatial configuration displayed. d. Participants first reported their subjective estimation of uncertainty (i.e., how confident they are about the location of the hidden circle given the information displayed on the screen). After this, the credits on offer appeared (displayed inside the two purple circles on the side). Participants could decide to accept or reject the offer to locate the hidden circle. d. At the end of the experiment, participants had to play 10 of the accepted offers by placing a blue disc on top of where they thought the hidden circle was located. These trials determined the scores in the task. The score was equal to the reward on offer minus a penalty reflecting their localisation error (i.e., how far the blue disc centre was from the centre of the hidden circle).
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[171, 145, 824, 290]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 307, 870, 471]]<|/det|> +
Figure 3: Task paradigm (Exp. 3) – Effort-based decision making task. The task was similar in design to Circle Quest passive choices task. First, calibration of the hand-held dynamometer was done based on each participant's maximum voluntary contraction. Participants were then familiarised with the effort levels they would encounter in the task by asking them to squeeze the handle up to the effort level indicated by the yellow line: the higher the line the more effort they needed to exert. They did this twice for each effort level. Next, participants made decisions indicating whether the reward (apples) on offer is worth the effort assigned to it. They were told that at the end of the experiment, 10 of their decisions would be selected randomly and that they would have to play them in order to obtain the apples.
+ +<|ref|>sub_title<|/ref|><|det|>[[105, 499, 446, 518]]<|/det|> +## Exp. 3 – Effort-based decision making + +<|ref|>text<|/ref|><|det|>[[99, 539, 871, 905]]<|/det|> +To investigate effort- based decision making we used a modified version of a well- validated effort- based decision making task that has been extensively used in healthy people and different patient groups (Aridan et al., 2019; Bonnelle et al., 2016, 2015; Chong et al., 2016; Le Heron et al., 2018b; Saleh et al., 2021). This task had a similar design as the passive choices task used in Exp. 2 to investigate reward valuation against uncertainty (Figure 2a). Reward was represented as apples on trees that participants were asked to weigh against physical effort levels that they needed to exert in order to obtain the apples (Figure 7). Physical effort in the task pertains to squeezing a hand- held dynamometer up to various force levels. There were five effort levels corresponding to \(16, 32, 48, 64\) and \(80\%\) of participant's maximal voluntary contraction (MVC) measured at the beginning of the task. After a familiarisation period with these effort levels, participants made Accept- Reject decisions for various reward- effort combinations. Similar to the passive version of Circle Quest task, individuals were instructed + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[100, 136, 870, 220]]<|/det|> +1 that some of the offers they accepted would be randomly selected at the end of the experiment 2 to be carried out, and that they would receive monetary rewards based on the number of apples 3 they collect. + +<|ref|>sub_title<|/ref|><|det|>[[102, 240, 601, 260]]<|/det|> +## Exp. 4 - Effort-based decision making under uncertainty + +<|ref|>text<|/ref|><|det|>[[100, 280, 872, 460]]<|/det|> +5 In the fourth experiment (Exp. 4), participants were re- invited to perform a third version of 6 the Circle Quest paradigm but this time designed to investigate effort- based decision- making 7 under uncertainty. The task was similar to those in Exps. 2 and 3. However, instead of making 8 decisions (accept/reject) based on two attributes (reward vs. uncertainty as in Exp. 2 and reward 9 vs. effort in Exp. 3), participants now had to make decisions under the three attributes together 10 (reward, uncertainty and effort simultaneously) (Figure 4a.). + +<|ref|>text<|/ref|><|det|>[[100, 469, 872, 648]]<|/det|> +11 Reward was presented as credits that could be won if participants completed two steps. 12 First, they had to achieve the required level of effort (as in Exp. 3). Second, they had to find 13 the location of the hidden circle without errors (as in Exp. 1 & 2). But the blue disc used to 14 localise the hidden circle appeared only when the required level of effort was achieved. If the 15 required effort level was met, participants could then win credits depending on how accurate 16 their localisation using the blue disc was from the true location of the hidden purple circle. + +<|ref|>text<|/ref|><|det|>[[100, 658, 872, 900]]<|/det|> +17 Thus, similar to Exp. 2, on each trial, participants were presented with a number of credits 18 on offer (same levels as in Exp. 2) that they could achieve if they managed to perfectly localise 19 the hidden circle given the configuration of dots on display. However, in order to make the 20 localisation disc appear, they now had to achieve the effort level assigned to the trial (same levels 21 as in Exp. 3). Importantly, uncertainty was omitted on half of the trials by showing the true 22 location of the hidden circle. On the other half where uncertainty was present, it corresponded 23 to the mid- range of expected error (EE) used in Exp. 2 (EE: 31.8- 73.95 pixels). Participants 24 also reported their subjective estimates of uncertainty separately. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[130, 160, 833, 470]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 496, 868, 952]]<|/det|> +
Figure 4: Task paradigm (Exp. 4) – Effort-based decision making under uncertainty. a. Once training was complete, participants made accept/reject decisions (200 trials) during the decision making phase weighing reward (credits) on offer against different effort levels required. They also had to take into consideration whether decisions were made under uncertainty or not, i.e., they had to decide whether the reward on offer, given the level of uncertainty, was worth making the effort. Uncertainty pertains to the location of the hidden circle which participants estimate from the configuration of dots on the screen (see Methods). Absence of uncertainty was indicated simply by displaying the purple circle at its precise location. b. At the end of the experiment, participants played 24 trials that were randomly selected from the decision phase. They had to make the physical effort in order to be given the opportunity to place the blue circle where they thought the purple circle was hidden (in 12 of the trials, the location of the purple circle was shown; in the other 12 it was not). Performance accuracy determined the credits participants eventually won. Participants were familiarised with the task environment and scoring function in three stages: i) following an interactive tutorial explaining Circle Quest task as in Exps. 1 & 2, they were required to place the blue disc for fixed levels of uncertainty with credits displayed on the side. This exposed them to the scoring function and served as a control task measuring localisation accuracy (Figure 10a.). ii) Participants reported their confidence about the location of the hidden circle using a rating scale ranging between zero and 100. The configuration of dots used were the ones they would face in the decision making phase as well as in catch trials, which were added to widen the uncertainty range used to characterise subjective estimations. Subjective uncertainty estimates were obtained by simply sign-flipping and z-scoring these confidence ratings (Figure 10b.). iii) Effort calibration and familiarisation were the same as in Exp. 3. Maximum voluntary contraction (MVC) was first obtained by asking participants to squeeze the effort handle as hard as they could and then effort levels were calibrated based on MVC (five levels).
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[102, 134, 272, 156]]<|/det|> +## 1 Demographics + +<|ref|>text<|/ref|><|det|>[[99, 177, 872, 480]]<|/det|> +2 Demographics and group characteristics are summarised in Tables S1 and S2. ALE patients had lower cognitive scores on Addenbrooke's cognitive examination (controls: \(\mu = 97.52, SD =\) \(\pm = 2.03\) , ALE: \(\mu = 93.42\) , \(SD = \pm 5.64\) , \(t_{36} = 2.99\) , \(95\% CI = [1.31, 6.89]\) , \(p < 0.01\) ). These cognitive differences were seen mainly in two domains of ACE- III: memory (controls: \(\mu = 24.79, = \pm = 1.36\) , ALE: \(\mu = 23.16\) , \(SD = \pm 2.54\) , \(z = 2.19\) , \(p = 0.02\) ) and fluency (controls: \(\mu = 13.32\) , \(SD = \pm = 1.36\) , ALE: \(\mu = 11.21\) , \(SD = \pm 2.69\) , \(z = 2.81\) , \(p < 0.01\) ). The other three domains including language, visuospatial abilities and attention were not significantly different between the two groups. There was no significant difference in measures of executive function (DS), apathy (AMI), fatigue (FSS), depression (BDI- II) or hedonic experience (SHAPS). + +<|ref|>sub_title<|/ref|><|det|>[[100, 502, 797, 525]]<|/det|> +## 2 Reduced sensitivity to changes in information cost in ALE patients. + +<|ref|>text<|/ref|><|det|>[[95, 545, 872, 930]]<|/det|> +Participants in both groups acquired samples to reduce uncertainty. Similar to previous reports (Attaallah et al., 2022; Petitet et al., 2021), reduction in uncertainty followed an exponential decay as a function of the number of samples acquired, indicating purposeful sampling abiding with task rules (Figure 5a.). Both patients and healthy controls behaved rationally, sampling less when acquiring samples was more expensive (Main effect of \(\eta_{s}\) on the number of samples acquired: \(\beta = - 0.11\) , \(t_{2272} = - 6.25\) , \(p < 0.0001\) , Table S3), and not responding to changes in initial reward reserve (Main effect of \(R_{0}\) on the number of samples acquired: \(\beta = 0.023\) , \(t_{2272} = 1.29\) , \(p = 0.20\) ). Furthermore, there was a significant interaction between the effect of sampling cost and the effect of initial reward reserve ( \(\eta_{s} \times R_{0}\) : \(\beta = - 0.030\) , \(t_{2272} = - 2.07\) , \(p = 0.038\) , Figure 5b., Table S3), indicating that higher initial reward reserves blunted the aversive effect of the sampling cost on the number of samples acquired. This interaction effect was significantly greater + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[98, 133, 872, 285]]<|/det|> +1 in patients than controls \((A L E\times \eta_{s}\times R_{0};\beta = 0.054,t_{2272} = 2.68,p< 0.01\) , Table S3). In other words, patients were less sensitive to changes in sampling cost when initial reward reserve was high and thus kept acquiring a lot of samples when there was a conflict between large reward reserve but expensive samples (Group difference in the number of samples acquired in the high \(R_{0}\) high \(\eta_{s}\) condition: \(z = - 2.2484\) , \(p = 0.024\) ). + +<|ref|>text<|/ref|><|det|>[[98, 293, 872, 534]]<|/det|> +Next, patients' and controls' performance in the active task was evaluated with regard to optimal sampling behaviour to determine whether they tended to under- or over- sample (Figure 5c- d.). Optimal sampling refers to the number of samples, \(s^{\star}\) , that maximises expected return, given the current cost- benefit structure \((R_{0}, \eta_{s}, \eta_{e})\) and search efficiency (i.e., the rate at which participants reduce uncertainty from one sample to the next, parameterised as the information extraction rate, \(\alpha\) , see Methods). In our task, uncertainty decreases exponentially over successive samples, and for each participant, sampling efficiency captures how steep this decline is (Figure 5a.). + +<|ref|>text<|/ref|><|det|>[[98, 544, 872, 722]]<|/det|> +The optimal number of samples – which takes into consideration such differences in sample utility – thus provides a more parsimonious account of how sampling behaviour is influenced by economic changes. It provides a useful measure against which to benchmark the willingness to give up rewards in exchange for information. For samples acquired beyond \(s^{\star}\) , the cost of acquiring a new sample is objectively larger than its benefit. Conversely, for samples acquired before \(s^{\star}\) , the benefit of acquiring a new sample is objectively larger than its cost (Figure 5c.). + +<|ref|>text<|/ref|><|det|>[[98, 732, 870, 912]]<|/det|> +Both ALE patients and healthy controls over- sampled when sampling cost was high (Deviation from optimal; ALE: \(\beta = 3.93\) , \(t_{1138} = 4.32\) , \(p < 0.0001\) ; Controls: \(\beta = 1.93\) , \(t_{1138} = 3.485\) , \(p < 0.001\) , see also Table S4), but patients over- sampled to a greater extent than controls when the initial reward reserve was high in these conditions ( \(z = 2.267\) , \(p = 0.023\) , Figure 5d.). When sampling cost was low, performance of ALE patients approached optimal behaviour ( \(\beta = - 0.94\) , \(t_{1138} = - 0.76\) , \(p = 0.44\) ) while controls under- sampled ( \(\beta =\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[100, 135, 870, 189]]<|/det|> +\(- 1.78, t_{1138} = - 2.59, p < 0.01\) . However, there was no significant difference between the two groups \((t_{36} = 0.579, p = 0.56)\) . + +<|ref|>text<|/ref|><|det|>[[100, 199, 871, 377]]<|/det|> +These findings suggest that ALE patients' sampling behaviour demonstrates, at least partially, blunted sensitivity to sampling cost leading to over- sampling (i.e., giving up more reward than needed in exchange for information) (see Supplementary materials for a computational model characterising this effect, Figure S1). This had consequences in terms of total reward received as patients' scores suffered to a greater extent than controls' when the sampling cost increased \((ALE \times \eta_s : \beta = - 3.66, t_{t2272} = - 2.00, p = 0.046, \text{Figure 5e., Table S3})\) . + +<|ref|>sub_title<|/ref|><|det|>[[100, 398, 847, 422]]<|/det|> +## ALE patients are less sensitive to changes in reward against uncertainty. + +<|ref|>text<|/ref|><|det|>[[100, 441, 870, 558]]<|/det|> +The passive task paradigm offers a reliable mechanistic delineation between response to reward and uncertainty when making value- based decisions. This helps to answer whether oversampling in ALE patients is indeed related to lower sensitivity to changes in reward, rather than increased sensitivity to uncertainty. + +<|ref|>text<|/ref|><|det|>[[100, 568, 871, 905]]<|/det|> +A generalised logistic mixed- effects model \((\mathrm{L}_g\mathrm{MM})\) with maximal randomness was used to analyse the accept- reject choice data (Table S7). As expected, participants (patients and controls) adjusted their decisions rationally according to offer attributes: accepting more offers with higher rewards and lower uncertainty (Main effect of reward on offer acceptance: \(\beta = 1.41, t_{3748} = 7.16, p < 0.001\) ; Main effect of uncertainty on offer acceptance: \(\beta = - 2.73, t_{3748} = - 8.72, p < 0.001\) , Figure 6c.). There was no significant interaction between the effect of reward and uncertainty \((\mathrm{R} \times \mathrm{EE}: \beta = 0.065, t_{3748} = 0.53, p = 0.60)\) . Patients and controls did not significantly differ in the time they took to accept \((z = 3.79, p = 0.70)\) or reject offers \((t_{36} = - 0.34, p = 0.73)\) . However, they differed with regards to the influence reward exerted on the decision to accept the offer. Compared to controls, ALE patients were overall less influenced by reward on offer \((\mathrm{ALE} \times \mathrm{Reward}: \beta = - 0.983, t_{3748} = - 3.58, p < 0.001)\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[170, 152, 825, 620]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 632, 870, 907]]<|/det|> +
Figure 5: Exp. 1 – Reduced sensitivity to changes in information cost in ALE patients. a. Uncertainty (indexed as expected error, \(EE\) ) decreases with sampling and follows an exponential decay slope on average in both patients and controls. b. ALE patients, compared to controls, sampled more when initial reward reserve and sampling cost were both high. c-d. The optimal number of samples \((s^{\star})\) is the number of samples (s) at which the maximum return (Expected Value, \(EV\) ) could be achieved. Obtaining more samples before \(s^{\star}\) results in increases in \(EV\) , while acquiring samples beyond \(s^{\star}\) results in lower \(EVs\) . ALE patients and healthy controls over-sampled when sampling cost was high. Patients, however, over-sampled to a greater extent than controls, mainly when initial reward reserve and sampling cost both increased. There was no significant difference between the two groups at low-cost conditions. Lines in c. show the individual EV timecourses centred on their peaks (optimal number of samples to acquire). Ellipses contain \(90\% CI\) for each participant. \(\eta_{s}\) : Sampling cost. \(R_{0}\) : Initial reward reserve. e. Patients achieved lower scores than controls, especially at higher sampling cost, where patients deviated more than controls from optimal sampling. Error bars show \(\pm \mathrm{SEM}\) . \*:p <0.05. See Tables S3 & S4 for full statistical details.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[98, 135, 872, 252]]<|/det|> +1 By contrast, their sensitivity to uncertainty was not significantly different from controls' (ALE \(\times EE: \beta = 0.336, t_{3748} = 0.76, p = 0.45\) ). Taken together, this means that patients accepted fewer of the high- value offers (high- reward and low- uncertainty) \((z = - 2.14, p = 0.03 \& z = - 2.93, p < 0.01\) , for the two offers of the highest value, Figure 2b- c.). + +<|ref|>text<|/ref|><|det|>[[98, 262, 872, 595]]<|/det|> +Next, we investigated whether sensitivity to changes in reward and uncertainty was associated with differences observed in sampling behaviour in Exp. 1. Sensitivity to reward and uncertainty were extracted from a \(\mathrm{L}_{g}\mathrm{MM}\) that included the two attributes and their interaction as predictors of offer acceptance (i.e., the same model used above but with no group effect). For each participant, reward and uncertainty sensitivity correspond to model- derived parameter estimates that capture how decisions are influenced by changes in these two offer attributes. A robust regression model showed that reward, but not uncertainty sensitivity, correlated significantly with the sensitivity to sampling cost at high initial reward reserve in the active task, i.e., the effect differentiating patients from controls in Exp. 1 (indexed by difference in number of samples between low and high sampling cost conditions) \((R^{2} = 0.23, t_{17} = 2.26 p = 0.03\) ; Figure 6d.). + +<|ref|>text<|/ref|><|det|>[[98, 607, 870, 721]]<|/det|> +In brief, the findings from Exps. 1 & 2 converge to indicate that ALE patients are less responsive to changes in reward under conditions of uncertainty in active and passive contexts. This is evidenced by less flexible sampling in response to changes in sampling cost in the active task, and reduced sensitivity to changes in reward on offer in the passive task. + +<|ref|>sub_title<|/ref|><|det|>[[98, 744, 646, 767]]<|/det|> +## 20 Intact effort-based decision making in ALE patients + +<|ref|>text<|/ref|><|det|>[[98, 787, 870, 901]]<|/det|> +In Exp. 3, we asked whether blunted reward sensitivity observed in ALE patients for decisions involving a trade- off with uncertainty was also evident for a different discounting attribute – physical effort. In other words, is this a generalised phenomenon? A novel version of a well- validated paradigm measuring effort and reward sensitivity was used to examine effort- based + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 201, 865, 600]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 612, 869, 888]]<|/det|> +
Figure 6: Exp. 2 – ALE patients are less sensitive to changes in reward under uncertainty. q. Patients and healthy controls adjusted their decisions according to the reward and uncertainty on offer. The influence of reward on offer acceptance was blunted in hippocampal patients when compared to controls (shallower reward slope). Level 1 indicates lowest reward/uncertainty level on offer. b. Controls accepted more of the high-value offers (blue region) when compared to hippocampal patients. c. Investigation of the group effect using logistic regression model with mixed effects ( \(\mathrm{L}_{\theta}\mathrm{MM}\) ) revealed that patients had significantly lower sensitivity to reward than controls but did not significantly differ in their sensitivity to uncertainty. Additionally, it showed that the impact of uncertainty on decision making was more significant than the impact of reward (histogram on the corner). d. Lower sensitivity to reward, but not uncertainty, in the passive task is associated with lower sensitivity to sampling cost in the active sampling task (Exp. 1) driving group differences in number of samples collected. Colour bar indicates the contribution of each data point to the model. Blue dots represent controls and are added for visual comparison. Error bars in a. show \(\pm\) SEM. ***: \(\mathrm{p}< 0.001\) . Shaded area in d. show \(95\%\) CI. See Table S7 for full statistical details.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[101, 135, 377, 155]]<|/det|> +1 decision making (see Methods). + +<|ref|>text<|/ref|><|det|>[[100, 163, 872, 700]]<|/det|> +2 A \(\mathrm{L}_{\theta}\mathrm{MM}\) with full randomness was used to analyse the choice data (Table S9). The results showed that, as expected, participants from both groups accepted more offers (showed more willingness to allocate effort) when reward on offer increased and required effort decreased (Main effect of reward on offer acceptance: \(\beta = 2.97\) , \(t_{4739} = 11.12\) , \(p < 0.001\) , Main effect of physical effort on offer acceptance: \(\beta = - 2.82\) , \(t_{4739} = - 8.45\) , \(p < 0.001\) ). However, neither reward nor effort sensitivity differed significantly between patients and controls (ALE \(\times\) Reward and ALE \(\times\) Effort both \(p > 0.05\) ). To quantify the evidence in favour of this null result, the same analysis was run using Bayesian mixed modelling. Once again, this analysis did not suggest differences between the two groups in reward (or effort) valuation ( \(BF = 3.78\) for null effect of ALE \(\times\) reward, \(BF = 5.28\) for null effect ALE \(\times\) effort, Figure S3, Table S10). Note that there was also no significant difference in total acceptance of offers (Effect of disease (ALE) on offer acceptance: \(\beta = - 0.40\) , \(t_{3748} = - 0.88\) , \(p = 0.45\) ) as well as in decision times (Accept decisions: \(t_{36} = 0.87\) , \(p = 0.38\) , Reject decision: \(t_{36} = 0.99\) , \(p = 0.32\) ). Finally, the group effect on reward sensitivity across Exps. 2 and 3 was examined using a generalised mixed model after combining choices from both tasks (Tables S11 & S12). As expected, reward sensitivity in ALE patients compared to controls was significantly blunted in Exp. 2 compared to Exp. 3 (ALE \(\times\) Reward \(\times\) Task \(= \beta = 0.467\) , \(t_{8495} = 4.15\) , \(p < 0.0001\) , Table S11). + +<|ref|>text<|/ref|><|det|>[[100, 701, 870, 753]]<|/det|> +These results are consistent with a sparing of reward sensitivity in ALE patients when reward had to be weighed against effort, without uncertainty being considered. + +<|ref|>sub_title<|/ref|><|det|>[[100, 775, 850, 800]]<|/det|> +## Blunted reward and effort sensitivity under uncertainty in ALE patients + +<|ref|>text<|/ref|><|det|>[[99, 819, 870, 903]]<|/det|> +One might argue that the comparison between reward sensitivity in Exps. 2 & 3 is not fully matched due to the difference in task cues and environment (e.g., reward as credits vs. virtual apples). These concerns were addressed using a novel version of Circle Quest that was designed + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[140, 150, 864, 352]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[121, 367, 870, 477]]<|/det|> +
Figure 7: Exp. 3 – Intact reward valuation against effort in ALE patients. a. There was no significant difference in offer acceptance between patients and controls. b. The estimates of these acceptance slopes indicate how sensitive participants are to reward/effort changes of offers. These sensitivity estimates were extracted from a generalised mixed model with full randomness and plotted for visualisation. Error bars show \(\pm \mathrm{SEM}\) . \(^{*}\mathrm{p}< 0.05\) . See Tables S9 & S10 for full statistical details.
+ +<|ref|>text<|/ref|><|det|>[[100, 504, 870, 644]]<|/det|> +1 to examine effort- based decision making under uncertainty. The task had the same reward levels and cues used in Exp. 2 and the same effort levels and cues used in Exp. 3. Participants also made the decisions either with or without uncertainty. Thus, the task overall examined how participants adjusted their decisions taking into consideration these three attributes (reward, effort and uncertainty). + +<|ref|>text<|/ref|><|det|>[[100, 659, 870, 903]]<|/det|> +Choice data were analysed using the same approach as in Exp. 2 & 3 with generalised mixed- effects models (Table S13). The results showed that participants from both groups generally made decisions reflecting changes in offer attributes, i.e., accepting more offers with increasing reward, decreasing effort and absent uncertainty (Main effect of reward on choice: \(\beta = +1.73\) , \(t_{4184} = 5.21\) , \(p < 0.0001\) ; Main effect of effort: \(\beta = - 2.45\) , \(t_{4184} = - 5.38\) , \(p < 0.0001\) ; Main effect of uncertainty: \(\beta = - 1.9\) , \(t_{4184} = - 3.49\) , \(p < 0.001\) ). However, ALE patients were significantly less sensitive to changes in reward and effort compared to healthy controls (ALE \(\times\) reward: \(\beta = - 1.41\) , \(t_{4184} = - 2.83\) , \(p < 0.01\) ; ALE \(\times\) effort: \(\beta = +1.24\) , \(t_{4184} =\) + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[169, 133, 818, 335]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[121, 350, 869, 479]]<|/det|> +
Figure 8: Exp. 4 – Blunted reward and effort sensitivity under uncertainty in ALE patients. In Exp. 4, participants were required to accept/reject offers taking into consideration three attributes: reward, physical effort, and uncertainty. The results showed that ALE patients, compared to controls, were less sensitive to changes in reward and effort while having intact sensitivity to uncertainty. Such results are consistent with findings from Exps. 1 & 2, highlighting disrupted reward and cost valuation in ALE patients under uncertainty. Error bars show \(\pm \mathrm{SEM}\) . \*: \(p< 0.05\) . See Table S13 for full statistical details.
+ +<|ref|>text<|/ref|><|det|>[[100, 504, 870, 902]]<|/det|> +1. \(2.99 p < 0.01\) ; Figure 8). On the other hand, patients and controls did not differ in their sensitivity to uncertainty \((p = 0.21)\) . The two-way interaction between uncertainty and reward was also significant, indicating that the presence of uncertainty resulted in blunted sensitivity to changes in rewards (Reward \(\times\) uncertainty: \(\beta = -0.67\) , \(t_{4184} = -3.61\) , \(p < 0.001\) ). This effect was significantly more prominent in ALE patients than matched controls (Reward \(\times\) uncertainty \(\times\) ALE: \(\beta = +0.568\) , \(t_{4184} = 2.09\) , \(p = 0.036\) ). These findings are consistent with the results from Exps. 1 & 2 showing intact sensitivity to uncertainty and reduced sensitivity to reward in presence of uncertainty in ALE patients. They also suggest that effort sensitivity can also be blunted in ALE patients under conditions requiring uncertainty consideration. Thus overall, while healthy controls managed to flexibly adjust their decision taking into consideration the three attributes of the offers, ALE patients were generally more responsive to the uncertainty component of the offers, with less emphasis on other economic attributes such as reward and effort. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[101, 134, 867, 181]]<|/det|> +## 1 Severity of hippocampal atrophy correlates with decreased reward sensitivity under uncertainty + +<|ref|>text<|/ref|><|det|>[[100, 201, 870, 440]]<|/det|> +3 A whole- brain voxel- based morphometry (VBM) analysis was performed. Compared to healthy controls, ALE patients had lower grey matter intensity in three clusters involving limbic, thalamic and temporal regions (Table 1). As expected, the largest cluster (limbic region) included mainly the hippocampal and para- hippocampal regions (Figure 9a.). Hippocampal atrophy was also demonstrated by comparing extracted total hippocampal volume using Freesurfer analysis pipeline, showing reduced right whole hippocampal volumes in ALE patients (Table S1). Note that a few participants had also severely atrophied left hippocampal (with and without right hippocampal atrophy, see Table S2). + +<|ref|>text<|/ref|><|det|>[[100, 451, 870, 660]]<|/det|> +Next, robust regression analysis was performed to examine the relationship between hippocampal atrophy and reduced reward sensitivity observed in ALE patients. For this purpose, reward and uncertainty sensitivities from Exp. 2 were used as key behavioural markers characterising performance of hippocampal patients when compared to controls. The results showed that sensitivity to changes in reward was associated with total average hippocampal volumes in patients (Model \(R^{2} = 0.41\) , \(t_{13} = 3.05\) , \(p < 0.01\) ; Figure 9b.). This correlation remained significant after controlling for age and gender (Model \(R^{2} = 0.38\) , \(t_{11} = 2.28\) , \(p = 0.043\) ). + +<|ref|>text<|/ref|><|det|>[[100, 671, 870, 755]]<|/det|> +Repeating the same analysis for reward sensitivity from Exp. 3 revealed no significant correlation between total hippocampal volume and reward sensitivity against effort ( \(p = 0.88\) ). Due to limitation of sample size, this analysis was not performed for Exp. 4. + +<|ref|>text<|/ref|><|det|>[[100, 767, 870, 881]]<|/det|> +To assess the anatomical specificity of this result, the volume of the amygdala – another limbic brain region that was highlighted in VBM analysis – was also extracted and the same analysis was run on this metric. This showed no significant correlation between the amygdala volume and sensitivity to reward or uncertainty (Figure S4). + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[123, 157, 870, 211]]<|/det|> + +
ClusterMain regions involvedVoxelsMAX X (mm)MAX Y (mm)MAX Z (mm)
1Right Limbic (Hippocampus, Para-hippocampus, Amygdala)19922-32-14
2Thalamus (Bilateral)1202-14-2
3Right Temporal Lobe (STG, MTG, AG)6568-406
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 223, 870, 295]]<|/det|> +Table 1: VBM analysis controls vs. patients. ALE patients had lower grey matter volumes compared to controls in three main clusters (1 -3). The largest difference was seen in the limbic region which includes mainly hippocampal regions (Figure 9a.) STG: Superior Temporal Gyrus. MTG: Middle Temporal Gyrus. AG: Angular Gyrus. + +<|ref|>image<|/ref|><|det|>[[170, 360, 825, 777]]<|/det|> + +<|ref|>image_caption<|/ref|><|det|>[[123, 789, 870, 896]]<|/det|> +
Figure 9: Severity of hippocampal atrophy correlates with decreased reward sensitivity against uncertainty. a. VBM analysis shows that ALE patients have significantly reduced grey matter intensity in right hippocampal region (cluster 1, Table 1). b. Patients with more severely atrophied hippocampi were less sensitive to reward when traded against uncertainty. By contrast, hippocampal volumes were not significantly correlated with sensitivity to uncertainty which was preserved in ALE patients.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 134, 747, 157]]<|/det|> +## Intact localisation and uncertainty estimation in ALE patients + +<|ref|>text<|/ref|><|det|>[[110, 175, 872, 578]]<|/det|> +Two control analyses were performed to examine possible factors that might influence response to uncertainty in the Circle Quest task. First, participants might differ in their motor performance and accuracy of placing the blue disc for given uncertainty levels, which might bias their estimation and valuation. To examine this, we analysed performance in the training task where participants were required to move the blue disc for fixed levels of uncertainty. Distance to the hidden circle was compared between the two groups. There was no significant difference between ALE patients and controls in this metric, indicating similar placement performance on the task (Difference between ALE and controls in Exps. 1 & 2: \(z = 1.10\) , \(p = 0.26\) ; Exp. 4: \(z = 0.10\) , \(p = 0.91\) ; Figure 10a.). To compare localisation performance on trials that did not feature the same levels of uncertainty, the distance to the optimal placement point (the centroid of the posterior belief of all the possible locations of the hidden circle) was calculated. This measure across all versions of Circle Quest was not significantly different between the two groups (Figure S5). + +<|ref|>text<|/ref|><|det|>[[110, 586, 872, 923]]<|/det|> +Second, different participants might have different estimations of uncertainty for the same visual displays, ultimately affecting their performance on the task as they gather information and make decisions. To examine inter- individual differences in uncertainty estimation, participants provided these estimates as confidence ratings before making decisions in Exp. 2 and during training in Exp. 4 (see Methods). A generalised mixed- effects model indicated that in both experiments there was no significant difference in subjective uncertainty estimation between patients and controls (Exp. 2; \(\mathrm{ALE}\times \mathrm{EE}\) : \(\beta = - 0.054\) , \(t_{3752} = - 0.77\) , \(p = 0.44\) ; Exp. 4; \(\mathrm{ALE}\times \mathrm{EE}\) : \(\beta = - 0.022\) , \(t_{2306} = - 0.16\) , \(p = 0.87\) , Figure 10b., Table S14). The results indicate that subjective estimates of uncertainty mapped well onto experimentally defined uncertainty across study participants. As a result, choice performance results did not change when these subjective estimates were used to analyse performance instead of objective uncertainty ( \(EE\) ) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[102, 138, 308, 155]]<|/det|> +1 (Figure S6, Table S16). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[223, 225, 732, 666]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 686, 869, 833]]<|/det|> +
Figure 10: Exps. 1, 2 & 4 – Intact localisation and subjective uncertainty estimation in ALE. a. ALE patients and controls did not differ in their localisation error (distance between the centre of the blue disc and hidden circle) for fixed levels of uncertainty, indicating similar motor and localisation performance. b. Subjective uncertainty is z-scored signed-flipped confidence ratings that participants reported before seeing the reward on offer (Exp. 2) or during training (Exp. 4). There was no significant difference between ALE patients and controls in this measure, indicating intact uncertainty estimation. Error bars in a. show \(\pm\) SEM. Shaded area show \(95\%\) CI. See Table S14 for full statistical details.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 133, 253, 157]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[98, 181, 874, 520]]<|/det|> +The studies presented here assessed acute limbic encephalitis (ALE), which is associated with damage to the hippocampus. In four experiments, patients were assessed on how they evaluate reward, uncertainty, and effort when making value- based decisions. The results converged to indicate that ALE patients had intact uncertainty processing across different contexts such as passive reward valuation (Figures 6 & 8) and active information gathering prior to decisions (Figure 5). On the other hand, whenever uncertainty was a factor that needed to be considered, sensitivity to other attributes (reward and physical effort) was blunted (Figure 6 & 8), despite intact valuation of these attributes in a context that does not feature uncertainty (Figure 7). Reduced sensitivity to changes in reward in the context of uncertainty correlated with severity of hippocampal atrophy in ALE patients (Figure 9). Thus, the results indicate a specific role of the hippocampus in processing uncertain rewards and costs. + +<|ref|>text<|/ref|><|det|>[[98, 536, 113, 546]]<|/det|> +13 + +<|ref|>text<|/ref|><|det|>[[98, 560, 872, 928]]<|/det|> +Previous decision- making studies demonstrated a potential role of the hippocampus in different forms of goal- directed behaviour. For example, several reports indicated that the hippocampus might be critical for inter- temporal decision making, especially when participants are required to imagine future outcomes (Benoit et al., 2011; Lebreton et al., 2013; Peters and Bui, 2011). The results of other investigations demonstrated that the hippocampus might be needed for deliberation prior to value- based decisions but not sensory discrimination (Bakkour et al., 2018), suggesting that it supports valuation by sampling from memories and previous experiences. Similarly, some researchers demonstrated hippocampal involvement in inferring values of novel stimuli from previously encountered cues and stimuli (Barron et al., 2013; Hassabis et al., 2007; Wimmer and Shohamy, 2012). These functions are thought to be related to the established role of the hippocampus in episodic thinking and prospection (Buckner and Carroll, 2007; Gilbert and Wilson, 2007; Schacter et al., 2007, 2012, 2017). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[98, 135, 872, 470]]<|/det|> +A common theme of such decision making scenarios is that they critically involve uncertainty regarding future rewards that agents consider to guide their decisions. For example, several previous reports highlighted similarities between delay and probabilistic discounting (i.e., decisions under uncertainty), suggesting that they might feature a common cognitive process (Green and Myerson, 1996; Myerson et al., 2003; Prelec and Loewenstein, 1991; Rachlin et al., 1991; Stevenson, 1986). These similarities are of special relevance when considering hippocampal contribution to inter-temporal decision making as its role seems to be specific to temporal discounting that requires simulated future trajectories (Palombo et al., 2015b; Peters and Buchel, 2010; Ye et al., 2021). According to this perspective, exaggerated delay discounting observed with hippocampal damage in previous studies might reflect intolerance to uncertainty of future reward. + +<|ref|>text<|/ref|><|det|>[[98, 480, 872, 787]]<|/det|> +Such reports highlighting a potential role of the hippocampus in uncertainty processing are supported by functional neuroimaging studies which have demonstrated hippocampal activation that correlates with the degree of uncertainty (entropy) of sensory stimuli when making decisions (Harrison et al., 2006; Rigoli et al., 2019; Strange et al., 2005; Tobia et al., 2012). These investigations align with the view that regards uncertainty as a threatening stimulus (i.e., carries risk signals) processed by hippocampus- centred behavioural inhibition system (BIS) (Gray and McNaughton, 2003). The hippocampus, according to this view, is considered to work as a mismatch detection system comparing expectation with perceived stimuli and triggering behavioural avoidance when confronted by uncertainty (or other anxiety- inducing stimuli) (Gray and McNaughton, 2003). + +<|ref|>text<|/ref|><|det|>[[98, 797, 870, 911]]<|/det|> +These features of uncertainty as probabilistically discounted risk signal offer a key distinction from other costs such as physical effort, which is more deterministic in nature. The results presented in this study provide evidence that the hippocampus is critically involved in valuation processes under the first context (uncertainty) but not the second (physical effort), + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[100, 135, 872, 252]]<|/det|> +providing specific insights into the role of the hippocampus in value- based decision making. Counter- intuitively, hippocampal damage was associated with preservation of uncertainty estimation and valuation, but blunted sensitivity to other decision attributes (e.g., reward and effort) if simultaneously considered with uncertainty. + +<|ref|>text<|/ref|><|det|>[[100, 260, 872, 757]]<|/det|> +These findings suggest that hippocampal damage might be related to a specific deficit in the integration of relatively intact uncertainty signal with other attributes that contribute to the value space. Note that this is unlikely to reflect a general cognitive deficit in decision- making or value computation, as participants demonstrated intact effort- based decision making in Exp. 3 (Figure 7). One possible explanation for this might be that people with hippocampal atrophy possess limited computational resources that prioritise uncertainty processing (and perhaps other risk- related signals) over other value determinants when computing subjective values to guide behaviour and decisions. In fact, the results from the active search experiment (Exp. 1) could also be interpreted as evidence of uncertainty prioritisation, as ALE patients sampled faster and more extensively than controls to abolish uncertainty before committing to their decisions (Figure S2), disregarding changes in sampling cost. The hippocampal role therefore might be to infer decision values by multiplying probability of outcomes (i.e., uncertainty) with other economic attributes such as reward and effort. These signals might then be related to other regions involved in subjective value estimation such ACC and OFC (Bakkour et al., 2019; Chen, 2021; Gluth et al., 2015; Ito et al., 2008; LeGates et al., 2018; Maller et al., 2019; Mizrak et al., 2021; Pessiglione et al., 2018; Rudebeck et al., 2006; Wimmer and Shohamy, 2012). + +<|ref|>text<|/ref|><|det|>[[100, 799, 872, 911]]<|/det|> +It is however difficult to fully determine whether the prioritisation of uncertainty in this process reflects a specific computational property of the hippocampus versus general disruption of cognitive processing that might be observed with other brain lesions. Two factors make this possibility unlikely: i) the correlation between behaviour and severity of hippocampal atrophy, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[98, 135, 872, 440]]<|/det|> +rather than other closely related regions such as the amygdala, which might have been affected by the disease process as well (see Figures 9 & S4) and ii) the results do not change when controlling for overall cognitive dysfunction indexed by ACE- III scores or meta- cognitive deficits in uncertainty estimation (Table S17). In a similar vein, it could be argued that the results might merely reflect a difficulty effect imposed by uncertainty cues in the task which biases participants to focus on uncertainty when evaluating it again other attributes. If this was the case, one would expect that patients would take longer than controls to make their decisions, which was not the case. More importantly, these effects persisted in the uncertainty- free condition in Exp. 4 where participants were shown the true location of the hidden circle and they did not need to process uncertainty when making effort- based decisions. + +<|ref|>text<|/ref|><|det|>[[98, 450, 872, 753]]<|/det|> +These findings resonate with previous reports that highlighted the hippocampus as part of a wider brain valuation network, which includes regions that have established roles in processing reward and costs such as dopaminergic brain regions (e.g., ventral striatum), ventromedial prefrontal cortex and anterior cingulate cortex (Haber, 2017). The hippocampus shares functional and structural connections with these regions and is thought to provide contextual information that support the valuation process (Bakkour et al., 2019; Gluth et al., 2015; Ito et al., 2008; LeGates et al., 2018; Maller et al., 2019; Mizrak et al., 2021; Wimmer and Shohamy, 2012). The behavioural results in this chapter indicate that hippocampal processing of uncertainty signals might be a key determinant of how different brain regions evaluate rewards and costs when computing subjective values. + +<|ref|>text<|/ref|><|det|>[[98, 765, 870, 911]]<|/det|> +The neural mechanism underlying how hippocampal signals support reward- related behaviours is not yet fully established and will require further research. However, previous studies demonstrated that the hippocampus might share future- related signals (e.g., preplay and look- ahead signals) with dopaminergic regions involved in reward evaluation and processing (Freyja Ólafsdóttir et al., 2015; Johnson and Redish, 2007; Pfeiffer and Foster, 2013). Similarly, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[98, 135, 872, 504]]<|/det|> +several forms of reward representation in the hippocampal formation and its extended networks have been reported, correlating with various reward- related behaviours and cognitive processes such as appetitive responses to rewards (Jarrard, 1973; Tracy et al., 2001), approach- avoid decisions (Ito and Lee, 2016), and reward- guided exploration (Sosa and Giocomo, 2021). While hippocampal- lesioned animals might show behavioural adjustments in responses to changes in reward in the environment (Flaherty et al., 1998; Kelley and Mittleman, 1999), these findings might differ between contexts and depend on the way reward is manipulated (e.g., whether animals move from high reward environment to low, or the opposite) (Tracy et al., 2001). Consumatory responses of lesioned animals seem to be unaffected despite the changes in behaviour, challenging the notion that the role of the hippocampus in reward processing is of a hedonic nature (Kelley and Mittleman, 1999), and further substantiating its goal- related and context- dependent contribution. + +<|ref|>text<|/ref|><|det|>[[95, 513, 872, 911]]<|/det|> +This uncertainty- sensitive hippocampal contribution might serve to support not only passive valuation but also active goal- directed behaviours such as information seeking to resolve uncertainty. While results from a previous investigation have shown that people with hippocampal damage exhibit a less structured and stochastic visual information exploration compared to controls (Lucas et al., 2019), it was unclear how economic factors come into play. In this study, less optimal decision making when uncertainty- related value computations had to occur was evident in hippocampal patients when they actively gathered information and sequentially updated the expected values of their decisions, as well as when they made passive decisions under uncertainty. The source of this disruption was related to less flexible decision adjustments based on reward changes in the environment while maintaining sensitivity to uncertainty. Thus, the results provide insights into how the hippocampus contributes to the economics of information gathering, in addition to its potential exploratory role (Johnson et al., 2012), which was not strongly affected in ALE patients as they managed to reduce uncertainty as efficiently as + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[100, 136, 258, 155]]<|/det|> +1 healthy controls. + +<|ref|>text<|/ref|><|det|>[[100, 170, 115, 181]]<|/det|> +2 + +<|ref|>text<|/ref|><|det|>[[100, 198, 872, 662]]<|/det|> +A positive correlation was observed between total hippocampal volumes and reward sensitivity under uncertainty. Future investigations might build on this to investigate the drivers of this relationship. For example, such an association might be related to specific hippocampal subfields and sub- circuits. In rats, reward cells have been described in the subiculum and CA1 subfields (Gauthier and Tank, 2018), which might suggest that atrophy or disruption of these regions might have a more specific role in the process. Moreover, distinct hippocampal subfields and regions might have differential functional connectivity profiles with other cortical and subcortical regions that might be contributing to motivation and decision making (Attaallah et al., 2022; Dalton et al., 2019; de Flores et al., 2017; Vos De Wael et al., 2018; Zeidman et al., 2015). Hippocampal damage observed in ALE is likely to be associated with more widespread disturbances of such networks contributing to reward valuation under uncertainty (Argyropoulos et al., 2019; Heine et al., 2018; Navarro et al., 2016; Qiao et al., 2020). With advanced neuroimaging acquisition and analysis techniques, it would be crucial to try to answer these questions at the subfield level and provide a more comprehensive account of hippocampal contribution to motivation. + +<|ref|>text<|/ref|><|det|>[[100, 678, 115, 688]]<|/det|> +18 + +<|ref|>text<|/ref|><|det|>[[100, 704, 872, 912]]<|/det|> +The present study has a few limitations. First, it is possible that the dissociation between effort and uncertainty in Exp. 2 and Exp. 3 might be not well- delineated. For example, in the Circle Quest paradigm one might potentially need to take into account effort costs such as moving the blue localisation disc on the screen or additional cognitive effort required to translate the configuration of dots into uncertainty estimates, etc. Such costs can also have an impact on active sampling behaviour influencing the speed and efficiency of gathering information (Petitet et al., 2021) (see also supplementary materials for a computational model characterising + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[98, 135, 872, 504]]<|/det|> +these effects). Similarly, the effort task (Exp. 3) might theoretically involve some elements of uncertainty (e.g., aligning visualised effort levels to subjective cost estimates). Second, while Exp. 4 was designed to address such limitations, only a subset of the participants performed it which might not be strictly representative of the larger group. This task also introduced a more complex decision structure that might require more planning (two steps to reach goals) and additional cognitive load on memory and attention. It is important to discuss and consider the results from one experiment in the context of the other experiments performed to obtain a bigger picture that paints how the hippocampus might be contributing to value processing. Finally, it might also be beneficial for future studies to obtain more objective measures of such processes, e.g., pupillometry for reward sensitivity (Le Heron et al., 2018a; Muhammed et al., 2016), which could help to bridge the gap with conclusions based on subjective estimates obtained from decision making and goal- directed behaviour. + +<|ref|>text<|/ref|><|det|>[[98, 520, 113, 531]]<|/det|> +13 + +<|ref|>text<|/ref|><|det|>[[98, 545, 870, 660]]<|/det|> +In conclusion, the results presented here provide evidence from human participants that the hippocampus plays a crucial role in decision making. This contribution appears to be specific to contexts that involve uncertainty, influencing how people evaluate other economic attributes such as reward and effort. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[102, 132, 408, 157]]<|/det|> +## Materials and Methods + +<|ref|>sub_title<|/ref|><|det|>[[101, 173, 250, 195]]<|/det|> +## Participants + +<|ref|>text<|/ref|><|det|>[[99, 214, 872, 680]]<|/det|> +In Exps. 1- 3, 19 individuals with a previously established diagnosis of ALE (age: \(\mu =\) \(60.00,SD = \pm 11.36,\) 13 males) were tested along with 19 healthy age- and gender- matched controls (age: \(\mu = 61.16,SD = \pm 11.71,\) 13 males). In Exp. 4, eight ALE patients and 12 controls completed an additional follow- up task. Sample size was determined based on previous comparable work in ALE patients (Hanert et al., 2019; Spano et al., 2020a,b) as well as previous research using the behavioural paradigms used in the study (Attaallah et al., 2022; Le Heron et al., 2018a,b; Petitet et al., 2021). All participants gave written consent to take part in the study and were offered monetary compensation for their time. The study was approved by the University of Oxford ethics committee (RAS ID: 248379, Ethics Approval Reference: 18/SC/0448). Tables S1 & S2 show the demographics and characteristics of the study groups. Due to a technical error during testing, two blocks (out of five) were missing from the passive choices task for one patient (code 14 in Table S2). Analyses were conducted with and without this patient's data and there was no difference in the results or conclusions. After completing a practice session, participants had to answer correctly all the questions of a task comprehension quiz in order to be eligible to do the task and continue the experiment. + +<|ref|>sub_title<|/ref|><|det|>[[100, 699, 314, 720]]<|/det|> +## External measures + +<|ref|>text<|/ref|><|det|>[[95, 740, 872, 920]]<|/det|> +All participants underwent a cognitive assessment using Addenbrooke's Cognitive Examination III (ACE III) (Hsieh et al., 2013) and executive function assessment with digit span (DS). In addition, they completed self- report questionnaires of apathy (Apathy Motivation Index, AMI (Ang et al., 2017)), depression (Beck Depression Inventory- II, BDI- II (Beck et al., 1996)), fatigue (Fatigue Severity Scale, FSS (Krupp et al., 1989)), and hedonic experience (Snaith- Hamilton Pleasure Scale, SHAPS (Snaith et al., 1995)). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[102, 135, 231, 156]]<|/det|> +## 1 Procedure + +<|ref|>text<|/ref|><|det|>[[100, 178, 872, 355]]<|/det|> +2 The tasks were presented on a 17- inch touchscreen PC using MATLAB (The MathWorks inc., version 2018b) and Psychtoolbox(Brainard, 1997; Kleiner et al., 2007) version 3. Testing was done in a quiet room with an experimenter present at all times. Participants sat within reaching distance of the screen ( \(\sim 50 \mathrm{cm}\) ) and were instructed to use the index finger of their dominant hand to respond. The task environment was adjusted according to handedness (e.g., uncertainty rating on the side of the dominant hand in Exps. 2 & 3). + +<|ref|>sub_title<|/ref|><|det|>[[101, 377, 510, 400]]<|/det|> +## 8 Experimental paradigm - Exps. 1 & 2 + +<|ref|>text<|/ref|><|det|>[[99, 420, 872, 788]]<|/det|> +9 A modified, shorter, version of "Circle Quest" task was used (described in detail in Petitet et al., 2021). In Exp. 1, participants performed the active sampling version of the task designed to test active information gathering prior to committing to decisions. In Exp. 2, participants performed the passive choices/decisions version designed to test decision making under fixed, experimentally defined levels of uncertainty and reward. Participants were told that their goal in the two parts of the task was to maximise reward. All participants performed a training task followed by the active task and then the passive task. The purpose of this order was to ensure that by the time participants performed the passive choices version of the task, they had extensive training and exposure to the task environment and scoring function through their interaction with the task during the active version. The average total duration of the testing session was approximately one hour (Average duration in minutes \(\pm \mathrm{SD}\) ; training: \(7.38 \pm 2.96\) , active sampling: \(30.11 \pm 5.20\) , passive choices: \(22.86 \pm 1.48\) ). + +<|ref|>text<|/ref|><|det|>[[98, 799, 870, 913]]<|/det|> +Training and exposure. The task was explained to participants using an interactive tutorial in the presence of the experimenter. In this interactive tutorial, participants were simply required to localise a hidden purple circle on the screen. This circle had a fixed size on all trials (radius: 130 Px; area: \(5.80\%\) of the search space). Two purple circles of the same size were always + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[98, 135, 872, 443]]<|/det|> +1 present on the right and left sides of the screen as a visual reminder of the circle on quest. This 2 served to limit the memory demands of the task. Clues about the location of the hidden circle 3 could be obtained by touching the screen. If a purple dot appeared where they touched (radius: 4 Px), this indicated that the location was located inside the hidden circle. Alternatively, if 5 a white dot appeared where they touched, the location was situated outside the hidden circle. 6 After completing the search, they were asked to move a blue disc of the same size as the hidden 7 circle on top of where they thought the hidden circle was located, based on the information 8 they gathered. During this tutorial, participants performed five trials with no constraints on 9 the number of samples they could acquire and without any sampling penalty. They were also 10 encouraged to ask the experimenter in case they had any questions. + +<|ref|>text<|/ref|><|det|>[[98, 450, 872, 911]]<|/det|> +Following this short introduction to the game, participants performed a training task that included 20 trials. The goal of this task was to: (1) practice localising the hidden circle for various levels of uncertainty, and (2) expose participants to the scoring function. On each trial of this training task, a configuration of eight dots (four purple and four white) was displayed on the screen, and participants were required to move the blue disc on top of where they thought the hidden circle was located. Different configurations mapped onto different levels of uncertainty. For example, displays with spaced-out purple dots represented lower levels of uncertainty than displays in which the purple dots were clustered more closely. The former configuration had a lower number of possible circle placements that were compatible with the dots displayed on the screen (purple dots should be inside the hidden circle), and consequently, the expected localisation error ( \(EE\) , a quantitative measure of uncertainty) for such a configuration was smaller. \(EE\) was calculated as the probability-weighted average of all possible errors a participant could incur by placing the blue disc at the best possible location (centroid of posterior belief) (Figure 2b., for more details see supplementary materials). The two circles on the right and left side of the search space contained the reward at stake which participants could obtain if they managed + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[99, 135, 872, 313]]<|/det|> +1 to perfectly localise the hidden circle. After placing the blue disc, the location of the hidden circle was revealed and the score they obtained for this localisation appeared. The score was calculated as the reward at stake minus the localisation error penalty upon placing the blue disc \((\eta_{e}.e)\) , where \(e\) is the distance between the centre of the blue disc and the centre of the hidden circle in pixels. \(\eta_{e}\) is the spatial error cost per pixel which was constant and equal to 1.2 credits/pixel. + +<|ref|>text<|/ref|><|det|>[[98, 325, 872, 784]]<|/det|> +7 Active sampling task (Exp. 1). In the active version of the task (Figure 1), participants could reduce their uncertainty about the location of the hidden circle by actively sampling the search space, i.e., touching the screen to obtain information. Similar to the training tutorial, this provided them with binary information about the location of that sample in relation to the hidden circle. If where they touched was inside the hidden circle, the sample was purple. Otherwise, the sample was white. At the beginning of every trial, participants had 18 seconds to sample whenever, wherever, and how much they wanted. After this, they were required to move the blue disc to where they thought the hidden circle was located in order to collect monetary credits. Participants started each trial with an initial reward reserve, \(R_{0}\) , from which they lost credits each time they acquired a new sample depending on the cost of sampling, \(\eta_{s}\) , on that trial. There were two levels of initial reward ( \(R_{0}\) : low = 95 credits and high = 130 credits) and two levels of sampling cost ( \(\eta_{s}\) : low = - 1 credit/sample and high = - 5 credits/sample) giving rise to four conditions in four blocks that were counterbalanced between participants. Each condition included 15 trials. The score (in credits) participants obtained on each trial was calculated as follows: + +<|ref|>equation<|/ref|><|det|>[[390, 828, 866, 848]]<|/det|> +\[Score = R_{0} - s.\eta_{s} - e.\eta_{e} \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[98, 867, 870, 919]]<|/det|> +Where \(R_{0}\) is the initial reward reserve, \(s\) is the number of samples obtained, \(\eta_{s}\) is the cost of acquiring a new sample, \(e\) is the placement error, \(\eta_{e}\) is the error cost per pixel which was fixed + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[102, 137, 350, 156]]<|/det|> +1 and equal to 1.2 credit/pixel. + +<|ref|>text<|/ref|><|det|>[[101, 166, 868, 219]]<|/det|> +2 Quantifying deviations from optimal behaviour. The expected value, \(EV\) , changed dynamically with each sample, \(s\) , as follows: + +<|ref|>equation<|/ref|><|det|>[[357, 260, 866, 281]]<|/det|> +\[EV(s) = R_{0} - s.\eta_{s} - EE(s).\eta_{e} \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[100, 300, 870, 604]]<|/det|> +5 Where \(EV(s)\) is the expected value at the \(s\) - th sample, which is calculated as what is left 6 of the initial reward reserve \(R_{0}\) after subtracting the credits lost during sampling \((s.\eta_{s})\) and 7 the expected localisation error penalty \((EE(s).\eta_{e})\) . The optimal number of samples to acquire 8 corresponds to the number of samples, \(s^{\star}\) , that maximises the expected value. Deviation from 9 optimal sampling (either over- or under- sampling) is the difference between the number of 10 samples obtained and \(s^{\star}\) . Note that in the equation above, the rate at which \(EE\) decays from 11 one sample to the next depends on the participant's choices of sampling locations (Figure 5a.). 12 Therefore, the dynamics of \(EE\) over successive samples (i.e., the efficiency of the search) may 13 vary across trials and participants. Sampling efficiency was parameterised as the information 14 extraction rate, \(\alpha\) , which was computed for each trial as follows: + +<|ref|>equation<|/ref|><|det|>[[290, 644, 866, 750]]<|/det|> +\[\begin{array}{l}{{\hat{E}\hat{E}_{(n,1)}=\frac{\sum_{i=1}^{60}E E_{(i,1)}}{60}}}\\ {{\hat{E}\hat{E}_{(n,s)}=(\hat{E}\hat{E}_{(n,1)}-\hat{E}\hat{E}_{\infty}).(1-\alpha_{n})^{s-1}+\hat{E}\hat{E}_{\infty}}}\\ {{0<\alpha<1\& \hat{E}\hat{E}_{\infty}>0}}\end{array} \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[99, 761, 870, 907]]<|/det|> +16 Where \(n\) is the trial number, \(s\) is the sample number within the trial (the \(0^{th}\) sample is the 17 sample displayed on the screen at the beginning of the trial, before participants touched the 18 screen, Figure 1), and \(\hat{E}\hat{E}_{\infty}\) is the asymptotic \(EE\) level reflecting limitations in uncertainty 19 reduction. This model was fitted using fmincon in MATLAB (The MathWorks inc., version 20 2019a) with a minimum mean square error method. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[98, 135, 872, 440]]<|/det|> +1 Passive choices task (Exp. 2). Each trial of the passive version of the task had two parts 2 (Figure 2c- d.). First, participants saw a configuration of dots on the screen (four purple dots and 3 four white dots) that mapped onto an experimentally defined level of uncertainty, \(EE\) . Based 4 on this configuration, participants were required to indicate how confident they were about the 5 location of the hidden circle. They could do this by touching on a rating scale on the side of 6 the screen ranging between zero and 100. A zero on this scale meant that the participant had no 7 idea where the hidden circle was, and 100 meant that the participant knew exactly where it was. 8 Metacognitive accuracy, a measure of how objective uncertainty is translated into subjective 9 estimates, was defined as the slope of the relationship between expected error and sign- flipped 10 confidence ratings. + +<|ref|>text<|/ref|><|det|>[[98, 450, 872, 742]]<|/det|> +Next, once participants reported their confidence, the reward on offer appeared. They then were required to make "Yes/No" decisions based on whether they wished to place the blue disc given the reward on offer and the uncertainty they have just rated. There were four reward levels ( \(R\) : 40, 65, 90, 115 credits) and five uncertainty levels ( \(EE\) : 16.3- 24.4, 27.1- 38.9, 57.5- 58.9, 91.9- 93.3 pixels). Participants were told that ten of the offers they accepted would be randomly selected at the end of the experiment, and they would be required to play them (i.e., localise the hidden circle using the blue disc). The credits they collected on these ten trials determined the monetary rewards they won in this version of the task. The score was calculated in the same way as the training task. Participants were rewarded at a rate of £1 per 150 credits (Table S18). + +<|ref|>sub_title<|/ref|><|det|>[[98, 777, 457, 799]]<|/det|> +## Experimental paradigm - Exp. 3 + +<|ref|>text<|/ref|><|det|>[[98, 820, 870, 903]]<|/det|> +A modified version of a well- validated effort- based decision making task was used (Aridan et al., 2019; Bonnelle et al., 2016, 2015; Chong et al., 2016; Le Heron et al., 2018b; Saleh et al., 2021). This task had a similar design as Circle Quest passive choices task, however, instead + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[95, 130, 874, 730]]<|/det|> +1 of uncertainty as the discounting attribute, participants evaluated reward against physical effort (Figure 7a.). Reward in the task was represented as apples on trees and effort levels were indicated by bars on the trunk of the trees. The higher the effort bar, the more effort participants needed to exert in order to obtain the apples. Effort in the task was exerted by squeezing a handheld dynamometer. Participants were first asked to squeeze the handle as hard as they could in order to measure their maximal voluntary contraction (MVC). Crucially, the effort handle was calibrated based on MVC for each participant. They were then familiarised with the different effort levels that they would encounter when making decisions. These effort levels corresponded to 16, 32, 48, 64 and \(0.8\%\) of MVC. Participants experienced each effort level twice before performing the decisions phase of the task where they had to evaluate the worthiness of reward (apples) against these effort levels. There were five reward levels corresponding to different numbers of apples (1, 4, 7, 10, and 13). Participants could indicate whether they would like to accept or reject the offer on the screen by pressing either the left or right arrow on the keyboard to select either "Yes" or "No", which were displayed on the sides of the screen. The positions of "Yes" and "No" changed randomly between trials. They were told that 10 of their decisions would be randomly selected at the end of the experiment and that they would have to play them (i.e., squeeze the effort handle to obtain reward). They were rewarded based on perfromance on these trials at a rate £1 per 10 apples. Before making their decisions, participants had the chance to perform five practice decisions. + +<|ref|>sub_title<|/ref|><|det|>[[121, 745, 457, 768]]<|/det|> +## Experimental paradigm - Exp. 4 + +<|ref|>text<|/ref|><|det|>[[95, 788, 872, 902]]<|/det|> +In Exp. 4, a novel version of Circle Quest (Exps. 1 & 2) was designed to investigate effort- based decision making under uncertainty (Figure 4). Participants were familiarised with uncertainty, reward and effort cues using the same training as in Exps. 1–3. After training, they were required to respond to (accept or reject) offers that had three attributes: reward, uncertainty + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[95, 123, 875, 792]]<|/det|> +1 and effort (Figure 4 a.). Reward was presented as credits that participants could win if they managed to complete two steps: i) achieve the required level of effort (as in Exp. 3) and ii) find the location of the hidden circle without errors (as in Exps. 1 & 2). The blue disc used to localise the hidden circle appeared only when the required level of effort was achieved. If effort level was met, participants then could win credits depending on how far their localisation using the blue disc was from the true location of the hidden purple circle. There were four levels of reward (R: 40, 65, 90, 115 credits; same as in Exp. 2), five levels of effort (16, 32, 48, 64 and \(0.8\%\) of MVC; same as in Exp. 3) and two levels of uncertainty (present and absent). Absence of uncertainty was indicated by showing the true location of the hidden circle on the screen when offers were presented. On trials in which uncertainty was present, it corresponded to the midrange of expected error (EE) used in Exp. 2 ( \(EE\) : 31.8- 73.95 pixels). Additional catch trails (20 in total) were added to increase the range of uncertainty during confidence rating ( \(EE\) : 13.2 - 93.3) to fully capture subjective estimation of uncertainty as in Exp. 2. Unlike Exp. 2, confidence ratings were blocked and reported during the training phase rather than prior to each decision. This was done to ensure that offers with and without uncertainty were experimentally matched during the decision phase. The decision phase involved 40 different trial types (four reward levels \(\times\) five effort levels \(\times\) two uncertainty levels), and each was repeated five times over ten blocks (200 trials in total). Participants were told that at the end of the decision phase, 24 trials (12 with uncertainty) will be randomly selected for them to play. Performance on these trials decided the reward that participants eventually won. Similar to Exp. 2, they were rewarded at a rate of £1 per 150 credits (Table S18). + +<|ref|>sub_title<|/ref|><|det|>[[123, 809, 516, 831]]<|/det|> +## Statistical analyses of behavioural data + +<|ref|>text<|/ref|><|det|>[[95, 852, 870, 902]]<|/det|> +Statistical analyses and modelling were done in MATLAB R2019a or R version 4.0.2. Generalised mixed- effects models and robust regression models were fitted using fitglm and fitlm func + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[100, 135, 872, 315]]<|/det|> +1 tions in MATLAB, respectively. Baysian mixed- effects models was performed using Stan computational framework (http://mc- stan.org/) accessed using brms in R (Bürkner, 2017). Bayes factors (BF) were calculated using brms hypothesis function. For group comparisons, student t- test was used if parametric assumptions were fulfilled and Wilcoxon rank sum test if not. All statistical tests were two- tailed with a testing level (alpha) 0.05. Full description of mixed effects models used and statistical results are reported in Supplementary Materials. + +<|ref|>sub_title<|/ref|><|det|>[[101, 336, 500, 359]]<|/det|> +## 7 Magnetic Resonance data acquisition + +<|ref|>text<|/ref|><|det|>[[98, 378, 872, 652]]<|/det|> +8 MRI scans were obtained at Acute Vascular Imaging Centre (AVIC) at John Radcliff Hospital (Oxford) using SIEMENS Verio 3T scanner. High- resolution T1- weighted structural MR images (MPRAGE; 208 sagittal slices of 1 mm thickness, voxel size = 1 mm isotropic, TR/TE = 2000/1.94 ms; flip angle = 8°, FOV read = 256, iPAT =2, prescan- normalise) and T2 weighted fluid- attenuated inversion recovery (FLAIR) images (192 sagittal slices of 1.05 mm thickness, voxel size = \(1\times 1\times 1.1\) , TR/TE = 5000/397 ms; FOV read = 256; iPAT = 2, partial Fourier = 7/8, fat saturation, prescan- normalise) were acquired. Four patients and two controls were not MRI compatible or did not consent to be scanned, therefore, imaging data was acquired for 15/19 patients and 17/19 controls. + +<|ref|>sub_title<|/ref|><|det|>[[100, 675, 470, 697]]<|/det|> +## 7 Magnetic Resonance data analysis + +<|ref|>text<|/ref|><|det|>[[98, 716, 870, 833]]<|/det|> +18 Whole hippocampal volumes were extracted using T1 and T2 imaging in Freesurfer version 7.1 (http://surfer.nmr.mgh.harvard.edu/). The automated standard segmentation protocol was used. Hippocampal volumes were adjusted for intracranial volume ( \(ICV\) ) using the following equation (Jack et al., 1998; Voevodskaya, 2014): + +<|ref|>equation<|/ref|><|det|>[[366, 856, 867, 879]]<|/det|> +\[V_{adj} = V - \beta \times (ICV - \overline{ICV}) \quad (4)\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[100, 135, 870, 283]]<|/det|> +1 where \(V_{adj}\) and \(V\) are the adjusted and observed volumes respectively, \(\beta\) is the slope of the relationship between \(ICV\) and \(V\) in a larger sample of healthy controls with similar demographics ( \(\mathrm{N} = 31\) , including the study sample and participants recruited for a different study), and \(\overline{ICV}\) is the mean intracranial volume in this control sample. Amygdala volumes were also extracted and used as a control comparison region (Figure S4). + +<|ref|>sub_title<|/ref|><|det|>[[101, 305, 387, 326]]<|/det|> +## 6 Data and code availability + +<|ref|>text<|/ref|><|det|>[[100, 348, 868, 400]]<|/det|> +7 Anonymised data and code for replicating the main results in the manuscript have been deposited on the Open Science Framework platform: https://osf.io/u4n2a/. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[100, 175, 259, 199]]<|/det|> +## 2 References + +<|ref|>text<|/ref|><|det|>[[100, 225, 870, 309]]<|/det|> +3 Acerbi L, Ma WJ. Practical Bayesian optimization for model fitting with Bayesian adaptive direct search. In: Advances in Neural Information Processing Systems, vol. 2017- Decem; 5 2017. p. 1837- 1847. + +<|ref|>text<|/ref|><|det|>[[100, 333, 870, 385]]<|/det|> +6 Ang YS, Lockwood P, Apps MAJ, Muhammed K, Husain M. Distinct Subtypes of Apathy Revealed by the Apathy Motivation Index. 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Decision Making Over Time and Under Uncertainty: A Common Approach. http://dx.doi.org/101287/mnsc377770. 1991 7; 37(7):770- 786. + +<|ref|>text<|/ref|><|det|>[[100, 286, 870, 338]]<|/det|> +5 Qiao J, Zhao X, Wang S, Li A, Wang Z, Cao C, Wang Q. Functional and Structural Brain Alterations in Encephalitis With LGI1 Antibodies. Frontiers in Neuroscience. 2020 4; 14:304. + +<|ref|>text<|/ref|><|det|>[[100, 361, 870, 413]]<|/det|> +7 Rachlin H, Raineri A, Cross D. SUBJECTIVE PROBABILITY AND DELAY. Journal of the Experimental Analysis of Behavior. 1991 3; 55(2):233- 244. + +<|ref|>text<|/ref|><|det|>[[100, 437, 870, 489]]<|/det|> +9 Rigoli F, Michely J, Friston KJ, Dolan RJ. The role of the hippocampus in weighting expectations during inference under uncertainty. Cortex. 2019 6; 115:1- 14. + +<|ref|>text<|/ref|><|det|>[[100, 513, 870, 595]]<|/det|> +11 Rudebeck PH, Walton ME, Smyth AN, Bannerman DM, Rushworth MFS. Separate neural pathways process different decision costs. Nature Neuroscience 2006 9:9. 2006 8; 9(9):1161- 1168. + +<|ref|>text<|/ref|><|det|>[[100, 620, 870, 701]]<|/det|> +14 Saleh Y, Le Heron C, Petitet P, Veldsman M, Drew D, Plant O, Schulz U, Sen A, Rothwell PM, Manohar S, Husain M. Apathy in small vessel cerebrovascular disease is associated with deficits in effort- based decision making. Brain. 2021 5; 144(4):1247- 1262. + +<|ref|>text<|/ref|><|det|>[[100, 726, 870, 778]]<|/det|> +17 Schacter DL, Addis DR, Buckner RL, Remembering the past to imagine the future: The prospective brain. Nature Publishing Group; 2007. + +<|ref|>text<|/ref|><|det|>[[100, 802, 870, 854]]<|/det|> +19 Schacter DL, Addis DR, Hassabis D, Martin VC, Spreng RN, Szpunar KK. The Future of Memory: Remembering, Imagining, and the Brain. Neuron. 2012 11; 76(4):677- 694. + +<|ref|>text<|/ref|><|det|>[[100, 878, 870, 899]]<|/det|> +21 Schacter DL, Addis DR, Szpunar KK. Escaping the Past: Contributions of the Hippocampus + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[100, 135, 870, 192]]<|/det|> +1 to Future Thinking and Imagination. In: The Hippocampus from Cells to Systems Cham: Springer International Publishing; 2017. p. 439- 465. + +<|ref|>text<|/ref|><|det|>[[100, 211, 870, 296]]<|/det|> +3 Schacter DL, Benoit RG, De Brigard F, Szpunar KK. Episodic future thinking and episodic counterfactual thinking: Intersections between memory and decisions. Neurobiology of learning and memory. 2015 1; 117:14. + +<|ref|>text<|/ref|><|det|>[[100, 319, 870, 402]]<|/det|> +6 Snaith RP, Hamilton M, Morley S, Humayan A, Hargreaves D, Trigwell P. A scale for the assessment of hedonic tone. The Snaith- Hamilton Pleasure Scale. British Journal of Psychiatry. 1995; 167(JULY):99- 103. + +<|ref|>text<|/ref|><|det|>[[100, 424, 870, 444]]<|/det|> +9 Sosa M, Giocomo LM. Navigating for reward. Nature reviews Neuroscience. 2021 7; p. 1- 16. + +<|ref|>text<|/ref|><|det|>[[100, 468, 870, 523]]<|/det|> +10 Spano G, Pizzamiglio G, McCormick C, Clark IA, De Felice S, Miller TD, Edgin JO, Rosenthal CR, Maguire EA. Dreaming with hippocampal damage. eLife. 2020; 9:1- 15. + +<|ref|>text<|/ref|><|det|>[[100, 545, 870, 599]]<|/det|> +12 Spano G, Weber FD, Pizzamiglio G, Rosenthal CR, Edgin JO, Correspondence EAM. Sleeping with Hippocampal Damage. Current Biology. 2020; 30. + +<|ref|>text<|/ref|><|det|>[[100, 621, 870, 675]]<|/det|> +14 Stevenson MK. A Discounting Model for Decisions With Delayed Positive or Negative Outcomes. Journal of Experimental Psychology: General. 1986 6; 115(2):131- 154. + +<|ref|>text<|/ref|><|det|>[[100, 696, 870, 780]]<|/det|> +16 Strange BA, Duggins A, Penny W, Dolan RJ, Friston KJ. Information theory, novelty and hippocampal responses: Unpredicted or unpredictable? Neural Networks. 2005 4; 18(3):225- 230. + +<|ref|>text<|/ref|><|det|>[[100, 803, 870, 888]]<|/det|> +19 Szots M, Marton A, Kover F, Kiss T, Berki T, Nagy F, Illes Z. Natural course of LGI1 encephalitis: 3- 5 years of follow- up without immunotherapy. Journal of the Neurological Sciences. 2014 8; 343(1- 2):198- 202. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[100, 135, 870, 188]]<|/det|> +Tobia MJ, Iacovella V, Davis B, Hasson U. Neural systems mediating recognition of changes in statistical regularities. NeuroImage. 2012 11; 63(3):1730- 1742. + +<|ref|>text<|/ref|><|det|>[[100, 211, 870, 263]]<|/det|> +Tracy AL, Jarrard LE, Davidson TL. The hippocampus and motivation revisited: appetite and activity. Behavioural Brain Research. 2001 12; 127(1- 2):13- 23. + +<|ref|>text<|/ref|><|det|>[[100, 287, 870, 339]]<|/det|> +Tversky A, Kahneman D. Judgment under Uncertainty: Heuristics and Biases. New Series. 1974; 185(4157):1124- 1131. + +<|ref|>text<|/ref|><|det|>[[100, 362, 870, 446]]<|/det|> +Voevodskaya O. The effects of intracranial volume adjustment approaches on multiple regional MRI volumes in healthy aging and Alzheimer's disease. Frontiers in Aging Neuroscience. 2014; 6(OCT). + +<|ref|>text<|/ref|><|det|>[[100, 469, 870, 584]]<|/det|> +Vos De Wael R, Larivière S, Caldairou B, Hong SJ, Margulies DS, Jefferies E, Bernasconi A, Smallwood J, Bernasconi N, Bernhardt BC. Anatomical and microstructural determinants of hippocampal subfield functional connectome embedding. Proceedings of the National Academy of Sciences of the United States of America. 2018 10; 115(40):10154- 10159. + +<|ref|>text<|/ref|><|det|>[[100, 608, 870, 660]]<|/det|> +Wimmer GE, Shohamy D. Preference by Association: How Memory Mechanisms in the Hippocampus Bias Decisions. Science. 2012 10; 338(6104):270- 273. + +<|ref|>text<|/ref|><|det|>[[100, 683, 870, 766]]<|/det|> +Ye JY, Ding QY, Cui JF, Liu Z, Jia LX, Qin XJ, Xu H, Wang Y. A meta- analysis of the effects of episodic future thinking on delay discounting. Quarterly journal of experimental psychology (2006). 2021 12; p. 17470218211066282. + +<|ref|>text<|/ref|><|det|>[[100, 790, 870, 842]]<|/det|> +Zeidman P, Lutti A, Maguire EA. Investigating the functions of subregions within anterior hippocampus. Cortex. 2015 12; 73:240- 256. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[102, 133, 348, 158]]<|/det|> +## 1 Acknowledgments + +<|ref|>text<|/ref|><|det|>[[100, 183, 870, 300]]<|/det|> +2 We thank Dr Kinan Muhammed for helping in recruiting some of the patients for the study. This work was supported by a Wellcome Trust grant to M.H. (206330/Z/17/Z) and a Rhodes scholarship awarded to B.A. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. + +<|ref|>text<|/ref|><|det|>[[100, 315, 113, 326]]<|/det|> +6 + +<|ref|>text<|/ref|><|det|>[[100, 341, 870, 423]]<|/det|> +7 Author contributions. B.A., P.P and M.H. designed the study. B.A, R.Z., S.I and M.H recruited the patients. B.A, S.T., M.R.M., A.G-D., and R.Z collected the data. B.A, P.P. and S.M. analysed the data. B.A. and M.H. wrote the paper. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[102, 133, 432, 158]]<|/det|> +## 1 Supplementary materials + +<|ref|>sub_title<|/ref|><|det|>[[101, 173, 444, 195]]<|/det|> +## 2 Expected error and uncertainty + +<|ref|>text<|/ref|><|det|>[[100, 216, 870, 362]]<|/det|> +3 Objective uncertainty in Circle Quest task was quantified as the expected error of localisation (EE). This is equal to the error the ideal agent would obtain on average by placing the blue disc at the best possible location given the information on the screen. For each location \(\lambda\) on the screen the probability that this location is the centre of the hidden circle given the observation \(o\) at location \(\sigma\) can be calculated using Bayes' rule as follows: + +<|ref|>equation<|/ref|><|det|>[[375, 400, 867, 468]]<|/det|> +\[\begin{array}{l}{{p_{s}(\lambda|o,\sigma)=\frac{p_{s}(\lambda).p_{s}(o,\sigma|\lambda)}{p_{s}(o,\sigma)}}}\\ {{p_{s+1}(\lambda)=p_{s}(\lambda|o,\sigma)}}\end{array} \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[100, 479, 870, 655]]<|/det|> +With successive sampling, this rule is applied sequentially. Therefore, the posterior probability \(p_{s}(\lambda |o,\sigma)\) becomes the prior probability \(p_{s + 1}(\lambda)\) from one sample to the next. Given the rules of the task and that there is no uncertainty regarding the radius of the hidden circle, the likelihood of observing a purple dot \((o^{+})\) at a location \(\sigma\) is 1 for locations within one radius distance of \(\sigma\) , and zero otherwise. The opposite is true for the likelihood of observing a white dot \((o^{- })\) . Thus, the likelihood function can be expressed mathematically as: + +<|ref|>equation<|/ref|><|det|>[[355, 666, 866, 739]]<|/det|> +\[\left\{ \begin{array}{l l}{p_{s}(o^{+},\sigma |\lambda) = 1\mathrm{~if~}|\lambda -\sigma |\leq r}\\ {p_{s}(o^{+},\sigma |\lambda) = 0\mathrm{~if~}|\lambda -\sigma | > r}\\ {p_{s}(o^{-},\sigma |\lambda) = 1 - p_{s}(o^{+},\sigma |\lambda)} \end{array} \right. \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[98, 748, 568, 768]]<|/det|> +Where \(r\) is the radius of the hidden circle which is fixed. + +<|ref|>text<|/ref|><|det|>[[98, 780, 869, 860]]<|/det|> +The probability of the observation \(o\) at the sampling location \(\sigma\) is the sum over all possible hidden circle centres \(\lambda\) of the probability of the observation given \(\lambda\) , weighted by the probability of \(\lambda\) to be the hidden circle centre: + +<|ref|>equation<|/ref|><|det|>[[368, 884, 866, 925]]<|/det|> +\[p_{s}(o,\sigma) = \sum_{\lambda}p_{s}(o,\sigma |\lambda).p_{s}(\lambda) \quad (3)\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[150, 137, 786, 157]]<|/det|> +Thus, for every possible circle placement, an expected error can be computed as: + +<|ref|>equation<|/ref|><|det|>[[375, 177, 867, 216]]<|/det|> +\[E E_{s}(\lambda) = \sum_{i}p_{s}(\lambda_{i}).|\lambda -\lambda_{i}| \quad (4)\] + +<|ref|>sub_title<|/ref|><|det|>[[101, 250, 701, 273]]<|/det|> +## Computational modelling of active information gathering + +<|ref|>text<|/ref|><|det|>[[101, 293, 870, 377]]<|/det|> +To further characterise active information sampling performance in Exp. 1, we analysed the behaviour using a well- validated computational model previously implemented in healthy and patient groups (Attaallah et al., 2022; Petitet et al., 2021). + +<|ref|>text<|/ref|><|det|>[[100, 388, 870, 567]]<|/det|> +The model calculates the expected utility of a sample \((E U_{s})\) accounting for economic and hidden cognitive effort costs to return five parameter estimates per participant. The first two parameters represent the weights participants assign to sample costs \((w_{s})\) and benefits \((w_{e})\) . Two parameters describe the cognitive cost function \(\eta_{c}(ISI,\alpha)\) in terms of a penalty for sampling speed \((w_{speed})\) and efficiency \((w_{\alpha})\) . The fifth parameter represents an intercept per participant describing their baseline valuation of samples \((w_{0})\) . + +<|ref|>text<|/ref|><|det|>[[101, 578, 516, 597]]<|/det|> +This was formalised quantitatively as follows: + +<|ref|>equation<|/ref|><|det|>[[179, 608, 870, 666]]<|/det|> +\[\begin{array}{r l} & {E U_{s}(I S I,\alpha ,t_{m a x}) = E U_{s - 1} + p(s|I S I,t_{m a x}).[w_{e}.\eta_{e}.(1 - \alpha).(E E_{s - 1} - E\hat{E}_{\infty})}\\ & {~ - w_{s}.\eta_{s}^{1 + \gamma .s} - \eta_{c}(I S I,\alpha)]} \end{array} \quad (5)\] + +<|ref|>text<|/ref|><|det|>[[101, 666, 870, 750]]<|/det|> +Previous EU + Probability of acquiring the sample given the current time. [Expected information benefit - Sampling cost - Cognitive effort cost] where \(\eta_{e}\) is the placement error penalty (1.2 credits/pixel) and \(t_{max}\) is the allowed search time per trial (18 seconds). \(E\hat{E}_{\infty}\) is the per- individual information sampling asymptotic limit estimated beforehand to take into consideration inter- participant variations in asymptotic information sampling performance. + +<|ref|>text<|/ref|><|det|>[[100, 750, 870, 864]]<|/det|> +where \(\eta_{e}\) is the placement error penalty (1.2 credits/pixel) and \(t_{max}\) is the allowed search time per trial (18 seconds). \(E\hat{E}_{\infty}\) is the per- individual information sampling asymptotic limit estimated beforehand to take into consideration inter- participant variations in asymptotic information sampling performance. + +<|ref|>text<|/ref|><|det|>[[100, 875, 868, 895]]<|/det|> +Based on previous work (Attaallah et al., 2022; Petitet et al., 2021), we used quadratic + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[102, 137, 395, 155]]<|/det|> +1 cognitive cost function as follows: + +<|ref|>equation<|/ref|><|det|>[[295, 172, 866, 214]]<|/det|> +\[\eta_{c}(ISI,\alpha) = w_{0} + w_{speed}\times \frac{1}{ISI^{2}} +w_{\alpha}\times \alpha^{2} \quad (6)\] + +<|ref|>text<|/ref|><|det|>[[101, 229, 868, 280]]<|/det|> +3 To obtain the likelihood function, softmax function was applied over the 3- dimensional 4 space of \(EU\) ( \(EU\) depends on \(ISI,\alpha ,s)\) for a given task condition as follows: + +<|ref|>equation<|/ref|><|det|>[[266, 295, 866, 339]]<|/det|> +\[p_{s}(stop|ISI,\alpha ,t_{max}) = \frac{\exp(EU_{s}(ISI,\alpha,t_{max}))}{\sum_{i}\sum_{a}\sum_{t}^{t_{max}}\exp(EU_{s}(i,a,t))} \quad (7)\] + +<|ref|>text<|/ref|><|det|>[[100, 353, 868, 404]]<|/det|> +6 For each individual, model fitting involved findings the parameters that achieved the low- 7 est negative log- likelihood of observing the multivariate distribution of the number of samples + +<|ref|>text<|/ref|><|det|>[[100, 415, 675, 436]]<|/det|> +8 acquired \((s)\) , inter- sampling interval (ISI) and sampling efficiency \((\alpha)\) + +<|ref|>text<|/ref|><|det|>[[100, 446, 868, 560]]<|/det|> +9 Optimisation of parameters was performed in MATLAB (The MathWorks inc., version 2019a) using Bayesian Adaptive Direct Search (BADS (Acerbi and Ma, 2017)). Further information about this modelling framework is provided in Attaallah et al. (2022); Petitet et al. (2021). + +<|ref|>text<|/ref|><|det|>[[99, 572, 869, 750]]<|/det|> +13 After the exclusion of potential outliers (1 patient with values \(>3SD\) ), comparing parameter estimates between two groups showed that ALE patients had lower weights assigned to sampling cost compared to controls ( \(t_{35} = - 2.24\) , \(p = 0.031\) ; Figure S1). There was no significant difference between the two groups in any of the other parameters. These results thus represent a computational formalisation of the findings from Exp. 1 suggesting that ALE patients have lower sensitivity to the cost of sampling. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[293, 335, 705, 604]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 622, 869, 735]]<|/det|> +
Figure S1: Computational modelling of active information sampling (Exp. 1). Compared to healthy matched controls, ALE patients assigned lower economic costs \((w_{s})\) to sample acquisition. All other model parameters including weights assigned to sample benefit \((w_{e})\) , efficiency \(w_{\alpha}\) , and speed \((w_{speed})\) were not significantly different between patients and controls. \(w_{0}\) captures a subjective fixed cost of sampling that is not explicitly specified in the task (e.g., cost of the motor action). This was not significantly different between the two groups. \(*:p< 0.05\)
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[102, 135, 362, 158]]<|/det|> +## Supplementary Figures + +<|ref|>text<|/ref|><|det|>[[102, 180, 240, 197]]<|/det|> +2 Figs. S2 to S6 + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[188, 163, 844, 604]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 633, 868, 907]]<|/det|> +
Figure S2: Active sampling (Exp. 1) – Hippocampal patients commit to decisions at similar uncertainty levels as controls. a. Final uncertainty is the expected error (EE) in pixels (Px) that a participant is likely to obtain at the end of their search. In the experimental condition where hippocampal patients over-sampled more than controls, there was no significant difference between hippocampal patients and controls in this measure. b. Similarly, the actual error that participants obtained upon localising the circle (distance to hidden circle in pixels) was not significantly different between patients and controls. These two results indicate that hippocampal patients wasted monetary resources on samples with limited utility (i.e., over-sampled). c. In the same condition, hippocampal patients gathered information at a significantly faster rate than controls. d. Sampling behaviour in hippocampal patients and controls was characterised by a speed-efficiency trade-off whereby faster sampling rates (shorter \(ISI\) ) were associated with lower sampling efficiency (smaller \(\alpha\) ). The figure shows this trade-off for the same condition in which patients over-sampled more than controls, demonstrating that hippocampal patients were also both faster and less efficient than controls. Error bars show \(\pm\) SEM. \*:p<0.05. See Tables S3, S5 & S6 for additional statistical details.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[163, 348, 812, 621]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 646, 868, 720]]<|/det|> +
Figure S3: Baysian mixed-effects model. The purple dots show the median of the posterior distributed with \(95\%\) credible intervals (thin green line) and \(50\%\) posterior interval (thick dark red lines). Model was specified as follows:choice \(\sim 1 + \mathrm{group}^{*}\mathrm{Reward} + \mathrm{group}^{*}\mathrm{Effort} + \mathrm{Re}-\) ward\*Effort + group:Reward:Effort + (1 + reward\*Effort |participant).
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[153, 178, 802, 541]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 566, 868, 620]]<|/det|> +
Figure S4: Amygdala as control region. No significant correlation was detected between amygdala volume and sensitivity to reward or uncertainty. Note also there was no significant difference in amygdala volumes between patients and controls.
+ +<|ref|>image<|/ref|><|det|>[[225, 640, 775, 809]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 828, 868, 920]]<|/det|> +
Figure S5: Intact localisation performance. Distance to optimal placement is the distance between the centre of the blue disc and the best localisation given the configuration of the dots on display. Across the three versions of Circle Quest (Exps. 1, 2 & 3), there was no significant difference between ALE patients and controls in this measure, indicating intact localisation performance. Error bars show \(\pm\) SEM.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[225, 352, 770, 555]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[123, 564, 870, 707]]<|/det|> +
Figure S6: Passive choices as a function of reward and subjective uncertainty estimates. There is no change in choice performance results when subjective estimates of uncertainty are used instead of expected error \((EE)\) in the analysis. ALE patients demonstrate lower sensitivity to reward and intact sensitivity to uncertainty when compared to healthy controls. Reward levels 1-4 correspond to the number of credits on display \((R: 40, 65, 90, 115\) credits). Subjective uncertainty levels were calculated by binning sign-flipped z-scored confidence ratings into five bins. level five describes the lowest level of subjective uncertainty estimate. For statistical details see Table S16.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[102, 135, 351, 157]]<|/det|> +## Supplementary Tables + +<|ref|>text<|/ref|><|det|>[[102, 180, 263, 197]]<|/det|> +2 Tables S1 to S18 + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[248, 153, 744, 340]]<|/det|> + +
Count (M/F) VariableControlsPatients
MeanSDMeanSDp-value
Age61.1611.7160.0011.360.76
ACE-III97.522.0393.425.640.005
DS18.053.4919.795.000.21
AMI1.180.401.250.490.65
FSS3.061.123.361.860.59
BDI-II5.95.2610.9510.150.06
SHAPS18.844.5621.584.850.08
Lt. Hipp Volume*3495.06199.413357.71722.710.457
Rt. Hipp Volume*3662.99167.063288.50675.580.034
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 353, 868, 423]]<|/det|> +Table S1: Demographics. ACE-III: Addenbrooke's Cognitive Examination. DS: Digit Span.AMI: Apathy Motivation Index. FSS: Fatigue Severity Scale. BDI-II: Beck Depression Inven-tory. SHAPS: Snaith-Hamilton Pleasure Scale. Hipp: Adjusted Hippocampal Volumes.* 15patients and 17 controls. + +<|ref|>table<|/ref|><|det|>[[142, 476, 857, 800]]<|/det|> + +
CodeAgeGenderAbsLt. Hipp.Rt. Hipp.Years Since
Diagnosis
VolumePercentileVolumePercentile
153FLGI13559.64293547.33213.38
247FLGI13157.676c2717.64\(<2.5^{c}\)3.44
359FLGI12332.83\(<2.5\)2255.53\(<2.5\)3.49
463FLGI12860.65\(<2.5\)3744.13432.59
572FLGI13170.72\(17^{c}\)2495.43\(<2.5^{c}\)7.67
664MLGI1----4.66
755MLGI14835.20974125.12462.56
853MLGI13663.60193754.24172.36
966MLGI14109.11724341.71801.18
1065MLGI13488.01183379.2093.27
1172MLGI12973.8352905.0431.82
1226MLGI1----0.96
1368MCASPR23050.2743364.09101.03
1477MCASPR2----10.52
1565MCASPR23455.68163411.77105.29
1667MCASPR24118.84743942.82484.30
1758FLGI1/CASPR23670.25413073.4143.26
1858MLGI1/CASPR2----7.16
1952MSeronegative2370.64\(<2.5^{c}\)2752.98\(<2.5^{c}\)1.99
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 810, 868, 900]]<|/det|> +Table S2: Patients Characteristics. Abs: Autoantibodies. Lt. Hipp.: Left Hippocampus. Rt.Hipp.: Right Hippocampus. Percentile is determined by plotting raw hippocampal volumes against normative brain volumes from UK biobank data (Nobis et al., 2019). c: Describes percentiles outside the age range of the UK biobank nomograms. Percentiles according to the closest age value within the UK biobank range was used instead. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[140, 198, 851, 760]]<|/det|> +
SscoreEEErrorISI
(Intercept)β = +2.18β = +65.7β = +20.3β = +2.06β = +1.5
SE = 0.0817SE = 1.39SE = 2.12SE = 0.0873SE = 0.0902
t2272 = +26.72t2272 = +47.32t2272 = +9.57t2272 = +23.64t2272 = +16.67
p<0.0001p<0.0001p<0.0001p<0.0001
ALEβ = +0.153β = -7.93β = -0.683β = +0.162β = -0.211
SE = 0.116SE = 1.96SE = 2.99SE = 0.123SE = 0.128
t2272 = +1.32t2272 = -4.04t2272 = -0.23t2272 = +1.32t2272 = -1.65
p = 0.19p<0.0001p = 0.82p = 0.19p = 0.10
ALE:R0β = +0.0247β = -1.66β = -0.952β = -0.0265β = -0.0393
SE = 0.0251SE = 1.06SE = 0.819SE = 0.0376SE = 0.027
t2272 = +0.98t2272 = -1.56t2272 = -1.16t2272 = -0.70t2272 = -1.45
p = 0.33p = 0.12p = 0.25p = 0.48p = 0.15
ALE:ηsβ = +0.0318β = -3.66β = -0.924β = -0.0277β = -0.0719
SE = 0.0247SE = 1.84SE = 0.665SE = 0.0318SE = 0.0337
t2272 = +1.29t2272 = -2.00t2272 = -1.39t2272 = -0.87t2272 = -2.14
p = 0.20p = 0.046p = 0.16p = 0.38p = 0.033
ALE:ηs:R0β = +0.0542β = -0.403β = -0.881β = -0.0463β = -0.0273
SE = 0.0202SE = 1.04SE = 0.602SE = 0.0302SE = 0.0231
t2272 = +2.68t2272 = -0.39t2272 = -1.46t2272 = -1.53t2272 = -1.18
p = 0.0074p = 0.70p = 0.14p = 0.13p = 0.24
R0β = +0.0231β = +17.6β = -0.169β = +0.0104β = +0.00487
SE = 0.018SE = 0.751SE = 0.579SE = 0.0266SE = 0.0191
t2272 = +1.29t2272 = +23.45t2272 = -0.29t2272 = +0.39t2272 = +0.25
p = 0.20p<0.0001p = 0.77p = 0.70p = 0.80
ηsβ = -0.111β = -18.7β = +2.07β = +0.0919β = +0.12
SE = 0.0177SE = 1.3SE = 0.47SE = 0.0225SE = 0.0238
t2272 = -6.25t2272 = -14.41t2272 = +4.40t2272 = +4.09t2272 = +5.04
p<0.0001p<0.0001p<0.0001p<0.0001
ηs:R0β = -0.0301β = -0.654β = +0.477β = +0.0211β = +0.0135
SE = 0.0145SE = 0.732SE = 0.426SE = 0.0213SE = 0.0164
t2272 = -2.07t2272 = -0.89t2272 = +1.12t2272 = +0.99t2272 = +0.83
p = 0.038p = 0.37p = 0.26p = 0.32p = 0.41
adj - R20.790.710.580.270.71
Nobs22802280228022802280
AIC295.8419913.9016521.034656.471163.69
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 771, 870, 862]]<|/det|> +Table S3: Active Search (Exp. 1) – Generalised mixed-effects models of the effect of the group (ALE) on performance. Models were specified as follows. Predicted variable ~ 1 + group*ηs + group*R0 + ηs*R0 + group:ηs:R0 + (1 + ηs*R0 |participant). S: Raw number of samples. EE: Expected Error (Uncertainty). Error: Distance to hidden circle. ISI: Inter-Sampling Interval. ηs : Sampling Cost. R0: Initial Reward Reserve. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[310, 167, 682, 485]]<|/det|> + +
Condition (R/hs)ControlsALE
Low/Low\(\beta =-2.24\)\(\beta =-1.6\)
\(SE=0.715\)\(SE=1.23\)
\(t_{1136}=-3.14\)\(t_{1136}=-1.30\)
\(p=0.0018\)\(p=0.19\)
Low/High\(\beta =+1.97\)\(\beta =+3.14\)
\(SE=0.543\)\(SE=0.81\)
\(t_{1136}=+3.63\)\(t_{1136}=+3.88\)
\(p=0.00029\)\(p=0.00011\)
High/Low\(\beta =-1.32\)\(\beta =-0.277\)
\(SE=0.689\)\(SE=1.34\)
\(t_{1136}=-1.92\)\(t_{1136}=-0.21\)
\(p=0.06\)\(p=0.84\)
High/High\(\beta =+1.9\)\(\beta =+4.74\)
\(SE=0.625\)\(SE=1.05\)
\(t_{1136}=+3.04\)\(t_{1136}=+4.52\)
\(p=0.0024\)\(p<0.0001\)
adj-R2
\(N_{obs}\) AIC		0.860.84
11401140
4273.505545.49
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 496, 870, 550]]<|/det|> +Table S4: Active Search (Exp. 1) - Deviation from optimal number of samples.. Models were specified as follows: Deviation $\sim $ Condition + (Condition |participant). $R$ : Initial reward reserve. $\eta _{s}:$ Sampling cost. + +<|ref|>table<|/ref|><|det|>[[325, 627, 666, 824]]<|/det|> + +
ControlsALE
(Intercept)\(\beta =-1.67\)\(\beta =-1.9\)
\(SE=0.0721\)\(SE=0.0881\)
\(t_{1138}=-23.12\)\(t_{1138}=-21.57\)
\(p<0.0001\)\(p<0.0001\)
ISI\(\beta =+0.137\)\(\beta =+0.26\)
\(SE=0.0463\)\(SE=0.054\)
\(t_{1138}=+2.96\)\(t_{1138}=+4.82\)
\(p=0.0032\)\(p<0.0001\)
adj-R2
\(N_{obs}\) BIC		0.220.27
11401140
570.28858.72
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 836, 870, 890]]<|/det|> +Table S5: Active Search (Exp. 1) - Relationship between inter-sampling interval and in-formation extraction rate. Model was specified as follows: $\alpha \sim 1+ISI+(1|trial)+(1+ISI$ condition) $+(1+ISI|participant)$ + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[390, 327, 601, 647]]<|/det|> + +
Exp. 1
(Intercept)\(\beta =+0.244\) \(SE=0.00934\) \(t_{566}=+26.11\) \(p<0.0001\)
ALE\(\beta =-0.026\) \(SE=0.0131\) \(t_{566}=-1.98\) \(p=0.048\)
ALE:ISI\(\beta =-0.00165\) \(SE=0.0128\) \(t_{566}=-0.13\) \(p=0.90\)
ISI\(\beta =+0.0122\) \(SE=0.00905\) \(t_{566}=+1.35\) \(p=0.18\)
\(adj-R^{2}\)0.25
\(N_{obs}\)570
AIC-1256.45
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 658, 870, 729]]<|/det|> +Table S6: Active Search (Exp. 1) - Generalised mixed-effects model investigating the effect of ALE on efficiency (α) in the condition with high sampling cost and high initial reward reserve, i.e., the condition where ALE patients over-sampled more than controls. Model was specified as follows.: \(\alpha \sim 1+\mathrm {group}^{*}\mathrm {ISI}+(1|\mathrm {trial})+(1+\mathrm {ISI}|\mathrm {participant}).\) + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[386, 199, 606, 757]]<|/det|> + +
Exp. 2
(Intercept)\(\beta =-0.423\)
\(SE=0.323\) \(t_{3748}=-1.31\) \(p=0.19\)
ALE\(\beta =-0.402\)
\(SE=0.457\) \(t_{3748}=-0.88\) \(p=0.38\)
ALE:EE\(\beta =+0.336\)
\(SE=0.442\) \(t_{3748}=+0.76\) \(p=0.45\)
ALE:R\(\beta =-0.983\)
\(SE=0.275\) \(t_{3748}=-3.58\) \(p=0.00035\)
ALE:R:EE\(\beta =+0.162\)
\(SE=0.17\) \(t_{3748}=+0.95\) \(p=0.34\)
EE\(\beta =-2.73\)
\(SE=0.313\) \(t_{3748}=-8.72\) \(p<0.0001\)
R\(\beta =+1.41\)
\(SE=0.198\) \(t_{3748}=+7.16\) \(p<0.0001\)
R:EE\(\beta =+0.0659\)
\(SE=0.125\) \(t_{3748}=+0.53\) \(p=0.60\)
adj-R20.93
\(N_{obs}\)3756
AIC2938.44
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 770, 870, 860]]<|/det|> +Table S7: Reward against uncertainty (Exp. 2) - Generalised mixed-effects model examin-ing effect of reward and uncertainty on choices as well as differences between ALE group and controls. Model was specified as follows: choice $\sim 1+\mathrm {grou}\mathrm {p}^{*}R+\mathrm {grou}\mathrm {p}^{*}EE+R^{*}EE$ +group: $R:EE+(1+R^{*}EE$ |participant). Control group was set as the reference group. EE: Expected Error. R: Reward. ALE: Autoimmune Limbic Encephalitis. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[340, 204, 649, 770]]<|/det|> +
Exp. 2 with outlier removed
(Intercept)β = -0.59
SE = 0.315
t3648 = -1.87
p = 0.06
LEβ = -0.226
SE = 0.439
t3648 = -0.52
p = 0.61
LE:EEβ = +0.529
SE = 0.412
t3648 = +1.28
p = 0.20
LE:Rβ = -0.868
SE = 0.259
t3648 = -3.35
p = 0.00081
LE:R:EEβ = +0.18
SE = 0.173
t3648 = +1.04
p = 0.30
EEβ = -2.9
SE = 0.298
t3648 = -9.73
p <0.0001
Rβ = +1.31
SE = 0.189
t3648 = +6.97
p <0.0001
R:EEβ = +0.843
SE = 0.128
t3648 = +0.66
p = 0.51
adj - R²
Nobs
AIC		0.91
3656
2879.44
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 780, 870, 853]]<|/det|> +Table S8: Generalised mixed-effects model of the effect of the group (ALE vs. Controls) on choices with one outlier removed from the control group. Model was specified as follows: choice ~ 1 + group*R + group*EE + R*EE + group:R:EE + (1 + R*EE |participant). EE : Expected Error. R : Reward. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[355, 188, 636, 750]]<|/det|> + +
(Exp. 3)
(Intercept)\(\beta =+1.96\)
\(SE=0.442\) \(t_{4739}=+4.43\) \(p<0.0001\)
ALE\(\beta =-0.137\)
\(SE=0.622\) \(t_{4739}=-0.22\) \(p=0.83\)
ALE:Effort\(\beta =+0.306\)
\(SE=0.467\) \(t_{4739}=+0.66\) \(p=0.51\)
ALE:Reward\(\beta =-0.48\)
\(SE=0.37\) \(t_{4739}=-1.30\) \(p=0.19\)
ALE:Reward:Effort\(\beta =-0.22\)
\(SE=0.408\) \(t_{4739}=-0.54\) \(p=0.59\)
Effort\(\beta =-2.82\)
\(SE=0.334\) \(t_{4739}=-8.45\) \(p<0.0001\)
Reward\(\beta =+2.97\)
\(SE=0.267\) \(t_{4739}=+11.12\) \(p<0.0001\)
Reward:Effort\(\beta =-0.281\)
\(SE=0.292\) \(t_{4739}=-0.96\) \(p=0.34\)
adj-R20.97
\(N_{obs}\)4747
AIC2775.57
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 760, 870, 870]]<|/det|> +Table S9: Effort-based decision making (Exp. 3)- Generalised mixed-effects models exam-ining effect of reward and effort on choices as well as differences between ALE group and controls. Models were specified as follows. Effort-based choices: choice $\sim 1+group*Reward$ +group*Effort + Reward*Effort + group:Reward:Effort + (1 + Reward*Effort |participant). Controls group was set as the reference group. ALE: ALE: Autoimmune Limbic Encephalitis group. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[202, 399, 789, 575]]<|/det|> + +
ParameterRhatn. effmeansd2.5%50%97.5%
Intercept1.029061.60.50.61.62.5
Reward1.067862.80.32.22.83.5
Effort1.05174-2.70.4-3.5-2.7-2.0
Reward:Effort1.07592-0.20.3-0.8-0.20.4
ALE1.030970.00.6-1.3-0.01.3
Reward:ALE1.06647-0.30.4-1.1-0.30.5
Effort:ALE1.049450.20.5-0.80.21.1
Reward:Effort:ALE1.07563-0.20.4-1.0-0.20.5
+ +<|ref|>text<|/ref|><|det|>[[123, 585, 870, 660]]<|/det|> +Table S10: Baysian mixed- effects modelling of effort- based choices data (Exp. 3) – Posterior summary statistics. Model was specified as follows: choice \(\sim 1 + \mathrm{group}^{*}R + \mathrm{group}^{*}EE + R^{*}EE + \mathrm{group}:R:EE + (1 + R^{*}EE | \mathrm{participant})\) . To improve convergence and guard against over- fitting, mildly informative conservative priors were specified. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[360, 206, 631, 768]]<|/det|> + +
Exps. 2 &amp; 3
(Intercept)\(\beta =+0.0983\)
\(SE=0.141\)
\(t_{8495}=+0.70\)
\(p=0.49\)
Group\(\beta =-0.235\)
\(SE=0.202\)
\(t_{8495}=-1.17\)
\(p=0.24\)
Group:Task\(\beta =+0.0471\)
\(SE=0.102\)
\(t_{8495}=+0.46\)
\(p=0.65\)
Group:Task:Reward\(\beta =+0.467\)
\(SE=0.113\)
\(t_{8495}=+4.15\)
\(p<0.0001\)
Group:Reward\(\beta =-0.569\)
\(SE=0.159\)
\(t_{8495}=-3.58\)
\(p=0.00035\)
Task\(\beta =+0.534\)
\(SE=0.0737\)
\(t_{8495}=+7.25\)
\(p<0.0001\)
Task:Reward\(\beta =+0.544\)
\(SE=0.0815\)
\(t_{8495}=+6.68\)
\(p<0.0001\)
Reward\(\beta =+0.665\)
\(SE=0.112\)
\(t_{8495}=+5.94\)
\(p<0.0001\)
adj-R20.39
\(N_{obs}\)8503
AIC9824.54
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 777, 869, 850]]<|/det|> +Table S11: Generalised mixed-effects model examining the effect of Group (ALE) and Task on reward sensitivity in Exps. 2 & 3. Models were specified as follows. Exps. 2 3: choice $\sim 1+Group*Task+Group*Reward+Task*Reward+Group:Task:Reward+(1+$Reward $|Participant)+(1+Reward|Task).$ + +<|ref|>text<|/ref|><|det|>[[123, 808, 869, 849]]<|/det|> +3: choice \(\sim 1+Group*Task+Group*Reward+Task*Reward+Group:Task:Reward+(1+\) Reward |Participant) + (1 + Reward |Task). + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[350, 338, 641, 655]]<|/det|> + +
Exp.s. 2 &amp; 3 (ALE only)
(Intercept)\(\beta =-0.137\) \(SE=0.144\) \(t_{4224}=-0.95\) \(p=0.34\)
Task\(\beta =+0.582\) \(SE=0.0709\) \(t_{4224}=+8.20\) \(p<0.0001\)
Task:Reward\(\beta =+1.01\) \(SE=0.0778\) \(t_{4224}=+13.03\) \(p<0.0001\)
Reward\(\beta =+0.0975\) \(SE=0.12\) \(t_{4224}=+0.81\) \(p=0.42\)
\(adj-R^{2}\)0.37
\(N_{obs}\)4228
AIC4973.27
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 666, 868, 720]]<|/det|> +Table S12: Generalised mixed-effects model examining the effect of task on reward sensi-tivity in ALE patients. Models were specified as follows: choice $\sim 1+Task\ast Reward+(1+$ Reward |Participant) $+(1+Reward|Task).$ + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[373, 145, 620, 771]]<|/det|> +
Exp. 4
(Intercept)β = +1.93
SE = 0.579
t4184 = +3.34
p = 0.00084
LEβ = -1.03
SE = 0.927
t4184 = -1.12
p = 0.26
ALE:Uncertaintyβ = +1.09
SE = 0.875
t4184 = +1.25
p = 0.21
ALE:Uncertainty:Effortβ = -0.257
SE = 0.239
t4184 = -1.08
p = 0.28
ALE:Uncertainty:Rewardβ = +0.568
SE = 0.272
t4184 = +2.09
p = 0.036
ALE:Uncertainty:Reward:Effortβ = -0.123
SE = 0.24
t4184 = -0.51
p = 0.61
ALE:Effortβ = +1.24
SE = 0.415
t4184 = +2.99
p = 0.0028
ALE:Rewardβ = -1.41
SE = 0.499
t4184 = -2.83
p = 0.0046
ALE:Reward:Effortβ = -0.146
SE = 0.209
t4184 = -0.70
p = 0.48
Uncertaintyβ = -1.9
SE = 0.545
t4184 = -3.49
p = 0.0005
Uncertainty:Effortβ = +0.251
SE = 0.164
t4184 = +1.53
p = 0.13
Uncertainty:Rewardβ = -0.671
SE = 0.186
t4184 = -3.61
p = 0.0003
Uncertainty:Reward:Effortβ = +0.0537
SE = 0.164
t4184 = +0.33
p = 0.74
Effortβ = -1.45
SE = 0.269
t4184 = -5.38
p < 0.0001
Rewardβ = +1.73
SE = 0.327
t4184 = +5.31
p < 0.0001
Reward:Effortβ = +0.207
SE = 0.141
t4184 = +1.47
p = 0.14
adj - R²0.99
Nobs4200
AC3352.10
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 782, 870, 909]]<|/det|> +Table S13: Generalised mixed-effects model of the effect of the group (ALE vs. controls) on effort-based decisions under uncertainty (Exp. 4). Models were specified as follows: choice ~ 1 + group*Uncertainty + group*Reward + Uncertainty*Reward + group*Effort + Uncertainty*Effort + Reward*Effort + group:Uncertainty:Reward + group:Uncertainty:Effort + group:Reward:Effort + Uncertainty:Reward:Effort + group:Uncertainty:Reward:Effort + (1 + Uncertainty*Reward + Uncertainty*Effort + Reward*Effort + Uncertainty:Reward:Effort |participant). + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[330, 336, 660, 655]]<|/det|> + +
Exp.s. 1 &amp; 2Exp. 4
(Intercept)\(\beta =-0.00398\)\(\beta =-0.0106\)
\(SE=0.0903\)\(SE=0.174\)
\(t_{3752}=-0.04\)\(t_{2306}=-0.06\)
\(p=0.96\)\(p=0.95\)
EE\(\beta =+0.647\)\(\beta =+0.476\)
\(SE=0.0502\)\(SE=0.0873\)
\(t_{3752}=+12.88\)\(t_{2306}=+5.45\)
\(p<0.0001\)\(p<0.0001\)
ALE\(\beta =-0.009\)\(\beta =+0.028\)
\(SE=0.127\)\(SE=0.281\)
\(t_{3752}=-0.07\)\(t_{2306}=+0.10\)
\(p=0.94\)\(p=0.92\)
ALE:EE\(\beta =-0.0546\)\(\beta =-0.0226\)
\(SE=0.0711\)\(SE=0.141\)
\(t_{3752}=-0.77\)\(t_{2306}=-0.16\)
\(p=0.44\)\(p=0.87\)
\(adj-R^{2}\)0.580.71
\(N_{obs}\)37562310
AIC7645.653940.63
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 667, 868, 721]]<|/det|> +Table S14: Generalised mixed-effects model of the effect of the group (ALE vs. controls)on flexibility of uncertainty estimation. Models were specified as follows: Uncertainty Score~1+group*EE+(1+EE|participant)+(1|trial). + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[312, 328, 680, 647]]<|/det|> + +
Subjective Uncertainty non-z-scored
(Intercept)\(\beta =-0.407\) \(SE=0.022\) \(t_{3752}=-18.54\) \(p<0.0001\)
EE\(\beta =+0.157\) \(SE=0.0122\) \(t_{3752}=+12.88\) \(p<0.0001\)
Group\(\beta =-0.00219\) \(SE=0.031\) \(t_{3752}=-0.07\) \(p=0.94\)
Group:EE\(\beta =-0.0133\) \(SE=0.0173\) \(t_{3752}=-0.77\) \(p=0.44\)
adj-R20.58
\(N_{obs}\)3756
AIC-2972.29
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 659, 867, 730]]<|/det|> +Table S15: Generalised mixed-effects model examining the effect Group (ALE vs. Con-trols) on non-z-scored values of subjective uncertainty. Models were specified as follows.Subjective Uncertainty non-z-scored: Uncertainty (non-z-scored) $\sim 1+group*EE+(1+EE$ participant) $+(1|trial_{a}ll).$ + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[268, 205, 723, 770]]<|/det|> + +
Choices with Confidence (Exp. 2)
(Intercept)\(\beta =+0.361\)
\(SE=0.367\)
\(t_{3748}=+0.98\)
\(p=0.33\)
Group\(\beta =-0.157\)
\(SE=0.518\)
\(t_{3748}=-0.30\)
\(p=0.76\)
Group:Reward\(\beta =-1.13\)
\(SE=0.279\)
\(t_{3748}=-4.06\)
\(p<0.0001\)
Group:Reward:Confidence\(\beta =-0.305\)
\(SE=0.22\)
\(t_{3748}=-1.38\)
\(p=0.17\)
Group:Confidence\(\beta =-0.573\)
\(SE=0.574\)
\(t_{3748}=-1.00\)
\(p=0.32\)
Reward\(\beta =+1.49\)
\(SE=0.2\)
\(t_{3748}=+7.47\)
\(p<0.0001\)
Reward:Confidence\(\beta =+0.228\)
\(SE=0.165\)
\(t_{3748}=+1.38\)
\(p=0.17\)
Confidence\(\beta =+3.94\)
\(SE=0.41\)
\(t_{3748}=+9.62\)
\(p<0.0001\)
adj-R²0.98
\(N_{obs}\)3756
AIC2436.28
+ +<|ref|>table_caption<|/ref|><|det|>[[124, 778, 870, 850]]<|/det|> +Table S16: Generalised mixed-effects model examining the effect of Group (ALE), Reward and Subjective Confidence on choices (Exp. 2). Models were specified as follows. Choices with Confidence: choice $\sim 1+Group\ast Reward+Group\ast Confidence+Reward\ast Confidence+$ Group:Reward:Confidence $+(1+Reward\ast Confidence$ Participant). + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[308, 160, 684, 530]]<|/det|> + +
Choice Model Controlled for Metacognitive Differences
(Intercept)\(\beta =-0.457\)
\(SE=0.330\)
\(t_{3747}=-1.34\)
\(p=0.18\)
Group\(\beta =-0.351\)
\(SE=0.481\)
\(t_{3747}=-0.73\)
\(p=0.47\)
Group:EE\(\beta =+0.31\)
\(SE=0.430\)
\(t_{3747}=+0.70\)
\(p=0.48\)
Group:Reward\(\beta =-0.976\)
\(SE=0.273\)
\(t_{3747}=-3.57\)
\(p=0.00036\)
Group:Reward:EE\(\beta =+0.165\)
\(SE=0.172\)
\(t_{3747}=+0.96\)
\(p=0.34\)
EE\(\beta =-2.72\)
\(SE=0.311\)
\(t_{3747}=-8.73\)
\(p<0.0001\)
Metacognitive differences\(\beta =+0.276\)
\(SE=0.153\)
\(t_{3747}=+1.81\)
\(p=0.07\)
Reward\(\beta =+1.4\)
\(SE=0.196\)
\(t_{3747}=+7.15\)
\(p<0.0001\)
Reward:EE\(\beta =+0.0658\)
\(SE=0.125\)
\(t_{3747}=+0.53\)
\(p=0.60\)
\(a_{dj}-R^{2}\) \(N_{obs}\) AIC0.93
3756
2938.62
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 544, 870, 653]]<|/det|> +Table S17: Generalised mixed-effects model examining choice behaviour in Exp. 2 while controlling for differences in uncertainty estimation. Models were specified as follows. Choice Model Controlled for Metacognitive Differences: Choice $\sim 1+Metacognitive$ differences + Group*Reward + Group*EE + Reward*EE + Group:Reward:EE + (1 + Reward*EE |participant). Metacognitive differences represent the slope of the correlation between subjec-tive and objective uncertainty estimates. + +<|ref|>table<|/ref|><|det|>[[137, 716, 857, 812]]<|/det|> + +
GroupExp. 1 (cds)Exp. 2 (cds)Exp. 3 (apples)Exp. 4 (cds)
ControlsALEControlsALEControlsALEControlsALE
Mean Total Score3941.873466.06595.11555.953.5651.11932.64693.68
SD211.14483.42106.33141.729.029.26155.97387.51
p-value\(<0.001**\)0.550.080.42
Reward in £*£1 per 150 cds£1 per 10 apples£1 per 150 cds
+ +<|ref|>table_caption<|/ref|><|det|>[[123, 825, 870, 894]]<|/det|> +Table S18: Scores in Exps. 1-4. * While participants were told that this is the reward structure of the tasks, most of them were paid a maximum of £5 per experiment. Similar to Exp. 2,participants were paid for 1/10 of the trials in Exp. 1. ** Please refer to Table S3 for more details about the effect of different conditions on scores in Exp. 1. + +<--- Page Split ---> diff --git a/preprint/preprint__06076107a582a7db1a993272bc8d0902287719a4fcc5b06c29c18d0dc685d84a/preprint__06076107a582a7db1a993272bc8d0902287719a4fcc5b06c29c18d0dc685d84a.mmd b/preprint/preprint__06076107a582a7db1a993272bc8d0902287719a4fcc5b06c29c18d0dc685d84a/preprint__06076107a582a7db1a993272bc8d0902287719a4fcc5b06c29c18d0dc685d84a.mmd new file mode 100644 index 0000000000000000000000000000000000000000..5e4c21b1060200770c32062e6fd7a444a94a9cd7 --- /dev/null +++ b/preprint/preprint__06076107a582a7db1a993272bc8d0902287719a4fcc5b06c29c18d0dc685d84a/preprint__06076107a582a7db1a993272bc8d0902287719a4fcc5b06c29c18d0dc685d84a.mmd @@ -0,0 +1,233 @@ + +# Pan-cancer copy number variant analysis identifies optimized size thresholds and co-occurrence models for individualized risk-stratification + +David Raleigh + +david.raleigh@ucsf.edu + +University of California San Francisco https://orcid.org/0000- 0001- 9299- 8864 + +Minh Nguyen University of California San Francisco + +William Chen UCSF https://orcid.org/0000- 0001- 8924- 5853 + +Naomi Zakimi Univeristy of California San Francisco + +Kanish Mirchia Univeristy of California San Francisco https://orcid.org/0000- 0002- 7371- 7059 + +Calixto- Hope Lucas + +Johns Hopkins University https://orcid.org/0000- 0002- 8347- 9592 + +## Brief Communication + +Keywords: + +Posted Date: January 11th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 3443805/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on July 2nd, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 61063- y. + +<--- Page Split ---> + +## Abstract + +Chromosome instability leading to accumulation of copy number gains or losses is a hallmark of cancer. Copy number variant (CNV) signatures are increasingly used for clinical risk- stratification, but size thresholds for defining CNVs are variable and the biological or clinical implications of CNV size heterogeneity or co- occurrence patterns are incompletely understood. Here we analyze CNV and clinical data from 565 meningiomas and 9,885 tumors from The Cancer Genome Atlas (TCGA) to develop tumor- and chromosome- specific CNV size- dependent and co- occurrence models for clinical outcomes. Our results reveal prognostic CNVs with optimized size thresholds and co- occurrence patterns that refine risk- stratification across a diversity of human cancers. + +## Main + +Chromosome instability contributes to the genomic complexity of cancer1 and is implicated in tumorigenesis, progression, metastasis, and resistance to therapy2- 4. As a marker of chromosome instability, CNV signatures are increasingly used for clinical risk- stratification of diverse cancer types5,6, and pan- cancer databases such as TCGA7 have been used to derive prognostic models based on CNVs6,8. There is no consensus on the optimal size threshold for defining or reporting CNVs, and CNV co- occurrence patterns that may improve risk- stratification models are incompletely understood. + +To test the hypothesis that size- dependent CNV models and co- occurrence patterns may improve clinical risk- stratification, CNV size- dependence was investigated in meningiomas, a tumor that is not represented in TCGA datasets but is associated with recurrent CNVs that can be used for risk- stratification9,10. Loss of chromosomes 1p, 6q, and others distinguish biologically aggressive meningiomas9,10, but published models have applied inconsistent size thresholds ranging from 5–80% of individual chromosome arms to define meningioma CNVs9–12. Using a previously described cohort of 565 meningiomas with long- term clinical outcomes data11, we used DNA methylation arrays to define CNVs ranging from individual CpG loci to entire chromosome arms (Extended Data Fig. 1). Next, we used CNVs ranging from 5–95% of each chromosome arm to generate univariate Cox proportional hazards models for postoperative local freedom from recurrence (LFFR) or overall survival (OS). These analyses revealed “size- dependent” CNVs (Fig. 1a), defined as having a maximum area under the curve (AUC) for 5- year LFFR or OS of at least 0.60 that decreased by at least 5% from the maximum AUC as CNV threshold varied (Supplementary Table 1). + +The implications of CNV size- dependence for meningioma risk- stratification were investigated using 2 robust models that rely on CNVs to predict postoperative meningioma LFFR. The first, integrated grade, is based on copy number losses of chromosomes 1p, 3p, 4p/q, 6p/q, 10p/q, 14q, 18p/q, and 19p/q at a uniform threshold of 50% of each chromosome arm plus CDKN2A loss and mitotic count from histology9. The second, integrated score, is based on copy number losses of chromosomes 1p, 6q, and 14q at a uniform threshold of 5% of each chromosome arm plus DNA methylation family13 and World + +<--- Page Split ---> + +Health Organization (WHO) histological grade \(^{10}\) . We tested each model on our cohort of 565 meningiomas using CNV thresholds ranging from 5–95% (Fig. 1b). Integrated grade reached a maximum AUC for 5- year LFFR of 0.78 at a uniform CNV threshold of 20%, and a maximum AUC for OS of 0.77 at a uniform threshold of 30%. Integrated score reached a maximum AUC for LFFR or OS of 0.76 at a uniform CNV threshold of 5%. The performance of each model degraded with varying CNV size thresholds (Fig. 1b), suggesting that CNV size heterogeneity influences risk- stratification for the most common primary intracranial tumor \(^{14}\) . + +To determine if models based on chromosome- specific CNV size thresholds could improve meningioma risk- stratification, LASSO and elastic net regularized Cox models were trained using optimized CNVs thresholds across the 565 meningiomas in our cohort (Extended Data Fig. 2). Cross- validated AUCs for 5- year LFFR or OS were 0.76 for LASSO models and 0.77–0.78 for elastic net models. CNV size- dependent models identified prognostic chromosome arms that were not included in either integrated grade or integrated score, such as gain of 1q or 17q and loss of 4p, 9p, 10q, or 12q for LFFR, and gain of 1q, 9q, or 10p and loss of 3q, 5p/q, 6p, 9p, 10q, 11p, 13q, 14q, or 18p/q for OS (Fig. 1c), many of which have been previously associated with biologically aggressive meningiomas \(^{11}\) . There were numerous areas of focal deletion across chromosome arms with size- dependent CNVs that correlated with decreased expression of genes mapping to these loci from RNA sequencing of 502 meningiomas (Fig. 1d, e and Supplementary Table 2). Ontology analysis of genes mapping to focal CNVs revealed dysregulation of metabolic and hormone signaling pathways (Fig. 1f), both of which have been implicated in meningiomas through mechanisms that are poorly understood \(^{15–18}\) . + +Prognostic CNVs from integrated grade, integrated score, and size- dependent LASSO or elastic net models (Fig. 1c) tended to co- occur in individual meningiomas (Fig. 2a). Regularized Cox regression models using co- occurrent CNV pairs identified 1p/22q and 9p/14q co- deletion as important predictors of postoperative LFFR or OS, respectively (Extended Data Fig. 3a). These findings remained significant when accounting for the total number of CNVs per meningioma ("CNV burden") on multivariate modeling (Supplementary Table 3), and meningiomas with 1p/22q or 9p/14q co- deletion, as defined using optimized CNV size- thresholds, had significantly worse clinical outcomes than meningiomas with these CNVs in isolation of one another (Fig. 2b). + +Chromosome 22q loss is a common early alteration in meningiomas \(^{19}\) , but the prognostic significance of this CNV is limited as subsequent genomic alterations lead to divergent meningioma phenotypes, such as immune infiltration or cell cycle misactivation \(^{11}\) . Thus, we hypothesized that CNV accumulation in meningiomas may occur sequentially, with some CNVs like loss of chromosome 22q occurring early during tumorigenesis and other CNVs developing later in tumor progression. In support of this hypothesis, hierarchical clustering of meningiomas, binned by CNV burden using optimized size- thresholds, revealed 3 clusters (Fig. 2c, Extended Data Fig. 3b, c). "Early" cluster CNVs, such as loss of 22q, 1p, and 14q, were prevalent regardless of total CNV burden. "Late" cluster CNVs, such as loss of 9p or gain of 1q, were prevalent in samples with higher CNV burden. The third cluster contained uncommon + +<--- Page Split ---> + +CNVs that did not correlate with total CNV burden. Meningioma CNV burden was associated with worse clinical outcomes, suggesting that progressive destabilization and development of late CNVs is associated with worse prognosis (Extended Data Fig. 3d, e). + +To test the broader implications of CNV size thresholds and co- occurrence patterns on cancer risk- stratification, SNP array- derived CNV profiles and clinical outcome data were obtained for 9,885 tumors in TCGA7. Nine cancer types, comprising approximately half of TCGA samples analyzed, were identified with CNV size- dependence, which was again defined using prognostic CNV- based models with a maximum AUC for 5- year local PFS or OS of at least 0.60 that decreased by at least 5% from the maximum AUC as CNV threshold varied (Fig. 3a, Supplementary Table 4). There were areas of focal deletion or amplification on size- dependent CNVs across these 9 cancer types, such as gain of 1q and loss of 17q or 21q that were not identified in size- independent cancers (Supplementary Table 5). Ontology analysis of genes mapping to focal CNVs across these 9 cancer types revealed dysregulation of metabolic, developmental, differentiation, biosynthetic, cytoskeletal, and enzymatic pathways (Extended Data Fig. 4). + +As in meningioma, size- dependent CNVs for 2 cancer types, glioblastoma (GBM) and cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), were used as inputs for co- occurrence models (Fig. 3b). In GBM, concurrent 16q loss and 7p gain was associated with worse OS than these CNVs in isolation (Fig. 3c, d). In CESC, concurrent 13q gain and 19p loss, as well as 19p/21q co- deletion, were both significant predictors of OS (Fig. 3c, d). These CNV co- occurrences remained significant predictors for GBM or CESC outcomes in multivariate models that accounted for total CNV burden (Supplementary Table 6). These findings support the clinical relevance of CNV size- dependence and co- occurrence in developing risk- stratification models for human cancer. + +In sum, our results demonstrate that CNVs exhibit size- dependence with respect to their prognostic value across multiple cancer types. We find cancer risk- stratification systems using CNVs with chromosome- specific size thresholds and co- occurrence patterns may refine risk- stratification across a diversity of human cancers. + +## Methods + +## Inclusion and ethics + +This study complied with all relevant ethical regulations and was approved by the UCSF Institutional Review Board (13- 12587, 17- 22324, 17- 23196 and 18- 24633). As part of routine clinical practice at UCSF, all patients included in this study signed a waiver of informed consent to contribute deidentified data to research. + +## Meningioma samples and clinical data + +<--- Page Split ---> + +Meningioma samples were collected from two sites, UCSF and Hong Kong University. Samples from the UCSF cohort \((n = 200)\) were selected from the UCSF Brain Tumor Center Biorepository and Pathology Core in 2017, and comprised all available WHO grade 2 and 3 meningioma frozen samples, WHO grade 1 frozen samples with clinical follow- up of greater than 10 years \((n = 40)\) or those with the longest available clinical follow- up less than 10 years \((n = 47)\) . The electronic medical record was reviewed for all patients in late 2018, and paper charts were reviewed in early 2019 for patients treated before the advent of the electronic medical record. The Hong Kong University cohort \((n = 365)\) comprised consecutive meningiomas from patients treated at Hong Kong University from 2000 to 2019 with frozen tissue that was sufficient for DNA methylation profiling. The medical record was reviewed for all patients in late 2019. For both cohorts, meningioma recurrence was defined as new radiographic tumor on magnetic resonance imaging after gross total resection, or progression of residual meningioma on magnetic resonance imaging after subtotal resection. + +## Meningioma DNA methylation profiling and analysis + +DNA methylation profiling was performed as previously described \(^{11}\) using the Illumina Methylation EPIC 850k Beadchip (WG- 317- 1003, Illumina) according to manufacturer instructions. Pre- processing and \(\beta\) - value calculations were performed using the SeSAME (v1.12.9) pipeline (BioConductor 3.13) with default settings. All DNA methylation profiling was performed at the Molecular Genomics Core at the University of Southern California. Assignment of meningiomas to DNA methylation groups or DNA methylation subgroups was performed using support vector models (https://william- cchen.shinyapps.io/MeninMethylClassApp/) \(^{11,20}\) . + +## TCGA CNV and clinical outcomes data + +TCGA data was collected from the TCGA PanCanAtlas (https://gdc.cancer.gov/about- data/publications/pancanatlas) \(^{21}\) . Copy number information was obtained using the Copy Number dataset (broad.mit.edu_PANCAN_Genome_Wide_SNP_6_whitelisted.seg). Only primary tumor samples were included by filtering TCGA Biospecimen Core Resource (BCR) barcodes for sample numbers containing the "01" designator. Clinical information was obtained from the TCGA- Clinical Data Resource (CDR) Outcome dataset (TCGA- CDR- SupplementalTableS1.xlsx) and was matched to CNV data by BCR barcode. + +## CNV analysis + +CNV profiles were generated from DNA methylation data using the SeSaMe package as previously described \(^{11}\) . The "cnSegmentation" command with default settings and the 'EPIC.5. normal' dataset as a copy- number normal control were used. + +For both meningioma methylation data and TCGA SNP array data, chromosome segments with mean intensity values less than - 0.1 were defined as lost. Mean intensity values greater than 0.15 were defined as gained. CNV profiling excluded sex chromosomes and p arms of acrocentric chromosomes (13p, 14p, 15p, 21p and 22p). CNV threshold analysis for each CNV profile was performed by measuring + +<--- Page Split ---> + +the mean intensity value at intervals of 30000 bases along each chromosome arm and summing nonconsecutive gains and losses. The total number of CNV profiles which met each threshold of gain or loss from \(5 - 95\%\) by \(5\%\) increments of the chromosome arm were counted. 5- year AUC for meningioma LFFR and OS, and TCGA PFS and OS, were calculated for each threshold using the survivalROC package (v1.0.3.1) in R, and the optimal threshold for each CNV was chosen based on the highest AUC for each clinical endpoint. Size- dependent CNVs were defined as those with a maximum 5- year AUC of at least 0.6 with another threshold of less than \(95\%\) of that maximum AUC. + +CNV network plots were constructed using the igraph package (v1.5.1) in R. Plots were constructed using the CNVs selected from regression models, as well as from those identified in the previously published integrated grade \(^{9}\) and integrated score \(^{10}\) models for meningioma. CNVs were called using their optimal thresholds. In the case of TCGA cancer data, network plots were constructed using size- dependent CNVs and the most important predictors identified in LASSO and Elastic Net Cox regression co- occurrence models. Co- occurrence analysis was limited to pairs of CNVs as sample size was insufficient to analyze the high number of predictors involved when using 3 or more CNVs. + +Cluster analysis was performed using CNVs defined with the optimal size- threshold for predicting LFFR. Clustering was done using the factoextra (v1.0.7) and cluster (v2.1.4) packages in R and visualized with the ComplexHeatmap package (v2.15.4). + +## Survival analysis and modelling + +CNV profiles using the optimal threshold for each CNV were used to train regression models on all available meningioma samples, and for all TCGA samples for size- dependent cancer types (BRCA, CESC, GBM, HNSCC, LGG, LUAD, OV, PRAD, and UCEC). LASSO and Elastic net regularized Cox regression models were trained with the concordance index (c- index) for each target endpoint, using the glmnet and cv.glmnet functions from the glmnet package (v4.1- 8) in R. Elastic net model selection was performed by selecting an optimal alpha value from a range of 0.05 to 0.95 (0.6 for meningioma LFFR, 0.2 for meningioma OS, 0.85 for TCGA PFS, 0.9 for TCGA OS). Model training was performed using 10- fold cross validation. CNV predictors for each model were identified within 1 standard error of the model achieving maximal c- index to reduce over- fitting. A risk metric was calculated for each sample, defined as the product of the regression coefficients and the normalized counts. Model performance was measured with 5- year cross- validation AUC for each model's respective clinical endpoint using the same training dataset with no hold out validation cohort. + +Integrated grade \(^{9}\) was assigned to meningioma samples using CNV calls for each threshold, mitoses per 10 high- power fields, and CDKN2A/B loss. Integrated score \(^{10}\) was assigned using CNV calls for each threshold, WHO grade, and methylation family \(^{13}\) , the latter which had been previously assigned independently by the authors who developed of this system. + +Multivariate Cox proportional hazards analysis was performed using the survival package (v3.5- 7) in R. Focal genomic and ontology analysis + +<--- Page Split ---> + +CNV pileup plots demonstrating the proportion of tumors with gains or losses at each position along the chromosome arm were constructed using the ggplot2 (v3.4.3) package in R. Focal regions of loss were selected by selecting loci along the chromosome arm with a higher proportion of samples demonstrating deletion compared to the surrounding regions. Genes present in regions of interest were identified by cross- referencing positions along the chromosome with the Ensembl (release 109)22 database using the biomaRt (v2.54.1) package in R. + +Meningioma gene expression analysis was performed using RNA- Seq data as previously described16. Briefly, RNA sequencing was performed on all 200 of the UCSF samples and 302 of the HKU samples meeting quality metrics. For UCSF samples, library preparation was performed using either the TruSeq RNA Library Prep Kit v2 (RS- 122- 2001, Illumina), sequencing was done on an Illumina HiSeq 4000 to a mean of 42 million reads per sample at the UCSF IHG Genomics Core, Quality control of FASTQ files was performed with FASTQC (v0.11.9), and 50 bp single- end reads were mapped to the human reference genome GRCh38 using HISAT2 (v2.1.0) with default parameters. For HKU samples, library preparation was performed using the TruSeq Standard mRNA Kit (20020595, Illumina) and 150 bp paired- end reads were sequenced on an Illumina NovaSeq 6000 to a mean of 100 million reads per sample at MedGenome Inc. Analysis was performed using a pipeline comprised of FastQC for quality control, and Kallisto for reading pseudo alignment and transcript abundance quantification using the default settings (v0.46.2). + +Gene ontology and interaction analysis were performed using Cytoscape. In brief, Gene Set Enrichment Analysis (GSEA, v4.3.2) was performed and gene rank scores were calculated using the formula \(\mathrm{sign}(\log_2\mathrm{fold - change})\times -\log 10(\mathrm{p - value})\) . Pathways were defined using the gene set file Human_GOBP_AllPathways_no_GO_iea_December_01_2022_symbol.gmt, which is maintained by the Bader laboratory. Gene set size was limited to range between 15 and 500, and positive and negative enrichment files were generated using 2000 permutations. The EnrichmentMap App (v3.3.4) in Cytoscape (v3.7.2) was used to visualize the results of pathway analysis. Nodes with FDR q value \(< 0.05\) and p- value \(< 0.05\) , and nodes sharing gene overlaps with Jaccard + Overlap Combined (JOC) threshold of 0.375 were connected by blue lines (edges) to generate network maps. Clusters of related pathways were identified and annotated using the AutoAnnotate app (v1.3.5) in Cytoscape that uses a Markov Cluster algorithm to connect pathways by shared keywords in the description of each pathway. The resulting groups of pathways were designated as the consensus pathways in a circle. + +## Statistics + +All experiments were performed with independent biological replicates and repeated, and statistics were derived from biological replicates. Biological replicates are indicated in each figure panel or figure legend. No statistical methods were used to predetermine sample sizes, but sample sizes in this study are similar or larger to those reported in previous publications. Data distribution was assumed to be normal, but this was not formally tested. Investigators were blinded to conditions during clinical data collection and analysis. Bioinformatic analyses were performed blind to clinical features, outcomes or + +<--- Page Split ---> + +molecular characteristics. The clinical samples used in this study were retrospective and nonrandomized with no intervention, and all samples were interrogated equally. Thus, controlling for covariates among clinical samples is not relevant. No data points were excluded from the analyses. Statistical analyses were conducted in R (v4.2.2). + +## Declarations + +## Reporting summary + +Further information on research design is available in the Nature Research Reporting Summary linked to this article. + +## Data availability + +DNA methylation (n=565) and RNA sequencing (n=502) of the meningiomas analyzed in this manuscript have been deposited in the NCBI Gene Expression Omnibus under the accessions GSE183656 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE183656), GSE101638 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE101638), and GSE212666 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE212666). The publicly available GRCh38 (hg38, https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.39/), and Kallisto index v10 (https://github.com/pachterlab/kallisto-transcriptome-indices/releases) datasets were used in this study. TCGA data was collected from the publicly available TCGA PanCanAtlas (https://gdc.cancer.gov/about-data/publications/pancanatlas). Copy number information was obtained using the Copy Number dataset (broad.mit.edu_PANCAN_Genome_Wide_SNP_6_whitelisted.seg). Clinical information was obtained from the TCGA- Clinical Data Resource (CDR) Outcome dataset (TCGA- CDR-SupplementalTableS1.xlsx). Source data are provided with this paper. + +## Code availability + +No custom software, tools, or packages were used. The open- source software, tools, and packages used for data analysis in this study are referenced in the methods where applicable and include R (v4.2.2), FASTQC (v0.11.9), HISAT2 (v2.1.0), Kallisto (v0.46.2), SeSAME (v1.12.9) (BioConductor 3.13), survival R package (v3.5- 7), survivalROC R package (v1.0.3.1), biomaRt R package (v2.54.1), glmnet R package (v4.1- 8), igraph R package (v1.5.1), factoextra R package (v1.0.7), cluster R package (v2.1.4), ComplexHeatmap R package (v2.15.4), GSEA (v4.3.2), and EnrichmentMap App (v3.3.4) and AutoAnnotate app (v1.3.5) in Cytoscape (v3.7.2). + +## Acknowledgements + +This study was supported by funding from American Brain Tumor Association Jack & Fay Netchin Medical Student Summer Fellowship in memory of Rose Digang to M.P.N., K12 CA260225 and the Chan Zuckerburg Biohub Physician Scientist Fellowship to W.C.C., and R01 CA262311, P50 CA097257, the UCSF Wolfe Meningioma Program Project and the Trenchard Family Charitable Fund, to D.R.R. The + +<--- Page Split ---> + +results shown here are in part based upon data generated by the TCGA Research Network (https://www.cancer.gov/tcga). + +## Author contributions statement + +All authors made substantial contributions to the conception or design of the study; the acquisition, analysis, or interpretation of data; or drafting or revising the manuscript. All authors approved the manuscript. All authors agree to be personally accountable for individual contributions and to ensure that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved and the resolution documented in the literature. M.P.N. and W.C.C. conceived and designed the study and analyzed bioinformatic data with supervision from D.R.R. N.Z. performed gene ontology analyses with supervision from D.R.R. K.M. and C-H.G.L. provided guidance and feedback on study design, analysis, and presentation. + +## Competing interests statement + +The authors declare no competing interests. + +## References + +1. Hanahan, D. Hallmarks of Cancer: New Dimensions. Cancer Discov 12, 31–46 (2022). +2. Nguyen, B. et al. Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients. Cell 185, 563-575.e11 (2022). +3. Lukow, D. A. et al. Chromosomal instability accelerates the evolution of resistance to anti-cancer therapies. Dev Cell 56, 2427-2439.e4 (2021). +4. Bakhoum, S. F. et al. Numerical chromosomal instability mediates susceptibility to radiation treatment. Nat Commun 6, 5990 (2015). +5. Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010). +6. Steele, C. D. et al. Signatures of copy number alterations in human cancer. Nature 606, 984–991 (2022). +7. Weinstein, J. N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). +8. van Dijk, E. et al. Chromosomal copy number heterogeneity predicts survival rates across cancers. Nat Commun 12, 3188 (2021). +9. Driver, J. et al. A molecularly integrated grade for meningioma. Neuro Oncol 24, 796–808 (2022). +10. Maas, S. L. N. et al. Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated. J Clin Oncol 39, 3839–3852 (2021). + +<--- Page Split ---> + +11. Choudhury, A. et al. Meningioma DNA methylation groups identify biological drivers and therapeutic vulnerabilities. Nat Genet 54, 649–659 (2022). + +12. Youngblood, M. W. et al. Associations of meningioma molecular subgroup and tumor recurrence. Neuro Oncol 23, 783–794 (2021). + +13. Sahm, F. et al. DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis. Lancet Oncol 18, 682–694 (2017). + +14. Ostrom, Q. T. et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2015-2019. Neuro Oncol 24, v1–v95 (2022). + +15. Nassiri, F. et al. A clinically applicable integrative molecular classification of meningiomas. Nature 597, 119–125 (2021). + +16. Choudhury, A. et al. Hypermitotic meningiomas harbor DNA methylation subgroups with distinct biological and clinical features. Neuro-Oncology 25, 520–530 (2023). + +17. Miyagishima, D. F., Moliterno, J., Claus, E. & Günel, M. Hormone therapies in meningioma-where are we? J Neurooncol 161, 297–308 (2023). + +18. Walsh, K. M. et al. Pleiotropic MLLT10 variation confers risk of meningioma and estrogen-mediated cancers. Neurooncol Adv 4, vdac044 (2022). + +19. Magill, S. T. et al. Multiplatform genomic profiling and magnetic resonance imaging identify mechanisms underlying intratumor heterogeneity in meningioma. Nat Commun 11, 4803 (2020). + +20. Chang, C.-W. et al. Identification of Human Housekeeping Genes and Tissue-Selective Genes by Microarray Meta-Analysis. PLoS One 6, e22859 (2011). + +21. Smith, J. C. & Sheltzer, J. M. Genome-wide identification and analysis of prognostic features in human cancers. Cell Reports 38, (2022). + +22. Cunningham, F. et al. Ensembl 2022. Nucleic Acids Research 50, D988–D995 (2022). + +## Figures + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1
+ +Meningioma risk- stratification models demonstrate CNV size- dependence. a, Heatmaps showing area under the curve for univariate Cox models of LFFR or OS based on individual copy number gains (left, red) or individual copy number losses (right, blue). Models were trained using sequential size thresholds requiring \(\geq 5\%\) to \(\geq 95\%\) of chromosome arms to be gained or lost to define CNVs. Boxes show peak AUCs for "size- dependent" CNVs, defined as having a maximum area under the curve (AUC) for 5- year LFFR or OS of at least 0.60 that decreased by at least \(5\%\) from the maximum AUC as CNV threshold was varied. n=565 meningiomas. b, Previously published meningioma risk- stratification models incorporating CNVs (integrated grade based on histology and a \(\geq 50\%\) CNV threshold, or integrated score based on histology, + +<--- Page Split ---> + +DNA methylation profiling, and a \(\geq 5\%\) CNV threshold) show decreasing AUC with varying size-thresholds. n=565 meningiomas. c, CNVs from previously published meningioma risk-stratification models or from newly-derived size-dependent LASSO or elastic net models for meningioma LFFR or OS. n=565 meningiomas. d, CNV profile plots demonstrating focal copy number losses in size-dependent CNVs from LASSO or elastic net models. Chromosomes 14 and 20 are shown as examples of broad/non-focal CNVs. n=565 meningiomas. e, Average RNA sequencing expression of genes mapping to regions of focal copy number loss on size-dependent CNVs from LASSO or elastic net models versus genes mapping to other regions on the same chromosomes. n=502 meningiomas. Error bars show standard error of the mean. Student's t test, p≤0.0001. f, Network of gene circuits distinguishing genes mapping to regions of focal copy number loss. Nodes represent pathways and edges represent shared genes between pathways (p≤0.05, FDR≤0.05). n=502 meningiomas. + +![](images/Figure_3.jpg) + + +<--- Page Split ---> + +Size- dependent CNV co- occurrence is prognostic for meningioma outcomes. a, Network diagrams demonstrating co- occurrence of prognostic size- dependent CNVs from Fig. 1c. b, Kaplan- Meier curves comparing meningioma LFFR or OS according to individual CNVs versus co- occurrent CNV pairs identified as the most important predictors of postoperative outcomes in LASSO Cox models from Extended Data Fig. 3a using optimized thresholds for defining CNVs from Fig. 1a. Log- rank tests. n=565 meningiomas. c, Heatmap showing unsupervised hierarchical clustering of individual CNVs according to the total number of CNVs per meningioma. CNVs were defined using optimal size thresholds for LFFR or OS models from Fig. 1a. + +![PLACEHOLDER_12_0] + +
Figure 3
+ +Pan- cancer analyses reveal size- dependent CNV co- occurrence risk- stratification models for half of human cancers. a, TCGA SNP- array and clinical outcomes data used in pan- cancer analyses. Cancers with size- dependent prognostic CNVs were defined as having a CNV with a univariate Cox AUC for either PFS or OS of at least 0.60 that dropped by at least \(5\%\) from the maximum AUC when varying the size threshold for defining CNVs. b, Network diagrams demonstrating co- occurrence of prognostic size- dependent CNVs for GBM or CESC from Supplementary Table 4. c, LASSO Cox model coefficients using + +<--- Page Split ---> + +size- dependent CNV co- occurrence to predict postoperative OS in GBM or CESC. d, Kaplan- Meier curves comparing OS for GBM or CESC with individual CNVs versus co- occurrent CNV pairs identified as the most important predictors of postoperative outcomes in LASSO Cox models. Log- rank tests. \(n = 571\) GBM and 294 CESC. + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- NguyenChenNatGenetEDFigv7.docx- NguyenChenNatGenetSupplementaryTablesv7.xlsx + +<--- Page Split ---> diff --git a/preprint/preprint__06076107a582a7db1a993272bc8d0902287719a4fcc5b06c29c18d0dc685d84a/preprint__06076107a582a7db1a993272bc8d0902287719a4fcc5b06c29c18d0dc685d84a_det.mmd b/preprint/preprint__06076107a582a7db1a993272bc8d0902287719a4fcc5b06c29c18d0dc685d84a/preprint__06076107a582a7db1a993272bc8d0902287719a4fcc5b06c29c18d0dc685d84a_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..7cf9b9bdb4330a2d11473fe89e5182d3c33ad27b --- /dev/null +++ b/preprint/preprint__06076107a582a7db1a993272bc8d0902287719a4fcc5b06c29c18d0dc685d84a/preprint__06076107a582a7db1a993272bc8d0902287719a4fcc5b06c29c18d0dc685d84a_det.mmd @@ -0,0 +1,312 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 930, 208]]<|/det|> +# Pan-cancer copy number variant analysis identifies optimized size thresholds and co-occurrence models for individualized risk-stratification + +<|ref|>text<|/ref|><|det|>[[44, 230, 156, 248]]<|/det|> +David Raleigh + +<|ref|>text<|/ref|><|det|>[[54, 257, 300, 274]]<|/det|> +david.raleigh@ucsf.edu + +<|ref|>text<|/ref|><|det|>[[50, 303, 741, 323]]<|/det|> +University of California San Francisco https://orcid.org/0000- 0001- 9299- 8864 + +<|ref|>text<|/ref|><|det|>[[44, 328, 383, 368]]<|/det|> +Minh Nguyen University of California San Francisco + +<|ref|>text<|/ref|><|det|>[[44, 374, 465, 415]]<|/det|> +William Chen UCSF https://orcid.org/0000- 0001- 8924- 5853 + +<|ref|>text<|/ref|><|det|>[[44, 420, 383, 460]]<|/det|> +Naomi Zakimi Univeristy of California San Francisco + +<|ref|>text<|/ref|><|det|>[[44, 466, 741, 507]]<|/det|> +Kanish Mirchia Univeristy of California San Francisco https://orcid.org/0000- 0002- 7371- 7059 + +<|ref|>text<|/ref|><|det|>[[44, 512, 202, 530]]<|/det|> +Calixto- Hope Lucas + +<|ref|>text<|/ref|><|det|>[[52, 534, 639, 553]]<|/det|> +Johns Hopkins University https://orcid.org/0000- 0002- 8347- 9592 + +<|ref|>sub_title<|/ref|><|det|>[[44, 595, 230, 614]]<|/det|> +## Brief Communication + +<|ref|>text<|/ref|><|det|>[[44, 634, 136, 652]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 671, 330, 691]]<|/det|> +Posted Date: January 11th, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 710, 475, 728]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3443805/v1 + +<|ref|>text<|/ref|><|det|>[[42, 746, 914, 789]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 807, 535, 826]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 862, 904, 906]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on July 2nd, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 61063- y. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 158, 68]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[41, 82, 954, 264]]<|/det|> +Chromosome instability leading to accumulation of copy number gains or losses is a hallmark of cancer. Copy number variant (CNV) signatures are increasingly used for clinical risk- stratification, but size thresholds for defining CNVs are variable and the biological or clinical implications of CNV size heterogeneity or co- occurrence patterns are incompletely understood. Here we analyze CNV and clinical data from 565 meningiomas and 9,885 tumors from The Cancer Genome Atlas (TCGA) to develop tumor- and chromosome- specific CNV size- dependent and co- occurrence models for clinical outcomes. Our results reveal prognostic CNVs with optimized size thresholds and co- occurrence patterns that refine risk- stratification across a diversity of human cancers. + +<|ref|>sub_title<|/ref|><|det|>[[43, 287, 111, 312]]<|/det|> +## Main + +<|ref|>text<|/ref|><|det|>[[41, 327, 940, 472]]<|/det|> +Chromosome instability contributes to the genomic complexity of cancer1 and is implicated in tumorigenesis, progression, metastasis, and resistance to therapy2- 4. As a marker of chromosome instability, CNV signatures are increasingly used for clinical risk- stratification of diverse cancer types5,6, and pan- cancer databases such as TCGA7 have been used to derive prognostic models based on CNVs6,8. There is no consensus on the optimal size threshold for defining or reporting CNVs, and CNV co- occurrence patterns that may improve risk- stratification models are incompletely understood. + +<|ref|>text<|/ref|><|det|>[[39, 488, 951, 792]]<|/det|> +To test the hypothesis that size- dependent CNV models and co- occurrence patterns may improve clinical risk- stratification, CNV size- dependence was investigated in meningiomas, a tumor that is not represented in TCGA datasets but is associated with recurrent CNVs that can be used for risk- stratification9,10. Loss of chromosomes 1p, 6q, and others distinguish biologically aggressive meningiomas9,10, but published models have applied inconsistent size thresholds ranging from 5–80% of individual chromosome arms to define meningioma CNVs9–12. Using a previously described cohort of 565 meningiomas with long- term clinical outcomes data11, we used DNA methylation arrays to define CNVs ranging from individual CpG loci to entire chromosome arms (Extended Data Fig. 1). Next, we used CNVs ranging from 5–95% of each chromosome arm to generate univariate Cox proportional hazards models for postoperative local freedom from recurrence (LFFR) or overall survival (OS). These analyses revealed “size- dependent” CNVs (Fig. 1a), defined as having a maximum area under the curve (AUC) for 5- year LFFR or OS of at least 0.60 that decreased by at least 5% from the maximum AUC as CNV threshold varied (Supplementary Table 1). + +<|ref|>text<|/ref|><|det|>[[41, 807, 940, 947]]<|/det|> +The implications of CNV size- dependence for meningioma risk- stratification were investigated using 2 robust models that rely on CNVs to predict postoperative meningioma LFFR. The first, integrated grade, is based on copy number losses of chromosomes 1p, 3p, 4p/q, 6p/q, 10p/q, 14q, 18p/q, and 19p/q at a uniform threshold of 50% of each chromosome arm plus CDKN2A loss and mitotic count from histology9. The second, integrated score, is based on copy number losses of chromosomes 1p, 6q, and 14q at a uniform threshold of 5% of each chromosome arm plus DNA methylation family13 and World + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 45, 953, 205]]<|/det|> +Health Organization (WHO) histological grade \(^{10}\) . We tested each model on our cohort of 565 meningiomas using CNV thresholds ranging from 5–95% (Fig. 1b). Integrated grade reached a maximum AUC for 5- year LFFR of 0.78 at a uniform CNV threshold of 20%, and a maximum AUC for OS of 0.77 at a uniform threshold of 30%. Integrated score reached a maximum AUC for LFFR or OS of 0.76 at a uniform CNV threshold of 5%. The performance of each model degraded with varying CNV size thresholds (Fig. 1b), suggesting that CNV size heterogeneity influences risk- stratification for the most common primary intracranial tumor \(^{14}\) . + +<|ref|>text<|/ref|><|det|>[[40, 223, 950, 522]]<|/det|> +To determine if models based on chromosome- specific CNV size thresholds could improve meningioma risk- stratification, LASSO and elastic net regularized Cox models were trained using optimized CNVs thresholds across the 565 meningiomas in our cohort (Extended Data Fig. 2). Cross- validated AUCs for 5- year LFFR or OS were 0.76 for LASSO models and 0.77–0.78 for elastic net models. CNV size- dependent models identified prognostic chromosome arms that were not included in either integrated grade or integrated score, such as gain of 1q or 17q and loss of 4p, 9p, 10q, or 12q for LFFR, and gain of 1q, 9q, or 10p and loss of 3q, 5p/q, 6p, 9p, 10q, 11p, 13q, 14q, or 18p/q for OS (Fig. 1c), many of which have been previously associated with biologically aggressive meningiomas \(^{11}\) . There were numerous areas of focal deletion across chromosome arms with size- dependent CNVs that correlated with decreased expression of genes mapping to these loci from RNA sequencing of 502 meningiomas (Fig. 1d, e and Supplementary Table 2). Ontology analysis of genes mapping to focal CNVs revealed dysregulation of metabolic and hormone signaling pathways (Fig. 1f), both of which have been implicated in meningiomas through mechanisms that are poorly understood \(^{15–18}\) . + +<|ref|>text<|/ref|><|det|>[[41, 538, 953, 717]]<|/det|> +Prognostic CNVs from integrated grade, integrated score, and size- dependent LASSO or elastic net models (Fig. 1c) tended to co- occur in individual meningiomas (Fig. 2a). Regularized Cox regression models using co- occurrent CNV pairs identified 1p/22q and 9p/14q co- deletion as important predictors of postoperative LFFR or OS, respectively (Extended Data Fig. 3a). These findings remained significant when accounting for the total number of CNVs per meningioma ("CNV burden") on multivariate modeling (Supplementary Table 3), and meningiomas with 1p/22q or 9p/14q co- deletion, as defined using optimized CNV size- thresholds, had significantly worse clinical outcomes than meningiomas with these CNVs in isolation of one another (Fig. 2b). + +<|ref|>text<|/ref|><|det|>[[41, 735, 950, 943]]<|/det|> +Chromosome 22q loss is a common early alteration in meningiomas \(^{19}\) , but the prognostic significance of this CNV is limited as subsequent genomic alterations lead to divergent meningioma phenotypes, such as immune infiltration or cell cycle misactivation \(^{11}\) . Thus, we hypothesized that CNV accumulation in meningiomas may occur sequentially, with some CNVs like loss of chromosome 22q occurring early during tumorigenesis and other CNVs developing later in tumor progression. In support of this hypothesis, hierarchical clustering of meningiomas, binned by CNV burden using optimized size- thresholds, revealed 3 clusters (Fig. 2c, Extended Data Fig. 3b, c). "Early" cluster CNVs, such as loss of 22q, 1p, and 14q, were prevalent regardless of total CNV burden. "Late" cluster CNVs, such as loss of 9p or gain of 1q, were prevalent in samples with higher CNV burden. The third cluster contained uncommon + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 945, 111]]<|/det|> +CNVs that did not correlate with total CNV burden. Meningioma CNV burden was associated with worse clinical outcomes, suggesting that progressive destabilization and development of late CNVs is associated with worse prognosis (Extended Data Fig. 3d, e). + +<|ref|>text<|/ref|><|det|>[[40, 128, 940, 378]]<|/det|> +To test the broader implications of CNV size thresholds and co- occurrence patterns on cancer risk- stratification, SNP array- derived CNV profiles and clinical outcome data were obtained for 9,885 tumors in TCGA7. Nine cancer types, comprising approximately half of TCGA samples analyzed, were identified with CNV size- dependence, which was again defined using prognostic CNV- based models with a maximum AUC for 5- year local PFS or OS of at least 0.60 that decreased by at least 5% from the maximum AUC as CNV threshold varied (Fig. 3a, Supplementary Table 4). There were areas of focal deletion or amplification on size- dependent CNVs across these 9 cancer types, such as gain of 1q and loss of 17q or 21q that were not identified in size- independent cancers (Supplementary Table 5). Ontology analysis of genes mapping to focal CNVs across these 9 cancer types revealed dysregulation of metabolic, developmental, differentiation, biosynthetic, cytoskeletal, and enzymatic pathways (Extended Data Fig. 4). + +<|ref|>text<|/ref|><|det|>[[40, 394, 953, 575]]<|/det|> +As in meningioma, size- dependent CNVs for 2 cancer types, glioblastoma (GBM) and cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), were used as inputs for co- occurrence models (Fig. 3b). In GBM, concurrent 16q loss and 7p gain was associated with worse OS than these CNVs in isolation (Fig. 3c, d). In CESC, concurrent 13q gain and 19p loss, as well as 19p/21q co- deletion, were both significant predictors of OS (Fig. 3c, d). These CNV co- occurrences remained significant predictors for GBM or CESC outcomes in multivariate models that accounted for total CNV burden (Supplementary Table 6). These findings support the clinical relevance of CNV size- dependence and co- occurrence in developing risk- stratification models for human cancer. + +<|ref|>text<|/ref|><|det|>[[42, 592, 945, 680]]<|/det|> +In sum, our results demonstrate that CNVs exhibit size- dependence with respect to their prognostic value across multiple cancer types. We find cancer risk- stratification systems using CNVs with chromosome- specific size thresholds and co- occurrence patterns may refine risk- stratification across a diversity of human cancers. + +<|ref|>sub_title<|/ref|><|det|>[[44, 703, 163, 728]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[44, 742, 351, 772]]<|/det|> +## Inclusion and ethics + +<|ref|>text<|/ref|><|det|>[[42, 789, 951, 876]]<|/det|> +This study complied with all relevant ethical regulations and was approved by the UCSF Institutional Review Board (13- 12587, 17- 22324, 17- 23196 and 18- 24633). As part of routine clinical practice at UCSF, all patients included in this study signed a waiver of informed consent to contribute deidentified data to research. + +<|ref|>sub_title<|/ref|><|det|>[[44, 879, 632, 910]]<|/det|> +## Meningioma samples and clinical data + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 44, 955, 315]]<|/det|> +Meningioma samples were collected from two sites, UCSF and Hong Kong University. Samples from the UCSF cohort \((n = 200)\) were selected from the UCSF Brain Tumor Center Biorepository and Pathology Core in 2017, and comprised all available WHO grade 2 and 3 meningioma frozen samples, WHO grade 1 frozen samples with clinical follow- up of greater than 10 years \((n = 40)\) or those with the longest available clinical follow- up less than 10 years \((n = 47)\) . The electronic medical record was reviewed for all patients in late 2018, and paper charts were reviewed in early 2019 for patients treated before the advent of the electronic medical record. The Hong Kong University cohort \((n = 365)\) comprised consecutive meningiomas from patients treated at Hong Kong University from 2000 to 2019 with frozen tissue that was sufficient for DNA methylation profiling. The medical record was reviewed for all patients in late 2019. For both cohorts, meningioma recurrence was defined as new radiographic tumor on magnetic resonance imaging after gross total resection, or progression of residual meningioma on magnetic resonance imaging after subtotal resection. + +<|ref|>sub_title<|/ref|><|det|>[[45, 316, 835, 348]]<|/det|> +## Meningioma DNA methylation profiling and analysis + +<|ref|>text<|/ref|><|det|>[[41, 362, 951, 520]]<|/det|> +DNA methylation profiling was performed as previously described \(^{11}\) using the Illumina Methylation EPIC 850k Beadchip (WG- 317- 1003, Illumina) according to manufacturer instructions. Pre- processing and \(\beta\) - value calculations were performed using the SeSAME (v1.12.9) pipeline (BioConductor 3.13) with default settings. All DNA methylation profiling was performed at the Molecular Genomics Core at the University of Southern California. Assignment of meningiomas to DNA methylation groups or DNA methylation subgroups was performed using support vector models (https://william- cchen.shinyapps.io/MeninMethylClassApp/) \(^{11,20}\) . + +<|ref|>sub_title<|/ref|><|det|>[[45, 522, 630, 553]]<|/det|> +## TCGA CNV and clinical outcomes data + +<|ref|>text<|/ref|><|det|>[[42, 570, 942, 727]]<|/det|> +TCGA data was collected from the TCGA PanCanAtlas (https://gdc.cancer.gov/about- data/publications/pancanatlas) \(^{21}\) . Copy number information was obtained using the Copy Number dataset (broad.mit.edu_PANCAN_Genome_Wide_SNP_6_whitelisted.seg). Only primary tumor samples were included by filtering TCGA Biospecimen Core Resource (BCR) barcodes for sample numbers containing the "01" designator. Clinical information was obtained from the TCGA- Clinical Data Resource (CDR) Outcome dataset (TCGA- CDR- SupplementalTableS1.xlsx) and was matched to CNV data by BCR barcode. + +<|ref|>sub_title<|/ref|><|det|>[[45, 730, 248, 760]]<|/det|> +## CNV analysis + +<|ref|>text<|/ref|><|det|>[[42, 776, 945, 844]]<|/det|> +CNV profiles were generated from DNA methylation data using the SeSaMe package as previously described \(^{11}\) . The "cnSegmentation" command with default settings and the 'EPIC.5. normal' dataset as a copy- number normal control were used. + +<|ref|>text<|/ref|><|det|>[[42, 861, 944, 952]]<|/det|> +For both meningioma methylation data and TCGA SNP array data, chromosome segments with mean intensity values less than - 0.1 were defined as lost. Mean intensity values greater than 0.15 were defined as gained. CNV profiling excluded sex chromosomes and p arms of acrocentric chromosomes (13p, 14p, 15p, 21p and 22p). CNV threshold analysis for each CNV profile was performed by measuring + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 44, 947, 201]]<|/det|> +the mean intensity value at intervals of 30000 bases along each chromosome arm and summing nonconsecutive gains and losses. The total number of CNV profiles which met each threshold of gain or loss from \(5 - 95\%\) by \(5\%\) increments of the chromosome arm were counted. 5- year AUC for meningioma LFFR and OS, and TCGA PFS and OS, were calculated for each threshold using the survivalROC package (v1.0.3.1) in R, and the optimal threshold for each CNV was chosen based on the highest AUC for each clinical endpoint. Size- dependent CNVs were defined as those with a maximum 5- year AUC of at least 0.6 with another threshold of less than \(95\%\) of that maximum AUC. + +<|ref|>text<|/ref|><|det|>[[41, 218, 940, 378]]<|/det|> +CNV network plots were constructed using the igraph package (v1.5.1) in R. Plots were constructed using the CNVs selected from regression models, as well as from those identified in the previously published integrated grade \(^{9}\) and integrated score \(^{10}\) models for meningioma. CNVs were called using their optimal thresholds. In the case of TCGA cancer data, network plots were constructed using size- dependent CNVs and the most important predictors identified in LASSO and Elastic Net Cox regression co- occurrence models. Co- occurrence analysis was limited to pairs of CNVs as sample size was insufficient to analyze the high number of predictors involved when using 3 or more CNVs. + +<|ref|>text<|/ref|><|det|>[[42, 394, 944, 461]]<|/det|> +Cluster analysis was performed using CNVs defined with the optimal size- threshold for predicting LFFR. Clustering was done using the factoextra (v1.0.7) and cluster (v2.1.4) packages in R and visualized with the ComplexHeatmap package (v2.15.4). + +<|ref|>sub_title<|/ref|><|det|>[[42, 461, 525, 492]]<|/det|> +## Survival analysis and modelling + +<|ref|>text<|/ref|><|det|>[[41, 507, 951, 779]]<|/det|> +CNV profiles using the optimal threshold for each CNV were used to train regression models on all available meningioma samples, and for all TCGA samples for size- dependent cancer types (BRCA, CESC, GBM, HNSCC, LGG, LUAD, OV, PRAD, and UCEC). LASSO and Elastic net regularized Cox regression models were trained with the concordance index (c- index) for each target endpoint, using the glmnet and cv.glmnet functions from the glmnet package (v4.1- 8) in R. Elastic net model selection was performed by selecting an optimal alpha value from a range of 0.05 to 0.95 (0.6 for meningioma LFFR, 0.2 for meningioma OS, 0.85 for TCGA PFS, 0.9 for TCGA OS). Model training was performed using 10- fold cross validation. CNV predictors for each model were identified within 1 standard error of the model achieving maximal c- index to reduce over- fitting. A risk metric was calculated for each sample, defined as the product of the regression coefficients and the normalized counts. Model performance was measured with 5- year cross- validation AUC for each model's respective clinical endpoint using the same training dataset with no hold out validation cohort. + +<|ref|>text<|/ref|><|det|>[[42, 797, 952, 892]]<|/det|> +Integrated grade \(^{9}\) was assigned to meningioma samples using CNV calls for each threshold, mitoses per 10 high- power fields, and CDKN2A/B loss. Integrated score \(^{10}\) was assigned using CNV calls for each threshold, WHO grade, and methylation family \(^{13}\) , the latter which had been previously assigned independently by the authors who developed of this system. + +<|ref|>text<|/ref|><|det|>[[42, 908, 936, 960]]<|/det|> +Multivariate Cox proportional hazards analysis was performed using the survival package (v3.5- 7) in R. Focal genomic and ontology analysis + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 44, 946, 181]]<|/det|> +CNV pileup plots demonstrating the proportion of tumors with gains or losses at each position along the chromosome arm were constructed using the ggplot2 (v3.4.3) package in R. Focal regions of loss were selected by selecting loci along the chromosome arm with a higher proportion of samples demonstrating deletion compared to the surrounding regions. Genes present in regions of interest were identified by cross- referencing positions along the chromosome with the Ensembl (release 109)22 database using the biomaRt (v2.54.1) package in R. + +<|ref|>text<|/ref|><|det|>[[40, 199, 950, 472]]<|/det|> +Meningioma gene expression analysis was performed using RNA- Seq data as previously described16. Briefly, RNA sequencing was performed on all 200 of the UCSF samples and 302 of the HKU samples meeting quality metrics. For UCSF samples, library preparation was performed using either the TruSeq RNA Library Prep Kit v2 (RS- 122- 2001, Illumina), sequencing was done on an Illumina HiSeq 4000 to a mean of 42 million reads per sample at the UCSF IHG Genomics Core, Quality control of FASTQ files was performed with FASTQC (v0.11.9), and 50 bp single- end reads were mapped to the human reference genome GRCh38 using HISAT2 (v2.1.0) with default parameters. For HKU samples, library preparation was performed using the TruSeq Standard mRNA Kit (20020595, Illumina) and 150 bp paired- end reads were sequenced on an Illumina NovaSeq 6000 to a mean of 100 million reads per sample at MedGenome Inc. Analysis was performed using a pipeline comprised of FastQC for quality control, and Kallisto for reading pseudo alignment and transcript abundance quantification using the default settings (v0.46.2). + +<|ref|>text<|/ref|><|det|>[[40, 488, 944, 761]]<|/det|> +Gene ontology and interaction analysis were performed using Cytoscape. In brief, Gene Set Enrichment Analysis (GSEA, v4.3.2) was performed and gene rank scores were calculated using the formula \(\mathrm{sign}(\log_2\mathrm{fold - change})\times -\log 10(\mathrm{p - value})\) . Pathways were defined using the gene set file Human_GOBP_AllPathways_no_GO_iea_December_01_2022_symbol.gmt, which is maintained by the Bader laboratory. Gene set size was limited to range between 15 and 500, and positive and negative enrichment files were generated using 2000 permutations. The EnrichmentMap App (v3.3.4) in Cytoscape (v3.7.2) was used to visualize the results of pathway analysis. Nodes with FDR q value \(< 0.05\) and p- value \(< 0.05\) , and nodes sharing gene overlaps with Jaccard + Overlap Combined (JOC) threshold of 0.375 were connected by blue lines (edges) to generate network maps. Clusters of related pathways were identified and annotated using the AutoAnnotate app (v1.3.5) in Cytoscape that uses a Markov Cluster algorithm to connect pathways by shared keywords in the description of each pathway. The resulting groups of pathways were designated as the consensus pathways in a circle. + +<|ref|>sub_title<|/ref|><|det|>[[44, 763, 189, 790]]<|/det|> +## Statistics + +<|ref|>text<|/ref|><|det|>[[41, 808, 949, 943]]<|/det|> +All experiments were performed with independent biological replicates and repeated, and statistics were derived from biological replicates. Biological replicates are indicated in each figure panel or figure legend. No statistical methods were used to predetermine sample sizes, but sample sizes in this study are similar or larger to those reported in previous publications. Data distribution was assumed to be normal, but this was not formally tested. Investigators were blinded to conditions during clinical data collection and analysis. Bioinformatic analyses were performed blind to clinical features, outcomes or + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 955, 134]]<|/det|> +molecular characteristics. The clinical samples used in this study were retrospective and nonrandomized with no intervention, and all samples were interrogated equally. Thus, controlling for covariates among clinical samples is not relevant. No data points were excluded from the analyses. Statistical analyses were conducted in R (v4.2.2). + +<|ref|>sub_title<|/ref|><|det|>[[44, 156, 210, 182]]<|/det|> +## Declarations + +<|ref|>sub_title<|/ref|><|det|>[[44, 198, 218, 218]]<|/det|> +## Reporting summary + +<|ref|>text<|/ref|><|det|>[[42, 235, 949, 278]]<|/det|> +Further information on research design is available in the Nature Research Reporting Summary linked to this article. + +<|ref|>sub_title<|/ref|><|det|>[[44, 296, 183, 316]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[40, 333, 950, 605]]<|/det|> +DNA methylation (n=565) and RNA sequencing (n=502) of the meningiomas analyzed in this manuscript have been deposited in the NCBI Gene Expression Omnibus under the accessions GSE183656 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE183656), GSE101638 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE101638), and GSE212666 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE212666). The publicly available GRCh38 (hg38, https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.39/), and Kallisto index v10 (https://github.com/pachterlab/kallisto-transcriptome-indices/releases) datasets were used in this study. TCGA data was collected from the publicly available TCGA PanCanAtlas (https://gdc.cancer.gov/about-data/publications/pancanatlas). Copy number information was obtained using the Copy Number dataset (broad.mit.edu_PANCAN_Genome_Wide_SNP_6_whitelisted.seg). Clinical information was obtained from the TCGA- Clinical Data Resource (CDR) Outcome dataset (TCGA- CDR-SupplementalTableS1.xlsx). Source data are provided with this paper. + +<|ref|>sub_title<|/ref|><|det|>[[44, 621, 188, 641]]<|/det|> +## Code availability + +<|ref|>text<|/ref|><|det|>[[42, 659, 950, 815]]<|/det|> +No custom software, tools, or packages were used. The open- source software, tools, and packages used for data analysis in this study are referenced in the methods where applicable and include R (v4.2.2), FASTQC (v0.11.9), HISAT2 (v2.1.0), Kallisto (v0.46.2), SeSAME (v1.12.9) (BioConductor 3.13), survival R package (v3.5- 7), survivalROC R package (v1.0.3.1), biomaRt R package (v2.54.1), glmnet R package (v4.1- 8), igraph R package (v1.5.1), factoextra R package (v1.0.7), cluster R package (v2.1.4), ComplexHeatmap R package (v2.15.4), GSEA (v4.3.2), and EnrichmentMap App (v3.3.4) and AutoAnnotate app (v1.3.5) in Cytoscape (v3.7.2). + +<|ref|>sub_title<|/ref|><|det|>[[44, 833, 218, 852]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[42, 871, 944, 958]]<|/det|> +This study was supported by funding from American Brain Tumor Association Jack & Fay Netchin Medical Student Summer Fellowship in memory of Rose Digang to M.P.N., K12 CA260225 and the Chan Zuckerburg Biohub Physician Scientist Fellowship to W.C.C., and R01 CA262311, P50 CA097257, the UCSF Wolfe Meningioma Program Project and the Trenchard Family Charitable Fund, to D.R.R. The + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 824, 90]]<|/det|> +results shown here are in part based upon data generated by the TCGA Research Network (https://www.cancer.gov/tcga). + +<|ref|>sub_title<|/ref|><|det|>[[44, 106, 319, 125]]<|/det|> +## Author contributions statement + +<|ref|>text<|/ref|><|det|>[[41, 144, 950, 323]]<|/det|> +All authors made substantial contributions to the conception or design of the study; the acquisition, analysis, or interpretation of data; or drafting or revising the manuscript. All authors approved the manuscript. All authors agree to be personally accountable for individual contributions and to ensure that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved and the resolution documented in the literature. M.P.N. and W.C.C. conceived and designed the study and analyzed bioinformatic data with supervision from D.R.R. N.Z. performed gene ontology analyses with supervision from D.R.R. K.M. and C-H.G.L. provided guidance and feedback on study design, analysis, and presentation. + +<|ref|>sub_title<|/ref|><|det|>[[44, 340, 317, 360]]<|/det|> +## Competing interests statement + +<|ref|>text<|/ref|><|det|>[[44, 379, 428, 398]]<|/det|> +The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[44, 421, 193, 447]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[52, 461, 940, 930]]<|/det|> +1. Hanahan, D. Hallmarks of Cancer: New Dimensions. Cancer Discov 12, 31–46 (2022). +2. Nguyen, B. et al. Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients. Cell 185, 563-575.e11 (2022). +3. Lukow, D. A. et al. Chromosomal instability accelerates the evolution of resistance to anti-cancer therapies. Dev Cell 56, 2427-2439.e4 (2021). +4. Bakhoum, S. F. et al. Numerical chromosomal instability mediates susceptibility to radiation treatment. Nat Commun 6, 5990 (2015). +5. Beroukhim, R. et al. The landscape of somatic copy-number alteration across human cancers. Nature 463, 899–905 (2010). +6. Steele, C. D. et al. Signatures of copy number alterations in human cancer. Nature 606, 984–991 (2022). +7. Weinstein, J. N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). +8. van Dijk, E. et al. Chromosomal copy number heterogeneity predicts survival rates across cancers. Nat Commun 12, 3188 (2021). +9. Driver, J. et al. A molecularly integrated grade for meningioma. Neuro Oncol 24, 796–808 (2022). +10. Maas, S. L. N. et al. Integrated Molecular-Morphologic Meningioma Classification: A Multicenter Retrospective Analysis, Retrospectively and Prospectively Validated. J Clin Oncol 39, 3839–3852 (2021). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[48, 45, 945, 90]]<|/det|> +11. Choudhury, A. et al. Meningioma DNA methylation groups identify biological drivers and therapeutic vulnerabilities. Nat Genet 54, 649–659 (2022). + +<|ref|>text<|/ref|><|det|>[[48, 95, 930, 139]]<|/det|> +12. Youngblood, M. W. et al. Associations of meningioma molecular subgroup and tumor recurrence. Neuro Oncol 23, 783–794 (2021). + +<|ref|>text<|/ref|><|det|>[[48, 144, 880, 188]]<|/det|> +13. Sahm, F. et al. DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis. Lancet Oncol 18, 682–694 (2017). + +<|ref|>text<|/ref|><|det|>[[48, 193, 914, 238]]<|/det|> +14. Ostrom, Q. T. et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2015-2019. Neuro Oncol 24, v1–v95 (2022). + +<|ref|>text<|/ref|><|det|>[[48, 242, 936, 286]]<|/det|> +15. Nassiri, F. et al. A clinically applicable integrative molecular classification of meningiomas. Nature 597, 119–125 (2021). + +<|ref|>text<|/ref|><|det|>[[48, 291, 920, 336]]<|/det|> +16. Choudhury, A. et al. Hypermitotic meningiomas harbor DNA methylation subgroups with distinct biological and clinical features. Neuro-Oncology 25, 520–530 (2023). + +<|ref|>text<|/ref|><|det|>[[48, 340, 940, 385]]<|/det|> +17. Miyagishima, D. F., Moliterno, J., Claus, E. & Günel, M. Hormone therapies in meningioma-where are we? J Neurooncol 161, 297–308 (2023). + +<|ref|>text<|/ref|><|det|>[[48, 389, 941, 434]]<|/det|> +18. Walsh, K. M. et al. Pleiotropic MLLT10 variation confers risk of meningioma and estrogen-mediated cancers. Neurooncol Adv 4, vdac044 (2022). + +<|ref|>text<|/ref|><|det|>[[48, 439, 920, 484]]<|/det|> +19. Magill, S. T. et al. Multiplatform genomic profiling and magnetic resonance imaging identify mechanisms underlying intratumor heterogeneity in meningioma. Nat Commun 11, 4803 (2020). + +<|ref|>text<|/ref|><|det|>[[48, 488, 912, 533]]<|/det|> +20. Chang, C.-W. et al. Identification of Human Housekeeping Genes and Tissue-Selective Genes by Microarray Meta-Analysis. PLoS One 6, e22859 (2011). + +<|ref|>text<|/ref|><|det|>[[48, 537, 905, 582]]<|/det|> +21. Smith, J. C. & Sheltzer, J. M. Genome-wide identification and analysis of prognostic features in human cancers. Cell Reports 38, (2022). + +<|ref|>text<|/ref|><|det|>[[48, 586, 816, 608]]<|/det|> +22. Cunningham, F. et al. Ensembl 2022. Nucleic Acids Research 50, D988–D995 (2022). + +<|ref|>sub_title<|/ref|><|det|>[[44, 630, 143, 656]]<|/det|> +## Figures + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[40, 37, 950, 714]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 725, 115, 743]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[41, 765, 952, 946]]<|/det|> +Meningioma risk- stratification models demonstrate CNV size- dependence. a, Heatmaps showing area under the curve for univariate Cox models of LFFR or OS based on individual copy number gains (left, red) or individual copy number losses (right, blue). Models were trained using sequential size thresholds requiring \(\geq 5\%\) to \(\geq 95\%\) of chromosome arms to be gained or lost to define CNVs. Boxes show peak AUCs for "size- dependent" CNVs, defined as having a maximum area under the curve (AUC) for 5- year LFFR or OS of at least 0.60 that decreased by at least \(5\%\) from the maximum AUC as CNV threshold was varied. n=565 meningiomas. b, Previously published meningioma risk- stratification models incorporating CNVs (integrated grade based on histology and a \(\geq 50\%\) CNV threshold, or integrated score based on histology, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 45, 950, 291]]<|/det|> +DNA methylation profiling, and a \(\geq 5\%\) CNV threshold) show decreasing AUC with varying size-thresholds. n=565 meningiomas. c, CNVs from previously published meningioma risk-stratification models or from newly-derived size-dependent LASSO or elastic net models for meningioma LFFR or OS. n=565 meningiomas. d, CNV profile plots demonstrating focal copy number losses in size-dependent CNVs from LASSO or elastic net models. Chromosomes 14 and 20 are shown as examples of broad/non-focal CNVs. n=565 meningiomas. e, Average RNA sequencing expression of genes mapping to regions of focal copy number loss on size-dependent CNVs from LASSO or elastic net models versus genes mapping to other regions on the same chromosomes. n=502 meningiomas. Error bars show standard error of the mean. Student's t test, p≤0.0001. f, Network of gene circuits distinguishing genes mapping to regions of focal copy number loss. Nodes represent pathways and edges represent shared genes between pathways (p≤0.05, FDR≤0.05). n=502 meningiomas. + +<|ref|>image<|/ref|><|det|>[[40, 290, 940, 960]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 84, 950, 264]]<|/det|> +Size- dependent CNV co- occurrence is prognostic for meningioma outcomes. a, Network diagrams demonstrating co- occurrence of prognostic size- dependent CNVs from Fig. 1c. b, Kaplan- Meier curves comparing meningioma LFFR or OS according to individual CNVs versus co- occurrent CNV pairs identified as the most important predictors of postoperative outcomes in LASSO Cox models from Extended Data Fig. 3a using optimized thresholds for defining CNVs from Fig. 1a. Log- rank tests. n=565 meningiomas. c, Heatmap showing unsupervised hierarchical clustering of individual CNVs according to the total number of CNVs per meningioma. CNVs were defined using optimal size thresholds for LFFR or OS models from Fig. 1a. + +<|ref|>image<|/ref|><|det|>[[42, 268, 952, 768]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 781, 116, 800]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[40, 825, 949, 960]]<|/det|> +Pan- cancer analyses reveal size- dependent CNV co- occurrence risk- stratification models for half of human cancers. a, TCGA SNP- array and clinical outcomes data used in pan- cancer analyses. Cancers with size- dependent prognostic CNVs were defined as having a CNV with a univariate Cox AUC for either PFS or OS of at least 0.60 that dropped by at least \(5\%\) from the maximum AUC when varying the size threshold for defining CNVs. b, Network diagrams demonstrating co- occurrence of prognostic size- dependent CNVs for GBM or CESC from Supplementary Table 4. c, LASSO Cox model coefficients using + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 945, 134]]<|/det|> +size- dependent CNV co- occurrence to predict postoperative OS in GBM or CESC. d, Kaplan- Meier curves comparing OS for GBM or CESC with individual CNVs versus co- occurrent CNV pairs identified as the most important predictors of postoperative outcomes in LASSO Cox models. Log- rank tests. \(n = 571\) GBM and 294 CESC. + +<|ref|>sub_title<|/ref|><|det|>[[44, 157, 312, 185]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 207, 768, 227]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[59, 245, 528, 292]]<|/det|> +- NguyenChenNatGenetEDFigv7.docx- NguyenChenNatGenetSupplementaryTablesv7.xlsx + +<--- Page Split ---> diff --git a/preprint/preprint__068ba17c74cb5962b82a0a7630cb5df2fe0b1a5cb30c6a3e3e705e8c6aa53848/images_list.json b/preprint/preprint__068ba17c74cb5962b82a0a7630cb5df2fe0b1a5cb30c6a3e3e705e8c6aa53848/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/preprint/preprint__068ba17c74cb5962b82a0a7630cb5df2fe0b1a5cb30c6a3e3e705e8c6aa53848/images_list.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/preprint/preprint__068ba17c74cb5962b82a0a7630cb5df2fe0b1a5cb30c6a3e3e705e8c6aa53848/preprint__068ba17c74cb5962b82a0a7630cb5df2fe0b1a5cb30c6a3e3e705e8c6aa53848.mmd b/preprint/preprint__068ba17c74cb5962b82a0a7630cb5df2fe0b1a5cb30c6a3e3e705e8c6aa53848/preprint__068ba17c74cb5962b82a0a7630cb5df2fe0b1a5cb30c6a3e3e705e8c6aa53848.mmd new file mode 100644 index 0000000000000000000000000000000000000000..3e6fbb1d46ef512f0ad8783bb3b623ae7c2526bd --- /dev/null +++ b/preprint/preprint__068ba17c74cb5962b82a0a7630cb5df2fe0b1a5cb30c6a3e3e705e8c6aa53848/preprint__068ba17c74cb5962b82a0a7630cb5df2fe0b1a5cb30c6a3e3e705e8c6aa53848.mmd @@ -0,0 +1,319 @@ + +# 4000-year-old Mycobacterium lepromatosis genomes from Chile reveal long-establishment of Hansen's Disease in the Americas + +Dario Ramirez Universidad Nacional de Cordoba https://orcid.org/0000- 0001- 8982- 2550 + +T. Sitter Max Planck Institute for Evolutionary Anthropology https://orcid.org/0000- 0002- 7160- 6240 + +Sanni Oversti Max Planck Institute for Geoanthropology + +Maria Herrera-Soto Universidad de Buenos Aires + +Nicolás Pastor Instituto de Antropología de Córdoba, Consejo Nacional de Investigaciones Científicas y Técnicas, Córdoba, Argentina https://orcid.org/0000- 0001- 8971- 7910 + +Oscar Fontana Silva Museo Arqueológico de La Serena + +Casey Kirkpatrick Simon Fraser University https://orcid.org/0000- 0001- 9755- 6459 + +José Castelleti Dellepiane Independent researcher + +Rodrigo Nores Instituto de Antropología de Córdoba https://orcid.org/0000- 0002- 9153- 0626 + +Kirsten Bos + +kirsten_bos@eva.mpg.de + +Max Planck Institute for Evolutionary Anthropology https://orcid.org/0000- 0003- 2937- 3006 + +Article + +Keywords: + +Posted Date: May 28th, 2025 + +DOI: https://doi.org/10.21203/rs.3.rs- 5776739/v1 + +<--- Page Split ---> + +License: © ① This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Ecology & Evolution on June 30th, 2025. See the published version at https://doi.org/10.1038/s41559-025-02771- y. + +<--- Page Split ---> + +# 4000-year-old Mycobacterium lepromatosis genomes from Chile reveal long- establishment of Hansen's Disease in the Americas + +Darío A. Ramirez*1, T. Lesley Sitter*2, Sanni Översti3, María José Herrera-Soto4, Nicolás Pastor5, Oscar Eduardo Fontana Silva6, Casey L. Kirkpatrick2,7,8, José Castelletti Dellepiane9, Rodrigo Nores1, and Kirsten Bos2 + +1 Instituto de Antropología de Córdoba CONICET-UNC, Museo de Antropologías, Departamento de Antropología, Facultad de Filosofía y Humanidades, Universidad Nacional de Córdoba. Av. Hipólito Yrigoyen 174, Córdoba, Argentina. + +2 Max Planck Institute for Evolutionary Anthropology. Deutscher Platz 6, Leipzig, Germany. + +3 Max Planck Institute of Geoanthropology. Kahlaische Strasse 10, Jena, Germany. + +4 Facultad de Filosofía y Letras, Universidad de Buenos Aires, Argentina. + +5 Instituto de Antropología de Córdoba CONICET-UNC, Museo de Antropologías, Facultad de Filosofía y Humanidades y Departamento de Diversidad Biológica y Ecología, Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba. Av. Hipólito Yrigoyen 174, Córdoba, Argentina. + +6 Museo Arqueológico de La Serena. Gregorio Cordovez esquina Cienfuegos, S/N, La Serena, Chile. + +7 Simon Fraser University, Dept. of Archaeology, 8888 University Drive, Burnaby, B.C., Canada + +8 Western University, Dept. of Anthropology, 1151 Richmond St., London, Ontario, Canada. + +9 Independent researcher, Chile. + +*These authors contributed equally to this work + +Address correspondence to: kirsten_bos@eva.mpg.de, or rodrigonores@ffyh.unc.edu.ar + +<--- Page Split ---> + +1 Abstract2 Mycobacterium lepromatosis is a recently identified cause of Hansen's Disease, and3 is associated with the more severe and potentially lethal presentations of diffuse4 lepromatous leprosy (DLL) and Lucio's phenomenon (LP). Detection of this infection5 has been limited to a small number of individuals, leaving much to be learned about6 its global distribution and transmissibility. Its discovery in wild rodent populations in the7 United Kingdom and Ireland also raises questions about its zoonotic potential. Here8 we raise further awareness of this disease via analyses of two exceptionally well9 preserved M. lepromatosis genomes obtained from 4000- year- old human remains of10 two adult males from the archaeological sites of El Cerrito and La Herradura in11 Northern Chile. This formed the basis of genomic comparisons between ancient and12 modern forms of the pathogen. We demonstrate an unexpected long history of M.13 lepromatosis in the Americas, which contrasts with the more recent Eurasian history14 of the closely related Mycobacterium leprae. We offer relevant perspectives on its15 evolution while providing an incentive for further disease monitoring in both humans16 and other potential reservoir species in the Americas and elsewhere.17 + +<--- Page Split ---> + +## Introduction + +Hansen's disease, more commonly known as leprosy, is caused by the unculturable bacteria Mycobacterium leprae and the recently described Mycobacterium lepromatosis1. Transmission occurs via prolonged exposure to respiratory droplets from of an infected person2. Untreated individuals can develop a chronic peripheral neuropathy with associated physical impairment3. Many infected remain asymptomatic, which can obscure diagnoses and control measures4. The availability of curative multidrug treatments has decreased worldwide prevalence5; regardless, the disease persists in more than 100 countries, with up to 174,000 new cases reported globally in 2022 alone6. Risk of infection is closely correlated with conditions of overcrowding, poverty, malnutrition and an immunocompromised state7. + +Written accounts describe the impact of disfiguring diseases presumed to be Hansen's Disease on Eurasian populations throughout the historic period8. As skeletal involvement occurs in advanced stages, past infections have been identified in archaeological tissues as early as 5,000 years ago in Europe, Asia, and Oceania9- 14. For M. leprae, analyses of ancient genomic data provide further support for its infectious potential having spanned several millennia16. While humans are regarded as the principal host of Hansen's Disease, maintenance of the causative bacteria in other animal species have raised concerns over their potential as zoonotic reservoirs from a One Health perspective. Nine- banded armadillos are known sources of M. leprae, where transmission may occur through human consumption16. Red squirrels in Britain and Ireland can harbour both M. leprae and M. lepromatosis17, and recent identification of M. leprae in archaeological rodent bone demonstrates cross- species infectivity in historical periods18. Detection of M. leprae in several species of non- human primates further demonstrates the broad host range of this pathogen19- 21. Viability of M. leprae in ticks and amoebae for several months opens the possibility of environmental reservoirs as well22,23. Unlike many bacterial diseases, presentation of symptoms and the development of its more severe multibacillary or lepromatous forms seem highly dependent on host immunological status2,24. While the few available reports tend to associate M. lepromatosis with severe disease presentation such as Diffuse Lepromatous Leprosy (DLL) and the potentially fatal Lucio's phenomenon (LP), a set of clinically defined criteria that distinguish it from M. leprae infection has yet to be established25. + +Understanding of M. lepromatosis distribution and evolutionary history is limited as few examples of the infection have been molecularly confirmed. PCR- based detections demonstrate its presence in the Americas (Mexico, the Caribbean)1,26, as well as Southeast Asia (Myanmar, Singapore)27, consistent with the global occurrence of DLL25,28. Genome- level analyses are limited in scope: the available modern genomes suggest a deep divergence of M. lepromatosis and M. leprae, though with retention of genomic features that contribute to some similarities in disease presentation29. While investigations that draw upon both modern and ancient genomic data consistently support an origin for M. leprae outside the Americas15, the identification of M. lepromatosis in archaeological contexts has not been reported, though its modern association with Latin American contexts could suggest its endemicity in the continent in the pre- colonial period28,29. + +Paleogenomic investigations of this disease are currently restricted to the recovery of M. leprae genomes, and are dominated by investigations that are limited to a Eurasian + +<--- Page Split ---> + +context. Here we present two high- coverage M. lepromatosis genomes reconstructed from skeletal remains of individuals from distinct archaeological contexts from Chile, both dated to ca. 4,000 years ago. These data indicate a long and previously undocumented history of this infectious disease in the Americas. + +## Archaeological context, morphology, and molecular recovery + +To investigate infectious disease in the American pre- colonial period from a molecular perspective, we sampled 35 teeth and 19 bones with pathological lesions suggestive of active infection belonging to 41 individuals from five archaeological sites representing various time periods and subsistence strategies in the semi- arid region of Chile (Supplementary Information section 1). Both teeth and pathological bone were selected to permit identification of pathogens that contribute to either acute or chronic infection, and when available both tissue types were selected from an individual. Approximately 50mg of each tissue was extracted and converted into a single- stranded DNA library for sequencing on an Illumina HiSeq 4000 to a depth of ca. 5 million reads. Data were computationally screened for a variety of pathogenic bacteria and viruses following a hypothesis- free method using the MALT and HOPS platforms implemented through the nf- core EAGER 2 analysis pipeline \(^{30 - 32}\) . This process revealed several thousand DNA fragments with homology to M. lepromatosis in each of two archaeological tissues, representing the neighbouring sites of La Herradura (a tibia from an individual referred to here as "ECR001") and El Cerrito (a tooth from an individual referred to here as "ECR003") (Figure 1, Tables S1 and S2, Figures S1- S6). Radiocarbon dating of both skeletal elements indicate them to be roughly contemporaneous, from approximately 4,200 - 4,300 years ago (Figure 1). + +Currently there is little information on the osteological manifestations of M. lepromatosis infection, but most reported examples are associated with the DLL and LP forms of Hansen's Disease \(^{1}\) . DLL primarily affects the skin and peripheral nerves but it can also cause ocular damage, rhinitis, destruction of the nasal septum causing saddle or crooked nose (usually without affecting the nasal bones), damage to the larynx, organ damage or failure, and sepsis. Generalized hypoesthesia or anesthesia resulting from neuritis can contribute to secondary injury of the extremities, which may result in bony changes. LP is a rare reaction most commonly associated with DLL that manifests as acute, necrotizing cutaneous vasculitis, generally affecting the legs, arms, torso, and face \(^{33}\) . Although LP does not necessarily affect the bones, the resulting inflammation and possible secondary infections could potentially cause osteological changes. Genetically- confirmed M. lepromatosis infections have also been associated with borderline lepromatous leprosy and lepromatous leprosy \(^{25}\) , the latter being the most common form of Hansen's Disease to cause osteological changes \(^{34}\) . While its modern presentation may differ from the spectrum of pathology observed in the past, both individuals display pathological lesions that are consistent with, though not diagnostic of Hansen's Disease, as well as additional changes that are associated with unrelated afflictions (see SI for complete descriptions of the remains). For example, skeleton ECR001 (male 35- 40 years, Figures S2 - S4) exhibits a slight widening of the nasal aperture compared to other individuals in the population, with rounding of the margins and possible osteolytic processes in the area. This individual also has slight recession of the alveolar bone of the anterior teeth (though this may be in part due to taphonomic breakage or in response to other pathological processes), as well as pitting on the palatine process and on the ribs. The right fibula and tibia are affected by mostly- healed lamellar periostosis and slight thickening and + +<--- Page Split ---> + +bowing of the right tibial diaphysis. The small tubular bones of the hands display pitting, abnormal foramina, and periosteal new bone on the palmar surfaces, but no concentric resorption or evidence of hyperflexion, and there are pronounced osteolytic lesions on the right calcaneus. Skeleton ECR003 (male, 40- 44 years) has fewer preserved skeletal elements but also displays rounding of the inferior margins of the nasal aperture and slight thickening and bowing of the tibial diaphysis (Figures 1, S5). Although the aforementioned osteological changes in both individuals could be associated with Hansen's Disease (though not necessarily with the DLL or LP forms), they could equally be caused by other diseases, both infectious and non- infectious. For this reason we do not attempt a differential diagnosis based on osteological criteria, nor do we propose any new diagnostic criteria from these limited examples. + +To explore the suitability of genomic reconstruction, DNA libraries were enriched via in- solution capture through use of a probe set designed from a modern M. leprae reference panel35, and sequenced to a read depth of 20 million fragments, as above. Distinction between several mycobacterial species was accomplished via a competitive mapping approach, which demonstrated much higher homology and hence high confidence in their assignment to M. lepromatosis (Table S3). Both genomes are of exceptional quality, yielding average genomic coverages of 45- and 74- fold for ECR001 and ECR003, respectively, when mapped against the modern FJ924 M. lepromatosis genomic reference (CP083405) (Table S4), isolated from a patient in Mexico29. The distribution of heterozygous positions is consistent with a single source of M. lepromatosis DNA for each individual, though with a detectable level of chemical damage and possibly sporadic reads of non- target origin in the mapped datasets (Figure S7), as expected of metagenomically- sourced ancient bacterial DNA. The spectrum of DNA damage from both pathogen and host (Figures S6, S8) is consistent with their contemporaneous antiquity as determined from radiocarbon data (Figure 1). An analysis of human DNA also indicates an exclusively American Indigenous host source (Table S5). Negative controls were free of M. lepromatosis DNA (Table S6). + +<--- Page Split ---> + +## M. Iepromatosis pangenome and comparisons against M. leprae + +Despite our use of an M. leprae capture panel, we observed a 278- and 23- fold increase in M. lepromatosis DNA content between the shotgun and enriched datasets, with \(83\%\) and \(88\%\) of the genome covered at four- fold read support for genomes ECR001 and ECR003, respectively (Tables S3 and S4, Figure 2). To investigate possible enrichment biases over individual regions, probes were mapped with high sensitivity against the M. lepromatosis reference and probe coverage was compared to that observed in the two ancient genomes over annotated coding regions (Figures 2, S9, and S10). Both ancient genomes include coverage over regions of the M. lepromatosis reference that were not included in probe design, and hence were not enriched. Coverage across these regions is higher for genome ECR003, which may be due to a higher abundance of M. lepromatosis DNA in the non- enriched fraction (Table S3). Importantly, we identify several regions with limited mapping reads in both ancient genomes where probe coverage is abundant. Further investigation revealed these regions to have asymmetric representation across host- associated modern genomes, which could indicate lineage- specific losses unrelated to host adaptation. This also reveals no pattern of gene loss/acquisition that distinguishes ancient from modern forms (Figure S10). + +This analysis was complemented by evaluation of the two reconstructed ancient genomes alongside 16 modern M. lepromatosis (Table S7) based on a common mapping to the FJ924 reference (Table S8, Figure S11 and S12). No consistent pattern of gene acquisition or loss across the full annotated coding region distinguishes the human- associated strains from those associated with red squirrel populations in the north of the United Kingdom or Ireland (Figure S12). This implies that any long- term changes related to host- specificity are influenced by either nucleotide substitution, disruptions in synteny, or changes outside of the mapped coding regions that are undetected via the methods employed here. This analysis also revealed a surprisingly low coverage for genome FJ924_S_4 reported in Singh et al 202336 as a first example of M. lepromatosis in India (Table S9, Figures S11 and S12). A competitive mapping approach revealed this genome to show far greater homology to M. leprae, thus questioning the accuracy of its assignment to M. lepromatosis (Table S9). + +Given the established observation of genome decay and reduction in M. leprae over evolutionary timescales37, divergence between M. lepromatosis and M. leprae were investigated on a gene level. There are currently four chromosomally resolved modern M. leprae genomes available, representative of branches 1 \((n = 2)\) , 3 \((n = 1)\) , and 4 \((n = 1)\) . A pangenomic analysis carried out in Roary38 indicated a strong level of divergence between the two pathogens, with 2000 (approximately half) of the 4097 protein coding regions identified in Prokka showing a minimum of \(50\%\) sequence homology between the two pathogens (Figure S13). This demonstrates a high sequence divergence despite M. leprae having been identified as the most closely related organism to M. lepromatosis29. This is further demonstrated via a mapping- based approach, which reveals the two to share only \(\sim 25\%\) nucleotide identity (Table S10). An alignment of the genomes using LASTZ39 and MAUVE40 shows several large rearrangements and approximately 0.5 Mbp ( \(\sim 12\%\) of the genome) present in M. lepromatosis FJ924 that is absent in M. leprae MRHRU- 235- G, either through acquisition in the former, decay in the latter, or a nucleotide homology that is too low for alignment. Less similarity is observed with the more distantly related Mycobacterium haemophilum (Figures S13 + +<--- Page Split ---> + +- S15). This would leave only disparate regions of similarity upon which to perform downstream genome-level analyses where M. leprae or another Mycobacterium representative are included. + +## Phylogenetic analysis + +The relationship of M. lepromatosis to other pathogenic mycobacteria was first determined through investigation of the 16S rRNA locus (Figure 3B), which confirmed M. leprae to be its closest relative despite extensive genomic divergence described above. This was complemented by a conservative approach to genome- level phylogenetic reconstruction, where focus was restricted to diversity within M. lepromatosis. These data are limited to the two ancient genomes presented here, four modern human genomes from Mexico, and six modern genomes isolated from red squirrels in Ireland and the United Kingdom. SNPs were called at 4- fold read support, and regions of low complexity, along with additional regions identified as potentially drawing background signal from co- enriched metagenomic DNA, were removed (Table S11). While M. leprae has not been observed to undergo recombination, Gubbins41 was applied to investigate this phenomenon in this sparsely studied organism (Table S11). These various filters resulted in 650 variant positions upon which to base the phylogeny (Figure 3A, Tables S12 and S13). A maximum parsimony tree was generated in MEGA 1142 with 100 bootstraps, mid- point rooting, and branch- length estimation (Figure 3C). The phylogeny supports a robust separation between the human and rodent- associated lineages, where the two ancient genomes form a sister clade to the cluster of all human M. lepromatosis thus far sequenced at the genomic level. For all polymorphic positions, 104 occur uniquely in the ancient genomes of which 43 correspond to non- synonymous changes with potential functional significance (Table S12). + +## Emergence scenarios for M. lepromatosis + +Reconstruction of the first ancient M. lepromatosis genomes with such deep chronology offers an unprecedented opportunity to explore the species' evolutionary history. Using the radiocarbon ages of skeletal elements from ECR001 and ECR003 and the collection year for all modern genomes (Table S14), time- calibrated phylogenetic trees were constructed to estimate divergence times and evolutionary rates using the BEAST v2.7.7 software package43. Topology of the Bayesian phylogeny agrees with that inferred from parsimony (Figure 3D). For thorough comparison we considered both strict and optimized uncorrelated relaxed lognormal clock models44 along with both the Bayesian skyline plot (BSP)45 and the coalescent constant population size model for demographic inference (Supplementary section 7.1). Model selection strongly supported a relaxed clock with BSP indicating rate heterogeneity among branches (Table S15a), which may reflect host- specific adaptations within human- and rodent- associated lineages. + +Strength of temporal signal in the data was investigated via date randomization test (DRT)46 (Figures S16 and S17). Simulations here showed a small proportion of overlap in the clock rate parameter (Figure S18), which indicated that a Bayesian framework may not estimate evolutionary rates and timescales with high confidence. This limitation likely arises from the small number of available genomes. We, therefore, chose to apply a prior distribution for the rate parameter based on previous estimates inferred for M. leprae (Supplementary text 8.3). The best- supported model estimates + +<--- Page Split ---> + +an evolutionary rate of \(6.91\mathrm{e}^{- 09}\) subst./site/year (95% HDPI: \(0.34\mathrm{e}^{- 09} - 15.64\mathrm{e}^{- 09}\) subst./site/year) for M. lepromatosis, which agrees closely with estimates obtained via other models (Table S15b), as well as previous estimates for M. leprae genomic substitution rates (Table S16). From this, we estimate the median time for the most recent common ancestor (tMRCA) of M. lepromatosis to be approximately 26,800 years ago (95% HPDI range of 4,206 ... 115,340 yBP) (Table S15b). Genomes obtained from human hosts yield a divergence estimate of ca. 12,600 years (95% HPDI: 5,304 ... 49,659 yBP), while the tMRCA for the red squirrel clade is ca. 440 years (95% HPDI: 73 ... 2,063 yBP) (Table S15b). The estimates proposed here are highly consistent with results obtained from all iterations tested, supporting robustness across different demographic and molecular clock models (Table S15b, Figures S18- S21). Our tMRCA for M. lepromatosis closely aligns with results presented elsewhere based on modern data17, though with broader temporal intervals resulting from either our inclusion of ancient genomes or our selection of more permissive models. Further refinement of the origin, evolution, and relationship between the ancient strains and those from the regions where the disease is found today is expected to come with additional genomic examples made available through increased awareness for its detection in both clinical and archaeological contexts. + +Recent investigations of M. leprae, as well as several other bacterial pathogens where ancient genomes are available, place their extrapolated coalescence date in the last 6000 years, which correlates with cultural adaptations such as the adoption of agriculture and animal husbandry in the Neolithic that are regarded as conducive to the emergence and maintenance of novel pathogens in human groups15,47- 50. The current analysis reveals a very different evolutionary history for M. lepromatosis: although based on only a small number of genomes, multiple simulations suggest a common ancestor for the human- associated lineages that temporally aligns with the Pleistocene- Holocene transition. This encompasses a warming period wherein human movements were less impeded by large ice sheets that covered 25% of the earth's land surface during the Last Glacial Maximum. Further exploration of the vast territories of the American continent soon followed, as demonstrated by the sudden increase in archaeological sites that indicate human activity51. Our finding of two M. lepromatosis infections in South America, prior to the periods of known contact with either Oceanian or European populations, implies either movement of the pathogen within human groups during an early peopling event, or its previously established endemicity in the continent in a separate reservoir species eventually acquired by humans. The latter would imply that its current distribution arises from a post- colonial dissemination, and would make it one of the few global diseases known to have emerged in the Americas52. Its presence in the continent has thus far remained undetected based on morphological analyses of human archaeological tissues, where skeletal lesions ascribed to Hansen's disease are limited to examples from the post- colonial period53, with the exception of two potential infections from the northern Pacific Coast that await molecular characterization and confirmation of their possible pre- AD1492 status54. Additional ancient genomes from either human or faunal remains may eventually disentangle the current mystery of its origin, and possible means of acquisition among the hunting- gathering- fishing groups studied here. It may also assist in the establishment of morphological diagnostic criteria for disease identification in the archaeological record. + +<--- Page Split ---> + +While we observe a deep divergence between the human- and rodent- associated lineages, current data from non- human sources are limited to modern rodent lineages within a restrictive geographic spread in Ireland and the United Kingdom55, from a single introductory event of unknown origin within the last 500 years. While surveillance has as yet failed to identify M. lepromatosis or M. leprae in multiple squirrel species in mainland Europe56, analogous efforts in other parts of the world are needed to explore its ecological distribution in broader scale. Greater awareness of this pathogen and its potential for zoonotic transmission from armadillos is also being explored given that they are known reservoirs of M. leprae in the Americas. Previous contact with these animals (handling or consumption) has been reported in two individuals with confirmed M. lepromatosis infection in Mexico57. Screening efforts of multiple species of armadillos has also begun in Brazil, where human infections with M. lepromatosis represent greater than 10% of reported instances of Hansen's Disease58. Of note, both individuals studied here come from archaeological contexts in Chile that are outside the current range of armadillos. + +## Modern M. lepromatosis in perspective + +Since its discovery in 2008, M. lepromatosis has been regarded as a second causal pathogen for Hansen's disease. While associated with the more severe forms of DLL and LP, these presentations are equally considered within the clinical spectrum of M. leprae infection25. Distinction between the two pathogens through use of the recently validated species- specific PCR assay59 has the potential to elucidate the true global prevalence of M. lepromatosis. Here we aim to raise awareness of M. lepromatosis infection through demonstration of its previously unknown health impact along the Pacific Coast of South America several millennia in the past. This region currently has a low incidence of Hansen's Disease where occasional reported cases, thus far attributed to M. leprae, are thought to result from travel to regions within Latin America where disease incidence is high60,61. Its restricted modern geographic distribution may in part be due to its decreased transmissibility in comparison to other globally dispersed pathogens. Management of human infections in living populations remains a principal concern, and adoption of a One Health perspective could provide the means to elucidate the zoonotic potential of this disease both in the present as well as the past18. Available data suggest that squirrel populations in Britain and Ireland may be the sole non- human reservoir for these pathogens in West Eurasia55,56. The results of such screenings from rodent populations in East Eurasia have yet to be reported, and recent evidence suggests wild rodents may be a natural source of M. leprae in Brazil62. This highlights the need for broader- scale investigations into potential wild reservoirs for both M. leprae and M. lepromatosis. The capacity of armadillos to harbour M. lepromatosis infection in Latin American countries, especially those where DLL representation is high such as Mexico and the Caribbean25, should also be considered. Given the narrow known host range for M. leprae, susceptibility in rodents, armadillos, or other animals may be related to their possible maintenance of M. lepromatosis in the past. Further contributions are also expected to come from paleogenomic analyses that continue to explore past disease landscapes represented in both human and animal remains. + +<--- Page Split ---> + +## Acknowledgements + +AcknowledgementsSkeletal elements for this work were obtained under permits \(N^{\circ} 43.341\) dated August 31 2022 of the Consejo de Monumentos Nacionales issued by the Ministerio de las Culturas, las Artes y el Patrimonio of Chile. We thank the Museo Arqueológico de La Serena, Chile and the laboratory processing teams of the Max Planck Institute of Evolutionary Anthropology at both the Jena satellite laboratory and the Leipzig Core Unit for their assistance in data generation. We also thank Alexander Herbig for helpful comments on an earlier draft of this manuscript, and support from the ancient pathogen research team at the Max Planck Institute for Evolutionary Anthropology. + +## Author contributions + +Author contributionsKIB, RN, and DAR conceived of the investigation. MJH- S, OEFS, JCD, and CLK performed archaeological and osteological analyses. TLS, SÖ, NP, DAR, RN, and KIB performed analyses. KIB, DAR, and RN wrote the manuscript with contributions from all coauthors. + +## Funding + +FundingThe Max Planck Society, European Research Council Starting Grant CoDisEASe (805268) to KIB, the German Academic Exchange Service (DAAD) to DAR under Short Term Grant number 57588366, the Social Sciences and Humanities Research Council of Canada postdoctoral fellowship no. 756- 2023- 0246 to CLK, Agencia Nacional de Investigación y Desarrollo de Chile (ANID) Doctorado Becas Chile Scholarship no. 2018- 72190531 to MJH- S, and the Secretaría de Ciencia y Tecnología (UNC) to RN. + +## Data availability + +Data are accessible via the ENA project ID ERR13916540 and ERR13916541. + +<--- Page Split ---> + +## Figure captions + +Figure 1. A) Map of the Semi-arid region of Chile showing the location of the two archaeological sites under study. B) Skeletal elements that yielded the two ancient genomes of M. lepromatosis. Left: tibia from ECR001 (bar = 5 cm). Right: tooth from ECR003 (bar = 0.5 cm). C) Modeled radiocarbon dates of the individuals ECR001 and ECR003 from La Herradura and El Cerrito sites, respectively. + +Figure 2 - Overview of the recovery status of the newly identified ancient M. lepromatosis genomes. A) Indication of the genomic regions with a depth range between 0 and 5- fold depth averaged over bins of 1000 bp. B) Genome coverage of the M. leprae probes mapped to the M. lepromatosis FJ924 reference genome averaged over bins of 1000 bp (top based on permitted 3bp mismatch and bottom based on permitted 7bp mismatch). C) Visual representation of the location of the non- reference loci recovered for ECR001 and ECR003. + +Figure 3 - Phylogenetic representation of ancient and modern M. lepromatosis. A) Network showing the number of SNPs that distinguish individual groupings. 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Schilling, A.-K., Del-Pozo, J, Lurz, P.W.W., et al. Leprosy in red squirrels in the UK. Vet Rec. 184, 416 (2019). doi:10.1136/vr.11385 + +56. Tió-Coma, M., Sprong, H, Kik, M., et al. Lack of evidence for the presence of leprosy bacilli in red squirrels from North-West Europe. Transbound Emerg Dis. 67, 1032-1034 (2020). + +<--- Page Split ---> + +57. Deps, P. & Collin, S.M. *Mycobacterium lepromatosis* as a Second Agent of Hansen's Disease. *Frontiers in Microbiology.* 12, 698588 (2021). +58. Monsalve-Lara, J., Drummond, M., Romero-Alvarez, D. et al. Prevalence of *Mycobacterium leprae* and *Mycobacterium lepromatosis* in roadkill armadillos in Brazil. *Acta Tropica.* 258, 107333 (2024). doi.org/10.1016/j.actatropica.2024.107333 +59. Sharma, R., Singh, P., McCoy, R.C., et al. Isolation of *Mycobacterium lepromatosis* and development of molecular diagnostic assays to distinguish *Mycobacterium leprae* and *M. lepromatosis*. *Clin Infect Dis.* 71, e262-9 (2020). +60. San Martín, A., Carrasco, C., Fica, A., Navarrete, M., Velásquez, J. & Herrera, T. Enfermedad de Hansen. Una condición emergente en Chile. *Rev Chilena Infectol.* 35, 722-8 (2018). doi: 10.4067/S0716-10182018000600722 +61. Armijo, D., Aguirre, F., Raimann, M. V., da Costa, F. & Barría, C. Enfermedad de Hansen. Comunicación de un caso de lepra tuberculoide en Chile. *Revista chilena de infectología.* 39, 80-85 (2002). https://dx.doi.org/10.4067/S0716-10182022000100080 +62. Lima de, M., F., Silvestre, M. do P. S., A, Santos, E. C. D., et al. The Presence of *Mycobacterium leprae* in Wild Rodents. *Microorganisms.* 28, 1114 (2022). + +<--- Page Split ---> +![PLACEHOLDER_18_0] + + +<--- Page Split ---> +![PLACEHOLDER_19_0] + + +<--- Page Split ---> +![PLACEHOLDER_20_0] + + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- RamirezSupplementaryTablesR1.xlsx- RamirezSitterSupplementaryMaterialR1.docx + +<--- Page Split ---> diff --git a/preprint/preprint__068ba17c74cb5962b82a0a7630cb5df2fe0b1a5cb30c6a3e3e705e8c6aa53848/preprint__068ba17c74cb5962b82a0a7630cb5df2fe0b1a5cb30c6a3e3e705e8c6aa53848_det.mmd b/preprint/preprint__068ba17c74cb5962b82a0a7630cb5df2fe0b1a5cb30c6a3e3e705e8c6aa53848/preprint__068ba17c74cb5962b82a0a7630cb5df2fe0b1a5cb30c6a3e3e705e8c6aa53848_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..3fdb7ff6dc76ad7773bc219e115bccee732fd76b --- /dev/null +++ b/preprint/preprint__068ba17c74cb5962b82a0a7630cb5df2fe0b1a5cb30c6a3e3e705e8c6aa53848/preprint__068ba17c74cb5962b82a0a7630cb5df2fe0b1a5cb30c6a3e3e705e8c6aa53848_det.mmd @@ -0,0 +1,429 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 904, 206]]<|/det|> +# 4000-year-old Mycobacterium lepromatosis genomes from Chile reveal long-establishment of Hansen's Disease in the Americas + +<|ref|>text<|/ref|><|det|>[[44, 229, 860, 272]]<|/det|> +Dario Ramirez Universidad Nacional de Cordoba https://orcid.org/0000- 0001- 8982- 2550 + +<|ref|>text<|/ref|><|det|>[[44, 277, 860, 319]]<|/det|> +T. Sitter Max Planck Institute for Evolutionary Anthropology https://orcid.org/0000- 0002- 7160- 6240 + +<|ref|>text<|/ref|><|det|>[[44, 323, 421, 365]]<|/det|> +Sanni Oversti Max Planck Institute for Geoanthropology + +<|ref|>text<|/ref|><|det|>[[44, 370, 308, 410]]<|/det|> +Maria Herrera-Soto Universidad de Buenos Aires + +<|ref|>text<|/ref|><|det|>[[44, 416, 905, 480]]<|/det|> +Nicolás Pastor Instituto de Antropología de Córdoba, Consejo Nacional de Investigaciones Científicas y Técnicas, Córdoba, Argentina https://orcid.org/0000- 0001- 8971- 7910 + +<|ref|>text<|/ref|><|det|>[[44, 484, 357, 525]]<|/det|> +Oscar Fontana Silva Museo Arqueológico de La Serena + +<|ref|>text<|/ref|><|det|>[[44, 531, 622, 572]]<|/det|> +Casey Kirkpatrick Simon Fraser University https://orcid.org/0000- 0001- 9755- 6459 + +<|ref|>text<|/ref|><|det|>[[44, 576, 270, 617]]<|/det|> +José Castelleti Dellepiane Independent researcher + +<|ref|>text<|/ref|><|det|>[[44, 623, 750, 665]]<|/det|> +Rodrigo Nores Instituto de Antropología de Córdoba https://orcid.org/0000- 0002- 9153- 0626 + +<|ref|>text<|/ref|><|det|>[[44, 670, 146, 688]]<|/det|> +Kirsten Bos + +<|ref|>text<|/ref|><|det|>[[55, 697, 300, 714]]<|/det|> +kirsten_bos@eva.mpg.de + +<|ref|>text<|/ref|><|det|>[[52, 742, 857, 762]]<|/det|> +Max Planck Institute for Evolutionary Anthropology https://orcid.org/0000- 0003- 2937- 3006 + +<|ref|>text<|/ref|><|det|>[[44, 803, 103, 821]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 841, 135, 859]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 879, 297, 898]]<|/det|> +Posted Date: May 28th, 2025 + +<|ref|>text<|/ref|><|det|>[[44, 916, 474, 935]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 5776739/v1 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 916, 87]]<|/det|> +License: © ① This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 105, 535, 125]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 161, 938, 205]]<|/det|> +Version of Record: A version of this preprint was published at Nature Ecology & Evolution on June 30th, 2025. See the published version at https://doi.org/10.1038/s41559-025-02771- y. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[130, 100, 864, 133]]<|/det|> +# 4000-year-old Mycobacterium lepromatosis genomes from Chile reveal long- establishment of Hansen's Disease in the Americas + +<|ref|>text<|/ref|><|det|>[[116, 150, 835, 198]]<|/det|> +Darío A. Ramirez*1, T. Lesley Sitter*2, Sanni Översti3, María José Herrera-Soto4, Nicolás Pastor5, Oscar Eduardo Fontana Silva6, Casey L. Kirkpatrick2,7,8, José Castelletti Dellepiane9, Rodrigo Nores1, and Kirsten Bos2 + +<|ref|>text<|/ref|><|det|>[[116, 216, 877, 264]]<|/det|> +1 Instituto de Antropología de Córdoba CONICET-UNC, Museo de Antropologías, Departamento de Antropología, Facultad de Filosofía y Humanidades, Universidad Nacional de Córdoba. Av. Hipólito Yrigoyen 174, Córdoba, Argentina. + +<|ref|>text<|/ref|><|det|>[[116, 281, 877, 313]]<|/det|> +2 Max Planck Institute for Evolutionary Anthropology. Deutscher Platz 6, Leipzig, Germany. + +<|ref|>text<|/ref|><|det|>[[116, 330, 864, 345]]<|/det|> +3 Max Planck Institute of Geoanthropology. Kahlaische Strasse 10, Jena, Germany. + +<|ref|>text<|/ref|><|det|>[[116, 363, 775, 377]]<|/det|> +4 Facultad de Filosofía y Letras, Universidad de Buenos Aires, Argentina. + +<|ref|>text<|/ref|><|det|>[[116, 395, 877, 459]]<|/det|> +5 Instituto de Antropología de Córdoba CONICET-UNC, Museo de Antropologías, Facultad de Filosofía y Humanidades y Departamento de Diversidad Biológica y Ecología, Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba. Av. Hipólito Yrigoyen 174, Córdoba, Argentina. + +<|ref|>text<|/ref|><|det|>[[116, 477, 876, 508]]<|/det|> +6 Museo Arqueológico de La Serena. Gregorio Cordovez esquina Cienfuegos, S/N, La Serena, Chile. + +<|ref|>text<|/ref|><|det|>[[116, 526, 876, 557]]<|/det|> +7 Simon Fraser University, Dept. of Archaeology, 8888 University Drive, Burnaby, B.C., Canada + +<|ref|>text<|/ref|><|det|>[[116, 576, 876, 606]]<|/det|> +8 Western University, Dept. of Anthropology, 1151 Richmond St., London, Ontario, Canada. + +<|ref|>text<|/ref|><|det|>[[116, 625, 412, 639]]<|/det|> +9 Independent researcher, Chile. + +<|ref|>text<|/ref|><|det|>[[117, 674, 539, 688]]<|/det|> +*These authors contributed equally to this work + +<|ref|>text<|/ref|><|det|>[[116, 706, 636, 737]]<|/det|> +Address correspondence to: kirsten_bos@eva.mpg.de, or rodrigonores@ffyh.unc.edu.ar + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[70, 85, 880, 355]]<|/det|> +1 Abstract2 Mycobacterium lepromatosis is a recently identified cause of Hansen's Disease, and3 is associated with the more severe and potentially lethal presentations of diffuse4 lepromatous leprosy (DLL) and Lucio's phenomenon (LP). Detection of this infection5 has been limited to a small number of individuals, leaving much to be learned about6 its global distribution and transmissibility. Its discovery in wild rodent populations in the7 United Kingdom and Ireland also raises questions about its zoonotic potential. Here8 we raise further awareness of this disease via analyses of two exceptionally well9 preserved M. lepromatosis genomes obtained from 4000- year- old human remains of10 two adult males from the archaeological sites of El Cerrito and La Herradura in11 Northern Chile. This formed the basis of genomic comparisons between ancient and12 modern forms of the pathogen. We demonstrate an unexpected long history of M.13 lepromatosis in the Americas, which contrasts with the more recent Eurasian history14 of the closely related Mycobacterium leprae. We offer relevant perspectives on its15 evolution while providing an incentive for further disease monitoring in both humans16 and other potential reservoir species in the Americas and elsewhere.17 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 85, 240, 100]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[118, 101, 880, 266]]<|/det|> +Hansen's disease, more commonly known as leprosy, is caused by the unculturable bacteria Mycobacterium leprae and the recently described Mycobacterium lepromatosis1. Transmission occurs via prolonged exposure to respiratory droplets from of an infected person2. Untreated individuals can develop a chronic peripheral neuropathy with associated physical impairment3. Many infected remain asymptomatic, which can obscure diagnoses and control measures4. The availability of curative multidrug treatments has decreased worldwide prevalence5; regardless, the disease persists in more than 100 countries, with up to 174,000 new cases reported globally in 2022 alone6. Risk of infection is closely correlated with conditions of overcrowding, poverty, malnutrition and an immunocompromised state7. + +<|ref|>text<|/ref|><|det|>[[115, 280, 880, 641]]<|/det|> +Written accounts describe the impact of disfiguring diseases presumed to be Hansen's Disease on Eurasian populations throughout the historic period8. As skeletal involvement occurs in advanced stages, past infections have been identified in archaeological tissues as early as 5,000 years ago in Europe, Asia, and Oceania9- 14. For M. leprae, analyses of ancient genomic data provide further support for its infectious potential having spanned several millennia16. While humans are regarded as the principal host of Hansen's Disease, maintenance of the causative bacteria in other animal species have raised concerns over their potential as zoonotic reservoirs from a One Health perspective. Nine- banded armadillos are known sources of M. leprae, where transmission may occur through human consumption16. Red squirrels in Britain and Ireland can harbour both M. leprae and M. lepromatosis17, and recent identification of M. leprae in archaeological rodent bone demonstrates cross- species infectivity in historical periods18. Detection of M. leprae in several species of non- human primates further demonstrates the broad host range of this pathogen19- 21. Viability of M. leprae in ticks and amoebae for several months opens the possibility of environmental reservoirs as well22,23. Unlike many bacterial diseases, presentation of symptoms and the development of its more severe multibacillary or lepromatous forms seem highly dependent on host immunological status2,24. While the few available reports tend to associate M. lepromatosis with severe disease presentation such as Diffuse Lepromatous Leprosy (DLL) and the potentially fatal Lucio's phenomenon (LP), a set of clinically defined criteria that distinguish it from M. leprae infection has yet to be established25. + +<|ref|>text<|/ref|><|det|>[[115, 657, 880, 853]]<|/det|> +Understanding of M. lepromatosis distribution and evolutionary history is limited as few examples of the infection have been molecularly confirmed. PCR- based detections demonstrate its presence in the Americas (Mexico, the Caribbean)1,26, as well as Southeast Asia (Myanmar, Singapore)27, consistent with the global occurrence of DLL25,28. Genome- level analyses are limited in scope: the available modern genomes suggest a deep divergence of M. lepromatosis and M. leprae, though with retention of genomic features that contribute to some similarities in disease presentation29. While investigations that draw upon both modern and ancient genomic data consistently support an origin for M. leprae outside the Americas15, the identification of M. lepromatosis in archaeological contexts has not been reported, though its modern association with Latin American contexts could suggest its endemicity in the continent in the pre- colonial period28,29. + +<|ref|>text<|/ref|><|det|>[[115, 870, 880, 904]]<|/det|> +Paleogenomic investigations of this disease are currently restricted to the recovery of M. leprae genomes, and are dominated by investigations that are limited to a Eurasian + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 85, 880, 152]]<|/det|> +context. Here we present two high- coverage M. lepromatosis genomes reconstructed from skeletal remains of individuals from distinct archaeological contexts from Chile, both dated to ca. 4,000 years ago. These data indicate a long and previously undocumented history of this infectious disease in the Americas. + +<|ref|>sub_title<|/ref|><|det|>[[118, 167, 710, 185]]<|/det|> +## Archaeological context, morphology, and molecular recovery + +<|ref|>text<|/ref|><|det|>[[115, 185, 880, 479]]<|/det|> +To investigate infectious disease in the American pre- colonial period from a molecular perspective, we sampled 35 teeth and 19 bones with pathological lesions suggestive of active infection belonging to 41 individuals from five archaeological sites representing various time periods and subsistence strategies in the semi- arid region of Chile (Supplementary Information section 1). Both teeth and pathological bone were selected to permit identification of pathogens that contribute to either acute or chronic infection, and when available both tissue types were selected from an individual. Approximately 50mg of each tissue was extracted and converted into a single- stranded DNA library for sequencing on an Illumina HiSeq 4000 to a depth of ca. 5 million reads. Data were computationally screened for a variety of pathogenic bacteria and viruses following a hypothesis- free method using the MALT and HOPS platforms implemented through the nf- core EAGER 2 analysis pipeline \(^{30 - 32}\) . This process revealed several thousand DNA fragments with homology to M. lepromatosis in each of two archaeological tissues, representing the neighbouring sites of La Herradura (a tibia from an individual referred to here as "ECR001") and El Cerrito (a tooth from an individual referred to here as "ECR003") (Figure 1, Tables S1 and S2, Figures S1- S6). Radiocarbon dating of both skeletal elements indicate them to be roughly contemporaneous, from approximately 4,200 - 4,300 years ago (Figure 1). + +<|ref|>text<|/ref|><|det|>[[115, 494, 880, 905]]<|/det|> +Currently there is little information on the osteological manifestations of M. lepromatosis infection, but most reported examples are associated with the DLL and LP forms of Hansen's Disease \(^{1}\) . DLL primarily affects the skin and peripheral nerves but it can also cause ocular damage, rhinitis, destruction of the nasal septum causing saddle or crooked nose (usually without affecting the nasal bones), damage to the larynx, organ damage or failure, and sepsis. Generalized hypoesthesia or anesthesia resulting from neuritis can contribute to secondary injury of the extremities, which may result in bony changes. LP is a rare reaction most commonly associated with DLL that manifests as acute, necrotizing cutaneous vasculitis, generally affecting the legs, arms, torso, and face \(^{33}\) . Although LP does not necessarily affect the bones, the resulting inflammation and possible secondary infections could potentially cause osteological changes. Genetically- confirmed M. lepromatosis infections have also been associated with borderline lepromatous leprosy and lepromatous leprosy \(^{25}\) , the latter being the most common form of Hansen's Disease to cause osteological changes \(^{34}\) . While its modern presentation may differ from the spectrum of pathology observed in the past, both individuals display pathological lesions that are consistent with, though not diagnostic of Hansen's Disease, as well as additional changes that are associated with unrelated afflictions (see SI for complete descriptions of the remains). For example, skeleton ECR001 (male 35- 40 years, Figures S2 - S4) exhibits a slight widening of the nasal aperture compared to other individuals in the population, with rounding of the margins and possible osteolytic processes in the area. This individual also has slight recession of the alveolar bone of the anterior teeth (though this may be in part due to taphonomic breakage or in response to other pathological processes), as well as pitting on the palatine process and on the ribs. The right fibula and tibia are affected by mostly- healed lamellar periostosis and slight thickening and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 84, 880, 266]]<|/det|> +bowing of the right tibial diaphysis. The small tubular bones of the hands display pitting, abnormal foramina, and periosteal new bone on the palmar surfaces, but no concentric resorption or evidence of hyperflexion, and there are pronounced osteolytic lesions on the right calcaneus. Skeleton ECR003 (male, 40- 44 years) has fewer preserved skeletal elements but also displays rounding of the inferior margins of the nasal aperture and slight thickening and bowing of the tibial diaphysis (Figures 1, S5). Although the aforementioned osteological changes in both individuals could be associated with Hansen's Disease (though not necessarily with the DLL or LP forms), they could equally be caused by other diseases, both infectious and non- infectious. For this reason we do not attempt a differential diagnosis based on osteological criteria, nor do we propose any new diagnostic criteria from these limited examples. + +<|ref|>text<|/ref|><|det|>[[115, 281, 880, 578]]<|/det|> +To explore the suitability of genomic reconstruction, DNA libraries were enriched via in- solution capture through use of a probe set designed from a modern M. leprae reference panel35, and sequenced to a read depth of 20 million fragments, as above. Distinction between several mycobacterial species was accomplished via a competitive mapping approach, which demonstrated much higher homology and hence high confidence in their assignment to M. lepromatosis (Table S3). Both genomes are of exceptional quality, yielding average genomic coverages of 45- and 74- fold for ECR001 and ECR003, respectively, when mapped against the modern FJ924 M. lepromatosis genomic reference (CP083405) (Table S4), isolated from a patient in Mexico29. The distribution of heterozygous positions is consistent with a single source of M. lepromatosis DNA for each individual, though with a detectable level of chemical damage and possibly sporadic reads of non- target origin in the mapped datasets (Figure S7), as expected of metagenomically- sourced ancient bacterial DNA. The spectrum of DNA damage from both pathogen and host (Figures S6, S8) is consistent with their contemporaneous antiquity as determined from radiocarbon data (Figure 1). An analysis of human DNA also indicates an exclusively American Indigenous host source (Table S5). Negative controls were free of M. lepromatosis DNA (Table S6). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 85, 733, 101]]<|/det|> +## M. Iepromatosis pangenome and comparisons against M. leprae + +<|ref|>text<|/ref|><|det|>[[115, 101, 880, 381]]<|/det|> +Despite our use of an M. leprae capture panel, we observed a 278- and 23- fold increase in M. lepromatosis DNA content between the shotgun and enriched datasets, with \(83\%\) and \(88\%\) of the genome covered at four- fold read support for genomes ECR001 and ECR003, respectively (Tables S3 and S4, Figure 2). To investigate possible enrichment biases over individual regions, probes were mapped with high sensitivity against the M. lepromatosis reference and probe coverage was compared to that observed in the two ancient genomes over annotated coding regions (Figures 2, S9, and S10). Both ancient genomes include coverage over regions of the M. lepromatosis reference that were not included in probe design, and hence were not enriched. Coverage across these regions is higher for genome ECR003, which may be due to a higher abundance of M. lepromatosis DNA in the non- enriched fraction (Table S3). Importantly, we identify several regions with limited mapping reads in both ancient genomes where probe coverage is abundant. Further investigation revealed these regions to have asymmetric representation across host- associated modern genomes, which could indicate lineage- specific losses unrelated to host adaptation. This also reveals no pattern of gene loss/acquisition that distinguishes ancient from modern forms (Figure S10). + +<|ref|>text<|/ref|><|det|>[[115, 395, 880, 625]]<|/det|> +This analysis was complemented by evaluation of the two reconstructed ancient genomes alongside 16 modern M. lepromatosis (Table S7) based on a common mapping to the FJ924 reference (Table S8, Figure S11 and S12). No consistent pattern of gene acquisition or loss across the full annotated coding region distinguishes the human- associated strains from those associated with red squirrel populations in the north of the United Kingdom or Ireland (Figure S12). This implies that any long- term changes related to host- specificity are influenced by either nucleotide substitution, disruptions in synteny, or changes outside of the mapped coding regions that are undetected via the methods employed here. This analysis also revealed a surprisingly low coverage for genome FJ924_S_4 reported in Singh et al 202336 as a first example of M. lepromatosis in India (Table S9, Figures S11 and S12). A competitive mapping approach revealed this genome to show far greater homology to M. leprae, thus questioning the accuracy of its assignment to M. lepromatosis (Table S9). + +<|ref|>text<|/ref|><|det|>[[115, 640, 880, 904]]<|/det|> +Given the established observation of genome decay and reduction in M. leprae over evolutionary timescales37, divergence between M. lepromatosis and M. leprae were investigated on a gene level. There are currently four chromosomally resolved modern M. leprae genomes available, representative of branches 1 \((n = 2)\) , 3 \((n = 1)\) , and 4 \((n = 1)\) . A pangenomic analysis carried out in Roary38 indicated a strong level of divergence between the two pathogens, with 2000 (approximately half) of the 4097 protein coding regions identified in Prokka showing a minimum of \(50\%\) sequence homology between the two pathogens (Figure S13). This demonstrates a high sequence divergence despite M. leprae having been identified as the most closely related organism to M. lepromatosis29. This is further demonstrated via a mapping- based approach, which reveals the two to share only \(\sim 25\%\) nucleotide identity (Table S10). An alignment of the genomes using LASTZ39 and MAUVE40 shows several large rearrangements and approximately 0.5 Mbp ( \(\sim 12\%\) of the genome) present in M. lepromatosis FJ924 that is absent in M. leprae MRHRU- 235- G, either through acquisition in the former, decay in the latter, or a nucleotide homology that is too low for alignment. Less similarity is observed with the more distantly related Mycobacterium haemophilum (Figures S13 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 84, 879, 134]]<|/det|> +- S15). This would leave only disparate regions of similarity upon which to perform downstream genome-level analyses where M. leprae or another Mycobacterium representative are included. + +<|ref|>sub_title<|/ref|><|det|>[[119, 150, 331, 166]]<|/det|> +## Phylogenetic analysis + +<|ref|>text<|/ref|><|det|>[[115, 166, 880, 511]]<|/det|> +The relationship of M. lepromatosis to other pathogenic mycobacteria was first determined through investigation of the 16S rRNA locus (Figure 3B), which confirmed M. leprae to be its closest relative despite extensive genomic divergence described above. This was complemented by a conservative approach to genome- level phylogenetic reconstruction, where focus was restricted to diversity within M. lepromatosis. These data are limited to the two ancient genomes presented here, four modern human genomes from Mexico, and six modern genomes isolated from red squirrels in Ireland and the United Kingdom. SNPs were called at 4- fold read support, and regions of low complexity, along with additional regions identified as potentially drawing background signal from co- enriched metagenomic DNA, were removed (Table S11). While M. leprae has not been observed to undergo recombination, Gubbins41 was applied to investigate this phenomenon in this sparsely studied organism (Table S11). These various filters resulted in 650 variant positions upon which to base the phylogeny (Figure 3A, Tables S12 and S13). A maximum parsimony tree was generated in MEGA 1142 with 100 bootstraps, mid- point rooting, and branch- length estimation (Figure 3C). The phylogeny supports a robust separation between the human and rodent- associated lineages, where the two ancient genomes form a sister clade to the cluster of all human M. lepromatosis thus far sequenced at the genomic level. For all polymorphic positions, 104 occur uniquely in the ancient genomes of which 43 correspond to non- synonymous changes with potential functional significance (Table S12). + +<|ref|>sub_title<|/ref|><|det|>[[119, 546, 519, 562]]<|/det|> +## Emergence scenarios for M. lepromatosis + +<|ref|>text<|/ref|><|det|>[[118, 571, 880, 785]]<|/det|> +Reconstruction of the first ancient M. lepromatosis genomes with such deep chronology offers an unprecedented opportunity to explore the species' evolutionary history. Using the radiocarbon ages of skeletal elements from ECR001 and ECR003 and the collection year for all modern genomes (Table S14), time- calibrated phylogenetic trees were constructed to estimate divergence times and evolutionary rates using the BEAST v2.7.7 software package43. Topology of the Bayesian phylogeny agrees with that inferred from parsimony (Figure 3D). For thorough comparison we considered both strict and optimized uncorrelated relaxed lognormal clock models44 along with both the Bayesian skyline plot (BSP)45 and the coalescent constant population size model for demographic inference (Supplementary section 7.1). Model selection strongly supported a relaxed clock with BSP indicating rate heterogeneity among branches (Table S15a), which may reflect host- specific adaptations within human- and rodent- associated lineages. + +<|ref|>text<|/ref|><|det|>[[118, 794, 880, 910]]<|/det|> +Strength of temporal signal in the data was investigated via date randomization test (DRT)46 (Figures S16 and S17). Simulations here showed a small proportion of overlap in the clock rate parameter (Figure S18), which indicated that a Bayesian framework may not estimate evolutionary rates and timescales with high confidence. This limitation likely arises from the small number of available genomes. We, therefore, chose to apply a prior distribution for the rate parameter based on previous estimates inferred for M. leprae (Supplementary text 8.3). The best- supported model estimates + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[66, 82, 880, 382]]<|/det|> +an evolutionary rate of \(6.91\mathrm{e}^{- 09}\) subst./site/year (95% HDPI: \(0.34\mathrm{e}^{- 09} - 15.64\mathrm{e}^{- 09}\) subst./site/year) for M. lepromatosis, which agrees closely with estimates obtained via other models (Table S15b), as well as previous estimates for M. leprae genomic substitution rates (Table S16). From this, we estimate the median time for the most recent common ancestor (tMRCA) of M. lepromatosis to be approximately 26,800 years ago (95% HPDI range of 4,206 ... 115,340 yBP) (Table S15b). Genomes obtained from human hosts yield a divergence estimate of ca. 12,600 years (95% HPDI: 5,304 ... 49,659 yBP), while the tMRCA for the red squirrel clade is ca. 440 years (95% HPDI: 73 ... 2,063 yBP) (Table S15b). The estimates proposed here are highly consistent with results obtained from all iterations tested, supporting robustness across different demographic and molecular clock models (Table S15b, Figures S18- S21). Our tMRCA for M. lepromatosis closely aligns with results presented elsewhere based on modern data17, though with broader temporal intervals resulting from either our inclusion of ancient genomes or our selection of more permissive models. Further refinement of the origin, evolution, and relationship between the ancient strains and those from the regions where the disease is found today is expected to come with additional genomic examples made available through increased awareness for its detection in both clinical and archaeological contexts. + +<|ref|>text<|/ref|><|det|>[[66, 400, 880, 884]]<|/det|> +Recent investigations of M. leprae, as well as several other bacterial pathogens where ancient genomes are available, place their extrapolated coalescence date in the last 6000 years, which correlates with cultural adaptations such as the adoption of agriculture and animal husbandry in the Neolithic that are regarded as conducive to the emergence and maintenance of novel pathogens in human groups15,47- 50. The current analysis reveals a very different evolutionary history for M. lepromatosis: although based on only a small number of genomes, multiple simulations suggest a common ancestor for the human- associated lineages that temporally aligns with the Pleistocene- Holocene transition. This encompasses a warming period wherein human movements were less impeded by large ice sheets that covered 25% of the earth's land surface during the Last Glacial Maximum. Further exploration of the vast territories of the American continent soon followed, as demonstrated by the sudden increase in archaeological sites that indicate human activity51. Our finding of two M. lepromatosis infections in South America, prior to the periods of known contact with either Oceanian or European populations, implies either movement of the pathogen within human groups during an early peopling event, or its previously established endemicity in the continent in a separate reservoir species eventually acquired by humans. The latter would imply that its current distribution arises from a post- colonial dissemination, and would make it one of the few global diseases known to have emerged in the Americas52. Its presence in the continent has thus far remained undetected based on morphological analyses of human archaeological tissues, where skeletal lesions ascribed to Hansen's disease are limited to examples from the post- colonial period53, with the exception of two potential infections from the northern Pacific Coast that await molecular characterization and confirmation of their possible pre- AD1492 status54. Additional ancient genomes from either human or faunal remains may eventually disentangle the current mystery of its origin, and possible means of acquisition among the hunting- gathering- fishing groups studied here. It may also assist in the establishment of morphological diagnostic criteria for disease identification in the archaeological record. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 84, 880, 331]]<|/det|> +While we observe a deep divergence between the human- and rodent- associated lineages, current data from non- human sources are limited to modern rodent lineages within a restrictive geographic spread in Ireland and the United Kingdom55, from a single introductory event of unknown origin within the last 500 years. While surveillance has as yet failed to identify M. lepromatosis or M. leprae in multiple squirrel species in mainland Europe56, analogous efforts in other parts of the world are needed to explore its ecological distribution in broader scale. Greater awareness of this pathogen and its potential for zoonotic transmission from armadillos is also being explored given that they are known reservoirs of M. leprae in the Americas. Previous contact with these animals (handling or consumption) has been reported in two individuals with confirmed M. lepromatosis infection in Mexico57. Screening efforts of multiple species of armadillos has also begun in Brazil, where human infections with M. lepromatosis represent greater than 10% of reported instances of Hansen's Disease58. Of note, both individuals studied here come from archaeological contexts in Chile that are outside the current range of armadillos. + +<|ref|>sub_title<|/ref|><|det|>[[118, 346, 495, 363]]<|/det|> +## Modern M. lepromatosis in perspective + +<|ref|>text<|/ref|><|det|>[[115, 362, 880, 821]]<|/det|> +Since its discovery in 2008, M. lepromatosis has been regarded as a second causal pathogen for Hansen's disease. While associated with the more severe forms of DLL and LP, these presentations are equally considered within the clinical spectrum of M. leprae infection25. Distinction between the two pathogens through use of the recently validated species- specific PCR assay59 has the potential to elucidate the true global prevalence of M. lepromatosis. Here we aim to raise awareness of M. lepromatosis infection through demonstration of its previously unknown health impact along the Pacific Coast of South America several millennia in the past. This region currently has a low incidence of Hansen's Disease where occasional reported cases, thus far attributed to M. leprae, are thought to result from travel to regions within Latin America where disease incidence is high60,61. Its restricted modern geographic distribution may in part be due to its decreased transmissibility in comparison to other globally dispersed pathogens. Management of human infections in living populations remains a principal concern, and adoption of a One Health perspective could provide the means to elucidate the zoonotic potential of this disease both in the present as well as the past18. Available data suggest that squirrel populations in Britain and Ireland may be the sole non- human reservoir for these pathogens in West Eurasia55,56. The results of such screenings from rodent populations in East Eurasia have yet to be reported, and recent evidence suggests wild rodents may be a natural source of M. leprae in Brazil62. This highlights the need for broader- scale investigations into potential wild reservoirs for both M. leprae and M. lepromatosis. The capacity of armadillos to harbour M. lepromatosis infection in Latin American countries, especially those where DLL representation is high such as Mexico and the Caribbean25, should also be considered. Given the narrow known host range for M. leprae, susceptibility in rodents, armadillos, or other animals may be related to their possible maintenance of M. lepromatosis in the past. Further contributions are also expected to come from paleogenomic analyses that continue to explore past disease landscapes represented in both human and animal remains. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 85, 310, 101]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[116, 101, 881, 234]]<|/det|> +AcknowledgementsSkeletal elements for this work were obtained under permits \(N^{\circ} 43.341\) dated August 31 2022 of the Consejo de Monumentos Nacionales issued by the Ministerio de las Culturas, las Artes y el Patrimonio of Chile. We thank the Museo Arqueológico de La Serena, Chile and the laboratory processing teams of the Max Planck Institute of Evolutionary Anthropology at both the Jena satellite laboratory and the Leipzig Core Unit for their assistance in data generation. We also thank Alexander Herbig for helpful comments on an earlier draft of this manuscript, and support from the ancient pathogen research team at the Max Planck Institute for Evolutionary Anthropology. + +<|ref|>sub_title<|/ref|><|det|>[[118, 250, 320, 265]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[118, 265, 880, 330]]<|/det|> +Author contributionsKIB, RN, and DAR conceived of the investigation. MJH- S, OEFS, JCD, and CLK performed archaeological and osteological analyses. TLS, SÖ, NP, DAR, RN, and KIB performed analyses. KIB, DAR, and RN wrote the manuscript with contributions from all coauthors. + +<|ref|>sub_title<|/ref|><|det|>[[118, 347, 200, 362]]<|/det|> +## Funding + +<|ref|>text<|/ref|><|det|>[[118, 363, 880, 478]]<|/det|> +FundingThe Max Planck Society, European Research Council Starting Grant CoDisEASe (805268) to KIB, the German Academic Exchange Service (DAAD) to DAR under Short Term Grant number 57588366, the Social Sciences and Humanities Research Council of Canada postdoctoral fellowship no. 756- 2023- 0246 to CLK, Agencia Nacional de Investigación y Desarrollo de Chile (ANID) Doctorado Becas Chile Scholarship no. 2018- 72190531 to MJH- S, and the Secretaría de Ciencia y Tecnología (UNC) to RN. + +<|ref|>sub_title<|/ref|><|det|>[[118, 510, 272, 525]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[118, 526, 820, 543]]<|/det|> +Data are accessible via the ENA project ID ERR13916540 and ERR13916541. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 85, 270, 102]]<|/det|> +## Figure captions + +<|ref|>text<|/ref|><|det|>[[118, 120, 870, 215]]<|/det|> +Figure 1. A) Map of the Semi-arid region of Chile showing the location of the two archaeological sites under study. B) Skeletal elements that yielded the two ancient genomes of M. lepromatosis. Left: tibia from ECR001 (bar = 5 cm). Right: tooth from ECR003 (bar = 0.5 cm). C) Modeled radiocarbon dates of the individuals ECR001 and ECR003 from La Herradura and El Cerrito sites, respectively. + +<|ref|>text<|/ref|><|det|>[[118, 253, 880, 370]]<|/det|> +Figure 2 - Overview of the recovery status of the newly identified ancient M. lepromatosis genomes. A) Indication of the genomic regions with a depth range between 0 and 5- fold depth averaged over bins of 1000 bp. B) Genome coverage of the M. leprae probes mapped to the M. lepromatosis FJ924 reference genome averaged over bins of 1000 bp (top based on permitted 3bp mismatch and bottom based on permitted 7bp mismatch). C) Visual representation of the location of the non- reference loci recovered for ECR001 and ECR003. + +<|ref|>text<|/ref|><|det|>[[118, 400, 880, 582]]<|/det|> +Figure 3 - Phylogenetic representation of ancient and modern M. lepromatosis. A) Network showing the number of SNPs that distinguish individual groupings. B) and C) Maximum parsimony trees (with branch length estimation) constructed in Mega X v11.0.11 with 1000 bootstrap iterations based on a 16S rRNA alignment of several mycobacterial representatives with ambiguous sites masked in the lower coverage genome ECR001 (B), and 650 full genomic alleles called at four- fold read support (C); D) Maximum clade credibility (MCC) tree with median heights, reconstructed using the Bayesian skyline plot and relaxed clock. Branches in (D) are color- coded based on the median rate estimates from the optimised relaxed clock model, with blue indicating lower rates and red indicating higher rates. 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Prates, L., Politis, G.G. & Perez, S.I. Rapid radiation of humans in South America after the last glacial maximum: A radiocarbon-based study. PLOS ONE. 15, e0236023 (2020). + +<|ref|>text<|/ref|><|det|>[[205, 671, 874, 718]]<|/det|> +52. Barquera, R., Sitter, T.L., Kirkpatrick, C.L., et al. A deep history of treponemal disease in the America revealed through ancient genomic analyses. Nature (2024). https://doi.org/10.1038/s41586-024-08515-5 + +<|ref|>text<|/ref|><|det|>[[205, 731, 808, 762]]<|/det|> +53. Ortner, D.J. Identification of pathological conditions in human skeletal remains. Florida: Academic Press (2003). + +<|ref|>text<|/ref|><|det|>[[205, 776, 812, 792]]<|/det|> +54. Leprosy Past and Present. Gainsville: Florida University Press (2020). + +<|ref|>text<|/ref|><|det|>[[205, 806, 855, 837]]<|/det|> +55. Schilling, A.-K., Del-Pozo, J, Lurz, P.W.W., et al. Leprosy in red squirrels in the UK. Vet Rec. 184, 416 (2019). doi:10.1136/vr.11385 + +<|ref|>text<|/ref|><|det|>[[205, 851, 868, 897]]<|/det|> +56. Tió-Coma, M., Sprong, H, Kik, M., et al. Lack of evidence for the presence of leprosy bacilli in red squirrels from North-West Europe. Transbound Emerg Dis. 67, 1032-1034 (2020). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[207, 84, 872, 459]]<|/det|> +57. Deps, P. & Collin, S.M. *Mycobacterium lepromatosis* as a Second Agent of Hansen's Disease. *Frontiers in Microbiology.* 12, 698588 (2021). +58. Monsalve-Lara, J., Drummond, M., Romero-Alvarez, D. et al. Prevalence of *Mycobacterium leprae* and *Mycobacterium lepromatosis* in roadkill armadillos in Brazil. *Acta Tropica.* 258, 107333 (2024). doi.org/10.1016/j.actatropica.2024.107333 +59. Sharma, R., Singh, P., McCoy, R.C., et al. Isolation of *Mycobacterium lepromatosis* and development of molecular diagnostic assays to distinguish *Mycobacterium leprae* and *M. lepromatosis*. *Clin Infect Dis.* 71, e262-9 (2020). +60. San Martín, A., Carrasco, C., Fica, A., Navarrete, M., Velásquez, J. & Herrera, T. Enfermedad de Hansen. Una condición emergente en Chile. *Rev Chilena Infectol.* 35, 722-8 (2018). doi: 10.4067/S0716-10182018000600722 +61. Armijo, D., Aguirre, F., Raimann, M. V., da Costa, F. & Barría, C. Enfermedad de Hansen. Comunicación de un caso de lepra tuberculoide en Chile. *Revista chilena de infectología.* 39, 80-85 (2002). https://dx.doi.org/10.4067/S0716-10182022000100080 +62. Lima de, M., F., Silvestre, M. do P. S., A, Santos, E. C. D., et al. The Presence of *Mycobacterium leprae* in Wild Rodents. *Microorganisms.* 28, 1114 (2022). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[0, 0, 999, 999]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[0, 266, 999, 721]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[0, 0, 999, 999]]<|/det|> + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[42, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[42, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[59, 131, 476, 178]]<|/det|> +- RamirezSupplementaryTablesR1.xlsx- RamirezSitterSupplementaryMaterialR1.docx + +<--- Page Split ---> diff --git a/preprint/preprint__06bb8347202433bb91b22abd4d31eeb57d2b64b25f7eb3fe4e53e20191446658/images_list.json b/preprint/preprint__06bb8347202433bb91b22abd4d31eeb57d2b64b25f7eb3fe4e53e20191446658/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..6c48a8808a7f0d5d824ced3780738c47ab91771b --- /dev/null +++ b/preprint/preprint__06bb8347202433bb91b22abd4d31eeb57d2b64b25f7eb3fe4e53e20191446658/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig.1 | Comparison of UDD-AL and MD-AL approaches for a glycine test case. a Schematic", + "footnote": [], + "bbox": [ + [ + 113, + 80, + 860, + 720 + ] + ], + "page_idx": 11 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 | 2D representation of glycine conformational space processed by UMAP dimensionality reduction technique. a 50ns test set. Heat-map represents the relative DFT energy. Glycine insets denote the corresponding conformational region. In panels b-d, data sets and scans are placed over the 50ns test set (gray). b Conformational paths through -COOH (cyan and purple) and -NH2 (red and green) rotations. c N-H (red) and C=O (cyan) bond length scans. d Comparison of training sets sampled by 350K MD-AL (blue) and 350K UDD-AL (orange). Green circle denotes the inner high-energy region. Red triangles denote the scan of -OH rotation around the C-O bond. e Comparison of training sets sampled by 350K MD-AL (blue) and 600K MD-AL (green). f Comparison of training sets sampled by 350K MD-AL (blue) and 1000K MD-AL (cyan).", + "footnote": [], + "bbox": [ + [ + 115, + 120, + 880, + 459 + ] + ], + "page_idx": 13 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 | Glycine interatomic distance distributions in MD-AL and UDD-AL data sets. Each subplot shows a comparison of bond length distributions in 350K MD-AL (blue), 350K UDD-AL (orange), 600K MD-AL (green), and 1000K MD-AL (cyan) data sets. Each subplot also lists the bond length standard deviation, in the legend. a O-H. b N-H. c C-H. d C=O, e C-N. f C-C.", + "footnote": [], + "bbox": [ + [ + 115, + 87, + 881, + 348 + ] + ], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 | Ensemble uncertainty and UDD in acetylacetone. a Acetylacetone molecule. Red circle denotes a hydrogen atom involved in a proton transfer. b Log-normalized map of disagreement \\(\\rho\\) of ANI-1x model ensemble with respect to a position of a circled hydrogen. c Log-normalized map of physical energy. d Log-normalized map of summed physical and bias energy. e and f show a comparison of C-H bond length distributions in the methyl and central groups, respectively, for 350K MD (blue), 350K UDD (orange), and 620K MD simulations. Red ellipse denotes a bond under consideration. Each subplot also lists the bond length standard deviation from the equilibrium distance per legend.", + "footnote": [], + "bbox": [ + [ + 112, + 123, + 875, + 624 + ] + ], + "page_idx": 19 + } +] \ No newline at end of file diff --git a/preprint/preprint__06bb8347202433bb91b22abd4d31eeb57d2b64b25f7eb3fe4e53e20191446658/preprint__06bb8347202433bb91b22abd4d31eeb57d2b64b25f7eb3fe4e53e20191446658.mmd b/preprint/preprint__06bb8347202433bb91b22abd4d31eeb57d2b64b25f7eb3fe4e53e20191446658/preprint__06bb8347202433bb91b22abd4d31eeb57d2b64b25f7eb3fe4e53e20191446658.mmd new file mode 100644 index 0000000000000000000000000000000000000000..a985e655436807c0cbaac60951eef950bff4f2f3 --- /dev/null +++ b/preprint/preprint__06bb8347202433bb91b22abd4d31eeb57d2b64b25f7eb3fe4e53e20191446658/preprint__06bb8347202433bb91b22abd4d31eeb57d2b64b25f7eb3fe4e53e20191446658.mmd @@ -0,0 +1,402 @@ + +# Uncertainty Driven Dynamics for Active Learning of Interatomic Potentials + +Maksim Kulichenko ( mbulichenko@gmail.com) Los Alamos National Laboratory https://orcid.org/0000- 0002- 6194- 3008 + +Kipton Barros Los Alamos National Laboratory + +Nicholas Lubbers Los Alamos National Laboratory + +Ying Wai Li Los Alamos National Laboratory + +Richard Messerly Los Alamos National Laboratory + +Sergei Tretiak Los Alamos National Laboratory https://orcid.org/0000- 0001- 5547- 3647 + +Justin Smith Los Alamos National Laboratory + +Benjamin Nebgen Los Alamos National Laboratory + +Article + +Keywords: + +Posted Date: October 3rd, 2022 + +DOI: https://doi.org/10.21203/rs.3.rs- 2109927/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> + +## Uncertainty Driven Dynamics for Active Learning of Interatomic Potentials. + +Maksim Kulichenko1\*, Kipton Barros1,2, Nicholas Lubbers3, Ying Wai Li3, Richard Messerly1, Sergei Tretiak1,2,4, Justin S. Smith1,5,\*, Benjamin Nebgen1,\* + +1 Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States 2 Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States 3 Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States 4 Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States 5 Nvidia Corporation, Santa Clara, CA 9505, United States + +\* Corresponding authors: maxim@lanl.gov, jusmith@nvidia.com, bnebgen@lanl.gov + +## Abstract + +Machine learning (ML) models, if trained to datasets of high- fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse datasets. In this approach, the ML model provides an uncertainty estimate along with its prediction for each new atomic configuration. If the uncertainty estimate passes a certain threshold, then the configuration is included in the dataset. A key challenge in this process is locating structures for which the model lacks underlying training data. Here, we develop a strategy to more rapidly discover configurations that meaningfully augment the training dataset. The approach, uncertainty driven dynamics for active learning (UDD- AL), modifies the potential energy surface used in molecular dynamics simulations to favor regions of configuration space for which there is large model uncertainty. Performance of UDD- AL is demonstrated for two challenging AL tasks: sampling the conformational space of glycine and sampling the promotion of proton transfer in acetylacetone. The method is shown to efficiently explore chemically relevant configuration + +<--- Page Split ---> + +space, which may be inaccessible using regular dynamical sampling at target temperature conditions. + +## Main + +Machine learning (ML) is a firmly established approach in chemical science that demonstrates great promise for the acceleration of physical simulations. A particular strength of ML models is a robust representation of potential energy surfaces of molecular and materials systems, when trained to large and diverse datasets of high- fidelity quantum chemistry simulations. For example, ML based potentials \(^{1 - 11}\) approach ab initio \(^{12,13}\) or density functional theory (DFT) \(^{14}\) levels of accuracy at a computational cost near that of classical force fields. \(^{15 - 17}\) Over the recent years, various ML- models — such as neural networks (NNs), \(^{18 - 24}\) Gaussian approximation potentials, \(^{25}\) spectral neighbor analysis potentials, \(^{26}\) moment tensor potentials, \(^{27}\) symmetric gradient domain machine learning \(^{28,29}\) — have demonstrated remarkable success in the field atomic- scale discovery. + +No matter how sophisticated the ML model architecture, however, the quality and diversity of the training data remains crucial to ultimate model accuracy since ML models are known to extrapolate poorly to unseen data. Therefore, training sets for ML potentials need to span as much phase (structural) space as possible to perform meaningful simulations. Additionally, the training set needs to be as diverse as possible to avoid overfitting towards excessively represented training data (e.g., near- equilibrium configurations in MD trajectories). + +Entropy- maximization techniques \(^{30,31}\) help to partially overcome these problems by maximizing the structural diversity of a data set. When acquiring new data, these methods are focused on the structural dissimilarity compared to the existing data. However, these methods usually require training of a separate Gaussian process model and significantly rely on the structural representation in latent space. There are other opportunities for improvement as well. Active learning (AL) \(^{32,33}\) attempts to expand the dataset in areas where the ML model is most + +<--- Page Split ---> + +uncertain, which leads to more rapid model improvement. Another feature of AL is that it can employ physically meaningful dynamical trajectories for the sampling of configurations. In the present work, we illustrate how to keep these benefits of AL, while accelerating the rate of new data collection. + +AL32,33,34 aims to iteratively collect diverse training datasets addressing any weaknesses identified in an ML model prediction. For this, it is necessary to estimate uncertainty for a model's predictions.35- 45 A well- established practical strategy for AL with NN potentials is Query by Committee46 (QBC); here, the estimate of uncertainty is the disagreement between a collection of models within an ensemble. Typically, there are 5 to 10 NNs in an ensemble, and these share the same architecture and hyperparameters but, crucially, use a different initial randomization of the model parameters prior to training, and different splits of the training/validation data. It is empirically observed that the variance of the ensemble predictions correlates strongly with actual prediction error,36 suggesting that the prediction task requires extrapolation beyond the range of the training data. In the QBC strategy, if this ensemble variance is observed to be large, then the training set will be augmented with new quantum simulation data. + +AL estimates uncertainty in properties predicted for structures generated by an underlying sampler at each iteration. Molecular dynamics (MD) is the most popular method for sampling chemically meaningful potential energy surfaces. However, MD is susceptible to trapping in near- minimum conformations and only rarely enters chemically important regions such as transition states, which are key data for reactive simulations of chemical processes. In general, capturing thermodynamically rare events is a challenging task for any sampler. For example, metadynamics47- 50 is an effective method of potential energy surface exploration, which operates on the concept of collective variables (CVs). However, CVs require manual selection, and their number is limited in practice. The user needs to "look" at each specific molecule to determine the desired structural parameters to be scanned. Therefore, this approach is not suitable for automatic sampling. + +<--- Page Split ---> + +Here, following the idea of QBC and ensemble uncertainty, we propose a new AL sampling algorithm biased towards regions of high uncertainty - Uncertainty Driven Dynamics (UDD). Due to model random initializations and the stochastic nature of training, regions of chemical space with low ensemble uncertainty will typically arise when similar regions are prevalent in the current training dataset, such that every member of the ensemble is making an accurate inference. Thus, biasing molecular dynamics in the direction of high ensemble uncertainty encourages the dynamics to visit new configurations, which are relevant for improving the diversity of the training set. + +One can regard the uncertainty- based bias potential as similar to metadynamics in the sense that the sampling trajectories are pushed towards less- visited configurational regions. Here, however, CVs need not be defined. We show that within MD- based AL data acquisition, UDD helps substantially reduce the MD simulation time required to enter the high uncertainty region. Most importantly, the proposed approach enables efficient conformational and configurational sampling at low- temperature (low- T) conditions, making this approach essential for temperature sensitive molecules. UDD assists in sampling the chemically relevant subspace of high- energy space which contains important data such as transition states. + +The value of the proposed approach is demonstrated in two test cases. First, UDD- AL is used for conformational sampling of a glycine molecule. We find that the bias potential technique generates a diverse dataset covering both low and high energy regions. This contrasts with high- temperature (high- T) MD- AL, which tends to skip over low energy regions. Second, in tests with acetylacetone at low- T conditions, the bias potential is observed to encourage the sampling of the phase space relevant to a proton transfer. Here we find that, in contrast with regular high- T MD, the bias potential technique encourages the reactive transition with very little distortion to the distribution of other degrees of freedom in the system. + +<--- Page Split ---> + +## Results + +## Uncertainty driven dynamics for active learning (UDD-AL) + +Before introducing UDD- AL, let us first set the context by reviewing the related method of metadynamics. Here the CVs \(s(q)\) are user- defined structural parameters being scanned by external Gaussian bias potentials. Usually, \(3N - 6\) dimensional atomic coordinates \(q\) of the simulated system are mapped to CVs \(s(q)\) . The corresponding energy function is defined as + +\[E_{\mathrm{metadynamics}}(s,t) = \sum_{k\tau < t}W(k\tau)\mathrm{exp}\big[-\sum_{i = 1}^{N_{CV}}\frac{1}{2b_{i}^{2}} (s_{i} - s_{i}(q(k\tau)))^{2}\big], \quad (1)\] + +where \(b_{i}\) is the width of the Gaussian function for the \(i^{\mathrm{th}}\) collective variable, \(W(k\tau)\) is the height of the Gaussian at the simulation time \(t = k\tau\) , which is constant in the case of standard metadynamics, \(\tau\) is the deposition rate of the Gaussian functions, and \(N_{\mathrm{CV}}\) is the number of CVs. During simulations, more Gaussians are added, thus discouraging the system to go back to its previous steps. + +Like metadynamics, UDD- AL method modifies the physical energy by adding a bias potential \(E_{\mathrm{bias}}\) . Here, however, \(E_{\mathrm{bias}}\) will be defined in terms of the model uncertainty rather than CVs. Such uncertainty estimates can be used to assist in the sampling of atomistic data. \(^{44}\) In the QBC approach, an ensemble of NN- potentials is trained, and the level of agreement between the NN predictions serves as the estimate of overall model uncertainty. The QBC uncertainty estimate is proportional to the variance in the energy \((\sigma_{E}^{2})\) + +\[\sigma_{E}^{2} = \frac{1}{2}\Sigma_{i}^{N_{M}}(\hat{E}_{i} - \hat{E})^{2}, \quad (2)\] + +where \(\hat{E}_{i}\) is the energy predicted by an ensemble member, \(\hat{E}\) is its ensemble average, and \(N_{\mathrm{M}}\) is the number of ensemble members, i.e., NN- potentials. Here, ensembles of ANI potentials are prepared using an 8- fold cross validation split of the data set which yields \(N_{\mathrm{M}} = 8\) ensemble members (see Methods). In previous applications of QBC- based AL, new data is collected when the uncertainty estimator + +\[\rho = \sqrt{2 / N_{M}N_{A}}\sigma_{E} \quad (3)\] + +<--- Page Split ---> + +exceeds a threshold, where \(N_{A}\) is the number of atoms in a configuration. + +We seek to construct a bias energy \(E_{\mathrm{bias}}(\sigma_{E}^{2})\) that favors configurations with larger uncertainties \(\rho \propto \sigma_{E}\) ; such configurations are expected to correlate with regions that are underrepresented in the training data. A reasonable choice is the Gaussian function, + +\[E_{\mathrm{bias}}(\sigma_{E}^{2}) = A\left[\exp \left(-\frac{\sigma_{E}^{2}}{N_{M}N_{A}B^{2}}\right) - 1\right]. \quad (4)\] + +The magnitude \(A\) and width \(B\) of the biasing should be selected empirically. The bias potential goes to zero in the absence of uncertainty, \(E_{\mathrm{bias}}(0) = 0\) . Configurations with large uncertainty, \(\rho \gg B\) , are favored by a bias energy of magnitude \(E_{\mathrm{bias}} \approx - A\) . Forces derived from the bias potential are strongest when the uncertainty \(\rho\) is of the same order as the parameter \(B\) . + +The combined potential \(\hat{E} + E_{\mathrm{bias}}\) is used to define an uncertainty driven dynamics (UDD). In applications to AL the overall strategy will be denoted UDD- AL. The schematic workflow of UDD- AL is depicted in Fig. 1a. It should be compared to the usual MD based approach (MD- AL), which does not incorporate the \(E_{\mathrm{bias}}\) term. + +The optimal parameters \(A\) and \(B\) will be context dependent. For example, our applications to glycine and acetylacetone tests discussed below suggest that the bias magnitude \(A\) should be at least of the order of the energy barriers of interest. The bias width, \(B\) , should also be carefully selected. If \(B\) is either too small or too large, the bias forces could become negligibly small for the typical uncertainties \(\rho\) that are observed in the AL sampling. + +Effective bias forces on an atom at position \(r\) can be calculated using the chain rule, \[ -\frac{\partial}{\partial r} E_{\mathrm{bias}}(\sigma_{E}^{2}) = -E_{\mathrm{bias}}(\sigma_{E}^{2})'\frac{\partial}{\partial r}\sigma_{E}^{2} \] and \[ -\frac{\partial}{\partial r}\sigma_{E}^{2} = -\sum_{i}^{M}(\hat{E}_{i} - \hat{E})\frac{\partial}{\partial r}(\hat{E}_{i} - \hat{E}) = \sum_{i}^{M}(\hat{E}_{i} - \hat{E})(\hat{f}_{i} - \hat{f}) \] + +\[-\frac{\partial}{\partial r}\sigma_{E}^{2} = -\sum_{i}^{M}(\hat{E}_{i} - \hat{E})\frac{\partial}{\partial r} (\hat{E}_{i} - \hat{E}) = \sum_{i}^{M}(\hat{E}_{i} - \hat{E})(\hat{f}_{i} - \hat{f}) \quad (6)\] + +<--- Page Split ---> + +where \(\hat{f}_{t}\) denotes the force vector predicted by an ensemble member and \(\hat{f}\) is the ensemble- averaged prediction. Since \(\sigma_{E}^{2}\) , \(\hat{E}_{t}\) , and \(\hat{f}_{t}\) are calculated at each MD step in any AL approach, including the bias potential has a negligible effect on the computational time. + +## Glycine conformational space sampling + +The glycine molecule is shown in Fig. 1a. We are interested in sampling the conformational space without bond- breaking events using various AL protocols. Dihedral rotations of \(- \mathrm{NH}_{2}\) and \(- \mathrm{COOH}\) groups correspond to 2.5- 3.5 kcal/mol barriers. Our numerical tests have shown that the bias magnitude \(A\) approximately five times higher than the average barrier of interest provides the best results for the glycine test. It helps keep the bias essentially high at higher uncertainty values. It also helps overcome possible barrier bottlenecks caused by possible overestimation of barrier heights by ML model. The geometries near global energy minimum (GM) of a glycine are already present in the initial training set, and each sampling MD simulation starts with this kind of structure. Depending on the AL iteration, near- GM structures have \(\rho\) of \(0.024 \pm 0.005 [\mathrm{kcal} \times \mathrm{mol}^{- 1} \times (\sqrt{\mathrm{N}_{\mathrm{A}}})^{- 1}]\) \((\sigma_{E} = 0.15 \pm 0.03 \mathrm{kcal / mol})\) . Thus, we selected \(A\) equal to 15.4 kcal/mol which corresponds to the bias potential value of \(- 15.0 \mathrm{kcal / mol}\) at near- GM values of \(\rho\) (with \(B = 0.12 \mathrm{kcal / mol}\) ). We further increase \(A\) by \(15\%\) at 140ps time step if the uncertainty criterion is not met at this simulation stage. Our tests also show that best results are achieved for \(B\) being close to near- GM uncertainty \(\sigma_{E}\) . Here we use \(B = 0.12 \mathrm{kcal / mol}\) for glycine. + +We next compare the two active learning approaches – UDD- AL and MD- AL – for the task of collecting a dataset of the glycine conformational space. Each AL iteration performs 16 MD simulations with 200ps time limit and 1fs step (see Methods). An ensemble of NN- potentials for the first AL iteration is trained on the initial data set of 125 conformers that span near- equilibrium structures of the glycine global minimum. At each subsequent AL iteration, the MD simulation employs an ensemble of ANI- type NN- potentials (see Methods and Ref. 51), trained on the initial + +<--- Page Split ---> + +data and data accumulated on all previous AL iterations. The starting geometries for MD simulations and the initial training set contain only near- equilibrium geometries of a glycine global minimum (Extended Data Fig. 1). Stated differently, NNs have no initial information about higher energy conformers, and MD simulations have to reach them from the bottom of the potential energy surface. Each MD simulation is terminated when the system meets the uncertainty selection criteria \(\rho\) of \(0.35 [\mathrm{kcal} \times \mathrm{mol}^{- 1} \times (\sqrt{N_{\mathrm{a}}})^{- 1}]\) (see Methods). If the MD simulation reaches the time limit, then the structure from the trajectory with the highest uncertainty is selected. Density Functional Theory (DFT) reference data (see Methods) is then computed for final conformations and added to the training set for the next iteration of the AL process. + +Figure 1b shows the average MD simulation time required to meet the uncertainty criterion in different AL approaches with respect to AL iteration. The MD- AL at low- T conditions (350K) reaches the MD time limit at \(\sim 20^{\mathrm{th}}\) iteration which is continued until the final AL iteration (Fig. 1b, blue line). This means that the specified uncertainty criterion is almost never met, and the sampler returns the geometry of maximum available uncertainty from the MD trajectory. The uncertainty bias potential is introduced in low- T (350K) MD simulations (orange line). We do not activate the bias potential at earlier AL iterations for two reasons. First, the low- T MD- AL (Fig. 1b, blue line) does not reach the MD simulation time limit up to \(15 - 20^{\mathrm{th}}\) iteration. Thus, at this stage, the regular MD- AL manages to acquire new data satisfying the uncertainty criteria. Second, the NN potential might be unstable or not smooth at earlier iterations due to the lack of data. Therefore, the bias is activated at the \(15^{\mathrm{th}}\) iteration to avoid moving systems toward unphysical configurations. When the uncertainty bias potential is on (UDD- AL regime), it reduces the number of MD steps needed to meet the desired uncertainty. Moreover, the MD time limit plateau is still not reached till the final iteration. + +Perhaps the most common way to accelerate sampling of high- energy states is to run high- T MD. Thus, to illustrate the difference between the bias potential and a simple temperature + +<--- Page Split ---> + +increase, we also compare the low- T 350K UDD- AL with the high- T MD- ALs at 600K and 1000K simulation conditions (Fig. 1b). Like in case of UDD- AL sampling, the temperature is increased at the 15th iteration. 600K MD- AL approaches the MD time limit at \(\sim 40\) th iteration while UDD- AL reaches the time limit plateau by the end of AL procedure. Thus, 600K MD- AL does not perform as well as UDD- AL in terms of simulation time. On the other hand, the 1000K MD- AL (cyan line) exhibits a faster sampling, and the average MD time does not exceed 35ps even at the last AL iteration. + +Energy ranges sampled by each AL approach are depicted in Fig.1c. Expectedly, high- T MD- AL data (green and cyan histograms) span wider energy ranges compared to low- T 350K MD- AL (blue histogram). What is more interesting is that the energy distribution of data from the 350K UDD- AL (orange histogram) is very similar to the shape of 600K MD- AL data; an advantage of UDD- AL is that many fewer MD steps are required to collect these samples. + +After completing the entire AL procedure, the final models are trained on 1280 glycine conformers collected during the entire AL procedure (+125 conformers in the initial training set). In order to access the accuracy of the four models, we use a test set comprising 50,000 glycine structures from a 50ns MD simulation via the ANI- 1ccx potential run at 400K with a 0.5fs time step (see Methods).19 As depicted in Fig. 1d, all models perform reasonably well with root- mean- square deviations (RMSEs) less than 0.3 kcal/mol. However, the RMSE of the model trained on low- T 350K MD- AL data is slightly yet systematically lower than RMSEs of the rest of the models with \(\sim 0.11\) kcal/mol difference. This is likely because MD simulations tend to oscillate in near- equilibrium positions, which is why this test set is dominated by low- energy geometries. Low- T 350K MD- AL, in turn, densely spans a narrow low- energy range, which might explain a slightly better performance of this model on a test set derived from a 50ns MD. Indeed, normalized histograms in Fig.1e point out that this seemingly large 50ns test set spans the energy region closest (but even lower) to the one covered by low- T 350K MD- AL data. + +<--- Page Split ---> + +Note that the 50ns test set is a MD trajectory with no AL involved while MD- AL data comprises structures with high uncertainties. High uncertainty usually corresponds to a higher energy due to poor sampling in normal low- T MD. In other words, the 50ns MD test set is biased towards near- equilibrium oscillations, but MD- AL data are selectively augmented with higher energy isomers. Similar RMSE shift is observed for low energy rotations of \(- \text{COOH}\) and \(- \text{NH}_2\) functional groups (Extended Data Fig.2). Therefore, the various models should also be tested on higher energy pathways. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig.1 | Comparison of UDD-AL and MD-AL approaches for a glycine test case. a Schematic
+ +representation of UDD- AL workflow. b Average MD time required to meet the uncertainty criterion vs. AL iteration for four different MD simulation types: 350K MD- AL (blue), 600K MD- AL (green), 1000K MD- AL (cyan), and 350K UDD- AL (orange). c Energy distribution histograms of four datasets sampled by 350K MD- AL (blue), 600K AL MD- AL (green), 1000K MD- AL (cyan), and 350K UDD- AL (orange). Data from iterations 0- 14 is omitted because the bias is off, or the temperature is not increased at this stage. d + +<--- Page Split ---> + +Comparison of potential energy RMSE obtained on the 50ns test set vs. AL iteration (i.e., training set size, 16 new glycine conformations per iteration). The legend shows RMSEs for models trained on data from the entire AL procedure. e Normalized energy distribution histograms of 50ns test set (red) and training set sampled by 350K MD- AL (blue). Lines in b and d, are averaged over three ensembles, each trained on data from an independent AL procedure. + +To illustrate what types of chemical processes appear in the system, and how each sampling method covers them, we next visualize the glycine conformational space using dimensionality reduction. We project samples to a 2D plane using the Uniform Manifold Approximation and Projection (UMAP) \(^{52}\) technique, where each conformation is characterized by the 672- D vector of activations (concatenated atomic environment vectors) in the first layer of an independent pre- trained ANI- 1x model. \(^{19}\) Figure 2a shows the 50ns MD dataset spanning four low- lying glycine conformers represented by four regions in the 2D space. Torsional conformations, N- H bond scan, and C=O bond scan are depicted in Fig.2b- c to illustrate structural profiles in 2D space. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2 | 2D representation of glycine conformational space processed by UMAP dimensionality reduction technique. a 50ns test set. Heat-map represents the relative DFT energy. Glycine insets denote the corresponding conformational region. In panels b-d, data sets and scans are placed over the 50ns test set (gray). b Conformational paths through -COOH (cyan and purple) and -NH2 (red and green) rotations. c N-H (red) and C=O (cyan) bond length scans. d Comparison of training sets sampled by 350K MD-AL (blue) and 350K UDD-AL (orange). Green circle denotes the inner high-energy region. Red triangles denote the scan of -OH rotation around the C-O bond. e Comparison of training sets sampled by 350K MD-AL (blue) and 600K MD-AL (green). f Comparison of training sets sampled by 350K MD-AL (blue) and 1000K MD-AL (cyan).
+ +Figure 2d- f depicts the four datasets visualized over the 50ns test set. Figure 2d provides a visual comparison of data sampled by MD- AL and UDD- AL at 350K. Both datasets cover the 50ns MD data reasonably well. However, there are three key differences. First, a high- energy configurational space (points inside the green circle) is more densely sampled in the UDD- AL dataset: there are 289 points in this non- equilibrium region in the UDD- AL sampling, compared to + +<--- Page Split ---> + +105 points in 350K MD- AL. Second, UDD- AL encountered a new conformational path in the top right corner of the Fig.2d which was not accessed by 350K MD- AL. This region corresponds to the rotation of the - OH group around the C- O bond which is a distinct conformational transition and a high- energy profile with a 15 kcal/mol barrier. + +Figure 2e presents a visual comparison of the sampling performance of MD- ALs at 350K and 600K. The 2D representation of 600K data is quite similar with the one of 350 UDD- AL data: there are 290 data points inside the inner circled region and a good coverage of - OH rotation region. As expected, MD- AL at the extreme temperature of 1000K (Fig.2f) samples the inner high- energy region even more densely (394 data points) as well as the - OH rotation region. This, however, comes at a cost. The low energy region in the lower left of Fig.2f clearly demonstrates lack of sampling. This is the primary deficiency of using high- T MD: as temperature increases, the system spends less time near low energy regions, since in these regions the kinetic energy is typically the greatest. Thus, it will be possible to 'skip over' regions of high stability thus resulting in a poor data coverage of near equilibrium region. On the other hand, UDD- AL sampling does not run this risk by sufficiently sampling any relevant region. + +Thus, Figure 2 indicates that the UDD- AL is a good, balanced way of sampling chemical space, reaching most of the high energy points achieved with 1000K MD sampling without losing data density in low energy regions. However, alone, it is not clear that biased sampling presents advantages over unbiased high- T sampling; UDD- AL appears to sample similar configurations to 600K MD- AL. Therefore, we performed additional tests of high- energy pathways that illuminate the differences between 600K MD- AL and 350K UDD- AL. The discussion on high energy profiles - angle and bond scans - can be found in Supplementary Information. The overall trend is that the 350K UDD- AL model exhibits much better accuracy than the model trained on low- T 350K MD- AL data. When comparing the UDD- AL with 600K and 1000K MD- AL models, the former results in a better or, at least, comparable accuracy. + +<--- Page Split ---> + +We also provide an overall assessment of the performance of the sampling strategy by cross- testing the associated models on the data from all sampling strategies in Supplementary information. Supplementary Table 2 summarizes RMSEs of the four models on the test sets accumulated by each AL sampler: 350K MD- AL, 350K UDD- AL, 600K MD- AL, and 1000K MD- AL. When testing models on data sets that are not generated by the same corresponding sampler, the UDD- AL model outperforms all other models. A detailed discussion on models' cross testing can be found in Supplementary Information. + +Ultimately, when looking at a variety of bond rotations and stretches, the most accurate energy profile changes depending on the energy range of the specific scan. Low energy profiles tend to be modeled better by the low- T dataset while higher energy scans are accessed better by the high- T dataset. However, the UDD- AL sampling method yields a model that performs well on a wide range of energy profiles, while also maintaining a low error on each sampling method's held out test set (Table 2 in Supplementary Information). This difference suggests that UDD- AL is able to avoid the higher- energy and less chemically relevant structural distortions, which are typical at very high temperatures. Meanwhile, chemically relevant data present in the UDD- AL data set enables efficient extrapolation to higher- energy structures present in 1000K MD- AL data. As can be seen in Fig. 3, the shapes of interatomic distance distributions in UDD- AL closely mimic sharp distributions in low- T 350K MD- AL, although having a larger standard deviation. This deviation, however, is lower than in 600K and 1000K MD- AL data sets that span a wider distance range. This, in turn, further suggests that the UDD sampler tends to avoid random distortions found in high- T regimes. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3 | Glycine interatomic distance distributions in MD-AL and UDD-AL data sets. Each subplot shows a comparison of bond length distributions in 350K MD-AL (blue), 350K UDD-AL (orange), 600K MD-AL (green), and 1000K MD-AL (cyan) data sets. Each subplot also lists the bond length standard deviation, in the legend. a O-H. b N-H. c C-H. d C=O, e C-N. f C-C.
+ +Since all models used the same hyperparameters, it is possible that each model could perform better if individual hyperparameter searches are carried out. Perhaps, data sets that cover a broader chemical space need more learnable parameters to be flexible enough to fit the effectively larger degrees of freedom they are being trained to. This will be a subject of future studies. + +## Proton transfer in acetylacetone + +We further examine the UDD performance for sampling of a reactive pathway in an acetylacetone enol tautomer depicted in Fig. 4a. Particularly, we are interested in the proton transfer between the two oxygen atoms, considering the proton position as a free variable. Instead of using AL techniques, here we use an ensemble of pre- trained ANI- 1x interatomic potentials,19 which were not trained on bond- breaking reactions, and analyze trajectories from UDD and MD simulations. ANI- 1x was trained on wB97x DFT level of theory which yields a 4.7 kcal/mol barrier. However, ANI- 1x significantly overestimates the barrier giving the value of 6.3 kcal/mol. This value + +<--- Page Split ---> + +is then selected as a bias magnitude \(A\) . Uncertainty values \(\rho\) of near-equilibrium acetylacetone structures within ANI- 1x model are higher by the order of magnitude than the ones produced by the newly trained model for glycine. Therefore, we set a higher value of bias width \(B = 0.45\) kcal/mol, being an empirically adjusted parameter. + +Figure 4b shows log- normalized uncertainty \(\rho\) of the acetylacetone system with respect to the position of the proton. The dark region (near \(x = 0\) Å and \(y = - 1.5\) Å) demonstrates that there is a high- uncertainty region between oxygen atoms which corresponds to a proton transfer transition state. Figure 4c depicts the log- normalized relative potential energy (ANI- 1x) of the system with respect to the position of the free proton. The dark region (near \(x = - 0.5\) Å and \(y = - 1.5\) Å) indicates that the lowest energy corresponds to a proton position near the oxygen atom. This is an expected result since the hydrogen bound to the oxygen atom is the most stable geometry. However, as shown in Fig. 4d, the energy minimum can be shifted to the central position between the oxygen atoms (dark region near \(x = 0\) Å) when the bias potential is applied. For illustrative purposes, here we use a high value of bias magnitude \(A = 56.0\) kcal/mol. In practice, we use \(A = 6.3\) kcal/mol for the MD simulation discussed below. The effect of different \(A\) values on the total energy landscape is depicted in Extended Data Fig. 3. + +We further analyze results from 0.5ns trajectories obtained using UDD and regular MD simulation techniques. No proton transfer occurs during the regular 350K MD simulation. Meanwhile, the uncertainty bias can direct the proton toward a high- uncertainty region between two oxygen atoms - 90 proton transitions observed in 350K UDD simulation. Finally, unbiased high- T 620K MD results in 48 proton transitions. Although at a lower rate compared to UDD, increased temperature also facilitates the proton transfer. Time traces of the two O- H distances can be further found in Extended Data Fig. 4. + +A key difference between low- T UDD and regular high- T MDs can be found when analyzing oscillations of interatomic distances. Certainly, high- T conditions will affect the entire molecule causing larger distance deviations compared to low- T conditions. Indeed, the overall + +<--- Page Split ---> + +spread of O- H distances is comparable using 350K MD (Extended Data Fig. 4), but far wider at 620K MD, even in segments of the trajectory without proton transfer. Further analysis of C- H distances in the molecule, shown in Fig. 4e- f, confirms this phenomenon. Figure 4e shows C- H distance distributions in the methyl group in 350K MD, 350K UDD, and 620K MD simulations. 620K MD exhibits higher deviations from the equilibrium C- H bond length compared to low- T UDD. The standard deviation of the methyl C- H distance in 620K MD is 0.04 Å against 0.03 Å in 350K UDD. Notably, the low- T 350K MD trajectory has the standard deviation of 0.03 Å, same as in low- T UDD. The same picture holds for the central C- H bond shown in Fig. 4f. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4 | Ensemble uncertainty and UDD in acetylacetone. a Acetylacetone molecule. Red circle denotes a hydrogen atom involved in a proton transfer. b Log-normalized map of disagreement \(\rho\) of ANI-1x model ensemble with respect to a position of a circled hydrogen. c Log-normalized map of physical energy. d Log-normalized map of summed physical and bias energy. e and f show a comparison of C-H bond length distributions in the methyl and central groups, respectively, for 350K MD (blue), 350K UDD (orange), and 620K MD simulations. Red ellipse denotes a bond under consideration. Each subplot also lists the bond length standard deviation from the equilibrium distance per legend.
+ +These observations confirm that although high- T sampling promotes the activation of reactive pathways, it has a global effect on all degrees of freedom in the system, whereas the + +<--- Page Split ---> + +UDD technique allows to sample the reactive pathway without significant changes to equilibrium distributions of other degrees of freedom. This is likely due to the composition of ANI- 1x training data, which has information on non- equilibrium extended bonds. However, a hydrogen which is equidistant between two oxygen atoms is not a commonly encountered configurational data. Thus, the UDD potential promotes sampling of this specific region. + +## Discussion + +In this work, we present a MD- based active learning (AL) algorithm assisted by the uncertainty- based bias potential. The algorithm is abbreviated as UDD- AL (uncertainty driven dynamics for active learning) and is compared to MD- based AL (MD- AL) in common use. We show that an uncertainty metric provided by an ensemble of NN- potentials can be used to construct the bias potential. The resulting energy term encourages the system to move toward underrepresented chemical regions, accelerating and improving sampling of high- energy regions. + +The ANI model, trained on glycine conformational data from low- T UDD- AL, is shown to properly reproduce conformational pathways not accessible by low- T MD- AL. The resulting ANI model exhibits high accuracy for important high- energy conformational profiles. Importantly, while this accuracy is better than or comparable to the results for models trained on high- T MD- AL data, a low- T UDD- AL training set spans an energy range narrower than that in high- T sets. However, a model trained on low- T UDD- AL data exhibits excellent accuracy on data sampled at unbiased high- T conditions which is not the case in the reverse test. This, in turn, suggests that the UDD- AL approach avoids oversampling the extreme structural distortions that are common at very high temperatures. + +The test case of acetylacetone shows that bias potential applied to a pre- trained ANI- 1x model promotes proton transfer in the enol tautomer keeping oscillations in the rest of the bond distances identical to low- T conditions. Opposite situation is observed for the unbiased high- T MD where interatomic distances exhibit larger deviations from equilibrium positions with a lower rate + +<--- Page Split ---> + +of a proton transfer. The analysis of interatomic distances clearly indicates an advantage of the bias potential over the high- T approach: bias potential facilitates sampling of important underrepresented chemical data without random structural distortions caused by high- T conditions. This feature can be used for efficient sampling of conformational and/or configurational space of temperature sensitive systems. + +Our tests indicate that the bias potential can facilitate sampling of high energy chemical space without sacrificing the sampling of low energy configurations. This means that UDD will produce robust datasets that are applicable to both lower energy, near global minimum data and high energy chemical space which usually corresponds to important reactive structural data such as transition states and intermediates. + +As the results show, uncertainty- based bias potential is a promising technique for sampling rare events while being relatively faithful to the physical equilibrium distribution. The uncertainty driven dynamics is similar to metadynamics47- 50 in its use a bias potential but avoids the need to manually select collective variables or to identify basins of attraction. In a way, it defines the best CV for the purpose of AL: training a more general and robust ML potential. The approach requires selection of two parameters: the bias magnitude and width; developing a method which can tune these algorithmically would be a productive future activity. Additionally, the algorithm for automatic selection of uncertainty criteria could improve the sampling efficiency. + +## Methods + +## Active learning + +For glycine simulations, we use ANI deep learning model51 to generate ensembles of NN potentials prepared using an 8- fold cross validation split of the data set. The empirical value 0.23 \([kcal \times mol^{- 1} \times (\sqrt{N_A})^{- 1}]\) for the uncertainty selection criteria \(\rho\) , equation (3), provided in the original work on active learning for organic molecules36 turned out to be too low for the purposes + +<--- Page Split ---> + +of training on one chemical system. It causes unnecessarily dense sampling of glycine conformational space which, in turn, hinders the MD simulation to reach higher energy regions. Therefore, we use a higher value of \(0.35 [kcal \times mol^{- 1} \times (\sqrt{N_A})^{- 1}]\) for this test case. Overall, automatic selection of uncertainty criteria is a nontrivial question which deserves a separate discussion and goes beyond the scope of this work. Each MD simulation is terminated when the system meets the uncertainty selection criteria \(\rho\) . + +The initial training set consists of 125 glycine geometries that span near- equilibrium structures of the glycine global minimum. This data is acquired from a separate 5ps MD trajectory at 350K with a 0.5 fs timestep, initialized from the glycine global minimum. Every \(80^{\text{th}}\) MD step is included in the initial data training set. The MD simulation for the initial training set is carried out using the pre- trained ANI- 1x potential. \(^{51}\) + +## MD Simulations + +In all discussed cases, the Atomic Simulation Environment (ASE) Langevin thermostat was used to maintain temperature with a friction coefficient of 0.01 a.u. Each AL iteration performs 16 MD simulations with 1fs time step and a 200 000 steps limit (200ps). At each AL iteration, the MD is driven by an ensemble of ANI- type ML potentials, trained on initial data and data accumulated on previous AL iterations. The NN- based MD is interfaced with ASE code. \(^{53}\) The final data set has 1280 data points sampled in AL procedure + 125 data points from the initial data set. + +The set of seed geometries for MD simulations comprises 25 structures that correspond to near- equilibrium geometries of a glycine global minimum (GM). These are selected as the first 25 structures from the initial training set. AL sampler randomly selects one of them for each MD initialization. Energies and forces of new conformers are calculated using the WB97X- D/cc- pVTZ \(^{54,55}\) level of theory as implemented in PSI4 code. \(^{56}\) + +<--- Page Split ---> + +## NN architecture + +NN architectureParameters for the atomic environment vector \(^{51}\) (a numerical vector used to encode the atomic local environment in ANI) used during the AL process were constant. 32 evenly spaced shifting parameters are used for the radial part of the vector with 4.6 Å cutoff radius and a total of 8 radial and 8 angular shifting parameters are used for the angular part with 3.5 Å cutoff radius. With four atom types, this gives 768 elements in the descriptor. The first atom- centered function is shifted to 0.8 Å from the atomic center. The ANI potential used in this work contains three hidden layers and has the following architecture: 768:32:16:8:1, each number describing the number of neurons at each subsequent layer in the network. + +The ANI potential used in this work contains three hidden layers and has the following architecture: 768:32:16:8:1. Gaussian activation functions are used in hidden layers and linear activation in the final layer. + +## Acknowledgements + +AcknowledgementsK. B., N. L., R. M., S. T., and B. N. acknowledge support from the US DOE, Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division under Triad National Security, LLC ("Triad") contract Grant 89233218CNA000001 (FWP: LANLE3F2). M. K. and Y. W. L. acknowledge support from the Los Alamos National Laboratory (LANL) Directed Research and Development funds. This work was performed in part at the Center for Nonlinear Studies and the Center for Integrated Nanotechnology, a US Department of Energy (DOE) and Office of Basic Energy Sciences user facility. This research used resources provided by the LANL Institutional Computing Program, which is supported by the US DOE National Nuclear Security Administration under Contract 89233218CNA000001. We also acknowledge the CCS- 7 Darwin cluster at LANL for additional computing resources. + +<--- Page Split ---> + +## Data Availability + +Source data are provided with this Paper. + +## Code Availability + +Two implementations of the ANI neural network architecture are available online: + +TorchANI (https://github.com/aiqm/torchani) and NeuroChem (https://github.com/ + +atomistic- ml/neurochem). + +## References + +1. Kulichenko, M. et al. The Rise of Neural Networks for Materials and Chemical Dynamics. J. Phys. Chem. Lett. 12, 6227–6243 (2021). +2. Dral, P. O. 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Query by committee. in Proceedings of the fifth annual workshop on Computational learning theory 287-294 (Association for Computing Machinery, 1992). doi:10.1145/130385.130417. + +47. Laio, A. & Parrinello, M. Escaping free-energy minima. Proceedings of the National Academy of Sciences 99, 12562-12566 (2002). + +48. Laio, A. & Gervasio, F. L. Metadynamics: a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science. Rep. Prog. Phys. 71, 126601 (2008). + +49. Sutto, L., Marsili, S. & Gervasio, F. L. New advances in metadynamics. WIREs Computational Molecular Science 2, 771-779 (2012). + +50. Valsson, O., Tiwary, P. & Parrinello, M. Enhancing Important Fluctuations: Rare Events and Metadynamics from a Conceptual Viewpoint. Annual Review of Physical Chemistry 67, 159-184 (2016). + +51. Smith, J. S., Isayev, O. & Roitberg, A. E. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci. 8, 3192-3203 (2017). + +52. Sainburg, T., McInnes, L. & Gentner, T. Q. Parametric UMAP embeddings for representation and semi-supervised learning. Preprint at + +<--- Page Split ---> + +https://doi.org/10.48550/arXiv.2009.12981 (2021). + +53. Larsen, A. H. et al. The atomic simulation environment—a Python library for working with atoms. J. Phys.: Condens. Matter 29, 273002 (2017). + +54. Chai, J.-D. & Head-Gordon, M. Long-range corrected hybrid density functionals with damped atom–atom dispersion corrections. Phys. Chem. Chem. Phys. 10, 6615–6620 (2008). + +55. Dunning, T. H. Gaussian basis sets for use in correlated molecular calculations. I. The atoms boron through neon and hydrogen. J. Chem. Phys. 90, 1007–1023 (1989). + +56. Smith, D. G. A. et al. PSI4 1.4: Open-source software for high-throughput quantum chemistry. J. Chem. Phys. 152, 184108 (2020). + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- Sl.docx- ExtendedDataFig1.jpg- ExtendedDataFig2.jpg- ExtendedDataFig3.jpg- ExtendedDataFig4.jpg + +<--- Page Split ---> diff --git a/preprint/preprint__06bb8347202433bb91b22abd4d31eeb57d2b64b25f7eb3fe4e53e20191446658/preprint__06bb8347202433bb91b22abd4d31eeb57d2b64b25f7eb3fe4e53e20191446658_det.mmd b/preprint/preprint__06bb8347202433bb91b22abd4d31eeb57d2b64b25f7eb3fe4e53e20191446658/preprint__06bb8347202433bb91b22abd4d31eeb57d2b64b25f7eb3fe4e53e20191446658_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..6adda46132582b807d3b714f28c39d1f17ea0ebd --- /dev/null +++ b/preprint/preprint__06bb8347202433bb91b22abd4d31eeb57d2b64b25f7eb3fe4e53e20191446658/preprint__06bb8347202433bb91b22abd4d31eeb57d2b64b25f7eb3fe4e53e20191446658_det.mmd @@ -0,0 +1,535 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 938, 175]]<|/det|> +# Uncertainty Driven Dynamics for Active Learning of Interatomic Potentials + +<|ref|>text<|/ref|><|det|>[[44, 195, 697, 238]]<|/det|> +Maksim Kulichenko ( mbulichenko@gmail.com) Los Alamos National Laboratory https://orcid.org/0000- 0002- 6194- 3008 + +<|ref|>text<|/ref|><|det|>[[44, 243, 340, 285]]<|/det|> +Kipton Barros Los Alamos National Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 291, 340, 332]]<|/det|> +Nicholas Lubbers Los Alamos National Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 338, 340, 379]]<|/det|> +Ying Wai Li Los Alamos National Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 384, 340, 425]]<|/det|> +Richard Messerly Los Alamos National Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 430, 697, 471]]<|/det|> +Sergei Tretiak Los Alamos National Laboratory https://orcid.org/0000- 0001- 5547- 3647 + +<|ref|>text<|/ref|><|det|>[[44, 476, 340, 517]]<|/det|> +Justin Smith Los Alamos National Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 522, 340, 563]]<|/det|> +Benjamin Nebgen Los Alamos National Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 603, 102, 620]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 641, 137, 659]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 678, 315, 697]]<|/det|> +Posted Date: October 3rd, 2022 + +<|ref|>text<|/ref|><|det|>[[44, 716, 475, 735]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 2109927/v1 + +<|ref|>text<|/ref|><|det|>[[44, 754, 910, 797]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 800, 144]]<|/det|> +## Uncertainty Driven Dynamics for Active Learning of Interatomic Potentials. + +<|ref|>text<|/ref|><|det|>[[115, 167, 840, 206]]<|/det|> +Maksim Kulichenko1\*, Kipton Barros1,2, Nicholas Lubbers3, Ying Wai Li3, Richard Messerly1, Sergei Tretiak1,2,4, Justin S. Smith1,5,\*, Benjamin Nebgen1,\* + +<|ref|>text<|/ref|><|det|>[[113, 223, 881, 391]]<|/det|> +1 Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States 2 Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States 3 Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States 4 Center for Integrated Nanotechnologies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States 5 Nvidia Corporation, Santa Clara, CA 9505, United States + +<|ref|>text<|/ref|><|det|>[[115, 408, 785, 427]]<|/det|> +\* Corresponding authors: maxim@lanl.gov, jusmith@nvidia.com, bnebgen@lanl.gov + +<|ref|>sub_title<|/ref|><|det|>[[115, 453, 215, 474]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[112, 483, 886, 858]]<|/det|> +Machine learning (ML) models, if trained to datasets of high- fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse datasets. In this approach, the ML model provides an uncertainty estimate along with its prediction for each new atomic configuration. If the uncertainty estimate passes a certain threshold, then the configuration is included in the dataset. A key challenge in this process is locating structures for which the model lacks underlying training data. Here, we develop a strategy to more rapidly discover configurations that meaningfully augment the training dataset. The approach, uncertainty driven dynamics for active learning (UDD- AL), modifies the potential energy surface used in molecular dynamics simulations to favor regions of configuration space for which there is large model uncertainty. Performance of UDD- AL is demonstrated for two challenging AL tasks: sampling the conformational space of glycine and sampling the promotion of proton transfer in acetylacetone. The method is shown to efficiently explore chemically relevant configuration + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 139]]<|/det|> +space, which may be inaccessible using regular dynamical sampling at target temperature conditions. + +<|ref|>sub_title<|/ref|><|det|>[[115, 180, 175, 201]]<|/det|> +## Main + +<|ref|>text<|/ref|><|det|>[[113, 210, 886, 520]]<|/det|> +Machine learning (ML) is a firmly established approach in chemical science that demonstrates great promise for the acceleration of physical simulations. A particular strength of ML models is a robust representation of potential energy surfaces of molecular and materials systems, when trained to large and diverse datasets of high- fidelity quantum chemistry simulations. For example, ML based potentials \(^{1 - 11}\) approach ab initio \(^{12,13}\) or density functional theory (DFT) \(^{14}\) levels of accuracy at a computational cost near that of classical force fields. \(^{15 - 17}\) Over the recent years, various ML- models — such as neural networks (NNs), \(^{18 - 24}\) Gaussian approximation potentials, \(^{25}\) spectral neighbor analysis potentials, \(^{26}\) moment tensor potentials, \(^{27}\) symmetric gradient domain machine learning \(^{28,29}\) — have demonstrated remarkable success in the field atomic- scale discovery. + +<|ref|>text<|/ref|><|det|>[[113, 530, 885, 710]]<|/det|> +No matter how sophisticated the ML model architecture, however, the quality and diversity of the training data remains crucial to ultimate model accuracy since ML models are known to extrapolate poorly to unseen data. Therefore, training sets for ML potentials need to span as much phase (structural) space as possible to perform meaningful simulations. Additionally, the training set needs to be as diverse as possible to avoid overfitting towards excessively represented training data (e.g., near- equilibrium configurations in MD trajectories). + +<|ref|>text<|/ref|><|det|>[[113, 722, 885, 902]]<|/det|> +Entropy- maximization techniques \(^{30,31}\) help to partially overcome these problems by maximizing the structural diversity of a data set. When acquiring new data, these methods are focused on the structural dissimilarity compared to the existing data. However, these methods usually require training of a separate Gaussian process model and significantly rely on the structural representation in latent space. There are other opportunities for improvement as well. Active learning (AL) \(^{32,33}\) attempts to expand the dataset in areas where the ML model is most + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 884, 202]]<|/det|> +uncertain, which leads to more rapid model improvement. Another feature of AL is that it can employ physically meaningful dynamical trajectories for the sampling of configurations. In the present work, we illustrate how to keep these benefits of AL, while accelerating the rate of new data collection. + +<|ref|>text<|/ref|><|det|>[[112, 214, 886, 556]]<|/det|> +AL32,33,34 aims to iteratively collect diverse training datasets addressing any weaknesses identified in an ML model prediction. For this, it is necessary to estimate uncertainty for a model's predictions.35- 45 A well- established practical strategy for AL with NN potentials is Query by Committee46 (QBC); here, the estimate of uncertainty is the disagreement between a collection of models within an ensemble. Typically, there are 5 to 10 NNs in an ensemble, and these share the same architecture and hyperparameters but, crucially, use a different initial randomization of the model parameters prior to training, and different splits of the training/validation data. It is empirically observed that the variance of the ensemble predictions correlates strongly with actual prediction error,36 suggesting that the prediction task requires extrapolation beyond the range of the training data. In the QBC strategy, if this ensemble variance is observed to be large, then the training set will be augmented with new quantum simulation data. + +<|ref|>text<|/ref|><|det|>[[112, 567, 886, 907]]<|/det|> +AL estimates uncertainty in properties predicted for structures generated by an underlying sampler at each iteration. Molecular dynamics (MD) is the most popular method for sampling chemically meaningful potential energy surfaces. However, MD is susceptible to trapping in near- minimum conformations and only rarely enters chemically important regions such as transition states, which are key data for reactive simulations of chemical processes. In general, capturing thermodynamically rare events is a challenging task for any sampler. For example, metadynamics47- 50 is an effective method of potential energy surface exploration, which operates on the concept of collective variables (CVs). However, CVs require manual selection, and their number is limited in practice. The user needs to "look" at each specific molecule to determine the desired structural parameters to be scanned. Therefore, this approach is not suitable for automatic sampling. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 885, 331]]<|/det|> +Here, following the idea of QBC and ensemble uncertainty, we propose a new AL sampling algorithm biased towards regions of high uncertainty - Uncertainty Driven Dynamics (UDD). Due to model random initializations and the stochastic nature of training, regions of chemical space with low ensemble uncertainty will typically arise when similar regions are prevalent in the current training dataset, such that every member of the ensemble is making an accurate inference. Thus, biasing molecular dynamics in the direction of high ensemble uncertainty encourages the dynamics to visit new configurations, which are relevant for improving the diversity of the training set. + +<|ref|>text<|/ref|><|det|>[[113, 343, 885, 587]]<|/det|> +One can regard the uncertainty- based bias potential as similar to metadynamics in the sense that the sampling trajectories are pushed towards less- visited configurational regions. Here, however, CVs need not be defined. We show that within MD- based AL data acquisition, UDD helps substantially reduce the MD simulation time required to enter the high uncertainty region. Most importantly, the proposed approach enables efficient conformational and configurational sampling at low- temperature (low- T) conditions, making this approach essential for temperature sensitive molecules. UDD assists in sampling the chemically relevant subspace of high- energy space which contains important data such as transition states. + +<|ref|>text<|/ref|><|det|>[[113, 599, 885, 842]]<|/det|> +The value of the proposed approach is demonstrated in two test cases. First, UDD- AL is used for conformational sampling of a glycine molecule. We find that the bias potential technique generates a diverse dataset covering both low and high energy regions. This contrasts with high- temperature (high- T) MD- AL, which tends to skip over low energy regions. Second, in tests with acetylacetone at low- T conditions, the bias potential is observed to encourage the sampling of the phase space relevant to a proton transfer. Here we find that, in contrast with regular high- T MD, the bias potential technique encourages the reactive transition with very little distortion to the distribution of other degrees of freedom in the system. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 91, 206, 112]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[115, 123, 700, 146]]<|/det|> +## Uncertainty driven dynamics for active learning (UDD-AL) + +<|ref|>text<|/ref|><|det|>[[113, 153, 886, 290]]<|/det|> +Before introducing UDD- AL, let us first set the context by reviewing the related method of metadynamics. Here the CVs \(s(q)\) are user- defined structural parameters being scanned by external Gaussian bias potentials. Usually, \(3N - 6\) dimensional atomic coordinates \(q\) of the simulated system are mapped to CVs \(s(q)\) . The corresponding energy function is defined as + +<|ref|>equation<|/ref|><|det|>[[113, 280, 845, 315]]<|/det|> +\[E_{\mathrm{metadynamics}}(s,t) = \sum_{k\tau < t}W(k\tau)\mathrm{exp}\big[-\sum_{i = 1}^{N_{CV}}\frac{1}{2b_{i}^{2}} (s_{i} - s_{i}(q(k\tau)))^{2}\big], \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[113, 325, 886, 476]]<|/det|> +where \(b_{i}\) is the width of the Gaussian function for the \(i^{\mathrm{th}}\) collective variable, \(W(k\tau)\) is the height of the Gaussian at the simulation time \(t = k\tau\) , which is constant in the case of standard metadynamics, \(\tau\) is the deposition rate of the Gaussian functions, and \(N_{\mathrm{CV}}\) is the number of CVs. During simulations, more Gaussians are added, thus discouraging the system to go back to its previous steps. + +<|ref|>text<|/ref|><|det|>[[112, 486, 886, 670]]<|/det|> +Like metadynamics, UDD- AL method modifies the physical energy by adding a bias potential \(E_{\mathrm{bias}}\) . Here, however, \(E_{\mathrm{bias}}\) will be defined in terms of the model uncertainty rather than CVs. Such uncertainty estimates can be used to assist in the sampling of atomistic data. \(^{44}\) In the QBC approach, an ensemble of NN- potentials is trained, and the level of agreement between the NN predictions serves as the estimate of overall model uncertainty. The QBC uncertainty estimate is proportional to the variance in the energy \((\sigma_{E}^{2})\) + +<|ref|>equation<|/ref|><|det|>[[113, 678, 845, 708]]<|/det|> +\[\sigma_{E}^{2} = \frac{1}{2}\Sigma_{i}^{N_{M}}(\hat{E}_{i} - \hat{E})^{2}, \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[112, 720, 886, 870]]<|/det|> +where \(\hat{E}_{i}\) is the energy predicted by an ensemble member, \(\hat{E}\) is its ensemble average, and \(N_{\mathrm{M}}\) is the number of ensemble members, i.e., NN- potentials. Here, ensembles of ANI potentials are prepared using an 8- fold cross validation split of the data set which yields \(N_{\mathrm{M}} = 8\) ensemble members (see Methods). In previous applications of QBC- based AL, new data is collected when the uncertainty estimator + +<|ref|>equation<|/ref|><|det|>[[112, 881, 845, 907]]<|/det|> +\[\rho = \sqrt{2 / N_{M}N_{A}}\sigma_{E} \quad (3)\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 698, 108]]<|/det|> +exceeds a threshold, where \(N_{A}\) is the number of atoms in a configuration. + +<|ref|>text<|/ref|><|det|>[[113, 120, 884, 202]]<|/det|> +We seek to construct a bias energy \(E_{\mathrm{bias}}(\sigma_{E}^{2})\) that favors configurations with larger uncertainties \(\rho \propto \sigma_{E}\) ; such configurations are expected to correlate with regions that are underrepresented in the training data. A reasonable choice is the Gaussian function, + +<|ref|>equation<|/ref|><|det|>[[113, 214, 845, 250]]<|/det|> +\[E_{\mathrm{bias}}(\sigma_{E}^{2}) = A\left[\exp \left(-\frac{\sigma_{E}^{2}}{N_{M}N_{A}B^{2}}\right) - 1\right]. \quad (4)\] + +<|ref|>text<|/ref|><|det|>[[113, 262, 884, 380]]<|/det|> +The magnitude \(A\) and width \(B\) of the biasing should be selected empirically. The bias potential goes to zero in the absence of uncertainty, \(E_{\mathrm{bias}}(0) = 0\) . Configurations with large uncertainty, \(\rho \gg B\) , are favored by a bias energy of magnitude \(E_{\mathrm{bias}} \approx - A\) . Forces derived from the bias potential are strongest when the uncertainty \(\rho\) is of the same order as the parameter \(B\) . + +<|ref|>text<|/ref|><|det|>[[113, 393, 884, 510]]<|/det|> +The combined potential \(\hat{E} + E_{\mathrm{bias}}\) is used to define an uncertainty driven dynamics (UDD). In applications to AL the overall strategy will be denoted UDD- AL. The schematic workflow of UDD- AL is depicted in Fig. 1a. It should be compared to the usual MD based approach (MD- AL), which does not incorporate the \(E_{\mathrm{bias}}\) term. + +<|ref|>text<|/ref|><|det|>[[113, 522, 884, 670]]<|/det|> +The optimal parameters \(A\) and \(B\) will be context dependent. For example, our applications to glycine and acetylacetone tests discussed below suggest that the bias magnitude \(A\) should be at least of the order of the energy barriers of interest. The bias width, \(B\) , should also be carefully selected. If \(B\) is either too small or too large, the bias forces could become negligibly small for the typical uncertainties \(\rho\) that are observed in the AL sampling. + +<|ref|>text<|/ref|><|det|>[[113, 683, 844, 802]]<|/det|> +Effective bias forces on an atom at position \(r\) can be calculated using the chain rule, \[ -\frac{\partial}{\partial r} E_{\mathrm{bias}}(\sigma_{E}^{2}) = -E_{\mathrm{bias}}(\sigma_{E}^{2})'\frac{\partial}{\partial r}\sigma_{E}^{2} \] and \[ -\frac{\partial}{\partial r}\sigma_{E}^{2} = -\sum_{i}^{M}(\hat{E}_{i} - \hat{E})\frac{\partial}{\partial r}(\hat{E}_{i} - \hat{E}) = \sum_{i}^{M}(\hat{E}_{i} - \hat{E})(\hat{f}_{i} - \hat{f}) \] + +<|ref|>equation<|/ref|><|det|>[[113, 789, 845, 820]]<|/det|> +\[-\frac{\partial}{\partial r}\sigma_{E}^{2} = -\sum_{i}^{M}(\hat{E}_{i} - \hat{E})\frac{\partial}{\partial r} (\hat{E}_{i} - \hat{E}) = \sum_{i}^{M}(\hat{E}_{i} - \hat{E})(\hat{f}_{i} - \hat{f}) \quad (6)\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 884, 175]]<|/det|> +where \(\hat{f}_{t}\) denotes the force vector predicted by an ensemble member and \(\hat{f}\) is the ensemble- averaged prediction. Since \(\sigma_{E}^{2}\) , \(\hat{E}_{t}\) , and \(\hat{f}_{t}\) are calculated at each MD step in any AL approach, including the bias potential has a negligible effect on the computational time. + +<|ref|>sub_title<|/ref|><|det|>[[114, 211, 517, 233]]<|/det|> +## Glycine conformational space sampling + +<|ref|>text<|/ref|><|det|>[[112, 240, 886, 708]]<|/det|> +The glycine molecule is shown in Fig. 1a. We are interested in sampling the conformational space without bond- breaking events using various AL protocols. Dihedral rotations of \(- \mathrm{NH}_{2}\) and \(- \mathrm{COOH}\) groups correspond to 2.5- 3.5 kcal/mol barriers. Our numerical tests have shown that the bias magnitude \(A\) approximately five times higher than the average barrier of interest provides the best results for the glycine test. It helps keep the bias essentially high at higher uncertainty values. It also helps overcome possible barrier bottlenecks caused by possible overestimation of barrier heights by ML model. The geometries near global energy minimum (GM) of a glycine are already present in the initial training set, and each sampling MD simulation starts with this kind of structure. Depending on the AL iteration, near- GM structures have \(\rho\) of \(0.024 \pm 0.005 [\mathrm{kcal} \times \mathrm{mol}^{- 1} \times (\sqrt{\mathrm{N}_{\mathrm{A}}})^{- 1}]\) \((\sigma_{E} = 0.15 \pm 0.03 \mathrm{kcal / mol})\) . Thus, we selected \(A\) equal to 15.4 kcal/mol which corresponds to the bias potential value of \(- 15.0 \mathrm{kcal / mol}\) at near- GM values of \(\rho\) (with \(B = 0.12 \mathrm{kcal / mol}\) ). We further increase \(A\) by \(15\%\) at 140ps time step if the uncertainty criterion is not met at this simulation stage. Our tests also show that best results are achieved for \(B\) being close to near- GM uncertainty \(\sigma_{E}\) . Here we use \(B = 0.12 \mathrm{kcal / mol}\) for glycine. + +<|ref|>text<|/ref|><|det|>[[113, 718, 886, 899]]<|/det|> +We next compare the two active learning approaches – UDD- AL and MD- AL – for the task of collecting a dataset of the glycine conformational space. Each AL iteration performs 16 MD simulations with 200ps time limit and 1fs step (see Methods). An ensemble of NN- potentials for the first AL iteration is trained on the initial data set of 125 conformers that span near- equilibrium structures of the glycine global minimum. At each subsequent AL iteration, the MD simulation employs an ensemble of ANI- type NN- potentials (see Methods and Ref. 51), trained on the initial + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 885, 377]]<|/det|> +data and data accumulated on all previous AL iterations. The starting geometries for MD simulations and the initial training set contain only near- equilibrium geometries of a glycine global minimum (Extended Data Fig. 1). Stated differently, NNs have no initial information about higher energy conformers, and MD simulations have to reach them from the bottom of the potential energy surface. Each MD simulation is terminated when the system meets the uncertainty selection criteria \(\rho\) of \(0.35 [\mathrm{kcal} \times \mathrm{mol}^{- 1} \times (\sqrt{N_{\mathrm{a}}})^{- 1}]\) (see Methods). If the MD simulation reaches the time limit, then the structure from the trajectory with the highest uncertainty is selected. Density Functional Theory (DFT) reference data (see Methods) is then computed for final conformations and added to the training set for the next iteration of the AL process. + +<|ref|>text<|/ref|><|det|>[[112, 389, 886, 824]]<|/det|> +Figure 1b shows the average MD simulation time required to meet the uncertainty criterion in different AL approaches with respect to AL iteration. The MD- AL at low- T conditions (350K) reaches the MD time limit at \(\sim 20^{\mathrm{th}}\) iteration which is continued until the final AL iteration (Fig. 1b, blue line). This means that the specified uncertainty criterion is almost never met, and the sampler returns the geometry of maximum available uncertainty from the MD trajectory. The uncertainty bias potential is introduced in low- T (350K) MD simulations (orange line). We do not activate the bias potential at earlier AL iterations for two reasons. First, the low- T MD- AL (Fig. 1b, blue line) does not reach the MD simulation time limit up to \(15 - 20^{\mathrm{th}}\) iteration. Thus, at this stage, the regular MD- AL manages to acquire new data satisfying the uncertainty criteria. Second, the NN potential might be unstable or not smooth at earlier iterations due to the lack of data. Therefore, the bias is activated at the \(15^{\mathrm{th}}\) iteration to avoid moving systems toward unphysical configurations. When the uncertainty bias potential is on (UDD- AL regime), it reduces the number of MD steps needed to meet the desired uncertainty. Moreover, the MD time limit plateau is still not reached till the final iteration. + +<|ref|>text<|/ref|><|det|>[[114, 836, 883, 888]]<|/det|> +Perhaps the most common way to accelerate sampling of high- energy states is to run high- T MD. Thus, to illustrate the difference between the bias potential and a simple temperature + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 885, 299]]<|/det|> +increase, we also compare the low- T 350K UDD- AL with the high- T MD- ALs at 600K and 1000K simulation conditions (Fig. 1b). Like in case of UDD- AL sampling, the temperature is increased at the 15th iteration. 600K MD- AL approaches the MD time limit at \(\sim 40\) th iteration while UDD- AL reaches the time limit plateau by the end of AL procedure. Thus, 600K MD- AL does not perform as well as UDD- AL in terms of simulation time. On the other hand, the 1000K MD- AL (cyan line) exhibits a faster sampling, and the average MD time does not exceed 35ps even at the last AL iteration. + +<|ref|>text<|/ref|><|det|>[[113, 311, 885, 458]]<|/det|> +Energy ranges sampled by each AL approach are depicted in Fig.1c. Expectedly, high- T MD- AL data (green and cyan histograms) span wider energy ranges compared to low- T 350K MD- AL (blue histogram). What is more interesting is that the energy distribution of data from the 350K UDD- AL (orange histogram) is very similar to the shape of 600K MD- AL data; an advantage of UDD- AL is that many fewer MD steps are required to collect these samples. + +<|ref|>text<|/ref|><|det|>[[112, 471, 886, 876]]<|/det|> +After completing the entire AL procedure, the final models are trained on 1280 glycine conformers collected during the entire AL procedure (+125 conformers in the initial training set). In order to access the accuracy of the four models, we use a test set comprising 50,000 glycine structures from a 50ns MD simulation via the ANI- 1ccx potential run at 400K with a 0.5fs time step (see Methods).19 As depicted in Fig. 1d, all models perform reasonably well with root- mean- square deviations (RMSEs) less than 0.3 kcal/mol. However, the RMSE of the model trained on low- T 350K MD- AL data is slightly yet systematically lower than RMSEs of the rest of the models with \(\sim 0.11\) kcal/mol difference. This is likely because MD simulations tend to oscillate in near- equilibrium positions, which is why this test set is dominated by low- energy geometries. Low- T 350K MD- AL, in turn, densely spans a narrow low- energy range, which might explain a slightly better performance of this model on a test set derived from a 50ns MD. Indeed, normalized histograms in Fig.1e point out that this seemingly large 50ns test set spans the energy region closest (but even lower) to the one covered by low- T 350K MD- AL data. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 886, 300]]<|/det|> +Note that the 50ns test set is a MD trajectory with no AL involved while MD- AL data comprises structures with high uncertainties. High uncertainty usually corresponds to a higher energy due to poor sampling in normal low- T MD. In other words, the 50ns MD test set is biased towards near- equilibrium oscillations, but MD- AL data are selectively augmented with higher energy isomers. Similar RMSE shift is observed for low energy rotations of \(- \text{COOH}\) and \(- \text{NH}_2\) functional groups (Extended Data Fig.2). Therefore, the various models should also be tested on higher energy pathways. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[113, 80, 860, 720]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 719, 816, 737]]<|/det|> +
Fig.1 | Comparison of UDD-AL and MD-AL approaches for a glycine test case. a Schematic
+ +<|ref|>text<|/ref|><|det|>[[112, 746, 872, 882]]<|/det|> +representation of UDD- AL workflow. b Average MD time required to meet the uncertainty criterion vs. AL iteration for four different MD simulation types: 350K MD- AL (blue), 600K MD- AL (green), 1000K MD- AL (cyan), and 350K UDD- AL (orange). c Energy distribution histograms of four datasets sampled by 350K MD- AL (blue), 600K AL MD- AL (green), 1000K MD- AL (cyan), and 350K UDD- AL (orange). Data from iterations 0- 14 is omitted because the bias is off, or the temperature is not increased at this stage. d + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 870, 222]]<|/det|> +Comparison of potential energy RMSE obtained on the 50ns test set vs. AL iteration (i.e., training set size, 16 new glycine conformations per iteration). The legend shows RMSEs for models trained on data from the entire AL procedure. e Normalized energy distribution histograms of 50ns test set (red) and training set sampled by 350K MD- AL (blue). Lines in b and d, are averaged over three ensembles, each trained on data from an independent AL procedure. + +<|ref|>text<|/ref|><|det|>[[112, 258, 886, 535]]<|/det|> +To illustrate what types of chemical processes appear in the system, and how each sampling method covers them, we next visualize the glycine conformational space using dimensionality reduction. We project samples to a 2D plane using the Uniform Manifold Approximation and Projection (UMAP) \(^{52}\) technique, where each conformation is characterized by the 672- D vector of activations (concatenated atomic environment vectors) in the first layer of an independent pre- trained ANI- 1x model. \(^{19}\) Figure 2a shows the 50ns MD dataset spanning four low- lying glycine conformers represented by four regions in the 2D space. Torsional conformations, N- H bond scan, and C=O bond scan are depicted in Fig.2b- c to illustrate structural profiles in 2D space. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 120, 880, 459]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 462, 884, 715]]<|/det|> +
Fig. 2 | 2D representation of glycine conformational space processed by UMAP dimensionality reduction technique. a 50ns test set. Heat-map represents the relative DFT energy. Glycine insets denote the corresponding conformational region. In panels b-d, data sets and scans are placed over the 50ns test set (gray). b Conformational paths through -COOH (cyan and purple) and -NH2 (red and green) rotations. c N-H (red) and C=O (cyan) bond length scans. d Comparison of training sets sampled by 350K MD-AL (blue) and 350K UDD-AL (orange). Green circle denotes the inner high-energy region. Red triangles denote the scan of -OH rotation around the C-O bond. e Comparison of training sets sampled by 350K MD-AL (blue) and 600K MD-AL (green). f Comparison of training sets sampled by 350K MD-AL (blue) and 1000K MD-AL (cyan).
+ +<|ref|>text<|/ref|><|det|>[[113, 742, 885, 891]]<|/det|> +Figure 2d- f depicts the four datasets visualized over the 50ns test set. Figure 2d provides a visual comparison of data sampled by MD- AL and UDD- AL at 350K. Both datasets cover the 50ns MD data reasonably well. However, there are three key differences. First, a high- energy configurational space (points inside the green circle) is more densely sampled in the UDD- AL dataset: there are 289 points in this non- equilibrium region in the UDD- AL sampling, compared to + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 202]]<|/det|> +105 points in 350K MD- AL. Second, UDD- AL encountered a new conformational path in the top right corner of the Fig.2d which was not accessed by 350K MD- AL. This region corresponds to the rotation of the - OH group around the C- O bond which is a distinct conformational transition and a high- energy profile with a 15 kcal/mol barrier. + +<|ref|>text<|/ref|><|det|>[[112, 216, 885, 555]]<|/det|> +Figure 2e presents a visual comparison of the sampling performance of MD- ALs at 350K and 600K. The 2D representation of 600K data is quite similar with the one of 350 UDD- AL data: there are 290 data points inside the inner circled region and a good coverage of - OH rotation region. As expected, MD- AL at the extreme temperature of 1000K (Fig.2f) samples the inner high- energy region even more densely (394 data points) as well as the - OH rotation region. This, however, comes at a cost. The low energy region in the lower left of Fig.2f clearly demonstrates lack of sampling. This is the primary deficiency of using high- T MD: as temperature increases, the system spends less time near low energy regions, since in these regions the kinetic energy is typically the greatest. Thus, it will be possible to 'skip over' regions of high stability thus resulting in a poor data coverage of near equilibrium region. On the other hand, UDD- AL sampling does not run this risk by sufficiently sampling any relevant region. + +<|ref|>text<|/ref|><|det|>[[112, 567, 885, 875]]<|/det|> +Thus, Figure 2 indicates that the UDD- AL is a good, balanced way of sampling chemical space, reaching most of the high energy points achieved with 1000K MD sampling without losing data density in low energy regions. However, alone, it is not clear that biased sampling presents advantages over unbiased high- T sampling; UDD- AL appears to sample similar configurations to 600K MD- AL. Therefore, we performed additional tests of high- energy pathways that illuminate the differences between 600K MD- AL and 350K UDD- AL. The discussion on high energy profiles - angle and bond scans - can be found in Supplementary Information. The overall trend is that the 350K UDD- AL model exhibits much better accuracy than the model trained on low- T 350K MD- AL data. When comparing the UDD- AL with 600K and 1000K MD- AL models, the former results in a better or, at least, comparable accuracy. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 885, 299]]<|/det|> +We also provide an overall assessment of the performance of the sampling strategy by cross- testing the associated models on the data from all sampling strategies in Supplementary information. Supplementary Table 2 summarizes RMSEs of the four models on the test sets accumulated by each AL sampler: 350K MD- AL, 350K UDD- AL, 600K MD- AL, and 1000K MD- AL. When testing models on data sets that are not generated by the same corresponding sampler, the UDD- AL model outperforms all other models. A detailed discussion on models' cross testing can be found in Supplementary Information. + +<|ref|>text<|/ref|><|det|>[[112, 310, 886, 748]]<|/det|> +Ultimately, when looking at a variety of bond rotations and stretches, the most accurate energy profile changes depending on the energy range of the specific scan. Low energy profiles tend to be modeled better by the low- T dataset while higher energy scans are accessed better by the high- T dataset. However, the UDD- AL sampling method yields a model that performs well on a wide range of energy profiles, while also maintaining a low error on each sampling method's held out test set (Table 2 in Supplementary Information). This difference suggests that UDD- AL is able to avoid the higher- energy and less chemically relevant structural distortions, which are typical at very high temperatures. Meanwhile, chemically relevant data present in the UDD- AL data set enables efficient extrapolation to higher- energy structures present in 1000K MD- AL data. As can be seen in Fig. 3, the shapes of interatomic distance distributions in UDD- AL closely mimic sharp distributions in low- T 350K MD- AL, although having a larger standard deviation. This deviation, however, is lower than in 600K and 1000K MD- AL data sets that span a wider distance range. This, in turn, further suggests that the UDD sampler tends to avoid random distortions found in high- T regimes. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 87, 881, 348]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 351, 860, 457]]<|/det|> +
Fig. 3 | Glycine interatomic distance distributions in MD-AL and UDD-AL data sets. Each subplot shows a comparison of bond length distributions in 350K MD-AL (blue), 350K UDD-AL (orange), 600K MD-AL (green), and 1000K MD-AL (cyan) data sets. Each subplot also lists the bond length standard deviation, in the legend. a O-H. b N-H. c C-H. d C=O, e C-N. f C-C.
+ +<|ref|>text<|/ref|><|det|>[[113, 485, 885, 630]]<|/det|> +Since all models used the same hyperparameters, it is possible that each model could perform better if individual hyperparameter searches are carried out. Perhaps, data sets that cover a broader chemical space need more learnable parameters to be flexible enough to fit the effectively larger degrees of freedom they are being trained to. This will be a subject of future studies. + +<|ref|>sub_title<|/ref|><|det|>[[115, 669, 441, 689]]<|/det|> +## Proton transfer in acetylacetone + +<|ref|>text<|/ref|><|det|>[[113, 698, 886, 910]]<|/det|> +We further examine the UDD performance for sampling of a reactive pathway in an acetylacetone enol tautomer depicted in Fig. 4a. Particularly, we are interested in the proton transfer between the two oxygen atoms, considering the proton position as a free variable. Instead of using AL techniques, here we use an ensemble of pre- trained ANI- 1x interatomic potentials,19 which were not trained on bond- breaking reactions, and analyze trajectories from UDD and MD simulations. ANI- 1x was trained on wB97x DFT level of theory which yields a 4.7 kcal/mol barrier. However, ANI- 1x significantly overestimates the barrier giving the value of 6.3 kcal/mol. This value + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 203]]<|/det|> +is then selected as a bias magnitude \(A\) . Uncertainty values \(\rho\) of near-equilibrium acetylacetone structures within ANI- 1x model are higher by the order of magnitude than the ones produced by the newly trained model for glycine. Therefore, we set a higher value of bias width \(B = 0.45\) kcal/mol, being an empirically adjusted parameter. + +<|ref|>text<|/ref|><|det|>[[112, 216, 886, 589]]<|/det|> +Figure 4b shows log- normalized uncertainty \(\rho\) of the acetylacetone system with respect to the position of the proton. The dark region (near \(x = 0\) Å and \(y = - 1.5\) Å) demonstrates that there is a high- uncertainty region between oxygen atoms which corresponds to a proton transfer transition state. Figure 4c depicts the log- normalized relative potential energy (ANI- 1x) of the system with respect to the position of the free proton. The dark region (near \(x = - 0.5\) Å and \(y = - 1.5\) Å) indicates that the lowest energy corresponds to a proton position near the oxygen atom. This is an expected result since the hydrogen bound to the oxygen atom is the most stable geometry. However, as shown in Fig. 4d, the energy minimum can be shifted to the central position between the oxygen atoms (dark region near \(x = 0\) Å) when the bias potential is applied. For illustrative purposes, here we use a high value of bias magnitude \(A = 56.0\) kcal/mol. In practice, we use \(A = 6.3\) kcal/mol for the MD simulation discussed below. The effect of different \(A\) values on the total energy landscape is depicted in Extended Data Fig. 3. + +<|ref|>text<|/ref|><|det|>[[113, 600, 885, 810]]<|/det|> +We further analyze results from 0.5ns trajectories obtained using UDD and regular MD simulation techniques. No proton transfer occurs during the regular 350K MD simulation. Meanwhile, the uncertainty bias can direct the proton toward a high- uncertainty region between two oxygen atoms - 90 proton transitions observed in 350K UDD simulation. Finally, unbiased high- T 620K MD results in 48 proton transitions. Although at a lower rate compared to UDD, increased temperature also facilitates the proton transfer. Time traces of the two O- H distances can be further found in Extended Data Fig. 4. + +<|ref|>text<|/ref|><|det|>[[113, 823, 884, 906]]<|/det|> +A key difference between low- T UDD and regular high- T MDs can be found when analyzing oscillations of interatomic distances. Certainly, high- T conditions will affect the entire molecule causing larger distance deviations compared to low- T conditions. Indeed, the overall + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 333]]<|/det|> +spread of O- H distances is comparable using 350K MD (Extended Data Fig. 4), but far wider at 620K MD, even in segments of the trajectory without proton transfer. Further analysis of C- H distances in the molecule, shown in Fig. 4e- f, confirms this phenomenon. Figure 4e shows C- H distance distributions in the methyl group in 350K MD, 350K UDD, and 620K MD simulations. 620K MD exhibits higher deviations from the equilibrium C- H bond length compared to low- T UDD. The standard deviation of the methyl C- H distance in 620K MD is 0.04 Å against 0.03 Å in 350K UDD. Notably, the low- T 350K MD trajectory has the standard deviation of 0.03 Å, same as in low- T UDD. The same picture holds for the central C- H bond shown in Fig. 4f. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 123, 875, 624]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 628, 884, 821]]<|/det|> +
Fig. 4 | Ensemble uncertainty and UDD in acetylacetone. a Acetylacetone molecule. Red circle denotes a hydrogen atom involved in a proton transfer. b Log-normalized map of disagreement \(\rho\) of ANI-1x model ensemble with respect to a position of a circled hydrogen. c Log-normalized map of physical energy. d Log-normalized map of summed physical and bias energy. e and f show a comparison of C-H bond length distributions in the methyl and central groups, respectively, for 350K MD (blue), 350K UDD (orange), and 620K MD simulations. Red ellipse denotes a bond under consideration. Each subplot also lists the bond length standard deviation from the equilibrium distance per legend.
+ +<|ref|>text<|/ref|><|det|>[[114, 850, 884, 902]]<|/det|> +These observations confirm that although high- T sampling promotes the activation of reactive pathways, it has a global effect on all degrees of freedom in the system, whereas the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 236]]<|/det|> +UDD technique allows to sample the reactive pathway without significant changes to equilibrium distributions of other degrees of freedom. This is likely due to the composition of ANI- 1x training data, which has information on non- equilibrium extended bonds. However, a hydrogen which is equidistant between two oxygen atoms is not a commonly encountered configurational data. Thus, the UDD potential promotes sampling of this specific region. + +<|ref|>sub_title<|/ref|><|det|>[[115, 275, 244, 297]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[113, 307, 885, 487]]<|/det|> +In this work, we present a MD- based active learning (AL) algorithm assisted by the uncertainty- based bias potential. The algorithm is abbreviated as UDD- AL (uncertainty driven dynamics for active learning) and is compared to MD- based AL (MD- AL) in common use. We show that an uncertainty metric provided by an ensemble of NN- potentials can be used to construct the bias potential. The resulting energy term encourages the system to move toward underrepresented chemical regions, accelerating and improving sampling of high- energy regions. + +<|ref|>text<|/ref|><|det|>[[112, 498, 885, 774]]<|/det|> +The ANI model, trained on glycine conformational data from low- T UDD- AL, is shown to properly reproduce conformational pathways not accessible by low- T MD- AL. The resulting ANI model exhibits high accuracy for important high- energy conformational profiles. Importantly, while this accuracy is better than or comparable to the results for models trained on high- T MD- AL data, a low- T UDD- AL training set spans an energy range narrower than that in high- T sets. However, a model trained on low- T UDD- AL data exhibits excellent accuracy on data sampled at unbiased high- T conditions which is not the case in the reverse test. This, in turn, suggests that the UDD- AL approach avoids oversampling the extreme structural distortions that are common at very high temperatures. + +<|ref|>text<|/ref|><|det|>[[113, 786, 884, 902]]<|/det|> +The test case of acetylacetone shows that bias potential applied to a pre- trained ANI- 1x model promotes proton transfer in the enol tautomer keeping oscillations in the rest of the bond distances identical to low- T conditions. Opposite situation is observed for the unbiased high- T MD where interatomic distances exhibit larger deviations from equilibrium positions with a lower rate + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 884, 235]]<|/det|> +of a proton transfer. The analysis of interatomic distances clearly indicates an advantage of the bias potential over the high- T approach: bias potential facilitates sampling of important underrepresented chemical data without random structural distortions caused by high- T conditions. This feature can be used for efficient sampling of conformational and/or configurational space of temperature sensitive systems. + +<|ref|>text<|/ref|><|det|>[[113, 248, 884, 395]]<|/det|> +Our tests indicate that the bias potential can facilitate sampling of high energy chemical space without sacrificing the sampling of low energy configurations. This means that UDD will produce robust datasets that are applicable to both lower energy, near global minimum data and high energy chemical space which usually corresponds to important reactive structural data such as transition states and intermediates. + +<|ref|>text<|/ref|><|det|>[[113, 408, 885, 650]]<|/det|> +As the results show, uncertainty- based bias potential is a promising technique for sampling rare events while being relatively faithful to the physical equilibrium distribution. The uncertainty driven dynamics is similar to metadynamics47- 50 in its use a bias potential but avoids the need to manually select collective variables or to identify basins of attraction. In a way, it defines the best CV for the purpose of AL: training a more general and robust ML potential. The approach requires selection of two parameters: the bias magnitude and width; developing a method which can tune these algorithmically would be a productive future activity. Additionally, the algorithm for automatic selection of uncertainty criteria could improve the sampling efficiency. + +<|ref|>sub_title<|/ref|><|det|>[[115, 690, 218, 711]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[115, 723, 268, 744]]<|/det|> +## Active learning + +<|ref|>text<|/ref|><|det|>[[113, 753, 884, 883]]<|/det|> +For glycine simulations, we use ANI deep learning model51 to generate ensembles of NN potentials prepared using an 8- fold cross validation split of the data set. The empirical value 0.23 \([kcal \times mol^{- 1} \times (\sqrt{N_A})^{- 1}]\) for the uncertainty selection criteria \(\rho\) , equation (3), provided in the original work on active learning for organic molecules36 turned out to be too low for the purposes + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 884, 283]]<|/det|> +of training on one chemical system. It causes unnecessarily dense sampling of glycine conformational space which, in turn, hinders the MD simulation to reach higher energy regions. Therefore, we use a higher value of \(0.35 [kcal \times mol^{- 1} \times (\sqrt{N_A})^{- 1}]\) for this test case. Overall, automatic selection of uncertainty criteria is a nontrivial question which deserves a separate discussion and goes beyond the scope of this work. Each MD simulation is terminated when the system meets the uncertainty selection criteria \(\rho\) . + +<|ref|>text<|/ref|><|det|>[[113, 293, 885, 442]]<|/det|> +The initial training set consists of 125 glycine geometries that span near- equilibrium structures of the glycine global minimum. This data is acquired from a separate 5ps MD trajectory at 350K with a 0.5 fs timestep, initialized from the glycine global minimum. Every \(80^{\text{th}}\) MD step is included in the initial data training set. The MD simulation for the initial training set is carried out using the pre- trained ANI- 1x potential. \(^{51}\) + +<|ref|>sub_title<|/ref|><|det|>[[114, 478, 279, 499]]<|/det|> +## MD Simulations + +<|ref|>text<|/ref|><|det|>[[113, 507, 885, 719]]<|/det|> +In all discussed cases, the Atomic Simulation Environment (ASE) Langevin thermostat was used to maintain temperature with a friction coefficient of 0.01 a.u. Each AL iteration performs 16 MD simulations with 1fs time step and a 200 000 steps limit (200ps). At each AL iteration, the MD is driven by an ensemble of ANI- type ML potentials, trained on initial data and data accumulated on previous AL iterations. The NN- based MD is interfaced with ASE code. \(^{53}\) The final data set has 1280 data points sampled in AL procedure + 125 data points from the initial data set. + +<|ref|>text<|/ref|><|det|>[[113, 730, 885, 879]]<|/det|> +The set of seed geometries for MD simulations comprises 25 structures that correspond to near- equilibrium geometries of a glycine global minimum (GM). These are selected as the first 25 structures from the initial training set. AL sampler randomly selects one of them for each MD initialization. Energies and forces of new conformers are calculated using the WB97X- D/cc- pVTZ \(^{54,55}\) level of theory as implemented in PSI4 code. \(^{56}\) + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 277, 110]]<|/det|> +## NN architecture + +<|ref|>text<|/ref|><|det|>[[113, 119, 886, 363]]<|/det|> +NN architectureParameters for the atomic environment vector \(^{51}\) (a numerical vector used to encode the atomic local environment in ANI) used during the AL process were constant. 32 evenly spaced shifting parameters are used for the radial part of the vector with 4.6 Å cutoff radius and a total of 8 radial and 8 angular shifting parameters are used for the angular part with 3.5 Å cutoff radius. With four atom types, this gives 768 elements in the descriptor. The first atom- centered function is shifted to 0.8 Å from the atomic center. The ANI potential used in this work contains three hidden layers and has the following architecture: 768:32:16:8:1, each number describing the number of neurons at each subsequent layer in the network. + +<|ref|>text<|/ref|><|det|>[[114, 374, 884, 459]]<|/det|> +The ANI potential used in this work contains three hidden layers and has the following architecture: 768:32:16:8:1. Gaussian activation functions are used in hidden layers and linear activation in the final layer. + +<|ref|>sub_title<|/ref|><|det|>[[115, 497, 345, 520]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[112, 528, 886, 885]]<|/det|> +AcknowledgementsK. B., N. L., R. M., S. T., and B. N. acknowledge support from the US DOE, Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division under Triad National Security, LLC ("Triad") contract Grant 89233218CNA000001 (FWP: LANLE3F2). M. K. and Y. W. L. acknowledge support from the Los Alamos National Laboratory (LANL) Directed Research and Development funds. This work was performed in part at the Center for Nonlinear Studies and the Center for Integrated Nanotechnology, a US Department of Energy (DOE) and Office of Basic Energy Sciences user facility. This research used resources provided by the LANL Institutional Computing Program, which is supported by the US DOE National Nuclear Security Administration under Contract 89233218CNA000001. We also acknowledge the CCS- 7 Darwin cluster at LANL for additional computing resources. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 304, 113]]<|/det|> +## Data Availability + +<|ref|>text<|/ref|><|det|>[[115, 123, 461, 141]]<|/det|> +Source data are provided with this Paper. + +<|ref|>sub_title<|/ref|><|det|>[[115, 168, 311, 192]]<|/det|> +## Code Availability + +<|ref|>text<|/ref|><|det|>[[115, 201, 790, 220]]<|/det|> +Two implementations of the ANI neural network architecture are available online: + +<|ref|>text<|/ref|><|det|>[[115, 235, 790, 254]]<|/det|> +TorchANI (https://github.com/aiqm/torchani) and NeuroChem (https://github.com/ + +<|ref|>text<|/ref|><|det|>[[115, 269, 328, 287]]<|/det|> +atomistic- ml/neurochem). + +<|ref|>sub_title<|/ref|><|det|>[[115, 328, 252, 351]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[112, 360, 870, 900]]<|/det|> +1. Kulichenko, M. et al. The Rise of Neural Networks for Materials and Chemical Dynamics. J. Phys. 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Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 131, 280, 258]]<|/det|> +- Sl.docx- ExtendedDataFig1.jpg- ExtendedDataFig2.jpg- ExtendedDataFig3.jpg- ExtendedDataFig4.jpg + +<--- Page Split ---> diff --git a/preprint/preprint__06ef6f0a131181318c342eb6f78c7a4f17407366557c7de9dbe200f81c0614f5/images_list.json b/preprint/preprint__06ef6f0a131181318c342eb6f78c7a4f17407366557c7de9dbe200f81c0614f5/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..58b6c8c2f5dbeb3c37ce98c1ebd75829bd7b134d --- /dev/null +++ b/preprint/preprint__06ef6f0a131181318c342eb6f78c7a4f17407366557c7de9dbe200f81c0614f5/images_list.json @@ -0,0 +1,107 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [ + [ + 50, + 92, + 950, + 470 + ] + ], + "page_idx": 26 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 48, + 66, + 911, + 707 + ] + ], + "page_idx": 27 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [ + [ + 45, + 55, + 737, + 780 + ] + ], + "page_idx": 28 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4", + "footnote": [], + "bbox": [ + [ + 50, + 234, + 955, + 803 + ] + ], + "page_idx": 29 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5", + "footnote": [], + "bbox": [ + [ + 45, + 120, + 680, + 845 + ] + ], + "page_idx": 30 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6", + "footnote": [], + "bbox": [ + [ + 48, + 68, + 789, + 780 + ] + ], + "page_idx": 32 + }, + { + "type": "image", + "img_path": "images/Figure_7.jpg", + "caption": "Figure 7", + "footnote": [], + "bbox": [ + [ + 45, + 70, + 772, + 789 + ] + ], + "page_idx": 34 + } +] \ No newline at end of file diff --git a/preprint/preprint__06ef6f0a131181318c342eb6f78c7a4f17407366557c7de9dbe200f81c0614f5/preprint__06ef6f0a131181318c342eb6f78c7a4f17407366557c7de9dbe200f81c0614f5.mmd b/preprint/preprint__06ef6f0a131181318c342eb6f78c7a4f17407366557c7de9dbe200f81c0614f5/preprint__06ef6f0a131181318c342eb6f78c7a4f17407366557c7de9dbe200f81c0614f5.mmd new file mode 100644 index 0000000000000000000000000000000000000000..612020f0182bb5821c9e5a32f40f881ba2fd0b85 --- /dev/null +++ b/preprint/preprint__06ef6f0a131181318c342eb6f78c7a4f17407366557c7de9dbe200f81c0614f5/preprint__06ef6f0a131181318c342eb6f78c7a4f17407366557c7de9dbe200f81c0614f5.mmd @@ -0,0 +1,460 @@ + +# dcHiC: differential compartment analysis of Hi-C datasets + +Abhijit Chakraborty La Jolla Institute for Allergy and Immunology + +Jeffrey Wang Harvard College https://orcid.org/0000- 0002- 5707- 5113 + +Ferhat Ay (☑ ferhatay@lji.org) La Jolla Institute for Allergy and Immunology https://orcid.org/0000- 0002- 0708- 6914 + +## Article + +# Keywords: + +Posted Date: April 1st, 2022 + +DOI: https://doi.org/10.21203/rs.3.rs- 1483135/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on November 11th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 34626- 6. + +<--- Page Split ---> + +# dcHiC: differential compartment analysis of Hi-C datasets + +Abhijit Chakraborty \(^{1,\# *}\) , Jeffrey Wang \(^{1,2,\# ,S}\) , Ferhat Ay \(^{1,3*}\) + +\(^{1}\) La Jolla Institute for Immunology, La Jolla, California, USA. \(^{2}\) The Bishop's School, La Jolla, California, USA. \(^{3}\) School of Medicine, University of California San Diego, La Jolla, California, USA. \(^{\#}\) Equal contribution \(^{\S}\) Current address: Harvard College, Cambridge, Massachusetts, USA. \(^{*}\) Co- corresponding authors: abhijit@lji.org, ferhatay@lji.org + +## Abstract + +Compartmental organization of chromatin and its changes play important roles in distinct biological processes carried out by mammalian genomes. However, differential compartment analyses have been mostly limited to pairwise comparisons and with main focus on only the compartment flips (e.g., A- to- B). Here, we introduce dcHiC, which utilizes quantile normalized compartment scores and a multivariate distance measure to identify significant changes in compartmentalization among multiple contact maps. Evaluating dcHiC on three collections of Hi- C contact maps from mouse neural differentiation \((n = 3)\) , mouse hematopoiesis \((n = 10)\) and human LCL cell lines \((n = 20)\) , we show its effectiveness and sensitivity in detecting biologically relevant differences, including those validated by orthogonal experiments. Across these experiments, dcHiC reported regions with dynamically regulated genes associated with cell identity, along with correlated changes in chromatin states, replication timing and lamin B1 association. With its efficient implementation, dcHiC not only enables high- resolution compartment analysis but also includes a suite of additional features, including standalone browser visualization, differential interaction identification, and time- series clustering. As such, it is an essential addition to the Hi- C analysis toolbox for the ever- growing number of contact maps being generated. dcHiC is freely available at https://github.com/ay- lab/dcHiC and examples from this paper can be seen at https://ay- lab.github.io/dcHiC. + +<--- Page Split ---> + +## BACKGROUND + +The three- dimensional organization of chromatin in the nucleus has been of interest to scientist for more than a century now. The observation that different chromosomes occupy a defined space in the nucleus dates back to Carl Rabl's work in animal cells in 1885 [1]. Since then, many experimental techniques have been developed to image and map chromatin allowing us to look at chromatin organization at an ever- increasing resolution. The greatest strides in this area have been made in the past decade following the advent of genome- wide conformation capture techniques. We now know that interphase chromosomes are folded into multiple layers of hierarchical structures. Each layer contributes to the establishment and maintenance of the epigenetic landscape that controls cellular state and function. + +Among these, the megabase- scale compartmental organization of eukaryotic genomes has been shown to play a critical role in transcription, DNA replication, accumulation of mutations, and DNA methylation [2- 12]. In broad terms, two types of compartments divide the genome into regions of open and active chromatin (compartment A) versus inactive and closed chromatin (compartment B) [13]. Further analysis of each compartment revealed subsets of regions with markedly different properties within each class called sub- compartments [14, 15] as well as to a putative third class (intermediate or I) that is at the interface between A and B and is reorganized in tumors [9]. + +The main method to extract compartment information has been to analyze high- throughput chromosome conformation capture (Hi- C) contact maps using Principal Components Analysis (PCA) [13, 16, 17]. Briefly, this process involves distance normalization (Observed/Expected for each genomic distance) of the Hi- C contact map for each chromosome at a particular resolution (generally between 100Kb to 1Mb) followed by transformation into a correlation matrix, where each entry \((i,j)\) denotes the correlation of row \(i\) and row \(j\) (or column \(i\) and \(j\) since symmetric) of the distance- normalized Hi- C map. The eigenvalue decomposition of the correlation matrix provides the eigenvectors, and the first eigenvector or principal component (PC1) typically represents the genomic compartments A and B. If PC1 corresponds to chromosome arms or other broad patterns in the Hi- C map (e.g., copy number differences), the second principal component (PC2) is likely to represent A and B compartments. The A/B compartment labels are assigned to the positive/negative stretches of the selected PC; however, depending on the implementation of eigenvalue decomposition, it may be necessary to re- orient these assignments correctly using GC content or gene density. + +Whether one is interested in the two major compartments or their more nuanced subsets, the magnitude and sign of eigenvalues derived from PCA have been the major determinants of compartment type. However, standard PCA is limited in analyzing each Hi- C contact map individually, and to date, there is no method to compare compartmentalization across multiple (>2) Hi- C datasets systematically. This is becoming + +<--- Page Split ---> + +an obstacle in analyzing the ever- increasing chromatin conformation data, either from Hi- C or its variants [18- 25], generated across many cell types and conditions [26]. Technical challenges such as selecting the correct PC and sign that represents A/B compartments, and their scaling across different datasets become larger problems while comparing many Hi- C contact maps. Thus far, comparative compartment analysis has been mainly limited to examining compartment flips between two Hi- C maps at a time [27, 28]. + +Here, we introduce dcHiC (differential compartment analysis of Hi- C), a method that identifies statistically significant differences in compartmentalization among two or more contact maps, including changes that are not accompanied by a compartment flip. Our method implements a memory- efficient and parallelized singular value decomposition (SVD) to derive principal components (i.e., eigenvectors) followed by quantile normalization to get the comparable compartment scores across two or more than two Hi- C maps at a time (Figure 1, Step1). dcHiC then utilizes the normalized component scores to derive a multivariate distance measure [29] (Figure 1, Step2) to estimate the statistical significance of compartment differences. If available, dcHiC utilizes variance among Hi- C replicates as covariates for Independent Hypothesis Weighting (IHW) [30] to correct for multiple testing. With our methodology, compartment analysis can be conducted on Hi- C maps with or without replicates at resolutions up to 10Kb for human and mouse genomes. Further downstream, dcHiC provides a raft of analysis features, including standalone IGV browser [31] visualization of results, detection of differential interactions involving significant differential compartments, time- series clustering of compartment scores, as well as a module for determining enriched Gene Ontology terms from differential compartments. + +To assess the biological relevance of the identified differences, we applied dcHiC to several different collections of Hi- C datasets across various biological conditions, including mouse neuronal development \((n = 3)\) , mouse hematopoiesis \((n = 10)\) , and a set of lymphoblastoid cell lines (LCLs) from different human populations \((n = 20)\) . Analyzing each Hi- C dataset at 100Kb and 40Kb resolution, we identified relevant compartmentalization differences reflecting the underlying biology in the respective scenarios. In the mouse neuronal differentiation model, dcHiC identified compartmental changes for loci involving critical genes associated with cellular identities in mouse embryonic stem cells (mESC) and neuronal differentiation such as Dppa2/4, Zfp42, Ephb1, and Ptn as well as GO term enrichments consistent with these cellular identities. In a ten- way comparison \((n = 10)\) of key cell types from mouse hematopoiesis; across stem cells, progenitor cells, and terminally differentiated cells, dcHiC revealed significant compartmental changes involving key genes like Sox6, Meis1, Runx2, Klf5, and many others. Across both neural and hematopoietic differentiation models, our results also highlight the importance of generally ignored compartmentalization changes within the same compartment type (within A or within B - Figure 1). We also demonstrate the biological significance of our differential calls through strong correlations with cell- type specific differences in lamin B1 association, histone modifications and gene expression. For human LCLs, comparing twenty Hi- C maps from a diverse set of donors, dcHiC confirmed the previous findings, + +<--- Page Split ---> + +with significant enrichment of various biological signals within the differential compartments across the population. + +Overall, dcHiC provides an integrative framework and an easy- to- use tool for comparative analysis of Hi- C maps and identifies biologically relevant differences in compartmentalization across multiple cell types. With immediate application to hundreds of publicly available Hi- C datasets, dcHiC will play an essential role in providing deeper insights into dynamic genome organization and its downstream effects. + +## RESULTS + +## dcHiC identifies compartments consistent with the PCA-based approach + +As more complex experimental designs emerge that compare different Hi- C profiles, a comprehensive method to compare the spatial organization of the genome is necessary. To do this, dcHiC first employs a time- and memory- efficient R implementation of singular value decomposition (SVD) to achieve the eigenvalue decomposition of each Hi- C contact map [32]. This is followed by the automated selection to find the principal component and its sign (reoriented if needed) that best correlates with gene density and GC content per sample (Methods). The resulting compartment scores are quantile normalized and a multivariate score (Mahalanobis distance) is computed based on an initial covariance estimation. We then refine the null distribution by removing outliers before calculating new covariance estimates that will be used for computing the final statistical significance (Chi- square test) of differences in compartmentalization (Methods). dcHiC provides standalone browser visualization as well as several other features facilitating the interpretation of its results. Figure 1 summarizes the overall workflow of dcHiC. + +In order to establish the validity of dcHiC results, we first compared our implementation of the eigenvalue decomposition to commonly used PCA- based approaches, a representative of which is implemented in HOMER[33]. Beyond a few differences in pre- filtering of low coverage regions, the resulting compartment scores were highly similar between dcHiC and HOMER for the 100Kb resolution (replicates combined) mouse ESC (Pearson's \(r = 0.98\) , Figure 2A) and for mouse neuronal progenitor cell (NPC) Hi- C map (Pearson's \(r = 0.96\) , Figure 2D). Similar to A/B compartment decomposition from Hi- C data, association with the nuclear lamina (or radial position) is another strong indicator of a broad- level chromatin state with heterochromatin localizing at the periphery and euchromatin at the nucleus center. Such organization is a conserved feature of eukaryotic genomes across most cell types except special cases [34, 35]. Here we used lamin B1 association profiles of ESC and NPC cell types as an independent measure of compartmentalization and compared the lamin B1 signal distribution with dcHiC and HOMER scores. As expected, both our compartment scores and HOMER results showed a strong negative correlation with lamin B1 association, confirming the previous findings [27, 36] (Figure 2B- C, 2E- F). We further plotted the chromosome 16 compartment score + +<--- Page Split ---> + +of ESC and NPC from dcHiC, HOMER, and lamin B1 association signal. Figure 2G- H shows the lamin B1 signal and compartment features captured by dcHiC and HOMER at a genome- wide scale in ESC and NPC cell types. These results establish that dcHiC, like the existing PCA- based (HOMER) approach, accurately captures compartment patterns. + +## Pairwise differential compartment analysis of mouse neuronal differentiation model + +Previous studies have reported substantial compartment flips during mouse embryonic cells (ESC) to neuronal progenitor cell (NPC) transition, a well- studied in vitro differentiation system [37, 38]. These differences have been studied further using replication timing profiling, lamin B1 association mapping, and fluorescence in situ hybridization (DNA FISH) [8, 27, 36]. Therefore, we chose these two cell types to demonstrate dcHiC's utility in a pairwise comparison to replicate known compartment flips and identify significant changes that do not involve flips from one compartment type to another. We also compared differential compartment calls from dcHiC and HOMER in this pairwise setting since HOMER does not readily allow multi- way comparisons. + +Overall, dcHiC identified 1981 100Kb bins with statistically significant differential compartmentalization (FDR < 0.1), covering up to 7.5% of the genome. For ESC and NPC, these differences constituted around \(\sim 37\%\) (72.8 Mb) and \(\sim 51\%\) (101.6 Mb) of A (active) compartments, respectively. The differential compartments are further subdivided into flipping (A \(\rightarrow\) B or B \(\rightarrow\) A) or matching (A \(\rightarrow\) A or B \(\rightarrow\) B) compartment transitions. We observed that \(\sim 74\%\) of all the differential compartments were flips from A to B ( \(\sim 30\%\) ) or B to A ( \(\sim 44\%\) ) compartments during ESC to NPC transition whereas the remaining \(\sim 26\%\) were within matching compartments (Figure 3A). We further classified significant changes within the same compartments (A to A or B to B) based on whether the compartment scores were higher in ESC or NPC (Figure 3B- E). For the resulting set of six different types of differential compartments, we plotted the distributions of compartment scores (Figure 3B), lamin B1 association (Figure 3C), replication timing (Figure 3D) and gene expression (Figure 3E). As expected, more euchromatic compartments were associated with lower lamin B1 attachment, early replication timing and higher gene expression. These trends were consistent for compartment flips as well as changes within matched compartments (e.g., strong A in ESC to weak A in NPC). + +Next, we compared the differential ESC vs NPC compartments from dcHiC to those from HOMER. HOMER reported a total of 3,042 100Kb bins with significant differential compartmentalization (FDR < 0.05). Only 1,355 of these 100Kb bins were found to be overlapping with dcHiC differential calls (+/- 1 bin slack; Figure 3F). To compare the calls made by the two different methods, we plotted the absolute differences of laminB1 signal, replication timing and log2 gene expression values of the reported differential compartments. Figure 3G shows the absolute difference distribution of all the respective signals from all the differential compartments between ESC and NPC, while Figure 3H shows the same but only for differential compartments exclusively identified by one method. These results show that dcHiC differential compartments are significantly + +<--- Page Split ---> + +(unpaired t- test p- values \(< 0.05\) ) enriched for regions with higher ESC, NPC differentials for lamin association and replication timing signals. We also performed differential expression analysis between ESC and NPC to map the differentially expressed (DE) genes (DEseq2[39], FDR \(< 0.05\) , fold change \(>4\) ) on the differential compartments. We observed that dcHiC differential compartment bins were enriched in DE genes compared to HOMER (Figure 3l). The trend was similar for bins reported exclusively by each method (Figure 3l). These observations imply that the differential calls made by dcHiC are accompanied by larger changes between ESC and NPC in other biological signals relevant to compartmentalization. + +To also show utility of our tool in detecting differences at higher resolution, we ran dcHiC at 10Kb resolution to call differential compartments between ESC and NPC. We found a total of 16,581 10Kb- bins i.e., 165.81Mb differential compartments between the conditions. Among the 1,981 100Kb dcHiC differential bins, \(72\%\) exactly overlapped at least one 10Kb differential bin (over \(86\%\) within \(+ / - 200\mathrm{kb}\) ). This suggest a significant overlap across resolutions but also highlights the prevalence of regions that are detectable only at higher or lower resolution compartment analysis (Supplementary Figure 1). We also evaluated the potential of false positive discoveries from dcHiC by running it to compare replicates of the same conditions/sample. We used all four biological Hi- C replicates available for ESC in different combinations (all 1 vs 3 and 2 vs 2 combinations of splitting the replicates). When we ran dcHiC on these combinations, the number of significant compartment changes (i.e., false positives) ranged from 1 to 32 with a median value of 2 bins (compared to 1,981 100Kb bins when ESC is compared to NPC), suggesting a low false positive rate for identifying differential compartments. When we ran the same analysis using 10kb bins we identified a median value of 751 differential bins ( \(\sim 0.2\%\) of the genome) suggesting higher resolution differential analysis may be more prone to false positives. + +Example genes from ESC vs NPC differential compartments: Within dcHiC's calls, we also analyzed a set of key genes known for their critical role in ESC or NPC state that have been studied extensively for changes in their nuclear organization during the transition. For instance, we analyzed a set of genes for which Fluorescence in situ Hybridization (FISH) experiments were performed to study changes in radial positioning during the ESC to NPC transition. These included pluripotency markers specifically expressed in ESCs (e.g., Zfp42 or REX1 and Dppa2/4) as well as EPH Receptor B1 (Ephb1) and other marker genes specific to neuronal differentiation. Figure 3J shows the Dppa2/4 region in mouse chromosome 16 that is shown to change radial positioning, chromatin state, lamin B1 association and replication timing during differentiation [36, 40]. Consistent with these data, both dcHiC and HOMER reports a significant shift from A (active) to B (inactive) compartment during mouse ESC to NPC differentiation (Figure 3J). In addition, dcHiC reported significant compartment changes for several other important genes that HOMER missed. Figure 3K- L displays two genes, namely Dach1 and Nedd9, which are known to play a critical role in organogenesis and signal- transduction pathways for mouse neuronal development [41, 42]. We also detected these + +<--- Page Split ---> + +genes in our differential gene expression analysis of ESC vs NPC as significantly upregulated in NPC (FDR<0.05; \(>160x\) for Dac1 and \(>30x\) for Nedd9). Dac1 lies in a compartment reported as flipped from ESC- B to NPC- A by dcHiC (Figure 3K). Nedd9 gene overlaps A- compartment in both cell types but with stronger compartmentalization in NPC that is detected as a significant change by dcHiC (Figure 3L). + +To see whether the compartmental changes are accompanied by specific differences in local chromatin interactions, we implemented an extension of our comparative approach to identify differences in contact counts involving the differential compartments (Methods). This feature allows users to input a set of significant chromatin interactions (e.g., from Fit- Hi- C[43]) or chromatin loops (e.g., from HiCCUPS or Mustache), which will then be filtered for their overlap with differential compartments and tested for their difference across the compared conditions. The black square boxes in Figure 4A represent the dcHiC identified differential interactions (ESC vs NPC) that are anchored in the DppA2/4 region. These interactions are identified among FitHiC2 calls [44] (FDR < 0.05) that are reported as significant in at least one replicate of ESC and/or NPC datasets. The results show that the DppA2/4 domain in NPC has specifically gained interactions with its upstream region compared to ESC while the interactions with adjacent downstream region remained unchanged, a change that can be visualized on the Hi- C map (Figure 4A). Previous studies on Ephb1 have demonstrated a significant subnuclear repositioning of the gene from the periphery to the nuclear center during ESC to NPC differentiation [36] accompanied by a higher gene expression later. A similar analysis of the Ephb1 region shows that it has enriched interactions with a pair of upstream B compartments in ESCs which are weakened in NPC where Ephb1 is transitioned to the A compartment (Figure 4B). In addition, the same region gained interactions with a downstream A compartment in NPC. These results highlight the value of differential interaction analysis coupled with differential compartmentalization to better delineate important changes in the local chromatin environment. Finally, even though the above examples highlight cases where gene expression is tightly correlated with compartment changes and radial positioning, this is not necessarily the case for all genes. Figure 4C shows the pluripotency marker gene Pou5f1/Oct4 region with ESC- specific gene expression. The radial positioning of this gene locus was shown to remain unchanged during ESC to NPC transition[36] consistent with our results (Figure 4C). Overall, dcHiC identified both known compartment flips (A to B or B to A) as well as novel compartmentalization differences within the same compartment for important genes. + +## Multi-cell-type differential compartment analysis of mouse neuronal system + +The same in vitro system used to differentiate from ESC to NPC also allows further differentiation of NPCs to cortical neurons or CNs [38]. This developmental lineage provides an approach to demonstrate how dcHiC uses a multivariate distance measure to compare the compartmentalization of more than two cell types simultaneously. For such multiway comparisons, dcHiC provides a quick and straightforward approach to detect outliers in compartment scores and associated differential interactions, an + +<--- Page Split ---> + +approach far easier with many experiments than the traditional paradigm of taking pairwise comparisons. In this section, we first illustrate the biological significance of \(dcHiC\) 's differential compartments using multiple lines of biological data. We then demonstrate functional term enrichments, and then show specific differential genes that illustrate the application's breadth of analysis. + +Applying \(dcHiC\) at 100Kb resolution on intra- chromosomal Hi- C data from ESC, NPC, and CN samples, we identified a total of 5,055 significant differential bins covering about \(19.2\%\) of the genome. Compartment A and B were evenly split for NPC and CN, whereas ESC has \(\sim 63\%\) B compartments. Overall, regions in the B compartment for each cell type were more likely to exhibit statistically significant compartment changes compared to A compartment ( \(21 - 23\%\) vs \(16 - 18\%\) ). Figure 5A summarizes the number of differential compartment bins that involve flips (A \(\rightarrow\) B or B \(\rightarrow\) A) or remained within the same compartment throughput the lineage transition. Consistent with the literature [2, 5, 45], we showed that compartmental dynamics are strongly associated with variability of gene expression and histone modifications (Figure 5B, Methods). + +To further analyze these changes simultaneously, rather than one transition (or pair) at a time, we utilized time- series analysis to cluster the compartmentalization score patterns of these differential bins across (Figure 5C) and plotted the expression pattern of the overlapping genes in each cluster across three different time- points. In order to focus on relative changes in compartmentalization, we further z- transformed the quantile normalized PCA scores for each 100Kb bin across the three cell types and applied TC- seq[46] to identify 6 major clusters (Methods). Two major clusters corresponded to regions that progressively became more euchromatic (cluster 1 and 6) and one corresponded to more heterochromatic (cluster 4). We observed other clusters that corresponded to one cell type showing highly different compartmentalization with respect to the other two (e.g., clusters 3 and 5 with NPC- specific patterns). To link these compartmentalization patterns to gene function, we identified genes overlapping with each differential compartment bin for each cluster. Performing functional enrichment analysis on these gene sets[47], we identified signatures that are consistent with cellular identity of the cell type with the highest compartment z- scores (i.e., more euchromatic). For instance, for the genes overlapping with clusters 1 and 6 with compartment scores increasing from ESC to NPC to CN, the enriched terms include neurogenesis and neuronal development (Figure 5D). For cluster 3, where CN compartment scores were highest, the enriched terms (cell- cell adhesion, biological adhesion, and others) were consistent with a general pattern for genes involved in regulating cell- type specific migration and development. We also observed that cluster 3 overlapped with an important class of gene family known as protocadherins [48]. Protocadherins are highly conserved genes across species and most of them are clustered in a single genomic locus in vertebrates [49]. They are shown to be differentially expressed in individual neurons and involved in diverse neurodevelopmental processes [50]. When we repeated the functional enrichment analysis per cell type using genes overlapping A compartments with the highest compartmentalization score for that cell type compared to the other two, we also + +<--- Page Split ---> + +observed cellular identity- related annotation terms (Supplementary Data 1). While annotations related to cell adhesion were enriched in ESC as well as CN, CN specifically showed enrichment for neurogenesis, neuron differentiation and development (Supplementary Data 1). CN, but not NPC, also showed enrichment for synaptic signaling, synapse organization and neuron projection development, potentially related to its further differentiated state with respect to NPC. + +Example genes from ESC- NPC- CN differential compartments: The differential compartments captured by dcHiC encompass a variety of traditionally studied as well as more nuanced scenarios. For instance, similar to Dppa2/4, Zfp42/Rex1 is a well- studied pluripotency marker primarily expressed in undifferentiated stem cells (Figure 5E). As is the case for Dppa2/4, Zfp42 is also in a small A compartment region surrounded by large stretches of B compartments in ESC. As expected, this region flipped into B compartment in NPC and stayed that way in CN consistent with lack of gene expression in these two cell types (Figure 5F). Ptn or Pleiotrophin, on the other hand, exhibits mitogenic and trophic effects on dopaminergic neurons, and is instead a marker gene for neuronal lineage. dcHiC reported this gene in a differential compartment that is B in ESC but A in NPC and CN, in concordance with gene expression (Figure 5G- H), which fits the compartmentalization pattern of cluster 1 (Figure 5C). These two examples represent strong compartment flips from A to B or B to A. An example of a more gradual compartmental change is the CN- specific Ctnna2 gene, which functions as a linker between cadherin adhesion receptors and the cytoskeleton to regulate cell- cell adhesion and differentiation in the nervous system. The B compartment encompassing Ctnna2 in ESC gradually weakens during the ESC- NPC- CN transition leading to transcription- permissive A compartment that starts in NPC and expands further in CN (Figure 5I- J). + +Compartment shifts within the same compartment are also captured by dcHiC (Figure 5K- L). Etv5 encodes for a transcription factor that plays an important role in the segregation between epiblast and primitive endoderm specification during ESC differentiation [51]. Etv5 is highly expressed in ESC but gradually loses its expression (Figure 5L) as well as strong compartmentalization during ESC- NPC- CN transition while remaining in the A compartment all the time. This locus belongs to cluster 2 with enrichment for more euchromatic association specifically in ESC consistent with the highest expression for Etv5 for this cell type. Beyond Etv5, we also found a list of 199 other genes within A compartment throughout ESC- NPC- CN transition, for which the variation in expression profile strongly correlated with changes in compartmentalization (Pearson correlation \(> 0.7\) ; Supplementary Data 2). A similar analysis within differential B compartments revealed 245 genes with strong positive correlation between expression and compartmentalization change (Supplementary Data 2). Overall, our results demonstrate that dcHiC can comprehensively analyze multiple different Hi- C maps simultaneously and identify compartmental changes involving abrupt (e.g., compartment flips) as well as gradual changes. + +Differential compartment analysis of mouse hematopoietic system + +<--- Page Split ---> + +The hematopoietic system is a developmentally regulated and well- characterized cell differentiation model [52, 53]. This system provides an opportunity to understand the dynamic changes in the chromatin structure together with transcriptional and other epigenetic changes during differentiation in detail. The study of the genome organization changes during this complex process—involving many different progenitors and differentiated cell types—requires a systematic approach. A recent study by Zhang et al. [28] profiled the chromatin organization in the classic hematopoietic model with ten primary stem, progenitor, and terminally differentiated cell populations from mouse bone marrow (Figure 6A). In this model, long- term hematopoietic stem cells (LT- HSC) represent the starting point of the hematopoietic hierarchy with self- renewal and multilineage differentiation capability. LT- HSC first differentiates into short- term hematopoietic stem cells (ST- HSC) and then multipotent progenitor cells (MPP). MPP cells differentiate into either common lymphoid progenitor (CLP) or common myeloid progenitor (CMP) cells. CMP then further branches out into granulocyte- macrophage progenitors (GMP) and megakaryocyte- erythrocyte progenitors (MEP). The GMP cells are then terminally differentiated into granulocytes (GR), while MEP cells are further differentiated into megakaryocyte progenitors (MKP) and then terminally differentiated into megakaryocytes (MK). + +Using the Hi- C data from this system, we carried out multivariate differential analysis using dcHiC at 100Kb resolution. We detected a total of 6,061 (60.61 Mb of the genome) differential compartment bins across the ten cell types encompassing many of the genomic regions previously shown to undergo hematopoiesis- related dynamic changes [28]. Figure 6A shows an overall summary of the significant compartment changes identified by dcHiC across these cell types. We observed that the number of A to B transitions keeps increasing from the LT- HSC stage to the MEP and GMP progenitor stages. The differentiation of CMP into MEP and GMP cells represent two of the most frequent A to B transitions ( \(\sim 27.4\%\) and \(\sim 15.7\%\) A \(\rightarrow\) B transition, respectively) within the hematopoietic hierarchy, likely reflecting the need for suppression of certain transcriptional profiles for commitment into each branch. This is consistent with the largest proportion of differential B compartments in MEP ( \(\sim 46.5\%\) ) and GMP ( \(\sim 42\%\) ) compared to all other cell types. With respect to the top of the hematopoietic tree (i.e., LT- HSC), early progenitors such as MPP has 571 100Kb bins with a significant compartment flip (either A to B or B to A), whereas the differentiated cells such as MK and GR had 949, and 1,212 such bins, respectively. This confirms the gradual divergence of chromatin compartmentalization from hematopoietic stem cells as cell progress further into differentiation. + +Next, similar to ESC- NPC- CN transition, we also carried out functional enrichment analysis of differential regions with the highest A compartment score in each group and specific cell type. Figure 6B- E show these enrichments for four different stages of hematopoiesis (pre- bifurcation stage: LT- HSC, ST- HSC, progenitor stage: MPP, CMP, granulocyte branch: GMP, GR and the terminally differentiated Granulocytes or GR) with respect to the rest and for a specific cell type within each of these stages highlighting + +<--- Page Split ---> + +biologically relevant processes in each case. For example, morphogenesis and development- related biological processes were enriched in the overall pre- bifurcation stage (set of genes with the highest A compartment score in either LT- HSC, ST- HSC) (Figure 6B) and the progenitor stage cells were enriched in morphogenesis, adhesion and migration related terms (Figure 6C). The granulocyte branch (GMP and GR) as well as the terminally differentiated granulocytes (GR) showed significant enrichments related to activation and regulation of neutrophils and granulocytes (Figure 6D- E). For the megakaryocyte branch (MEP, MKP, MK), however, we did not observe any statistically significant GO term biological process enrichments. + +Example genes from mouse hematopoiesis differential compartments: After investigating the significance of the differential compartments from a high level, we examined the genes overlapping with the differential compartments involved in hematopoietic lineage differentiation and chromatin dynamics [54]. Figure 6F shows a set of important genes overlapping with differential compartments from our multivariate analysis. Zhang et al. showed that increased gene- body associating domain (GAD) scores are linked to active transcription and indicate cell- type specific features. We identified 12 out of 16 such differential GAD genes between ST- HSC and GR as part of dcHiC differential compartments identified across the system (FDR < 0.1; Figure 6F, marked by cyan stars). In addition, previous analysis by Lara- Astiaso et al. [54] also reported a set of critical genes for hematopoietic lineage differentiation. We identified 12 of these 26 genes within differential compartments (FDR < 0.1; Figure 6F, marked by red stars) supporting dcHiC's ability to pick up changes in regions harboring genes that are dynamically regulated during hematopoiesis. Among these genes, one example is the transmembrane transporter gene Abca13, which was the exclusive differential A compartment within GR but in the B compartment for all other cell types (Figure 6G). Other notable examples include Meis1, a transcription factor required to maintain hematopoiesis under stress and over the long term [55]. Notably, this particular example was a significant change solely within the A compartment (Figure 6H). Apart from Meis1, dcHiC also detected differences for other transcription factors like Runx2 and Sox6 that are essential for progenitor cell differentiation (Figure 6F) [56, 57]. We also identified Myc, known for its role in balancing hematopoietic stem cell self- renewal and differentiation [58] adjacent to a significant change within the A compartment that encompasses the Pvt1 gene. The long non- coding RNA Pvt1 harbors intronic enhancers that interact with Myc and promote Myc expression during tumorigenesis [59]. Overall, this complex system demonstrates the utility of dcHiC's multivariate compartment analysis, which discovers important changes in compartmentalization without requiring a large number of pairwise comparisons. + +## Multiway differential compartment analysis across human-derived cell lines + +Measuring the extent to which genetic variation across individuals influences chromatin features including 3D organization has significant implications in our understanding of human disease. Previous studies have revealed that the presence of variations such as + +<--- Page Split ---> + +quantitative trait loci (QTLs) can affect histone modifications, transcription factor binding, and enhancer activity across populations [60, 61]. More recent work by Gorkin et al. [62] studied variation in chromatin conformation across individuals from different human populations. Using dilution Hi- C, they profiled lymphoblastoid cell lines (LCLs) derived from 13 Yoruban individuals, one Puerto Rican trio, one Han Chinese trio, and one European LCL (GM12878). They measured significant differences in 3D genome organization across individuals using different metrics, including Directionality Index (DI), Insulation Score (INS), Frequently Interacting REgions (FIREs), and compartment scores [62]. The study also carried out differential analysis of compartments across individuals and provided both compartment scores and "variable regions" at 40kb resolution (except for chromosomes 1, 9, 14, 19 and X). In order to minimize technical variation and ensure a fair comparison, we started directly from the 40Kb compartment scores reported by Gorkin et al. and ran \(dcHiC\) on these values (starting from quantile normalization). \(dcHiC\) allows direct utilization of pre- computed compartment scores, such as in this case, when available. + +The Venn diagram (Figure 7A) of differential compartments from \(dcHiC\) and Gorkin et al. using the same set of 40Kb genomic bins shows a large overlap between the methods. A large fraction of \(dcHiC\) calls (7524 out of 7,876 or \(\sim 96\%\) ) were also reported by the original paper. However, Gorkin et al. reported an additional 765Mb of the human genome as variable compartment regions (Additional_file_4.xlsx from the original publication filtered for phenotype \(= PC1\) and discover_set \(= 20\) LCLs), which amounts to \(\sim 11K\) more bins at 40Kb resolution. To further study the overlap and differences between the two approaches, we plotted two statistical significance score distributions (- log10 of the adjusted p- value calculated by Gorkin et al.) for regions that the Gorkin study reported as differential, one with regions overlapping with \(dcHiC\) calls and the other of non- overlapping regions (Figure 7B). Variable compartments from the previous study that were not deemed significant by \(dcHiC\) have substantially lower statistical significance, as computed by the original paper suggesting \(dcHiC\) calls are enriched for stronger differences. Next, we compared the full- set of differential compartments called by both methods and their fraction covering each individual chromosome (Figure 7C). The figure shows that Gorkin et al. calls cover a larger fraction of smaller chromosomes, with more than half the entire length reported as a significant variable compartment for some chromosomes (e.g., chr18). \(dcHiC\) , on the other hand, has a more uniform representation of differential compartments across chromosomes with differential fractions ranging between \(10\%\) to \(20\%\) for most chromosomes. Lastly, we compared the top 5000 differential compartment bins ranked by their significance scores from each approach. Figure 7D shows that about \(\sim 61\%\) of these top 5000 differential bins are identical, suggesting substantial differences in each approach's ranking with respect to statistical significance (Spearman rank correlation of 0.55). Although the ranking is substantially different between the methods, the overlapping fraction of the top 5000 differential compartments for \(dcHiC\) had more significant differences (Figure 7E). Using other variable chromatin organization metrics from the Gorkin paper, we observed that \(dcHiC\) calls are more enriched in FIRE- QTLs (Figure 7F) as well as DI- QTLs (Figure 7G). + +<--- Page Split ---> + +Preferential enrichment of such signals suggests a better concordance of \(dChIC\) identified compartmental differences and chromatin organization variability at other levels across individuals. + +Example genes from differential compartments among human- derived LCLs: Figures 7H- I show two examples of a variable region (overlapping with NR2F2, and THEMIS/PTPRK genes) identified by \(dChIC\) . The NR2F2 region was investigated using FISH by Gorkin et al., which confirmed individual- specific changes in 3D chromatin conformation. Two of the individuals from the cohort (YRI- 4 and YRI- 8) showed enriched interaction between the NR2F2 FISH and another placed upstream as compared to YRI- 3 and YRI- 5. The variability of 3D genome organization among individuals is also apparent from compartment scores for this region. The NR2F2 locus across the cohort is found to be a part of strong B- compartment for all Yoruban individuals except for YRI- 4, YRI- 8 and YRI- 9 (Figure 7H). Figure 7I shows another example of such variable region with coordinated changes in epigenetic marks across individuals with support from differential compartments documented in the previous paper. Gorkin et al. have identified variations in different epigenetic marks like H3K4me1 and H3K27ac, binding of CTCF and most importantly gene expression pattern within this region across different individuals (YRI- 2 and 13 vs 11 and 12). The PC score track in Figure 7I also supports the previous findings as some of the individuals from YRI population, especially YRI- 3, YRI- 5, YRI- 11, YRI- 12 showed a clear flip from B to A compartment and both our approach and Gorkin et al. labeled this region as a differential compartment. Taken together, \(dChIC\) identified fewer differential compartment bins with enrichment towards capturing regions with higher variability in different levels of chromatin organization and those with additional evidence for difference among individuals. + +## DISCUSSION + +This paper presents a new application, \(dChIC\) , to compare compartmentalization across Hi- C datasets. \(dChIC\) employs principal component analysis followed by quantile normalization of the compartment scores and a multivariate distance measure to systematically identify significant compartmentalization changes among multiple contact maps. By facilitating comparative analysis across multiple integrated datasets, it helps identify biologically relevant differential compartments with statistical confidence scores. Along with conventional pairwise differential analysis, \(dChIC\) allows a single multivariate differential comparison of Hi- C datasets, utilizing replicates when available, and provides an efficient approach to analyze multiple Hi- C maps without the need for generating many different combinations. + +We applied \(dChIC\) to various biological scenarios, ranging from neuronal and hematopoietic stem cell differentiation in mice to Hi- C data from different human populations. Our results confirmed that \(dChIC\) detects known compartmental changes among cell types, including those previously validated to play a role in neuronal and hematopoietic differentiation. When comparing \(dChIC\) to existing approaches, we showed that it identifies regions with higher differences in replication timing, Lamin B1 signals, + +<--- Page Split ---> + +and differentially expressed genes, suggesting better prioritization of relevant biological regions. Even though differences of compartmentalization between ESC and NPC generally aligned with changes in Lamin B1 association, a recent work highlighted the importance of nucleolus association in revealing layers of compartmentalization with distinct repressive chromatin states [63]. Our initial analysis showed that over \(10\%\) of all significant compartment differences we found between ESC and NPC belongs to nucleolus associated domains (NADs) that were deemed exclusive to either ESC or NPC [63] providing an explanation for a subset of differences in compartmentalization during differentiation. Expanding to a three- way \((n = 3)\) mouse neuronal differentiation model, we showed that dcHiC continues to systematically identify critical biological marker genes and can recover cell- specific functions from differential compartment analysis alone. Across dcHiC's differential compartments, we observed significant and relevant enrichment of biological processes such as neuron differentiation in NPC and CN cells. More broadly, dcHiC's differential compartments also compellingly aligned with changes in Lamin B1, gene expression, and histone modification data. Taken together, these results demonstrate dcHiC's ability to find regions with the most biologically variable compartmentalization across the genome. + +The hierarchical mouse hematopoietic stem cell differentiation model, consisting of ten different cell types with Hi- C data, provided a unique opportunity to demonstrate the utility of dcHiC. A ten- way multivariate differential comparison of the hematopoietic system revealed previously known lineage- specific critical genes encompassing the differential compartments. Notably, we identified vital transcription factors like Sox6, Runx2, Meis1, Foxo1, and many other critical genes like Abca13, by solely analyzing the differential calls. Our functional enrichment analysis of gene sets overlapping with the lineage- specific differential compartments reported from the apex to the bottom of the hematopoietic model- tree reconfirmed that genome compartments play a contributory role in determining the accessibility of genes in specific cell types. Measuring the extent of compartment variability across twenty- cell human types also highlighted our method's novel utility and strength. Most dcHiC calls overlapped with a subset of variable compartments reported by the previous study, but dcHiC calls were enriched for higher variability. Similarly, the regions encompassing Frequently Interacting Region (FIRE)- QTLS and Directionality Index (DI)- QTLS defined by the previous study were more enriched in the top differential compartment calls of dcHiC compared to the top calls defined in the previous study. The analysis also demonstrated an important feature of dcHiC: the ability to directly utilize previously computed compartment scores to run differential compartmentalization analysis. + +The framework we developed here provides a systematic way to identify differential compartments and visualize these differences in different scenarios, including multi- way, hierarchical and time- series setting. Although we focused on human and mouse Hi- C data in this work, our method is readily applicable to Hi- C data or its variants (e.g., Micro- C [64]) derived from any organism with compartmental genome organization. With hundreds of publicly available Hi- C datasets in the 4D Nucleome Data Portal and others published every day, dcHiC will play an essential role in comparative analysis of high- level genome + +<--- Page Split ---> + +organization. As single- cell Hi- C data starts providing better resolution, dcHiC and methods derive from it will be critical to enable compartment comparison across thousands of cells. + +## METHODS + +## Data processing, result generation, and visualization + +Hi- C data: All the Hi- C data, except for the Gorkin et. al. 2019 were mapped on mm10 reference genome and processed using HiCpro (v2.7.9) pipeline [65]. The raw Hi- C interaction maps retrieved after HiC- Pro processing are used for downstream compartment score calculation by dcHiC. In the section analyzing data from the Gorkin et. al. study, we used the provided compartment scores (40Kb resolution) across all samples mapped on hg19 reference genome [62]. Statistically significant interactions were called using FitHiC2 [44] with default parameters and an FDR threshold of 0.05 for each replicate and/or each sample. + +RNA- seq data: The RNA- seq data from Bonev et. al. 2017 [38] study concerning mouse neural development were processed using our in- house and open- source RNA- seq processing pipeline (https://github.com/ay- lab/LJI RNA_SEQ_PIPELINE_V2.git), which utilizes STAR [66]. The differential gene expression analysis between mouse ESC and NPC cell lines (two- replicates each) was performed using DESeq2 method [39] with all the default parameter settings. + +ChIP- seq data: For ChIP- seq peak calling (H3K27ac, H3K4me3 and H3K4me1 histone marks), we first mapped the respective fastq files on the mm10 genome using the bowtie2 [67] and generated the corresponding bam files (MAPQ \(>20\) ). The aligned files were then used as input to the MACS2 program [68] to call peaks (p- value \(< 1e - 5\) ) against their respective input controls. The continuous ChIP- seq peaks were then merged and the unique set was mapped to the 100Kb differential compartments to calculate the average number of peaks. The enrichment of signal difference is calculated by first quantifying the absolute difference in signal (number of ChIP- seq peaks and gene expression TPM values) within ESC to NPC differential and non- differential compartments. The enrichment of absolute signal difference between the differential and non- differential compartments between ESC and NPC was then compared by un- paired T- test. + +Time- series analysis: The time- series clustering was generated using the TCseq package [46]. For gene- term enrichment analysis, the differential compartments are scanned against the gene coordinates of the respected genome defined by the user using 'bedtools map' function [69]. The unique overlapping set of genes are then extracted and are used for GO biological function enrichment analysis using the ToppGene suite API function or directly from their webserver [47]. + +<--- Page Split ---> + +IGV Browser visualization: dcHiC generates a Javascript based stand-alone dynamic IGV-HTML page to visualize the compartments and differential compartment calls, with an option to add additional tracks. + +## Computation and quantile normalization of compartment scores for comparison + +To perform principal component analysis (PCA) on Hi- C maps dcHiC utilizes the singular value decomposition (SVD) implementation of the bigstatsr R package [32]. The input to SVD is \(K\) different distance- normalized chromosome- wise correlation matrices \((X_{1},X_{2},X_{3}\ldots X_{K})\) for each Hi- C data. For each such matrix, dcHiC finds the decomposition: + +\[X_{K} = U_{K}\cdot \Gamma_{K}\cdot V_{K}^{T}\quad with U_{K}^{T}\cdot U_{K} = V_{K}^{T}\cdot V_{K} = I \quad (1)\] + +The matrices \(U_{K}\) and \(V_{K}\) store the left and right singular vectors of the matrix \(X_{K}\) . The singular values of \(X_{K}\) are stored in the diagonal matrix \(\Gamma_{K}\) . The principal components for each matrix are then obtained as: + +\[P C_{K} = X_{K}\cdot \mathrm{V}_{K} \quad (2)\] + +The eigen- decomposition of the \(K^{th}\) correlation matrix provides the eigenvectors, and the sign of the first eigenvector or principal component \((PC1_{K})\) typically represents the genomic compartments A and B for the \(K^{th}\) chromosome. If \(PC1_{K}\) corresponds to chromosome arms or other broad patterns in the Hi- C matrix, the second principal component \((PC2_{K})\) may represent A and B compartments. The A/B compartment labels are assigned to the positive/negative stretches of the selected \(PC_{K}\) depending on the implementation of eigen- decomposition. It may be necessary to re- orient these assignments and select the correct \(PC_{K}\) using GC content or gene density. Thus, before the quantile normalization step, dcHiC performs an intermediate correlation analysis of the first two principal component scores (user- defined) of each chromosome per sample against the GC content and gene density of that chromosome. The principal component which obtains the highest sum of GC content and gene density correlation is considered the compartment score, and the A/B compartments of the selected principal components are assigned based on the GC content correlation (A compartment and positive values representing higher GC content). These generate a set of compartment score vectors representing each sample (M samples) for a given chromosome \((C_{1},C_{2},C_{3}\ldots C_{M})\) . Once the properly labeled compartment scores are obtained, dcHiC performs quantile normalization (QN) using the limma package [70] on the set \((C_{1},C_{2},C_{3}\ldots C_{M})\) per chromosome to even out the scaling across the group for downstream analysis. + +\[(q_{1},q_{2},q_{3}\ldots q_{M}) = QN(C_{1},C_{2},C_{3}\ldots C_{M}) \quad (3)\] + +In the case of samples with replicates, dcHiC performs the above steps by including each replicate from each sample (i.e., quantile normalize all replicates together). dcHiC then + +<--- Page Split ---> + +calculates the average of quantile normalized values of each genomic bin across all the replicates of a given sample to represent sample- wise compartment scores. + +## Differential compartment identification + +Mahalanobis distance (MD) is a multivariate statistical measure of the extent to which the multivariate data points are marked as outliers, based on a Chi- square distribution [71]. The Mahalanobis distance of a point \(i\) from a multi- dimensional distribution defined by set \(s\) (sample) and its center \(\mu\) is defined as: + +\[M D_{s a m p l e}^{i} = (s_{i} - \mu_{i})^{T}\cdot \Sigma^{-1}\cdot (s_{i} - \mu_{i}) \quad (4)\] + +Where \(s_{i} = (q_{1}^{i}, q_{2}^{i}, q_{3}^{i} \ldots q_{M}^{i})\) is the set of quantile normalized compartment score distributions and \(\mu_{i} = (\mu_{1}^{i}, \mu_{2}^{i}, \mu_{3}^{i} \ldots \mu_{M}^{i})\) is the set of weighted centers for each point \(i\) from set \(s\) . The inverse of the covariance matrix of set \(s\) is represented as \(\Sigma^{- 1}\) . The weighted centers \(\mu_{i}\) is calculated as: + +\[\mu_{i} = s_{i} * w_{i} \quad (5)\] + +Where \(0 \leq w_{i} \leq 1\) is the cumulative Normal distribution probability associated with the maximum z- score among the z- scores of all samples for \(i\) : + +\[w_{i} = \max \{Pr(Z_{M}^{i})\} \quad (6)\] + +Where \(Z_{N}^{i}\) , the z- score for point \(i\) for sample \(N\) is computed as: + +\[Z_{N}^{i} = \frac{(d_{N}^{i} - \overline{d_{N}})}{\sigma(d_{N})} \quad (7)\] + +Here \(\overline{d_{N}}\) and \(\sigma (d_{N})\) represent the average distance and standard deviation within sample \(N\) among all \(d_{N}^{i}\) values that are computed as: + +\[d_{N}^{i} = \frac{\sqrt{\Sigma_{t = 1}^{M}(q_{t}^{i} - q_{N}^{i})^{2}}}{(M - 1)} \quad (8)\] + +Essentially, the approach provides more weight to the points that are distant from others among the samples (further from the diagonal) than to points that are closer together in the multi- dimensional space (close to the diagonal). Equation (4) is the standard MD formulation, which we modify using the weighted centers as computed through Equations (6) to (8). + +In order to increase the sensitivity of our difference detection, we implemented an outlier removal step that eliminates genomic bins (or points) with high MD (as computed above) at the initial pass (1st pass). We use a pre- defined upper- tail critical value of the chi- square distribution with \(df\) degrees of freedom as our threshold for outlier removal (default value we used is: \(MD threshold \sim \chi_{0.90,df}^{2}\) ). We then recompute the covariance matrix \(\Sigma^{- 1}\) after + +<--- Page Split ---> + +removal of these outliers and calculate the MD (through Equation (4)) one more time for each point \((2^{\mathrm{nd}}\) pass). The significance of the corresponding \(MD_{sample}^{i}\) \((2^{\mathrm{nd}}\) pass) is calculated from the critical chi- square distribution table as \(\chi^{2}(MD_{sample}^{i},df)\) using \(pchisq\) function of R programming language followed by multiple testing correction to retrieve adjusted p- values. + +In case of samples \((s)\) with replicates \((r)\) \(dchIC\) calculates an additional covariate \(MD_{replicate}\) and applies Independent Hypothesis Weighting (IHW) to adjust the \(p\) - values. + +The covariate is calculated as: + +\[MD_{repl}^{i} = \{(s_{i}^{T} - s_{i}^{T}\mu_{i})^{T}\cdot (diag(\Sigma^{-1}))\cdot (s_{i}^{T} - s_{i}^{T}\mu_{i})|s\in (1,2\dots M),r\in (1,2\dots R)\} (9)\] + +Where \(R\) is the total number of all replicates combined across all samples, \(s_{i}^{T} =\) \((r_{1}^{i},r_{2}^{i},r_{3}^{i},\dots r_{R}^{i})\) is the set of quantile normalized compartment score distributions of all replicates from samples \(s\in (1,2\dots M)\) and \(\bar{s}_{i}^{T}\mu_{i} = (\frac{1}{s}\mu_{i},\frac{2}{s}\mu_{i},\frac{3}{s}\mu_{i},\dots \frac{5}{s}\mu_{i})\) is the is the set of weighted centers for each point \(i\) from \(R\) replicates. \(diag\) is an operation that masks all non- diagonal entries (sets to zero) of the covariance matrix. + +The weighted centers \(\bar{s}_{i}^{T}\mu_{i}\) are calculated as: + +\[\bar{s}_{i}^{T}\mu_{i} = s_{i}^{T}*(1 - \bar{s}_{i}^{T}w_{i}) \quad (10)\] + +Where \(0\leq \bar{s}_{i}^{T}w_{i}\leq 1\) is calculated as: + +\[\bar{s}_{i}^{T}w_{i} = max\{Pr(\bar{s}_{i}^{T}Z_{R})\} \quad (11)\] + +And \(\bar{s}_{i}^{T}Z_{i}\) for replicate \(r\) of sample \(s\) is computed as: + +\[\begin{array}{l}{{\bar{s}_{i}^{T}d_{i}=\frac{\sqrt{\Sigma_{t=1}^{R}(r_{i}^{t}-r_{R}^{t})^{2}}}{(R-1)}}}\\ {{\bar{s}_{i}^{T}Z_{i}=\frac{(\bar{s}_{i}^{T}d_{i}-\overline{\bar{s}_{i}^{d}})}{\sigma(\bar{s}_{i}^{d})}}}\end{array} \quad (13)\] + +Here the variables are defined as similar to Equations (7) and (8) and \(R\) is used to represent the number of replicates of the same sample (i.e., distances across replicates of different samples are not taken into account). + +This approach provides more weight to the features that are closer to each other within replicates of a sample (close to the diagonal) and as opposed to the calculation across different samples (Equation (5)) where higher weights were given to the points with samples distant from each other (far from the diagonal). The significance of the corresponding \(MD\) for each point are calculated using chi- square distribution as mentioned above. \(dchIC\) applies the IHW approach to adjust the p- values using FDR + +<--- Page Split ---> + +correction obtain from \(MD_{sample}\) using \(MD_{replicate}\) replicate variation measure as a covariate. + +## Differential interaction identification + +Using the same Mahalanobis distance (MD) measure, \(dcHiC\) enables the user to find differential interactions across samples that are either linking two differential compartments together or a differential compartment with other parts of the same chromosome. The goal of this feature is to provide more information on the chromatin organization changes related to or correlated with compartmental differences. For this analysis, we have used FitHiC2 to call significant interactions (FDR \(5\%\) ) for each sample or replicate (when available), but users are free to provide their own set of interaction or loop calls from any other tool. Using these calls, \(dcHiC\) first finds the interaction subset that overlaps with differential compartments (on either end or both) using the bedtools 'paitobed' function. \(dcHiC\) utilizes the \(log2(Observed / Expected)\) values of a chromatin interaction \(i\) from to perform differential interaction calling as: + +\[MD_{interaction}^{i} = (oe_{i}^{s} - \mu^{s})^{T}\cdot \Sigma^{-1}\cdot (oe_{i}^{s} - \mu^{s})\mid s\in (1,2,3\dots M) \quad (14)\] + +where \(oe_{i}^{s}\) represents \(log2(Observed / Expected)\) values for chromatin interactions of locus pair \(i\) for sample \(s\) and \(\mu^{s}\) represents the vector of centers of distance normalized interactions from sample \(s\) . Here \(\Sigma^{- 1}\) represents the inverse of covariance matrix of interactions among the samples. The approach provides more weight to the interactions that are distant from the expected interaction strength among the samples than to the interactions that are closer to the expected range in the multi- dimensional space. The significance of the corresponding \(MD_{interaction}^{i}\) is calculated from the critical chi- square distribution table as \(\chi^{2}(MD_{interaction}^{i},df)\) using \(pchisq\) function embedded within R programming environment followed by the FDR correction to retrieve adjusted p- values. + +## Availability of data and materials + +A Python/R implementation of \(dcHiC\) is freely available at https://github.com/ay- lab/dcHiC. This application is compatible with Hi- C data in HiC- Pro, .hic, and .cool formats. The data used in this study are available at the following GEO accession numbers: GSE96107 (mESC- NPC- CN), GSE152918 (mouse hematopoiesis), GSE128678 (human LCLs). These are also available in Supplemental Table 1 (see Supplementary Information). All reported compartments for all cell lines, multivariate differential scores, RNA- seq, and ChIP- seq data used in this manuscript can be viewed interactively at: ay- lab.github.io/dcHiC. These standalone HTML files employ \(dcHiC\) 's visualization utility through IGV browser. + +<--- Page Split ---> + +## FIGURE LEGENDS + +Figure 1: Outline of the method. The figure panel shows the dcHiC workflow in two steps. In step 1, dcHiC calculates the principal components followed by the quantile normalization of the compartment scores across all input Hi- C data. In the step 2, for each genomic bin, dcHiC calculates a Mahalanobis distance, which is a statistical measure of the extent to which each bin is a multivariate outlier with respect to the overall multivariate compartment score distribution across all input Hi- C maps (Methods). dcHiC then utilizes the Mahalanobis distance to assign a statistical significance using Chi- square test (p- value) for each compartment bin and employs independent hypothesis weighting (IHW - when there are replicate samples) or FDR (when no replicates are available) correction on these p- values. dcHiC outputs a standalone dynamic IGV web browser view and enables the user to integrate other datasets into the same view for an integrated visualization. + +Figure 2: Comparison of dcHiC compartment scores with HOMER compartment scores and Lamin B1 association data. (A- B) Genome- wide comparison of dcHiC compartment scores against HOMER compartment scores and Lamin B1 profiles for mouse ESC. (C) Comparison between HOMER compartment scores and Lamin B1 association. (D- F) Plots similar to (A- C) but for NPC Hi- C data. Pearson correlation value between two axes are reported for each plot. (G- H) Browser views of the compartment scores and Lamin B1 signal for a chromosome 16 region with arrows pointing to some small differences among the compartment scores of dcHiC and HOMER. + +Figure 3: Pairwise differential compartment analysis between ESC and NPC. (A) The breakdown of the numbers of differential compartment calls (100Kb resolution) belonging to different types. (B- E) The distributions of compartment scores, Lamin B1, replication timing, association and gene expression across different subtypes of differential compartment calls made by dcHiC. Strong (s) and Weak (w) were used to indicate the relative compartment strength (or absolute value) between the two cell types. (F) The Venn- diagram shows the overlap between dcHiC and HOMER differential compartment calls. (G- H) The absolute difference of Lamin B1, replication timing signal and gene expression values (TPM) overlapping with all and exclusive differential compartments identified by dcHiC and HOMER, respectively (statistical significance was calculated by unpaired T- test). (I) The average number of differential (DE) genes overlapping with differential compartment bins (100Kb) identified by dcHiC and HOMER. (J- L) dcHiC differential compartments involving three DE genes: Dppa2/4, Dach1 and Nedd9. + +Figure 4: Differences in local chromatin interactions of differential compartments. Detailed browser views (top), Hi- C contact maps (mid) and differential chromatin interactions (bottom) of three gene loci – (A) Dppa2/4, (B) Ephb1 and (C) Oct4. Visible changes in interactions involving Dppa2/4 locus and Ephb1 locus are highlighted through each plot. Although Oct4 shows a dramatic change in gene expression, the region does + +<--- Page Split ---> + +not alter its radial position within nucleus (FISH experiments) which is also consistent with the lack of change in compartmentalization as reported by \(dcHiC\) . + +Figure 5: Three- way differential compartment analysis of ESC, NPC, and CN. (A) The breakdown of the numbers of differential compartment calls belonging to different types. (B) The enrichment of signal difference in different histone marks and gene expression in \(dcHiC\) differential bins with compartment flips (A \(\rightarrow\) B or B \(\rightarrow\) A) compared to bins with non- significant compartment flips. (C) Time- series clustering of normalized compartment scores into six different clusters from the three cell types along with their overlapping gene expression profile. For the clustering analysis, the quantile normalized PCA scores for each 100Kb bin across ESC- NPC- CN were further z- transformed to focus on relative changes in compartmentalization. (D) Gene term enrichment results of GO biological functions from genes overlapping with cluster 1, 3 and 6 compartments. (E- L) Differential compartments overlapping with representative genes in each of the cell types shown along with the differential chromatin interactions involving the respective compartments and gene expression values (TPM) across the ESC- NPC- CN transition for four example genes each representing one cluster pattern. + +Figure 6: Ten- way multivariate differential compartment analysis of mouse hematopoiesis. (A) Summary of overall compartment decomposition and significant compartment changes observed across the 10 cell types. The orange and blue arrows represent A to B and B to A compartment flips, respectively. The numbers next to the arrows represent the total number of flipping compartments and the numbers within the parentheses next to it shows the significantly differential flipping compartments. The bottom- right plot shows the proportion of A and B bins among \(dcHiC\) differential compartments for each cell type. Figure adopted from Zhang et. al. [28]. (B- E) The functional enrichment of genes overlapping with differential compartments from 10- way comparison that have the strongest A compartment scores in either (B) LT- HSC or ST- HSC, (C) MPP or CMP, (D) GMP or GR, or (E) GR alone. (F) Differential compartments identified by \(dcHiC\) overlap a set of critical genes previously known to play role in mouse hematopoiesis. (G) An IGV browser snapshot of Abca13 gene and its overlapping differential compartment across the ten cell types. The Abca13 gene is exclusively found to be a part of A- compartment in GR while in the B- compartment in all other cell types. (H) An IGV browser view surrounding Meis1 genic region. This region is overlapping with the A compartment for all the cell types but with varying magnitude of strength. \(dcHiC\) captured this region as a differential compartment. + +Figure 7: Twenty- way multivariate differential compartment analysis of human lymphoblastoid cell lines (LCLs). (A) A Venn diagram of the overlap of differential compartments called by \(dcHiC\) and the variable compartment regions by a previous study (Gorkin et. al, 2019). (B) The distribution of - log10(p- adj) values of \(dcHiC\) - overlapping and non- overlapping variable regions calculated by the previous study. (C) The total number of chromosome- wise differential compartments and the fraction of each chromosome (except those filtered by Gorkin et. al. 2019) covered by such calls for \(dcHiC\) and the + +<--- Page Split ---> + +previous study. 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Castiglioni V, Faedo A, Onorati M, Bocchi VD, Li Z, Iennaco R, Vuono R, Bulfamante GP, Muzio L, Martino G, et al: Dynamic and Cell-Specific DACH1 Expression in Human Neocortical and Striatal Development. Cereb Cortex 2019, 29:2115-2124. +43. Ay F, Bailey TL, Noble WS: Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts. Genome Res 2014, 24:999-1011. +44. Kaul A, Bhattacharyya S, Ay F: Identifying statistically significant chromatin contacts from Hi-C data with FithHiC2. Nat Protoc 2020, 15:991-1012. +45. Bonev B, Cavalli G: Organization and function of the 3D genome. Nat Rev Genet 2016, 17:661-678. +46. Wu M, Gu L: TCseq: Time course sequencing data analysis. R package version 1180 2021. +47. Chen J, Bardes EE, Aronow BJ, Jegga AG: ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 2009, 37:W305-311. +48. Chen WV, Maniatis T: Clustered protocadherins. Development 2013, 140:3297-3302. +49. Chen WV, Alvarez FJ, Lefebvre JL, Friedman B, Nwakeze C, Geiman E, Smith C, Thu CA, Tapia JC, Tasic B, et al: Functional significance of isoform diversification in the protocadherin gamma gene cluster. Neuron 2012, 75:402-409. +50. Hirabayashi T, Yagi T: Protocadherins in neurological diseases. Adv Neurobiol 2014, 8:293-314. +51. Zhang J, Cao H, Xie J, Fan C, Xie Y, He X, Liao M, Zhang S, Wang H: The oncogene Etv5 promotes MET in somatic reprogramming and orchestrates epiblast/primitive endoderm specification during mESCs differentiation. Cell Death Dis 2018, 9:224. +52. Yoder MC: Embryonic hematopoiesis in mice and humans. Acta Paediatr Suppl 2002, 91:5-8. +53. Doulatov S, Notta F, Laurenti E, Dick JE: Hematopoiesis: a human perspective. Cell Stem Cell 2012, 10:120-136. +54. Lara-Astiaso D, Weiner A, Lorenzo-Vivas E, Zaretsky I, Jaitin DA, David E, Keren-Shaul H, Mildner A, Winter D, Jung S, et al: Immunogenetics. Chromatin state dynamics during blood formation. Science 2014, 345:943-949. +55. Unnisa Z, Clark JP, Roychoudhury J, Thomas E, Tessarollo L, Copeland NG, Jenkins NA, Grimes HL, Kumar AR: Meis1 preserves hematopoietic stem cells in mice by limiting oxidative stress. Blood 2012, 120:4973-4981. + +<--- Page Split ---> + +56. de Bruijn M, Dzierzak E: Runx transcription factors in the development and function of the definitive hematopoietic system. Blood 2017, 129:2061-2069.57. Dumitriu B, Patrick MR, Petschek JP, Cherukuri S, Klingmuller U, Fox PL, Lefebvre V: Sox6 cell-autonomously stimulates erythroid cell survival, proliferation, and terminal maturation and is thereby an important enhancer of definitive erythropoiesis during mouse development. Blood 2006, 108:1198-1207.58. Wilson A, Murphy MJ, Oskarsson T, Kaloulis K, Bettess MD, Oser GM, Pasche AC, Knabenhans C, Macdonald HR, Trumpp A: c-Myc controls the balance between hematopoietic stem cell self-renewal and differentiation. Genes Dev 2004, 18:2747-2763.59. Jin K, Wang S, Zhang Y, Xia M, Mo Y, Li X, Li G, Zeng Z, Xiong W, He Y: Long non-coding RNA PVT1 interacts with MYC and its downstream molecules to synergistically promote tumorigenesis. Cell Mol Life Sci 2019, 76:4275-4289.60. McVicker G, van de Geijn B, Degner JF, Cain CE, Banovich NE, Raj A, Lewellen N, Myrthil M, Gilad Y, Pritchard JK: Identification of genetic variants that affect histone modifications in human cells. Science 2013, 342:747-749.61. Kasowski M, Kyriazopoulou-Panagiotopoulou S, Grubert F, Zaugg JB, Kundaje A, Liu Y, Boyle AP, Zhang QC, Zakharia F, Spacek DV, et al: Extensive variation in chromatin states across humans. Science 2013, 342:750-752.62. Gorkin DU, Qiu Y, Hu M, Fletez-Brant K, Liu T, Schmitt AD, Noor A, Chiou J, Gaulton KJ, Sebat J, et al: Common DNA sequence variation influences 3-dimensional conformation of the human genome. Genome Biol 2019, 20:255.63. Bersaglieri C, Kresoja-Rakic J, Gupta S, Bar D, Kuzyakiv R, Panatta M, Santoro R: Genome-wide maps of nucleolus interactions reveal distinct layers of repressive chromatin domains. Nat Commun 2022, 13:1483.64. Hsieh TH, Weiner A, Lajoie B, Dekker J, Friedman N, Rando OJ: Mapping Nucleosome Resolution Chromosome Folding in Yeast by Micro-C. Cell 2015, 162:108-119.65. Servant N, Varoquaux N, Lajoie BR, Viara E, Chen CJ, Vert JP, Heard E, Dekker J, Barillot E: HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol 2015, 16:259.66. Dobin A, Gingeras TR: Mapping RNA-seq Reads with STAR. Curr Protoc Bioinformatics 2015, 51:11-14 11-11 14 19.67. Langmead B, Salzberg SL: Fast gapped-read alignment with Bowtie 2. Nat Methods 2012, 9:357-359.68. Feng J, Liu T, Qin B, Zhang Y, Liu XS: Identifying ChIP-seq enrichment using MACS. Nat Protoc 2012, 7:1728-1740.69. Quinlan AR, Hall IM: BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 2010, 26:841-842.70. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015, 43:e47.71. Brereton RG: The Mahalanobis distance and its relationship to principal component scores. Journal of Chemometrics 2015. + +<--- Page Split ---> + +## Figures + +![](images/Figure_1.jpg) + +
Figure 1
+ +Outline of the method. The figure panel shows the dcHiC workflow in two steps. In step 1, dcHiC calculates the principal components followed by the quantile normalization of the compartment scores across all input Hi- C data. In the step 2, for each genomic bin, dcHiC calculates a Mahalanobis distance, which is a statistical measure of the extent to which each bin is a multivariate outlier with respect to the overall multivariate compartment score distribution across all input Hi- C maps (Methods). dcHiC then utilizes the Mahalanobis distance to assign a statistical significance using Chi- square test (p- value) for each compartment bin and employs independent hypothesis weighting (IHW – when there are replicate samples) or FDR (when no replicates are available) correction on these p- values. dcHiC outputs a standalone dynamic IGV web browser view and enables the user to integrate other datasets into the same view for an integrated visualization. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2
+ +Comparison of dChIC compartment scores with HOMER compartment scores and Lamin B1 association data. (A- B) Genome- wide comparison of dChIC compartment scores against HOMER compartment scores and Lamin B1 profiles for mouse ESC. (C) Comparison between HOMER compartment scores and Lamin B1 association. (D- F) Plots similar to (A- C) but for NPC Hi- C data. Pearson correlation value between two axes are reported for each plot. (G- H) Browser views of the compartment scores and Lamin B1 signal for a chromosome 16 region with arrows pointing to some small differences among the compartment scores of dChIC and HOMER. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3
+ +Pairwise differential compartment analysis between ESC and NPC. (A) The breakdown of the numbers of differential compartment calls (100Kb resolution) belonging to different types. (B- E) The distributions of compartment scores, Lamin B1, replication timing, association and gene expression across different subtypes of differential compartment calls made by \(dcHiC\) . Strong (s) and Weak (w) were used to indicate the relative compartment strength (or absolute value) between the two cell types. (F) The Venn- + +<--- Page Split ---> + +diagram shows the overlap between dcHiC and HOMER differential compartment calls. (G-H) The absolute difference of Lamin B1, replication timing signal and gene expression values (TPM) overlapping with all and exclusive differential compartments identified by dcHiC and HOMER, respectively (statistical significance was calculated by unpaired T-test). (I) The average number of differential (DE) genes overlapping with differential compartment bins (100Kb) identified by dcHiC and HOMER. (J-L) dcHiC differential compartments involving three DE genes: DppA2/4, Dach1 and Nedd9. + +![](images/Figure_4.jpg) + +
Figure 4
+ +Differences in local chromatin interactions of differential compartments. Detailed browser views (top), HiC contact maps (mid) and differential chromatin interactions (bottom) of three gene loci – (A) DppA2/4, (B) Ephb1 and (C) Oct4. Visible changes in interactions involving DppA2/4 locus and Ephb1 locus are highlighted through each plot. Although Oct4 shows a dramatic change in gene expression, the region + +<--- Page Split ---> + +does not alter its radial position within nucleus (FISH experiments) which is also consistent with the lack of change in compartmentalization as reported by \(dcHiC\) . + +![](images/Figure_5.jpg) + +
Figure 5
+ +Three- way differential compartment analysis of ESC, NPC, and CN. (A) The breakdown of the numbers of differential compartment calls belonging to different types. (B) The enrichment of signal difference in + +<--- Page Split ---> + +different histone marks and gene expression in dcHiC differential bins with compartment flips (A à B or B à A) compared to bins with non- significant compartment flips. (C) Time- series clustering of normalized compartment scores into six different clusters from the three cell types along with their overlapping gene expression profile. For the clustering analysis, the quantile normalized PCA scores for each 100Kb bin across ESC- NPC- CN were further z- transformed to focus on relative changes in compartmentalization. (D) Gene term enrichment results of GO biological functions from genes overlapping with cluster 1, 3 and 6 compartments. (E- L) Differential compartments overlapping with representative genes in each of the cell types shown along with the differential chromatin interactions involving the respective compartments and gene expression values (TPM) across the ESC- NPC- CN transition for four example genes each representing one cluster pattern. + +<--- Page Split ---> +![](images/Figure_6.jpg) + +
Figure 6
+ +Ten- way multivariate differential compartment analysis of mouse hematopoiesis. (A) Summary of overall compartment decomposition and significant compartment changes observed across the 10 cell types. The orange and blue arrows represent A to B and B to A compartment flips, respectively. The numbers next to the arrows represent the total number of flipping compartments and the numbers within the parentheses next to it shows the significantly differential flipping compartments. The bottom- right + +<--- Page Split ---> + +plot shows the proportion of A and B bins among dcHiC differential compartments for each cell type. Figure adopted from Zhang et. al. [28]. (B- E) The functional enrichment of genes overlapping with differential compartments from 10- way comparison that have the strongest A compartment scores in either (B) LTHSC or ST- HSC, (C) MPP or CMP, (D) GMP or GR, or (E) GR alone. (F) Differential compartments identified by dcHiC overlap a set of critical genes previously known to play role in mouse hematopoiesis. (G) An IGV browser snapshot of Abca13 gene and its overlapping differential compartment across the ten cell types. The Abca13 gene is exclusively found to be a part of A- compartment in GR while in the B- compartment in all other cell types. (H) An IGV browser view surrounding Meis1 genic region. This region is overlapping with the A compartment for all the cell types but with varying magnitude of strength. dcHiC captured this region as a differential compartment. + +<--- Page Split ---> +![](images/Figure_7.jpg) + +
Figure 7
+ +Twenty- way multivariate differential compartment analysis of human lymphoblastoid cell lines (LCLs). (A) A Venn diagram of the overlap of differential compartments called by \(dcHiC\) and the variable compartment regions by a previous study (Gorkin et. al, 2019). (B) The distribution of - log10(p- adj) values of \(dcHiC\) overlapping and non- overlapping variable regions calculated by the previous study. (C) The total number of chromosome- wise differential compartments and the fraction of each chromosome (except + +<--- Page Split ---> + +those filtered by Gorkin et. al. 2019) covered by such calls for \(dcHiC\) and the previous study. (D- E) Venn diagrams of overlapping compartments of the top 5000 differential region from both the approaches and the \(- \log 10(p\) - adj) value distribution of the overlapping and non- overlapping set from \(dcHiC\) (F- G) The cumulative number of FIRE- QTLs and DI- QTLs overlapping the top 5000 differential compartment calls by \(dcHiC\) and Gorkin et al. (H- I) Two differential compartments overlapping genic regions of \(NR2F2\) and THEMIS/PTPRK. Both of the genes and especially \(NR2F2\) region was shown to be a variable region across the population through FISH experiments in the previous study (Gorkin et. al, 2019). + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementaryData1. xlsx SupplementaryData2. xlsx Reportingsummary002. pdf dcHiCdemo.zip SupplementaryInformation.docx + +<--- Page Split ---> diff --git a/preprint/preprint__06ef6f0a131181318c342eb6f78c7a4f17407366557c7de9dbe200f81c0614f5/preprint__06ef6f0a131181318c342eb6f78c7a4f17407366557c7de9dbe200f81c0614f5_det.mmd b/preprint/preprint__06ef6f0a131181318c342eb6f78c7a4f17407366557c7de9dbe200f81c0614f5/preprint__06ef6f0a131181318c342eb6f78c7a4f17407366557c7de9dbe200f81c0614f5_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..721f5f3c6839655191cb3a11000cbe23b4522eb7 --- /dev/null +++ b/preprint/preprint__06ef6f0a131181318c342eb6f78c7a4f17407366557c7de9dbe200f81c0614f5/preprint__06ef6f0a131181318c342eb6f78c7a4f17407366557c7de9dbe200f81c0614f5_det.mmd @@ -0,0 +1,593 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 894, 175]]<|/det|> +# dcHiC: differential compartment analysis of Hi-C datasets + +<|ref|>text<|/ref|><|det|>[[44, 195, 450, 238]]<|/det|> +Abhijit Chakraborty La Jolla Institute for Allergy and Immunology + +<|ref|>text<|/ref|><|det|>[[44, 243, 553, 285]]<|/det|> +Jeffrey Wang Harvard College https://orcid.org/0000- 0002- 5707- 5113 + +<|ref|>text<|/ref|><|det|>[[44, 289, 808, 331]]<|/det|> +Ferhat Ay (☑ ferhatay@lji.org) La Jolla Institute for Allergy and Immunology https://orcid.org/0000- 0002- 0708- 6914 + +<|ref|>sub_title<|/ref|><|det|>[[44, 371, 102, 388]]<|/det|> +## Article + +<|ref|>title<|/ref|><|det|>[[44, 409, 137, 427]]<|/det|> +# Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 446, 287, 466]]<|/det|> +Posted Date: April 1st, 2022 + +<|ref|>text<|/ref|><|det|>[[44, 485, 474, 504]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 1483135/v1 + +<|ref|>text<|/ref|><|det|>[[44, 521, 908, 565]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 583, 530, 603]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 639, 955, 683]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on November 11th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 34626- 6. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[114, 117, 742, 140]]<|/det|> +# dcHiC: differential compartment analysis of Hi-C datasets + +<|ref|>text<|/ref|><|det|>[[114, 148, 597, 169]]<|/det|> +Abhijit Chakraborty \(^{1,\# *}\) , Jeffrey Wang \(^{1,2,\# ,S}\) , Ferhat Ay \(^{1,3*}\) + +<|ref|>text<|/ref|><|det|>[[114, 207, 765, 320]]<|/det|> +\(^{1}\) La Jolla Institute for Immunology, La Jolla, California, USA. \(^{2}\) The Bishop's School, La Jolla, California, USA. \(^{3}\) School of Medicine, University of California San Diego, La Jolla, California, USA. \(^{\#}\) Equal contribution \(^{\S}\) Current address: Harvard College, Cambridge, Massachusetts, USA. \(^{*}\) Co- corresponding authors: abhijit@lji.org, ferhatay@lji.org + +<|ref|>sub_title<|/ref|><|det|>[[115, 352, 198, 370]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[114, 380, 883, 718]]<|/det|> +Compartmental organization of chromatin and its changes play important roles in distinct biological processes carried out by mammalian genomes. However, differential compartment analyses have been mostly limited to pairwise comparisons and with main focus on only the compartment flips (e.g., A- to- B). Here, we introduce dcHiC, which utilizes quantile normalized compartment scores and a multivariate distance measure to identify significant changes in compartmentalization among multiple contact maps. Evaluating dcHiC on three collections of Hi- C contact maps from mouse neural differentiation \((n = 3)\) , mouse hematopoiesis \((n = 10)\) and human LCL cell lines \((n = 20)\) , we show its effectiveness and sensitivity in detecting biologically relevant differences, including those validated by orthogonal experiments. Across these experiments, dcHiC reported regions with dynamically regulated genes associated with cell identity, along with correlated changes in chromatin states, replication timing and lamin B1 association. With its efficient implementation, dcHiC not only enables high- resolution compartment analysis but also includes a suite of additional features, including standalone browser visualization, differential interaction identification, and time- series clustering. As such, it is an essential addition to the Hi- C analysis toolbox for the ever- growing number of contact maps being generated. dcHiC is freely available at https://github.com/ay- lab/dcHiC and examples from this paper can be seen at https://ay- lab.github.io/dcHiC. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 117, 260, 136]]<|/det|> +## BACKGROUND + +<|ref|>text<|/ref|><|det|>[[115, 146, 882, 335]]<|/det|> +The three- dimensional organization of chromatin in the nucleus has been of interest to scientist for more than a century now. The observation that different chromosomes occupy a defined space in the nucleus dates back to Carl Rabl's work in animal cells in 1885 [1]. Since then, many experimental techniques have been developed to image and map chromatin allowing us to look at chromatin organization at an ever- increasing resolution. The greatest strides in this area have been made in the past decade following the advent of genome- wide conformation capture techniques. We now know that interphase chromosomes are folded into multiple layers of hierarchical structures. Each layer contributes to the establishment and maintenance of the epigenetic landscape that controls cellular state and function. + +<|ref|>text<|/ref|><|det|>[[115, 345, 882, 495]]<|/det|> +Among these, the megabase- scale compartmental organization of eukaryotic genomes has been shown to play a critical role in transcription, DNA replication, accumulation of mutations, and DNA methylation [2- 12]. In broad terms, two types of compartments divide the genome into regions of open and active chromatin (compartment A) versus inactive and closed chromatin (compartment B) [13]. Further analysis of each compartment revealed subsets of regions with markedly different properties within each class called sub- compartments [14, 15] as well as to a putative third class (intermediate or I) that is at the interface between A and B and is reorganized in tumors [9]. + +<|ref|>text<|/ref|><|det|>[[115, 505, 882, 787]]<|/det|> +The main method to extract compartment information has been to analyze high- throughput chromosome conformation capture (Hi- C) contact maps using Principal Components Analysis (PCA) [13, 16, 17]. Briefly, this process involves distance normalization (Observed/Expected for each genomic distance) of the Hi- C contact map for each chromosome at a particular resolution (generally between 100Kb to 1Mb) followed by transformation into a correlation matrix, where each entry \((i,j)\) denotes the correlation of row \(i\) and row \(j\) (or column \(i\) and \(j\) since symmetric) of the distance- normalized Hi- C map. The eigenvalue decomposition of the correlation matrix provides the eigenvectors, and the first eigenvector or principal component (PC1) typically represents the genomic compartments A and B. If PC1 corresponds to chromosome arms or other broad patterns in the Hi- C map (e.g., copy number differences), the second principal component (PC2) is likely to represent A and B compartments. The A/B compartment labels are assigned to the positive/negative stretches of the selected PC; however, depending on the implementation of eigenvalue decomposition, it may be necessary to re- orient these assignments correctly using GC content or gene density. + +<|ref|>text<|/ref|><|det|>[[115, 797, 882, 891]]<|/det|> +Whether one is interested in the two major compartments or their more nuanced subsets, the magnitude and sign of eigenvalues derived from PCA have been the major determinants of compartment type. However, standard PCA is limited in analyzing each Hi- C contact map individually, and to date, there is no method to compare compartmentalization across multiple (>2) Hi- C datasets systematically. This is becoming + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 882, 201]]<|/det|> +an obstacle in analyzing the ever- increasing chromatin conformation data, either from Hi- C or its variants [18- 25], generated across many cell types and conditions [26]. Technical challenges such as selecting the correct PC and sign that represents A/B compartments, and their scaling across different datasets become larger problems while comparing many Hi- C contact maps. Thus far, comparative compartment analysis has been mainly limited to examining compartment flips between two Hi- C maps at a time [27, 28]. + +<|ref|>text<|/ref|><|det|>[[114, 211, 882, 532]]<|/det|> +Here, we introduce dcHiC (differential compartment analysis of Hi- C), a method that identifies statistically significant differences in compartmentalization among two or more contact maps, including changes that are not accompanied by a compartment flip. Our method implements a memory- efficient and parallelized singular value decomposition (SVD) to derive principal components (i.e., eigenvectors) followed by quantile normalization to get the comparable compartment scores across two or more than two Hi- C maps at a time (Figure 1, Step1). dcHiC then utilizes the normalized component scores to derive a multivariate distance measure [29] (Figure 1, Step2) to estimate the statistical significance of compartment differences. If available, dcHiC utilizes variance among Hi- C replicates as covariates for Independent Hypothesis Weighting (IHW) [30] to correct for multiple testing. With our methodology, compartment analysis can be conducted on Hi- C maps with or without replicates at resolutions up to 10Kb for human and mouse genomes. Further downstream, dcHiC provides a raft of analysis features, including standalone IGV browser [31] visualization of results, detection of differential interactions involving significant differential compartments, time- series clustering of compartment scores, as well as a module for determining enriched Gene Ontology terms from differential compartments. + +<|ref|>text<|/ref|><|det|>[[114, 542, 882, 899]]<|/det|> +To assess the biological relevance of the identified differences, we applied dcHiC to several different collections of Hi- C datasets across various biological conditions, including mouse neuronal development \((n = 3)\) , mouse hematopoiesis \((n = 10)\) , and a set of lymphoblastoid cell lines (LCLs) from different human populations \((n = 20)\) . Analyzing each Hi- C dataset at 100Kb and 40Kb resolution, we identified relevant compartmentalization differences reflecting the underlying biology in the respective scenarios. In the mouse neuronal differentiation model, dcHiC identified compartmental changes for loci involving critical genes associated with cellular identities in mouse embryonic stem cells (mESC) and neuronal differentiation such as Dppa2/4, Zfp42, Ephb1, and Ptn as well as GO term enrichments consistent with these cellular identities. In a ten- way comparison \((n = 10)\) of key cell types from mouse hematopoiesis; across stem cells, progenitor cells, and terminally differentiated cells, dcHiC revealed significant compartmental changes involving key genes like Sox6, Meis1, Runx2, Klf5, and many others. Across both neural and hematopoietic differentiation models, our results also highlight the importance of generally ignored compartmentalization changes within the same compartment type (within A or within B - Figure 1). We also demonstrate the biological significance of our differential calls through strong correlations with cell- type specific differences in lamin B1 association, histone modifications and gene expression. For human LCLs, comparing twenty Hi- C maps from a diverse set of donors, dcHiC confirmed the previous findings, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 882, 127]]<|/det|> +with significant enrichment of various biological signals within the differential compartments across the population. + +<|ref|>text<|/ref|><|det|>[[115, 136, 882, 231]]<|/det|> +Overall, dcHiC provides an integrative framework and an easy- to- use tool for comparative analysis of Hi- C maps and identifies biologically relevant differences in compartmentalization across multiple cell types. With immediate application to hundreds of publicly available Hi- C datasets, dcHiC will play an essential role in providing deeper insights into dynamic genome organization and its downstream effects. + +<|ref|>sub_title<|/ref|><|det|>[[115, 270, 210, 288]]<|/det|> +## RESULTS + +<|ref|>sub_title<|/ref|><|det|>[[115, 298, 792, 318]]<|/det|> +## dcHiC identifies compartments consistent with the PCA-based approach + +<|ref|>text<|/ref|><|det|>[[114, 327, 882, 590]]<|/det|> +As more complex experimental designs emerge that compare different Hi- C profiles, a comprehensive method to compare the spatial organization of the genome is necessary. To do this, dcHiC first employs a time- and memory- efficient R implementation of singular value decomposition (SVD) to achieve the eigenvalue decomposition of each Hi- C contact map [32]. This is followed by the automated selection to find the principal component and its sign (reoriented if needed) that best correlates with gene density and GC content per sample (Methods). The resulting compartment scores are quantile normalized and a multivariate score (Mahalanobis distance) is computed based on an initial covariance estimation. We then refine the null distribution by removing outliers before calculating new covariance estimates that will be used for computing the final statistical significance (Chi- square test) of differences in compartmentalization (Methods). dcHiC provides standalone browser visualization as well as several other features facilitating the interpretation of its results. Figure 1 summarizes the overall workflow of dcHiC. + +<|ref|>text<|/ref|><|det|>[[114, 600, 882, 902]]<|/det|> +In order to establish the validity of dcHiC results, we first compared our implementation of the eigenvalue decomposition to commonly used PCA- based approaches, a representative of which is implemented in HOMER[33]. Beyond a few differences in pre- filtering of low coverage regions, the resulting compartment scores were highly similar between dcHiC and HOMER for the 100Kb resolution (replicates combined) mouse ESC (Pearson's \(r = 0.98\) , Figure 2A) and for mouse neuronal progenitor cell (NPC) Hi- C map (Pearson's \(r = 0.96\) , Figure 2D). Similar to A/B compartment decomposition from Hi- C data, association with the nuclear lamina (or radial position) is another strong indicator of a broad- level chromatin state with heterochromatin localizing at the periphery and euchromatin at the nucleus center. Such organization is a conserved feature of eukaryotic genomes across most cell types except special cases [34, 35]. Here we used lamin B1 association profiles of ESC and NPC cell types as an independent measure of compartmentalization and compared the lamin B1 signal distribution with dcHiC and HOMER scores. As expected, both our compartment scores and HOMER results showed a strong negative correlation with lamin B1 association, confirming the previous findings [27, 36] (Figure 2B- C, 2E- F). We further plotted the chromosome 16 compartment score + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 882, 165]]<|/det|> +of ESC and NPC from dcHiC, HOMER, and lamin B1 association signal. Figure 2G- H shows the lamin B1 signal and compartment features captured by dcHiC and HOMER at a genome- wide scale in ESC and NPC cell types. These results establish that dcHiC, like the existing PCA- based (HOMER) approach, accurately captures compartment patterns. + +<|ref|>sub_title<|/ref|><|det|>[[115, 175, 882, 212]]<|/det|> +## Pairwise differential compartment analysis of mouse neuronal differentiation model + +<|ref|>text<|/ref|><|det|>[[115, 222, 882, 390]]<|/det|> +Previous studies have reported substantial compartment flips during mouse embryonic cells (ESC) to neuronal progenitor cell (NPC) transition, a well- studied in vitro differentiation system [37, 38]. These differences have been studied further using replication timing profiling, lamin B1 association mapping, and fluorescence in situ hybridization (DNA FISH) [8, 27, 36]. Therefore, we chose these two cell types to demonstrate dcHiC's utility in a pairwise comparison to replicate known compartment flips and identify significant changes that do not involve flips from one compartment type to another. We also compared differential compartment calls from dcHiC and HOMER in this pairwise setting since HOMER does not readily allow multi- way comparisons. + +<|ref|>text<|/ref|><|det|>[[114, 400, 882, 701]]<|/det|> +Overall, dcHiC identified 1981 100Kb bins with statistically significant differential compartmentalization (FDR < 0.1), covering up to 7.5% of the genome. For ESC and NPC, these differences constituted around \(\sim 37\%\) (72.8 Mb) and \(\sim 51\%\) (101.6 Mb) of A (active) compartments, respectively. The differential compartments are further subdivided into flipping (A \(\rightarrow\) B or B \(\rightarrow\) A) or matching (A \(\rightarrow\) A or B \(\rightarrow\) B) compartment transitions. We observed that \(\sim 74\%\) of all the differential compartments were flips from A to B ( \(\sim 30\%\) ) or B to A ( \(\sim 44\%\) ) compartments during ESC to NPC transition whereas the remaining \(\sim 26\%\) were within matching compartments (Figure 3A). We further classified significant changes within the same compartments (A to A or B to B) based on whether the compartment scores were higher in ESC or NPC (Figure 3B- E). For the resulting set of six different types of differential compartments, we plotted the distributions of compartment scores (Figure 3B), lamin B1 association (Figure 3C), replication timing (Figure 3D) and gene expression (Figure 3E). As expected, more euchromatic compartments were associated with lower lamin B1 attachment, early replication timing and higher gene expression. These trends were consistent for compartment flips as well as changes within matched compartments (e.g., strong A in ESC to weak A in NPC). + +<|ref|>text<|/ref|><|det|>[[115, 710, 882, 900]]<|/det|> +Next, we compared the differential ESC vs NPC compartments from dcHiC to those from HOMER. HOMER reported a total of 3,042 100Kb bins with significant differential compartmentalization (FDR < 0.05). Only 1,355 of these 100Kb bins were found to be overlapping with dcHiC differential calls (+/- 1 bin slack; Figure 3F). To compare the calls made by the two different methods, we plotted the absolute differences of laminB1 signal, replication timing and log2 gene expression values of the reported differential compartments. Figure 3G shows the absolute difference distribution of all the respective signals from all the differential compartments between ESC and NPC, while Figure 3H shows the same but only for differential compartments exclusively identified by one method. These results show that dcHiC differential compartments are significantly + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 882, 258]]<|/det|> +(unpaired t- test p- values \(< 0.05\) ) enriched for regions with higher ESC, NPC differentials for lamin association and replication timing signals. We also performed differential expression analysis between ESC and NPC to map the differentially expressed (DE) genes (DEseq2[39], FDR \(< 0.05\) , fold change \(>4\) ) on the differential compartments. We observed that dcHiC differential compartment bins were enriched in DE genes compared to HOMER (Figure 3l). The trend was similar for bins reported exclusively by each method (Figure 3l). These observations imply that the differential calls made by dcHiC are accompanied by larger changes between ESC and NPC in other biological signals relevant to compartmentalization. + +<|ref|>text<|/ref|><|det|>[[114, 268, 882, 588]]<|/det|> +To also show utility of our tool in detecting differences at higher resolution, we ran dcHiC at 10Kb resolution to call differential compartments between ESC and NPC. We found a total of 16,581 10Kb- bins i.e., 165.81Mb differential compartments between the conditions. Among the 1,981 100Kb dcHiC differential bins, \(72\%\) exactly overlapped at least one 10Kb differential bin (over \(86\%\) within \(+ / - 200\mathrm{kb}\) ). This suggest a significant overlap across resolutions but also highlights the prevalence of regions that are detectable only at higher or lower resolution compartment analysis (Supplementary Figure 1). We also evaluated the potential of false positive discoveries from dcHiC by running it to compare replicates of the same conditions/sample. We used all four biological Hi- C replicates available for ESC in different combinations (all 1 vs 3 and 2 vs 2 combinations of splitting the replicates). When we ran dcHiC on these combinations, the number of significant compartment changes (i.e., false positives) ranged from 1 to 32 with a median value of 2 bins (compared to 1,981 100Kb bins when ESC is compared to NPC), suggesting a low false positive rate for identifying differential compartments. When we ran the same analysis using 10kb bins we identified a median value of 751 differential bins ( \(\sim 0.2\%\) of the genome) suggesting higher resolution differential analysis may be more prone to false positives. + +<|ref|>text<|/ref|><|det|>[[114, 598, 882, 899]]<|/det|> +Example genes from ESC vs NPC differential compartments: Within dcHiC's calls, we also analyzed a set of key genes known for their critical role in ESC or NPC state that have been studied extensively for changes in their nuclear organization during the transition. For instance, we analyzed a set of genes for which Fluorescence in situ Hybridization (FISH) experiments were performed to study changes in radial positioning during the ESC to NPC transition. These included pluripotency markers specifically expressed in ESCs (e.g., Zfp42 or REX1 and Dppa2/4) as well as EPH Receptor B1 (Ephb1) and other marker genes specific to neuronal differentiation. Figure 3J shows the Dppa2/4 region in mouse chromosome 16 that is shown to change radial positioning, chromatin state, lamin B1 association and replication timing during differentiation [36, 40]. Consistent with these data, both dcHiC and HOMER reports a significant shift from A (active) to B (inactive) compartment during mouse ESC to NPC differentiation (Figure 3J). In addition, dcHiC reported significant compartment changes for several other important genes that HOMER missed. Figure 3K- L displays two genes, namely Dach1 and Nedd9, which are known to play a critical role in organogenesis and signal- transduction pathways for mouse neuronal development [41, 42]. We also detected these + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 882, 184]]<|/det|> +genes in our differential gene expression analysis of ESC vs NPC as significantly upregulated in NPC (FDR<0.05; \(>160x\) for Dac1 and \(>30x\) for Nedd9). Dac1 lies in a compartment reported as flipped from ESC- B to NPC- A by dcHiC (Figure 3K). Nedd9 gene overlaps A- compartment in both cell types but with stronger compartmentalization in NPC that is detected as a significant change by dcHiC (Figure 3L). + +<|ref|>text<|/ref|><|det|>[[114, 193, 882, 738]]<|/det|> +To see whether the compartmental changes are accompanied by specific differences in local chromatin interactions, we implemented an extension of our comparative approach to identify differences in contact counts involving the differential compartments (Methods). This feature allows users to input a set of significant chromatin interactions (e.g., from Fit- Hi- C[43]) or chromatin loops (e.g., from HiCCUPS or Mustache), which will then be filtered for their overlap with differential compartments and tested for their difference across the compared conditions. The black square boxes in Figure 4A represent the dcHiC identified differential interactions (ESC vs NPC) that are anchored in the DppA2/4 region. These interactions are identified among FitHiC2 calls [44] (FDR < 0.05) that are reported as significant in at least one replicate of ESC and/or NPC datasets. The results show that the DppA2/4 domain in NPC has specifically gained interactions with its upstream region compared to ESC while the interactions with adjacent downstream region remained unchanged, a change that can be visualized on the Hi- C map (Figure 4A). Previous studies on Ephb1 have demonstrated a significant subnuclear repositioning of the gene from the periphery to the nuclear center during ESC to NPC differentiation [36] accompanied by a higher gene expression later. A similar analysis of the Ephb1 region shows that it has enriched interactions with a pair of upstream B compartments in ESCs which are weakened in NPC where Ephb1 is transitioned to the A compartment (Figure 4B). In addition, the same region gained interactions with a downstream A compartment in NPC. These results highlight the value of differential interaction analysis coupled with differential compartmentalization to better delineate important changes in the local chromatin environment. Finally, even though the above examples highlight cases where gene expression is tightly correlated with compartment changes and radial positioning, this is not necessarily the case for all genes. Figure 4C shows the pluripotency marker gene Pou5f1/Oct4 region with ESC- specific gene expression. The radial positioning of this gene locus was shown to remain unchanged during ESC to NPC transition[36] consistent with our results (Figure 4C). Overall, dcHiC identified both known compartment flips (A to B or B to A) as well as novel compartmentalization differences within the same compartment for important genes. + +<|ref|>sub_title<|/ref|><|det|>[[116, 747, 820, 767]]<|/det|> +## Multi-cell-type differential compartment analysis of mouse neuronal system + +<|ref|>text<|/ref|><|det|>[[115, 777, 882, 889]]<|/det|> +The same in vitro system used to differentiate from ESC to NPC also allows further differentiation of NPCs to cortical neurons or CNs [38]. This developmental lineage provides an approach to demonstrate how dcHiC uses a multivariate distance measure to compare the compartmentalization of more than two cell types simultaneously. For such multiway comparisons, dcHiC provides a quick and straightforward approach to detect outliers in compartment scores and associated differential interactions, an + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 883, 184]]<|/det|> +approach far easier with many experiments than the traditional paradigm of taking pairwise comparisons. In this section, we first illustrate the biological significance of \(dcHiC\) 's differential compartments using multiple lines of biological data. We then demonstrate functional term enrichments, and then show specific differential genes that illustrate the application's breadth of analysis. + +<|ref|>text<|/ref|><|det|>[[115, 192, 883, 383]]<|/det|> +Applying \(dcHiC\) at 100Kb resolution on intra- chromosomal Hi- C data from ESC, NPC, and CN samples, we identified a total of 5,055 significant differential bins covering about \(19.2\%\) of the genome. Compartment A and B were evenly split for NPC and CN, whereas ESC has \(\sim 63\%\) B compartments. Overall, regions in the B compartment for each cell type were more likely to exhibit statistically significant compartment changes compared to A compartment ( \(21 - 23\%\) vs \(16 - 18\%\) ). Figure 5A summarizes the number of differential compartment bins that involve flips (A \(\rightarrow\) B or B \(\rightarrow\) A) or remained within the same compartment throughput the lineage transition. Consistent with the literature [2, 5, 45], we showed that compartmental dynamics are strongly associated with variability of gene expression and histone modifications (Figure 5B, Methods). + +<|ref|>text<|/ref|><|det|>[[114, 390, 883, 900]]<|/det|> +To further analyze these changes simultaneously, rather than one transition (or pair) at a time, we utilized time- series analysis to cluster the compartmentalization score patterns of these differential bins across (Figure 5C) and plotted the expression pattern of the overlapping genes in each cluster across three different time- points. In order to focus on relative changes in compartmentalization, we further z- transformed the quantile normalized PCA scores for each 100Kb bin across the three cell types and applied TC- seq[46] to identify 6 major clusters (Methods). Two major clusters corresponded to regions that progressively became more euchromatic (cluster 1 and 6) and one corresponded to more heterochromatic (cluster 4). We observed other clusters that corresponded to one cell type showing highly different compartmentalization with respect to the other two (e.g., clusters 3 and 5 with NPC- specific patterns). To link these compartmentalization patterns to gene function, we identified genes overlapping with each differential compartment bin for each cluster. Performing functional enrichment analysis on these gene sets[47], we identified signatures that are consistent with cellular identity of the cell type with the highest compartment z- scores (i.e., more euchromatic). For instance, for the genes overlapping with clusters 1 and 6 with compartment scores increasing from ESC to NPC to CN, the enriched terms include neurogenesis and neuronal development (Figure 5D). For cluster 3, where CN compartment scores were highest, the enriched terms (cell- cell adhesion, biological adhesion, and others) were consistent with a general pattern for genes involved in regulating cell- type specific migration and development. We also observed that cluster 3 overlapped with an important class of gene family known as protocadherins [48]. Protocadherins are highly conserved genes across species and most of them are clustered in a single genomic locus in vertebrates [49]. They are shown to be differentially expressed in individual neurons and involved in diverse neurodevelopmental processes [50]. When we repeated the functional enrichment analysis per cell type using genes overlapping A compartments with the highest compartmentalization score for that cell type compared to the other two, we also + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 882, 202]]<|/det|> +observed cellular identity- related annotation terms (Supplementary Data 1). While annotations related to cell adhesion were enriched in ESC as well as CN, CN specifically showed enrichment for neurogenesis, neuron differentiation and development (Supplementary Data 1). CN, but not NPC, also showed enrichment for synaptic signaling, synapse organization and neuron projection development, potentially related to its further differentiated state with respect to NPC. + +<|ref|>text<|/ref|><|det|>[[114, 211, 882, 551]]<|/det|> +Example genes from ESC- NPC- CN differential compartments: The differential compartments captured by dcHiC encompass a variety of traditionally studied as well as more nuanced scenarios. For instance, similar to Dppa2/4, Zfp42/Rex1 is a well- studied pluripotency marker primarily expressed in undifferentiated stem cells (Figure 5E). As is the case for Dppa2/4, Zfp42 is also in a small A compartment region surrounded by large stretches of B compartments in ESC. As expected, this region flipped into B compartment in NPC and stayed that way in CN consistent with lack of gene expression in these two cell types (Figure 5F). Ptn or Pleiotrophin, on the other hand, exhibits mitogenic and trophic effects on dopaminergic neurons, and is instead a marker gene for neuronal lineage. dcHiC reported this gene in a differential compartment that is B in ESC but A in NPC and CN, in concordance with gene expression (Figure 5G- H), which fits the compartmentalization pattern of cluster 1 (Figure 5C). These two examples represent strong compartment flips from A to B or B to A. An example of a more gradual compartmental change is the CN- specific Ctnna2 gene, which functions as a linker between cadherin adhesion receptors and the cytoskeleton to regulate cell- cell adhesion and differentiation in the nervous system. The B compartment encompassing Ctnna2 in ESC gradually weakens during the ESC- NPC- CN transition leading to transcription- permissive A compartment that starts in NPC and expands further in CN (Figure 5I- J). + +<|ref|>text<|/ref|><|det|>[[114, 559, 882, 860]]<|/det|> +Compartment shifts within the same compartment are also captured by dcHiC (Figure 5K- L). Etv5 encodes for a transcription factor that plays an important role in the segregation between epiblast and primitive endoderm specification during ESC differentiation [51]. Etv5 is highly expressed in ESC but gradually loses its expression (Figure 5L) as well as strong compartmentalization during ESC- NPC- CN transition while remaining in the A compartment all the time. This locus belongs to cluster 2 with enrichment for more euchromatic association specifically in ESC consistent with the highest expression for Etv5 for this cell type. Beyond Etv5, we also found a list of 199 other genes within A compartment throughout ESC- NPC- CN transition, for which the variation in expression profile strongly correlated with changes in compartmentalization (Pearson correlation \(> 0.7\) ; Supplementary Data 2). A similar analysis within differential B compartments revealed 245 genes with strong positive correlation between expression and compartmentalization change (Supplementary Data 2). Overall, our results demonstrate that dcHiC can comprehensively analyze multiple different Hi- C maps simultaneously and identify compartmental changes involving abrupt (e.g., compartment flips) as well as gradual changes. + +<|ref|>text<|/ref|><|det|>[[115, 871, 736, 890]]<|/det|> +Differential compartment analysis of mouse hematopoietic system + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 882, 427]]<|/det|> +The hematopoietic system is a developmentally regulated and well- characterized cell differentiation model [52, 53]. This system provides an opportunity to understand the dynamic changes in the chromatin structure together with transcriptional and other epigenetic changes during differentiation in detail. The study of the genome organization changes during this complex process—involving many different progenitors and differentiated cell types—requires a systematic approach. A recent study by Zhang et al. [28] profiled the chromatin organization in the classic hematopoietic model with ten primary stem, progenitor, and terminally differentiated cell populations from mouse bone marrow (Figure 6A). In this model, long- term hematopoietic stem cells (LT- HSC) represent the starting point of the hematopoietic hierarchy with self- renewal and multilineage differentiation capability. LT- HSC first differentiates into short- term hematopoietic stem cells (ST- HSC) and then multipotent progenitor cells (MPP). MPP cells differentiate into either common lymphoid progenitor (CLP) or common myeloid progenitor (CMP) cells. CMP then further branches out into granulocyte- macrophage progenitors (GMP) and megakaryocyte- erythrocyte progenitors (MEP). The GMP cells are then terminally differentiated into granulocytes (GR), while MEP cells are further differentiated into megakaryocyte progenitors (MKP) and then terminally differentiated into megakaryocytes (MK). + +<|ref|>text<|/ref|><|det|>[[114, 437, 882, 775]]<|/det|> +Using the Hi- C data from this system, we carried out multivariate differential analysis using dcHiC at 100Kb resolution. We detected a total of 6,061 (60.61 Mb of the genome) differential compartment bins across the ten cell types encompassing many of the genomic regions previously shown to undergo hematopoiesis- related dynamic changes [28]. Figure 6A shows an overall summary of the significant compartment changes identified by dcHiC across these cell types. We observed that the number of A to B transitions keeps increasing from the LT- HSC stage to the MEP and GMP progenitor stages. The differentiation of CMP into MEP and GMP cells represent two of the most frequent A to B transitions ( \(\sim 27.4\%\) and \(\sim 15.7\%\) A \(\rightarrow\) B transition, respectively) within the hematopoietic hierarchy, likely reflecting the need for suppression of certain transcriptional profiles for commitment into each branch. This is consistent with the largest proportion of differential B compartments in MEP ( \(\sim 46.5\%\) ) and GMP ( \(\sim 42\%\) ) compared to all other cell types. With respect to the top of the hematopoietic tree (i.e., LT- HSC), early progenitors such as MPP has 571 100Kb bins with a significant compartment flip (either A to B or B to A), whereas the differentiated cells such as MK and GR had 949, and 1,212 such bins, respectively. This confirms the gradual divergence of chromatin compartmentalization from hematopoietic stem cells as cell progress further into differentiation. + +<|ref|>text<|/ref|><|det|>[[115, 785, 882, 899]]<|/det|> +Next, similar to ESC- NPC- CN transition, we also carried out functional enrichment analysis of differential regions with the highest A compartment score in each group and specific cell type. Figure 6B- E show these enrichments for four different stages of hematopoiesis (pre- bifurcation stage: LT- HSC, ST- HSC, progenitor stage: MPP, CMP, granulocyte branch: GMP, GR and the terminally differentiated Granulocytes or GR) with respect to the rest and for a specific cell type within each of these stages highlighting + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 89, 882, 259]]<|/det|> +biologically relevant processes in each case. For example, morphogenesis and development- related biological processes were enriched in the overall pre- bifurcation stage (set of genes with the highest A compartment score in either LT- HSC, ST- HSC) (Figure 6B) and the progenitor stage cells were enriched in morphogenesis, adhesion and migration related terms (Figure 6C). The granulocyte branch (GMP and GR) as well as the terminally differentiated granulocytes (GR) showed significant enrichments related to activation and regulation of neutrophils and granulocytes (Figure 6D- E). For the megakaryocyte branch (MEP, MKP, MK), however, we did not observe any statistically significant GO term biological process enrichments. + +<|ref|>text<|/ref|><|det|>[[113, 268, 882, 794]]<|/det|> +Example genes from mouse hematopoiesis differential compartments: After investigating the significance of the differential compartments from a high level, we examined the genes overlapping with the differential compartments involved in hematopoietic lineage differentiation and chromatin dynamics [54]. Figure 6F shows a set of important genes overlapping with differential compartments from our multivariate analysis. Zhang et al. showed that increased gene- body associating domain (GAD) scores are linked to active transcription and indicate cell- type specific features. We identified 12 out of 16 such differential GAD genes between ST- HSC and GR as part of dcHiC differential compartments identified across the system (FDR < 0.1; Figure 6F, marked by cyan stars). In addition, previous analysis by Lara- Astiaso et al. [54] also reported a set of critical genes for hematopoietic lineage differentiation. We identified 12 of these 26 genes within differential compartments (FDR < 0.1; Figure 6F, marked by red stars) supporting dcHiC's ability to pick up changes in regions harboring genes that are dynamically regulated during hematopoiesis. Among these genes, one example is the transmembrane transporter gene Abca13, which was the exclusive differential A compartment within GR but in the B compartment for all other cell types (Figure 6G). Other notable examples include Meis1, a transcription factor required to maintain hematopoiesis under stress and over the long term [55]. Notably, this particular example was a significant change solely within the A compartment (Figure 6H). Apart from Meis1, dcHiC also detected differences for other transcription factors like Runx2 and Sox6 that are essential for progenitor cell differentiation (Figure 6F) [56, 57]. We also identified Myc, known for its role in balancing hematopoietic stem cell self- renewal and differentiation [58] adjacent to a significant change within the A compartment that encompasses the Pvt1 gene. The long non- coding RNA Pvt1 harbors intronic enhancers that interact with Myc and promote Myc expression during tumorigenesis [59]. Overall, this complex system demonstrates the utility of dcHiC's multivariate compartment analysis, which discovers important changes in compartmentalization without requiring a large number of pairwise comparisons. + +<|ref|>sub_title<|/ref|><|det|>[[116, 804, 820, 823]]<|/det|> +## Multiway differential compartment analysis across human-derived cell lines + +<|ref|>text<|/ref|><|det|>[[115, 833, 882, 888]]<|/det|> +Measuring the extent to which genetic variation across individuals influences chromatin features including 3D organization has significant implications in our understanding of human disease. Previous studies have revealed that the presence of variations such as + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 882, 371]]<|/det|> +quantitative trait loci (QTLs) can affect histone modifications, transcription factor binding, and enhancer activity across populations [60, 61]. More recent work by Gorkin et al. [62] studied variation in chromatin conformation across individuals from different human populations. Using dilution Hi- C, they profiled lymphoblastoid cell lines (LCLs) derived from 13 Yoruban individuals, one Puerto Rican trio, one Han Chinese trio, and one European LCL (GM12878). They measured significant differences in 3D genome organization across individuals using different metrics, including Directionality Index (DI), Insulation Score (INS), Frequently Interacting REgions (FIREs), and compartment scores [62]. The study also carried out differential analysis of compartments across individuals and provided both compartment scores and "variable regions" at 40kb resolution (except for chromosomes 1, 9, 14, 19 and X). In order to minimize technical variation and ensure a fair comparison, we started directly from the 40Kb compartment scores reported by Gorkin et al. and ran \(dcHiC\) on these values (starting from quantile normalization). \(dcHiC\) allows direct utilization of pre- computed compartment scores, such as in this case, when available. + +<|ref|>text<|/ref|><|det|>[[114, 377, 882, 909]]<|/det|> +The Venn diagram (Figure 7A) of differential compartments from \(dcHiC\) and Gorkin et al. using the same set of 40Kb genomic bins shows a large overlap between the methods. A large fraction of \(dcHiC\) calls (7524 out of 7,876 or \(\sim 96\%\) ) were also reported by the original paper. However, Gorkin et al. reported an additional 765Mb of the human genome as variable compartment regions (Additional_file_4.xlsx from the original publication filtered for phenotype \(= PC1\) and discover_set \(= 20\) LCLs), which amounts to \(\sim 11K\) more bins at 40Kb resolution. To further study the overlap and differences between the two approaches, we plotted two statistical significance score distributions (- log10 of the adjusted p- value calculated by Gorkin et al.) for regions that the Gorkin study reported as differential, one with regions overlapping with \(dcHiC\) calls and the other of non- overlapping regions (Figure 7B). Variable compartments from the previous study that were not deemed significant by \(dcHiC\) have substantially lower statistical significance, as computed by the original paper suggesting \(dcHiC\) calls are enriched for stronger differences. Next, we compared the full- set of differential compartments called by both methods and their fraction covering each individual chromosome (Figure 7C). The figure shows that Gorkin et al. calls cover a larger fraction of smaller chromosomes, with more than half the entire length reported as a significant variable compartment for some chromosomes (e.g., chr18). \(dcHiC\) , on the other hand, has a more uniform representation of differential compartments across chromosomes with differential fractions ranging between \(10\%\) to \(20\%\) for most chromosomes. Lastly, we compared the top 5000 differential compartment bins ranked by their significance scores from each approach. Figure 7D shows that about \(\sim 61\%\) of these top 5000 differential bins are identical, suggesting substantial differences in each approach's ranking with respect to statistical significance (Spearman rank correlation of 0.55). Although the ranking is substantially different between the methods, the overlapping fraction of the top 5000 differential compartments for \(dcHiC\) had more significant differences (Figure 7E). Using other variable chromatin organization metrics from the Gorkin paper, we observed that \(dcHiC\) calls are more enriched in FIRE- QTLs (Figure 7F) as well as DI- QTLs (Figure 7G). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 882, 145]]<|/det|> +Preferential enrichment of such signals suggests a better concordance of \(dChIC\) identified compartmental differences and chromatin organization variability at other levels across individuals. + +<|ref|>text<|/ref|><|det|>[[113, 156, 882, 551]]<|/det|> +Example genes from differential compartments among human- derived LCLs: Figures 7H- I show two examples of a variable region (overlapping with NR2F2, and THEMIS/PTPRK genes) identified by \(dChIC\) . The NR2F2 region was investigated using FISH by Gorkin et al., which confirmed individual- specific changes in 3D chromatin conformation. Two of the individuals from the cohort (YRI- 4 and YRI- 8) showed enriched interaction between the NR2F2 FISH and another placed upstream as compared to YRI- 3 and YRI- 5. The variability of 3D genome organization among individuals is also apparent from compartment scores for this region. The NR2F2 locus across the cohort is found to be a part of strong B- compartment for all Yoruban individuals except for YRI- 4, YRI- 8 and YRI- 9 (Figure 7H). Figure 7I shows another example of such variable region with coordinated changes in epigenetic marks across individuals with support from differential compartments documented in the previous paper. Gorkin et al. have identified variations in different epigenetic marks like H3K4me1 and H3K27ac, binding of CTCF and most importantly gene expression pattern within this region across different individuals (YRI- 2 and 13 vs 11 and 12). The PC score track in Figure 7I also supports the previous findings as some of the individuals from YRI population, especially YRI- 3, YRI- 5, YRI- 11, YRI- 12 showed a clear flip from B to A compartment and both our approach and Gorkin et al. labeled this region as a differential compartment. Taken together, \(dChIC\) identified fewer differential compartment bins with enrichment towards capturing regions with higher variability in different levels of chromatin organization and those with additional evidence for difference among individuals. + +<|ref|>sub_title<|/ref|><|det|>[[115, 560, 240, 578]]<|/det|> +## DISCUSSION + +<|ref|>text<|/ref|><|det|>[[115, 589, 882, 777]]<|/det|> +This paper presents a new application, \(dChIC\) , to compare compartmentalization across Hi- C datasets. \(dChIC\) employs principal component analysis followed by quantile normalization of the compartment scores and a multivariate distance measure to systematically identify significant compartmentalization changes among multiple contact maps. By facilitating comparative analysis across multiple integrated datasets, it helps identify biologically relevant differential compartments with statistical confidence scores. Along with conventional pairwise differential analysis, \(dChIC\) allows a single multivariate differential comparison of Hi- C datasets, utilizing replicates when available, and provides an efficient approach to analyze multiple Hi- C maps without the need for generating many different combinations. + +<|ref|>text<|/ref|><|det|>[[115, 787, 882, 900]]<|/det|> +We applied \(dChIC\) to various biological scenarios, ranging from neuronal and hematopoietic stem cell differentiation in mice to Hi- C data from different human populations. Our results confirmed that \(dChIC\) detects known compartmental changes among cell types, including those previously validated to play a role in neuronal and hematopoietic differentiation. When comparing \(dChIC\) to existing approaches, we showed that it identifies regions with higher differences in replication timing, Lamin B1 signals, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 882, 383]]<|/det|> +and differentially expressed genes, suggesting better prioritization of relevant biological regions. Even though differences of compartmentalization between ESC and NPC generally aligned with changes in Lamin B1 association, a recent work highlighted the importance of nucleolus association in revealing layers of compartmentalization with distinct repressive chromatin states [63]. Our initial analysis showed that over \(10\%\) of all significant compartment differences we found between ESC and NPC belongs to nucleolus associated domains (NADs) that were deemed exclusive to either ESC or NPC [63] providing an explanation for a subset of differences in compartmentalization during differentiation. Expanding to a three- way \((n = 3)\) mouse neuronal differentiation model, we showed that dcHiC continues to systematically identify critical biological marker genes and can recover cell- specific functions from differential compartment analysis alone. Across dcHiC's differential compartments, we observed significant and relevant enrichment of biological processes such as neuron differentiation in NPC and CN cells. More broadly, dcHiC's differential compartments also compellingly aligned with changes in Lamin B1, gene expression, and histone modification data. Taken together, these results demonstrate dcHiC's ability to find regions with the most biologically variable compartmentalization across the genome. + +<|ref|>text<|/ref|><|det|>[[114, 391, 882, 750]]<|/det|> +The hierarchical mouse hematopoietic stem cell differentiation model, consisting of ten different cell types with Hi- C data, provided a unique opportunity to demonstrate the utility of dcHiC. A ten- way multivariate differential comparison of the hematopoietic system revealed previously known lineage- specific critical genes encompassing the differential compartments. Notably, we identified vital transcription factors like Sox6, Runx2, Meis1, Foxo1, and many other critical genes like Abca13, by solely analyzing the differential calls. Our functional enrichment analysis of gene sets overlapping with the lineage- specific differential compartments reported from the apex to the bottom of the hematopoietic model- tree reconfirmed that genome compartments play a contributory role in determining the accessibility of genes in specific cell types. Measuring the extent of compartment variability across twenty- cell human types also highlighted our method's novel utility and strength. Most dcHiC calls overlapped with a subset of variable compartments reported by the previous study, but dcHiC calls were enriched for higher variability. Similarly, the regions encompassing Frequently Interacting Region (FIRE)- QTLS and Directionality Index (DI)- QTLS defined by the previous study were more enriched in the top differential compartment calls of dcHiC compared to the top calls defined in the previous study. The analysis also demonstrated an important feature of dcHiC: the ability to directly utilize previously computed compartment scores to run differential compartmentalization analysis. + +<|ref|>text<|/ref|><|det|>[[115, 759, 882, 891]]<|/det|> +The framework we developed here provides a systematic way to identify differential compartments and visualize these differences in different scenarios, including multi- way, hierarchical and time- series setting. Although we focused on human and mouse Hi- C data in this work, our method is readily applicable to Hi- C data or its variants (e.g., Micro- C [64]) derived from any organism with compartmental genome organization. With hundreds of publicly available Hi- C datasets in the 4D Nucleome Data Portal and others published every day, dcHiC will play an essential role in comparative analysis of high- level genome + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 882, 145]]<|/det|> +organization. As single- cell Hi- C data starts providing better resolution, dcHiC and methods derive from it will be critical to enable compartment comparison across thousands of cells. + +<|ref|>sub_title<|/ref|><|det|>[[115, 185, 215, 203]]<|/det|> +## METHODS + +<|ref|>sub_title<|/ref|><|det|>[[115, 215, 608, 234]]<|/det|> +## Data processing, result generation, and visualization + +<|ref|>text<|/ref|><|det|>[[115, 245, 883, 404]]<|/det|> +Hi- C data: All the Hi- C data, except for the Gorkin et. al. 2019 were mapped on mm10 reference genome and processed using HiCpro (v2.7.9) pipeline [65]. The raw Hi- C interaction maps retrieved after HiC- Pro processing are used for downstream compartment score calculation by dcHiC. In the section analyzing data from the Gorkin et. al. study, we used the provided compartment scores (40Kb resolution) across all samples mapped on hg19 reference genome [62]. Statistically significant interactions were called using FitHiC2 [44] with default parameters and an FDR threshold of 0.05 for each replicate and/or each sample. + +<|ref|>text<|/ref|><|det|>[[115, 415, 883, 535]]<|/det|> +RNA- seq data: The RNA- seq data from Bonev et. al. 2017 [38] study concerning mouse neural development were processed using our in- house and open- source RNA- seq processing pipeline (https://github.com/ay- lab/LJI RNA_SEQ_PIPELINE_V2.git), which utilizes STAR [66]. The differential gene expression analysis between mouse ESC and NPC cell lines (two- replicates each) was performed using DESeq2 method [39] with all the default parameter settings. + +<|ref|>text<|/ref|><|det|>[[115, 544, 883, 765]]<|/det|> +ChIP- seq data: For ChIP- seq peak calling (H3K27ac, H3K4me3 and H3K4me1 histone marks), we first mapped the respective fastq files on the mm10 genome using the bowtie2 [67] and generated the corresponding bam files (MAPQ \(>20\) ). The aligned files were then used as input to the MACS2 program [68] to call peaks (p- value \(< 1e - 5\) ) against their respective input controls. The continuous ChIP- seq peaks were then merged and the unique set was mapped to the 100Kb differential compartments to calculate the average number of peaks. The enrichment of signal difference is calculated by first quantifying the absolute difference in signal (number of ChIP- seq peaks and gene expression TPM values) within ESC to NPC differential and non- differential compartments. The enrichment of absolute signal difference between the differential and non- differential compartments between ESC and NPC was then compared by un- paired T- test. + +<|ref|>text<|/ref|><|det|>[[115, 776, 883, 895]]<|/det|> +Time- series analysis: The time- series clustering was generated using the TCseq package [46]. For gene- term enrichment analysis, the differential compartments are scanned against the gene coordinates of the respected genome defined by the user using 'bedtools map' function [69]. The unique overlapping set of genes are then extracted and are used for GO biological function enrichment analysis using the ToppGene suite API function or directly from their webserver [47]. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 149]]<|/det|> +IGV Browser visualization: dcHiC generates a Javascript based stand-alone dynamic IGV-HTML page to visualize the compartments and differential compartment calls, with an option to add additional tracks. + +<|ref|>sub_title<|/ref|><|det|>[[115, 159, 864, 180]]<|/det|> +## Computation and quantile normalization of compartment scores for comparison + +<|ref|>text<|/ref|><|det|>[[114, 189, 883, 270]]<|/det|> +To perform principal component analysis (PCA) on Hi- C maps dcHiC utilizes the singular value decomposition (SVD) implementation of the bigstatsr R package [32]. The input to SVD is \(K\) different distance- normalized chromosome- wise correlation matrices \((X_{1},X_{2},X_{3}\ldots X_{K})\) for each Hi- C data. For each such matrix, dcHiC finds the decomposition: + +<|ref|>equation<|/ref|><|det|>[[288, 277, 848, 302]]<|/det|> +\[X_{K} = U_{K}\cdot \Gamma_{K}\cdot V_{K}^{T}\quad with U_{K}^{T}\cdot U_{K} = V_{K}^{T}\cdot V_{K} = I \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[114, 311, 883, 372]]<|/det|> +The matrices \(U_{K}\) and \(V_{K}\) store the left and right singular vectors of the matrix \(X_{K}\) . The singular values of \(X_{K}\) are stored in the diagonal matrix \(\Gamma_{K}\) . The principal components for each matrix are then obtained as: + +<|ref|>equation<|/ref|><|det|>[[405, 382, 848, 404]]<|/det|> +\[P C_{K} = X_{K}\cdot \mathrm{V}_{K} \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[113, 412, 883, 799]]<|/det|> +The eigen- decomposition of the \(K^{th}\) correlation matrix provides the eigenvectors, and the sign of the first eigenvector or principal component \((PC1_{K})\) typically represents the genomic compartments A and B for the \(K^{th}\) chromosome. If \(PC1_{K}\) corresponds to chromosome arms or other broad patterns in the Hi- C matrix, the second principal component \((PC2_{K})\) may represent A and B compartments. The A/B compartment labels are assigned to the positive/negative stretches of the selected \(PC_{K}\) depending on the implementation of eigen- decomposition. It may be necessary to re- orient these assignments and select the correct \(PC_{K}\) using GC content or gene density. Thus, before the quantile normalization step, dcHiC performs an intermediate correlation analysis of the first two principal component scores (user- defined) of each chromosome per sample against the GC content and gene density of that chromosome. The principal component which obtains the highest sum of GC content and gene density correlation is considered the compartment score, and the A/B compartments of the selected principal components are assigned based on the GC content correlation (A compartment and positive values representing higher GC content). These generate a set of compartment score vectors representing each sample (M samples) for a given chromosome \((C_{1},C_{2},C_{3}\ldots C_{M})\) . Once the properly labeled compartment scores are obtained, dcHiC performs quantile normalization (QN) using the limma package [70] on the set \((C_{1},C_{2},C_{3}\ldots C_{M})\) per chromosome to even out the scaling across the group for downstream analysis. + +<|ref|>equation<|/ref|><|det|>[[348, 807, 848, 829]]<|/det|> +\[(q_{1},q_{2},q_{3}\ldots q_{M}) = QN(C_{1},C_{2},C_{3}\ldots C_{M}) \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[114, 838, 883, 879]]<|/det|> +In the case of samples with replicates, dcHiC performs the above steps by including each replicate from each sample (i.e., quantile normalize all replicates together). dcHiC then + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 128]]<|/det|> +calculates the average of quantile normalized values of each genomic bin across all the replicates of a given sample to represent sample- wise compartment scores. + +<|ref|>sub_title<|/ref|><|det|>[[114, 138, 477, 158]]<|/det|> +## Differential compartment identification + +<|ref|>text<|/ref|><|det|>[[114, 168, 883, 250]]<|/det|> +Mahalanobis distance (MD) is a multivariate statistical measure of the extent to which the multivariate data points are marked as outliers, based on a Chi- square distribution [71]. The Mahalanobis distance of a point \(i\) from a multi- dimensional distribution defined by set \(s\) (sample) and its center \(\mu\) is defined as: + +<|ref|>equation<|/ref|><|det|>[[346, 258, 848, 282]]<|/det|> +\[M D_{s a m p l e}^{i} = (s_{i} - \mu_{i})^{T}\cdot \Sigma^{-1}\cdot (s_{i} - \mu_{i}) \quad (4)\] + +<|ref|>text<|/ref|><|det|>[[114, 293, 883, 380]]<|/det|> +Where \(s_{i} = (q_{1}^{i}, q_{2}^{i}, q_{3}^{i} \ldots q_{M}^{i})\) is the set of quantile normalized compartment score distributions and \(\mu_{i} = (\mu_{1}^{i}, \mu_{2}^{i}, \mu_{3}^{i} \ldots \mu_{M}^{i})\) is the set of weighted centers for each point \(i\) from set \(s\) . The inverse of the covariance matrix of set \(s\) is represented as \(\Sigma^{- 1}\) . The weighted centers \(\mu_{i}\) is calculated as: + +<|ref|>equation<|/ref|><|det|>[[346, 392, 848, 412]]<|/det|> +\[\mu_{i} = s_{i} * w_{i} \quad (5)\] + +<|ref|>text<|/ref|><|det|>[[114, 421, 883, 461]]<|/det|> +Where \(0 \leq w_{i} \leq 1\) is the cumulative Normal distribution probability associated with the maximum z- score among the z- scores of all samples for \(i\) : + +<|ref|>equation<|/ref|><|det|>[[346, 470, 672, 494]]<|/det|> +\[w_{i} = \max \{Pr(Z_{M}^{i})\} \quad (6)\] + +<|ref|>text<|/ref|><|det|>[[114, 506, 660, 527]]<|/det|> +Where \(Z_{N}^{i}\) , the z- score for point \(i\) for sample \(N\) is computed as: + +<|ref|>equation<|/ref|><|det|>[[346, 535, 848, 571]]<|/det|> +\[Z_{N}^{i} = \frac{(d_{N}^{i} - \overline{d_{N}})}{\sigma(d_{N})} \quad (7)\] + +<|ref|>text<|/ref|><|det|>[[114, 581, 883, 624]]<|/det|> +Here \(\overline{d_{N}}\) and \(\sigma (d_{N})\) represent the average distance and standard deviation within sample \(N\) among all \(d_{N}^{i}\) values that are computed as: + +<|ref|>equation<|/ref|><|det|>[[346, 633, 848, 680]]<|/det|> +\[d_{N}^{i} = \frac{\sqrt{\Sigma_{t = 1}^{M}(q_{t}^{i} - q_{N}^{i})^{2}}}{(M - 1)} \quad (8)\] + +<|ref|>text<|/ref|><|det|>[[114, 689, 883, 790]]<|/det|> +Essentially, the approach provides more weight to the points that are distant from others among the samples (further from the diagonal) than to points that are closer together in the multi- dimensional space (close to the diagonal). Equation (4) is the standard MD formulation, which we modify using the weighted centers as computed through Equations (6) to (8). + +<|ref|>text<|/ref|><|det|>[[114, 799, 883, 902]]<|/det|> +In order to increase the sensitivity of our difference detection, we implemented an outlier removal step that eliminates genomic bins (or points) with high MD (as computed above) at the initial pass (1st pass). We use a pre- defined upper- tail critical value of the chi- square distribution with \(df\) degrees of freedom as our threshold for outlier removal (default value we used is: \(MD threshold \sim \chi_{0.90,df}^{2}\) ). We then recompute the covariance matrix \(\Sigma^{- 1}\) after + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 87, 883, 197]]<|/det|> +removal of these outliers and calculate the MD (through Equation (4)) one more time for each point \((2^{\mathrm{nd}}\) pass). The significance of the corresponding \(MD_{sample}^{i}\) \((2^{\mathrm{nd}}\) pass) is calculated from the critical chi- square distribution table as \(\chi^{2}(MD_{sample}^{i},df)\) using \(pchisq\) function of R programming language followed by multiple testing correction to retrieve adjusted p- values. + +<|ref|>text<|/ref|><|det|>[[114, 207, 883, 249]]<|/det|> +In case of samples \((s)\) with replicates \((r)\) \(dchIC\) calculates an additional covariate \(MD_{replicate}\) and applies Independent Hypothesis Weighting (IHW) to adjust the \(p\) - values. + +<|ref|>text<|/ref|><|det|>[[115, 260, 382, 279]]<|/det|> +The covariate is calculated as: + +<|ref|>equation<|/ref|><|det|>[[124, 290, 854, 315]]<|/det|> +\[MD_{repl}^{i} = \{(s_{i}^{T} - s_{i}^{T}\mu_{i})^{T}\cdot (diag(\Sigma^{-1}))\cdot (s_{i}^{T} - s_{i}^{T}\mu_{i})|s\in (1,2\dots M),r\in (1,2\dots R)\} (9)\] + +<|ref|>text<|/ref|><|det|>[[114, 323, 884, 428]]<|/det|> +Where \(R\) is the total number of all replicates combined across all samples, \(s_{i}^{T} =\) \((r_{1}^{i},r_{2}^{i},r_{3}^{i},\dots r_{R}^{i})\) is the set of quantile normalized compartment score distributions of all replicates from samples \(s\in (1,2\dots M)\) and \(\bar{s}_{i}^{T}\mu_{i} = (\frac{1}{s}\mu_{i},\frac{2}{s}\mu_{i},\frac{3}{s}\mu_{i},\dots \frac{5}{s}\mu_{i})\) is the is the set of weighted centers for each point \(i\) from \(R\) replicates. \(diag\) is an operation that masks all non- diagonal entries (sets to zero) of the covariance matrix. + +<|ref|>text<|/ref|><|det|>[[115, 438, 492, 458]]<|/det|> +The weighted centers \(\bar{s}_{i}^{T}\mu_{i}\) are calculated as: + +<|ref|>equation<|/ref|><|det|>[[350, 468, 858, 491]]<|/det|> +\[\bar{s}_{i}^{T}\mu_{i} = s_{i}^{T}*(1 - \bar{s}_{i}^{T}w_{i}) \quad (10)\] + +<|ref|>text<|/ref|><|det|>[[114, 500, 435, 520]]<|/det|> +Where \(0\leq \bar{s}_{i}^{T}w_{i}\leq 1\) is calculated as: + +<|ref|>equation<|/ref|><|det|>[[350, 529, 741, 551]]<|/det|> +\[\bar{s}_{i}^{T}w_{i} = max\{Pr(\bar{s}_{i}^{T}Z_{R})\} \quad (11)\] + +<|ref|>text<|/ref|><|det|>[[113, 560, 558, 580]]<|/det|> +And \(\bar{s}_{i}^{T}Z_{i}\) for replicate \(r\) of sample \(s\) is computed as: + +<|ref|>equation<|/ref|><|det|>[[350, 590, 858, 680]]<|/det|> +\[\begin{array}{l}{{\bar{s}_{i}^{T}d_{i}=\frac{\sqrt{\Sigma_{t=1}^{R}(r_{i}^{t}-r_{R}^{t})^{2}}}{(R-1)}}}\\ {{\bar{s}_{i}^{T}Z_{i}=\frac{(\bar{s}_{i}^{T}d_{i}-\overline{\bar{s}_{i}^{d}})}{\sigma(\bar{s}_{i}^{d})}}}\end{array} \quad (13)\] + +<|ref|>text<|/ref|><|det|>[[114, 690, 883, 751]]<|/det|> +Here the variables are defined as similar to Equations (7) and (8) and \(R\) is used to represent the number of replicates of the same sample (i.e., distances across replicates of different samples are not taken into account). + +<|ref|>text<|/ref|><|det|>[[114, 762, 883, 882]]<|/det|> +This approach provides more weight to the features that are closer to each other within replicates of a sample (close to the diagonal) and as opposed to the calculation across different samples (Equation (5)) where higher weights were given to the points with samples distant from each other (far from the diagonal). The significance of the corresponding \(MD\) for each point are calculated using chi- square distribution as mentioned above. \(dchIC\) applies the IHW approach to adjust the p- values using FDR + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 883, 130]]<|/det|> +correction obtain from \(MD_{sample}\) using \(MD_{replicate}\) replicate variation measure as a covariate. + +<|ref|>sub_title<|/ref|><|det|>[[115, 142, 454, 161]]<|/det|> +## Differential interaction identification + +<|ref|>text<|/ref|><|det|>[[114, 171, 883, 400]]<|/det|> +Using the same Mahalanobis distance (MD) measure, \(dcHiC\) enables the user to find differential interactions across samples that are either linking two differential compartments together or a differential compartment with other parts of the same chromosome. The goal of this feature is to provide more information on the chromatin organization changes related to or correlated with compartmental differences. For this analysis, we have used FitHiC2 to call significant interactions (FDR \(5\%\) ) for each sample or replicate (when available), but users are free to provide their own set of interaction or loop calls from any other tool. Using these calls, \(dcHiC\) first finds the interaction subset that overlaps with differential compartments (on either end or both) using the bedtools 'paitobed' function. \(dcHiC\) utilizes the \(log2(Observed / Expected)\) values of a chromatin interaction \(i\) from to perform differential interaction calling as: + +<|ref|>equation<|/ref|><|det|>[[229, 409, 859, 435]]<|/det|> +\[MD_{interaction}^{i} = (oe_{i}^{s} - \mu^{s})^{T}\cdot \Sigma^{-1}\cdot (oe_{i}^{s} - \mu^{s})\mid s\in (1,2,3\dots M) \quad (14)\] + +<|ref|>text<|/ref|><|det|>[[114, 441, 884, 640]]<|/det|> +where \(oe_{i}^{s}\) represents \(log2(Observed / Expected)\) values for chromatin interactions of locus pair \(i\) for sample \(s\) and \(\mu^{s}\) represents the vector of centers of distance normalized interactions from sample \(s\) . Here \(\Sigma^{- 1}\) represents the inverse of covariance matrix of interactions among the samples. The approach provides more weight to the interactions that are distant from the expected interaction strength among the samples than to the interactions that are closer to the expected range in the multi- dimensional space. The significance of the corresponding \(MD_{interaction}^{i}\) is calculated from the critical chi- square distribution table as \(\chi^{2}(MD_{interaction}^{i},df)\) using \(pchisq\) function embedded within R programming environment followed by the FDR correction to retrieve adjusted p- values. + +<|ref|>sub_title<|/ref|><|det|>[[115, 680, 422, 699]]<|/det|> +## Availability of data and materials + +<|ref|>text<|/ref|><|det|>[[114, 709, 883, 890]]<|/det|> +A Python/R implementation of \(dcHiC\) is freely available at https://github.com/ay- lab/dcHiC. This application is compatible with Hi- C data in HiC- Pro, .hic, and .cool formats. The data used in this study are available at the following GEO accession numbers: GSE96107 (mESC- NPC- CN), GSE152918 (mouse hematopoiesis), GSE128678 (human LCLs). These are also available in Supplemental Table 1 (see Supplementary Information). All reported compartments for all cell lines, multivariate differential scores, RNA- seq, and ChIP- seq data used in this manuscript can be viewed interactively at: ay- lab.github.io/dcHiC. These standalone HTML files employ \(dcHiC\) 's visualization utility through IGV browser. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 89, 291, 107]]<|/det|> +## FIGURE LEGENDS + +<|ref|>text<|/ref|><|det|>[[114, 117, 883, 343]]<|/det|> +Figure 1: Outline of the method. The figure panel shows the dcHiC workflow in two steps. In step 1, dcHiC calculates the principal components followed by the quantile normalization of the compartment scores across all input Hi- C data. In the step 2, for each genomic bin, dcHiC calculates a Mahalanobis distance, which is a statistical measure of the extent to which each bin is a multivariate outlier with respect to the overall multivariate compartment score distribution across all input Hi- C maps (Methods). dcHiC then utilizes the Mahalanobis distance to assign a statistical significance using Chi- square test (p- value) for each compartment bin and employs independent hypothesis weighting (IHW - when there are replicate samples) or FDR (when no replicates are available) correction on these p- values. dcHiC outputs a standalone dynamic IGV web browser view and enables the user to integrate other datasets into the same view for an integrated visualization. + +<|ref|>text<|/ref|><|det|>[[114, 353, 883, 504]]<|/det|> +Figure 2: Comparison of dcHiC compartment scores with HOMER compartment scores and Lamin B1 association data. (A- B) Genome- wide comparison of dcHiC compartment scores against HOMER compartment scores and Lamin B1 profiles for mouse ESC. (C) Comparison between HOMER compartment scores and Lamin B1 association. (D- F) Plots similar to (A- C) but for NPC Hi- C data. Pearson correlation value between two axes are reported for each plot. (G- H) Browser views of the compartment scores and Lamin B1 signal for a chromosome 16 region with arrows pointing to some small differences among the compartment scores of dcHiC and HOMER. + +<|ref|>text<|/ref|><|det|>[[113, 513, 883, 777]]<|/det|> +Figure 3: Pairwise differential compartment analysis between ESC and NPC. (A) The breakdown of the numbers of differential compartment calls (100Kb resolution) belonging to different types. (B- E) The distributions of compartment scores, Lamin B1, replication timing, association and gene expression across different subtypes of differential compartment calls made by dcHiC. Strong (s) and Weak (w) were used to indicate the relative compartment strength (or absolute value) between the two cell types. (F) The Venn- diagram shows the overlap between dcHiC and HOMER differential compartment calls. (G- H) The absolute difference of Lamin B1, replication timing signal and gene expression values (TPM) overlapping with all and exclusive differential compartments identified by dcHiC and HOMER, respectively (statistical significance was calculated by unpaired T- test). (I) The average number of differential (DE) genes overlapping with differential compartment bins (100Kb) identified by dcHiC and HOMER. (J- L) dcHiC differential compartments involving three DE genes: Dppa2/4, Dach1 and Nedd9. + +<|ref|>text<|/ref|><|det|>[[115, 787, 882, 881]]<|/det|> +Figure 4: Differences in local chromatin interactions of differential compartments. Detailed browser views (top), Hi- C contact maps (mid) and differential chromatin interactions (bottom) of three gene loci – (A) Dppa2/4, (B) Ephb1 and (C) Oct4. Visible changes in interactions involving Dppa2/4 locus and Ephb1 locus are highlighted through each plot. Although Oct4 shows a dramatic change in gene expression, the region does + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 881, 127]]<|/det|> +not alter its radial position within nucleus (FISH experiments) which is also consistent with the lack of change in compartmentalization as reported by \(dcHiC\) . + +<|ref|>text<|/ref|><|det|>[[114, 137, 883, 400]]<|/det|> +Figure 5: Three- way differential compartment analysis of ESC, NPC, and CN. (A) The breakdown of the numbers of differential compartment calls belonging to different types. (B) The enrichment of signal difference in different histone marks and gene expression in \(dcHiC\) differential bins with compartment flips (A \(\rightarrow\) B or B \(\rightarrow\) A) compared to bins with non- significant compartment flips. (C) Time- series clustering of normalized compartment scores into six different clusters from the three cell types along with their overlapping gene expression profile. For the clustering analysis, the quantile normalized PCA scores for each 100Kb bin across ESC- NPC- CN were further z- transformed to focus on relative changes in compartmentalization. (D) Gene term enrichment results of GO biological functions from genes overlapping with cluster 1, 3 and 6 compartments. (E- L) Differential compartments overlapping with representative genes in each of the cell types shown along with the differential chromatin interactions involving the respective compartments and gene expression values (TPM) across the ESC- NPC- CN transition for four example genes each representing one cluster pattern. + +<|ref|>text<|/ref|><|det|>[[114, 410, 883, 748]]<|/det|> +Figure 6: Ten- way multivariate differential compartment analysis of mouse hematopoiesis. (A) Summary of overall compartment decomposition and significant compartment changes observed across the 10 cell types. The orange and blue arrows represent A to B and B to A compartment flips, respectively. The numbers next to the arrows represent the total number of flipping compartments and the numbers within the parentheses next to it shows the significantly differential flipping compartments. The bottom- right plot shows the proportion of A and B bins among \(dcHiC\) differential compartments for each cell type. Figure adopted from Zhang et. al. [28]. (B- E) The functional enrichment of genes overlapping with differential compartments from 10- way comparison that have the strongest A compartment scores in either (B) LT- HSC or ST- HSC, (C) MPP or CMP, (D) GMP or GR, or (E) GR alone. (F) Differential compartments identified by \(dcHiC\) overlap a set of critical genes previously known to play role in mouse hematopoiesis. (G) An IGV browser snapshot of Abca13 gene and its overlapping differential compartment across the ten cell types. The Abca13 gene is exclusively found to be a part of A- compartment in GR while in the B- compartment in all other cell types. (H) An IGV browser view surrounding Meis1 genic region. This region is overlapping with the A compartment for all the cell types but with varying magnitude of strength. \(dcHiC\) captured this region as a differential compartment. + +<|ref|>text<|/ref|><|det|>[[114, 758, 883, 889]]<|/det|> +Figure 7: Twenty- way multivariate differential compartment analysis of human lymphoblastoid cell lines (LCLs). (A) A Venn diagram of the overlap of differential compartments called by \(dcHiC\) and the variable compartment regions by a previous study (Gorkin et. al, 2019). (B) The distribution of - log10(p- adj) values of \(dcHiC\) - overlapping and non- overlapping variable regions calculated by the previous study. (C) The total number of chromosome- wise differential compartments and the fraction of each chromosome (except those filtered by Gorkin et. al. 2019) covered by such calls for \(dcHiC\) and the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 241]]<|/det|> +previous study. (D- E) Venn diagrams of overlapping compartments of the top 5000 differential region from both the approaches and the \(- \log 10(p - adj)\) value distribution of the overlapping and non- overlapping set from \(dcHiC\) . (F- G) The cumulative number of FIRE- QTLs and DI- QTLs overlapping the top 5000 differential compartment calls by \(dcHiC\) and Gorkin et al. (H- I) Two differential compartments overlapping genic regions of \(NR2F2\) and THEMIS/PTPRK. Both of the genes and especially \(NR2F2\) region was shown to be a variable region across the population through FISH experiments in the previous study (Gorkin et. al, 2019). + +<|ref|>sub_title<|/ref|><|det|>[[115, 279, 251, 297]]<|/det|> +## REFERENCES + +<|ref|>text<|/ref|><|det|>[[110, 303, 886, 888]]<|/det|> +1. C R: Über Zelltheilung. Morphologisches Jahrbuch Gegenbaur C (ed) 1885, 10:214-330. +2. Oudelaar AM, Higgs DR: The relationship between genome structure and function. Nat Rev Genet 2020. +3. 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Feng J, Liu T, Qin B, Zhang Y, Liu XS: Identifying ChIP-seq enrichment using MACS. Nat Protoc 2012, 7:1728-1740.69. Quinlan AR, Hall IM: BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 2010, 26:841-842.70. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015, 43:e47.71. Brereton RG: The Mahalanobis distance and its relationship to principal component scores. Journal of Chemometrics 2015. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 44, 143, 70]]<|/det|> +## Figures + +<|ref|>image<|/ref|><|det|>[[50, 92, 950, 470]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 494, 115, 513]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[41, 536, 949, 763]]<|/det|> +Outline of the method. The figure panel shows the dcHiC workflow in two steps. In step 1, dcHiC calculates the principal components followed by the quantile normalization of the compartment scores across all input Hi- C data. In the step 2, for each genomic bin, dcHiC calculates a Mahalanobis distance, which is a statistical measure of the extent to which each bin is a multivariate outlier with respect to the overall multivariate compartment score distribution across all input Hi- C maps (Methods). dcHiC then utilizes the Mahalanobis distance to assign a statistical significance using Chi- square test (p- value) for each compartment bin and employs independent hypothesis weighting (IHW – when there are replicate samples) or FDR (when no replicates are available) correction on these p- values. dcHiC outputs a standalone dynamic IGV web browser view and enables the user to integrate other datasets into the same view for an integrated visualization. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[48, 66, 911, 707]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[52, 48, 150, 68]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[40, 761, 958, 921]]<|/det|> +Comparison of dChIC compartment scores with HOMER compartment scores and Lamin B1 association data. (A- B) Genome- wide comparison of dChIC compartment scores against HOMER compartment scores and Lamin B1 profiles for mouse ESC. (C) Comparison between HOMER compartment scores and Lamin B1 association. (D- F) Plots similar to (A- C) but for NPC Hi- C data. Pearson correlation value between two axes are reported for each plot. (G- H) Browser views of the compartment scores and Lamin B1 signal for a chromosome 16 region with arrows pointing to some small differences among the compartment scores of dChIC and HOMER. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[45, 55, 737, 780]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[45, 802, 117, 820]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[42, 841, 950, 955]]<|/det|> +Pairwise differential compartment analysis between ESC and NPC. (A) The breakdown of the numbers of differential compartment calls (100Kb resolution) belonging to different types. (B- E) The distributions of compartment scores, Lamin B1, replication timing, association and gene expression across different subtypes of differential compartment calls made by \(dcHiC\) . Strong (s) and Weak (w) were used to indicate the relative compartment strength (or absolute value) between the two cell types. (F) The Venn- + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 951, 180]]<|/det|> +diagram shows the overlap between dcHiC and HOMER differential compartment calls. (G-H) The absolute difference of Lamin B1, replication timing signal and gene expression values (TPM) overlapping with all and exclusive differential compartments identified by dcHiC and HOMER, respectively (statistical significance was calculated by unpaired T-test). (I) The average number of differential (DE) genes overlapping with differential compartment bins (100Kb) identified by dcHiC and HOMER. (J-L) dcHiC differential compartments involving three DE genes: DppA2/4, Dach1 and Nedd9. + +<|ref|>image<|/ref|><|det|>[[50, 234, 955, 803]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 207, 184, 230]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[42, 868, 953, 959]]<|/det|> +Differences in local chromatin interactions of differential compartments. Detailed browser views (top), HiC contact maps (mid) and differential chromatin interactions (bottom) of three gene loci – (A) DppA2/4, (B) Ephb1 and (C) Oct4. Visible changes in interactions involving DppA2/4 locus and Ephb1 locus are highlighted through each plot. Although Oct4 shows a dramatic change in gene expression, the region + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 945, 88]]<|/det|> +does not alter its radial position within nucleus (FISH experiments) which is also consistent with the lack of change in compartmentalization as reported by \(dcHiC\) . + +<|ref|>image<|/ref|><|det|>[[45, 120, 680, 845]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[45, 860, 118, 878]]<|/det|> +
Figure 5
+ +<|ref|>text<|/ref|><|det|>[[42, 902, 950, 946]]<|/det|> +Three- way differential compartment analysis of ESC, NPC, and CN. (A) The breakdown of the numbers of differential compartment calls belonging to different types. (B) The enrichment of signal difference in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 44, 955, 270]]<|/det|> +different histone marks and gene expression in dcHiC differential bins with compartment flips (A à B or B à A) compared to bins with non- significant compartment flips. (C) Time- series clustering of normalized compartment scores into six different clusters from the three cell types along with their overlapping gene expression profile. For the clustering analysis, the quantile normalized PCA scores for each 100Kb bin across ESC- NPC- CN were further z- transformed to focus on relative changes in compartmentalization. (D) Gene term enrichment results of GO biological functions from genes overlapping with cluster 1, 3 and 6 compartments. (E- L) Differential compartments overlapping with representative genes in each of the cell types shown along with the differential chromatin interactions involving the respective compartments and gene expression values (TPM) across the ESC- NPC- CN transition for four example genes each representing one cluster pattern. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[48, 68, 789, 780]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 802, 117, 820]]<|/det|> +
Figure 6
+ +<|ref|>text<|/ref|><|det|>[[41, 841, 949, 955]]<|/det|> +Ten- way multivariate differential compartment analysis of mouse hematopoiesis. (A) Summary of overall compartment decomposition and significant compartment changes observed across the 10 cell types. The orange and blue arrows represent A to B and B to A compartment flips, respectively. The numbers next to the arrows represent the total number of flipping compartments and the numbers within the parentheses next to it shows the significantly differential flipping compartments. The bottom- right + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[40, 44, 940, 271]]<|/det|> +plot shows the proportion of A and B bins among dcHiC differential compartments for each cell type. Figure adopted from Zhang et. al. [28]. (B- E) The functional enrichment of genes overlapping with differential compartments from 10- way comparison that have the strongest A compartment scores in either (B) LTHSC or ST- HSC, (C) MPP or CMP, (D) GMP or GR, or (E) GR alone. (F) Differential compartments identified by dcHiC overlap a set of critical genes previously known to play role in mouse hematopoiesis. (G) An IGV browser snapshot of Abca13 gene and its overlapping differential compartment across the ten cell types. The Abca13 gene is exclusively found to be a part of A- compartment in GR while in the B- compartment in all other cell types. (H) An IGV browser view surrounding Meis1 genic region. This region is overlapping with the A compartment for all the cell types but with varying magnitude of strength. dcHiC captured this region as a differential compartment. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[45, 70, 772, 789]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[47, 805, 117, 824]]<|/det|> +
Figure 7
+ +<|ref|>text<|/ref|><|det|>[[40, 841, 950, 955]]<|/det|> +Twenty- way multivariate differential compartment analysis of human lymphoblastoid cell lines (LCLs). (A) A Venn diagram of the overlap of differential compartments called by \(dcHiC\) and the variable compartment regions by a previous study (Gorkin et. al, 2019). (B) The distribution of - log10(p- adj) values of \(dcHiC\) overlapping and non- overlapping variable regions calculated by the previous study. (C) The total number of chromosome- wise differential compartments and the fraction of each chromosome (except + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 44, 951, 202]]<|/det|> +those filtered by Gorkin et. al. 2019) covered by such calls for \(dcHiC\) and the previous study. (D- E) Venn diagrams of overlapping compartments of the top 5000 differential region from both the approaches and the \(- \log 10(p\) - adj) value distribution of the overlapping and non- overlapping set from \(dcHiC\) (F- G) The cumulative number of FIRE- QTLs and DI- QTLs overlapping the top 5000 differential compartment calls by \(dcHiC\) and Gorkin et al. (H- I) Two differential compartments overlapping genic regions of \(NR2F2\) and THEMIS/PTPRK. Both of the genes and especially \(NR2F2\) region was shown to be a variable region across the population through FISH experiments in the previous study (Gorkin et. al, 2019). + +<|ref|>sub_title<|/ref|><|det|>[[44, 224, 310, 251]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 274, 765, 295]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 312, 366, 440]]<|/det|> +SupplementaryData1. xlsx SupplementaryData2. xlsx Reportingsummary002. pdf dcHiCdemo.zip SupplementaryInformation.docx + +<--- Page Split ---> diff --git a/preprint/preprint__070706503cff2b0c5895a72491f41d6927ffe121f452603fa7f93aef50b5eb1a/images_list.json b/preprint/preprint__070706503cff2b0c5895a72491f41d6927ffe121f452603fa7f93aef50b5eb1a/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..bb46ebd30ba3a689fea8daa50660bf0a2d79624b --- /dev/null +++ b/preprint/preprint__070706503cff2b0c5895a72491f41d6927ffe121f452603fa7f93aef50b5eb1a/images_list.json @@ -0,0 +1,168 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1. Construction of PuuE tube via NIPAD. | a, AF2 prediction of the heterodimeric peptide pair, M3L2 (yellow) and p66α (blue). b, Crystal structure of PuuE (PDB ID: 3CL6). C-terminus positions are circled. Detailed structure, face, side, and back are shown for clarity. c, Schematic diagram of the protein sequence (top) and the AF2-predicted structures of PuuE-M and PuuE-p (bottom). PuuE-M and PuuE-p are coloured yellow and blue to match the respective peptides and overall structure to clear the tube structure (d). The peptide parts, M3L2 and p66α, are highlighted in darker colours. d, Left, predicted model of the tubular assembly consisting of PuuE-M and PuuE-p. Right, brief schematic diagram of how many proteins (n) form a system of tube structures. e, nsTEM images of tubular assemblies constructed from PuuE-M and PuuE-p; 12.5 μM PuuE-M and 12.5 μM PuuE-p in NaCl (+) buffer was incubated at 40 °C for 24 h and imaged via nsTEM. Scale bars, 1 μm (white), 50 nm (black).", + "footnote": [], + "bbox": [ + [ + 113, + 87, + 880, + 445 + ] + ], + "page_idx": 12 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2. Condition optimisation for PuuE tube assembly. | a, b, The kinetics of tubular assembly. nsTEM images of tubular assembly (a) and length analysis (b). c, nsTEM images of tubular assemblies with varying NaCl concentration. d, nsTEM images showing the reversibility of tube structures with changing NaCl concentration. e, Tube length analysis of nsTEM images. For tube length analysis, tubes were picked up and calculated from 5k images at each step; 150 tubes from the longest tube length were used at each data point. \\*\\*\\* p<0.001 (Welch's t-test). Scale bar, 1 μm.", + "footnote": [], + "bbox": [ + [ + 293, + 90, + 705, + 744 + ] + ], + "page_idx": 13 + }, + { + "type": "image", + "img_path": "images/Supplementary_Figure_1b.jpg", + "caption": "Fig. 3. Structural characterisation of PuuE tube. | a, 2D class-averaged images of tube structures. The population of each structure was determined from the total pickings of 206,658 tube segments. Scale bar, 500 Å. b 3D reconstructed models of tube structures with \\(C_4\\) , \\(C_5\\) , and \\(C_6\\) symmetries. The fitting results suggest that PuuE-p is less likely to fit into units located inside the tube structure and more likely to fit into units located on the outside. Based on the predictions, the units were colour-coded as shown in Fig. 1c. For visibility, only the molecular model of the PuuE (PDB ID: 3CL6) is overlayed on the 3D reconstructed model. c, Time-lapse images of random bending of the tube structures monitored by TIRFM. Top: snapshots at the starting point (0 sec) and after 4 sec (top). Bottom: enlarged images of tubes in green or orange rectangles in the top images, showing the dynamic flexibility of tube structures between 0 to 4 sec (0.4 sec per image). Scale bar, 5 μm. d, Left, a relationship between contour length (L) and mean square of end-to-end distance () of the tube structures for estimation of the persistence length (Lp). The continued lines represent fitting curves (black for PuuE tube, red for actin filament) to experimental data (black open circle for PuuE tube, red cross mark for actin filament). Right, comparison of persistence length with cytoskeletal elements. PuuE tube (PT, black) and actin filaments (AF, red) were determined in this study (A wider range of plots is shown in Supplementary Fig. 1b). Intermediate filaments (IF, blue) and microtubules (MT, green) are taken from ref. 41 and 38, respectively.", + "footnote": [], + "bbox": [ + [ + 115, + 90, + 881, + 480 + ] + ], + "page_idx": 14 + }, + { + "type": "image", + "img_path": "images/Extended_Data_Figure_9.jpg", + "caption": "Fig. 4. Emulation of actin filament by D-loop grafting. | a, Schematic representations of PuuE(D-loop)-M. The position of D-loop graft (red) is indicated by protein sequence (top) and the AF2-predicted structure (bottom). b, nsTEM images of tubes with a helical conformation composed of PuuE(D-loop)-M and PuuE-p. The helical pattern of two (centre) or three (right) intertwined tubes is shown in the high-magnification image. c, nsTEM images showing the reversibility of tube structure with helical conformations by temperature change. d, Representative cryo-EM images (top) and 2D class-averaged images (bottom) of helical tube structures. e, Representative cryo-EM image (top) and 2D class-averaged image of tube structure with \\(C_3\\) symmetry. Tube structures with other symmetries found in this study are shown in Extended Data Fig. 9. f, 3D reconstructed model of tube structure with \\(C_3\\) symmetry. For visibility, only the PuuE structure (PDB ID: 3CL6) is overlayed on the 3D reconstructed model. g, Fitting of AF2-predicted model of PuuE-p into the 3D reconstructed model. The fitting results suggest that PuuE-p is unlikely to fit in the units located inside the tube structure; it is better accommodated by the units on the outside. Based on this prediction, the units in f are colour-coded as described in Fig. 1c. 6xHis-TEVcs region of the PuuE-p model is not shown to improve visibility. Scale bars, 1 \\(\\mu \\mathrm{m}\\) (white), 100 nm (black), 10 nm (grey).", + "footnote": [], + "bbox": [ + [ + 120, + 95, + 886, + 520 + ] + ], + "page_idx": 15 + }, + { + "type": "image", + "img_path": "images/Extended_Data_Figure_2.jpg", + "caption": "Extended Data Fig. 2. | nsTEM characterisation of PuuE-M, PuuE-p, and the mixture for PuuE-M and PuuE-p. a, 12.5 \\(\\mu \\mathrm{M}\\) PuuE-M or b, 12.5 \\(\\mu \\mathrm{M}\\) PuuE-p in NaCl (+) buffer was incubated at \\(40^{\\circ}\\mathrm{C}\\) for 24 h. Scale bars, 200 nm (white), 50 nm (black). c, Dependency of PuuE tube assemblies on protein concentration. 250 nM (top), 2.5 \\(\\mu \\mathrm{M}\\) (middle), and 12.5 \\(\\mu \\mathrm{M}\\) (bottom) of PuuE-M and PuuE-p each in NaCl (+) buffer was incubated at \\(40^{\\circ}\\mathrm{C}\\) for 24 h and imaged by nsTEM. The tube structure observed in the nsTEM images was flexible as it was curved and collapsed. Scale bars, 1 \\(\\mu \\mathrm{m}\\) (white), 50 nm (black).", + "footnote": [], + "bbox": [], + "page_idx": 23 + }, + { + "type": "image", + "img_path": "images/Extended_Data_Figure_3.jpg", + "caption": "Extended Data Fig. 3. | Time dependence of PuuE tube assemblies and their stability over time. a, 12.5 \\(\\mu \\mathrm{M}\\) of PuuE-M and PuuE-p each in \\(\\mathrm{NaCl}(+)\\) buffer was incubated at \\(40^{\\circ}\\mathrm{C}\\) for indicated time points and imaged via nsTEM. b, After \\(24\\mathrm{h}\\) of tube formation, the sample was kept at \\(25\\pm 1^{\\circ}\\mathrm{C}\\) for the indicated time and imaged using nsTEM. Tube structures remained unchanged after 2 weeks and even after 1 month, suggesting stability. Scale bars, \\(1\\mu \\mathrm{m}\\) .", + "footnote": [], + "bbox": [ + [ + 123, + 115, + 880, + 744 + ] + ], + "page_idx": 24 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "C4 tube: 2D classifications (2 rounds): 51,590 segments", + "footnote": [], + "bbox": [], + "page_idx": 25 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_1.jpg", + "caption": "C5 tube: 2D classifications (2 rounds): 44,868 segments", + "footnote": [], + "bbox": [ + [ + 210, + 268, + 520, + 415 + ] + ], + "page_idx": 26 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_2.jpg", + "caption": "3D Refinement \\((C_4)\\) : 12,052 segments Post-processing", + "footnote": [], + "bbox": [ + [ + 536, + 310, + 808, + 416 + ] + ], + "page_idx": 27 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_3.jpg", + "caption": "C6 tube: 2D classification (1 round): 117,636 segments", + "footnote": [], + "bbox": [ + [ + 210, + 434, + 500, + 592 + ] + ], + "page_idx": 28 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_4.jpg", + "caption": "3D Refinement \\((C_5)\\) : 12,572 segments Post-processing", + "footnote": [], + "bbox": [ + [ + 536, + 434, + 808, + 592 + ] + ], + "page_idx": 28 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_5.jpg", + "caption": "3D classification \\((C_1)\\) : 39,841 segments", + "footnote": [], + "bbox": [ + [ + 177, + 633, + 808, + 805 + ] + ], + "page_idx": 28 + } +] \ No newline at end of file diff --git a/preprint/preprint__070706503cff2b0c5895a72491f41d6927ffe121f452603fa7f93aef50b5eb1a/preprint__070706503cff2b0c5895a72491f41d6927ffe121f452603fa7f93aef50b5eb1a.mmd b/preprint/preprint__070706503cff2b0c5895a72491f41d6927ffe121f452603fa7f93aef50b5eb1a/preprint__070706503cff2b0c5895a72491f41d6927ffe121f452603fa7f93aef50b5eb1a.mmd new file mode 100644 index 0000000000000000000000000000000000000000..7a8a921a3c38a3863a694d93b43c357f3166c954 --- /dev/null +++ b/preprint/preprint__070706503cff2b0c5895a72491f41d6927ffe121f452603fa7f93aef50b5eb1a/preprint__070706503cff2b0c5895a72491f41d6927ffe121f452603fa7f93aef50b5eb1a.mmd @@ -0,0 +1,371 @@ + +# Protein design of two-component tubular assemblies like cytoskeletons + +Yuta Suzuki suzuki.yuta.2m@kyoto- u.ac.jp + +Kyoto University https://orcid.org/0000- 0002- 4863- 4585 Masahiro Noji Kyoto University Yukihiko Sugita Institute for Life and Medical Sciences, Kyoto University https://orcid.org/0000- 0001- 6861- 4840 Yosuke Yamazaki RIKEN Makito Miyazaki RIKEN https://orcid.org/0000- 0002- 4603- 851X + +## Article + +# Keywords: + +Posted Date: October 21st, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4976952/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on July 22nd, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 62076- 3. + +<--- Page Split ---> + +# Protein design of two-component tubular assemblies like cytoskeletons + +2 + +3 Masahiro Noji \(^{1,2,3}\) , Yukihiko Sugita \(^{4,5,6}\) , Yosuke Yamazaki \(^{7,8}\) , Makito Miyazaki \(^{6,7,8,9}\) , and Yuta Suzuki \(^{3,6,9}\) .\* + +4 \(^{1}\) Research Fellow of Japan Society for the Promotion of Science, Japan; \(^{2}\) Graduate School of Human and Environmental Studies, Kyoto University, Kyoto, Japan; \(^{3}\) Institute for Integrated Cell- Material Sciences, Kyoto University, Kyoto, Japan; \(^{4}\) Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan; \(^{5}\) Graduate School of Biostudies, Kyoto University, Kyoto, Japan; \(^{6}\) Hakubi Center for Advanced Research, Kyoto University, Kyoto, Japan; \(^{7}\) Graduate School of Science, Kyoto University, Kyoto, Japan; \(^{8}\) RIKEN Center for Biosystems Dynamics Research, Yokohama, Japan; \(^{9}\) PRESTO, JST, Saitama, Japan + +Recent advances in protein design have ushered in an era of constructing intricate higher- order structures \(^{1}\) . Nonetheless, orchestrating the assembly of diverse protein units into cohesive artificial structures akin to biological assembly systems, especially in tubular forms, remains elusive. To this end, here, we introduce the Nature- Inspired Protein Assembly Design (NIPAD), a novel methodology that utilises two distinct protein units to create unique tubular structures under carefully designed conditions. These structures demonstrate dynamic flexibility similar to that of actin filaments, with cryo- electron microscopy revealing diverse morphologies, like microtubules. By mimicking actin filaments, helical conformations were incorporated into tubular assemblies, thereby enriching their structural diversity. Notably, these assemblies can be reversibly disassembled and reassembled in response to environmental stimuli, including changes in salt concentration and temperature, mirroring the dynamic behaviour of natural systems. NIPAD combines rational protein design with biophysical insights, leading to the creation of biomimetic, adaptable, and reversible higher- order assemblies. This approach deepens our understanding of protein assembly design and complex biological structures. Concurrently, it broadens the horizons of synthetic biology and material science, holding significant implications for unravelling life's fundamental processes and pioneering new applications. + +Life phenomena rely on the dynamic and reversible assembly and disassembly of various higher- order protein assemblies. Actin filaments \(^{2,3}\) and microtubules \(^{4,5}\) in the cytoskeleton and the capsid proteins of viruses \(^{6,7}\) are examples of such naturally occurring structures. These are tightly regulated in function and complexity. Synthesising higher- order structures of heterogeneous protein units poses a significant challenge, particularly regarding replicating the diversity and flexibility inherent to natural assemblies. Although recent advances in computational design have enabled the creation of artificial higher- order + +<--- Page Split ---> + +protein structures from two protein components8- 10, the design of heterogeneous higher- order protein assemblies with the flexibility and reversible assembly/disassembly characteristics of natural structures, especially tube structures reminiscent of the cytoskeleton, remains a formidable challenge. + +Herein, we introduced Nature- Inspired Protein Assembly Design (NIPAD), a novel methodology that draws inspiration from the principles underlying natural protein complexes. By integrating rational protein design with biophysical insights to optimise assembly conditions, NIPAD recapitulates flexibility and reversible assembly principles. We employed NIPAD to create a novel assembly of two distinct protein units, successfully forming unique two- component tube structures. This development represents a significant step toward replicating the properties of complex natural structures at the molecular level. + +## Results and discussion + +## The concept of NIPAD + +In developing the protein components for NIPAD, we employed rational design principles with hints from natural biological systems, integrating naturally occurring ‘heterolinkers’ with ‘scaffold proteins’ to streamline design. For the heterolinker, we chose the heterodimeric peptide pair ‘MBD3L2 (M3L2)/p66α’ (Fig. 1a). Our choice of M3L2/p66α was influenced by its role in the MBD2- NuRD complex, where the ‘MBD2/p66α’ anti- parallel coiled- coil domain is essential for complex assembly11. Given the moderate denaturation midpoint temperature \((T_{\mathrm{m}})\) of M3L2/p66α \((T_{\mathrm{m}} = 35^{\circ}\mathrm{C})\) compared to MBD2/p66α \((T_{\mathrm{m}} = 65^{\circ}\mathrm{C})^{12}\) , we anticipated that M3L2/p66α would provide a balance between stability and reversible assembly control through temperature modulation. We then sought to identify a scaffold protein that could connect the heterolinker in the simplest manner possible. The positions of connecting sites at the corners of such scaffold proteins facilitate the desired assembly formation13. Therefore, we chose the ‘Pseudomonas fluorescens PuuE allantoinase (PuuE)’, a homotetramer with \(C_4\) symmetry where each C- terminus is located at each vertex of the quaternary structure (Fig. 1b)14. This arrangement enabled straightforward genetic fusion of heterolinkers to the scaffold’s C- termini, leveraging specificity and reversibility of heterolinker interactions to drive assembly formation. This approach simplifies the assembly process and enhances expression and purification efficiency for each protein unit, preventing spontaneous assembly and ensuring the controlled formation of higher- order structures. + +We constructed protein units ‘PuuE- M’ and ‘PuuE- p’ through genetic engineering, fusing M3L2 and p66α to the C- terminus of PuuE, respectively (Fig. 1c). AlphaFold2 (AF2)15,16 modelling suggested a configuration with a relatively flexible orientation of M3L2 in PuuE- M, whereas a highly constrained orientation of p66α in PuuE- p (Extended Data Fig. 1). Owing to the constrained orientation of PuuE- p, an + +<--- Page Split ---> + +angular interface was formed between PuuE- M and PuuE- p, and we predicted that assembly of these two units would form a tubular structure (Fig. 1d). Additionally, depending on the number of PuuE- M and PuuE- p units, different tubular structures were expected. Protein expression in Escherichia coli provided both constructs in a soluble form, facilitating their purification. In isolation, neither protein unit exhibits self- assembly (Extended Data Fig. 2a, b). However, when combined under optimised conditions (discussed in the following section), we successfully observed the expected chessboard- patterned tube (PuuE tube) using negative- stain transmission electron microscopy (nsTEM) (Fig. 1e, Extended Data Fig. 2c). Although previous studies have assembled cages \(^{8,9}\) , sheets \(^{10}\) , and three- dimensional (3D) crystals \(^{17}\) using two- component protein systems, this study is unique in that tube structures were successfully created. + +## The condition design of tubular assemblies + +Based on established principles observed in biological systems, including actin filaments \(^{18,19}\) , microtubules \(^{20,21}\) , and amyloid fibrils \(^{22,23}\) , protein concentration, temperature, time, and salinity have a significant influence on assembly formation. Thus, we carefully tailored assembly conditions to exploit the complex interactions between these factors. This approach allowed us to optimise experimental conditions for constructing the desired tubular structures. + +First, we focused on the dependency of PuuE tube assembly on protein concentration (Extended Data Fig. 2c). Mixing PuuE- M and PuuE- p at a concentration of \(250~\mathrm{nM}\) each (considering tetramer equivalence) led to the formation of tubular structures after an incubation period of \(24\mathrm{~h}\) at \(40^{\circ}\mathrm{C}\) , consistent with the dissociation constant \((K_{\mathrm{d}})\) for M3L2/p66α dimer formation, which is approximately \(268~\mathrm{nM}^{12}\) . Increasing protein concentration to \(2.5~\mu \mathrm{M}\) markedly enhanced the quantity and length of formed tubular structures. Elevating the concentration to \(12.5~\mu \mathrm{M}\) for each component significantly increased tube formation efficiency, underscoring the concentration- dependent nature of PuuE- M- and PuuE- p- facilitated tubular assembly. + +Next, PuuE tube formation kinetics were investigated. The incubation of mixtures containing \(12.5~\mu \mathrm{M}\) of each protein at \(40^{\circ}\mathrm{C}\) resulted in the formation of nascent tube structures within \(30~\mathrm{min}\) , evolving into distinguishable tubes spanning several hundred nanometres to \(1\mu \mathrm{m}\) in length within \(1 - 2\mathrm{~h}\) (Fig. 2a, b, Extended Data Fig. 3a). Over time, these tubes elongated, reaching several micrometres in length after \(24\mathrm{~h}\) and extending up to approximately \(5\mu \mathrm{m}\) after \(48\mathrm{~h}\) . Once formed, the tubes remained structurally stable for at least 1 month at \(25\pm 1^{\circ}\mathrm{C}\) (Extended Data Fig. 3b). + +<--- Page Split ---> + +We then explored the influence of temperature on PnuE tube formation (Extended Data Fig. 4a). While the melting temperature of the M3L2/p66α dimer is around \(35^{\circ}\mathrm{C}\) , tube assembly was hardly observed at sufficiently lower temperatures of \(20 - 25^{\circ}\mathrm{C}\) , even after \(24\mathrm{h}\) of incubation. Conversely, temperatures near \(T_{\mathrm{m}}\) , specifically between 30 and \(40^{\circ}\mathrm{C}\) , markedly promoted tube formation. Therefore, temperatures below \(T_{\mathrm{m}}\) may excessively enhance the binding force between M3L2 and p66α, causing kinetic entrapment of assemblies. However, temperatures close to \(T_{\mathrm{m}}\) modulate this binding force, allowing the dynamic rearrangement of M3L2/p66α interactions under thermal fluctuations, thus facilitating the assembly of thermodynamically stable, ordered structures. This principle is consistent with general crystallisation theories \(^{24,25}\) and reports on the formation of ordered structures in natural protein assemblies \(^{22,23,26}\) . Importantly, temperatures above \(45^{\circ}\mathrm{C}\) led to thermal denaturation and aggregation of PnuE- M ( \(T_{\mathrm{m}} = 46.2^{\circ}\mathrm{C}\) ) and PnuE- p ( \(T_{\mathrm{m}} = 48.1^{\circ}\mathrm{C}\) ), significantly diminishing tube formation capabilities (Extended Data Fig. 4b, c). This finding implies that the original concept of tube formation with reversible temperature control was not realised. + +## Reversibility of tubular assemblies + +Finally, we examined the effects of salt concentration on PnuE tube assembly. We prepared mixtures with different NaCl concentrations ranging from 0 to \(400\mathrm{mM}\) and incubated them at \(40^{\circ}\mathrm{C}\) for \(24\mathrm{h}\) . Tube formation was clearly observed within the NaCl concentration window of \(50 - 200\mathrm{mM}\) , with no tube formation detected outside this range (Fig. 2c, Extended Data Fig. 5a). Since both PnuE- M ( \(\mathrm{pI} = 6.44\) ) and PnuE- p ( \(\mathrm{pI} = 6.02\) ) were similarly charged under \(\mathrm{pH}8.0\) , tube formation at low salt concentrations was likely inhibited by electrostatic repulsion. Conversely, moderate electrostatic shielding facilitated by \(50 - 200\mathrm{mM}\) NaCl likely provided conducive conditions for tube assembly, whereas higher NaCl concentrations may have induced excessive shielding or aggregation due to salting out, inhibiting tube formation. This observation aligns with known phenomena in protein crystallisation, where electrostatic shielding above a certain threshold can prevent crystal growth \(^{17,27 - 29}\) , although crystals formed by a combination of electrostatic and hydrophobic interactions can remain stable up to approximately \(200\mathrm{mM}\) NaCl \(^{30}\) . The association of M3L2/p66α involves both electrostatic and hydrophobic interactions \(^{12}\) , consistent with the latter scenario. + +The salt- dependent PnuE tube formation and the dynamic nature of PnuE- M/PnuE- p interactions near their \(T_{\mathrm{m}}(35^{\circ}\mathrm{C})\) led us to hypothesise that tubes could undergo reversible disassembly and reassembly in response to changes in NaCl concentration. Confirming our hypothesis, tubes initially formed in \(100\mathrm{mM}\) NaCl solution were significantly shortened when subjected to solvent exchange with \(0\mathrm{mM}\) NaCl buffer (NaCl (- ) buffer) and subsequent incubation at \(40^{\circ}\mathrm{C}\) for \(24\mathrm{h}\) (Fig. 2d, e, Extended Data Fig. 5b). Subsequent + +<--- Page Split ---> + +solvent exchange with \(100\mathrm{mMNaCl}\) buffer \((\mathrm{NaCl} + )\) buffer) resulted in notable tube reassembly. This salt- concentration- driven reversibility, although divergent from the initial temperature- controlled reversibility hypothesis, marks a significant advance in artificial protein assembly design, allowing for the biomimetic replication of dynamic structural changes under relatively mild conditions, akin to the behaviour of actin filaments in cellular structures \(^{18,19}\) . Unlike the irreversible aggregation observed in amyloid structures, our assemblies exhibit a reversible and dynamic assembly process akin to the cytoskeleton behaviour, successfully demonstrating the potential for the biomimetic replication of natural cellular dynamics under controlled conditions. + +## Diversity and flexibility of tubes + +Based on these findings, we determined the ideal conditions for PuuE tube formation in \(100\mathrm{mMNaCl}\) at \(40^{\circ}\mathrm{C}\) for \(24\mathrm{h}\) . To further characterise the structural features of tubes formed under these conditions, cryo- electron microscopy (cryo- EM) was employed (Extended Data Fig. 6, Extended Data Table 1). Analysis of 2D class- averaged images revealed a spectrum of tube diameters and symmetries similar to the diversity observed in microtubules \(^{31 - 34}\) (Fig. 3a). From these images, we successfully reconstructed the 3D structures with \(C_4\) , \(C_5\) , and \(C_6\) symmetries within tube structures (Fig. 3b). Insights from the PuuE crystal structure \(^{14}\) , notably its unique central indentation on the back surface (Fig 1b), allowed us to deduce that PuuE units are alternately oriented face- to- back across all 3D models. Additionally, cryo- EM analysis suggested that connection flexibility allowed the contraction of the entire tube structure (Extended Data Fig. 6, Supplementary Movie 1). Although definitive conclusions are difficult owing to its inherent flexibility, the comparison of the cryo- EM 3D reconstruction with the AF2- predicted model of PuuE- p suggests that PuuE- p is less likely to fit inside the tube structure and instead fits better on the outside (Supplementary Movie 2). Furthermore, tubes with larger diameters, presumably having \(C_7\) to \(C_{10}\) symmetries, were identified at low resolution, likely owing to the flexibility of connection sites influencing tube structure. In fact, nsTEM and cryo- EM images frequently showed tubes appearing bent or compressed (Extended Data Fig. 2–6). In contrast to prior strategies by engineering on scaffold proteins itself to create higher- order protein assemblies \(^{8 - 10,13,35}\) , NIPAD integrates a flexible linker with the scaffold protein, resulting in varied structures and arrangements among higher- order assemblies. This variation in tube diameter, akin to that observed in microtubules \(^{31 - 34}\) , is presumably a hallmark of NIPAD. + +To further explore PuuE tube structure flexibility, we labelled tubes with Alexa Fluor 488 succinimidyl ester and observed them in real- time using total internal reflection fluorescence microscopy (TIRFM). Tube structures were constrained in the evanescent field by the depletion effect of methylcellulose contained in the observation buffer and underwent thermally driven two- dimensional random bending (Fig. 3c, + +<--- Page Split ---> + +Supplementary Fig. 1a, Supplementary Movie 3). Analysis of the fluctuation in shape yielded the persistence length \((L_{\mathrm{p}})\) of \(19.7 \mu \mathrm{m}\) (Fig. 3d, Supplementary Fig. 1b). \(L_{\mathrm{p}}\) is the mean length over which a semiflexible polymer remains straight, characterising polymer stiffness36. The \(L_{\mathrm{p}}\) value of the tube structures is nearly equal to that of actin filaments measured in this study, \(12.5 \mu \mathrm{m}\) (Fig. 3d), and previously reported values of \(9 - 20 \mu \mathrm{m}^{37}\) . Microtubules have much longer persistence lengths \((0.1 - 10 \mathrm{mm})^{38 - 40}\) . Conversely, intermediate filaments, another cytoskeletal fibre structure, typically have shorter persistence lengths \((< 1 \mu \mathrm{m})^{41}\) . Therefore, the tube structure is as flexible as actin filaments, more flexible than microtubules, and stiffer than intermediate filaments. + +## Emulation of actin filaments + +Finally, we sought to modify the morphology of PuuE tube assemblies. Specifically, we hypothesised that grafting the D- loop of actin onto PuuE- M would produce tubes with a helical conformation reminiscent of actin filaments. The D- loop plays an important role in helical actin filament formation via hydrophobic pockets42- 45. The hydrophobic nature of a prominent indentation on the 'back' side of PuuE (Extended Data Fig. 7a) guided our hypothesis. The loop structure on the back side of PuuE- M was chosen as the grafting site for the D- loop, and the 'PuuE(D- loop)- M' fusion construct was constructed (Fig. 4a). When PuuE(D- loop)- M was expressed in \(E\) . coli, it was found in the soluble fraction and was purified as PuuE- M. Since PuuE(D- loop)- M has a lower thermal stability \((T_{\mathrm{m}} = 35.6^{\circ}\mathrm{C})\) than PuuE- M \((T_{\mathrm{m}} = 46.2^{\circ}\mathrm{C}\) , Extended Data Fig. 4b, 7b), we performed sample incubation at a lower temperature \((30^{\circ}\mathrm{C})\) . Although PuuE(D- loop)- M alone did not assemble, its combination with PuuE- p replicated the PuuE tube and introduced novel helical patterns, with two or three tubes intertwined (PuuE D- loop tube), as verified via nsTEM (Fig. 4b, Extended Data Fig. 7c). The emergence of helical formations, absent in the PuuE- M and PuuE- p mixtures, clearly stems from D- loop integration. While the D- loop likely plays a crucial role in the helical formation of actin filaments42- 45, its complete mechanism remains unclear. Our study, by successfully grafting the D- loop to replicate actin- like helical structures, offers a novel perspective on its significance. This approach confirms the critical role of the D- loop in helical conformations and opens new avenues for understanding the intricate design principles of actin filaments. + +As mentioned above, the helical conformation of tube structures is thought to arise from hydrophobic interactions, which are inherently sensitive to temperature and weaken at lower temperatures46- 48. This led us to posit that alterations in temperature can serve as reversible switches for disassembly and reassembly. Notably, exposing the samples to \(0^{\circ}\mathrm{C}\) for 1 h suggested a dissociation of the helical conformations and hinted at a possible breakdown of the tubular structures (Fig. 4c, Extended Data Fig. 7d). Remarkably, when these disassembled samples were reintroduced to \(30^{\circ}\mathrm{C}\) for \(24 \mathrm{h}\) , the elongated tubular formations + +<--- Page Split ---> + +with helical conformations were restored. By grafting D- loop, the tube structure could form helical conformations and acquired a new temperature- dependent reversibility. This thermal responsiveness parallels the behaviour of microtubules20,21,49, underscoring the ability of the NIPAD approach to mimic the dynamic properties of biomolecular assemblies in artificial protein design to create complex higher- order protein structures. This dual responsiveness (salt and temperature dependence) enhances the biomimetic potential of our design, which is a promising avenue for advanced applications in synthetic biology and materials science. + +To determine the intricate helical configurations, structural analyses were performed using cryo- EM (Fig. 4d, Extended Data Fig. 8, Extended Data Table 1). In addition to the inherent flexibility of the tube structure, the ability of tubes to form helical bundles introduces an additional layer of complexity to the structural analysis. This complexity is underscored by cryo- EM results, which render a detailed analysis of these higher- order structures particularly challenging. However, analysis of 2D class- averaged images of the helical structures revealed double and triple helical tubes, which was consistent with the nsTEM observation (Fig. 4b, d, Extended Data Fig. 7c, 8). Moreover, the tube structures forming these helices seem to show a thinner diameter of approximately \(24~\mathrm{nm}\) , which does not align with any of the original PuuE tubes with diameters starting at \(28.6~\mathrm{nm}\) (Fig. 3b, Extended Data Fig. 6). Therefore, an attempt was made to elucidate the characteristics of the tube structures forming the helical conformations by employing temperature- induced structural disassembly (Fig. 4c). The cryo- EM sample was initially prepared at \(25 \pm 1^{\circ}\mathrm{C}\) to prevent disassembly; however, to unwind the helical structures, the sample was briefly chilled on ice for approximately \(1\mathrm{~h}\) . By observing these chilled samples with cryo- EM, we successfully identified a new tube structure with \(C_3\) symmetry (Fig. 4e, f, Extended Data Table 1) in addition to the previously observed structures (Extended Data Fig. 9). A comparison of this tube structure with \(C_3\) symmetry with the structures forming the helical conformations indicated a match, suggesting that the tubes forming the helical conformation have indeed \(C_3\) symmetry (Fig. 4d bottom). Additionally, the diameter of approximately \(23.6\mathrm{~nm}\) , as determined by cryo- EM 3D reconstruction, corresponds to the tubes forming helical structures, further supporting these findings. Considering its inherent flexibility, it is challenging to reach a definitive conclusion, but further examination of the tube structure with \(C_3\) symmetry suggests that PuuE- p is likely positioned on the outside (Fig. 4g, Supplementary Movie 4), consistent with the original PuuE tube structures (Fig. 3b, Supplementary Movie 2). This arrangement indicates that the D- loop of PuuE(D- loop)- M appears on the exterior of the tubes, which is crucial for forming helical structures not observed in PuuE tubes lacking the D- loop. The \(C_3\) symmetry enhances the exposure of internal PuuE(D- loop)- M on the outer surface compared to structures with \(C_4\) or higher symmetry, enabling hydrophobic interactions between tubes. Therefore, the formation of the \(C_3\) symmetric tube structure likely facilitated + +<--- Page Split ---> + +the creation of the helical conformations. Furthermore, the lack of \(C_3\) symmetry in PuuE tubes (Fig. 3a, b) suggests that they are unstable as single tubes without forming helical conformations. The formation of helical conformations may stabilise the structure with \(C_3\) symmetry, as evidenced by its successful identification in tube structures with helical conformations. Additionally, the temperature- induced degradation leading to the rapid collapse of tubes with \(C_3\) symmetry suggests that helical structure stabilisation is essential for maintaining structural integrity under physiological conditions. + +## Conclusions + +We introduce NIPAD, a pioneering approach that intricately weaves together protein unit design and assembly, drawing inspiration from the complexity and adaptability of natural protein assemblies. By employing NIPAD, we created a unique higher- order tubular assembly composed of two protein units, exhibiting the reversible, flexible, and diverse characteristics of natural structures. A noteworthy highlight of our study was the successful induction of helical conformations within these tube assemblies, akin to those observed in actin filaments, achieved through strategic integration of the D- loop into assembly design. + +This advance in protein assembly highlights the complexity of emulating the dynamic behaviour observed in biological systems. The design and assembly of protein structures in vitro, although closely controlled, cannot fully replicate the complex cellular environment. In vivo, myriad factors, including macromolecular crowding, post- translational modifications, and interactions with other cellular components can significantly influence protein behaviour50. Our designed protein assemblies exhibit remarkable biomimicry regarding flexibility, reversibility, and structural diversity, but have yet to be demonstrated and validated in biological systems, where the true complexity of biological interactions is present. Furthermore, our approach, which focuses on the assembly of tubular structures inspired by cytoskeletal elements, including actin filaments and microtubules, does not address the full range of complex protein structures found within biological systems. Natural protein assemblies contain structural and functional diversity, and much remains to be explored. Computational methods have an important role to play in improving the accuracy and breadth of protein assembly design1,8- 10. By utilising computational predictions about protein interactions and assembly outcomes, our design would be refined into more complex and functional biomimetic structures, with applications ranging from novel biomaterials and nanodevices to therapeutic innovations. + +Our research extends the boundaries of protein assembly design and provides new insights into its applications in synthetic biology and life sciences. This research encourages a comprehensive approach that bridges the divide between the biological and materials sciences and suggests that the exploration of + +<--- Page Split ---> + +nature's complex systems has the potential to transform science and technology. As we continue to explore this intersection of life and materials sciences, we anticipate that future investigations will provide fundamental insights into the natural world, heralding a new era of scientific discoveries and technological breakthroughs. + +# References + +Zhu, J. et al. Protein Assembly by Design. Chem Rev 121, 13701- 13796 (2021). Korn, E. D., Carlier, M. F. & Pantaloni, D. Actin polymerization and ATP hydrolysis. Science 238, 638- 644 (1987). Pollard, T. D. & Cooper, J. A. Actin, a central player in cell shape and movement. Science 326, 1208- 1212 (2009). Desai, A. & Mitchison, T. J. Microtubule polymerization dynamics. Annu Rev Cell Dev Biol 13, 83- 117 (1997). Gudimchuk, N. B. & McIntosh, J. R. 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Fig. 1. Construction of PuuE tube via NIPAD. | a, AF2 prediction of the heterodimeric peptide pair, M3L2 (yellow) and p66α (blue). b, Crystal structure of PuuE (PDB ID: 3CL6). C-terminus positions are circled. Detailed structure, face, side, and back are shown for clarity. c, Schematic diagram of the protein sequence (top) and the AF2-predicted structures of PuuE-M and PuuE-p (bottom). PuuE-M and PuuE-p are coloured yellow and blue to match the respective peptides and overall structure to clear the tube structure (d). The peptide parts, M3L2 and p66α, are highlighted in darker colours. d, Left, predicted model of the tubular assembly consisting of PuuE-M and PuuE-p. Right, brief schematic diagram of how many proteins (n) form a system of tube structures. e, nsTEM images of tubular assemblies constructed from PuuE-M and PuuE-p; 12.5 μM PuuE-M and 12.5 μM PuuE-p in NaCl (+) buffer was incubated at 40 °C for 24 h and imaged via nsTEM. Scale bars, 1 μm (white), 50 nm (black).
+ +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2. Condition optimisation for PuuE tube assembly. | a, b, The kinetics of tubular assembly. nsTEM images of tubular assembly (a) and length analysis (b). c, nsTEM images of tubular assemblies with varying NaCl concentration. d, nsTEM images showing the reversibility of tube structures with changing NaCl concentration. e, Tube length analysis of nsTEM images. For tube length analysis, tubes were picked up and calculated from 5k images at each step; 150 tubes from the longest tube length were used at each data point. \*\*\* p<0.001 (Welch's t-test). Scale bar, 1 μm.
+ +<--- Page Split ---> +![](images/Supplementary_Figure_1b.jpg) + +
Fig. 3. Structural characterisation of PuuE tube. | a, 2D class-averaged images of tube structures. The population of each structure was determined from the total pickings of 206,658 tube segments. Scale bar, 500 Å. b 3D reconstructed models of tube structures with \(C_4\) , \(C_5\) , and \(C_6\) symmetries. The fitting results suggest that PuuE-p is less likely to fit into units located inside the tube structure and more likely to fit into units located on the outside. Based on the predictions, the units were colour-coded as shown in Fig. 1c. For visibility, only the molecular model of the PuuE (PDB ID: 3CL6) is overlayed on the 3D reconstructed model. c, Time-lapse images of random bending of the tube structures monitored by TIRFM. Top: snapshots at the starting point (0 sec) and after 4 sec (top). Bottom: enlarged images of tubes in green or orange rectangles in the top images, showing the dynamic flexibility of tube structures between 0 to 4 sec (0.4 sec per image). Scale bar, 5 μm. d, Left, a relationship between contour length (L) and mean square of end-to-end distance () of the tube structures for estimation of the persistence length (Lp). The continued lines represent fitting curves (black for PuuE tube, red for actin filament) to experimental data (black open circle for PuuE tube, red cross mark for actin filament). Right, comparison of persistence length with cytoskeletal elements. PuuE tube (PT, black) and actin filaments (AF, red) were determined in this study (A wider range of plots is shown in Supplementary Fig. 1b). Intermediate filaments (IF, blue) and microtubules (MT, green) are taken from ref. 41 and 38, respectively.
+ +<--- Page Split ---> +![](images/Extended_Data_Figure_9.jpg) + +
Fig. 4. Emulation of actin filament by D-loop grafting. | a, Schematic representations of PuuE(D-loop)-M. The position of D-loop graft (red) is indicated by protein sequence (top) and the AF2-predicted structure (bottom). b, nsTEM images of tubes with a helical conformation composed of PuuE(D-loop)-M and PuuE-p. The helical pattern of two (centre) or three (right) intertwined tubes is shown in the high-magnification image. c, nsTEM images showing the reversibility of tube structure with helical conformations by temperature change. d, Representative cryo-EM images (top) and 2D class-averaged images (bottom) of helical tube structures. e, Representative cryo-EM image (top) and 2D class-averaged image of tube structure with \(C_3\) symmetry. Tube structures with other symmetries found in this study are shown in Extended Data Fig. 9. f, 3D reconstructed model of tube structure with \(C_3\) symmetry. For visibility, only the PuuE structure (PDB ID: 3CL6) is overlayed on the 3D reconstructed model. g, Fitting of AF2-predicted model of PuuE-p into the 3D reconstructed model. The fitting results suggest that PuuE-p is unlikely to fit in the units located inside the tube structure; it is better accommodated by the units on the outside. Based on this prediction, the units in f are colour-coded as described in Fig. 1c. 6xHis-TEVcs region of the PuuE-p model is not shown to improve visibility. Scale bars, 1 \(\mu \mathrm{m}\) (white), 100 nm (black), 10 nm (grey).
+ +<--- Page Split ---> + +## Methods + +## Plasmids and cloning + +Primers for cloning and synthetic genes of N- terminal 6xHis- tagged PuuE- M and PuuE- p were purchased from Eurofins Genomics. PCRs were performed using the PrimeSTAR Max DNA Polymerase (Takara Bio) according to the manufacturer's protocol. Sizes of PCR products were verified using standard agarose gel electrophoresis. The In- Fusion Snap Assembly (Takara Bio) was used as the standard method for cloning according to the manufacturer's protocol, and each amplified gene fragment was ligated between the Ndel and BamHI multicoloring sites of the pET11a expression vector (Novagen). Primers for cloning and a synthetic DNA fragment of D- loop were purchased from Eurofins Genomics. The plasmid encoding N- terminal 6xHis- tagged PuuE- D- loop- M was generated from the PuuE- M plasmid following the same procedures as above. All plasmids were amplified in E. coli strain DH5α (NIPPON GENE) and extracted using the NucleoSpin Plasmid EasyPure (MACHEREY- NAGEL) according to the manufacturer's protocol. DNA sequences were confirmed by a sequencing service (Eurofins Genomics). + +## Protein expression and purification + +The recombinant proteins were expressed using E. coli strain BL21 (DE3) (NIPPON GENE) cotransformed with a pGro7 chaperone plasmid (Takara Bio) and purified as follows. After transformation with plasmid DNA, colonies grown overnight on LB agar plates supplemented with \(100~\mu \mathrm{g / mL}\) ampicillin (Amp) and \(20~\mu \mathrm{g / mL}\) chloramphenicol (Crm) at \(37^{\circ}\mathrm{C}\) were picked to inoculate \(5\mathrm{mL}\) of liquid LB- AmpCrm broth and grown overnight at \(37^{\circ}\mathrm{C}\) and \(200~\mathrm{rpm}\) . Overnight cultures were diluted in \(1\mathrm{L}\) of liquid LB- Amp- Crm broth supplemented with \(0.5\mathrm{mg / mL}\) L- arabinose and grown at \(37^{\circ}\mathrm{C}\) and \(200~\mathrm{rpm}\) until reaching an optical density at \(600\mathrm{nm}\) of 0.6- 0.8. Protein synthesis was induced by adding \(0.1\mathrm{mM}\) isopropyl- \(\beta\) - D- thiogalactopyranoside and the cultures were grown at \(16^{\circ}\mathrm{C}\) for \(16 - 20\mathrm{h}\) . Cells were harvested by centrifugation at \(15,317\mathrm{g}\) and \(4^{\circ}\mathrm{C}\) for \(5\mathrm{min}\) and then frozen at - 80 °C. Cell pellets were thawed at \(25\pm 1^{\circ}\mathrm{C}\) , resuspended in \(60~\mathrm{mL}\) of ice- cold purification buffer ( \(20\mathrm{mM}\) Tris- HCl, \(\mathrm{pH}8.0\) , containing \(300\mathrm{mM}\) NaCl), and lysed using sonication ( \(9\mathrm{min}\) with 1:2 on/off cycles and \(70\%\) amplitude; SFX250, Branson) on ice. Cell debris was cleared by centrifugation at \(15,317\mathrm{g}\) and \(4^{\circ}\mathrm{C}\) for \(30\mathrm{min}\) . The supernatant (i.e., crude protein) was filtered through a \(0.45 - \mu \mathrm{m}\) pore size membrane filter (Merck), applied onto HisTrap FF crude column (Cytiva) pre- equilibrated with the purification buffer and washed with \(5\mathrm{cm}\) volumes of \(2\%\) elution buffer ( \(20\mathrm{mM}\) Tris- HCl, \(\mathrm{pH}8.0\) , containing \(300\mathrm{mM}\) NaCl and \(1\mathrm{M}\) imidazole; \(2\%\) means \(20\mathrm{mM}\) imidazole). 6xHis- tagged proteins were eluted with \(10\mathrm{cm}\) volumes of elution buffer with a linear gradient of \(2 - 40\%\) (i.e., \(20 - 400\mathrm{mM}\) imidazole). The fractions containing the proteins confirmed by means of UV absorption and SDS- PAGE were again collected and dialysed against 50- fold volume of NaCl (+) or NaCl (- ) buffer ( \(50\mathrm{mM}\) Tris- HCl, \(\mathrm{pH}8.0\) , containing \(\pm 100\mathrm{mM}\) NaCl and \(0.5\mathrm{mM}\) EDTA) at \(4^{\circ}\mathrm{C}\) twice. + +<--- Page Split ---> + +Each of the purified proteins was concentrated by an Amicon Ultra centrifugal filter unit (Merck) with an appropriate molecular weight cutoff followed by filtration through a \(0.45 \mu \mathrm{m}\) pore size membrane filter (Merck). Protein concentration was determined by absorbance measurements at \(280 \mathrm{nm}\) using a NanoDrop OneC spectrophotometer (Thermo Scientific). The molar extinction coefficients at \(280 \mathrm{nm}\) for the proteins were calculated from the basis of amino acid composition51. The concentrated proteins were frozen in liquid nitrogen and stored at \(- 80^{\circ} \mathrm{C}\) before experiments. + +## Sample preparation + +All proteins were thawed immediately before tube formation experiments on ice. Each sample was prepared in a \(1.5 \mathrm{mL}\) microtube using an appropriate buffer to adjust the concentration described in the manuscript and the volume to \(200 \mu \mathrm{L}\) at \(25 \pm 1^{\circ} \mathrm{C}\) . Except for the NaCl concentration- dependent experiments, NaCl (+) protein stock solution and buffer were used. For the NaCl concentration- dependent experiments, \(50 \mathrm{mM}\) Tris- HCl (pH 8.0), \(1 \mathrm{M} \mathrm{NaCl}\) , and \(0.5 \mathrm{mM}\) EDTA were used in addition to NaCl (- ) protein stock solution and buffer. Incubation of the samples was carried out using a ThermoMixer C (Eppendorf) or a MATRIX Orbital Delta Plus (IKA) with shaking of \(300 \mathrm{rpm}\) at the temperature described in the manuscript. For the disassembly and reassembly experiments, buffer substitution procedures were conducted using NaCl (- ) and NaCl (+) buffer, respectively, with Microcon 50 centrifugal filter units (Merck) according to the manufacturer's protocol four times at each step. + +## Negative-stain transmission electron microscopy (nSTEM) + +A naked G600TT copper grid (Nisshin EM) was carbon- coated using a VE- 2030 (VACUUM DEVICE). The grid was glow- discharged using a PIB- 10 (VACUUM DEVICE). Then, a \(5 - \mu \mathrm{L}\) aliquot of the sample solution was placed on the grid for \(1 \mathrm{min}\) , and the remaining solution was removed with filter paper (No. 2, ADVANTEC) followed by rinsing thrice with a \(5 - \mu \mathrm{L}\) aliquot of Milli- Q water. After blotting off the water with filter paper, the sample was stained briefly with a \(3 - \mu \mathrm{L}\) aliquot of \(2\%\) (w/v) uranyl acetate solution three times. The remaining solution was removed with filter paper and the grid was dried on the bench- top. TEM observation was performed using a transmission electron microscope HT- 7700 (Hitachi) with an acceleration voltage of \(80 \mathrm{kV}\) . The images were recorded using HT- 7700 control software (Hitachi). + +## Tube length analysis + +Hundreds of discriminable tubes were picked up manually on 5k- magnification TEM images. The tube lengths were calculated as half of the perimeter analysed with ImageJ (Fiji)52. The plots were drawn by selecting 150 tubes from the longer lengths using Igor Pro 9 (WaveMetrics). For the disassembly and reassembly analysis, Welch's t- test was carried out. + +<--- Page Split ---> + +## Circular dichroism (CD) spectrum measurements + +All proteins were thawed immediately before CD measurements on ice. Each sample was prepared in a 1.5- mL microtube using \(\mathrm{NaCl(+)}\) buffer to adjust the concentration to \(2.5\mu \mathrm{M}\) and the volume to \(200\mu \mathrm{L}\) at 25 \(\pm 1^{\circ}\mathrm{C}\) . Far- UV CD spectra were obtained at a wavelength of \(200–250\mathrm{nm}\) using a J- 1100 spectropolarimeter (JASCO) with a quartz cell with a light path of \(1\mathrm{mm}\) . Thermal denaturation was performed at a temperature change rate of \(1^{\circ}\mathrm{C / min}\) . The CD spectral data were collected using Spectra Manager (version 2.5, JASCO). All CD data were expressed as mean residue ellipticity. The \(T_{\mathrm{m}}\) of each protein was calculated from the thermal denaturation curve at a wavelength of \(222\mathrm{nm}\) by sigmoid fitting using Igor Pro 9 (WaveMetrics). + +## Cryo-EM structural analysis + +All proteins were thawed immediately before tube formation on ice. Each sample was prepared in a 1.5- mL microtube using \(\mathrm{NaCl(+)}\) buffer to adjust the concentration to \(12.5\mu \mathrm{M}\) and the volume to \(200\mu \mathrm{L}\) at 25 \(\pm 1^{\circ}\mathrm{C}\) . Incubation of the samples was carried out as described above for \(24\mathrm{h}\) at \(40^{\circ}\mathrm{C}\) for the PnuE tube and \(30^{\circ}\mathrm{C}\) for the PnuE D- loop tube, and then the samples were provided for grid preparation. For unwinding the helical structures of the PnuE D- loop tube, additional incubation was carried out for \(1\mathrm{h}\) on ice immediately before grid preparation. + +The PnuE D- loop tube sample prepared at \(25\pm 1^{\circ}\mathrm{C}\) was used at the original concentration. In contrast, the PnuE tube and the PnuE D- loop tube preincubated on ice were diluted to one- third and one- sixth of their original concentrations, respectively. Quantifoil R1.2/1.3 Cu 300 grids coated with a holey carbon film (Quantifoil) were treated for hydrophilisation using a JEC- 3000FC Auto Fine Coater (JEOL) at \(20\mathrm{Pa}\) and \(10\mathrm{mA}\) for \(30\mathrm{s}\) . Subsequently, \(2.5\mathrm{- }\mu \mathrm{L}\) aliquots of the respective diluted samples were applied to the prepared grids. After blotting off excess solution, the grids were rapidly immersed in liquid ethane for vitrification using a Vitrobot Mark IV (Thermo Fisher Scientific). Vitrobot was set at \(4^{\circ}\mathrm{C}\) and \(100\%\) humidity for PnuE and PnuE D- loop samples preincubated on ice, and \(25^{\circ}\mathrm{C}\) and \(100\%\) humidity for PnuE D- loop sample prepared at \(25\pm 1^{\circ}\mathrm{C}\) . + +Sample screening and data acquisition were performed using a Glacios cryo- transmission electron microscope (Thermo Fisher Scientific) operated at an accelerated voltage of \(200\mathrm{kV}\) , equipped with a Falcon4EC camera, at the Institute of Life and Medical Sciences, Kyoto University. Images were automatically acquired using the EPU software as movies with nominal magnifications and corresponding calibrated pixel sizes of \(120,000\mathrm{x}\) (1.22 A/pixel) for the PnuE sample, and \(150,000\mathrm{x}\) (0.925 A/pixel) for PnuE D- loop samples. + +## Cryo-EM image processing + +<--- Page Split ---> + +Image analysis was conducted using similar workflows for each dataset of the three samples with the software package RELION 5.0beta53,54. + +For the PuuE tube sample, 4,346 movies were subjected to motion- correction using RELION's algorithm, and the contrast transfer function (CTF) was estimated using CTFFIND45. Tube coordinates were manually registered, and 709,722 segments were extracted with 3x binning into 260×260- pixel boxes (approximately 950x950 Å) with an inter- box spacing of 80 Å. The extracted segments were subjected to two rounds of 2D classification, and the resulting class averages were visually inspected to categorise the segments based on tube diameters. In parallel, an additional round of 2D classification with 10 classes was performed to assess the structural diversity of the tubes roughly. Each subset of segments, categorised by diameter, was then re- extracted and subjected to further 2D classifications to remove junk images. 3D classification with symmetry search was performed on each subset without imposing symmetry (C1). Finally, 3D refinement was carried out for the subsets with the three smallest diameters, applying \(C_4\) , \(C_5\) , and \(C_6\) symmetries, respectively. The subsets with larger diameters exhibited significant heterogeneity and did not yield reliable 3D reconstructions. + +For the PuuE D- loop tube sample prepared at \(25 \pm 1^{\circ}\mathrm{C}\) , 4,871 movies were motion- corrected and CTF- estimated using RELION and CTFFIND4, respectively. A total of 126,987 segments were extracted with 5x binning into 320×320- pixel or 640×640- pixel segmented boxes (1480x1480 or 2960x2960 Å) with an inter- box spacing of 60 Å. The extracted segments were subjected to six rounds of 2D classification, yielding class averages displaying single, double, and triple helical tube architectures. + +For the PuuE D- loop tube sample, preincubated on ice to unwind the helical structures, 4,346 movies were subjected to motion correction and CTF estimation. A total of 709,722 segments were extracted with 3x binning into 360×360- pixel boxes (approximately 1000x1000 Å) with an inter- box spacing of 80 Å. The extracted segments were subjected to two rounds of 2D classification, and the resulting class averages were visually inspected to categorise the segments based on tube diameters. In parallel, two rounds of 2D classification were performed to assess the structural diversity of the tubes. Each subset of segments, categorised by diameter, was re- extracted and subjected to further 2D classifications to remove junk images. 3D classification with symmetry search was performed on each subset without imposing symmetry (C1). During the 3D classification of the initially selected C5- tube subset, C6 tubes were found to be present and were subsequently combined with the C6- tube subset from the 2D classification. Finally, 3D refinement was carried out for the subsets with the four smallest diameters, applying \(C_3\) , \(C_4\) , \(C_5\) , and \(C_6\) symmetries, respectively. As observed in the PuuE dataset, the subsets with larger diameters displayed considerable heterogeneity and failed to yield reliable 3D reconstructions. Detailed image processing workflows are depicted in Extended Data Figures. 6, 8, and 9. + +<--- Page Split ---> + +## Fluorescent labelling + +Fluorescent labellingTube formation was conducted as described above under the optimised condition described in the manuscript. Labelling reaction was achieved by adding Alexa Fluor 488 succinimidyl ester dissolved in dimethyl sulfoxide (DMSO) to the tube solution at a final concentration of \(0.7 \mathrm{mM}\) . The reaction was then incubated at \(25 \pm 1^{\circ} \mathrm{C}\) for \(1 \mathrm{~h}\) with gentle shaking under shading. The excess dye was removed using NaCl (+) buffer with Microcon 300 centrifugal filter units (Merck) according to the manufacturer's protocol four times. The labelled tubes were then stored under shading at \(25 \pm 1^{\circ} \mathrm{C}\) until further experiments. + +## Fluorescence microscopy + +Fluorescence microscopyAn observation chamber was assembled by placing two double- sided tapes (thickness \(\sim 100 \mu \mathrm{m}\) ) onto a silicone- coated coverslip \((24 \times 36 \mathrm{~mm}^2\) , thickness No. 1; Matsunami) with another coverslip \((18 \times 18 \mathrm{~mm}^2\) , thickness No. 1; Matsunami) on top. To passivate the surface of the coverslips against nonspecific adhesion of protein, the chamber was filled with \(10 \mathrm{mg} \mathrm{mL}^{- 1}\) of Pluronic F- 127 (Sigma- Aldrich) dissolved in distilled water for more than 10 minutes at \(25^{\circ} \mathrm{C}\) . After washing out Pluronic F- 127 solution with 5 chamber volumes of \(\mathrm{NaCl}\) (+) buffer, the chamber was filled with TIRFM buffer ( \(50 \mathrm{mM}\) Tris- HCl pH 8.0, \(100 \mathrm{mM}\) NaCl, \(0.5 \mathrm{mM}\) EDTA, \(0.2\%\) (w/v) methylcellulose (1500 cP, Wako), \(1 \mathrm{mM}\) DTT, \(2 \mathrm{mM}\) Trolox). Next, the Alexa488- tube solution was diluted to \(1 / 10\) in \(\mathrm{NaCl}\) (+) solution, and further diluted to \(1 / 10\) (final \(1 / 100\) dilution) in TIRFM buffer. Then, the diluted tube solution was perfused into the observation chamber and sealed by Valap to prevent flow. The fluorescence images of tube structures were acquired at intervals of \(40 \mathrm{ms}\) with an inverted microscope (IX- 71, Olympus) equipped with a \(60 \times\) objective lens (PlanApo NA 1.45 oil, Olympus), an EMCCD camera (iXon3, Andor Technology) and an excitation laser with the wavelength at \(488 \mathrm{nm}\) (OBIS 488- 60- LS, COHERENT). All observations were performed at \(25 \pm 1^{\circ} \mathrm{C}\) . + +## Mechanical property analysis + +Mechanical property analysisThe persistence length of the tube structures was estimated as follows. First, the fluorescence images were converted to 8- bit images using the ImageJ function. Then, the skeletons of the tube structures were tracked using ImageJ plugin, JFilament6. Distances between adjacent nodes composing the skeletons were set as 1 pixel. Next, the contour length \((L)\) and end- to- end distance \((R)\) of the tube structures at each frame were calculated using the coordinates of the nodes with custom- written Python scripts. The mean square of \(R\) \((\langle R^2 \rangle)\) of each tube structure was calculated by averaging \(R^2\) along 100–200 frames. \(\langle R^2 \rangle\) and \(L\) follow the following equation when the shape fluctuation is driven thermally36. \(\langle R^2 \rangle = 4L_{\mathrm{p}}^2 [2\exp (- L / 2L_{\mathrm{p}}) - 2 + L / L_{\mathrm{p}}]\) , where \(L_{\mathrm{p}}\) is the persistence length of the tube structure. The \(L_{\mathrm{p}}\) values of the tube structures were estimated by fitting this equation to the experimental data using 'curve_fit' function of Python package + +by fitting this equation to the experimental data using 'curve_fit' function of Python package + +<--- Page Split ---> + +'scipy.optimize'. \(L_{\mathrm{p}}\) of actin filaments was estimated by the same analysis. Totally, 55 tube structures and 37 actin filaments were analysed. + +## Molecular modelling + +All predicted protein structures were generated by AlphaFold 2.2 or 2.3 multimer- mode (DeepMind) \(^{15,16}\) . Cartoon models of the proteins were drawn using PyMOL 2.5 (Schrödinger) \(^{57}\) and UCSF ChimeraX (UCSF RBVI and NIH) \(^{58}\) . Isoelectric points of PuuE- M and PuuE- p were calculated from the basis of amino acid composition \(^{59}\) . Surface hydrophobicity of PuuE was drawn using Color_h script (PyMOL Wiki) based on the hydrophobicity scale \(^{60}\) . + +## Data availability + +The cryo- EM structures have been deposited in the Electron Microscopy Data Bank (EMDB) with the following accession codes: EMD- 60617, EMD- 60618, and EMD- 60619 for the PuuE tubes with C4, C5, and C6 symmetry, respectively; and EMD- 60620, EMD- 60621, EMD- 60622, and EMD- 60623 for the PuuE D- loop tubes with C3, C4, C5, and C6 symmetry, respectively. + +## Acknowledgements + +This work was supported by JSPS KAKENHI (grant nos. 19H02832, 19K22253, and 21H05116 to Y. Suzuki; 21H05117 to Y. Suzuki and Y. Sugita; and 20K22628, 21J00530, and 22KJ1644 to M.N.), JST PRESTO (grant no. JPMJPR22A7 to Y. Suzuki and JPMJPR20ED to M.M.), Takeda Science Foundation to Y. Suzuki, Chubei Itoh Foundation to Y. Suzuki, and The Hakubi Center for Advanced Research to Y. Sugita, M.M., and Y. Suzuki. + +## Author contributions + +Y. Suzuki directed the project. Y. Suzuki and M.N. conceived and designed the overall study. M.N. conducted experiment works with contributions from Y. Suzuki, Y. Sugita, and Y.Y.. Y. Sugita and M.N. performed cryo- EM data collection and analysed data. Y.Y. conducted TIRFM experiments, and Y.Y. and M.M. analysed mechanical properties. M.N. and Y. Suzuki wrote the manuscript with contributions from Y. Sugita, Y.Y., and M.M. + +## Competing interest declaration + +Y. Suzuki and M.N. are inventors of a provisional patent submitted by Kyoto University for ‘Protein Assembly Structure’. + +<--- Page Split ---> + +# Additional information + +Supplementary Information is available for this paper. + +Correspondence and requests for materials should be addressed to Yuta Suzuki. + +Tel: +81- 75- 753- 9766 + +E- mail address: suzuki.yuta.2m@kyoto- u.ac.jp + +<--- Page Split ---> +![](images/Extended_Data_Figure_2.jpg) + + +Extended Data Fig. 1. | AF2 prediction of PuuE- M and PuuE- p. a, Fifty prediction models overlapped for PuuE- M (top) and PuuE- p (bottom). Peptide parts, M3L2 and p66α, are indicated with a red box. b, Predicted local distance difference test plots for the most reliable prediction models for PuuE- M (top) and PuuE- p (bottom). Arrows indicate the N- terminal region of M3L2 and p66α. For these regions, PuuE- M has a lower predictive reliability than does PuuE- p, suggesting that the structure may be more flexible. c, The most reliable prediction model for PuuE- p. The region from the C- terminus of PuuE to the N- terminus of p66α (i.e. \(^{313}\mathrm{HPYTPE}^{318}\) ) is depicted by a stick model. The two Pro residues highlighted in red are thought to be responsible for the rigidity of the PuuE- p structure. Because of the rigidity of PuuE- p, the final product of the mixture was predicted to be a tube, as shown in Fig. 1d. + +<--- Page Split ---> +![](images/Extended_Data_Figure_3.jpg) + +
Extended Data Fig. 2. | nsTEM characterisation of PuuE-M, PuuE-p, and the mixture for PuuE-M and PuuE-p. a, 12.5 \(\mu \mathrm{M}\) PuuE-M or b, 12.5 \(\mu \mathrm{M}\) PuuE-p in NaCl (+) buffer was incubated at \(40^{\circ}\mathrm{C}\) for 24 h. Scale bars, 200 nm (white), 50 nm (black). c, Dependency of PuuE tube assemblies on protein concentration. 250 nM (top), 2.5 \(\mu \mathrm{M}\) (middle), and 12.5 \(\mu \mathrm{M}\) (bottom) of PuuE-M and PuuE-p each in NaCl (+) buffer was incubated at \(40^{\circ}\mathrm{C}\) for 24 h and imaged by nsTEM. The tube structure observed in the nsTEM images was flexible as it was curved and collapsed. Scale bars, 1 \(\mu \mathrm{m}\) (white), 50 nm (black).
+ +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + +
Extended Data Fig. 3. | Time dependence of PuuE tube assemblies and their stability over time. a, 12.5 \(\mu \mathrm{M}\) of PuuE-M and PuuE-p each in \(\mathrm{NaCl}(+)\) buffer was incubated at \(40^{\circ}\mathrm{C}\) for indicated time points and imaged via nsTEM. b, After \(24\mathrm{h}\) of tube formation, the sample was kept at \(25\pm 1^{\circ}\mathrm{C}\) for the indicated time and imaged using nsTEM. Tube structures remained unchanged after 2 weeks and even after 1 month, suggesting stability. Scale bars, \(1\mu \mathrm{m}\) .
+ +<--- Page Split ---> +![](images/Figure_unknown_1.jpg) + + +Extended Data Fig. 4. | Temperature dependence of PuuE tube assemblies and determination of \(T_{\mathrm{m}}\) for PuuE- M and PuuE- p. a, 12.5 \(\mu \mathrm{M}\) of PuuE- M and PuuE- p each in NaCl (+), buffer was incubated at the indicated temperature for 24 h and imaged via nsTEM. Scale bars, 1 \(\mu \mathrm{m}\) . b, c, \(T_{\mathrm{m}}\) measurements using CD for PuuE- M (b) and PuuE- p (c). 2.5 \(\mu \mathrm{M}\) of PuuE- M or PuuE- p in NaCl (+), buffer was incubated from 25 to 55 °C with temperature change of 1 °C/min. Left panel, overall CD spectra; right panel, thermal denaturation profiles. + +<--- Page Split ---> +![](images/Figure_unknown_2.jpg) + + +Extended Data Fig. 5. | Salt concentration dependence and reversibility of PuuE tube assemblies. a, Left, \(12.5 \mu \mathrm{M}\) of PuuE- M and PuuE- p each in \(\mathrm{NaCl}(+)\) buffer was incubated at \(40^{\circ} \mathrm{C}\) for \(24 \mathrm{~h}\) with indicated \(\mathrm{NaCl}\) concentration and imaged via nsTEM. Right: diagram of salt concentration effects described in the main text. b, Additional images in Fig. 2d prove the reversibility of tubular assemblies. These images were used for statistical analysis of tube length, as shown in Fig. 2e. Scale bars, \(1 \mu \mathrm{m}\) (white). + +<--- Page Split ---> +![](images/Figure_unknown_3.jpg) + + +![](images/Figure_unknown_4.jpg) + +
C4 tube: 2D classifications (2 rounds): 51,590 segments
+ +![](images/Figure_unknown_5.jpg) + +
C5 tube: 2D classifications (2 rounds): 44,868 segments
+ +![PLACEHOLDER_28_3] + +
3D Refinement \((C_4)\) : 12,052 segments Post-processing
+ +![PLACEHOLDER_28_4] + +
C6 tube: 2D classification (1 round): 117,636 segments
+ +![PLACEHOLDER_28_5] + +
3D Refinement \((C_5)\) : 12,572 segments Post-processing
+ +![PLACEHOLDER_28_6] + +
3D classification \((C_1)\) : 39,841 segments
+ +Extended Data Fig. 6. | Cryo- EM image processing workflow of the PuuE tubes. Flowchart illustrating the image processing steps. Scale bars: \(100 \mathrm{nm}\) (white), \(250 \mathrm{\AA}\) (black). Gold- standard Fourier shell correlation (FSC) curve of the independently refined half maps indicating a global resolution at the 0.143 threshold. + +<--- Page Split ---> +![PLACEHOLDER_29_0] + + +Extended Data Fig. 7. D- loop grafting to emulate actin filaments. a, Surface hydrophobicity calculation for PuuE. D- loop was grafted into the 'back' side of PuuE owing to the hydrophobic nature of a prominent indentation. b, \(T_{\mathrm{m}}\) measurement of PuuE(D- loop)- M via CD. \(2.5 \mu \mathrm{M}\) of PuuE(D- loop)- M in NaCl (+) buffer was incubated from 25 to \(55^{\circ}\mathrm{C}\) with temperature change of \(1^{\circ}\mathrm{C / min}\) . Top: overall CD spectra; bottom: thermal denaturation profiles, respectively. c, \(12.5 \mu \mathrm{M}\) of PuuE(D- loop)- M and PuuE- p in NaCl (+) buffer were incubated at \(30^{\circ}\mathrm{C}\) for \(24 \mathrm{h}\) and imaged using nsTEM. A novel helical pattern of two or three intertwined tubes was clearly observed. Flexibility was also noted when curved structures were observed. Scale bars, \(1 \mu \mathrm{m}\) (white), \(100 \mathrm{nm}\) (black). d, Additional images for reversibility of tube formation depends on temperature changes in Fig. 4c. For this observation, we focused on the presence of tube structures with helical conformations. After \(1 \mathrm{h}\) at \(0^{\circ}\mathrm{C}\) , there were no such structures observed via nsTEM. + +<--- Page Split ---> +![PLACEHOLDER_30_0] + + +![PLACEHOLDER_30_1] + + +2D classifications (6 rounds) with 2960x2960- Å segments: 9,989 segments + +![PLACEHOLDER_30_2] + + +718 + +719 Extended Data Fig. 8. | Cryo- EM image processing workflow of PuuE D- loop tubes prepared at \(25 \pm 720\) 1 °C. Flowchart illustrating the image processing steps. Scale bars: 100 nm (white), 500 Å (black). + +<--- Page Split ---> +![PLACEHOLDER_31_0] + + +![PLACEHOLDER_31_1] + + +Extended Data Fig. 9. | Cryo- EM image processing workflow of PuuE D- loop tubes preincubated on ice to unwind the helical structures. Flowchart illustrating the image processing steps. Scale bars: 100 nm (white), 250 Å (black). Gold- standard Fourier shell correlation (FSC) curve of the independently refined half maps indicating a global resolution at the 0.143 threshold. + +<--- Page Split ---> + +
#1 C4 tube (EMD-60617)#2 C5 tube (EMD-60618)#3 C6 tube (EMD-60619)
Data collection and processing
Magnification120,000
Voltage (kV)200
Electron exposure (e-/Ų)40
Defocus range (μm)-0.8 to -1.6
Pixel size (Å)1.22
Symmetry imposedC4 helicalC5 helicalC6 helical
Initial helical segments (no.)709,722709,722709,722
Final helical segments (no.)12,05212,57239,841
Map resolution (Å)11.320.617.5
FSC threshold0.1430.1430.143
+ +727 + +2D analysis of the PuuE D-loop tube prepared at 25 ± 1 °C + +
#1 Tubes
Data collection and processing
Magnification150,000
Voltage (kV)200
Electron exposure (e-/Ų)40
Defocus range (μm)-0.8 to -1.6
Pixel size (Å)0.925
Symmetry imposedNo
Initial helical segments (no.)126,987
Final helical segments (no.)104,748
+ +729 + +730 + +PuuE D-loop tube preincubated on ice to unwind the helical structures + +
#1 C3 tube (EMD-60620)#2 C4 tube (EMD-60621)#3 C5 tube (EMD-60622)#4 C6 tube (EMD-60623)
Data collection and processing
Magnification150,000
Voltage (kV)200
Electron exposure (e-/Ų)40
Defocus range (μm)-0.8 to -1.6
Pixel size (Å)0.925
Symmetry imposedC3 helicalC4 helicalC5 helicalC6 helical
Initial helical segments (no.)397,778397,778397,778397,778
Final helical segments (no.)2,6753,2622,9981,291
Map resolution (Å)9.714.618.226.0
FSC threshold0.1430.1430.1430.143
+ +734 + +Extended Data Table 1. | Cryo-EM data collection, refinement, and validation statistics + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementaryInformation.pdf Supplementarymovie1. mov Supplementarymovie2. mov Supplementarymovie3. mov Supplementarymovie4. mov + +<--- Page Split ---> diff --git a/preprint/preprint__070706503cff2b0c5895a72491f41d6927ffe121f452603fa7f93aef50b5eb1a/preprint__070706503cff2b0c5895a72491f41d6927ffe121f452603fa7f93aef50b5eb1a_det.mmd b/preprint/preprint__070706503cff2b0c5895a72491f41d6927ffe121f452603fa7f93aef50b5eb1a/preprint__070706503cff2b0c5895a72491f41d6927ffe121f452603fa7f93aef50b5eb1a_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..ba567cec1662f9f480ca63411d414c1be3395101 --- /dev/null +++ b/preprint/preprint__070706503cff2b0c5895a72491f41d6927ffe121f452603fa7f93aef50b5eb1a/preprint__070706503cff2b0c5895a72491f41d6927ffe121f452603fa7f93aef50b5eb1a_det.mmd @@ -0,0 +1,459 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 760, 175]]<|/det|> +# Protein design of two-component tubular assemblies like cytoskeletons + +<|ref|>text<|/ref|><|det|>[[44, 195, 368, 240]]<|/det|> +Yuta Suzuki suzuki.yuta.2m@kyoto- u.ac.jp + +<|ref|>text<|/ref|><|det|>[[44, 268, 907, 479]]<|/det|> +Kyoto University https://orcid.org/0000- 0002- 4863- 4585 Masahiro Noji Kyoto University Yukihiko Sugita Institute for Life and Medical Sciences, Kyoto University https://orcid.org/0000- 0001- 6861- 4840 Yosuke Yamazaki RIKEN Makito Miyazaki RIKEN https://orcid.org/0000- 0002- 4603- 851X + +<|ref|>sub_title<|/ref|><|det|>[[44, 515, 103, 532]]<|/det|> +## Article + +<|ref|>title<|/ref|><|det|>[[44, 553, 135, 570]]<|/det|> +# Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 590, 328, 608]]<|/det|> +Posted Date: October 21st, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 628, 473, 647]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4976952/v1 + +<|ref|>text<|/ref|><|det|>[[42, 666, 914, 708]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 727, 535, 747]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 783, 914, 825]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on July 22nd, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 62076- 3. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[67, 90, 660, 110]]<|/det|> +# Protein design of two-component tubular assemblies like cytoskeletons + +<|ref|>text<|/ref|><|det|>[[66, 115, 860, 137]]<|/det|> +2 + +<|ref|>text<|/ref|><|det|>[[66, 140, 857, 161]]<|/det|> +3 Masahiro Noji \(^{1,2,3}\) , Yukihiko Sugita \(^{4,5,6}\) , Yosuke Yamazaki \(^{7,8}\) , Makito Miyazaki \(^{6,7,8,9}\) , and Yuta Suzuki \(^{3,6,9}\) .\* + +<|ref|>text<|/ref|><|det|>[[66, 185, 888, 325]]<|/det|> +4 \(^{1}\) Research Fellow of Japan Society for the Promotion of Science, Japan; \(^{2}\) Graduate School of Human and Environmental Studies, Kyoto University, Kyoto, Japan; \(^{3}\) Institute for Integrated Cell- Material Sciences, Kyoto University, Kyoto, Japan; \(^{4}\) Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan; \(^{5}\) Graduate School of Biostudies, Kyoto University, Kyoto, Japan; \(^{6}\) Hakubi Center for Advanced Research, Kyoto University, Kyoto, Japan; \(^{7}\) Graduate School of Science, Kyoto University, Kyoto, Japan; \(^{8}\) RIKEN Center for Biosystems Dynamics Research, Yokohama, Japan; \(^{9}\) PRESTO, JST, Saitama, Japan + +<|ref|>text<|/ref|><|det|>[[66, 352, 888, 732]]<|/det|> +Recent advances in protein design have ushered in an era of constructing intricate higher- order structures \(^{1}\) . Nonetheless, orchestrating the assembly of diverse protein units into cohesive artificial structures akin to biological assembly systems, especially in tubular forms, remains elusive. To this end, here, we introduce the Nature- Inspired Protein Assembly Design (NIPAD), a novel methodology that utilises two distinct protein units to create unique tubular structures under carefully designed conditions. These structures demonstrate dynamic flexibility similar to that of actin filaments, with cryo- electron microscopy revealing diverse morphologies, like microtubules. By mimicking actin filaments, helical conformations were incorporated into tubular assemblies, thereby enriching their structural diversity. Notably, these assemblies can be reversibly disassembled and reassembled in response to environmental stimuli, including changes in salt concentration and temperature, mirroring the dynamic behaviour of natural systems. NIPAD combines rational protein design with biophysical insights, leading to the creation of biomimetic, adaptable, and reversible higher- order assemblies. This approach deepens our understanding of protein assembly design and complex biological structures. Concurrently, it broadens the horizons of synthetic biology and material science, holding significant implications for unravelling life's fundamental processes and pioneering new applications. + +<|ref|>text<|/ref|><|det|>[[66, 760, 888, 900]]<|/det|> +Life phenomena rely on the dynamic and reversible assembly and disassembly of various higher- order protein assemblies. Actin filaments \(^{2,3}\) and microtubules \(^{4,5}\) in the cytoskeleton and the capsid proteins of viruses \(^{6,7}\) are examples of such naturally occurring structures. These are tightly regulated in function and complexity. Synthesising higher- order structures of heterogeneous protein units poses a significant challenge, particularly regarding replicating the diversity and flexibility inherent to natural assemblies. Although recent advances in computational design have enabled the creation of artificial higher- order + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 886, 157]]<|/det|> +protein structures from two protein components8- 10, the design of heterogeneous higher- order protein assemblies with the flexibility and reversible assembly/disassembly characteristics of natural structures, especially tube structures reminiscent of the cytoskeleton, remains a formidable challenge. + +<|ref|>text<|/ref|><|det|>[[115, 185, 886, 325]]<|/det|> +Herein, we introduced Nature- Inspired Protein Assembly Design (NIPAD), a novel methodology that draws inspiration from the principles underlying natural protein complexes. By integrating rational protein design with biophysical insights to optimise assembly conditions, NIPAD recapitulates flexibility and reversible assembly principles. We employed NIPAD to create a novel assembly of two distinct protein units, successfully forming unique two- component tube structures. This development represents a significant step toward replicating the properties of complex natural structures at the molecular level. + +<|ref|>sub_title<|/ref|><|det|>[[115, 355, 292, 371]]<|/det|> +## Results and discussion + +<|ref|>sub_title<|/ref|><|det|>[[116, 378, 293, 395]]<|/det|> +## The concept of NIPAD + +<|ref|>text<|/ref|><|det|>[[112, 400, 886, 781]]<|/det|> +In developing the protein components for NIPAD, we employed rational design principles with hints from natural biological systems, integrating naturally occurring ‘heterolinkers’ with ‘scaffold proteins’ to streamline design. For the heterolinker, we chose the heterodimeric peptide pair ‘MBD3L2 (M3L2)/p66α’ (Fig. 1a). Our choice of M3L2/p66α was influenced by its role in the MBD2- NuRD complex, where the ‘MBD2/p66α’ anti- parallel coiled- coil domain is essential for complex assembly11. Given the moderate denaturation midpoint temperature \((T_{\mathrm{m}})\) of M3L2/p66α \((T_{\mathrm{m}} = 35^{\circ}\mathrm{C})\) compared to MBD2/p66α \((T_{\mathrm{m}} = 65^{\circ}\mathrm{C})^{12}\) , we anticipated that M3L2/p66α would provide a balance between stability and reversible assembly control through temperature modulation. We then sought to identify a scaffold protein that could connect the heterolinker in the simplest manner possible. The positions of connecting sites at the corners of such scaffold proteins facilitate the desired assembly formation13. Therefore, we chose the ‘Pseudomonas fluorescens PuuE allantoinase (PuuE)’, a homotetramer with \(C_4\) symmetry where each C- terminus is located at each vertex of the quaternary structure (Fig. 1b)14. This arrangement enabled straightforward genetic fusion of heterolinkers to the scaffold’s C- termini, leveraging specificity and reversibility of heterolinker interactions to drive assembly formation. This approach simplifies the assembly process and enhances expression and purification efficiency for each protein unit, preventing spontaneous assembly and ensuring the controlled formation of higher- order structures. + +<|ref|>text<|/ref|><|det|>[[115, 809, 886, 900]]<|/det|> +We constructed protein units ‘PuuE- M’ and ‘PuuE- p’ through genetic engineering, fusing M3L2 and p66α to the C- terminus of PuuE, respectively (Fig. 1c). AlphaFold2 (AF2)15,16 modelling suggested a configuration with a relatively flexible orientation of M3L2 in PuuE- M, whereas a highly constrained orientation of p66α in PuuE- p (Extended Data Fig. 1). Owing to the constrained orientation of PuuE- p, an + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 886, 300]]<|/det|> +angular interface was formed between PuuE- M and PuuE- p, and we predicted that assembly of these two units would form a tubular structure (Fig. 1d). Additionally, depending on the number of PuuE- M and PuuE- p units, different tubular structures were expected. Protein expression in Escherichia coli provided both constructs in a soluble form, facilitating their purification. In isolation, neither protein unit exhibits self- assembly (Extended Data Fig. 2a, b). However, when combined under optimised conditions (discussed in the following section), we successfully observed the expected chessboard- patterned tube (PuuE tube) using negative- stain transmission electron microscopy (nsTEM) (Fig. 1e, Extended Data Fig. 2c). Although previous studies have assembled cages \(^{8,9}\) , sheets \(^{10}\) , and three- dimensional (3D) crystals \(^{17}\) using two- component protein systems, this study is unique in that tube structures were successfully created. + +<|ref|>sub_title<|/ref|><|det|>[[115, 330, 447, 348]]<|/det|> +## The condition design of tubular assemblies + +<|ref|>text<|/ref|><|det|>[[113, 352, 886, 468]]<|/det|> +Based on established principles observed in biological systems, including actin filaments \(^{18,19}\) , microtubules \(^{20,21}\) , and amyloid fibrils \(^{22,23}\) , protein concentration, temperature, time, and salinity have a significant influence on assembly formation. Thus, we carefully tailored assembly conditions to exploit the complex interactions between these factors. This approach allowed us to optimise experimental conditions for constructing the desired tubular structures. + +<|ref|>text<|/ref|><|det|>[[112, 496, 886, 682]]<|/det|> +First, we focused on the dependency of PuuE tube assembly on protein concentration (Extended Data Fig. 2c). Mixing PuuE- M and PuuE- p at a concentration of \(250~\mathrm{nM}\) each (considering tetramer equivalence) led to the formation of tubular structures after an incubation period of \(24\mathrm{~h}\) at \(40^{\circ}\mathrm{C}\) , consistent with the dissociation constant \((K_{\mathrm{d}})\) for M3L2/p66α dimer formation, which is approximately \(268~\mathrm{nM}^{12}\) . Increasing protein concentration to \(2.5~\mu \mathrm{M}\) markedly enhanced the quantity and length of formed tubular structures. Elevating the concentration to \(12.5~\mu \mathrm{M}\) for each component significantly increased tube formation efficiency, underscoring the concentration- dependent nature of PuuE- M- and PuuE- p- facilitated tubular assembly. + +<|ref|>text<|/ref|><|det|>[[112, 712, 886, 852]]<|/det|> +Next, PuuE tube formation kinetics were investigated. The incubation of mixtures containing \(12.5~\mu \mathrm{M}\) of each protein at \(40^{\circ}\mathrm{C}\) resulted in the formation of nascent tube structures within \(30~\mathrm{min}\) , evolving into distinguishable tubes spanning several hundred nanometres to \(1\mu \mathrm{m}\) in length within \(1 - 2\mathrm{~h}\) (Fig. 2a, b, Extended Data Fig. 3a). Over time, these tubes elongated, reaching several micrometres in length after \(24\mathrm{~h}\) and extending up to approximately \(5\mu \mathrm{m}\) after \(48\mathrm{~h}\) . Once formed, the tubes remained structurally stable for at least 1 month at \(25\pm 1^{\circ}\mathrm{C}\) (Extended Data Fig. 3b). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 888, 396]]<|/det|> +We then explored the influence of temperature on PnuE tube formation (Extended Data Fig. 4a). While the melting temperature of the M3L2/p66α dimer is around \(35^{\circ}\mathrm{C}\) , tube assembly was hardly observed at sufficiently lower temperatures of \(20 - 25^{\circ}\mathrm{C}\) , even after \(24\mathrm{h}\) of incubation. Conversely, temperatures near \(T_{\mathrm{m}}\) , specifically between 30 and \(40^{\circ}\mathrm{C}\) , markedly promoted tube formation. Therefore, temperatures below \(T_{\mathrm{m}}\) may excessively enhance the binding force between M3L2 and p66α, causing kinetic entrapment of assemblies. However, temperatures close to \(T_{\mathrm{m}}\) modulate this binding force, allowing the dynamic rearrangement of M3L2/p66α interactions under thermal fluctuations, thus facilitating the assembly of thermodynamically stable, ordered structures. This principle is consistent with general crystallisation theories \(^{24,25}\) and reports on the formation of ordered structures in natural protein assemblies \(^{22,23,26}\) . Importantly, temperatures above \(45^{\circ}\mathrm{C}\) led to thermal denaturation and aggregation of PnuE- M ( \(T_{\mathrm{m}} = 46.2^{\circ}\mathrm{C}\) ) and PnuE- p ( \(T_{\mathrm{m}} = 48.1^{\circ}\mathrm{C}\) ), significantly diminishing tube formation capabilities (Extended Data Fig. 4b, c). This finding implies that the original concept of tube formation with reversible temperature control was not realised. + +<|ref|>sub_title<|/ref|><|det|>[[115, 425, 384, 443]]<|/det|> +## Reversibility of tubular assemblies + +<|ref|>text<|/ref|><|det|>[[112, 448, 886, 754]]<|/det|> +Finally, we examined the effects of salt concentration on PnuE tube assembly. We prepared mixtures with different NaCl concentrations ranging from 0 to \(400\mathrm{mM}\) and incubated them at \(40^{\circ}\mathrm{C}\) for \(24\mathrm{h}\) . Tube formation was clearly observed within the NaCl concentration window of \(50 - 200\mathrm{mM}\) , with no tube formation detected outside this range (Fig. 2c, Extended Data Fig. 5a). Since both PnuE- M ( \(\mathrm{pI} = 6.44\) ) and PnuE- p ( \(\mathrm{pI} = 6.02\) ) were similarly charged under \(\mathrm{pH}8.0\) , tube formation at low salt concentrations was likely inhibited by electrostatic repulsion. Conversely, moderate electrostatic shielding facilitated by \(50 - 200\mathrm{mM}\) NaCl likely provided conducive conditions for tube assembly, whereas higher NaCl concentrations may have induced excessive shielding or aggregation due to salting out, inhibiting tube formation. This observation aligns with known phenomena in protein crystallisation, where electrostatic shielding above a certain threshold can prevent crystal growth \(^{17,27 - 29}\) , although crystals formed by a combination of electrostatic and hydrophobic interactions can remain stable up to approximately \(200\mathrm{mM}\) NaCl \(^{30}\) . The association of M3L2/p66α involves both electrostatic and hydrophobic interactions \(^{12}\) , consistent with the latter scenario. + +<|ref|>text<|/ref|><|det|>[[113, 785, 886, 900]]<|/det|> +The salt- dependent PnuE tube formation and the dynamic nature of PnuE- M/PnuE- p interactions near their \(T_{\mathrm{m}}(35^{\circ}\mathrm{C})\) led us to hypothesise that tubes could undergo reversible disassembly and reassembly in response to changes in NaCl concentration. Confirming our hypothesis, tubes initially formed in \(100\mathrm{mM}\) NaCl solution were significantly shortened when subjected to solvent exchange with \(0\mathrm{mM}\) NaCl buffer (NaCl (- ) buffer) and subsequent incubation at \(40^{\circ}\mathrm{C}\) for \(24\mathrm{h}\) (Fig. 2d, e, Extended Data Fig. 5b). Subsequent + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 886, 277]]<|/det|> +solvent exchange with \(100\mathrm{mMNaCl}\) buffer \((\mathrm{NaCl} + )\) buffer) resulted in notable tube reassembly. This salt- concentration- driven reversibility, although divergent from the initial temperature- controlled reversibility hypothesis, marks a significant advance in artificial protein assembly design, allowing for the biomimetic replication of dynamic structural changes under relatively mild conditions, akin to the behaviour of actin filaments in cellular structures \(^{18,19}\) . Unlike the irreversible aggregation observed in amyloid structures, our assemblies exhibit a reversible and dynamic assembly process akin to the cytoskeleton behaviour, successfully demonstrating the potential for the biomimetic replication of natural cellular dynamics under controlled conditions. + +<|ref|>sub_title<|/ref|><|det|>[[115, 306, 365, 323]]<|/det|> +## Diversity and flexibility of tubes + +<|ref|>text<|/ref|><|det|>[[112, 329, 886, 781]]<|/det|> +Based on these findings, we determined the ideal conditions for PuuE tube formation in \(100\mathrm{mMNaCl}\) at \(40^{\circ}\mathrm{C}\) for \(24\mathrm{h}\) . To further characterise the structural features of tubes formed under these conditions, cryo- electron microscopy (cryo- EM) was employed (Extended Data Fig. 6, Extended Data Table 1). Analysis of 2D class- averaged images revealed a spectrum of tube diameters and symmetries similar to the diversity observed in microtubules \(^{31 - 34}\) (Fig. 3a). From these images, we successfully reconstructed the 3D structures with \(C_4\) , \(C_5\) , and \(C_6\) symmetries within tube structures (Fig. 3b). Insights from the PuuE crystal structure \(^{14}\) , notably its unique central indentation on the back surface (Fig 1b), allowed us to deduce that PuuE units are alternately oriented face- to- back across all 3D models. Additionally, cryo- EM analysis suggested that connection flexibility allowed the contraction of the entire tube structure (Extended Data Fig. 6, Supplementary Movie 1). Although definitive conclusions are difficult owing to its inherent flexibility, the comparison of the cryo- EM 3D reconstruction with the AF2- predicted model of PuuE- p suggests that PuuE- p is less likely to fit inside the tube structure and instead fits better on the outside (Supplementary Movie 2). Furthermore, tubes with larger diameters, presumably having \(C_7\) to \(C_{10}\) symmetries, were identified at low resolution, likely owing to the flexibility of connection sites influencing tube structure. In fact, nsTEM and cryo- EM images frequently showed tubes appearing bent or compressed (Extended Data Fig. 2–6). In contrast to prior strategies by engineering on scaffold proteins itself to create higher- order protein assemblies \(^{8 - 10,13,35}\) , NIPAD integrates a flexible linker with the scaffold protein, resulting in varied structures and arrangements among higher- order assemblies. This variation in tube diameter, akin to that observed in microtubules \(^{31 - 34}\) , is presumably a hallmark of NIPAD. + +<|ref|>text<|/ref|><|det|>[[115, 808, 886, 899]]<|/det|> +To further explore PuuE tube structure flexibility, we labelled tubes with Alexa Fluor 488 succinimidyl ester and observed them in real- time using total internal reflection fluorescence microscopy (TIRFM). Tube structures were constrained in the evanescent field by the depletion effect of methylcellulose contained in the observation buffer and underwent thermally driven two- dimensional random bending (Fig. 3c, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 886, 277]]<|/det|> +Supplementary Fig. 1a, Supplementary Movie 3). Analysis of the fluctuation in shape yielded the persistence length \((L_{\mathrm{p}})\) of \(19.7 \mu \mathrm{m}\) (Fig. 3d, Supplementary Fig. 1b). \(L_{\mathrm{p}}\) is the mean length over which a semiflexible polymer remains straight, characterising polymer stiffness36. The \(L_{\mathrm{p}}\) value of the tube structures is nearly equal to that of actin filaments measured in this study, \(12.5 \mu \mathrm{m}\) (Fig. 3d), and previously reported values of \(9 - 20 \mu \mathrm{m}^{37}\) . Microtubules have much longer persistence lengths \((0.1 - 10 \mathrm{mm})^{38 - 40}\) . Conversely, intermediate filaments, another cytoskeletal fibre structure, typically have shorter persistence lengths \((< 1 \mu \mathrm{m})^{41}\) . Therefore, the tube structure is as flexible as actin filaments, more flexible than microtubules, and stiffer than intermediate filaments. + +<|ref|>sub_title<|/ref|><|det|>[[116, 306, 338, 323]]<|/det|> +## Emulation of actin filaments + +<|ref|>text<|/ref|><|det|>[[111, 328, 888, 732]]<|/det|> +Finally, we sought to modify the morphology of PuuE tube assemblies. Specifically, we hypothesised that grafting the D- loop of actin onto PuuE- M would produce tubes with a helical conformation reminiscent of actin filaments. The D- loop plays an important role in helical actin filament formation via hydrophobic pockets42- 45. The hydrophobic nature of a prominent indentation on the 'back' side of PuuE (Extended Data Fig. 7a) guided our hypothesis. The loop structure on the back side of PuuE- M was chosen as the grafting site for the D- loop, and the 'PuuE(D- loop)- M' fusion construct was constructed (Fig. 4a). When PuuE(D- loop)- M was expressed in \(E\) . coli, it was found in the soluble fraction and was purified as PuuE- M. Since PuuE(D- loop)- M has a lower thermal stability \((T_{\mathrm{m}} = 35.6^{\circ}\mathrm{C})\) than PuuE- M \((T_{\mathrm{m}} = 46.2^{\circ}\mathrm{C}\) , Extended Data Fig. 4b, 7b), we performed sample incubation at a lower temperature \((30^{\circ}\mathrm{C})\) . Although PuuE(D- loop)- M alone did not assemble, its combination with PuuE- p replicated the PuuE tube and introduced novel helical patterns, with two or three tubes intertwined (PuuE D- loop tube), as verified via nsTEM (Fig. 4b, Extended Data Fig. 7c). The emergence of helical formations, absent in the PuuE- M and PuuE- p mixtures, clearly stems from D- loop integration. While the D- loop likely plays a crucial role in the helical formation of actin filaments42- 45, its complete mechanism remains unclear. Our study, by successfully grafting the D- loop to replicate actin- like helical structures, offers a novel perspective on its significance. This approach confirms the critical role of the D- loop in helical conformations and opens new avenues for understanding the intricate design principles of actin filaments. + +<|ref|>text<|/ref|><|det|>[[114, 760, 886, 899]]<|/det|> +As mentioned above, the helical conformation of tube structures is thought to arise from hydrophobic interactions, which are inherently sensitive to temperature and weaken at lower temperatures46- 48. This led us to posit that alterations in temperature can serve as reversible switches for disassembly and reassembly. Notably, exposing the samples to \(0^{\circ}\mathrm{C}\) for 1 h suggested a dissociation of the helical conformations and hinted at a possible breakdown of the tubular structures (Fig. 4c, Extended Data Fig. 7d). Remarkably, when these disassembled samples were reintroduced to \(30^{\circ}\mathrm{C}\) for \(24 \mathrm{h}\) , the elongated tubular formations + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 886, 252]]<|/det|> +with helical conformations were restored. By grafting D- loop, the tube structure could form helical conformations and acquired a new temperature- dependent reversibility. This thermal responsiveness parallels the behaviour of microtubules20,21,49, underscoring the ability of the NIPAD approach to mimic the dynamic properties of biomolecular assemblies in artificial protein design to create complex higher- order protein structures. This dual responsiveness (salt and temperature dependence) enhances the biomimetic potential of our design, which is a promising avenue for advanced applications in synthetic biology and materials science. + +<|ref|>text<|/ref|><|det|>[[112, 280, 886, 903]]<|/det|> +To determine the intricate helical configurations, structural analyses were performed using cryo- EM (Fig. 4d, Extended Data Fig. 8, Extended Data Table 1). In addition to the inherent flexibility of the tube structure, the ability of tubes to form helical bundles introduces an additional layer of complexity to the structural analysis. This complexity is underscored by cryo- EM results, which render a detailed analysis of these higher- order structures particularly challenging. However, analysis of 2D class- averaged images of the helical structures revealed double and triple helical tubes, which was consistent with the nsTEM observation (Fig. 4b, d, Extended Data Fig. 7c, 8). Moreover, the tube structures forming these helices seem to show a thinner diameter of approximately \(24~\mathrm{nm}\) , which does not align with any of the original PuuE tubes with diameters starting at \(28.6~\mathrm{nm}\) (Fig. 3b, Extended Data Fig. 6). Therefore, an attempt was made to elucidate the characteristics of the tube structures forming the helical conformations by employing temperature- induced structural disassembly (Fig. 4c). The cryo- EM sample was initially prepared at \(25 \pm 1^{\circ}\mathrm{C}\) to prevent disassembly; however, to unwind the helical structures, the sample was briefly chilled on ice for approximately \(1\mathrm{~h}\) . By observing these chilled samples with cryo- EM, we successfully identified a new tube structure with \(C_3\) symmetry (Fig. 4e, f, Extended Data Table 1) in addition to the previously observed structures (Extended Data Fig. 9). A comparison of this tube structure with \(C_3\) symmetry with the structures forming the helical conformations indicated a match, suggesting that the tubes forming the helical conformation have indeed \(C_3\) symmetry (Fig. 4d bottom). Additionally, the diameter of approximately \(23.6\mathrm{~nm}\) , as determined by cryo- EM 3D reconstruction, corresponds to the tubes forming helical structures, further supporting these findings. Considering its inherent flexibility, it is challenging to reach a definitive conclusion, but further examination of the tube structure with \(C_3\) symmetry suggests that PuuE- p is likely positioned on the outside (Fig. 4g, Supplementary Movie 4), consistent with the original PuuE tube structures (Fig. 3b, Supplementary Movie 2). This arrangement indicates that the D- loop of PuuE(D- loop)- M appears on the exterior of the tubes, which is crucial for forming helical structures not observed in PuuE tubes lacking the D- loop. The \(C_3\) symmetry enhances the exposure of internal PuuE(D- loop)- M on the outer surface compared to structures with \(C_4\) or higher symmetry, enabling hydrophobic interactions between tubes. Therefore, the formation of the \(C_3\) symmetric tube structure likely facilitated + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 886, 228]]<|/det|> +the creation of the helical conformations. Furthermore, the lack of \(C_3\) symmetry in PuuE tubes (Fig. 3a, b) suggests that they are unstable as single tubes without forming helical conformations. The formation of helical conformations may stabilise the structure with \(C_3\) symmetry, as evidenced by its successful identification in tube structures with helical conformations. Additionally, the temperature- induced degradation leading to the rapid collapse of tubes with \(C_3\) symmetry suggests that helical structure stabilisation is essential for maintaining structural integrity under physiological conditions. + +<|ref|>sub_title<|/ref|><|det|>[[115, 260, 213, 276]]<|/det|> +## Conclusions + +<|ref|>text<|/ref|><|det|>[[115, 281, 886, 420]]<|/det|> +We introduce NIPAD, a pioneering approach that intricately weaves together protein unit design and assembly, drawing inspiration from the complexity and adaptability of natural protein assemblies. By employing NIPAD, we created a unique higher- order tubular assembly composed of two protein units, exhibiting the reversible, flexible, and diverse characteristics of natural structures. A noteworthy highlight of our study was the successful induction of helical conformations within these tube assemblies, akin to those observed in actin filaments, achieved through strategic integration of the D- loop into assembly design. + +<|ref|>text<|/ref|><|det|>[[113, 448, 886, 803]]<|/det|> +This advance in protein assembly highlights the complexity of emulating the dynamic behaviour observed in biological systems. The design and assembly of protein structures in vitro, although closely controlled, cannot fully replicate the complex cellular environment. In vivo, myriad factors, including macromolecular crowding, post- translational modifications, and interactions with other cellular components can significantly influence protein behaviour50. Our designed protein assemblies exhibit remarkable biomimicry regarding flexibility, reversibility, and structural diversity, but have yet to be demonstrated and validated in biological systems, where the true complexity of biological interactions is present. Furthermore, our approach, which focuses on the assembly of tubular structures inspired by cytoskeletal elements, including actin filaments and microtubules, does not address the full range of complex protein structures found within biological systems. Natural protein assemblies contain structural and functional diversity, and much remains to be explored. Computational methods have an important role to play in improving the accuracy and breadth of protein assembly design1,8- 10. By utilising computational predictions about protein interactions and assembly outcomes, our design would be refined into more complex and functional biomimetic structures, with applications ranging from novel biomaterials and nanodevices to therapeutic innovations. + +<|ref|>text<|/ref|><|det|>[[115, 832, 886, 900]]<|/det|> +Our research extends the boundaries of protein assembly design and provides new insights into its applications in synthetic biology and life sciences. This research encourages a comprehensive approach that bridges the divide between the biological and materials sciences and suggests that the exploration of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 90, 886, 180]]<|/det|> +nature's complex systems has the potential to transform science and technology. As we continue to explore this intersection of life and materials sciences, we anticipate that future investigations will provide fundamental insights into the natural world, heralding a new era of scientific discoveries and technological breakthroughs. + +<|ref|>title<|/ref|><|det|>[[115, 211, 202, 226]]<|/det|> +# References + +<|ref|>text<|/ref|><|det|>[[111, 230, 884, 910]]<|/det|> +Zhu, J. et al. Protein Assembly by Design. Chem Rev 121, 13701- 13796 (2021). Korn, E. D., Carlier, M. F. & Pantaloni, D. Actin polymerization and ATP hydrolysis. Science 238, 638- 644 (1987). Pollard, T. D. & Cooper, J. A. Actin, a central player in cell shape and movement. Science 326, 1208- 1212 (2009). Desai, A. & Mitchison, T. J. Microtubule polymerization dynamics. Annu Rev Cell Dev Biol 13, 83- 117 (1997). Gudimchuk, N. B. & McIntosh, J. R. Regulation of microtubule dynamics, mechanics and function through the growing tip. Nat Rev Mol Cell Biol 22, 777- 795 (2021). Perlmutter, J. D. & Hagan, M. 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Physical properties of 359 cytoplasmic intermediate filaments. Biochim Biophys Acta 1853, 3053- 3064 (2015). 360 Oda, T., Iwasa, M., Aihara, T., Maeda, Y. & Narita, A. The nature of the globular- to fibrous- actin 361 transition. Nature 457, 441- 445 (2009). 362 Murakami, K. et al. Structural basis for actin assembly, activation of ATP hydrolysis, and delayed 363 phosphate release. Cell 143, 275- 287 (2010). 364 Durer, Z. A. et al. Structural states and dynamics of the D- loop in actin. Biophys J 103, 930- 939 365 (2012). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[56, 90, 883, 585]]<|/det|> +370 45 Das, S. et al. D- loop Dynamics and Near- Atomic- Resolution Cryo- EM Structure of Phalloidin- 371 Bound F- Actin. Structure 28, 586- 593 e583 (2020). 372 46 Pratt, L. R., Chaudhari, M. I. & Rempe, S. B. Statistical Analyses of Hydrophobic Interactions: A 373 Mini- Review. J Phys Chem B 120, 6455- 6460 (2016). 374 47 Koga, K. & Yamamoto, N. Hydrophobicity Varying with Temperature, Pressure, and Salt 375 Concentration. J Phys Chem B 122, 3655- 3665 (2018). 376 48 Sun, Q., Fu, Y. F. & Wang, W. Q. Temperature effects on hydrophobic interactions: Implications 377 for protein unfolding. Chem Phys 559 (2022). 378 49 Echandia, E. L. & Piezzi, R. S. Microtubules in the nerve fibers of the toad Bufo arenarum 379 Hensel. Effect of low temperature on the sciatic nerve. J Cell Biol 39, 491- 497 (1968). 380 50 Nakajima, K. et al. Macromolecular crowding and supersaturation protect hemodialysis patients 381 from the onset of dialysis- related amyloidosis. Nat Commun 13, 5689 (2022). 382 51 Pace, C. N., Vajdos, F., Fee, L., Grimsley, G. & Gray, T. How to measure and predict the molar 383 absorption coefficient of a protein. Protein Sci 4, 2411- 2423 (1995). 384 52 Schindelin, J. et al. Fiji: an open- source platform for biological- image analysis. Nat Methods 9, 385 676- 682 (2012). 386 53 Scheres, S. H. A Bayesian view on cryo- EM structure determination. J Mol Biol 415, 406- 418 387 (2012). 388 54 He, S. & Scheres, S. H. W. Helical reconstruction in RELION. J Struct Biol 198, 163- 176 (2017). 389 55 Rohou, A. & Grigorieff, N. CTFFIND4: Fast and accurate defocus estimation from electron 390 micrographs. J Struct Biol 192, 216- 221 (2015). 391 56 Smith, M. B. et al. Segmentation and tracking of cytoskeletal filaments using open active 392 contours. Cytoskeleton (Hoboken) 67, 693- 705 (2010). 393 57 Schrodinger, LLC. The PyMOL Molecular Graphics System, Version 2.5. 394 58 Meng, E. C. et al. UCSF ChimeraX: Tools for structure building and analysis. Protein Sci 32, 395 e4792 (2023). 396 59 Bjellqvist, B. et al. The focusing positions of polypeptides in immobilized pH gradients can be 397 predicted from their amino acid sequences. Electrophoresis 14, 1023- 1031 (1993). 398 60 Eisenberg, D., Schwarz, E., Komaromy, M. & Wall, R. Analysis of membrane and surface protein 399 sequences with the hydrophobic moment plot. J Mol Biol 179, 125- 142 (1984). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[113, 87, 880, 445]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 453, 886, 688]]<|/det|> +
Fig. 1. Construction of PuuE tube via NIPAD. | a, AF2 prediction of the heterodimeric peptide pair, M3L2 (yellow) and p66α (blue). b, Crystal structure of PuuE (PDB ID: 3CL6). C-terminus positions are circled. Detailed structure, face, side, and back are shown for clarity. c, Schematic diagram of the protein sequence (top) and the AF2-predicted structures of PuuE-M and PuuE-p (bottom). PuuE-M and PuuE-p are coloured yellow and blue to match the respective peptides and overall structure to clear the tube structure (d). The peptide parts, M3L2 and p66α, are highlighted in darker colours. d, Left, predicted model of the tubular assembly consisting of PuuE-M and PuuE-p. Right, brief schematic diagram of how many proteins (n) form a system of tube structures. e, nsTEM images of tubular assemblies constructed from PuuE-M and PuuE-p; 12.5 μM PuuE-M and 12.5 μM PuuE-p in NaCl (+) buffer was incubated at 40 °C for 24 h and imaged via nsTEM. Scale bars, 1 μm (white), 50 nm (black).
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[293, 90, 705, 744]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 766, 886, 905]]<|/det|> +
Fig. 2. Condition optimisation for PuuE tube assembly. | a, b, The kinetics of tubular assembly. nsTEM images of tubular assembly (a) and length analysis (b). c, nsTEM images of tubular assemblies with varying NaCl concentration. d, nsTEM images showing the reversibility of tube structures with changing NaCl concentration. e, Tube length analysis of nsTEM images. For tube length analysis, tubes were picked up and calculated from 5k images at each step; 150 tubes from the longest tube length were used at each data point. \*\*\* p<0.001 (Welch's t-test). Scale bar, 1 μm.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 90, 881, 480]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 485, 886, 863]]<|/det|> +
Fig. 3. Structural characterisation of PuuE tube. | a, 2D class-averaged images of tube structures. The population of each structure was determined from the total pickings of 206,658 tube segments. Scale bar, 500 Å. b 3D reconstructed models of tube structures with \(C_4\) , \(C_5\) , and \(C_6\) symmetries. The fitting results suggest that PuuE-p is less likely to fit into units located inside the tube structure and more likely to fit into units located on the outside. Based on the predictions, the units were colour-coded as shown in Fig. 1c. For visibility, only the molecular model of the PuuE (PDB ID: 3CL6) is overlayed on the 3D reconstructed model. c, Time-lapse images of random bending of the tube structures monitored by TIRFM. Top: snapshots at the starting point (0 sec) and after 4 sec (top). Bottom: enlarged images of tubes in green or orange rectangles in the top images, showing the dynamic flexibility of tube structures between 0 to 4 sec (0.4 sec per image). Scale bar, 5 μm. d, Left, a relationship between contour length (L) and mean square of end-to-end distance () of the tube structures for estimation of the persistence length (Lp). The continued lines represent fitting curves (black for PuuE tube, red for actin filament) to experimental data (black open circle for PuuE tube, red cross mark for actin filament). Right, comparison of persistence length with cytoskeletal elements. PuuE tube (PT, black) and actin filaments (AF, red) were determined in this study (A wider range of plots is shown in Supplementary Fig. 1b). Intermediate filaments (IF, blue) and microtubules (MT, green) are taken from ref. 41 and 38, respectively.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 95, 886, 520]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 528, 886, 585]]<|/det|> +
Fig. 4. Emulation of actin filament by D-loop grafting. | a, Schematic representations of PuuE(D-loop)-M. The position of D-loop graft (red) is indicated by protein sequence (top) and the AF2-predicted structure (bottom). b, nsTEM images of tubes with a helical conformation composed of PuuE(D-loop)-M and PuuE-p. The helical pattern of two (centre) or three (right) intertwined tubes is shown in the high-magnification image. c, nsTEM images showing the reversibility of tube structure with helical conformations by temperature change. d, Representative cryo-EM images (top) and 2D class-averaged images (bottom) of helical tube structures. e, Representative cryo-EM image (top) and 2D class-averaged image of tube structure with \(C_3\) symmetry. Tube structures with other symmetries found in this study are shown in Extended Data Fig. 9. f, 3D reconstructed model of tube structure with \(C_3\) symmetry. For visibility, only the PuuE structure (PDB ID: 3CL6) is overlayed on the 3D reconstructed model. g, Fitting of AF2-predicted model of PuuE-p into the 3D reconstructed model. The fitting results suggest that PuuE-p is unlikely to fit in the units located inside the tube structure; it is better accommodated by the units on the outside. Based on this prediction, the units in f are colour-coded as described in Fig. 1c. 6xHis-TEVcs region of the PuuE-p model is not shown to improve visibility. Scale bars, 1 \(\mu \mathrm{m}\) (white), 100 nm (black), 10 nm (grey).
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 92, 186, 107]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[115, 115, 283, 131]]<|/det|> +## Plasmids and cloning + +<|ref|>text<|/ref|><|det|>[[112, 137, 886, 397]]<|/det|> +Primers for cloning and synthetic genes of N- terminal 6xHis- tagged PuuE- M and PuuE- p were purchased from Eurofins Genomics. PCRs were performed using the PrimeSTAR Max DNA Polymerase (Takara Bio) according to the manufacturer's protocol. Sizes of PCR products were verified using standard agarose gel electrophoresis. The In- Fusion Snap Assembly (Takara Bio) was used as the standard method for cloning according to the manufacturer's protocol, and each amplified gene fragment was ligated between the Ndel and BamHI multicoloring sites of the pET11a expression vector (Novagen). Primers for cloning and a synthetic DNA fragment of D- loop were purchased from Eurofins Genomics. The plasmid encoding N- terminal 6xHis- tagged PuuE- D- loop- M was generated from the PuuE- M plasmid following the same procedures as above. All plasmids were amplified in E. coli strain DH5α (NIPPON GENE) and extracted using the NucleoSpin Plasmid EasyPure (MACHEREY- NAGEL) according to the manufacturer's protocol. DNA sequences were confirmed by a sequencing service (Eurofins Genomics). + +<|ref|>sub_title<|/ref|><|det|>[[115, 426, 390, 443]]<|/det|> +## Protein expression and purification + +<|ref|>text<|/ref|><|det|>[[111, 448, 886, 902]]<|/det|> +The recombinant proteins were expressed using E. coli strain BL21 (DE3) (NIPPON GENE) cotransformed with a pGro7 chaperone plasmid (Takara Bio) and purified as follows. After transformation with plasmid DNA, colonies grown overnight on LB agar plates supplemented with \(100~\mu \mathrm{g / mL}\) ampicillin (Amp) and \(20~\mu \mathrm{g / mL}\) chloramphenicol (Crm) at \(37^{\circ}\mathrm{C}\) were picked to inoculate \(5\mathrm{mL}\) of liquid LB- AmpCrm broth and grown overnight at \(37^{\circ}\mathrm{C}\) and \(200~\mathrm{rpm}\) . Overnight cultures were diluted in \(1\mathrm{L}\) of liquid LB- Amp- Crm broth supplemented with \(0.5\mathrm{mg / mL}\) L- arabinose and grown at \(37^{\circ}\mathrm{C}\) and \(200~\mathrm{rpm}\) until reaching an optical density at \(600\mathrm{nm}\) of 0.6- 0.8. Protein synthesis was induced by adding \(0.1\mathrm{mM}\) isopropyl- \(\beta\) - D- thiogalactopyranoside and the cultures were grown at \(16^{\circ}\mathrm{C}\) for \(16 - 20\mathrm{h}\) . Cells were harvested by centrifugation at \(15,317\mathrm{g}\) and \(4^{\circ}\mathrm{C}\) for \(5\mathrm{min}\) and then frozen at - 80 °C. Cell pellets were thawed at \(25\pm 1^{\circ}\mathrm{C}\) , resuspended in \(60~\mathrm{mL}\) of ice- cold purification buffer ( \(20\mathrm{mM}\) Tris- HCl, \(\mathrm{pH}8.0\) , containing \(300\mathrm{mM}\) NaCl), and lysed using sonication ( \(9\mathrm{min}\) with 1:2 on/off cycles and \(70\%\) amplitude; SFX250, Branson) on ice. Cell debris was cleared by centrifugation at \(15,317\mathrm{g}\) and \(4^{\circ}\mathrm{C}\) for \(30\mathrm{min}\) . The supernatant (i.e., crude protein) was filtered through a \(0.45 - \mu \mathrm{m}\) pore size membrane filter (Merck), applied onto HisTrap FF crude column (Cytiva) pre- equilibrated with the purification buffer and washed with \(5\mathrm{cm}\) volumes of \(2\%\) elution buffer ( \(20\mathrm{mM}\) Tris- HCl, \(\mathrm{pH}8.0\) , containing \(300\mathrm{mM}\) NaCl and \(1\mathrm{M}\) imidazole; \(2\%\) means \(20\mathrm{mM}\) imidazole). 6xHis- tagged proteins were eluted with \(10\mathrm{cm}\) volumes of elution buffer with a linear gradient of \(2 - 40\%\) (i.e., \(20 - 400\mathrm{mM}\) imidazole). The fractions containing the proteins confirmed by means of UV absorption and SDS- PAGE were again collected and dialysed against 50- fold volume of NaCl (+) or NaCl (- ) buffer ( \(50\mathrm{mM}\) Tris- HCl, \(\mathrm{pH}8.0\) , containing \(\pm 100\mathrm{mM}\) NaCl and \(0.5\mathrm{mM}\) EDTA) at \(4^{\circ}\mathrm{C}\) twice. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 89, 886, 227]]<|/det|> +Each of the purified proteins was concentrated by an Amicon Ultra centrifugal filter unit (Merck) with an appropriate molecular weight cutoff followed by filtration through a \(0.45 \mu \mathrm{m}\) pore size membrane filter (Merck). Protein concentration was determined by absorbance measurements at \(280 \mathrm{nm}\) using a NanoDrop OneC spectrophotometer (Thermo Scientific). The molar extinction coefficients at \(280 \mathrm{nm}\) for the proteins were calculated from the basis of amino acid composition51. The concentrated proteins were frozen in liquid nitrogen and stored at \(- 80^{\circ} \mathrm{C}\) before experiments. + +<|ref|>sub_title<|/ref|><|det|>[[115, 259, 272, 276]]<|/det|> +## Sample preparation + +<|ref|>text<|/ref|><|det|>[[113, 281, 886, 515]]<|/det|> +All proteins were thawed immediately before tube formation experiments on ice. Each sample was prepared in a \(1.5 \mathrm{mL}\) microtube using an appropriate buffer to adjust the concentration described in the manuscript and the volume to \(200 \mu \mathrm{L}\) at \(25 \pm 1^{\circ} \mathrm{C}\) . Except for the NaCl concentration- dependent experiments, NaCl (+) protein stock solution and buffer were used. For the NaCl concentration- dependent experiments, \(50 \mathrm{mM}\) Tris- HCl (pH 8.0), \(1 \mathrm{M} \mathrm{NaCl}\) , and \(0.5 \mathrm{mM}\) EDTA were used in addition to NaCl (- ) protein stock solution and buffer. Incubation of the samples was carried out using a ThermoMixer C (Eppendorf) or a MATRIX Orbital Delta Plus (IKA) with shaking of \(300 \mathrm{rpm}\) at the temperature described in the manuscript. For the disassembly and reassembly experiments, buffer substitution procedures were conducted using NaCl (- ) and NaCl (+) buffer, respectively, with Microcon 50 centrifugal filter units (Merck) according to the manufacturer's protocol four times at each step. + +<|ref|>sub_title<|/ref|><|det|>[[115, 547, 564, 564]]<|/det|> +## Negative-stain transmission electron microscopy (nSTEM) + +<|ref|>text<|/ref|><|det|>[[113, 568, 886, 754]]<|/det|> +A naked G600TT copper grid (Nisshin EM) was carbon- coated using a VE- 2030 (VACUUM DEVICE). The grid was glow- discharged using a PIB- 10 (VACUUM DEVICE). Then, a \(5 - \mu \mathrm{L}\) aliquot of the sample solution was placed on the grid for \(1 \mathrm{min}\) , and the remaining solution was removed with filter paper (No. 2, ADVANTEC) followed by rinsing thrice with a \(5 - \mu \mathrm{L}\) aliquot of Milli- Q water. After blotting off the water with filter paper, the sample was stained briefly with a \(3 - \mu \mathrm{L}\) aliquot of \(2\%\) (w/v) uranyl acetate solution three times. The remaining solution was removed with filter paper and the grid was dried on the bench- top. TEM observation was performed using a transmission electron microscope HT- 7700 (Hitachi) with an acceleration voltage of \(80 \mathrm{kV}\) . The images were recorded using HT- 7700 control software (Hitachi). + +<|ref|>sub_title<|/ref|><|det|>[[115, 787, 275, 803]]<|/det|> +## Tube length analysis + +<|ref|>text<|/ref|><|det|>[[115, 809, 886, 898]]<|/det|> +Hundreds of discriminable tubes were picked up manually on 5k- magnification TEM images. The tube lengths were calculated as half of the perimeter analysed with ImageJ (Fiji)52. The plots were drawn by selecting 150 tubes from the longer lengths using Igor Pro 9 (WaveMetrics). For the disassembly and reassembly analysis, Welch's t- test was carried out. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 115, 500, 132]]<|/det|> +## Circular dichroism (CD) spectrum measurements + +<|ref|>text<|/ref|><|det|>[[115, 137, 886, 302]]<|/det|> +All proteins were thawed immediately before CD measurements on ice. Each sample was prepared in a 1.5- mL microtube using \(\mathrm{NaCl(+)}\) buffer to adjust the concentration to \(2.5\mu \mathrm{M}\) and the volume to \(200\mu \mathrm{L}\) at 25 \(\pm 1^{\circ}\mathrm{C}\) . Far- UV CD spectra were obtained at a wavelength of \(200–250\mathrm{nm}\) using a J- 1100 spectropolarimeter (JASCO) with a quartz cell with a light path of \(1\mathrm{mm}\) . Thermal denaturation was performed at a temperature change rate of \(1^{\circ}\mathrm{C / min}\) . The CD spectral data were collected using Spectra Manager (version 2.5, JASCO). All CD data were expressed as mean residue ellipticity. The \(T_{\mathrm{m}}\) of each protein was calculated from the thermal denaturation curve at a wavelength of \(222\mathrm{nm}\) by sigmoid fitting using Igor Pro 9 (WaveMetrics). + +<|ref|>sub_title<|/ref|><|det|>[[115, 330, 341, 347]]<|/det|> +## Cryo-EM structural analysis + +<|ref|>text<|/ref|><|det|>[[115, 353, 886, 491]]<|/det|> +All proteins were thawed immediately before tube formation on ice. Each sample was prepared in a 1.5- mL microtube using \(\mathrm{NaCl(+)}\) buffer to adjust the concentration to \(12.5\mu \mathrm{M}\) and the volume to \(200\mu \mathrm{L}\) at 25 \(\pm 1^{\circ}\mathrm{C}\) . Incubation of the samples was carried out as described above for \(24\mathrm{h}\) at \(40^{\circ}\mathrm{C}\) for the PnuE tube and \(30^{\circ}\mathrm{C}\) for the PnuE D- loop tube, and then the samples were provided for grid preparation. For unwinding the helical structures of the PnuE D- loop tube, additional incubation was carried out for \(1\mathrm{h}\) on ice immediately before grid preparation. + +<|ref|>text<|/ref|><|det|>[[115, 495, 886, 707]]<|/det|> +The PnuE D- loop tube sample prepared at \(25\pm 1^{\circ}\mathrm{C}\) was used at the original concentration. In contrast, the PnuE tube and the PnuE D- loop tube preincubated on ice were diluted to one- third and one- sixth of their original concentrations, respectively. Quantifoil R1.2/1.3 Cu 300 grids coated with a holey carbon film (Quantifoil) were treated for hydrophilisation using a JEC- 3000FC Auto Fine Coater (JEOL) at \(20\mathrm{Pa}\) and \(10\mathrm{mA}\) for \(30\mathrm{s}\) . Subsequently, \(2.5\mathrm{- }\mu \mathrm{L}\) aliquots of the respective diluted samples were applied to the prepared grids. After blotting off excess solution, the grids were rapidly immersed in liquid ethane for vitrification using a Vitrobot Mark IV (Thermo Fisher Scientific). Vitrobot was set at \(4^{\circ}\mathrm{C}\) and \(100\%\) humidity for PnuE and PnuE D- loop samples preincubated on ice, and \(25^{\circ}\mathrm{C}\) and \(100\%\) humidity for PnuE D- loop sample prepared at \(25\pm 1^{\circ}\mathrm{C}\) . + +<|ref|>text<|/ref|><|det|>[[115, 712, 886, 850]]<|/det|> +Sample screening and data acquisition were performed using a Glacios cryo- transmission electron microscope (Thermo Fisher Scientific) operated at an accelerated voltage of \(200\mathrm{kV}\) , equipped with a Falcon4EC camera, at the Institute of Life and Medical Sciences, Kyoto University. Images were automatically acquired using the EPU software as movies with nominal magnifications and corresponding calibrated pixel sizes of \(120,000\mathrm{x}\) (1.22 A/pixel) for the PnuE sample, and \(150,000\mathrm{x}\) (0.925 A/pixel) for PnuE D- loop samples. + +<|ref|>sub_title<|/ref|><|det|>[[115, 880, 330, 897]]<|/det|> +## Cryo-EM image processing + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 90, 884, 132]]<|/det|> +Image analysis was conducted using similar workflows for each dataset of the three samples with the software package RELION 5.0beta53,54. + +<|ref|>text<|/ref|><|det|>[[112, 137, 886, 417]]<|/det|> +For the PuuE tube sample, 4,346 movies were subjected to motion- correction using RELION's algorithm, and the contrast transfer function (CTF) was estimated using CTFFIND45. Tube coordinates were manually registered, and 709,722 segments were extracted with 3x binning into 260×260- pixel boxes (approximately 950x950 Å) with an inter- box spacing of 80 Å. The extracted segments were subjected to two rounds of 2D classification, and the resulting class averages were visually inspected to categorise the segments based on tube diameters. In parallel, an additional round of 2D classification with 10 classes was performed to assess the structural diversity of the tubes roughly. Each subset of segments, categorised by diameter, was then re- extracted and subjected to further 2D classifications to remove junk images. 3D classification with symmetry search was performed on each subset without imposing symmetry (C1). Finally, 3D refinement was carried out for the subsets with the three smallest diameters, applying \(C_4\) , \(C_5\) , and \(C_6\) symmetries, respectively. The subsets with larger diameters exhibited significant heterogeneity and did not yield reliable 3D reconstructions. + +<|ref|>text<|/ref|><|det|>[[112, 422, 886, 539]]<|/det|> +For the PuuE D- loop tube sample prepared at \(25 \pm 1^{\circ}\mathrm{C}\) , 4,871 movies were motion- corrected and CTF- estimated using RELION and CTFFIND4, respectively. A total of 126,987 segments were extracted with 5x binning into 320×320- pixel or 640×640- pixel segmented boxes (1480x1480 or 2960x2960 Å) with an inter- box spacing of 60 Å. The extracted segments were subjected to six rounds of 2D classification, yielding class averages displaying single, double, and triple helical tube architectures. + +<|ref|>text<|/ref|><|det|>[[112, 543, 886, 875]]<|/det|> +For the PuuE D- loop tube sample, preincubated on ice to unwind the helical structures, 4,346 movies were subjected to motion correction and CTF estimation. A total of 709,722 segments were extracted with 3x binning into 360×360- pixel boxes (approximately 1000x1000 Å) with an inter- box spacing of 80 Å. The extracted segments were subjected to two rounds of 2D classification, and the resulting class averages were visually inspected to categorise the segments based on tube diameters. In parallel, two rounds of 2D classification were performed to assess the structural diversity of the tubes. Each subset of segments, categorised by diameter, was re- extracted and subjected to further 2D classifications to remove junk images. 3D classification with symmetry search was performed on each subset without imposing symmetry (C1). During the 3D classification of the initially selected C5- tube subset, C6 tubes were found to be present and were subsequently combined with the C6- tube subset from the 2D classification. Finally, 3D refinement was carried out for the subsets with the four smallest diameters, applying \(C_3\) , \(C_4\) , \(C_5\) , and \(C_6\) symmetries, respectively. As observed in the PuuE dataset, the subsets with larger diameters displayed considerable heterogeneity and failed to yield reliable 3D reconstructions. Detailed image processing workflows are depicted in Extended Data Figures. 6, 8, and 9. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 91, 280, 108]]<|/det|> +## Fluorescent labelling + +<|ref|>text<|/ref|><|det|>[[115, 113, 886, 252]]<|/det|> +Fluorescent labellingTube formation was conducted as described above under the optimised condition described in the manuscript. Labelling reaction was achieved by adding Alexa Fluor 488 succinimidyl ester dissolved in dimethyl sulfoxide (DMSO) to the tube solution at a final concentration of \(0.7 \mathrm{mM}\) . The reaction was then incubated at \(25 \pm 1^{\circ} \mathrm{C}\) for \(1 \mathrm{~h}\) with gentle shaking under shading. The excess dye was removed using NaCl (+) buffer with Microcon 300 centrifugal filter units (Merck) according to the manufacturer's protocol four times. The labelled tubes were then stored under shading at \(25 \pm 1^{\circ} \mathrm{C}\) until further experiments. + +<|ref|>sub_title<|/ref|><|det|>[[115, 282, 311, 299]]<|/det|> +## Fluorescence microscopy + +<|ref|>text<|/ref|><|det|>[[112, 304, 886, 614]]<|/det|> +Fluorescence microscopyAn observation chamber was assembled by placing two double- sided tapes (thickness \(\sim 100 \mu \mathrm{m}\) ) onto a silicone- coated coverslip \((24 \times 36 \mathrm{~mm}^2\) , thickness No. 1; Matsunami) with another coverslip \((18 \times 18 \mathrm{~mm}^2\) , thickness No. 1; Matsunami) on top. To passivate the surface of the coverslips against nonspecific adhesion of protein, the chamber was filled with \(10 \mathrm{mg} \mathrm{mL}^{- 1}\) of Pluronic F- 127 (Sigma- Aldrich) dissolved in distilled water for more than 10 minutes at \(25^{\circ} \mathrm{C}\) . After washing out Pluronic F- 127 solution with 5 chamber volumes of \(\mathrm{NaCl}\) (+) buffer, the chamber was filled with TIRFM buffer ( \(50 \mathrm{mM}\) Tris- HCl pH 8.0, \(100 \mathrm{mM}\) NaCl, \(0.5 \mathrm{mM}\) EDTA, \(0.2\%\) (w/v) methylcellulose (1500 cP, Wako), \(1 \mathrm{mM}\) DTT, \(2 \mathrm{mM}\) Trolox). Next, the Alexa488- tube solution was diluted to \(1 / 10\) in \(\mathrm{NaCl}\) (+) solution, and further diluted to \(1 / 10\) (final \(1 / 100\) dilution) in TIRFM buffer. Then, the diluted tube solution was perfused into the observation chamber and sealed by Valap to prevent flow. The fluorescence images of tube structures were acquired at intervals of \(40 \mathrm{ms}\) with an inverted microscope (IX- 71, Olympus) equipped with a \(60 \times\) objective lens (PlanApo NA 1.45 oil, Olympus), an EMCCD camera (iXon3, Andor Technology) and an excitation laser with the wavelength at \(488 \mathrm{nm}\) (OBIS 488- 60- LS, COHERENT). All observations were performed at \(25 \pm 1^{\circ} \mathrm{C}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 642, 346, 660]]<|/det|> +## Mechanical property analysis + +<|ref|>text<|/ref|><|det|>[[112, 664, 886, 880]]<|/det|> +Mechanical property analysisThe persistence length of the tube structures was estimated as follows. First, the fluorescence images were converted to 8- bit images using the ImageJ function. Then, the skeletons of the tube structures were tracked using ImageJ plugin, JFilament6. Distances between adjacent nodes composing the skeletons were set as 1 pixel. Next, the contour length \((L)\) and end- to- end distance \((R)\) of the tube structures at each frame were calculated using the coordinates of the nodes with custom- written Python scripts. The mean square of \(R\) \((\langle R^2 \rangle)\) of each tube structure was calculated by averaging \(R^2\) along 100–200 frames. \(\langle R^2 \rangle\) and \(L\) follow the following equation when the shape fluctuation is driven thermally36. \(\langle R^2 \rangle = 4L_{\mathrm{p}}^2 [2\exp (- L / 2L_{\mathrm{p}}) - 2 + L / L_{\mathrm{p}}]\) , where \(L_{\mathrm{p}}\) is the persistence length of the tube structure. The \(L_{\mathrm{p}}\) values of the tube structures were estimated by fitting this equation to the experimental data using 'curve_fit' function of Python package + +<|ref|>text<|/ref|><|det|>[[112, 861, 886, 902]]<|/det|> +by fitting this equation to the experimental data using 'curve_fit' function of Python package + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 884, 132]]<|/det|> +'scipy.optimize'. \(L_{\mathrm{p}}\) of actin filaments was estimated by the same analysis. Totally, 55 tube structures and 37 actin filaments were analysed. + +<|ref|>sub_title<|/ref|><|det|>[[115, 162, 280, 179]]<|/det|> +## Molecular modelling + +<|ref|>text<|/ref|><|det|>[[115, 185, 884, 300]]<|/det|> +All predicted protein structures were generated by AlphaFold 2.2 or 2.3 multimer- mode (DeepMind) \(^{15,16}\) . Cartoon models of the proteins were drawn using PyMOL 2.5 (Schrödinger) \(^{57}\) and UCSF ChimeraX (UCSF RBVI and NIH) \(^{58}\) . Isoelectric points of PuuE- M and PuuE- p were calculated from the basis of amino acid composition \(^{59}\) . Surface hydrophobicity of PuuE was drawn using Color_h script (PyMOL Wiki) based on the hydrophobicity scale \(^{60}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 331, 247, 347]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[115, 353, 884, 444]]<|/det|> +The cryo- EM structures have been deposited in the Electron Microscopy Data Bank (EMDB) with the following accession codes: EMD- 60617, EMD- 60618, and EMD- 60619 for the PuuE tubes with C4, C5, and C6 symmetry, respectively; and EMD- 60620, EMD- 60621, EMD- 60622, and EMD- 60623 for the PuuE D- loop tubes with C3, C4, C5, and C6 symmetry, respectively. + +<|ref|>sub_title<|/ref|><|det|>[[115, 475, 268, 491]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[115, 497, 884, 610]]<|/det|> +This work was supported by JSPS KAKENHI (grant nos. 19H02832, 19K22253, and 21H05116 to Y. Suzuki; 21H05117 to Y. Suzuki and Y. Sugita; and 20K22628, 21J00530, and 22KJ1644 to M.N.), JST PRESTO (grant no. JPMJPR22A7 to Y. Suzuki and JPMJPR20ED to M.M.), Takeda Science Foundation to Y. Suzuki, Chubei Itoh Foundation to Y. Suzuki, and The Hakubi Center for Advanced Research to Y. Sugita, M.M., and Y. Suzuki. + +<|ref|>sub_title<|/ref|><|det|>[[115, 642, 282, 658]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[115, 664, 884, 778]]<|/det|> +Y. Suzuki directed the project. Y. Suzuki and M.N. conceived and designed the overall study. M.N. conducted experiment works with contributions from Y. Suzuki, Y. Sugita, and Y.Y.. Y. Sugita and M.N. performed cryo- EM data collection and analysed data. Y.Y. conducted TIRFM experiments, and Y.Y. and M.M. analysed mechanical properties. M.N. and Y. Suzuki wrote the manuscript with contributions from Y. Sugita, Y.Y., and M.M. + +<|ref|>sub_title<|/ref|><|det|>[[115, 810, 357, 827]]<|/det|> +## Competing interest declaration + +<|ref|>text<|/ref|><|det|>[[115, 833, 884, 875]]<|/det|> +Y. Suzuki and M.N. are inventors of a provisional patent submitted by Kyoto University for ‘Protein Assembly Structure’. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[115, 91, 297, 107]]<|/det|> +# Additional information + +<|ref|>text<|/ref|><|det|>[[115, 114, 510, 132]]<|/det|> +Supplementary Information is available for this paper. + +<|ref|>text<|/ref|><|det|>[[115, 138, 688, 156]]<|/det|> +Correspondence and requests for materials should be addressed to Yuta Suzuki. + +<|ref|>text<|/ref|><|det|>[[115, 163, 280, 179]]<|/det|> +Tel: +81- 75- 753- 9766 + +<|ref|>text<|/ref|><|det|>[[115, 187, 457, 204]]<|/det|> +E- mail address: suzuki.yuta.2m@kyoto- u.ac.jp + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[216, 123, 714, 690]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[112, 696, 886, 907]]<|/det|> +Extended Data Fig. 1. | AF2 prediction of PuuE- M and PuuE- p. a, Fifty prediction models overlapped for PuuE- M (top) and PuuE- p (bottom). Peptide parts, M3L2 and p66α, are indicated with a red box. b, Predicted local distance difference test plots for the most reliable prediction models for PuuE- M (top) and PuuE- p (bottom). Arrows indicate the N- terminal region of M3L2 and p66α. For these regions, PuuE- M has a lower predictive reliability than does PuuE- p, suggesting that the structure may be more flexible. c, The most reliable prediction model for PuuE- p. The region from the C- terminus of PuuE to the N- terminus of p66α (i.e. \(^{313}\mathrm{HPYTPE}^{318}\) ) is depicted by a stick model. The two Pro residues highlighted in red are thought to be responsible for the rigidity of the PuuE- p structure. Because of the rigidity of PuuE- p, the final product of the mixture was predicted to be a tube, as shown in Fig. 1d. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[201, 95, 797, 765]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 772, 884, 910]]<|/det|> +
Extended Data Fig. 2. | nsTEM characterisation of PuuE-M, PuuE-p, and the mixture for PuuE-M and PuuE-p. a, 12.5 \(\mu \mathrm{M}\) PuuE-M or b, 12.5 \(\mu \mathrm{M}\) PuuE-p in NaCl (+) buffer was incubated at \(40^{\circ}\mathrm{C}\) for 24 h. Scale bars, 200 nm (white), 50 nm (black). c, Dependency of PuuE tube assemblies on protein concentration. 250 nM (top), 2.5 \(\mu \mathrm{M}\) (middle), and 12.5 \(\mu \mathrm{M}\) (bottom) of PuuE-M and PuuE-p each in NaCl (+) buffer was incubated at \(40^{\circ}\mathrm{C}\) for 24 h and imaged by nsTEM. The tube structure observed in the nsTEM images was flexible as it was curved and collapsed. Scale bars, 1 \(\mu \mathrm{m}\) (white), 50 nm (black).
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 115, 880, 744]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 744, 884, 862]]<|/det|> +
Extended Data Fig. 3. | Time dependence of PuuE tube assemblies and their stability over time. a, 12.5 \(\mu \mathrm{M}\) of PuuE-M and PuuE-p each in \(\mathrm{NaCl}(+)\) buffer was incubated at \(40^{\circ}\mathrm{C}\) for indicated time points and imaged via nsTEM. b, After \(24\mathrm{h}\) of tube formation, the sample was kept at \(25\pm 1^{\circ}\mathrm{C}\) for the indicated time and imaged using nsTEM. Tube structures remained unchanged after 2 weeks and even after 1 month, suggesting stability. Scale bars, \(1\mu \mathrm{m}\) .
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 90, 882, 650]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[111, 653, 886, 789]]<|/det|> +Extended Data Fig. 4. | Temperature dependence of PuuE tube assemblies and determination of \(T_{\mathrm{m}}\) for PuuE- M and PuuE- p. a, 12.5 \(\mu \mathrm{M}\) of PuuE- M and PuuE- p each in NaCl (+), buffer was incubated at the indicated temperature for 24 h and imaged via nsTEM. Scale bars, 1 \(\mu \mathrm{m}\) . b, c, \(T_{\mathrm{m}}\) measurements using CD for PuuE- M (b) and PuuE- p (c). 2.5 \(\mu \mathrm{M}\) of PuuE- M or PuuE- p in NaCl (+), buffer was incubated from 25 to 55 °C with temperature change of 1 °C/min. Left panel, overall CD spectra; right panel, thermal denaturation profiles. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 88, 880, 750]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[113, 765, 886, 880]]<|/det|> +Extended Data Fig. 5. | Salt concentration dependence and reversibility of PuuE tube assemblies. a, Left, \(12.5 \mu \mathrm{M}\) of PuuE- M and PuuE- p each in \(\mathrm{NaCl}(+)\) buffer was incubated at \(40^{\circ} \mathrm{C}\) for \(24 \mathrm{~h}\) with indicated \(\mathrm{NaCl}\) concentration and imaged via nsTEM. Right: diagram of salt concentration effects described in the main text. b, Additional images in Fig. 2d prove the reversibility of tubular assemblies. These images were used for statistical analysis of tube length, as shown in Fig. 2e. Scale bars, \(1 \mu \mathrm{m}\) (white). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[175, 92, 828, 190]]<|/det|> + +<|ref|>image<|/ref|><|det|>[[190, 210, 595, 250]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[192, 252, 512, 266]]<|/det|> +
C4 tube: 2D classifications (2 rounds): 51,590 segments
+ +<|ref|>image<|/ref|><|det|>[[210, 268, 520, 415]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[192, 418, 500, 432]]<|/det|> +
C5 tube: 2D classifications (2 rounds): 44,868 segments
+ +<|ref|>image<|/ref|><|det|>[[536, 310, 808, 416]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[536, 280, 756, 308]]<|/det|> +
3D Refinement \((C_4)\) : 12,052 segments Post-processing
+ +<|ref|>image<|/ref|><|det|>[[210, 434, 500, 592]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[192, 595, 490, 610]]<|/det|> +
C6 tube: 2D classification (1 round): 117,636 segments
+ +<|ref|>image<|/ref|><|det|>[[536, 434, 808, 592]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[536, 447, 756, 475]]<|/det|> +
3D Refinement \((C_5)\) : 12,572 segments Post-processing
+ +<|ref|>image<|/ref|><|det|>[[177, 633, 808, 805]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[192, 612, 440, 626]]<|/det|> +
3D classification \((C_1)\) : 39,841 segments
+ +<|ref|>text<|/ref|><|det|>[[113, 817, 886, 909]]<|/det|> +Extended Data Fig. 6. | Cryo- EM image processing workflow of the PuuE tubes. Flowchart illustrating the image processing steps. Scale bars: \(100 \mathrm{nm}\) (white), \(250 \mathrm{\AA}\) (black). Gold- standard Fourier shell correlation (FSC) curve of the independently refined half maps indicating a global resolution at the 0.143 threshold. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[133, 88, 860, 660]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[112, 666, 886, 899]]<|/det|> +Extended Data Fig. 7. D- loop grafting to emulate actin filaments. a, Surface hydrophobicity calculation for PuuE. D- loop was grafted into the 'back' side of PuuE owing to the hydrophobic nature of a prominent indentation. b, \(T_{\mathrm{m}}\) measurement of PuuE(D- loop)- M via CD. \(2.5 \mu \mathrm{M}\) of PuuE(D- loop)- M in NaCl (+) buffer was incubated from 25 to \(55^{\circ}\mathrm{C}\) with temperature change of \(1^{\circ}\mathrm{C / min}\) . Top: overall CD spectra; bottom: thermal denaturation profiles, respectively. c, \(12.5 \mu \mathrm{M}\) of PuuE(D- loop)- M and PuuE- p in NaCl (+) buffer were incubated at \(30^{\circ}\mathrm{C}\) for \(24 \mathrm{h}\) and imaged using nsTEM. A novel helical pattern of two or three intertwined tubes was clearly observed. Flexibility was also noted when curved structures were observed. Scale bars, \(1 \mu \mathrm{m}\) (white), \(100 \mathrm{nm}\) (black). d, Additional images for reversibility of tube formation depends on temperature changes in Fig. 4c. For this observation, we focused on the presence of tube structures with helical conformations. After \(1 \mathrm{h}\) at \(0^{\circ}\mathrm{C}\) , there were no such structures observed via nsTEM. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[137, 88, 844, 300]]<|/det|> + +<|ref|>image<|/ref|><|det|>[[155, 306, 780, 411]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[155, 419, 644, 435]]<|/det|> +2D classifications (6 rounds) with 2960x2960- Å segments: 9,989 segments + +<|ref|>image<|/ref|><|det|>[[210, 444, 784, 808]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[60, 800, 90, 814]]<|/det|> +718 + +<|ref|>text<|/ref|><|det|>[[60, 817, 884, 835]]<|/det|> +719 Extended Data Fig. 8. | Cryo- EM image processing workflow of PuuE D- loop tubes prepared at \(25 \pm 720\) 1 °C. Flowchart illustrating the image processing steps. Scale bars: 100 nm (white), 500 Å (black). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[180, 92, 824, 190]]<|/det|> + +<|ref|>image<|/ref|><|det|>[[180, 201, 789, 811]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[113, 816, 886, 909]]<|/det|> +Extended Data Fig. 9. | Cryo- EM image processing workflow of PuuE D- loop tubes preincubated on ice to unwind the helical structures. Flowchart illustrating the image processing steps. Scale bars: 100 nm (white), 250 Å (black). Gold- standard Fourier shell correlation (FSC) curve of the independently refined half maps indicating a global resolution at the 0.143 threshold. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[115, 106, 765, 305]]<|/det|> +
#1 C4 tube (EMD-60617)#2 C5 tube (EMD-60618)#3 C6 tube (EMD-60619)
Data collection and processing
Magnification120,000
Voltage (kV)200
Electron exposure (e-/Ų)40
Defocus range (μm)-0.8 to -1.6
Pixel size (Å)1.22
Symmetry imposedC4 helicalC5 helicalC6 helical
Initial helical segments (no.)709,722709,722709,722
Final helical segments (no.)12,05212,57239,841
Map resolution (Å)11.320.617.5
FSC threshold0.1430.1430.143
+ +<|ref|>text<|/ref|><|det|>[[60, 310, 92, 323]]<|/det|> +727 + +<|ref|>text<|/ref|><|det|>[[60, 328, 568, 344]]<|/det|> +2D analysis of the PuuE D-loop tube prepared at 25 ± 1 °C + +<|ref|>table<|/ref|><|det|>[[115, 344, 460, 492]]<|/det|> +
#1 Tubes
Data collection and processing
Magnification150,000
Voltage (kV)200
Electron exposure (e-/Ų)40
Defocus range (μm)-0.8 to -1.6
Pixel size (Å)0.925
Symmetry imposedNo
Initial helical segments (no.)126,987
Final helical segments (no.)104,748
+ +<|ref|>text<|/ref|><|det|>[[60, 495, 92, 508]]<|/det|> +729 + +<|ref|>text<|/ref|><|det|>[[60, 513, 92, 526]]<|/det|> +730 + +<|ref|>text<|/ref|><|det|>[[115, 512, 671, 528]]<|/det|> +PuuE D-loop tube preincubated on ice to unwind the helical structures + +<|ref|>table<|/ref|><|det|>[[115, 528, 857, 728]]<|/det|> +
#1 C3 tube (EMD-60620)#2 C4 tube (EMD-60621)#3 C5 tube (EMD-60622)#4 C6 tube (EMD-60623)
Data collection and processing
Magnification150,000
Voltage (kV)200
Electron exposure (e-/Ų)40
Defocus range (μm)-0.8 to -1.6
Pixel size (Å)0.925
Symmetry imposedC3 helicalC4 helicalC5 helicalC6 helical
Initial helical segments (no.)397,778397,778397,778397,778
Final helical segments (no.)2,6753,2622,9981,291
Map resolution (Å)9.714.618.226.0
FSC threshold0.1430.1430.1430.143
+ +<|ref|>text<|/ref|><|det|>[[60, 730, 92, 743]]<|/det|> +734 + +<|ref|>text<|/ref|><|det|>[[60, 750, 783, 766]]<|/det|> +Extended Data Table 1. | Cryo-EM data collection, refinement, and validation statistics + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[59, 130, 353, 257]]<|/det|> +SupplementaryInformation.pdf Supplementarymovie1. mov Supplementarymovie2. mov Supplementarymovie3. mov Supplementarymovie4. mov + +<--- Page Split ---> diff --git a/preprint/preprint__07090632be97150b9482692b3758503018ba474d5a059546c8cd240472d00789/images_list.json b/preprint/preprint__07090632be97150b9482692b3758503018ba474d5a059546c8cd240472d00789/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..b8a8a39eaf960888cd071760ab39e0b6d465e62c --- /dev/null +++ b/preprint/preprint__07090632be97150b9482692b3758503018ba474d5a059546c8cd240472d00789/images_list.json @@ -0,0 +1,152 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 | Disulphide-locked BamA variants and Fab1 binding impair BAM-mediated OMP folding in vitro. (a) BAM-P5L (G393C/G584C) is expected to lock BamA in the lateral-open conformation (PDB code 5LJO8), while (b) BAM-LL (E435C/S665C) is expected to lock BamA in the lateral-closed conformation (PDB code 5DOO6). BamA POTRAs 1-4 and BamBCDE are rendered semi-transparent for emphasis on the BamA \\(\\beta\\) -barrel and POTRA-5. The position of the disulphide bond is shown as a yellow bar. Figure made in PyMOL v1.7.2.3. (c and d) Quantification of folded and unfolded bands from SDS-PAGE band-shift", + "footnote": [], + "bbox": [ + [ + 112, + 78, + 870, + 760 + ] + ], + "page_idx": 23 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 | CryoEM resolves two conformations of BAM-LL in detergent. (a) 4.1 A cryoEM map of the BAM-LL lateral-closed conformation at a contour of \\(10\\sigma\\) , coloured by subunit. The lateral-gate is closed and POTRA-5 does not block the BamA barrel (schematic inset). (b) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on the segmented density for the barrel and POTRA-5 of BamA. \\(\\beta 1\\) and \\(\\beta 16\\) contact to close the gate. (c) The same density viewed from the periplasmic side, showing the open lumen of the BamA barrel in this conformation. (d) 4.8 Å cryoEM map of the BAM-LL lateral-open conformation at a contour of \\(10\\sigma\\) , coloured by subunit. The lateral-gate is open and POTRA-5 occludes the BamA barrel (schematic inset). (e) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on segmented density for the barrel and POTRA-5 of BamA. To satisfy the disulphide in this conformation, eL1 must bend back into the barrel to contact eL6. (f) The same density viewed from the periplasmic side, showing that the BamA lumen is blocked by POTRA-5 in this conformation. Fig. made in UCSF ChimeraX76. Segmenting and colouring performed with corresponding atomic models. Less well resolved regions and the micelle have been masked.", + "footnote": [], + "bbox": [ + [ + 115, + 81, + 880, + 490 + ] + ], + "page_idx": 25 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 | Fab1-bound BAM is in a lateral-open conformation. (a) 5.1 Å cryoEM map of the BAM-Fab1 complex in a lateral-open conformation at a contour of \\(10\\sigma\\) , coloured by subunit. The lateral-gate is fully-open and POTRA-5 occludes the BamA barrel (schematic inset). (b) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on the segmented density for the barrel and POTRA-5 of BamA. \\(\\beta 1\\) is in a conformation that makes limited contact with \\(\\beta 16\\) . (c) The same density viewed from the periplasmic side, showing that the BamA lumen is blocked by POTRA-5 in this conformation. Panels made using UCSF ChimeraX76. Segmenting and colouring performed with corresponding atomic models. Less well resolved regions and the micelle have been masked. (d) Close up of the BamA-Fab1 interface region highlighting the Fab1 CDRs (red) interacting with eL4 of BamA (dark blue). Other regions of BamA are rendered semi-transparent to highlight eL4. Heavy and light chains of Fab1 are coloured cyan and pink, respectively. (e) The \\(\\mathrm{V_L}\\) and \\(\\mathrm{V_H}\\) domains of Fab1 variable form a complementary binding surface for eL4 of BamA involving residues Y550, E554 and H555.", + "footnote": [], + "bbox": [ + [ + 115, + 80, + 875, + 550 + ] + ], + "page_idx": 26 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 | Additive effect of BAM inhibition by disulphide-locking and binding of Fab1. (a) 7.1 Å cryoEM map of the Fab1-bound LL-BAM in a lateral-open conformation at a contour of 9.5 σ, coloured by subunit. The lateral-gate is open and POTRA-5 occludes the BamA barrel (schematic inset). (b) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on the segmented density for the β-barrel and", + "footnote": [], + "bbox": [ + [ + 115, + 80, + 870, + 789 + ] + ], + "page_idx": 27 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5 | BAM variants reduce the phase transition temperature of DMPC liposomes. Global lipid phase transition behaviour for each BAM variant and BamA in DMPC proteoliposomes, with an empty liposomes control measured using laurdan fluorescence. (a) The ratio of laurdan fluorescence at \\(440 \\text{nm}\\) and \\(490 \\text{nm}\\) was plotted as generalised polarisation (GP, see Methods) against temperature for \\(0.8 \\mu \\text{M BAM/BamA proteoliposome}\\) suspensions at a \\(1600:1\\) (mol/mol) lipid-to-protein ratio (LPR) with added laurdan (at a \\(305:1\\) lipid-to-laurdan ratio) in TBS pH 8.0. (b) The first derivative of data shown in (a) showing the transition temperature for each liposome suspension as the point of steepest (most negative) gradient. Whilst empty DMPC (grey) and BamA proteoliposomes (purple) have a transition temperature of \\(24 \\text{‰}\\) , the presence of WT BAM (black), BAM-Fab1 (red), BAM-P5L (green), BAM-LL (blue), BAM-P5L + Fab1 (orange) and BAM-LL + Fab1 (yellow)", + "footnote": [], + "bbox": [ + [ + 122, + 100, + 820, + 680 + ] + ], + "page_idx": 29 + }, + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [ + [ + 66, + 108, + 800, + 830 + ] + ], + "page_idx": 36 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 72, + 295, + 884, + 770 + ] + ], + "page_idx": 37 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [ + [ + 60, + 260, + 901, + 817 + ] + ], + "page_idx": 38 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4", + "footnote": [], + "bbox": [ + [ + 71, + 50, + 820, + 785 + ] + ], + "page_idx": 40 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5", + "footnote": [], + "bbox": [ + [ + 75, + 68, + 830, + 300 + ] + ], + "page_idx": 42 + } +] \ No newline at end of file diff --git a/preprint/preprint__07090632be97150b9482692b3758503018ba474d5a059546c8cd240472d00789/preprint__07090632be97150b9482692b3758503018ba474d5a059546c8cd240472d00789.mmd b/preprint/preprint__07090632be97150b9482692b3758503018ba474d5a059546c8cd240472d00789/preprint__07090632be97150b9482692b3758503018ba474d5a059546c8cd240472d00789.mmd new file mode 100644 index 0000000000000000000000000000000000000000..3385067db4b7f8dd36acf595312855375a54fed7 --- /dev/null +++ b/preprint/preprint__07090632be97150b9482692b3758503018ba474d5a059546c8cd240472d00789/preprint__07090632be97150b9482692b3758503018ba474d5a059546c8cd240472d00789.mmd @@ -0,0 +1,343 @@ + +# The role of membrane destabilisation and protein dynamics in BAM catalysed OMP folding. + +Paul White University of Leeds Samuel Haysom University of Leeds https://orcid.org/0000- 0002- 8769- 090X Matthew ladanza University of Leeds Anna Higgins University of Leeds Jonathan Machin University of Leeds Jim Home University of Leeds https://orcid.org/0000- 0001- 5260- 2634 Bob Schiffrin University of Leeds Charlotte Carpenter-Platt University of Leeds James Whitehouse University of Leeds Kelly Storek Genentech Inc Steven Rutherford Genentech Inc https://orcid.org/0000- 0002- 4758- 4248 David Brockwell University of Leeds https://orcid.org/0000- 0002- 0802- 5937 Neil Ranson University of Leeds https://orcid.org/0000- 0002- 3640- 5275 Sheena Radford ( s.e.radford@leeds.ac.uk ) University of Leeds https://orcid.org/0000- 0002- 3079- 8039 + +## Article + +Keywords: outer membrane proteins (OMPs), \(\beta\) - barrel assembly machinery (BAM), OMP folding. + +<--- Page Split ---> + +**Posted Date:** February 2nd, 2021 + +**DOI:** https://doi.org/10.21203/rs.3.rs-155135/v1 + +**License:** © This work is licensed under a Creative Commons Attribution 4.0 International License. +Read Full License + +**Version of Record:** A version of this preprint was published at Nature Communications on July 7th, 2021. +See the published version at https://doi.org/10.1038/s41467-021-24432-x. + +<--- Page Split ---> + +1 The role of membrane destabilisation and protein dynamics in BAM catalysed OMP folding 3 4 Paul White1\\*, Samuel F. Haysom1\\*, Matthew G. Iadanza1\\*, Anna J. Higgins1, Jonathan M. Machin1, Jim E. Horne1#, Bob Schiffrin1, Charlotte Carpenter-Platt1, James M. Whitehouse1, Kelly M. Storek2, Steven T. Rutherford2, David J. Brockwell1, Neil A. Ranson1\\*, Sheena E. Radford1\* 8 9 1 Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, 10 Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK 11 2 Department of Infectious Diseases, Genentech Inc., South San Francisco, CA 94080 12 4 Contributed equally 13 \* Current affiliation Scientific Computing Department, Science and Technology Facilities 14 Council, Research Complex at Harwell, Didcot, OX11 0FA, UK 15 # Current affiliation: Department of Biochemistry, University of Oxford, Oxford, OX1 3QU, UK 16 17 \*Correspondence: n.a.ranson@leeds.ac.uk; s.e.radford@leeds.ac.uk 18 + +<--- Page Split ---> + +## 19 Abstract + +The folding of \(\beta\) - barrel outer membrane proteins (OMPs) in Gram- negative bacteria is catalysed by the \(\beta\) - barrel assembly machinery (BAM). How lateral opening in the \(\beta\) - barrel of the major subunit BamA assists in OMP folding, and the contribution of membrane disruption to BAM catalysis remain unresolved. Here, we use an anti- BamA monoclonal antibody fragment (Fab1) and two disulphide- crosslinked BAM variants (lid- locked (LL), and POTRA- 5- locked (P5L)) to dissect these roles. Despite being lethal in vivo, we show that all complexes catalyse folding in vitro, albeit less efficiently than wild- type BAM. CryoEM revealed that while Fab1 and BAM- P5L trap an open- barrel state, BAM- LL contains a mixture of closed and contorted, partially- open structures. Finally, all three complexes globally destabilise the lipid bilayer, while BamA does not, revealing that the BAM lipoproteins are required for this function. Together the results provide new insights into the role of BAM structure and lipid dynamics in OMP folding. + +<--- Page Split ---> + +Outer membrane proteins (OMPs) in Gram negative bacteria are functionally diverse, but share a common \(\beta\) - barrel fold involving between 8 and 36 \(\beta\) - strands1. The folding and membrane insertion of OMPs is catalysed by the essential \(\beta\) - barrel assembly machinery (BAM)2- 4 which in E. coli comprises five proteins (BamABCDE). The major conserved subunit, BamA, is a 16- stranded Omp85 family member that contains five N- terminal polypeptide transport associated (POTRA) domains that extend into the periplasm to scaffold four lipoprotein BamB- E5- 8, all of which are required for maximally- efficient OMP folding9,10. BAM is essential for bacterial survival, highly conserved, and surface accessible via the extracellular loops of BamA, making the complex an attractive target for small molecule11- 13, peptide14,15 and antibody- based antibiotics16,17. + +BAM exists in an ensemble of conformations, with one of the most notable differences between published structures occurring around the seam or 'lateral gate' involving \(\beta\) - strands 1 ( \(\beta 1\) ) and 16 ( \(\beta 16\) ) in the BamA barrel6- 18,20. In the 'lateral- open' conformation, as captured by cryoEM of the intact complex8 and X- ray crystallography of the BamACDE subcomplex5,6, \(\beta 1\) and \(\beta 16\) are separated. In contrast, crystal structures of the intact BAM complex are in a 'lateral- closed' conformation in both in the absence6,7 or presence of substrates21,22, wherein \(\beta 1\) and \(\beta 16\) are hydrogen bonded, albeit with fewer hydrogen bonds than exist between the other strands in the barrel1. The POTRA domains are also dynamically organised, with motions of POTRA- 5 being tightly correlated with gate conformation, with POTRA- 5 plugging entrance to the BamA \(\beta\) - barrel lumen only in the lateral- open state18. These conformational changes are essential for cell viability as disulphide bonds that purportedly lock BamA in either conformation have a lethal phenotype that is rescued by reducing agent6,19. Such variants include those that lock the lateral gate closed (e.g. G433C/N805C linking \(\beta 1\) to \(\beta 16^{8,19}\) , or E435C/S665C locking extracellular loop 1 (eL1) to eL66,19), or those that lock the BamA lateral gate in an open conformation by introducing a disulphide bond between POTRA- 5 and \(\beta\) - turn between \(\beta 8\) and \(\beta 9\) at the base of the barrel (e.g. G393C/G584C6). Disulphide bonds which restrict flexibility between POTRA domains 2 and 3 also impair growth23; how, or if, these motions correlate with structural changes at the BamA \(\beta\) - barrel is unclear. + +Models of BAM- catalysed OMP insertion and folding broadly invoke two distinct roles for BAM (reviewed in24). Firstly conformational changes in BAM, and protein- protein interactions between BAM and substrate OMPs are thought to be involved in catalysing folding25- 29. These models all involve a folding intermediate in which the C- terminal \(\beta\) - strand of the substrate is associated with BamA- \(\beta 1\) , as supported by crosslinking26,27, a recent cryoEM structure of a hybrid barrel formed between BAM and tBamA (the transmembrane domain of a BamA substrate)29, and crystal structures of BAM covalently tethered to the C- terminal \(\beta\) - strands of OMP substrates OmpA and OmpLA22. Variations of these models include the + +<--- Page Split ---> + +'barrel elongation'25 and 'swing'27 models which suggest that folding begins in the periplasm, and also 'budding' models1,3,25 wherein OMPs are thought to enter the lumen of the BamA barrel and fold via sequential addition of \(\beta\) - hairpin units26. This is akin to the role proposed for the mitochondrial homologue Sam50 of the sorting and assembly machinery (SAM) complex26. An alternative model proposes that BAM may disorder its lipid environment, lowering the kinetic barrier to OMP folding, potentially allowing OMPs to fold and insert into the outer membrane without direct interaction with the \(\beta 1 - \beta 16\) seam. This 'BamA- assisted' model18,30- 32 is supported by molecular dynamics (MD) simulations which show lipid disordering and bilayer thinning by BamA20,25,30- 35, and by BAM- mediated distortion of a nanodisc18. Both protein dynamics and lipid disordering may act synergistically to maximise the efficiency of OMP folding, and different OMPs may depend on each effect to different degrees. However, little mechanistic insight is available, beyond that which has been inferred from the observation of a lethal phenotype. + +Here, we investigate the roles of BAM structure/dynamics and membrane stability in OMP folding by exploiting two disulphide- locked variants termed lid- lock (LL) and POTRA- 5- lock (P5L) which are lethal in vivo6,19, and purportedly lock BamA's barrel closed and open, respectively. We also investigate a bactericidal Fab fragment (Fab1), that binds to eL4 of BamA16. We report cryoEM structures for the two disulphide locked BAM variants and the BAM- Fab1 complex, revealing that BAM- P5L and Fab1 stabilise a lateral- open conformation, whilst BAM- LL adopts both a lateral- closed state and a distorted, partially- open conformation. Despite being lethal in vivo, the two disulphide variants and the Fab1- BAM complex are all able to catalyse the folding of the 8- stranded OMPs OmpX and tOmpA (the transmembrane region of OmpA) in vitro, though less efficiently than wild- type BAM, and by combining Fab1 and disulphide- locking, BAM is further inactivated. We also demonstrate that all BAM variants studied lower the phase transition temperature of their lipid environment, but that BamA alone does not, providing direct experimental evidence that lipid disordering by BAM requires the presence of its lipoproteins. The results provide new insights into the structural features of BAM's catalytic mechanism and suggest that even subtle disruption of BAM activity may provide an effective route to the development of novel antibiotics. + +<--- Page Split ---> + +## Results + +## Disulphide-locked and Fab1-bound BAM can catalyse OMP folding in vitro + +To assess the relationship between bacterial lethality and the catalytic ability of BAM we determined the in vitro folding activity of two paired cysteine mutations in BamA that are bactericidal6,19. In the BAM- P5L variant (BamA G393C/G584C)6, tethering of POTRA- 5 to the base of the BamA barrel is expected to stabilise a lateral- open conformation (Fig. 1a). By contrast, the BAM- LL variant, (BamA E435C/S665C)19 is expected to lock eL1 to eL6, and stabilise a lateral- closed conformation (Fig. 1b). The BAM- LL and BAM- P5L variants were made in a BAM construct in which the two Cys of BamA that naturally form a disulphide bond (C690 and C700), are replaced with Ser (Cys- free BAM). This variant is able to complement WT BamA in E. coli19,36 and has little effect on BAM- catalysed OMP folding rates in vitro9. We also investigated how a bactericidal anti- BamA binding antibody Fab fragment, known as Fab16,37, affects OMP folding in vitro. BAM- P5L, BAM- LL and the BAM- Fab1 complex were each reconstituted into liposomes comprised of E. coli polar lipids, and their ability to fold the 8- stranded OMPs, OmpX and tOmpA, in the presence of SurA was determined by SDS- PAGE band- shift assays38. In each case, BamA was folded (as judged by a band- shift relative to the boiled (denatured) BamA band) and all four BAM lipoproteins were present (Supplementary Fig. 1). Interestingly, Fab1 formed a stable, SDS- resistant complex with BamA (Supplementary Fig. 1b), consistent with its IC50 of 0.095 nM determined for \(\Delta waaD E. coli^{16}\) . Disulphide bond formation in BAM- P5L and BAM- LL was confirmed by the lack of fluorescein- C5- maleimide labelling, and electrophoretic band- shifts in oxidising/reducing conditions (Supplementary Fig. 2). Both tOmpA or OmpX do not fold spontaneously into the liposomes formed from E. coli polar lipids, but fold rapidly and efficiently into liposomes formed from the same lipids containing WT BAM (Fig. 1c and d). Remarkably, considering their in vivo lethality6,16,19, the efficiency of folding and membrane insertion of tOmpA and OmpX is reduced, but not abolished, by BAM- P5L, BAM- LL and BAM- Fab1, with folding yields of \(\sim 50\%\) for tOmpA and \(\sim 15 - 30\%\) for OmpX after 3 hours at 25 °C (note that tOmpA folds more rapidly than OmpX with WT BAM) (Fig. 1c and d, and Supplementary Fig. 3 and 4). Relative to WT BAM, the initial rates of folding for BAM- Fab1, BAM- LL and BAM- P5L ranged from 16- 20% for tOmpA, and 8- 29% for OmpX (Fig. 1e and f, respectively, and Supplementary Table 1). When the disulphide bond in BAM- P5L and BAM- LL is reduced with DTT, folding activity surpassed that of WT BAM. This effect was not observed for WT BAM, or Cys- free BAM (Supplementary Fig. 5). Folding into proteoliposomes containing BamA alone was much slower than observed with BAM- P5L, BAM- LL, or BAM- Fab1, with initial folding rates for both substrates reaching \(\sim 3\%\) of that WT BAM, highlighting the importance of the accessory lipoproteins for efficient catalysis of folding of these OMPs39. Importantly, the inhibited BAM variants were able to fold their OMP substrates to 80- 100% completion after 24 hours, whilst incubation with BamA alone resulted in folding yields of only 50% and 16% for tOmpA and OmpX, respectively, after 24 + +<--- Page Split ---> + +hours (note that both substrates were unable to fold into empty liposomes even on these extended timescales) (Supplementary Table 2). Collectively, these results show that although both Fab1 binding and disulphide- locking of BamA are lethal in vivo6,16,19, the BAM- catalysed folding of OmpX and tOmpA is only partially inhibited in vitro. + +## Lid-locked BAM exists in two conformations + +To understand the molecular basis of inhibition, we determined the structure of BAM- LL in DDM detergent micelles using cryoEM. We predicted, based on the lethality of this mutation and the crystal/cryoEM structures of BAM in its different conformational states5- 8, that the formation of a disulphide bond between C435 and C665 would trap BAM in a lateral- closed state (Fig. 1b). However, 3D classification of cryoEM data of this construct revealed two distinct, approximately equally populated, structures (Fig. 2 and Supplementary Fig. 6). The first structure (at 4.1 Å resolution) is similar to the crystal structure of intact BAM in the lateral- closed conformation, with pairing of β1 and β16 (Fig. 2a,b) and displacement of POTRA- 5 from beneath the barrel (Fig. 2c). The second structure (at 4.8 Å) has β1 and β16 separated (Fig. 2d, e) and POTRA- 5 occludes the periplasmic face of the BamA barrel (Fig. 2f), and is thus consistent with a lateral- open conformation. In all previous lateral- open structures5,6,8, extracellular loop 1 (eL1) bends away from the BamA β- barrel, separating the lid- lock cysteine positions (C435 and C665) by \(\sim 20\) Å. Given the unequivocal in vitro biochemical evidence for formation of the lid- lock disulphide (Supplementary Fig. 2), eL1 must be distorted to allow disulphide bond formation with eL6. However, poor resolution in this region of the map, itself indicative of mobility, prevented modelling of this new eL1 conformation. We therefore used molecular dynamics- based flexible fitting (MDFF)40 to morph the lateral- closed BAM- LL atomic model into the density observed in the second conformation, whilst maintaining the disulphide link. This generated a chemically plausible loop conformation (Fig. 2e), but this is not constrained by the EM density. The difference between eL1 conformations in the two BAM- LL structures is striking, and suggests that this region must be highly malleable to allow disulphide bond formation within the BamA β- barrel. Interestingly, the 'contorted open' BAM- LL structure closely resembles a recent structure of WT BAM in saponin nanodiscs22 in which eL1 adopts this inward conformation in the absence of disulphide tethering. In accord with this idea, eL1 can adopt a wide range of conformations in lateral- open BAM structures (Supplementary Fig. 7). Overall, these data suggest that the lid- lock disulphide biases the conformational ensemble toward a lateral- closed conformation, but cannot completely pull the conformational equilibrium over to that state, consistent with BAM adopting only the lateral- open state in DDM detergent8. + +<--- Page Split ---> + +## Fab1-bound BAM and BAM-P5L adopt a lateral-open state + +Inspired by the findings that MAB1 (and Fab1) binding is lethal in vivo \(^{16}\) and also retards OMP folding rates in vitro (Figure 1), we next investigated the effect of Fab1 binding on the conformation of BAM using cryoEM. The structure of BAM in complex with a bactericidal molecule (Fab1) was solved in DDM micelles to 5.1 Å resolution. The cryoEM map contained unambiguous density for Fab1 bound to the extracellular region of BamA (Fig. 3a, Supplementary Fig. 8), and revealed that BAM is in a lateral-open conformation when bound to Fab1, as defined by the position of POTRA- 5, the shape of the BamA \(\beta\) - barrel, and the orientation of \(\beta 1\) and \(\beta 16\) (Fig. 3b and c). The structure of Fab1 alone was also solved by X- ray diffraction to \(\sim 3.0\) Å resolution and this structure was flexibly fitted into the EM density map (Supplementary Table 3). In agreement with mutagenesis data \(^{16}\) , Fab1 binds specifically to eL4 (Fig. 3d) (contributing 98% of the total interface area of 934 Å \(^{2}\) as determined by PISA interface analysis \(^{41}\) ), and the complementarity determining regions (CDRs) bind to residues Y550, E554 and H555 in BamA (Fig. 3e). Interestingly, a BamA- specific nanobody (nanoE6) has also been found to bind eL4 (involving E554) and also influences dynamics in the lateral gate \(^{17}\) . However, since binding of Fab1 to BAM (and nanoE6 to BamA \(^{17}\) ) does not drastically alter the conformation of eL4 from that seen in lateral- closed structures, how Fab1 binding stabilises a lateral- open conformation remains obscure. Finally, we determined the cryoEM structure of BAM- P5L at lower resolution (10.3 Å; Supplementary Figs. 9 and 10), and although the conformation of the lateral gate is not clearly observed at this resolution (Supplementary Fig. 10a), POTRA- 5 unambiguously occludes the BamA barrel suggesting that BAM- P5L is in a "lateral- open"- like state (Supplementary Fig. 10b). Cross- correlation of the BAM- P5L, WT BAM \(^{8}\) (open) and BAM- LL (closed) density maps, as well as comparison of the shapes of the BamA barrel in the different structures add further evidence that BAM- P5L is indeed in a lateral- open state, as expected from the design of the Cys mutants, (Supplementary Figure 10d,e). + +## Fab1 binding to disulphide-locked BAM further inhibits OMP folding + +As BAM can populate a lateral- open conformation in the presence or absence of Fab1, we determined the cryoEM structure of BAM- LL bound to Fab1 to ascertain whether Fab1 binding could further stabilise a lateral- open conformation, potentially further blocking the conformational changes required for BAM's catalytic action. In contrast with BAM- LL, the cryoEM structure of the BAM- LL: Fab1 complex (at 7.1 Å resolution) contains a single structure which is in a lateral- open conformation (Fig. 4a, Supplementary Fig. 11), consistent with Fab1 biasing BamA's conformational equilibrium towards a lateral- open state (Fig. 4b) in which POTRA- 5 occludes the barrel (Fig. 4c). Further evidence for the lateral + +<--- Page Split ---> + +closed state being incompatible with Fab1 binding was observed by SDS- PAGE, where the SDS- resistant BamA- Fab1 band observed for WT BAM- Fab1 was weaker for BAM- LL- Fab1, with a compensating increase in the band corresponding to non- complexed BamA, suggestive of the BAM- LL- Fab1 complex being less stable under SDS- PAGE conditions (Supplementary Fig. 12a). Interestingly, since MAB1 binds to BAM in the E. coli OM16, this suggests that a lateral- open conformation is formed in situ in the OM, consistent with previous data36. Conversely, the Fab1- bound BAM- P5L complex produces an SDS- resistant band, consistent with stable binding to its lateral- open state (Supplementary Fig. 12b). tOmpA and OmpX folding assays revealed that the addition of Fab1 to BAM- P5L or BAM- LL each resulted in increased inhibition, with folding yields of \(\sim 10 - 20\%\) for tOmpA (Fig. 4d, Supplementary Fig. 13a) and \(5 - 10\%\) for OmpX (Fig. 4e, Supplementary Fig. 13b) after 3 hours at \(25^{\circ}C\) , and initial folding rates of only \(1 - 3\%\) and \(1 - 6\%\) of that of WT BAM for tOmpA and OmpX, respectively (Fig. 4f and g). This additive inhibition could arise from a synergistic reduction in conformational dynamics within the BAM complex, or from Fab1 binding and disulphide locking inhibiting distinct mechanisms of BAM- mediated folding catalysis. + +## BAM lipoproteins mediate destabilisation of the lipid bilayer + +In vitro studies have shown that spontaneous OMP folding rates and efficiencies are increased in membranes with decreased thickness, increased fluidity, or containing bilayer defects42- 45. As well as directly interacting with its substrate OMPs27,29, BAM is also thought to reduce the stability of the lipid bilayer to facilitate folding, due to asymmetry in the hydrophobic thickness of the BamA \(\beta\) - barrel (which is narrowest in the vicinity of the lateral gate)18,32. Evidence for membrane destabilisation has been provided by molecular dynamics (MD) simulations of BamA in lipid bilayers20,24,25,30- 35 and by cryoEM and MD simulations of BAM in nanodiscs formed from E. coli polar lipids18. To determine how the different conformational states of BAM affect bilayer stability more directly, we measured the effect of the different BAM complexes studied above on the lipid phase transition of liposomes formed from 1,2- dimyristoyl- sn- glycero- 3- phosphocholine (DMPC, \(d / C_{14:0}PC\) ) using the fluorescent lipid probe laurdan (Supplementary Fig. 14), the fluorescence emission spectrum of which depends on lipid phase46. DMPC was chosen for these experiments as it undergoes a gel- liquid phase transition with a midpoint of \(\sim 24^{\circ}C\) , compared with \(\sim 3^{\circ}C\) for E. coli polar lipid47 and BAM has been shown to be active in DMPC liposomes48. As expected, a phase transition for empty DMPC liposomes was observed at \(24^{\circ}C\) (Fig. 5a, see also Supplementary Fig. 15). Interestingly, the transition phase temperature (Tm) was not affected by the presence of BamA alone (Fig. 5a), demonstrating that the asymmetric BamA \(\beta\) - barrel does not itself cause this global perturbation of the lipid bilayer, at least as judged by this assay. By contrast, in all proteoliposomes containing the full BAM complex, + +<--- Page Split ---> + +regardless of whether that complex is inhibited, the gel- liquid phase transition occurred at a lower temperature ( \(\sim 22 - 23^{\circ}C\) ) and over a broader temperature range (Fig. 5b). These results thus demonstrate that BAM disrupts bilayer stability independently of the structure of the \(\beta 1 - \beta 16\) seam and shows that the BamB- E lipoproteins are essential for this perturbation of the membrane. + +## Discussion + +Protein- protein interactions between BAM and substrate OMPs, and lipid disordering have both been implicated as important features in BAM function3,24, but how these different facets of BAM are balanced to enable OMP folding remained unclear. Here, we have used structural, biochemical and kinetic refolding analyses to dissect these two roles, at least for the 8- stranded OMPs, tOmpA and OmpX. BAM is well- known to be conformationally dynamic, with cryo- EM and X- ray structures capturing the complex in lateral- open5,6,8 and lateral- closed6,7,21,22 conformations, and a recent cryoEM, MD and single molecule FRET study demonstrating dynamics of the complex in nanodiscs18. Furthermore, recent X- ray structures have demonstrated that the C- terminal strand of the OMP substrates tOMPa and OMPLA forms an antiparallel \(\beta\) - strand pairing with lateral- closed BamA \(\beta 1\) , possibly capturing an early stage intermediate in OMP assembly22. A recent cryoEM structure of a BAM:tBamA complex revealed that the tBamA substrate forms a \(\beta\) - strand pairing with lateral- open BamA \(\beta 1\) of BAM, whilst making a side- chain mediated interface involving BamA \(\beta 16\) , to form a hybrid barrel29 that presumably mimics a late- stage assembly intermediate. This observation is consistent with crosslinking studies of EspP27 and LptD28 to BAM, and Por1 to SAM26. Given these insights, it is perhaps unsurprising that trapping BamA in the BAM complex in an open or closed conformation by disulphide bonding has a profound effect on bacterial viability, akin to the observations found using nanobodies17, small molecules and peptidomimetic antibiotics, which also have a lethal outcome11,12. Remarkably, we show here that this in vivo lethality masks a more subtle effect on BAM activity that is revealed by in vitro activity assays. Both disulphide- locking and Fab1 binding inhibit, but do not abolish, BAM- catalysed folding of tOmpA and OmpX in vitro (Fig. 1, and Supplementary Tables 1 and 2). The finding that these inhibitory effects are distinct and additive (Fig. 4) highlights the importance of different, presumably parallel, facets of BAM action for OMP folding catalysis. Our cryoEM structures confirm that in solution, both BAM- P5L and Fab1 lock BamA in a lateral- open conformation (Figs. 3, 4, and Supplementary Fig. 10). Presumably this prevents substrate access and pairing to BamA \(\beta 1\) which recent structures suggest initially occurs to a lateral- closed conformation22. It may also inhibit substrate binding by occlusion of entry to the BamA barrel by POTRA- 5. Consistent with this, it has recently been shown that the BAM substrate, RcsF, binds in the lumen of the BamA \(\beta\) - barrel only in the lateral- closed conformation21, and that the essential mediator of + +<--- Page Split ---> + +LPS assembly, LptD, contacts the internal lumen of BamA during folding28. An inability to assemble larger and essential BAM-dependent substrates, such as LptD, could explain why disulphide locking/Fab1 binding are lethal in vivo6,16,19, despite smaller OMPs potentially remaining able to fold and insert into the OM, albeit more slowly than with WT BAM. For the latter OMPs, lethality may result from a reduced flux through the OMP biogenesis pathway when BAM is impaired, inducing cell envelope stress caused by accumulation of unfolded OMPs in the periplasmid. Indeed, increased envelope stress was observed upon addition of MAB1 to \(\Delta \text{waaD} E\) . coli16. Moreover, a small molecule inhibitor of the regulator of sigma E protease (RseP)49, that is a key component of this pathway, has a lethal outcome by blocking the \(\sigma^{\mathrm{F}}\) stress response that normally responds to envelope stress by increasing BAM expression50, decreasing OMP expression51, and increasing protein degradation52. The extent to which the folding of larger OMPs is inhibited by the BAM variants examined here remains unclear, but we speculate that for these proteins there could be a greater dependence on a direct interaction with BAM for successful insertion and folding, with BAM being unable to destabilise membranes sufficiently to allow larger OMPs to fold solely via this route. + +Despite the apparent incompatibility of BAM- LL's disulphide bond and a lateral- open conformation6,8, both open- like and closed structures are present in approximately equal populations in solution. The BAM- LL structures presented here thus provide direct evidence that at least \(\beta 1\) and \(\beta 2\) of BamA are malleable in the lateral- open state, being able to bend inwards towards the barrel lumen (Supplementary Fig. 7). Such plasticity appears to be functionally relevant, especially considering the more severe outward motion observed when BAM is engaged with tBamA as a substrate29 (Supplementary Fig. 7). Such an extended conformation would presumably be impossible in BAM- LL, perhaps explaining the partial inhibitory effects observed here for OmpX and tOmpA. Superposition of all the lateral- open BAM structures reported to date thus support a model in which the N- terminal half of the BamA barrel is conformationally dynamic, whilst the C- terminal half provides a stable scaffold that supports these functionally important conformational changes. + +Lipid destabilisation by BAM has been proposed previously as a potentially important facet of the catalysis of OMP folding and insertion into the OM3,25,53. This has been supported by MD simulations that reveal destabilisation of the membrane surrounding BamA20,24,25,30- 35, and a recent cryoEM structure of BAM in a nanodiscs containing \(E\) . coli polar lipids that shows distortion of the bilayer adjacent to the lateral gate18. Whilst these effects are localised to the BamA barrel, the laurdan fluorescence data provide direct biochemical evidence that BAM causes global destabilisation of a bilayer, as revealed by a reduction in the lipid phase transition temperature of DMPC liposomes (Fig. 5). They also reveal that this is mediated by lipoproteins BamB- E, since BamA alone had no discernible effect. This is consistent with cryoEM structures which have identified interactions between BamB, BamD and BamE and detergent micelles8 as well as with lipid in nanodiscs18, whilst BamC is + +<--- Page Split ---> + +thought to span the membrane necessary for surface exposure of the two C- terminal helix- grip domains54. In addition to the roles of BamB- E in substrate recognition15,55, in mediating BAM oligomerisation into 'precincts'56, and coordinating conformational changes in BamA36,57, the results presented here highlight the importance of these lipoproteins in mediating changes in membrane stability. + +In summary, the results presented allow different facets of BAM- mediated catalysis of OMP folding and membrane insertion to be discerned. By structural analysis of Fab1- bound and two different disulphide locked BAM complexes we reveal a remarkable structural malleability of the BamA barrel, and show that interconversion between these different structures is not essential for folding and membrane insertion of the 8- stranded tOmpA and OmpX substrates in vitro. In addition, we provide direct biochemical evidence that BAM causes global destabilisation of a lipid bilayer and reveal that this is not endowed by asymmetry in the depth of the BamA barrel, but instead requires the presence of BamB- E, demonstrating a new role for its lipoproteins. Finally, by demonstrating a significant, but reduced folding capacity of the Fab1- bound and disulphide- locked BAM variants in vitro, we provide evidence in support of models that suggest that bacterial viability depends on a delicate balance between the rates of OMP synthesis and their chaperone- dependent delivery to BAM, with the catalytic power of BAM to insert OMPs into the OM. Perturbing this balance thus offers exciting opportunities to create new antibacterial agents by targeting the different protein complexes required for OMP biogenesis. + +<--- Page Split ---> + +## Methods + +## Expression and purification of WT and disulphide-locked BAM complexes + +BAM- LL (BamA(E435C/S665C/C690S/C700S)BCDE- His) and BAM- P5L (BamA(G393C/G584C/C690S/C700S)BCDE- His) in a pTrc99a vector were generated using Q5 site- directed mutagenesis (New England BioLabs) using plasmid pJH114 (kindly provided by Harris Bernstein58) as a template. WT BAM, BAM- LL and BAM- P5L were expressed in E. coli BL21(DE3) cells and were purified from the membrane fraction using a combination of Ni- affinity and size exclusion chromatography, as described previously8. + +## Expression and purification of BamA, OmpX and tOmpA + +BamA, OmpX and tOmpA were expressed as inclusion bodies in E. coli BL21(DE3) cells, using a procedure modified from McMorran et al.50. Briefly, inclusion bodies were solubilised in \(25~\mathrm{mM}\) Tris- HCl pH 8.0, \(6M\) guanidine- HCl and were centrifuged (20,000 g, \(20\mathrm{min}\) , \(4^{\circ}\mathrm{C}\) ) to remove remaining insoluble material. The solubilised inclusion bodies were purified by SEC using a Superdex 75 HiLoad 26/60 column (GE Healthcare) for tOmpA and OmpX, and Sephacryl 200 26/60 column for BamA, equilibrated in \(25~\mathrm{mM}\) Tris- HCl pH 8.0, \(6M\) guanidine- HCl. For folding experiments, OmpX and tOmpA were buffer exchanged into Tris- buffered saline (TBS, \(20~\mathrm{mM}\) Tris- HCl, \(150~\mathrm{mM}\) NaCl) pH 8.0, \(8M\) urea using ZebaTM Spin Desalting Columns, \(7k\) MWCO, \(0.5~\mathrm{mL}\) (Thermo Scientific). BamA was refolded in LDAO detergent prior to reconstitution into proteoliposomes, as described previously59. + +## Refolding of BamA + +BamA was refolded as described by Hartmann et al.59. Briefly, BamA added dropwise into ice- cold \(50~\mathrm{mM}\) Tris- HCl pH 8.0, \(300~\mathrm{mM}\) NaCl, \(500~\mathrm{mM}\) arginine, \(0.5\%\) (w/v) LDAO, \(10~\mathrm{mM}\) DTT whilst rapidly stirring. Following 24 hours incubation, BamA was dialysed against 50 mM Tris- HCl pH 8.0, \(0.1\%\) (w/v) LDAO overnight before loading on a \(5~\mathrm{mL}\) HiTrap Q (GE Healthcare) anion exchange column and eluting in a NaCl gradient. Folded BamA was separated from unfolded and degraded BamA, as judged by SDS- PAGE, and used for reconstitution into liposomes containing E. coli polar lipid or DMPC, as required. + +## Expression and purification of SurA + +SurA with an N- terminal 6x His- tag and a TEV cleavage site was expressed and purified using a modified protocol described previously60. Briefly, SurA was expressed in E. coli BL21(DE3) cells and was purified on a \(5~\mathrm{mL}\) HisTrap FF column (GE Healthcare). SurA was denatured on- column in \(25~\mathrm{mM}\) Tris- HCl pH 7.2, \(6M\) guanidine- HCl, washed in the same + +<--- Page Split ---> + +buffer and then refolded on- column in \(25~\mathrm{mM}\) Tris- HCl pH 7.2, \(150~\mathrm{mM}\) NaCl, \(20~\mathrm{mM}\) imidazole before elution in \(25~\mathrm{mM}\) Tris- HCl pH 7.2, \(150~\mathrm{mM}\) NaCl, \(500~\mathrm{mM}\) imidazole. The His- tag was cleaved by addition of His- tagged TEV protease and \(14.3~\mathrm{mM}\) 2- mercaptoethanol, produced as previously described31, and the cleaved His- tag and TEV protease were removed on a \(5~\mathrm{mL}\) HisTrap FF column. Purified SurA was dialysed against 5 L TBS pH 8.0, concentrated to \(\sim 200~\mu \mathrm{M}\) using Vivaspin 20 MWCO \(10~\mathrm{kDa}\) concentrators (Sartorius, UK), aliquoted, snap- frozen in liquid nitrogen, and stored at \(- 80~^\circ \mathrm{C}\) . + +## Monoclonal antibody Fab production + +Fabs were cloned and expressed in \(E\) . coli as previously described61,62. Cell paste containing the expressed Fab was resuspended in PBS buffer containing \(25~\mathrm{mM}\) EDTA and \(1~\mathrm{mM}\) PMSF. The mixture was homogenised and then passed twice through a microfluidiser. The suspension was then centrifuged at \(21,500g\) for \(60~\mathrm{min}\) . The supernatant was loaded onto a Protein G column equilibrated with PBS at \(5~\mathrm{mL / min}\) . The column was washed with PBS to baseline and proteins were eluted with \(0.6\%\) (v/v) acetic acid. Fractions containing Fabs, assayed by SDS- PAGE, were pooled and loaded onto a \(50~\mathrm{mL}\) SP Sepharose column equilibrated in \(20~\mathrm{mM}\) MES, pH 5.5. The column was washed with \(20~\mathrm{mM}\) MES, pH 5.5 for 2 column volumes and the protein was then eluted with a linear gradient to \(0.5\mathrm{M}\) NaCl in the same buffer. For final purification, Fab- containing fractions from the ion exchange column were concentrated and run on a Superdex 75 size exclusion column (GE Healthcare) in PBS buffer. + +## Reconstitution of BAM complex variants and BamA into \(E\) . coli polar lipid proteoliposomes + +\(E\) . coli polar lipid extract, purchased as powder from Avanti Polar Lipids (Alabaster, AL), was dissolved in \(80:20\) (v/v) chloroform/methanol at \(20~\mathrm{mg / mL}\) . Appropriate volumes were dried to thin films in clean Pyrex tubes at \(42~^\circ \mathrm{C}\) under \(\mathrm{N}_2\) gas, and were further dried by vacuum desiccation for at least 3 hours. WT BAM, BAM- LL and BAM- P5L in TBS pH 8.0, \(0.05\%\) (w/v) DDM were mixed with \(E\) . coli polar lipid extract films solubilized in TBS pH 8.0, \(0.05\%\) (w/v) DDM in a 1:2 (w/w) ratio. For formation of BAM- Fab1 proteoliposomes, a 2- fold molar excess of Fab1 was added to WT BAM, BAM- P5L or BAM- LL in TBS pH 8.0, \(0.05\%\) (w/v) DDM before mixing with lipid. For BamA proteoliposomes, refolded BamA was added to \(E\) . coli polar lipid films solubilised in TBS pH 8.0, \(0.1\%\) (w/v) LDAO in a 1:2 (w/w) ratio. Empty liposomes were prepared by mixing lipid with an equivalent volume of buffer. To remove detergent and promote liposome formation, the mixtures were dialyzed against \(2~\mathrm{L}\) of \(20~\mathrm{mM}\) Tris- HCl pH 8.0, \(150~\mathrm{mM}\) KCl using 12- 14 kDa MWCO D- Tube™ Maxi Dialyzers (Merck) at room temperature for 48 hours with a total of four buffer changes. Following dialysis, the + +<--- Page Split ---> + +proteoliposomes were pelleted twice by ultracentrifugation at 100,000 \(g\) for 30 mins at \(4^{\circ}C\) (the supernatants referred to as wash 1 and wash 2 in Supplementary Figures) and were resuspended in TBS pH 8.0. Protein concentration was determined using a BCA assay (ThermoScientific) and successful reconstitution was determined by SDS- PAGE. + +## Fluorescein-C5-maleimide labelling of free thiols in BAM disulphide variants + +WT BAM, BAM- LL and BAM- P5L proteoliposome preparations (containing 5 \(\mu \mathrm{M}\) BAM) in TBS pH 8.0 were treated with 1 mM TCEP or 0.1 mM diamide, along with an untreated control, for 45 mins at room temperature. The proteoliposomes were then diluted 10- fold into TBS pH 7.5, 8 M urea containing 100 \(\mu \mathrm{M}\) fluorescein- C5- maleimide and were incubated overnight at \(25^{\circ}C\) . The products of the labelling reaction were then analysed by SDS- PAGE on \(15\%\) (w/v) acrylamide/bis- acrylamide (37.5:1) Tris- tricine SDS- PAGE gels run at \(60\mathrm{mA}\) per gel for 90 mins at \(25^{\circ}C\) , and imaged under 460 nm light using an Alliance Q9 Advanced gel doc (UVITEC, Cambridge, UK). Subsequently gels were stained with Coomassie Blue to visualise all protein bands. + +## BAM-mediated folding of OMPs by SDS-PAGE band-shift assays + +Solutions of \(20\mu \mathrm{M}\) tOmpA or OmpX denatured in TBS pH 8.0 containing 8 M urea were diluted 5- fold into a \(20\mu \mathrm{M}\) solution of SurA. This mixture was then immediately diluted 2- fold into BAM, BamA or empty proteoliposomes to initiate the folding reaction, maintained at 25 \(^\circ \mathrm{C}\) . Final concentrations were \(1\mu \mathrm{M}\) BAM, \(2\mu \mathrm{M}\) tOmpA/OmpX, \(10\mu \mathrm{M}\) SurA, \(0.8\mathrm{M}\) urea in TBS pH 8.0. DTT was included in the relevant folding reactions at a final concentration of 25 mM. Samples of the folding reaction were taken periodically and were quenched in SDS- PAGE loading buffer (final concentrations: \(50\mathrm{mM}\) Tris- HCl pH 6.8, \(10\%\) (v/v) glycerol, \(1.5\%\) (w/v) SDS, \(0.001\%\) (w/v) bromophenol blue). The samples, including a boiled control (10 mins at \(>95^{\circ}C\) ), were run on \(15\%\) (w/v) SDS- PAGE gels as described above. The gels were stained in InstantBlue™ (Experion) and were imaged using an Alliance Q9 Advanced gel doc (UVITEC, Cambridge, UK). Folded and unfolded band intensities were quantified using ImageJ software (Fiji) and were plotted as a fraction folded ( \(\mathrm{I_F / (I_F + I_{UF})}\) ) against time. Folding data were fitted to a single exponential function in Igor Pro (V8.04) and initial rates calculated by applying a linear fit to data within the first \(5\%\) of the time- course (540 seconds). + +## CryoEM grid preparation + +Samples for grid preparation were prepared as follows. Purified BAM- LL or BAM- P5L in 50 mM Tris- HCl pH 8.0, \(150\mathrm{mM}\) NaCl and \(0.05\%\) (w/v) DDM were diluted to \(3.3\mathrm{mg / mL}\) or 2.3 + +<--- Page Split ---> + +mg/mL, respectively. For the BAM- Fab1 complex, purified WT BAM was mixed with a 2- fold molar excess of Fab1 and run on a Superdex 200 10/300 column in TBS pH 8.0, 0.05% (w/v) DDM to isolate a stoichiometric complex from excess free Fab1. Fractions corresponding to the complex were concentrated to 4.8 \(\mu \mathrm{M}\) in Vivaspin 500 concentrator MWCO 30k (Sartorius). To assemble the Fab1- bound BAM- LL complex, stock solutions of purified BAM- LL and Fab1 were first diluted to 5.9 \(\mu \mathrm{M}\) in 20 mM Tris- HCl pH 8.0, 150 mM NaCl and 0.05% (w/v) DDM and mixed in a 1:1 molar ratio, before dilution in detergent- free buffer to a total protein concentration of 0.9 mg/mL and a total DDM concentration of 0.03% (w/v). The detergent concentration was lowered to combat a tendency for very thin ice on the resulting grids. + +CryoEM grids were prepared as follows. For the BAM- Fab1 complex, 4 \(\mu \mathrm{L}\) protein was applied to gold UltraUfoil R2/2 200 mesh grids, previously glow discharged for 60 sec at 20 mA in a GlowQube Plus (Electron Microscopy Sciences) in the presence of amylamine vapor. For BAM- LL, BAM- P5L and BAM- LL in complex with Fab1, 3 \(\mu \mathrm{L}\) of sample was applied to copper QUANTIFOIL R1.2/1.3 300 mesh, copper QUANTIFOIL R0.6/1 400 mesh and gold UltraUfoil R1.2/1.3 300 mesh grids (Electron Microscopy Sciences), respectively, that were previously glow discharged for 30 sec at 60 mA in a GlowQube Plus (Electron Microscopy Sciences). Grids were blotted for 6 sec with Whatman #1 filter paper at 4 °C and 80- 100% relative humidity, before plunge freezing in liquid ethane using a Vitrobot Mark IV (ThermoFisher). + +## CryoEM Imaging + +Data were collected on a 300 KeV Titan Krios (ThermoFisher) EM in the Astbury Biostructure Laboratory in automated fashion using EPU software (ThermoFisher). Micrographs were recorded on an energy- filtered K2 detector (Gatan inc.) in counting mode, using a 100 \(\mu \mathrm{m}\) objective aperture. For BAM- LL, 6,456 micrographs were collected from a single grid over two sessions. For the Fab1- bound BAM- LL complex, 2,780 micrographs were collected from a single grid. For BAM- P5L, two grids were imaged in separate sessions, resulting in 2150 total micrographs. For the BAM- Fab1 complex, a single grid was imaged over three sessions, resulting in 4197 total micrographs. Full data collection parameters for each sample are shown in Supplementary Table 4. + +## Image Processing + +All processing was performed in RELION 3.063 (BAM- LL, BAM- Fab1, Fab1- bound BAM- LL) or 3.164 (BAM- P5L) unless otherwise stated. Dose- fractionated micrographs were motion- corrected and dose- weighted by MotionCor65, before estimation of contrast transfer function parameters by Gct66 using the motion corrected and dose- weighted micrographs, + +<--- Page Split ---> + +apart from the BAM- Fab1 complex where motion corrected, but non- dose weighted, micrographs were used. + +For BAM- LL, the two datasets were initially processed separately in a similar manner (Supplementary Fig. 6). For dataset 1, 299,458 particles were first picked using the general model in crYOLO 1.3.567, and extracted in 300 pixel (321 Å) boxes with two- fold binning, before removal of false positives through two rounds of 2D classification. The resulting 234,598 particles were then used to generate an initial model by stochastic gradient descent68, which was used as the starting model for a 3D classification. Two high resolution classes corresponding to different conformations of BAM- LL were obtained, one termed lateral- closed (86,615 particles) and one lateral- open (83,803 particles). Particles corresponding to each class were then re- extracted unbinned, and autorefined with a mask excluding bulk solvent. After masking and sharpening, resolutions of 5.0 Å (lateral- closed) and 5.9 Å (lateral- open) were obtained. Processing of dataset 2 proceeded similarly and resulted in comparable resolutions for both conformations. To achieve higher resolution, one round of CTF refinement followed by Bayesian polishing were then employed for each dataset, following which the particles corresponding to the same conformation were combined, resulting in 160,118 lateral- closed and 141,612 lateral- open particles. Finally these particle stacks were subject to separate non- uniform refinements in cryoSPARC v2.2.068,69. Masking and sharpening of the resulting half- maps in RELION resulted in resolutions of 4.1 Å (lateral- closed) and 4.8 Å (lateral- open). B- factors of - 107 Å2 and - 127 Å2 were applied to the final lateral- closed and lateral- open reconstructions, respectively. Local resolution was estimated using RELION. + +For the BAM- Fab1 complex (Supplementary Fig. 8), particles were autopicked in RELION 363 using class averages from a previous reconstruction8 filtered to 30 Å as search templates. Individual particles were extracted in 350 pixel (374.5 Å) boxes and culled with multiple rounds of 2D and 3D classification. The resulting particle stack containing 131,853 particles was further refined using the non- uniform refinement function in CryoSPARC v2.2.068,69. The reconstruction was performed on independent subsets and final resolution of 5.2 Å determined by 'gold standard' FSC70. A B- factor of - 167 Å2 was applied to the final reconstruction. + +For BAM- P5L (Supplementary Figs. 9 and 10), particles were picked in crYOLO 1.4.1 using the general model. For dataset 1: 41, 316 particles were picked and extracted in a 280 pixel (300 Å) box, for dataset 2: 54, 532 particles were picked and extracted into 352 pixel (300 Å) boxes. Both used twofold binning. The extracted particles were combined into a single dataset and the resulting 95,848 particles passed through 2D classification. The best 21, 483 particles were used to construct an initial model by stochastic gradient descent68, which was used as a reference for 3D classification of the 43,280 good particles from 2D + +<--- Page Split ---> + +classification. The resulting 24, 101 particles were autorefined, and re- extracted as unbinned particles and subject to 3D classification using the autorefined model as the reference. The resulting 19,044 particles were autorefined with a mask to a resolution of 10.3 Å. A B- factor of - 671 Ų was applied to the final reconstruction + +For the Fab1- bound BAM- LL complex (Supplementary Fig. 11), particles were picked in crYOLO 1.4.1 using a model trained with 11 handpicked micrographs spanning the defoci range. The resulting 162,844 particles were extracted in 300 (321 Å) pixel boxes with twofold binning. One round of 2D classification was used to cull the particle set to 108,096 particles which was then subject to 3D classification, using an initial model generated by stochastic gradient descent68 from the best 32,645 particles in that stack as a template. From this 3D classification run, only one conformer was observed, corresponding to a lateral- open, BAM- LL bound to Fab1. The 71,675 particles in the highest resolution class were autorefined, re- extracted as unbinned particles and subject to 3D classification using the autorefined model as the reference, further culling the particle stack. Autorefinement and sharpening of the resulting 61,777 good particles gave a resolution of 7.3 Å. Finally, one round of CTF refinement followed by Bayesian polishing was carried out, and the resulting particle stacks were subject to non- uniform refinement in cryoSPARC v2.2.068,69. Masking and sharpening of the resulting half- maps in RELION resulted in a resolution of 7.1 Å. A B- factor of - 274 Ų was applied to the final reconstruction. + +## CryoEM model building and refinement + +For LL- BAM in the lateral- closed cryoEM map, an existing crystal structure of intact BAM in a lateral- closed conformation (PDB ID: 5D0O5) was first edited to both remove the two natural cysteines in BamA and to insert the lid- lock disulphide bond. This starting model was fitted to the density as a rigid body in Chimera71, before performing several iterations of real- space refinement in PHENIX 1.1472 with secondary structure restraints followed by manual refinement in COOT73, until satisfactory geometry and fit between model and map was obtained as assessed using MolProbity74. The extracellular region of eL6 (BamA675- 702, C- terminal globular domains of BamC (BamC89- 344), and regions at the chain termini of BamABCDE were insufficiently resolved and were not modelled. The final model contains BamA24- 675, 702- 810 BamB31- 391, BamC30- 85, BamD27- 244, BamE29- 111. + +As the resolution of the other structures was insufficient for the above approach, Molecular Dynamics Flexible Fitting (MDFF)40 was used to flexibly fit these conformations. For BAM- LL lateral- open, cascade MDFF (cMDFF) simulations of the lateral- closed atomic model with BamA truncated after residue 809 were first used to derive an initial fit to the lid- lock lateral- open cryoEM map. Here, a series of Gaussian blurred density maps were generated using the volutil function in VMD (halfwidths \(\sigma = 0, 1, \ldots , 6 \text{Å}\) ). The atomic model was then simulated in vacuum and subject to an external potential derived from most blurred density + +<--- Page Split ---> + +map, causing it to be flexibly fit into the density. 2 ps of minimisation followed by 100 ps of equilibration were run with a gscale of 1.0 defining the strength of the external potential derived from the density map. Consecutive 100 ps simulations were then run into maps of decreasing blurring, where the end coordinates from the previous simulation were used as input for the next, until reaching the unblurred map. At each step, isomerism, chirality and secondary structure restraints were applied. Several repeats were run, taking advantage of the stochastic nature of the simulation to generate different fits. Additionally, a second MDFF simulation was also run into the unblurred map using PDB- 5LJO8 as a starting model, to derive better conformations for BamA720- 734 and BamA807, 808. These models were then manually combined to give best mainchain fit to the density, before minimising against the unblurred map for 40 ps. In the combined model, BamA429- 440, corresponding to eL1 and the extracellular sides of \(\beta 1\) and \(\beta 2\) , was fitting into micelle density rather than protein density due to the low resolution in this region. A final set of 500 ps MDFF simulations were therefore run with this combined model against the unblurred map, in which BamA429- 440 was not subject to the external potential. The best fitting structure from these runs was then minimised for 40 ps against the unblurred map and real space refined in PHENIX 1.1472 with secondary structure restraints to generate the final atomic model. + +For the Fab1- bound wild- type BAM complex, an initial model was created from the BAM complex PDB entry 5LJO8, with BamA687- 700 from 5EKQ5, and the Fab1 crystal structure determined here (PDB 7BM5). The C- terminal globular domains of BamC were truncated, leaving only the lasso75 region (residues 25- 83) resulting in a starting model containing BamA24- 806, BamB22- 392, BamC25- 83, BamD26- 243, and BamE24- 110. The starting model was fitted into each EM density as a rigid body using UCSF Chimera71 and flexibly fit using cMDFF40. This was followed by real space refinement in PHENIX 1.1472 using secondary structure restraints to generate the final atomic model, with the Fab1 crystal structure used as a reference model to generate additional restraints. + +For the Fab1- bound lid- locked BAM complex, the final lid- locked lateral- open structure and the Fab1 crystal structure were rigid body fitted into the EM density using UCSF Chimera and flexibly fit using a round of MDFF into the unblurred map. This was followed by real space refinement in PHENIX 1.14 with secondary structure restraints to generate the final atomic model, with the Fab1 crystal structure and the final lid- locked lateral- open structures used as reference models to generate additional restraints. During the simulation eL1 of BamA (BamA429- 440) was not subject to the external potential to prevent overfitting to micelle density in this region. Model building statistics for all cryoEM conformers are shown in Supplementary Table 5. + +## Crystallisation and structure determination of Fab1 + +<--- Page Split ---> + +Fab1 at 6.5 mg/mL was crystallised by the sitting drop vapour diffusion method in 96- well SWISSC1 3- drop plates at \(20^{\circ}C\) . Drops consisted of 100 nL protein and 100 nL crystallisation solution were dispensed using a Mosquito robot (TTP Labtech). Crystals were grown in 0.16 M lithium chloride, \(22\%\) (w/v) PEG6000, 0.1 M MES pH 6.0 and were harvested after 21 days. Crystals were cryo- protected in the crystallisation solution supplemented with \(20\%\) (v/v) ethylene glycol before flash- cooling into liquid nitrogen. X- ray data were collected at Diamond Light Source on beamline I24 from a single cryo- cooled crystal (100 K) using a Pilatus3 6M detector. Diffraction data were collected for a total of \(180^{\circ}\) up to a resolution of \(2.5 \AA\) with a \(0.2^{\circ}\) oscillation using an exposure time of 0.04 seconds at \(100\%\) transmission. X- ray diffraction data were indexed and integrated by autoPROC and STARANISO \(^{76}\) and were scaled to \(2.96 \AA\) in Aimless \(^{77}\) using the I24 beamline autoprocessing pipeline. The crystals belonged to a monoclinic space group \(P12_{1}1\) with unit cell parameters a = 92.0 Å, b = 130.1 Å, c = 138.9 Å, \(\alpha = 90.00^{\circ}\) , \(\beta = 106.1^{\circ}\) , \(\gamma = 90.00^{\circ}\) . The structure was solved by molecular replacement using Phaser \(^{78}\) and the C \(_{H}\) domain of the anti- NFG Fab as the search model (PDB accession number 1ZAN \(^{79}\) ). Crystallographic refinement was performed using PHENIX- 1.9 \(^{72,80}\) and model building was carried out in Coot \(^{73}\) . MolProbity \(^{74}\) was used for structure validation and quality assessment. The final model coordinates and structure factors are deposited in the PDB under the accession number 7BM5. + +## Reconstitution of BamA and different BAM complexes into DMPC proteoliposomes + +DMPC (dIC14:0PC), purchased as powder from Avanti Polar Lipids (Alabaster, AL), was dissolved in \(80:20\) (v/v) chloroform/methanol mixture at \(25 \text{mg/mL}\) . Appropriate volumes were dried to thin films in clean Pyrex tubes at \(42^{\circ}C\) under \(\mathsf{N}_2\) gas, and were further dried by vacuum desiccation for \(>3\) hours. BAM WT, BAM- LL and BAM- P5L or a 2:1 (mol/mol) mixture of Fab1 and BAM in TBS pH 8.0, \(0.05\%\) (w/v) DDM were mixed with DMPC lipid solubilized in TBS pH 8.0, \(0.05\%\) (w/v) DMD at a lipid to protein ratio (LPR) of 1600:1 (mol/mol). For BamA, DMPC lipid was first solubilised in TBS pH 8.0, \(0.1\%\) (w/v) LDAO. Empty liposomes were prepared by mixing DDM- solubilised lipid with an equivalent volume of buffer. Dialysis was performed as described for the preparation of E. coli polar lipid proteoliposomes, except that a temperature of \(30^{\circ}C\) was used (above the DMPC transition temperature). Following dialysis, the proteoliposomes were pelleted twice by ultracentrifugation at \(100,000 \text{g}\) for 30 min at \(4^{\circ}C\) and resuspended in TBS pH 8.0. The proteoliposomes were then extruded with 21 passes through a \(0.1 \mu \text{m}\) polycarbonate membrane using a mini- extruder (Avanti) pre- equilibrated at \(30^{\circ}C\) . Following ultracentrifugation as before, proteoliposomes were resuspended in TBS pH 8.0, protein concentration was determined using a BCA assay (ThermoScientific) and successful reconstitution was confirmed using SDS- PAGE. + +<--- Page Split ---> + +## Probing lipid disorder using laurdan + +Probing lipid disorder using laurdanLaurdan (Cambridge Bioscience) dissolved in DMSO was added to a final concentration of \(4.2 \mu M\) (final DMSO concentration of \(0.15\%\) (v/v)) to a \(0.8 \mu M\) suspension of BAM-, BamA- or empty- DMPC proteoliposomes (LPR 1600:1 mol/mol). The proteoliposomes were incubated at \(25 ^{\circ} C\) overnight to allow random partitioning of the laurdan probe into the membrane. Fluorescence emission was measured at \(440 nm\) and \(490 nm\) for a total time of 10 sec following excitation of laurdan fluorescence at \(340 nm\) in quartz cuvettes using a PTI QuantaMaster fluorimeter with a \(1 nm\) bandwidth and 1 second integration time. Excitation and emission slit widths were set to \(0.1 nm\) . Spectra were acquired at increasing temperature intervals from \(6 ^{\circ} C\) to \(40 ^{\circ} C\) , and to test reversibility, from \(40 ^{\circ} C\) to \(6 ^{\circ} C\) , allowing the sample to equilibrate at each temperature for 3 min. Generalised polarisation (GP) \(^{46}\) was calculated from the ratio of fluorescence intensity at \(440 nm\) and \(490 nm\) , averaged over the 10 second acquisition, using the formula GP = \((l_{440} - l_{490}) / (l_{440} + l_{490})\) , and was plotted against temperature. Mid- points and gradients of the transitions were determined by calculating the first derivative of the curve. + +## Data availability + +Data availabilityRaw micrographs for each dataset are deposited at EMPIAR under accession numbers XXXX (BAM- LL), XXXX (BAM Fab1 complex), XXXX (BAM- P5L), XXXX (BAM- LL Fab1 complex). The final density maps are deposited in the EMDB under accession numbers XXXX (BAM- LL lateral- closed), XXXX (BAM- LL lateral- open), XXXX (BAM Fab1 complex), XXXX (BAM- P5L) and XXXX (BAM- LL Fab1 complex). Final model coordinates have been deposited in the PDB under accession numbers XXXX (BAM- LL lateral- closed), XXXX (BAM- LL lateral- open), XXXX (BAM Fab1 complex) and XXXX (BAM- LL Fab1 complex). The crystal structure of Fab1 has been deposited in the PDB under accession number 7BM5, and crystallographic data are available at https://doi.org/10.2210/pdb7BM5/pdb. Data supporting this study are freely available at the University of Leeds Data Repository: https://doi.org/10.5518/835. + +## Acknowledgements + +AcknowledgementsWe thank members of the Radford, Ranson, Brockwell and Rutherford labs for helpful discussions, and Nasir Khan for technical support. CryoEM data were collected at the Astbury Biostructure Laboratory, funded by the University of Leeds and the Wellcome Trust (108466/Z/15/Z). We thank Diamond Light Source for access to Beamline i24 (MX19248). P.W and M.G.I acknowledge funding from the Medical Research Council UK + +<--- Page Split ---> + +(MR/P018491/1). S.H and J.E.H are funded by the White Rose BBSRC DTP (BB/M011151/1) J.M. and A.J.H acknowledge support from the Wellcome Trust (222373/Z/21/Z and 105220/Z/14/Z, respectively). B.S acknowledges support from the BBSRC (BB/N007603/1 and BB/T000635/1). For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. J.W. is funded by an EOS Excellence in Research Program of the FWO and FRS-FNRS (G0G0818N). SER holds a Royal Society Professorial Fellowship (RSRP/R1/211057). + +## Author contributions + +P.W, S.F.H, M.G.I and A.J.H designed and performed the experiments and analysed the data. P.W, S.F.H, J.M.M, A.J.H, B.S and C.C.P carried out BAM functional assays. P.W prepared protein samples for cryoEM. S.F.H, M.G.I and J.M.M performed cryoEM experiments and determined BAM cryoEM structures. P.W solved the X-ray structure of Fab1. P.W, S.F.H, J.E.H, B.S, C.C.P and J.M.W produced proteins required for the study. J.E.H developed the BAM laurdan fluorescence assay. K.M.S and S.T.R developed and produced the anti- BamA Fab fragment (Fab1). S.E.R, N.A.R and D.J.B supervised the research. P.W, S.F.H and M.G.I wrote the manuscript with comments and edits provided from all authors. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1 | Disulphide-locked BamA variants and Fab1 binding impair BAM-mediated OMP folding in vitro. (a) BAM-P5L (G393C/G584C) is expected to lock BamA in the lateral-open conformation (PDB code 5LJO8), while (b) BAM-LL (E435C/S665C) is expected to lock BamA in the lateral-closed conformation (PDB code 5DOO6). BamA POTRAs 1-4 and BamBCDE are rendered semi-transparent for emphasis on the BamA \(\beta\) -barrel and POTRA-5. The position of the disulphide bond is shown as a yellow bar. Figure made in PyMOL v1.7.2.3. (c and d) Quantification of folded and unfolded bands from SDS-PAGE band-shift
+ +<--- Page Split ---> + +assays (Supplementary Figs. 3 and 4) plotted as fraction folded against time for tOmpA or OmpX, respectively. Data are fitted to a single exponential function. (e and f) The initial rates of folding (determined by applying a linear fit to the first \(5\%\) of folding data) normalised as a percentage of the initial rate obtained for WT BAM, are shown for (e) tOmpA and (f) OmpX folding (see also Supplementary Table 1). Folding assays were repeated to assess reproducibility, with errors for replicate initial rate measurements listed in Supplementary Table 1. Folding yields after 24 hours are reported in Supplementary Table 2. Figures labelled with "BAM" refer to the full BAM complex (BamABCDE), whilst "BamA" is just BamA alone. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2 | CryoEM resolves two conformations of BAM-LL in detergent. (a) 4.1 A cryoEM map of the BAM-LL lateral-closed conformation at a contour of \(10\sigma\) , coloured by subunit. The lateral-gate is closed and POTRA-5 does not block the BamA barrel (schematic inset). (b) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on the segmented density for the barrel and POTRA-5 of BamA. \(\beta 1\) and \(\beta 16\) contact to close the gate. (c) The same density viewed from the periplasmic side, showing the open lumen of the BamA barrel in this conformation. (d) 4.8 Å cryoEM map of the BAM-LL lateral-open conformation at a contour of \(10\sigma\) , coloured by subunit. The lateral-gate is open and POTRA-5 occludes the BamA barrel (schematic inset). (e) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on segmented density for the barrel and POTRA-5 of BamA. To satisfy the disulphide in this conformation, eL1 must bend back into the barrel to contact eL6. (f) The same density viewed from the periplasmic side, showing that the BamA lumen is blocked by POTRA-5 in this conformation. Fig. made in UCSF ChimeraX76. Segmenting and colouring performed with corresponding atomic models. Less well resolved regions and the micelle have been masked.
+ +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3 | Fab1-bound BAM is in a lateral-open conformation. (a) 5.1 Å cryoEM map of the BAM-Fab1 complex in a lateral-open conformation at a contour of \(10\sigma\) , coloured by subunit. The lateral-gate is fully-open and POTRA-5 occludes the BamA barrel (schematic inset). (b) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on the segmented density for the barrel and POTRA-5 of BamA. \(\beta 1\) is in a conformation that makes limited contact with \(\beta 16\) . (c) The same density viewed from the periplasmic side, showing that the BamA lumen is blocked by POTRA-5 in this conformation. Panels made using UCSF ChimeraX76. Segmenting and colouring performed with corresponding atomic models. Less well resolved regions and the micelle have been masked. (d) Close up of the BamA-Fab1 interface region highlighting the Fab1 CDRs (red) interacting with eL4 of BamA (dark blue). Other regions of BamA are rendered semi-transparent to highlight eL4. Heavy and light chains of Fab1 are coloured cyan and pink, respectively. (e) The \(\mathrm{V_L}\) and \(\mathrm{V_H}\) domains of Fab1 variable form a complementary binding surface for eL4 of BamA involving residues Y550, E554 and H555.
+ +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4 | Additive effect of BAM inhibition by disulphide-locking and binding of Fab1. (a) 7.1 Å cryoEM map of the Fab1-bound LL-BAM in a lateral-open conformation at a contour of 9.5 σ, coloured by subunit. The lateral-gate is open and POTRA-5 occludes the BamA barrel (schematic inset). (b) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on the segmented density for the β-barrel and
+ +<--- Page Split ---> + +POTRA- 5 of BamA. To satisfy the disulphide in this conformation, eL1 must bend back into the barrel to contact eL6. (c) The same density viewed from the periplasmic side, showing that the BamA lumen is blocked by POTRA- 5 in this conformation. Structural panels made using UCSF ChimeraX76. Segmenting and colouring performed with corresponding atomic models. Less well resolved regions and the micelle have been masked. (d and e) Quantification of SDS- PAGE band- shift assays shown in Supplementary Fig. 13 for (d) tOmpA and (e) OmpX folding catalysed by BAM- P5L (green), BAM- LL (blue) and WT BAM (black), each with and without Fab1 (solid and open circles, respectively). (f and g) The initial rates, calculated by applying a linear fit to the first 5% of fitted folding data, were normalised to that of WT BAM, and are shown for (f) tOmpA and (g) OmpX folding (see also Supplementary Table 1). Folding assays were conducted twice for reproducibility with data for replicate initial rate measurements listed in Supplementary Table 1. Folding yields after 24 hours are reported in Supplementary Table 2. Figures labelled with "BAM" refer to the full BAM complex (BamABCDE). + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Fig. 5 | BAM variants reduce the phase transition temperature of DMPC liposomes. Global lipid phase transition behaviour for each BAM variant and BamA in DMPC proteoliposomes, with an empty liposomes control measured using laurdan fluorescence. (a) The ratio of laurdan fluorescence at \(440 \text{nm}\) and \(490 \text{nm}\) was plotted as generalised polarisation (GP, see Methods) against temperature for \(0.8 \mu \text{M BAM/BamA proteoliposome}\) suspensions at a \(1600:1\) (mol/mol) lipid-to-protein ratio (LPR) with added laurdan (at a \(305:1\) lipid-to-laurdan ratio) in TBS pH 8.0. (b) The first derivative of data shown in (a) showing the transition temperature for each liposome suspension as the point of steepest (most negative) gradient. Whilst empty DMPC (grey) and BamA proteoliposomes (purple) have a transition temperature of \(24 \text{‰}\) , the presence of WT BAM (black), BAM-Fab1 (red), BAM-P5L (green), BAM-LL (blue), BAM-P5L + Fab1 (orange) and BAM-LL + Fab1 (yellow)
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PHENIX: A comprehensive Python- based system for 988 macromolecular structure solution. Acta Crystallogr. Sect. D Biol. Crystallogr. 66, 989 213- 221 (2010). 990 + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1
+ +Disulphide- locked BamA variants and Fab1 binding impair BAM- mediated OMP folding in vitro. (a) BAM- P5L (G393C/G584C) is expected to lock BamA in the lateral- open conformation (PDB code 5LJ08), while (b) BAM- LL (E435C/S665C) is expected to lock BamA in the lateral- closed conformation (PDB code + +<--- Page Split ---> + +5D006). BamA POTRAs 1- 4 and BamBCDE are rendered semi- transparent for emphasis on the BamA β- barrel and POTRA- 5. The position of the disulphide bond is shown as a yellow bar. Figure made in PyMOL v1.7.2.3. (c and d) Quantification of folded and unfolded bands from SDS- PAGE band- shift assays (Supplementary Figs. 3 and 4) plotted as fraction folded against time for tOmpA or OmpX, respectively. Data are fitted to a single exponential function. (e and f) The initial rates of folding (determined by applying a linear fit to the first 5% of folding data) normalised as a percentage of the initial rate obtained for WT BAM, are shown for (e) tOmpA and (f) OmpX folding (see also Supplementary Table 1). Folding assays were repeated to assess reproducibility, with errors for replicate initial rate measurements listed in Supplementary Table 1. Folding yields after 24 hours are reported in Supplementary Table 2. Figures labelled with "BAM" refer to the full BAM complex (BamABCDE), whilst "BamA" is just BamA alone. + +![](images/Figure_2.jpg) + +
Figure 2
+ +CryoEM resolves two conformations of BAM- LL in detergent. (a) 4.1 723 Å cryoEM map of the BAM- LL lateral- closed conformation at a contour of 10 σ, coloured by subunit. The lateral- gate is closed and POTRA- 5 does not block the BamA barrel (schematic inset). (b) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on the segmented density for the barrel and POTRA- 5 of BamA. β1 and β16 contact to close the gate. (c) The same density viewed from the + +<--- Page Split ---> + +periplasmic side, showing the open lumen of the BamA barrel in this conformation. (d) 4.8 Å cryoEM map of the BAM- LL lateral-open conformation at a contour of 10 σ, coloured by subunit. The lateral- gate is open and POTRA- 5 occludes the BamA barrel (schematic inset). (e) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on segmented density for the barrel and POTRA- 5 of BamA. To satisfy the disulphide in this conformation, eL1 must bend back into the barrel to contact eL6. (f) The same density viewed from the periplasmic side, showing that the BamA lumen is blocked by POTRA- 5 in this conformation. Fig. made in UCSF ChimeraX76. Segmenting and colouring performed with corresponding atomic models. Less well resolved regions and the micelle have been masked. + +![](images/Figure_3.jpg) + +
Figure 3
+ +Fab1- bound BAM is in a lateral- open conformation. (a) 5.1 Å cryoEM map of the BAM- Fab1 complex in a lateral- open conformation at a contour of 10 σ, coloured by subunit. The lateral- gate is fully- open and + +<--- Page Split ---> + +POTRA- 5 occludes the BamA barrel (schematic inset). (b) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on the segmented density for the barrel and POTRA- 5 of BamA. \(\beta 1\) is in a conformation that makes limited contact with \(\beta 16\) . (c) The same density viewed from the periplasmic side, showing that the BamA lumen is blocked by POTRA- 5 in this conformation. Panels made using UCSF ChimeraX76. Segmenting and colouring performed with corresponding atomic models. Less well resolved regions and the micelle have been masked. (d) Close up of the BamA- Fab1 interface region highlighting the Fab1 CDRs (red) interacting with eL4 of BamA (dark blue). Other regions of BamA are rendered semi- transparent to highlight eL4. Heavy and light chains of Fab1 are coloured cyan and pink, respectively. (e) The VL and VH domains of Fab1 variable form a complementary binding surface for eL4 of BamA involving residues Y550, E554 and H555. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4
+ +Additive effect of BAM inhibition by disulphide- locking and binding of Fab1. (a) 7.1 Å cryoEM map of the Fab1- bound LL- BAM in a lateral- open conformation at a contour of 9.5 σ, coloured by subunit. The lateral- gate is open and POTRA- 5 occludes the BamA barrel (schematic inset). (b) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on the segmented density for the β- barrel and POTRA- 5 of BamA. To satisfy the disulphide in this conformation, eL1 must bend back into the barrel + +<--- Page Split ---> + +to contact eL6. (c) The same density viewed from the periplasmic side, showing that the BamA lumen is blocked by POTRA- 5 in this conformation. Structural panels made using UCSF ChimeraX76. Segmenting and colouring performed with corresponding atomic models. Less well resolved regions and the micelle have been masked. (d and e) Quantification of SDS- PAGE band- shift assays shown in Supplementary Fig. 13 for (d) tOmpA and (e) OmpX folding catalysed by BAM- P5L (green), BAM- LL (blue) and WT BAM (black), each with and without Fab1 (solid and open circles, respectively). (f and g) The initial rates, calculated by applying a linear fit to the first \(5\%\) of fitted folding data, were normalised to that of WT BAM, and are shown for (f) tOmpA and (g) OmpX folding (see also Supplementary Table 1). Folding assays were conducted twice for reproducibility with data for replicate initial rate measurements listed in Supplementary Table 1. Folding yields after 24 hours are reported in Supplementary Table 2. Figures labelled with "BAM" refer to the full BAM complex (BamABCDE). + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Figure 5
+ +![PLACEHOLDER_42_1] + + +<--- Page Split ---> + +305:1 lipid- to- laurdan ratio) in TBS pH 8.0. (b) The first derivative of data shown in (a) showing the transition temperature for each liposome suspension as the point of steepest (most negative) gradient. Whilst empty DMPC (grey) and BamA proteoliposomes (purple) have a transition temperature of \(24^{\circ}C\) , the presence of WT BAM (black), BAM- Fab1 (red), BAM- P5L (green), BAM- LL (blue), BAM- P5L + Fab1 (orange) and BAM- LL + Fab1 (yellow) broaden the phase transition and lower the transition temperature. Figures labelled with "BAM" refer to the full BAM complex (BamABCDE), whilst "BamA" is just BamA alone. + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementaryInformationsubmitted.pdf ValidationReports.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__07090632be97150b9482692b3758503018ba474d5a059546c8cd240472d00789/preprint__07090632be97150b9482692b3758503018ba474d5a059546c8cd240472d00789_det.mmd b/preprint/preprint__07090632be97150b9482692b3758503018ba474d5a059546c8cd240472d00789/preprint__07090632be97150b9482692b3758503018ba474d5a059546c8cd240472d00789_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..8eea5bd01a70ac1d5b566615f4a3143048e815da --- /dev/null +++ b/preprint/preprint__07090632be97150b9482692b3758503018ba474d5a059546c8cd240472d00789/preprint__07090632be97150b9482692b3758503018ba474d5a059546c8cd240472d00789_det.mmd @@ -0,0 +1,415 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 904, 177]]<|/det|> +# The role of membrane destabilisation and protein dynamics in BAM catalysed OMP folding. + +<|ref|>text<|/ref|><|det|>[[44, 196, 580, 848]]<|/det|> +Paul White University of Leeds Samuel Haysom University of Leeds https://orcid.org/0000- 0002- 8769- 090X Matthew ladanza University of Leeds Anna Higgins University of Leeds Jonathan Machin University of Leeds Jim Home University of Leeds https://orcid.org/0000- 0001- 5260- 2634 Bob Schiffrin University of Leeds Charlotte Carpenter-Platt University of Leeds James Whitehouse University of Leeds Kelly Storek Genentech Inc Steven Rutherford Genentech Inc https://orcid.org/0000- 0002- 4758- 4248 David Brockwell University of Leeds https://orcid.org/0000- 0002- 0802- 5937 Neil Ranson University of Leeds https://orcid.org/0000- 0002- 3640- 5275 Sheena Radford ( s.e.radford@leeds.ac.uk ) University of Leeds https://orcid.org/0000- 0002- 3079- 8039 + +<|ref|>sub_title<|/ref|><|det|>[[44, 882, 101, 899]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 918, 870, 939]]<|/det|> +Keywords: outer membrane proteins (OMPs), \(\beta\) - barrel assembly machinery (BAM), OMP folding. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[44, 46, 327, 64]]<|/det|> +**Posted Date:** February 2nd, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 85, 465, 102]]<|/det|> +**DOI:** https://doi.org/10.21203/rs.3.rs-155135/v1 + +<|ref|>text<|/ref|><|det|>[[44, 122, 909, 163]]<|/det|> +**License:** © This work is licensed under a Creative Commons Attribution 4.0 International License. +Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 201, 949, 241]]<|/det|> +**Version of Record:** A version of this preprint was published at Nature Communications on July 7th, 2021. +See the published version at https://doi.org/10.1038/s41467-021-24432-x. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[71, 80, 884, 540]]<|/det|> +1 The role of membrane destabilisation and protein dynamics in BAM catalysed OMP folding 3 4 Paul White1\\*, Samuel F. Haysom1\\*, Matthew G. Iadanza1\\*, Anna J. Higgins1, Jonathan M. Machin1, Jim E. Horne1#, Bob Schiffrin1, Charlotte Carpenter-Platt1, James M. Whitehouse1, Kelly M. Storek2, Steven T. Rutherford2, David J. Brockwell1, Neil A. Ranson1\\*, Sheena E. Radford1\* 8 9 1 Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, 10 Faculty of Biological Sciences, University of Leeds, Leeds, LS2 9JT, UK 11 2 Department of Infectious Diseases, Genentech Inc., South San Francisco, CA 94080 12 4 Contributed equally 13 \* Current affiliation Scientific Computing Department, Science and Technology Facilities 14 Council, Research Complex at Harwell, Didcot, OX11 0FA, UK 15 # Current affiliation: Department of Biochemistry, University of Oxford, Oxford, OX1 3QU, UK 16 17 \*Correspondence: n.a.ranson@leeds.ac.uk; s.e.radford@leeds.ac.uk 18 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[70, 83, 197, 100]]<|/det|> +## 19 Abstract + +<|ref|>text<|/ref|><|det|>[[115, 110, 882, 362]]<|/det|> +The folding of \(\beta\) - barrel outer membrane proteins (OMPs) in Gram- negative bacteria is catalysed by the \(\beta\) - barrel assembly machinery (BAM). How lateral opening in the \(\beta\) - barrel of the major subunit BamA assists in OMP folding, and the contribution of membrane disruption to BAM catalysis remain unresolved. Here, we use an anti- BamA monoclonal antibody fragment (Fab1) and two disulphide- crosslinked BAM variants (lid- locked (LL), and POTRA- 5- locked (P5L)) to dissect these roles. Despite being lethal in vivo, we show that all complexes catalyse folding in vitro, albeit less efficiently than wild- type BAM. CryoEM revealed that while Fab1 and BAM- P5L trap an open- barrel state, BAM- LL contains a mixture of closed and contorted, partially- open structures. Finally, all three complexes globally destabilise the lipid bilayer, while BamA does not, revealing that the BAM lipoproteins are required for this function. Together the results provide new insights into the role of BAM structure and lipid dynamics in OMP folding. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[66, 108, 883, 320]]<|/det|> +Outer membrane proteins (OMPs) in Gram negative bacteria are functionally diverse, but share a common \(\beta\) - barrel fold involving between 8 and 36 \(\beta\) - strands1. The folding and membrane insertion of OMPs is catalysed by the essential \(\beta\) - barrel assembly machinery (BAM)2- 4 which in E. coli comprises five proteins (BamABCDE). The major conserved subunit, BamA, is a 16- stranded Omp85 family member that contains five N- terminal polypeptide transport associated (POTRA) domains that extend into the periplasm to scaffold four lipoprotein BamB- E5- 8, all of which are required for maximally- efficient OMP folding9,10. BAM is essential for bacterial survival, highly conserved, and surface accessible via the extracellular loops of BamA, making the complex an attractive target for small molecule11- 13, peptide14,15 and antibody- based antibiotics16,17. + +<|ref|>text<|/ref|><|det|>[[66, 328, 883, 728]]<|/det|> +BAM exists in an ensemble of conformations, with one of the most notable differences between published structures occurring around the seam or 'lateral gate' involving \(\beta\) - strands 1 ( \(\beta 1\) ) and 16 ( \(\beta 16\) ) in the BamA barrel6- 18,20. In the 'lateral- open' conformation, as captured by cryoEM of the intact complex8 and X- ray crystallography of the BamACDE subcomplex5,6, \(\beta 1\) and \(\beta 16\) are separated. In contrast, crystal structures of the intact BAM complex are in a 'lateral- closed' conformation in both in the absence6,7 or presence of substrates21,22, wherein \(\beta 1\) and \(\beta 16\) are hydrogen bonded, albeit with fewer hydrogen bonds than exist between the other strands in the barrel1. The POTRA domains are also dynamically organised, with motions of POTRA- 5 being tightly correlated with gate conformation, with POTRA- 5 plugging entrance to the BamA \(\beta\) - barrel lumen only in the lateral- open state18. These conformational changes are essential for cell viability as disulphide bonds that purportedly lock BamA in either conformation have a lethal phenotype that is rescued by reducing agent6,19. Such variants include those that lock the lateral gate closed (e.g. G433C/N805C linking \(\beta 1\) to \(\beta 16^{8,19}\) , or E435C/S665C locking extracellular loop 1 (eL1) to eL66,19), or those that lock the BamA lateral gate in an open conformation by introducing a disulphide bond between POTRA- 5 and \(\beta\) - turn between \(\beta 8\) and \(\beta 9\) at the base of the barrel (e.g. G393C/G584C6). Disulphide bonds which restrict flexibility between POTRA domains 2 and 3 also impair growth23; how, or if, these motions correlate with structural changes at the BamA \(\beta\) - barrel is unclear. + +<|ref|>text<|/ref|><|det|>[[66, 736, 883, 903]]<|/det|> +Models of BAM- catalysed OMP insertion and folding broadly invoke two distinct roles for BAM (reviewed in24). Firstly conformational changes in BAM, and protein- protein interactions between BAM and substrate OMPs are thought to be involved in catalysing folding25- 29. These models all involve a folding intermediate in which the C- terminal \(\beta\) - strand of the substrate is associated with BamA- \(\beta 1\) , as supported by crosslinking26,27, a recent cryoEM structure of a hybrid barrel formed between BAM and tBamA (the transmembrane domain of a BamA substrate)29, and crystal structures of BAM covalently tethered to the C- terminal \(\beta\) - strands of OMP substrates OmpA and OmpLA22. Variations of these models include the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 81, 881, 356]]<|/det|> +'barrel elongation'25 and 'swing'27 models which suggest that folding begins in the periplasm, and also 'budding' models1,3,25 wherein OMPs are thought to enter the lumen of the BamA barrel and fold via sequential addition of \(\beta\) - hairpin units26. This is akin to the role proposed for the mitochondrial homologue Sam50 of the sorting and assembly machinery (SAM) complex26. An alternative model proposes that BAM may disorder its lipid environment, lowering the kinetic barrier to OMP folding, potentially allowing OMPs to fold and insert into the outer membrane without direct interaction with the \(\beta 1 - \beta 16\) seam. This 'BamA- assisted' model18,30- 32 is supported by molecular dynamics (MD) simulations which show lipid disordering and bilayer thinning by BamA20,25,30- 35, and by BAM- mediated distortion of a nanodisc18. Both protein dynamics and lipid disordering may act synergistically to maximise the efficiency of OMP folding, and different OMPs may depend on each effect to different degrees. However, little mechanistic insight is available, beyond that which has been inferred from the observation of a lethal phenotype. + +<|ref|>text<|/ref|><|det|>[[112, 363, 881, 718]]<|/det|> +Here, we investigate the roles of BAM structure/dynamics and membrane stability in OMP folding by exploiting two disulphide- locked variants termed lid- lock (LL) and POTRA- 5- lock (P5L) which are lethal in vivo6,19, and purportedly lock BamA's barrel closed and open, respectively. We also investigate a bactericidal Fab fragment (Fab1), that binds to eL4 of BamA16. We report cryoEM structures for the two disulphide locked BAM variants and the BAM- Fab1 complex, revealing that BAM- P5L and Fab1 stabilise a lateral- open conformation, whilst BAM- LL adopts both a lateral- closed state and a distorted, partially- open conformation. Despite being lethal in vivo, the two disulphide variants and the Fab1- BAM complex are all able to catalyse the folding of the 8- stranded OMPs OmpX and tOmpA (the transmembrane region of OmpA) in vitro, though less efficiently than wild- type BAM, and by combining Fab1 and disulphide- locking, BAM is further inactivated. We also demonstrate that all BAM variants studied lower the phase transition temperature of their lipid environment, but that BamA alone does not, providing direct experimental evidence that lipid disordering by BAM requires the presence of its lipoproteins. The results provide new insights into the structural features of BAM's catalytic mechanism and suggest that even subtle disruption of BAM activity may provide an effective route to the development of novel antibiotics. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 84, 189, 100]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[118, 111, 774, 130]]<|/det|> +## Disulphide-locked and Fab1-bound BAM can catalyse OMP folding in vitro + +<|ref|>text<|/ref|><|det|>[[110, 135, 881, 916]]<|/det|> +To assess the relationship between bacterial lethality and the catalytic ability of BAM we determined the in vitro folding activity of two paired cysteine mutations in BamA that are bactericidal6,19. In the BAM- P5L variant (BamA G393C/G584C)6, tethering of POTRA- 5 to the base of the BamA barrel is expected to stabilise a lateral- open conformation (Fig. 1a). By contrast, the BAM- LL variant, (BamA E435C/S665C)19 is expected to lock eL1 to eL6, and stabilise a lateral- closed conformation (Fig. 1b). The BAM- LL and BAM- P5L variants were made in a BAM construct in which the two Cys of BamA that naturally form a disulphide bond (C690 and C700), are replaced with Ser (Cys- free BAM). This variant is able to complement WT BamA in E. coli19,36 and has little effect on BAM- catalysed OMP folding rates in vitro9. We also investigated how a bactericidal anti- BamA binding antibody Fab fragment, known as Fab16,37, affects OMP folding in vitro. BAM- P5L, BAM- LL and the BAM- Fab1 complex were each reconstituted into liposomes comprised of E. coli polar lipids, and their ability to fold the 8- stranded OMPs, OmpX and tOmpA, in the presence of SurA was determined by SDS- PAGE band- shift assays38. In each case, BamA was folded (as judged by a band- shift relative to the boiled (denatured) BamA band) and all four BAM lipoproteins were present (Supplementary Fig. 1). Interestingly, Fab1 formed a stable, SDS- resistant complex with BamA (Supplementary Fig. 1b), consistent with its IC50 of 0.095 nM determined for \(\Delta waaD E. coli^{16}\) . Disulphide bond formation in BAM- P5L and BAM- LL was confirmed by the lack of fluorescein- C5- maleimide labelling, and electrophoretic band- shifts in oxidising/reducing conditions (Supplementary Fig. 2). Both tOmpA or OmpX do not fold spontaneously into the liposomes formed from E. coli polar lipids, but fold rapidly and efficiently into liposomes formed from the same lipids containing WT BAM (Fig. 1c and d). Remarkably, considering their in vivo lethality6,16,19, the efficiency of folding and membrane insertion of tOmpA and OmpX is reduced, but not abolished, by BAM- P5L, BAM- LL and BAM- Fab1, with folding yields of \(\sim 50\%\) for tOmpA and \(\sim 15 - 30\%\) for OmpX after 3 hours at 25 °C (note that tOmpA folds more rapidly than OmpX with WT BAM) (Fig. 1c and d, and Supplementary Fig. 3 and 4). Relative to WT BAM, the initial rates of folding for BAM- Fab1, BAM- LL and BAM- P5L ranged from 16- 20% for tOmpA, and 8- 29% for OmpX (Fig. 1e and f, respectively, and Supplementary Table 1). When the disulphide bond in BAM- P5L and BAM- LL is reduced with DTT, folding activity surpassed that of WT BAM. This effect was not observed for WT BAM, or Cys- free BAM (Supplementary Fig. 5). Folding into proteoliposomes containing BamA alone was much slower than observed with BAM- P5L, BAM- LL, or BAM- Fab1, with initial folding rates for both substrates reaching \(\sim 3\%\) of that WT BAM, highlighting the importance of the accessory lipoproteins for efficient catalysis of folding of these OMPs39. Importantly, the inhibited BAM variants were able to fold their OMP substrates to 80- 100% completion after 24 hours, whilst incubation with BamA alone resulted in folding yields of only 50% and 16% for tOmpA and OmpX, respectively, after 24 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 81, 880, 165]]<|/det|> +hours (note that both substrates were unable to fold into empty liposomes even on these extended timescales) (Supplementary Table 2). Collectively, these results show that although both Fab1 binding and disulphide- locking of BamA are lethal in vivo6,16,19, the BAM- catalysed folding of OmpX and tOmpA is only partially inhibited in vitro. + +<|ref|>sub_title<|/ref|><|det|>[[118, 203, 512, 220]]<|/det|> +## Lid-locked BAM exists in two conformations + +<|ref|>text<|/ref|><|det|>[[113, 225, 881, 844]]<|/det|> +To understand the molecular basis of inhibition, we determined the structure of BAM- LL in DDM detergent micelles using cryoEM. We predicted, based on the lethality of this mutation and the crystal/cryoEM structures of BAM in its different conformational states5- 8, that the formation of a disulphide bond between C435 and C665 would trap BAM in a lateral- closed state (Fig. 1b). However, 3D classification of cryoEM data of this construct revealed two distinct, approximately equally populated, structures (Fig. 2 and Supplementary Fig. 6). The first structure (at 4.1 Å resolution) is similar to the crystal structure of intact BAM in the lateral- closed conformation, with pairing of β1 and β16 (Fig. 2a,b) and displacement of POTRA- 5 from beneath the barrel (Fig. 2c). The second structure (at 4.8 Å) has β1 and β16 separated (Fig. 2d, e) and POTRA- 5 occludes the periplasmic face of the BamA barrel (Fig. 2f), and is thus consistent with a lateral- open conformation. In all previous lateral- open structures5,6,8, extracellular loop 1 (eL1) bends away from the BamA β- barrel, separating the lid- lock cysteine positions (C435 and C665) by \(\sim 20\) Å. Given the unequivocal in vitro biochemical evidence for formation of the lid- lock disulphide (Supplementary Fig. 2), eL1 must be distorted to allow disulphide bond formation with eL6. However, poor resolution in this region of the map, itself indicative of mobility, prevented modelling of this new eL1 conformation. We therefore used molecular dynamics- based flexible fitting (MDFF)40 to morph the lateral- closed BAM- LL atomic model into the density observed in the second conformation, whilst maintaining the disulphide link. This generated a chemically plausible loop conformation (Fig. 2e), but this is not constrained by the EM density. The difference between eL1 conformations in the two BAM- LL structures is striking, and suggests that this region must be highly malleable to allow disulphide bond formation within the BamA β- barrel. Interestingly, the 'contorted open' BAM- LL structure closely resembles a recent structure of WT BAM in saponin nanodiscs22 in which eL1 adopts this inward conformation in the absence of disulphide tethering. In accord with this idea, eL1 can adopt a wide range of conformations in lateral- open BAM structures (Supplementary Fig. 7). Overall, these data suggest that the lid- lock disulphide biases the conformational ensemble toward a lateral- closed conformation, but cannot completely pull the conformational equilibrium over to that state, consistent with BAM adopting only the lateral- open state in DDM detergent8. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 82, 630, 100]]<|/det|> +## Fab1-bound BAM and BAM-P5L adopt a lateral-open state + +<|ref|>text<|/ref|><|det|>[[115, 110, 882, 675]]<|/det|> +Inspired by the findings that MAB1 (and Fab1) binding is lethal in vivo \(^{16}\) and also retards OMP folding rates in vitro (Figure 1), we next investigated the effect of Fab1 binding on the conformation of BAM using cryoEM. The structure of BAM in complex with a bactericidal molecule (Fab1) was solved in DDM micelles to 5.1 Å resolution. The cryoEM map contained unambiguous density for Fab1 bound to the extracellular region of BamA (Fig. 3a, Supplementary Fig. 8), and revealed that BAM is in a lateral-open conformation when bound to Fab1, as defined by the position of POTRA- 5, the shape of the BamA \(\beta\) - barrel, and the orientation of \(\beta 1\) and \(\beta 16\) (Fig. 3b and c). The structure of Fab1 alone was also solved by X- ray diffraction to \(\sim 3.0\) Å resolution and this structure was flexibly fitted into the EM density map (Supplementary Table 3). In agreement with mutagenesis data \(^{16}\) , Fab1 binds specifically to eL4 (Fig. 3d) (contributing 98% of the total interface area of 934 Å \(^{2}\) as determined by PISA interface analysis \(^{41}\) ), and the complementarity determining regions (CDRs) bind to residues Y550, E554 and H555 in BamA (Fig. 3e). Interestingly, a BamA- specific nanobody (nanoE6) has also been found to bind eL4 (involving E554) and also influences dynamics in the lateral gate \(^{17}\) . However, since binding of Fab1 to BAM (and nanoE6 to BamA \(^{17}\) ) does not drastically alter the conformation of eL4 from that seen in lateral- closed structures, how Fab1 binding stabilises a lateral- open conformation remains obscure. Finally, we determined the cryoEM structure of BAM- P5L at lower resolution (10.3 Å; Supplementary Figs. 9 and 10), and although the conformation of the lateral gate is not clearly observed at this resolution (Supplementary Fig. 10a), POTRA- 5 unambiguously occludes the BamA barrel suggesting that BAM- P5L is in a "lateral- open"- like state (Supplementary Fig. 10b). Cross- correlation of the BAM- P5L, WT BAM \(^{8}\) (open) and BAM- LL (closed) density maps, as well as comparison of the shapes of the BamA barrel in the different structures add further evidence that BAM- P5L is indeed in a lateral- open state, as expected from the design of the Cys mutants, (Supplementary Figure 10d,e). + +<|ref|>sub_title<|/ref|><|det|>[[118, 712, 718, 730]]<|/det|> +## Fab1 binding to disulphide-locked BAM further inhibits OMP folding + +<|ref|>text<|/ref|><|det|>[[115, 740, 882, 905]]<|/det|> +As BAM can populate a lateral- open conformation in the presence or absence of Fab1, we determined the cryoEM structure of BAM- LL bound to Fab1 to ascertain whether Fab1 binding could further stabilise a lateral- open conformation, potentially further blocking the conformational changes required for BAM's catalytic action. In contrast with BAM- LL, the cryoEM structure of the BAM- LL: Fab1 complex (at 7.1 Å resolution) contains a single structure which is in a lateral- open conformation (Fig. 4a, Supplementary Fig. 11), consistent with Fab1 biasing BamA's conformational equilibrium towards a lateral- open state (Fig. 4b) in which POTRA- 5 occludes the barrel (Fig. 4c). Further evidence for the lateral + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 881, 416]]<|/det|> +closed state being incompatible with Fab1 binding was observed by SDS- PAGE, where the SDS- resistant BamA- Fab1 band observed for WT BAM- Fab1 was weaker for BAM- LL- Fab1, with a compensating increase in the band corresponding to non- complexed BamA, suggestive of the BAM- LL- Fab1 complex being less stable under SDS- PAGE conditions (Supplementary Fig. 12a). Interestingly, since MAB1 binds to BAM in the E. coli OM16, this suggests that a lateral- open conformation is formed in situ in the OM, consistent with previous data36. Conversely, the Fab1- bound BAM- P5L complex produces an SDS- resistant band, consistent with stable binding to its lateral- open state (Supplementary Fig. 12b). tOmpA and OmpX folding assays revealed that the addition of Fab1 to BAM- P5L or BAM- LL each resulted in increased inhibition, with folding yields of \(\sim 10 - 20\%\) for tOmpA (Fig. 4d, Supplementary Fig. 13a) and \(5 - 10\%\) for OmpX (Fig. 4e, Supplementary Fig. 13b) after 3 hours at \(25^{\circ}C\) , and initial folding rates of only \(1 - 3\%\) and \(1 - 6\%\) of that of WT BAM for tOmpA and OmpX, respectively (Fig. 4f and g). This additive inhibition could arise from a synergistic reduction in conformational dynamics within the BAM complex, or from Fab1 binding and disulphide locking inhibiting distinct mechanisms of BAM- mediated folding catalysis. + +<|ref|>sub_title<|/ref|><|det|>[[118, 454, 647, 473]]<|/det|> +## BAM lipoproteins mediate destabilisation of the lipid bilayer + +<|ref|>text<|/ref|><|det|>[[115, 482, 881, 904]]<|/det|> +In vitro studies have shown that spontaneous OMP folding rates and efficiencies are increased in membranes with decreased thickness, increased fluidity, or containing bilayer defects42- 45. As well as directly interacting with its substrate OMPs27,29, BAM is also thought to reduce the stability of the lipid bilayer to facilitate folding, due to asymmetry in the hydrophobic thickness of the BamA \(\beta\) - barrel (which is narrowest in the vicinity of the lateral gate)18,32. Evidence for membrane destabilisation has been provided by molecular dynamics (MD) simulations of BamA in lipid bilayers20,24,25,30- 35 and by cryoEM and MD simulations of BAM in nanodiscs formed from E. coli polar lipids18. To determine how the different conformational states of BAM affect bilayer stability more directly, we measured the effect of the different BAM complexes studied above on the lipid phase transition of liposomes formed from 1,2- dimyristoyl- sn- glycero- 3- phosphocholine (DMPC, \(d / C_{14:0}PC\) ) using the fluorescent lipid probe laurdan (Supplementary Fig. 14), the fluorescence emission spectrum of which depends on lipid phase46. DMPC was chosen for these experiments as it undergoes a gel- liquid phase transition with a midpoint of \(\sim 24^{\circ}C\) , compared with \(\sim 3^{\circ}C\) for E. coli polar lipid47 and BAM has been shown to be active in DMPC liposomes48. As expected, a phase transition for empty DMPC liposomes was observed at \(24^{\circ}C\) (Fig. 5a, see also Supplementary Fig. 15). Interestingly, the transition phase temperature (Tm) was not affected by the presence of BamA alone (Fig. 5a), demonstrating that the asymmetric BamA \(\beta\) - barrel does not itself cause this global perturbation of the lipid bilayer, at least as judged by this assay. By contrast, in all proteoliposomes containing the full BAM complex, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 881, 185]]<|/det|> +regardless of whether that complex is inhibited, the gel- liquid phase transition occurred at a lower temperature ( \(\sim 22 - 23^{\circ}C\) ) and over a broader temperature range (Fig. 5b). These results thus demonstrate that BAM disrupts bilayer stability independently of the structure of the \(\beta 1 - \beta 16\) seam and shows that the BamB- E lipoproteins are essential for this perturbation of the membrane. + +<|ref|>sub_title<|/ref|><|det|>[[118, 232, 220, 248]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[111, 252, 881, 912]]<|/det|> +Protein- protein interactions between BAM and substrate OMPs, and lipid disordering have both been implicated as important features in BAM function3,24, but how these different facets of BAM are balanced to enable OMP folding remained unclear. Here, we have used structural, biochemical and kinetic refolding analyses to dissect these two roles, at least for the 8- stranded OMPs, tOmpA and OmpX. BAM is well- known to be conformationally dynamic, with cryo- EM and X- ray structures capturing the complex in lateral- open5,6,8 and lateral- closed6,7,21,22 conformations, and a recent cryoEM, MD and single molecule FRET study demonstrating dynamics of the complex in nanodiscs18. Furthermore, recent X- ray structures have demonstrated that the C- terminal strand of the OMP substrates tOMPa and OMPLA forms an antiparallel \(\beta\) - strand pairing with lateral- closed BamA \(\beta 1\) , possibly capturing an early stage intermediate in OMP assembly22. A recent cryoEM structure of a BAM:tBamA complex revealed that the tBamA substrate forms a \(\beta\) - strand pairing with lateral- open BamA \(\beta 1\) of BAM, whilst making a side- chain mediated interface involving BamA \(\beta 16\) , to form a hybrid barrel29 that presumably mimics a late- stage assembly intermediate. This observation is consistent with crosslinking studies of EspP27 and LptD28 to BAM, and Por1 to SAM26. Given these insights, it is perhaps unsurprising that trapping BamA in the BAM complex in an open or closed conformation by disulphide bonding has a profound effect on bacterial viability, akin to the observations found using nanobodies17, small molecules and peptidomimetic antibiotics, which also have a lethal outcome11,12. Remarkably, we show here that this in vivo lethality masks a more subtle effect on BAM activity that is revealed by in vitro activity assays. Both disulphide- locking and Fab1 binding inhibit, but do not abolish, BAM- catalysed folding of tOmpA and OmpX in vitro (Fig. 1, and Supplementary Tables 1 and 2). The finding that these inhibitory effects are distinct and additive (Fig. 4) highlights the importance of different, presumably parallel, facets of BAM action for OMP folding catalysis. Our cryoEM structures confirm that in solution, both BAM- P5L and Fab1 lock BamA in a lateral- open conformation (Figs. 3, 4, and Supplementary Fig. 10). Presumably this prevents substrate access and pairing to BamA \(\beta 1\) which recent structures suggest initially occurs to a lateral- closed conformation22. It may also inhibit substrate binding by occlusion of entry to the BamA barrel by POTRA- 5. Consistent with this, it has recently been shown that the BAM substrate, RcsF, binds in the lumen of the BamA \(\beta\) - barrel only in the lateral- closed conformation21, and that the essential mediator of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 81, 881, 416]]<|/det|> +LPS assembly, LptD, contacts the internal lumen of BamA during folding28. An inability to assemble larger and essential BAM-dependent substrates, such as LptD, could explain why disulphide locking/Fab1 binding are lethal in vivo6,16,19, despite smaller OMPs potentially remaining able to fold and insert into the OM, albeit more slowly than with WT BAM. For the latter OMPs, lethality may result from a reduced flux through the OMP biogenesis pathway when BAM is impaired, inducing cell envelope stress caused by accumulation of unfolded OMPs in the periplasmid. Indeed, increased envelope stress was observed upon addition of MAB1 to \(\Delta \text{waaD} E\) . coli16. Moreover, a small molecule inhibitor of the regulator of sigma E protease (RseP)49, that is a key component of this pathway, has a lethal outcome by blocking the \(\sigma^{\mathrm{F}}\) stress response that normally responds to envelope stress by increasing BAM expression50, decreasing OMP expression51, and increasing protein degradation52. The extent to which the folding of larger OMPs is inhibited by the BAM variants examined here remains unclear, but we speculate that for these proteins there could be a greater dependence on a direct interaction with BAM for successful insertion and folding, with BAM being unable to destabilise membranes sufficiently to allow larger OMPs to fold solely via this route. + +<|ref|>text<|/ref|><|det|>[[115, 424, 881, 677]]<|/det|> +Despite the apparent incompatibility of BAM- LL's disulphide bond and a lateral- open conformation6,8, both open- like and closed structures are present in approximately equal populations in solution. The BAM- LL structures presented here thus provide direct evidence that at least \(\beta 1\) and \(\beta 2\) of BamA are malleable in the lateral- open state, being able to bend inwards towards the barrel lumen (Supplementary Fig. 7). Such plasticity appears to be functionally relevant, especially considering the more severe outward motion observed when BAM is engaged with tBamA as a substrate29 (Supplementary Fig. 7). Such an extended conformation would presumably be impossible in BAM- LL, perhaps explaining the partial inhibitory effects observed here for OmpX and tOmpA. Superposition of all the lateral- open BAM structures reported to date thus support a model in which the N- terminal half of the BamA barrel is conformationally dynamic, whilst the C- terminal half provides a stable scaffold that supports these functionally important conformational changes. + +<|ref|>text<|/ref|><|det|>[[115, 685, 881, 913]]<|/det|> +Lipid destabilisation by BAM has been proposed previously as a potentially important facet of the catalysis of OMP folding and insertion into the OM3,25,53. This has been supported by MD simulations that reveal destabilisation of the membrane surrounding BamA20,24,25,30- 35, and a recent cryoEM structure of BAM in a nanodiscs containing \(E\) . coli polar lipids that shows distortion of the bilayer adjacent to the lateral gate18. Whilst these effects are localised to the BamA barrel, the laurdan fluorescence data provide direct biochemical evidence that BAM causes global destabilisation of a bilayer, as revealed by a reduction in the lipid phase transition temperature of DMPC liposomes (Fig. 5). They also reveal that this is mediated by lipoproteins BamB- E, since BamA alone had no discernible effect. This is consistent with cryoEM structures which have identified interactions between BamB, BamD and BamE and detergent micelles8 as well as with lipid in nanodiscs18, whilst BamC is + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 881, 185]]<|/det|> +thought to span the membrane necessary for surface exposure of the two C- terminal helix- grip domains54. In addition to the roles of BamB- E in substrate recognition15,55, in mediating BAM oligomerisation into 'precincts'56, and coordinating conformational changes in BamA36,57, the results presented here highlight the importance of these lipoproteins in mediating changes in membrane stability. + +<|ref|>text<|/ref|><|det|>[[115, 194, 882, 509]]<|/det|> +In summary, the results presented allow different facets of BAM- mediated catalysis of OMP folding and membrane insertion to be discerned. By structural analysis of Fab1- bound and two different disulphide locked BAM complexes we reveal a remarkable structural malleability of the BamA barrel, and show that interconversion between these different structures is not essential for folding and membrane insertion of the 8- stranded tOmpA and OmpX substrates in vitro. In addition, we provide direct biochemical evidence that BAM causes global destabilisation of a lipid bilayer and reveal that this is not endowed by asymmetry in the depth of the BamA barrel, but instead requires the presence of BamB- E, demonstrating a new role for its lipoproteins. Finally, by demonstrating a significant, but reduced folding capacity of the Fab1- bound and disulphide- locked BAM variants in vitro, we provide evidence in support of models that suggest that bacterial viability depends on a delicate balance between the rates of OMP synthesis and their chaperone- dependent delivery to BAM, with the catalytic power of BAM to insert OMPs into the OM. Perturbing this balance thus offers exciting opportunities to create new antibacterial agents by targeting the different protein complexes required for OMP biogenesis. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 84, 198, 100]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[118, 111, 770, 130]]<|/det|> +## Expression and purification of WT and disulphide-locked BAM complexes + +<|ref|>text<|/ref|><|det|>[[118, 138, 881, 263]]<|/det|> +BAM- LL (BamA(E435C/S665C/C690S/C700S)BCDE- His) and BAM- P5L (BamA(G393C/G584C/C690S/C700S)BCDE- His) in a pTrc99a vector were generated using Q5 site- directed mutagenesis (New England BioLabs) using plasmid pJH114 (kindly provided by Harris Bernstein58) as a template. WT BAM, BAM- LL and BAM- P5L were expressed in E. coli BL21(DE3) cells and were purified from the membrane fraction using a combination of Ni- affinity and size exclusion chromatography, as described previously8. + +<|ref|>sub_title<|/ref|><|det|>[[118, 300, 616, 319]]<|/det|> +## Expression and purification of BamA, OmpX and tOmpA + +<|ref|>text<|/ref|><|det|>[[116, 327, 882, 536]]<|/det|> +BamA, OmpX and tOmpA were expressed as inclusion bodies in E. coli BL21(DE3) cells, using a procedure modified from McMorran et al.50. Briefly, inclusion bodies were solubilised in \(25~\mathrm{mM}\) Tris- HCl pH 8.0, \(6M\) guanidine- HCl and were centrifuged (20,000 g, \(20\mathrm{min}\) , \(4^{\circ}\mathrm{C}\) ) to remove remaining insoluble material. The solubilised inclusion bodies were purified by SEC using a Superdex 75 HiLoad 26/60 column (GE Healthcare) for tOmpA and OmpX, and Sephacryl 200 26/60 column for BamA, equilibrated in \(25~\mathrm{mM}\) Tris- HCl pH 8.0, \(6M\) guanidine- HCl. For folding experiments, OmpX and tOmpA were buffer exchanged into Tris- buffered saline (TBS, \(20~\mathrm{mM}\) Tris- HCl, \(150~\mathrm{mM}\) NaCl) pH 8.0, \(8M\) urea using ZebaTM Spin Desalting Columns, \(7k\) MWCO, \(0.5~\mathrm{mL}\) (Thermo Scientific). BamA was refolded in LDAO detergent prior to reconstitution into proteoliposomes, as described previously59. + +<|ref|>sub_title<|/ref|><|det|>[[118, 575, 288, 592]]<|/det|> +## Refolding of BamA + +<|ref|>text<|/ref|><|det|>[[116, 601, 882, 747]]<|/det|> +BamA was refolded as described by Hartmann et al.59. Briefly, BamA added dropwise into ice- cold \(50~\mathrm{mM}\) Tris- HCl pH 8.0, \(300~\mathrm{mM}\) NaCl, \(500~\mathrm{mM}\) arginine, \(0.5\%\) (w/v) LDAO, \(10~\mathrm{mM}\) DTT whilst rapidly stirring. Following 24 hours incubation, BamA was dialysed against 50 mM Tris- HCl pH 8.0, \(0.1\%\) (w/v) LDAO overnight before loading on a \(5~\mathrm{mL}\) HiTrap Q (GE Healthcare) anion exchange column and eluting in a NaCl gradient. Folded BamA was separated from unfolded and degraded BamA, as judged by SDS- PAGE, and used for reconstitution into liposomes containing E. coli polar lipid or DMPC, as required. + +<|ref|>sub_title<|/ref|><|det|>[[118, 785, 437, 803]]<|/det|> +## Expression and purification of SurA + +<|ref|>text<|/ref|><|det|>[[116, 812, 882, 893]]<|/det|> +SurA with an N- terminal 6x His- tag and a TEV cleavage site was expressed and purified using a modified protocol described previously60. Briefly, SurA was expressed in E. coli BL21(DE3) cells and was purified on a \(5~\mathrm{mL}\) HisTrap FF column (GE Healthcare). SurA was denatured on- column in \(25~\mathrm{mM}\) Tris- HCl pH 7.2, \(6M\) guanidine- HCl, washed in the same + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 881, 227]]<|/det|> +buffer and then refolded on- column in \(25~\mathrm{mM}\) Tris- HCl pH 7.2, \(150~\mathrm{mM}\) NaCl, \(20~\mathrm{mM}\) imidazole before elution in \(25~\mathrm{mM}\) Tris- HCl pH 7.2, \(150~\mathrm{mM}\) NaCl, \(500~\mathrm{mM}\) imidazole. The His- tag was cleaved by addition of His- tagged TEV protease and \(14.3~\mathrm{mM}\) 2- mercaptoethanol, produced as previously described31, and the cleaved His- tag and TEV protease were removed on a \(5~\mathrm{mL}\) HisTrap FF column. Purified SurA was dialysed against 5 L TBS pH 8.0, concentrated to \(\sim 200~\mu \mathrm{M}\) using Vivaspin 20 MWCO \(10~\mathrm{kDa}\) concentrators (Sartorius, UK), aliquoted, snap- frozen in liquid nitrogen, and stored at \(- 80~^\circ \mathrm{C}\) . + +<|ref|>sub_title<|/ref|><|det|>[[118, 266, 446, 284]]<|/det|> +## Monoclonal antibody Fab production + +<|ref|>text<|/ref|><|det|>[[115, 293, 881, 542]]<|/det|> +Fabs were cloned and expressed in \(E\) . coli as previously described61,62. Cell paste containing the expressed Fab was resuspended in PBS buffer containing \(25~\mathrm{mM}\) EDTA and \(1~\mathrm{mM}\) PMSF. The mixture was homogenised and then passed twice through a microfluidiser. The suspension was then centrifuged at \(21,500g\) for \(60~\mathrm{min}\) . The supernatant was loaded onto a Protein G column equilibrated with PBS at \(5~\mathrm{mL / min}\) . The column was washed with PBS to baseline and proteins were eluted with \(0.6\%\) (v/v) acetic acid. Fractions containing Fabs, assayed by SDS- PAGE, were pooled and loaded onto a \(50~\mathrm{mL}\) SP Sepharose column equilibrated in \(20~\mathrm{mM}\) MES, pH 5.5. The column was washed with \(20~\mathrm{mM}\) MES, pH 5.5 for 2 column volumes and the protein was then eluted with a linear gradient to \(0.5\mathrm{M}\) NaCl in the same buffer. For final purification, Fab- containing fractions from the ion exchange column were concentrated and run on a Superdex 75 size exclusion column (GE Healthcare) in PBS buffer. + +<|ref|>sub_title<|/ref|><|det|>[[118, 580, 880, 619]]<|/det|> +## Reconstitution of BAM complex variants and BamA into \(E\) . coli polar lipid proteoliposomes + +<|ref|>text<|/ref|><|det|>[[115, 628, 881, 901]]<|/det|> +\(E\) . coli polar lipid extract, purchased as powder from Avanti Polar Lipids (Alabaster, AL), was dissolved in \(80:20\) (v/v) chloroform/methanol at \(20~\mathrm{mg / mL}\) . Appropriate volumes were dried to thin films in clean Pyrex tubes at \(42~^\circ \mathrm{C}\) under \(\mathrm{N}_2\) gas, and were further dried by vacuum desiccation for at least 3 hours. WT BAM, BAM- LL and BAM- P5L in TBS pH 8.0, \(0.05\%\) (w/v) DDM were mixed with \(E\) . coli polar lipid extract films solubilized in TBS pH 8.0, \(0.05\%\) (w/v) DDM in a 1:2 (w/w) ratio. For formation of BAM- Fab1 proteoliposomes, a 2- fold molar excess of Fab1 was added to WT BAM, BAM- P5L or BAM- LL in TBS pH 8.0, \(0.05\%\) (w/v) DDM before mixing with lipid. For BamA proteoliposomes, refolded BamA was added to \(E\) . coli polar lipid films solubilised in TBS pH 8.0, \(0.1\%\) (w/v) LDAO in a 1:2 (w/w) ratio. Empty liposomes were prepared by mixing lipid with an equivalent volume of buffer. To remove detergent and promote liposome formation, the mixtures were dialyzed against \(2~\mathrm{L}\) of \(20~\mathrm{mM}\) Tris- HCl pH 8.0, \(150~\mathrm{mM}\) KCl using 12- 14 kDa MWCO D- Tube™ Maxi Dialyzers (Merck) at room temperature for 48 hours with a total of four buffer changes. Following dialysis, the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 881, 164]]<|/det|> +proteoliposomes were pelleted twice by ultracentrifugation at 100,000 \(g\) for 30 mins at \(4^{\circ}C\) (the supernatants referred to as wash 1 and wash 2 in Supplementary Figures) and were resuspended in TBS pH 8.0. Protein concentration was determined using a BCA assay (ThermoScientific) and successful reconstitution was determined by SDS- PAGE. + +<|ref|>sub_title<|/ref|><|det|>[[115, 201, 790, 220]]<|/det|> +## Fluorescein-C5-maleimide labelling of free thiols in BAM disulphide variants + +<|ref|>text<|/ref|><|det|>[[115, 229, 882, 416]]<|/det|> +WT BAM, BAM- LL and BAM- P5L proteoliposome preparations (containing 5 \(\mu \mathrm{M}\) BAM) in TBS pH 8.0 were treated with 1 mM TCEP or 0.1 mM diamide, along with an untreated control, for 45 mins at room temperature. The proteoliposomes were then diluted 10- fold into TBS pH 7.5, 8 M urea containing 100 \(\mu \mathrm{M}\) fluorescein- C5- maleimide and were incubated overnight at \(25^{\circ}C\) . The products of the labelling reaction were then analysed by SDS- PAGE on \(15\%\) (w/v) acrylamide/bis- acrylamide (37.5:1) Tris- tricine SDS- PAGE gels run at \(60\mathrm{mA}\) per gel for 90 mins at \(25^{\circ}C\) , and imaged under 460 nm light using an Alliance Q9 Advanced gel doc (UVITEC, Cambridge, UK). Subsequently gels were stained with Coomassie Blue to visualise all protein bands. + +<|ref|>sub_title<|/ref|><|det|>[[117, 454, 687, 473]]<|/det|> +## BAM-mediated folding of OMPs by SDS-PAGE band-shift assays + +<|ref|>text<|/ref|><|det|>[[115, 482, 882, 794]]<|/det|> +Solutions of \(20\mu \mathrm{M}\) tOmpA or OmpX denatured in TBS pH 8.0 containing 8 M urea were diluted 5- fold into a \(20\mu \mathrm{M}\) solution of SurA. This mixture was then immediately diluted 2- fold into BAM, BamA or empty proteoliposomes to initiate the folding reaction, maintained at 25 \(^\circ \mathrm{C}\) . Final concentrations were \(1\mu \mathrm{M}\) BAM, \(2\mu \mathrm{M}\) tOmpA/OmpX, \(10\mu \mathrm{M}\) SurA, \(0.8\mathrm{M}\) urea in TBS pH 8.0. DTT was included in the relevant folding reactions at a final concentration of 25 mM. Samples of the folding reaction were taken periodically and were quenched in SDS- PAGE loading buffer (final concentrations: \(50\mathrm{mM}\) Tris- HCl pH 6.8, \(10\%\) (v/v) glycerol, \(1.5\%\) (w/v) SDS, \(0.001\%\) (w/v) bromophenol blue). The samples, including a boiled control (10 mins at \(>95^{\circ}C\) ), were run on \(15\%\) (w/v) SDS- PAGE gels as described above. The gels were stained in InstantBlue™ (Experion) and were imaged using an Alliance Q9 Advanced gel doc (UVITEC, Cambridge, UK). Folded and unfolded band intensities were quantified using ImageJ software (Fiji) and were plotted as a fraction folded ( \(\mathrm{I_F / (I_F + I_{UF})}\) ) against time. Folding data were fitted to a single exponential function in Igor Pro (V8.04) and initial rates calculated by applying a linear fit to data within the first \(5\%\) of the time- course (540 seconds). + +<|ref|>sub_title<|/ref|><|det|>[[118, 834, 338, 852]]<|/det|> +## CryoEM grid preparation + +<|ref|>text<|/ref|><|det|>[[115, 861, 881, 901]]<|/det|> +Samples for grid preparation were prepared as follows. Purified BAM- LL or BAM- P5L in 50 mM Tris- HCl pH 8.0, \(150\mathrm{mM}\) NaCl and \(0.05\%\) (w/v) DDM were diluted to \(3.3\mathrm{mg / mL}\) or 2.3 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 881, 290]]<|/det|> +mg/mL, respectively. For the BAM- Fab1 complex, purified WT BAM was mixed with a 2- fold molar excess of Fab1 and run on a Superdex 200 10/300 column in TBS pH 8.0, 0.05% (w/v) DDM to isolate a stoichiometric complex from excess free Fab1. Fractions corresponding to the complex were concentrated to 4.8 \(\mu \mathrm{M}\) in Vivaspin 500 concentrator MWCO 30k (Sartorius). To assemble the Fab1- bound BAM- LL complex, stock solutions of purified BAM- LL and Fab1 were first diluted to 5.9 \(\mu \mathrm{M}\) in 20 mM Tris- HCl pH 8.0, 150 mM NaCl and 0.05% (w/v) DDM and mixed in a 1:1 molar ratio, before dilution in detergent- free buffer to a total protein concentration of 0.9 mg/mL and a total DDM concentration of 0.03% (w/v). The detergent concentration was lowered to combat a tendency for very thin ice on the resulting grids. + +<|ref|>text<|/ref|><|det|>[[115, 300, 881, 507]]<|/det|> +CryoEM grids were prepared as follows. For the BAM- Fab1 complex, 4 \(\mu \mathrm{L}\) protein was applied to gold UltraUfoil R2/2 200 mesh grids, previously glow discharged for 60 sec at 20 mA in a GlowQube Plus (Electron Microscopy Sciences) in the presence of amylamine vapor. For BAM- LL, BAM- P5L and BAM- LL in complex with Fab1, 3 \(\mu \mathrm{L}\) of sample was applied to copper QUANTIFOIL R1.2/1.3 300 mesh, copper QUANTIFOIL R0.6/1 400 mesh and gold UltraUfoil R1.2/1.3 300 mesh grids (Electron Microscopy Sciences), respectively, that were previously glow discharged for 30 sec at 60 mA in a GlowQube Plus (Electron Microscopy Sciences). Grids were blotted for 6 sec with Whatman #1 filter paper at 4 °C and 80- 100% relative humidity, before plunge freezing in liquid ethane using a Vitrobot Mark IV (ThermoFisher). + +<|ref|>sub_title<|/ref|><|det|>[[118, 547, 266, 564]]<|/det|> +## CryoEM Imaging + +<|ref|>text<|/ref|><|det|>[[115, 574, 881, 759]]<|/det|> +Data were collected on a 300 KeV Titan Krios (ThermoFisher) EM in the Astbury Biostructure Laboratory in automated fashion using EPU software (ThermoFisher). Micrographs were recorded on an energy- filtered K2 detector (Gatan inc.) in counting mode, using a 100 \(\mu \mathrm{m}\) objective aperture. For BAM- LL, 6,456 micrographs were collected from a single grid over two sessions. For the Fab1- bound BAM- LL complex, 2,780 micrographs were collected from a single grid. For BAM- P5L, two grids were imaged in separate sessions, resulting in 2150 total micrographs. For the BAM- Fab1 complex, a single grid was imaged over three sessions, resulting in 4197 total micrographs. Full data collection parameters for each sample are shown in Supplementary Table 4. + +<|ref|>sub_title<|/ref|><|det|>[[118, 800, 279, 816]]<|/det|> +## Image Processing + +<|ref|>text<|/ref|><|det|>[[115, 826, 881, 907]]<|/det|> +All processing was performed in RELION 3.063 (BAM- LL, BAM- Fab1, Fab1- bound BAM- LL) or 3.164 (BAM- P5L) unless otherwise stated. Dose- fractionated micrographs were motion- corrected and dose- weighted by MotionCor65, before estimation of contrast transfer function parameters by Gct66 using the motion corrected and dose- weighted micrographs, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 879, 121]]<|/det|> +apart from the BAM- Fab1 complex where motion corrected, but non- dose weighted, micrographs were used. + +<|ref|>text<|/ref|><|det|>[[115, 131, 881, 550]]<|/det|> +For BAM- LL, the two datasets were initially processed separately in a similar manner (Supplementary Fig. 6). For dataset 1, 299,458 particles were first picked using the general model in crYOLO 1.3.567, and extracted in 300 pixel (321 Å) boxes with two- fold binning, before removal of false positives through two rounds of 2D classification. The resulting 234,598 particles were then used to generate an initial model by stochastic gradient descent68, which was used as the starting model for a 3D classification. Two high resolution classes corresponding to different conformations of BAM- LL were obtained, one termed lateral- closed (86,615 particles) and one lateral- open (83,803 particles). Particles corresponding to each class were then re- extracted unbinned, and autorefined with a mask excluding bulk solvent. After masking and sharpening, resolutions of 5.0 Å (lateral- closed) and 5.9 Å (lateral- open) were obtained. Processing of dataset 2 proceeded similarly and resulted in comparable resolutions for both conformations. To achieve higher resolution, one round of CTF refinement followed by Bayesian polishing were then employed for each dataset, following which the particles corresponding to the same conformation were combined, resulting in 160,118 lateral- closed and 141,612 lateral- open particles. Finally these particle stacks were subject to separate non- uniform refinements in cryoSPARC v2.2.068,69. Masking and sharpening of the resulting half- maps in RELION resulted in resolutions of 4.1 Å (lateral- closed) and 4.8 Å (lateral- open). B- factors of - 107 Å2 and - 127 Å2 were applied to the final lateral- closed and lateral- open reconstructions, respectively. Local resolution was estimated using RELION. + +<|ref|>text<|/ref|><|det|>[[115, 558, 881, 724]]<|/det|> +For the BAM- Fab1 complex (Supplementary Fig. 8), particles were autopicked in RELION 363 using class averages from a previous reconstruction8 filtered to 30 Å as search templates. Individual particles were extracted in 350 pixel (374.5 Å) boxes and culled with multiple rounds of 2D and 3D classification. The resulting particle stack containing 131,853 particles was further refined using the non- uniform refinement function in CryoSPARC v2.2.068,69. The reconstruction was performed on independent subsets and final resolution of 5.2 Å determined by 'gold standard' FSC70. A B- factor of - 167 Å2 was applied to the final reconstruction. + +<|ref|>text<|/ref|><|det|>[[115, 763, 881, 907]]<|/det|> +For BAM- P5L (Supplementary Figs. 9 and 10), particles were picked in crYOLO 1.4.1 using the general model. For dataset 1: 41, 316 particles were picked and extracted in a 280 pixel (300 Å) box, for dataset 2: 54, 532 particles were picked and extracted into 352 pixel (300 Å) boxes. Both used twofold binning. The extracted particles were combined into a single dataset and the resulting 95,848 particles passed through 2D classification. The best 21, 483 particles were used to construct an initial model by stochastic gradient descent68, which was used as a reference for 3D classification of the 43,280 good particles from 2D + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 881, 165]]<|/det|> +classification. The resulting 24, 101 particles were autorefined, and re- extracted as unbinned particles and subject to 3D classification using the autorefined model as the reference. The resulting 19,044 particles were autorefined with a mask to a resolution of 10.3 Å. A B- factor of - 671 Ų was applied to the final reconstruction + +<|ref|>text<|/ref|><|det|>[[115, 173, 882, 487]]<|/det|> +For the Fab1- bound BAM- LL complex (Supplementary Fig. 11), particles were picked in crYOLO 1.4.1 using a model trained with 11 handpicked micrographs spanning the defoci range. The resulting 162,844 particles were extracted in 300 (321 Å) pixel boxes with twofold binning. One round of 2D classification was used to cull the particle set to 108,096 particles which was then subject to 3D classification, using an initial model generated by stochastic gradient descent68 from the best 32,645 particles in that stack as a template. From this 3D classification run, only one conformer was observed, corresponding to a lateral- open, BAM- LL bound to Fab1. The 71,675 particles in the highest resolution class were autorefined, re- extracted as unbinned particles and subject to 3D classification using the autorefined model as the reference, further culling the particle stack. Autorefinement and sharpening of the resulting 61,777 good particles gave a resolution of 7.3 Å. Finally, one round of CTF refinement followed by Bayesian polishing was carried out, and the resulting particle stacks were subject to non- uniform refinement in cryoSPARC v2.2.068,69. Masking and sharpening of the resulting half- maps in RELION resulted in a resolution of 7.1 Å. A B- factor of - 274 Ų was applied to the final reconstruction. + +<|ref|>sub_title<|/ref|><|det|>[[118, 525, 465, 542]]<|/det|> +## CryoEM model building and refinement + +<|ref|>text<|/ref|><|det|>[[115, 552, 882, 761]]<|/det|> +For LL- BAM in the lateral- closed cryoEM map, an existing crystal structure of intact BAM in a lateral- closed conformation (PDB ID: 5D0O5) was first edited to both remove the two natural cysteines in BamA and to insert the lid- lock disulphide bond. This starting model was fitted to the density as a rigid body in Chimera71, before performing several iterations of real- space refinement in PHENIX 1.1472 with secondary structure restraints followed by manual refinement in COOT73, until satisfactory geometry and fit between model and map was obtained as assessed using MolProbity74. The extracellular region of eL6 (BamA675- 702, C- terminal globular domains of BamC (BamC89- 344), and regions at the chain termini of BamABCDE were insufficiently resolved and were not modelled. The final model contains BamA24- 675, 702- 810 BamB31- 391, BamC30- 85, BamD27- 244, BamE29- 111. + +<|ref|>text<|/ref|><|det|>[[115, 770, 881, 914]]<|/det|> +As the resolution of the other structures was insufficient for the above approach, Molecular Dynamics Flexible Fitting (MDFF)40 was used to flexibly fit these conformations. For BAM- LL lateral- open, cascade MDFF (cMDFF) simulations of the lateral- closed atomic model with BamA truncated after residue 809 were first used to derive an initial fit to the lid- lock lateral- open cryoEM map. Here, a series of Gaussian blurred density maps were generated using the volutil function in VMD (halfwidths \(\sigma = 0, 1, \ldots , 6 \text{Å}\) ). The atomic model was then simulated in vacuum and subject to an external potential derived from most blurred density + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 881, 437]]<|/det|> +map, causing it to be flexibly fit into the density. 2 ps of minimisation followed by 100 ps of equilibration were run with a gscale of 1.0 defining the strength of the external potential derived from the density map. Consecutive 100 ps simulations were then run into maps of decreasing blurring, where the end coordinates from the previous simulation were used as input for the next, until reaching the unblurred map. At each step, isomerism, chirality and secondary structure restraints were applied. Several repeats were run, taking advantage of the stochastic nature of the simulation to generate different fits. Additionally, a second MDFF simulation was also run into the unblurred map using PDB- 5LJO8 as a starting model, to derive better conformations for BamA720- 734 and BamA807, 808. These models were then manually combined to give best mainchain fit to the density, before minimising against the unblurred map for 40 ps. In the combined model, BamA429- 440, corresponding to eL1 and the extracellular sides of \(\beta 1\) and \(\beta 2\) , was fitting into micelle density rather than protein density due to the low resolution in this region. A final set of 500 ps MDFF simulations were therefore run with this combined model against the unblurred map, in which BamA429- 440 was not subject to the external potential. The best fitting structure from these runs was then minimised for 40 ps against the unblurred map and real space refined in PHENIX 1.1472 with secondary structure restraints to generate the final atomic model. + +<|ref|>text<|/ref|><|det|>[[115, 447, 881, 633]]<|/det|> +For the Fab1- bound wild- type BAM complex, an initial model was created from the BAM complex PDB entry 5LJO8, with BamA687- 700 from 5EKQ5, and the Fab1 crystal structure determined here (PDB 7BM5). The C- terminal globular domains of BamC were truncated, leaving only the lasso75 region (residues 25- 83) resulting in a starting model containing BamA24- 806, BamB22- 392, BamC25- 83, BamD26- 243, and BamE24- 110. The starting model was fitted into each EM density as a rigid body using UCSF Chimera71 and flexibly fit using cMDFF40. This was followed by real space refinement in PHENIX 1.1472 using secondary structure restraints to generate the final atomic model, with the Fab1 crystal structure used as a reference model to generate additional restraints. + +<|ref|>text<|/ref|><|det|>[[115, 643, 881, 825]]<|/det|> +For the Fab1- bound lid- locked BAM complex, the final lid- locked lateral- open structure and the Fab1 crystal structure were rigid body fitted into the EM density using UCSF Chimera and flexibly fit using a round of MDFF into the unblurred map. This was followed by real space refinement in PHENIX 1.14 with secondary structure restraints to generate the final atomic model, with the Fab1 crystal structure and the final lid- locked lateral- open structures used as reference models to generate additional restraints. During the simulation eL1 of BamA (BamA429- 440) was not subject to the external potential to prevent overfitting to micelle density in this region. Model building statistics for all cryoEM conformers are shown in Supplementary Table 5. + +<|ref|>sub_title<|/ref|><|det|>[[117, 869, 568, 886]]<|/det|> +## Crystallisation and structure determination of Fab1 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 81, 881, 480]]<|/det|> +Fab1 at 6.5 mg/mL was crystallised by the sitting drop vapour diffusion method in 96- well SWISSC1 3- drop plates at \(20^{\circ}C\) . Drops consisted of 100 nL protein and 100 nL crystallisation solution were dispensed using a Mosquito robot (TTP Labtech). Crystals were grown in 0.16 M lithium chloride, \(22\%\) (w/v) PEG6000, 0.1 M MES pH 6.0 and were harvested after 21 days. Crystals were cryo- protected in the crystallisation solution supplemented with \(20\%\) (v/v) ethylene glycol before flash- cooling into liquid nitrogen. X- ray data were collected at Diamond Light Source on beamline I24 from a single cryo- cooled crystal (100 K) using a Pilatus3 6M detector. Diffraction data were collected for a total of \(180^{\circ}\) up to a resolution of \(2.5 \AA\) with a \(0.2^{\circ}\) oscillation using an exposure time of 0.04 seconds at \(100\%\) transmission. X- ray diffraction data were indexed and integrated by autoPROC and STARANISO \(^{76}\) and were scaled to \(2.96 \AA\) in Aimless \(^{77}\) using the I24 beamline autoprocessing pipeline. The crystals belonged to a monoclinic space group \(P12_{1}1\) with unit cell parameters a = 92.0 Å, b = 130.1 Å, c = 138.9 Å, \(\alpha = 90.00^{\circ}\) , \(\beta = 106.1^{\circ}\) , \(\gamma = 90.00^{\circ}\) . The structure was solved by molecular replacement using Phaser \(^{78}\) and the C \(_{H}\) domain of the anti- NFG Fab as the search model (PDB accession number 1ZAN \(^{79}\) ). Crystallographic refinement was performed using PHENIX- 1.9 \(^{72,80}\) and model building was carried out in Coot \(^{73}\) . MolProbity \(^{74}\) was used for structure validation and quality assessment. The final model coordinates and structure factors are deposited in the PDB under the accession number 7BM5. + +<|ref|>sub_title<|/ref|><|det|>[[115, 516, 850, 536]]<|/det|> +## Reconstitution of BamA and different BAM complexes into DMPC proteoliposomes + +<|ref|>text<|/ref|><|det|>[[113, 545, 881, 902]]<|/det|> +DMPC (dIC14:0PC), purchased as powder from Avanti Polar Lipids (Alabaster, AL), was dissolved in \(80:20\) (v/v) chloroform/methanol mixture at \(25 \text{mg/mL}\) . Appropriate volumes were dried to thin films in clean Pyrex tubes at \(42^{\circ}C\) under \(\mathsf{N}_2\) gas, and were further dried by vacuum desiccation for \(>3\) hours. BAM WT, BAM- LL and BAM- P5L or a 2:1 (mol/mol) mixture of Fab1 and BAM in TBS pH 8.0, \(0.05\%\) (w/v) DDM were mixed with DMPC lipid solubilized in TBS pH 8.0, \(0.05\%\) (w/v) DMD at a lipid to protein ratio (LPR) of 1600:1 (mol/mol). For BamA, DMPC lipid was first solubilised in TBS pH 8.0, \(0.1\%\) (w/v) LDAO. Empty liposomes were prepared by mixing DDM- solubilised lipid with an equivalent volume of buffer. Dialysis was performed as described for the preparation of E. coli polar lipid proteoliposomes, except that a temperature of \(30^{\circ}C\) was used (above the DMPC transition temperature). Following dialysis, the proteoliposomes were pelleted twice by ultracentrifugation at \(100,000 \text{g}\) for 30 min at \(4^{\circ}C\) and resuspended in TBS pH 8.0. The proteoliposomes were then extruded with 21 passes through a \(0.1 \mu \text{m}\) polycarbonate membrane using a mini- extruder (Avanti) pre- equilibrated at \(30^{\circ}C\) . Following ultracentrifugation as before, proteoliposomes were resuspended in TBS pH 8.0, protein concentration was determined using a BCA assay (ThermoScientific) and successful reconstitution was confirmed using SDS- PAGE. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 111, 440, 129]]<|/det|> +## Probing lipid disorder using laurdan + +<|ref|>text<|/ref|><|det|>[[115, 138, 881, 430]]<|/det|> +Probing lipid disorder using laurdanLaurdan (Cambridge Bioscience) dissolved in DMSO was added to a final concentration of \(4.2 \mu M\) (final DMSO concentration of \(0.15\%\) (v/v)) to a \(0.8 \mu M\) suspension of BAM-, BamA- or empty- DMPC proteoliposomes (LPR 1600:1 mol/mol). The proteoliposomes were incubated at \(25 ^{\circ} C\) overnight to allow random partitioning of the laurdan probe into the membrane. Fluorescence emission was measured at \(440 nm\) and \(490 nm\) for a total time of 10 sec following excitation of laurdan fluorescence at \(340 nm\) in quartz cuvettes using a PTI QuantaMaster fluorimeter with a \(1 nm\) bandwidth and 1 second integration time. Excitation and emission slit widths were set to \(0.1 nm\) . Spectra were acquired at increasing temperature intervals from \(6 ^{\circ} C\) to \(40 ^{\circ} C\) , and to test reversibility, from \(40 ^{\circ} C\) to \(6 ^{\circ} C\) , allowing the sample to equilibrate at each temperature for 3 min. Generalised polarisation (GP) \(^{46}\) was calculated from the ratio of fluorescence intensity at \(440 nm\) and \(490 nm\) , averaged over the 10 second acquisition, using the formula GP = \((l_{440} - l_{490}) / (l_{440} + l_{490})\) , and was plotted against temperature. Mid- points and gradients of the transitions were determined by calculating the first derivative of the curve. + +<|ref|>sub_title<|/ref|><|det|>[[118, 469, 260, 485]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[115, 496, 881, 725]]<|/det|> +Data availabilityRaw micrographs for each dataset are deposited at EMPIAR under accession numbers XXXX (BAM- LL), XXXX (BAM Fab1 complex), XXXX (BAM- P5L), XXXX (BAM- LL Fab1 complex). The final density maps are deposited in the EMDB under accession numbers XXXX (BAM- LL lateral- closed), XXXX (BAM- LL lateral- open), XXXX (BAM Fab1 complex), XXXX (BAM- P5L) and XXXX (BAM- LL Fab1 complex). Final model coordinates have been deposited in the PDB under accession numbers XXXX (BAM- LL lateral- closed), XXXX (BAM- LL lateral- open), XXXX (BAM Fab1 complex) and XXXX (BAM- LL Fab1 complex). The crystal structure of Fab1 has been deposited in the PDB under accession number 7BM5, and crystallographic data are available at https://doi.org/10.2210/pdb7BM5/pdb. Data supporting this study are freely available at the University of Leeds Data Repository: https://doi.org/10.5518/835. + +<|ref|>sub_title<|/ref|><|det|>[[118, 763, 295, 780]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[115, 790, 881, 898]]<|/det|> +AcknowledgementsWe thank members of the Radford, Ranson, Brockwell and Rutherford labs for helpful discussions, and Nasir Khan for technical support. CryoEM data were collected at the Astbury Biostructure Laboratory, funded by the University of Leeds and the Wellcome Trust (108466/Z/15/Z). We thank Diamond Light Source for access to Beamline i24 (MX19248). P.W and M.G.I acknowledge funding from the Medical Research Council UK + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 81, 882, 260]]<|/det|> +(MR/P018491/1). S.H and J.E.H are funded by the White Rose BBSRC DTP (BB/M011151/1) J.M. and A.J.H acknowledge support from the Wellcome Trust (222373/Z/21/Z and 105220/Z/14/Z, respectively). B.S acknowledges support from the BBSRC (BB/N007603/1 and BB/T000635/1). For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. J.W. is funded by an EOS Excellence in Research Program of the FWO and FRS-FNRS (G0G0818N). SER holds a Royal Society Professorial Fellowship (RSRP/R1/211057). + +<|ref|>sub_title<|/ref|><|det|>[[120, 278, 305, 295]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[115, 306, 882, 492]]<|/det|> +P.W, S.F.H, M.G.I and A.J.H designed and performed the experiments and analysed the data. P.W, S.F.H, J.M.M, A.J.H, B.S and C.C.P carried out BAM functional assays. P.W prepared protein samples for cryoEM. S.F.H, M.G.I and J.M.M performed cryoEM experiments and determined BAM cryoEM structures. P.W solved the X-ray structure of Fab1. P.W, S.F.H, J.E.H, B.S, C.C.P and J.M.W produced proteins required for the study. J.E.H developed the BAM laurdan fluorescence assay. K.M.S and S.T.R developed and produced the anti- BamA Fab fragment (Fab1). S.E.R, N.A.R and D.J.B supervised the research. P.W, S.F.H and M.G.I wrote the manuscript with comments and edits provided from all authors. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 78, 870, 760]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 760, 881, 904]]<|/det|> +
Fig. 1 | Disulphide-locked BamA variants and Fab1 binding impair BAM-mediated OMP folding in vitro. (a) BAM-P5L (G393C/G584C) is expected to lock BamA in the lateral-open conformation (PDB code 5LJO8), while (b) BAM-LL (E435C/S665C) is expected to lock BamA in the lateral-closed conformation (PDB code 5DOO6). BamA POTRAs 1-4 and BamBCDE are rendered semi-transparent for emphasis on the BamA \(\beta\) -barrel and POTRA-5. The position of the disulphide bond is shown as a yellow bar. Figure made in PyMOL v1.7.2.3. (c and d) Quantification of folded and unfolded bands from SDS-PAGE band-shift
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 81, 883, 271]]<|/det|> +assays (Supplementary Figs. 3 and 4) plotted as fraction folded against time for tOmpA or OmpX, respectively. Data are fitted to a single exponential function. (e and f) The initial rates of folding (determined by applying a linear fit to the first \(5\%\) of folding data) normalised as a percentage of the initial rate obtained for WT BAM, are shown for (e) tOmpA and (f) OmpX folding (see also Supplementary Table 1). Folding assays were repeated to assess reproducibility, with errors for replicate initial rate measurements listed in Supplementary Table 1. Folding yields after 24 hours are reported in Supplementary Table 2. Figures labelled with "BAM" refer to the full BAM complex (BamABCDE), whilst "BamA" is just BamA alone. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 81, 880, 490]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 504, 881, 863]]<|/det|> +
Fig. 2 | CryoEM resolves two conformations of BAM-LL in detergent. (a) 4.1 A cryoEM map of the BAM-LL lateral-closed conformation at a contour of \(10\sigma\) , coloured by subunit. The lateral-gate is closed and POTRA-5 does not block the BamA barrel (schematic inset). (b) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on the segmented density for the barrel and POTRA-5 of BamA. \(\beta 1\) and \(\beta 16\) contact to close the gate. (c) The same density viewed from the periplasmic side, showing the open lumen of the BamA barrel in this conformation. (d) 4.8 Å cryoEM map of the BAM-LL lateral-open conformation at a contour of \(10\sigma\) , coloured by subunit. The lateral-gate is open and POTRA-5 occludes the BamA barrel (schematic inset). (e) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on segmented density for the barrel and POTRA-5 of BamA. To satisfy the disulphide in this conformation, eL1 must bend back into the barrel to contact eL6. (f) The same density viewed from the periplasmic side, showing that the BamA lumen is blocked by POTRA-5 in this conformation. Fig. made in UCSF ChimeraX76. Segmenting and colouring performed with corresponding atomic models. Less well resolved regions and the micelle have been masked.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 80, 875, 550]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 575, 881, 866]]<|/det|> +
Fig. 3 | Fab1-bound BAM is in a lateral-open conformation. (a) 5.1 Å cryoEM map of the BAM-Fab1 complex in a lateral-open conformation at a contour of \(10\sigma\) , coloured by subunit. The lateral-gate is fully-open and POTRA-5 occludes the BamA barrel (schematic inset). (b) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on the segmented density for the barrel and POTRA-5 of BamA. \(\beta 1\) is in a conformation that makes limited contact with \(\beta 16\) . (c) The same density viewed from the periplasmic side, showing that the BamA lumen is blocked by POTRA-5 in this conformation. Panels made using UCSF ChimeraX76. Segmenting and colouring performed with corresponding atomic models. Less well resolved regions and the micelle have been masked. (d) Close up of the BamA-Fab1 interface region highlighting the Fab1 CDRs (red) interacting with eL4 of BamA (dark blue). Other regions of BamA are rendered semi-transparent to highlight eL4. Heavy and light chains of Fab1 are coloured cyan and pink, respectively. (e) The \(\mathrm{V_L}\) and \(\mathrm{V_H}\) domains of Fab1 variable form a complementary binding surface for eL4 of BamA involving residues Y550, E554 and H555.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 80, 870, 789]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 791, 880, 894]]<|/det|> +
Fig. 4 | Additive effect of BAM inhibition by disulphide-locking and binding of Fab1. (a) 7.1 Å cryoEM map of the Fab1-bound LL-BAM in a lateral-open conformation at a contour of 9.5 σ, coloured by subunit. The lateral-gate is open and POTRA-5 occludes the BamA barrel (schematic inset). (b) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on the segmented density for the β-barrel and
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[66, 81, 883, 377]]<|/det|> +POTRA- 5 of BamA. To satisfy the disulphide in this conformation, eL1 must bend back into the barrel to contact eL6. (c) The same density viewed from the periplasmic side, showing that the BamA lumen is blocked by POTRA- 5 in this conformation. Structural panels made using UCSF ChimeraX76. Segmenting and colouring performed with corresponding atomic models. Less well resolved regions and the micelle have been masked. (d and e) Quantification of SDS- PAGE band- shift assays shown in Supplementary Fig. 13 for (d) tOmpA and (e) OmpX folding catalysed by BAM- P5L (green), BAM- LL (blue) and WT BAM (black), each with and without Fab1 (solid and open circles, respectively). (f and g) The initial rates, calculated by applying a linear fit to the first 5% of fitted folding data, were normalised to that of WT BAM, and are shown for (f) tOmpA and (g) OmpX folding (see also Supplementary Table 1). Folding assays were conducted twice for reproducibility with data for replicate initial rate measurements listed in Supplementary Table 1. Folding yields after 24 hours are reported in Supplementary Table 2. Figures labelled with "BAM" refer to the full BAM complex (BamABCDE). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[122, 100, 820, 680]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 685, 880, 912]]<|/det|> +
Fig. 5 | BAM variants reduce the phase transition temperature of DMPC liposomes. Global lipid phase transition behaviour for each BAM variant and BamA in DMPC proteoliposomes, with an empty liposomes control measured using laurdan fluorescence. (a) The ratio of laurdan fluorescence at \(440 \text{nm}\) and \(490 \text{nm}\) was plotted as generalised polarisation (GP, see Methods) against temperature for \(0.8 \mu \text{M BAM/BamA proteoliposome}\) suspensions at a \(1600:1\) (mol/mol) lipid-to-protein ratio (LPR) with added laurdan (at a \(305:1\) lipid-to-laurdan ratio) in TBS pH 8.0. (b) The first derivative of data shown in (a) showing the transition temperature for each liposome suspension as the point of steepest (most negative) gradient. Whilst empty DMPC (grey) and BamA proteoliposomes (purple) have a transition temperature of \(24 \text{‰}\) , the presence of WT BAM (black), BAM-Fab1 (red), BAM-P5L (green), BAM-LL (blue), BAM-P5L + Fab1 (orange) and BAM-LL + Fab1 (yellow)
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[58, 82, 880, 123]]<|/det|> +792 broaden the phase transition and lower the transition temperature. Figures labelled with 793 “BAM” refer to the full BAM complex (BamABCDE), whilst “BamA” is just BamA alone. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[58, 100, 884, 905]]<|/det|> +796 1. Horne, J. E., Brockwell, D. J. & Radford, S. E. Role of the lipid bilayer in outer 797 membrane protein folding in Gram-negative bacteria. J. Biol. Chem. 295, 10340- 798 10367 (2020). 799 2. Noinaj, N., Gumbart, J. C. & Buchanan, S. K. The \(\beta\) - barrel assembly machinery in 800 motion. Nat. Rev. Microbiol. 15, 197- 204 (2017). 801 3. Konovalova, A., Kahne, D. E. & Silhavy, T. J. Outer membrane biogenesis. Annu. 802 Rev. Microbiol. 71, 539- 556 (2017). 803 4. Voulhoux, R., Bos, M. P., Geurtsen, J., Mols, M. & Tommassen, J. Role of a highly 804 conserved bacterial protein in outer membrane protein assembly. 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Formation of a \(\beta\) - barrel membrane protein is catalyzed by the interior859 surface of the assembly machine protein BamA. Elife 8, (2019).860 29. Tomasek, D. et al. Structure of a nascent membrane protein as it folds on the BAM861 complex. Nature 583, 473- 478 (2020).862 30. Noinaj, N. et al. Structural insight into the biogenesis of \(\beta\) - barrel membrane proteins.863 Nature 501, 385- 390 (2013).864 31. Schiffrin, B. et al. Effects of periplasmic chaperones and membrane thickness on865 BamA- catalyzed outer- membrane protein folding. J. Mol. Biol. 429, 3776- 3792 (2017).866 32. Liu, J. & Gumbart, J. C. Membrane thinning and lateral gating are consistent features867 of BamA across multiple species. PLOS Comput. Biol. 16, e1008355 (2020).868 33. Patel, G. J. & Kleinschmidt, J. H. The lipid bilayer- inserted membrane protein BamA869 of Escherichia coli facilitates insertion and folding of outer membrane protein A from870 its complex with Skp. 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Dissecting NGF interactions with TrkA and p75 receptors by 985 structural and functional studies of an anti- NGF neutralizing antibody. J. Mol. Biol. 986 381, 881- 896 (2008). 987 80. Adams, P. D. et al. PHENIX: A comprehensive Python- based system for 988 macromolecular structure solution. Acta Crystallogr. Sect. D Biol. Crystallogr. 66, 989 213- 221 (2010). 990 + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[66, 108, 800, 830]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 849, 115, 868]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[42, 889, 953, 956]]<|/det|> +Disulphide- locked BamA variants and Fab1 binding impair BAM- mediated OMP folding in vitro. (a) BAM- P5L (G393C/G584C) is expected to lock BamA in the lateral- open conformation (PDB code 5LJ08), while (b) BAM- LL (E435C/S665C) is expected to lock BamA in the lateral- closed conformation (PDB code + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 45, 955, 271]]<|/det|> +5D006). BamA POTRAs 1- 4 and BamBCDE are rendered semi- transparent for emphasis on the BamA β- barrel and POTRA- 5. The position of the disulphide bond is shown as a yellow bar. Figure made in PyMOL v1.7.2.3. (c and d) Quantification of folded and unfolded bands from SDS- PAGE band- shift assays (Supplementary Figs. 3 and 4) plotted as fraction folded against time for tOmpA or OmpX, respectively. Data are fitted to a single exponential function. (e and f) The initial rates of folding (determined by applying a linear fit to the first 5% of folding data) normalised as a percentage of the initial rate obtained for WT BAM, are shown for (e) tOmpA and (f) OmpX folding (see also Supplementary Table 1). Folding assays were repeated to assess reproducibility, with errors for replicate initial rate measurements listed in Supplementary Table 1. Folding yields after 24 hours are reported in Supplementary Table 2. Figures labelled with "BAM" refer to the full BAM complex (BamABCDE), whilst "BamA" is just BamA alone. + +<|ref|>image<|/ref|><|det|>[[72, 295, 884, 770]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 802, 117, 821]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[41, 841, 930, 956]]<|/det|> +CryoEM resolves two conformations of BAM- LL in detergent. (a) 4.1 723 Å cryoEM map of the BAM- LL lateral- closed conformation at a contour of 10 σ, coloured by subunit. The lateral- gate is closed and POTRA- 5 does not block the BamA barrel (schematic inset). (b) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on the segmented density for the barrel and POTRA- 5 of BamA. β1 and β16 contact to close the gate. (c) The same density viewed from the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[40, 44, 944, 247]]<|/det|> +periplasmic side, showing the open lumen of the BamA barrel in this conformation. (d) 4.8 Å cryoEM map of the BAM- LL lateral-open conformation at a contour of 10 σ, coloured by subunit. The lateral- gate is open and POTRA- 5 occludes the BamA barrel (schematic inset). (e) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on segmented density for the barrel and POTRA- 5 of BamA. To satisfy the disulphide in this conformation, eL1 must bend back into the barrel to contact eL6. (f) The same density viewed from the periplasmic side, showing that the BamA lumen is blocked by POTRA- 5 in this conformation. Fig. made in UCSF ChimeraX76. Segmenting and colouring performed with corresponding atomic models. Less well resolved regions and the micelle have been masked. + +<|ref|>image<|/ref|><|det|>[[60, 260, 901, 817]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 863, 117, 881]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[42, 903, 946, 947]]<|/det|> +Fab1- bound BAM is in a lateral- open conformation. (a) 5.1 Å cryoEM map of the BAM- Fab1 complex in a lateral- open conformation at a contour of 10 σ, coloured by subunit. The lateral- gate is fully- open and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[40, 45, 955, 271]]<|/det|> +POTRA- 5 occludes the BamA barrel (schematic inset). (b) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on the segmented density for the barrel and POTRA- 5 of BamA. \(\beta 1\) is in a conformation that makes limited contact with \(\beta 16\) . (c) The same density viewed from the periplasmic side, showing that the BamA lumen is blocked by POTRA- 5 in this conformation. Panels made using UCSF ChimeraX76. Segmenting and colouring performed with corresponding atomic models. Less well resolved regions and the micelle have been masked. (d) Close up of the BamA- Fab1 interface region highlighting the Fab1 CDRs (red) interacting with eL4 of BamA (dark blue). Other regions of BamA are rendered semi- transparent to highlight eL4. Heavy and light chains of Fab1 are coloured cyan and pink, respectively. (e) The VL and VH domains of Fab1 variable form a complementary binding surface for eL4 of BamA involving residues Y550, E554 and H555. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[71, 50, 820, 785]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[43, 800, 118, 820]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[41, 840, 955, 955]]<|/det|> +Additive effect of BAM inhibition by disulphide- locking and binding of Fab1. (a) 7.1 Å cryoEM map of the Fab1- bound LL- BAM in a lateral- open conformation at a contour of 9.5 σ, coloured by subunit. The lateral- gate is open and POTRA- 5 occludes the BamA barrel (schematic inset). (b) Cartoon representation of the corresponding atomic model at the lateral gate, superimposed on the segmented density for the β- barrel and POTRA- 5 of BamA. To satisfy the disulphide in this conformation, eL1 must bend back into the barrel + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 44, 945, 293]]<|/det|> +to contact eL6. (c) The same density viewed from the periplasmic side, showing that the BamA lumen is blocked by POTRA- 5 in this conformation. Structural panels made using UCSF ChimeraX76. Segmenting and colouring performed with corresponding atomic models. Less well resolved regions and the micelle have been masked. (d and e) Quantification of SDS- PAGE band- shift assays shown in Supplementary Fig. 13 for (d) tOmpA and (e) OmpX folding catalysed by BAM- P5L (green), BAM- LL (blue) and WT BAM (black), each with and without Fab1 (solid and open circles, respectively). (f and g) The initial rates, calculated by applying a linear fit to the first \(5\%\) of fitted folding data, were normalised to that of WT BAM, and are shown for (f) tOmpA and (g) OmpX folding (see also Supplementary Table 1). Folding assays were conducted twice for reproducibility with data for replicate initial rate measurements listed in Supplementary Table 1. Folding yields after 24 hours are reported in Supplementary Table 2. Figures labelled with "BAM" refer to the full BAM complex (BamABCDE). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[75, 68, 830, 300]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 800, 120, 820]]<|/det|> +
Figure 5
+ +<|ref|>image<|/ref|><|det|>[[75, 460, 830, 770]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 44, 944, 200]]<|/det|> +305:1 lipid- to- laurdan ratio) in TBS pH 8.0. (b) The first derivative of data shown in (a) showing the transition temperature for each liposome suspension as the point of steepest (most negative) gradient. Whilst empty DMPC (grey) and BamA proteoliposomes (purple) have a transition temperature of \(24^{\circ}C\) , the presence of WT BAM (black), BAM- Fab1 (red), BAM- P5L (green), BAM- LL (blue), BAM- P5L + Fab1 (orange) and BAM- LL + Fab1 (yellow) broaden the phase transition and lower the transition temperature. Figures labelled with "BAM" refer to the full BAM complex (BamABCDE), whilst "BamA" is just BamA alone. + +<|ref|>sub_title<|/ref|><|det|>[[44, 223, 310, 251]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 274, 765, 295]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 312, 441, 358]]<|/det|> +SupplementaryInformationsubmitted.pdf ValidationReports.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__07208eea83c232a05dca70cd64270d5ee1a24fdbc00dcd03cf4ddc8b21562a44/images_list.json b/preprint/preprint__07208eea83c232a05dca70cd64270d5ee1a24fdbc00dcd03cf4ddc8b21562a44/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..21f5d04f15ae18e71a2bfd1a6a19172ebad59ecf --- /dev/null +++ b/preprint/preprint__07208eea83c232a05dca70cd64270d5ee1a24fdbc00dcd03cf4ddc8b21562a44/images_list.json @@ -0,0 +1,47 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1: Schematic diagram of a remote focusing system implemented in light-sheet microscopy and its performance. a) Three different modalities to acquire volumetric imaging of the sample along the focus direction. Either the sample or objective lens can be moved for axial refocusing. Alternatively, both the sample and objective lens can remain stationary by using a remote focusing system. b) Implementation of the remote-focusing system on the detection arm of the light sheet microscope. In this configuration, objective lenses 1 and 2 are pupil-matched through two lenses to form a perfect imaging system. Combined with mirror M3 and a polarizing beam splitter (PBS), the whole system works as a remote focusing system. The novel design of this remote-focusing system is implementation in the detection arm for unpolarized fluorescent light emitted from the sample. To do this, two tilted mirrors M1 and M2 are utilized to direct both S and P-polarized beams toward Objective lens 2 and then combine the reflected beams from mirror M3 to create an image by S and P-polarized beams onto the camera by focusing through the tube lens. The mirror M3 is attached to the linear focus actuator (LFA), moving back and forth to scan the sample in the Z-direction to acquire a 3D image. In the illumination arm, the generated light sheet by a cylindrical lens is translated by a galvanometric scan mirror (GSM) along the detection arm. To focus the detection path on the plane of the light sheet, the synchronization of GSM and LFA is carried out by sawtooth signals. Simultaneous dual-channel imaging of the cell is achieved in \\(40 \\mu \\mathrm{m} \\times 150 \\mu \\mathrm{m}\\) FOV over \\(70 \\mu \\mathrm{m}\\) in the Z-direction. c) The polarization state of the incoming beams changes after reflection from mirror M3 (S to P, and P to S). d) The reflected beams from mirror M3 have a different polarization state compared to the incoming beams; therefore, they exist from a different side of the PBS than the incoming beams. e) The point spread function (PSF) of 200 nm beads formed by S, P, and S+P polarized beams. The microscope performs at the diffraction limit, \\(394 \\mathrm{nm}\\) resolution, for S, P, and S+P in the lateral directions (X-Y), while it maintains a resolution of \\(654 \\mathrm{nm}\\) in the axial direction (Z).", + "footnote": [], + "bbox": [ + [ + 128, + 280, + 863, + 675 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2: Ray tracing of the setup and resolution assessment. a) Ray tracing of the detection path. L: image size, f: effective focal length, S: image or object position relative to the lens, unit: mm. b) Calibration of lateral magnification at various object positions, a target illuminated by a white light LED is imaged for magnification measurement. c) Maximum intensity projections of data acquired on \\(200 \\mathrm{nm}\\) beads from 10 slices spaced \\(500 \\mathrm{nm}\\) in the Z-direction. The images show orthogonal views of the MIPs across scan range for S,P and S+P. The elongated PSF in the Z direction exhibits less resolution in the axial direction controlled by the light sheet waist. d) The FWHM of the \\(200 \\mathrm{nm}\\) beads in the lateral and axial directions over the scan range. The minimum lateral resolution, \\(394 \\mathrm{nm}\\) , occurs at the center of the scan range and increases by moving away from the center. These plots show a constant axial resolution of \\(650 \\mathrm{nm}\\) over the axial scan range. The microscope functions in the scan range of \\(70 \\mu \\mathrm{m}\\) .", + "footnote": [], + "bbox": [ + [ + 120, + 285, + 860, + 707 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3: Dual-Color volumetric imaging of live RBL cells. (a-d) Dual-color volumetric imaging of granule motions in a live RBL-2H3 GFP-FasL cell, where the cell membrane is labeled with IgE-CF640R and granules contain GFP-FasL, at an imaging speed of \\(\\sim 0.6\\) volumes \\((80\\times 15\\times 40\\) \\(\\mu \\mathrm{m}^3\\) in XYZ) per second for 80 volumes, for a total imaging time of \\(\\sim 2\\) minutes. (a) Maximum intensity projection views of the cell images at one-time point and overlay with representative trajectories of granule movement (orange lines). (b) Time series of the trajectories in a. (c, d) Histograms of estimated diffusion coefficients and velocities of all trajectories found in cell 1 and cell 2. (e-g) Dual-color volumetric imaging of live RBL-2H3 GFP-FasL cell, where the cell membrane is labeled with CellMask DeepRed and the granules contain GFP-FasL using an imaging speed of \\(\\sim 8.3\\) volumes/s for 80 volumes for a total time of \\(10\\mathrm{s}\\) . (e) Maximum intensity projection views of the cell images at one-time point and overlay with representative trajectories of granule movement (orange lines). (f) Time series of the trajectories in e. (g) Histograms of estimated diffusion coefficients of all trajectories in the cell. (h) Cumulative probability of the estimated diffusion coefficients under normal (a-d) and stressed (e-g) imaging conditions. 400-500 trajectories with a diffusion coefficient \\(>0.001\\mu \\mathrm{m}^2 /\\mathrm{s}\\) from four cells under each condition are selected.", + "footnote": [], + "bbox": [ + [ + 130, + 92, + 876, + 734 + ] + ], + "page_idx": 8 + } +] \ No newline at end of file diff --git a/preprint/preprint__07208eea83c232a05dca70cd64270d5ee1a24fdbc00dcd03cf4ddc8b21562a44/preprint__07208eea83c232a05dca70cd64270d5ee1a24fdbc00dcd03cf4ddc8b21562a44.mmd b/preprint/preprint__07208eea83c232a05dca70cd64270d5ee1a24fdbc00dcd03cf4ddc8b21562a44/preprint__07208eea83c232a05dca70cd64270d5ee1a24fdbc00dcd03cf4ddc8b21562a44.mmd new file mode 100644 index 0000000000000000000000000000000000000000..6483ba6ee60b4f3c3d36fe8b37fdda15cc95b2b4 --- /dev/null +++ b/preprint/preprint__07208eea83c232a05dca70cd64270d5ee1a24fdbc00dcd03cf4ddc8b21562a44/preprint__07208eea83c232a05dca70cd64270d5ee1a24fdbc00dcd03cf4ddc8b21562a44.mmd @@ -0,0 +1,336 @@ + +# Axial de-scanning using remote focusing in the detection arm of light-sheet microscopy + +Tommy Chakraborty tchakraborty@umn.edu + +University of New Mexico HASSAN DIBAJI University of New Mexico Ali KAZEMI NASABAN SHOTORBAN University of New Mexico https://orcid.org/0000- 0002- 8513- 1784 + +MAHSA HABIBI University of New Mexico + +RACHEL GRATTAN Comprehensive Cancer Center, University of New Mexico Health Sciences Center, + +SHAYNA LUCERO Comprehensive Cancer Center, University of New Mexico Health Sciences Center, + +DAVID SCHODT University of New Mexico + +Keith A. Lidke The University of New Mexico https://orcid.org/0000- 0002- 9328- 4318 + +JONATHAN PETRUCCELLI Department of Physics, University at Albany- State University of New York + +DIANE LIDKE Comprehensive Cancer Center, University of New Mexico Health Sciences Center, + +SHENG LIU University of New Mexico + +Article + +Keywords: + +Posted Date: October 3rd, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 3338831/v1 + +<--- Page Split ---> + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: Yes there is potential Competing Interest. T.C. and H.D. have filed a patent application (United States Patent and Trademark Office application number 63/397,714) for the remote focusing setup mentioned here. + +Version of Record: A version of this preprint was published at Nature Communications on June 12th, 2024. See the published version at https://doi.org/10.1038/s41467-024-49291-0. + +<--- Page Split ---> + +# Axial de-scanning using remote focusing in the detection arm of light-sheet microscopy + +HASSAN DIBAJI \(^{1}\) , ALI KAZEMI NASABAN SHOTORBAN \(^{1}\) , MAHSA HABIBI \(^{1}\) , RACHEL M GRATTAN \(^{2,3}\) , SHAYNA LUCERO \(^{2,3}\) , DAVID J. SCHODT \(^{1}\) , KEITH A. LIDKE \(^{1,2}\) , JONATHAN PETRUCCELLI \(^{4}\) , DIANE S. LIDKE \(^{2,3}\) , SHENG LIU \(^{1}\) , AND TONMOY CHAKRABORTY \(^{1,2,*}\) + +\(^{1}\) Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico 87131, USA \(^{2}\) Comprehensive Cancer Center, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, 87131, USA \(^{3}\) Department of Pathology, University of New Mexico Health Science Center, Albuquerque, NM, USA \(^{4}\) Department of Physics, University at Albany- State University of New York, 1400 Washington Avenue, Albany, NY 12222, USA \(^*\) tchakraborty@unm.edu + +Abstract: The ability to image at high speeds is necessary in biological imaging to capture fast- moving or transient events or to efficiently image large samples. However, due to the lack of rigidity of biological specimens, carrying out fast, high- resolution volumetric imaging without moving and agitating the sample has been a challenging problem. Pupil- matched remote focusing has been promising for high NA imaging systems with their low aberrations and wavelength independence, making it suitable for multicolor imaging. However, owing to the incoherent and unpolarized nature of the fluorescence signal, manipulating this emission light through remote focusing is challenging. Therefore, remote focusing has been primarily limited to the illumination arm, using polarized laser light for facilitating coupling in and out of the remote focusing optics. Here we introduce a novel optical design that can de- scan the axial focus movement in the detection arm of a microscope. Our method splits the fluorescence signal into S and P- polarized light and lets them pass through the remote focusing module separately and combines them with the camera. This allows us to use only one focusing element to perform aberration- free, multi- color, volumetric imaging without (a) compromising the fluorescent signal and (b) needing to perform sample/detection- objective translation. We demonstrate the capabilities of this scheme by acquiring fast dual- color 4D (3D space + time) image stacks, with an axial range of \(70 \mu \mathrm{m}\) and camera limited acquisition speed. Owing to its general nature, we believe this technique will find its application to many other microscopy techniques that currently use an adjustable Z- stage to carry out volumetric imaging such as confocal, 2- photon, and light sheet variants. + +## MAIN + +Fast 3D positioning or scanning of an optical system's focal point or focal plane has the potential to transform many areas of BioPhotonics, especially those that require studying the complex dynamics of living organisms. Processes like investigation of neuronal activities of the brain, blood flow in the heart, and cell signaling require high- speed volumetric imaging \(^{1 - 3}\) . However, volumetric imaging requires an axial scan either through the translation of the sample or the detection objective lens (Fig. 1a). Such axial translations result in imaging modalities that are often slow with speeds limited to a few hundred \(\mathrm{Hz}^{4 - 6}\) . Additionally, with fragile samples, such as an expanded sample in hydrogel \(^{7}\) , fast movements of the sample stage may agitate the sample and induce distortions when collecting volumetric images. To avoid the slow translation of bulky objectives or the sample stages, several attempts, employing variable- focus (vari- focus) lenses, mechanical mirrors, and acousto- optics modulators have been proposed to refocus the light for 3D imaging. However, they all suffer from unacceptable aberrations introduced by the focusing elements. A large category of those techniques utilize different types of tunable lenses such as ferroelectric liquid crystal (LC), acoustic waves (TAG lens), and acoustic optics modulators (AOM) \(^{8}\) to achieve fast focal shifts (~1kHz). Ferroelectric LC and TAG lenses introduce a focal shift by varying the gradient of the refractive index of the liquid medium, however, the generated phase variation only approximates the defocus phase, leading to increased spherical aberration at large focal shifts \(^{9 - 11}\) . AOM- based vari- focus techniques on the other hand use two AOMs with counterpropagating acoustic waves to cancel out the transverse scan but can only achieve focus shift in one dimension (acting as a cylindrical lens) \(^{12,13}\) . + +Adaptive optics- based vari- focus techniques overcome these limitations through accurate wavefront control using either a spatial light modulator (SLM) or a deformable mirror (DM), which can achieve a response rate of ~1 kHz and 20 kHz respectively. However, SLMs are polarization and wavelength- dependent and cannot model a continuous wavefront of the defocus phase due to its limited phase modulation depth. Large phase shifts are generated through multiple phase- wrapping of \(2\pi\) . With finite fly- back at the phase- wrapping borders, part of the incident light is not correctly modulated and results in decreased intensity at the focus \(^{14}\) . DMs are not polarization and wavelength- dependent and can model a continuous defocus wavefront. However, the axial scan range of a DM is limited by the + +<--- Page Split ---> + +stroke length of the DM actuators. For example, for an objective with a numerical aperture (NA) of 0.8, the maximum axial scan range that DM based techniques can generate is \(- 40 \mu \mathrm{m}^{15}\) . Furthermore, using DM for focus control requires accurate alignment and complicated calibration of the DM to reduce the aberrations caused by imaging samples out of the nominal focal plane of the objective9. + +Unlike the adaptive optics or DM- based approaches that require correcting the defocus plane- by- plane, pupil- matched remote focusing (pmRF), pioneered by Botcherby et al.16,17, instantaneously corrects defocus across 3D volumes for high- NA optics thereby conserving the microscope's temporal bandwidth16- 26. In addition, because pmRF allows precise mapping of the wavefront coupled into the back- pupil of the objective, where the angular magnification is unity, such techniques have been routinely used to carry out aberration- free high- quality axial focus control16- 26. In pmRF techniques, a fast axial scan is achieved by the translation of a small mirror in front of the remote objective using a focus actuator18,19,23 or by a lateral scan of a galvo mirror in combination to a step or tilted mirror at the remote objective27. Because of the fast response time of the focus actuator or the galvo mirror, an axial scan rate of 1- 5 kHz or 12 kHz can be achieved respectively. However, current pmRF techniques for focus control are primarily limited to the illumination path. This is because pmRF uses the concept of optical isolators28, where the polarization of the returning beam is rotated orthogonally to the incoming beam so that it can be separated from the incoming beam at the polarized beam splitter (PBS) (Supplementary Fig. 1a). This configuration ensures minimum light loss through the pmRF module but requires the incoming beam to be polarized, which is why this method is primarily used in the illumination arm where illumination laser light is usually polarized in nature and its manipulation through the optical isolator can be easily done. In the detection arm, however, the emitted fluorescence is unpolarized in nature. To the best of our knowledge, because, using purely linear optical elements, lossless conversion of unpolarized light into a single polarized state is not yet possible29,30 (Supplementary Note 1), manipulating the fluorescent light using the optical isolators is unfeasible. As a result, microscopes that use pmRF to carry out axial scanning, incur 50% light loss due to one state of the polarized light being discarded after the PBS16,21,24 (Supplementary Fig. 1a). + +A straightforward method to mitigate this problem is to have another copy of the pmRF module at the unused port of the PBS (Supplementary Fig. 1b) to collect the other half of the fluorescent light. However, this would require precise synchronization of two linear- focus- actuators (LFA), which is not only a difficult task at high speeds but also will be expensive since this method warrants two such LFAs. In this article, we present a novel optical design that overcomes these problems and presents a modular setup that can perform remote focusing on the detection arm of a fluorescent microscope without incurring polarization- induced losses. When attached to a light- sheet microscope, this technique allows optical refocusing without requiring the movement of the sample, or the detection objective (Fig. 1b and Supplementary Fig. 1c). As a result the microscope can acquire 3D volumetric data limited by camera speed. This technique is applicable to many other microscopy techniques that currently use an adjustable \(Z\) - stage to carry out volumetric imaging such as confocal, 2- photon, and light sheet variants. + +## Results + +## Concept and microscope layout + +Optical axial- refocusing: Our refocusing unit is shown in Fig. 1b. Here, the water immersion detection objective (Obj1) is pupil matched to a second air objective (Obj2) through two intermediate lenses following the original design by Botcherby et al.16,17. However, unlike traditional refocusing geometry, we split the collected unpolarized fluorescence into S and P- polarized light using a polarizing beam splitter cube (PBS) in the infinity space of Obj2. The generated orthogonal paths are then projected onto Obj2 using two angled mirrors M1 and M2. Because of this angular launch in infinity space, Obj2 forms two distinct laterally shifted images at its nominal focal plane. A small mirror placed on an LFA reflects the light back through the path it came from where a quarter wave plate (QWP) converts the S- polarized light to P on its way back (and P- polarized light to S) after being reflected from the mirror (Fig. 1c). When the returning light (in each arm) reaches the PBS, it now acts as an optical valve where the S path (which was initially P) gets reflected while the P- polarized light (which was initially S) gets transmitted by the PBS. As a result, both S and P polarized light exits the PBS through the fourth and unused face of the PBS cube (Fig. 1d). This light after passing through a tube lens forms identical images, one with S and another with P, at the sCMOS camera. A precise alignment using mirrors M1 and M2 overlays the two images, thereby resulting in a combined image by simply an incoherent addition without any interference artifacts. + +There are a few important design considerations that need to be considered for our de- scanning setup. Firstly, it is essential that mirror M3 consistently moves in parallel with the focal plane of Obj2 during the LFA's oscillatory motion. This prevents any unwanted focal shifts between the S and P paths, ensuring that the resulting image from + +<--- Page Split ---> + +both S and P polarizations remain focused on the camera at the same time. This arrangement ensures that both beams return through their incoming paths, resulting in easier alignment for overlaying the final images formed by the S and P- polarized beams. + +Secondly, it is advantageous that 0 (angle between S and P polarized beam hitting the Obj2) (Supplementary Fig. 2) be as small as possible because this directly controls the distance between the two focal points at M3 (depicted by \(\Delta L\) in Fig. 1c). A smaller \(\Delta L\) ensures: (1) a smaller mirror could be utilized to carry out the remote- focusing, reducing the inertial load on the LFA, and enhancing its efficiency; (2) The alignment becomes less sensitive to tip- tilt misalignment of M3; and (3) This guarantees that both images fit within Obj2's field of view (FOV). + +Thirdly, there exists an inverse relationship between the angle \(\theta\) and the distance between Obj2 and the PBS (inset of Supplementary Fig.2). Therefore, this gives us an option: either adhere to the 4f system or minimize \(\theta\) . We found that for our matching objectives Obj1 and Obj2 the 4f system (with matching lenses L1 and L2) resulted in a \(\theta\) of \(20^{\circ}\) (inset of Supplementary Fig.2). However, operating in this range poses a risk as it is challenging to ensure that both reflected beams are entirely captured by Obj2. Hence, there is a balance between adhering to the 4f system and minimizing the angle \(\theta\) . We found that with our current design, we can still achieve diffraction limited resolution (Fig. 1e). + +Finally, because we generated two identical images on the camera using S and P- polarized light, it was crucial to overlay these images with precision higher than the diffraction- limited resolution to produce the final image. To do this, we developed a cross- correlation- based algorithm that quantifies the shift between overlayed S and P images in real- time with sub- pixel accuracy, allowing interactive adjustment of the mirrors M1 and M2 during system alignment. + +Implementation in a light- sheet system: In order to test the performance of our design we implemented this setup into the detection arm of a light- sheet microscope with orthogonal illumination and detection objectives. The system layout is shown in (Fig.1b and Supplementary Fig.2). The sample is illuminated by a sheet of light generated with a cylindrical lens in the illumination arm, and the emitted fluorescence from the sample is collected by the detection objective lens, which is set orthogonal to the illumination objective lens to capture 2D information from the sample. A galvanometric scan mirror (GSM) in the illumination arm translates the light- sheet in the Z- direction. Because the position of the LFA in the detection arm determines the focal plane of the detection objective lens, we synchronized the GSM and LFA with the sawtooth signal to ensure that the detection path is always focused on the plane of the light- sheet (Supplementary Fig.3). This allowed us to carry out volumetric imaging by acquiring a sequence of images from different focal planes. The LFA moves back and forth rapidly, synchronized with the movement of the GSM enabling us to quickly collect 3D image stacks. + +The optical correction of defocus in our high- NA microscope allowed fast de- scanning of a 3D volume over an axial range of \(\sim 70 \mu \mathrm{m}\) at speeds limited primarily by the camera framerate ( \(\sim\) in our case 799 camera frames/s at \(2304 \times 256\) pixels using Hamamatsu Orca- fusion BT). We employed a dual- color imaging strategy by partitioning the FOV, enabling simultaneous capture of two distinct fluorescent labels within each slice without sacrificing imaging speed. To do this we used a pair of dichroic mirrors to separate the emitted wavelengths from the two labels into side- by- side dual- color images (Supplementary Fig.2). Once acquired, these separate image sets are then precisely registered and merged to generate 4D (X, Y, Z, and \(\lambda\) ) stacks. By sequentially capturing 4D stacks, we generated 5D (X, Y, Z, \(\lambda\) , and time) datasets that allowed us to track the dynamic behavior of biological processes. It is important to note that our setup is wavelength- independent, an attribute not feasible with technologies like diffractive tunable lenses or spatial light modulators. + +## Characterization of the optical system + +To understand the image formation of the proposed setup, we simulated the ray tracing of the detection path (Fig. 2a). The ray tracing assumes all rays satisfy paraxial approximation and all lenses are simple lenses. The detection objective is a water immersion objective, we calculated its effective focal length as \(f_{\mathrm{obj}} = f_{\mathrm{Tube}} n / M_{\mathrm{obj}}\) , where \(f_{\mathrm{Tube}}\) is the focal length of the designed tube lens, \(M_{\mathrm{obj}}\) is the magnification of the objective, and \(n\) is the refractive index of water. Here we have \(f_{\mathrm{obj}}\) equal to 6.65 mm. The pmRF module (from the beam splitter to LFA) is modeled two times to simulate the forward and backward transmission through the module. The LFA is omitted from the simulation, instead, we change the distance between the two copies of the pmRF objectives so that the distance \((S_{3})\) of the image plane to the second pmRF objective remains as a constant. We simulated with an object of \(100 \mu \mathrm{m}\) , the image size after the pmRF objective is \(\sim 140 \mu \mathrm{m}\) , indicating a lateral magnification of 1.4, which is close to the requirement of perfect imaging with \(M_{\mathrm{lateral}} = n_{\mathrm{water}} / n_{\mathrm{air}} = 1.33\) . The small deviation is limited by the geometry of the pmRF + +<--- Page Split ---> + +module: the separation \((\Delta L)\) of the S and P- images formed by the pmRF objective is approximate to \(\Delta L = f_{\mathrm{RFobj}}\theta\) where \(f_{\mathrm{RFobj}} = 10 \mathrm{mm}\) is the effective focal length of the RF objective and \(\theta\) is the angle between the S and P- polarized rays meeting at the RF objective. The larger the \(\Delta L\) , the larger the aberration introduced by the pmRF objective. To reduce \(\Delta L\) , the pmRF objective is located \(\sim 500 \mathrm{mm}\) from the PBS, therefore, the pmRF module is no longer an exact 4f system, the magnification, \(M_{\mathrm{lateral}}\) , varies with the axial position of the object. Furthermore, the beam path from the detection objective to the tube lens is also not a 4f system, where the tube lens is \(\sim 100 \mathrm{mm}\) away from the detection objective. The combination of the two non- 4f systems can partially reduce the axial dependence of the magnification. Fig. 2b shows the change of the lateral magnification with respect to the galvo position (the axial position of the light sheet) from both ray tracing and the experimental data. There is a \(\sim 5\%\) magnification change over an axial range of \(80 \mu \mathrm{m}\) . This magnification change can be further reduced by optimizing the axial position of the tube lens. + +![](images/Figure_1.jpg) + +
Figure 1: Schematic diagram of a remote focusing system implemented in light-sheet microscopy and its performance. a) Three different modalities to acquire volumetric imaging of the sample along the focus direction. Either the sample or objective lens can be moved for axial refocusing. Alternatively, both the sample and objective lens can remain stationary by using a remote focusing system. b) Implementation of the remote-focusing system on the detection arm of the light sheet microscope. In this configuration, objective lenses 1 and 2 are pupil-matched through two lenses to form a perfect imaging system. Combined with mirror M3 and a polarizing beam splitter (PBS), the whole system works as a remote focusing system. The novel design of this remote-focusing system is implementation in the detection arm for unpolarized fluorescent light emitted from the sample. To do this, two tilted mirrors M1 and M2 are utilized to direct both S and P-polarized beams toward Objective lens 2 and then combine the reflected beams from mirror M3 to create an image by S and P-polarized beams onto the camera by focusing through the tube lens. The mirror M3 is attached to the linear focus actuator (LFA), moving back and forth to scan the sample in the Z-direction to acquire a 3D image. In the illumination arm, the generated light sheet by a cylindrical lens is translated by a galvanometric scan mirror (GSM) along the detection arm. To focus the detection path on the plane of the light sheet, the synchronization of GSM and LFA is carried out by sawtooth signals. Simultaneous dual-channel imaging of the cell is achieved in \(40 \mu \mathrm{m} \times 150 \mu \mathrm{m}\) FOV over \(70 \mu \mathrm{m}\) in the Z-direction. c) The polarization state of the incoming beams changes after reflection from mirror M3 (S to P, and P to S). d) The reflected beams from mirror M3 have a different polarization state compared to the incoming beams; therefore, they exist from a different side of the PBS than the incoming beams. e) The point spread function (PSF) of 200 nm beads formed by S, P, and S+P polarized beams. The microscope performs at the diffraction limit, \(394 \mathrm{nm}\) resolution, for S, P, and S+P in the lateral directions (X-Y), while it maintains a resolution of \(654 \mathrm{nm}\) in the axial direction (Z).
+ +<--- Page Split ---> + +To quantify the performance of the proposed scheme, we used full width at half max (FWHM) measurements of 3D point spread function (PSF) to validate that the incoherent addition of S and P images was not compromising the resolution. To do this, we measured the PSF of each polarization component individually and compared it with the PSF of the unified S+P image. As illustrated in Fig.1e, both the S and P- polarized images rendered onto the camera exhibit identical FHWM, resulting in an equivalent resolution for the combined S+P image. Further quantification involving 10 randomly chosen beads, reveals that the microscope achieved diffraction- limited resolutions: \(394 \pm 31 \mathrm{nm}\) laterally (X- Y) and \(654 \pm 130 \mathrm{nm}\) axially (Z). These measurements were performed in proximity to the nominal focal plane (MIP of 10 slices, each separated 500 nm). + +To evaluate the performance of the de- scanning system, we imaged 3D volumes of \(200 \mathrm{nm}\) beads embedded in a \(2\%\) + +![](images/Figure_2.jpg) + +
Figure 2: Ray tracing of the setup and resolution assessment. a) Ray tracing of the detection path. L: image size, f: effective focal length, S: image or object position relative to the lens, unit: mm. b) Calibration of lateral magnification at various object positions, a target illuminated by a white light LED is imaged for magnification measurement. c) Maximum intensity projections of data acquired on \(200 \mathrm{nm}\) beads from 10 slices spaced \(500 \mathrm{nm}\) in the Z-direction. The images show orthogonal views of the MIPs across scan range for S,P and S+P. The elongated PSF in the Z direction exhibits less resolution in the axial direction controlled by the light sheet waist. d) The FWHM of the \(200 \mathrm{nm}\) beads in the lateral and axial directions over the scan range. The minimum lateral resolution, \(394 \mathrm{nm}\) , occurs at the center of the scan range and increases by moving away from the center. These plots show a constant axial resolution of \(650 \mathrm{nm}\) over the axial scan range. The microscope functions in the scan range of \(70 \mu \mathrm{m}\) .
+ +agarose cube across the scan range and accessed the quality of the generated PSFs. Fig. 2c shows the maximum intensity projection (MIP) of beads (from 10 axial slices, each slice spaced \(500 \mathrm{nm}\) ) separated by \(30 \mu \mathrm{m}\) for S, P, and S+P across the scan range, after 10 iterations of Richardson- Lucy (RL) deconvolution. We found that our remote focusing setup demonstrated close to diffraction- limited performance over a scan range of \(\sim 70 \mathrm{um}\) . As evident from + +<--- Page Split ---> + +the 'S' and 'P' images the quality of the beads visually appears similar across the entire scan range thereby resulting in an identical 'S+P' image. In the axial direction (the \(YZ\) view) the PSFs are limited by the Gaussian light sheet's waist (beads from red boxes in \(XY\) view), which was determined by the tradeoff that exists between the FOV and \(Z\) resolution. We found that in order to image an entire cell, we needed a lightsheet that would generate a FOV of \(\sim 8\) \(\mu \mathrm{m}\) (Supplementary Fig. 4). As a result, we reduced the NA of the illumination objective and chose a light sheet whose waist was at FWHMz of \(\sim 650 \mathrm{nm}\) after deconvolution (850 nm before deconvolution). + +Figure 2d displays the measured FWHMs from \(200 \mathrm{nm}\) beads after RL deconvolution for S, P, and S+P polarize images in the lateral (XY) and axial (Z) directions across the entire scan range. The figure shows a minimum lateral FWHM of \(394 \mathrm{nm}\) at the center of the scan range which slowly increases as the beads move away from the nominal focal plane. This can be attributed to residue index mismatch aberrations that were not corrected by the remote focusing system21. Additionally, we found that the S polarization path suffered more in lateral resolution compared to the P polarization path and the trend is different along \(X\) and \(Y\) directions. This asymmetric FWHMs (X-Y) across scan range (Z) and the discrepancy between S and P paths is likely due to field- dependent aberrations from Obj2, where the S and P images were formed at different field points of Obj2 (Fig. 1b). Furthermore, our microscope shows a constant axial FWHM of \(\sim 650 \mathrm{nm}\) over the entire scan range as the axial resolution is mainly determined by the lightsheet waist. + +## Fast 3D live cell imaging + +As a first demonstration of the 3D cellular imaging capabilities, we monitored the 3D motion of secretory granules in living mast cells. Mast cells possess distinct secretory granules that contain the mediators of the allergic response and are released upon mast cell activation by allergen31. These granules are distributed across the cytosol and have been shown to undergo both Brownian diffusion and directed motion31. Upon activation of the membrane receptor, FcεRI, via crosslinking by multivalent antigen32,33, the granules undergo increased directed motion that moves them to the plasma membrane where they will fuse and release mediators that regulate allergic responses31,34. + +We applied the developed system for dual- color, volumetric imaging of live cells and tracked the 3D motion of green fluorescent protein- labeled Fas ligand (GFP- FasL) loaded secretory granules in the cytosol of RBL- 2H3 mast cells31. IgE- bound FcεRI was simultaneously imaged by addition of anti- DNP IgE- CF640R. With addition of the antigen- mimic, DNP- conjugated to BSA (DNP- BSA), FcεRI aggregates and undergoes endocytosis as seen in Figure 3a. During data acquisition, the light sheet is parallel to the \(XY\) plane and scans along the \(Z\) direction. Within the lightsheet region, the \(XY\) and \(XZ\) maximum intensity projections (Fig.3a) of the cell image show GFP- FasL granules in three dimensions. The cells were imaged at \(\sim 0.6\) volumes \((80 \times 15 \times 40 \mu \mathrm{m}^3\) in \(XYZ\) per second for 80 volumes, for a total imaging time of \(\sim 2\) minutes (Fig.3a- d). To quantify the granule dynamics, isolated granules were identified and tracked in 3D using the U- track3D software35. We calculated the mean square displacement (MSD) of each trajectory over time and extracted the diffusion coefficient, \(D\) , and velocity, \(v\) , by fitting the MSD curve with \(MSD(t) = 6Dt^2 + v^2 t^2 + o\) , where \(o\) is an offset related to localization and tracking uncertainties36,37 (Fig.3c,d). We found that most granules undergo Brownian Diffusion and a few exhibited directed motion, consistent with granules being transported along the microtubules (Fig.3a,b)31. The measured transport velocities of the two trajectories indicated in Fig.3a,b are \(\sim 0.1 \mu \mathrm{m / s}\) , consistent with previous work that performed tracking in 2D31. + +To test the limits of the new system in terms of speed, we set out to image Brownian motion on the microscopic level. For this, we stressed the cells by incubating them in Hank's balanced salt solution (HBSS) (Method) at room temperature for over 1 hour, which induced cell blebbing. This also caused more rapid diffusion of the granules that we were able to capture using an imaging speed of \(\sim 8.3\) volumes/s for 80 volumes for a total time of \(10 \mathrm{s}\) . With this imaging speed, we retained good signal- to- noise and the ability to track the 3D motion of individual granules (Fig 3e- g). Under these non- physiological conditions, average granule diffusion was increased by \(\sim 41\) times (Fig.3h). Two tracks shown in Fig. 3e have diffusion coefficients of \(0.41 \mu \mathrm{m}^2 / \mathrm{s}\) and \(0.64 \mu \mathrm{m}^2 / \mathrm{s}\) . + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3: Dual-Color volumetric imaging of live RBL cells. (a-d) Dual-color volumetric imaging of granule motions in a live RBL-2H3 GFP-FasL cell, where the cell membrane is labeled with IgE-CF640R and granules contain GFP-FasL, at an imaging speed of \(\sim 0.6\) volumes \((80\times 15\times 40\) \(\mu \mathrm{m}^3\) in XYZ) per second for 80 volumes, for a total imaging time of \(\sim 2\) minutes. (a) Maximum intensity projection views of the cell images at one-time point and overlay with representative trajectories of granule movement (orange lines). (b) Time series of the trajectories in a. (c, d) Histograms of estimated diffusion coefficients and velocities of all trajectories found in cell 1 and cell 2. (e-g) Dual-color volumetric imaging of live RBL-2H3 GFP-FasL cell, where the cell membrane is labeled with CellMask DeepRed and the granules contain GFP-FasL using an imaging speed of \(\sim 8.3\) volumes/s for 80 volumes for a total time of \(10\mathrm{s}\) . (e) Maximum intensity projection views of the cell images at one-time point and overlay with representative trajectories of granule movement (orange lines). (f) Time series of the trajectories in e. (g) Histograms of estimated diffusion coefficients of all trajectories in the cell. (h) Cumulative probability of the estimated diffusion coefficients under normal (a-d) and stressed (e-g) imaging conditions. 400-500 trajectories with a diffusion coefficient \(>0.001\mu \mathrm{m}^2 /\mathrm{s}\) from four cells under each condition are selected.
+ +<--- Page Split ---> + +## Discussion + +In this work, we developed an axial scanning module in the detection path of a light- sheet microscope utilizing the pmRF technique proposed by Botcherby et al.16,17. While inheriting all the benefits from the pmRF technique, such as fast scanning and all- optical aberration compensation (no wavefront control element), our design overcomes a critical limitation of the original pmRF technique, as in, the loss of \(50\%\) of the emitted fluorescence in the detection path21,24,38. Here we engineered a new optical design, where we split the emitted fluorescence into S and P polarized light to carryout remote focusing and then seamlessly combine them to achieve minimum light loss. We demonstrated our implementation of the developed scanning module through a light- sheet microscope with two orthogonally arranged objectives. We can perform simultaneous two- color imaging at 8.3 volumes ( \(80\times 15\times 40\mu \mathrm{m}^{3}\) in XYZ) per second with a lateral resolution of \(394\mathrm{nm}\) and an axial resolution of \(650\mathrm{nm}\) (after deconvolution). As our method is fully optical, the imaging speed scales with advancements in LFA technology and camera acquisition speed. + +The S and P polarized beams are directed at an oblique angle into the remote objective (Fig. 1b). This angled approach creates two separate images at the mirror attached to the LFA (M3). However, there are limitations to this angular arrangement. The two images formed away from the optical axis are prone to aberrations. To reduce the image separation, the remote objective must be positioned further from the PBS to reduce the incident angles of S and P- polarized lights. However, this increased distance breaks the 4f configuration between the two objectives (detection and remote objectives) that is critical to achieving aberration- free imaging. Future studies will investigate into more compact designs that will better satisfy the 4f condition and will reduce the separation between the two foci at M3. + +Of note, our approach offers several advantages over the existing axial refocusing methods. First, it provides an extended, aberration- free scan range for high numerical aperture (NA) optics. This is a significant benefit when compared to techniques based on deformable mirrors (DMs), where our method approximately doubles the axial scan range of DMs15. Second, it is wavelength independent, which makes it suited for simultaneous multicolor imaging when compared to SLMs and tunable lenses. Additionally, unlike SLMs which depend on polarization, our arrangement is not dependent on the polarization of the fluorescence. Furthermore, unlike SLMs, which are typically slow (especially the nematic liquid crystal ones), and even their faster counterparts (ferroelectrics) tend to be less effective, our method allows for imaging speed that are only limited by the sCMOS's framerate. + +Although recent advancements in single- objective oblique plane microscopy (OPM) have achieved speeds comparable to our method, our technique presents several notable advantages. In OPM, the de- scanning of the returning fluorescent light leads to skewed images. Before these images can be viewed, they require intensive de- skewing processes23,39- 43. On the other hand, our approach captures 3D volumes in a conventional orthogonal setup. This is achieved by recording high- speed images while sweeping the light- sheet through the sample. Each frame captured by the camera represents an optical cross- section of the specimen. As a result, the 3D image stacks generated using our method are immediately available for viewing. They may benefit from an optional deconvolution, but there's no delay caused by necessary post- processing. Furthermore, the OPM setup necessitates a third objective, which in the latest setups require expensive objectives like 'Snouty' or 'King Snout'2,39- 42. Our setup on the other hand does not have this requirement and our secondary objective performs the role of a tertiary objective. Moreover, while not demonstrated explicitly here, our method can be employed to achieve isotropic resolution, a feat the OPM cannot achieve. + +Compared with the original Botcherby's remote focusing setup, our pmRF module folds the beam path between the detection and the remote objectives. This configuration complicated the optical alignment. A potential solution is to arrange both objectives inline in a 4f configuration. Furthermore, we note that although an all- optical design has its merit of simplicity and robustness, using an objective lens in the pmRF module introduces \(\sim 30 - 40\%\) light loss (Supplementary Fig.5) compared with the axial scanning techniques based on DMs, future development of objective with high- transmission efficiency is desirable. + +Finally, it is our firm belief that owing to its generalized design, we envision our method has the potential to transform many popular microscope modalities like confocal, 2- photon, and the rapidly emerging field of light sheet microscopy, by reinventing how they perform scanning in the axial dimension. + +## Acknowledgements + +This work was supported by University of New Mexico (Start- up Grant) (TC), NIH R35GM151152 (TC), NIH P30CA118100 (TC and KL) and NIH R35GM126934 (DSL). We thank Derek Rinaldi for generating the IgE + +<--- Page Split ---> + +CF640R. This work was conducted with support from the University of New Mexico Office of the Vice President for Research Program for Enhancing Research Capacity, was supported by grants from NVIDIA and utilized an NVIDIA A6000 GPU. + +## Author contributions + +T.C. conceived the idea of lossless remote focusing in detection arm. H.D. and T.C. designed and built the remote focusing unit. H.D. and T.C. designed, built, and operated the microscope. H.D. and Sh.Li performed image analysis. Sh. Li and J.P. have theoretically demonstrated that achieving \(100\%\) conversion from unpolarized to polarized light is not feasible. D.J.S and K.A.L provided the MATLAB code for the fine alignment. H.D., T.C. and M.H. designed the chamber and sample holder. D.S.L., Sh.Lu., and R.M.G prepared RBL cells for imaging. H.D. imaged RBL cells labeled with DeepRed CellMask. H.D. and A.K.N.Sh imaged the RBL cells labeled with IgE- CF640R. H.D., Sh.Li., and T.C. wrote the manuscript. All authors read and provided feedback on the final manuscript. + +## Competing interests + +T.C. and H.D. have filed a patent application (United States Patent and Trademark Office application number 63/397,714) for the remote focusing setup mentioned here. + +## Methods + +## Optical setup + +The illumination arm consists of two laser sources (Coherent Sapphire \(488~\mathrm{nm}\) and Obis LX \(637~\mathrm{nm}\) ) which were combined with a dichroic beam splitter (LM01- 503- 25, Semrock). To clean up the beams, the beams were focused through a \(50 - \mu \mathrm{m}\) pinhole (P50D, Thorlabs) by a \(45 - \mathrm{mm}\) achromatic doublet (AC254- 045- A, Thorlabs) and then recollimated using a \(150 - \mathrm{mm}\) achromatic doublet (AC254- 150- A- ML, Thorlabs). The original beams were expanded by 9 folds with a \(3\times\) Galilean beam expander (GBE03- A) before being focused with a cylindrical lens (ACY254- 50- A, Thorlabs), onto a resonant mirror galvanometer (CRS \(4\mathrm{kHz}\) , Cambridge Technology), driven by a 12- volt power supply (A12MT400, Acopian), to wobble the light sheet. One- dimensional focus was then recollimated with a 100- mm achromatic doublet (AC254- 100- A- ML, Thorlabs) and hit the galvanometric scan mirror (GSM) (GVS111, Thorlabs), driven by a 15- volt power supply (GPS011, Thorlabs), for rapid shifting of the light sheet along the detection arm. This galvanometric mirror was conjugated to the back pupil of the objective lens (Nikon \(40\times 0.8\mathrm{NA}\) ) through \(100 - \mathrm{mm}\) and \(200 - \mathrm{mm}\) achromatic doublet (AC508- 100- A- ML and AC508- 200- A- ML, Thorlabs). + +In the detection arm, the same objective lens (Nikon \(40\times 0.8\mathrm{NA}\) ) in an orthogonal setup was used and pupil- matched to the scanning objective lens (Nikon Plan Apo \(20\times 0.75\mathrm{NA}\) ) through a \(200 - \mathrm{mm}\) tube lens (TTL200- A, Thorlabs) and a \(300 - \mathrm{mm}\) achromatic doublet (AC508- 300- A- ML, Thorlabs). A \(50:50\) polarizing beam splitter (PBS) (10FC16PB.3, Newport), splits the beam in S and P polarized light. Using mirrors M1 and M2 these light paths where then launched at an angle towards the Obj2 (Supplementary Fig.2). It is extremely critical to minimize the angle of the launch. Both experiment and simulation predicted that we used 8 degrees as the launch angle (called 0) (Fig.2a, inset of Supplementary Fig.2). The S and P polarized light passed through a quarter waveplate (AQWP10M, Thorlabs) and were focused onto a mirror positioned at the focus of the scanning objective lens. The mirror (PF03- 03- P01 - 07.0 mm Protected Silver Mirror, Thorlabs) was attached to a voice coil with a travel of \(10\mathrm{mm}\) , positional repeatability of fewer than 50 nanometers, and a response time of fewer than 3 milliseconds (LFA- 2010, Equipment Solutions). Then the reflected light was recaptured by the same scanning objective lens and quarter- wave plate to rotate the beam's polarization state. Afterward, the light was directed toward an sCMOS camera (Hamamatsu Orca- fusion BT) by reflecting from the same cube polarizing beam splitter and a \(300 - \mathrm{mm}\) achromatic doublet (AC508- 300- A- ML, Thorlabs). For emission filters, we used two long- pass filters (FF01- 525/30- 25, and BLP01- 647R- 25, Semrock), for blue, and far- red, respectively. To image dual channels simultaneously, the field of view (FOV) was separated into half using dichroic mirrors (DMLP605R, Thorlabs) between the \(300 - \mathrm{mm}\) achromatic doublet and the camera. We immersed the specimen and the illumination and detection objectives in a chamber designed using Adobe Inventor and machined through Protolabs (R). The LFA and GSM, in the detection and illumination arms respectively, were synchronized together to always keep the translated light sheet in the focus of the detection objective lens to acquire a 3D stack of the specimen. + +<--- Page Split ---> + +## Overlaying S and P images with sub-pixel accuracy + +Two identical images - - one corresponding to S and another to P polarized light - - are formed at the camera and added incoherently to generate the final image. We used a custom- written MATLAB script to monitor the offset between the two images in near real- time while adjusting the positions of M1 and M2 (Supplementary Fig.6). + +The offset between the two images using a cross- correlation- based algorithm as was used in Wester, M.J. et al44, which achieves sub- pixel accuracy by fitting second- order polynomials through the peak of the scaled cross- correlation between the S and P polarized images. + +Initially, an image is acquired as a reference by obstructing one optical path (either S or P). Subsequently, the alternative optical path is used to collect new images. The shift between each new image and the reference image is then measured using the method described in Wester, M.J. et al44 and is available as the MIC_Reg3DTrans.findStackOffset method in the matlab- instrument- control toolbox45. And while new images are collecting, mirrors M1 and M2 are adjusted to minimize the shift. + +## Microscope control + +A Dell Precision 7920 computer with two processors Intel(R) Xenon(R) Silver 4210R CPU having a processing speed of 2.40 GHz and \(2.39\mathrm{GHz}\) and was integrated with 128 GB RAM was used to acquire the microscope's data. An NVIDIA Quadro RTX 4000 Graphics processing unit (GPU) with dedicated memory of 8 GB and shared memory of 63.8 GB (GPU memory of 71.8 GB) was also integrated into the system. 64- bit operating system \(\times 64\) - based processor facilities the system to operate. LabView 2020 64- bit allowed us to work with the required software, including the LabView Run- Time Engine, Vision Run- Time Module, Vision Development Module, and other required drivers like NI- RIO drivers (National Instruments). DCAM- API software was used for the Active Silicon Firebird frame- grabber to actively interfere with the scientific complementary metal- oxide semiconductor (sCMOS) camera (ORCA- Fusion BT Digital CMOS camera, model: C15440- 20UP) manufactured by Hamamatsu, Japan. It generated deterministic transistor logic (TTL) trigger sequences through 150 Watts shutter instrument (100- 240 V\~50/60 Hz; model: MP- 285A) with a field programmable gate array (FPGA) (PCIe 7852R, National Instruments). The generated triggers controlled the resonant mirror galvanometers, placement of the stage, voice coils, blanking and modulation of laser, firing camera, and other external triggers. K- Hyper Terminal software facilitated engaging LFA with the system hardware. Some key features along with some routines under the agreement of material transfer were licensed by the Howard Hughes Medical Institute's Janelia Farms Research Campus. + +## Sample preparation + +Bead sample:200 nm beads embedded in \(2\%\) agarose gel was used for microscope resolution assessment. To make \(2\%\) agarose gel, 2 g of agarose powder (A9045- 25G, SIGMA life science) was mixed with \(100\mathrm{mL}\) water and swirled thoroughly before putting into the microwave oven to heat. Once the solution boiled and got completely clear and the agarose was dissolved, we should remove the solution from the oven and let it cool down. Then, \(200\mathrm{nm}\) beads (YG, Polysciences) were mixed with water with a ratio of \(1 / 100\) to form a solution of the \(200\mathrm{nm}\) beads. It was sonicated before mixing with the molten \(2\%\) agarose gel with a volumetric ratio of \(1 / 10\) . Then this molten combination was poured into the cubic mold where the sample holder was placed there and sat there for a few minutes to dry and form a \(1\mathrm{cm}^3\) cubic sample (200 nm beads embedded into \(2\%\) agarose gel) attached to the sample holder. + +Cell samples: RBL- 2H3 GFP- FasL cells were cultured in Gibco Minimum Essential Media (MEM) media supplemented with \(10\%\) heat- inactivated Fetal Bovine Serum (FBS), \(1\%\) Penicillin/Streptomycin, and \(1\%\) L- glutamine \(^{31}\) . The cells were primed with \(1\mu \mathrm{g / ml}\) anti- DNP- IgE \(^{46}\) (Fig. 3e- g) or anti- DNP- IgE- CF640R (Fig. 3a- d) and seeded at a density of \(100,000\) cells per well in 12 well dish over \(5\mathrm{mm}\) glass coverslips and incubated with \(5\%\) \(\mathrm{CO_2}\) at \(37^{\circ}\mathrm{C}\) overnight. IgE- CF640R was prepared using CF640R NHS- ester (Biotium #92108). For fast imaging experiments, the cellular membranes of anti- DNP- IgE primed cells were labeled with CellMask™ Deep Red Plasma Membrane Stain (Thermo Fisher Scientific #C10046, \(5\mathrm{mg / ml}\) , 1000X) according to manufacturer's instruction for 10 minutes in modified Hank's balanced salt solution (HBSS) (additional \(10\mathrm{mM}\) Hepes, \(0.05\%\) w/v BSA, \(5.45\mathrm{mM}\) glucose, 0.88 + +<--- Page Split ---> + +mM MgSO4, \(1.79\mathrm{mM}\) CaCl2, \(16.67\mathrm{mM}\) NaHCO3) and rinsed with HBSS. Cells were stimulated with \(1\mu \mathrm{g / ml}\) DNPBSA in the sample chamber. Data was acquired in 2 min captures for up to 15 minutes post antigen treatment. + +## Sample mounting + +Cells samples on \(5\mathrm{mm}\) coverslips were loaded onto the holder as depicted in Supplementary Fig.7. In the sample holder, two metal wires were designed to clamp the coverslip tightly. This sample holder was attached to the XYZ Translation Stage with Standard Micrometers using a rotation mount. As a result, the coverslip had four degrees of freedom, including the translation on the \(X - Y - Z\) axis to locate the cells while imaging and the rotation around the X- axis to face the coverslip with the desired angle relative to the illumination and detection objectives. Here, the coverslips were faced 8 degrees relative to the optic axis of the detection objective (Supplementary Fig.7). In order to minimize the buffer volume for live cell imaging, the \(6\mathrm{ml}\) chamber was designed to immerse the sample, illumination, and detection of objective lenses into it (Supplementary Fig.7). + +## Image processing pipeline + +Data were analyzed with the custom script written in Matlab. The procedure for quantifying the microscope's resolution from fluorescence bead data is as follows: 1) A 3D- PSF model was generated from the raw data. 2) The light- sheet region was cropped from each slice of the raw data. The width of the light- sheet region was defined by the distance to the waist of the light- sheet where the axial resolution increases by 2 times. We sat the light- sheet width to be \(\sim 6\mathrm{mm}\) . Note that the light- sheet region translated along its width direction (the \(Y\) - axis) while it was being scanned in the axial direction (Z- axis relative to the detection objective) (Supplementary Fig.8). Therefore, the light- sheet region to be cropped was also shifted in \(Y\) accordingly (Supplementary Video 1). 3) the cropped region was deconvolved with the 3D- PSF model using Richard- Lucy deconvolution from ImageJ. 4) the deconvolved data stack was divided into segments with an axial dimension of \(5\mu \mathrm{m}\) . For each segment, candidate beads were selected and their FWHMs along each dimension were estimated from Gaussian fitting of their intensity profiles along that dimension. 5) the measured FWHMs were used to quantify the resolution of the microscope as shown in Fig. 2d. + +Dual- color live- cell data was processed as follows: 1) cell signal from each color channel was cropped with a user- selected region. 2) for each color channel, the XYZ drifts of the data stack at each time point relative to the reference data stack were estimated, where the maximum- intensity projection (MIP) along each dimension of the two data stacks was generated and the 2D shift between each pair of the MIP images was calculated through cross- correlation. 3) an average of the XYZ shifts from both channels was used to correct the drift between time points. 4) the XYZ shift between the two- color channels was calculated by first averaging over the time dimension for each color channel, then estimating the shift from the MIP images as in step 3. Then register the two channels by applying the estimated shift. 5) after drift correction and channel registration, the resulting image stacks were deconvolved with the 3D- PSF model generated from the bead data using Richard- Lucy deconvolution from Matlab. 6) to reduce noise and correct photobleaching, the deconvolved images were subtracted by a background value with negative pixel values set to zero and divided by a normalization factor equal to the 99.95 quantiles of all pixel values in the corresponding time points and color channel. + +## Quantification of Light-sheet dimension + +To quantify the light- sheet dimension, bead data in agarose gel were collected at different slit widths. At each slit width, we estimated the FWHMs in XYZ for all selected beads as described above, however, here we used the full FOV of the color channel for bead imaging. As the position of the light- sheet waist shifted in y with respect to the axial dimension, we corrected the y coordinates of the selected bead by \(y_{Cor}^{\prime} = y_{Cor} - az_{Cor}\) , where \(a\) is the y shift by moving one pixel in \(Z\) (Supplementary Fig.8). Then we fitted the \(FWHM_{Z}\) verse \(y_{Cor}^{\prime}\) for all selected beads with a polynomial function (Supplementary Fig.4). The length of the light- sheet was found when the \(FWHM_{Z}\) was twice the minimum from the polynomial fit. + +## Magnification calibration + +<--- Page Split ---> + +One brightfield image of the calibration target was captured at each of the galvo positions from \(- 40\) to \(40\mu \mathrm{m}\) with a step size of \(10\mu \mathrm{m}\) . The target image consisted of parallel line segments, we cropped a region of \(700\mathrm{x}700\) pixels from each image (Supplementary Fig.9a- b). We then calculated the affine transformation (from the Dipimage toolbox) of each image with respect to a reference image. The zoom factors from affine transformation were used to quantify the relative magnification between each image to the reference image. The absolute magnification of one image was calculated as follows: crop a narrow section of multiple parallel lines, obtain the intensity profile by averaging over the line dimension, smooth the intensity profile by applying a running average with a window size of 30 pixels, find all peaks from the smoothed intensity profile (Supplementary Fig.9c), calculate the average distance (in pixels, denoted as \(\Delta d\) ) between consecutive peaks, as the distance between consecutive parallel lines is \(10\mathrm{mm}\) , then the pixel size at the sample plane can be estimated from \(10 / \Delta d\) mm, therefore the magnification can be calculated from the pixel size of the camera divided by pixel size at the sample plane. + +## Ray tracing + +Ray tracing was based on geometric optics with paraxial approximation. The ray propagation was calculated using the ABCD matrices. Two matrices were used, the translation matrix, + +\[M_{d} = \left[ \begin{array}{cc}1 & 0\\ d / n & 1 \end{array} \right],\] + +and the matrix of a thin lens, + +\[M_{f} = \left[ \begin{array}{cc}1 & -n / f\\ 0 & 1 \end{array} \right],\] + +where \(d\) is the translation distance, \(f\) is the focal length of the thin lens and \(n\) is the refractive index of the propagation medium. For our system, \(n\) is 1.33 before the detection objective (including the objective) and \(n\) equals 1 for the rest of the ray tracing. The starting point of each ray was represented by a vector of \([n\alpha ,y]^{T}\) , where \(\alpha\) and \(y\) are the angle and the y position of the ray with respect to the optical axis. The propagation of the ray is then calculated from + +\[\left[ \begin{array}{c}n\alpha '\\ y' \end{array} \right] = M\left[ \begin{array}{c}n\alpha '\\ y \end{array} \right].\] + +For a defined FOV, we selected three field points, two mark the edge of the FOV and one at the optical axis. For each field point, we generated three rays at different angles that will intersect three points at the pupil plane, where two points mark the edge of the pupil and one at the center of the pupil. Rays from the same field point were colored the same. The optical axis after the polarizing beam splitter was rotated by 45 degree to be along the splitting plane of the PBS. The ray tracing after the PBS was done by first transforming the ray coordinates to the ones defined by the optical axis and propagating the ray with the ABCD matrix, then transforming back to the global coordinates. Except for the distance between the tube lens and the detection objective (denoted as \(d_{1}\) ), the rest distances between consecutive optical elements were measured with a ruler. The angle between the chief rays of the S and P- polarization \((\theta\) in Fig.2a) was set when the input and output beam diameters at the remote focusing objective were minimum. The distance \(d_{1}\) was set when the relative magnifications from ray tracing match with the measured ones (Fig. 2b). The central position of the scan range, the distance of the objective to the detection objective (denoted as \(S_{1}\) ), was set when the absolute magnification from ray tracing matches with measured one. Here \(S_{1} = 6.695\mathrm{mm}\) , which was \(45\mu \mathrm{m}\) away from the designed focal plane of the detection objective. + +## References + +1. Bruzzone, M. et al. Whole brain functional recordings at cellular resolution in zebrafish larvae with 3D scanning multiphoton microscopy. Sci. 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Traffic 4, 302-312 (2003). + +32. Siraganian, R. P., de Castro, R. O., Barbu, E. A. & Zhang, J. Mast cell signaling: The role of protein tyrosine kinase Syk, its activation and screening methods for new pathway participants. FEBS Lett. 584, 4933-4940 (2010). + +33. Andrews, N. L. et al. Small, Mobile FcεRI Receptor Aggregates Are Signaling Competent. Immunity 31, 469-479 (2009). + +34. Mechanisms of Granule Membrane Recapture following Exocytosis in Intact Mast Cells - ScienceDirect. + +https://www.sciencedirect.com/science/article/pii/S0021925820456005?via%3Dihub. + +35. Roudot, P. et al. u-track 3D: measuring and interrogating dense particle dynamics in three dimensions. 2020.11.30.404814 Preprint at https://doi.org/10.1101/2020.11.30.404814 (2022). + +36. Dupont, A. et al. Three-dimensional single-particle tracking in live cells: news from the third dimension. New J. Phys. 15, 075008 (2013). + +37. Michalet, X. Mean square displacement analysis of single-particle trajectories with localization error: Brownian motion in an isotropic medium. Phys. Rev. E 82, 041914 (2010). + +38. Rupprecht, P., Prendergast, A., Wyart, C. & Friedrich, R. W. Remote z-scanning with a macroscopic voice coil motor for fast 3D multiphoton laser scanning microscopy. Biomed. Opt. Express 7, 1656-1671 (2016). + +39. Bouchard, M. B. et al. Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms. Nat. Photonics 9, 113-119 (2015). + +40. Yang, B. et al. DaXi—high-resolution, large imaging volume and multi-view single-objective light-sheet microscopy. Nat. Methods 19, 461-469 (2022). + +41. Sapoznik, E. et al. A versatile oblique plane microscope for large-scale and high-resolution imaging of subcellular dynamics. eLife 9, e57681 (2020). + +42. Chen, B. et al. Resolution doubling in light-sheet microscopy via oblique plane structured illumination. Nat. Methods 19, 1419-1426 (2022). + +43. Sparks, H. et al. Dual-view oblique plane microscopy (dOPM). Biomed. Opt. Express 11, 7204-7220 (2020). + +44. Wester, M. J. et al. Robust, fiducial-free drift correction for super-resolution imaging. Sci. Rep. 11, 23672 (2021). + +45. matlab-instrument-control. (2023). + +46. Liu, F. T. et al. Monoclonal dinitrophenyl-specific murine IgE antibody: preparation, isolation, and characterization. J. Immunol. 124, 2728-2737 (1980). + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementaryVideo1. avi SupplementaryFileSept1. pdf + +<--- Page Split ---> diff --git a/preprint/preprint__07208eea83c232a05dca70cd64270d5ee1a24fdbc00dcd03cf4ddc8b21562a44/preprint__07208eea83c232a05dca70cd64270d5ee1a24fdbc00dcd03cf4ddc8b21562a44_det.mmd b/preprint/preprint__07208eea83c232a05dca70cd64270d5ee1a24fdbc00dcd03cf4ddc8b21562a44/preprint__07208eea83c232a05dca70cd64270d5ee1a24fdbc00dcd03cf4ddc8b21562a44_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..a8606714dee552cb061498b19c1e38ea55c4f6d7 --- /dev/null +++ b/preprint/preprint__07208eea83c232a05dca70cd64270d5ee1a24fdbc00dcd03cf4ddc8b21562a44/preprint__07208eea83c232a05dca70cd64270d5ee1a24fdbc00dcd03cf4ddc8b21562a44_det.mmd @@ -0,0 +1,464 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 860, 177]]<|/det|> +# Axial de-scanning using remote focusing in the detection arm of light-sheet microscopy + +<|ref|>text<|/ref|><|det|>[[44, 195, 280, 240]]<|/det|> +Tommy Chakraborty tchakraborty@umn.edu + +<|ref|>text<|/ref|><|det|>[[44, 270, 640, 380]]<|/det|> +University of New Mexico HASSAN DIBAJI University of New Mexico Ali KAZEMI NASABAN SHOTORBAN University of New Mexico https://orcid.org/0000- 0002- 8513- 1784 + +<|ref|>text<|/ref|><|det|>[[44, 384, 280, 426]]<|/det|> +MAHSA HABIBI University of New Mexico + +<|ref|>text<|/ref|><|det|>[[44, 431, 761, 472]]<|/det|> +RACHEL GRATTAN Comprehensive Cancer Center, University of New Mexico Health Sciences Center, + +<|ref|>text<|/ref|><|det|>[[44, 476, 761, 519]]<|/det|> +SHAYNA LUCERO Comprehensive Cancer Center, University of New Mexico Health Sciences Center, + +<|ref|>text<|/ref|><|det|>[[44, 523, 280, 565]]<|/det|> +DAVID SCHODT University of New Mexico + +<|ref|>text<|/ref|><|det|>[[44, 570, 677, 612]]<|/det|> +Keith A. Lidke The University of New Mexico https://orcid.org/0000- 0002- 9328- 4318 + +<|ref|>text<|/ref|><|det|>[[44, 616, 692, 658]]<|/det|> +JONATHAN PETRUCCELLI Department of Physics, University at Albany- State University of New York + +<|ref|>text<|/ref|><|det|>[[44, 662, 761, 704]]<|/det|> +DIANE LIDKE Comprehensive Cancer Center, University of New Mexico Health Sciences Center, + +<|ref|>text<|/ref|><|det|>[[44, 709, 280, 750]]<|/det|> +SHENG LIU University of New Mexico + +<|ref|>text<|/ref|><|det|>[[44, 792, 103, 810]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 830, 137, 848]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 867, 317, 886]]<|/det|> +Posted Date: October 3rd, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 905, 473, 924]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3338831/v1 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 916, 87]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 105, 937, 171]]<|/det|> +Additional Declarations: Yes there is potential Competing Interest. T.C. and H.D. have filed a patent application (United States Patent and Trademark Office application number 63/397,714) for the remote focusing setup mentioned here. + +<|ref|>text<|/ref|><|det|>[[42, 207, 917, 250]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on June 12th, 2024. See the published version at https://doi.org/10.1038/s41467-024-49291-0. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[118, 89, 875, 137]]<|/det|> +# Axial de-scanning using remote focusing in the detection arm of light-sheet microscopy + +<|ref|>text<|/ref|><|det|>[[115, 150, 797, 204]]<|/det|> +HASSAN DIBAJI \(^{1}\) , ALI KAZEMI NASABAN SHOTORBAN \(^{1}\) , MAHSA HABIBI \(^{1}\) , RACHEL M GRATTAN \(^{2,3}\) , SHAYNA LUCERO \(^{2,3}\) , DAVID J. SCHODT \(^{1}\) , KEITH A. LIDKE \(^{1,2}\) , JONATHAN PETRUCCELLI \(^{4}\) , DIANE S. LIDKE \(^{2,3}\) , SHENG LIU \(^{1}\) , AND TONMOY CHAKRABORTY \(^{1,2,*}\) + +<|ref|>text<|/ref|><|det|>[[115, 208, 861, 272]]<|/det|> +\(^{1}\) Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico 87131, USA \(^{2}\) Comprehensive Cancer Center, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, 87131, USA \(^{3}\) Department of Pathology, University of New Mexico Health Science Center, Albuquerque, NM, USA \(^{4}\) Department of Physics, University at Albany- State University of New York, 1400 Washington Avenue, Albany, NY 12222, USA \(^*\) tchakraborty@unm.edu + +<|ref|>text<|/ref|><|det|>[[115, 280, 882, 514]]<|/det|> +Abstract: The ability to image at high speeds is necessary in biological imaging to capture fast- moving or transient events or to efficiently image large samples. However, due to the lack of rigidity of biological specimens, carrying out fast, high- resolution volumetric imaging without moving and agitating the sample has been a challenging problem. Pupil- matched remote focusing has been promising for high NA imaging systems with their low aberrations and wavelength independence, making it suitable for multicolor imaging. However, owing to the incoherent and unpolarized nature of the fluorescence signal, manipulating this emission light through remote focusing is challenging. Therefore, remote focusing has been primarily limited to the illumination arm, using polarized laser light for facilitating coupling in and out of the remote focusing optics. Here we introduce a novel optical design that can de- scan the axial focus movement in the detection arm of a microscope. Our method splits the fluorescence signal into S and P- polarized light and lets them pass through the remote focusing module separately and combines them with the camera. This allows us to use only one focusing element to perform aberration- free, multi- color, volumetric imaging without (a) compromising the fluorescent signal and (b) needing to perform sample/detection- objective translation. We demonstrate the capabilities of this scheme by acquiring fast dual- color 4D (3D space + time) image stacks, with an axial range of \(70 \mu \mathrm{m}\) and camera limited acquisition speed. Owing to its general nature, we believe this technique will find its application to many other microscopy techniques that currently use an adjustable Z- stage to carry out volumetric imaging such as confocal, 2- photon, and light sheet variants. + +<|ref|>sub_title<|/ref|><|det|>[[115, 544, 162, 558]]<|/det|> +## MAIN + +<|ref|>text<|/ref|><|det|>[[115, 565, 882, 799]]<|/det|> +Fast 3D positioning or scanning of an optical system's focal point or focal plane has the potential to transform many areas of BioPhotonics, especially those that require studying the complex dynamics of living organisms. Processes like investigation of neuronal activities of the brain, blood flow in the heart, and cell signaling require high- speed volumetric imaging \(^{1 - 3}\) . However, volumetric imaging requires an axial scan either through the translation of the sample or the detection objective lens (Fig. 1a). Such axial translations result in imaging modalities that are often slow with speeds limited to a few hundred \(\mathrm{Hz}^{4 - 6}\) . Additionally, with fragile samples, such as an expanded sample in hydrogel \(^{7}\) , fast movements of the sample stage may agitate the sample and induce distortions when collecting volumetric images. To avoid the slow translation of bulky objectives or the sample stages, several attempts, employing variable- focus (vari- focus) lenses, mechanical mirrors, and acousto- optics modulators have been proposed to refocus the light for 3D imaging. However, they all suffer from unacceptable aberrations introduced by the focusing elements. A large category of those techniques utilize different types of tunable lenses such as ferroelectric liquid crystal (LC), acoustic waves (TAG lens), and acoustic optics modulators (AOM) \(^{8}\) to achieve fast focal shifts (~1kHz). Ferroelectric LC and TAG lenses introduce a focal shift by varying the gradient of the refractive index of the liquid medium, however, the generated phase variation only approximates the defocus phase, leading to increased spherical aberration at large focal shifts \(^{9 - 11}\) . AOM- based vari- focus techniques on the other hand use two AOMs with counterpropagating acoustic waves to cancel out the transverse scan but can only achieve focus shift in one dimension (acting as a cylindrical lens) \(^{12,13}\) . + +<|ref|>text<|/ref|><|det|>[[115, 805, 881, 908]]<|/det|> +Adaptive optics- based vari- focus techniques overcome these limitations through accurate wavefront control using either a spatial light modulator (SLM) or a deformable mirror (DM), which can achieve a response rate of ~1 kHz and 20 kHz respectively. However, SLMs are polarization and wavelength- dependent and cannot model a continuous wavefront of the defocus phase due to its limited phase modulation depth. Large phase shifts are generated through multiple phase- wrapping of \(2\pi\) . With finite fly- back at the phase- wrapping borders, part of the incident light is not correctly modulated and results in decreased intensity at the focus \(^{14}\) . DMs are not polarization and wavelength- dependent and can model a continuous defocus wavefront. However, the axial scan range of a DM is limited by the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 882, 147]]<|/det|> +stroke length of the DM actuators. For example, for an objective with a numerical aperture (NA) of 0.8, the maximum axial scan range that DM based techniques can generate is \(- 40 \mu \mathrm{m}^{15}\) . Furthermore, using DM for focus control requires accurate alignment and complicated calibration of the DM to reduce the aberrations caused by imaging samples out of the nominal focal plane of the objective9. + +<|ref|>text<|/ref|><|det|>[[115, 161, 882, 438]]<|/det|> +Unlike the adaptive optics or DM- based approaches that require correcting the defocus plane- by- plane, pupil- matched remote focusing (pmRF), pioneered by Botcherby et al.16,17, instantaneously corrects defocus across 3D volumes for high- NA optics thereby conserving the microscope's temporal bandwidth16- 26. In addition, because pmRF allows precise mapping of the wavefront coupled into the back- pupil of the objective, where the angular magnification is unity, such techniques have been routinely used to carry out aberration- free high- quality axial focus control16- 26. In pmRF techniques, a fast axial scan is achieved by the translation of a small mirror in front of the remote objective using a focus actuator18,19,23 or by a lateral scan of a galvo mirror in combination to a step or tilted mirror at the remote objective27. Because of the fast response time of the focus actuator or the galvo mirror, an axial scan rate of 1- 5 kHz or 12 kHz can be achieved respectively. However, current pmRF techniques for focus control are primarily limited to the illumination path. This is because pmRF uses the concept of optical isolators28, where the polarization of the returning beam is rotated orthogonally to the incoming beam so that it can be separated from the incoming beam at the polarized beam splitter (PBS) (Supplementary Fig. 1a). This configuration ensures minimum light loss through the pmRF module but requires the incoming beam to be polarized, which is why this method is primarily used in the illumination arm where illumination laser light is usually polarized in nature and its manipulation through the optical isolator can be easily done. In the detection arm, however, the emitted fluorescence is unpolarized in nature. To the best of our knowledge, because, using purely linear optical elements, lossless conversion of unpolarized light into a single polarized state is not yet possible29,30 (Supplementary Note 1), manipulating the fluorescent light using the optical isolators is unfeasible. As a result, microscopes that use pmRF to carry out axial scanning, incur 50% light loss due to one state of the polarized light being discarded after the PBS16,21,24 (Supplementary Fig. 1a). + +<|ref|>text<|/ref|><|det|>[[115, 451, 882, 596]]<|/det|> +A straightforward method to mitigate this problem is to have another copy of the pmRF module at the unused port of the PBS (Supplementary Fig. 1b) to collect the other half of the fluorescent light. However, this would require precise synchronization of two linear- focus- actuators (LFA), which is not only a difficult task at high speeds but also will be expensive since this method warrants two such LFAs. In this article, we present a novel optical design that overcomes these problems and presents a modular setup that can perform remote focusing on the detection arm of a fluorescent microscope without incurring polarization- induced losses. When attached to a light- sheet microscope, this technique allows optical refocusing without requiring the movement of the sample, or the detection objective (Fig. 1b and Supplementary Fig. 1c). As a result the microscope can acquire 3D volumetric data limited by camera speed. This technique is applicable to many other microscopy techniques that currently use an adjustable \(Z\) - stage to carry out volumetric imaging such as confocal, 2- photon, and light sheet variants. + +<|ref|>sub_title<|/ref|><|det|>[[115, 605, 170, 618]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[116, 627, 336, 641]]<|/det|> +## Concept and microscope layout + +<|ref|>text<|/ref|><|det|>[[115, 648, 882, 852]]<|/det|> +Optical axial- refocusing: Our refocusing unit is shown in Fig. 1b. Here, the water immersion detection objective (Obj1) is pupil matched to a second air objective (Obj2) through two intermediate lenses following the original design by Botcherby et al.16,17. However, unlike traditional refocusing geometry, we split the collected unpolarized fluorescence into S and P- polarized light using a polarizing beam splitter cube (PBS) in the infinity space of Obj2. The generated orthogonal paths are then projected onto Obj2 using two angled mirrors M1 and M2. Because of this angular launch in infinity space, Obj2 forms two distinct laterally shifted images at its nominal focal plane. A small mirror placed on an LFA reflects the light back through the path it came from where a quarter wave plate (QWP) converts the S- polarized light to P on its way back (and P- polarized light to S) after being reflected from the mirror (Fig. 1c). When the returning light (in each arm) reaches the PBS, it now acts as an optical valve where the S path (which was initially P) gets reflected while the P- polarized light (which was initially S) gets transmitted by the PBS. As a result, both S and P polarized light exits the PBS through the fourth and unused face of the PBS cube (Fig. 1d). This light after passing through a tube lens forms identical images, one with S and another with P, at the sCMOS camera. A precise alignment using mirrors M1 and M2 overlays the two images, thereby resulting in a combined image by simply an incoherent addition without any interference artifacts. + +<|ref|>text<|/ref|><|det|>[[116, 859, 882, 902]]<|/det|> +There are a few important design considerations that need to be considered for our de- scanning setup. Firstly, it is essential that mirror M3 consistently moves in parallel with the focal plane of Obj2 during the LFA's oscillatory motion. This prevents any unwanted focal shifts between the S and P paths, ensuring that the resulting image from + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 882, 133]]<|/det|> +both S and P polarizations remain focused on the camera at the same time. This arrangement ensures that both beams return through their incoming paths, resulting in easier alignment for overlaying the final images formed by the S and P- polarized beams. + +<|ref|>text<|/ref|><|det|>[[115, 140, 882, 215]]<|/det|> +Secondly, it is advantageous that 0 (angle between S and P polarized beam hitting the Obj2) (Supplementary Fig. 2) be as small as possible because this directly controls the distance between the two focal points at M3 (depicted by \(\Delta L\) in Fig. 1c). A smaller \(\Delta L\) ensures: (1) a smaller mirror could be utilized to carry out the remote- focusing, reducing the inertial load on the LFA, and enhancing its efficiency; (2) The alignment becomes less sensitive to tip- tilt misalignment of M3; and (3) This guarantees that both images fit within Obj2's field of view (FOV). + +<|ref|>text<|/ref|><|det|>[[115, 221, 882, 309]]<|/det|> +Thirdly, there exists an inverse relationship between the angle \(\theta\) and the distance between Obj2 and the PBS (inset of Supplementary Fig.2). Therefore, this gives us an option: either adhere to the 4f system or minimize \(\theta\) . We found that for our matching objectives Obj1 and Obj2 the 4f system (with matching lenses L1 and L2) resulted in a \(\theta\) of \(20^{\circ}\) (inset of Supplementary Fig.2). However, operating in this range poses a risk as it is challenging to ensure that both reflected beams are entirely captured by Obj2. Hence, there is a balance between adhering to the 4f system and minimizing the angle \(\theta\) . We found that with our current design, we can still achieve diffraction limited resolution (Fig. 1e). + +<|ref|>text<|/ref|><|det|>[[115, 316, 882, 375]]<|/det|> +Finally, because we generated two identical images on the camera using S and P- polarized light, it was crucial to overlay these images with precision higher than the diffraction- limited resolution to produce the final image. To do this, we developed a cross- correlation- based algorithm that quantifies the shift between overlayed S and P images in real- time with sub- pixel accuracy, allowing interactive adjustment of the mirrors M1 and M2 during system alignment. + +<|ref|>text<|/ref|><|det|>[[115, 388, 882, 550]]<|/det|> +Implementation in a light- sheet system: In order to test the performance of our design we implemented this setup into the detection arm of a light- sheet microscope with orthogonal illumination and detection objectives. The system layout is shown in (Fig.1b and Supplementary Fig.2). The sample is illuminated by a sheet of light generated with a cylindrical lens in the illumination arm, and the emitted fluorescence from the sample is collected by the detection objective lens, which is set orthogonal to the illumination objective lens to capture 2D information from the sample. A galvanometric scan mirror (GSM) in the illumination arm translates the light- sheet in the Z- direction. Because the position of the LFA in the detection arm determines the focal plane of the detection objective lens, we synchronized the GSM and LFA with the sawtooth signal to ensure that the detection path is always focused on the plane of the light- sheet (Supplementary Fig.3). This allowed us to carry out volumetric imaging by acquiring a sequence of images from different focal planes. The LFA moves back and forth rapidly, synchronized with the movement of the GSM enabling us to quickly collect 3D image stacks. + +<|ref|>text<|/ref|><|det|>[[115, 562, 882, 710]]<|/det|> +The optical correction of defocus in our high- NA microscope allowed fast de- scanning of a 3D volume over an axial range of \(\sim 70 \mu \mathrm{m}\) at speeds limited primarily by the camera framerate ( \(\sim\) in our case 799 camera frames/s at \(2304 \times 256\) pixels using Hamamatsu Orca- fusion BT). We employed a dual- color imaging strategy by partitioning the FOV, enabling simultaneous capture of two distinct fluorescent labels within each slice without sacrificing imaging speed. To do this we used a pair of dichroic mirrors to separate the emitted wavelengths from the two labels into side- by- side dual- color images (Supplementary Fig.2). Once acquired, these separate image sets are then precisely registered and merged to generate 4D (X, Y, Z, and \(\lambda\) ) stacks. By sequentially capturing 4D stacks, we generated 5D (X, Y, Z, \(\lambda\) , and time) datasets that allowed us to track the dynamic behavior of biological processes. It is important to note that our setup is wavelength- independent, an attribute not feasible with technologies like diffractive tunable lenses or spatial light modulators. + +<|ref|>sub_title<|/ref|><|det|>[[116, 730, 381, 745]]<|/det|> +## Characterization of the optical system + +<|ref|>text<|/ref|><|det|>[[115, 752, 882, 905]]<|/det|> +To understand the image formation of the proposed setup, we simulated the ray tracing of the detection path (Fig. 2a). The ray tracing assumes all rays satisfy paraxial approximation and all lenses are simple lenses. The detection objective is a water immersion objective, we calculated its effective focal length as \(f_{\mathrm{obj}} = f_{\mathrm{Tube}} n / M_{\mathrm{obj}}\) , where \(f_{\mathrm{Tube}}\) is the focal length of the designed tube lens, \(M_{\mathrm{obj}}\) is the magnification of the objective, and \(n\) is the refractive index of water. Here we have \(f_{\mathrm{obj}}\) equal to 6.65 mm. The pmRF module (from the beam splitter to LFA) is modeled two times to simulate the forward and backward transmission through the module. The LFA is omitted from the simulation, instead, we change the distance between the two copies of the pmRF objectives so that the distance \((S_{3})\) of the image plane to the second pmRF objective remains as a constant. We simulated with an object of \(100 \mu \mathrm{m}\) , the image size after the pmRF objective is \(\sim 140 \mu \mathrm{m}\) , indicating a lateral magnification of 1.4, which is close to the requirement of perfect imaging with \(M_{\mathrm{lateral}} = n_{\mathrm{water}} / n_{\mathrm{air}} = 1.33\) . The small deviation is limited by the geometry of the pmRF + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 883, 255]]<|/det|> +module: the separation \((\Delta L)\) of the S and P- images formed by the pmRF objective is approximate to \(\Delta L = f_{\mathrm{RFobj}}\theta\) where \(f_{\mathrm{RFobj}} = 10 \mathrm{mm}\) is the effective focal length of the RF objective and \(\theta\) is the angle between the S and P- polarized rays meeting at the RF objective. The larger the \(\Delta L\) , the larger the aberration introduced by the pmRF objective. To reduce \(\Delta L\) , the pmRF objective is located \(\sim 500 \mathrm{mm}\) from the PBS, therefore, the pmRF module is no longer an exact 4f system, the magnification, \(M_{\mathrm{lateral}}\) , varies with the axial position of the object. Furthermore, the beam path from the detection objective to the tube lens is also not a 4f system, where the tube lens is \(\sim 100 \mathrm{mm}\) away from the detection objective. The combination of the two non- 4f systems can partially reduce the axial dependence of the magnification. Fig. 2b shows the change of the lateral magnification with respect to the galvo position (the axial position of the light sheet) from both ray tracing and the experimental data. There is a \(\sim 5\%\) magnification change over an axial range of \(80 \mu \mathrm{m}\) . This magnification change can be further reduced by optimizing the axial position of the tube lens. + +<|ref|>image<|/ref|><|det|>[[128, 280, 863, 675]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 686, 883, 886]]<|/det|> +
Figure 1: Schematic diagram of a remote focusing system implemented in light-sheet microscopy and its performance. a) Three different modalities to acquire volumetric imaging of the sample along the focus direction. Either the sample or objective lens can be moved for axial refocusing. Alternatively, both the sample and objective lens can remain stationary by using a remote focusing system. b) Implementation of the remote-focusing system on the detection arm of the light sheet microscope. In this configuration, objective lenses 1 and 2 are pupil-matched through two lenses to form a perfect imaging system. Combined with mirror M3 and a polarizing beam splitter (PBS), the whole system works as a remote focusing system. The novel design of this remote-focusing system is implementation in the detection arm for unpolarized fluorescent light emitted from the sample. To do this, two tilted mirrors M1 and M2 are utilized to direct both S and P-polarized beams toward Objective lens 2 and then combine the reflected beams from mirror M3 to create an image by S and P-polarized beams onto the camera by focusing through the tube lens. The mirror M3 is attached to the linear focus actuator (LFA), moving back and forth to scan the sample in the Z-direction to acquire a 3D image. In the illumination arm, the generated light sheet by a cylindrical lens is translated by a galvanometric scan mirror (GSM) along the detection arm. To focus the detection path on the plane of the light sheet, the synchronization of GSM and LFA is carried out by sawtooth signals. Simultaneous dual-channel imaging of the cell is achieved in \(40 \mu \mathrm{m} \times 150 \mu \mathrm{m}\) FOV over \(70 \mu \mathrm{m}\) in the Z-direction. c) The polarization state of the incoming beams changes after reflection from mirror M3 (S to P, and P to S). d) The reflected beams from mirror M3 have a different polarization state compared to the incoming beams; therefore, they exist from a different side of the PBS than the incoming beams. e) The point spread function (PSF) of 200 nm beads formed by S, P, and S+P polarized beams. The microscope performs at the diffraction limit, \(394 \mathrm{nm}\) resolution, for S, P, and S+P in the lateral directions (X-Y), while it maintains a resolution of \(654 \mathrm{nm}\) in the axial direction (Z).
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 117, 883, 242]]<|/det|> +To quantify the performance of the proposed scheme, we used full width at half max (FWHM) measurements of 3D point spread function (PSF) to validate that the incoherent addition of S and P images was not compromising the resolution. To do this, we measured the PSF of each polarization component individually and compared it with the PSF of the unified S+P image. As illustrated in Fig.1e, both the S and P- polarized images rendered onto the camera exhibit identical FHWM, resulting in an equivalent resolution for the combined S+P image. Further quantification involving 10 randomly chosen beads, reveals that the microscope achieved diffraction- limited resolutions: \(394 \pm 31 \mathrm{nm}\) laterally (X- Y) and \(654 \pm 130 \mathrm{nm}\) axially (Z). These measurements were performed in proximity to the nominal focal plane (MIP of 10 slices, each separated 500 nm). + +<|ref|>text<|/ref|><|det|>[[115, 254, 883, 270]]<|/det|> +To evaluate the performance of the de- scanning system, we imaged 3D volumes of \(200 \mathrm{nm}\) beads embedded in a \(2\%\) + +<|ref|>image<|/ref|><|det|>[[120, 285, 860, 707]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 723, 870, 831]]<|/det|> +
Figure 2: Ray tracing of the setup and resolution assessment. a) Ray tracing of the detection path. L: image size, f: effective focal length, S: image or object position relative to the lens, unit: mm. b) Calibration of lateral magnification at various object positions, a target illuminated by a white light LED is imaged for magnification measurement. c) Maximum intensity projections of data acquired on \(200 \mathrm{nm}\) beads from 10 slices spaced \(500 \mathrm{nm}\) in the Z-direction. The images show orthogonal views of the MIPs across scan range for S,P and S+P. The elongated PSF in the Z direction exhibits less resolution in the axial direction controlled by the light sheet waist. d) The FWHM of the \(200 \mathrm{nm}\) beads in the lateral and axial directions over the scan range. The minimum lateral resolution, \(394 \mathrm{nm}\) , occurs at the center of the scan range and increases by moving away from the center. These plots show a constant axial resolution of \(650 \mathrm{nm}\) over the axial scan range. The microscope functions in the scan range of \(70 \mu \mathrm{m}\) .
+ +<|ref|>text<|/ref|><|det|>[[115, 835, 882, 895]]<|/det|> +agarose cube across the scan range and accessed the quality of the generated PSFs. Fig. 2c shows the maximum intensity projection (MIP) of beads (from 10 axial slices, each slice spaced \(500 \mathrm{nm}\) ) separated by \(30 \mu \mathrm{m}\) for S, P, and S+P across the scan range, after 10 iterations of Richardson- Lucy (RL) deconvolution. We found that our remote focusing setup demonstrated close to diffraction- limited performance over a scan range of \(\sim 70 \mathrm{um}\) . As evident from + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 88, 882, 177]]<|/det|> +the 'S' and 'P' images the quality of the beads visually appears similar across the entire scan range thereby resulting in an identical 'S+P' image. In the axial direction (the \(YZ\) view) the PSFs are limited by the Gaussian light sheet's waist (beads from red boxes in \(XY\) view), which was determined by the tradeoff that exists between the FOV and \(Z\) resolution. We found that in order to image an entire cell, we needed a lightsheet that would generate a FOV of \(\sim 8\) \(\mu \mathrm{m}\) (Supplementary Fig. 4). As a result, we reduced the NA of the illumination objective and chose a light sheet whose waist was at FWHMz of \(\sim 650 \mathrm{nm}\) after deconvolution (850 nm before deconvolution). + +<|ref|>text<|/ref|><|det|>[[115, 190, 882, 336]]<|/det|> +Figure 2d displays the measured FWHMs from \(200 \mathrm{nm}\) beads after RL deconvolution for S, P, and S+P polarize images in the lateral (XY) and axial (Z) directions across the entire scan range. The figure shows a minimum lateral FWHM of \(394 \mathrm{nm}\) at the center of the scan range which slowly increases as the beads move away from the nominal focal plane. This can be attributed to residue index mismatch aberrations that were not corrected by the remote focusing system21. Additionally, we found that the S polarization path suffered more in lateral resolution compared to the P polarization path and the trend is different along \(X\) and \(Y\) directions. This asymmetric FWHMs (X-Y) across scan range (Z) and the discrepancy between S and P paths is likely due to field- dependent aberrations from Obj2, where the S and P images were formed at different field points of Obj2 (Fig. 1b). Furthermore, our microscope shows a constant axial FWHM of \(\sim 650 \mathrm{nm}\) over the entire scan range as the axial resolution is mainly determined by the lightsheet waist. + +<|ref|>sub_title<|/ref|><|det|>[[116, 365, 287, 380]]<|/det|> +## Fast 3D live cell imaging + +<|ref|>text<|/ref|><|det|>[[115, 393, 882, 481]]<|/det|> +As a first demonstration of the 3D cellular imaging capabilities, we monitored the 3D motion of secretory granules in living mast cells. Mast cells possess distinct secretory granules that contain the mediators of the allergic response and are released upon mast cell activation by allergen31. These granules are distributed across the cytosol and have been shown to undergo both Brownian diffusion and directed motion31. Upon activation of the membrane receptor, FcεRI, via crosslinking by multivalent antigen32,33, the granules undergo increased directed motion that moves them to the plasma membrane where they will fuse and release mediators that regulate allergic responses31,34. + +<|ref|>text<|/ref|><|det|>[[115, 494, 882, 700]]<|/det|> +We applied the developed system for dual- color, volumetric imaging of live cells and tracked the 3D motion of green fluorescent protein- labeled Fas ligand (GFP- FasL) loaded secretory granules in the cytosol of RBL- 2H3 mast cells31. IgE- bound FcεRI was simultaneously imaged by addition of anti- DNP IgE- CF640R. With addition of the antigen- mimic, DNP- conjugated to BSA (DNP- BSA), FcεRI aggregates and undergoes endocytosis as seen in Figure 3a. During data acquisition, the light sheet is parallel to the \(XY\) plane and scans along the \(Z\) direction. Within the lightsheet region, the \(XY\) and \(XZ\) maximum intensity projections (Fig.3a) of the cell image show GFP- FasL granules in three dimensions. The cells were imaged at \(\sim 0.6\) volumes \((80 \times 15 \times 40 \mu \mathrm{m}^3\) in \(XYZ\) per second for 80 volumes, for a total imaging time of \(\sim 2\) minutes (Fig.3a- d). To quantify the granule dynamics, isolated granules were identified and tracked in 3D using the U- track3D software35. We calculated the mean square displacement (MSD) of each trajectory over time and extracted the diffusion coefficient, \(D\) , and velocity, \(v\) , by fitting the MSD curve with \(MSD(t) = 6Dt^2 + v^2 t^2 + o\) , where \(o\) is an offset related to localization and tracking uncertainties36,37 (Fig.3c,d). We found that most granules undergo Brownian Diffusion and a few exhibited directed motion, consistent with granules being transported along the microtubules (Fig.3a,b)31. The measured transport velocities of the two trajectories indicated in Fig.3a,b are \(\sim 0.1 \mu \mathrm{m / s}\) , consistent with previous work that performed tracking in 2D31. + +<|ref|>text<|/ref|><|det|>[[115, 713, 882, 816]]<|/det|> +To test the limits of the new system in terms of speed, we set out to image Brownian motion on the microscopic level. For this, we stressed the cells by incubating them in Hank's balanced salt solution (HBSS) (Method) at room temperature for over 1 hour, which induced cell blebbing. This also caused more rapid diffusion of the granules that we were able to capture using an imaging speed of \(\sim 8.3\) volumes/s for 80 volumes for a total time of \(10 \mathrm{s}\) . With this imaging speed, we retained good signal- to- noise and the ability to track the 3D motion of individual granules (Fig 3e- g). Under these non- physiological conditions, average granule diffusion was increased by \(\sim 41\) times (Fig.3h). Two tracks shown in Fig. 3e have diffusion coefficients of \(0.41 \mu \mathrm{m}^2 / \mathrm{s}\) and \(0.64 \mu \mathrm{m}^2 / \mathrm{s}\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[130, 92, 876, 734]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[125, 742, 901, 878]]<|/det|> +
Figure 3: Dual-Color volumetric imaging of live RBL cells. (a-d) Dual-color volumetric imaging of granule motions in a live RBL-2H3 GFP-FasL cell, where the cell membrane is labeled with IgE-CF640R and granules contain GFP-FasL, at an imaging speed of \(\sim 0.6\) volumes \((80\times 15\times 40\) \(\mu \mathrm{m}^3\) in XYZ) per second for 80 volumes, for a total imaging time of \(\sim 2\) minutes. (a) Maximum intensity projection views of the cell images at one-time point and overlay with representative trajectories of granule movement (orange lines). (b) Time series of the trajectories in a. (c, d) Histograms of estimated diffusion coefficients and velocities of all trajectories found in cell 1 and cell 2. (e-g) Dual-color volumetric imaging of live RBL-2H3 GFP-FasL cell, where the cell membrane is labeled with CellMask DeepRed and the granules contain GFP-FasL using an imaging speed of \(\sim 8.3\) volumes/s for 80 volumes for a total time of \(10\mathrm{s}\) . (e) Maximum intensity projection views of the cell images at one-time point and overlay with representative trajectories of granule movement (orange lines). (f) Time series of the trajectories in e. (g) Histograms of estimated diffusion coefficients of all trajectories in the cell. (h) Cumulative probability of the estimated diffusion coefficients under normal (a-d) and stressed (e-g) imaging conditions. 400-500 trajectories with a diffusion coefficient \(>0.001\mu \mathrm{m}^2 /\mathrm{s}\) from four cells under each condition are selected.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 190, 104]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[115, 117, 882, 264]]<|/det|> +In this work, we developed an axial scanning module in the detection path of a light- sheet microscope utilizing the pmRF technique proposed by Botcherby et al.16,17. While inheriting all the benefits from the pmRF technique, such as fast scanning and all- optical aberration compensation (no wavefront control element), our design overcomes a critical limitation of the original pmRF technique, as in, the loss of \(50\%\) of the emitted fluorescence in the detection path21,24,38. Here we engineered a new optical design, where we split the emitted fluorescence into S and P polarized light to carryout remote focusing and then seamlessly combine them to achieve minimum light loss. We demonstrated our implementation of the developed scanning module through a light- sheet microscope with two orthogonally arranged objectives. We can perform simultaneous two- color imaging at 8.3 volumes ( \(80\times 15\times 40\mu \mathrm{m}^{3}\) in XYZ) per second with a lateral resolution of \(394\mathrm{nm}\) and an axial resolution of \(650\mathrm{nm}\) (after deconvolution). As our method is fully optical, the imaging speed scales with advancements in LFA technology and camera acquisition speed. + +<|ref|>text<|/ref|><|det|>[[115, 277, 882, 380]]<|/det|> +The S and P polarized beams are directed at an oblique angle into the remote objective (Fig. 1b). This angled approach creates two separate images at the mirror attached to the LFA (M3). However, there are limitations to this angular arrangement. The two images formed away from the optical axis are prone to aberrations. To reduce the image separation, the remote objective must be positioned further from the PBS to reduce the incident angles of S and P- polarized lights. However, this increased distance breaks the 4f configuration between the two objectives (detection and remote objectives) that is critical to achieving aberration- free imaging. Future studies will investigate into more compact designs that will better satisfy the 4f condition and will reduce the separation between the two foci at M3. + +<|ref|>text<|/ref|><|det|>[[115, 393, 882, 510]]<|/det|> +Of note, our approach offers several advantages over the existing axial refocusing methods. First, it provides an extended, aberration- free scan range for high numerical aperture (NA) optics. This is a significant benefit when compared to techniques based on deformable mirrors (DMs), where our method approximately doubles the axial scan range of DMs15. Second, it is wavelength independent, which makes it suited for simultaneous multicolor imaging when compared to SLMs and tunable lenses. Additionally, unlike SLMs which depend on polarization, our arrangement is not dependent on the polarization of the fluorescence. Furthermore, unlike SLMs, which are typically slow (especially the nematic liquid crystal ones), and even their faster counterparts (ferroelectrics) tend to be less effective, our method allows for imaging speed that are only limited by the sCMOS's framerate. + +<|ref|>text<|/ref|><|det|>[[115, 523, 882, 685]]<|/det|> +Although recent advancements in single- objective oblique plane microscopy (OPM) have achieved speeds comparable to our method, our technique presents several notable advantages. In OPM, the de- scanning of the returning fluorescent light leads to skewed images. Before these images can be viewed, they require intensive de- skewing processes23,39- 43. On the other hand, our approach captures 3D volumes in a conventional orthogonal setup. This is achieved by recording high- speed images while sweeping the light- sheet through the sample. Each frame captured by the camera represents an optical cross- section of the specimen. As a result, the 3D image stacks generated using our method are immediately available for viewing. They may benefit from an optional deconvolution, but there's no delay caused by necessary post- processing. Furthermore, the OPM setup necessitates a third objective, which in the latest setups require expensive objectives like 'Snouty' or 'King Snout'2,39- 42. Our setup on the other hand does not have this requirement and our secondary objective performs the role of a tertiary objective. Moreover, while not demonstrated explicitly here, our method can be employed to achieve isotropic resolution, a feat the OPM cannot achieve. + +<|ref|>text<|/ref|><|det|>[[115, 698, 882, 787]]<|/det|> +Compared with the original Botcherby's remote focusing setup, our pmRF module folds the beam path between the detection and the remote objectives. This configuration complicated the optical alignment. A potential solution is to arrange both objectives inline in a 4f configuration. Furthermore, we note that although an all- optical design has its merit of simplicity and robustness, using an objective lens in the pmRF module introduces \(\sim 30 - 40\%\) light loss (Supplementary Fig.5) compared with the axial scanning techniques based on DMs, future development of objective with high- transmission efficiency is desirable. + +<|ref|>text<|/ref|><|det|>[[115, 800, 882, 844]]<|/det|> +Finally, it is our firm belief that owing to its generalized design, we envision our method has the potential to transform many popular microscope modalities like confocal, 2- photon, and the rapidly emerging field of light sheet microscopy, by reinventing how they perform scanning in the axial dimension. + +<|ref|>sub_title<|/ref|><|det|>[[116, 860, 252, 873]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[115, 873, 881, 902]]<|/det|> +This work was supported by University of New Mexico (Start- up Grant) (TC), NIH R35GM151152 (TC), NIH P30CA118100 (TC and KL) and NIH R35GM126934 (DSL). We thank Derek Rinaldi for generating the IgE + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 881, 133]]<|/det|> +CF640R. This work was conducted with support from the University of New Mexico Office of the Vice President for Research Program for Enhancing Research Capacity, was supported by grants from NVIDIA and utilized an NVIDIA A6000 GPU. + +<|ref|>sub_title<|/ref|><|det|>[[116, 149, 265, 162]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[115, 163, 881, 265]]<|/det|> +T.C. conceived the idea of lossless remote focusing in detection arm. H.D. and T.C. designed and built the remote focusing unit. H.D. and T.C. designed, built, and operated the microscope. H.D. and Sh.Li performed image analysis. Sh. Li and J.P. have theoretically demonstrated that achieving \(100\%\) conversion from unpolarized to polarized light is not feasible. D.J.S and K.A.L provided the MATLAB code for the fine alignment. H.D., T.C. and M.H. designed the chamber and sample holder. D.S.L., Sh.Lu., and R.M.G prepared RBL cells for imaging. H.D. imaged RBL cells labeled with DeepRed CellMask. H.D. and A.K.N.Sh imaged the RBL cells labeled with IgE- CF640R. H.D., Sh.Li., and T.C. wrote the manuscript. All authors read and provided feedback on the final manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[115, 280, 258, 293]]<|/det|> +## Competing interests + +<|ref|>text<|/ref|><|det|>[[115, 293, 881, 322]]<|/det|> +T.C. and H.D. have filed a patent application (United States Patent and Trademark Office application number 63/397,714) for the remote focusing setup mentioned here. + +<|ref|>sub_title<|/ref|><|det|>[[115, 351, 177, 365]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[115, 380, 210, 396]]<|/det|> +## Optical setup + +<|ref|>text<|/ref|><|det|>[[115, 408, 882, 578]]<|/det|> +The illumination arm consists of two laser sources (Coherent Sapphire \(488~\mathrm{nm}\) and Obis LX \(637~\mathrm{nm}\) ) which were combined with a dichroic beam splitter (LM01- 503- 25, Semrock). To clean up the beams, the beams were focused through a \(50 - \mu \mathrm{m}\) pinhole (P50D, Thorlabs) by a \(45 - \mathrm{mm}\) achromatic doublet (AC254- 045- A, Thorlabs) and then recollimated using a \(150 - \mathrm{mm}\) achromatic doublet (AC254- 150- A- ML, Thorlabs). The original beams were expanded by 9 folds with a \(3\times\) Galilean beam expander (GBE03- A) before being focused with a cylindrical lens (ACY254- 50- A, Thorlabs), onto a resonant mirror galvanometer (CRS \(4\mathrm{kHz}\) , Cambridge Technology), driven by a 12- volt power supply (A12MT400, Acopian), to wobble the light sheet. One- dimensional focus was then recollimated with a 100- mm achromatic doublet (AC254- 100- A- ML, Thorlabs) and hit the galvanometric scan mirror (GSM) (GVS111, Thorlabs), driven by a 15- volt power supply (GPS011, Thorlabs), for rapid shifting of the light sheet along the detection arm. This galvanometric mirror was conjugated to the back pupil of the objective lens (Nikon \(40\times 0.8\mathrm{NA}\) ) through \(100 - \mathrm{mm}\) and \(200 - \mathrm{mm}\) achromatic doublet (AC508- 100- A- ML and AC508- 200- A- ML, Thorlabs). + +<|ref|>text<|/ref|><|det|>[[115, 591, 882, 884]]<|/det|> +In the detection arm, the same objective lens (Nikon \(40\times 0.8\mathrm{NA}\) ) in an orthogonal setup was used and pupil- matched to the scanning objective lens (Nikon Plan Apo \(20\times 0.75\mathrm{NA}\) ) through a \(200 - \mathrm{mm}\) tube lens (TTL200- A, Thorlabs) and a \(300 - \mathrm{mm}\) achromatic doublet (AC508- 300- A- ML, Thorlabs). A \(50:50\) polarizing beam splitter (PBS) (10FC16PB.3, Newport), splits the beam in S and P polarized light. Using mirrors M1 and M2 these light paths where then launched at an angle towards the Obj2 (Supplementary Fig.2). It is extremely critical to minimize the angle of the launch. Both experiment and simulation predicted that we used 8 degrees as the launch angle (called 0) (Fig.2a, inset of Supplementary Fig.2). The S and P polarized light passed through a quarter waveplate (AQWP10M, Thorlabs) and were focused onto a mirror positioned at the focus of the scanning objective lens. The mirror (PF03- 03- P01 - 07.0 mm Protected Silver Mirror, Thorlabs) was attached to a voice coil with a travel of \(10\mathrm{mm}\) , positional repeatability of fewer than 50 nanometers, and a response time of fewer than 3 milliseconds (LFA- 2010, Equipment Solutions). Then the reflected light was recaptured by the same scanning objective lens and quarter- wave plate to rotate the beam's polarization state. Afterward, the light was directed toward an sCMOS camera (Hamamatsu Orca- fusion BT) by reflecting from the same cube polarizing beam splitter and a \(300 - \mathrm{mm}\) achromatic doublet (AC508- 300- A- ML, Thorlabs). For emission filters, we used two long- pass filters (FF01- 525/30- 25, and BLP01- 647R- 25, Semrock), for blue, and far- red, respectively. To image dual channels simultaneously, the field of view (FOV) was separated into half using dichroic mirrors (DMLP605R, Thorlabs) between the \(300 - \mathrm{mm}\) achromatic doublet and the camera. We immersed the specimen and the illumination and detection objectives in a chamber designed using Adobe Inventor and machined through Protolabs (R). The LFA and GSM, in the detection and illumination arms respectively, were synchronized together to always keep the translated light sheet in the focus of the detection objective lens to acquire a 3D stack of the specimen. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 473, 105]]<|/det|> +## Overlaying S and P images with sub-pixel accuracy + +<|ref|>text<|/ref|><|det|>[[115, 119, 882, 164]]<|/det|> +Two identical images - - one corresponding to S and another to P polarized light - - are formed at the camera and added incoherently to generate the final image. We used a custom- written MATLAB script to monitor the offset between the two images in near real- time while adjusting the positions of M1 and M2 (Supplementary Fig.6). + +<|ref|>text<|/ref|><|det|>[[115, 176, 882, 220]]<|/det|> +The offset between the two images using a cross- correlation- based algorithm as was used in Wester, M.J. et al44, which achieves sub- pixel accuracy by fitting second- order polynomials through the peak of the scaled cross- correlation between the S and P polarized images. + +<|ref|>text<|/ref|><|det|>[[115, 231, 882, 308]]<|/det|> +Initially, an image is acquired as a reference by obstructing one optical path (either S or P). Subsequently, the alternative optical path is used to collect new images. The shift between each new image and the reference image is then measured using the method described in Wester, M.J. et al44 and is available as the MIC_Reg3DTrans.findStackOffset method in the matlab- instrument- control toolbox45. And while new images are collecting, mirrors M1 and M2 are adjusted to minimize the shift. + +<|ref|>sub_title<|/ref|><|det|>[[115, 323, 252, 337]]<|/det|> +## Microscope control + +<|ref|>text<|/ref|><|det|>[[114, 347, 882, 582]]<|/det|> +A Dell Precision 7920 computer with two processors Intel(R) Xenon(R) Silver 4210R CPU having a processing speed of 2.40 GHz and \(2.39\mathrm{GHz}\) and was integrated with 128 GB RAM was used to acquire the microscope's data. An NVIDIA Quadro RTX 4000 Graphics processing unit (GPU) with dedicated memory of 8 GB and shared memory of 63.8 GB (GPU memory of 71.8 GB) was also integrated into the system. 64- bit operating system \(\times 64\) - based processor facilities the system to operate. LabView 2020 64- bit allowed us to work with the required software, including the LabView Run- Time Engine, Vision Run- Time Module, Vision Development Module, and other required drivers like NI- RIO drivers (National Instruments). DCAM- API software was used for the Active Silicon Firebird frame- grabber to actively interfere with the scientific complementary metal- oxide semiconductor (sCMOS) camera (ORCA- Fusion BT Digital CMOS camera, model: C15440- 20UP) manufactured by Hamamatsu, Japan. It generated deterministic transistor logic (TTL) trigger sequences through 150 Watts shutter instrument (100- 240 V\~50/60 Hz; model: MP- 285A) with a field programmable gate array (FPGA) (PCIe 7852R, National Instruments). The generated triggers controlled the resonant mirror galvanometers, placement of the stage, voice coils, blanking and modulation of laser, firing camera, and other external triggers. K- Hyper Terminal software facilitated engaging LFA with the system hardware. Some key features along with some routines under the agreement of material transfer were licensed by the Howard Hughes Medical Institute's Janelia Farms Research Campus. + +<|ref|>sub_title<|/ref|><|det|>[[115, 593, 255, 608]]<|/det|> +## Sample preparation + +<|ref|>text<|/ref|><|det|>[[115, 622, 882, 746]]<|/det|> +Bead sample:200 nm beads embedded in \(2\%\) agarose gel was used for microscope resolution assessment. To make \(2\%\) agarose gel, 2 g of agarose powder (A9045- 25G, SIGMA life science) was mixed with \(100\mathrm{mL}\) water and swirled thoroughly before putting into the microwave oven to heat. Once the solution boiled and got completely clear and the agarose was dissolved, we should remove the solution from the oven and let it cool down. Then, \(200\mathrm{nm}\) beads (YG, Polysciences) were mixed with water with a ratio of \(1 / 100\) to form a solution of the \(200\mathrm{nm}\) beads. It was sonicated before mixing with the molten \(2\%\) agarose gel with a volumetric ratio of \(1 / 10\) . Then this molten combination was poured into the cubic mold where the sample holder was placed there and sat there for a few minutes to dry and form a \(1\mathrm{cm}^3\) cubic sample (200 nm beads embedded into \(2\%\) agarose gel) attached to the sample holder. + +<|ref|>text<|/ref|><|det|>[[115, 761, 882, 882]]<|/det|> +Cell samples: RBL- 2H3 GFP- FasL cells were cultured in Gibco Minimum Essential Media (MEM) media supplemented with \(10\%\) heat- inactivated Fetal Bovine Serum (FBS), \(1\%\) Penicillin/Streptomycin, and \(1\%\) L- glutamine \(^{31}\) . The cells were primed with \(1\mu \mathrm{g / ml}\) anti- DNP- IgE \(^{46}\) (Fig. 3e- g) or anti- DNP- IgE- CF640R (Fig. 3a- d) and seeded at a density of \(100,000\) cells per well in 12 well dish over \(5\mathrm{mm}\) glass coverslips and incubated with \(5\%\) \(\mathrm{CO_2}\) at \(37^{\circ}\mathrm{C}\) overnight. IgE- CF640R was prepared using CF640R NHS- ester (Biotium #92108). For fast imaging experiments, the cellular membranes of anti- DNP- IgE primed cells were labeled with CellMask™ Deep Red Plasma Membrane Stain (Thermo Fisher Scientific #C10046, \(5\mathrm{mg / ml}\) , 1000X) according to manufacturer's instruction for 10 minutes in modified Hank's balanced salt solution (HBSS) (additional \(10\mathrm{mM}\) Hepes, \(0.05\%\) w/v BSA, \(5.45\mathrm{mM}\) glucose, 0.88 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 881, 120]]<|/det|> +mM MgSO4, \(1.79\mathrm{mM}\) CaCl2, \(16.67\mathrm{mM}\) NaHCO3) and rinsed with HBSS. Cells were stimulated with \(1\mu \mathrm{g / ml}\) DNPBSA in the sample chamber. Data was acquired in 2 min captures for up to 15 minutes post antigen treatment. + +<|ref|>sub_title<|/ref|><|det|>[[115, 137, 240, 152]]<|/det|> +## Sample mounting + +<|ref|>text<|/ref|><|det|>[[115, 166, 882, 285]]<|/det|> +Cells samples on \(5\mathrm{mm}\) coverslips were loaded onto the holder as depicted in Supplementary Fig.7. In the sample holder, two metal wires were designed to clamp the coverslip tightly. This sample holder was attached to the XYZ Translation Stage with Standard Micrometers using a rotation mount. As a result, the coverslip had four degrees of freedom, including the translation on the \(X - Y - Z\) axis to locate the cells while imaging and the rotation around the X- axis to face the coverslip with the desired angle relative to the illumination and detection objectives. Here, the coverslips were faced 8 degrees relative to the optic axis of the detection objective (Supplementary Fig.7). In order to minimize the buffer volume for live cell imaging, the \(6\mathrm{ml}\) chamber was designed to immerse the sample, illumination, and detection of objective lenses into it (Supplementary Fig.7). + +<|ref|>sub_title<|/ref|><|det|>[[115, 312, 297, 327]]<|/det|> +## Image processing pipeline + +<|ref|>text<|/ref|><|det|>[[115, 338, 882, 500]]<|/det|> +Data were analyzed with the custom script written in Matlab. The procedure for quantifying the microscope's resolution from fluorescence bead data is as follows: 1) A 3D- PSF model was generated from the raw data. 2) The light- sheet region was cropped from each slice of the raw data. The width of the light- sheet region was defined by the distance to the waist of the light- sheet where the axial resolution increases by 2 times. We sat the light- sheet width to be \(\sim 6\mathrm{mm}\) . Note that the light- sheet region translated along its width direction (the \(Y\) - axis) while it was being scanned in the axial direction (Z- axis relative to the detection objective) (Supplementary Fig.8). Therefore, the light- sheet region to be cropped was also shifted in \(Y\) accordingly (Supplementary Video 1). 3) the cropped region was deconvolved with the 3D- PSF model using Richard- Lucy deconvolution from ImageJ. 4) the deconvolved data stack was divided into segments with an axial dimension of \(5\mu \mathrm{m}\) . For each segment, candidate beads were selected and their FWHMs along each dimension were estimated from Gaussian fitting of their intensity profiles along that dimension. 5) the measured FWHMs were used to quantify the resolution of the microscope as shown in Fig. 2d. + +<|ref|>text<|/ref|><|det|>[[115, 510, 882, 698]]<|/det|> +Dual- color live- cell data was processed as follows: 1) cell signal from each color channel was cropped with a user- selected region. 2) for each color channel, the XYZ drifts of the data stack at each time point relative to the reference data stack were estimated, where the maximum- intensity projection (MIP) along each dimension of the two data stacks was generated and the 2D shift between each pair of the MIP images was calculated through cross- correlation. 3) an average of the XYZ shifts from both channels was used to correct the drift between time points. 4) the XYZ shift between the two- color channels was calculated by first averaging over the time dimension for each color channel, then estimating the shift from the MIP images as in step 3. Then register the two channels by applying the estimated shift. 5) after drift correction and channel registration, the resulting image stacks were deconvolved with the 3D- PSF model generated from the bead data using Richard- Lucy deconvolution from Matlab. 6) to reduce noise and correct photobleaching, the deconvolved images were subtracted by a background value with negative pixel values set to zero and divided by a normalization factor equal to the 99.95 quantiles of all pixel values in the corresponding time points and color channel. + +<|ref|>sub_title<|/ref|><|det|>[[116, 711, 395, 726]]<|/det|> +## Quantification of Light-sheet dimension + +<|ref|>text<|/ref|><|det|>[[115, 738, 882, 848]]<|/det|> +To quantify the light- sheet dimension, bead data in agarose gel were collected at different slit widths. At each slit width, we estimated the FWHMs in XYZ for all selected beads as described above, however, here we used the full FOV of the color channel for bead imaging. As the position of the light- sheet waist shifted in y with respect to the axial dimension, we corrected the y coordinates of the selected bead by \(y_{Cor}^{\prime} = y_{Cor} - az_{Cor}\) , where \(a\) is the y shift by moving one pixel in \(Z\) (Supplementary Fig.8). Then we fitted the \(FWHM_{Z}\) verse \(y_{Cor}^{\prime}\) for all selected beads with a polynomial function (Supplementary Fig.4). The length of the light- sheet was found when the \(FWHM_{Z}\) was twice the minimum from the polynomial fit. + +<|ref|>sub_title<|/ref|><|det|>[[116, 862, 295, 876]]<|/det|> +## Magnification calibration + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 89, 883, 260]]<|/det|> +One brightfield image of the calibration target was captured at each of the galvo positions from \(- 40\) to \(40\mu \mathrm{m}\) with a step size of \(10\mu \mathrm{m}\) . The target image consisted of parallel line segments, we cropped a region of \(700\mathrm{x}700\) pixels from each image (Supplementary Fig.9a- b). We then calculated the affine transformation (from the Dipimage toolbox) of each image with respect to a reference image. The zoom factors from affine transformation were used to quantify the relative magnification between each image to the reference image. The absolute magnification of one image was calculated as follows: crop a narrow section of multiple parallel lines, obtain the intensity profile by averaging over the line dimension, smooth the intensity profile by applying a running average with a window size of 30 pixels, find all peaks from the smoothed intensity profile (Supplementary Fig.9c), calculate the average distance (in pixels, denoted as \(\Delta d\) ) between consecutive peaks, as the distance between consecutive parallel lines is \(10\mathrm{mm}\) , then the pixel size at the sample plane can be estimated from \(10 / \Delta d\) mm, therefore the magnification can be calculated from the pixel size of the camera divided by pixel size at the sample plane. + +<|ref|>sub_title<|/ref|><|det|>[[115, 274, 200, 289]]<|/det|> +## Ray tracing + +<|ref|>text<|/ref|><|det|>[[115, 301, 880, 331]]<|/det|> +Ray tracing was based on geometric optics with paraxial approximation. The ray propagation was calculated using the ABCD matrices. Two matrices were used, the translation matrix, + +<|ref|>equation<|/ref|><|det|>[[440, 329, 558, 361]]<|/det|> +\[M_{d} = \left[ \begin{array}{cc}1 & 0\\ d / n & 1 \end{array} \right],\] + +<|ref|>text<|/ref|><|det|>[[115, 360, 304, 374]]<|/det|> +and the matrix of a thin lens, + +<|ref|>equation<|/ref|><|det|>[[435, 371, 561, 401]]<|/det|> +\[M_{f} = \left[ \begin{array}{cc}1 & -n / f\\ 0 & 1 \end{array} \right],\] + +<|ref|>text<|/ref|><|det|>[[115, 400, 881, 460]]<|/det|> +where \(d\) is the translation distance, \(f\) is the focal length of the thin lens and \(n\) is the refractive index of the propagation medium. For our system, \(n\) is 1.33 before the detection objective (including the objective) and \(n\) equals 1 for the rest of the ray tracing. The starting point of each ray was represented by a vector of \([n\alpha ,y]^{T}\) , where \(\alpha\) and \(y\) are the angle and the y position of the ray with respect to the optical axis. The propagation of the ray is then calculated from + +<|ref|>equation<|/ref|><|det|>[[440, 458, 556, 489]]<|/det|> +\[\left[ \begin{array}{c}n\alpha '\\ y' \end{array} \right] = M\left[ \begin{array}{c}n\alpha '\\ y \end{array} \right].\] + +<|ref|>text<|/ref|><|det|>[[114, 487, 882, 678]]<|/det|> +For a defined FOV, we selected three field points, two mark the edge of the FOV and one at the optical axis. For each field point, we generated three rays at different angles that will intersect three points at the pupil plane, where two points mark the edge of the pupil and one at the center of the pupil. Rays from the same field point were colored the same. The optical axis after the polarizing beam splitter was rotated by 45 degree to be along the splitting plane of the PBS. The ray tracing after the PBS was done by first transforming the ray coordinates to the ones defined by the optical axis and propagating the ray with the ABCD matrix, then transforming back to the global coordinates. Except for the distance between the tube lens and the detection objective (denoted as \(d_{1}\) ), the rest distances between consecutive optical elements were measured with a ruler. The angle between the chief rays of the S and P- polarization \((\theta\) in Fig.2a) was set when the input and output beam diameters at the remote focusing objective were minimum. The distance \(d_{1}\) was set when the relative magnifications from ray tracing match with the measured ones (Fig. 2b). The central position of the scan range, the distance of the objective to the detection objective (denoted as \(S_{1}\) ), was set when the absolute magnification from ray tracing matches with measured one. Here \(S_{1} = 6.695\mathrm{mm}\) , which was \(45\mu \mathrm{m}\) away from the designed focal plane of the detection objective. + +<|ref|>sub_title<|/ref|><|det|>[[115, 705, 196, 718]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[112, 747, 875, 900]]<|/det|> +1. Bruzzone, M. et al. Whole brain functional recordings at cellular resolution in zebrafish larvae with 3D scanning multiphoton microscopy. Sci. Rep. 11, 11048 (2021). +2. Voleti, V. et al. Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0. Nat. Methods 16, 1054–1062 (2019). +3. Wagner, N. et al. Instantaneous isotropic volumetric imaging of fast biological processes. Nat. Methods 16, 497–500 (2019). +4. A Protocol For Real-Time 3d Single Particle Tracking - Video. https://www.jove.com/t/56711/a-protocol-for-real-time-3d-single-particle-tracking. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 870, 125]]<|/det|> +5. Panier, T. et al. 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Analysis of the Three-Dimensional Focal Positioning Capability of Adaptive Optic Elements. Int. J. Optomechatronics 7, 1-14 (2013). + +<|ref|>text<|/ref|><|det|>[[113, 459, 861, 496]]<|/det|> +15. Shain, W. J., Vickers, N. A., Goldberg, B. B., Bifano, T. & Mertz, J. Extended depth-of-field microscopy with a high-speed deformable mirror. Opt. Lett. 42, 995-998 (2017). + +<|ref|>text<|/ref|><|det|>[[113, 505, 880, 542]]<|/det|> +16. Botcherby, E. J., Juškaitis, R., Booth, M. J. & Wilson, T. An optical technique for remote focusing in microscopy. Opt. Commun. 281, 880-887 (2008). + +<|ref|>text<|/ref|><|det|>[[113, 551, 866, 589]]<|/det|> +17. Botcherby, E. J., Juškaitis, R., Booth, M. J. & Wilson, T. Aberration-free optical refocusing in high numerical aperture microscopy. Opt. Lett. 32, 2007 (2007). + +<|ref|>text<|/ref|><|det|>[[113, 598, 870, 636]]<|/det|> +18. Dibaji, H., Prince, M. N. H., Yi, Y., Zhao, H. & Chakraborty, T. 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Remote-refocusing light-sheet fluorescence microscopy enables 3D imaging of electromechanical coupling of hiPSC-derived and adult cardiomyocytes in co-culture | Scientific Reports. https://www.nature.com/articles/s41598-023-29419-w. + +<|ref|>text<|/ref|><|det|>[[112, 133, 860, 171]]<|/det|> +27. Chakraborty, T. et al. Converting lateral scanning into axial focusing to speed up three-dimensional microscopy. Light Sci. Appl. 9, 165 (2020). + +<|ref|>text<|/ref|><|det|>[[112, 180, 500, 195]]<|/det|> +28. Encyclopedia of Physical Science and Technology. ScienceDirect + +<|ref|>text<|/ref|><|det|>[[141, 204, 770, 219]]<|/det|> +http://www.sciencedirect.com:5070/referencework/9780122274107/encyclopedia-of-physical-science-and-technology. + +<|ref|>text<|/ref|><|det|>[[112, 227, 845, 266]]<|/det|> +29. Heebner, J. E., Bennink, R. S., Boyd, R. W. & Fisher, R. A. Conversion of unpolarized light to polarized light with greater than 50% efficiency by photorefractive two-beam coupling. Opt. 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Mechanisms of Granule Membrane Recapture following Exocytosis in Intact Mast Cells - ScienceDirect. + +<|ref|>text<|/ref|><|det|>[[142, 414, 597, 428]]<|/det|> +https://www.sciencedirect.com/science/article/pii/S0021925820456005?via%3Dihub. + +<|ref|>text<|/ref|><|det|>[[112, 437, 850, 475]]<|/det|> +35. Roudot, P. et al. u-track 3D: measuring and interrogating dense particle dynamics in three dimensions. 2020.11.30.404814 Preprint at https://doi.org/10.1101/2020.11.30.404814 (2022). + +<|ref|>text<|/ref|><|det|>[[112, 483, 870, 498]]<|/det|> +36. Dupont, A. et al. Three-dimensional single-particle tracking in live cells: news from the third dimension. New J. Phys. 15, 075008 (2013). + +<|ref|>text<|/ref|><|det|>[[112, 506, 857, 545]]<|/det|> +37. Michalet, X. Mean square displacement analysis of single-particle trajectories with localization error: Brownian motion in an isotropic medium. Phys. Rev. E 82, 041914 (2010). + +<|ref|>text<|/ref|><|det|>[[112, 553, 827, 592]]<|/det|> +38. Rupprecht, P., Prendergast, A., Wyart, C. & Friedrich, R. W. Remote z-scanning with a macroscopic voice coil motor for fast 3D multiphoton laser scanning microscopy. Biomed. Opt. Express 7, 1656-1671 (2016). + +<|ref|>text<|/ref|><|det|>[[112, 600, 863, 639]]<|/det|> +39. Bouchard, M. B. et al. Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms. Nat. Photonics 9, 113-119 (2015). + +<|ref|>text<|/ref|><|det|>[[112, 647, 860, 685]]<|/det|> +40. Yang, B. et al. DaXi—high-resolution, large imaging volume and multi-view single-objective light-sheet microscopy. Nat. Methods 19, 461-469 (2022). + +<|ref|>text<|/ref|><|det|>[[112, 693, 848, 731]]<|/det|> +41. Sapoznik, E. et al. A versatile oblique plane microscope for large-scale and high-resolution imaging of subcellular dynamics. eLife 9, e57681 (2020). + +<|ref|>text<|/ref|><|det|>[[112, 740, 847, 778]]<|/det|> +42. Chen, B. et al. Resolution doubling in light-sheet microscopy via oblique plane structured illumination. Nat. Methods 19, 1419-1426 (2022). + +<|ref|>text<|/ref|><|det|>[[112, 787, 721, 801]]<|/det|> +43. Sparks, H. et al. Dual-view oblique plane microscopy (dOPM). Biomed. Opt. Express 11, 7204-7220 (2020). + +<|ref|>text<|/ref|><|det|>[[112, 810, 744, 824]]<|/det|> +44. Wester, M. J. et al. Robust, fiducial-free drift correction for super-resolution imaging. Sci. Rep. 11, 23672 (2021). + +<|ref|>text<|/ref|><|det|>[[112, 833, 331, 846]]<|/det|> +45. matlab-instrument-control. (2023). + +<|ref|>text<|/ref|><|det|>[[112, 856, 849, 894]]<|/det|> +46. Liu, F. T. et al. Monoclonal dinitrophenyl-specific murine IgE antibody: preparation, isolation, and characterization. J. Immunol. 124, 2728-2737 (1980). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[42, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 333, 177]]<|/det|> +SupplementaryVideo1. avi SupplementaryFileSept1. pdf + +<--- Page Split ---> diff --git a/preprint/preprint__074d5ca947dfa0b3c0d61ffc91ee2ad07daa2a3b9cd623deb20d79df8add57d4/images_list.json b/preprint/preprint__074d5ca947dfa0b3c0d61ffc91ee2ad07daa2a3b9cd623deb20d79df8add57d4/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..38df3247235585c5014adfd648a751f1a8a39bbf --- /dev/null +++ b/preprint/preprint__074d5ca947dfa0b3c0d61ffc91ee2ad07daa2a3b9cd623deb20d79df8add57d4/images_list.json @@ -0,0 +1,47 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1: Temporal behaviour of the fractions of Searches (red, left \\(y\\) -axis) and General News (blue, right \\(y\\) -axis) for the keyword coronavirus in Italy from early December 2019 to the end of August 2020. Searches are reported as a percentage of the maximum observed in the monitored period. General News is represented by the daily fraction of articles containing at least three keyword occurrences (see Materials and Methods). The Improved Model (black line) leverages the past General News and Searches, together with present Searches, to infer the dynamics of General News.", + "footnote": [], + "bbox": [ + [ + 239, + 336, + 768, + 565 + ] + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2: The ranked components of \\(\\mathbf{S}_{tot}\\) (centre red), representing coronavirus sub-domains sorted by total news demand over the observed time. On the sides of each keyword, a tag indicates the rank in \\(\\mathbf{N}_{tot}\\) for General News, on the left, and in \\(\\mathbf{FN}_{tot}\\) for Fake News, on the right. Tags are distanced from the centre by the amount of rank mismatch to Searches ranks. Tags are coloured to highlight the rank closest to the Searches rank: blue for General News and green for Fake News.", + "footnote": [], + "bbox": [ + [ + 252, + 220, + 700, + 590 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3: The time series of news annotated as Fake, normalised through the total number of coronavirus-related News compared with the Combined Index for disinformation. The Combined Index is defined as a linear combination of the weighted modelling error for the local fitting of News within the improved Vector Auto-Regression model and the cosine distance between the semantic vectors of Searches and News. The parameters of the combination were fitted in the training set and then tested in the validation set.", + "footnote": [], + "bbox": [ + [ + 277, + 280, + 712, + 533 + ] + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/preprint/preprint__074d5ca947dfa0b3c0d61ffc91ee2ad07daa2a3b9cd623deb20d79df8add57d4/preprint__074d5ca947dfa0b3c0d61ffc91ee2ad07daa2a3b9cd623deb20d79df8add57d4.mmd b/preprint/preprint__074d5ca947dfa0b3c0d61ffc91ee2ad07daa2a3b9cd623deb20d79df8add57d4/preprint__074d5ca947dfa0b3c0d61ffc91ee2ad07daa2a3b9cd623deb20d79df8add57d4.mmd new file mode 100644 index 0000000000000000000000000000000000000000..540be04096dd150ec89a9e2827391a9658ec9c9c --- /dev/null +++ b/preprint/preprint__074d5ca947dfa0b3c0d61ffc91ee2ad07daa2a3b9cd623deb20d79df8add57d4/preprint__074d5ca947dfa0b3c0d61ffc91ee2ad07daa2a3b9cd623deb20d79df8add57d4.mmd @@ -0,0 +1,337 @@ + +# Assessing disinformation through the dynamics of supply and demand in the news ecosystem + +Pietro Gravino ( \(\boxed{ \begin{array}{r l} \end{array} }\) pietro.gravino@sony.com) Sony CSL Paris Giulio Prevedello Sony CSL Paris Martina Galletti Sony CSL Paris Vittorio Loreto Sony CSL Paris + +## Article + +Keywords: SARS- CoV- 2, social dialogue, information technology + +Posted Date: June 1st, 2021 + +DOI: https://doi.org/10.21203/rs.3. rs- 577571/v1 + +License: © \(\circledast\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Human Behaviour on May 23rd, 2022. See the published version at https://doi.org/10.1038/s41562- 022- 01353- 3. + +<--- Page Split ---> + +# Assessing disinformation through the dynamics of supply and demand in the news ecosystem + +Pietro Gravino\*a, Giulio Prevedelloa, Martina Gallettia, and Vittorio Loretoa,b,c + +aSony Computer Science Laboratories, 75005 Paris, France bSapienza University of Rome, Physics Department, 00185, Rome, Italy cComplexity Science Hub Vienna, A- 1080 Vienna, Austria \*Corresponding author: pietro.gravino@sony.com + +May 31, 2021 + +## Abstract + +Social dialogue, the foundation of our democracies, is currently threatened by disinformation and partisanship, with their disrupting role on individual and collective awareness and detrimental effects on decision- making processes. Despite a great deal of attention to the news sphere itself, little is known about the subtle interplay between the offer and the demand for information. Still, a broader perspective on the news ecosystem, including both the producers and the consumers of information, is needed to build new tools to assess the health of the infosphere. Here, we combine in the same framework news supply, as mirrored by a fairly complete Italian news database - partially annotated for fake news, and news demand, as captured through the Google Trends data for Italy. Our investigation focuses on the temporal and semantic interplay of news, fake news, and searches in several domains, including the virus SARS- CoV- 2 pandemic. Two main results emerge. First, disinformation is extremely reactive to people's interests and tends to thrive, especially when there is a mismatch between what people are interested in and what news outlets provide. Second, a suitably defined index can assess the level of disinformation only based on the available volumes of news and searches. Although our results mainly concern the Coronavirus subject, we provide hints that the same findings can have more general applications. We contend these results can be a powerful asset in informing campaigns against disinformation and providing news outlets and institutions with potentially relevant strategies. + +<--- Page Split ---> + +## Introduction + +The Covid- 19 crisis evidenced once more that disinformation stands as one of the major plagues of the Information Age. In the last decades, many national and international institutions started to implement a vast plethora of strategies to tackle this issue [14] and mitigate its effects. Still, the mechanisms underlying the role and phenomenology of disinformation are largely unclear. + +Only in recent times the complex ecosystem of information massively attracted the interest of the scientific community. Disinformation went under investigation, from its very definition [13] to its psychological mechanisms [3], and its spreading dynamics [7]. Detection and forecast of disinformation were also among the relevant topics explored by the scientific community [28]. These studies raised questions about how to identify statistical markers in the news content [8] or about the diffusion mechanisms [33]. + +A meaningful part of the research effort focused on the impact of disinformation on diverse fields of human activities, such as consumers' behaviour [34], political elections [2], sustainability [31] or health [20]. During the Covid- 19 pandemic, particularly, the effect of disinformation on social behaviours became so compelling that the term "Infodemic" made a comeback from the SARS epidemic of 2003 [25], to describe the spreading of false or incorrect information about the virus SARS- CoV- 2. The consequences were disastrous [17] and led to dangerous behaviours that further aggravated the epidemic crisis. + +While disinformation is always under the spotlight, the complex ecosystem of information, which is the substrate for disinformation, attracted much less interest. It is important to stress that the infosphere relies on the subtle interplay of two types of actors: news producers on the one hand and news consumers on the other. In this structure, the supply and the demand of information stand in a market- like relationship. The study of their interplay is essential to unveil the mechanisms of information dynamics. It also provides a broader view in which disinformation can be contextualised and analysed. + +The news supply can be identified with the overall news production, mainly consisting of officially recognised newspapers. The general news production had been primarily studied in linguistics [10], while analyses of news content [21] and coverage [26, 29] were often focusing on particular countries or topics. Other works investigated the impacts of news and its consumption on, for example, reading behaviour [30], finance [12], and political opinions [16]. + +News demand, instead, is more difficult to pinpoint. In the literature, surveys and lab studies are usual procedures of investigation [32, 30, 18], but, unlike general news production, they cannot scale up to the population level. Thus, different solutions have to be adopted. The tracking of reading behaviours, for example, had been used to study the demands and interests of readers [4]. However, such a methodology is biased by the very existence of news since the interest for topics not covered by news cannot be recorded. + +An independent way to track people interests that gained popularity in the + +<--- Page Split ---> + +scientific community is the Google Trends service \(^{1}\) [19]. It provides an index proportional to the number of searches made with the Google Search engine, enabling the quantitative comparison of searched queries. In the last decade, Google Trends has been mainly used as a marker, and a predictor, of people's behaviours in different contexts, like finance [9, 24], epidemiology [22, 11] or socio- economic indicators [6, 5]. Interestingly, its intrinsic value as a proxy for people's interest was perhaps overlooked. In the framework of news, the Google Trends index has been mainly adopted for forecasting [35], without delving into the comprehension of the dynamics of the news ecosystem. + +Here we comprehend, in a unique framework, the supply and demand for information and analyse their dynamical interplay with the final goal of understanding the main mechanisms of the information ecosystem dynamics and extracting hints about the determinants of disinformation. To this end, we focused on the general production of news in Italy, from early December 2019 to the end of August 2020, as the reference for the news supply. For the same period, the Google Trend index served as a proxy for the general public's information demand. + +We adopted Vector Auto- Regression (VAR) models to study the interplay between news demand and supply, evidencing different causal relationships for distinct subjects. We presented an improved modelling scheme that allows for a quantitative description of the dependencies in the time series evolution for information demand and supply. + +The new framework also permitted to study and compare the disinformation dynamics within the general information system, highlighting behavioural differences in reactivity and modelling efficacy. We observed, in particular, that the semantic misalignment between information supply and demand is higher than the misalignment between disinformation supply and demand. + +These discrepancies could be exploited to aggregate a disinformation risk indicator that is independent of fake news annotations. We contend this index could provide a reliable and independent assessment tool for the news supply's health status. + +## Results + +## Dynamics of news supply and demand + +Information systems feature two main drivers: news supply and news demand. As a reference for the news supply, we looked at the whole Italian production of information, termed General News, from early December 2019 to the end of August 2020. For the same period, the Google Trend index served as a proxy for the news demand from the Italian general public, thus termed Searches (refer to Materials and Methods for more details). + +To investigate the nature of the relation between supply and demand of news about a certain subject, six keywords, referring to the most searched subjects + +<--- Page Split ---> + +in Italy over the entire observation period, were selected: coronavirus, regional, playstation, papa francesco, eurovision, sondaggi (Supplemental Fig. S1)). General News and Searches for coronavirus are reported in Fig. 1. For each keyword, the time series of the daily appearances in the General News and the daily volume of queries in the Searches were simultaneously fit by Vector Auto- Regression (VAR) linear modelling [15]. VAR models with different lag parameters, which encapsulate the system's memory, were considered, and the best parameters were identified via the Akaike criterion [1] (see Materials and Methods). For all keywords, best- fitting lags ranged between 2 and 4, suggesting a typical, short- memory timescale in the system (see Supplemental Fig. S2)). + +![](images/Figure_1.jpg) + +
Figure 1: Temporal behaviour of the fractions of Searches (red, left \(y\) -axis) and General News (blue, right \(y\) -axis) for the keyword coronavirus in Italy from early December 2019 to the end of August 2020. Searches are reported as a percentage of the maximum observed in the monitored period. General News is represented by the daily fraction of articles containing at least three keyword occurrences (see Materials and Methods). The Improved Model (black line) leverages the past General News and Searches, together with present Searches, to infer the dynamics of General News.
+ +Within the VAR framework, we performed the test for Granger- causality [15] to illustrate which time series, between General News and Searches, contributed more to the prediction of the other, and if any contribution was significant. For the majority of keywords, the contribution of past Searches to present General News was most significant (i.e. coronavirus, regional, playstation, papa francesco) (see Supplemental Fig. S2)). We could safely assume that Searches anticipates General News and use this assumption to improve the model of the temporal behaviour of the latter. We modified the VAR equation for the evo + +<--- Page Split ---> + + +Table 1: The parameters and the \(R^{2}\) resulting from the improved linear model of equation 1 for the 4 selected keywords. As a reference, in brackets, we report the value for \(R^{2}\) of a trivial model with equation \(N(t) = \alpha N(t - 1)\) , i.e., a model where every day depends only on the day before. The value of \(R^{2}\) of the improved linear model is systematically larger than that of the trivial model. Starred values are those not significantly different from zero. + +
coronavirusregionaliplaystationpapa francesco
α10.820.650.180.54
α20.260.19
β00.0700.00820.000550.0038
β1-0.0340.003*0.00035
β2-0.00640.00068
R²0.996 (0.991)0.89 (0.86)0.54 (0.29)0.73 (0.63)
+ +lution of General News by inserting Searches' role. More precisely, let \(S(t)\) and \(N(t)\) be, respectively, the values of Searches and General News at day \(t\) , then the new equation for the evolution of \(N(t)\) reads: + +\[N(t) = \sum_{i = 1}^{d}(\alpha_{i}N(t - i) + \beta_{i}S(t - i)) + \beta_{0}S(t). \quad (1)\] + +where the coefficients \(\alpha_{i}\) , \(\beta_{0}\) and \(\beta_{i}\) were fitted, while the Akaike criterion provides the optimal lag \(d\) . This Improved Model closely reproduced the data, particularly in correspondence with the peaks (Fig. 1 for coronavirus and Supplemental Fig. S3). + +The model's parameters also provided a quantitative insight on the interplay between General News and Searches (Tab. 1): + +\(\alpha_{1}\) was larger than other \(\alpha\) parameters, indicating a strong dependence of General News on the previous day activity. This evidence is a sign of an inertial behaviour of the news supply. \(\beta_{0}\) , the weight of present Searches, was typically larger than other \(\beta\) parameters and significantly non- zero, supporting the assumption of present Searches role for the Improved Model. The remaining parameters were smaller though almost always significant. For two keywords (coronavirus and regionali), the parameters \(\beta_{d}\) (for \(d \geq 1\) ) were negative. This result suggests that General News depends on the different quotient of Searches, together with the volume of Searches itself. + +Of note, a direct comparison between \(\alpha\) and \(\beta\) parameters was not possible, as Searches and General News were scaled differently (Google Trends does not disclose the absolute scale of queries volume). + +<--- Page Split ---> + +## The different behaviours of General News and Fake News + +The Improved Model quantifies the information supply dynamics and enables the comparison between General News and disinformation supply. We applied this methodology to the topic coronavirus, since it dominated the landscape of information (Supplemental Fig. S1), and due to the direct impact of disinformation on the response to the 2020 pandemic. To this end, we extended our analysis to the news items that were annotated as false or misleading, thus named Fake News (see Material and Methods). + +We exploited the Improved Model 1 to compare General News and Fake News through their best- fitting coefficients \(\alpha\) and \(\beta\) . To this end, we paralleled the variable \(N(t)\) , the daily proportion of coronavirus- related General News at day \(t\) , and \(FN(t)\) , the daily proportion of coronavirus- related Fake News at day \(t\) (Tab. 2). + +Compared to General News, coronavirus- related Fake News shows a meaningfully lower Inertia term, \(\alpha_{1}\) , and a non- significant \(\beta_{1}\) indicating a greater reactivity to \(S(t)\) . These pieces of evidence and the lower prediction score (adjusted \(R^{2}\) ) suggest that disinformation presents a different behaviour than General News, to the points that it distorts the dynamics of the news ecosystem and leads to impaired modelling performance. + +Table 2: Coefficients from the Improved Model fitting of General News and of Fake News having at least one occurrence of the keyword coronavirus (see Materials and Methods). Starred coefficients do not differ significantly from zero. + +
General NewsFake News
α1 (Inertia)0.860 ± 0.0160.758 ± 0.039
β00.460 ± 0.0350.294 ± 0.086
β1-0.248 ± 0.042-0.081* ± 0.091
R²0.9950.931
+ +Another difference in the behaviour of news and disinformation emerged at a semantic level. We focused on the most queried keywords searched together with coronavirus in Google Search (see Materials and Methods). Each of these related queries provided a time series of news demand about a sub- domain that co- occurs with, and therefore is semantically linked to, coronavirus. We quantified the co- occurrence of these terms with the coronavirus keyword also in the news items, for both General News and Fake News. In this way, we defined \(\mathbf{S}(t), \mathbf{N}(t), \mathbf{FN}(t)\) as the daily semantic vectors for coronavirus- related Searches, General News, and Fake News, respectively. Each vector has seventeen entries, one per sub- domain (see Materials and Methods for more details). + +We calculated \(\mathbf{S}_{tot} = \sum_{t} \mathbf{S}(t)\) and sort its components to rank the different sub- domains by the total news demand over the period considered (Fig. 2). To assess the difference between information and disinformation with respect to the matching of news demand for different sub- domains, we challenged the + +<--- Page Split ---> + +components' rankings of \(\mathbf{N}_{tot} = \sum_{t}\mathbf{N}(t)\) and \(\mathbf{FN}_{tot} = \sum_{t}\mathbf{FN}(t)\) against the corresponding ones of \(\mathbf{S}_{tot}\) (Fig. 2). + +![](images/Figure_2.jpg) + +
Figure 2: The ranked components of \(\mathbf{S}_{tot}\) (centre red), representing coronavirus sub-domains sorted by total news demand over the observed time. On the sides of each keyword, a tag indicates the rank in \(\mathbf{N}_{tot}\) for General News, on the left, and in \(\mathbf{FN}_{tot}\) for Fake News, on the right. Tags are distanced from the centre by the amount of rank mismatch to Searches ranks. Tags are coloured to highlight the rank closest to the Searches rank: blue for General News and green for Fake News.
+ +Given the coronavirus- related keywords ranked from the Searches as a reference, Fake News ranking shows fewer and minor mismatches compared to General News. We quantified this difference in behaviour through Spearman's Correlation. \(\mathbf{S}_{tot}\) and \(\mathbf{N}_{tot}\) components resulted positively correlated ( \(r = 0.52\) , with a p- value of 0.031) but \(\mathbf{S}_{tot}\) and \(\mathbf{FN}_{tot}\) correlated more ( \(r = 0.67\) , with a p- value of 0.0032). + +The semantic difference in the behaviour of Fake News and General News holds not only at the aggregated level but also at a daily level. This was mea + +<--- Page Split ---> + +sured through the cosine distance \(\mathrm{d}(\cdot ,\cdot)\) on their daily vectors \(\mathbf{S}(t)\) , \(\mathbf{N}(t)\) and \(\mathbf{FN}(t)\) (see Materials and Methods). Again, Searches were taken as reference and we calculated its cosine distance from General News, \(\mathrm{d}(\mathbf{S}(t),\mathbf{N}(t))\) , and from Fake News, \(\mathrm{d}(\mathbf{S}(t),\mathbf{FN}(t))\) . The daily relative difference between the cosine distances of Searches- Fake News and Searches- General News + +\[\frac{\mathrm{d}(\mathbf{S}(t),\mathbf{FN}(t)) - \mathrm{d}(\mathbf{S}(t),\mathbf{N}(t))}{\mathrm{d}(\mathbf{S}(t),\mathbf{N}(t))} \quad (2)\] + +resulted in negatives values in most days \(t\) (Supplemental Fig. S5)). In fact, both the Mean \((- 0.13)\) and Median \((- 0.15)\) were negative, indicating that the cosine distance Searches- Fake News is generally smaller than that of Searches- General News. This result shows how Fake News meets news demand better than General News. + +## Independent detection of Fake News concentration + +The observed differences between General News and Fake News dynamics can be exploited to assess disinformation about the topic coronavirus. + +The difference in modelling Fake and General News suggests that when Fake News concentration on a topic rises, the General News dynamics, which includes Fake News, becomes perturbed. We hypothesise that this perturbation is expected to impair the General News modelling performance. To test this hypothesis, the Improved Model was fit to General News locally on a time window of 14 days, sliding over the entire data time range (see Materials and Methods). For each window, centred in \(t\) , we computed the local modelling error defined as: + +\[E(t) = (1 - R^{2}(t))\cdot \langle N\rangle (t), \quad (3)\] + +where \(R^{2}(t)\) , the \(R^{2}\) score for the model fitted to the window, is weighted by \(\langle N\rangle (t)\) , the average volume of news produced in that time window. + +Although formulated without exploiting disinformation annotations, \(E(t)\) significantly correlates with the concentration of Fake News on the coronavirus subject, \(FN(t) / N(t)\) (Spearman's \(r = 0.47\) , with a p- value of \(3.9\cdot 10^{- 13}\) (see Materials and Methods). This result supports the hypothesis that loss of predictability from the General News dynamics co- occurs with disinformation spikes. As a consequence, \(E(t)\) stands as a very promising proxy for the concentration of disinformation about the topic coronavirus. + +The semantic difference between General News and Fake News suggests that disinformation might receive greater attention thanks to its ability to match the news demand semantically unsatisfied. We hypothesized that as General News becomes more semantically distant from Searches, Fake News would fill that gap. This hypothesis was tested all over the time range measuring the daily cosine distance between the semantic vectors of Searches and General News \(K(t) = \mathrm{d}(\mathbf{S}(t),\mathbf{N}(t))\) . We then checked the correlation of \(K(t)\) with the daily concentration of Fake News, \(FN(t) / N(t)\) , about coronavirus. The correlation turns out to be positive and significant (Spearman's \(r = 0.58\) , with a p- value of + +<--- Page Split ---> + +\(1.6 \cdot 10^{- 21}\) ), supporting the hypothesis. This result allows us to adopt \(K\) as a second independent indicator for Fake News concentration assessment. + +To test the effectiveness of our indicators \(E\) and \(K\) in assessing disinformation concentration, we combined them in a Combined Index for disinformation (see Material and Methods). We fit them linearly on a training set composed of approximately the first \(25\%\) of data from the time series, providing the best linear combination of the two (see Fig. 3). + +![](images/Figure_3.jpg) + +
Figure 3: The time series of news annotated as Fake, normalised through the total number of coronavirus-related News compared with the Combined Index for disinformation. The Combined Index is defined as a linear combination of the weighted modelling error for the local fitting of News within the improved Vector Auto-Regression model and the cosine distance between the semantic vectors of Searches and News. The parameters of the combination were fitted in the training set and then tested in the validation set.
+ +The Combined Index was then tested against the validation set, achieving substantial accuracy (reduced chi- squared statistic of 0.945). All these findings suggest that the Combined Index provides a valuable measure for detecting disinformation concerning SARS- CoV- 2. + +The methodology could also be applied to different topics, and to the aggregation of several topics, to assess the health status of the news ecosystem at a more general level. To challenge this claim, we considered the set of the 4 keywords modelled. We aggregated them to create a synthetic macro- topic, for which they individually represented the analogous of the related queries we have seen before. We judged the adoption of the first indicator, i.e. the weighted modelling error for the local fitting, to be pointless since the macro- subject dynamics is largely dominated by the topic coronavirus. This would have resulted + +<--- Page Split ---> + +in an indicator similar to the modelling of the coronavirus component alone. We thus focused only on the second indicator, i.e., the cosine distance between the semantic vectors of Searches and News, where the components of the vectors are now the values of General News, Fake News, and Searches for the 4 keywords. The daily value of cosine distance between General News and Searches of the synthetic subject correlates positively and meaningfully with the concentration of disinformation on the synthetic subject (Spearman's correlation of 0.44 with p- value \(= 1.8 \cdot 10^{- 11}\) ). This result supports the plausibility of the application of our methodology in wider contexts. + +## Discussion + +Information quality is a fundamental challenge for the Information Age, especially during a pandemic. Studying the general news system and comparing it with the subset of news labelled as disinformation, we found that pandemic- related Fake News production seems more reactive and precise than General News supply in addressing people's news demand. We exploited such a difference to develop an index for vulnerability of specific topics to disinformation takeover. + +The analysis of Searches and General News for coronavirus and a set of other coronavirus- unrelated highly queried keywords exposed the relation between supply and demand of news: (i) a linear modelling scheme was effective in almost all cases; (ii) the memory of the process seems to be very short (2- 4 days) in all cases; (iii) causality was more commonly directed from Searches to General News (e.g. for coronavirus). Thanks to these considerations, an improved descriptive model could be developed to better describe the relationship between supply and demand for information. This modelling framework allowed us to discern how the inertia of news suppliers is one of the main traits of the dynamics for all the studied keywords. Also, the negative dependence on previous days Searches observed in some cases suggests a dynamics where the trend of the interest is more important than the interest itself for news producers. + +The comparison of coronavirus- related General News and Fake News through the improved linear model's lens exposed that Fake News feature lower inertia and a different dependence on Searches, quantifying their more reactive behaviour. We can speculate that this behavioural difference could be a consequence of the different production environments of General News and Fake News. The firsts are mainly produced by a large and well- established community of professional journalists while the latter are the outcome of by a scattered multitude of small, unorganized actors. The community size effect might be responsible for the different inertial behaviour observed. + +The semantic analysis revealed another key difference between the dynamics of General News and Fake News. Looking at the shares of the most queried keywords co- occurring with coronavirus we discovered that Fake News is better aligned to Searches than General News not only at a cumulative level but also daily over the entire observation period. This result suggests that disinformation + +<--- Page Split ---> + +tends to be more semantically precise than information in matching people's interests. This difference might be explained considering the different aims of the two communities. While they are both interested in answering people's demand for information, general news producers also have the ambition for complete coverage of topics, while Fake News producers can focus on chasing the people's attention. + +We exploited the modelling and the semantic mismatch between General News and Fake News to detect bursts of disinformation on coronavirus. It is worth mentioning how this measure was performed without any information about FakeNews, looking instead only at the time- series of General News and Searches. The modelling difference resulted in a destabilizing effect of Fake News peaks on the modelling of General News as a function of Searches. Such a perturbation was measured as the local weighted modelling error in the Improved Model for General News as a function of Searches. A higher value of this indicator means that the normal relation between General News and Searches has been altered, probably by Fake News presence. The semantic difference was estimated from daily misalignment itself between General News and Searches. In this case, a higher value of this indicator means that semantic imprecision leaves an unsatisfied interest in readers, possibly fostering Fake News. + +The positive and meaningful correlation of both indicators with Fake News concentration on Coronavirus supports both the hypothesis: (i) disinformation perturbs the normal relation between General News and Searches and (ii) is fueled by the semantic misalignment between the two. + +We blend the two markers into a single Combined Index for disinformation by adopting a training set for its definition and testing it on a validation set with good results. Given its independence from Fake News annotations and the hints supporting a possible generalization of the result to other topics, the Combined Index can be a powerful tool for journalists and editors as well as for news monitoring authorities to detect in real- time vulnerabilities to disinformation. Our results also suggest, as a possible strategy to face these vulnerabilities, a timely refocus of General News supply to better meet the information demand of the public. + +Information vulnerabilities are a major risk factor for our societies, as they have a direct impact on individuals. For example, the solution to the Coronavirus crisis depends mainly on individual behaviours which, in turn, are directly affected by the news. The approach presented here, far from being conclusive, represents a first contribution towards a deeper understanding of the phenomenology of disinformation as part of the information ecosystem's general dynamics. Further studies will be needed to test the conclusions and to generalize the results to different countries, languages, domains, and time periods. Moreover, the diffusion layer should be added to the analysis of the dynamics of the infosphere, with particular attention to the social media spreading of news. In our opinion, a paradigm shift in facing disinformation is no more an option, is a necessity for information societies. We contend that the presented work contributes to the shift of scientific research towards a more concrete view, aiming to provide policymakers with knowledge and tools to fight disinformation. + +<--- Page Split ---> + +## Materials and Methods + +## Searches Data + +The information demand about a specific subject was obtained from Google Trends, a platform providing access to an anonymous sample of actual search requests made in Google Search engine, from a selected location and time interval. + +For each given keyword, Google Search returns a time- series with values proportional to the number of times the keyword was searched each day. Since Google Search does not disclose the actual number of searches, the time series values are rendered as percentages of the maximum number returned. As a result, data consist of integers ranging in the interval \((0,100)\) . The time series of one keyword was referred to as the "Searches" for that keyword and provided a measure of the interest it received. + +The use of "pytrends" library for Python \(^2\) enabled the interaction with the Google Trends platform. The terms from Supplemental Fig. S1 were requested separately, for the time ranging from the 6th of December 2019 to the 31st of August 2020 in Italy. These were: coronavirus, regionali (regional elections), playstation, papa francesco (Pope Francesco), eurovision (the European music contest), sondaggi (polls). + +Google Trends also provided information about queries most searched with a specific keyword. In particular, the most popular queries related to the keyword coronavirus (e.g., coronavirus news ) were gathered. Such list is capped by Google Trends at a maximum of 25 related keywords, ordered by most searched to least, and denoted \(q_{1}(t),\ldots ,q_{25}(t)\) respectively ( \(t\) indicating the time), with \(q_{0}(t)\) the time series of coronavirus searches. + +To compare the searches of a given keyword with its related keywords, it is necessary to put them on the same scale. To this end, searched items were queried in pairs. In this way, Google Trends normalized the two resulting time series for the highest of the maximums of the two. Given the two times- series per request \((q_{i - 1}(t),q_{i}(t))\) , with \(i = 1,\ldots ,25\) , a coefficient \(\alpha_{i} = \max_{t}(q_{i - 1}(t)) / \max_{t}(q_{i}(t))\) was calculated. Thus, all the time series \(q_{i}\) could be set on the same scale of \(q_{0}\) , multiplying by \(\prod_{j = 1}^{i}\alpha_{j}\) . This procedure is needed not to lose resolution on keywords with a small number of queries. Having queried for pairs \((q_{0}(t),q_{i}(t))\) , would have resulted in a rounding at 0 performed by Google Trends. + +coronavirus- related queries were then aggregated by summing up their time- series. Thus, coronavirus oggi (Coronavirus today), coronavirus notizie (Coronavirus news), coronavirus ultime (Coronavirus latest), coronavirus ultime notizie (Coronavirus latest news) and coronavirus news, were all aggregated into coronavirus news. Subsequently, we removed all the queries that returned the same search results as another query. These were coronavirus contagi (Coronavirus infections) and coronavirus in italia (Coronavirus in Italy), duplicates + +<--- Page Split ---> + +of contagi coronavirus (Coronavirus infections), and coronavirus italia (Coronavirus Italy), respectively. Also, the query corona was excluded because it has other meanings in Italian, namely "crown", and it is also a famous brand of beer. Finally, the list of queries associated with coronavirus, ordered by the amount of searches, was: news, italia (Italy), lombardia (Lombardy), sintomi (symptoms), contagi (infections), casi (cases), morti (deaths), bollettino (bulletin), roma (Rome), dati (data), mondo (world), mappa (map), sicilia (Sicily), veneto, campania, decreto (decree), milano (Milan), piemonte (Piedmont). + +## News Data + +To analyse the news supply, we investigated the data provided by AGCOM, the Italian Authority for Communications Guarantees, which granted us access to the content of a vast number of Italian news sources published online and offline from the 6th of December 2019 to the 31st of August 2020 in Italy. These data included articles from printed and digital newspapers and information agencies, TV, radio sites, and scientific sources. Moreover, the data had a specific annotation for "fake news" sources. The list disinformation sources, vetted by the Authority, is provided by fact- checking organizations like bufale.net and butac.it. It had already been employed for other studies on disinformation [33]. + +After pre- processing the data for duplicates and incomplete logs elimination, the General News data consisted of almost 7 million entries from 554 different news sources. Each data entry has a unique ID and contains, among other information, the title and the content of the piece of news, its date, its source, and the annotation of belonging to the disinformation sources list). + +Needing to imitate the rationale underlying Google Trends data, where daily search counts refer to the query of specific keywords, we sought to find counts of daily keywords also in the news data. To do so, given a keyword (e.g., coronavirus), we defined three different metrics: the piece of news containing the keyword at least once, those having the keyword at least three times, and finally, all the occurrences of a specific keyword. These three metrics were then normalized on the total number of news' sources per day to level the press activity during weekends. For each model, we chose the metric with the best modelling performances. For the improved version of the VAR model described in equation 1 from the Results section, the metric with at least three occurrences was selected, even if the other two showed similar performances. Instead, the most inclusive metric (at least one occurrence) was adopted when dealing with disinformation. This procedure was necessary to enhance the signal, given the low number of Fake News items encountered. For consistency, the overall General News was considered with the same metric (at least one occurrence) when comparing it with the Fake News time series. + +Following the same rationale, we adopted the first metric to filter for the keywords related to the Coronavirus subject described in the previous subsection. To do so, we selected the piece of news containing the keyword coronavirus at least once, and, in this subset, we counted the ones featuring the desired related keyword at least once. The values found were normalized on the total + +<--- Page Split ---> + +number of news pieces having the keyword coronavirus at least once per day to get a proxy for the share of Coronavirus piece of information focused on the related keyword sub- domain. We repeated this analysis for the subset of news mentioning the keyword coronavirus at least once and marked as Fake News in the data. We then used the values extracted from this analysis to investigate the disinformation supply in the Coronavirus context. + +## Time Series Analysis + +Time series of Searches and General News, from Supplemental Fig. S1, were investigated using the VAR model [15], using Python's statsmodels package for time series analysis [27]. Data were regularized via \(x \mapsto \log (1 + x)\) transformation before fitting. For the VAR modelling, the number of lags \(d\) was determined as the parameter that minimized the Akaike information criterion [1], with \(d\) ranging in the interval \((1, 14)\) . This modelling strategy was chosen to ensure the interpretability of the fitted model and its regression coefficients. + +From the VAR model, we computed Granger- causality [15] to test whether queries' values provided meaningful information to the prediction of news volumes and vice versa. Since two tests were performed on the same data from a given subject (for the null hypotheses "Searches do not Granger- cause General News" and "General News do not Granger- cause Searches"), resulting p- values were corrected by the Holm- Bonferroni method [23] Thus, pairs of p- values in Supplemental Fig. S2 were multiplied by 2, to control for family- wise error rate and to maintain comparability. + +In Fig. 1 and Supplemental Fig. S3, the Improved Models for regression of the General News were derived adjusting the VAR models to include Searches at time \(t\) (Supplemental Fig. S3). Lags were re- elaborated through the Akaike criterion as before, with similar results. These models were then compared against a null model that forecasts one day proportionally to the value of the day before to benchmark how beneficial the addition of regressing variables was to General News's prediction (see Tab. 1). + +To assess the semantic misalignment between General News and Searches from Supplemental Fig. S5, the cosine distance was calculated as \(\mathrm{d}(\mathbf{S}(t), \mathbf{N}(t)) = 1 - \mathbf{S}(t) \cdot \mathbf{N}(t) / |\mathbf{S}(t)| |\mathbf{N}(t)|\) , on the vectors \(\mathbf{S}(t) = (S_{1}(t), \ldots , S_{k}(t))\) , \(\mathbf{N}(t) = (N_{1}(t), \ldots , N_{k}(t))\) where \(S_{i}(t)\) and \(N_{i}(t)\) represented the volumes of searches and news, respectively, at time \(t\) for the \(i\) - th keyword associated to coronavirus, with \(\cdot\) being the dot product and \(|\cdot |\) the Euclidean norm. Cosine distance was suitable to compare high- dimensional vectors at different scales, and returned values in \((0, 1)\) for vectors with non- negative entries such as \(\mathbf{S}(t)\) and \(\mathbf{N}(t)\) . + +## Combined Index Validation + +To define and validate the Combined Index from Fig. 3, we split the daily data from Fake News concentration on coronavirus into a training set (from the 29th of January 2020 to the 20th of March 2020) and a validation set (from the 21st of March 2020 on). + +<--- Page Split ---> + +Thus, we defined the Combined Index as a linear combination of the two starting indexes that best fitted the Fake News concentration, using a linear model with Gaussian noise on the training data. The ordinary least squares estimate \(\hat{\sigma}\) for the variance of the Gaussian noise was then calculated as the Mean Squared Error (MSE) divided by the statistical degrees of freedom \(k\) (i.e., the number of observations minus 2, the number of parameters in the model). + +To assess the predictive potential of the Combined Index, we adopted the trained model to forecast the concentration of Fake News in the validation set. This prediction's goodness was tested through the reduced chi- squared statistic, which is calculated as the MSE on the validation set divided by \(\hat{\sigma}\) . This statistic is approximately distributed as a \(\chi^{2}\) with as many degrees of freedom as the size of the validation set (i.e., 51), leading to a p- value of about 0.945. As such, the null hypothesis, that the concentration of Fake News for the keyword coronavirus is distributed in agreement with the trained model, cannot be rejected. + +## Acknowledgements + +The Authors wish to warmly thank Marco Delmastro of AGCOM for insightful discussions about the Italian news ecosystem as well as for providing the database of Italian news. The database was shared in the framework of the Task Force on "Digital Platforms and Big Data - Covid- 19 Emergency", established by the AGCOM to contribute, among other things, to the fight against online disinformation on issues related to the SARS- CoV- 2 crisis. + +## References + +[1] Hirotugu Akaike. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6):716- 723, 1974. + +[2] Hunt Allcott and Matthew Gentzkow. Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2):211- 36, 2017. + +[3] Vian Bakir and Andrew McStay. Fake news and the economy of emotions: Problems, causes, solutions. Digital journalism, 6(2):154- 175, 2018. + +[4] Pablo J Boczkowski and Limor Peer. The choice gap: The divergent online news preferences of journalists and consumers. Journal of communication, 61(5):857- 876, 2011. + +[5] Daniel Borup and Erik Christian Montes Schütte. In search of a job: Forecasting employment growth using google trends, 2020. + +[6] Hyunyoung Choi and Hal Varian. Predicting the present with google trends. Economic record, 88:2- 9, 2012. + +<--- Page Split ---> + +[7] Matteo Cinelli, Walter Quattrociocchi, Alessandro Galeazzi, Carlo Michele Valensise, Emanuele Brugnoli, Ana Lucia Schmidt, Paola Zola, Fabiana Zollo, and Antonio Scala. The covid- 19 social media infodemic, 2020. + +[8] Nadia K Conroy, Victoria L Rubin, and Yimin Chen. Automatic deception detection: Methods for finding fake news. Proceedings of the Association for Information Science and Technology, 52(1):1- 4, 2015. + +[9] Zhi Da, Joseph Engelberg, and Pengjie Gao. The sum of all fears investor sentiment and asset prices. The Review of Financial Studies, 28(1):1- 32, 2015. + +[10] Emma Dafouz- Mihe. The pragmatic role of textual and interpersonal metadiscourse markers in the construction and attainment of persuasion: A cross- linguistic study of newspaper discourse. Journal of Pragmatics, 40:95- 113, 01 2008. + +[11] Andrea Freyer Dugas, Mehdi Jalalpour, Yulia Gel, Scott Levin, Fred Torcaso, Takeru Igusa, and Richard E Rothman. Influenza forecasting with google flu trends. PloS one, 8(2):e56176, 2013. + +[12] Robert F Engle and Victor K Ng. Measuring and testing the impact of news on volatility. The journal of finance, 48(5):1749- 1778, 1993. + +[13] Don Fallis. What is disinformation? Library Trends, 63(3):401- 426, 2015. + +[14] Daniel Funke and Daniela Flamini. A guide to anti- misinformation actions around the world, 2018. + +[15] James Douglas Hamilton. Time Series Analysis. Princeton University Press, 1 edition, 1994. + +[16] Eelco Harteveld, Joep Schaper, Sarah L De Lange, and Wouter Van Der Brug. Blaming brussels? the impact of (news about) the refugee crisis on attitudes towards the eu and national politics. JCMS: Journal of Common Market Studies, 56(1):157- 177, 2018. + +[17] Md Saiful Islam, Tonmoy Sarkar, Sazzad Hossain Khan, Abu- Hena Mostofa Kamal, SM Murshid Hasan, Alamgir Kabir, Dalia Yeasmin, Mohammad Ariful Islam, Kamal Ibne Amin Chowdhury, Kazi Selim Anwar, et al. Covid- 19- related infodemic and its impact on public health: a global social media analysis. The American Journal of Tropical Medicine and Hygiene, 103(4):1621- 1629, 2020. + +[18] Shanto Iyengar, Helmut Norpoth, and Kyu S Hahn. Consumer demand for election news: The horserace sells. The Journal of Politics, 66(1):157- 175, 2004. + +<--- Page Split ---> + +[19] Seung- Pyo Jun, Hyoung Sun Yoo, and San Choi. Ten years of research change using google trends: From the perspective of big data utilizations and applications. Technological forecasting and social change, 130:69- 87, 2018. + +[20] Anna Kata. A postmodern pandora's box: anti- vaccination misinformation on the internet. Vaccine, 28(7):1709- 1716, 2010. + +[21] Maksym Korobchinsky, Liliya Chyrun, Lyubomyr Chyrun, and Victoria Vysotska. Peculiarities of content forming and analysis in internet newspaper covering music news. In 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), volume 1, pages 52- 57. IEEE, 2017. + +[22] Vasileios Lampos, Andrew C Miller, Steve Crossan, and Christian Stefansen. Advances in nowcasting influenza- like illness rates using search query logs. Scientific reports, 5(1):1- 10, 2015. + +[23] Erich L Lehmann and Joseph P Romano. Testing statistical hypotheses. Springer- Verlag New York, 3 edition, 2006. + +[24] Tobias Preis, Helen Susannah Moat, and H Eugene Stanley. Quantifying trading behavior in financial markets using google trends. Scientific reports, 3:1684, 2013. + +[25] David J Rothkopf. When the buzz bites back. The Washington Post, 11:B1- B5, 2003. + +[26] Andreas Schmidt, Ana Ivanova, and Mike S Schafer. Media attention for climate change around the world: A comparative analysis of newspaper coverage in 27 countries. Global Environmental Change, 23(5):1233- 1248, 2013. + +[27] Skipper Seabold and Josef Perktold. statsmodels: Econometric and statistical modeling with python. In 9th Python in Science Conference, 2010. + +[28] Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter, 19(1):22- 36, 2017. + +[29] Sharon R Sznitman and Nehama Lewis. Is cannabis an illicit drug or a medicine? a quantitative framing analysis of israeli newspaper coverage. International Journal of Drug Policy, 26(5):446- 452, 2015. + +[30] David Tewksbury. What do americans really want to know? tracking the behavior of news readers on the internet. Journal of communication, 53(4):694- 710, 2003. + +[31] Kathie M d'I Treen, Hywel TP Williams, and Saffron J O'Neill. Online misinformation about climate change. Wiley Interdisciplinary Reviews: Climate Change, 11(5):e665, 2020. + +<--- Page Split ---> + +[32] Marc Trussler and Stuart Soroka. Consumer demand for cynical and negative news frames. The International Journal of Press/Politics, 19(3):360- 379, 2014. + +[33] Michela Del Vicario, Walter Quattrociocchi, Antonio Scala, and Fabiana Zollo. Polarization and fake news: Early warning of potential misinformation targets. ACM Transactions on the Web (TWEB), 13(2):1- 22, 2019. + +[34] Marco Visentin, Gabriele Pizzi, and Marco Pichierri. Fake news, real problems for brands: The impact of content truthfulness and source credibility on consumers' behavioral intentions toward the advertised brands. Journal of Interactive Marketing, 45:99- 112, 2019. + +[35] Brian Weeks and Brian Southwell. The symbiosis of news coverage and aggregate online search behavior: Obama, rumors, and presidential politics. Mass communication and society, 13(4):341- 360, 2010. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +GravinoetalSl.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__074d5ca947dfa0b3c0d61ffc91ee2ad07daa2a3b9cd623deb20d79df8add57d4/preprint__074d5ca947dfa0b3c0d61ffc91ee2ad07daa2a3b9cd623deb20d79df8add57d4_det.mmd b/preprint/preprint__074d5ca947dfa0b3c0d61ffc91ee2ad07daa2a3b9cd623deb20d79df8add57d4/preprint__074d5ca947dfa0b3c0d61ffc91ee2ad07daa2a3b9cd623deb20d79df8add57d4_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..4815d2fbaa81d03b8e7bd1bd9b60014d60d2ba03 --- /dev/null +++ b/preprint/preprint__074d5ca947dfa0b3c0d61ffc91ee2ad07daa2a3b9cd623deb20d79df8add57d4/preprint__074d5ca947dfa0b3c0d61ffc91ee2ad07daa2a3b9cd623deb20d79df8add57d4_det.mmd @@ -0,0 +1,458 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 933, 177]]<|/det|> +# Assessing disinformation through the dynamics of supply and demand in the news ecosystem + +<|ref|>text<|/ref|><|det|>[[44, 196, 448, 377]]<|/det|> +Pietro Gravino ( \(\boxed{ \begin{array}{r l} \end{array} }\) pietro.gravino@sony.com) Sony CSL Paris Giulio Prevedello Sony CSL Paris Martina Galletti Sony CSL Paris Vittorio Loreto Sony CSL Paris + +<|ref|>sub_title<|/ref|><|det|>[[44, 417, 102, 435]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 455, 597, 475]]<|/det|> +Keywords: SARS- CoV- 2, social dialogue, information technology + +<|ref|>text<|/ref|><|det|>[[44, 494, 288, 512]]<|/det|> +Posted Date: June 1st, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 531, 463, 550]]<|/det|> +DOI: https://doi.org/10.21203/rs.3. rs- 577571/v1 + +<|ref|>text<|/ref|><|det|>[[44, 568, 910, 610]]<|/det|> +License: © \(\circledast\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 648, 914, 690]]<|/det|> +Version of Record: A version of this preprint was published at Nature Human Behaviour on May 23rd, 2022. See the published version at https://doi.org/10.1038/s41562- 022- 01353- 3. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[216, 202, 781, 255]]<|/det|> +# Assessing disinformation through the dynamics of supply and demand in the news ecosystem + +<|ref|>text<|/ref|><|det|>[[243, 271, 752, 306]]<|/det|> +Pietro Gravino\*a, Giulio Prevedelloa, Martina Gallettia, and Vittorio Loretoa,b,c + +<|ref|>text<|/ref|><|det|>[[219, 319, 777, 405]]<|/det|> +aSony Computer Science Laboratories, 75005 Paris, France bSapienza University of Rome, Physics Department, 00185, Rome, Italy cComplexity Science Hub Vienna, A- 1080 Vienna, Austria \*Corresponding author: pietro.gravino@sony.com + +<|ref|>text<|/ref|><|det|>[[441, 430, 553, 447]]<|/det|> +May 31, 2021 + +<|ref|>sub_title<|/ref|><|det|>[[465, 478, 530, 491]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[256, 498, 740, 817]]<|/det|> +Social dialogue, the foundation of our democracies, is currently threatened by disinformation and partisanship, with their disrupting role on individual and collective awareness and detrimental effects on decision- making processes. Despite a great deal of attention to the news sphere itself, little is known about the subtle interplay between the offer and the demand for information. Still, a broader perspective on the news ecosystem, including both the producers and the consumers of information, is needed to build new tools to assess the health of the infosphere. Here, we combine in the same framework news supply, as mirrored by a fairly complete Italian news database - partially annotated for fake news, and news demand, as captured through the Google Trends data for Italy. Our investigation focuses on the temporal and semantic interplay of news, fake news, and searches in several domains, including the virus SARS- CoV- 2 pandemic. Two main results emerge. First, disinformation is extremely reactive to people's interests and tends to thrive, especially when there is a mismatch between what people are interested in and what news outlets provide. Second, a suitably defined index can assess the level of disinformation only based on the available volumes of news and searches. Although our results mainly concern the Coronavirus subject, we provide hints that the same findings can have more general applications. We contend these results can be a powerful asset in informing campaigns against disinformation and providing news outlets and institutions with potentially relevant strategies. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[216, 153, 362, 173]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[216, 184, 780, 259]]<|/det|> +The Covid- 19 crisis evidenced once more that disinformation stands as one of the major plagues of the Information Age. In the last decades, many national and international institutions started to implement a vast plethora of strategies to tackle this issue [14] and mitigate its effects. Still, the mechanisms underlying the role and phenomenology of disinformation are largely unclear. + +<|ref|>text<|/ref|><|det|>[[216, 260, 780, 366]]<|/det|> +Only in recent times the complex ecosystem of information massively attracted the interest of the scientific community. Disinformation went under investigation, from its very definition [13] to its psychological mechanisms [3], and its spreading dynamics [7]. Detection and forecast of disinformation were also among the relevant topics explored by the scientific community [28]. These studies raised questions about how to identify statistical markers in the news content [8] or about the diffusion mechanisms [33]. + +<|ref|>text<|/ref|><|det|>[[216, 366, 780, 486]]<|/det|> +A meaningful part of the research effort focused on the impact of disinformation on diverse fields of human activities, such as consumers' behaviour [34], political elections [2], sustainability [31] or health [20]. During the Covid- 19 pandemic, particularly, the effect of disinformation on social behaviours became so compelling that the term "Infodemic" made a comeback from the SARS epidemic of 2003 [25], to describe the spreading of false or incorrect information about the virus SARS- CoV- 2. The consequences were disastrous [17] and led to dangerous behaviours that further aggravated the epidemic crisis. + +<|ref|>text<|/ref|><|det|>[[216, 486, 780, 606]]<|/det|> +While disinformation is always under the spotlight, the complex ecosystem of information, which is the substrate for disinformation, attracted much less interest. It is important to stress that the infosphere relies on the subtle interplay of two types of actors: news producers on the one hand and news consumers on the other. In this structure, the supply and the demand of information stand in a market- like relationship. The study of their interplay is essential to unveil the mechanisms of information dynamics. It also provides a broader view in which disinformation can be contextualised and analysed. + +<|ref|>text<|/ref|><|det|>[[216, 607, 780, 697]]<|/det|> +The news supply can be identified with the overall news production, mainly consisting of officially recognised newspapers. The general news production had been primarily studied in linguistics [10], while analyses of news content [21] and coverage [26, 29] were often focusing on particular countries or topics. Other works investigated the impacts of news and its consumption on, for example, reading behaviour [30], finance [12], and political opinions [16]. + +<|ref|>text<|/ref|><|det|>[[216, 697, 780, 802]]<|/det|> +News demand, instead, is more difficult to pinpoint. In the literature, surveys and lab studies are usual procedures of investigation [32, 30, 18], but, unlike general news production, they cannot scale up to the population level. Thus, different solutions have to be adopted. The tracking of reading behaviours, for example, had been used to study the demands and interests of readers [4]. However, such a methodology is biased by the very existence of news since the interest for topics not covered by news cannot be recorded. + +<|ref|>text<|/ref|><|det|>[[238, 803, 780, 818]]<|/det|> +An independent way to track people interests that gained popularity in the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[216, 157, 780, 293]]<|/det|> +scientific community is the Google Trends service \(^{1}\) [19]. It provides an index proportional to the number of searches made with the Google Search engine, enabling the quantitative comparison of searched queries. In the last decade, Google Trends has been mainly used as a marker, and a predictor, of people's behaviours in different contexts, like finance [9, 24], epidemiology [22, 11] or socio- economic indicators [6, 5]. Interestingly, its intrinsic value as a proxy for people's interest was perhaps overlooked. In the framework of news, the Google Trends index has been mainly adopted for forecasting [35], without delving into the comprehension of the dynamics of the news ecosystem. + +<|ref|>text<|/ref|><|det|>[[216, 293, 780, 413]]<|/det|> +Here we comprehend, in a unique framework, the supply and demand for information and analyse their dynamical interplay with the final goal of understanding the main mechanisms of the information ecosystem dynamics and extracting hints about the determinants of disinformation. To this end, we focused on the general production of news in Italy, from early December 2019 to the end of August 2020, as the reference for the news supply. For the same period, the Google Trend index served as a proxy for the general public's information demand. + +<|ref|>text<|/ref|><|det|>[[216, 413, 780, 488]]<|/det|> +We adopted Vector Auto- Regression (VAR) models to study the interplay between news demand and supply, evidencing different causal relationships for distinct subjects. We presented an improved modelling scheme that allows for a quantitative description of the dependencies in the time series evolution for information demand and supply. + +<|ref|>text<|/ref|><|det|>[[216, 489, 780, 564]]<|/det|> +The new framework also permitted to study and compare the disinformation dynamics within the general information system, highlighting behavioural differences in reactivity and modelling efficacy. We observed, in particular, that the semantic misalignment between information supply and demand is higher than the misalignment between disinformation supply and demand. + +<|ref|>text<|/ref|><|det|>[[216, 564, 780, 624]]<|/det|> +These discrepancies could be exploited to aggregate a disinformation risk indicator that is independent of fake news annotations. We contend this index could provide a reliable and independent assessment tool for the news supply's health status. + +<|ref|>sub_title<|/ref|><|det|>[[216, 647, 303, 666]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[216, 679, 586, 697]]<|/det|> +## Dynamics of news supply and demand + +<|ref|>text<|/ref|><|det|>[[216, 705, 780, 795]]<|/det|> +Information systems feature two main drivers: news supply and news demand. As a reference for the news supply, we looked at the whole Italian production of information, termed General News, from early December 2019 to the end of August 2020. For the same period, the Google Trend index served as a proxy for the news demand from the Italian general public, thus termed Searches (refer to Materials and Methods for more details). + +<|ref|>text<|/ref|><|det|>[[216, 796, 780, 825]]<|/det|> +To investigate the nature of the relation between supply and demand of news about a certain subject, six keywords, referring to the most searched subjects + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[216, 157, 780, 309]]<|/det|> +in Italy over the entire observation period, were selected: coronavirus, regional, playstation, papa francesco, eurovision, sondaggi (Supplemental Fig. S1)). General News and Searches for coronavirus are reported in Fig. 1. For each keyword, the time series of the daily appearances in the General News and the daily volume of queries in the Searches were simultaneously fit by Vector Auto- Regression (VAR) linear modelling [15]. VAR models with different lag parameters, which encapsulate the system's memory, were considered, and the best parameters were identified via the Akaike criterion [1] (see Materials and Methods). For all keywords, best- fitting lags ranged between 2 and 4, suggesting a typical, short- memory timescale in the system (see Supplemental Fig. S2)). + +<|ref|>image<|/ref|><|det|>[[239, 336, 768, 565]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[216, 595, 780, 717]]<|/det|> +
Figure 1: Temporal behaviour of the fractions of Searches (red, left \(y\) -axis) and General News (blue, right \(y\) -axis) for the keyword coronavirus in Italy from early December 2019 to the end of August 2020. Searches are reported as a percentage of the maximum observed in the monitored period. General News is represented by the daily fraction of articles containing at least three keyword occurrences (see Materials and Methods). The Improved Model (black line) leverages the past General News and Searches, together with present Searches, to infer the dynamics of General News.
+ +<|ref|>text<|/ref|><|det|>[[216, 730, 780, 850]]<|/det|> +Within the VAR framework, we performed the test for Granger- causality [15] to illustrate which time series, between General News and Searches, contributed more to the prediction of the other, and if any contribution was significant. For the majority of keywords, the contribution of past Searches to present General News was most significant (i.e. coronavirus, regional, playstation, papa francesco) (see Supplemental Fig. S2)). We could safely assume that Searches anticipates General News and use this assumption to improve the model of the temporal behaviour of the latter. We modified the VAR equation for the evo + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[258, 258, 730, 380]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[215, 165, 781, 256]]<|/det|> +Table 1: The parameters and the \(R^{2}\) resulting from the improved linear model of equation 1 for the 4 selected keywords. As a reference, in brackets, we report the value for \(R^{2}\) of a trivial model with equation \(N(t) = \alpha N(t - 1)\) , i.e., a model where every day depends only on the day before. The value of \(R^{2}\) of the improved linear model is systematically larger than that of the trivial model. Starred values are those not significantly different from zero. + +
coronavirusregionaliplaystationpapa francesco
α10.820.650.180.54
α20.260.19
β00.0700.00820.000550.0038
β1-0.0340.003*0.00035
β2-0.00640.00068
R²0.996 (0.991)0.89 (0.86)0.54 (0.29)0.73 (0.63)
+ +<|ref|>text<|/ref|><|det|>[[215, 404, 780, 450]]<|/det|> +lution of General News by inserting Searches' role. More precisely, let \(S(t)\) and \(N(t)\) be, respectively, the values of Searches and General News at day \(t\) , then the new equation for the evolution of \(N(t)\) reads: + +<|ref|>equation<|/ref|><|det|>[[333, 460, 778, 503]]<|/det|> +\[N(t) = \sum_{i = 1}^{d}(\alpha_{i}N(t - i) + \beta_{i}S(t - i)) + \beta_{0}S(t). \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[216, 512, 780, 573]]<|/det|> +where the coefficients \(\alpha_{i}\) , \(\beta_{0}\) and \(\beta_{i}\) were fitted, while the Akaike criterion provides the optimal lag \(d\) . This Improved Model closely reproduced the data, particularly in correspondence with the peaks (Fig. 1 for coronavirus and Supplemental Fig. S3). + +<|ref|>text<|/ref|><|det|>[[216, 574, 780, 604]]<|/det|> +The model's parameters also provided a quantitative insight on the interplay between General News and Searches (Tab. 1): + +<|ref|>text<|/ref|><|det|>[[238, 613, 781, 787]]<|/det|> +\(\alpha_{1}\) was larger than other \(\alpha\) parameters, indicating a strong dependence of General News on the previous day activity. This evidence is a sign of an inertial behaviour of the news supply. \(\beta_{0}\) , the weight of present Searches, was typically larger than other \(\beta\) parameters and significantly non- zero, supporting the assumption of present Searches role for the Improved Model. The remaining parameters were smaller though almost always significant. For two keywords (coronavirus and regionali), the parameters \(\beta_{d}\) (for \(d \geq 1\) ) were negative. This result suggests that General News depends on the different quotient of Searches, together with the volume of Searches itself. + +<|ref|>text<|/ref|><|det|>[[216, 796, 780, 841]]<|/det|> +Of note, a direct comparison between \(\alpha\) and \(\beta\) parameters was not possible, as Searches and General News were scaled differently (Google Trends does not disclose the absolute scale of queries volume). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[216, 154, 773, 171]]<|/det|> +## The different behaviours of General News and Fake News + +<|ref|>text<|/ref|><|det|>[[216, 179, 780, 286]]<|/det|> +The Improved Model quantifies the information supply dynamics and enables the comparison between General News and disinformation supply. We applied this methodology to the topic coronavirus, since it dominated the landscape of information (Supplemental Fig. S1), and due to the direct impact of disinformation on the response to the 2020 pandemic. To this end, we extended our analysis to the news items that were annotated as false or misleading, thus named Fake News (see Material and Methods). + +<|ref|>text<|/ref|><|det|>[[216, 286, 780, 361]]<|/det|> +We exploited the Improved Model 1 to compare General News and Fake News through their best- fitting coefficients \(\alpha\) and \(\beta\) . To this end, we paralleled the variable \(N(t)\) , the daily proportion of coronavirus- related General News at day \(t\) , and \(FN(t)\) , the daily proportion of coronavirus- related Fake News at day \(t\) (Tab. 2). + +<|ref|>text<|/ref|><|det|>[[216, 361, 780, 452]]<|/det|> +Compared to General News, coronavirus- related Fake News shows a meaningfully lower Inertia term, \(\alpha_{1}\) , and a non- significant \(\beta_{1}\) indicating a greater reactivity to \(S(t)\) . These pieces of evidence and the lower prediction score (adjusted \(R^{2}\) ) suggest that disinformation presents a different behaviour than General News, to the points that it distorts the dynamics of the news ecosystem and leads to impaired modelling performance. + +<|ref|>table<|/ref|><|det|>[[310, 530, 680, 626]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[216, 474, 780, 534]]<|/det|> +Table 2: Coefficients from the Improved Model fitting of General News and of Fake News having at least one occurrence of the keyword coronavirus (see Materials and Methods). Starred coefficients do not differ significantly from zero. + +
General NewsFake News
α1 (Inertia)0.860 ± 0.0160.758 ± 0.039
β00.460 ± 0.0350.294 ± 0.086
β1-0.248 ± 0.042-0.081* ± 0.091
R²0.9950.931
+ +<|ref|>text<|/ref|><|det|>[[216, 640, 780, 791]]<|/det|> +Another difference in the behaviour of news and disinformation emerged at a semantic level. We focused on the most queried keywords searched together with coronavirus in Google Search (see Materials and Methods). Each of these related queries provided a time series of news demand about a sub- domain that co- occurs with, and therefore is semantically linked to, coronavirus. We quantified the co- occurrence of these terms with the coronavirus keyword also in the news items, for both General News and Fake News. In this way, we defined \(\mathbf{S}(t), \mathbf{N}(t), \mathbf{FN}(t)\) as the daily semantic vectors for coronavirus- related Searches, General News, and Fake News, respectively. Each vector has seventeen entries, one per sub- domain (see Materials and Methods for more details). + +<|ref|>text<|/ref|><|det|>[[216, 791, 780, 851]]<|/det|> +We calculated \(\mathbf{S}_{tot} = \sum_{t} \mathbf{S}(t)\) and sort its components to rank the different sub- domains by the total news demand over the period considered (Fig. 2). To assess the difference between information and disinformation with respect to the matching of news demand for different sub- domains, we challenged the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[215, 155, 780, 188]]<|/det|> +components' rankings of \(\mathbf{N}_{tot} = \sum_{t}\mathbf{N}(t)\) and \(\mathbf{FN}_{tot} = \sum_{t}\mathbf{FN}(t)\) against the corresponding ones of \(\mathbf{S}_{tot}\) (Fig. 2). + +<|ref|>image<|/ref|><|det|>[[252, 220, 700, 590]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[215, 607, 781, 713]]<|/det|> +
Figure 2: The ranked components of \(\mathbf{S}_{tot}\) (centre red), representing coronavirus sub-domains sorted by total news demand over the observed time. On the sides of each keyword, a tag indicates the rank in \(\mathbf{N}_{tot}\) for General News, on the left, and in \(\mathbf{FN}_{tot}\) for Fake News, on the right. Tags are distanced from the centre by the amount of rank mismatch to Searches ranks. Tags are coloured to highlight the rank closest to the Searches rank: blue for General News and green for Fake News.
+ +<|ref|>text<|/ref|><|det|>[[216, 725, 780, 816]]<|/det|> +Given the coronavirus- related keywords ranked from the Searches as a reference, Fake News ranking shows fewer and minor mismatches compared to General News. We quantified this difference in behaviour through Spearman's Correlation. \(\mathbf{S}_{tot}\) and \(\mathbf{N}_{tot}\) components resulted positively correlated ( \(r = 0.52\) , with a p- value of 0.031) but \(\mathbf{S}_{tot}\) and \(\mathbf{FN}_{tot}\) correlated more ( \(r = 0.67\) , with a p- value of 0.0032). + +<|ref|>text<|/ref|><|det|>[[216, 817, 780, 847]]<|/det|> +The semantic difference in the behaviour of Fake News and General News holds not only at the aggregated level but also at a daily level. This was mea + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[216, 156, 780, 232]]<|/det|> +sured through the cosine distance \(\mathrm{d}(\cdot ,\cdot)\) on their daily vectors \(\mathbf{S}(t)\) , \(\mathbf{N}(t)\) and \(\mathbf{FN}(t)\) (see Materials and Methods). Again, Searches were taken as reference and we calculated its cosine distance from General News, \(\mathrm{d}(\mathbf{S}(t),\mathbf{N}(t))\) , and from Fake News, \(\mathrm{d}(\mathbf{S}(t),\mathbf{FN}(t))\) . The daily relative difference between the cosine distances of Searches- Fake News and Searches- General News + +<|ref|>equation<|/ref|><|det|>[[388, 238, 778, 273]]<|/det|> +\[\frac{\mathrm{d}(\mathbf{S}(t),\mathbf{FN}(t)) - \mathrm{d}(\mathbf{S}(t),\mathbf{N}(t))}{\mathrm{d}(\mathbf{S}(t),\mathbf{N}(t))} \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[216, 283, 780, 359]]<|/det|> +resulted in negatives values in most days \(t\) (Supplemental Fig. S5)). In fact, both the Mean \((- 0.13)\) and Median \((- 0.15)\) were negative, indicating that the cosine distance Searches- Fake News is generally smaller than that of Searches- General News. This result shows how Fake News meets news demand better than General News. + +<|ref|>sub_title<|/ref|><|det|>[[216, 377, 707, 395]]<|/det|> +## Independent detection of Fake News concentration + +<|ref|>text<|/ref|><|det|>[[216, 402, 780, 432]]<|/det|> +The observed differences between General News and Fake News dynamics can be exploited to assess disinformation about the topic coronavirus. + +<|ref|>text<|/ref|><|det|>[[216, 433, 780, 552]]<|/det|> +The difference in modelling Fake and General News suggests that when Fake News concentration on a topic rises, the General News dynamics, which includes Fake News, becomes perturbed. We hypothesise that this perturbation is expected to impair the General News modelling performance. To test this hypothesis, the Improved Model was fit to General News locally on a time window of 14 days, sliding over the entire data time range (see Materials and Methods). For each window, centred in \(t\) , we computed the local modelling error defined as: + +<|ref|>equation<|/ref|><|det|>[[398, 551, 778, 569]]<|/det|> +\[E(t) = (1 - R^{2}(t))\cdot \langle N\rangle (t), \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[216, 575, 780, 605]]<|/det|> +where \(R^{2}(t)\) , the \(R^{2}\) score for the model fitted to the window, is weighted by \(\langle N\rangle (t)\) , the average volume of news produced in that time window. + +<|ref|>text<|/ref|><|det|>[[216, 606, 781, 711]]<|/det|> +Although formulated without exploiting disinformation annotations, \(E(t)\) significantly correlates with the concentration of Fake News on the coronavirus subject, \(FN(t) / N(t)\) (Spearman's \(r = 0.47\) , with a p- value of \(3.9\cdot 10^{- 13}\) (see Materials and Methods). This result supports the hypothesis that loss of predictability from the General News dynamics co- occurs with disinformation spikes. As a consequence, \(E(t)\) stands as a very promising proxy for the concentration of disinformation about the topic coronavirus. + +<|ref|>text<|/ref|><|det|>[[216, 712, 781, 848]]<|/det|> +The semantic difference between General News and Fake News suggests that disinformation might receive greater attention thanks to its ability to match the news demand semantically unsatisfied. We hypothesized that as General News becomes more semantically distant from Searches, Fake News would fill that gap. This hypothesis was tested all over the time range measuring the daily cosine distance between the semantic vectors of Searches and General News \(K(t) = \mathrm{d}(\mathbf{S}(t),\mathbf{N}(t))\) . We then checked the correlation of \(K(t)\) with the daily concentration of Fake News, \(FN(t) / N(t)\) , about coronavirus. The correlation turns out to be positive and significant (Spearman's \(r = 0.58\) , with a p- value of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[216, 157, 780, 187]]<|/det|> +\(1.6 \cdot 10^{- 21}\) ), supporting the hypothesis. This result allows us to adopt \(K\) as a second independent indicator for Fake News concentration assessment. + +<|ref|>text<|/ref|><|det|>[[216, 188, 780, 263]]<|/det|> +To test the effectiveness of our indicators \(E\) and \(K\) in assessing disinformation concentration, we combined them in a Combined Index for disinformation (see Material and Methods). We fit them linearly on a training set composed of approximately the first \(25\%\) of data from the time series, providing the best linear combination of the two (see Fig. 3). + +<|ref|>image<|/ref|><|det|>[[277, 280, 712, 533]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[216, 552, 781, 658]]<|/det|> +
Figure 3: The time series of news annotated as Fake, normalised through the total number of coronavirus-related News compared with the Combined Index for disinformation. The Combined Index is defined as a linear combination of the weighted modelling error for the local fitting of News within the improved Vector Auto-Regression model and the cosine distance between the semantic vectors of Searches and News. The parameters of the combination were fitted in the training set and then tested in the validation set.
+ +<|ref|>text<|/ref|><|det|>[[216, 670, 780, 730]]<|/det|> +The Combined Index was then tested against the validation set, achieving substantial accuracy (reduced chi- squared statistic of 0.945). All these findings suggest that the Combined Index provides a valuable measure for detecting disinformation concerning SARS- CoV- 2. + +<|ref|>text<|/ref|><|det|>[[216, 732, 780, 848]]<|/det|> +The methodology could also be applied to different topics, and to the aggregation of several topics, to assess the health status of the news ecosystem at a more general level. To challenge this claim, we considered the set of the 4 keywords modelled. We aggregated them to create a synthetic macro- topic, for which they individually represented the analogous of the related queries we have seen before. We judged the adoption of the first indicator, i.e. the weighted modelling error for the local fitting, to be pointless since the macro- subject dynamics is largely dominated by the topic coronavirus. This would have resulted + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[216, 157, 780, 293]]<|/det|> +in an indicator similar to the modelling of the coronavirus component alone. We thus focused only on the second indicator, i.e., the cosine distance between the semantic vectors of Searches and News, where the components of the vectors are now the values of General News, Fake News, and Searches for the 4 keywords. The daily value of cosine distance between General News and Searches of the synthetic subject correlates positively and meaningfully with the concentration of disinformation on the synthetic subject (Spearman's correlation of 0.44 with p- value \(= 1.8 \cdot 10^{- 11}\) ). This result supports the plausibility of the application of our methodology in wider contexts. + +<|ref|>sub_title<|/ref|><|det|>[[216, 315, 337, 335]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[216, 346, 780, 452]]<|/det|> +Information quality is a fundamental challenge for the Information Age, especially during a pandemic. Studying the general news system and comparing it with the subset of news labelled as disinformation, we found that pandemic- related Fake News production seems more reactive and precise than General News supply in addressing people's news demand. We exploited such a difference to develop an index for vulnerability of specific topics to disinformation takeover. + +<|ref|>text<|/ref|><|det|>[[216, 453, 780, 633]]<|/det|> +The analysis of Searches and General News for coronavirus and a set of other coronavirus- unrelated highly queried keywords exposed the relation between supply and demand of news: (i) a linear modelling scheme was effective in almost all cases; (ii) the memory of the process seems to be very short (2- 4 days) in all cases; (iii) causality was more commonly directed from Searches to General News (e.g. for coronavirus). Thanks to these considerations, an improved descriptive model could be developed to better describe the relationship between supply and demand for information. This modelling framework allowed us to discern how the inertia of news suppliers is one of the main traits of the dynamics for all the studied keywords. Also, the negative dependence on previous days Searches observed in some cases suggests a dynamics where the trend of the interest is more important than the interest itself for news producers. + +<|ref|>text<|/ref|><|det|>[[216, 634, 780, 770]]<|/det|> +The comparison of coronavirus- related General News and Fake News through the improved linear model's lens exposed that Fake News feature lower inertia and a different dependence on Searches, quantifying their more reactive behaviour. We can speculate that this behavioural difference could be a consequence of the different production environments of General News and Fake News. The firsts are mainly produced by a large and well- established community of professional journalists while the latter are the outcome of by a scattered multitude of small, unorganized actors. The community size effect might be responsible for the different inertial behaviour observed. + +<|ref|>text<|/ref|><|det|>[[216, 770, 780, 845]]<|/det|> +The semantic analysis revealed another key difference between the dynamics of General News and Fake News. Looking at the shares of the most queried keywords co- occurring with coronavirus we discovered that Fake News is better aligned to Searches than General News not only at a cumulative level but also daily over the entire observation period. This result suggests that disinformation + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[216, 157, 780, 247]]<|/det|> +tends to be more semantically precise than information in matching people's interests. This difference might be explained considering the different aims of the two communities. While they are both interested in answering people's demand for information, general news producers also have the ambition for complete coverage of topics, while Fake News producers can focus on chasing the people's attention. + +<|ref|>text<|/ref|><|det|>[[216, 248, 780, 444]]<|/det|> +We exploited the modelling and the semantic mismatch between General News and Fake News to detect bursts of disinformation on coronavirus. It is worth mentioning how this measure was performed without any information about FakeNews, looking instead only at the time- series of General News and Searches. The modelling difference resulted in a destabilizing effect of Fake News peaks on the modelling of General News as a function of Searches. Such a perturbation was measured as the local weighted modelling error in the Improved Model for General News as a function of Searches. A higher value of this indicator means that the normal relation between General News and Searches has been altered, probably by Fake News presence. The semantic difference was estimated from daily misalignment itself between General News and Searches. In this case, a higher value of this indicator means that semantic imprecision leaves an unsatisfied interest in readers, possibly fostering Fake News. + +<|ref|>text<|/ref|><|det|>[[216, 445, 780, 504]]<|/det|> +The positive and meaningful correlation of both indicators with Fake News concentration on Coronavirus supports both the hypothesis: (i) disinformation perturbs the normal relation between General News and Searches and (ii) is fueled by the semantic misalignment between the two. + +<|ref|>text<|/ref|><|det|>[[216, 505, 780, 640]]<|/det|> +We blend the two markers into a single Combined Index for disinformation by adopting a training set for its definition and testing it on a validation set with good results. Given its independence from Fake News annotations and the hints supporting a possible generalization of the result to other topics, the Combined Index can be a powerful tool for journalists and editors as well as for news monitoring authorities to detect in real- time vulnerabilities to disinformation. Our results also suggest, as a possible strategy to face these vulnerabilities, a timely refocus of General News supply to better meet the information demand of the public. + +<|ref|>text<|/ref|><|det|>[[216, 641, 780, 851]]<|/det|> +Information vulnerabilities are a major risk factor for our societies, as they have a direct impact on individuals. For example, the solution to the Coronavirus crisis depends mainly on individual behaviours which, in turn, are directly affected by the news. The approach presented here, far from being conclusive, represents a first contribution towards a deeper understanding of the phenomenology of disinformation as part of the information ecosystem's general dynamics. Further studies will be needed to test the conclusions and to generalize the results to different countries, languages, domains, and time periods. Moreover, the diffusion layer should be added to the analysis of the dynamics of the infosphere, with particular attention to the social media spreading of news. In our opinion, a paradigm shift in facing disinformation is no more an option, is a necessity for information societies. We contend that the presented work contributes to the shift of scientific research towards a more concrete view, aiming to provide policymakers with knowledge and tools to fight disinformation. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[216, 153, 486, 172]]<|/det|> +## Materials and Methods + +<|ref|>sub_title<|/ref|><|det|>[[216, 185, 354, 202]]<|/det|> +## Searches Data + +<|ref|>text<|/ref|><|det|>[[216, 210, 780, 269]]<|/det|> +The information demand about a specific subject was obtained from Google Trends, a platform providing access to an anonymous sample of actual search requests made in Google Search engine, from a selected location and time interval. + +<|ref|>text<|/ref|><|det|>[[216, 270, 780, 375]]<|/det|> +For each given keyword, Google Search returns a time- series with values proportional to the number of times the keyword was searched each day. Since Google Search does not disclose the actual number of searches, the time series values are rendered as percentages of the maximum number returned. As a result, data consist of integers ranging in the interval \((0,100)\) . The time series of one keyword was referred to as the "Searches" for that keyword and provided a measure of the interest it received. + +<|ref|>text<|/ref|><|det|>[[216, 376, 780, 466]]<|/det|> +The use of "pytrends" library for Python \(^2\) enabled the interaction with the Google Trends platform. The terms from Supplemental Fig. S1 were requested separately, for the time ranging from the 6th of December 2019 to the 31st of August 2020 in Italy. These were: coronavirus, regionali (regional elections), playstation, papa francesco (Pope Francesco), eurovision (the European music contest), sondaggi (polls). + +<|ref|>text<|/ref|><|det|>[[216, 466, 780, 557]]<|/det|> +Google Trends also provided information about queries most searched with a specific keyword. In particular, the most popular queries related to the keyword coronavirus (e.g., coronavirus news ) were gathered. Such list is capped by Google Trends at a maximum of 25 related keywords, ordered by most searched to least, and denoted \(q_{1}(t),\ldots ,q_{25}(t)\) respectively ( \(t\) indicating the time), with \(q_{0}(t)\) the time series of coronavirus searches. + +<|ref|>text<|/ref|><|det|>[[216, 557, 780, 708]]<|/det|> +To compare the searches of a given keyword with its related keywords, it is necessary to put them on the same scale. To this end, searched items were queried in pairs. In this way, Google Trends normalized the two resulting time series for the highest of the maximums of the two. Given the two times- series per request \((q_{i - 1}(t),q_{i}(t))\) , with \(i = 1,\ldots ,25\) , a coefficient \(\alpha_{i} = \max_{t}(q_{i - 1}(t)) / \max_{t}(q_{i}(t))\) was calculated. Thus, all the time series \(q_{i}\) could be set on the same scale of \(q_{0}\) , multiplying by \(\prod_{j = 1}^{i}\alpha_{j}\) . This procedure is needed not to lose resolution on keywords with a small number of queries. Having queried for pairs \((q_{0}(t),q_{i}(t))\) , would have resulted in a rounding at 0 performed by Google Trends. + +<|ref|>text<|/ref|><|det|>[[216, 710, 780, 816]]<|/det|> +coronavirus- related queries were then aggregated by summing up their time- series. Thus, coronavirus oggi (Coronavirus today), coronavirus notizie (Coronavirus news), coronavirus ultime (Coronavirus latest), coronavirus ultime notizie (Coronavirus latest news) and coronavirus news, were all aggregated into coronavirus news. Subsequently, we removed all the queries that returned the same search results as another query. These were coronavirus contagi (Coronavirus infections) and coronavirus in italia (Coronavirus in Italy), duplicates + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[216, 157, 780, 278]]<|/det|> +of contagi coronavirus (Coronavirus infections), and coronavirus italia (Coronavirus Italy), respectively. Also, the query corona was excluded because it has other meanings in Italian, namely "crown", and it is also a famous brand of beer. Finally, the list of queries associated with coronavirus, ordered by the amount of searches, was: news, italia (Italy), lombardia (Lombardy), sintomi (symptoms), contagi (infections), casi (cases), morti (deaths), bollettino (bulletin), roma (Rome), dati (data), mondo (world), mappa (map), sicilia (Sicily), veneto, campania, decreto (decree), milano (Milan), piemonte (Piedmont). + +<|ref|>sub_title<|/ref|><|det|>[[216, 296, 323, 313]]<|/det|> +## News Data + +<|ref|>text<|/ref|><|det|>[[216, 320, 780, 456]]<|/det|> +To analyse the news supply, we investigated the data provided by AGCOM, the Italian Authority for Communications Guarantees, which granted us access to the content of a vast number of Italian news sources published online and offline from the 6th of December 2019 to the 31st of August 2020 in Italy. These data included articles from printed and digital newspapers and information agencies, TV, radio sites, and scientific sources. Moreover, the data had a specific annotation for "fake news" sources. The list disinformation sources, vetted by the Authority, is provided by fact- checking organizations like bufale.net and butac.it. It had already been employed for other studies on disinformation [33]. + +<|ref|>text<|/ref|><|det|>[[216, 457, 780, 531]]<|/det|> +After pre- processing the data for duplicates and incomplete logs elimination, the General News data consisted of almost 7 million entries from 554 different news sources. Each data entry has a unique ID and contains, among other information, the title and the content of the piece of news, its date, its source, and the annotation of belonging to the disinformation sources list). + +<|ref|>text<|/ref|><|det|>[[216, 532, 780, 773]]<|/det|> +Needing to imitate the rationale underlying Google Trends data, where daily search counts refer to the query of specific keywords, we sought to find counts of daily keywords also in the news data. To do so, given a keyword (e.g., coronavirus), we defined three different metrics: the piece of news containing the keyword at least once, those having the keyword at least three times, and finally, all the occurrences of a specific keyword. These three metrics were then normalized on the total number of news' sources per day to level the press activity during weekends. For each model, we chose the metric with the best modelling performances. For the improved version of the VAR model described in equation 1 from the Results section, the metric with at least three occurrences was selected, even if the other two showed similar performances. Instead, the most inclusive metric (at least one occurrence) was adopted when dealing with disinformation. This procedure was necessary to enhance the signal, given the low number of Fake News items encountered. For consistency, the overall General News was considered with the same metric (at least one occurrence) when comparing it with the Fake News time series. + +<|ref|>text<|/ref|><|det|>[[216, 774, 780, 848]]<|/det|> +Following the same rationale, we adopted the first metric to filter for the keywords related to the Coronavirus subject described in the previous subsection. To do so, we selected the piece of news containing the keyword coronavirus at least once, and, in this subset, we counted the ones featuring the desired related keyword at least once. The values found were normalized on the total + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[217, 157, 780, 248]]<|/det|> +number of news pieces having the keyword coronavirus at least once per day to get a proxy for the share of Coronavirus piece of information focused on the related keyword sub- domain. We repeated this analysis for the subset of news mentioning the keyword coronavirus at least once and marked as Fake News in the data. We then used the values extracted from this analysis to investigate the disinformation supply in the Coronavirus context. + +<|ref|>sub_title<|/ref|><|det|>[[217, 265, 419, 283]]<|/det|> +## Time Series Analysis + +<|ref|>text<|/ref|><|det|>[[217, 290, 780, 397]]<|/det|> +Time series of Searches and General News, from Supplemental Fig. S1, were investigated using the VAR model [15], using Python's statsmodels package for time series analysis [27]. Data were regularized via \(x \mapsto \log (1 + x)\) transformation before fitting. For the VAR modelling, the number of lags \(d\) was determined as the parameter that minimized the Akaike information criterion [1], with \(d\) ranging in the interval \((1, 14)\) . This modelling strategy was chosen to ensure the interpretability of the fitted model and its regression coefficients. + +<|ref|>text<|/ref|><|det|>[[217, 397, 780, 517]]<|/det|> +From the VAR model, we computed Granger- causality [15] to test whether queries' values provided meaningful information to the prediction of news volumes and vice versa. Since two tests were performed on the same data from a given subject (for the null hypotheses "Searches do not Granger- cause General News" and "General News do not Granger- cause Searches"), resulting p- values were corrected by the Holm- Bonferroni method [23] Thus, pairs of p- values in Supplemental Fig. S2 were multiplied by 2, to control for family- wise error rate and to maintain comparability. + +<|ref|>text<|/ref|><|det|>[[217, 517, 780, 622]]<|/det|> +In Fig. 1 and Supplemental Fig. S3, the Improved Models for regression of the General News were derived adjusting the VAR models to include Searches at time \(t\) (Supplemental Fig. S3). Lags were re- elaborated through the Akaike criterion as before, with similar results. These models were then compared against a null model that forecasts one day proportionally to the value of the day before to benchmark how beneficial the addition of regressing variables was to General News's prediction (see Tab. 1). + +<|ref|>text<|/ref|><|det|>[[217, 622, 780, 743]]<|/det|> +To assess the semantic misalignment between General News and Searches from Supplemental Fig. S5, the cosine distance was calculated as \(\mathrm{d}(\mathbf{S}(t), \mathbf{N}(t)) = 1 - \mathbf{S}(t) \cdot \mathbf{N}(t) / |\mathbf{S}(t)| |\mathbf{N}(t)|\) , on the vectors \(\mathbf{S}(t) = (S_{1}(t), \ldots , S_{k}(t))\) , \(\mathbf{N}(t) = (N_{1}(t), \ldots , N_{k}(t))\) where \(S_{i}(t)\) and \(N_{i}(t)\) represented the volumes of searches and news, respectively, at time \(t\) for the \(i\) - th keyword associated to coronavirus, with \(\cdot\) being the dot product and \(|\cdot |\) the Euclidean norm. Cosine distance was suitable to compare high- dimensional vectors at different scales, and returned values in \((0, 1)\) for vectors with non- negative entries such as \(\mathbf{S}(t)\) and \(\mathbf{N}(t)\) . + +<|ref|>sub_title<|/ref|><|det|>[[217, 761, 481, 779]]<|/det|> +## Combined Index Validation + +<|ref|>text<|/ref|><|det|>[[217, 787, 780, 847]]<|/det|> +To define and validate the Combined Index from Fig. 3, we split the daily data from Fake News concentration on coronavirus into a training set (from the 29th of January 2020 to the 20th of March 2020) and a validation set (from the 21st of March 2020 on). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[216, 157, 780, 248]]<|/det|> +Thus, we defined the Combined Index as a linear combination of the two starting indexes that best fitted the Fake News concentration, using a linear model with Gaussian noise on the training data. The ordinary least squares estimate \(\hat{\sigma}\) for the variance of the Gaussian noise was then calculated as the Mean Squared Error (MSE) divided by the statistical degrees of freedom \(k\) (i.e., the number of observations minus 2, the number of parameters in the model). + +<|ref|>text<|/ref|><|det|>[[216, 248, 780, 368]]<|/det|> +To assess the predictive potential of the Combined Index, we adopted the trained model to forecast the concentration of Fake News in the validation set. This prediction's goodness was tested through the reduced chi- squared statistic, which is calculated as the MSE on the validation set divided by \(\hat{\sigma}\) . This statistic is approximately distributed as a \(\chi^{2}\) with as many degrees of freedom as the size of the validation set (i.e., 51), leading to a p- value of about 0.945. As such, the null hypothesis, that the concentration of Fake News for the keyword coronavirus is distributed in agreement with the trained model, cannot be rejected. + +<|ref|>sub_title<|/ref|><|det|>[[217, 390, 436, 410]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[216, 421, 780, 512]]<|/det|> +The Authors wish to warmly thank Marco Delmastro of AGCOM for insightful discussions about the Italian news ecosystem as well as for providing the database of Italian news. The database was shared in the framework of the Task Force on "Digital Platforms and Big Data - Covid- 19 Emergency", established by the AGCOM to contribute, among other things, to the fight against online disinformation on issues related to the SARS- CoV- 2 crisis. + +<|ref|>sub_title<|/ref|><|det|>[[216, 535, 341, 555]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[222, 566, 781, 590]]<|/det|> +[1] Hirotugu Akaike. A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6):716- 723, 1974. + +<|ref|>text<|/ref|><|det|>[[222, 608, 780, 638]]<|/det|> +[2] Hunt Allcott and Matthew Gentzkow. Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2):211- 36, 2017. + +<|ref|>text<|/ref|><|det|>[[222, 647, 780, 678]]<|/det|> +[3] Vian Bakir and Andrew McStay. Fake news and the economy of emotions: Problems, causes, solutions. Digital journalism, 6(2):154- 175, 2018. + +<|ref|>text<|/ref|><|det|>[[222, 687, 780, 732]]<|/det|> +[4] Pablo J Boczkowski and Limor Peer. The choice gap: The divergent online news preferences of journalists and consumers. Journal of communication, 61(5):857- 876, 2011. + +<|ref|>text<|/ref|><|det|>[[222, 743, 780, 774]]<|/det|> +[5] Daniel Borup and Erik Christian Montes Schütte. In search of a job: Forecasting employment growth using google trends, 2020. + +<|ref|>text<|/ref|><|det|>[[222, 784, 780, 814]]<|/det|> +[6] Hyunyoung Choi and Hal Varian. 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The pragmatic role of textual and interpersonal metadiscourse markers in the construction and attainment of persuasion: A cross- linguistic study of newspaper discourse. Journal of Pragmatics, 40:95- 113, 01 2008. + +<|ref|>text<|/ref|><|det|>[[216, 393, 780, 439]]<|/det|> +[11] Andrea Freyer Dugas, Mehdi Jalalpour, Yulia Gel, Scott Levin, Fred Torcaso, Takeru Igusa, and Richard E Rothman. Influenza forecasting with google flu trends. PloS one, 8(2):e56176, 2013. + +<|ref|>text<|/ref|><|det|>[[216, 448, 780, 479]]<|/det|> +[12] Robert F Engle and Victor K Ng. Measuring and testing the impact of news on volatility. The journal of finance, 48(5):1749- 1778, 1993. + +<|ref|>text<|/ref|><|det|>[[216, 488, 778, 504]]<|/det|> +[13] Don Fallis. What is disinformation? Library Trends, 63(3):401- 426, 2015. + +<|ref|>text<|/ref|><|det|>[[216, 513, 780, 543]]<|/det|> +[14] Daniel Funke and Daniela Flamini. A guide to anti- misinformation actions around the world, 2018. + +<|ref|>text<|/ref|><|det|>[[216, 553, 780, 584]]<|/det|> +[15] James Douglas Hamilton. Time Series Analysis. Princeton University Press, 1 edition, 1994. + +<|ref|>text<|/ref|><|det|>[[216, 594, 781, 654]]<|/det|> +[16] Eelco Harteveld, Joep Schaper, Sarah L De Lange, and Wouter Van Der Brug. Blaming brussels? the impact of (news about) the refugee crisis on attitudes towards the eu and national politics. JCMS: Journal of Common Market Studies, 56(1):157- 177, 2018. + +<|ref|>text<|/ref|><|det|>[[216, 665, 780, 755]]<|/det|> +[17] Md Saiful Islam, Tonmoy Sarkar, Sazzad Hossain Khan, Abu- Hena Mostofa Kamal, SM Murshid Hasan, Alamgir Kabir, Dalia Yeasmin, Mohammad Ariful Islam, Kamal Ibne Amin Chowdhury, Kazi Selim Anwar, et al. Covid- 19- related infodemic and its impact on public health: a global social media analysis. The American Journal of Tropical Medicine and Hygiene, 103(4):1621- 1629, 2020. + +<|ref|>text<|/ref|><|det|>[[216, 765, 780, 810]]<|/det|> +[18] Shanto Iyengar, Helmut Norpoth, and Kyu S Hahn. Consumer demand for election news: The horserace sells. The Journal of Politics, 66(1):157- 175, 2004. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[215, 157, 780, 218]]<|/det|> +[19] Seung- Pyo Jun, Hyoung Sun Yoo, and San Choi. Ten years of research change using google trends: From the perspective of big data utilizations and applications. Technological forecasting and social change, 130:69- 87, 2018. + +<|ref|>text<|/ref|><|det|>[[216, 226, 780, 257]]<|/det|> +[20] Anna Kata. A postmodern pandora's box: anti- vaccination misinformation on the internet. Vaccine, 28(7):1709- 1716, 2010. + +<|ref|>text<|/ref|><|det|>[[216, 264, 780, 341]]<|/det|> +[21] Maksym Korobchinsky, Liliya Chyrun, Lyubomyr Chyrun, and Victoria Vysotska. Peculiarities of content forming and analysis in internet newspaper covering music news. In 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), volume 1, pages 52- 57. IEEE, 2017. + +<|ref|>text<|/ref|><|det|>[[216, 348, 780, 395]]<|/det|> +[22] Vasileios Lampos, Andrew C Miller, Steve Crossan, and Christian Stefansen. Advances in nowcasting influenza- like illness rates using search query logs. Scientific reports, 5(1):1- 10, 2015. + +<|ref|>text<|/ref|><|det|>[[216, 402, 779, 433]]<|/det|> +[23] Erich L Lehmann and Joseph P Romano. Testing statistical hypotheses. Springer- Verlag New York, 3 edition, 2006. + +<|ref|>text<|/ref|><|det|>[[216, 441, 780, 487]]<|/det|> +[24] Tobias Preis, Helen Susannah Moat, and H Eugene Stanley. Quantifying trading behavior in financial markets using google trends. Scientific reports, 3:1684, 2013. + +<|ref|>text<|/ref|><|det|>[[216, 495, 780, 525]]<|/det|> +[25] David J Rothkopf. When the buzz bites back. The Washington Post, 11:B1- B5, 2003. + +<|ref|>text<|/ref|><|det|>[[216, 534, 780, 594]]<|/det|> +[26] Andreas Schmidt, Ana Ivanova, and Mike S Schafer. Media attention for climate change around the world: A comparative analysis of newspaper coverage in 27 countries. Global Environmental Change, 23(5):1233- 1248, 2013. + +<|ref|>text<|/ref|><|det|>[[216, 603, 780, 634]]<|/det|> +[27] Skipper Seabold and Josef Perktold. statsmodels: Econometric and statistical modeling with python. In 9th Python in Science Conference, 2010. + +<|ref|>text<|/ref|><|det|>[[216, 642, 780, 688]]<|/det|> +[28] Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter, 19(1):22- 36, 2017. + +<|ref|>text<|/ref|><|det|>[[216, 696, 780, 742]]<|/det|> +[29] Sharon R Sznitman and Nehama Lewis. Is cannabis an illicit drug or a medicine? a quantitative framing analysis of israeli newspaper coverage. International Journal of Drug Policy, 26(5):446- 452, 2015. + +<|ref|>text<|/ref|><|det|>[[216, 751, 780, 796]]<|/det|> +[30] David Tewksbury. What do americans really want to know? tracking the behavior of news readers on the internet. Journal of communication, 53(4):694- 710, 2003. + +<|ref|>text<|/ref|><|det|>[[216, 805, 780, 850]]<|/det|> +[31] Kathie M d'I Treen, Hywel TP Williams, and Saffron J O'Neill. Online misinformation about climate change. Wiley Interdisciplinary Reviews: Climate Change, 11(5):e665, 2020. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[216, 157, 780, 202]]<|/det|> +[32] Marc Trussler and Stuart Soroka. Consumer demand for cynical and negative news frames. The International Journal of Press/Politics, 19(3):360- 379, 2014. + +<|ref|>text<|/ref|><|det|>[[216, 211, 780, 257]]<|/det|> +[33] Michela Del Vicario, Walter Quattrociocchi, Antonio Scala, and Fabiana Zollo. Polarization and fake news: Early warning of potential misinformation targets. ACM Transactions on the Web (TWEB), 13(2):1- 22, 2019. + +<|ref|>text<|/ref|><|det|>[[216, 267, 780, 328]]<|/det|> +[34] Marco Visentin, Gabriele Pizzi, and Marco Pichierri. Fake news, real problems for brands: The impact of content truthfulness and source credibility on consumers' behavioral intentions toward the advertised brands. Journal of Interactive Marketing, 45:99- 112, 2019. + +<|ref|>text<|/ref|><|det|>[[216, 338, 780, 383]]<|/det|> +[35] Brian Weeks and Brian Southwell. The symbiosis of news coverage and aggregate online search behavior: Obama, rumors, and presidential politics. Mass communication and society, 13(4):341- 360, 2010. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 43, 311, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 130, 237, 149]]<|/det|> +GravinoetalSl.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__075d82fd5d37a02cde8eab26a4d85528ea15df290443dfb4d60ca9ac191d6a24/images_list.json b/preprint/preprint__075d82fd5d37a02cde8eab26a4d85528ea15df290443dfb4d60ca9ac191d6a24/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..a6e2e6e462983f62e82584e2fc42bbbc476bb98d --- /dev/null +++ b/preprint/preprint__075d82fd5d37a02cde8eab26a4d85528ea15df290443dfb4d60ca9ac191d6a24/images_list.json @@ -0,0 +1,108 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig 1. Adjusted IRR and predicted case counts from multilevel negative binomial model assessing the impact of withdrawing IRS after 5 years of sustained use. The blue shaded region represents the \\(95\\%\\) confidence interval around the predicted case counts from the adjusted regression model. Vertical bars represent the \\(95\\%\\) confidence interval around adjusted IRR.", + "footnote": [], + "bbox": [ + [ + 112, + 545, + 875, + 765 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig 2. Adjusted IRR and predicted case counts from multilevel negative binomial model assessing the impact of restarting IRS with a single round. The blue shaded region represents the 95% CI around the predicted case counts from the adjusted regression model. Vertical bars represent the 95% CI around adjusted IRR.", + "footnote": [], + "bbox": [ + [ + 115, + 560, + 880, + 787 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig 3. Adjusted IRR and predicted case counts from multilevel negative binomial model assessing the impact of initiating and sustaining IRS. The blue shaded region represents the \\(95\\%\\) CI around the predicted case counts from the adjusted regression model. Vertical bars represent the \\(95\\%\\) CI around adjusted IRR.", + "footnote": [], + "bbox": [ + [ + 112, + 612, + 880, + 840 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig 4. Map of Uganda showing study sites and IRS districts.", + "footnote": [], + "bbox": [ + [ + 113, + 110, + 880, + 530 + ] + ], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [], + "page_idx": 18 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 44, + 516, + 951, + 783 + ] + ], + "page_idx": 30 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [ + [ + 45, + 45, + 951, + 312 + ] + ], + "page_idx": 30 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4", + "footnote": [], + "bbox": [], + "page_idx": 31 + } +] \ No newline at end of file diff --git a/preprint/preprint__075d82fd5d37a02cde8eab26a4d85528ea15df290443dfb4d60ca9ac191d6a24/preprint__075d82fd5d37a02cde8eab26a4d85528ea15df290443dfb4d60ca9ac191d6a24.mmd b/preprint/preprint__075d82fd5d37a02cde8eab26a4d85528ea15df290443dfb4d60ca9ac191d6a24/preprint__075d82fd5d37a02cde8eab26a4d85528ea15df290443dfb4d60ca9ac191d6a24.mmd new file mode 100644 index 0000000000000000000000000000000000000000..1d73bf42567bef99a269311558d6714cd5e076b4 --- /dev/null +++ b/preprint/preprint__075d82fd5d37a02cde8eab26a4d85528ea15df290443dfb4d60ca9ac191d6a24/preprint__075d82fd5d37a02cde8eab26a4d85528ea15df290443dfb4d60ca9ac191d6a24.mmd @@ -0,0 +1,409 @@ + +# The impact of stopping and starting indoor residual spraying on malaria burden in 14 districts of Uganda + +Jane Namuganga Infectious Diseases Research Collaboration Adrienne Epstein ( \(\boxed{}\) adrienne.epstein@ucsf.edu) University of California, San Francisco https://orcid.org/0000- 0002- 8253- 6102 + +Joaniter Nankabirwa Infectious Diseases Research Collaboration + +Arthur Mpimbaza Infectious Diseases Research Collaboration + +Moses Kiggundu Infectious Diseases Research Collaboration + +Asadu Sserwanga Infectious Diseases Research Collaboration + +James Kapisi Infectious Diseases Research Collaboration + +Emmanuel Arinaitwe Infectious Diseases Research Collaboration + +Samuel Gonahasa Infectious Diseases Research Collaboration + +Jimmy Opigo National Malaria Control Division + +Chris Ebong Infectious Diseases Research Collaboration + +Sarah Staedke London School of Hygiene & Tropical Medicine + +Josephat Shillul US President's Malaria Initiative - VectorLink Uganda Project + +Michael Okia US President's Malaria Initiative - VectorLink Uganda Project + +Damian Rutazaana National Malaria Control Division + +Catherine Maiteki- Ssebuguzi + +<--- Page Split ---> + +National Malaria Control Division + +Kassahun Belay US President's Malaria Initiative, USAID + +Moses Kamya Makerere University + +Grant Dorsey University of California, San Francisco + +Isabel Rodriguez- Barraquer University of California, San Francisco + +## Article + +Keywords: malaria, disease control, insecticide, vector control intervention + +Posted Date: December 29th, 2020 + +DOI: https://doi.org/10.21203/rs.3.rs- 126095/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on May 11th, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 22896- 5. + +<--- Page Split ---> + +# The impact of stopping and starting indoor residual spraying on malaria burden in 14 districts of Uganda + +Jane F. Namuganga1\\*, Adrienne Epstein2\\*, Joaniter I. Nankabirwa1,3, Arthur Mpimbaza1,4, Moses Kiggundu1, Asadu Serwanga1, James Kapisi1, Emmanuel Arinaitwe1, Samuel Gonahasa1, Jimmy Opigo5, Chris Ebong1, Sarah G. Staedke6, Josephat Shililu7, Michael Okia7, Damian Rutazaanas, Catherine Maiteki- Ssebuguzi5, Kassahun Belay8, Moses R. Kamya1,3, Grant Dorsey9, Isabel Rodriquez- Barraquer9 + +1 Infectious Diseases Research Collaboration, Kampala, Uganda + +2 Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America + +3 Department of Medicine, Makerere University, College of Health Sciences, Kampala, Uganda + +4 Child Health and Development Centre, Makerere University, College of Health Sciences, Kampala, Uganda + +5 National Malaria Control Division, Ministry of Health, Kampala, Uganda + +6 London School of Hygiene and Tropical Medicine, London, United Kingdom + +7 US President's Malaria Initiative - VectorLink Uganda Project, Kampala, Uganda + +8 US President's Malaria Initiative, USAID/Uganda Senior Malaria Advisor + +9 Department of Medicine, University of California San Francisco, San Francisco, California, United States of America + +\*Adrienne.Epstein@ucsf.edu + +\* These authors contributed equally to this work. + +<--- Page Split ---> + +## 40 Abstract + +The scale- up of malaria control efforts has led to marked reductions in malaria burden over the past twenty years, but progress has slowed. Implementation of indoor residual spraying (IRS) of insecticide, a proven vector control intervention, has been limited and difficult to sustain partly because questions remain on its added impact over widely accepted interventions such as bed nets. Using data from 14 enhanced surveillance health facilities in Uganda, a country with high bet net coverage yet high malaria burden, we estimate the impact of starting and stopping IRS. We show that stopping IRS resulted in a 5- fold increase in malaria incidence within 10 months, but reinstating IRS led to an over 5- fold decrease within 8 months. In areas where IRS was initiated and sustained, malaria incidence dropped by \(85\%\) after year 4. IRS could play a critical role in achieving global malaria targets, particularly in areas where progress has stalled. + +<--- Page Split ---> + +## Introduction + +Over the past twenty years the scale- up of malaria control efforts has led to marked reductions in morbidity and mortality \(^{1,2}\) . However, global progress has slowed in recent years, particularly in sub- Saharan Africa, which accounted for \(93\%\) of the world's 228 million cases in \(2018^{2}\) . Longlasting insecticidal nets (LLINs) and indoor residual spraying of insecticide (IRS) are the primary vector control interventions used for the prevention of malaria. The World Health Organization recommends universal coverage of LLINs for at- risk populations in sub- Saharan Africa, where the proportion of households owning at least one LLIN is estimated to have increased from \(47\%\) in 2010 to \(72\%\) in 2018. Until recently, pyrethroids were the only class of insecticides approved for use in LLINs and, given the emergence of widespread pyrethroid resistance \(^{3,4}\) , there is concern that the effectiveness of LLINs may be diminishing. Unlike LLINs, IRS has the advantage of utilizing multiple different classes of insecticides and combing IRS with LLINs may improve malaria control and slow the spread of pyrethroid resistance. However, few controlled trials have evaluated the effect of adding IRS to communities using LLINs and the evidence is mixed, with a few studies showing benefits when IRS included 'non- pyrethroid- like' insecticides \(^{5}\) . Other barriers to IRS delivery – including cost, logistics, and community acceptance – have limited its use \(^{6}\) , such that less than \(5\%\) of the population at risk in sub- Saharan Africa was protected by IRS in 2018, a decrease from over \(10\%\) coverage in \(2010^{2}\) . + +Uganda is illustrative of a country where the burden of malaria remains high and progress has slowed in recent years \(^{2}\) . Malaria control efforts in Uganda have primarily focused on LLINs. In 2013- 14 it became the first country to implement a universal LLIN distribution campaign, which was repeated in 2017- 18. In 2018- 19, Uganda had the highest coverage of LLINs in the world, + +<--- Page Split ---> + +with \(83\%\) of households reported owning at least one LLIN7. In contrast to LLINs, the implementation of IRS in Uganda has been focal and limited. In 2006, IRS was reintroduced into Uganda for the first time since the 1960s. In 2007- 09, the IRS program was shifted to 10 high burden districts in the north, leading to large reductions in malaria burden8,9. In 2014, the IRS program was relocated from these 10 northern districts to 14 districts in the eastern part of the country, where it has been sustained. The discontinuation of IRS in the 10 northern districts was followed by a marked resurgence in malaria cases10,11, prompting the implementation of a single round of IRS in these 10 districts in 2017. + +In this study, we used data from a network of health facility- based malaria surveillance sites to evaluate the impact of different IRS delivery scenarios in 14 districts in Uganda. This study had three objectives: (1) to estimate the impact of withdrawing IRS after five years of sustained use on the burden of malaria in three sites in Northern Uganda; (2) to estimate the impact of restarting IRS with a single round three to four years after IRS was discontinued on the burden of malaria in nine sites in Northern Uganda; and (3) to estimate the impact of five years of sustained IRS on the burden of malaria in five sites in Northern and Eastern Uganda. + +## Results + +## Impact of withdrawing IRS after sustained use + +Across the three sites included in the analysis, a total of 224,859 outpatient visits were observed (Table 1). During the baseline period, average monthly cases ranged from 104- 272 and TPR ranged from \(23.7\% - 25.9\%\) . This increased to 491- 751 and \(52.3\% - 78.0\%\) respectively, during the evaluation period (Supplementary Fig S1). + +<--- Page Split ---> + +Monthly adjusted IRRs and \(95\%\) confidence intervals (CI) for the three sites combined are presented in Fig 1 and Supplementary Table S1. These results showed an initial reduction in malaria cases after the final round of IRS relative to the baseline period until (adjusted IRR in the first month after \(\mathrm{IRS} = 0.19\) , \(95\%\) CI 0.09- 0.42) about four to five months after the final IRS campaign when malaria cases began to increase. Over the 10- 31 months after IRS was stopped, the number of malaria cases increased by over 5- fold relative to the baseline period (adjusted \(\mathrm{IRR} = 5.24\) , \(95\%\) CI 3.67- 7.50). This corresponds to predicted case counts of near zero immediately following final IRS campaign followed by an increase to about 1000 cases per month at each site (Fig 1). These results were consistent when considering only laboratory- confirmed cases unadjusted for testing rates (Supplementary Fig S2). + +![](images/Figure_1.jpg) + +
Fig 1. Adjusted IRR and predicted case counts from multilevel negative binomial model assessing the impact of withdrawing IRS after 5 years of sustained use. The blue shaded region represents the \(95\%\) confidence interval around the predicted case counts from the adjusted regression model. Vertical bars represent the \(95\%\) confidence interval around adjusted IRR.
+ +## Impact of restarting IRS with a single round + +A total of 858,380 outpatient visits were recorded across the analysis period for the nine sites. (Table 2). Mean monthly malaria cases ranged from 643- 1,569 and the TPR ranged from \(56.5\%\) - + +<--- Page Split ---> + +84.7% during the baseline period. These ranges were 501- 762 and 48.5%- 72.0% respectively during the evaluation period. Temporal trends of laboratory- confirmed malaria cases over time for the individual health facilities are presented in Supplementary Fig S3. + +Monthly adjusted IRRs and 95% CI for the nine sites combined are presented in Fig 2 and Supplementary Table S2. The single round of IRS led to a reduction in malaria cases until approximately 23 months post- IRS. Over the 8- 12 months after the single round of IRS, malaria cases decreased by over 5- fold relative to the baseline period (adjusted \(\mathrm{IRR} = 0.17\) , 95% CI 0.15- 0.20). After 23 months following the single round of IRS, malaria cases returned to a level similar to the baseline period before the single round of IRS (adjusted IRR for months \(23 - 31 = 1.06\) , 95% CI 0.92- 1.21). These results were consistent when considering only laboratory- confirmed cases unadjusted for testing rates (Supplementary Fig S4). + +![](images/Figure_2.jpg) + +
Fig 2. Adjusted IRR and predicted case counts from multilevel negative binomial model assessing the impact of restarting IRS with a single round. The blue shaded region represents the 95% CI around the predicted case counts from the adjusted regression model. Vertical bars represent the 95% CI around adjusted IRR.
+ +## Impact of initiating and sustaining IRS + +In total, 574,587 outpatient visits were observed across the five sites included in the analysis. + +(Table 3). During the baseline period, average monthly malaria cases adjusted for testing rates + +<--- Page Split ---> + +ranged from 286- 657 and the TPR ranged from \(25.4\% - 67.0\%\) . This range decreased to 85- 289 for malaria cases and \(13.8\% - 45.3\%\) for the TPR during the evaluation period. Temporal trends of laboratory- confirmed malaria cases over time for the individual health facilities are presented in Supplementary Fig S5. + +Monthly adjusted IRRs and \(95\%\) CI for the five sites combined are presented in Fig 3 and Supplementary Table S3. There was a modest overall reduction in malaria case counts in the first three years after IRS was initiated relative to the baseline period, with some peaks in case counts returning to near baseline levels just prior to when rounds of IRS were administered. However, after the third year of sustained use, malaria case counts dropped substantially and remained low relative to the period before IRS was initiated. In the \(4^{\text{th}}\) and \(5^{\text{th}}\) year after IRS was initiated and sustained, malaria cases dropped by \(85\%\) (adjusted \(\mathrm{IRR} = 0.15\) , \(95\%\) CI 0.12- 0.18). These results were consistent when considering only laboratory- confirmed cases unadjusted for testing rates (Supplementary Fig S6). + +![](images/Figure_3.jpg) + +
Fig 3. Adjusted IRR and predicted case counts from multilevel negative binomial model assessing the impact of initiating and sustaining IRS. The blue shaded region represents the \(95\%\) CI around the predicted case counts from the adjusted regression model. Vertical bars represent the \(95\%\) CI around adjusted IRR.
+ +<--- Page Split ---> + +## Discussion + +Uganda has been exceptionally successful in scaling- up coverage of LLINs. Following the mass distribution campaigns to deliver free LLINs in 2013- 14 and 2017- 18, \(90\%\) and \(83\%\) of households respectively reported ownership of at least one LLIN \(^{7,12}\) . However, despite this success, the burden of malaria remains high in much of the country. Uganda had the \(3^{\text{rd}}\) highest number of malaria cases reported in 2018, with reported case incidence increasing since \(2014^{2}\) . If Uganda is to achieve the goals established by the World Health Organization’s Global Technical Strategy for malaria including reducing malaria case incidence by at least \(90\%\) by 2030 as compared with \(2015^{13}\) , additional malaria control measures will be needed. This report highlights the critical role of IRS in substantially reducing the burden of malaria in areas where transmission remains high despite deployment of LLINs. Withdrawing IRS after five years of sustained use in three districts in northern Uganda resulted in a more than 5- fold increase in malaria cases within 10 months. Re- starting IRS with a single round in nine districts in Northern Uganda approximately three years after IRS had been stopped led to a transient but important (more than a 5- fold) decrease in malaria cases within 8- 12 months, returning to pre- IRS levels after 23 months. Initiating and sustaining IRS in five districts in Eastern Uganda led to a gradual reduction in malaria cases reaching almost a 7- fold reduction after 4- 5 years. + +Robust evidence supports the widespread use of LLINs for malaria control. In a systematic review of clinical trials conducted between 1987 and 2001, insecticide treated nets reduced all cause child mortality by \(17\%\) and the incidence of uncomplicated \(P\) . falciparum malaria by almost half \(^{14}\) . However, there is concern that the effectiveness of LLINs may be diminishing due to widespread resistance to pyrethroids which until recently were the only class of insecticides + +<--- Page Split ---> + +approved for LLINs. Similar to many other African countries, high- level resistance to pyrethroids among the principle Anopheles vectors has been reported recently throughout Uganda \(^{15 - 17}\) . In addition, behavioral changes in vector biting activity following the introduction of LLINs have been reported which could present new challenges for malaria control \(^{18 - 20}\) . Finally, the effectiveness of LLINs may be further compromised by poor adherence and waning coverage in the setting of free distribution campaigns done intermittently. In Uganda, less than \(18\%\) of households reported adequate coverage (defined as at least one LLIN per 2 residents) three years after the 2013- 14 distribution campaign \(^{21}\) and adequate coverage decreased from \(71\%\) to \(51\%\) between 6 and 18 months following the 2017- 18 distribution campaign \(^{22}\) . Although the World Health Organization recommends mass distribution campaigns every three years, mounting evidence suggests that LLINs should be distributed more frequently to sustain high coverage \(^{23 - 29}\) . + +Given concerns about the current effectiveness of pyrethroid- based LLINs and the persistently high burden of malaria despite aggressive scale up of LLINs in countries like Uganda, additional malaria control measures are needed. IRS is an attractive option. Historically, IRS programs were used to dramatically reduce and even eliminate malaria in many parts of the world. Thus, it is surprising that the evidence base from contemporary controlled trials on the impact of adding IRS to LLINs for vector control is limited. A recent systematic review of cluster randomized controlled trials conducted in sub- Saharan Africa since 2008, reported that adding IRS using a “pyrethroid- like” insecticide to LLINs did not provide any benefits, while adding IRS with a “non- pyrethroid- like” insecticide produced mixed results \(^{5}\) . Among the four trials comparing IRS plus LLINs with LLINs alone, three evaluated IRS with a carbamate (bendiocarb) and one + +<--- Page Split ---> + +evaluated a long- lasting organophosphate, pirimiphos- methyl (Actellic 300CS®) \(^{30 - 33}\) . Only two trials (both using bendiocarb) assessed malaria incidence; one from Sudan found a \(35\%\) reduction when adding IRS to LLINs \(^{31}\) , while another from Benin found no benefit of adding IRS \(^{30}\) . All four trials assessed parasite prevalence, with an overall non- significant trend towards a lower prevalence when adding IRS to LLINs (RR=0.67, 95% CI 0.35- 1.28) \(^{5}\) . However, when the analyses were restricted to include only the two studies with LLIN usage over \(50\%\) , adding IRS reduced parasite prevalence by over \(50\%\) (RR=0.47, 95% CI 0.33- 0.67) \(^{5}\) . Of note, none of the trials that evaluated the impact of adding IRS with a “non- pyrethroid- like” insecticide assessed outcomes beyond two years. More recently, a number of observational studies have reported benefits of using IRS with pirimiphos- methyl (Actellic 300CS®). In the Mopti Region of Mali, delivery of a single round of IRS with Actellic 300CS® was associated with a \(42\%\) decrease in the peak incidence of laboratory confirmed malaria cases reported at public health facilities \(^{34}\) . In the Koulikoro Region of Mali, villages that received a single round of IRS with Actellic 300CS® combined with LLINs observed a greater than \(50\%\) decrease in the incidence of malaria compared to villages that only received LLINs \(^{35}\) . In the Northern Region of Ghana, districts that received IRS with Actellic 300CS® reported 26- 58% fewer cases of laboratory confirmed malaria cases reported at public health facilities over a two- year period, compared to districts that did not receive IRS \(^{36}\) . In Northern Zambia, implementation of IRS with Actellic 300CS® targeting only high burden areas over a three year period was associated with a \(25\%\) decline in parasite prevalence during the rainy season, but no decline during the dry season \(^{37}\) . In Western Kenya, the introduction of a single round of IRS with Actellic 300CS® was associated with a 44- 65% decrease in district level malaria case counts over a 10 month period compared to pre- IRS levels \(^{38}\) . In addition, several recent reports have documented dramatic resurgences + +<--- Page Split ---> + +following the withdrawal of IRS with bendiocarb in Benin39, and the withdrawal of IRS with Actellic 300CS® in Mali and Ghana34,36. + +The results from this study provides additional support for the critical role IRS can play in reducing the burden of malaria in African countries with high LLINs coverage. We analyzed a large, rigorously collected dataset, which is a strength of study. Data were collected over nearly seven years through an enhanced health facility- based surveillance system covering 14 districts in Uganda where IRS was being withdrawn, re- started, and initiated. This enhanced surveillance system facilitated laboratory testing and provided prospectively collected, individual- level data, allowing for analyses of quantitative changes in laboratory- confirmed cases of malaria over time, controlling for temporal changes in rainfall, seasonal effects, diagnostic practices, and health seeking behavior. Previous work by our group documented a marked decrease in malaria test positivity rates after four years of sustained IRS with bendiocarb in one district of Northern Uganda followed by a rapid resurgence over an 18- month period after IRS was withdrawn11. In this study we expand on these findings by including data from three districts and covering a 31- month period following the withdrawal of IRS. We were able to quantify more than a 5- fold increase in malaria cases which was sustained over the 10- 31 months following the withdrawal of IRS. This marked resurgence occurred despite the fact the first universal LLIN distribution campaign was timed to occur right after IRS was withdrawn. Given the dramatic nature of the resurgence, the Ugandan government was able to procure funding for a single round of IRS with Actellic 300CS® approximately three years after IRS was withdrawn in 10 districts of Northern Uganda. In this study, we assessed the impact of this single round in nine of these districts. This single round was associated with over a 5- fold decrease in malaria cases after 8- 12 months, with + +<--- Page Split ---> + +malaria cases returning to pre- IRS levels after almost 2 years. These data suggest that IRS with longer- acting formulations such as Actellic 300CS® administered every 2 years may be a cost- effective strategy for mitigating the risk of resurgence following sustained IRS and/or enabling countries to expand coverage when resources are limited. This study also evaluated the impact of five years of sustained IRS in 5 districts of Eastern Uganda, starting first with bendiocarb and then switching to Actellic 300CS® after 18 months. Rounds of IRS were initially associated with marked decreases in malaria cases followed by peaks before subsequent rounds until the \(4^{\text{th}}\) and \(5^{\text{th}}\) years after IRS was initiated when there was a sustained decrease of almost 7- fold compared to pre- IRS level. Given the before- and- after nature of our study design, it is not clear whether the maximum sustained benefits of IRS seen after 4- 5 years were due to the cumulative effect of multiple rounds of IRS, the switch from bendiocarb to Actellic 300CS®, the second universal LLIN distribution campaign which occurred in this area in 2017, and/or other factors. + +This study had several limitations. First, we used an observational study design, with measures of impact based on comparisons made before- and- after key changes in IRS policy. Although cluster randomized controlled trials are the gold standard study design for estimating the impact of IRS, it could be argued that withholding IRS would be unethical, given what is known about its impact in Uganda. Second, our estimates of impact could have been confounded by secular trends in factors not accounted for in our analyses. However, we feel that our overall conclusions are robust given the large amount of data available from multiple sites over an extended period with multiple complementary objectives providing consistent findings. Third, we could not assess the impact of IRS independent of LLIN use and did not have access to measures of IRS or LLIN coverage from our study populations. However, we were able to provide a “real world” + +<--- Page Split ---> + +assessment of IRS in a setting where LLIN use is strongly supported by repeated universal distribution campaigns that are becoming increasingly common in sub- Saharan Africa. Finally, our study outcome was limited to case counts of laboratory confirmed malaria captured at health facilities. Thus, we were unable to measure the impact of IRS on other important indicators such as measures of transmission intensity, parasite prevalence, or mortality. + +There is a growing body of evidence that combining LLINs with IRS using "non- pyrethroid- like" insecticides, especially the long acting organophosphate Actellic 300CS®, is highly effective at reducing the burden of malaria in Uganda, and elsewhere in Africa. Despite these encouraging findings, IRS coverage in Africa has been moving in the wrong direction. The proportion of those at risk protected by IRS in Africa peaked at just over \(10\%\) in 2010. However, the spread of pyrethroid resistance has led many control programs to switch to more expensive formulations resulting in a \(53\%\) decrease in the number of houses sprayed between years of peak coverage and 2015 across 18 countries supported by the U.S. President's Malaria Initiative40 and an overall reduction in the proportion protected by IRS in Africa to less than \(5\%\) in 20182. Given the lack of recent progress in reducing the global burden of malaria coupled with challenges in funding, renewed commitments are needed to address the "high burden to high impact" approach now being advocated by the World Health Organization2. IRS is a widely available tool that could be scaled up, however demands currently exceed the availability of resources. Additional work is needed to optimize the use of IRS, prevent further spread of insecticide resistance, and better evaluate the cost effectiveness of IRS in the context of other control interventions. + +## Methods + +<--- Page Split ---> + +## Study sites and vector control interventions + +This study utilized data from 14 health facilities located in 14 districts in Northern and Eastern Uganda (Fig 4) which were part of a larger comprehensive malaria surveillance network called the Uganda Malaria Surveillance Program (UMSP). Between 2007 and 2009, IRS was implemented in 10 high burden districts in northern Uganda. DDT or pyrethroids were initially used but in 2010 the insecticide was changed to a carbamate (bendiocarb) due to concern regarding the spread of pyrethroid resistance. Rounds of bendiocarb were repeated approximately every 6 months until 2014 when the IRS program was discontinued, so that resources could be shifted to other high burden districts. In 2017, these 10 districts in northern Uganda received a single round of the organophosphate pirimiphos- methyl (Actellic 300CS®) following reports of malaria resurgence after IRS has been discontinued in 2014. Between 2014 and 2015, IRS with bendiocarb was implemented in 14 districts in the Northern and Eastern part of the country. Rounds of bendiocarb were repeated approximately every six months until 2016 when the formulation was changed to Actellic 300CS®, which continues to be administered once a year. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig 4. Map of Uganda showing study sites and IRS districts.
+ +Universal LLIN distribution campaigns were conducted in 2013- 14 and 2017- 18, where LLINs were distributed free- of- charge by the Uganda Ministry of Health targeting 1 LLIN for every two household residents. + +## Health-facility based surveillance + +Enhanced malaria surveillance was established by UMSP in 2006, as previously described 41. UMSP operates Malaria Reference Centers (MRCs) at 70 level III/IV public health facilities across Uganda. At each MRC, individual- level data from standardized registers for all patients presenting to the outpatient departments are entered into an Access database by on- site data entry officers. Variables include patient demographics, results of laboratory testing for malaria (rapid + +<--- Page Split ---> + +diagnostic test [RDT] or microscopy), diagnoses given, and treatments prescribed. Emphasis is placed on ensuring that patients with suspected malaria undergo testing, by either RDT or microscopy. + +This study utilized data from 14 MRCs located in districts that either previously had IRS or have ongoing IRS campaigns. We estimated the impact of withdrawing IRS using data from three sites in Northern Uganda that had at least six months of data preceding the final round of IRS administered in 2014. To estimate the impact of restarting IRS with a single round administered in 2017, we used data from nine sites in Northern Uganda. To estimate the impact of sustained IRS over five years, we used data from five sites in Eastern Uganda where IRS had been implemented since 2014- 15. + +## Measures + +Exposure. The exposure was specified as an indicator variable for each month since IRS was withdrawn or initiated relative to a baseline period (Fig 5 and Supplementary Fig S7). We also fit separate models with categorical exposure variables divided into distinct periods of months. To determine the impact of withdrawing IRS after at least five years of sustained use, the baseline period was defined as the final year of sustained IRS use, and the evaluation period lasted through 2016, prior to when an additional round of IRS was implemented. In order to determine the impact of restarting IRS with a single round of IRS, the baseline period was defined as one year prior to the single round of IRS and the evaluation period went through December 2019. To determine the impact of initiating and sustaining IRS, the baseline period was the year prior to IRS initiation, and the evaluation period lasted through December 2019. + +<--- Page Split ---> + +Fig 5. Timeline summarizing the dates of IRS campaigns, baseline and evaluation periods. Objective 1 is to assess the impact of withdrawing IRS after five years of sustained use; Objective 2 is to assess the impact of restarting IRS with a single round; and Objective 3 is to assess the impact of initiating and sustaining IRS. + +![](images/Figure_1.jpg) + + +Outcome. The primary outcome was the monthly count of laboratory- confirmed malaria cases at each MRC. The case count was corrected for testing rates by multiplying the number of individuals with suspected malaria but not tested each month by the test positivity rate (the number who tested positive divided by the total number tested) for that month and adding the result to the number of laboratory- confirmed positive cases. As a sensitivity analysis, we re- specified the models including only laboratory- confirmed case counts as the outcome. + +Covariates. We adjusted for time- varying variables that impact malaria burden and malaria case detection at the health facility. These variables included average rainfall at the health facility lagged by 1 month, indicator variables for month of the year (to adjust for seasonal effects), the proportion of tests that were RDTs in that month (vs. microscopy), and the number of individuals who attended the health facility but were not suspected of having malaria in that month (to adjust for care- seeking behaviors). + +<--- Page Split ---> + +## Statistical analysis + +For each objective, we specified mixed effects negative binomial regression models with random intercepts for health facility. Coefficients for the exposure variable were exponentiated to represent the incidence rate ratio (IRR) comparing the incidence of malaria in the month of interest relative to the baseline period. This method assumes that the underlying population has remained constant over the study period. + +<--- Page Split ---> + +386 References387 1 Bhatt, S. et al. The effect of malaria control on Plasmodium falciparum in Africa between2000 and 2015. Nature 526, 207- 211, doi:10.1038/nature15535 (2015).390 2 Organization, W. H. World Malaria Report 2019. (2019).391 3 Hemingway, J. et al. Averting a malaria disaster: will insecticide resistance derail malariacontrol? Lancet 387, 1785- 1788, doi:10.1016/S0140- 6736(15)00417- 1 (2016).392 4 Ranson, H. & Lissenden, N. Insecticide Resistance in African Anopheles Mosquitoes: AWorsening Situation that Needs Urgent Action to Maintain Malaria Control. Trends Parasitol32, 187- 196, doi:10.1016/j.pt.2015.11.010 (2016).393 5 Choi, L., Pryce, J. & Garner, P. Indoor residual spraying for preventing malaria incommunities using insecticide- treated nets. Cochrane database of systematic reviews (Online) 5,CD012688, doi:10.1002/14651858.CD012688.pub2 (2019).396 6 Sherrard- Smith, E. et al. Systematic review of indoor residual spray efficacy andeffectiveness against Plasmodium falciparum in Africa. Nat Commun 9, 4982,doi:10.1038/s41467- 018- 07357- w (2018).397 7 Uganda National Malaria Control Division (NMCD), Uganda Bureau of Statistics(UBOS) & ICF. Uganda Malaria Indicator Survey 2018- 19. (NMCD, UBOS, and ICF, Kampala,Uganda, and Rockville, Maryland, USA, 2020).398 8 Kigozi, R. et al. Indoor residual spraying of insecticide and malaria morbidity in a hightransmission intensity area of Uganda. PLoS One 7, e42857, doi:10.1371/journal.pone.0042857(2012). + +<--- Page Split ---> + +9 Steinhardt, L. C. et al. The effect of indoor residual spraying on malaria and anemia in a high- transmission area of northern Uganda. Am J Trop Med Hyg 88, 855- 861, doi:10.4269/ajtmh.12- 0747 (2013). + +10 Okullo, A. E. et al. Malaria incidence among children less than 5 years during and after cessation of indoor residual spraying in Northern Uganda. Malar J 16, 319, doi:10.1186/s12936- 017- 1966- x (2017). + +11 Raouf, S. et al. Resurgence of Malaria Following Discontinuation of Indoor Residual Spraying of Insecticide in an Area of Uganda With Previously High- Transmission Intensity. Clin Infect Dis 65, 453- 460, doi:10.1093/cid/cix251 (2017). + +12 Uganda Bureau of Statistics (UBOS) and ICF International. Uganda Malaria Indicator Survey 2014- 15. (UBOS and ICF International, Kampala, Uganda and Rockville, Maryland, USA, 2015). + +13 Organization, W. H. Framework for Implementing the Global Technical Strategy for Malaria 2016- 2030 in the African Region. (2016). + +14 Pryce, J., Richardson, M. & Lengeler, C. Insecticide- treated nets for preventing malaria. + +Cochrane database of systematic reviews (Online) 11, CD000363, + +doi:10.1002/14651858. CD000363. pub3 (2018). + +15 Echodu, R. et al. High insecticide resistances levels in Anopheles gambiae s.l. in + +northern Uganda and its relevance for future malaria control. BMC Res Notes 13, 348, + +doi:10.1186/s13104- 020- 05193- 0 (2020). + +16 Lynd, A. et al. LLIN Evaluation in Uganda Project (LLINEUP): a cross- sectional survey of species diversity and insecticide resistance in 48 districts of Uganda. Parasit Vectors 12, 94, doi:10.1186/s13071- 019- 3353- 7 (2019). + +<--- Page Split ---> + +17 Okia, M. et al. Insecticide resistance status of the malaria mosquitoes: Anopheles + +gambiae and Anopheles funestus in eastern and northern Uganda. Malar J 17, 157, + +doi:10.1186/s12936-018-2293-6 (2018). + +18 AFRO, W. H. O.-. Global AMDP database, + + ( + +19 Cooke, M. K. et al. 'A bite before bed': exposure to malaria vectors outside the times of net use in the highlands of western Kenya. Malar J 14, 259, doi:10.1186/s12936-015-0766-4 (2015). + +20 Sougoufara, S. et al. Biting by Anopheles funestus in broad daylight after use of long-lasting insecticidal nets: a new challenge to malaria elimination. Malar J 13, 125, + +doi:10.1186/1475-2875-13-125 (2014). + +21 Gonahasa, S. et al. LLIN Evaluation in Uganda Project (LLINEUP): factors associated with ownership and use of long-lasting insecticidal nets in Uganda: a cross-sectional survey of 48 districts. Malar J 17, 421, doi:10.1186/s12936-018-2571-3 (2018). + +22 Staedke, S. G. et al. Effect of long-lasting insecticidal nets with and without piperonyl butoxide on malaria indicators in Uganda (LLINEUP): a pragmatic, cluster-randomised trial + +embedded in a national LLIN distribution campaign. Lancet 395, 1292-1303, + +doi:10.1016/S0140-6736(20)30214-2 (2020). + +23 Rugnao, S. et al. LLIN Evaluation in Uganda Project (LLINEUP): factors associated with childhood parasitaemia and anaemia 3 years after a national long-lasting insecticidal net distribution campaign: a cross-sectional survey. Malar J 18, 207, doi:10.1186/s12936-019-2838-3 (2019). + +<--- Page Split ---> + +24 WHO. Achieving and maintaining universal coverage with long-lasting insecticidal nets for malaria control. (2017). + +25 Wills, A. B. et al. Physical durability of PermaNet 2.0 long-lasting insecticidal nets over three to 32 months of use in Ethiopia. Malar J 12, 242, doi:10.1186/1475-2875-12-242 (2013). + +26 Hakizimana, E. et al. Monitoring long-lasting insecticidal net (LLIN) durability to validate net serviceable life assumptions, in Rwanda. Malar J 13, 344, doi:10.1186/1475-2875-13-344 (2014). + +27 Massue, D. J. et al. Durability of Olyset campaign nets distributed between 2009 and 2011 in eight districts of Tanzania. Malar J 15, 176, doi:10.1186/s12936-016-1225-6 (2016). + +28 Tan, K. R. et al. A longitudinal study of the durability of long-lasting insecticidal nets in Zambia. Malar J 15, 106, doi:10.1186/s12936-016-1154-4 (2016). + +29 Randriamaherijaoana, S., Raharinjatovo, J. & Boyer, S. Durability monitoring of long-lasting insecticidal (mosquito) nets (LLINs) in Madagascar: physical integrity and insecticidal activity. Parasit Vectors 10, 564, doi:10.1186/s13071-017-2419-7 (2017). + +30 Corbel, V. et al. Combination of malaria vector control interventions in pyrethroid resistance area in Benin: a cluster randomised controlled trial. Lancet Infect Dis 12, 617-626, doi:10.1016/S1473-3099(12)70081-6 (2012). + +31 Kafy, H. T. et al. Impact of insecticide resistance in Anopheles arabiensis on malaria incidence and prevalence in Sudan and the costs of mitigation. Proc Natl Acad Sci U S A 114, E11267-E11275, doi:10.1073/pnas.1713814114 (2017). + +32 Protopopoff, N. et al. Effectiveness of a long-lasting piperonyl butoxide-treated insecticidal net and indoor residual spray interventions, separately and together, against malaria + +<--- Page Split ---> + +transmitted by pyrethroid-resistant mosquitoes: a cluster, randomised controlled, two-by-two factorial design trial. Lancet 391, 1577- 1588, doi:10.1016/S0140- 6736(18)30427- 6 (2018). + +West, P. A. et al. Indoor residual spraying in combination with insecticide- treated nets compared to insecticide- treated nets alone for protection against malaria: a cluster randomised trial in Tanzania. PLoS Med 11, e1001630, doi:10.1371/journal.pmed.1001630 (2014). + +Wagman, J. et al. Rapid reduction of malaria transmission following the introduction of indoor residual spraying in previously unsprayed districts: an observational analysis of Mopti Region, Mali, in 2017. Malar J 19, 340, doi:10.1186/s12936- 020- 03414- 2 (2020). + +Kane, F. et al. Performance of IRS on malaria prevalence and incidence using pirimiphos- methyl in the context of pyrethroid resistance in Koulikoro region, Mali. Malar J 19, 286, doi:10.1186/s12936- 020- 03357- 8 (2020). + +Gogue, C. et al. An observational analysis of the impact of indoor residual spraying in Northern, Upper East, and Upper West Regions of Ghana: 2014 through 2017. Malar J 19, 242, doi:10.1186/s12936- 020- 03318- 1 (2020). + +Hast, M. A. et al. The Impact of 3 Years of Targeted Indoor Residual Spraying With Pirimiphos- Methyl on Malaria Parasite Prevalence in a High- Transmission Area of Northern Zambia. Am J Epidemiol 188, 2120- 2130, doi:10.1093/aje/kwz107 (2019). + +Abong'o, B. et al. Impact of indoor residual spraying with pirimiphos- methyl (Actellic 300CS) on entomological indicators of transmission and malaria case burden in Migori County, western Kenya. Scientific reports 10, 4518, doi:10.1038/s41598- 020- 61350- 2 (2020). + +Aikpon, R. Y. et al. Upsurge of malaria transmission after indoor residual spraying withdrawal in Atacora region in Benin, West Africa. Malar J 19, 3, doi:10.1186/s12936- 019- 3086- 2 (2020). + +<--- Page Split ---> + +40 Oxborough, R. M. Trends in US President's Malaria Initiative- funded indoor residual spray coverage and insecticide choice in sub- Saharan Africa (2008- 2015): urgent need for affordable, long- lasting insecticides. Malar J 15, 146, doi:10.1186/s12936- 016- 1201- 1 (2016). 41 Sserwanga, A. et al. Improved malaria case management through the implementation of a health facility- based sentinel site surveillance system in Uganda. PLoS One 6, e16316, doi:10.1371/journal.pone.0016316 (2011). + +<--- Page Split ---> + +## Acknowledgements + +We would like to acknowledge the health workers at all 14 health facilities that contributed data for this study. We would like to thank the Ugandan Ministry of Health National Malaria Control Division, and USAID – President’s Malaria Initiative. This work was supported by the National Institutes of Health as part of the International Centers of Excellence in Malaria Research (ICMER) program (U19AI089674). AE is supported by the National Institute of Allergy and Infectious Diseases (F31AI150029). JIN is supported by the Fogarty International Center (Emerging Global Leader Award grant number K43TW010365). EA is supported by the Fogarty International Center of the National Institutes of Health under Award Number D43TW010526. + +## Author Contributions + +JFN, AE, GD, and IRB conceived of the study. JFN led the data collection activities with support from JIN, AM, MK, AS, JK, EA, SG, CE, SGS, CMS, and MRK. AE and IRB led the data analysis with support from GD. AE and JFN drafted the manuscript with support from GD, SGS, and IRB. All authors contributed to interpretation of the results and edited the manuscripts. All authors read and approved the final manuscript. + +## Competing Interests + +The authors declare no competing interests. + +## Materials and Correspondence + +Correspondence to Adrienne Epstein: Adrienne.Epstein@ucsf.edu + +<--- Page Split ---> + +Table 1. Summary statistics from health-facility based surveillance sites where IRS was stopped after sustained use. + +
MRC
(District)		Time periodNumber of
months
included		Total
outpatient
visits, n		Suspected
malaria
cases, n (%
of total)		Tested for
malaria, n
(% of
suspected)		RDT
performed
(versus
microscopy),
n(% of
tested)		Confirmed
malaria
cases, n (% of tested)		Confirmed
cases
adjusted for testing rate, n		Mean
monthly
confirmed
cases adjusted for testing rate, n
Aboke HCIV
(Kole)		Baseline914,0153,766 (26.9)3,735 (99.2)2,450 (65.6)923 (24.7)932104
Evaluation2546,85021,245 (45.3)18,185 (85.6)17,210 (94.6)14,200 (78.0)16,699668
Aduku HCIV
(Kwania)		Baseline1324,16413,742 (56.9)13,719 (99.8)1,049 (7.6)3,254 (23.7)3,529272
Evaluation3257,47030,035 (52.2)25,896 (86.2)10,731 (41.4)13,537 (52.3)15,717491
Anyeke
HCIV
(Oyam)		Baseline815,8593,514 (22.2)2,627 (74.8)2,604 (99.1)680 (25.9)918115
Evaluation2566,50128,755 (43.2)20,659 (71.8)16,147 (78.2)13,559 (65.6)18,774751
+ +<--- Page Split ---> + + +Table 2. Summary statistics from health-facility based surveillance sites that received a single round of IRS. + +
MRC (District)Time periodNumber of months includedTotal outpatient visits, nSuspected malaria cases, n (% of total)Tested for malaria, n (% of suspected)RDT performed (versus microscopy), n (% of tested)Confirmed malaria cases, n (% of tested)Confirmed cases adjusted for testing rate, nMean monthly confirmed cases adjusted for testing rate, n
Aboke HCIV (Kole)Baseline1421,18611,752 (55.5)9,613 (81.8)9,079 (94.5)7,297 (75.9)9,006643
Evaluation3454,82630,973 (56.5)30,674 (99.0)29,064 (94.8)22,097 (72.0)22,308656
Aduku HCIV (Kwania)Baseline1735,01720,645 (59.0)17,156 (83.1)7,938 (46.3)9,699 (56.5)11,465674
Evaluation3165,37932,260 (49.3)31,337 (97.1)20,385 (65.1)15,201 (48.5)15,534501
Anyeke HCIV (Oyam)Baseline1435,37818,445 (52.1)12,997 (70.5)9,151 (70.4)8,967 (69.0)12,595900
Evaluation3470,14933,618 (47.9)32,522 (96.7)31,208 (96.0)21,799 (67.0)22,375658
Awach HCIV (Gulu)Baseline1736,92321,920 (59.4)17,927 (82.0)17,736 (98.7)13,663 (76.0)16,749985
Evaluation3069,37536,760 (53.0)35,189 (95.7)34,070 (96.8)21,879 (62.2)22,851762
Lalogi HCIV (Omoro)Baseline1754,43632,642 (60.0)31,545 (96.6)31,490 (99.8)23,106 (73.2)23,9481,409
Evaluation3172,44941,846 (57.8)41,668 (99.6)40,804 (97.9)22,986 (55.2)23,060744
Patongo HCIII (Agago)Baseline1424,68615,453 (62.6)15,122 (97.9)14,758 (97.6)11,313 (74.8)11,487821
Evaluation3454,48634,482 (63.3)33,797 (98.0)32,176 (95.2)17,231 (51.0)17,440513
Atiak HCIV (Amuru)Baseline1438,91625,929 (66.6)22,418 (86.5)22,335 (99.6)18,978 (84.7)21,9661,569
Evaluation3460,75031,650 (52.1)30,754 (97.2)30,541 (99.3)19,766 (64.3)20,325598
Padibe HCIV (Lamwo)Baseline2029,74020,589 (69.0)20,427 (99.2)20,420 (99.9)17,031 (83.4)17,161858
Evaluation2850,11726,883 (53.6)26,831 (99.8)25,956 (96.7)15,199 (56.6)15,224544
Namokora HCIV (Kitgum)Baseline1727,80222,597 (81.3)19,990 (88.5)18,909 (94.6)12,294 (61.5)14,401847
Evaluation3156,76540,185 (70.8)39,966 (99.5)38,468 (96.3)21,958 (54.9)22,063712
+ +<--- Page Split ---> + + +Table 3. Summary statistics from health-facility based surveillance sites where IRS was initiated and sustained. + +
MRC (District)Time periodNumber of months includedTotal outpatient visits, nSuspected malaria cases, n (%)Tested for malaria, n (%)RDT performed (versus microscopy), n (% of tested)Confirmed malaria cases, n (%)Confirmed malaria cases adjusted for testing rate, nMean monthly confirmed cases adjusted for testing rate, n
Nagongera HCIV (TororoBaseline1322,85914,676 (64.2)14,516 (98.9)799 (5.5)3,682 (25.4)3,722286
Evaluation5997,01236,308 (37.4)36,069 (99.3)13,129 (36.4)4,984 (13.8)5,02285
Amolatar HCIV (Amolatar)Baseline1219,5528,547 (43.7)6,512 (76.2)5,923 (91.0)3,701 (56.8)4,845404
Evaluation5989,77924,889 (27.8)21,849 (87.9)19,459 (89.1)4,822 (22.1)5,85499
Dokolo HCIV (Dokolo)Baseline1225,57012,854 (50.3)8,875 (69.0)8,212 (92.5)5,211 (58.7)7,889657
Evaluation59129,24546,428 (35.9)44,972 (96.9)42,259 (94.0)10,210 (22.7)10,761183
Orum HCIV (Onuke)Baseline1116,1209,324 (57.8)8,929 (95.8)3,990 (44.7)5,974 (66.9)6,236567
Evaluation5965,03637,430 (57.6)36,371 (97.2)19,536 (53.7)16,481 (45.3)17,069289
Alebong HCIV (Alebong)Baseline815,3596,694 (43.6)4,789 (71.5)4,620 (96.5)3,209 (67.0)4,317540
Evaluation5994,05540,821 (43.0)36,211 (88.7)32,327 (89.3)12,037 (33.2)13,869235
+ +<--- Page Split ---> + +## Figures + +![](images/Figure_2.jpg) + +
Figure 1
+ +Adjusted IRR and predicted case counts from multilevel negative binomial model assessing the impact of withdrawing IRS after 5 years of sustained use. The blue shaded region represents the \(95\%\) confidence interval around the predicted case counts from the adjusted regression model. Vertical bars represent the \(95\%\) confidence interval around adjusted IRR. + +![](images/Figure_3.jpg) + +
Figure 2
+ +Adjusted IRR and predicted case counts from multilevel negative binomial model assessing the impact of restarting IRS with a single round. The blue shaded region represents the \(95\%\) CI around the predicted case counts from the adjusted regression model. Vertical bars represent the \(95\%\) CI around adjusted IRR. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 3
+ +Adjusted IRR and predicted case counts from multilevel negative binomial model assessing the impact of initiating and sustaining IRS. The blue shaded region represents the \(95\%\) CI around the predicted case counts from the adjusted regression model. Vertical bars represent the \(95\%\) CI around adjusted IRR. + +![PLACEHOLDER_31_1] + + +<--- Page Split ---> +![PLACEHOLDER_32_0] + +
Figure 4
+ +Timeline summarizing the dates of IRS campaigns, baseline 357 and evaluation periods. Objective 1 is to assess the impact of withdrawing IRS after five years of sustained use; Objective 2 is to assess the impact of restarting IRS with a single round; and Objective 3 is to assess the impact of initiating and sustaining IRS. + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- IRSprojectsupplements.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__075d82fd5d37a02cde8eab26a4d85528ea15df290443dfb4d60ca9ac191d6a24/preprint__075d82fd5d37a02cde8eab26a4d85528ea15df290443dfb4d60ca9ac191d6a24_det.mmd b/preprint/preprint__075d82fd5d37a02cde8eab26a4d85528ea15df290443dfb4d60ca9ac191d6a24/preprint__075d82fd5d37a02cde8eab26a4d85528ea15df290443dfb4d60ca9ac191d6a24_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..9d01bee424884cd77016e72bc008e7a8a44e57e1 --- /dev/null +++ b/preprint/preprint__075d82fd5d37a02cde8eab26a4d85528ea15df290443dfb4d60ca9ac191d6a24/preprint__075d82fd5d37a02cde8eab26a4d85528ea15df290443dfb4d60ca9ac191d6a24_det.mmd @@ -0,0 +1,529 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 941, 210]]<|/det|> +# The impact of stopping and starting indoor residual spraying on malaria burden in 14 districts of Uganda + +<|ref|>text<|/ref|><|det|>[[44, 230, 748, 317]]<|/det|> +Jane Namuganga Infectious Diseases Research Collaboration Adrienne Epstein ( \(\boxed{}\) adrienne.epstein@ucsf.edu) University of California, San Francisco https://orcid.org/0000- 0002- 8253- 6102 + +<|ref|>text<|/ref|><|det|>[[44, 323, 435, 365]]<|/det|> +Joaniter Nankabirwa Infectious Diseases Research Collaboration + +<|ref|>text<|/ref|><|det|>[[44, 371, 435, 411]]<|/det|> +Arthur Mpimbaza Infectious Diseases Research Collaboration + +<|ref|>text<|/ref|><|det|>[[44, 417, 435, 458]]<|/det|> +Moses Kiggundu Infectious Diseases Research Collaboration + +<|ref|>text<|/ref|><|det|>[[44, 464, 435, 504]]<|/det|> +Asadu Sserwanga Infectious Diseases Research Collaboration + +<|ref|>text<|/ref|><|det|>[[44, 510, 435, 550]]<|/det|> +James Kapisi Infectious Diseases Research Collaboration + +<|ref|>text<|/ref|><|det|>[[44, 556, 435, 596]]<|/det|> +Emmanuel Arinaitwe Infectious Diseases Research Collaboration + +<|ref|>text<|/ref|><|det|>[[44, 602, 435, 642]]<|/det|> +Samuel Gonahasa Infectious Diseases Research Collaboration + +<|ref|>text<|/ref|><|det|>[[44, 648, 345, 688]]<|/det|> +Jimmy Opigo National Malaria Control Division + +<|ref|>text<|/ref|><|det|>[[44, 694, 435, 734]]<|/det|> +Chris Ebong Infectious Diseases Research Collaboration + +<|ref|>text<|/ref|><|det|>[[44, 740, 465, 781]]<|/det|> +Sarah Staedke London School of Hygiene & Tropical Medicine + +<|ref|>text<|/ref|><|det|>[[44, 787, 585, 827]]<|/det|> +Josephat Shillul US President's Malaria Initiative - VectorLink Uganda Project + +<|ref|>text<|/ref|><|det|>[[44, 833, 585, 873]]<|/det|> +Michael Okia US President's Malaria Initiative - VectorLink Uganda Project + +<|ref|>text<|/ref|><|det|>[[44, 879, 345, 920]]<|/det|> +Damian Rutazaana National Malaria Control Division + +<|ref|>text<|/ref|><|det|>[[44, 926, 294, 944]]<|/det|> +Catherine Maiteki- Ssebuguzi + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[55, 46, 345, 65]]<|/det|> +National Malaria Control Division + +<|ref|>text<|/ref|><|det|>[[44, 70, 395, 110]]<|/det|> +Kassahun Belay US President's Malaria Initiative, USAID + +<|ref|>text<|/ref|><|det|>[[44, 116, 228, 156]]<|/det|> +Moses Kamya Makerere University + +<|ref|>text<|/ref|><|det|>[[44, 163, 390, 204]]<|/det|> +Grant Dorsey University of California, San Francisco + +<|ref|>text<|/ref|><|det|>[[44, 210, 390, 250]]<|/det|> +Isabel Rodriguez- Barraquer University of California, San Francisco + +<|ref|>sub_title<|/ref|><|det|>[[44, 291, 102, 309]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 328, 680, 349]]<|/det|> +Keywords: malaria, disease control, insecticide, vector control intervention + +<|ref|>text<|/ref|><|det|>[[44, 367, 346, 387]]<|/det|> +Posted Date: December 29th, 2020 + +<|ref|>text<|/ref|><|det|>[[44, 404, 463, 425]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 126095/v1 + +<|ref|>text<|/ref|><|det|>[[44, 441, 910, 485]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 520, 909, 564]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on May 11th, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 22896- 5. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[137, 90, 860, 135]]<|/det|> +# The impact of stopping and starting indoor residual spraying on malaria burden in 14 districts of Uganda + +<|ref|>text<|/ref|><|det|>[[112, 193, 884, 320]]<|/det|> +Jane F. Namuganga1\\*, Adrienne Epstein2\\*, Joaniter I. Nankabirwa1,3, Arthur Mpimbaza1,4, Moses Kiggundu1, Asadu Serwanga1, James Kapisi1, Emmanuel Arinaitwe1, Samuel Gonahasa1, Jimmy Opigo5, Chris Ebong1, Sarah G. Staedke6, Josephat Shililu7, Michael Okia7, Damian Rutazaanas, Catherine Maiteki- Ssebuguzi5, Kassahun Belay8, Moses R. Kamya1,3, Grant Dorsey9, Isabel Rodriquez- Barraquer9 + +<|ref|>text<|/ref|><|det|>[[112, 375, 869, 416]]<|/det|> +1 Infectious Diseases Research Collaboration, Kampala, Uganda + +<|ref|>text<|/ref|><|det|>[[112, 418, 870, 458]]<|/det|> +2 Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America + +<|ref|>text<|/ref|><|det|>[[112, 464, 870, 504]]<|/det|> +3 Department of Medicine, Makerere University, College of Health Sciences, Kampala, Uganda + +<|ref|>text<|/ref|><|det|>[[112, 499, 833, 539]]<|/det|> +4 Child Health and Development Centre, Makerere University, College of Health Sciences, Kampala, Uganda + +<|ref|>text<|/ref|><|det|>[[112, 550, 710, 571]]<|/det|> +5 National Malaria Control Division, Ministry of Health, Kampala, Uganda + +<|ref|>text<|/ref|><|det|>[[112, 584, 737, 605]]<|/det|> +6 London School of Hygiene and Tropical Medicine, London, United Kingdom + +<|ref|>text<|/ref|><|det|>[[112, 618, 775, 640]]<|/det|> +7 US President's Malaria Initiative - VectorLink Uganda Project, Kampala, Uganda + +<|ref|>text<|/ref|><|det|>[[112, 653, 712, 673]]<|/det|> +8 US President's Malaria Initiative, USAID/Uganda Senior Malaria Advisor + +<|ref|>text<|/ref|><|det|>[[112, 687, 850, 726]]<|/det|> +9 Department of Medicine, University of California San Francisco, San Francisco, California, United States of America + +<|ref|>text<|/ref|><|det|>[[112, 740, 349, 759]]<|/det|> +\*Adrienne.Epstein@ucsf.edu + +<|ref|>text<|/ref|><|det|>[[112, 775, 498, 796]]<|/det|> +\* These authors contributed equally to this work. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[66, 91, 203, 111]]<|/det|> +## 40 Abstract + +<|ref|>text<|/ref|><|det|>[[110, 128, 874, 465]]<|/det|> +The scale- up of malaria control efforts has led to marked reductions in malaria burden over the past twenty years, but progress has slowed. Implementation of indoor residual spraying (IRS) of insecticide, a proven vector control intervention, has been limited and difficult to sustain partly because questions remain on its added impact over widely accepted interventions such as bed nets. Using data from 14 enhanced surveillance health facilities in Uganda, a country with high bet net coverage yet high malaria burden, we estimate the impact of starting and stopping IRS. We show that stopping IRS resulted in a 5- fold increase in malaria incidence within 10 months, but reinstating IRS led to an over 5- fold decrease within 8 months. In areas where IRS was initiated and sustained, malaria incidence dropped by \(85\%\) after year 4. IRS could play a critical role in achieving global malaria targets, particularly in areas where progress has stalled. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 91, 243, 110]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[111, 123, 881, 712]]<|/det|> +Over the past twenty years the scale- up of malaria control efforts has led to marked reductions in morbidity and mortality \(^{1,2}\) . However, global progress has slowed in recent years, particularly in sub- Saharan Africa, which accounted for \(93\%\) of the world's 228 million cases in \(2018^{2}\) . Longlasting insecticidal nets (LLINs) and indoor residual spraying of insecticide (IRS) are the primary vector control interventions used for the prevention of malaria. The World Health Organization recommends universal coverage of LLINs for at- risk populations in sub- Saharan Africa, where the proportion of households owning at least one LLIN is estimated to have increased from \(47\%\) in 2010 to \(72\%\) in 2018. Until recently, pyrethroids were the only class of insecticides approved for use in LLINs and, given the emergence of widespread pyrethroid resistance \(^{3,4}\) , there is concern that the effectiveness of LLINs may be diminishing. Unlike LLINs, IRS has the advantage of utilizing multiple different classes of insecticides and combing IRS with LLINs may improve malaria control and slow the spread of pyrethroid resistance. However, few controlled trials have evaluated the effect of adding IRS to communities using LLINs and the evidence is mixed, with a few studies showing benefits when IRS included 'non- pyrethroid- like' insecticides \(^{5}\) . Other barriers to IRS delivery – including cost, logistics, and community acceptance – have limited its use \(^{6}\) , such that less than \(5\%\) of the population at risk in sub- Saharan Africa was protected by IRS in 2018, a decrease from over \(10\%\) coverage in \(2010^{2}\) . + +<|ref|>text<|/ref|><|det|>[[112, 755, 879, 881]]<|/det|> +Uganda is illustrative of a country where the burden of malaria remains high and progress has slowed in recent years \(^{2}\) . Malaria control efforts in Uganda have primarily focused on LLINs. In 2013- 14 it became the first country to implement a universal LLIN distribution campaign, which was repeated in 2017- 18. In 2018- 19, Uganda had the highest coverage of LLINs in the world, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 881, 352]]<|/det|> +with \(83\%\) of households reported owning at least one LLIN7. In contrast to LLINs, the implementation of IRS in Uganda has been focal and limited. In 2006, IRS was reintroduced into Uganda for the first time since the 1960s. In 2007- 09, the IRS program was shifted to 10 high burden districts in the north, leading to large reductions in malaria burden8,9. In 2014, the IRS program was relocated from these 10 northern districts to 14 districts in the eastern part of the country, where it has been sustained. The discontinuation of IRS in the 10 northern districts was followed by a marked resurgence in malaria cases10,11, prompting the implementation of a single round of IRS in these 10 districts in 2017. + +<|ref|>text<|/ref|><|det|>[[111, 403, 884, 632]]<|/det|> +In this study, we used data from a network of health facility- based malaria surveillance sites to evaluate the impact of different IRS delivery scenarios in 14 districts in Uganda. This study had three objectives: (1) to estimate the impact of withdrawing IRS after five years of sustained use on the burden of malaria in three sites in Northern Uganda; (2) to estimate the impact of restarting IRS with a single round three to four years after IRS was discontinued on the burden of malaria in nine sites in Northern Uganda; and (3) to estimate the impact of five years of sustained IRS on the burden of malaria in five sites in Northern and Eastern Uganda. + +<|ref|>sub_title<|/ref|><|det|>[[113, 683, 189, 701]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[113, 722, 510, 742]]<|/det|> +## Impact of withdrawing IRS after sustained use + +<|ref|>text<|/ref|><|det|>[[112, 755, 878, 882]]<|/det|> +Across the three sites included in the analysis, a total of 224,859 outpatient visits were observed (Table 1). During the baseline period, average monthly cases ranged from 104- 272 and TPR ranged from \(23.7\% - 25.9\%\) . This increased to 491- 751 and \(52.3\% - 78.0\%\) respectively, during the evaluation period (Supplementary Fig S1). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 123, 884, 460]]<|/det|> +Monthly adjusted IRRs and \(95\%\) confidence intervals (CI) for the three sites combined are presented in Fig 1 and Supplementary Table S1. These results showed an initial reduction in malaria cases after the final round of IRS relative to the baseline period until (adjusted IRR in the first month after \(\mathrm{IRS} = 0.19\) , \(95\%\) CI 0.09- 0.42) about four to five months after the final IRS campaign when malaria cases began to increase. Over the 10- 31 months after IRS was stopped, the number of malaria cases increased by over 5- fold relative to the baseline period (adjusted \(\mathrm{IRR} = 5.24\) , \(95\%\) CI 3.67- 7.50). This corresponds to predicted case counts of near zero immediately following final IRS campaign followed by an increase to about 1000 cases per month at each site (Fig 1). These results were consistent when considering only laboratory- confirmed cases unadjusted for testing rates (Supplementary Fig S2). + +<|ref|>image<|/ref|><|det|>[[112, 545, 875, 765]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 472, 881, 544]]<|/det|> +
Fig 1. Adjusted IRR and predicted case counts from multilevel negative binomial model assessing the impact of withdrawing IRS after 5 years of sustained use. The blue shaded region represents the \(95\%\) confidence interval around the predicted case counts from the adjusted regression model. Vertical bars represent the \(95\%\) confidence interval around adjusted IRR.
+ +<|ref|>sub_title<|/ref|><|det|>[[113, 817, 490, 837]]<|/det|> +## Impact of restarting IRS with a single round + +<|ref|>text<|/ref|><|det|>[[111, 851, 880, 906]]<|/det|> +A total of 858,380 outpatient visits were recorded across the analysis period for the nine sites. (Table 2). Mean monthly malaria cases ranged from 643- 1,569 and the TPR ranged from \(56.5\%\) - + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 864, 179]]<|/det|> +84.7% during the baseline period. These ranges were 501- 762 and 48.5%- 72.0% respectively during the evaluation period. Temporal trends of laboratory- confirmed malaria cases over time for the individual health facilities are presented in Supplementary Fig S3. + +<|ref|>text<|/ref|><|det|>[[111, 210, 881, 475]]<|/det|> +Monthly adjusted IRRs and 95% CI for the nine sites combined are presented in Fig 2 and Supplementary Table S2. The single round of IRS led to a reduction in malaria cases until approximately 23 months post- IRS. Over the 8- 12 months after the single round of IRS, malaria cases decreased by over 5- fold relative to the baseline period (adjusted \(\mathrm{IRR} = 0.17\) , 95% CI 0.15- 0.20). After 23 months following the single round of IRS, malaria cases returned to a level similar to the baseline period before the single round of IRS (adjusted IRR for months \(23 - 31 = 1.06\) , 95% CI 0.92- 1.21). These results were consistent when considering only laboratory- confirmed cases unadjusted for testing rates (Supplementary Fig S4). + +<|ref|>image<|/ref|><|det|>[[115, 560, 880, 787]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 490, 877, 560]]<|/det|> +
Fig 2. Adjusted IRR and predicted case counts from multilevel negative binomial model assessing the impact of restarting IRS with a single round. The blue shaded region represents the 95% CI around the predicted case counts from the adjusted regression model. Vertical bars represent the 95% CI around adjusted IRR.
+ +<|ref|>sub_title<|/ref|><|det|>[[113, 819, 445, 839]]<|/det|> +## Impact of initiating and sustaining IRS + +<|ref|>text<|/ref|><|det|>[[112, 853, 850, 874]]<|/det|> +In total, 574,587 outpatient visits were observed across the five sites included in the analysis. + +<|ref|>text<|/ref|><|det|>[[112, 888, 857, 908]]<|/det|> +(Table 3). During the baseline period, average monthly malaria cases adjusted for testing rates + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 881, 207]]<|/det|> +ranged from 286- 657 and the TPR ranged from \(25.4\% - 67.0\%\) . This range decreased to 85- 289 for malaria cases and \(13.8\% - 45.3\%\) for the TPR during the evaluation period. Temporal trends of laboratory- confirmed malaria cases over time for the individual health facilities are presented in Supplementary Fig S5. + +<|ref|>text<|/ref|><|det|>[[111, 228, 879, 528]]<|/det|> +Monthly adjusted IRRs and \(95\%\) CI for the five sites combined are presented in Fig 3 and Supplementary Table S3. There was a modest overall reduction in malaria case counts in the first three years after IRS was initiated relative to the baseline period, with some peaks in case counts returning to near baseline levels just prior to when rounds of IRS were administered. However, after the third year of sustained use, malaria case counts dropped substantially and remained low relative to the period before IRS was initiated. In the \(4^{\text{th}}\) and \(5^{\text{th}}\) year after IRS was initiated and sustained, malaria cases dropped by \(85\%\) (adjusted \(\mathrm{IRR} = 0.15\) , \(95\%\) CI 0.12- 0.18). These results were consistent when considering only laboratory- confirmed cases unadjusted for testing rates (Supplementary Fig S6). + +<|ref|>image<|/ref|><|det|>[[112, 612, 880, 840]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 543, 861, 614]]<|/det|> +
Fig 3. Adjusted IRR and predicted case counts from multilevel negative binomial model assessing the impact of initiating and sustaining IRS. The blue shaded region represents the \(95\%\) CI around the predicted case counts from the adjusted regression model. Vertical bars represent the \(95\%\) CI around adjusted IRR.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 91, 220, 111]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[111, 123, 884, 680]]<|/det|> +Uganda has been exceptionally successful in scaling- up coverage of LLINs. Following the mass distribution campaigns to deliver free LLINs in 2013- 14 and 2017- 18, \(90\%\) and \(83\%\) of households respectively reported ownership of at least one LLIN \(^{7,12}\) . However, despite this success, the burden of malaria remains high in much of the country. Uganda had the \(3^{\text{rd}}\) highest number of malaria cases reported in 2018, with reported case incidence increasing since \(2014^{2}\) . If Uganda is to achieve the goals established by the World Health Organization’s Global Technical Strategy for malaria including reducing malaria case incidence by at least \(90\%\) by 2030 as compared with \(2015^{13}\) , additional malaria control measures will be needed. This report highlights the critical role of IRS in substantially reducing the burden of malaria in areas where transmission remains high despite deployment of LLINs. Withdrawing IRS after five years of sustained use in three districts in northern Uganda resulted in a more than 5- fold increase in malaria cases within 10 months. Re- starting IRS with a single round in nine districts in Northern Uganda approximately three years after IRS had been stopped led to a transient but important (more than a 5- fold) decrease in malaria cases within 8- 12 months, returning to pre- IRS levels after 23 months. Initiating and sustaining IRS in five districts in Eastern Uganda led to a gradual reduction in malaria cases reaching almost a 7- fold reduction after 4- 5 years. + +<|ref|>text<|/ref|><|det|>[[112, 722, 884, 882]]<|/det|> +Robust evidence supports the widespread use of LLINs for malaria control. In a systematic review of clinical trials conducted between 1987 and 2001, insecticide treated nets reduced all cause child mortality by \(17\%\) and the incidence of uncomplicated \(P\) . falciparum malaria by almost half \(^{14}\) . However, there is concern that the effectiveness of LLINs may be diminishing due to widespread resistance to pyrethroids which until recently were the only class of insecticides + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 881, 494]]<|/det|> +approved for LLINs. Similar to many other African countries, high- level resistance to pyrethroids among the principle Anopheles vectors has been reported recently throughout Uganda \(^{15 - 17}\) . In addition, behavioral changes in vector biting activity following the introduction of LLINs have been reported which could present new challenges for malaria control \(^{18 - 20}\) . Finally, the effectiveness of LLINs may be further compromised by poor adherence and waning coverage in the setting of free distribution campaigns done intermittently. In Uganda, less than \(18\%\) of households reported adequate coverage (defined as at least one LLIN per 2 residents) three years after the 2013- 14 distribution campaign \(^{21}\) and adequate coverage decreased from \(71\%\) to \(51\%\) between 6 and 18 months following the 2017- 18 distribution campaign \(^{22}\) . Although the World Health Organization recommends mass distribution campaigns every three years, mounting evidence suggests that LLINs should be distributed more frequently to sustain high coverage \(^{23 - 29}\) . + +<|ref|>text<|/ref|><|det|>[[110, 541, 880, 877]]<|/det|> +Given concerns about the current effectiveness of pyrethroid- based LLINs and the persistently high burden of malaria despite aggressive scale up of LLINs in countries like Uganda, additional malaria control measures are needed. IRS is an attractive option. Historically, IRS programs were used to dramatically reduce and even eliminate malaria in many parts of the world. Thus, it is surprising that the evidence base from contemporary controlled trials on the impact of adding IRS to LLINs for vector control is limited. A recent systematic review of cluster randomized controlled trials conducted in sub- Saharan Africa since 2008, reported that adding IRS using a “pyrethroid- like” insecticide to LLINs did not provide any benefits, while adding IRS with a “non- pyrethroid- like” insecticide produced mixed results \(^{5}\) . Among the four trials comparing IRS plus LLINs with LLINs alone, three evaluated IRS with a carbamate (bendiocarb) and one + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 872, 876]]<|/det|> +evaluated a long- lasting organophosphate, pirimiphos- methyl (Actellic 300CS®) \(^{30 - 33}\) . Only two trials (both using bendiocarb) assessed malaria incidence; one from Sudan found a \(35\%\) reduction when adding IRS to LLINs \(^{31}\) , while another from Benin found no benefit of adding IRS \(^{30}\) . All four trials assessed parasite prevalence, with an overall non- significant trend towards a lower prevalence when adding IRS to LLINs (RR=0.67, 95% CI 0.35- 1.28) \(^{5}\) . However, when the analyses were restricted to include only the two studies with LLIN usage over \(50\%\) , adding IRS reduced parasite prevalence by over \(50\%\) (RR=0.47, 95% CI 0.33- 0.67) \(^{5}\) . Of note, none of the trials that evaluated the impact of adding IRS with a “non- pyrethroid- like” insecticide assessed outcomes beyond two years. More recently, a number of observational studies have reported benefits of using IRS with pirimiphos- methyl (Actellic 300CS®). In the Mopti Region of Mali, delivery of a single round of IRS with Actellic 300CS® was associated with a \(42\%\) decrease in the peak incidence of laboratory confirmed malaria cases reported at public health facilities \(^{34}\) . In the Koulikoro Region of Mali, villages that received a single round of IRS with Actellic 300CS® combined with LLINs observed a greater than \(50\%\) decrease in the incidence of malaria compared to villages that only received LLINs \(^{35}\) . In the Northern Region of Ghana, districts that received IRS with Actellic 300CS® reported 26- 58% fewer cases of laboratory confirmed malaria cases reported at public health facilities over a two- year period, compared to districts that did not receive IRS \(^{36}\) . In Northern Zambia, implementation of IRS with Actellic 300CS® targeting only high burden areas over a three year period was associated with a \(25\%\) decline in parasite prevalence during the rainy season, but no decline during the dry season \(^{37}\) . In Western Kenya, the introduction of a single round of IRS with Actellic 300CS® was associated with a 44- 65% decrease in district level malaria case counts over a 10 month period compared to pre- IRS levels \(^{38}\) . In addition, several recent reports have documented dramatic resurgences + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 848, 144]]<|/det|> +following the withdrawal of IRS with bendiocarb in Benin39, and the withdrawal of IRS with Actellic 300CS® in Mali and Ghana34,36. + +<|ref|>text<|/ref|><|det|>[[110, 191, 880, 880]]<|/det|> +The results from this study provides additional support for the critical role IRS can play in reducing the burden of malaria in African countries with high LLINs coverage. We analyzed a large, rigorously collected dataset, which is a strength of study. Data were collected over nearly seven years through an enhanced health facility- based surveillance system covering 14 districts in Uganda where IRS was being withdrawn, re- started, and initiated. This enhanced surveillance system facilitated laboratory testing and provided prospectively collected, individual- level data, allowing for analyses of quantitative changes in laboratory- confirmed cases of malaria over time, controlling for temporal changes in rainfall, seasonal effects, diagnostic practices, and health seeking behavior. Previous work by our group documented a marked decrease in malaria test positivity rates after four years of sustained IRS with bendiocarb in one district of Northern Uganda followed by a rapid resurgence over an 18- month period after IRS was withdrawn11. In this study we expand on these findings by including data from three districts and covering a 31- month period following the withdrawal of IRS. We were able to quantify more than a 5- fold increase in malaria cases which was sustained over the 10- 31 months following the withdrawal of IRS. This marked resurgence occurred despite the fact the first universal LLIN distribution campaign was timed to occur right after IRS was withdrawn. Given the dramatic nature of the resurgence, the Ugandan government was able to procure funding for a single round of IRS with Actellic 300CS® approximately three years after IRS was withdrawn in 10 districts of Northern Uganda. In this study, we assessed the impact of this single round in nine of these districts. This single round was associated with over a 5- fold decrease in malaria cases after 8- 12 months, with + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 883, 494]]<|/det|> +malaria cases returning to pre- IRS levels after almost 2 years. These data suggest that IRS with longer- acting formulations such as Actellic 300CS® administered every 2 years may be a cost- effective strategy for mitigating the risk of resurgence following sustained IRS and/or enabling countries to expand coverage when resources are limited. This study also evaluated the impact of five years of sustained IRS in 5 districts of Eastern Uganda, starting first with bendiocarb and then switching to Actellic 300CS® after 18 months. Rounds of IRS were initially associated with marked decreases in malaria cases followed by peaks before subsequent rounds until the \(4^{\text{th}}\) and \(5^{\text{th}}\) years after IRS was initiated when there was a sustained decrease of almost 7- fold compared to pre- IRS level. Given the before- and- after nature of our study design, it is not clear whether the maximum sustained benefits of IRS seen after 4- 5 years were due to the cumulative effect of multiple rounds of IRS, the switch from bendiocarb to Actellic 300CS®, the second universal LLIN distribution campaign which occurred in this area in 2017, and/or other factors. + +<|ref|>text<|/ref|><|det|>[[111, 541, 879, 876]]<|/det|> +This study had several limitations. First, we used an observational study design, with measures of impact based on comparisons made before- and- after key changes in IRS policy. Although cluster randomized controlled trials are the gold standard study design for estimating the impact of IRS, it could be argued that withholding IRS would be unethical, given what is known about its impact in Uganda. Second, our estimates of impact could have been confounded by secular trends in factors not accounted for in our analyses. However, we feel that our overall conclusions are robust given the large amount of data available from multiple sites over an extended period with multiple complementary objectives providing consistent findings. Third, we could not assess the impact of IRS independent of LLIN use and did not have access to measures of IRS or LLIN coverage from our study populations. However, we were able to provide a “real world” + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 877, 250]]<|/det|> +assessment of IRS in a setting where LLIN use is strongly supported by repeated universal distribution campaigns that are becoming increasingly common in sub- Saharan Africa. Finally, our study outcome was limited to case counts of laboratory confirmed malaria captured at health facilities. Thus, we were unable to measure the impact of IRS on other important indicators such as measures of transmission intensity, parasite prevalence, or mortality. + +<|ref|>text<|/ref|><|det|>[[111, 295, 880, 808]]<|/det|> +There is a growing body of evidence that combining LLINs with IRS using "non- pyrethroid- like" insecticides, especially the long acting organophosphate Actellic 300CS®, is highly effective at reducing the burden of malaria in Uganda, and elsewhere in Africa. Despite these encouraging findings, IRS coverage in Africa has been moving in the wrong direction. The proportion of those at risk protected by IRS in Africa peaked at just over \(10\%\) in 2010. However, the spread of pyrethroid resistance has led many control programs to switch to more expensive formulations resulting in a \(53\%\) decrease in the number of houses sprayed between years of peak coverage and 2015 across 18 countries supported by the U.S. President's Malaria Initiative40 and an overall reduction in the proportion protected by IRS in Africa to less than \(5\%\) in 20182. Given the lack of recent progress in reducing the global burden of malaria coupled with challenges in funding, renewed commitments are needed to address the "high burden to high impact" approach now being advocated by the World Health Organization2. IRS is a widely available tool that could be scaled up, however demands currently exceed the availability of resources. Additional work is needed to optimize the use of IRS, prevent further spread of insecticide resistance, and better evaluate the cost effectiveness of IRS in the context of other control interventions. + +<|ref|>sub_title<|/ref|><|det|>[[115, 857, 202, 876]]<|/det|> +## Methods + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[112, 90, 482, 109]]<|/det|> +## Study sites and vector control interventions + +<|ref|>text<|/ref|><|det|>[[110, 121, 880, 597]]<|/det|> +This study utilized data from 14 health facilities located in 14 districts in Northern and Eastern Uganda (Fig 4) which were part of a larger comprehensive malaria surveillance network called the Uganda Malaria Surveillance Program (UMSP). Between 2007 and 2009, IRS was implemented in 10 high burden districts in northern Uganda. DDT or pyrethroids were initially used but in 2010 the insecticide was changed to a carbamate (bendiocarb) due to concern regarding the spread of pyrethroid resistance. Rounds of bendiocarb were repeated approximately every 6 months until 2014 when the IRS program was discontinued, so that resources could be shifted to other high burden districts. In 2017, these 10 districts in northern Uganda received a single round of the organophosphate pirimiphos- methyl (Actellic 300CS®) following reports of malaria resurgence after IRS has been discontinued in 2014. Between 2014 and 2015, IRS with bendiocarb was implemented in 14 districts in the Northern and Eastern part of the country. Rounds of bendiocarb were repeated approximately every six months until 2016 when the formulation was changed to Actellic 300CS®, which continues to be administered once a year. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[113, 110, 880, 530]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 91, 616, 110]]<|/det|> +
Fig 4. Map of Uganda showing study sites and IRS districts.
+ +<|ref|>text<|/ref|><|det|>[[113, 575, 880, 666]]<|/det|> +Universal LLIN distribution campaigns were conducted in 2013- 14 and 2017- 18, where LLINs were distributed free- of- charge by the Uganda Ministry of Health targeting 1 LLIN for every two household residents. + +<|ref|>sub_title<|/ref|><|det|>[[115, 715, 397, 733]]<|/det|> +## Health-facility based surveillance + +<|ref|>text<|/ref|><|det|>[[112, 747, 880, 910]]<|/det|> +Enhanced malaria surveillance was established by UMSP in 2006, as previously described 41. UMSP operates Malaria Reference Centers (MRCs) at 70 level III/IV public health facilities across Uganda. At each MRC, individual- level data from standardized registers for all patients presenting to the outpatient departments are entered into an Access database by on- site data entry officers. Variables include patient demographics, results of laboratory testing for malaria (rapid + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 866, 180]]<|/det|> +diagnostic test [RDT] or microscopy), diagnoses given, and treatments prescribed. Emphasis is placed on ensuring that patients with suspected malaria undergo testing, by either RDT or microscopy. + +<|ref|>text<|/ref|><|det|>[[111, 228, 877, 459]]<|/det|> +This study utilized data from 14 MRCs located in districts that either previously had IRS or have ongoing IRS campaigns. We estimated the impact of withdrawing IRS using data from three sites in Northern Uganda that had at least six months of data preceding the final round of IRS administered in 2014. To estimate the impact of restarting IRS with a single round administered in 2017, we used data from nine sites in Northern Uganda. To estimate the impact of sustained IRS over five years, we used data from five sites in Eastern Uganda where IRS had been implemented since 2014- 15. + +<|ref|>sub_title<|/ref|><|det|>[[113, 509, 199, 526]]<|/det|> +## Measures + +<|ref|>text<|/ref|><|det|>[[111, 541, 884, 876]]<|/det|> +Exposure. The exposure was specified as an indicator variable for each month since IRS was withdrawn or initiated relative to a baseline period (Fig 5 and Supplementary Fig S7). We also fit separate models with categorical exposure variables divided into distinct periods of months. To determine the impact of withdrawing IRS after at least five years of sustained use, the baseline period was defined as the final year of sustained IRS use, and the evaluation period lasted through 2016, prior to when an additional round of IRS was implemented. In order to determine the impact of restarting IRS with a single round of IRS, the baseline period was defined as one year prior to the single round of IRS and the evaluation period went through December 2019. To determine the impact of initiating and sustaining IRS, the baseline period was the year prior to IRS initiation, and the evaluation period lasted through December 2019. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 90, 864, 162]]<|/det|> +Fig 5. Timeline summarizing the dates of IRS campaigns, baseline and evaluation periods. Objective 1 is to assess the impact of withdrawing IRS after five years of sustained use; Objective 2 is to assess the impact of restarting IRS with a single round; and Objective 3 is to assess the impact of initiating and sustaining IRS. + +<|ref|>image<|/ref|><|det|>[[130, 200, 875, 408]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[111, 430, 880, 625]]<|/det|> +Outcome. The primary outcome was the monthly count of laboratory- confirmed malaria cases at each MRC. The case count was corrected for testing rates by multiplying the number of individuals with suspected malaria but not tested each month by the test positivity rate (the number who tested positive divided by the total number tested) for that month and adding the result to the number of laboratory- confirmed positive cases. As a sensitivity analysis, we re- specified the models including only laboratory- confirmed case counts as the outcome. + +<|ref|>text<|/ref|><|det|>[[111, 675, 884, 868]]<|/det|> +Covariates. We adjusted for time- varying variables that impact malaria burden and malaria case detection at the health facility. These variables included average rainfall at the health facility lagged by 1 month, indicator variables for month of the year (to adjust for seasonal effects), the proportion of tests that were RDTs in that month (vs. microscopy), and the number of individuals who attended the health facility but were not suspected of having malaria in that month (to adjust for care- seeking behaviors). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 135, 272, 153]]<|/det|> +## Statistical analysis + +<|ref|>text<|/ref|><|det|>[[112, 167, 880, 328]]<|/det|> +For each objective, we specified mixed effects negative binomial regression models with random intercepts for health facility. Coefficients for the exposure variable were exponentiated to represent the incidence rate ratio (IRR) comparing the incidence of malaria in the month of interest relative to the baseline period. This method assumes that the underlying population has remained constant over the study period. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 90, 883, 811]]<|/det|> +386 References387 1 Bhatt, S. et al. The effect of malaria control on Plasmodium falciparum in Africa between2000 and 2015. Nature 526, 207- 211, doi:10.1038/nature15535 (2015).390 2 Organization, W. H. World Malaria Report 2019. (2019).391 3 Hemingway, J. et al. Averting a malaria disaster: will insecticide resistance derail malariacontrol? Lancet 387, 1785- 1788, doi:10.1016/S0140- 6736(15)00417- 1 (2016).392 4 Ranson, H. & Lissenden, N. Insecticide Resistance in African Anopheles Mosquitoes: AWorsening Situation that Needs Urgent Action to Maintain Malaria Control. Trends Parasitol32, 187- 196, doi:10.1016/j.pt.2015.11.010 (2016).393 5 Choi, L., Pryce, J. & Garner, P. Indoor residual spraying for preventing malaria incommunities using insecticide- treated nets. 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LLIN Evaluation in Uganda Project (LLINEUP): a cross- sectional survey of species diversity and insecticide resistance in 48 districts of Uganda. Parasit Vectors 12, 94, doi:10.1186/s13071- 019- 3353- 7 (2019). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 820, 108]]<|/det|> +17 Okia, M. et al. Insecticide resistance status of the malaria mosquitoes: Anopheles + +<|ref|>text<|/ref|><|det|>[[111, 124, 770, 143]]<|/det|> +gambiae and Anopheles funestus in eastern and northern Uganda. Malar J 17, 157, + +<|ref|>text<|/ref|><|det|>[[111, 160, 436, 178]]<|/det|> +doi:10.1186/s12936-018-2293-6 (2018). + +<|ref|>text<|/ref|><|det|>[[111, 195, 521, 213]]<|/det|> +18 AFRO, W. H. O.-. Global AMDP database, + +<|ref|>text<|/ref|><|det|>[[111, 230, 546, 248]]<|/det|> + ( + +<|ref|>text<|/ref|><|det|>[[111, 265, 864, 352]]<|/det|> +19 Cooke, M. K. et al. 'A bite before bed': exposure to malaria vectors outside the times of net use in the highlands of western Kenya. Malar J 14, 259, doi:10.1186/s12936-015-0766-4 (2015). + +<|ref|>text<|/ref|><|det|>[[111, 370, 853, 427]]<|/det|> +20 Sougoufara, S. et al. Biting by Anopheles funestus in broad daylight after use of long-lasting insecticidal nets: a new challenge to malaria elimination. Malar J 13, 125, + +<|ref|>text<|/ref|><|det|>[[111, 443, 427, 460]]<|/det|> +doi:10.1186/1475-2875-13-125 (2014). + +<|ref|>text<|/ref|><|det|>[[111, 477, 860, 563]]<|/det|> +21 Gonahasa, S. et al. LLIN Evaluation in Uganda Project (LLINEUP): factors associated with ownership and use of long-lasting insecticidal nets in Uganda: a cross-sectional survey of 48 districts. Malar J 17, 421, doi:10.1186/s12936-018-2571-3 (2018). + +<|ref|>text<|/ref|><|det|>[[111, 580, 852, 636]]<|/det|> +22 Staedke, S. G. et al. Effect of long-lasting insecticidal nets with and without piperonyl butoxide on malaria indicators in Uganda (LLINEUP): a pragmatic, cluster-randomised trial + +<|ref|>text<|/ref|><|det|>[[111, 653, 724, 671]]<|/det|> +embedded in a national LLIN distribution campaign. Lancet 395, 1292-1303, + +<|ref|>text<|/ref|><|det|>[[111, 687, 473, 705]]<|/det|> +doi:10.1016/S0140-6736(20)30214-2 (2020). + +<|ref|>text<|/ref|><|det|>[[111, 721, 880, 842]]<|/det|> +23 Rugnao, S. et al. LLIN Evaluation in Uganda Project (LLINEUP): factors associated with childhood parasitaemia and anaemia 3 years after a national long-lasting insecticidal net distribution campaign: a cross-sectional survey. Malar J 18, 207, doi:10.1186/s12936-019-2838-3 (2019). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 870, 144]]<|/det|> +24 WHO. Achieving and maintaining universal coverage with long-lasting insecticidal nets for malaria control. (2017). + +<|ref|>text<|/ref|><|det|>[[110, 159, 870, 214]]<|/det|> +25 Wills, A. B. et al. Physical durability of PermaNet 2.0 long-lasting insecticidal nets over three to 32 months of use in Ethiopia. Malar J 12, 242, doi:10.1186/1475-2875-12-242 (2013). + +<|ref|>text<|/ref|><|det|>[[110, 229, 870, 319]]<|/det|> +26 Hakizimana, E. et al. Monitoring long-lasting insecticidal net (LLIN) durability to validate net serviceable life assumptions, in Rwanda. Malar J 13, 344, doi:10.1186/1475-2875-13-344 (2014). + +<|ref|>text<|/ref|><|det|>[[110, 334, 870, 421]]<|/det|> +27 Massue, D. J. et al. Durability of Olyset campaign nets distributed between 2009 and 2011 in eight districts of Tanzania. Malar J 15, 176, doi:10.1186/s12936-016-1225-6 (2016). + +<|ref|>text<|/ref|><|det|>[[110, 404, 872, 440]]<|/det|> +28 Tan, K. R. et al. A longitudinal study of the durability of long-lasting insecticidal nets in Zambia. Malar J 15, 106, doi:10.1186/s12936-016-1154-4 (2016). + +<|ref|>text<|/ref|><|det|>[[110, 475, 857, 563]]<|/det|> +29 Randriamaherijaoana, S., Raharinjatovo, J. & Boyer, S. Durability monitoring of long-lasting insecticidal (mosquito) nets (LLINs) in Madagascar: physical integrity and insecticidal activity. Parasit Vectors 10, 564, doi:10.1186/s13071-017-2419-7 (2017). + +<|ref|>text<|/ref|><|det|>[[110, 578, 850, 667]]<|/det|> +30 Corbel, V. et al. Combination of malaria vector control interventions in pyrethroid resistance area in Benin: a cluster randomised controlled trial. Lancet Infect Dis 12, 617-626, doi:10.1016/S1473-3099(12)70081-6 (2012). + +<|ref|>text<|/ref|><|det|>[[110, 682, 856, 772]]<|/det|> +31 Kafy, H. T. et al. Impact of insecticide resistance in Anopheles arabiensis on malaria incidence and prevalence in Sudan and the costs of mitigation. Proc Natl Acad Sci U S A 114, E11267-E11275, doi:10.1073/pnas.1713814114 (2017). + +<|ref|>text<|/ref|><|det|>[[110, 787, 870, 843]]<|/det|> +32 Protopopoff, N. et al. Effectiveness of a long-lasting piperonyl butoxide-treated insecticidal net and indoor residual spray interventions, separately and together, against malaria + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 857, 145]]<|/det|> +transmitted by pyrethroid-resistant mosquitoes: a cluster, randomised controlled, two-by-two factorial design trial. Lancet 391, 1577- 1588, doi:10.1016/S0140- 6736(18)30427- 6 (2018). + +<|ref|>text<|/ref|><|det|>[[110, 157, 860, 248]]<|/det|> +West, P. A. et al. Indoor residual spraying in combination with insecticide- treated nets compared to insecticide- treated nets alone for protection against malaria: a cluster randomised trial in Tanzania. PLoS Med 11, e1001630, doi:10.1371/journal.pmed.1001630 (2014). + +<|ref|>text<|/ref|><|det|>[[110, 261, 870, 355]]<|/det|> +Wagman, J. et al. Rapid reduction of malaria transmission following the introduction of indoor residual spraying in previously unsprayed districts: an observational analysis of Mopti Region, Mali, in 2017. Malar J 19, 340, doi:10.1186/s12936- 020- 03414- 2 (2020). + +<|ref|>text<|/ref|><|det|>[[110, 367, 875, 460]]<|/det|> +Kane, F. et al. Performance of IRS on malaria prevalence and incidence using pirimiphos- methyl in the context of pyrethroid resistance in Koulikoro region, Mali. Malar J 19, 286, doi:10.1186/s12936- 020- 03357- 8 (2020). + +<|ref|>text<|/ref|><|det|>[[110, 472, 872, 565]]<|/det|> +Gogue, C. et al. An observational analysis of the impact of indoor residual spraying in Northern, Upper East, and Upper West Regions of Ghana: 2014 through 2017. Malar J 19, 242, doi:10.1186/s12936- 020- 03318- 1 (2020). + +<|ref|>text<|/ref|><|det|>[[110, 577, 848, 667]]<|/det|> +Hast, M. A. et al. The Impact of 3 Years of Targeted Indoor Residual Spraying With Pirimiphos- Methyl on Malaria Parasite Prevalence in a High- Transmission Area of Northern Zambia. Am J Epidemiol 188, 2120- 2130, doi:10.1093/aje/kwz107 (2019). + +<|ref|>text<|/ref|><|det|>[[110, 680, 870, 777]]<|/det|> +Abong'o, B. et al. Impact of indoor residual spraying with pirimiphos- methyl (Actellic 300CS) on entomological indicators of transmission and malaria case burden in Migori County, western Kenya. Scientific reports 10, 4518, doi:10.1038/s41598- 020- 61350- 2 (2020). + +<|ref|>text<|/ref|><|det|>[[110, 789, 850, 876]]<|/det|> +Aikpon, R. Y. et al. Upsurge of malaria transmission after indoor residual spraying withdrawal in Atacora region in Benin, West Africa. Malar J 19, 3, doi:10.1186/s12936- 019- 3086- 2 (2020). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 88, 884, 285]]<|/det|> +40 Oxborough, R. M. Trends in US President's Malaria Initiative- funded indoor residual spray coverage and insecticide choice in sub- Saharan Africa (2008- 2015): urgent need for affordable, long- lasting insecticides. Malar J 15, 146, doi:10.1186/s12936- 016- 1201- 1 (2016). 41 Sserwanga, A. et al. Improved malaria case management through the implementation of a health facility- based sentinel site surveillance system in Uganda. PLoS One 6, e16316, doi:10.1371/journal.pone.0016316 (2011). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 91, 280, 109]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[111, 123, 877, 395]]<|/det|> +We would like to acknowledge the health workers at all 14 health facilities that contributed data for this study. We would like to thank the Ugandan Ministry of Health National Malaria Control Division, and USAID – President’s Malaria Initiative. This work was supported by the National Institutes of Health as part of the International Centers of Excellence in Malaria Research (ICMER) program (U19AI089674). AE is supported by the National Institute of Allergy and Infectious Diseases (F31AI150029). JIN is supported by the Fogarty International Center (Emerging Global Leader Award grant number K43TW010365). EA is supported by the Fogarty International Center of the National Institutes of Health under Award Number D43TW010526. + +<|ref|>sub_title<|/ref|><|det|>[[115, 440, 302, 458]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[111, 471, 884, 632]]<|/det|> +JFN, AE, GD, and IRB conceived of the study. JFN led the data collection activities with support from JIN, AM, MK, AS, JK, EA, SG, CE, SGS, CMS, and MRK. AE and IRB led the data analysis with support from GD. AE and JFN drafted the manuscript with support from GD, SGS, and IRB. All authors contributed to interpretation of the results and edited the manuscripts. All authors read and approved the final manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[115, 682, 288, 700]]<|/det|> +## Competing Interests + +<|ref|>text<|/ref|><|det|>[[115, 733, 460, 752]]<|/det|> +The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[115, 823, 377, 841]]<|/det|> +## Materials and Correspondence + +<|ref|>text<|/ref|><|det|>[[115, 856, 638, 875]]<|/det|> +Correspondence to Adrienne Epstein: Adrienne.Epstein@ucsf.edu + +<--- Page Split ---> +<|ref|>table_caption<|/ref|><|det|>[[120, 94, 688, 103]]<|/det|> +Table 1. Summary statistics from health-facility based surveillance sites where IRS was stopped after sustained use. + +<|ref|>table<|/ref|><|det|>[[115, 110, 920, 290]]<|/det|> + +
MRC
(District)		Time periodNumber of
months
included		Total
outpatient
visits, n		Suspected
malaria
cases, n (%
of total)		Tested for
malaria, n
(% of
suspected)		RDT
performed
(versus
microscopy),
n(% of
tested)		Confirmed
malaria
cases, n (% of tested)		Confirmed
cases
adjusted for testing rate, n		Mean
monthly
confirmed
cases adjusted for testing rate, n
Aboke HCIV
(Kole)		Baseline914,0153,766 (26.9)3,735 (99.2)2,450 (65.6)923 (24.7)932104
Evaluation2546,85021,245 (45.3)18,185 (85.6)17,210 (94.6)14,200 (78.0)16,699668
Aduku HCIV
(Kwania)		Baseline1324,16413,742 (56.9)13,719 (99.8)1,049 (7.6)3,254 (23.7)3,529272
Evaluation3257,47030,035 (52.2)25,896 (86.2)10,731 (41.4)13,537 (52.3)15,717491
Anyeke
HCIV
(Oyam)		Baseline815,8593,514 (22.2)2,627 (74.8)2,604 (99.1)680 (25.9)918115
Evaluation2566,50128,755 (43.2)20,659 (71.8)16,147 (78.2)13,559 (65.6)18,774751
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[113, 110, 905, 533]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[123, 93, 649, 105]]<|/det|> +Table 2. Summary statistics from health-facility based surveillance sites that received a single round of IRS. + +
MRC (District)Time periodNumber of months includedTotal outpatient visits, nSuspected malaria cases, n (% of total)Tested for malaria, n (% of suspected)RDT performed (versus microscopy), n (% of tested)Confirmed malaria cases, n (% of tested)Confirmed cases adjusted for testing rate, nMean monthly confirmed cases adjusted for testing rate, n
Aboke HCIV (Kole)Baseline1421,18611,752 (55.5)9,613 (81.8)9,079 (94.5)7,297 (75.9)9,006643
Evaluation3454,82630,973 (56.5)30,674 (99.0)29,064 (94.8)22,097 (72.0)22,308656
Aduku HCIV (Kwania)Baseline1735,01720,645 (59.0)17,156 (83.1)7,938 (46.3)9,699 (56.5)11,465674
Evaluation3165,37932,260 (49.3)31,337 (97.1)20,385 (65.1)15,201 (48.5)15,534501
Anyeke HCIV (Oyam)Baseline1435,37818,445 (52.1)12,997 (70.5)9,151 (70.4)8,967 (69.0)12,595900
Evaluation3470,14933,618 (47.9)32,522 (96.7)31,208 (96.0)21,799 (67.0)22,375658
Awach HCIV (Gulu)Baseline1736,92321,920 (59.4)17,927 (82.0)17,736 (98.7)13,663 (76.0)16,749985
Evaluation3069,37536,760 (53.0)35,189 (95.7)34,070 (96.8)21,879 (62.2)22,851762
Lalogi HCIV (Omoro)Baseline1754,43632,642 (60.0)31,545 (96.6)31,490 (99.8)23,106 (73.2)23,9481,409
Evaluation3172,44941,846 (57.8)41,668 (99.6)40,804 (97.9)22,986 (55.2)23,060744
Patongo HCIII (Agago)Baseline1424,68615,453 (62.6)15,122 (97.9)14,758 (97.6)11,313 (74.8)11,487821
Evaluation3454,48634,482 (63.3)33,797 (98.0)32,176 (95.2)17,231 (51.0)17,440513
Atiak HCIV (Amuru)Baseline1438,91625,929 (66.6)22,418 (86.5)22,335 (99.6)18,978 (84.7)21,9661,569
Evaluation3460,75031,650 (52.1)30,754 (97.2)30,541 (99.3)19,766 (64.3)20,325598
Padibe HCIV (Lamwo)Baseline2029,74020,589 (69.0)20,427 (99.2)20,420 (99.9)17,031 (83.4)17,161858
Evaluation2850,11726,883 (53.6)26,831 (99.8)25,956 (96.7)15,199 (56.6)15,224544
Namokora HCIV (Kitgum)Baseline1727,80222,597 (81.3)19,990 (88.5)18,909 (94.6)12,294 (61.5)14,401847
Evaluation3156,76540,185 (70.8)39,966 (99.5)38,468 (96.3)21,958 (54.9)22,063712
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[113, 110, 920, 380]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[122, 95, 666, 107]]<|/det|> +Table 3. Summary statistics from health-facility based surveillance sites where IRS was initiated and sustained. + +
MRC (District)Time periodNumber of months includedTotal outpatient visits, nSuspected malaria cases, n (%)Tested for malaria, n (%)RDT performed (versus microscopy), n (% of tested)Confirmed malaria cases, n (%)Confirmed malaria cases adjusted for testing rate, nMean monthly confirmed cases adjusted for testing rate, n
Nagongera HCIV (TororoBaseline1322,85914,676 (64.2)14,516 (98.9)799 (5.5)3,682 (25.4)3,722286
Evaluation5997,01236,308 (37.4)36,069 (99.3)13,129 (36.4)4,984 (13.8)5,02285
Amolatar HCIV (Amolatar)Baseline1219,5528,547 (43.7)6,512 (76.2)5,923 (91.0)3,701 (56.8)4,845404
Evaluation5989,77924,889 (27.8)21,849 (87.9)19,459 (89.1)4,822 (22.1)5,85499
Dokolo HCIV (Dokolo)Baseline1225,57012,854 (50.3)8,875 (69.0)8,212 (92.5)5,211 (58.7)7,889657
Evaluation59129,24546,428 (35.9)44,972 (96.9)42,259 (94.0)10,210 (22.7)10,761183
Orum HCIV (Onuke)Baseline1116,1209,324 (57.8)8,929 (95.8)3,990 (44.7)5,974 (66.9)6,236567
Evaluation5965,03637,430 (57.6)36,371 (97.2)19,536 (53.7)16,481 (45.3)17,069289
Alebong HCIV (Alebong)Baseline815,3596,694 (43.6)4,789 (71.5)4,620 (96.5)3,209 (67.0)4,317540
Evaluation5994,05540,821 (43.0)36,211 (88.7)32,327 (89.3)12,037 (33.2)13,869235
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 44, 143, 69]]<|/det|> +## Figures + +<|ref|>image<|/ref|><|det|>[[44, 90, 950, 360]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 382, 115, 401]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[42, 423, 953, 512]]<|/det|> +Adjusted IRR and predicted case counts from multilevel negative binomial model assessing the impact of withdrawing IRS after 5 years of sustained use. The blue shaded region represents the \(95\%\) confidence interval around the predicted case counts from the adjusted regression model. Vertical bars represent the \(95\%\) confidence interval around adjusted IRR. + +<|ref|>image<|/ref|><|det|>[[44, 516, 951, 783]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 803, 117, 822]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[42, 845, 953, 910]]<|/det|> +Adjusted IRR and predicted case counts from multilevel negative binomial model assessing the impact of restarting IRS with a single round. The blue shaded region represents the \(95\%\) CI around the predicted case counts from the adjusted regression model. Vertical bars represent the \(95\%\) CI around adjusted IRR. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[45, 45, 951, 312]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 334, 117, 352]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[42, 375, 955, 440]]<|/det|> +Adjusted IRR and predicted case counts from multilevel negative binomial model assessing the impact of initiating and sustaining IRS. The blue shaded region represents the \(95\%\) CI around the predicted case counts from the adjusted regression model. Vertical bars represent the \(95\%\) CI around adjusted IRR. + +<|ref|>image<|/ref|><|det|>[[42, 444, 955, 938]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[62, 112, 944, 360]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 43, 117, 62]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[42, 420, 952, 518]]<|/det|> +Timeline summarizing the dates of IRS campaigns, baseline 357 and evaluation periods. Objective 1 is to assess the impact of withdrawing IRS after five years of sustained use; Objective 2 is to assess the impact of restarting IRS with a single round; and Objective 3 is to assess the impact of initiating and sustaining IRS. + +<|ref|>sub_title<|/ref|><|det|>[[44, 543, 311, 570]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 593, 765, 614]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 632, 320, 652]]<|/det|> +- IRSprojectsupplements.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__078ee3115bb7f9caf7ff08bd41325e1b3d5bc1c68c6da418cda25d198e700819/images_list.json b/preprint/preprint__078ee3115bb7f9caf7ff08bd41325e1b3d5bc1c68c6da418cda25d198e700819/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..7ee093ff5855751ea85a2ceba4f1b9788a0e8a51 --- /dev/null +++ b/preprint/preprint__078ee3115bb7f9caf7ff08bd41325e1b3d5bc1c68c6da418cda25d198e700819/images_list.json @@ -0,0 +1,77 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 ONE architecture and hardware implementations. (a) Illustration of processing branches and flows in the ONE architecture to predict output spatiotemporal output physical quantities from corresponding input and solve PDEs involving single or multiple physics. Illustrations of integrated and free-space implementations of reconfigurable (b) DONN and (c) XBAR structures.", + "footnote": [], + "bbox": [ + [ + 213, + 90, + 830, + 479 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 Solving Darcy flow and magnetostatic Poisson's equations. (a) Illustration of the Darcy flow equation describing a fluid flow through a porous medium. The ONE architecture learns the mapping between the permeability and pressure fields. (b) Training loss curves for input data with different resolutions. (c) Comparison of the training loss of different models at various resolutions. (d) Input permeability field, the expected ground truth of output pressure field, the predicted output pressure field, the absolute error between the expected and predicted outputs, and the relative error between the expected and predicted outputs, at 85 and 421 resolutions. (e) Illustration of the magnetostatic Poisson's equation calculating the demagnetizing field generated by the magnetization field. The ONE architecture learns the mapping between these two fields. (f) Validation loss curve for the ONE architecture solving the magnetostatic Poisson's equation and (g) corresponding input magnetization field, the expected ground truth of output demagnetizing field, the predicted output demagnetizing field, the absolute and normalized errors between the expected and predicted outputs.", + "footnote": [], + "bbox": [ + [ + 171, + 88, + 784, + 486 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 Solving time-dependent Navier-Stokes and Maxwell's equations. Illustrations of (a) Navier-Stokes equation for solving the time evolution of the vorticity field in a viscous, incompressible fluid in vorticity form on the unit torus and (b) Maxwell's equations for solving the time evolution of the electric field in a dielectric metasurface. Validation loss curves for (c) solving the Navier-Stokes equation and (d) Maxwell's equations using the ONE architecture. The expected ground truth field, the predicted field, and the absolute and relative errors between these two fields for (e) the Navier-Stokes equation and (f) Maxwell's equations, respectively.", + "footnote": [], + "bbox": [ + [ + 172, + 88, + 785, + 428 + ] + ], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 Solving multiphysics PDEs. (a) Illustration of solving coupled PDEs in an electrical heating problem involving electric current physics and heat transfer physics. (b) Validation loss curve. (c) A few representative 2D voltage profiles in the circuit. (d) The expected ground truth temperature profile, the predicted profile, and the absolute and relative errors between these two profiles.", + "footnote": [], + "bbox": [ + [ + 212, + 264, + 832, + 590 + ] + ], + "page_idx": 11 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5 Experimental demonstration. (a) Photo and schematic of a reconfigurable DONN experimental setup consisting of a reconfigurable input encoder, two reconfigurable diffractive layers, and a camera. Polarization components were used to configure SLMs in the phase modulation mode. (b) Output 2D data in one DONN kernel of the Fourier space processing branch in the ONE architecture obtained from model calculations and experimental measurements. (c) Validation loss curves at different noise levels in optical XBAR structures and (d) the loss at the final epoch as a function of noise level.", + "footnote": [], + "bbox": [ + [ + 175, + 225, + 770, + 610 + ] + ], + "page_idx": 12 + } +] \ No newline at end of file diff --git a/preprint/preprint__078ee3115bb7f9caf7ff08bd41325e1b3d5bc1c68c6da418cda25d198e700819/preprint__078ee3115bb7f9caf7ff08bd41325e1b3d5bc1c68c6da418cda25d198e700819.mmd b/preprint/preprint__078ee3115bb7f9caf7ff08bd41325e1b3d5bc1c68c6da418cda25d198e700819/preprint__078ee3115bb7f9caf7ff08bd41325e1b3d5bc1c68c6da418cda25d198e700819.mmd new file mode 100644 index 0000000000000000000000000000000000000000..64e02b54e4face1e01884ce3fd45ef6343a007fb --- /dev/null +++ b/preprint/preprint__078ee3115bb7f9caf7ff08bd41325e1b3d5bc1c68c6da418cda25d198e700819/preprint__078ee3115bb7f9caf7ff08bd41325e1b3d5bc1c68c6da418cda25d198e700819.mmd @@ -0,0 +1,399 @@ + +# Optical Neural Engine for Solving Scientific Partial Differential Equations + +Weilu Gao + +weilu.gao@utah.edu + +The University of Utah https://orcid.org/0000- 0003- 3139- 034X + +Yingheng Tang + +Lawrence Berkeley National Laboratory + +Ruiyang Chen + +The University of Utah https://orcid.org/0000- 0002- 1538- 1702 + +Minhan Lou + +The University of Utah + +Jichao Fan + +The University of Utah + +Cunxi Yu + +University of Maryland, College Park + +Andy Nonaka + +Lawrence Berkeley National Laboratory + +Zhi Yao + +Lawrence Berkeley National Laboratory + +Article + +Keywords: + +Posted Date: September 30th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 5061922/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> + +Version of Record: A version of this preprint was published at Nature Communications on May 17th, 2025. See the published version at https://doi.org/10.1038/s41467-025-59847-3. + +<--- Page Split ---> + +# Optical Neural Engine for Solving Scientific Partial Differential Equations + +Yingheng Tang \(^{1\ast}\) , Ruiyang Chen \(^{2\dagger}\) , Minhan Lou \(^{2}\) , Jichao Fan \(^{2}\) , Cunxi Yu \(^{3}\) , Andy Nonaka \(^{1}\) , Zhi (Jackie) Yao \(^{1*}\) , Weilu Gao \(^{2*}\) + +\(^{1}\) Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. \(^{2}\) Department of Electrical and Computer Engineering, The University of Utah, Salt Lake City, UT 84112, USA. \(^{3}\) Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA. + +\*Corresponding author(s). E- mail(s): ytang4@lbl.gov; jackie_zhiyao@lbl.gov; weilu.gao@utah.edu; †These authors contribute equally + +## Abstract + +Solving partial differential equations (PDEs) is the cornerstone of scientific research and development. Data- driven machine learning (ML) approaches are emerging to accelerate time- consuming and computation- intensive numerical simulations of PDEs. Although optical systems offer high- throughput and energy- efficient ML hardware, there is no demonstration of utilizing them for solving PDEs. Here, we present an optical neural engine (ONE) architecture combining diffractive optical neural networks for Fourier space processing and optical crossbar structures for real space processing to solve time- dependent and time- independent PDEs in diverse disciplines, including Darcy flow equation, the magnetostatic Poisson's equation in demagnetization, the Navier- Stokes equation in incompressible fluid, Maxwell's equations in nanophotonic metasurfaces, and coupled PDEs in a multiphysics system. We numerically and experimentally demonstrate the capability of the ONE architecture, which not only leverages the advantages of high- performance dual- space processing for outperforming traditional PDE solvers and being comparable with state- of- the- art ML models but also can be implemented using optical computing hardware with unique features of low- energy and highly parallel constant- time processing irrespective of model scales and real- time reconfigurability for tackling multiple tasks with the same architecture. The demonstrated architecture offers a versatile and powerful platform for large- scale scientific and engineering computations. + +<--- Page Split ---> + +## Introduction + +Partial differential equations (PDEs) derived from physical laws have been a powerful and faithful computational tool to accelerate the exploration and validation of scientific hypotheses instead of performing expensive and time- consuming real- world experiments [1]. Hence, numerically solving PDEs is essential for scientific research and development in nearly every scientific domain. For example, the interaction of electromagnetic waves with materials and engineered structures in broad applications such as communication, imaging, sensing, and quantum technologies is governed by Maxwell's equations [2]; automotive and flight aerodynamics for designing and manufacturing road vehicles and airplanes is determined by Navier- Stokes equation [3]; the Earth system including temperature, atmosphere, and ice sheets for understanding climate change and making policies is also described with a series of PDEs [4]. However, current numerical simulation methods to solve PDEs, such as finite difference/volume methods to solve Maxwell's and the Navier- Stokes equations, are costly in computing time and resources. + +Machine learning (ML) offers a new perspective on solving PDEs through data- driven approaches to enable fast and accurate simulations of many multiphysics and multiscale processes [5- 7]. However, the ML model deployment on electronic computing hardware requires substantial computing resources and consumes substantial energy. In the foreseeable future, the fundamental quantum mechanics limit will lead to a bottleneck of further reducing the energy consumption and simultaneously increasing the integration density of electronic circuits to catch up with the increasing scale of ML models in demand for solving complex problems [8, 9], thus urgently calling for new high- throughput and energy- efficient ML hardware accelerators. Recently, optical architectures, including photonic integrated circuits for matrix- vector multiplication (MVM) [10, 11], for neuro- inspired spiking neural networks [12, 13], and for photonic reservoir computing [14, 15], and free- space optical systems for MVM [16- 18] and diffractive optical neural networks (DONNs) [19- 22], are emerging as high- performance ML hardware accelerators by leveraging different particles - photons - to break down electronic bottleneck thanks to high parallelism and low static energy consumption of photons [23]. However, to date, there is no deployment of any optical computing systems for solving PDEs in any scientific domain. + +Here, we present a fully reconfigurable and scalable optical neural engine (ONE) architecture that combines DONN systems for processing data in Fourier space and optical crossbar (XBAR) structures for processing data in real space to solve two- dimensional (2D) spatiotemporal profiles in time- independent and time- dependent PDEs. The ONE architecture not only leverages the advantages of high- performance dual- space processing [24], but also can be implemented using optical computing hardware with unique features of low- energy and highly parallel constant- time processing irrespective of model scales, and real- time reconfigurability for tackling multiple tasks with the same architecture. We numerically and experimentally demonstrate the capability of the ONE architecture in solving a broad range of PDEs in diverse disciplines, including the Darcy flow equation in fluid dynamics, the magnetostatic Poisson's equation in micromagnetics, the Navier- Stokes equation in aerodynamics, Maxwell's equations in nanophotonics, and coupled electric current and heat transfer equations in + +<--- Page Split ---> + +a multiphysics electrical heating problem. The ONE architecture not only outperforms traditional PDE solvers because of its data- driven nature, but also shows comparable and better performance with other ML models while with substantial hardware advantages because of its implementation in the optical domain. The demonstrated ONE architecture is versatile and can be tailored with different combinations of DONN and XBAR structures for solving various PDEs, offering a transformative universal solution for large- scale scientific and engineering computations. + +## Results + +## ONE Architecture + +Figure 1a illustrates the ONE architecture, which takes the spatiotemporal data of an input physical quantity \(\mathbf{U}\) , described as a function \(u(x,y,t)\) in terms of positions \(x\) and \(y\) and time \(t\) , to predict the spatiotemporal data of an output physical quantity \(\mathbf{G}\) described using a function \(g(x,y,t)\) . The input and output quantities \(\mathbf{U}\) and \(\mathbf{G}\) can be connected through either a single- physics PDE or coupled multiphysics PDEs. There are three branches inside the ONE architecture, including (i) Fourier space processing branch, (ii) real space processing branch, and (iii) physics parameter processing branch. The combination of both real and Fourier space processing has been proven fast, powerful, and efficient in solving PDEs [24], and the incorporation of additional physics parameter processing enables the fusion of multimodal data for complex tasks [25]. More importantly, most operations in these branches can be deployed on optical computing hardware in both real and Fourier space, enabling solving PDEs in high- throughput and energy- efficient manners. The details of each branch are described below. + +In the first Fourier space processing branch, the core arithmetic operations are based on Fourier and inverse Fourier transformations to process input spatiotemporal data in the Fourier space. Their optical hardware implementations are mainly based on reconfigurable DONNs, which contain cascaded reconfigurable diffractive layers. Reconfigurable DONNs can be implemented in both integrated photonic chips [26, 27] and free space [19- 21]; see Fig. 1b. There are two fundamental operations in DONNs - optical diffraction and spatial light modulation. For the optical diffraction operation, an optical field right after the \(l\) - th diffractive layer, \(f_{l}\) , diffracts to the front of \((l + 1)\) - th layer, whose optical field, \(f_{\mathrm{in},l + 1}\) , is a convolution of \(f_{l}\) and the diffraction impulse function \(h(x,y)\) . Specifically, the complex- valued field at point \((x,y)\) on the input plane of \((l + 1)\) - th layer can be written as the convolution of all fields at the output plane of \(l\) - th layer as + +\[f_{\mathrm{in},l + 1}(x,y,z) = \iint f_{l}(x^{\prime},y^{\prime},0)h(x - x^{\prime},y - y^{\prime})dx^{\prime}dy^{\prime},\] + +where \(z\) is the distance between two diffractive layers and \(h(x,y)\) is the impulse response function of free space. By the convolution theorem, this 2D convolution can be efficiently calculated in Fourier space based on Fourier and inverse Fourier transformations. Specifically, the 2D Fourier transformation \(\mathcal{F}_{xy}\) of \(f\) and \(h\) , \(F\) and \(H\) , are + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1 ONE architecture and hardware implementations. (a) Illustration of processing branches and flows in the ONE architecture to predict output spatiotemporal output physical quantities from corresponding input and solve PDEs involving single or multiple physics. Illustrations of integrated and free-space implementations of reconfigurable (b) DONN and (c) XBAR structures.
+ +connected through + +\[\mathcal{F}_{xy}(f_{\mathrm{in},l + 1}(x,y,z)) = \mathcal{F}_{xy}(f_l(x,y,0))\mathcal{F}_{xy}(h(x,y)),\] \[F_{\mathrm{in},l + 1}(\alpha ,\beta ,z) = F_l(\alpha ,\beta ,0)H(\alpha ,\beta),\] + +where \(\alpha ,\beta\) are spatial domain indices. After diffraction, the 2D inverse Fourier transformation \(\mathcal{F}_{xy}^{- 1}\) of \(F_{\mathrm{in},l + 1}(\alpha ,\beta ,z)\) , \(f_{\mathrm{in},l + 1}(x,y,z)\) , is then spatially modulated. Each diffraction pixel at location \((x,y)\) has a complex- valued electric field transmission coefficient \(t(x,y,S)e^{\phi (x,y,S)}\) , where \(t(x,y,S)\) \((\phi (x,y,S))\) is the amplitude (phase) response as a function of external stimuli \(S\) , such as voltages. The spatial light modulation operation is expressed as a pixel- wise multiplication + +\[f_{l + 1}(x,y,z) = \mathcal{F}_{xy}^{-1}(F_{\mathrm{in},l + 1}(\alpha ,\beta ,z))t(x,y,S)e^{\phi (x,y,S)}\] + +<--- Page Split ---> + +\[= f_{\mathrm{in},l + 1}(x,y,z)t(x,y,S)e^{\phi (x,y,S)},\] + +where \(f_{l + 1}(x,y,z)\) is the near- field output field right after the \((l + 1)\) - th layer. More details can be found in Methods. + +Before and between DONN kernels, there is a linear transformation operation based on fully connected layers to scale up the number of channels and a channel mixing operation based on matrix multiplications [24]. The core arithmetic operations are based on MVM. Their optical hardware implementations are mainly based on reconfigurable optical XBAR structures, which encode element values of vector \(\mathbf{v}\) and matrix \(\mathbf{M}\) into light intensity through electro- optic modulators, perform multiplications through cascaded modulators, and add signals at the output detector array. The signals are routed to follow mathematical calculations in MVM so that the reading from the detector array represents the output vector \(\mathbf{o} = \mathbf{M}\times \mathbf{v}\) . Reconfigurable XBAR structures can also be implemented in both integrated photonic chips [10, 11] and free space [16- 18]; see Fig. 1c. More details on the operation mechanism can be found in Methods and Supplementary Fig. 1. + +The second real space processing branch contains fully connected layers, whose operations are also based on MVM and implemented with optical XBAR structures. The output from the Fourier space branch, \(\mathbf{F}(u)\) , and the output from the real space branch, \(\mathbf{R}(u)\) are added and further processed with a nonlinear operation. Note that the nonlinear operation is the only operation performed in electronic hardware in the ONE architecture. Moreover, this combination of real space, Fourier space, and nonlinear processing is scaled up, repeated four times, and cascaded in series. The third branch is to perform a linear transformation on other relevant physics parameters \(d(t)\) , which are time sequences instead of spatiotemporal data, based on fully connected layers. The obtained data \(\mathbf{T}(d)\) is multiplied and merged onto two other branches to have the final output \(g(x,y,t)\) . Hence, except nonlinear operations, all other operations can be done with DONN and optical XBAR systems. These two systems can be seamlessly assembled into a single integrated photonic chip or a single free- space optical system for all- optical operations without converting between optical and electronic hardware, fully leveraging the advantages of high throughput and high parallelism in optical computing systems. More details on the ONE architecture model are in Methods. + +## Darcy flow and magnetostatic Poisson's equations + +The first PDE we solved with the ONE architecture is the Darcy flow equation in fluid dynamics physics. This PDE describes a fluid flow through a porous medium as shown in Fig. 2a. Specifically, the equation is + +\[-\nabla \cdot (k(x,y)\nabla u(x,y)) = f(x,y),\] + +where \(k(x,y)\) is the permeability field of the medium, \(u(x,y)\) is the pressure field of the flow, and \(f(x,y)\) is the force function. The ONE architecture was trained to learn the mapping from the 2D function \(k(x,y)\) to function \(u(x,y)\) . More details about the equation dataset generation and training are in Methods. Figure 2b displays the training loss curves for inputs with different resolutions. The training loss + +<--- Page Split ---> + +is generally low for all resolutions and slightly increases at the highest 421 resolution. Figure 2c shows the comparison of the training loss of our ONE architecture with other PDE solving models, including fully convolution networks (FCN) [28], principal component analysis- based neural network (PCANN) [29], reduced biased method (RBM) [30], graph neural operator (GNO) [31], low- rank kernel decomposition neural operator (LNO) [25], multipole graph neural operator (MGNO) [32], and Fourier neural operator (FNO) [24]. The performance of the ONE architecture is comparable with the state- of- the- art neural operators including GNO, LNO, MGNO, and FNO, and is better than FCN. Further, from the hardware perspective, the ONE architecture is constructed based on high- throughput optical computing hardware platforms so that all operations can be performed in parallel within a single clock cycle. In addition, the ONE architecture can be practically implemented on a large scale. For example, free- space reconfigurable DONNs [20, 21, 33] and optical MVM [17] are typically implemented using spatial light modulators (SLMs) with a scale \(>1000 \times 1000\) . Hence, the execution cost of solving PDEs with different scales and resolutions is invariant, meaning \(\mathcal{O}(1)\) , if the scale of the optical hardware in the ONE architecture is large enough. Figure 2d displays the input permeability field \(k(x, y)\) , the expected ground truth of output pressure field \(u(x, y)\) , the predicted output pressure field, the absolute error between the expected and predicted outputs, and the relative error between the expected and predicted outputs, at the lowest 85 and the highest 421 resolutions, respectively. This visualization further validates the ONE architecture in solving PDEs. More data on other resolutions are shown in Supplementary Fig. 2. + +The second PDE we solved is the magnetostatic Poisson's equation of demagnetization in micromagnetics physics. This PDE calculates the demagnetizing field \(\mathbf{H}\) generated by the magnetization field \(\mathbf{M}\) as shown in Fig. 2e. Specifically, the equation is obtained from Maxwell's equation as + +\[\nabla \cdot \mathbf{H} = -\nabla \cdot \mathbf{M}.\] + +By defining an effective magnetic charge density \(\rho = - \nabla \cdot \mathbf{M}\) and a magnetic scalar potential \(\Phi\) assuming there is no free current, we can express the demagnetizing field \(\mathbf{H} = - \nabla \Phi\) and rewrite the previous equation as a Poisson's equation + +\[\nabla^{2}\Phi = -\rho .\] + +Similar to solving the Darcy flow equation, the ONE architecture was trained to learn the mapping from components of \(\mathbf{M}\) to \(\mathbf{H}\) vector fields. More details about the equation dataset generation and training are in Methods. Figure 2f shows the validation loss curve and Fig. 2g shows the input one component of \(\mathbf{M}\) field, the expected ground truth of output \(H_{x}\) component of \(\mathbf{H}\) field, the predicted output \(H_{x}\) component, the absolute error between the expected and predicted outputs, and normalized error between the expected and predicted outputs with respect to the maximum field strength in the ground truth. Both confirm a good performance of the ONE architecture in solving the magnetostatic Poisson's equation. More data on \(H_{y}\) and \(H_{z}\) components is shown in Supplementary Fig. 3. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2 Solving Darcy flow and magnetostatic Poisson's equations. (a) Illustration of the Darcy flow equation describing a fluid flow through a porous medium. The ONE architecture learns the mapping between the permeability and pressure fields. (b) Training loss curves for input data with different resolutions. (c) Comparison of the training loss of different models at various resolutions. (d) Input permeability field, the expected ground truth of output pressure field, the predicted output pressure field, the absolute error between the expected and predicted outputs, and the relative error between the expected and predicted outputs, at 85 and 421 resolutions. (e) Illustration of the magnetostatic Poisson's equation calculating the demagnetizing field generated by the magnetization field. The ONE architecture learns the mapping between these two fields. (f) Validation loss curve for the ONE architecture solving the magnetostatic Poisson's equation and (g) corresponding input magnetization field, the expected ground truth of output demagnetizing field, the predicted output demagnetizing field, the absolute and normalized errors between the expected and predicted outputs.
+ +## Navier-Stokes and Maxwell's equations + +In addition to steady- state Darcy flow and magnetostatic Poisson's equations without time evolution, we employed the ONE architecture to solve time- dependent PDEs, including the Navier- Stokes equation in fluid dynamics and Maxwell equations in electromagnetics and optics. In particular, the real- time reconfigurability of DONN and optical XBAR structures makes the ONE architecture suitable for such a purpose. + +<--- Page Split ---> + +Specifically, we solved a 2D Navier- Stokes equation for a viscous, incompressible fluid in vorticity form on the unit torus as shown in Fig. 3a. This PDE calculates the time evolution of vorticity described as + +\[\partial_{t}w(x,y,t) + u(x,y,t)\cdot \nabla w(x,y,t) = v\Delta w(x,y,t) + f(x,y),\] + +where \(u\) is the velocity field, \(w = \nabla \times u\) is the vorticity, \(\nu\) is the viscosity coefficient, \(f\) is the forcing function. The ONE architecture was trained to learn the mapping from \(w\) in a time range from 0 to \(t_0\) to \(w\) in a time range from \(t_0\) to \(t_1\) ( \(t_1 > t_0\) ). More details about the equation dataset generation and training are in Methods. Further, we also solved Maxwell's equations in a dielectric metasurface consisting of multiple cylindrical pillars in a unit cell of a periodic pattern as shown in Fig. 3b [34]. The general Maxwell's equations can calculate the time evolution of an electric field through the following equations + +\[\nabla \cdot \mathbf{D} = \rho ,\] \[\nabla \cdot \mathbf{B} = 0,\] \[\nabla \times \mathbf{E} = -\frac{\partial\mathbf{B}}{\partial t},\] \[\nabla \times \mathbf{H} = \mathbf{J} + \frac{\partial\mathbf{D}}{\partial t},\] + +where \(\mathbf{D}\) is the electric displacement field, \(\rho\) is the free charge density, \(\mathbf{B}\) is the magnetic flux density, \(\mathbf{E}\) is the electric field, \(\mathbf{H}\) is the magnetic field, and \(\mathbf{J}\) is the free current density. The ONE architecture was trained to learn the mapping from \(\mathbf{E}\) in a time range from 0 to \(t_0\) to \(\mathbf{E}\) in a time range from \(t_0\) to \(t_1\) ( \(t_1 > t_0\) ). More details about the dataset generation and training are in Methods. Figure 3c displays the validation loss curve for solving the Navier- Stokes equation with \(t_0 = 10\) and \(t_1 = 20\) . Figure 3d displays the validation loss curves for solving Maxwell's equations with \(t_0 = 10\) and \(t_1 = 20,30,40\) , respectively. Moreover, Figure 3e and 3f show the expected ground truth of \(w\) field and the \(E_x\) component of the \(\mathbf{E}\) field at \(t_1\) , the corresponding predicted fields at \(t_1\) , and the absolute and relative errors between ground truth and prediction for the Navier- Stokes equation and Maxwell's equations, respectively. All confirm a good performance in solving time- dependent PDEs using the ONE architecture. + +## Multiphysics PDEs + +Moreover, we employed the ONE architecture to solve coupled PDEs involving two physics. Specifically, we solved an electrical heating problem to obtain a temperature profile at an intermediate time step \(t_n\) , \(T(x,y,t_n)\) , in an electrical circuit when a time- dependent voltage signal was applied to the circuit pads, involving coupled electric current physics and heat transfer physics; see Fig. 4a. Specifically, for the electrical current physics, the corresponding PDE is + +\[Q_{e} = d\sigma \nabla_{t}V(x,y,t),\] + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3 Solving time-dependent Navier-Stokes and Maxwell's equations. Illustrations of (a) Navier-Stokes equation for solving the time evolution of the vorticity field in a viscous, incompressible fluid in vorticity form on the unit torus and (b) Maxwell's equations for solving the time evolution of the electric field in a dielectric metasurface. Validation loss curves for (c) solving the Navier-Stokes equation and (d) Maxwell's equations using the ONE architecture. The expected ground truth field, the predicted field, and the absolute and relative errors between these two fields for (e) the Navier-Stokes equation and (f) Maxwell's equations, respectively.
+ +\[V(x_{0},y_{0},t) = \mathrm{rect}(t),\] + +where \(Q_{e}\) is the heat rate per unit area from an electromagnetic heating source, \(d\) is the thickness of the heating layer, \(V(x,y,t)\) is the voltage profile in the circuit that is subjected to a voltage boundary condition defined in the pads \(V(x_{0},y_{0},t)\) , and \(V(x_{0},y_{0},t)\) is a pulse rectangular function \(\mathrm{rect}(t)\) with pulse height and width. For the heat transfer physics, the corresponding PDE is + +\[\rho C_{p}\frac{\partial T}{\partial t} +\rho C_{p}\mathbf{u}\cdot \nabla T - \nabla \cdot (k\nabla T) = Q_{e},\] + +where \(\rho\) is the mass density, \(C_{p}\) is the specific heat capacity, \(T\) is the absolute temperature, and \(k\) is the thermal conductivity. These two PDEs are connected through the quantity \(Q_{e}\) . The ONE architecture was trained to learn the mapping from \(V(x,y,t)\) + +<--- Page Split ---> + +in a time range spanning all time steps in input pulses to \(T(x,y,t_{n})\) at an intermediate pulse time step \(t_{n}\) . In contrast to previous examples, the pulse information, including pulse height and width, was processed through the physics parameter processing branch in the ONE architecture (Fig. 1a) and multiplied with the output from cascaded real space processing and Fourier space processing branches to yield the final output. More details about the dataset generation and training are in Methods. Figure 4b displays the validation loss curve and Fig. 4c shows a few representative input 2D data \(V(x,y,t)\) at various time steps. Figure 4d shows the expected ground truth of \(T(x,y,t_{n})\) , the corresponding predicted temperature profile, and the absolute and relative errors between ground truth and prediction. All confirm a good performance in solving multiphysics PDEs using the ONE architecture. + +![](images/Figure_4.jpg) + +
Fig. 4 Solving multiphysics PDEs. (a) Illustration of solving coupled PDEs in an electrical heating problem involving electric current physics and heat transfer physics. (b) Validation loss curve. (c) A few representative 2D voltage profiles in the circuit. (d) The expected ground truth temperature profile, the predicted profile, and the absolute and relative errors between these two profiles.
+ +## Experimental demonstration + +Finally, to demonstrate the experimental feasibility of the ONE architecture, we constructed a free- space reconfigurable DONN setup and evaluated the performance of + +<--- Page Split ---> + +solving the Darcy flow equation under different hardware noise levels in optical XBAR structures. Figure 5a displays a photo and schematic of the reconfigurable DONN setup, which contains a laser source, a reconfigurable input encoder, two reconfigurable diffractive layers, and a camera. The reconfigurable encoder and diffractive layers were built upon SLMs, which can modulate the amplitude and phase of transmitted light when applying voltage. Multiple light polarization components, including polarizers and half- wave plates, were also employed to manipulate polarization states to achieve large phase modulation ranges. More details on the experimental setup are in Methods. + +![](images/Figure_5.jpg) + +
Fig. 5 Experimental demonstration. (a) Photo and schematic of a reconfigurable DONN experimental setup consisting of a reconfigurable input encoder, two reconfigurable diffractive layers, and a camera. Polarization components were used to configure SLMs in the phase modulation mode. (b) Output 2D data in one DONN kernel of the Fourier space processing branch in the ONE architecture obtained from model calculations and experimental measurements. (c) Validation loss curves at different noise levels in optical XBAR structures and (d) the loss at the final epoch as a function of noise level.
+ +<--- Page Split ---> + +As shown in Supplementary Fig. 4, the experimentally measured amplitude and phase modulation responses of all three SLMs are not only discrete with respect to grey levels but also coupled and dependent. To leverage the gradient- based ML training algorithm, we utilized the Gumbel- softmax reparameterization technique to approximate a discrete distribution to a continuous distribution [21]. More details are described in Methods. Moreover, the values of input 2D data span both negative and positive values and were encoded as the grey level of the SLM in the reconfigurable input encoder (SLM0 in Fig. 5a). We performed the encoding through linear mapping from minimum and maximum values of input data to a grey- level range in the SLM. More details are described in Methods. In addition, we precisely aligned all SLMs with respect to each other within a range of a few pixels on the order of hundreds of \(\mu \mathrm{m}\) ; see Supplementary Fig. 5. Although the long optical path in the system makes the alignment sensitive to external variations, the system's full reconfigurability can enable fast adaptive pixel- by- pixel re- alignment. Figure 5b shows output 2D data in one DONN kernel of the Fourier space processing branch in the ONE architecture (Fig. 1a) obtained from model calculations and experimental measurements, showing good agreement and experimentally validating the feasibility of the ONE architecture in solving PDEs. More data is shown in Supplementary Fig. 6. There are some speckles in the background of measured images, which probably originate from high- order diffraction interference, leading to numerical errors in the ONE architecture for performing regression tasks. This discrepancy between models and experiments can be mitigated through hardware- software co- design, such as incorporating loss functions based on experimental results for gradient calculations as demonstrated in prior works [20, 33, 35]. + +We also evaluated the performance of the ONE architecture under different noise levels of optical XBAR structures. Specifically, we added random Gaussian noise with zero mean and varying standard deviation (Std) to the values obtained from matrix multiplications to represent hardware noise, such as shot noise in photodetectors [36]. The corresponding MVM results and histograms of different noise standard deviation values are shown in Supplementary Fig. 7, and more details can be found in Methods. As shown in Fig. 5c and Fig. 5d, the validation loss increases with the increasing noise standard deviation value. The current hardware implementation of optical XBAR structures with advanced components and calibration algorithms [16- 18], including the structure we demonstrated before [36], can achieve quite a small noise level similar or below the noise level corresponding to 0.5 Std. Hence, the noise influence in optical XBAR structures on the performance of the ONE architecture is not substantial. + +We further estimated the potential throughput and power consumption of the ONE architecture implemented using optical computing hardware for inference. The throughput is mainly determined by the SLM refresh rate and camera frame rate. Current commercial SLMs and cameras can have rates \(>1000\mathrm{Hz}\) , meaning that the inference time for one instance is \(< 1\mathrm{ms}\) . In contrast, it typically takes minutes to hours to numerically solve PDEs. Hence, the ONE architecture features \(>10^{5}\) (five orders of magnitude) acceleration compared to typical PDE solvers. This throughput is also comparable to the state- of- art ML model, such as FNO with a \(5\mathrm{ms}\) inference time [24]. Moreover, the system throughput can be substantially improved with device + +<--- Page Split ---> + +innovation. For example, an electro-optic SLM based on organic molecules can achieve \(> \mathrm{GHz}\) switching speed [37], and an ultrafast camera can achieve a trillion frames per second [38]. With these devices, the ONE architecture can achieve an inference time \(< 1 \mathrm{ns}\) . The power consumption is mainly determined by the leakage current of liquid crystal cells in SLMs. Because of the dielectric nature of liquid crystals and their high leakage resistance, the leakage current is typically \(< 1 \mu \mathrm{A}\) . Hence, assuming a \(10 \mathrm{V}\) driving voltage, the static power consumption of SLMs is \(\sim 10 \mu \mathrm{W}\) , which is nearly \(10^{7}\) (seven orders of magnitude) smaller than typical GPU inference power \(\sim 100 \mathrm{W}\) . + +## Discussion + +We have demonstrated the ONE architecture and validated its performance in solving a broad range of PDEs in diverse scientific domains. The ONE architecture is versatile and can be modified to reduce the interface and connection between DONN and optical XBAR structures and facilitate the hardware implementation of the whole system. Further, in a whole system, active learning and noise- aware training can be incorporated to mitigate the discrepancy between models and practical systems for accurate deployment. Moreover, in addition to solving PDEs, the ONE architecture can be tailored to accelerate ML models for other regression problems. + +## Methods + +DONN diffraction model – The diffraction impulse function \(h(x, y)\) was described using the Fresnel equation as + +\[h(x,y) = \frac{e^{ikz}}{i\lambda z} e^{\frac{ik}{2z} (x^{2} + y^{2})},\] + +where \(\lambda\) is the wavelength, \(k = 2\pi /\lambda\) is the free- space wavenumber, \((x, y)\) are positions within a plane perpendicular to the wave propagation direction, \(z\) is the distance along the propagation direction, and \(i\) is the imaginary unit. The 2D Fourier transformation was directly performed on \(h(x, y)\) for model training and evaluation. To match the experimental setup as described below, \(h(x, y)\) was first discretized with respect to a defined rectangular mesh grid in the convolution calculation and then converted into the Fourier space through 2D Fourier transformation. More details can be found in our prior work [21]. + +The operation mechanism of optical XBAR structures – Supplementary Fig. 1a shows the detailed schematic of an integrated photonic XBAR structure. Specifically, the element values of a \(n \times 1\) input vector \(\mathbf{v}\) are represented by the intensities of light at input waveguides, \(\{I_{1}, I_{2}, I_{3}, \ldots , I_{n}\}\) , which can be implemented by modulating an equally distributed laser intensity through a \(n \times 1\) array of electro- optic modulators (red squares in Supplementary Fig. 1a) at input waveguides. The light on each row waveguide is then equally distributed to the column waveguides connected to that row waveguide and modulated through an electro- optic modulator on the coupled curved waveguide (yellow squares in Supplementary Fig. 1a). The element values of a \(m \times n\) + +<--- Page Split ---> + +matrix \(\mathbf{M}\) are represented by the transmittance of modulators on curved waveguides, \(\{T_{i j}\} ,i\in [1,m],j\in [1,n]\) . At the end of each column waveguide, a photodetector collects all light intensity passing through the column waveguide. The obtained photocurrents or photovoltages of a \(m\times 1\) photodetector array represent the summation of multiplied input vector light intensity and matrix modulator transmittance, and the element values of output vector \(\mathbf{o}\) , \(O_{j} = \sum_{s = 1}^{n}T_{j s}I_{s},j\in [1,m]\) . Hence, this integrated photonic XBAR structure can implement MVM in the optical domain. + +Similarly, Supplementary Fig. 1b shows the detailed schematic of a free- space optical XBAR structure. Specifically, the element values of a \(n\times 1\) input vector \(\mathbf{v}\) are represented by the intensities of light, \(\{I_{1},I_{2},I_{3},\dots,I_{n}\}\) , which is implemented through a \(n\times 1\) array of free- space vector SLM. The output light is broadcast to a \(m\times n\) array of matrix SLM through lenses so that the light distribution from vector SLM is identical at each column of matrix SLM. The element values of a \(m\times n\) matrix \(\mathbf{M}\) are represented by the transmittance of matrix SLM, \(\{T_{i j}\} ,i\in [1,m],j\in [1,n]\) . Lenses are then used to focus the output light from each modulator on the same column of matrix SLM to a photodetector. The readings from a \(m\times 1\) photodetector array represent the element values of output vector \(\mathbf{o}\) , \(O_{j} = \sum_{s = 1}^{n}T_{j s}I_{s},j\in [1,m]\) . Hence, this free- space optical XBAR structure can also implement MVM in the optical domain. + +ONE architecture model – The ONE architecture model was constructed with two main modules – the DONN module processing data in the Fourier space and the optical XBAR module processing linear operations. The mathematical operations in DONN and optical XBAR structures have been described before and their accurate models have been implemented in our prior works, closely matching experimental results [21, 36]. Briefly, the DONN module was modeled by combining the Fresnel free- space diffraction with phase- only spatial light modulation in a range of \([0,2\pi ]\) in the model and coupled spatial light modulation as shown in Supplementary Fig. 4; the optical XBAR module was represented as matrix multiplication incorporating measurement noise. Both modules were implemented under the PyTorch 1.12 framework with graphics processing unit (GPU)- accelerated parallel computation and gradient backpropagation for training. The GPU used in this work was an Nvidia RTX 6000 card. + +Darcy flow equation dataset and training – A 2D Darcy flow equation on the unit box was employed as described in detail in Ref. [24]. The corresponding PDE is a second- order, linear, elliptic PDE as + +\[-\nabla \cdot (k(x,y)\nabla u(x,y)) = f(x,y), \qquad x\in (0,1),y\in (0,1),\] \[u(x) = 0, \qquad x\in \partial (0,1),y\in \partial (0,1)\] + +with a Dirichlet boundary condition. We used the Darcy flow dataset from the existing dataset in Ref. [24] with a boundary condition \(u(x,y) = 0\) on domain edges. The coefficient \(k(x,y)\) was generated based on a specific distribution with the value 12 for positive inputs and 3 for negative inputs. The forcing term was fixed at \(f(x,y) = 1\) . The solution \(u(x,y)\) was computed using a second- order finite difference method on a \(421 \times 421\) grid, and other resolutions were obtained with downsampling. We used a \(10:1\) ratio for the numbers of data in the training set and validation set, respectively. + +<--- Page Split ---> + +The model was trained with a total of 600 epochs and a batch size of 40. The learning rate was 0.1 for the trainable parameters in DONNs and 0.001 for all other trainable parameters with the Adam optimizer. + +Magnetostatic Poisson's equation dataset and training – The demagnetizing field \(\mathbf{H}\) originates from the magnetization within the material itself, which can be calculated as the convolution of \(\mathbf{M}\) with the demagnetization tensor \(\mathbf{N}\) as + +\[\mathbf{H}(\mathbf{r}) = \int \mathbf{N}(\mathbf{r} - \mathbf{r}^{\prime})\mathbf{M}(\mathbf{r}^{\prime})d\mathbf{r}^{\prime}.\] + +This convolution was computed through Fourier space representations of fields. Specifically, to create the dataset, we utilized the MagneX solver [39] to simulate the time evolution of magnetization in a thin magnetic film with dimensions of \(500 \times 125 \times 3.125 \mathrm{~nm}\) . The modeling incorporated both demagnetization and exchange interactions. Initially, we relaxed the magnetic field into a stable S- state before subjecting the system to varying external magnetic fields in different scenarios. We uniformly sampled 8 bias \(\mathbf{H}\) fields in the \(x\) and \(y\) directions, each with a magnitude of 19872 A/m. The system evolved for 1 ns, during which we collected paired data of \(\mathbf{M}\) and \(\mathbf{H}\) fields. Each field was represented by three channels corresponding to the field components in \(x\) , \(y\) , and \(z\) directions. The dataset was divided into training and testing sets with an \(8:2\) ratio. The training was conducted over 500 epochs with a batch size of 128. The learning rate was set to 1.0 for the trainable parameters in DONNs and 0.001 for all other trainable parameters with the Adam optimizer. + +Navier- Stokes equation dataset and training – A 2D Navier- Stokes equation for a viscous, incompressible fluid in vorticity form on the unit torus was used to generate spatiotemporal data for training the ONE architecture. The details are described in Ref. [24]. Specifically, the PDEs are + +\[\partial_{t}w(x,y,t) + u(x,y,t)\cdot \nabla w(x,y,t) = v\Delta w(x,y,t) + f(x,y),x\in (0,1),y\in (0,1),t\in (0,T]\] \[\nabla \cdot u(x,y,t) = 0,x\in (0,1),y\in (0,1),t\in (0,T]\] \[w(x,y,0) = w_{0}(x,y),x\in (0,1),y\in (0,1),\] + +where \(w_{0}(x,y)\) is the initial vorticity and boundary conditions were used. We utilized the existing dataset with the viscosity coefficient \(v = 10^{- 3}\) from Ref. [24] for training and inference. The samples in the dataset were recorded with a time step of \(10^{- 4}\) s. We used 1000 data as the training set and 100 data as the validation set. We trained the ONE architecture model with the first 10 vorticity fields \((w(x,y,t))\) to predict the time evolution of the next 10 vorticity fields. The model was trained with a total of 600 epochs and a batch size of 40. The learning rate was 0.1 for the trainable parameters in DONNs and 0.001 for all other trainable parameters with the Adam optimizer. + +Maxwell's equations dataset and training – We employed commercial Ansys Lumerical finite- difference- time- domain simulation software to generate an electric field dataset by solving Maxwell's equations in dielectric metasurfaces. Specifically, the dielectric metasurface had a periodic pattern and we used four silicon cylindrical rods as the unit cell and periodic boundary condition. Data were generated by randomly + +<--- Page Split ---> + +selecting the radii of four cylindrical rods. The radius was chosen from \(39.5\mu \mathrm{m}\) to \(44.5\mu \mathrm{m}\) with a step of \(0.25\mu \mathrm{m}\) . The simulation time was set as 300000 fs. We generated a total of 1200 data and used 1000 as the training set and the rest 200 as the validation set. The model was trained in an auto- regressive style for the \(E_{x}\) component processing. The \(E_{x}\) field data between 300000 fs to 160000 fs was backward fed into to the model to predict the next 40000 fs \(E_{x}\) field data. The model was trained with a total of 500 epochs and a batch size of 20. The learning rate was 0.1 for the trainable parameters in DONNs and 0.001 for all other trainable parameters with the Adam optimizer. + +Multiphysics dataset and training – We employed commercial COMSOL Multiphysics finite- element simulation software to generate a temperature profile dataset by solving coupled electric current and heat transfer PDEs in an electrical heating circuit. The circuit details can be found in Ref. [40]. Concisely, the circuit contained a serpentine- shaped Nichrome resistive layer with \(10\mu \mathrm{m}\) thick and \(5\mathrm{mm}\) wide on top of a glass plate. A silver contact pad with a dimension \(10\mathrm{mm}\times 10\mathrm{mm}\times 10\mu \mathrm{m}\) was attached at each end. The deposited side of the glass plate was in contact with the surrounding air at \(293.15\mathrm{K}\) and the back side was in contact with the heated fluid at \(353\mathrm{K}\) . Two coupled physics modules, electrical current in layered shells and heat transfer in layered shells, were used in COMSOL simulations. The input voltage pulse height was set from 5 to \(25\mathrm{V}\) with a step of \(1\mathrm{V}\) and the pulse width was set from 20 to \(60\mathrm{s}\) with a step of \(1\mathrm{s}\) . The simulation time range was from 0 to \(110\mathrm{s}\) . We generated a total number of 861 data and divided the data into training and testing set with the splitting ratio of \(8:2\) . The ONE architecture took the electric current layer data as the input spatiotemporal data and the input voltage pulse information was fed into the physics parameter data processing branch to predict temperature field data at \(55\mathrm{s}\) . The model was trained with a total of 100 epochs and a batch size of 40. The learning rate for the trainable parameters in DONNs was 0.1 and the learning rate for all other trainable parameters was 0.001 with the Adam optimizer. + +DONN experimental setup and alignment – The photo and schematic diagram of the DONN experimental setup are displayed in Fig. 5a. The laser diode with a center wavelength \(532\mathrm{nm}\) (CPS532 from Thorlabs, Inc.) was used as a source. The distance between SLMs and between the last SLM and camera was set as \(25.4\mathrm{cm}\) . The polarizers and half- wave plates before and after each SLM were configured so that each SLM operated with a strong modulation of the transmitted electric field phase (phase mode) together with a moderate modulation of light amplitude. The experimentally measured amplitude and phase modulation responses of three SLMs are shown in Supplementary Fig. 4. All transmissive SLMs are the LC 2012 model from HOLOEYE Photonics AG with a refresh rate of \(60\mathrm{Hz}\) . The analog- to- digital converter has 8- bit precision for liquid crystal driving voltage, so that the grey level of SLMs is from 0 to 255. The pixel size of SLMs is \(36\mu \mathrm{m}\times 36\mu \mathrm{m}\) . The output data was captured on a CMOS camera with a frame rate of 34.8 frames per second (CS165MU1 from Thorlabs, Inc.). + +We aligned the DONN setup by loading standard images on SLMs and comparing experimental results with simulation. Specifically, as shown in Supplementary Fig. 5a, standard Gaussian images, which were centered with a peak at 255 grey level and with a standard deviation of 6 pixels, were loaded in the input SLM and two diffractive + +<--- Page Split ---> + +SLMs. Supplementary Fig. 5b displays the simulation pattern for the perfectly aligned setup. During the alignment process, loaded images were moved up, down, left, and right pixel- by- pixel to match the captured images by the camera with the simulation pattern. Supplementary Fig. 5c displays the matched experimental diffraction pattern when the optical setup was aligned, while Supplementary Fig. 5d shows misaligned patterns when there was five- pixel misalignment in vertical and horizontal directions, respectively. + +DONN experimental training with reparameterization – The discrete look- up tables of device responses shown in Supplementary Fig. 4 break the gradient backpropagation in the ML training process in PyTorch. To solve this challenge, we utilized a differentiable reparameterization Gumbel- softmax technique, which was first introduced in Ref. [41] and demonstrated in our prior work [21]. Specifically, continuous noise from the Gumbel distribution was added to the discrete distribution. The argmax function was then used to find the optimized sample. The training problem after this Gumbel- argmax process is mathematically equivalent to the original training problem under one- hot representation [41]. Since the argmax function still breaks the gradient chain, it was replaced with the softmax function to enable differentiability. Hence, this Gumbel- softmax technique, which is also available in PyTorch, offers continuous and differentiable approximation to discrete distributions and the gradient can backpropagate to reduce the loss function. + +DONN experimental grey- level encoding – The global minimum and maximum values in input 2D data were calculated as \(d_{\mathrm{min}}\) and \(d_{\mathrm{max}}\) . A grey level range from 130 to 255 in the input encoder SLM was selected for a relatively large amplitude modulation range to have enough contrast. Hence, any value \(d\) in the input 2D data was converted into a grey level through a linear mapping as + +\[d = \operatorname {int}\left(\frac{255 - 130}{d_{\mathrm{max}} - d_{\mathrm{min}}} + 130\right),\] + +where the int(·) operation rounded the expression to the nearest integer since the SLM grey level must be an integer. + +Optical XBAR noise – The MVM results from an optical XBAR structure were uniformly randomly generated in a range of \(- 15\) to 15, which was the value range in the ONE architecture for solving the Darcy flow equation. The expected number \(o\) was then added with a randomly generated noise from a Gaussian distribution with a zero average and varying standard deviation. The noise- dressed number \(\bar{o}\) was used in ONE architecture calculations. Under different noise standard deviation levels, Supplementary Fig. 7a demonstrates \(\bar{o}\) with respect to \(o\) and Supplementary Fig. 7b displays histograms of \(\bar{o} - o\) . + +## Data availability + +Upon publication, all data that support the plots within this paper and other findings of this study will be available on a public GitHub repository. + +<--- Page Split ---> + +## Code availability + +Code availabilityUpon publication, all codes that support the plots within this paper and other findings of this study will be available on a public GitHub repository. + +## Acknowledgements + +AcknowledgementsR.C., C.Y., and W.G. acknowledge support from the National Science Foundation through Grants No. 2235276, No. 2316627, and No. 2428520. M.L., J.F., and W.G. also acknowledge support from the University of Utah start- up fund. Y.T., Z.Y., and A.N. were supported by Laboratory Directed Research and Development (LDRD) funding from Berkeley Lab, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE- AC02- 05CH11231. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE- AC02- 05CH11231 and under NERSC GenAI award under No. DDR- ERCAP0030541. + +## Author Contributions Statement + +Author Contributions StatementY.T. and W.G. conceived the idea and W.G. supervised the project. Y.T. constructed models and performed machine learning calculations with the help of M.L., J.F., and C.Y and under the support of A.N., Z.Y., and W.G. R.C constructed an optical experimental setup, performed experiments, and performed numerical calculations under the supervision of W.G. Y.T. and W.G. wrote the manuscript. + +## Competing Interests Statement + +The authors declare no competing interests. + +## References + +References[1] Azizzadenesheli, K., Kovachki, N., Li, Z., Liu- Schiaffini, M., Kossaifi, J., Anandkumar, A.: Neural operators for accelerating scientific simulations and design. Nat. Rev. Phys., 1- 9 (2024)[2] Griffiths, D.J.: Introduction to Electrodynamics. Cambridge University Press, New York (2023)[3] Batchelor, G.K.: An Introduction to Fluid Dynamics. Cambridge University Press, New York (2000)[4] Goosse, H.: Climate System Dynamics and Modeling. Cambridge University Press, New York (2015)[5] Jiang, J., Chen, M., Fan, J.A.: Deep neural networks for the evaluation and design of photonic devices. Nat. Rev. Mater., 1- 22 (2020) + +<--- Page Split ---> + +[6] Zobeiry, N., Humfeld, K.D.: A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications. Eng. Appl. Artif. 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Adv. 6(3), 6200 (2020) + +[39] Yao, Z., Kumar, P., Lepelch, J., Nonaka, A.: Code Repository for “MagneX”: https://github.com/AMReX- Microelectronics/MagneX + +[40] COMSOL Tutorial Model of a Heating Circuit. https://comsol.com/model/heating- circuit- 465/ + +<--- Page Split ---> + +[41] Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel- softmax. arXiv preprint arXiv:1611.01144 (2016) + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- SIFinal.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__078ee3115bb7f9caf7ff08bd41325e1b3d5bc1c68c6da418cda25d198e700819/preprint__078ee3115bb7f9caf7ff08bd41325e1b3d5bc1c68c6da418cda25d198e700819_det.mmd b/preprint/preprint__078ee3115bb7f9caf7ff08bd41325e1b3d5bc1c68c6da418cda25d198e700819/preprint__078ee3115bb7f9caf7ff08bd41325e1b3d5bc1c68c6da418cda25d198e700819_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..75a955376bb292a7e28b5a45f2cc3e1c0509fcd6 --- /dev/null +++ b/preprint/preprint__078ee3115bb7f9caf7ff08bd41325e1b3d5bc1c68c6da418cda25d198e700819/preprint__078ee3115bb7f9caf7ff08bd41325e1b3d5bc1c68c6da418cda25d198e700819_det.mmd @@ -0,0 +1,543 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 910, 175]]<|/det|> +# Optical Neural Engine for Solving Scientific Partial Differential Equations + +<|ref|>text<|/ref|><|det|>[[44, 195, 135, 213]]<|/det|> +Weilu Gao + +<|ref|>text<|/ref|><|det|>[[55, 222, 256, 240]]<|/det|> +weilu.gao@utah.edu + +<|ref|>text<|/ref|><|det|>[[44, 268, 610, 288]]<|/det|> +The University of Utah https://orcid.org/0000- 0003- 3139- 034X + +<|ref|>text<|/ref|><|det|>[[44, 293, 175, 312]]<|/det|> +Yingheng Tang + +<|ref|>text<|/ref|><|det|>[[53, 315, 399, 334]]<|/det|> +Lawrence Berkeley National Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 340, 165, 358]]<|/det|> +Ruiyang Chen + +<|ref|>text<|/ref|><|det|>[[53, 362, 608, 381]]<|/det|> +The University of Utah https://orcid.org/0000- 0002- 1538- 1702 + +<|ref|>text<|/ref|><|det|>[[44, 386, 147, 404]]<|/det|> +Minhan Lou + +<|ref|>text<|/ref|><|det|>[[53, 408, 250, 427]]<|/det|> +The University of Utah + +<|ref|>text<|/ref|><|det|>[[44, 433, 147, 450]]<|/det|> +Jichao Fan + +<|ref|>text<|/ref|><|det|>[[53, 455, 250, 473]]<|/det|> +The University of Utah + +<|ref|>text<|/ref|><|det|>[[44, 479, 122, 496]]<|/det|> +Cunxi Yu + +<|ref|>text<|/ref|><|det|>[[53, 500, 371, 519]]<|/det|> +University of Maryland, College Park + +<|ref|>text<|/ref|><|det|>[[44, 525, 162, 543]]<|/det|> +Andy Nonaka + +<|ref|>text<|/ref|><|det|>[[53, 547, 397, 566]]<|/det|> +Lawrence Berkeley National Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 572, 112, 589]]<|/det|> +Zhi Yao + +<|ref|>text<|/ref|><|det|>[[53, 593, 397, 612]]<|/det|> +Lawrence Berkeley National Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 653, 105, 671]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 691, 136, 710]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 729, 355, 749]]<|/det|> +Posted Date: September 30th, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 767, 475, 787]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 5061922/v1 + +<|ref|>text<|/ref|><|det|>[[44, 804, 914, 848]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 865, 535, 886]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 911, 88]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on May 17th, 2025. See the published version at https://doi.org/10.1038/s41467-025-59847-3. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[223, 155, 732, 206]]<|/det|> +# Optical Neural Engine for Solving Scientific Partial Differential Equations + +<|ref|>text<|/ref|><|det|>[[190, 226, 768, 263]]<|/det|> +Yingheng Tang \(^{1\ast}\) , Ruiyang Chen \(^{2\dagger}\) , Minhan Lou \(^{2}\) , Jichao Fan \(^{2}\) , Cunxi Yu \(^{3}\) , Andy Nonaka \(^{1}\) , Zhi (Jackie) Yao \(^{1*}\) , Weilu Gao \(^{2*}\) + +<|ref|>text<|/ref|><|det|>[[186, 270, 771, 353]]<|/det|> +\(^{1}\) Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. \(^{2}\) Department of Electrical and Computer Engineering, The University of Utah, Salt Lake City, UT 84112, USA. \(^{3}\) Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA. + +<|ref|>text<|/ref|><|det|>[[260, 380, 694, 428]]<|/det|> +\*Corresponding author(s). E- mail(s): ytang4@lbl.gov; jackie_zhiyao@lbl.gov; weilu.gao@utah.edu; †These authors contribute equally + +<|ref|>sub_title<|/ref|><|det|>[[443, 454, 512, 467]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[205, 470, 750, 735]]<|/det|> +Solving partial differential equations (PDEs) is the cornerstone of scientific research and development. Data- driven machine learning (ML) approaches are emerging to accelerate time- consuming and computation- intensive numerical simulations of PDEs. Although optical systems offer high- throughput and energy- efficient ML hardware, there is no demonstration of utilizing them for solving PDEs. Here, we present an optical neural engine (ONE) architecture combining diffractive optical neural networks for Fourier space processing and optical crossbar structures for real space processing to solve time- dependent and time- independent PDEs in diverse disciplines, including Darcy flow equation, the magnetostatic Poisson's equation in demagnetization, the Navier- Stokes equation in incompressible fluid, Maxwell's equations in nanophotonic metasurfaces, and coupled PDEs in a multiphysics system. We numerically and experimentally demonstrate the capability of the ONE architecture, which not only leverages the advantages of high- performance dual- space processing for outperforming traditional PDE solvers and being comparable with state- of- the- art ML models but also can be implemented using optical computing hardware with unique features of low- energy and highly parallel constant- time processing irrespective of model scales and real- time reconfigurability for tackling multiple tasks with the same architecture. The demonstrated architecture offers a versatile and powerful platform for large- scale scientific and engineering computations. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[207, 83, 357, 101]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[207, 111, 832, 310]]<|/det|> +Partial differential equations (PDEs) derived from physical laws have been a powerful and faithful computational tool to accelerate the exploration and validation of scientific hypotheses instead of performing expensive and time- consuming real- world experiments [1]. Hence, numerically solving PDEs is essential for scientific research and development in nearly every scientific domain. For example, the interaction of electromagnetic waves with materials and engineered structures in broad applications such as communication, imaging, sensing, and quantum technologies is governed by Maxwell's equations [2]; automotive and flight aerodynamics for designing and manufacturing road vehicles and airplanes is determined by Navier- Stokes equation [3]; the Earth system including temperature, atmosphere, and ice sheets for understanding climate change and making policies is also described with a series of PDEs [4]. However, current numerical simulation methods to solve PDEs, such as finite difference/volume methods to solve Maxwell's and the Navier- Stokes equations, are costly in computing time and resources. + +<|ref|>text<|/ref|><|det|>[[207, 311, 832, 555]]<|/det|> +Machine learning (ML) offers a new perspective on solving PDEs through data- driven approaches to enable fast and accurate simulations of many multiphysics and multiscale processes [5- 7]. However, the ML model deployment on electronic computing hardware requires substantial computing resources and consumes substantial energy. In the foreseeable future, the fundamental quantum mechanics limit will lead to a bottleneck of further reducing the energy consumption and simultaneously increasing the integration density of electronic circuits to catch up with the increasing scale of ML models in demand for solving complex problems [8, 9], thus urgently calling for new high- throughput and energy- efficient ML hardware accelerators. Recently, optical architectures, including photonic integrated circuits for matrix- vector multiplication (MVM) [10, 11], for neuro- inspired spiking neural networks [12, 13], and for photonic reservoir computing [14, 15], and free- space optical systems for MVM [16- 18] and diffractive optical neural networks (DONNs) [19- 22], are emerging as high- performance ML hardware accelerators by leveraging different particles - photons - to break down electronic bottleneck thanks to high parallelism and low static energy consumption of photons [23]. However, to date, there is no deployment of any optical computing systems for solving PDEs in any scientific domain. + +<|ref|>text<|/ref|><|det|>[[207, 555, 832, 739]]<|/det|> +Here, we present a fully reconfigurable and scalable optical neural engine (ONE) architecture that combines DONN systems for processing data in Fourier space and optical crossbar (XBAR) structures for processing data in real space to solve two- dimensional (2D) spatiotemporal profiles in time- independent and time- dependent PDEs. The ONE architecture not only leverages the advantages of high- performance dual- space processing [24], but also can be implemented using optical computing hardware with unique features of low- energy and highly parallel constant- time processing irrespective of model scales, and real- time reconfigurability for tackling multiple tasks with the same architecture. We numerically and experimentally demonstrate the capability of the ONE architecture in solving a broad range of PDEs in diverse disciplines, including the Darcy flow equation in fluid dynamics, the magnetostatic Poisson's equation in micromagnetics, the Navier- Stokes equation in aerodynamics, Maxwell's equations in nanophotonics, and coupled electric current and heat transfer equations in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[165, 86, 790, 188]]<|/det|> +a multiphysics electrical heating problem. The ONE architecture not only outperforms traditional PDE solvers because of its data- driven nature, but also shows comparable and better performance with other ML models while with substantial hardware advantages because of its implementation in the optical domain. The demonstrated ONE architecture is versatile and can be tailored with different combinations of DONN and XBAR structures for solving various PDEs, offering a transformative universal solution for large- scale scientific and engineering computations. + +<|ref|>sub_title<|/ref|><|det|>[[165, 202, 254, 220]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[165, 231, 348, 248]]<|/det|> +## ONE Architecture + +<|ref|>text<|/ref|><|det|>[[165, 254, 790, 454]]<|/det|> +Figure 1a illustrates the ONE architecture, which takes the spatiotemporal data of an input physical quantity \(\mathbf{U}\) , described as a function \(u(x,y,t)\) in terms of positions \(x\) and \(y\) and time \(t\) , to predict the spatiotemporal data of an output physical quantity \(\mathbf{G}\) described using a function \(g(x,y,t)\) . The input and output quantities \(\mathbf{U}\) and \(\mathbf{G}\) can be connected through either a single- physics PDE or coupled multiphysics PDEs. There are three branches inside the ONE architecture, including (i) Fourier space processing branch, (ii) real space processing branch, and (iii) physics parameter processing branch. The combination of both real and Fourier space processing has been proven fast, powerful, and efficient in solving PDEs [24], and the incorporation of additional physics parameter processing enables the fusion of multimodal data for complex tasks [25]. More importantly, most operations in these branches can be deployed on optical computing hardware in both real and Fourier space, enabling solving PDEs in high- throughput and energy- efficient manners. The details of each branch are described below. + +<|ref|>text<|/ref|><|det|>[[165, 454, 790, 624]]<|/det|> +In the first Fourier space processing branch, the core arithmetic operations are based on Fourier and inverse Fourier transformations to process input spatiotemporal data in the Fourier space. Their optical hardware implementations are mainly based on reconfigurable DONNs, which contain cascaded reconfigurable diffractive layers. Reconfigurable DONNs can be implemented in both integrated photonic chips [26, 27] and free space [19- 21]; see Fig. 1b. There are two fundamental operations in DONNs - optical diffraction and spatial light modulation. For the optical diffraction operation, an optical field right after the \(l\) - th diffractive layer, \(f_{l}\) , diffracts to the front of \((l + 1)\) - th layer, whose optical field, \(f_{\mathrm{in},l + 1}\) , is a convolution of \(f_{l}\) and the diffraction impulse function \(h(x,y)\) . Specifically, the complex- valued field at point \((x,y)\) on the input plane of \((l + 1)\) - th layer can be written as the convolution of all fields at the output plane of \(l\) - th layer as + +<|ref|>equation<|/ref|><|det|>[[278, 637, 675, 668]]<|/det|> +\[f_{\mathrm{in},l + 1}(x,y,z) = \iint f_{l}(x^{\prime},y^{\prime},0)h(x - x^{\prime},y - y^{\prime})dx^{\prime}dy^{\prime},\] + +<|ref|>text<|/ref|><|det|>[[165, 679, 790, 737]]<|/det|> +where \(z\) is the distance between two diffractive layers and \(h(x,y)\) is the impulse response function of free space. By the convolution theorem, this 2D convolution can be efficiently calculated in Fourier space based on Fourier and inverse Fourier transformations. Specifically, the 2D Fourier transformation \(\mathcal{F}_{xy}\) of \(f\) and \(h\) , \(F\) and \(H\) , are + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[213, 90, 830, 479]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[206, 485, 833, 534]]<|/det|> +
Fig. 1 ONE architecture and hardware implementations. (a) Illustration of processing branches and flows in the ONE architecture to predict output spatiotemporal output physical quantities from corresponding input and solve PDEs involving single or multiple physics. Illustrations of integrated and free-space implementations of reconfigurable (b) DONN and (c) XBAR structures.
+ +<|ref|>text<|/ref|><|det|>[[207, 552, 345, 565]]<|/det|> +connected through + +<|ref|>equation<|/ref|><|det|>[[333, 577, 702, 612]]<|/det|> +\[\mathcal{F}_{xy}(f_{\mathrm{in},l + 1}(x,y,z)) = \mathcal{F}_{xy}(f_l(x,y,0))\mathcal{F}_{xy}(h(x,y)),\] \[F_{\mathrm{in},l + 1}(\alpha ,\beta ,z) = F_l(\alpha ,\beta ,0)H(\alpha ,\beta),\] + +<|ref|>text<|/ref|><|det|>[[206, 622, 832, 710]]<|/det|> +where \(\alpha ,\beta\) are spatial domain indices. After diffraction, the 2D inverse Fourier transformation \(\mathcal{F}_{xy}^{- 1}\) of \(F_{\mathrm{in},l + 1}(\alpha ,\beta ,z)\) , \(f_{\mathrm{in},l + 1}(x,y,z)\) , is then spatially modulated. Each diffraction pixel at location \((x,y)\) has a complex- valued electric field transmission coefficient \(t(x,y,S)e^{\phi (x,y,S)}\) , where \(t(x,y,S)\) \((\phi (x,y,S))\) is the amplitude (phase) response as a function of external stimuli \(S\) , such as voltages. The spatial light modulation operation is expressed as a pixel- wise multiplication + +<|ref|>equation<|/ref|><|det|>[[327, 722, 708, 739]]<|/det|> +\[f_{l + 1}(x,y,z) = \mathcal{F}_{xy}^{-1}(F_{\mathrm{in},l + 1}(\alpha ,\beta ,z))t(x,y,S)e^{\phi (x,y,S)}\] + +<--- Page Split ---> +<|ref|>equation<|/ref|><|det|>[[375, 86, 622, 102]]<|/det|> +\[= f_{\mathrm{in},l + 1}(x,y,z)t(x,y,S)e^{\phi (x,y,S)},\] + +<|ref|>text<|/ref|><|det|>[[165, 114, 790, 144]]<|/det|> +where \(f_{l + 1}(x,y,z)\) is the near- field output field right after the \((l + 1)\) - th layer. More details can be found in Methods. + +<|ref|>text<|/ref|><|det|>[[165, 144, 790, 314]]<|/det|> +Before and between DONN kernels, there is a linear transformation operation based on fully connected layers to scale up the number of channels and a channel mixing operation based on matrix multiplications [24]. The core arithmetic operations are based on MVM. Their optical hardware implementations are mainly based on reconfigurable optical XBAR structures, which encode element values of vector \(\mathbf{v}\) and matrix \(\mathbf{M}\) into light intensity through electro- optic modulators, perform multiplications through cascaded modulators, and add signals at the output detector array. The signals are routed to follow mathematical calculations in MVM so that the reading from the detector array represents the output vector \(\mathbf{o} = \mathbf{M}\times \mathbf{v}\) . Reconfigurable XBAR structures can also be implemented in both integrated photonic chips [10, 11] and free space [16- 18]; see Fig. 1c. More details on the operation mechanism can be found in Methods and Supplementary Fig. 1. + +<|ref|>text<|/ref|><|det|>[[165, 314, 790, 544]]<|/det|> +The second real space processing branch contains fully connected layers, whose operations are also based on MVM and implemented with optical XBAR structures. The output from the Fourier space branch, \(\mathbf{F}(u)\) , and the output from the real space branch, \(\mathbf{R}(u)\) are added and further processed with a nonlinear operation. Note that the nonlinear operation is the only operation performed in electronic hardware in the ONE architecture. Moreover, this combination of real space, Fourier space, and nonlinear processing is scaled up, repeated four times, and cascaded in series. The third branch is to perform a linear transformation on other relevant physics parameters \(d(t)\) , which are time sequences instead of spatiotemporal data, based on fully connected layers. The obtained data \(\mathbf{T}(d)\) is multiplied and merged onto two other branches to have the final output \(g(x,y,t)\) . Hence, except nonlinear operations, all other operations can be done with DONN and optical XBAR systems. These two systems can be seamlessly assembled into a single integrated photonic chip or a single free- space optical system for all- optical operations without converting between optical and electronic hardware, fully leveraging the advantages of high throughput and high parallelism in optical computing systems. More details on the ONE architecture model are in Methods. + +<|ref|>sub_title<|/ref|><|det|>[[165, 557, 660, 574]]<|/det|> +## Darcy flow and magnetostatic Poisson's equations + +<|ref|>text<|/ref|><|det|>[[165, 580, 790, 624]]<|/det|> +The first PDE we solved with the ONE architecture is the Darcy flow equation in fluid dynamics physics. This PDE describes a fluid flow through a porous medium as shown in Fig. 2a. Specifically, the equation is + +<|ref|>equation<|/ref|><|det|>[[357, 640, 595, 654]]<|/det|> +\[-\nabla \cdot (k(x,y)\nabla u(x,y)) = f(x,y),\] + +<|ref|>text<|/ref|><|det|>[[165, 666, 790, 739]]<|/det|> +where \(k(x,y)\) is the permeability field of the medium, \(u(x,y)\) is the pressure field of the flow, and \(f(x,y)\) is the force function. The ONE architecture was trained to learn the mapping from the 2D function \(k(x,y)\) to function \(u(x,y)\) . More details about the equation dataset generation and training are in Methods. Figure 2b displays the training loss curves for inputs with different resolutions. The training loss + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[206, 86, 831, 400]]<|/det|> +is generally low for all resolutions and slightly increases at the highest 421 resolution. Figure 2c shows the comparison of the training loss of our ONE architecture with other PDE solving models, including fully convolution networks (FCN) [28], principal component analysis- based neural network (PCANN) [29], reduced biased method (RBM) [30], graph neural operator (GNO) [31], low- rank kernel decomposition neural operator (LNO) [25], multipole graph neural operator (MGNO) [32], and Fourier neural operator (FNO) [24]. The performance of the ONE architecture is comparable with the state- of- the- art neural operators including GNO, LNO, MGNO, and FNO, and is better than FCN. Further, from the hardware perspective, the ONE architecture is constructed based on high- throughput optical computing hardware platforms so that all operations can be performed in parallel within a single clock cycle. In addition, the ONE architecture can be practically implemented on a large scale. For example, free- space reconfigurable DONNs [20, 21, 33] and optical MVM [17] are typically implemented using spatial light modulators (SLMs) with a scale \(>1000 \times 1000\) . Hence, the execution cost of solving PDEs with different scales and resolutions is invariant, meaning \(\mathcal{O}(1)\) , if the scale of the optical hardware in the ONE architecture is large enough. Figure 2d displays the input permeability field \(k(x, y)\) , the expected ground truth of output pressure field \(u(x, y)\) , the predicted output pressure field, the absolute error between the expected and predicted outputs, and the relative error between the expected and predicted outputs, at the lowest 85 and the highest 421 resolutions, respectively. This visualization further validates the ONE architecture in solving PDEs. More data on other resolutions are shown in Supplementary Fig. 2. + +<|ref|>text<|/ref|><|det|>[[207, 400, 831, 457]]<|/det|> +The second PDE we solved is the magnetostatic Poisson's equation of demagnetization in micromagnetics physics. This PDE calculates the demagnetizing field \(\mathbf{H}\) generated by the magnetization field \(\mathbf{M}\) as shown in Fig. 2e. Specifically, the equation is obtained from Maxwell's equation as + +<|ref|>equation<|/ref|><|det|>[[455, 472, 581, 485]]<|/det|> +\[\nabla \cdot \mathbf{H} = -\nabla \cdot \mathbf{M}.\] + +<|ref|>text<|/ref|><|det|>[[206, 499, 831, 544]]<|/det|> +By defining an effective magnetic charge density \(\rho = - \nabla \cdot \mathbf{M}\) and a magnetic scalar potential \(\Phi\) assuming there is no free current, we can express the demagnetizing field \(\mathbf{H} = - \nabla \Phi\) and rewrite the previous equation as a Poisson's equation + +<|ref|>equation<|/ref|><|det|>[[475, 558, 560, 571]]<|/det|> +\[\nabla^{2}\Phi = -\rho .\] + +<|ref|>text<|/ref|><|det|>[[206, 584, 831, 728]]<|/det|> +Similar to solving the Darcy flow equation, the ONE architecture was trained to learn the mapping from components of \(\mathbf{M}\) to \(\mathbf{H}\) vector fields. More details about the equation dataset generation and training are in Methods. Figure 2f shows the validation loss curve and Fig. 2g shows the input one component of \(\mathbf{M}\) field, the expected ground truth of output \(H_{x}\) component of \(\mathbf{H}\) field, the predicted output \(H_{x}\) component, the absolute error between the expected and predicted outputs, and normalized error between the expected and predicted outputs with respect to the maximum field strength in the ground truth. Both confirm a good performance of the ONE architecture in solving the magnetostatic Poisson's equation. More data on \(H_{y}\) and \(H_{z}\) components is shown in Supplementary Fig. 3. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[171, 88, 784, 486]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[165, 494, 791, 631]]<|/det|> +
Fig. 2 Solving Darcy flow and magnetostatic Poisson's equations. (a) Illustration of the Darcy flow equation describing a fluid flow through a porous medium. The ONE architecture learns the mapping between the permeability and pressure fields. (b) Training loss curves for input data with different resolutions. (c) Comparison of the training loss of different models at various resolutions. (d) Input permeability field, the expected ground truth of output pressure field, the predicted output pressure field, the absolute error between the expected and predicted outputs, and the relative error between the expected and predicted outputs, at 85 and 421 resolutions. (e) Illustration of the magnetostatic Poisson's equation calculating the demagnetizing field generated by the magnetization field. The ONE architecture learns the mapping between these two fields. (f) Validation loss curve for the ONE architecture solving the magnetostatic Poisson's equation and (g) corresponding input magnetization field, the expected ground truth of output demagnetizing field, the predicted output demagnetizing field, the absolute and normalized errors between the expected and predicted outputs.
+ +<|ref|>sub_title<|/ref|><|det|>[[165, 646, 554, 663]]<|/det|> +## Navier-Stokes and Maxwell's equations + +<|ref|>text<|/ref|><|det|>[[165, 670, 790, 741]]<|/det|> +In addition to steady- state Darcy flow and magnetostatic Poisson's equations without time evolution, we employed the ONE architecture to solve time- dependent PDEs, including the Navier- Stokes equation in fluid dynamics and Maxwell equations in electromagnetics and optics. In particular, the real- time reconfigurability of DONN and optical XBAR structures makes the ONE architecture suitable for such a purpose. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[206, 86, 832, 129]]<|/det|> +Specifically, we solved a 2D Navier- Stokes equation for a viscous, incompressible fluid in vorticity form on the unit torus as shown in Fig. 3a. This PDE calculates the time evolution of vorticity described as + +<|ref|>equation<|/ref|><|det|>[[300, 145, 737, 159]]<|/det|> +\[\partial_{t}w(x,y,t) + u(x,y,t)\cdot \nabla w(x,y,t) = v\Delta w(x,y,t) + f(x,y),\] + +<|ref|>text<|/ref|><|det|>[[206, 171, 832, 286]]<|/det|> +where \(u\) is the velocity field, \(w = \nabla \times u\) is the vorticity, \(\nu\) is the viscosity coefficient, \(f\) is the forcing function. The ONE architecture was trained to learn the mapping from \(w\) in a time range from 0 to \(t_0\) to \(w\) in a time range from \(t_0\) to \(t_1\) ( \(t_1 > t_0\) ). More details about the equation dataset generation and training are in Methods. Further, we also solved Maxwell's equations in a dielectric metasurface consisting of multiple cylindrical pillars in a unit cell of a periodic pattern as shown in Fig. 3b [34]. The general Maxwell's equations can calculate the time evolution of an electric field through the following equations + +<|ref|>equation<|/ref|><|det|>[[448, 300, 588, 393]]<|/det|> +\[\nabla \cdot \mathbf{D} = \rho ,\] \[\nabla \cdot \mathbf{B} = 0,\] \[\nabla \times \mathbf{E} = -\frac{\partial\mathbf{B}}{\partial t},\] \[\nabla \times \mathbf{H} = \mathbf{J} + \frac{\partial\mathbf{D}}{\partial t},\] + +<|ref|>text<|/ref|><|det|>[[206, 403, 832, 575]]<|/det|> +where \(\mathbf{D}\) is the electric displacement field, \(\rho\) is the free charge density, \(\mathbf{B}\) is the magnetic flux density, \(\mathbf{E}\) is the electric field, \(\mathbf{H}\) is the magnetic field, and \(\mathbf{J}\) is the free current density. The ONE architecture was trained to learn the mapping from \(\mathbf{E}\) in a time range from 0 to \(t_0\) to \(\mathbf{E}\) in a time range from \(t_0\) to \(t_1\) ( \(t_1 > t_0\) ). More details about the dataset generation and training are in Methods. Figure 3c displays the validation loss curve for solving the Navier- Stokes equation with \(t_0 = 10\) and \(t_1 = 20\) . Figure 3d displays the validation loss curves for solving Maxwell's equations with \(t_0 = 10\) and \(t_1 = 20,30,40\) , respectively. Moreover, Figure 3e and 3f show the expected ground truth of \(w\) field and the \(E_x\) component of the \(\mathbf{E}\) field at \(t_1\) , the corresponding predicted fields at \(t_1\) , and the absolute and relative errors between ground truth and prediction for the Navier- Stokes equation and Maxwell's equations, respectively. All confirm a good performance in solving time- dependent PDEs using the ONE architecture. + +<|ref|>sub_title<|/ref|><|det|>[[207, 588, 399, 605]]<|/det|> +## Multiphysics PDEs + +<|ref|>text<|/ref|><|det|>[[207, 612, 832, 700]]<|/det|> +Moreover, we employed the ONE architecture to solve coupled PDEs involving two physics. Specifically, we solved an electrical heating problem to obtain a temperature profile at an intermediate time step \(t_n\) , \(T(x,y,t_n)\) , in an electrical circuit when a time- dependent voltage signal was applied to the circuit pads, involving coupled electric current physics and heat transfer physics; see Fig. 4a. Specifically, for the electrical current physics, the corresponding PDE is + +<|ref|>equation<|/ref|><|det|>[[471, 714, 625, 728]]<|/det|> +\[Q_{e} = d\sigma \nabla_{t}V(x,y,t),\] + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[172, 88, 785, 428]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[164, 450, 791, 530]]<|/det|> +
Fig. 3 Solving time-dependent Navier-Stokes and Maxwell's equations. Illustrations of (a) Navier-Stokes equation for solving the time evolution of the vorticity field in a viscous, incompressible fluid in vorticity form on the unit torus and (b) Maxwell's equations for solving the time evolution of the electric field in a dielectric metasurface. Validation loss curves for (c) solving the Navier-Stokes equation and (d) Maxwell's equations using the ONE architecture. The expected ground truth field, the predicted field, and the absolute and relative errors between these two fields for (e) the Navier-Stokes equation and (f) Maxwell's equations, respectively.
+ +<|ref|>equation<|/ref|><|det|>[[370, 547, 527, 561]]<|/det|> +\[V(x_{0},y_{0},t) = \mathrm{rect}(t),\] + +<|ref|>text<|/ref|><|det|>[[165, 574, 791, 647]]<|/det|> +where \(Q_{e}\) is the heat rate per unit area from an electromagnetic heating source, \(d\) is the thickness of the heating layer, \(V(x,y,t)\) is the voltage profile in the circuit that is subjected to a voltage boundary condition defined in the pads \(V(x_{0},y_{0},t)\) , and \(V(x_{0},y_{0},t)\) is a pulse rectangular function \(\mathrm{rect}(t)\) with pulse height and width. For the heat transfer physics, the corresponding PDE is + +<|ref|>equation<|/ref|><|det|>[[328, 659, 625, 686]]<|/det|> +\[\rho C_{p}\frac{\partial T}{\partial t} +\rho C_{p}\mathbf{u}\cdot \nabla T - \nabla \cdot (k\nabla T) = Q_{e},\] + +<|ref|>text<|/ref|><|det|>[[165, 697, 791, 740]]<|/det|> +where \(\rho\) is the mass density, \(C_{p}\) is the specific heat capacity, \(T\) is the absolute temperature, and \(k\) is the thermal conductivity. These two PDEs are connected through the quantity \(Q_{e}\) . The ONE architecture was trained to learn the mapping from \(V(x,y,t)\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[206, 86, 832, 244]]<|/det|> +in a time range spanning all time steps in input pulses to \(T(x,y,t_{n})\) at an intermediate pulse time step \(t_{n}\) . In contrast to previous examples, the pulse information, including pulse height and width, was processed through the physics parameter processing branch in the ONE architecture (Fig. 1a) and multiplied with the output from cascaded real space processing and Fourier space processing branches to yield the final output. More details about the dataset generation and training are in Methods. Figure 4b displays the validation loss curve and Fig. 4c shows a few representative input 2D data \(V(x,y,t)\) at various time steps. Figure 4d shows the expected ground truth of \(T(x,y,t_{n})\) , the corresponding predicted temperature profile, and the absolute and relative errors between ground truth and prediction. All confirm a good performance in solving multiphysics PDEs using the ONE architecture. + +<|ref|>image<|/ref|><|det|>[[212, 264, 832, 590]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[206, 610, 832, 658]]<|/det|> +
Fig. 4 Solving multiphysics PDEs. (a) Illustration of solving coupled PDEs in an electrical heating problem involving electric current physics and heat transfer physics. (b) Validation loss curve. (c) A few representative 2D voltage profiles in the circuit. (d) The expected ground truth temperature profile, the predicted profile, and the absolute and relative errors between these two profiles.
+ +<|ref|>sub_title<|/ref|><|det|>[[208, 690, 490, 706]]<|/det|> +## Experimental demonstration + +<|ref|>text<|/ref|><|det|>[[206, 713, 832, 741]]<|/det|> +Finally, to demonstrate the experimental feasibility of the ONE architecture, we constructed a free- space reconfigurable DONN setup and evaluated the performance of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[165, 86, 791, 201]]<|/det|> +solving the Darcy flow equation under different hardware noise levels in optical XBAR structures. Figure 5a displays a photo and schematic of the reconfigurable DONN setup, which contains a laser source, a reconfigurable input encoder, two reconfigurable diffractive layers, and a camera. The reconfigurable encoder and diffractive layers were built upon SLMs, which can modulate the amplitude and phase of transmitted light when applying voltage. Multiple light polarization components, including polarizers and half- wave plates, were also employed to manipulate polarization states to achieve large phase modulation ranges. More details on the experimental setup are in Methods. + +<|ref|>image<|/ref|><|det|>[[175, 225, 770, 610]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[165, 632, 791, 712]]<|/det|> +
Fig. 5 Experimental demonstration. (a) Photo and schematic of a reconfigurable DONN experimental setup consisting of a reconfigurable input encoder, two reconfigurable diffractive layers, and a camera. Polarization components were used to configure SLMs in the phase modulation mode. (b) Output 2D data in one DONN kernel of the Fourier space processing branch in the ONE architecture obtained from model calculations and experimental measurements. (c) Validation loss curves at different noise levels in optical XBAR structures and (d) the loss at the final epoch as a function of noise level.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[207, 87, 831, 428]]<|/det|> +As shown in Supplementary Fig. 4, the experimentally measured amplitude and phase modulation responses of all three SLMs are not only discrete with respect to grey levels but also coupled and dependent. To leverage the gradient- based ML training algorithm, we utilized the Gumbel- softmax reparameterization technique to approximate a discrete distribution to a continuous distribution [21]. More details are described in Methods. Moreover, the values of input 2D data span both negative and positive values and were encoded as the grey level of the SLM in the reconfigurable input encoder (SLM0 in Fig. 5a). We performed the encoding through linear mapping from minimum and maximum values of input data to a grey- level range in the SLM. More details are described in Methods. In addition, we precisely aligned all SLMs with respect to each other within a range of a few pixels on the order of hundreds of \(\mu \mathrm{m}\) ; see Supplementary Fig. 5. Although the long optical path in the system makes the alignment sensitive to external variations, the system's full reconfigurability can enable fast adaptive pixel- by- pixel re- alignment. Figure 5b shows output 2D data in one DONN kernel of the Fourier space processing branch in the ONE architecture (Fig. 1a) obtained from model calculations and experimental measurements, showing good agreement and experimentally validating the feasibility of the ONE architecture in solving PDEs. More data is shown in Supplementary Fig. 6. There are some speckles in the background of measured images, which probably originate from high- order diffraction interference, leading to numerical errors in the ONE architecture for performing regression tasks. This discrepancy between models and experiments can be mitigated through hardware- software co- design, such as incorporating loss functions based on experimental results for gradient calculations as demonstrated in prior works [20, 33, 35]. + +<|ref|>text<|/ref|><|det|>[[207, 429, 831, 599]]<|/det|> +We also evaluated the performance of the ONE architecture under different noise levels of optical XBAR structures. Specifically, we added random Gaussian noise with zero mean and varying standard deviation (Std) to the values obtained from matrix multiplications to represent hardware noise, such as shot noise in photodetectors [36]. The corresponding MVM results and histograms of different noise standard deviation values are shown in Supplementary Fig. 7, and more details can be found in Methods. As shown in Fig. 5c and Fig. 5d, the validation loss increases with the increasing noise standard deviation value. The current hardware implementation of optical XBAR structures with advanced components and calibration algorithms [16- 18], including the structure we demonstrated before [36], can achieve quite a small noise level similar or below the noise level corresponding to 0.5 Std. Hence, the noise influence in optical XBAR structures on the performance of the ONE architecture is not substantial. + +<|ref|>text<|/ref|><|det|>[[207, 600, 831, 728]]<|/det|> +We further estimated the potential throughput and power consumption of the ONE architecture implemented using optical computing hardware for inference. The throughput is mainly determined by the SLM refresh rate and camera frame rate. Current commercial SLMs and cameras can have rates \(>1000\mathrm{Hz}\) , meaning that the inference time for one instance is \(< 1\mathrm{ms}\) . In contrast, it typically takes minutes to hours to numerically solve PDEs. Hence, the ONE architecture features \(>10^{5}\) (five orders of magnitude) acceleration compared to typical PDE solvers. This throughput is also comparable to the state- of- art ML model, such as FNO with a \(5\mathrm{ms}\) inference time [24]. Moreover, the system throughput can be substantially improved with device + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[165, 86, 790, 201]]<|/det|> +innovation. For example, an electro-optic SLM based on organic molecules can achieve \(> \mathrm{GHz}\) switching speed [37], and an ultrafast camera can achieve a trillion frames per second [38]. With these devices, the ONE architecture can achieve an inference time \(< 1 \mathrm{ns}\) . The power consumption is mainly determined by the leakage current of liquid crystal cells in SLMs. Because of the dielectric nature of liquid crystals and their high leakage resistance, the leakage current is typically \(< 1 \mu \mathrm{A}\) . Hence, assuming a \(10 \mathrm{V}\) driving voltage, the static power consumption of SLMs is \(\sim 10 \mu \mathrm{W}\) , which is nearly \(10^{7}\) (seven orders of magnitude) smaller than typical GPU inference power \(\sim 100 \mathrm{W}\) . + +<|ref|>sub_title<|/ref|><|det|>[[165, 216, 290, 234]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[165, 244, 790, 359]]<|/det|> +We have demonstrated the ONE architecture and validated its performance in solving a broad range of PDEs in diverse scientific domains. The ONE architecture is versatile and can be modified to reduce the interface and connection between DONN and optical XBAR structures and facilitate the hardware implementation of the whole system. Further, in a whole system, active learning and noise- aware training can be incorporated to mitigate the discrepancy between models and practical systems for accurate deployment. Moreover, in addition to solving PDEs, the ONE architecture can be tailored to accelerate ML models for other regression problems. + +<|ref|>sub_title<|/ref|><|det|>[[165, 373, 270, 392]]<|/det|> +## Methods + +<|ref|>text<|/ref|><|det|>[[165, 405, 789, 435]]<|/det|> +DONN diffraction model – The diffraction impulse function \(h(x, y)\) was described using the Fresnel equation as + +<|ref|>equation<|/ref|><|det|>[[385, 446, 567, 476]]<|/det|> +\[h(x,y) = \frac{e^{ikz}}{i\lambda z} e^{\frac{ik}{2z} (x^{2} + y^{2})},\] + +<|ref|>text<|/ref|><|det|>[[165, 487, 790, 604]]<|/det|> +where \(\lambda\) is the wavelength, \(k = 2\pi /\lambda\) is the free- space wavenumber, \((x, y)\) are positions within a plane perpendicular to the wave propagation direction, \(z\) is the distance along the propagation direction, and \(i\) is the imaginary unit. The 2D Fourier transformation was directly performed on \(h(x, y)\) for model training and evaluation. To match the experimental setup as described below, \(h(x, y)\) was first discretized with respect to a defined rectangular mesh grid in the convolution calculation and then converted into the Fourier space through 2D Fourier transformation. More details can be found in our prior work [21]. + +<|ref|>text<|/ref|><|det|>[[165, 606, 790, 736]]<|/det|> +The operation mechanism of optical XBAR structures – Supplementary Fig. 1a shows the detailed schematic of an integrated photonic XBAR structure. Specifically, the element values of a \(n \times 1\) input vector \(\mathbf{v}\) are represented by the intensities of light at input waveguides, \(\{I_{1}, I_{2}, I_{3}, \ldots , I_{n}\}\) , which can be implemented by modulating an equally distributed laser intensity through a \(n \times 1\) array of electro- optic modulators (red squares in Supplementary Fig. 1a) at input waveguides. The light on each row waveguide is then equally distributed to the column waveguides connected to that row waveguide and modulated through an electro- optic modulator on the coupled curved waveguide (yellow squares in Supplementary Fig. 1a). The element values of a \(m \times n\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[207, 86, 832, 187]]<|/det|> +matrix \(\mathbf{M}\) are represented by the transmittance of modulators on curved waveguides, \(\{T_{i j}\} ,i\in [1,m],j\in [1,n]\) . At the end of each column waveguide, a photodetector collects all light intensity passing through the column waveguide. The obtained photocurrents or photovoltages of a \(m\times 1\) photodetector array represent the summation of multiplied input vector light intensity and matrix modulator transmittance, and the element values of output vector \(\mathbf{o}\) , \(O_{j} = \sum_{s = 1}^{n}T_{j s}I_{s},j\in [1,m]\) . Hence, this integrated photonic XBAR structure can implement MVM in the optical domain. + +<|ref|>text<|/ref|><|det|>[[207, 187, 832, 343]]<|/det|> +Similarly, Supplementary Fig. 1b shows the detailed schematic of a free- space optical XBAR structure. Specifically, the element values of a \(n\times 1\) input vector \(\mathbf{v}\) are represented by the intensities of light, \(\{I_{1},I_{2},I_{3},\dots,I_{n}\}\) , which is implemented through a \(n\times 1\) array of free- space vector SLM. The output light is broadcast to a \(m\times n\) array of matrix SLM through lenses so that the light distribution from vector SLM is identical at each column of matrix SLM. The element values of a \(m\times n\) matrix \(\mathbf{M}\) are represented by the transmittance of matrix SLM, \(\{T_{i j}\} ,i\in [1,m],j\in [1,n]\) . Lenses are then used to focus the output light from each modulator on the same column of matrix SLM to a photodetector. The readings from a \(m\times 1\) photodetector array represent the element values of output vector \(\mathbf{o}\) , \(O_{j} = \sum_{s = 1}^{n}T_{j s}I_{s},j\in [1,m]\) . Hence, this free- space optical XBAR structure can also implement MVM in the optical domain. + +<|ref|>text<|/ref|><|det|>[[207, 346, 832, 532]]<|/det|> +ONE architecture model – The ONE architecture model was constructed with two main modules – the DONN module processing data in the Fourier space and the optical XBAR module processing linear operations. The mathematical operations in DONN and optical XBAR structures have been described before and their accurate models have been implemented in our prior works, closely matching experimental results [21, 36]. Briefly, the DONN module was modeled by combining the Fresnel free- space diffraction with phase- only spatial light modulation in a range of \([0,2\pi ]\) in the model and coupled spatial light modulation as shown in Supplementary Fig. 4; the optical XBAR module was represented as matrix multiplication incorporating measurement noise. Both modules were implemented under the PyTorch 1.12 framework with graphics processing unit (GPU)- accelerated parallel computation and gradient backpropagation for training. The GPU used in this work was an Nvidia RTX 6000 card. + +<|ref|>text<|/ref|><|det|>[[207, 535, 832, 578]]<|/det|> +Darcy flow equation dataset and training – A 2D Darcy flow equation on the unit box was employed as described in detail in Ref. [24]. The corresponding PDE is a second- order, linear, elliptic PDE as + +<|ref|>equation<|/ref|><|det|>[[277, 591, 757, 627]]<|/det|> +\[-\nabla \cdot (k(x,y)\nabla u(x,y)) = f(x,y), \qquad x\in (0,1),y\in (0,1),\] \[u(x) = 0, \qquad x\in \partial (0,1),y\in \partial (0,1)\] + +<|ref|>text<|/ref|><|det|>[[207, 639, 832, 739]]<|/det|> +with a Dirichlet boundary condition. We used the Darcy flow dataset from the existing dataset in Ref. [24] with a boundary condition \(u(x,y) = 0\) on domain edges. The coefficient \(k(x,y)\) was generated based on a specific distribution with the value 12 for positive inputs and 3 for negative inputs. The forcing term was fixed at \(f(x,y) = 1\) . The solution \(u(x,y)\) was computed using a second- order finite difference method on a \(421 \times 421\) grid, and other resolutions were obtained with downsampling. We used a \(10:1\) ratio for the numbers of data in the training set and validation set, respectively. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[165, 86, 790, 129]]<|/det|> +The model was trained with a total of 600 epochs and a batch size of 40. The learning rate was 0.1 for the trainable parameters in DONNs and 0.001 for all other trainable parameters with the Adam optimizer. + +<|ref|>text<|/ref|><|det|>[[165, 133, 790, 176]]<|/det|> +Magnetostatic Poisson's equation dataset and training – The demagnetizing field \(\mathbf{H}\) originates from the magnetization within the material itself, which can be calculated as the convolution of \(\mathbf{M}\) with the demagnetization tensor \(\mathbf{N}\) as + +<|ref|>equation<|/ref|><|det|>[[368, 188, 586, 217]]<|/det|> +\[\mathbf{H}(\mathbf{r}) = \int \mathbf{N}(\mathbf{r} - \mathbf{r}^{\prime})\mathbf{M}(\mathbf{r}^{\prime})d\mathbf{r}^{\prime}.\] + +<|ref|>text<|/ref|><|det|>[[165, 226, 790, 414]]<|/det|> +This convolution was computed through Fourier space representations of fields. Specifically, to create the dataset, we utilized the MagneX solver [39] to simulate the time evolution of magnetization in a thin magnetic film with dimensions of \(500 \times 125 \times 3.125 \mathrm{~nm}\) . The modeling incorporated both demagnetization and exchange interactions. Initially, we relaxed the magnetic field into a stable S- state before subjecting the system to varying external magnetic fields in different scenarios. We uniformly sampled 8 bias \(\mathbf{H}\) fields in the \(x\) and \(y\) directions, each with a magnitude of 19872 A/m. The system evolved for 1 ns, during which we collected paired data of \(\mathbf{M}\) and \(\mathbf{H}\) fields. Each field was represented by three channels corresponding to the field components in \(x\) , \(y\) , and \(z\) directions. The dataset was divided into training and testing sets with an \(8:2\) ratio. The training was conducted over 500 epochs with a batch size of 128. The learning rate was set to 1.0 for the trainable parameters in DONNs and 0.001 for all other trainable parameters with the Adam optimizer. + +<|ref|>text<|/ref|><|det|>[[165, 417, 790, 475]]<|/det|> +Navier- Stokes equation dataset and training – A 2D Navier- Stokes equation for a viscous, incompressible fluid in vorticity form on the unit torus was used to generate spatiotemporal data for training the ONE architecture. The details are described in Ref. [24]. Specifically, the PDEs are + +<|ref|>equation<|/ref|><|det|>[[165, 487, 835, 542]]<|/det|> +\[\partial_{t}w(x,y,t) + u(x,y,t)\cdot \nabla w(x,y,t) = v\Delta w(x,y,t) + f(x,y),x\in (0,1),y\in (0,1),t\in (0,T]\] \[\nabla \cdot u(x,y,t) = 0,x\in (0,1),y\in (0,1),t\in (0,T]\] \[w(x,y,0) = w_{0}(x,y),x\in (0,1),y\in (0,1),\] + +<|ref|>text<|/ref|><|det|>[[165, 551, 790, 666]]<|/det|> +where \(w_{0}(x,y)\) is the initial vorticity and boundary conditions were used. We utilized the existing dataset with the viscosity coefficient \(v = 10^{- 3}\) from Ref. [24] for training and inference. The samples in the dataset were recorded with a time step of \(10^{- 4}\) s. We used 1000 data as the training set and 100 data as the validation set. We trained the ONE architecture model with the first 10 vorticity fields \((w(x,y,t))\) to predict the time evolution of the next 10 vorticity fields. The model was trained with a total of 600 epochs and a batch size of 40. The learning rate was 0.1 for the trainable parameters in DONNs and 0.001 for all other trainable parameters with the Adam optimizer. + +<|ref|>text<|/ref|><|det|>[[165, 669, 790, 741]]<|/det|> +Maxwell's equations dataset and training – We employed commercial Ansys Lumerical finite- difference- time- domain simulation software to generate an electric field dataset by solving Maxwell's equations in dielectric metasurfaces. Specifically, the dielectric metasurface had a periodic pattern and we used four silicon cylindrical rods as the unit cell and periodic boundary condition. Data were generated by randomly + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[207, 86, 832, 201]]<|/det|> +selecting the radii of four cylindrical rods. The radius was chosen from \(39.5\mu \mathrm{m}\) to \(44.5\mu \mathrm{m}\) with a step of \(0.25\mu \mathrm{m}\) . The simulation time was set as 300000 fs. We generated a total of 1200 data and used 1000 as the training set and the rest 200 as the validation set. The model was trained in an auto- regressive style for the \(E_{x}\) component processing. The \(E_{x}\) field data between 300000 fs to 160000 fs was backward fed into to the model to predict the next 40000 fs \(E_{x}\) field data. The model was trained with a total of 500 epochs and a batch size of 20. The learning rate was 0.1 for the trainable parameters in DONNs and 0.001 for all other trainable parameters with the Adam optimizer. + +<|ref|>text<|/ref|><|det|>[[207, 204, 832, 475]]<|/det|> +Multiphysics dataset and training – We employed commercial COMSOL Multiphysics finite- element simulation software to generate a temperature profile dataset by solving coupled electric current and heat transfer PDEs in an electrical heating circuit. The circuit details can be found in Ref. [40]. Concisely, the circuit contained a serpentine- shaped Nichrome resistive layer with \(10\mu \mathrm{m}\) thick and \(5\mathrm{mm}\) wide on top of a glass plate. A silver contact pad with a dimension \(10\mathrm{mm}\times 10\mathrm{mm}\times 10\mu \mathrm{m}\) was attached at each end. The deposited side of the glass plate was in contact with the surrounding air at \(293.15\mathrm{K}\) and the back side was in contact with the heated fluid at \(353\mathrm{K}\) . Two coupled physics modules, electrical current in layered shells and heat transfer in layered shells, were used in COMSOL simulations. The input voltage pulse height was set from 5 to \(25\mathrm{V}\) with a step of \(1\mathrm{V}\) and the pulse width was set from 20 to \(60\mathrm{s}\) with a step of \(1\mathrm{s}\) . The simulation time range was from 0 to \(110\mathrm{s}\) . We generated a total number of 861 data and divided the data into training and testing set with the splitting ratio of \(8:2\) . The ONE architecture took the electric current layer data as the input spatiotemporal data and the input voltage pulse information was fed into the physics parameter data processing branch to predict temperature field data at \(55\mathrm{s}\) . The model was trained with a total of 100 epochs and a batch size of 40. The learning rate for the trainable parameters in DONNs was 0.1 and the learning rate for all other trainable parameters was 0.001 with the Adam optimizer. + +<|ref|>text<|/ref|><|det|>[[207, 479, 832, 678]]<|/det|> +DONN experimental setup and alignment – The photo and schematic diagram of the DONN experimental setup are displayed in Fig. 5a. The laser diode with a center wavelength \(532\mathrm{nm}\) (CPS532 from Thorlabs, Inc.) was used as a source. The distance between SLMs and between the last SLM and camera was set as \(25.4\mathrm{cm}\) . The polarizers and half- wave plates before and after each SLM were configured so that each SLM operated with a strong modulation of the transmitted electric field phase (phase mode) together with a moderate modulation of light amplitude. The experimentally measured amplitude and phase modulation responses of three SLMs are shown in Supplementary Fig. 4. All transmissive SLMs are the LC 2012 model from HOLOEYE Photonics AG with a refresh rate of \(60\mathrm{Hz}\) . The analog- to- digital converter has 8- bit precision for liquid crystal driving voltage, so that the grey level of SLMs is from 0 to 255. The pixel size of SLMs is \(36\mu \mathrm{m}\times 36\mu \mathrm{m}\) . The output data was captured on a CMOS camera with a frame rate of 34.8 frames per second (CS165MU1 from Thorlabs, Inc.). + +<|ref|>text<|/ref|><|det|>[[207, 679, 832, 735]]<|/det|> +We aligned the DONN setup by loading standard images on SLMs and comparing experimental results with simulation. Specifically, as shown in Supplementary Fig. 5a, standard Gaussian images, which were centered with a peak at 255 grey level and with a standard deviation of 6 pixels, were loaded in the input SLM and two diffractive + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[165, 86, 790, 187]]<|/det|> +SLMs. Supplementary Fig. 5b displays the simulation pattern for the perfectly aligned setup. During the alignment process, loaded images were moved up, down, left, and right pixel- by- pixel to match the captured images by the camera with the simulation pattern. Supplementary Fig. 5c displays the matched experimental diffraction pattern when the optical setup was aligned, while Supplementary Fig. 5d shows misaligned patterns when there was five- pixel misalignment in vertical and horizontal directions, respectively. + +<|ref|>text<|/ref|><|det|>[[165, 190, 790, 374]]<|/det|> +DONN experimental training with reparameterization – The discrete look- up tables of device responses shown in Supplementary Fig. 4 break the gradient backpropagation in the ML training process in PyTorch. To solve this challenge, we utilized a differentiable reparameterization Gumbel- softmax technique, which was first introduced in Ref. [41] and demonstrated in our prior work [21]. Specifically, continuous noise from the Gumbel distribution was added to the discrete distribution. The argmax function was then used to find the optimized sample. The training problem after this Gumbel- argmax process is mathematically equivalent to the original training problem under one- hot representation [41]. Since the argmax function still breaks the gradient chain, it was replaced with the softmax function to enable differentiability. Hence, this Gumbel- softmax technique, which is also available in PyTorch, offers continuous and differentiable approximation to discrete distributions and the gradient can backpropagate to reduce the loss function. + +<|ref|>text<|/ref|><|det|>[[165, 377, 790, 450]]<|/det|> +DONN experimental grey- level encoding – The global minimum and maximum values in input 2D data were calculated as \(d_{\mathrm{min}}\) and \(d_{\mathrm{max}}\) . A grey level range from 130 to 255 in the input encoder SLM was selected for a relatively large amplitude modulation range to have enough contrast. Hence, any value \(d\) in the input 2D data was converted into a grey level through a linear mapping as + +<|ref|>equation<|/ref|><|det|>[[364, 464, 589, 496]]<|/det|> +\[d = \operatorname {int}\left(\frac{255 - 130}{d_{\mathrm{max}} - d_{\mathrm{min}}} + 130\right),\] + +<|ref|>text<|/ref|><|det|>[[165, 505, 790, 535]]<|/det|> +where the int(·) operation rounded the expression to the nearest integer since the SLM grey level must be an integer. + +<|ref|>text<|/ref|><|det|>[[165, 537, 790, 653]]<|/det|> +Optical XBAR noise – The MVM results from an optical XBAR structure were uniformly randomly generated in a range of \(- 15\) to 15, which was the value range in the ONE architecture for solving the Darcy flow equation. The expected number \(o\) was then added with a randomly generated noise from a Gaussian distribution with a zero average and varying standard deviation. The noise- dressed number \(\bar{o}\) was used in ONE architecture calculations. Under different noise standard deviation levels, Supplementary Fig. 7a demonstrates \(\bar{o}\) with respect to \(o\) and Supplementary Fig. 7b displays histograms of \(\bar{o} - o\) . + +<|ref|>sub_title<|/ref|><|det|>[[165, 666, 361, 686]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[165, 695, 790, 725]]<|/det|> +Upon publication, all data that support the plots within this paper and other findings of this study will be available on a public GitHub repository. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[207, 82, 406, 101]]<|/det|> +## Code availability + +<|ref|>text<|/ref|><|det|>[[207, 111, 831, 140]]<|/det|> +Code availabilityUpon publication, all codes that support the plots within this paper and other findings of this study will be available on a public GitHub repository. + +<|ref|>sub_title<|/ref|><|det|>[[208, 153, 433, 172]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[207, 181, 832, 325]]<|/det|> +AcknowledgementsR.C., C.Y., and W.G. acknowledge support from the National Science Foundation through Grants No. 2235276, No. 2316627, and No. 2428520. M.L., J.F., and W.G. also acknowledge support from the University of Utah start- up fund. Y.T., Z.Y., and A.N. were supported by Laboratory Directed Research and Development (LDRD) funding from Berkeley Lab, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE- AC02- 05CH11231. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE- AC02- 05CH11231 and under NERSC GenAI award under No. DDR- ERCAP0030541. + +<|ref|>sub_title<|/ref|><|det|>[[207, 338, 597, 357]]<|/det|> +## Author Contributions Statement + +<|ref|>text<|/ref|><|det|>[[207, 366, 832, 437]]<|/det|> +Author Contributions StatementY.T. and W.G. conceived the idea and W.G. supervised the project. Y.T. constructed models and performed machine learning calculations with the help of M.L., J.F., and C.Y and under the support of A.N., Z.Y., and W.G. R.C constructed an optical experimental setup, performed experiments, and performed numerical calculations under the supervision of W.G. Y.T. and W.G. wrote the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[207, 450, 580, 470]]<|/det|> +## Competing Interests Statement + +<|ref|>text<|/ref|><|det|>[[207, 480, 530, 494]]<|/det|> +The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[207, 507, 335, 525]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[212, 535, 832, 740]]<|/det|> +References[1] Azizzadenesheli, K., Kovachki, N., Li, Z., Liu- Schiaffini, M., Kossaifi, J., Anandkumar, A.: Neural operators for accelerating scientific simulations and design. Nat. Rev. Phys., 1- 9 (2024)[2] Griffiths, D.J.: Introduction to Electrodynamics. 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Adv. 6(3), 6200 (2020) + +<|ref|>text<|/ref|><|det|>[[165, 658, 790, 688]]<|/det|> +[39] Yao, Z., Kumar, P., Lepelch, J., Nonaka, A.: Code Repository for “MagneX”: https://github.com/AMReX- Microelectronics/MagneX + +<|ref|>text<|/ref|><|det|>[[165, 698, 790, 727]]<|/det|> +[40] COMSOL Tutorial Model of a Heating Circuit. https://comsol.com/model/heating- circuit- 465/ + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[206, 85, 831, 115]]<|/det|> +[41] Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel- softmax. arXiv preprint arXiv:1611.01144 (2016) + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 92, 768, 112]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 179, 149]]<|/det|> +- SIFinal.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__07bb49d179d74a99cfca97aa544930e840ce529d98ed5ba46742687ced243ffc/images_list.json b/preprint/preprint__07bb49d179d74a99cfca97aa544930e840ce529d98ed5ba46742687ced243ffc/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..99d76f30f7c3583265953fb7a51a8fa9065d0c6b --- /dev/null +++ b/preprint/preprint__07bb49d179d74a99cfca97aa544930e840ce529d98ed5ba46742687ced243ffc/images_list.json @@ -0,0 +1,101 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1. FTIR spectra (a), XRD pattern (b) of pristine PA1 (—), PA2 (—), PA3 (—) and TA (—). SEM micrographs of PA1 (c), PA2 (d), PA3 (e), and TA (f).", + "footnote": [], + "bbox": [], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2. Absorbance spectra of ninhydrin complexes of spermine before (—■—) and after adsorption of putrescine (—●—), spermidine (—▲—), spermine (—▼—), tryptamine (—◆—), tryptophan (—★—), followed by ninhydrin treatment, PA1 (a) and PA2 (b), PA3 (c), and TA (d). Absorbance spectra of the ninhydrin complexes of other amines are given in the supporting information (Figure S2), which also showed the same adsorption maxima. The inset images show the optical images of the ninhydrin complexes of spermine (i) before and (ii) after extractions with different PAs. PA absorbents (25 mg) were used for the extraction of amine solution at a concentration of 50 mg/L. All adsorption experiments were conducted at pH 7 and \\(22^{\\circ}\\mathrm{C}\\) .", + "footnote": [], + "bbox": [ + [ + 115, + 285, + 880, + 727 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3. Percentage removal efficiencies of putrescine (—), spermidine (—), spermine (—), tryptamine (—), tryptophan (—) of PA1 (a), PA2 (b), PA3 (c) and TA (d) polyaramides at different dosages. The extraction was done at room temperature for 300 min, and the amine concentrations were kept constant (50 mg/L). .All adsorption experiments were conducted at pH 7 and \\(22^{\\circ}C\\)", + "footnote": [], + "bbox": [ + [ + 137, + 81, + 857, + 504 + ] + ], + "page_idx": 9 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4. Effect of time \\((15 - 300\\mathrm{min})\\) and concentration \\((5 - 150\\mathrm{mg / L})\\) of putrescine ( \\(\\bullet\\) -), spermidine ( \\(\\bullet\\) -), spermine ( \\(\\bullet\\) -), tryptamine ( \\(\\bullet\\) -) and tryptophan ( \\(\\bullet\\) -) on removal efficiency, PA1 (a, b), PA2 (c, d), PA3 (e, f) and TA (g, h). The amount of all PAs used for the adsorption experiments was kept constant at \\(25\\mathrm{mg}\\) .", + "footnote": [], + "bbox": [], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5. Langmuir (a, c, e and g) and pseudo-second-order kinetics (b, d, f and h) plots for PA1 (a, b), PA2 (c, d), PA3 (e, f), TA (g, h) at neutral pH and 298 K using different concentrations (5 - 150 mg/mL) of the putrescine (- - -), spermidine (- - -), spermine (- - -), tryptamine (- - -) and tryptophan (- - -) and a fixed concentration of absorbents (25 mg). The Freundlich isotherm and pseudo-first order kinetic regressions for PAs 1 - 3 and TA, are given in the supporting information (Figure S3).", + "footnote": [], + "bbox": [], + "page_idx": 12 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6. Removal efficiencies of PA 1 - 3 after repeated adsorption-desorption cycles (cycle 1 (■), cycle 2 (■), cycle 3 (■), cycle 4 (■), and cycle 5 (■)). The concentration of putrescine (a), spermidine (b), spermine (c), and tryptamine (d) used was \\(50 \\mathrm{mg / L}\\) , a time of \\(300 \\mathrm{min}\\) , and an adsorbent dose of \\(25 \\mathrm{mg}\\) was kept constant. The regeneration data of PA 1 - 3 after adsorption of tryptophan are given in the supporting information (Figure S6). The regeneration data for TA using all amines are given in the supporting information (Figure S7).", + "footnote": [], + "bbox": [ + [ + 115, + 312, + 880, + 800 + ] + ], + "page_idx": 14 + }, + { + "type": "image", + "img_path": "images/Figure_7.jpg", + "caption": "Figure 7. (a) UV-Vis spectra of the ninhydrin treated extracts obtained from fish sample kept at different time points of 6 h, 24 h, and 48 h at room temperature before (6 h (-), 24 h (-), 24 h (-)) and after (6 h (-), 24 h (-), and 48h (-)) extraction with PA 1-3 samples (25 mg). Note that the absorbance of solutions after extraction with PA was almost zero, indicating a complete removal of amines. (b) The extraction efficiencies of PAs (50 mg) for the removal of amines from solutions collected after keeping the fish samples for 6 h, 12 h, and 24 h at room temperature. The extraction time was kept at 5 h for all samples. (c) LC traces of extract collected from fish sample (2 g) after 48 h at room temperature. Optical images of fish samples (2 g) and LCMS traces of 6h and 24 h fish extracts are given in the supporting information (Figure S8).", + "footnote": [], + "bbox": [ + [ + 60, + 155, + 870, + 655 + ] + ], + "page_idx": 15 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "Scheme 2. The complexation and extraction behaviour of PA with amines from solution", + "footnote": [], + "bbox": [ + [ + 118, + 95, + 820, + 380 + ] + ], + "page_idx": 17 + } +] \ No newline at end of file diff --git a/preprint/preprint__07bb49d179d74a99cfca97aa544930e840ce529d98ed5ba46742687ced243ffc/preprint__07bb49d179d74a99cfca97aa544930e840ce529d98ed5ba46742687ced243ffc.mmd b/preprint/preprint__07bb49d179d74a99cfca97aa544930e840ce529d98ed5ba46742687ced243ffc/preprint__07bb49d179d74a99cfca97aa544930e840ce529d98ed5ba46742687ced243ffc.mmd new file mode 100644 index 0000000000000000000000000000000000000000..52ab8d6673f49e6f5918f2585a37560b9e1726a7 --- /dev/null +++ b/preprint/preprint__07bb49d179d74a99cfca97aa544930e840ce529d98ed5ba46742687ced243ffc/preprint__07bb49d179d74a99cfca97aa544930e840ce529d98ed5ba46742687ced243ffc.mmd @@ -0,0 +1,403 @@ + +# Engineered Polyaramides for Extraction of Bioamines from Water + +Gomathi Mahadevan National University of Singapore Suresh Valiyaveettil chmsv@nus.edu.sg + +National University of Singapore + +## Article + +Keywords: Polyaramide, biogenic amines, extraction, adsorption efficiency, LCMS, environmental matrix + +Posted Date: August 11th, 2025 + +DOI: https://doi.org/10.21203/rs.3.rs- 7238623/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: No competing interests reported. + +Version of Record: A version of this preprint was published at Scientific Reports on October 21st, 2025. See the published version at https://doi.org/10.1038/s41598- 025- 20410- 1. + +<--- Page Split ---> + +# Engineered Polyaramides for Extraction of Bioamines from Water + +Gomathi Mahadevan and Suresh Valiyaveettil\* + +Department of Chemistry, National University of Singapore 3 Science Drive 3, Singapore 117543 Email: chmsv@nus.edu.sg + +## Abstract + +Biogenic amines (BAs) are prevalent in fermented foods, protein- rich meats, and brewed food additives. High concentrations of BAs can induce health issues such as headaches, high blood pressure, and palpitations. Polyaramides (PAs) prepared from the reaction of trimesoyl chloride with ethylene diamine (ED), \(p\) - xylene diamine (PXD), and \(m\) - xylene diamine (MXD) and characterized using different characterization techniques, FTIR, XRD, SEM, TGA, and DLS measurements. All PAs were used for the removal of five biogenic amines (i.e., putrescine, spermidine, spermine, tryptamine, and tryptophan) from water. All PAs showed high removal efficiencies for biogenic amines, and the data collected fit well with the Langmuir isotherm and pseudo- second- order kinetic models. Among three different polymers, PA3 showed exceptional adsorption capacities, achieving removal efficiencies of \(97.1 \pm 0.24\%\) (putrescine), \(99 \pm 0.68\%\) (spermidine), \(99.6 \pm 0.23\%\) (spermine), \(99 \pm 1.68\%\) (tryptamine), and \(97 \pm 0.08\%\) (tryptophan) at an equilibrium concentration of \(25 \mathrm{mg}\) PA and an adsorption time of 300 minutes. Furthermore, adsorbent regeneration was established via washing with an acid solution, and the removal efficiency was retained after five cycles of repeated washings and extractions. As a proof of concept, the synthesized PAs were used to extract the amines from a decaying fish sample for different periods, which showed \(99\%\) extraction and removal efficiencies. The chemical nature of the extracted BAs was identified using LCMS. Such synthetic polyaramides for removing pollutants from environmental matrices are interesting candidates for developing functional materials. + +Keywords: Polyaramide, biogenic amines, extraction, adsorption efficiency, LCMS, environmental matrix. + +<--- Page Split ---> + +## Introduction + +The freshness of meat or fish products in the market is difficult to measure, owing to the lack of suitable fast and reliable methods. Biogenic amines (BAs) such as cadaverine, putrescine, spermidine, spermine, phenylethylamine, tyramine, and histamine are present or generated in biological samples with aging or under stress. \(^{1,2}\) The production of BAs is primarily a result of the decarboxylation of amino acids catalysed by microorganisms. \(^{3}\) Microbial species in the environment degrade the biological tissues to release small molecular metabolites, including significant amounts of BAs. \(^{4}\) A high concentration of BAs in human body fluids causes health issues, such as asthma, headaches, and irregular blood pressure. \(^{5,6}\) Many aged foods, such as fish, wine, beer, meat products, and fermented foods, contain BAs, which accumulate with time due to microbial degradation. \(^{7}\) BAs from decaying old food products or effluents from fish markets or butcheries end up in local water bodies, posing adverse health risks to living organisms. \(^{6,8}\) Furthermore, studies have demonstrated that BA accumulates in algae during summertime blooming, which results in an abrupt increase in the concentration of amines in marine or coastal locations. \(^{1,9}\) A few reports are available on the extraction and identification of BAs from decaying fish samples by using the HPLC technique. \(^{5,10}\) + +New functional materials and methodologies are needed for the detection, removal, and quantification of the BAs from water. \(^{11,12}\) A few materials were used to detect the presence of biogenic amines in food and water. \(^{13 - 15}\) However, removing amines or other metabolites from different raw food materials has not been well studied due to the complexity of the matrix. Amines such as putrescine and spermine were extracted and removed from meat products using absorbents such as iron oxide and graphene, but multiple treatments were necessary to achieve high removal efficiencies. \(^{16 - 18}\) + +Water pollution caused by various contaminants such as heavy metals, pesticides, biological waste, and industrial chemicals poses a significant threat to human health and the environment. \(^{4}\) In recent years, polyamides have emerged as a promising material for water purification technology. Polyamides, a class of synthetic polymers, are widely employed in various industrial applications due to their excellent mechanical properties, thermal stability, and chemical resistance. \(^{19 - 21}\) Here, we use synthetic, highly branched polyamides (PAs) for the removal of BAs from spiked water samples to develop a purification system for future applications (Scheme 1). + +<--- Page Split ---> +![](images/Figure_1.jpg) + + +Scheme 1. Synthetic scheme (a) of PAs 1- 3 and triamide (TA), and chemical structure (b) of amines used for the extraction. + +In our design strategy, the electron- deficient amide groups, hydrophobic interactions, and the network structure of PA were employed for the extraction of organic amines from water. Our objectives include the extraction of amines from an aqueous environment and understanding the structure- property correlations of the three PAs. The synthetic strategy includes trimesoyl chloride (TMC) as a trifunctional monomer and one of the linear ethylene diamine (ED), \(p\) - xylene diamine (PXD), and bend \(m\) - xylene diamine (MXD) for the synthesis of PAs (Scheme 1a). Ethylene diamine (ED) is a flexible linear molecule, whereas aromatic amines such as \(p\) - xylene diamine (PXD) and \(m\) - xylene diamine (MXD) are rigid molecules. A + +<--- Page Split ---> + +model triamide (TA) was prepared using TMC and aniline (AN) for comparison of structure and properties. All three PAs with different structural features were utilized for the removal of BAs such as putrescine (i), spermidine (ii), spermine (iii), tryptamine (iv), and tryptophan (v), from water (Scheme 1b). The amide groups inside the polymer lattice are electron-deficient moieties and interact strongly with electron-rich pollutants (e.g. amines, anions) through electrostatic interactions. Impact of experimental factors such as concentration of PAs and amines, extraction time on adsorption efficiency, isotherm and kinetic model treatments of the data, regeneration of PAs, proof- of- concept demonstration of extracting amines from decaying fish sample, comparison of PAs with other adsorbents towards amine extraction, and mechanism of extraction are discussed in this manuscript. Although many polyamides have been reported in the literature, \(^{10,13,21}\) the PAs discussed here demonstrate significant efficiency towards the extraction of biogenic amines, making them suitable for separation and purification technologies. + +## Materials and Methods + +1,3,5- Trimesoyl chloride (TMC), ethylenediamine (ED), \(p\) - xylenediamine (PXD), \(m\) - xylenediamine (MXD), aniline, dimethylformamide (DMF, HPLC grade), and triethylamine (TEA) were purchased from Sigma Aldrich Pte Ltd. All chemicals were used as received without any further purification. Deionized water was used throughout the experiments. A Bruker ALPHA FT- IR spectrophotometer was used to record the FTIR spectra (500 – 4000 cm\(^{- 1}\)) both before and after the extraction of amines using KBr as the matrix. The size and surface charges of the materials were measured using the Malvern Zetasizer Nano- ZS90. The thermogravimetric analyses (TGA) were done using a Discovery TGA apparatus in a nitrogen atmosphere within the 25 – 1000 °C temperature range and a heating rate of 10 °C/min. The UV- Vis spectra of every sample were recorded using a Shimadzu- 1601 spectrophotometer. The synthesized PA’s morphologies were established using a JEOL JSM- 6701F scanning electron microscope (SEM). The crystallinity of the polyaramides was investigated with the help of a Bruker D8 Advance Powder Crystal X- ray diffractometer with Cu Kα radiation (λ = 0.154 nm) operating at 40 kV and 40 mA within the 20 range of 5 - 70°. + +## Synthesis and Characterisation of PAs + +All polyaramides (PAs) and the control molecules were synthesized and fully characterized using the reported procedures. \(^{22}\) Full details on the synthesis and characterisation of the PAs are given in the supporting information. + +<--- Page Split ---> + +## Removal of Amines Using PAs + +Commercially available amines (putrescine, spermidine, spermine, tryptamine, and tryptophan) were dissolved in Milli- Q water to prepare a stock solution (50 mg/L) and used for extraction studies. The extraction was carried out at room temperature (22 °C) and a neutral pH of 7. A calibration curve was prepared after mixing the amine solution of different concentrations with an appropriate amount of ninhydrin solution. Ninhydrin reacts with the amines to form a blue adduct, whose concentration is monitored using UV- Vis spectroscopy to quantify the amount of amine in the solution. Different amounts of PA absorbent (2- 50 mg) were added to the amine solution (6 mL), and the extraction was carried out for 300 min. The solution was centrifuged, and the supernatant was mixed with dilute ninhydrin solution (0.01M) to get a blue- colored solution after 30 min at pH 8. \(^{10}\) UV- Vis spectra of the blue solutions were recorded, and the concentration of amine was determined from a calibration curve. The removal efficiency was calculated from the spectroscopic data obtained before and after extraction using equation (1). \(^{23}\) + +\[\mathrm{Removal~efficiency~(\mathrm{RE})} = \frac{\mathrm{C_0 - C_e}}{\mathrm{C_0}}\cdot \mathrm{X}100\% \quad (1)\] + +where \(\mathrm{C_0}\) (mg/L) is the initial concentration and \(\mathrm{C_e}\) (mg/L) is the equilibrium concentration of amines. + +## Isotherm and Kinetic Model Studies + +The amines were extracted using an optimized amount (25 mg) of PA absorbents from dilute solutions (6 mL) containing varying concentrations (5 - 150 mg/L). Adsorption studies were repeated three times for each concentration, and the average value was reported. The collected data were used to analyse the common adsorption models, such as the Langmuir and the Freundlich isotherm models. The Langmuir isotherm model (Equation 2) fits the data well, indicating the formation of continuous monolayers on the surface of the PAs. \(^{24}\) + +\[\mathrm{Q_e} = \frac{\mathrm{Q_{max}}\mathrm{K_L}\mathrm{C_e}}{1 + \mathrm{K_L}\mathrm{C_e}} \quad (2)\] + +\(\mathrm{Q_{max}}\) (mg/g) is the maximum adsorption capacity of amines, and \(\mathrm{Q_e}\) (mg/g) is the equilibrium adsorption capacity. The equilibrium concentration is represented by the letter \(\mathrm{C_e}\) , and \(\mathrm{K_L}\) is the binding energy- related constant. Multilayer adsorption on heterogeneous surfaces is treated by the Freundlich empirical equation (3). \(^{24}\) + +<--- Page Split ---> + +\[\mathrm{Q}_{\mathrm{e}} = \mathrm{K}_{\mathrm{F}}\mathrm{C}_{\mathrm{e}}^{1 / \mathrm{n}} \quad (3)\] + +where \(\mathrm{C}_{\mathrm{e}}\) (mg/L) is the equilibrium concentration and \(\mathrm{Q}_{\mathrm{e}}\) (mg/g) is the adsorbent's quantity of absorbed amines per unit mass at equilibrium. \(\mathrm{K}_{\mathrm{F}}\) and \(\mathrm{n}\) represent adsorption capacity and intensity, respectively. In a linear form, Equation 3 could be extended as Equation 4.25 + +\[\mathrm{Log} \mathrm{Q}_{\mathrm{e}} = \log \mathrm{K}_{\mathrm{F}} + \frac{1}{n} \log C_{\mathrm{e}} \quad (4)\] + +The PA (25 mg) was mixed with the appropriate amine solution (50 mg/ L, 6 mL) at room temperature and shaken mechanically for different time points (15 - 300 min). The solutions were centrifuged, and the collected supernatant was filtered. The filtrate was analyzed to determine the extraction efficiency of the PA towards the amine. The adsorption experiments were repeated three times for each concentration, and the average value was used for calculations. The extraction data were shown using the pseudo- first- order (Equation 5) and pseudo- second- order (Equation 6) kinetic models.26,27 + +\[\begin{array}{l}{\ln (\mathrm{q}_{\mathrm{e}} - \mathrm{q}_{\mathrm{t}}) = \ln \mathrm{q}_{\mathrm{e}} - \mathrm{k}_{1}\mathrm{t}}\\ {\frac{\mathrm{t}}{\mathrm{q}_{\mathrm{t}}} = \frac{\mathrm{t}}{\mathrm{q}_{\mathrm{e}}} +\frac{1}{\mathrm{k}_{2}\mathrm{q}_{\mathrm{e}}^{2}}} \end{array} \quad (5)\] + +In this equation, \(\mathrm{q}_{\mathrm{e}}\) (mg/g) represents the amount of amine extracted at equilibrium, and \(\mathrm{q}_{\mathrm{t}}\) (mg/g) represents the number of pollutants absorbed at a given time t (min). The letters \(\mathrm{k}_{1}\) and \(\mathrm{k}_{2}\) are used to denote the rate constants in the pseudo- first- order and pseudo- second- order kinetic models. The slope and intercepts of the linear curves are used to calculate the values of \(\mathrm{k}_{1}\) , \(\mathrm{k}_{2}\) , and the adsorption capacity. + +## Regeneration Studies + +As described above, all synthesized PAs were used to extract different amines from the solution. The PAs with amines absorbed on the surface were regenerated using a dilute HCl solution (0.3 M, 10 mL). After acid washing, the PAs were washed with water to remove traces of acid on the surface, which was checked with pH paper. The thoroughly washed PAs were collected after filtration, dried at 70 °C, and used again for extracting the amines from water. The acid washings and amine extractions were successively done five times, and no significant changes in the removal efficiencies were observed. + +## Biogenic Amine Extraction from Mackerel (Scomber Japonicus) Sample + +<--- Page Split ---> + +To quantify the amount of biogenic amine generation over time due to microbial degradation, fresh fish tissues (2 g) purchased from a local supermarket were kept at room temperature (22°C) for different time durations (6 h, 24 h, and 48 h). After each exposure period, the fish tissue was subjected to a homogenization process with miliQ water (2 g, 10 mL) for 2 minutes. Subsequently, the homogenate was centrifuged for 10 minutes at 4 °C (5,000 rotations per minute) to obtain the supernatant. This extraction process was repeated once, and the resulting supernatants were combined and adjusted to a volume of 25 mL. The resulting extract was stored in a 50 mL centrifuge tube at 4 °C until further treatment. The extraction experiment was carried out by using the procedure mentioned above. Ninhydrin tests were used to quantify the extraction efficiency and the amine content before and after the extraction study. + +## Liquid Chromatography - Mass Spectrometry (LCMS) Analysis + +The mass spectra were obtained using the TSQ Quantum Discovery triple- quadrupole tandem mass spectrometer from Thermo Electron, coupled with a Surveyor high- performance liquid chromatography system from Thermo Finnigan. The system included a thermostated autosampler and a Phenomenex Gemini® 5 μm C18 110 Å 2.0 x 100 mm column kept at 30 °C, connected to a 4 × 2 mm polar RP precolumn from Phenomenex. A 5 μL sample was separated at a flow rate of 0.2 mL/min using a solvent system composed of water (A) and acetonitrile with 0.1% formic acid (B). A linear gradient was applied, increasing the concentration of B from 0% to 45% over 35 minutes and then to 100% over 13 minutes. The mass spectrometer operated in positive electrospray ionization mode (ESI+), with a spray needle voltage of 3.5 kV and a spray current of 5 μA. + +## Results and Discussion + +## Characterization of PAs + +All polyaramides were synthesized and fully characterized using the reported literature procedures.28,29 The design criteria involve the use of A3 (i.e., trimesoyl chloride) and B2 (i.e., diamine) monomers to create a branched polyaramide architecture. The choice of three structurally different amines provides a 3D polyaramide architecture with a significant number of electron- deficient amide groups along the polymer chain. The polyaramides are synthesized with amines with different degrees of flexibility and structural geometries – linear (i.e., EA, PX) vs bent (i.e., MX) structures. Also, the polyaramides from A3 and B2 monomers provide a branched architecture. In the FTIR spectra, all synthesized PAs showed a broad peak from + +<--- Page Split ---> + +3400 - 3485 \(\mathrm{cm^{- 1}}\) corresponding to the amide groups, peaks at \(3075\mathrm{cm^{- 1}}\) (C- H, aromatic), peaks around \(2925\mathrm{cm^{- 1}}\) and \(2852\mathrm{cm^{- 1}}\) representing the stretching vibrations of aliphatic - \(\mathrm{CH_2}\) along the polymer backbone (Figure. 1a). The strong peaks observed around \(1655\mathrm{cm^{- 1}}\) and \(1545\mathrm{cm^{- 1}}\) were assigned as \(>c = 0\) stretching of the amide group (i.e. amide I and amide II peaks). \(^{30,31}\) The other peaks in the FTIR spectra were accounted as \(1420\mathrm{cm^{- 1}}\) (in- plane bending vibration of \(>CH_2\) ), and \(720\mathrm{cm^{- 1}}\) ( \(>N\) - H stretching). \(^{30,31}\) The model compound TA showed a broad peak at \(3469 - 3525\mathrm{cm^{- 1}}\) , region and \(3252\mathrm{cm^{- 1}}\) for \(>N\) - H stretching vibration, peaks observed at \(3031\mathrm{cm^{- 1}}\) (Ar- H), and the peak position observed at \(2921\) and \(2843\mathrm{cm^{- 1}}\) for aliphatic - CH- stretching vibrations (Figure. 1a). The stretching vibrations of the \(>c = 0\) bond of the amide group were observed at \(1658\) and \(1545\mathrm{cm^{- 1}}\) . The peak positions at \(1446\) , \(1320\) , and \(722\mathrm{cm^{- 1}}\) indicated \(>CH_2\) in- plane bending vibration. \(^{31}\) + +![](images/Figure_2.jpg) + + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 1. FTIR spectra (a), XRD pattern (b) of pristine PA1 (—), PA2 (—), PA3 (—) and TA (—). SEM micrographs of PA1 (c), PA2 (d), PA3 (e), and TA (f).
+ +All three polymers showed broad XRD peaks at \(24.55^{\circ}\) (3.62 Å, PA1), \(24.28^{\circ}\) (3.66 Å, PA2), and \(24.94^{\circ}\) (3.56 Å, PA3) (Figure. 1b), which correspond to a disordered amorphous lattice. The small molecule showed many sharp XRD patterns at \(20 = 6.34^{\circ}\) (13.92 Å), \(12.64^{\circ}\) (6.99 Å), \(13.90^{\circ}\) (6.36 Å), \(21.18^{\circ}\) (4.19 Å), \(22.18^{\circ}\) (4.00 Å), which implies a highly crystalline lattice. The TGA data revealed that all polyamrides (PAs 1- 3) have excellent thermal stability, degrading at \(400 - 420^{\circ}\) C (Figure S1). From the TGA traces, the observed mass loss above \(400^{\circ}\) C was calculated as \(63.18 \pm 1.23\%\) (PA1), \(70.24 \pm 1.30\%\) (PA2), \(73.40 \pm 0.54\%\) (PA3), and \(75.33 \pm 0.10\%\) (TA) due to amide bond degradation. Since the TGA was done in a nitrogen atmosphere, the remaining solid could be a highly stable carbon analogue. + +PAs 1 - 3 were dispersed in water and showed zetapotentials of \((- )7.56 \pm 2.23 \mathrm{mV}\) , \((- )12.58 \pm 0.34 \mathrm{mV}\) , and \((- )18.98 \pm 1.24 \mathrm{mV}\) . Such negative values are attributed to the presence of carboxylate groups (- COO\(^{- }\)) on the surface,\(^{34,35}\) which have emerged from the acyl group of TMC. Biogenic amines are protonated in water at a neutral pH of \(6 - 7.^{36,37}\) The zetapotentials measurement of amine dissolved in water showed positive zetapotentials of \((- )6.56 \pm 0.56 \mathrm{mV}\) (putrescine), \((- )7.23 \pm 1.55 \mathrm{mV}\) (spermidine), \((- )8.82 \pm 0.78 \mathrm{mV}\) (spermine), \((- )4.87 \pm 0.66\) + +<--- Page Split ---> + +mv (tryptamine), and \((+1.87 \pm 0.34\) (tryptophan). The SEM images of PA1, PA2, and PA3 revealed spherical morphology structures (Figure 1c- e). However, the SEM images of crystalline small molecule TA showed a ribbon- type structure (Figure 1f). + +## Extraction of Amines from Water + +The synthesized PAs were used for the extraction of biogenic amines from water using a batch process and monitored using UV- Vis spectroscopy. + +![](images/Figure_4.jpg) + +
Figure 2. Absorbance spectra of ninhydrin complexes of spermine before (—■—) and after adsorption of putrescine (—●—), spermidine (—▲—), spermine (—▼—), tryptamine (—◆—), tryptophan (—★—), followed by ninhydrin treatment, PA1 (a) and PA2 (b), PA3 (c), and TA (d). Absorbance spectra of the ninhydrin complexes of other amines are given in the supporting information (Figure S2), which also showed the same adsorption maxima. The inset images show the optical images of the ninhydrin complexes of spermine (i) before and (ii) after extractions with different PAs. PA absorbents (25 mg) were used for the extraction of amine solution at a concentration of 50 mg/L. All adsorption experiments were conducted at pH 7 and \(22^{\circ}\mathrm{C}\) .
+ +<--- Page Split ---> + +The amine molecules present in the supernatant reacted with the carbonyl group of the ninhydrin and formed a blue- colored solution. The concentration of the complex and color of the solution are proportional to the amount of amine present in the solution. \(^{38}\) The intensity values at the adsorption maximum \((\lambda_{\mathrm{max}})\) is \(563 \mathrm{nm}\) in the UV- Vis spectra of the ninhydrin complex were measured, and the removal efficiencies of all PAs were calculated (Figure 2a- c). Strong electrostatic interactions between PAs and amines present in water facilitated the adsorption and enhanced the removal efficiencies \((\sim 96 - 99\%)\) . As expected, the removal efficiencies of the model compounds, triamide TA \((73.46 \pm 2.12\%)\) , Figure 2d, is lower than other PA polymers. + +## Effect of Different Dosages + +In a typical procedure, an appropriate concentration of amine solution \((50 \mathrm{mg / L}, 6 \mathrm{mL})\) was added to various amounts of PAs ranging from 2 to \(50 \mathrm{mg}\) . The extractions were carried out for \(300 \mathrm{min}\) , the mixture was centrifuged, and the remaining concentration of amine in the supernatant was determined using the ninhydrin test, and the extraction efficiency was reported in Figure 3a- d for PAs 1- 3 and TA. The maximum removal efficiencies (Equation 1) were obtained when the optimum amount of PAs \((25 \mathrm{mg})\) was used. PA1 (Figure 3a) showed removal efficiencies in the range of \(93.53 \pm 1.66 - 97.22 \pm 0.38\%\) , and PA2 (Figure 3b) showed \(93.48 \pm 0.70 - 98.74 \pm 0.12\%\) for the five amines tested. PA3 (Figure 3c) showed maximum removal efficiencies in the range of \(97.11 \pm 0.24 - 99.58 \pm 0.23\%\) for amines such as putrescine, spermidine, spermine, tryptamine, and tryptophan. The removal efficiencies of PAs remained constant, with a further increase in the adsorbent dosage of \(50 \mathrm{mg}\) due to the saturation of the surface. The saturation of the adsorbent surface occurs when all available active sites for adsorption are occupied by the target pollutants. When the adsorbate concentration is fixed, increasing the adsorbent amount increases adsorption efficiency by providing more active sites for binding. Initially, efficiency rises sharply with adsorbent dose, but increasing the adsorbent dose still increases the binding sites, and adsorption reaches equilibrium. However, beyond a certain dosage \((25 \mathrm{mg})\) , the removal efficiency is maximum because the pollutant concentration is fixed at \(50 \mathrm{mg / L}\) . + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Figure 3. Percentage removal efficiencies of putrescine (—), spermidine (—), spermine (—), tryptamine (—), tryptophan (—) of PA1 (a), PA2 (b), PA3 (c) and TA (d) polyaramides at different dosages. The extraction was done at room temperature for 300 min, and the amine concentrations were kept constant (50 mg/L). .All adsorption experiments were conducted at pH 7 and \(22^{\circ}C\)
+ +To understand the role of dimensionality of polymers in the extraction of amines from water, a structurally similar triamide small molecule (TA) was prepared from trimesoyl chloride (A3) and aniline (B1), for comparison. The adsorption experiments with small molecule TA as control at different dosages (2 to \(50\mathrm{mg}\) ) (Figure 3d) showed lower efficiencies towards the extraction of amines. At a lower dosage (2 mg) of TA, putrescine, spermidine, spermine, tryptamine, and tryptophan were removed with low efficiencies in the range of 3.97 \(\pm 0.56\%\) - \(10.43\pm 2.35\%\) (Figure 3d). The removal efficiencies of putrescine, spermidine, spermine, tryptamine, and tryptophan at high dosages of TA (25 mg) were in the range of 48 - 57%. This is much less ( \(\sim 80\%\) ) than the removal efficiency of PA3. The network- type structure of PAs 1- 3 has large numbers of electron- deficient amide groups for interacting with amines through hydrogen bonds. Such a network is absent in the case of small molecule TA. + +<--- Page Split ---> + +## Effects of Time and Concentrations of Amines on the Adsorption Efficiency + +The effects of different extraction time points (15 min, 30 min, 60 min, 90 min, 120 min, 180 min and 300 min) and amine concentrations (5, 10, 50, 100 and 150 mg/L) on the removal efficiency were examined. The amine solutions (6 mL) at different concentrations were shaken with fixed amounts of PAs (1- 3, and TAs 25 mg) at room temperature for 15 - 300 min intervals. Samples were analysed, and removal efficiencies were calculated (Figure 4a- h). The removal efficiencies of amines are low at short time points (15 - 90 min) for PA1 (Figure 4a), ranging from \(5.60 \pm 0.04\) to \(55.27 \pm 1.459\%\) . At a higher time duration (300 min), the removal efficiency was increased to \(70.04 - 89.60\%\) . At longer durations, higher removal efficiency was observed due to the increase in the contact time of amine and polyaramide. For amines, removal efficiencies decreased as the initial concentration increased from \(5 \mathrm{mg / L}\) to \(150 \mathrm{mg / L}\) (Figure 4b). Lower amine concentrations (5 mg/L) yielded \(61.40 - 89.74\%\) removal, whereas higher concentrations (150 mg/L) decreased efficiency due to surface saturation. This decrease is due to the saturation of adsorption sites on the surface, leading to decreased removal efficiencies. The initial adsorption rate was higher at lower concentrations, where particle uptake resistance decreased as mass transfer increased. Same way, removal efficiency of PA2 varied with different time points and amine concentrations. After 15 minutes, efficiency was in the range of \(20.84\% - 45.91\%\) , whereas the removal efficiency increased to \(75.83 - 96.44\%\) after 300 minutes for all amines tested. The PA2 showed lower removal efficiencies at high amine concentrations (150 mg/L; \(32.73 - 51.75\%\) ) and high removal efficiencies at low concentrations of PA2 (5 mg/L; \(74.49 - 95.24\%\) , (Figure 4c,d). + +<--- Page Split ---> +![](images/Figure_6.jpg) + + +<--- Page Split ---> +![](images/Figure_7.jpg) + +
Figure 4. Effect of time \((15 - 300\mathrm{min})\) and concentration \((5 - 150\mathrm{mg / L})\) of putrescine ( \(\bullet\) -), spermidine ( \(\bullet\) -), spermine ( \(\bullet\) -), tryptamine ( \(\bullet\) -) and tryptophan ( \(\bullet\) -) on removal efficiency, PA1 (a, b), PA2 (c, d), PA3 (e, f) and TA (g, h). The amount of all PAs used for the adsorption experiments was kept constant at \(25\mathrm{mg}\) .
+ +The polymer, PA3 showed different removal efficiencies of five amines in the range of \(26.39\%\) to \(54.95\%\) after \(15\mathrm{min}\) shaking on a mechanical shaker (Figure 4e). By increasing the shaking time to \(300\mathrm{min}\) , there was a significant improvement in the removal efficiencies to \(84.39 - 99.81\%\) (Figure 4e). Similarly, at low concentrations of amines at \(5\mathrm{mg / L}\) , the polymer PA3 was able to remove \(73.65 - 99.42\%\) of all amines from water (Figure 4f). However, removal efficiencies decreased to \(40.35 - 83.14\%\) at a high concentration of amines at \(150\mathrm{mg / L}\) . The network structure of PA1 - 3 enabled high removal efficiencies as compared to the small molecule TA, with equilibrium reached within \(200\mathrm{min}\) . After \(15\mathrm{minutes}\) , the removal efficiencies of TA were at \(3.93 - 17.15\%\) (Figure 4g), and increasing the extraction time to \(300\mathrm{min}\) , the removal efficiencies were improved to \(48.33 - 56.89\%\) . The low amine concentrations ( \(5\mathrm{mg / L}\) ) in water enhanced TA's efficiencies to \(54.62 - 85.16\%\) . At high concentrations ( \(150\mathrm{mg / L}\) ) of amines, the removal efficiencies are as low as \(4.34 - 11.69\%\) for TA (Figure 4h). The small size of TA contributes to lower removal efficiency compared to other PAs. + +## Isotherm Model Studies + +To understand the adsorption behaviour of amines on the PA surface, the data were analysed using both the Freundlich and Langmuir isotherm models (Figure 5a- h). All necessary model equations for kinetics and isotherms are given in the Materials and Methods section (equations 2 - 6). + +<--- Page Split ---> + +Table 1. Regression coefficient and isotherm parameters for the adsorption of amines (putrescine, spermidine, spermine, tryptamine, and tryptophan) on the PAs. + +
Amines used for
adsorption		Polyara
mides
(PAs)
used		LangmuirFreundlich
\(K_{L}\) (L/mg)\(Q_{max}\)\(R^{2}\)\(K_{r}\)n\(R^{2}\)
PutrescinePA12.9791100.2000.97146.12631.50640.9260
PA20.1657166.6670.96670.92561.09970.9350
PA30.5088243.9020.98511.68011.57190.9066
TA0.567651.8130.98561.51501.25660.9678
SpermidinePA10.095396.07990.99971.83998.34020.9172
PA20.0036183.8240.98141.03731.48300.9119
PA30.0027334.4480.96671.47281.12520.9326
TA0.009981.3000.98441.44021.27300.9767
SperminePA10.1304138.8890.98511.27701.68120.9066
PA20.0430163.9340.98591.30331.12650.9221
PA30.1925370.3720.98792.52001.42100.9178
TA0.1547119.0480.92062.28391.03220.9175
TryptaminePA10.4431102.0400.97888.73171.60340.9009
PA20.3743142.8570.98839.44711.70850.9146
PA30.1302270.1700.9982.93351.74890.9820
TA0.618583.33330.99172.33441.77630.9892
TryptophanPA10.133381.96720.96383.60081.45900.9182
PA20.0853119.0470.99237.26271.56760.9453
PA30.0502232.5580.98146.15741.58290.9556
TA0.139476.92300.97941.71391.29330.9554
+ +350 + +351 After analysis of the data, the Langmuir isotherm model provided a better fit (Table 1)(Figure 5a, c, e, and g) for each of the five amines when compared to Freundlich's model(Figure S3). The adsorption of amines on the PA surface is indicated by high values of \(K_{L}\) and + +<--- Page Split ---> + +n. Monolayer formation of all amines was observed on PA surfaces, as stated by the Langmuir model. This is expected due to the strong interaction between the negatively charged PA surface and positively charged amines at neutral pH. PA3 had a higher adsorption capacity \((Q_{\mathrm{max}})\) compared to PA1, PA2, and TA for putrescine, spermidine, spermine, tryptophan, and tryptamine, with respective removal amounts of \(243.9\mathrm{mg / g}\) , \(334.4\mathrm{mg / g}\) , \(370.3\mathrm{mg / g}\) , \(270.1\mathrm{mg / g}\) , and \(232.5\mathrm{mg / g}\) from water which is calculated using Langmuir model (Table 1). The corresponding values for other polymers are given in Table 1. + +![](images/Figure_unknown_0.jpg) + + +<--- Page Split ---> +![PLACEHOLDER_18_0] + +
Figure 5. Langmuir (a, c, e and g) and pseudo-second-order kinetics (b, d, f and h) plots for PA1 (a, b), PA2 (c, d), PA3 (e, f), TA (g, h) at neutral pH and 298 K using different concentrations (5 - 150 mg/mL) of the putrescine (- - -), spermidine (- - -), spermine (- - -), tryptamine (- - -) and tryptophan (- - -) and a fixed concentration of absorbents (25 mg). The Freundlich isotherm and pseudo-first order kinetic regressions for PAs 1 - 3 and TA, are given in the supporting information (Figure S3).
+ +## Kinetic Model Studies + +To study the mechanism, adsorption of a series of amines at different adsorption times was used (Figure 5, Table 2). The data collected from the five BAs used in the experiments were fitted with pseudo- first- order and pseudo- second- order kinetic models. The methods and materials section includes all equations relevant to both kinetic models. The practical and theoretical parameters determined from the data are given in Figure 5b, d, f and h and summarised in Table 2. The pseudo- second- order kinetic models fit the adsorption data for the five amines more closely than pseudo- first- order kinetic models for all PAs (Table 2). Higher \(\mathrm{R}^2\) values (putrescine - 0.9876, spermidine - 0.9878, spermine - 0.9999, tryptamine - 0.9986, + +<--- Page Split ---> + +tryptophan – 0.9980) were obtained for PA3 using the pseudo-second-order model (Table 2). + +Furthermore, the theoretical removal efficiencies (Qe, cal) calculated using the pseudo-second-order model for each of the five amines are in agreement with the experimental data (Qe, exp) obtained. + +386 + +Table 2. Kinetic parameters for the adsorption of amines, putrescine, spermidine, spermine, + +tryptamine, and tryptophan on PAs 1-3 and model compound TA. + +
Amines usedPolya
mide
used		Pseudo-first orderPseudo-second order
\(Q_{\mathrm {e}}\)cal\(K_{1}\)\(R^{2}\)\(Q_{\mathrm {e}}\)\(Q_{\mathrm {e}}\)\(K_{2}\)(g\(R^{2}\)
(mg g-1)(min-1)(exp.)
(mg g-1)		(cal.)
(mgg-1)		\(mg^{-1}\)
min-1)
PutrescinePA19.44130.0180.9104156.780112.360.02200.9986
PA29.24700.0240.9152159.094149.2540.01710.9873
PA38.05660.0510.9179211.345208.3330.00520.9876
TA6.24630.040.947883.34276.92310.00510.9903
SpermidinePA117.07130.0770.9172268.902256.410.00110.9997
PA219.09630.00950.9135290.112270.2780.01070.9999
PA320.57340.01220.9446303.124277.7780.00820.9878
TA17.40920.01020.9121195.564181.8110.00240.9863
SperminePA124.19870.00590.9066258.009263.1580.01660.9851
PA236.67520.0600.9591318.909287.3560.00950.9996
PA348.95980.0930.9671304.901294.1180.00370.9999
TA12.98770.00560.9477192.778185.1830.00440.9632
TryptaminePA19.649440.0020.9215112.45699.00990.03510.9778
PA212.99020.0320.9168124.098109.8900.01530.9987
PA319.74690.0350.9247134.678294.1170.06720.9986
TA6.78230.0220.831275.78568.02720.08820.9640
TryptophanPA18.693710.00460.9179298.0990.90900.01070.9998
PA211.14390.00340.8742183.980108.6950.01030.9999
PA318.34760.00940.9820301.333243.9020.00120.9980
TA12.77380.00760.949794.87883.3330.01170.9700
+ +<--- Page Split ---> + +## Analysis of PAs After Extraction of Commercial Amines from Spiked Samples + +The amine adsorption on the PAs influences the surface characteristics. Changes in the zetapotential of PAs are examined before and after the adsorption of amines. Before the amines were adsorbed, the PAs showed negative zetapotential values due to the surface - COO end groups on the polymer (Table 3). The positively charged amines are adsorbed on the negatively charged PA surface. After adsorption of amine (spermine), the zetapotential of PAs in water was measured at ambient temperature and showed positive zetapotentials for PA1 (+)14.93 ± 1.23, PA2 (+15.3 ± 2.33, and PA3 (+19.93 ± 0.45. The zetapotential for the adsorption of other amines is given in the supporting information (Table S2). + +Table 3. Particle size, zetapotential, and BET analysis data for PAs and TA. + +
Particle Size (nm)Pore Size (nm)Surface area (m²/g)ζ Potential (mV)
BeforeAfter(spermine)
PA11288 ±3.094.19110.843(-)7.56±2.23(+)14.93 ± 1.23
PA2918 ± 3.262.97814.048(-)12.58±0.34(+)15.3 ± 2.33
PA3875±1.243.23329.233(-)18.98±1.24(+)19.93 ± 0.45
TA529±3.095.7905.683(-)2.52±0.68(+)12.88 ± 1.89
+ +The Fourier transform infrared (FTIR) spectra of all PAs, after the adsorption of amines, showed a broad peak in the range of \(3100 - 3600\mathrm{cm}^{- 1}\) , which corresponds to the N- H stretching from the amide group on the polymer backbone and amine groups after adsorption process, (Figure S4). All PAs showed adsorption peaks corresponding to the aromatic and aliphatic - CH- groups on the polymer backbone (Figure S6a- c). Amide I and amide II peaks were observed around 1660 and \(1545\mathrm{cm}^{- 1}\) , which is slightly shifted to the higher wavelength from the peaks corresponding to the polyaramides before the adsorption of amines. This is expected due to the strong interactions of amine - NH- and amide \(> \mathrm{C} = \mathrm{O}\) groups. The other common peaks were observed around 1293, 917, and \(709\mathrm{cm}^{- 1}\) for PAs before and after adsorption. + +PA solids obtained after the adsorption of amines were examined using thermogravimetric analysis under a nitrogen atmosphere. The TGA analysis was conducted throughout a temperature range from \(22^{\circ}\mathrm{C}\) to \(1000^{\circ}\mathrm{C}\) , with a heating rate of \(10^{\circ}\mathrm{C}\) per minute (Figure S5). There was no specific change observed as the smaller amount of amines adsorbed onto the PAs. The ICP analysis of nitrogen content analysis indicates an increase in the + +<--- Page Split ---> + +percentage of nitrogen after adsorption of amines on PAs (Table S1). Pristine PAs showed 6.48, 6.68, 6.90, and 5.67 % of nitrogen content for PAs 1 – 3 and TA, respectively. The nitrogen content after adsorption of the amines on PAs was increased in the range of 9 to 14% (Table S1). Similar to N content, the percentages of C and H also increased after adsorption of the amines on PAs. Such results are complementary to the extraction data. + +## Regeneration Studies + +After adsorption of the amines on PAs, regeneration, and reuse of the same PAs were also attempted. + +![PLACEHOLDER_21_0] + +
Figure 6. Removal efficiencies of PA 1 - 3 after repeated adsorption-desorption cycles (cycle 1 (■), cycle 2 (■), cycle 3 (■), cycle 4 (■), and cycle 5 (■)). The concentration of putrescine (a), spermidine (b), spermine (c), and tryptamine (d) used was \(50 \mathrm{mg / L}\) , a time of \(300 \mathrm{min}\) , and an adsorbent dose of \(25 \mathrm{mg}\) was kept constant. The regeneration data of PA 1 - 3 after adsorption of tryptophan are given in the supporting information (Figure S6). The regeneration data for TA using all amines are given in the supporting information (Figure S7).
+ +<--- Page Split ---> + +PAs (25 mg) with adsorbed amines on the surface were washed with dilute HCl solution (0.5 M, \(10~\mathrm{mL}\) ) a few times. The resulting mixture was centrifuged, and the solid was washed with water to remove traces of acid and dried at \(60^{\circ}\mathrm{C}\) . The solid was then reused for the extraction of amines from water. The removal efficiencies of regenerated PAs for different amines were measured for five repeated cycles of washings and readortions of the amines. The regenerated PAs demonstrated consistent removal efficiencies of over \(90\%\) , suggesting that PAs are a very effective and reusable adsorbent (Figure 6a- d). PAs were recovered quickly from the solution through filtration after the adsorption. The regeneration process involves protonation (acid- base reaction) of the adsorbed amines (basic) by HCl (acid), converting them into water- soluble ammonium salts for desorption. The removed PAs were utilized directly in the subsequent adsorption procedure without the need for drying or grinding. + +## Extraction and Identification of Biogenic Amines from Fish Sample + +To explore the application of synthesized PA materials in environmental samples, common fish samples were purchased from supermarkets and kept at room temperature for natural degradation. Cleaned fish tissue (2 g) was kept at room temperature in an open environment at different time points (6 h, 24 h, and 48 h). After homogenization in water, the samples were centrifuged. The resulting supernatant fish extract was used for LCMS analysis. A fixed amount of PAs was mixed with an appropriate amount of fish extract solution and kept in a mechanical shaker. The mixture was centrifuged, and the supernatant was treated with ninhydrin reagent to convert the amines into a blue- coloured amine- ninhydrin complex. The UV spectra of the colored complex solution were recorded for solutions before and after extraction with PAs and are presented in Figure 7a. A standard calibration curve was prepared using the known solutions of commercially available amine solutions after treatment with ninhydrin solution. + +The concentration of extracted unknown amines in the solution was measured from the calibration curve. The amines generated inside the fish sample left for 6 hours in open air at ambient conditions were quantified as 2.566 mg/g, which increased to 18.45 and 34.56 mg/g with an increase in time of open- air degradation of 24 h and 48 h, respectively. A known volume (i.e. \(6~\mathrm{mL}\) ) was mixed with PA3 (50 mg) and kept on a mechanical shaker for 6 h. The removal efficiencies of the PAs were calculated from the UV adsorption spectra of the ninhydrin- treated extract solutions before and after adsorption (Figure 7b). PA3 showed high removal efficiencies + +<--- Page Split ---> + +466 of \(99.42 \pm 0.12 \%\) , \(97.00 \pm 1.43 \%\) , and \(95.05 \pm 0.92 \%\) for extract of fish samples after 6 h, 24 h, and 48 h, respectively. + +![PLACEHOLDER_23_0] + +
Figure 7. (a) UV-Vis spectra of the ninhydrin treated extracts obtained from fish sample kept at different time points of 6 h, 24 h, and 48 h at room temperature before (6 h (-), 24 h (-), 24 h (-)) and after (6 h (-), 24 h (-), and 48h (-)) extraction with PA 1-3 samples (25 mg). Note that the absorbance of solutions after extraction with PA was almost zero, indicating a complete removal of amines. (b) The extraction efficiencies of PAs (50 mg) for the removal of amines from solutions collected after keeping the fish samples for 6 h, 12 h, and 24 h at room temperature. The extraction time was kept at 5 h for all samples. (c) LC traces of extract collected from fish sample (2 g) after 48 h at room temperature. Optical images of fish samples (2 g) and LCMS traces of 6h and 24 h fish extracts are given in the supporting information (Figure S8).
+ +LCMS traces were utilized to identify and quantify the amines present in the extracts of fish tissues (Figure 7c). A few peaks were identified by comparing the retention time and + +<--- Page Split ---> + +mass of the commercial amine samples. The retention peak positions at 1.03 min and 1.98 min indicated the presence of putrescine and spermidine with mass (m/z) values of 88.10 and 146.20, respectively. Tryptamine, with a retention time of 8.87 min, was identified with an m/z value of 160.10. The next major peak, with a retention time of 13.66 min and a m/z value of 186.22, was identified as 1- O- alkylglycerols. Similarly, the next major peak with a retention time of 15.88 min and a m/z value of 241.28, indicates the presence of hexadecylamine. The peak with a retention time of 18.62 min and an m/z value of 256.30 is identified as palmitic acid, which is present in fish oil. The next major peak at 26.12 min with a m/z value of 248.48 corresponds to stearic acid. The corresponding mass spectrometry data of all the major peaks observed for the components in the fish extract are given in the supporting information (Figure S9). LCMS obtained from commercial BAs solutions were used for comparison and quantification (Figure S10). The mass spectra of five different commercially available BAs used in our extraction were given in the supporting information (Figure S11). + +## Comparison of the Removal Efficiency of PAs with Other Absorbents + +Biogenic amines are generated through fermentation and decaying of meat and fish samples. Due to relatively low levels of BAs present in water bodies, only a limited number of research have focused on their occurrence in surface and waste waters. Nevertheless, the excessive release or accumulation of organic matter (such as animal remains or discarded food) usually contaminates water and alters the taste, smell, and dissolved oxygen levels. Such deterioration of water quality induces negative consequences for aquatic life, potentially leading to reduced activity or even death of these organisms. Zhu et al. reported that the poly(ether-block- amide) removed 54 - 72 % of biogenic amines such as histamine, putrescine, cadaverine, and tyramine from water. Another group reported 82 - 100% removal efficiency for histamine, putrescine, cadaverine, spermidine, spermine, and tryramine from water using functionalized silica material. The methods of extraction and detection techniques can impact the comparison of adsorption efficiency. The choice of adsorption method plays a significant role in the results; for example, batch extraction may favour high- capacity adsorbents, while chromatography- based methods may highlight selectivity. Additionally, batch extraction is generally more scalable than chromatography- based methods. + +Here, we report a polyaramide- based adsorbents for the removal of biogenic amines from water. The removal process was thoroughly analyzed using kinetic studies, TGA, SEM, and FTIR spectra to gain valuable mechanistic insights. Furthermore, Table 4 presents a comprehensive comparison of various parameters, including different absorbents, removal + +<--- Page Split ---> + +methods, particle concentrations, and the removal efficiencies achieved through different analytical methods. Compared to the absorbent materials reported in the literature, the PAs 1 – 3 showed high removal efficiencies (i.e. \(\sim 99.96\%\) , Table 4). + +Table 4. Comparison of removal efficiency of PAs with other absorbents. + +
AdsorbentExperiment methodAdsorption percentage %Removal efficiency (mg/g)Ref.
1Graphene aerogelBatch extractionHIS 85.19%, CAD 74.1%, and SPD 70.11%-49
2Poly(ether-block-amide)Batch extractionHIS 54%, PUT 72%, CAD 68%, TYR 87%HIS 3.46, PUT 4.58, CAD 5.09, TYR 5.8647
3Functionalized silica materialLiquid chromatograph (LC) coupled to a mass spectrometer detectorHIS 95.0%, PUT 82.0%, CAD 88.7%, SPD 100%, SPM, 100%, TYR 13.3%-48
4Sulfamic acid functionalised blast furnace slagBatch extractionPUT 90%, TYR 70%, PEA 99%PEA 80.64, PUT 12.5 and TYR 64.5250
5Crown ether-modified mesoporous silicaHigh-Performance Liquid ChromatographTRP 40%, PUT 40%, HIS 12%, TYR 20%, SPD 98%51
7PA1Batch ExtractionPUT 94.82 ± 0.12%, SPD 95.48 ± 0.15%, SPM 97.22 ± 0.38%, TYA 95.68 ± 1.15%, TYP 93.53 ± 1.66%PUT 100.20, SPD 96.07, SPM 138.88, TYA 102.04, TYP 81.96This work
8PA2Batch ExtractionPUT 94.31±1.55%, SPD 97.45± 0.10%, SPM 98.64± 0.17%, TYA 98.04 ±PUT 166.66, SPD 183.82, SPM 163.93, TYA 142.85, TYP 119.04This work
+ +<--- Page Split ---> + +0.12%, TYP 93.48± 0.70%, + +
9PA3Batch ExtractionPUT 97.56 ± 0.24%, SPD 98.97 ± 0.68%, SPM 99.58 ± 0.23%, TYA 98.97 ± 1.68%, TYP 96.96 ± 0.08 %PUT 243.90, SPD 334.44, SPM 370.37, TYA 270.17, TYP 232.55This work
+ +522 Abbreviation: HIS- histamine, CAD - cadaverine, SPD - spermidine, PUT - putrescine, TYR +523 - tyramine, SPM - spermine, PEA - 2-phenylamine, TYA - tryptamine, TYP - tryptophan. + +## Mechanism of Adsorption + +Biogenic amines have been shown to accumulate due to microbial attacks on meat or fish products.52 53 Biogenic amines can harm aquatic life, disrupt water ecosystems, and pose health risks to humans, particularly those with histamine intolerance or certain medical conditions.54 Table 4 shows a list of the different absorbents that have been used for removing different biogenic amines from water. The current study used commercially available biological amines such as putrescine, spermine, spermidine, tryptamine, and tryptophan to understand the removal efficiencies of PAs. + +The PAs 1-3 synthesized during this study showed a negative zetapotential that is ideal for extracting the positively charged amines in water at a neutral pH. At an equilibrium concentration of PAs (25 mg) and an adsorption time of 300 min, high removal efficiencies of 97.11 ± 0.24 %, 98.97 ± 0.68 %, 99.58 ± 0.23 %, 98.97 ± 1.68 %, 96.96 ± 0.08 % for putrescine, spermidine, spermine, tryptamine, and tryptophan, respectively, were observed. The polyaramide (PA3) showed higher removal efficiency (~ 99%) than other polymers. PA3 has a greater surface area (29.23 m²/g) due to the incorporation of the nonlinear m-xylene diamine in the structure. + +<--- Page Split ---> +![PLACEHOLDER_27_0] + +
Scheme 2. The complexation and extraction behaviour of PA with amines from solution
+ +Also, the zetapotentials of amine solutions in water were measured at ambient temperature and neutral pH (7.0). The respective values obtained for putrescine, spermidine, spermine, tryptamine, and tryptophan were \((+6.56 \pm 0.56 \text{mV}, (+7.23 \pm 1.55 \text{mV}, (+8.82 \pm 0.78 \text{mV}, (+4.87 \pm 0.66 \text{mV}, and (+1.87 \pm 0.34 \text{mV. All synthesized PAs showed a negative zetapotential for the adsorption of positively charged amines. After the amine adsorption, the zetapotential of the PA surface changed from negative to positive values (Table 3). The negatively charged polyaramides (PAs) attract the protonated amine molecules on the surface via electrostatic forces and H- bonding. The zetapotential of the PA surface changed from negative to positive values after extracting the amines from the solution (Table 3, Scheme 2). In addition to electrostatic interaction, hydrogen bonds also play an important role towards the removal of amines from water. PA3 showed a higher adsorption capacity (Qmax) of amine 243.9 mg/g for putrescine, 334.4 mg/g spermidine, 370.3 mg/g for spermine, 270.1 mg/g for tryptamine, and 232.5 mg/g for tryptophan. Elemental analysis indicates an increase in nitrogen content for all PAs after adsorption of biogenic amines (Table S1). PAs absorbed with spermine have a higher percentage of nitrogen content, 6.89 - 14.43 %, compared to other biogenic amines due to higher adsorption efficiency. The order of removal efficiencies of biogenic amines is spermine > spermidine > putrescine > tryptamine > tryptophan. To understand and compare the removal efficiencies of PAs, other monoamines such as hexylamine, and phenyl ethylene amine were also used for the extraction studies. PA3 exhibited removal efficiencies of 87.61 ± 2.43 and 87.30 ± 1.02 % for hexyl amine (HA) and phenylene ethylene amine (PEA), + +<--- Page Split ---> + +respectively. Similarly, both PA2 and PA1 showed lower removal efficiencies of \(80 - 77\%\) for HA and \(70 - 50\%\) for PEA under same experimental conditions (Figure S12). Similarly, the small molecules (TA) showed \(57.70 - 73.46\%\) removal efficiencies, which are lower than that observed for PAs 1- 3. The synthesized PAs were used to extract amines and other degraded molecules in the decaying fish samples kept at room temperature for periods of \(6\mathrm{h}\) , \(24\mathrm{h}\) , and \(48\mathrm{h}\) . LCM technique and commercially available standard amine samples were used to determine the chemical identity of the compounds present in the fish extract. All three polyaramides have a new work structure, in particular, PA3 is expected to have a 3D architecture due to the bent structure of the diamine, MX. The network structure and negative zetapotential of PAs help to trap the positively charged amine molecules inside the solid lattice, which then enhances the adsorption capacities (Scheme 2). + +## Conclusion + +The amide- based porous network polymers effectively removed biogenic amines such as putrescin, spermidine, spermine, tryptamine, and tryptophan. The prepared PAs removed biogenic amines (BAs) from water with around \(99\%\) efficiency. All the PAs were characterized before and after adsorption of the amines. PA3 showed the highest adsorption efficiencies \((Q_{\mathrm{max}})\) as compared to the other three PAs ( \(244\mathrm{mg / g}\) putrescin, \(334\mathrm{mg / g}\) spermidine, \(370\mathrm{mg / g}\) spermine, \(270\mathrm{mg / g}\) tryptamine, and \(232\mathrm{mg / g}\) tryptophan). Absorbents (PA1- 3) and model compound TA were characterized using FTIR spectra, TGA, and SEM. The extraction data for the amines were analyzed using the Langmuir and Freundlich isotherm models and different kinetic models. The absorbents were regenerated and reused to extract amines from water. After five cycles, the PAs showed similar removal efficiencies, and there was no appreciable efficiency loss due to polymer degradation. Compared to triamide (TA), the PAs 1- 3 showed higher removal efficiencies towards various amines tested. The synthesised PAs were also used to extract amines generated by decaying natural fish tissues for \(6\mathrm{h}\) to \(48\mathrm{h}\) . The amounts of amines extracted from such fish tissues were in the range of \(2 - 35\mathrm{mg / g}\) which increased with an increase in time. Such easily accessible synthetic polymers are a great candidate for environmental remediation in the future. + +## Supporting Information + +Full synthetic details of polyaramides; PA1- 3 and small molecule TA; TGA (b) of PA1 (—), PA2 (—), PA3 (—) and SA1 (—) before adsorption; Absorbance spectra of ninhydrin complex of putrescin (—), spermidine (—), tryptamine (—), and tryptophan (—) at a concentration of \(50\mathrm{mg / L}\) ; Freundlich (a, c, e, g) and pseudo- first- order kinetics (b, d, f and h) + +<--- Page Split ---> + +plots for PA1 (a and b), PA2 (and d), PA3(e and f) and SA1 (g and h) at 298 K using different concentrations (5 - 150 mg/mL) of the putrescine (--), spermidine (--), spermine (--), tryptamine (--) and tryptophan (--). A fixed concentration (25 mg in 6 mL) of PAs and SA1 are used for all studies; FTIR spectra of PA1 (a), PA2 (b), PA3 (c) and SA1 (d) after adsorption of putrescine (--), spermidine (--), spermine (--), tryptamine (--) and tryptophan (--). KBr matrix was used for recording the spectra; TGA of PA1 (a), PA2 (b), PA3 (c), and SA1 (d) after adsorption of putrescine (--), spermidine (--), spermine (--), tryptamine (--), and tryptophan (--); Removal & regeneration efficiencies of PA 1 - 3 after repeated absorption-desorption cycles 1 (--), 2 (--), 3 (--), 4 (--), and 5 (--) using tryptophan as a model amine. The tryptophan concentration was 50 mg/L, extraction time of 300 min, and an adsorbent dose of 25 mg was kept constant; Removal efficiency of SA1 after repeated adsorption-desorption cycles (cycle 1 (--), cycle 2 (--), cycle 3 (--), cycle 4 (--), and cycle 5 (--). The concentration of Putrescine (a), Spermidine(b), Spermine(c), Tryptamine(d), and tryptophan(e) (50 mg/L), time (300 min), and adsorbent dose (25 mg) were kept constant. PUT-Putrescine, SPD-Spermidine, SPM-Spermine, TYA- Tryptamine, and TYP-Tryptophan; Optical images of fish samples at different time points of 6h (a), 24 h (b), and 48 h (c). LCMS traces of crude extracts collected from fish samples kept at 6h (d) and 24 h using a C-18 reverse phase column; The mass spectra of eluents with a retention time of 1.03 min (a),1.98 min (b), 8.87 min (c), 10.33 min (d),13.88 min (e),15.88 min (f), 18.62 min (g), 26.12 min (h) observed for the fish extract collected from 48h; LCMS of standard commercial amines, putrescine (1), spermidine (2), spermine (3), tryptamine (4) and tryptophan (5). The inset represents the enlarged view of putrescine (1) and spermidine (2) peaks; The mass spectra of commercially available standard samples of putrescine (1), spermidine (2), spermine (3), tryptamine (4) and tryptophan (5); Removal efficiencies of hexylamine (a)and phenylethylamine (b) at different concentrations (5 - 100 mg) of polyaramides PA1 (--), PA2 (--) and PA3 (--). The concentration of all PAs was kept constant at 25 mg. + +## Author contribution statement + +GM: Experimentation, collection of data, formal analyses, writing of the paper draft. SV: Resources, ideation, methodology, and revision of the manuscript. + +## Declaration of the competing interests + +The authors declare no known competing financial or personal relationships that could have influenced the work reported in this paper. + +## Funding + +The authors acknowledge the funding support from the National Research Foundation grant A- 0004151- 00- 00 and technical support from the Department of Chemistry at the National University of Singapore. + +## Data Availability + +<--- Page Split ---> + +The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information file. 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LWT 2024, 194, 115793- 115813. 816 https://doi.org/10.1016/j.lwt.2024.115793. + +<--- Page Split ---> + +# Engineered Polyamides for Extraction of Bioamines from Water + +Gomathi Mahadevan and Suresh Valiyaveettil\* Department of Chemistry, National University of Singapore 3 Science Drive 3, Singapore 117543 Email: chmsv@nus.edu.sg + +![PLACEHOLDER_34_0] + + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +Supportinginformation.docx + +<--- Page Split ---> diff --git a/preprint/preprint__07bb49d179d74a99cfca97aa544930e840ce529d98ed5ba46742687ced243ffc/preprint__07bb49d179d74a99cfca97aa544930e840ce529d98ed5ba46742687ced243ffc_det.mmd b/preprint/preprint__07bb49d179d74a99cfca97aa544930e840ce529d98ed5ba46742687ced243ffc/preprint__07bb49d179d74a99cfca97aa544930e840ce529d98ed5ba46742687ced243ffc_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..61d2a46f40d226112007bc2ee1cec40eb44d0267 --- /dev/null +++ b/preprint/preprint__07bb49d179d74a99cfca97aa544930e840ce529d98ed5ba46742687ced243ffc/preprint__07bb49d179d74a99cfca97aa544930e840ce529d98ed5ba46742687ced243ffc_det.mmd @@ -0,0 +1,502 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 780, 174]]<|/det|> +# Engineered Polyaramides for Extraction of Bioamines from Water + +<|ref|>text<|/ref|><|det|>[[44, 196, 339, 288]]<|/det|> +Gomathi Mahadevan National University of Singapore Suresh Valiyaveettil chmsv@nus.edu.sg + +<|ref|>text<|/ref|><|det|>[[55, 315, 339, 333]]<|/det|> +National University of Singapore + +<|ref|>sub_title<|/ref|><|det|>[[44, 375, 103, 393]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 413, 936, 433]]<|/det|> +Keywords: Polyaramide, biogenic amines, extraction, adsorption efficiency, LCMS, environmental matrix + +<|ref|>text<|/ref|><|det|>[[44, 451, 322, 470]]<|/det|> +Posted Date: August 11th, 2025 + +<|ref|>text<|/ref|><|det|>[[44, 489, 475, 508]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 7238623/v1 + +<|ref|>text<|/ref|><|det|>[[42, 526, 916, 570]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 588, 545, 607]]<|/det|> +Additional Declarations: No competing interests reported. + +<|ref|>text<|/ref|><|det|>[[42, 643, 935, 686]]<|/det|> +Version of Record: A version of this preprint was published at Scientific Reports on October 21st, 2025. See the published version at https://doi.org/10.1038/s41598- 025- 20410- 1. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[75, 87, 876, 157]]<|/det|> +# Engineered Polyaramides for Extraction of Bioamines from Water + +<|ref|>text<|/ref|><|det|>[[292, 169, 701, 186]]<|/det|> +Gomathi Mahadevan and Suresh Valiyaveettil\* + +<|ref|>text<|/ref|><|det|>[[255, 204, 740, 252]]<|/det|> +Department of Chemistry, National University of Singapore 3 Science Drive 3, Singapore 117543 Email: chmsv@nus.edu.sg + +<|ref|>sub_title<|/ref|><|det|>[[115, 289, 221, 309]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[112, 320, 882, 781]]<|/det|> +Biogenic amines (BAs) are prevalent in fermented foods, protein- rich meats, and brewed food additives. High concentrations of BAs can induce health issues such as headaches, high blood pressure, and palpitations. Polyaramides (PAs) prepared from the reaction of trimesoyl chloride with ethylene diamine (ED), \(p\) - xylene diamine (PXD), and \(m\) - xylene diamine (MXD) and characterized using different characterization techniques, FTIR, XRD, SEM, TGA, and DLS measurements. All PAs were used for the removal of five biogenic amines (i.e., putrescine, spermidine, spermine, tryptamine, and tryptophan) from water. All PAs showed high removal efficiencies for biogenic amines, and the data collected fit well with the Langmuir isotherm and pseudo- second- order kinetic models. Among three different polymers, PA3 showed exceptional adsorption capacities, achieving removal efficiencies of \(97.1 \pm 0.24\%\) (putrescine), \(99 \pm 0.68\%\) (spermidine), \(99.6 \pm 0.23\%\) (spermine), \(99 \pm 1.68\%\) (tryptamine), and \(97 \pm 0.08\%\) (tryptophan) at an equilibrium concentration of \(25 \mathrm{mg}\) PA and an adsorption time of 300 minutes. Furthermore, adsorbent regeneration was established via washing with an acid solution, and the removal efficiency was retained after five cycles of repeated washings and extractions. As a proof of concept, the synthesized PAs were used to extract the amines from a decaying fish sample for different periods, which showed \(99\%\) extraction and removal efficiencies. The chemical nature of the extracted BAs was identified using LCMS. Such synthetic polyaramides for removing pollutants from environmental matrices are interesting candidates for developing functional materials. + +<|ref|>text<|/ref|><|det|>[[115, 795, 880, 838]]<|/det|> +Keywords: Polyaramide, biogenic amines, extraction, adsorption efficiency, LCMS, environmental matrix. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 86, 266, 107]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[115, 116, 883, 504]]<|/det|> +The freshness of meat or fish products in the market is difficult to measure, owing to the lack of suitable fast and reliable methods. Biogenic amines (BAs) such as cadaverine, putrescine, spermidine, spermine, phenylethylamine, tyramine, and histamine are present or generated in biological samples with aging or under stress. \(^{1,2}\) The production of BAs is primarily a result of the decarboxylation of amino acids catalysed by microorganisms. \(^{3}\) Microbial species in the environment degrade the biological tissues to release small molecular metabolites, including significant amounts of BAs. \(^{4}\) A high concentration of BAs in human body fluids causes health issues, such as asthma, headaches, and irregular blood pressure. \(^{5,6}\) Many aged foods, such as fish, wine, beer, meat products, and fermented foods, contain BAs, which accumulate with time due to microbial degradation. \(^{7}\) BAs from decaying old food products or effluents from fish markets or butcheries end up in local water bodies, posing adverse health risks to living organisms. \(^{6,8}\) Furthermore, studies have demonstrated that BA accumulates in algae during summertime blooming, which results in an abrupt increase in the concentration of amines in marine or coastal locations. \(^{1,9}\) A few reports are available on the extraction and identification of BAs from decaying fish samples by using the HPLC technique. \(^{5,10}\) + +<|ref|>text<|/ref|><|det|>[[115, 519, 883, 686]]<|/det|> +New functional materials and methodologies are needed for the detection, removal, and quantification of the BAs from water. \(^{11,12}\) A few materials were used to detect the presence of biogenic amines in food and water. \(^{13 - 15}\) However, removing amines or other metabolites from different raw food materials has not been well studied due to the complexity of the matrix. Amines such as putrescine and spermine were extracted and removed from meat products using absorbents such as iron oxide and graphene, but multiple treatments were necessary to achieve high removal efficiencies. \(^{16 - 18}\) + +<|ref|>text<|/ref|><|det|>[[115, 700, 883, 892]]<|/det|> +Water pollution caused by various contaminants such as heavy metals, pesticides, biological waste, and industrial chemicals poses a significant threat to human health and the environment. \(^{4}\) In recent years, polyamides have emerged as a promising material for water purification technology. Polyamides, a class of synthetic polymers, are widely employed in various industrial applications due to their excellent mechanical properties, thermal stability, and chemical resistance. \(^{19 - 21}\) Here, we use synthetic, highly branched polyamides (PAs) for the removal of BAs from spiked water samples to develop a purification system for future applications (Scheme 1). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[170, 85, 860, 655]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[115, 664, 880, 701]]<|/det|> +Scheme 1. Synthetic scheme (a) of PAs 1- 3 and triamide (TA), and chemical structure (b) of amines used for the extraction. + +<|ref|>text<|/ref|><|det|>[[115, 710, 881, 900]]<|/det|> +In our design strategy, the electron- deficient amide groups, hydrophobic interactions, and the network structure of PA were employed for the extraction of organic amines from water. Our objectives include the extraction of amines from an aqueous environment and understanding the structure- property correlations of the three PAs. The synthetic strategy includes trimesoyl chloride (TMC) as a trifunctional monomer and one of the linear ethylene diamine (ED), \(p\) - xylene diamine (PXD), and bend \(m\) - xylene diamine (MXD) for the synthesis of PAs (Scheme 1a). Ethylene diamine (ED) is a flexible linear molecule, whereas aromatic amines such as \(p\) - xylene diamine (PXD) and \(m\) - xylene diamine (MXD) are rigid molecules. A + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 883, 398]]<|/det|> +model triamide (TA) was prepared using TMC and aniline (AN) for comparison of structure and properties. All three PAs with different structural features were utilized for the removal of BAs such as putrescine (i), spermidine (ii), spermine (iii), tryptamine (iv), and tryptophan (v), from water (Scheme 1b). The amide groups inside the polymer lattice are electron-deficient moieties and interact strongly with electron-rich pollutants (e.g. amines, anions) through electrostatic interactions. Impact of experimental factors such as concentration of PAs and amines, extraction time on adsorption efficiency, isotherm and kinetic model treatments of the data, regeneration of PAs, proof- of- concept demonstration of extracting amines from decaying fish sample, comparison of PAs with other adsorbents towards amine extraction, and mechanism of extraction are discussed in this manuscript. Although many polyamides have been reported in the literature, \(^{10,13,21}\) the PAs discussed here demonstrate significant efficiency towards the extraction of biogenic amines, making them suitable for separation and purification technologies. + +<|ref|>sub_title<|/ref|><|det|>[[118, 413, 355, 432]]<|/det|> +## Materials and Methods + +<|ref|>text<|/ref|><|det|>[[115, 442, 883, 781]]<|/det|> +1,3,5- Trimesoyl chloride (TMC), ethylenediamine (ED), \(p\) - xylenediamine (PXD), \(m\) - xylenediamine (MXD), aniline, dimethylformamide (DMF, HPLC grade), and triethylamine (TEA) were purchased from Sigma Aldrich Pte Ltd. All chemicals were used as received without any further purification. Deionized water was used throughout the experiments. A Bruker ALPHA FT- IR spectrophotometer was used to record the FTIR spectra (500 – 4000 cm\(^{- 1}\)) both before and after the extraction of amines using KBr as the matrix. The size and surface charges of the materials were measured using the Malvern Zetasizer Nano- ZS90. The thermogravimetric analyses (TGA) were done using a Discovery TGA apparatus in a nitrogen atmosphere within the 25 – 1000 °C temperature range and a heating rate of 10 °C/min. The UV- Vis spectra of every sample were recorded using a Shimadzu- 1601 spectrophotometer. The synthesized PA’s morphologies were established using a JEOL JSM- 6701F scanning electron microscope (SEM). The crystallinity of the polyaramides was investigated with the help of a Bruker D8 Advance Powder Crystal X- ray diffractometer with Cu Kα radiation (λ = 0.154 nm) operating at 40 kV and 40 mA within the 20 range of 5 - 70°. + +<|ref|>sub_title<|/ref|><|det|>[[115, 797, 505, 817]]<|/det|> +## Synthesis and Characterisation of PAs + +<|ref|>text<|/ref|><|det|>[[115, 825, 881, 893]]<|/det|> +All polyaramides (PAs) and the control molecules were synthesized and fully characterized using the reported procedures. \(^{22}\) Full details on the synthesis and characterisation of the PAs are given in the supporting information. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 108, 425, 129]]<|/det|> +## Removal of Amines Using PAs + +<|ref|>text<|/ref|><|det|>[[115, 137, 883, 450]]<|/det|> +Commercially available amines (putrescine, spermidine, spermine, tryptamine, and tryptophan) were dissolved in Milli- Q water to prepare a stock solution (50 mg/L) and used for extraction studies. The extraction was carried out at room temperature (22 °C) and a neutral pH of 7. A calibration curve was prepared after mixing the amine solution of different concentrations with an appropriate amount of ninhydrin solution. Ninhydrin reacts with the amines to form a blue adduct, whose concentration is monitored using UV- Vis spectroscopy to quantify the amount of amine in the solution. Different amounts of PA absorbent (2- 50 mg) were added to the amine solution (6 mL), and the extraction was carried out for 300 min. The solution was centrifuged, and the supernatant was mixed with dilute ninhydrin solution (0.01M) to get a blue- colored solution after 30 min at pH 8. \(^{10}\) UV- Vis spectra of the blue solutions were recorded, and the concentration of amine was determined from a calibration curve. The removal efficiency was calculated from the spectroscopic data obtained before and after extraction using equation (1). \(^{23}\) + +<|ref|>equation<|/ref|><|det|>[[175, 464, 720, 500]]<|/det|> +\[\mathrm{Removal~efficiency~(\mathrm{RE})} = \frac{\mathrm{C_0 - C_e}}{\mathrm{C_0}}\cdot \mathrm{X}100\% \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[117, 515, 881, 559]]<|/det|> +where \(\mathrm{C_0}\) (mg/L) is the initial concentration and \(\mathrm{C_e}\) (mg/L) is the equilibrium concentration of amines. + +<|ref|>sub_title<|/ref|><|det|>[[117, 576, 485, 596]]<|/det|> +## Isotherm and Kinetic Model Studies + +<|ref|>text<|/ref|><|det|>[[115, 603, 882, 746]]<|/det|> +The amines were extracted using an optimized amount (25 mg) of PA absorbents from dilute solutions (6 mL) containing varying concentrations (5 - 150 mg/L). Adsorption studies were repeated three times for each concentration, and the average value was reported. The collected data were used to analyse the common adsorption models, such as the Langmuir and the Freundlich isotherm models. The Langmuir isotherm model (Equation 2) fits the data well, indicating the formation of continuous monolayers on the surface of the PAs. \(^{24}\) + +<|ref|>equation<|/ref|><|det|>[[175, 750, 716, 789]]<|/det|> +\[\mathrm{Q_e} = \frac{\mathrm{Q_{max}}\mathrm{K_L}\mathrm{C_e}}{1 + \mathrm{K_L}\mathrm{C_e}} \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[115, 797, 881, 890]]<|/det|> +\(\mathrm{Q_{max}}\) (mg/g) is the maximum adsorption capacity of amines, and \(\mathrm{Q_e}\) (mg/g) is the equilibrium adsorption capacity. The equilibrium concentration is represented by the letter \(\mathrm{C_e}\) , and \(\mathrm{K_L}\) is the binding energy- related constant. Multilayer adsorption on heterogeneous surfaces is treated by the Freundlich empirical equation (3). \(^{24}\) + +<--- Page Split ---> +<|ref|>equation<|/ref|><|det|>[[177, 84, 721, 110]]<|/det|> +\[\mathrm{Q}_{\mathrm{e}} = \mathrm{K}_{\mathrm{F}}\mathrm{C}_{\mathrm{e}}^{1 / \mathrm{n}} \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[115, 116, 882, 185]]<|/det|> +where \(\mathrm{C}_{\mathrm{e}}\) (mg/L) is the equilibrium concentration and \(\mathrm{Q}_{\mathrm{e}}\) (mg/g) is the adsorbent's quantity of absorbed amines per unit mass at equilibrium. \(\mathrm{K}_{\mathrm{F}}\) and \(\mathrm{n}\) represent adsorption capacity and intensity, respectively. In a linear form, Equation 3 could be extended as Equation 4.25 + +<|ref|>equation<|/ref|><|det|>[[175, 189, 716, 222]]<|/det|> +\[\mathrm{Log} \mathrm{Q}_{\mathrm{e}} = \log \mathrm{K}_{\mathrm{F}} + \frac{1}{n} \log C_{\mathrm{e}} \quad (4)\] + +<|ref|>text<|/ref|><|det|>[[114, 228, 882, 395]]<|/det|> +The PA (25 mg) was mixed with the appropriate amine solution (50 mg/ L, 6 mL) at room temperature and shaken mechanically for different time points (15 - 300 min). The solutions were centrifuged, and the collected supernatant was filtered. The filtrate was analyzed to determine the extraction efficiency of the PA towards the amine. The adsorption experiments were repeated three times for each concentration, and the average value was used for calculations. The extraction data were shown using the pseudo- first- order (Equation 5) and pseudo- second- order (Equation 6) kinetic models.26,27 + +<|ref|>equation<|/ref|><|det|>[[175, 400, 716, 470]]<|/det|> +\[\begin{array}{l}{\ln (\mathrm{q}_{\mathrm{e}} - \mathrm{q}_{\mathrm{t}}) = \ln \mathrm{q}_{\mathrm{e}} - \mathrm{k}_{1}\mathrm{t}}\\ {\frac{\mathrm{t}}{\mathrm{q}_{\mathrm{t}}} = \frac{\mathrm{t}}{\mathrm{q}_{\mathrm{e}}} +\frac{1}{\mathrm{k}_{2}\mathrm{q}_{\mathrm{e}}^{2}}} \end{array} \quad (5)\] + +<|ref|>text<|/ref|><|det|>[[114, 500, 882, 620]]<|/det|> +In this equation, \(\mathrm{q}_{\mathrm{e}}\) (mg/g) represents the amount of amine extracted at equilibrium, and \(\mathrm{q}_{\mathrm{t}}\) (mg/g) represents the number of pollutants absorbed at a given time t (min). The letters \(\mathrm{k}_{1}\) and \(\mathrm{k}_{2}\) are used to denote the rate constants in the pseudo- first- order and pseudo- second- order kinetic models. The slope and intercepts of the linear curves are used to calculate the values of \(\mathrm{k}_{1}\) , \(\mathrm{k}_{2}\) , and the adsorption capacity. + +<|ref|>sub_title<|/ref|><|det|>[[117, 650, 336, 670]]<|/det|> +## Regeneration Studies + +<|ref|>text<|/ref|><|det|>[[115, 677, 882, 844]]<|/det|> +As described above, all synthesized PAs were used to extract different amines from the solution. The PAs with amines absorbed on the surface were regenerated using a dilute HCl solution (0.3 M, 10 mL). After acid washing, the PAs were washed with water to remove traces of acid on the surface, which was checked with pH paper. The thoroughly washed PAs were collected after filtration, dried at 70 °C, and used again for extracting the amines from water. The acid washings and amine extractions were successively done five times, and no significant changes in the removal efficiencies were observed. + +<|ref|>sub_title<|/ref|><|det|>[[112, 874, 842, 895]]<|/det|> +## Biogenic Amine Extraction from Mackerel (Scomber Japonicus) Sample + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 882, 349]]<|/det|> +To quantify the amount of biogenic amine generation over time due to microbial degradation, fresh fish tissues (2 g) purchased from a local supermarket were kept at room temperature (22°C) for different time durations (6 h, 24 h, and 48 h). After each exposure period, the fish tissue was subjected to a homogenization process with miliQ water (2 g, 10 mL) for 2 minutes. Subsequently, the homogenate was centrifuged for 10 minutes at 4 °C (5,000 rotations per minute) to obtain the supernatant. This extraction process was repeated once, and the resulting supernatants were combined and adjusted to a volume of 25 mL. The resulting extract was stored in a 50 mL centrifuge tube at 4 °C until further treatment. The extraction experiment was carried out by using the procedure mentioned above. Ninhydrin tests were used to quantify the extraction efficiency and the amine content before and after the extraction study. + +<|ref|>sub_title<|/ref|><|det|>[[118, 364, 769, 386]]<|/det|> +## Liquid Chromatography - Mass Spectrometry (LCMS) Analysis + +<|ref|>text<|/ref|><|det|>[[115, 393, 882, 634]]<|/det|> +The mass spectra were obtained using the TSQ Quantum Discovery triple- quadrupole tandem mass spectrometer from Thermo Electron, coupled with a Surveyor high- performance liquid chromatography system from Thermo Finnigan. The system included a thermostated autosampler and a Phenomenex Gemini® 5 μm C18 110 Å 2.0 x 100 mm column kept at 30 °C, connected to a 4 × 2 mm polar RP precolumn from Phenomenex. A 5 μL sample was separated at a flow rate of 0.2 mL/min using a solvent system composed of water (A) and acetonitrile with 0.1% formic acid (B). A linear gradient was applied, increasing the concentration of B from 0% to 45% over 35 minutes and then to 100% over 13 minutes. The mass spectrometer operated in positive electrospray ionization mode (ESI+), with a spray needle voltage of 3.5 kV and a spray current of 5 μA. + +<|ref|>sub_title<|/ref|><|det|>[[118, 650, 382, 671]]<|/det|> +## Results and Discussion + +<|ref|>sub_title<|/ref|><|det|>[[118, 680, 364, 700]]<|/det|> +## Characterization of PAs + +<|ref|>text<|/ref|><|det|>[[115, 708, 882, 899]]<|/det|> +All polyaramides were synthesized and fully characterized using the reported literature procedures.28,29 The design criteria involve the use of A3 (i.e., trimesoyl chloride) and B2 (i.e., diamine) monomers to create a branched polyaramide architecture. The choice of three structurally different amines provides a 3D polyaramide architecture with a significant number of electron- deficient amide groups along the polymer chain. The polyaramides are synthesized with amines with different degrees of flexibility and structural geometries – linear (i.e., EA, PX) vs bent (i.e., MX) structures. Also, the polyaramides from A3 and B2 monomers provide a branched architecture. In the FTIR spectra, all synthesized PAs showed a broad peak from + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 882, 348]]<|/det|> +3400 - 3485 \(\mathrm{cm^{- 1}}\) corresponding to the amide groups, peaks at \(3075\mathrm{cm^{- 1}}\) (C- H, aromatic), peaks around \(2925\mathrm{cm^{- 1}}\) and \(2852\mathrm{cm^{- 1}}\) representing the stretching vibrations of aliphatic - \(\mathrm{CH_2}\) along the polymer backbone (Figure. 1a). The strong peaks observed around \(1655\mathrm{cm^{- 1}}\) and \(1545\mathrm{cm^{- 1}}\) were assigned as \(>c = 0\) stretching of the amide group (i.e. amide I and amide II peaks). \(^{30,31}\) The other peaks in the FTIR spectra were accounted as \(1420\mathrm{cm^{- 1}}\) (in- plane bending vibration of \(>CH_2\) ), and \(720\mathrm{cm^{- 1}}\) ( \(>N\) - H stretching). \(^{30,31}\) The model compound TA showed a broad peak at \(3469 - 3525\mathrm{cm^{- 1}}\) , region and \(3252\mathrm{cm^{- 1}}\) for \(>N\) - H stretching vibration, peaks observed at \(3031\mathrm{cm^{- 1}}\) (Ar- H), and the peak position observed at \(2921\) and \(2843\mathrm{cm^{- 1}}\) for aliphatic - CH- stretching vibrations (Figure. 1a). The stretching vibrations of the \(>c = 0\) bond of the amide group were observed at \(1658\) and \(1545\mathrm{cm^{- 1}}\) . The peak positions at \(1446\) , \(1320\) , and \(722\mathrm{cm^{- 1}}\) indicated \(>CH_2\) in- plane bending vibration. \(^{31}\) + +<|ref|>image<|/ref|><|det|>[[150, 355, 842, 570]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[150, 84, 843, 488]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 500, 869, 536]]<|/det|> +
Figure 1. FTIR spectra (a), XRD pattern (b) of pristine PA1 (—), PA2 (—), PA3 (—) and TA (—). SEM micrographs of PA1 (c), PA2 (d), PA3 (e), and TA (f).
+ +<|ref|>text<|/ref|><|det|>[[115, 544, 882, 759]]<|/det|> +All three polymers showed broad XRD peaks at \(24.55^{\circ}\) (3.62 Å, PA1), \(24.28^{\circ}\) (3.66 Å, PA2), and \(24.94^{\circ}\) (3.56 Å, PA3) (Figure. 1b), which correspond to a disordered amorphous lattice. The small molecule showed many sharp XRD patterns at \(20 = 6.34^{\circ}\) (13.92 Å), \(12.64^{\circ}\) (6.99 Å), \(13.90^{\circ}\) (6.36 Å), \(21.18^{\circ}\) (4.19 Å), \(22.18^{\circ}\) (4.00 Å), which implies a highly crystalline lattice. The TGA data revealed that all polyamrides (PAs 1- 3) have excellent thermal stability, degrading at \(400 - 420^{\circ}\) C (Figure S1). From the TGA traces, the observed mass loss above \(400^{\circ}\) C was calculated as \(63.18 \pm 1.23\%\) (PA1), \(70.24 \pm 1.30\%\) (PA2), \(73.40 \pm 0.54\%\) (PA3), and \(75.33 \pm 0.10\%\) (TA) due to amide bond degradation. Since the TGA was done in a nitrogen atmosphere, the remaining solid could be a highly stable carbon analogue. + +<|ref|>text<|/ref|><|det|>[[115, 765, 882, 907]]<|/det|> +PAs 1 - 3 were dispersed in water and showed zetapotentials of \((- )7.56 \pm 2.23 \mathrm{mV}\) , \((- )12.58 \pm 0.34 \mathrm{mV}\) , and \((- )18.98 \pm 1.24 \mathrm{mV}\) . Such negative values are attributed to the presence of carboxylate groups (- COO\(^{- }\)) on the surface,\(^{34,35}\) which have emerged from the acyl group of TMC. Biogenic amines are protonated in water at a neutral pH of \(6 - 7.^{36,37}\) The zetapotentials measurement of amine dissolved in water showed positive zetapotentials of \((- )6.56 \pm 0.56 \mathrm{mV}\) (putrescine), \((- )7.23 \pm 1.55 \mathrm{mV}\) (spermidine), \((- )8.82 \pm 0.78 \mathrm{mV}\) (spermine), \((- )4.87 \pm 0.66\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 84, 881, 151]]<|/det|> +mv (tryptamine), and \((+1.87 \pm 0.34\) (tryptophan). The SEM images of PA1, PA2, and PA3 revealed spherical morphology structures (Figure 1c- e). However, the SEM images of crystalline small molecule TA showed a ribbon- type structure (Figure 1f). + +<|ref|>sub_title<|/ref|><|det|>[[118, 184, 457, 203]]<|/det|> +## Extraction of Amines from Water + +<|ref|>text<|/ref|><|det|>[[115, 213, 880, 255]]<|/det|> +The synthesized PAs were used for the extraction of biogenic amines from water using a batch process and monitored using UV- Vis spectroscopy. + +<|ref|>image<|/ref|><|det|>[[115, 285, 880, 727]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 730, 880, 890]]<|/det|> +
Figure 2. Absorbance spectra of ninhydrin complexes of spermine before (—■—) and after adsorption of putrescine (—●—), spermidine (—▲—), spermine (—▼—), tryptamine (—◆—), tryptophan (—★—), followed by ninhydrin treatment, PA1 (a) and PA2 (b), PA3 (c), and TA (d). Absorbance spectra of the ninhydrin complexes of other amines are given in the supporting information (Figure S2), which also showed the same adsorption maxima. The inset images show the optical images of the ninhydrin complexes of spermine (i) before and (ii) after extractions with different PAs. PA absorbents (25 mg) were used for the extraction of amine solution at a concentration of 50 mg/L. All adsorption experiments were conducted at pH 7 and \(22^{\circ}\mathrm{C}\) .
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 883, 301]]<|/det|> +The amine molecules present in the supernatant reacted with the carbonyl group of the ninhydrin and formed a blue- colored solution. The concentration of the complex and color of the solution are proportional to the amount of amine present in the solution. \(^{38}\) The intensity values at the adsorption maximum \((\lambda_{\mathrm{max}})\) is \(563 \mathrm{nm}\) in the UV- Vis spectra of the ninhydrin complex were measured, and the removal efficiencies of all PAs were calculated (Figure 2a- c). Strong electrostatic interactions between PAs and amines present in water facilitated the adsorption and enhanced the removal efficiencies \((\sim 96 - 99\%)\) . As expected, the removal efficiencies of the model compounds, triamide TA \((73.46 \pm 2.12\%)\) , Figure 2d, is lower than other PA polymers. + +<|ref|>sub_title<|/ref|><|det|>[[120, 317, 390, 337]]<|/det|> +## Effect of Different Dosages + +<|ref|>text<|/ref|><|det|>[[115, 345, 883, 784]]<|/det|> +In a typical procedure, an appropriate concentration of amine solution \((50 \mathrm{mg / L}, 6 \mathrm{mL})\) was added to various amounts of PAs ranging from 2 to \(50 \mathrm{mg}\) . The extractions were carried out for \(300 \mathrm{min}\) , the mixture was centrifuged, and the remaining concentration of amine in the supernatant was determined using the ninhydrin test, and the extraction efficiency was reported in Figure 3a- d for PAs 1- 3 and TA. The maximum removal efficiencies (Equation 1) were obtained when the optimum amount of PAs \((25 \mathrm{mg})\) was used. PA1 (Figure 3a) showed removal efficiencies in the range of \(93.53 \pm 1.66 - 97.22 \pm 0.38\%\) , and PA2 (Figure 3b) showed \(93.48 \pm 0.70 - 98.74 \pm 0.12\%\) for the five amines tested. PA3 (Figure 3c) showed maximum removal efficiencies in the range of \(97.11 \pm 0.24 - 99.58 \pm 0.23\%\) for amines such as putrescine, spermidine, spermine, tryptamine, and tryptophan. The removal efficiencies of PAs remained constant, with a further increase in the adsorbent dosage of \(50 \mathrm{mg}\) due to the saturation of the surface. The saturation of the adsorbent surface occurs when all available active sites for adsorption are occupied by the target pollutants. When the adsorbate concentration is fixed, increasing the adsorbent amount increases adsorption efficiency by providing more active sites for binding. Initially, efficiency rises sharply with adsorbent dose, but increasing the adsorbent dose still increases the binding sites, and adsorption reaches equilibrium. However, beyond a certain dosage \((25 \mathrm{mg})\) , the removal efficiency is maximum because the pollutant concentration is fixed at \(50 \mathrm{mg / L}\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[137, 81, 857, 504]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 514, 880, 598]]<|/det|> +
Figure 3. Percentage removal efficiencies of putrescine (—), spermidine (—), spermine (—), tryptamine (—), tryptophan (—) of PA1 (a), PA2 (b), PA3 (c) and TA (d) polyaramides at different dosages. The extraction was done at room temperature for 300 min, and the amine concentrations were kept constant (50 mg/L). .All adsorption experiments were conducted at pH 7 and \(22^{\circ}C\)
+ +<|ref|>text<|/ref|><|det|>[[115, 604, 881, 870]]<|/det|> +To understand the role of dimensionality of polymers in the extraction of amines from water, a structurally similar triamide small molecule (TA) was prepared from trimesoyl chloride (A3) and aniline (B1), for comparison. The adsorption experiments with small molecule TA as control at different dosages (2 to \(50\mathrm{mg}\) ) (Figure 3d) showed lower efficiencies towards the extraction of amines. At a lower dosage (2 mg) of TA, putrescine, spermidine, spermine, tryptamine, and tryptophan were removed with low efficiencies in the range of 3.97 \(\pm 0.56\%\) - \(10.43\pm 2.35\%\) (Figure 3d). The removal efficiencies of putrescine, spermidine, spermine, tryptamine, and tryptophan at high dosages of TA (25 mg) were in the range of 48 - 57%. This is much less ( \(\sim 80\%\) ) than the removal efficiency of PA3. The network- type structure of PAs 1- 3 has large numbers of electron- deficient amide groups for interacting with amines through hydrogen bonds. Such a network is absent in the case of small molecule TA. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 84, 875, 105]]<|/det|> +## Effects of Time and Concentrations of Amines on the Adsorption Efficiency + +<|ref|>text<|/ref|><|det|>[[115, 112, 883, 613]]<|/det|> +The effects of different extraction time points (15 min, 30 min, 60 min, 90 min, 120 min, 180 min and 300 min) and amine concentrations (5, 10, 50, 100 and 150 mg/L) on the removal efficiency were examined. The amine solutions (6 mL) at different concentrations were shaken with fixed amounts of PAs (1- 3, and TAs 25 mg) at room temperature for 15 - 300 min intervals. Samples were analysed, and removal efficiencies were calculated (Figure 4a- h). The removal efficiencies of amines are low at short time points (15 - 90 min) for PA1 (Figure 4a), ranging from \(5.60 \pm 0.04\) to \(55.27 \pm 1.459\%\) . At a higher time duration (300 min), the removal efficiency was increased to \(70.04 - 89.60\%\) . At longer durations, higher removal efficiency was observed due to the increase in the contact time of amine and polyaramide. For amines, removal efficiencies decreased as the initial concentration increased from \(5 \mathrm{mg / L}\) to \(150 \mathrm{mg / L}\) (Figure 4b). Lower amine concentrations (5 mg/L) yielded \(61.40 - 89.74\%\) removal, whereas higher concentrations (150 mg/L) decreased efficiency due to surface saturation. This decrease is due to the saturation of adsorption sites on the surface, leading to decreased removal efficiencies. The initial adsorption rate was higher at lower concentrations, where particle uptake resistance decreased as mass transfer increased. Same way, removal efficiency of PA2 varied with different time points and amine concentrations. After 15 minutes, efficiency was in the range of \(20.84\% - 45.91\%\) , whereas the removal efficiency increased to \(75.83 - 96.44\%\) after 300 minutes for all amines tested. The PA2 showed lower removal efficiencies at high amine concentrations (150 mg/L; \(32.73 - 51.75\%\) ) and high removal efficiencies at low concentrations of PA2 (5 mg/L; \(74.49 - 95.24\%\) , (Figure 4c,d). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 80, 825, 757]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[118, 82, 864, 310]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 340, 880, 414]]<|/det|> +
Figure 4. Effect of time \((15 - 300\mathrm{min})\) and concentration \((5 - 150\mathrm{mg / L})\) of putrescine ( \(\bullet\) -), spermidine ( \(\bullet\) -), spermine ( \(\bullet\) -), tryptamine ( \(\bullet\) -) and tryptophan ( \(\bullet\) -) on removal efficiency, PA1 (a, b), PA2 (c, d), PA3 (e, f) and TA (g, h). The amount of all PAs used for the adsorption experiments was kept constant at \(25\mathrm{mg}\) .
+ +<|ref|>text<|/ref|><|det|>[[115, 420, 882, 758]]<|/det|> +The polymer, PA3 showed different removal efficiencies of five amines in the range of \(26.39\%\) to \(54.95\%\) after \(15\mathrm{min}\) shaking on a mechanical shaker (Figure 4e). By increasing the shaking time to \(300\mathrm{min}\) , there was a significant improvement in the removal efficiencies to \(84.39 - 99.81\%\) (Figure 4e). Similarly, at low concentrations of amines at \(5\mathrm{mg / L}\) , the polymer PA3 was able to remove \(73.65 - 99.42\%\) of all amines from water (Figure 4f). However, removal efficiencies decreased to \(40.35 - 83.14\%\) at a high concentration of amines at \(150\mathrm{mg / L}\) . The network structure of PA1 - 3 enabled high removal efficiencies as compared to the small molecule TA, with equilibrium reached within \(200\mathrm{min}\) . After \(15\mathrm{minutes}\) , the removal efficiencies of TA were at \(3.93 - 17.15\%\) (Figure 4g), and increasing the extraction time to \(300\mathrm{min}\) , the removal efficiencies were improved to \(48.33 - 56.89\%\) . The low amine concentrations ( \(5\mathrm{mg / L}\) ) in water enhanced TA's efficiencies to \(54.62 - 85.16\%\) . At high concentrations ( \(150\mathrm{mg / L}\) ) of amines, the removal efficiencies are as low as \(4.34 - 11.69\%\) for TA (Figure 4h). The small size of TA contributes to lower removal efficiency compared to other PAs. + +<|ref|>sub_title<|/ref|><|det|>[[118, 775, 362, 794]]<|/det|> +## Isotherm Model Studies + +<|ref|>text<|/ref|><|det|>[[115, 803, 880, 894]]<|/det|> +To understand the adsorption behaviour of amines on the PA surface, the data were analysed using both the Freundlich and Langmuir isotherm models (Figure 5a- h). All necessary model equations for kinetics and isotherms are given in the Materials and Methods section (equations 2 - 6). + +<--- Page Split ---> +<|ref|>table_caption<|/ref|><|det|>[[67, 120, 880, 161]]<|/det|> +Table 1. Regression coefficient and isotherm parameters for the adsorption of amines (putrescine, spermidine, spermine, tryptamine, and tryptophan) on the PAs. + +<|ref|>table<|/ref|><|det|>[[117, 175, 855, 812]]<|/det|> + +
Amines used for
adsorption		Polyara
mides
(PAs)
used		LangmuirFreundlich
\(K_{L}\) (L/mg)\(Q_{max}\)\(R^{2}\)\(K_{r}\)n\(R^{2}\)
PutrescinePA12.9791100.2000.97146.12631.50640.9260
PA20.1657166.6670.96670.92561.09970.9350
PA30.5088243.9020.98511.68011.57190.9066
TA0.567651.8130.98561.51501.25660.9678
SpermidinePA10.095396.07990.99971.83998.34020.9172
PA20.0036183.8240.98141.03731.48300.9119
PA30.0027334.4480.96671.47281.12520.9326
TA0.009981.3000.98441.44021.27300.9767
SperminePA10.1304138.8890.98511.27701.68120.9066
PA20.0430163.9340.98591.30331.12650.9221
PA30.1925370.3720.98792.52001.42100.9178
TA0.1547119.0480.92062.28391.03220.9175
TryptaminePA10.4431102.0400.97888.73171.60340.9009
PA20.3743142.8570.98839.44711.70850.9146
PA30.1302270.1700.9982.93351.74890.9820
TA0.618583.33330.99172.33441.77630.9892
TryptophanPA10.133381.96720.96383.60081.45900.9182
PA20.0853119.0470.99237.26271.56760.9453
PA30.0502232.5580.98146.15741.58290.9556
TA0.139476.92300.97941.71391.29330.9554
+ +<|ref|>text<|/ref|><|det|>[[67, 815, 95, 826]]<|/det|> +350 + +<|ref|>text<|/ref|><|det|>[[67, 850, 880, 912]]<|/det|> +351 After analysis of the data, the Langmuir isotherm model provided a better fit (Table 1)(Figure 5a, c, e, and g) for each of the five amines when compared to Freundlich's model(Figure S3). The adsorption of amines on the PA surface is indicated by high values of \(K_{L}\) and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 881, 250]]<|/det|> +n. Monolayer formation of all amines was observed on PA surfaces, as stated by the Langmuir model. This is expected due to the strong interaction between the negatively charged PA surface and positively charged amines at neutral pH. PA3 had a higher adsorption capacity \((Q_{\mathrm{max}})\) compared to PA1, PA2, and TA for putrescine, spermidine, spermine, tryptophan, and tryptamine, with respective removal amounts of \(243.9\mathrm{mg / g}\) , \(334.4\mathrm{mg / g}\) , \(370.3\mathrm{mg / g}\) , \(270.1\mathrm{mg / g}\) , and \(232.5\mathrm{mg / g}\) from water which is calculated using Langmuir model (Table 1). The corresponding values for other polymers are given in Table 1. + +<|ref|>image<|/ref|><|det|>[[117, 300, 864, 768]]<|/det|> + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 84, 864, 528]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 539, 881, 644]]<|/det|> +
Figure 5. Langmuir (a, c, e and g) and pseudo-second-order kinetics (b, d, f and h) plots for PA1 (a, b), PA2 (c, d), PA3 (e, f), TA (g, h) at neutral pH and 298 K using different concentrations (5 - 150 mg/mL) of the putrescine (- - -), spermidine (- - -), spermine (- - -), tryptamine (- - -) and tryptophan (- - -) and a fixed concentration of absorbents (25 mg). The Freundlich isotherm and pseudo-first order kinetic regressions for PAs 1 - 3 and TA, are given in the supporting information (Figure S3).
+ +<|ref|>sub_title<|/ref|><|det|>[[118, 677, 344, 696]]<|/det|> +## Kinetic Model Studies + +<|ref|>text<|/ref|><|det|>[[115, 707, 881, 896]]<|/det|> +To study the mechanism, adsorption of a series of amines at different adsorption times was used (Figure 5, Table 2). The data collected from the five BAs used in the experiments were fitted with pseudo- first- order and pseudo- second- order kinetic models. The methods and materials section includes all equations relevant to both kinetic models. The practical and theoretical parameters determined from the data are given in Figure 5b, d, f and h and summarised in Table 2. The pseudo- second- order kinetic models fit the adsorption data for the five amines more closely than pseudo- first- order kinetic models for all PAs (Table 2). Higher \(\mathrm{R}^2\) values (putrescine - 0.9876, spermidine - 0.9878, spermine - 0.9999, tryptamine - 0.9986, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 85, 880, 101]]<|/det|> +tryptophan – 0.9980) were obtained for PA3 using the pseudo-second-order model (Table 2). + +<|ref|>text<|/ref|><|det|>[[60, 111, 880, 172]]<|/det|> +Furthermore, the theoretical removal efficiencies (Qe, cal) calculated using the pseudo-second-order model for each of the five amines are in agreement with the experimental data (Qe, exp) obtained. + +<|ref|>text<|/ref|><|det|>[[60, 187, 92, 198]]<|/det|> +386 + +<|ref|>text<|/ref|><|det|>[[60, 220, 880, 235]]<|/det|> +Table 2. Kinetic parameters for the adsorption of amines, putrescine, spermidine, spermine, + +<|ref|>text<|/ref|><|det|>[[60, 245, 661, 260]]<|/det|> +tryptamine, and tryptophan on PAs 1-3 and model compound TA. + +<|ref|>table<|/ref|><|det|>[[117, 273, 870, 863]]<|/det|> + +
Amines usedPolya
mide
used		Pseudo-first orderPseudo-second order
\(Q_{\mathrm {e}}\)cal\(K_{1}\)\(R^{2}\)\(Q_{\mathrm {e}}\)\(Q_{\mathrm {e}}\)\(K_{2}\)(g\(R^{2}\)
(mg g-1)(min-1)(exp.)
(mg g-1)		(cal.)
(mgg-1)		\(mg^{-1}\)
min-1)
PutrescinePA19.44130.0180.9104156.780112.360.02200.9986
PA29.24700.0240.9152159.094149.2540.01710.9873
PA38.05660.0510.9179211.345208.3330.00520.9876
TA6.24630.040.947883.34276.92310.00510.9903
SpermidinePA117.07130.0770.9172268.902256.410.00110.9997
PA219.09630.00950.9135290.112270.2780.01070.9999
PA320.57340.01220.9446303.124277.7780.00820.9878
TA17.40920.01020.9121195.564181.8110.00240.9863
SperminePA124.19870.00590.9066258.009263.1580.01660.9851
PA236.67520.0600.9591318.909287.3560.00950.9996
PA348.95980.0930.9671304.901294.1180.00370.9999
TA12.98770.00560.9477192.778185.1830.00440.9632
TryptaminePA19.649440.0020.9215112.45699.00990.03510.9778
PA212.99020.0320.9168124.098109.8900.01530.9987
PA319.74690.0350.9247134.678294.1170.06720.9986
TA6.78230.0220.831275.78568.02720.08820.9640
TryptophanPA18.693710.00460.9179298.0990.90900.01070.9998
PA211.14390.00340.8742183.980108.6950.01030.9999
PA318.34760.00940.9820301.333243.9020.00120.9980
TA12.77380.00760.949794.87883.3330.01170.9700
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[117, 84, 812, 126]]<|/det|> +## Analysis of PAs After Extraction of Commercial Amines from Spiked Samples + +<|ref|>text<|/ref|><|det|>[[116, 134, 883, 326]]<|/det|> +The amine adsorption on the PAs influences the surface characteristics. Changes in the zetapotential of PAs are examined before and after the adsorption of amines. Before the amines were adsorbed, the PAs showed negative zetapotential values due to the surface - COO end groups on the polymer (Table 3). The positively charged amines are adsorbed on the negatively charged PA surface. After adsorption of amine (spermine), the zetapotential of PAs in water was measured at ambient temperature and showed positive zetapotentials for PA1 (+)14.93 ± 1.23, PA2 (+15.3 ± 2.33, and PA3 (+19.93 ± 0.45. The zetapotential for the adsorption of other amines is given in the supporting information (Table S2). + +<|ref|>table<|/ref|><|det|>[[139, 372, 850, 528]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[115, 341, 725, 360]]<|/det|> +Table 3. Particle size, zetapotential, and BET analysis data for PAs and TA. + +
Particle Size (nm)Pore Size (nm)Surface area (m²/g)ζ Potential (mV)
BeforeAfter(spermine)
PA11288 ±3.094.19110.843(-)7.56±2.23(+)14.93 ± 1.23
PA2918 ± 3.262.97814.048(-)12.58±0.34(+)15.3 ± 2.33
PA3875±1.243.23329.233(-)18.98±1.24(+)19.93 ± 0.45
TA529±3.095.7905.683(-)2.52±0.68(+)12.88 ± 1.89
+ +<|ref|>text<|/ref|><|det|>[[115, 558, 883, 774]]<|/det|> +The Fourier transform infrared (FTIR) spectra of all PAs, after the adsorption of amines, showed a broad peak in the range of \(3100 - 3600\mathrm{cm}^{- 1}\) , which corresponds to the N- H stretching from the amide group on the polymer backbone and amine groups after adsorption process, (Figure S4). All PAs showed adsorption peaks corresponding to the aromatic and aliphatic - CH- groups on the polymer backbone (Figure S6a- c). Amide I and amide II peaks were observed around 1660 and \(1545\mathrm{cm}^{- 1}\) , which is slightly shifted to the higher wavelength from the peaks corresponding to the polyaramides before the adsorption of amines. This is expected due to the strong interactions of amine - NH- and amide \(> \mathrm{C} = \mathrm{O}\) groups. The other common peaks were observed around 1293, 917, and \(709\mathrm{cm}^{- 1}\) for PAs before and after adsorption. + +<|ref|>text<|/ref|><|det|>[[115, 789, 881, 907]]<|/det|> +PA solids obtained after the adsorption of amines were examined using thermogravimetric analysis under a nitrogen atmosphere. The TGA analysis was conducted throughout a temperature range from \(22^{\circ}\mathrm{C}\) to \(1000^{\circ}\mathrm{C}\) , with a heating rate of \(10^{\circ}\mathrm{C}\) per minute (Figure S5). There was no specific change observed as the smaller amount of amines adsorbed onto the PAs. The ICP analysis of nitrogen content analysis indicates an increase in the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 200]]<|/det|> +percentage of nitrogen after adsorption of amines on PAs (Table S1). Pristine PAs showed 6.48, 6.68, 6.90, and 5.67 % of nitrogen content for PAs 1 – 3 and TA, respectively. The nitrogen content after adsorption of the amines on PAs was increased in the range of 9 to 14% (Table S1). Similar to N content, the percentages of C and H also increased after adsorption of the amines on PAs. Such results are complementary to the extraction data. + +<|ref|>sub_title<|/ref|><|det|>[[118, 216, 335, 236]]<|/det|> +## Regeneration Studies + +<|ref|>text<|/ref|><|det|>[[115, 254, 880, 297]]<|/det|> +After adsorption of the amines on PAs, regeneration, and reuse of the same PAs were also attempted. + +<|ref|>image<|/ref|><|det|>[[115, 312, 880, 800]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 808, 881, 908]]<|/det|> +
Figure 6. Removal efficiencies of PA 1 - 3 after repeated adsorption-desorption cycles (cycle 1 (■), cycle 2 (■), cycle 3 (■), cycle 4 (■), and cycle 5 (■)). The concentration of putrescine (a), spermidine (b), spermine (c), and tryptamine (d) used was \(50 \mathrm{mg / L}\) , a time of \(300 \mathrm{min}\) , and an adsorbent dose of \(25 \mathrm{mg}\) was kept constant. The regeneration data of PA 1 - 3 after adsorption of tryptophan are given in the supporting information (Figure S6). The regeneration data for TA using all amines are given in the supporting information (Figure S7).
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 882, 349]]<|/det|> +PAs (25 mg) with adsorbed amines on the surface were washed with dilute HCl solution (0.5 M, \(10~\mathrm{mL}\) ) a few times. The resulting mixture was centrifuged, and the solid was washed with water to remove traces of acid and dried at \(60^{\circ}\mathrm{C}\) . The solid was then reused for the extraction of amines from water. The removal efficiencies of regenerated PAs for different amines were measured for five repeated cycles of washings and readortions of the amines. The regenerated PAs demonstrated consistent removal efficiencies of over \(90\%\) , suggesting that PAs are a very effective and reusable adsorbent (Figure 6a- d). PAs were recovered quickly from the solution through filtration after the adsorption. The regeneration process involves protonation (acid- base reaction) of the adsorbed amines (basic) by HCl (acid), converting them into water- soluble ammonium salts for desorption. The removed PAs were utilized directly in the subsequent adsorption procedure without the need for drying or grinding. + +<|ref|>sub_title<|/ref|><|det|>[[118, 365, 796, 386]]<|/det|> +## Extraction and Identification of Biogenic Amines from Fish Sample + +<|ref|>text<|/ref|><|det|>[[115, 402, 882, 690]]<|/det|> +To explore the application of synthesized PA materials in environmental samples, common fish samples were purchased from supermarkets and kept at room temperature for natural degradation. Cleaned fish tissue (2 g) was kept at room temperature in an open environment at different time points (6 h, 24 h, and 48 h). After homogenization in water, the samples were centrifuged. The resulting supernatant fish extract was used for LCMS analysis. A fixed amount of PAs was mixed with an appropriate amount of fish extract solution and kept in a mechanical shaker. The mixture was centrifuged, and the supernatant was treated with ninhydrin reagent to convert the amines into a blue- coloured amine- ninhydrin complex. The UV spectra of the colored complex solution were recorded for solutions before and after extraction with PAs and are presented in Figure 7a. A standard calibration curve was prepared using the known solutions of commercially available amine solutions after treatment with ninhydrin solution. + +<|ref|>text<|/ref|><|det|>[[115, 707, 882, 873]]<|/det|> +The concentration of extracted unknown amines in the solution was measured from the calibration curve. The amines generated inside the fish sample left for 6 hours in open air at ambient conditions were quantified as 2.566 mg/g, which increased to 18.45 and 34.56 mg/g with an increase in time of open- air degradation of 24 h and 48 h, respectively. A known volume (i.e. \(6~\mathrm{mL}\) ) was mixed with PA3 (50 mg) and kept on a mechanical shaker for 6 h. The removal efficiencies of the PAs were calculated from the UV adsorption spectra of the ninhydrin- treated extract solutions before and after adsorption (Figure 7b). PA3 showed high removal efficiencies + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 84, 880, 128]]<|/det|> +466 of \(99.42 \pm 0.12 \%\) , \(97.00 \pm 1.43 \%\) , and \(95.05 \pm 0.92 \%\) for extract of fish samples after 6 h, 24 h, and 48 h, respectively. + +<|ref|>image<|/ref|><|det|>[[60, 155, 870, 655]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 669, 881, 837]]<|/det|> +
Figure 7. (a) UV-Vis spectra of the ninhydrin treated extracts obtained from fish sample kept at different time points of 6 h, 24 h, and 48 h at room temperature before (6 h (-), 24 h (-), 24 h (-)) and after (6 h (-), 24 h (-), and 48h (-)) extraction with PA 1-3 samples (25 mg). Note that the absorbance of solutions after extraction with PA was almost zero, indicating a complete removal of amines. (b) The extraction efficiencies of PAs (50 mg) for the removal of amines from solutions collected after keeping the fish samples for 6 h, 12 h, and 24 h at room temperature. The extraction time was kept at 5 h for all samples. (c) LC traces of extract collected from fish sample (2 g) after 48 h at room temperature. Optical images of fish samples (2 g) and LCMS traces of 6h and 24 h fish extracts are given in the supporting information (Figure S8).
+ +<|ref|>text<|/ref|><|det|>[[115, 870, 880, 914]]<|/det|> +LCMS traces were utilized to identify and quantify the amines present in the extracts of fish tissues (Figure 7c). A few peaks were identified by comparing the retention time and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 883, 399]]<|/det|> +mass of the commercial amine samples. The retention peak positions at 1.03 min and 1.98 min indicated the presence of putrescine and spermidine with mass (m/z) values of 88.10 and 146.20, respectively. Tryptamine, with a retention time of 8.87 min, was identified with an m/z value of 160.10. The next major peak, with a retention time of 13.66 min and a m/z value of 186.22, was identified as 1- O- alkylglycerols. Similarly, the next major peak with a retention time of 15.88 min and a m/z value of 241.28, indicates the presence of hexadecylamine. The peak with a retention time of 18.62 min and an m/z value of 256.30 is identified as palmitic acid, which is present in fish oil. The next major peak at 26.12 min with a m/z value of 248.48 corresponds to stearic acid. The corresponding mass spectrometry data of all the major peaks observed for the components in the fish extract are given in the supporting information (Figure S9). LCMS obtained from commercial BAs solutions were used for comparison and quantification (Figure S10). The mass spectra of five different commercially available BAs used in our extraction were given in the supporting information (Figure S11). + +<|ref|>sub_title<|/ref|><|det|>[[118, 413, 815, 435]]<|/det|> +## Comparison of the Removal Efficiency of PAs with Other Absorbents + +<|ref|>text<|/ref|><|det|>[[115, 441, 883, 808]]<|/det|> +Biogenic amines are generated through fermentation and decaying of meat and fish samples. Due to relatively low levels of BAs present in water bodies, only a limited number of research have focused on their occurrence in surface and waste waters. Nevertheless, the excessive release or accumulation of organic matter (such as animal remains or discarded food) usually contaminates water and alters the taste, smell, and dissolved oxygen levels. Such deterioration of water quality induces negative consequences for aquatic life, potentially leading to reduced activity or even death of these organisms. Zhu et al. reported that the poly(ether-block- amide) removed 54 - 72 % of biogenic amines such as histamine, putrescine, cadaverine, and tyramine from water. Another group reported 82 - 100% removal efficiency for histamine, putrescine, cadaverine, spermidine, spermine, and tryramine from water using functionalized silica material. The methods of extraction and detection techniques can impact the comparison of adsorption efficiency. The choice of adsorption method plays a significant role in the results; for example, batch extraction may favour high- capacity adsorbents, while chromatography- based methods may highlight selectivity. Additionally, batch extraction is generally more scalable than chromatography- based methods. + +<|ref|>text<|/ref|><|det|>[[115, 814, 881, 907]]<|/det|> +Here, we report a polyaramide- based adsorbents for the removal of biogenic amines from water. The removal process was thoroughly analyzed using kinetic studies, TGA, SEM, and FTIR spectra to gain valuable mechanistic insights. Furthermore, Table 4 presents a comprehensive comparison of various parameters, including different absorbents, removal + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 85, 880, 150]]<|/det|> +methods, particle concentrations, and the removal efficiencies achieved through different analytical methods. Compared to the absorbent materials reported in the literature, the PAs 1 – 3 showed high removal efficiencies (i.e. \(\sim 99.96\%\) , Table 4). + +<|ref|>table_caption<|/ref|><|det|>[[115, 185, 712, 201]]<|/det|> +Table 4. Comparison of removal efficiency of PAs with other absorbents. + +<|ref|>table<|/ref|><|det|>[[115, 214, 880, 900]]<|/det|> +
AdsorbentExperiment methodAdsorption percentage %Removal efficiency (mg/g)Ref.
1Graphene aerogelBatch extractionHIS 85.19%, CAD 74.1%, and SPD 70.11%-49
2Poly(ether-block-amide)Batch extractionHIS 54%, PUT 72%, CAD 68%, TYR 87%HIS 3.46, PUT 4.58, CAD 5.09, TYR 5.8647
3Functionalized silica materialLiquid chromatograph (LC) coupled to a mass spectrometer detectorHIS 95.0%, PUT 82.0%, CAD 88.7%, SPD 100%, SPM, 100%, TYR 13.3%-48
4Sulfamic acid functionalised blast furnace slagBatch extractionPUT 90%, TYR 70%, PEA 99%PEA 80.64, PUT 12.5 and TYR 64.5250
5Crown ether-modified mesoporous silicaHigh-Performance Liquid ChromatographTRP 40%, PUT 40%, HIS 12%, TYR 20%, SPD 98%51
7PA1Batch ExtractionPUT 94.82 ± 0.12%, SPD 95.48 ± 0.15%, SPM 97.22 ± 0.38%, TYA 95.68 ± 1.15%, TYP 93.53 ± 1.66%PUT 100.20, SPD 96.07, SPM 138.88, TYA 102.04, TYP 81.96This work
8PA2Batch ExtractionPUT 94.31±1.55%, SPD 97.45± 0.10%, SPM 98.64± 0.17%, TYA 98.04 ±PUT 166.66, SPD 183.82, SPM 163.93, TYA 142.85, TYP 119.04This work
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[457, 83, 580, 116]]<|/det|> +0.12%, TYP 93.48± 0.70%, + +<|ref|>table<|/ref|><|det|>[[120, 131, 870, 266]]<|/det|> +
9PA3Batch ExtractionPUT 97.56 ± 0.24%, SPD 98.97 ± 0.68%, SPM 99.58 ± 0.23%, TYA 98.97 ± 1.68%, TYP 96.96 ± 0.08 %PUT 243.90, SPD 334.44, SPM 370.37, TYA 270.17, TYP 232.55This work
+ +<|ref|>text<|/ref|><|det|>[[67, 283, 870, 319]]<|/det|> +522 Abbreviation: HIS- histamine, CAD - cadaverine, SPD - spermidine, PUT - putrescine, TYR +523 - tyramine, SPM - spermine, PEA - 2-phenylamine, TYA - tryptamine, TYP - tryptophan. + +<|ref|>sub_title<|/ref|><|det|>[[117, 355, 383, 374]]<|/det|> +## Mechanism of Adsorption + +<|ref|>text<|/ref|><|det|>[[117, 385, 881, 550]]<|/det|> +Biogenic amines have been shown to accumulate due to microbial attacks on meat or fish products.52 53 Biogenic amines can harm aquatic life, disrupt water ecosystems, and pose health risks to humans, particularly those with histamine intolerance or certain medical conditions.54 Table 4 shows a list of the different absorbents that have been used for removing different biogenic amines from water. The current study used commercially available biological amines such as putrescine, spermine, spermidine, tryptamine, and tryptophan to understand the removal efficiencies of PAs. + +<|ref|>text<|/ref|><|det|>[[117, 567, 881, 755]]<|/det|> +The PAs 1-3 synthesized during this study showed a negative zetapotential that is ideal for extracting the positively charged amines in water at a neutral pH. At an equilibrium concentration of PAs (25 mg) and an adsorption time of 300 min, high removal efficiencies of 97.11 ± 0.24 %, 98.97 ± 0.68 %, 99.58 ± 0.23 %, 98.97 ± 1.68 %, 96.96 ± 0.08 % for putrescine, spermidine, spermine, tryptamine, and tryptophan, respectively, were observed. The polyaramide (PA3) showed higher removal efficiency (~ 99%) than other polymers. PA3 has a greater surface area (29.23 m²/g) due to the incorporation of the nonlinear m-xylene diamine in the structure. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[118, 95, 820, 380]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[117, 394, 829, 413]]<|/det|> +
Scheme 2. The complexation and extraction behaviour of PA with amines from solution
+ +<|ref|>text<|/ref|><|det|>[[115, 428, 883, 915]]<|/det|> +Also, the zetapotentials of amine solutions in water were measured at ambient temperature and neutral pH (7.0). The respective values obtained for putrescine, spermidine, spermine, tryptamine, and tryptophan were \((+6.56 \pm 0.56 \text{mV}, (+7.23 \pm 1.55 \text{mV}, (+8.82 \pm 0.78 \text{mV}, (+4.87 \pm 0.66 \text{mV}, and (+1.87 \pm 0.34 \text{mV. All synthesized PAs showed a negative zetapotential for the adsorption of positively charged amines. After the amine adsorption, the zetapotential of the PA surface changed from negative to positive values (Table 3). The negatively charged polyaramides (PAs) attract the protonated amine molecules on the surface via electrostatic forces and H- bonding. The zetapotential of the PA surface changed from negative to positive values after extracting the amines from the solution (Table 3, Scheme 2). In addition to electrostatic interaction, hydrogen bonds also play an important role towards the removal of amines from water. PA3 showed a higher adsorption capacity (Qmax) of amine 243.9 mg/g for putrescine, 334.4 mg/g spermidine, 370.3 mg/g for spermine, 270.1 mg/g for tryptamine, and 232.5 mg/g for tryptophan. Elemental analysis indicates an increase in nitrogen content for all PAs after adsorption of biogenic amines (Table S1). PAs absorbed with spermine have a higher percentage of nitrogen content, 6.89 - 14.43 %, compared to other biogenic amines due to higher adsorption efficiency. The order of removal efficiencies of biogenic amines is spermine > spermidine > putrescine > tryptamine > tryptophan. To understand and compare the removal efficiencies of PAs, other monoamines such as hexylamine, and phenyl ethylene amine were also used for the extraction studies. PA3 exhibited removal efficiencies of 87.61 ± 2.43 and 87.30 ± 1.02 % for hexyl amine (HA) and phenylene ethylene amine (PEA), + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 881, 349]]<|/det|> +respectively. Similarly, both PA2 and PA1 showed lower removal efficiencies of \(80 - 77\%\) for HA and \(70 - 50\%\) for PEA under same experimental conditions (Figure S12). Similarly, the small molecules (TA) showed \(57.70 - 73.46\%\) removal efficiencies, which are lower than that observed for PAs 1- 3. The synthesized PAs were used to extract amines and other degraded molecules in the decaying fish samples kept at room temperature for periods of \(6\mathrm{h}\) , \(24\mathrm{h}\) , and \(48\mathrm{h}\) . LCM technique and commercially available standard amine samples were used to determine the chemical identity of the compounds present in the fish extract. All three polyaramides have a new work structure, in particular, PA3 is expected to have a 3D architecture due to the bent structure of the diamine, MX. The network structure and negative zetapotential of PAs help to trap the positively charged amine molecules inside the solid lattice, which then enhances the adsorption capacities (Scheme 2). + +<|ref|>sub_title<|/ref|><|det|>[[118, 366, 234, 384]]<|/det|> +## Conclusion + +<|ref|>text<|/ref|><|det|>[[115, 393, 882, 782]]<|/det|> +The amide- based porous network polymers effectively removed biogenic amines such as putrescin, spermidine, spermine, tryptamine, and tryptophan. The prepared PAs removed biogenic amines (BAs) from water with around \(99\%\) efficiency. All the PAs were characterized before and after adsorption of the amines. PA3 showed the highest adsorption efficiencies \((Q_{\mathrm{max}})\) as compared to the other three PAs ( \(244\mathrm{mg / g}\) putrescin, \(334\mathrm{mg / g}\) spermidine, \(370\mathrm{mg / g}\) spermine, \(270\mathrm{mg / g}\) tryptamine, and \(232\mathrm{mg / g}\) tryptophan). Absorbents (PA1- 3) and model compound TA were characterized using FTIR spectra, TGA, and SEM. The extraction data for the amines were analyzed using the Langmuir and Freundlich isotherm models and different kinetic models. The absorbents were regenerated and reused to extract amines from water. After five cycles, the PAs showed similar removal efficiencies, and there was no appreciable efficiency loss due to polymer degradation. Compared to triamide (TA), the PAs 1- 3 showed higher removal efficiencies towards various amines tested. The synthesised PAs were also used to extract amines generated by decaying natural fish tissues for \(6\mathrm{h}\) to \(48\mathrm{h}\) . The amounts of amines extracted from such fish tissues were in the range of \(2 - 35\mathrm{mg / g}\) which increased with an increase in time. Such easily accessible synthetic polymers are a great candidate for environmental remediation in the future. + +<|ref|>sub_title<|/ref|><|det|>[[118, 798, 363, 818]]<|/det|> +## Supporting Information + +<|ref|>text<|/ref|><|det|>[[117, 834, 881, 907]]<|/det|> +Full synthetic details of polyaramides; PA1- 3 and small molecule TA; TGA (b) of PA1 (—), PA2 (—), PA3 (—) and SA1 (—) before adsorption; Absorbance spectra of ninhydrin complex of putrescin (—), spermidine (—), tryptamine (—), and tryptophan (—) at a concentration of \(50\mathrm{mg / L}\) ; Freundlich (a, c, e, g) and pseudo- first- order kinetics (b, d, f and h) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 84, 880, 549]]<|/det|> +plots for PA1 (a and b), PA2 (and d), PA3(e and f) and SA1 (g and h) at 298 K using different concentrations (5 - 150 mg/mL) of the putrescine (--), spermidine (--), spermine (--), tryptamine (--) and tryptophan (--). A fixed concentration (25 mg in 6 mL) of PAs and SA1 are used for all studies; FTIR spectra of PA1 (a), PA2 (b), PA3 (c) and SA1 (d) after adsorption of putrescine (--), spermidine (--), spermine (--), tryptamine (--) and tryptophan (--). KBr matrix was used for recording the spectra; TGA of PA1 (a), PA2 (b), PA3 (c), and SA1 (d) after adsorption of putrescine (--), spermidine (--), spermine (--), tryptamine (--), and tryptophan (--); Removal & regeneration efficiencies of PA 1 - 3 after repeated absorption-desorption cycles 1 (--), 2 (--), 3 (--), 4 (--), and 5 (--) using tryptophan as a model amine. The tryptophan concentration was 50 mg/L, extraction time of 300 min, and an adsorbent dose of 25 mg was kept constant; Removal efficiency of SA1 after repeated adsorption-desorption cycles (cycle 1 (--), cycle 2 (--), cycle 3 (--), cycle 4 (--), and cycle 5 (--). The concentration of Putrescine (a), Spermidine(b), Spermine(c), Tryptamine(d), and tryptophan(e) (50 mg/L), time (300 min), and adsorbent dose (25 mg) were kept constant. PUT-Putrescine, SPD-Spermidine, SPM-Spermine, TYA- Tryptamine, and TYP-Tryptophan; Optical images of fish samples at different time points of 6h (a), 24 h (b), and 48 h (c). LCMS traces of crude extracts collected from fish samples kept at 6h (d) and 24 h using a C-18 reverse phase column; The mass spectra of eluents with a retention time of 1.03 min (a),1.98 min (b), 8.87 min (c), 10.33 min (d),13.88 min (e),15.88 min (f), 18.62 min (g), 26.12 min (h) observed for the fish extract collected from 48h; LCMS of standard commercial amines, putrescine (1), spermidine (2), spermine (3), tryptamine (4) and tryptophan (5). The inset represents the enlarged view of putrescine (1) and spermidine (2) peaks; The mass spectra of commercially available standard samples of putrescine (1), spermidine (2), spermine (3), tryptamine (4) and tryptophan (5); Removal efficiencies of hexylamine (a)and phenylethylamine (b) at different concentrations (5 - 100 mg) of polyaramides PA1 (--), PA2 (--) and PA3 (--). The concentration of all PAs was kept constant at 25 mg. + +<|ref|>sub_title<|/ref|><|det|>[[118, 588, 429, 606]]<|/det|> +## Author contribution statement + +<|ref|>text<|/ref|><|det|>[[118, 624, 802, 658]]<|/det|> +GM: Experimentation, collection of data, formal analyses, writing of the paper draft. SV: Resources, ideation, methodology, and revision of the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[118, 668, 500, 687]]<|/det|> +## Declaration of the competing interests + +<|ref|>text<|/ref|><|det|>[[118, 705, 880, 740]]<|/det|> +The authors declare no known competing financial or personal relationships that could have influenced the work reported in this paper. + +<|ref|>sub_title<|/ref|><|det|>[[118, 749, 207, 768]]<|/det|> +## Funding + +<|ref|>text<|/ref|><|det|>[[118, 778, 863, 830]]<|/det|> +The authors acknowledge the funding support from the National Research Foundation grant A- 0004151- 00- 00 and technical support from the Department of Chemistry at the National University of Singapore. + +<|ref|>sub_title<|/ref|><|det|>[[118, 841, 289, 859]]<|/det|> +## Data Availability + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[66, 85, 874, 138]]<|/det|> +The authors declare that the data supporting the findings of this study are available within the paper and its Supplementary Information file. All data that support the findings of this study are available from the corresponding author upon reasonable request. + +<|ref|>sub_title<|/ref|><|det|>[[67, 148, 230, 167]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[66, 174, 880, 912]]<|/det|> +(1) Abril, A. G.; Calo-Mata, P.; Villa, T. G.; Böhme, K.; Barros-Velazquez, J.; Sánchez-Pérez, Á.; Pazos, M.; Carrera, M. High-Resolution Comparative and Quantitative Proteomics of Biogenic-Amine-Producing Bacteria and Virulence Factors Present in Seafood. J. Agric. Food Chem. 2024, 72 (8), 4448-4463. https://doi.org/10.1021/acs.jafc.3c06607. +(2) Zhu, H.; Yang, S.; Zhang, Y.; Fang, G.; Wang, S. Simultaneous Detection of Fifteen Biogenic Amines in Animal Derived Products by HPLC-FLD with Solid-Phase Extraction after Derivatization with Dansyl Chloride. Anal. Methods 2016, 8 (18), 3747-3755. https://doi.org/10.1039/C6AY00010J. +(3) Liao, S.; Lu, Y.; He, Q.; Chi, Y. 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Biogenic Amines Are Important Indices for Characterizing the Freshness and 815 Hygienic Quality of Aquatic Products: A Review. LWT 2024, 194, 115793- 115813. 816 https://doi.org/10.1016/j.lwt.2024.115793. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[130, 140, 866, 191]]<|/det|> +# Engineered Polyamides for Extraction of Bioamines from Water + +<|ref|>text<|/ref|><|det|>[[275, 203, 720, 268]]<|/det|> +Gomathi Mahadevan and Suresh Valiyaveettil\* Department of Chemistry, National University of Singapore 3 Science Drive 3, Singapore 117543 Email: chmsv@nus.edu.sg + +<|ref|>image<|/ref|><|det|>[[140, 345, 870, 696]]<|/det|> + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 330, 150]]<|/det|> +Supportinginformation.docx + +<--- Page Split ---> diff --git a/preprint/preprint__07ca7119489ce742f39828987bc35bcbb114b2fcae1faac252a175d7615d08d8/images_list.json b/preprint/preprint__07ca7119489ce742f39828987bc35bcbb114b2fcae1faac252a175d7615d08d8/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..4178bc5147223098c88bb9ed78a3ade2a5467eda --- /dev/null +++ b/preprint/preprint__07ca7119489ce742f39828987bc35bcbb114b2fcae1faac252a175d7615d08d8/images_list.json @@ -0,0 +1,92 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1: Time traces of, from top to bottom: line averaged density; input power; electron (solid) and ion (dotted) temperature; stored energy; injected boron mass rate (dotted, left axis) and BV spectroscopic line (solid, right axis). Red color for shots with B powder injection with IPD, blue for reference shots (no IPD). Main plasma ion is deuterium for cases a,b and hydrogen for c,d. The magnetic field direction for d is reversed with respect to a-c.", + "footnote": [], + "bbox": [ + [ + 123, + 88, + 875, + 435 + ] + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2: Measured radial profiles of a) electron density \\(n_{e}\\) b) electron temperature \\(T_{e}\\) c) ion temperature \\(T_{i}\\) , averaged over a 100 ms time window, in the case with (red) and without (blue) B powder injection. Polynomial fits of the profiles are shown with solid lines. d) \\(T_{e}\\) from EMC3-EIRENE simulations for one case (#167234), together with a B powder grain trajectories (black) computed by the DUSTT code, with the resulting B neutral atom source(dots). e) B neutral source remapped on the normalized radial coordinate \\(r_{eff} / a_{99}\\) .", + "footnote": [], + "bbox": [ + [ + 128, + 95, + 880, + 395 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3: Time evolution of difference of normalized gradient between a powder injection shot (#166256) and its reference (#166254) for electron density (a) and temperature (b). c) Peak value of divertor density measured by Langmuir probes (black) and \\(H_{\\alpha}\\) radiation (red). d) Evolution of spectroscopic lines CVI (red), FeXVI (blue) and BV (black). e) Average value of the density fluctuation amplitude measured by PCI. Solid lines are for the powder injection shot #166256, and dotted lines for the reference #166254. Periodic oscillations at 3.33 Hz are due to the pulsed operation of diagnostic NB.", + "footnote": [], + "bbox": [ + [ + 238, + 103, + 763, + 498 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4: Radial profiles of a) turbulent fluctuation amplitude and b) their perpendicular velocity for a powder injection shot (#166256, red) and its reference (#166254, blue), at \\(t = 5.2 \\mathrm{s}\\) (before injection, dashed lines) and \\(t = 8.5 \\mathrm{s}\\) (during injection, solid lines). Radially resolved power spectral density in terms of wave number for #166256 before (c) and during (d) powder injection, and their ratio (e).", + "footnote": [], + "bbox": [ + [ + 120, + 105, + 884, + 377 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5: Spectrogram of the line integrated PCI signal for #166256 (a) and #167234 (b). The white dashed line indicates the approximate time of the boron powder entering the plasma. c,d) Comparison of time averaged turbulence spectrum for the B injection shot (red) and their reference shot (blue), at time before (dashed lines) and during (solid lines) powder injection.", + "footnote": [], + "bbox": [ + [ + 130, + 540, + 844, + 825 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6: Radial profiles of heat conductivities for ions (a) and electrons (b), comparing a discharge with powder injection (red) with a reference discharge (blue). c) Measured energy confinement time \\(\\tau_{E}\\) during B powder injection (red squares), and for the reference shots at the same time (blue diamonds). The experimental \\(\\tau_{E}\\) is plotted against the predicted energy confinement time from the international stellarator scaling [28].", + "footnote": [], + "bbox": [ + [ + 140, + 555, + 800, + 808 + ] + ], + "page_idx": 8 + } +] \ No newline at end of file diff --git a/preprint/preprint__07ca7119489ce742f39828987bc35bcbb114b2fcae1faac252a175d7615d08d8/preprint__07ca7119489ce742f39828987bc35bcbb114b2fcae1faac252a175d7615d08d8.mmd b/preprint/preprint__07ca7119489ce742f39828987bc35bcbb114b2fcae1faac252a175d7615d08d8/preprint__07ca7119489ce742f39828987bc35bcbb114b2fcae1faac252a175d7615d08d8.mmd new file mode 100644 index 0000000000000000000000000000000000000000..9dd26653740fdeb76a84a369768001d6aa261f0d --- /dev/null +++ b/preprint/preprint__07ca7119489ce742f39828987bc35bcbb114b2fcae1faac252a175d7615d08d8/preprint__07ca7119489ce742f39828987bc35bcbb114b2fcae1faac252a175d7615d08d8.mmd @@ -0,0 +1,290 @@ + +# Observation of a novel reduced-turbulence regime with boron powder injection in a stellarator + +Federico Nespoli ( \(\boxed{\pi}\) fnespoli@pppl.gov) Princeton Plasma Physics Laboratory https://orcid.org/0000- 0001- 7644- 751X + +Suguru Masuzaki National Institute for Fusion Science https://orcid.org/0000- 0003- 0161- 0938 + +Kenji Tanaka National Institute for Fusion Science + +Naoko Ashikawa National Institute for Fusion Science + +Mamoru Shoji National Institute for Fusion Science + +Erik Gilson Princeton Plasma Physics Laboratory + +Robert Lunsford Princeton Plasma Physics Laboratory + +Tetsutaro Oishi National Institute for Fusion Science + +Katsumi Ida National Institute for Fusion Science + +Mikirou Yoshinuma National Institute for Fusion Science + +Yuki Takemura National Institute for Fusion Science + +Toshiki Kinoshita Kyushu University https://orcid.org/0000- 0003- 3930- 4434 + +Gen Motojima National Institute for Fusion Science + +Naoki Kenmochi National Institute for Fusion Science + +Gakushi Kawamura National Institute for Fusion Science + +Chihiro Suzuki National Institute for Fusion Science + +Alex Nagy + +<--- Page Split ---> + +Princeton Plasma Physics Laboratory + +Alessandro Bortolon Princeton Plasma Physics Laboratory + +Novimir Pablant Princeton Plasma Physics Laboratory + +Albert Mollen Princeton Plasma Physics Laboratory + +Naoki Tamura National Institute for Fusion Science + +David Gates Princeton University https://orcid.org/0000- 0001- 5679- 3124 + +Tomohiro Morisaki National Institute for Fusion Science + +## Article + +Keywords: Confinement Regime, Turbulent Fluctuations, Line Averaged Electron Density, Resonant Radio Frequency, Hydrogen and Deuterium Plasmas + +Posted Date: June 30th, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 614131/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Physics on January 10th, 2022. See the published version at https://doi.org/10.1038/s41567- 021- 01460- 4. + +<--- Page Split ---> + +# Observation of a novel reduced-turbulence regime with boron powder injection in a stellarator + +F. Nespoli\*,1, +S. Masuzaki2,3, +K. Tanaka2,4, +N. Ashikawa2,3, +M. Shoji2, +E.P. Gilson1, +R. Lunsford1, +T. Oishi2,3, +K. Ida2,3, +M. Yoshinuma2,3, +Y. Takemura2,3, +T. Kinoshita4, +G. Motojima2,3, +N. Kenmochi2,3, +G. Kawamura2,3, +C. Suzuki2, +A. Nagy1, +A. Bortolon1, +N.A. Pablant1, +A. Mollen1, +N. Tamura2, +D.A. Gates1, +T. Morisaki2,3 + +1 Princeton Plasma Physics Laboratory, 100 Stellarator Road, Princeton, NJ 08540, United States of America 2 National Institute for Fusion Science, 322- 6 Oroshi- cho Toki, Gifu 509- 5292, Japan 3 The Graduate University for Advanced Studies, SOKENDAI, 322- 6 Oroshi- cho Toki, Gifu 509- 5292, Japan 4 Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga, Fukuoka, 816- 8580, Japan \* Corresponding author. Email: fnespoli@pppl.gov + +## Abstract + +We report the first observation of a novel confinement regime in a stellarator plasma, characterized by increased confinement and reduced turbulent fluctuations. The transition to this new regime is driven by the injection of sub- millimetric boron powder grains into the plasma. With the line averaged electron density being kept constant, substantial increase of stored energy, electron and ion temperature have been observed. At the same time, the amplitude of the plasma turbulent fluctuations is halved. While lower frequency fluctuations are damped, higher frequency modes in the range \(100 \leq f[kHz] \leq 200\) are excited. The access to this regime has been observed for different heating schemes, namely with both electron and ion cyclotron resonant radio frequency, and neutral beams, for both directions of the magnetic field, and for both hydrogen and deuterium plasmas. + +## 1 Introduction + +Stellarators are one of the most promising concepts for magnetic confined nuclear fusion, which could provide a clean alternative to fossil fuels and nuclear fission for mass energy production. Unlike tokamaks, their 3D magnetic filed is provided entirely by external coils, removing the need for a current to flow into the plasma, which makes it prone to instabilities and violent disruptions. This also allows the magnetic field to be tailored to minimize neoclassical transport and improve confinement. At present, the biggest degradation of confinement is given by plasma turbulence, resulting in an increased "anomalous" transport. While in principle possible, optimizing the stellarator magnetic field to reduce turbulent transport is extremely challenging due to the huge computational cost of 3D turbulence simulations. It is therefore fundamental to reduce turbulence in order to maximise the plasma confinement, finally determining the amount of fusion reactions. + +In this article, we report the observation of a novel confinement regime in the Large Helical Device (LHD) stellarator [1], characterized by widespread reduction of turbulence across the plasma cross- section. In this regime, the line- averaged density remains unvaried, and both ion and electron temperature increase remarkably, without ELM- like bursts typical of H- mode [2]. The transition to this new regime, observed at constant heating power and for different heating sources, is triggered by the injection of sub- millimetric boron (B) powder into the plasma. + +<--- Page Split ---> + +## 2 Motivation + +The impurity powder dropper (IPD) is a device for injecting controlled amounts of sub- millimeter powder grains into the plasma under the action of gravity [3]. Its main applications is to perform a real- time boronization by injecting B powder (and B composites such as BN and \(\mathrm{B_4C}\) ) into the plasma. The powder, penetrating into the hot plasma, evaporates, and the resulting B ions are eventually deposited on the plasma facing surfaces creating a thin boron layer. Advantages over the standard glow discharge boronization include no need for the toxic diborane gas \(B_{2}H_{6}\) , and to interrupt the plasma operation. This technique has been shown to improve wall conditioning already in tokamaks [4, 5, 6, 7], reducing wall recycling and impurity content, and in general increasing the plasma performances by accessing lower plasma collisionalities. Furthermore, the IPD has revealed itself an effective tool for ELMs suppression, allowing the access ELM- free H- mode [8]. A similar powder injection technique has been recently employed on the W7- X stellarator, showing an improvement of confinement [9], most likely induced by the modification of the plasma profiles and in a change of the radial electric field. + +The IPD has recently been installed on LHD, with the final goals of improving the plasma performances, and assessing the viability of the real time boronization technique in steady- state operation. Indeed, LHD is capable of extremely long discharges, up to one hour. The installation of the IPD on LHD has been guided by predictive simulations with the coupled EMC3- EIRENE and DUSTT codes [10], maximising the penetration of the powder into the plasma. The successful injection of B and BN powder in the unique LHD plasma configuration, featuring a double- null like cross section with predominantly poloidal magnetic field in the divertor, coupled to the confined plasma by a thick ergodic layer, has been demonstrated in 4- seconds- long plasmas [11], for a wide range of mass injection rates, plasma density, and heating power. + +## 3 Results + +A new set of powder injection experiments has been performed on LHD, featuring longer plasma duration, in the "inward shifted" magnetic configuration (position of the magnetic axis \(R_{ax} = 3.6\) m). B powder grains with diameter \(d = 150 \mu \mathrm{m}\) have been injected for a duration \(t_{d} \geq 5 \mathrm{s}\) in plasmas with different heating sources. A few seconds into powder injection, the plasma performance is observed to improve, with marked increase of both electron and ion temperature ( \(T_{e}\) , \(T_{i}\) ) and plasma stored energy \(W_{p}\) . Four different examples are shown in Fig. 1, corresponding to different heating schemes: both electron and ion cyclotron resonant heating (ECH, ICH) for subplot a, ECH only in b (but with perpendicular diagnostic neutral beam (pNB) for charge exchange spectroscopy (CXS)), \(\sim 3.5 \mathrm{MW}\) of NB in c, and \(\sim 6 \mathrm{MW}\) of NB in d. The magnetic field direction for case d is reversed with respect to cases a- c. The main plasma ion is deuterium (D) in cases a and b, while it is hydrogen (H) in cases c and d. For all cases, the line averaged density is comprised in the range \(2.7 \leq n_{e,av}[10^{19} m^{- 3}] \leq 3.7\) . The relative increase of \(T_{e}\) , \(T_{i}\) and \(W_{p}\) in between before and during B powder injection is, on average, \(\langle \Delta T_{e} / T_{e} \rangle = 27\%\) , \(\langle \Delta T_{i} / T_{i} \rangle = 25\%\) , \(\langle \Delta W_{p} / W_{p} \rangle = 17\%\) . + +For all cases, a discharge with B powder injection (red) is compared with a reference discharge without powder (blue). We remark how in cases b- d, the pulsed operation of the diagnostic beam results in periodic variations on most considered quantities. In case c, huge variations in the plasma at \(t \sim 5.3 \mathrm{s}\) and \(t \sim 7.2 \mathrm{s}\) are due to the change of NB, varying from counter- current direction to co- current and vice- versa. + +The effect of B powder on the plasma can be better appreciated in case a, where the gas puff is provided in feed- forward. The powder is dropped at \(t \sim 22 \mathrm{s}\) , and approximately after 1 s of free fall + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1: Time traces of, from top to bottom: line averaged density; input power; electron (solid) and ion (dotted) temperature; stored energy; injected boron mass rate (dotted, left axis) and BV spectroscopic line (solid, right axis). Red color for shots with B powder injection with IPD, blue for reference shots (no IPD). Main plasma ion is deuterium for cases a,b and hydrogen for c,d. The magnetic field direction for d is reversed with respect to a-c.
+ +it enters the plasma, where it is heated and evaporates. The effective injection and vaporization of the powder is confirmed by the sharp increase in the BV line measured by ultraviolet spectroscopy [12]. Due to the extra electron source, the line- averaged electron density \(n_{e,av}\) starts to increase. After a few seconds though, \(n_{e,av}\) decreases below the reference level: this is an effect of the real- time wall conditioning provided by the deposited boron, effectively reducing the recycling at the wall. In the other cases (b- d) the gas puff is operated with a feedback to keep the line averaged density constant, and this effect is masked. + +Even though \(n_{e,av}\) remains unvaried, the powder injection changes the shape of the electron density \(n_{e}\) profile, as it is shown for one of the previously discussed shots in Fig. 2. During powder injection, \(n_{e}\) is increased in the edge region \(0.7 < r_{eff} / a_{99} < 1\) , while \(n_{e}\) is slightly decreased for \(r_{eff} / a_{99} < 0.7\) , rendering the profile more hollow. Here, \(r_{eff}\) the effective minor radius, and \(a_{99}\) the minor radius of the flux surface enclosing \(99\%\) of the stored energy. Correspondingly, the slope of the electron temperature profile \(T_{e}\) is more strongly increased in the edge region. A similar change is observed for the profiles of ion temperature \(T_{i}\) as well. The increase of \(n_{e}\) in the edge region is consistent with the powder being vaporized around the LCFS, as it results from coupled EMC3- EIRENE [13] and DUSTT [14] simulations (Fig. 2d). Indeed the powder particles, initially dropped vertically, are deflected by the plasma flow in the divertor leg; nevertheless, they reach the main plasma where they are completely evaporated, depositing neutral B atoms in the range \(1 \leq r_{eff} / a_{99} \leq 1.06\) , as shown in Fig. 2e, where the B neutral source is remapped on the normalized radial coordinate \(r_{eff} / a_{99}\) for all the discussed cases. In addition to the extra electron + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2: Measured radial profiles of a) electron density \(n_{e}\) b) electron temperature \(T_{e}\) c) ion temperature \(T_{i}\) , averaged over a 100 ms time window, in the case with (red) and without (blue) B powder injection. Polynomial fits of the profiles are shown with solid lines. d) \(T_{e}\) from EMC3-EIRENE simulations for one case (#167234), together with a B powder grain trajectories (black) computed by the DUSTT code, with the resulting B neutral atom source(dots). e) B neutral source remapped on the normalized radial coordinate \(r_{eff} / a_{99}\) .
+ +source provided by the powder, the created B ions are deposited on the plasma facing surfaces, reducing hydrogen recycling and impurity influx. This results in a two- fold reduction of \(n_{e}\) in the divertor, as measured by embedded Langmuir probe arrays. The time evolution of the peak value of \(n_{e}\) at the strike point is plotted in black in Fig. 3c for a shot with powder injection (solid line) and for the reference discharge (dotted line). \(H_{\alpha}\) radiation is also decreased (red traces), suggesting a reduction of recycling. Another indication of the effective real- time boronization is the reduction of impurity influx from the plasma facing components (Fig. 3d), as suggested from the decrease in CVI (red) and FeXVI (blue) radiation lines. The decrease in C concentration is also confirmed by CXS measurements. The above mentioned increase of \(n_{e}\) close to the LCFS due to the powder injection, combined with the decrease of \(n_{e}\) at the divertor, causes the density profile to steepen in the plasma edge, as shown in Fig. 3a, where the difference in normalized gradient \((dn_{e} / d\rho) / n_{e}\) in between the powder injection shot and the no- powder reference is plotted, with \(\rho = r_{eff} / a_{99}\) . The steepening of the gradient appears to originate around \(r_{eff} / a_{99} \geq 1\) and propagate inwards, together with an increase of the electron temperature gradient (Fig. 3b). + +Simultaneously, the turbulent fluctuation level in the confined plasma is observed to be reduced to approximately half its value before powder injection, as it is shown in Fig. 3e, displaying the amplitude of the the density fluctuations measured by 2D phase contrast imaging (PCI) [15, 16], averaged over the whole plasma cross section. + +The radially resolved profiles of the density fluctuation amplitude and their velocity in the direction perpendicular to the field line \(v_{\perp}\) are plotted in Fig. 4a,b, before (dashed lines) and during B powder injection (solid lines). A discharge with B powder injection (red) is compared with a + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3: Time evolution of difference of normalized gradient between a powder injection shot (#166256) and its reference (#166254) for electron density (a) and temperature (b). c) Peak value of divertor density measured by Langmuir probes (black) and \(H_{\alpha}\) radiation (red). d) Evolution of spectroscopic lines CVI (red), FeXVI (blue) and BV (black). e) Average value of the density fluctuation amplitude measured by PCI. Solid lines are for the powder injection shot #166256, and dotted lines for the reference #166254. Periodic oscillations at 3.33 Hz are due to the pulsed operation of diagnostic NB.
+ +reference shot (blue). The shaded area accounts for the variation in time over a time window of 0.4 s, to avoid instantaneous variations caused by the pulsed diagnostic NB. As the turbulent fluctuation amplitude is substantially reduced across the whole cross section (no measurements are available for \(|r_{eff} / a_{99}| < 0.37\) ), their velocity \(v_{\perp}\) is doubled in the edge of the plasma ( \(v_{\perp}\) is directed in the e- diamagnetic direction, resulting in positive/negative values in the lab frame when measured at the bottom/top of the plasma \(r_{eff} / a_{99} \lesssim 0\) ). Radially resolved power spectral density in terms of perpendicular wave number \(k_{\perp}\) are shown in Fig. 4c,d before and during B powder injection respectively. Before the powder injection, the PSD peaks for wave- numbers in the range \(0.2 \leq k_{\perp} [mm^{- 1}] \leq 0.4\) , consistent with ion temperature gradient (ITG) driven instabilities. In Refs. [17, 18], similar PCI measurements were compared with gyrokinetic simulations, determining that the observed fluctuations are indeed due to ITG turbulence. Dedicated gyrokinetic simulations are needed to confirm this result for the cases exposed here, and are foreseen for future works. During B powder injection, these ITG- like fluctuations are substantially suppressed in the confined plasma \(r_{eff} / a_{99} < 1\) , and in general across the whole spectrum. Conversely, turbulence is slightly enhanced + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4: Radial profiles of a) turbulent fluctuation amplitude and b) their perpendicular velocity for a powder injection shot (#166256, red) and its reference (#166254, blue), at \(t = 5.2 \mathrm{s}\) (before injection, dashed lines) and \(t = 8.5 \mathrm{s}\) (during injection, solid lines). Radially resolved power spectral density in terms of wave number for #166256 before (c) and during (d) powder injection, and their ratio (e).
+ +![](images/Figure_5.jpg) + +
Figure 5: Spectrogram of the line integrated PCI signal for #166256 (a) and #167234 (b). The white dashed line indicates the approximate time of the boron powder entering the plasma. c,d) Comparison of time averaged turbulence spectrum for the B injection shot (red) and their reference shot (blue), at time before (dashed lines) and during (solid lines) powder injection.
+ +<--- Page Split ---> + +around the LCFS \(r_{e f f} / a_{9 9}\sim 1\) , as it emerges from Fig. 4e, displaying the logarithm of the ratio of the PSD during ( \(t = t_{2} = 8.5\pm 0.2\) s) and before ( \(t = t_{1} = 5.2\pm 0.2\) s) B powder injection, + +\[C = \log_{10}\frac{PSD(t = t_{2})}{PSD(t = t_{1})} \quad (1)\] + +so that \(C< 0\) and \(C > 0\) means reduction and increase of turbulence, respectively. Turbulence is therefore reduced on most of the plasma cross section, practically suppressed in correspondence of the peaks before injection ( \(k_{\perp}\sim 0.3 m m^{- 1}\) ), and slightly enhanced around the LCFS ( \(r_{e f f} / a_{9 9}\sim 1\) ). The additional enhancement for \(k_{\perp}\leq 0.1 m m^{- 1}\) close to the plasma core \(r_{e f f} / a_{9 9}\leq 0.5\) could be an artifact due to insufficient diagnostic resolution for small \(k\) values. + +This change is reflected in the time evolution of the power spectrum of the line integrated PCI signal (probing all \(r_{e f f} / a_{9 9} > 0.4\) ), shown in Fig. 5a,b for shot #166256 (D, ECH+pNB heated) and #167234 (H, NB- heated). As powder is injected (white dashed line), the dominant low- frequency fluctuations are suppressed, and a new mode emerges in the range \(100\leq f[kHz]\leq 200\) . Time averaged spectra over a window of 0.4 s, in order to attenuate the variations due to the pulsed diagnostic NB, are plotted in Fig. 5c,d for the same shots as in a,b (red) and compared to their reference shot (blue), at times before (dashed lines) and during (solid lines) powder injection. Once again, fluctuations in the range \(10\leq f[kHz]\leq 100\) are damped, while a new peak emerges in the spectrum for \(f\sim 100 - 200\mathrm{kHz}\) . In Ref. [26], a similar difference in PCI spectra has been observed in between isotope mixing and non- mixing discharges. In the first case, the spectrum peaks at \(\sim 20\) kHz, identified with ITG turbulence. At lower collisionalities, ITG turbulence is stabilized and trapped electron modes (TEM) are destabilized instead at the edge of the plasma, where gradients are steeper, resulting in a peak at \(\sim 80\mathrm{kHz}\) in the PCI spectrum. + +The decrease in turbulence is observed on the whole cross section, except close to the LCFS region \(r_{e f f} / a_{9 9}\sim 1\) . Conversely to the case of a H- mode, where turbulence is suppressed in the vicinity + +![](images/Figure_6.jpg) + +
Figure 6: Radial profiles of heat conductivities for ions (a) and electrons (b), comparing a discharge with powder injection (red) with a reference discharge (blue). c) Measured energy confinement time \(\tau_{E}\) during B powder injection (red squares), and for the reference shots at the same time (blue diamonds). The experimental \(\tau_{E}\) is plotted against the predicted energy confinement time from the international stellarator scaling [28].
+ +<--- Page Split ---> + +of the LCFS only, due to a increased shear in poloidal velocity and therefore in the radial electric field \(E_{r}\) , since the poloidal velocity is mainly given by the \(\mathbf{E} \times \mathbf{B}\) drift. In Ref. [9], confinement improvement has been observed in W7- X during \(\mathrm{B}_{4} \mathrm{C}\) powder injection, which has been judged to be consistent with a change in \(E_{r}\) . In our case, no significant change in radial electric field is expected in the phase where the powder is injected into the plasma. The ambipolar radial electric field has been computed using the neoclassical transport code SFINCS [19], for all cases (except #164645 where no \(T_{i}\) measurement is available), comparing plasma with and without powder injection. In all cases, no change in \(E_{r}\) that could result in an improvement of confinement emerges from the simulation results. The increase in \(T_{e}\) , \(T_{i}\) , \(W_{p}\) observed during powder injection is associated with a reduced energy transport in the edge of the plasma, and an increase in energy confinement time. The heat conductivities for ions and electrons, \(\chi_{i}\) and \(\chi_{e}\) respectively, are computed using the DYTRANS module of TASK3D- a [27], performing a dynamics transport analysis. As a result (shown in Fig. 6 for a NBI heated case), both \(\chi_{i}\) and \(\chi_{e}\) are reduced in the plasma edge during powder injection. \(\chi_{i}\) is reduced by up to \(40\%\) for \(r_{eff} / a_{99} > 0.4\) , while \(\chi_{e}\) is reduced by up to \(50\%\) for \(r_{eff} / a_{99} > 0.5\) . The DYTRANS results are consistent with the reduction of turbulent transport in the edge of the plasma during B powder injection. The measured energy confinement time \(\tau_{E}\) (red squares in Fig. 6, plotted against the value predicted from the international stellarator scaling \(\tau_{E,IS04}\) [28]), is also increased during B powder injection, the improvement being in between \(17\%\) and \(25\%\) when compared to the reference shot at the same time (blue diamonds). + +## 4 Conclusions + +The Impurity Powder Dropper has been used to inject sub- millimetric boron (B) powder into the LHD plasma. During powder injection, the electron and ion temperature \(T_{e}\) , \(T_{i}\) and the plasma stored energy \(W_{p}\) have been observed to increase by approximately \(27\%\) , \(25\%\) and \(17\%\) respectively. The improvement has been observed for different heating schemes (ECH+ICH, ECH+pNB, NBH), for both directions of the magnetic field, and for both H and D plasmas. At the same time, the turbulent fluctuation amplitude measured by PCI, most possibly due to ITG type of turbulence, is observed to decrease up to a factor 2, and a secondary peak in the power spectrum of the line integrated signal has been observed to emerge at higher frequencies \(f > 100 \mathrm{kHz}\) . When powder is injected, the density profile is steepened in the edge region and made more hollow in the center, due to an additional electron source for \(r_{eff} / a_{99} \geq 1\) provided by the vaporization of the powder itself, as determined by EMC3- EIRENE and DUSTT simulations. Simultaneously, the B ions deposit on the plasma facing components, changing the wall conditions in real time: the impurity influx from the wall is reduced, together with the recycling at the divertor plates. The latter results in lower plasma density in the divertor region, contributing to increase the density gradient. The modification of the \(n_{e}\) profile at the edge is followed by the steepening of \(T_{e}\) and \(T_{i}\) in the same region, resulting finally in an increase in their value on axis. Accordingly, the analysis of dynamic transport exhibits a reduction of the heat conductivities for electrons and ions \(\chi_{e}\) and \(\chi_{i}\) in the plasma edge. The measured energy confinement time \(\tau_{E}\) is also observed to increase. As the \(n_{e}\) , \(T_{e}\) and \(T_{i}\) profiles are changed, the ambipolar radial electric field computed by the SFINCS code remains mainly unvaried. This, together with reduction of the turbulent fluctuations across the whole plasma cross section, are in contrast with an H- mode type increase of confinement, where the turbulent fluctuations are reduced in the vicinity of the separatrix due to an increased \(\mathbf{E} \times \mathbf{B}\) shear, and suggest a different underlying mechanism. + +While the reason of the observed improvement of confinement is not yet clear, we suspect it to be due to the suppression of ITG turbulence. This might be an effect of the change in the profiles of the + +<--- Page Split ---> + +plasma quantities, in particular of the peaking of \(n_{e}\) in the edge, resulting in a more hollow profile in the center of the plasma. Hollow density profiles have been reported to increase ITG stability, reducing turbulence fluctuations [18, 21]. Another possibility is turbulence being reduced by the increased effective charge \(Z_{eff}\) due to B injection, also referred to as plasma dilution. Indeed, an increase \(Z_{eff}\) has also been reported to have a stabilizing effect on ITG turbulence [22, 23, 24]. More likely, the combination of the two above mentioned effects might occur in our case, similarly to what reported in Ref. [25]. Dedicated gyrokinetic simulations will be necessary to verify those hypotheses, and are planned for future works. + +Furthermore, additional experiments are foreseen on LHD, with the aim of better defining the parameter space where this new regime is observed, and to assess whether it is compatible with conditions relevant to future fusion reactors, i.e. with higher power and density plasmas. + +## 5 Methods + +Evaluation of plasma quantities and profiles + +Throughout the paper, the line averaged electron density \(n_{e,av}\) is measured by means of far infrared interferometry (FIR). Radially resolved profiles of electron density and temperature \(n_{e}\) , \(T_{e}\) are measured by Thomson scattering (TS). Radially resolved profiles of ion temperature \(T_{i}\) are measured by charge exchange spectroscopy (CXS). Using magnetic reconstruction from the VMEC code, all profiles are remapped onto the normalized coordinate \(\rho = r_{eff} / a_{99}\) , with \(r_{eff}\) the effective minor radius, and \(a_{99}\) the minor radius of the flux surface enclosing 99% of the stored energy. For each time step, the \(n_{e}\) , \(T_{e}\) , \(T_{i}\) profiles are fitted with polynomials including only even power terms, ensuring zero derivative at \(r_{eff} / a_{99} = 0\) . This provides smooth spatial profiles and gradients. The values of temperatures on axis (Fig. 1) correspond to the fitted profiles evaluated at \(r_{eff} / a_{99} = 0\) . For discharges #164644 and #164645 in Fig. 1A, the diagnostic neutral beam was not operated and no \(T_{i}\) measurement from CXS is available. + +Powder injection and evaluation of mass rate + +The impurity powder dropper features four independent feeders, composed of a powder reservoir and a vibrating tray. The latter is vibrated by piezoelectric blades through a driving voltage, allowing to control the amplitude of the vibrations and finally the amount of powder delivered to the plasma. Each independent feeder includes an accelerometer, measuring the vibration of the tray actuated by piezoelectric blades, finally determining the injection rate of the powder into the plasma. Calibration curves have been acquired in the laboratory, allowing to convert the amplitude of the accelerometer signal into injected mass rates. + +Simulations with EMC3- EIRENE and DUSTT + +Interpretative simulations are performed with the coupled EMC3- EIRENE [13] and DUSTT [14] codes, after Ref. [10]. EMC3 is a fully 3D Monte Carlo code modelling the plasma transport in the edge and scrape- off layer, and it is coupled to EIRENE, describing the neutrals dynamics. The diffusion and thermal conductivities coefficients are set by matching the experimental profiles of plasma density and ion and electron temperatures. Electron density and temperature are measured by Thomson scattering, while the ion temperature is measured by charge exchange spectroscopy. The 3D plasma solutions from the EMC3- EIRENE simulations are then used as the background for the DUSTT simulations, computing the trajectory of a powder grain injected into the plasma. For those simulations, the powder grains are injected vertically at the actual IPD location with an initial velocity of 5 m/s directed downwards, consistent with the free fall of the powder grains prior + +<--- Page Split ---> + +to entering the plasma. The powder material is B, and the size of the modelled powder grain is 150 \(\mu \mathrm{m}\) , matching the ones used in the experiments. As the powder grains enter the plasma, they are progressively heated up to evaporating temperature, providing a localized source of neutral B atoms. + +Measurement of turbulent fluctuations + +In this work, the turbulent fluctuations characteristics are measured by means of two- dimensional Phase Contrast Imaging (PCI). The diagnostic system and the spectral analysis technique are detailed in Refs. [15, 16]. The amplitude of the density fluctuation is computed from the power spectrum integrated over \(\omega\) and \(k\) as \(\sqrt{\bar{n}^{2}}\) . The cutoff wave- number of the PCI system has been investigated in detail in Ref. [29], showing how fluctuations with wave- number in the range \(0.1 \leq k [mm^{- 1}] \leq 0.8\) are measured, with an attenuated contributions to the final spectrum for \(k < 0.2mm^{- 1}\) . + +Energy confinement time + +The confinement time is computed from the time trace of the plasma stored energy \(W_{p}\) , smoothed in time to average over the variations provided by the perpendicular NBI. For ECH, all port- through power is assumed to be absorbed by the plasma. For ICH, it is assumed that \(75\%\) of the input power is absorbed. For NBIs, the absorbed power is assumed to be \(P_{ab} = P_{in}[1 - \exp \left(- \sigma n_{e,av}l\right)]\) , where \(\sigma = 0.43\) for H plasmas and \(l = 1.86 \mathrm{m}\) [30]. + +## Acknowledgments + +The authors wish to thank the LHD experiment group for the excellent support of this work, and Drs. R. Seki and M. Yokoyama (NIFS) for executing TASK3D- a suite, allowing to conduct transport analyses. This work was conducted within the framework of the NIFS/PPPL International Collaboration, and it is supported by the U.S. DOE under Contract No. DE- AC02- 09CH11466 with Princeton University. + +## Author Contributions + +N.A., E.P.G, R.L., S.M., M.S, F.N., A.N. set up and performed the experiments. T.O., K.I., M.Y., Y.T., K.T., T.K., C.S. set up and operated the diagnostics used in the experiments and ran preliminary analysis. F.N., M.S., G.K., G.M., N.K., N.A.P, A.M. performed numerical modeling of the experiments. F.N., S.M., K.T., K.I, A.B., D.A.G further analyzed and interpreted the data. F.N. prepared the figures and wrote the manuscript. D.A.G. and T.M. supervised the project. All authors reviewed the manuscript and contributed to discussions. + +## References + +[1] A. Iiyoshi et al., Overview of the Large Helical Device project, Nuclear Fusion 39 (1999) pp.1245- 1256. + +[2] F. Wagner, A quarter- century of H- mode studies, Plasma Physics and Controlled Fusion 49 (2007) + +[3] A. Nagy et al., A multi- species powder dropper for magnetic fusion applications, Review of Scientific Instruments 89 (2018), 10K121 + +<--- Page Split ---> + +[4] A. Bortolon et al., Real- time wall conditioning by controlled injection of boron and boron nitride powder in full tungsten wall ASDEX Upgrade, Nuclear Materials and Energy 19 (2019) 384- 389 + +[5] R. Lunsford et al., Active conditioning of ASDEX Upgrade tungsten plasma- facing components and discharge enhancement through boron and boron nitride particulate injection, Nuclear Fusion 59 (2019) 126034 + +[6] A. Bortolon et al., Observations of wall conditioning by means of boron powder injection in DIII D H- mode plasmas, Nuclear Fusion 60 (2020) 126010 + +[7] E. P. Gilson et al., Wall Conditioning with Boron Nitride and Boron Powder Injection in KSTAR, submitted to Nuclear Materials and Energy (2021) + +[8] Z. Sun et al., Suppression of edge localized modes with real- time boron injection using the tungsten divertor in EAST, Nuclear Fusion 61 (2021) 014002 + +[9] R. Lunsford et al., Characterization of injection and confinement improvement through impurity induced profile modifications on the Wendelstein 7- X stellarator, submitted to Physics of Plasmas (2021) + +[10] M. Shoji et al., Full- torus impurity transport simulation for optimizing plasma discharge operation using a multi- species impurity powder dropper in the large helical device, Contributions to Plasma Physics (2019) e201900101. https://doi.org/10.1002/ctpp.201900101 + +[11] F. Nespoli et al., First impurity powder injection experiment in LHD, Nuclear Materials and Energy 25 (2020) 100842 + +[12] T. Oishi et al., Line identification of boron and nitrogen emissions in EUV and VUV wavelength ranges in the impurity powder dropping experiments of LHD and its application to spectroscopic diagnostics, Plasma Science and Technology (2021) https://doi.org/10.1088/2058- 6272/abfd88 + +[13] Y. Feng et al., Fluid features of the stochastic layer transport in LHD, Nuclear Fusion 48 (2008) 024012 + +[14] R.D. Smirnov et al., Plasma Physics and Controlled Fusion 49 (2007) 347- 371 + +[15] K. Tanaka et al., Two- dimensional phase contrast imaging for local turbulence measurements in large helical device (invited) Review of Scientific Instruments 79, 10E702 (2008) + +[16] C. A. Michael, K. Tanaka, L. Vyacheslavov, A. Sanin, and K. Kawahata, Two- dimensional wave- number spectral analysis techniques for phase contrast imaging turbulence imaging data on large helical device, Review of Scientific Instruments 86, 093503 (2015) + +[17] M. Nunami et al., Linear Gyrokinetic Analyses of ITG Modes and Zonal Flows in LHD with High Ion Temperature, Plasma and Fusion Research 6 (2011) 1403001 + +[18] K. Tanaka et al., Extended investigations of isotope effects on ECRH plasma in LHD, Plasma Physics and Controlled Fusion 62 (2020) 024006 + +[19] M. Landreman, et al., Comparison of particle trajectories and collision operators for collisional transport in nonaxisymmetric plasmas, Phys. Plasmas 21 (2014) 042503. + +<--- Page Split ---> + +[20] M. Yokoyama et al., Extended capability of the integrated transport analysis suite, TASK3D- a, for LHD experiment, Nuclear Fusion 57 (2017) 126016 + +[21] M. Nakata et al., Gyrokinetic microinstability analysis of high- Ti and high- Te isotope plasmas in Large Helical Device, Plasma Phys. Control. Fusion 61 (2019) 014016 + +[22] D. R. Mikkelsen et al., Quasilinear carbon transport in an impurity hole plasma in LHD, Physics of Plasmas 21, 082302 (2014) + +[23] P. Ennever et al., The effects of dilution on turbulence and transport in C- Mod ohmic plasmas and comparisons with gyrokinetic simulations Physics of Plasmas 22, 072507 (2015) + +[24] P. Ennever et al., The effects of main- ion dilution on turbulence in low q95 C- Mod ohmic plasmas, and comparisons with nonlinear GYRO, Phys. Plasmas 23, 082509 (2016) + +[25] T. Kinoshita et al., submitted to Plasma Physics and Controlled Fusion (2021) + +[26] K. Ida et al., Characteristics of plasma parameters and turbulence in the isotope- mixing and the non- mixing states in hydrogen- deuterium mixture plasmas in the large helical device, Nucl. Fusion 61 (2021) 016012 + +[27] M. Yokoyama et al., Extended capability of the integrated transport analysis suite, TASK3D- a, for LHD experiment, Nucl. Fusion 57 (2017) 126016 + +[28] H. Yamada et al., Characterization of energy confinement in net- current free plasmas using the extended International Stellarator Database, Nuclear Fusion 45 (2005) 1684- 1693 + +[29] T. Kinoshita et al., Determination of absolute turbulence amplitude by CO2 laser phase contrast imaging, JINST 15 C01045 (2020) + +[30] Y. Takeiri et al., High- ion temperature experiments with negative- ion- based neutral beam injection heating in large helical device, Nucl. Fusion 45 (2005) 565- 573. + +<--- Page Split ---> diff --git a/preprint/preprint__07ca7119489ce742f39828987bc35bcbb114b2fcae1faac252a175d7615d08d8/preprint__07ca7119489ce742f39828987bc35bcbb114b2fcae1faac252a175d7615d08d8_det.mmd b/preprint/preprint__07ca7119489ce742f39828987bc35bcbb114b2fcae1faac252a175d7615d08d8/preprint__07ca7119489ce742f39828987bc35bcbb114b2fcae1faac252a175d7615d08d8_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..fb471fdf703afcc3a294324bc011361f5cbceedf --- /dev/null +++ b/preprint/preprint__07ca7119489ce742f39828987bc35bcbb114b2fcae1faac252a175d7615d08d8/preprint__07ca7119489ce742f39828987bc35bcbb114b2fcae1faac252a175d7615d08d8_det.mmd @@ -0,0 +1,390 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 920, 175]]<|/det|> +# Observation of a novel reduced-turbulence regime with boron powder injection in a stellarator + +<|ref|>text<|/ref|><|det|>[[44, 195, 744, 238]]<|/det|> +Federico Nespoli ( \(\boxed{\pi}\) fnespoli@pppl.gov) Princeton Plasma Physics Laboratory https://orcid.org/0000- 0001- 7644- 751X + +<|ref|>text<|/ref|><|det|>[[44, 243, 732, 285]]<|/det|> +Suguru Masuzaki National Institute for Fusion Science https://orcid.org/0000- 0003- 0161- 0938 + +<|ref|>text<|/ref|><|det|>[[44, 290, 375, 332]]<|/det|> +Kenji Tanaka National Institute for Fusion Science + +<|ref|>text<|/ref|><|det|>[[44, 338, 375, 379]]<|/det|> +Naoko Ashikawa National Institute for Fusion Science + +<|ref|>text<|/ref|><|det|>[[44, 384, 375, 425]]<|/det|> +Mamoru Shoji National Institute for Fusion Science + +<|ref|>text<|/ref|><|det|>[[44, 430, 383, 472]]<|/det|> +Erik Gilson Princeton Plasma Physics Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 477, 383, 518]]<|/det|> +Robert Lunsford Princeton Plasma Physics Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 523, 375, 564]]<|/det|> +Tetsutaro Oishi National Institute for Fusion Science + +<|ref|>text<|/ref|><|det|>[[44, 570, 375, 610]]<|/det|> +Katsumi Ida National Institute for Fusion Science + +<|ref|>text<|/ref|><|det|>[[44, 616, 375, 657]]<|/det|> +Mikirou Yoshinuma National Institute for Fusion Science + +<|ref|>text<|/ref|><|det|>[[44, 662, 375, 703]]<|/det|> +Yuki Takemura National Institute for Fusion Science + +<|ref|>text<|/ref|><|det|>[[44, 708, 570, 750]]<|/det|> +Toshiki Kinoshita Kyushu University https://orcid.org/0000- 0003- 3930- 4434 + +<|ref|>text<|/ref|><|det|>[[44, 755, 375, 796]]<|/det|> +Gen Motojima National Institute for Fusion Science + +<|ref|>text<|/ref|><|det|>[[44, 801, 375, 842]]<|/det|> +Naoki Kenmochi National Institute for Fusion Science + +<|ref|>text<|/ref|><|det|>[[44, 847, 375, 888]]<|/det|> +Gakushi Kawamura National Institute for Fusion Science + +<|ref|>text<|/ref|><|det|>[[44, 893, 375, 934]]<|/det|> +Chihiro Suzuki National Institute for Fusion Science + +<|ref|>text<|/ref|><|det|>[[44, 940, 133, 958]]<|/det|> +Alex Nagy + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[52, 45, 383, 64]]<|/det|> +Princeton Plasma Physics Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 70, 383, 109]]<|/det|> +Alessandro Bortolon Princeton Plasma Physics Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 115, 385, 156]]<|/det|> +Novimir Pablant Princeton Plasma Physics Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 162, 383, 202]]<|/det|> +Albert Mollen Princeton Plasma Physics Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 208, 375, 248]]<|/det|> +Naoki Tamura National Institute for Fusion Science + +<|ref|>text<|/ref|><|det|>[[44, 254, 588, 295]]<|/det|> +David Gates Princeton University https://orcid.org/0000- 0001- 5679- 3124 + +<|ref|>text<|/ref|><|det|>[[44, 300, 375, 341]]<|/det|> +Tomohiro Morisaki National Institute for Fusion Science + +<|ref|>sub_title<|/ref|><|det|>[[44, 383, 102, 401]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[42, 420, 946, 465]]<|/det|> +Keywords: Confinement Regime, Turbulent Fluctuations, Line Averaged Electron Density, Resonant Radio Frequency, Hydrogen and Deuterium Plasmas + +<|ref|>text<|/ref|><|det|>[[44, 482, 300, 501]]<|/det|> +Posted Date: June 30th, 2021 + +<|ref|>text<|/ref|><|det|>[[42, 520, 463, 540]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 614131/v1 + +<|ref|>text<|/ref|><|det|>[[42, 557, 909, 600]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 636, 950, 679]]<|/det|> +Version of Record: A version of this preprint was published at Nature Physics on January 10th, 2022. See the published version at https://doi.org/10.1038/s41567- 021- 01460- 4. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[115, 87, 880, 123]]<|/det|> +# Observation of a novel reduced-turbulence regime with boron powder injection in a stellarator + +<|ref|>text<|/ref|><|det|>[[112, 123, 884, 191]]<|/det|> +F. Nespoli\*,1, +S. Masuzaki2,3, +K. Tanaka2,4, +N. Ashikawa2,3, +M. Shoji2, +E.P. Gilson1, +R. Lunsford1, +T. Oishi2,3, +K. Ida2,3, +M. Yoshinuma2,3, +Y. Takemura2,3, +T. Kinoshita4, +G. Motojima2,3, +N. Kenmochi2,3, +G. Kawamura2,3, +C. Suzuki2, +A. Nagy1, +A. Bortolon1, +N.A. Pablant1, +A. Mollen1, +N. Tamura2, +D.A. Gates1, +T. Morisaki2,3 + +<|ref|>text<|/ref|><|det|>[[112, 205, 886, 295]]<|/det|> +1 Princeton Plasma Physics Laboratory, 100 Stellarator Road, Princeton, NJ 08540, United States of America 2 National Institute for Fusion Science, 322- 6 Oroshi- cho Toki, Gifu 509- 5292, Japan 3 The Graduate University for Advanced Studies, SOKENDAI, 322- 6 Oroshi- cho Toki, Gifu 509- 5292, Japan 4 Interdisciplinary Graduate School of Engineering Sciences, Kyushu University, Kasuga, Fukuoka, 816- 8580, Japan \* Corresponding author. Email: fnespoli@pppl.gov + +<|ref|>sub_title<|/ref|><|det|>[[114, 320, 215, 340]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[112, 354, 884, 508]]<|/det|> +We report the first observation of a novel confinement regime in a stellarator plasma, characterized by increased confinement and reduced turbulent fluctuations. The transition to this new regime is driven by the injection of sub- millimetric boron powder grains into the plasma. With the line averaged electron density being kept constant, substantial increase of stored energy, electron and ion temperature have been observed. At the same time, the amplitude of the plasma turbulent fluctuations is halved. While lower frequency fluctuations are damped, higher frequency modes in the range \(100 \leq f[kHz] \leq 200\) are excited. The access to this regime has been observed for different heating schemes, namely with both electron and ion cyclotron resonant radio frequency, and neutral beams, for both directions of the magnetic field, and for both hydrogen and deuterium plasmas. + +<|ref|>sub_title<|/ref|><|det|>[[114, 548, 300, 568]]<|/det|> +## 1 Introduction + +<|ref|>text<|/ref|><|det|>[[112, 582, 884, 753]]<|/det|> +Stellarators are one of the most promising concepts for magnetic confined nuclear fusion, which could provide a clean alternative to fossil fuels and nuclear fission for mass energy production. Unlike tokamaks, their 3D magnetic filed is provided entirely by external coils, removing the need for a current to flow into the plasma, which makes it prone to instabilities and violent disruptions. This also allows the magnetic field to be tailored to minimize neoclassical transport and improve confinement. At present, the biggest degradation of confinement is given by plasma turbulence, resulting in an increased "anomalous" transport. While in principle possible, optimizing the stellarator magnetic field to reduce turbulent transport is extremely challenging due to the huge computational cost of 3D turbulence simulations. It is therefore fundamental to reduce turbulence in order to maximise the plasma confinement, finally determining the amount of fusion reactions. + +<|ref|>text<|/ref|><|det|>[[112, 754, 884, 856]]<|/det|> +In this article, we report the observation of a novel confinement regime in the Large Helical Device (LHD) stellarator [1], characterized by widespread reduction of turbulence across the plasma cross- section. In this regime, the line- averaged density remains unvaried, and both ion and electron temperature increase remarkably, without ELM- like bursts typical of H- mode [2]. The transition to this new regime, observed at constant heating power and for different heating sources, is triggered by the injection of sub- millimetric boron (B) powder into the plasma. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 86, 281, 106]]<|/det|> +## 2 Motivation + +<|ref|>text<|/ref|><|det|>[[112, 118, 884, 343]]<|/det|> +The impurity powder dropper (IPD) is a device for injecting controlled amounts of sub- millimeter powder grains into the plasma under the action of gravity [3]. Its main applications is to perform a real- time boronization by injecting B powder (and B composites such as BN and \(\mathrm{B_4C}\) ) into the plasma. The powder, penetrating into the hot plasma, evaporates, and the resulting B ions are eventually deposited on the plasma facing surfaces creating a thin boron layer. Advantages over the standard glow discharge boronization include no need for the toxic diborane gas \(B_{2}H_{6}\) , and to interrupt the plasma operation. This technique has been shown to improve wall conditioning already in tokamaks [4, 5, 6, 7], reducing wall recycling and impurity content, and in general increasing the plasma performances by accessing lower plasma collisionalities. Furthermore, the IPD has revealed itself an effective tool for ELMs suppression, allowing the access ELM- free H- mode [8]. A similar powder injection technique has been recently employed on the W7- X stellarator, showing an improvement of confinement [9], most likely induced by the modification of the plasma profiles and in a change of the radial electric field. + +<|ref|>text<|/ref|><|det|>[[112, 342, 884, 496]]<|/det|> +The IPD has recently been installed on LHD, with the final goals of improving the plasma performances, and assessing the viability of the real time boronization technique in steady- state operation. Indeed, LHD is capable of extremely long discharges, up to one hour. The installation of the IPD on LHD has been guided by predictive simulations with the coupled EMC3- EIRENE and DUSTT codes [10], maximising the penetration of the powder into the plasma. The successful injection of B and BN powder in the unique LHD plasma configuration, featuring a double- null like cross section with predominantly poloidal magnetic field in the divertor, coupled to the confined plasma by a thick ergodic layer, has been demonstrated in 4- seconds- long plasmas [11], for a wide range of mass injection rates, plasma density, and heating power. + +<|ref|>sub_title<|/ref|><|det|>[[112, 519, 240, 539]]<|/det|> +## 3 Results + +<|ref|>text<|/ref|><|det|>[[112, 552, 884, 777]]<|/det|> +A new set of powder injection experiments has been performed on LHD, featuring longer plasma duration, in the "inward shifted" magnetic configuration (position of the magnetic axis \(R_{ax} = 3.6\) m). B powder grains with diameter \(d = 150 \mu \mathrm{m}\) have been injected for a duration \(t_{d} \geq 5 \mathrm{s}\) in plasmas with different heating sources. A few seconds into powder injection, the plasma performance is observed to improve, with marked increase of both electron and ion temperature ( \(T_{e}\) , \(T_{i}\) ) and plasma stored energy \(W_{p}\) . Four different examples are shown in Fig. 1, corresponding to different heating schemes: both electron and ion cyclotron resonant heating (ECH, ICH) for subplot a, ECH only in b (but with perpendicular diagnostic neutral beam (pNB) for charge exchange spectroscopy (CXS)), \(\sim 3.5 \mathrm{MW}\) of NB in c, and \(\sim 6 \mathrm{MW}\) of NB in d. The magnetic field direction for case d is reversed with respect to cases a- c. The main plasma ion is deuterium (D) in cases a and b, while it is hydrogen (H) in cases c and d. For all cases, the line averaged density is comprised in the range \(2.7 \leq n_{e,av}[10^{19} m^{- 3}] \leq 3.7\) . The relative increase of \(T_{e}\) , \(T_{i}\) and \(W_{p}\) in between before and during B powder injection is, on average, \(\langle \Delta T_{e} / T_{e} \rangle = 27\%\) , \(\langle \Delta T_{i} / T_{i} \rangle = 25\%\) , \(\langle \Delta W_{p} / W_{p} \rangle = 17\%\) . + +<|ref|>text<|/ref|><|det|>[[112, 776, 883, 861]]<|/det|> +For all cases, a discharge with B powder injection (red) is compared with a reference discharge without powder (blue). We remark how in cases b- d, the pulsed operation of the diagnostic beam results in periodic variations on most considered quantities. In case c, huge variations in the plasma at \(t \sim 5.3 \mathrm{s}\) and \(t \sim 7.2 \mathrm{s}\) are due to the change of NB, varying from counter- current direction to co- current and vice- versa. + +<|ref|>text<|/ref|><|det|>[[112, 878, 884, 913]]<|/det|> +The effect of B powder on the plasma can be better appreciated in case a, where the gas puff is provided in feed- forward. The powder is dropped at \(t \sim 22 \mathrm{s}\) , and approximately after 1 s of free fall + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 88, 875, 435]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 445, 884, 532]]<|/det|> +
Figure 1: Time traces of, from top to bottom: line averaged density; input power; electron (solid) and ion (dotted) temperature; stored energy; injected boron mass rate (dotted, left axis) and BV spectroscopic line (solid, right axis). Red color for shots with B powder injection with IPD, blue for reference shots (no IPD). Main plasma ion is deuterium for cases a,b and hydrogen for c,d. The magnetic field direction for d is reversed with respect to a-c.
+ +<|ref|>text<|/ref|><|det|>[[113, 557, 884, 678]]<|/det|> +it enters the plasma, where it is heated and evaporates. The effective injection and vaporization of the powder is confirmed by the sharp increase in the BV line measured by ultraviolet spectroscopy [12]. Due to the extra electron source, the line- averaged electron density \(n_{e,av}\) starts to increase. After a few seconds though, \(n_{e,av}\) decreases below the reference level: this is an effect of the real- time wall conditioning provided by the deposited boron, effectively reducing the recycling at the wall. In the other cases (b- d) the gas puff is operated with a feedback to keep the line averaged density constant, and this effect is masked. + +<|ref|>text<|/ref|><|det|>[[113, 679, 884, 901]]<|/det|> +Even though \(n_{e,av}\) remains unvaried, the powder injection changes the shape of the electron density \(n_{e}\) profile, as it is shown for one of the previously discussed shots in Fig. 2. During powder injection, \(n_{e}\) is increased in the edge region \(0.7 < r_{eff} / a_{99} < 1\) , while \(n_{e}\) is slightly decreased for \(r_{eff} / a_{99} < 0.7\) , rendering the profile more hollow. Here, \(r_{eff}\) the effective minor radius, and \(a_{99}\) the minor radius of the flux surface enclosing \(99\%\) of the stored energy. Correspondingly, the slope of the electron temperature profile \(T_{e}\) is more strongly increased in the edge region. A similar change is observed for the profiles of ion temperature \(T_{i}\) as well. The increase of \(n_{e}\) in the edge region is consistent with the powder being vaporized around the LCFS, as it results from coupled EMC3- EIRENE [13] and DUSTT [14] simulations (Fig. 2d). Indeed the powder particles, initially dropped vertically, are deflected by the plasma flow in the divertor leg; nevertheless, they reach the main plasma where they are completely evaporated, depositing neutral B atoms in the range \(1 \leq r_{eff} / a_{99} \leq 1.06\) , as shown in Fig. 2e, where the B neutral source is remapped on the normalized radial coordinate \(r_{eff} / a_{99}\) for all the discussed cases. In addition to the extra electron + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[128, 95, 880, 395]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 408, 884, 512]]<|/det|> +
Figure 2: Measured radial profiles of a) electron density \(n_{e}\) b) electron temperature \(T_{e}\) c) ion temperature \(T_{i}\) , averaged over a 100 ms time window, in the case with (red) and without (blue) B powder injection. Polynomial fits of the profiles are shown with solid lines. d) \(T_{e}\) from EMC3-EIRENE simulations for one case (#167234), together with a B powder grain trajectories (black) computed by the DUSTT code, with the resulting B neutral atom source(dots). e) B neutral source remapped on the normalized radial coordinate \(r_{eff} / a_{99}\) .
+ +<|ref|>text<|/ref|><|det|>[[113, 536, 884, 777]]<|/det|> +source provided by the powder, the created B ions are deposited on the plasma facing surfaces, reducing hydrogen recycling and impurity influx. This results in a two- fold reduction of \(n_{e}\) in the divertor, as measured by embedded Langmuir probe arrays. The time evolution of the peak value of \(n_{e}\) at the strike point is plotted in black in Fig. 3c for a shot with powder injection (solid line) and for the reference discharge (dotted line). \(H_{\alpha}\) radiation is also decreased (red traces), suggesting a reduction of recycling. Another indication of the effective real- time boronization is the reduction of impurity influx from the plasma facing components (Fig. 3d), as suggested from the decrease in CVI (red) and FeXVI (blue) radiation lines. The decrease in C concentration is also confirmed by CXS measurements. The above mentioned increase of \(n_{e}\) close to the LCFS due to the powder injection, combined with the decrease of \(n_{e}\) at the divertor, causes the density profile to steepen in the plasma edge, as shown in Fig. 3a, where the difference in normalized gradient \((dn_{e} / d\rho) / n_{e}\) in between the powder injection shot and the no- powder reference is plotted, with \(\rho = r_{eff} / a_{99}\) . The steepening of the gradient appears to originate around \(r_{eff} / a_{99} \geq 1\) and propagate inwards, together with an increase of the electron temperature gradient (Fig. 3b). + +<|ref|>text<|/ref|><|det|>[[113, 777, 884, 845]]<|/det|> +Simultaneously, the turbulent fluctuation level in the confined plasma is observed to be reduced to approximately half its value before powder injection, as it is shown in Fig. 3e, displaying the amplitude of the the density fluctuations measured by 2D phase contrast imaging (PCI) [15, 16], averaged over the whole plasma cross section. + +<|ref|>text<|/ref|><|det|>[[113, 846, 884, 898]]<|/det|> +The radially resolved profiles of the density fluctuation amplitude and their velocity in the direction perpendicular to the field line \(v_{\perp}\) are plotted in Fig. 4a,b, before (dashed lines) and during B powder injection (solid lines). A discharge with B powder injection (red) is compared with a + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[238, 103, 763, 498]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 511, 884, 633]]<|/det|> +
Figure 3: Time evolution of difference of normalized gradient between a powder injection shot (#166256) and its reference (#166254) for electron density (a) and temperature (b). c) Peak value of divertor density measured by Langmuir probes (black) and \(H_{\alpha}\) radiation (red). d) Evolution of spectroscopic lines CVI (red), FeXVI (blue) and BV (black). e) Average value of the density fluctuation amplitude measured by PCI. Solid lines are for the powder injection shot #166256, and dotted lines for the reference #166254. Periodic oscillations at 3.33 Hz are due to the pulsed operation of diagnostic NB.
+ +<|ref|>text<|/ref|><|det|>[[113, 659, 883, 899]]<|/det|> +reference shot (blue). The shaded area accounts for the variation in time over a time window of 0.4 s, to avoid instantaneous variations caused by the pulsed diagnostic NB. As the turbulent fluctuation amplitude is substantially reduced across the whole cross section (no measurements are available for \(|r_{eff} / a_{99}| < 0.37\) ), their velocity \(v_{\perp}\) is doubled in the edge of the plasma ( \(v_{\perp}\) is directed in the e- diamagnetic direction, resulting in positive/negative values in the lab frame when measured at the bottom/top of the plasma \(r_{eff} / a_{99} \lesssim 0\) ). Radially resolved power spectral density in terms of perpendicular wave number \(k_{\perp}\) are shown in Fig. 4c,d before and during B powder injection respectively. Before the powder injection, the PSD peaks for wave- numbers in the range \(0.2 \leq k_{\perp} [mm^{- 1}] \leq 0.4\) , consistent with ion temperature gradient (ITG) driven instabilities. In Refs. [17, 18], similar PCI measurements were compared with gyrokinetic simulations, determining that the observed fluctuations are indeed due to ITG turbulence. Dedicated gyrokinetic simulations are needed to confirm this result for the cases exposed here, and are foreseen for future works. During B powder injection, these ITG- like fluctuations are substantially suppressed in the confined plasma \(r_{eff} / a_{99} < 1\) , and in general across the whole spectrum. Conversely, turbulence is slightly enhanced + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 105, 884, 377]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 386, 884, 473]]<|/det|> +
Figure 4: Radial profiles of a) turbulent fluctuation amplitude and b) their perpendicular velocity for a powder injection shot (#166256, red) and its reference (#166254, blue), at \(t = 5.2 \mathrm{s}\) (before injection, dashed lines) and \(t = 8.5 \mathrm{s}\) (during injection, solid lines). Radially resolved power spectral density in terms of wave number for #166256 before (c) and during (d) powder injection, and their ratio (e).
+ +<|ref|>image<|/ref|><|det|>[[130, 540, 844, 825]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 837, 884, 908]]<|/det|> +
Figure 5: Spectrogram of the line integrated PCI signal for #166256 (a) and #167234 (b). The white dashed line indicates the approximate time of the boron powder entering the plasma. c,d) Comparison of time averaged turbulence spectrum for the B injection shot (red) and their reference shot (blue), at time before (dashed lines) and during (solid lines) powder injection.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 89, 886, 124]]<|/det|> +around the LCFS \(r_{e f f} / a_{9 9}\sim 1\) , as it emerges from Fig. 4e, displaying the logarithm of the ratio of the PSD during ( \(t = t_{2} = 8.5\pm 0.2\) s) and before ( \(t = t_{1} = 5.2\pm 0.2\) s) B powder injection, + +<|ref|>equation<|/ref|><|det|>[[405, 133, 882, 170]]<|/det|> +\[C = \log_{10}\frac{PSD(t = t_{2})}{PSD(t = t_{1})} \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[112, 180, 884, 268]]<|/det|> +so that \(C< 0\) and \(C > 0\) means reduction and increase of turbulence, respectively. Turbulence is therefore reduced on most of the plasma cross section, practically suppressed in correspondence of the peaks before injection ( \(k_{\perp}\sim 0.3 m m^{- 1}\) ), and slightly enhanced around the LCFS ( \(r_{e f f} / a_{9 9}\sim 1\) ). The additional enhancement for \(k_{\perp}\leq 0.1 m m^{- 1}\) close to the plasma core \(r_{e f f} / a_{9 9}\leq 0.5\) could be an artifact due to insufficient diagnostic resolution for small \(k\) values. + +<|ref|>text<|/ref|><|det|>[[112, 268, 884, 490]]<|/det|> +This change is reflected in the time evolution of the power spectrum of the line integrated PCI signal (probing all \(r_{e f f} / a_{9 9} > 0.4\) ), shown in Fig. 5a,b for shot #166256 (D, ECH+pNB heated) and #167234 (H, NB- heated). As powder is injected (white dashed line), the dominant low- frequency fluctuations are suppressed, and a new mode emerges in the range \(100\leq f[kHz]\leq 200\) . Time averaged spectra over a window of 0.4 s, in order to attenuate the variations due to the pulsed diagnostic NB, are plotted in Fig. 5c,d for the same shots as in a,b (red) and compared to their reference shot (blue), at times before (dashed lines) and during (solid lines) powder injection. Once again, fluctuations in the range \(10\leq f[kHz]\leq 100\) are damped, while a new peak emerges in the spectrum for \(f\sim 100 - 200\mathrm{kHz}\) . In Ref. [26], a similar difference in PCI spectra has been observed in between isotope mixing and non- mixing discharges. In the first case, the spectrum peaks at \(\sim 20\) kHz, identified with ITG turbulence. At lower collisionalities, ITG turbulence is stabilized and trapped electron modes (TEM) are destabilized instead at the edge of the plasma, where gradients are steeper, resulting in a peak at \(\sim 80\mathrm{kHz}\) in the PCI spectrum. + +<|ref|>text<|/ref|><|det|>[[112, 490, 884, 525]]<|/det|> +The decrease in turbulence is observed on the whole cross section, except close to the LCFS region \(r_{e f f} / a_{9 9}\sim 1\) . Conversely to the case of a H- mode, where turbulence is suppressed in the vicinity + +<|ref|>image<|/ref|><|det|>[[140, 555, 800, 808]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 818, 884, 904]]<|/det|> +
Figure 6: Radial profiles of heat conductivities for ions (a) and electrons (b), comparing a discharge with powder injection (red) with a reference discharge (blue). c) Measured energy confinement time \(\tau_{E}\) during B powder injection (red squares), and for the reference shots at the same time (blue diamonds). The experimental \(\tau_{E}\) is plotted against the predicted energy confinement time from the international stellarator scaling [28].
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 884, 413]]<|/det|> +of the LCFS only, due to a increased shear in poloidal velocity and therefore in the radial electric field \(E_{r}\) , since the poloidal velocity is mainly given by the \(\mathbf{E} \times \mathbf{B}\) drift. In Ref. [9], confinement improvement has been observed in W7- X during \(\mathrm{B}_{4} \mathrm{C}\) powder injection, which has been judged to be consistent with a change in \(E_{r}\) . In our case, no significant change in radial electric field is expected in the phase where the powder is injected into the plasma. The ambipolar radial electric field has been computed using the neoclassical transport code SFINCS [19], for all cases (except #164645 where no \(T_{i}\) measurement is available), comparing plasma with and without powder injection. In all cases, no change in \(E_{r}\) that could result in an improvement of confinement emerges from the simulation results. The increase in \(T_{e}\) , \(T_{i}\) , \(W_{p}\) observed during powder injection is associated with a reduced energy transport in the edge of the plasma, and an increase in energy confinement time. The heat conductivities for ions and electrons, \(\chi_{i}\) and \(\chi_{e}\) respectively, are computed using the DYTRANS module of TASK3D- a [27], performing a dynamics transport analysis. As a result (shown in Fig. 6 for a NBI heated case), both \(\chi_{i}\) and \(\chi_{e}\) are reduced in the plasma edge during powder injection. \(\chi_{i}\) is reduced by up to \(40\%\) for \(r_{eff} / a_{99} > 0.4\) , while \(\chi_{e}\) is reduced by up to \(50\%\) for \(r_{eff} / a_{99} > 0.5\) . The DYTRANS results are consistent with the reduction of turbulent transport in the edge of the plasma during B powder injection. The measured energy confinement time \(\tau_{E}\) (red squares in Fig. 6, plotted against the value predicted from the international stellarator scaling \(\tau_{E,IS04}\) [28]), is also increased during B powder injection, the improvement being in between \(17\%\) and \(25\%\) when compared to the reference shot at the same time (blue diamonds). + +<|ref|>sub_title<|/ref|><|det|>[[113, 436, 291, 457]]<|/det|> +## 4 Conclusions + +<|ref|>text<|/ref|><|det|>[[112, 470, 884, 864]]<|/det|> +The Impurity Powder Dropper has been used to inject sub- millimetric boron (B) powder into the LHD plasma. During powder injection, the electron and ion temperature \(T_{e}\) , \(T_{i}\) and the plasma stored energy \(W_{p}\) have been observed to increase by approximately \(27\%\) , \(25\%\) and \(17\%\) respectively. The improvement has been observed for different heating schemes (ECH+ICH, ECH+pNB, NBH), for both directions of the magnetic field, and for both H and D plasmas. At the same time, the turbulent fluctuation amplitude measured by PCI, most possibly due to ITG type of turbulence, is observed to decrease up to a factor 2, and a secondary peak in the power spectrum of the line integrated signal has been observed to emerge at higher frequencies \(f > 100 \mathrm{kHz}\) . When powder is injected, the density profile is steepened in the edge region and made more hollow in the center, due to an additional electron source for \(r_{eff} / a_{99} \geq 1\) provided by the vaporization of the powder itself, as determined by EMC3- EIRENE and DUSTT simulations. Simultaneously, the B ions deposit on the plasma facing components, changing the wall conditions in real time: the impurity influx from the wall is reduced, together with the recycling at the divertor plates. The latter results in lower plasma density in the divertor region, contributing to increase the density gradient. The modification of the \(n_{e}\) profile at the edge is followed by the steepening of \(T_{e}\) and \(T_{i}\) in the same region, resulting finally in an increase in their value on axis. Accordingly, the analysis of dynamic transport exhibits a reduction of the heat conductivities for electrons and ions \(\chi_{e}\) and \(\chi_{i}\) in the plasma edge. The measured energy confinement time \(\tau_{E}\) is also observed to increase. As the \(n_{e}\) , \(T_{e}\) and \(T_{i}\) profiles are changed, the ambipolar radial electric field computed by the SFINCS code remains mainly unvaried. This, together with reduction of the turbulent fluctuations across the whole plasma cross section, are in contrast with an H- mode type increase of confinement, where the turbulent fluctuations are reduced in the vicinity of the separatrix due to an increased \(\mathbf{E} \times \mathbf{B}\) shear, and suggest a different underlying mechanism. + +<|ref|>text<|/ref|><|det|>[[112, 863, 884, 898]]<|/det|> +While the reason of the observed improvement of confinement is not yet clear, we suspect it to be due to the suppression of ITG turbulence. This might be an effect of the change in the profiles of the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 89, 884, 225]]<|/det|> +plasma quantities, in particular of the peaking of \(n_{e}\) in the edge, resulting in a more hollow profile in the center of the plasma. Hollow density profiles have been reported to increase ITG stability, reducing turbulence fluctuations [18, 21]. Another possibility is turbulence being reduced by the increased effective charge \(Z_{eff}\) due to B injection, also referred to as plasma dilution. Indeed, an increase \(Z_{eff}\) has also been reported to have a stabilizing effect on ITG turbulence [22, 23, 24]. More likely, the combination of the two above mentioned effects might occur in our case, similarly to what reported in Ref. [25]. Dedicated gyrokinetic simulations will be necessary to verify those hypotheses, and are planned for future works. + +<|ref|>text<|/ref|><|det|>[[114, 226, 884, 277]]<|/det|> +Furthermore, additional experiments are foreseen on LHD, with the aim of better defining the parameter space where this new regime is observed, and to assess whether it is compatible with conditions relevant to future fusion reactors, i.e. with higher power and density plasmas. + +<|ref|>sub_title<|/ref|><|det|>[[114, 299, 256, 319]]<|/det|> +## 5 Methods + +<|ref|>text<|/ref|><|det|>[[113, 333, 460, 350]]<|/det|> +Evaluation of plasma quantities and profiles + +<|ref|>text<|/ref|><|det|>[[113, 350, 884, 540]]<|/det|> +Throughout the paper, the line averaged electron density \(n_{e,av}\) is measured by means of far infrared interferometry (FIR). Radially resolved profiles of electron density and temperature \(n_{e}\) , \(T_{e}\) are measured by Thomson scattering (TS). Radially resolved profiles of ion temperature \(T_{i}\) are measured by charge exchange spectroscopy (CXS). Using magnetic reconstruction from the VMEC code, all profiles are remapped onto the normalized coordinate \(\rho = r_{eff} / a_{99}\) , with \(r_{eff}\) the effective minor radius, and \(a_{99}\) the minor radius of the flux surface enclosing 99% of the stored energy. For each time step, the \(n_{e}\) , \(T_{e}\) , \(T_{i}\) profiles are fitted with polynomials including only even power terms, ensuring zero derivative at \(r_{eff} / a_{99} = 0\) . This provides smooth spatial profiles and gradients. The values of temperatures on axis (Fig. 1) correspond to the fitted profiles evaluated at \(r_{eff} / a_{99} = 0\) . For discharges #164644 and #164645 in Fig. 1A, the diagnostic neutral beam was not operated and no \(T_{i}\) measurement from CXS is available. + +<|ref|>text<|/ref|><|det|>[[113, 556, 467, 572]]<|/det|> +Powder injection and evaluation of mass rate + +<|ref|>text<|/ref|><|det|>[[113, 573, 884, 693]]<|/det|> +The impurity powder dropper features four independent feeders, composed of a powder reservoir and a vibrating tray. The latter is vibrated by piezoelectric blades through a driving voltage, allowing to control the amplitude of the vibrations and finally the amount of powder delivered to the plasma. Each independent feeder includes an accelerometer, measuring the vibration of the tray actuated by piezoelectric blades, finally determining the injection rate of the powder into the plasma. Calibration curves have been acquired in the laboratory, allowing to convert the amplitude of the accelerometer signal into injected mass rates. + +<|ref|>text<|/ref|><|det|>[[113, 710, 480, 726]]<|/det|> +Simulations with EMC3- EIRENE and DUSTT + +<|ref|>text<|/ref|><|det|>[[113, 727, 884, 898]]<|/det|> +Interpretative simulations are performed with the coupled EMC3- EIRENE [13] and DUSTT [14] codes, after Ref. [10]. EMC3 is a fully 3D Monte Carlo code modelling the plasma transport in the edge and scrape- off layer, and it is coupled to EIRENE, describing the neutrals dynamics. The diffusion and thermal conductivities coefficients are set by matching the experimental profiles of plasma density and ion and electron temperatures. Electron density and temperature are measured by Thomson scattering, while the ion temperature is measured by charge exchange spectroscopy. The 3D plasma solutions from the EMC3- EIRENE simulations are then used as the background for the DUSTT simulations, computing the trajectory of a powder grain injected into the plasma. For those simulations, the powder grains are injected vertically at the actual IPD location with an initial velocity of 5 m/s directed downwards, consistent with the free fall of the powder grains prior + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 89, 884, 141]]<|/det|> +to entering the plasma. The powder material is B, and the size of the modelled powder grain is 150 \(\mu \mathrm{m}\) , matching the ones used in the experiments. As the powder grains enter the plasma, they are progressively heated up to evaporating temperature, providing a localized source of neutral B atoms. + +<|ref|>text<|/ref|><|det|>[[113, 158, 416, 174]]<|/det|> +Measurement of turbulent fluctuations + +<|ref|>text<|/ref|><|det|>[[112, 175, 884, 295]]<|/det|> +In this work, the turbulent fluctuations characteristics are measured by means of two- dimensional Phase Contrast Imaging (PCI). The diagnostic system and the spectral analysis technique are detailed in Refs. [15, 16]. The amplitude of the density fluctuation is computed from the power spectrum integrated over \(\omega\) and \(k\) as \(\sqrt{\bar{n}^{2}}\) . The cutoff wave- number of the PCI system has been investigated in detail in Ref. [29], showing how fluctuations with wave- number in the range \(0.1 \leq k [mm^{- 1}] \leq 0.8\) are measured, with an attenuated contributions to the final spectrum for \(k < 0.2mm^{- 1}\) . + +<|ref|>text<|/ref|><|det|>[[113, 312, 312, 328]]<|/det|> +Energy confinement time + +<|ref|>text<|/ref|><|det|>[[113, 329, 884, 414]]<|/det|> +The confinement time is computed from the time trace of the plasma stored energy \(W_{p}\) , smoothed in time to average over the variations provided by the perpendicular NBI. For ECH, all port- through power is assumed to be absorbed by the plasma. For ICH, it is assumed that \(75\%\) of the input power is absorbed. For NBIs, the absorbed power is assumed to be \(P_{ab} = P_{in}[1 - \exp \left(- \sigma n_{e,av}l\right)]\) , where \(\sigma = 0.43\) for H plasmas and \(l = 1.86 \mathrm{m}\) [30]. + +<|ref|>sub_title<|/ref|><|det|>[[114, 437, 321, 458]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[113, 470, 884, 556]]<|/det|> +The authors wish to thank the LHD experiment group for the excellent support of this work, and Drs. R. Seki and M. Yokoyama (NIFS) for executing TASK3D- a suite, allowing to conduct transport analyses. This work was conducted within the framework of the NIFS/PPPL International Collaboration, and it is supported by the U.S. DOE under Contract No. DE- AC02- 09CH11466 with Princeton University. + +<|ref|>sub_title<|/ref|><|det|>[[114, 579, 367, 599]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[112, 612, 884, 715]]<|/det|> +N.A., E.P.G, R.L., S.M., M.S, F.N., A.N. set up and performed the experiments. T.O., K.I., M.Y., Y.T., K.T., T.K., C.S. set up and operated the diagnostics used in the experiments and ran preliminary analysis. F.N., M.S., G.K., G.M., N.K., N.A.P, A.M. performed numerical modeling of the experiments. F.N., S.M., K.T., K.I, A.B., D.A.G further analyzed and interpreted the data. F.N. prepared the figures and wrote the manuscript. D.A.G. and T.M. supervised the project. All authors reviewed the manuscript and contributed to discussions. + +<|ref|>sub_title<|/ref|><|det|>[[113, 739, 238, 758]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[130, 772, 884, 802]]<|/det|> +[1] A. Iiyoshi et al., Overview of the Large Helical Device project, Nuclear Fusion 39 (1999) pp.1245- 1256. + +<|ref|>text<|/ref|><|det|>[[130, 817, 884, 852]]<|/det|> +[2] F. Wagner, A quarter- century of H- mode studies, Plasma Physics and Controlled Fusion 49 (2007) + +<|ref|>text<|/ref|><|det|>[[130, 863, 884, 898]]<|/det|> +[3] A. Nagy et al., A multi- species powder dropper for magnetic fusion applications, Review of Scientific Instruments 89 (2018), 10K121 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[128, 88, 886, 140]]<|/det|> +[4] A. Bortolon et al., Real- time wall conditioning by controlled injection of boron and boron nitride powder in full tungsten wall ASDEX Upgrade, Nuclear Materials and Energy 19 (2019) 384- 389 + +<|ref|>text<|/ref|><|det|>[[128, 151, 884, 203]]<|/det|> +[5] R. Lunsford et al., Active conditioning of ASDEX Upgrade tungsten plasma- facing components and discharge enhancement through boron and boron nitride particulate injection, Nuclear Fusion 59 (2019) 126034 + +<|ref|>text<|/ref|><|det|>[[128, 214, 884, 249]]<|/det|> +[6] A. Bortolon et al., Observations of wall conditioning by means of boron powder injection in DIII D H- mode plasmas, Nuclear Fusion 60 (2020) 126010 + +<|ref|>text<|/ref|><|det|>[[128, 259, 884, 294]]<|/det|> +[7] E. P. Gilson et al., Wall Conditioning with Boron Nitride and Boron Powder Injection in KSTAR, submitted to Nuclear Materials and Energy (2021) + +<|ref|>text<|/ref|><|det|>[[128, 304, 884, 340]]<|/det|> +[8] Z. Sun et al., Suppression of edge localized modes with real- time boron injection using the tungsten divertor in EAST, Nuclear Fusion 61 (2021) 014002 + +<|ref|>text<|/ref|><|det|>[[128, 350, 884, 402]]<|/det|> +[9] R. Lunsford et al., Characterization of injection and confinement improvement through impurity induced profile modifications on the Wendelstein 7- X stellarator, submitted to Physics of Plasmas (2021) + +<|ref|>text<|/ref|><|det|>[[123, 413, 884, 465]]<|/det|> +[10] M. Shoji et al., Full- torus impurity transport simulation for optimizing plasma discharge operation using a multi- species impurity powder dropper in the large helical device, Contributions to Plasma Physics (2019) e201900101. https://doi.org/10.1002/ctpp.201900101 + +<|ref|>text<|/ref|><|det|>[[123, 476, 884, 511]]<|/det|> +[11] F. Nespoli et al., First impurity powder injection experiment in LHD, Nuclear Materials and Energy 25 (2020) 100842 + +<|ref|>text<|/ref|><|det|>[[123, 521, 884, 590]]<|/det|> +[12] T. Oishi et al., Line identification of boron and nitrogen emissions in EUV and VUV wavelength ranges in the impurity powder dropping experiments of LHD and its application to spectroscopic diagnostics, Plasma Science and Technology (2021) https://doi.org/10.1088/2058- 6272/abfd88 + +<|ref|>text<|/ref|><|det|>[[123, 601, 884, 636]]<|/det|> +[13] Y. Feng et al., Fluid features of the stochastic layer transport in LHD, Nuclear Fusion 48 (2008) 024012 + +<|ref|>text<|/ref|><|det|>[[123, 647, 771, 665]]<|/det|> +[14] R.D. Smirnov et al., Plasma Physics and Controlled Fusion 49 (2007) 347- 371 + +<|ref|>text<|/ref|><|det|>[[123, 675, 884, 710]]<|/det|> +[15] K. Tanaka et al., Two- dimensional phase contrast imaging for local turbulence measurements in large helical device (invited) Review of Scientific Instruments 79, 10E702 (2008) + +<|ref|>text<|/ref|><|det|>[[123, 720, 884, 772]]<|/det|> +[16] C. A. Michael, K. Tanaka, L. Vyacheslavov, A. Sanin, and K. Kawahata, Two- dimensional wave- number spectral analysis techniques for phase contrast imaging turbulence imaging data on large helical device, Review of Scientific Instruments 86, 093503 (2015) + +<|ref|>text<|/ref|><|det|>[[123, 783, 884, 818]]<|/det|> +[17] M. Nunami et al., Linear Gyrokinetic Analyses of ITG Modes and Zonal Flows in LHD with High Ion Temperature, Plasma and Fusion Research 6 (2011) 1403001 + +<|ref|>text<|/ref|><|det|>[[123, 829, 884, 863]]<|/det|> +[18] K. Tanaka et al., Extended investigations of isotope effects on ECRH plasma in LHD, Plasma Physics and Controlled Fusion 62 (2020) 024006 + +<|ref|>text<|/ref|><|det|>[[123, 874, 884, 909]]<|/det|> +[19] M. Landreman, et al., Comparison of particle trajectories and collision operators for collisional transport in nonaxisymmetric plasmas, Phys. Plasmas 21 (2014) 042503. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[120, 88, 886, 123]]<|/det|> +[20] M. Yokoyama et al., Extended capability of the integrated transport analysis suite, TASK3D- a, for LHD experiment, Nuclear Fusion 57 (2017) 126016 + +<|ref|>text<|/ref|><|det|>[[120, 133, 886, 169]]<|/det|> +[21] M. Nakata et al., Gyrokinetic microinstability analysis of high- Ti and high- Te isotope plasmas in Large Helical Device, Plasma Phys. Control. Fusion 61 (2019) 014016 + +<|ref|>text<|/ref|><|det|>[[120, 179, 886, 214]]<|/det|> +[22] D. R. Mikkelsen et al., Quasilinear carbon transport in an impurity hole plasma in LHD, Physics of Plasmas 21, 082302 (2014) + +<|ref|>text<|/ref|><|det|>[[120, 225, 886, 260]]<|/det|> +[23] P. Ennever et al., The effects of dilution on turbulence and transport in C- Mod ohmic plasmas and comparisons with gyrokinetic simulations Physics of Plasmas 22, 072507 (2015) + +<|ref|>text<|/ref|><|det|>[[120, 270, 886, 306]]<|/det|> +[24] P. Ennever et al., The effects of main- ion dilution on turbulence in low q95 C- Mod ohmic plasmas, and comparisons with nonlinear GYRO, Phys. Plasmas 23, 082509 (2016) + +<|ref|>text<|/ref|><|det|>[[120, 316, 778, 334]]<|/det|> +[25] T. 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Kinoshita et al., Determination of absolute turbulence amplitude by CO2 laser phase contrast imaging, JINST 15 C01045 (2020) + +<|ref|>text<|/ref|><|det|>[[120, 544, 886, 579]]<|/det|> +[30] Y. Takeiri et al., High- ion temperature experiments with negative- ion- based neutral beam injection heating in large helical device, Nucl. Fusion 45 (2005) 565- 573. + +<--- Page Split ---> diff --git a/preprint/preprint__07d4a546b84977cbe671fd9636cce724b9118c33a913f81362f52341cbf8a751/images_list.json b/preprint/preprint__07d4a546b84977cbe671fd9636cce724b9118c33a913f81362f52341cbf8a751/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..4355501cb0bd8cb82dbbdd8474be580b799bac09 --- /dev/null +++ b/preprint/preprint__07d4a546b84977cbe671fd9636cce724b9118c33a913f81362f52341cbf8a751/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig.1 Structure and magnetic properties of CrPS4 and the Hall bar device for spin Seebeck effect (SSE) measurement. a Crystal structure of CrPS4. The red and blue arrows indicate the direction of the magnetic moment. b The magnetic measurements at \\(15\\mathrm{K}\\) are taken both along and perpendicular to the \\(c\\) -axis. The spin-flop and spin-flip transitions appear when the magnetic field is aligned with the \\(c\\) -axis. In contrast, only the spin-flip transition occurs when the field is applied perpendicularly to the \\(c\\) -axis, c Schematic of the Hall bar devices for the longitudinal spin Seebeck effect. The alternating current heats the sample, creating a vertical heat gradient and generating a spin current perpendicular to the sample plane. d Angular dependence (in the \\(xz\\) plane) of \\(R_{xy}^{2\\omega}\\) at different fields at a temperature of \\(15\\mathrm{K}\\) and an applied current of \\(1\\mathrm{mA}\\) (peak value). e Applied current dependence of \\(R_{xy}^{2\\omega}\\) (at \\(9\\mathrm{T}\\) ) at \\(15\\mathrm{K}\\) . The dashed-dot line is the linear fit.", + "footnote": [], + "bbox": [ + [ + 150, + 100, + 844, + 390 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 Temperature dependence of the SSE in CrPS4/Pt and CrPS4/Ta. a and d The schematics of spin Seebeck effect in CrPS4 in contact with Pt and Ta, the differing signs of the spin Hall angle result in a change in the sign of the SSE. b and e Field dependence \\((\\mu_{0}H_{x})\\) of \\(R_{xy}^{2\\omega}\\) at various temperatures for both CrPS4/Pt (5 nm) (applied current of 1 mA) and CrPS4/Ta (11 nm) (applied current of 0.6 mA). c Temperature dependence of the SSE effective resistance in CrPS4/Pt at 9 T, along with the magnetization as a function of temperature under a 50 mT applied field. The Néel temperature \\((T_{\\mathrm{N}})\\) is identified as 36 K, however, the SSE signal continues to be present even above \\(T_{\\mathrm{N}}\\) . f The field of the \\(R_{xy}^{2\\omega}\\) peak decreases with increasing temperature (blue star and red square), which is similar to the temperature dependence of the spin-flip transition field (black circle).", + "footnote": [], + "bbox": [ + [ + 150, + 293, + 848, + 565 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 Origin of SSE peak at the spin-flip field. a Comparison of the field dependence of \\(R_{xy}^{2\\omega}\\) in CrPS4/Pt (obtained at 15 K) and magnetic moment CrPS4 flake (measured at 20 K). b Angular dependence (in the \\(xz\\) plane) of \\(R_{xy}^{2\\omega}\\) when the applied field approaches the spin-flip field at a temperature of 15 K and an applied current of 1 mA. c Magnon mode edges \\((k = 0)\\) as a function of the applied field perpendicular to the \\(c\\) axis. The inset shows the simulated magnetic moment as a function of the magnetic field. d The canted magnetization of \\(\\omega_{\\alpha}\\) mode precesses around the applied field, while that of \\(\\omega_{\\beta}\\) mode oscillates in the direction of the applied field.", + "footnote": [], + "bbox": [ + [ + 261, + 98, + 761, + 373 + ] + ], + "page_idx": 9 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 Nonlocal SSE measurement. a Schematics of nonlocal SSE measurement. b Field dependence of SSE at different angles at \\(5\\mathrm{K}\\) with the applied current of \\(1\\mathrm{mA}\\) . Inset shows the field dependence SSE when the applied field is slightly off the \\(c\\) -axis ( \\(z\\) -axis).", + "footnote": [], + "bbox": [ + [ + 225, + 523, + 767, + 703 + ] + ], + "page_idx": 12 + } +] \ No newline at end of file diff --git a/preprint/preprint__07d4a546b84977cbe671fd9636cce724b9118c33a913f81362f52341cbf8a751/preprint__07d4a546b84977cbe671fd9636cce724b9118c33a913f81362f52341cbf8a751.mmd b/preprint/preprint__07d4a546b84977cbe671fd9636cce724b9118c33a913f81362f52341cbf8a751/preprint__07d4a546b84977cbe671fd9636cce724b9118c33a913f81362f52341cbf8a751.mmd new file mode 100644 index 0000000000000000000000000000000000000000..9f5cd8df51deb01dfa8e596319102e8b6083faaa --- /dev/null +++ b/preprint/preprint__07d4a546b84977cbe671fd9636cce724b9118c33a913f81362f52341cbf8a751/preprint__07d4a546b84977cbe671fd9636cce724b9118c33a913f81362f52341cbf8a751.mmd @@ -0,0 +1,301 @@ + +# Spin Seebeck in the weak exchange coupled van der Waals antiferromagnet + +Rui Wu ruiwu001@scut.edu.cn + +South China University of Technology https://orcid.org/0000- 0003- 2010- 5961 + +Xue He South China University of Technology + +Shilei Ding https://orcid.org/0000- 0002- 5534- 6901 + +Hans Gill Norwegian University of Science and Technology + +Jicheng Wang South China University of Technology + +Zhongchong Lin Peking University + +Zhongyu Liang Peking university https://orcid.org/0000- 0002- 7872- 7571 + +Jinbo Yang State Key Laboratory for Mesoscopic Physics, School of Physics, Peking University, Beijing 100871 https://orcid.org/0000- 0003- 3517- 9701 + +Mathias Kläui Johannes Gutenberg University of Mainz https://orcid.org/0000- 0002- 4848- 2569 + +Arne Brataas Norwegian University of Science and Technology https://orcid.org/0000- 0003- 0867- 6323 + +Yanglong Hou Shenzhen Campus of Sun Yat- Sen University + +## Article + +Keywords: + +Posted Date: October 28th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 5308219/v1 + +<--- Page Split ---> + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on March 28th, 2025. See the published version at https://doi.org/10.1038/s41467-025-58306-3. + +<--- Page Split ---> + +# Spin Seebeck in the weak exchange coupled van der Waals antiferromagnet + +Xue He \(^{1}\) , Shilei Ding \(^{2*}\) , Hans Glockner Gill \(^{3}\) , Jicheng Wang \(^{1}\) , Zhongchong Lin \(^{4}\) , Zhongyu Liang \(^{4}\) , Jinbo Yang \(^{4*}\) , Mathias Klaui \(^{3,5}\) , Arne Brataas \(^{3}\) , Yanglong Hou \(^{6,7*}\) , Rui Wu \(^{1*}\) + +1. Spin-X Institute, School of Physics and Optoelectronics, State Key Laboratory of Luminescent Materials and Devices, and Guangdong-Hong Kong-Macao Joint Laboratory of Optoelectronic and Magnetic Functional Materials, South China University of Technology, Guangzhou 511442, China + +2. Department of Materials, ETH Zurich, 8093 Zurich, Switzerland + +3. Center for Quantum Spintronics, Norwegian University of Science and Technology, Trondheim 7491, Norway + +4. State Key Laboratory for Mesoscopic Physics, School of Physics, Peking University, Beijing 100871, P.R. China + +5. Institute of Physics, Johannes Gutenberg-University Mainz, Staudingerweg 7, Mainz 55128, Germany + +6. School of Materials, Shenzhen Campus of Sun Yat-Sen University, Shenzhen 518107, China + +7. School of Materials Science and Engineering, Beijing Key Laboratory for Magnetoelectric Materials and Devices, Peking University, Beijing 100871, China + +\*Corresponding author: shilei.ding@mat.ethz.ch, jbyang@pku.edu.cn, hou@sysu.edu.cn, and ruiwu001@scut.edu.cn. + +Spin Seebeck effect (SSE) refers to the creation of spin currents due to a temperature gradient in the magnetic materials or across magnet- normal metal interfaces, which can be electrically detected through the inverse spin Hall effect (ISHE) when in contact with heavy metals. It offers fundamental insights into the magnetic properties of materials, including the magnetic phase transition, static magnetic order, and magnon excitations. However, the SSE in van der Waals antiferromagnet is still elusive, especially across the spin- flip transition. Here, we demonstrate the SSE in the + +<--- Page Split ---> + +weak exchange coupled van der Waals antiferromagnet CrPS4. The SSE increases as the magnetic field increases before the spin- flip transition due to the enhancement of the thermal spin current as a function of the applied field. A peak of SSE is observed at the spin- flip field, which is related to the magnon mode edges across the spin- flip field. Our results extend SSE research to van der Waals antiferromagnets and demonstrate an enhancement of SSE at the spin- flip transition. + +Thermoelectricity combines heat transfer and electric voltage in solid materials, presenting a promising option for green energy production by harnessing waste heat with a simple device design1. In particular, thermal spintronics effect utilize nonequilibrium magnon transport phenomena in the presence of a heat gradient, enabling magnetic insulators to serve as effective thermoelectric devices2. The spin Seebeck effect (SSE) has therefore drawn significant interest, where a temperature gradient ( \(\nabla T\) ) in magnetic materials leads to the generation of spin currents ( \(J_{\mathrm{s}}\) ). SSE can be subsequently detected via the inverse spin Hall effect (ISHE) in a heavy metal contact with strong spin- orbit coupling3- 24. The phenomenon was initially discovered in 20085, and various configurations have been suggested to explore the SSE, such as transverse SSE6, longitudinal SSE7, and nonlocal SSE8. Additionally, it has been examined in a range of magnetically ordered systems, including ferromagnets5,9, ferrimagnets6,10, antiferromagnets11- 15, paramagnets16, chiral helimagnets17, and quantum magnets18, where the magnon excitations play critical roles regardless of long- range or short- range magnetic interactions. + +In ferromagnet/heavy metal bilayers, the SSE observed below the Curie temperature is associated with the spin current generated by thermally excited magnons that exhibit only right- handed chirality19. The SSE mechanism in antiferromagnetic heterostructures is more complex due to two magnetic sublattices, which result in different magnon modes20- 23. In a uniaxial antiferromagnet, there are two magnon branches with opposite chirality carrying opposite angular momentum. These modes are degenerate at zero magnetic field, meaning there is no net magnon current until a field is applied to lift this degeneracy. A change in the sign of the SSE was observed during the spin- flop transition14,15, which is attributed to the change in the chirality of the thermally excited magnon mode, which dominates. Additionally, the interfacial Néel coupling and spin conductance can influence the sign and + +<--- Page Split ---> + +magnitude of the SSE \(^{21,23}\) . Nonetheless, the spin Seebeck effect in van der Waals antiferromagnets requires further investigation \(^{25,26}\) , particularly in the van der Waals system with interlayer antiferromagnetic coupling, which typically suggests weak exchange coupling and a low spin- flip field. + +CrPS \(_4\) is an antiferromagnetic van der Waals material constituted of chains of chromium octahedra interconnected through phosphorus \(^{27 - 33}\) as shown in Fig. 1a. Due to the chemical composition and multi- bonded crystal structures, CrPS \(_4\) is a comparably air- stable material that makes the device fabrication easier compared with other van der Waals materials \(^{34}\) . It shows a sizeable Néel temperature ( \(T_{\mathrm{N}} = 36 \mathrm{~K}\) ) and A- type antiferromagnetic ordering \(^{27}\) . Unlike the conventional bulk antiferromagnetic materials, CrPS \(_4\) with a layered structure exhibits extremely weak interlayer interactions between sublattice spins, where spins within each monolayer are aligned ferromagnetically out of the plane, subsequently leading to the weak spin- flop field (0.8 T at 5 K) and spin- flip field (7 T at 5 K) as shown in Fig. 1b. This characteristic also significantly lowers the frequency of antiferromagnetic magnons to the GHz range \(^{35}\) . As a result, it provides easier access to antiferromagnetic dynamics. Notably, it improves the efficiency of the thermal magnon population compared to traditional antiferromagnets with a large magnon gap, making CrPS \(_4\) an excellent candidate for investigating the mechanism of SSE in antiferromagnets. + +Here, we demonstrate the SSE in CrPS \(_4\) in contact with a heavy metal. A vertical temperature gradient in CrPS \(_4\) drives the magnon current in the longitudinal SSE configuration. The SSE increases as a function of the applied field before the spin- flip transition. The enhancement of the canted magnetization leads to pronounced magnon pumping. At the spin- flip field, a peak and saturation of SSE is observed that further disappears above the Néel temperature. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig.1 Structure and magnetic properties of CrPS4 and the Hall bar device for spin Seebeck effect (SSE) measurement. a Crystal structure of CrPS4. The red and blue arrows indicate the direction of the magnetic moment. b The magnetic measurements at \(15\mathrm{K}\) are taken both along and perpendicular to the \(c\) -axis. The spin-flop and spin-flip transitions appear when the magnetic field is aligned with the \(c\) -axis. In contrast, only the spin-flip transition occurs when the field is applied perpendicularly to the \(c\) -axis, c Schematic of the Hall bar devices for the longitudinal spin Seebeck effect. The alternating current heats the sample, creating a vertical heat gradient and generating a spin current perpendicular to the sample plane. d Angular dependence (in the \(xz\) plane) of \(R_{xy}^{2\omega}\) at different fields at a temperature of \(15\mathrm{K}\) and an applied current of \(1\mathrm{mA}\) (peak value). e Applied current dependence of \(R_{xy}^{2\omega}\) (at \(9\mathrm{T}\) ) at \(15\mathrm{K}\) . The dashed-dot line is the linear fit.
+ +## Results + +Longitudinal SSE in CrPS4/Pt(Ta). To obtain CrPS4/Pt heterostructures for the SSE measurements, we deposited \(5\mathrm{nm}\) Pt on top of exfoliated CrPS4 flakes and subsequently fabricated Hall bar devices (see methods for details and schematic in Fig. 1c). The structure and phase of the CrPS4 are characterized with X- ray Diffractometer and Raman spectroscopy (details see Supplemental Material S1). The microscopic picture of the + +<--- Page Split ---> + +\(\mathrm{CrPS_4 / Pt}\) Hall bar device can be found in Supplemental Material S2, where one could obtain the thickness of the \(\mathrm{CrPS_4}\) flake to be \(75\mathrm{nm}\) . An alternating current \((\tilde{I})\) is applied to the Hall bar to generate vertical temperature gradient \(\nabla T\) , leading to the population of spin current \(\pmb{J}_{\mathrm{s}} = - \pmb {S}\nabla T\) . \(S\) is the SSE coefficient. By further applying a magnetic field, it is possible to observe the SSE detected via the inverse spin Hall effect. The resultant electric field \(\pmb{E}_{\mathrm{ISHE}}\) is given by \(^3\) + +\[\pmb{E}_{\mathrm{ISHE}}\propto \theta_{\mathrm{SH}}\pmb{J}_{\mathrm{s}}\times \pmb {\sigma}, \quad (1)\] + +Where \(\theta_{\mathrm{SH}}\) is the spin Hall angle. \(\sigma\) is the spin polarization direction, which is parallel to the equilibrium magnetization \(\pmb{M}\) . Since the temperature gradient results from the heating power of \(\mathrm{Pt}\) , which is proportional to \(\tilde{I}^2\) , it is expected that the thermal signal can be detected through the second harmonic response \(R_{xy}^{2\omega}\) . + +In the magnetic material/Pt bilayer system, \(R_{xy}^{2\omega}\) typically involves different factors, including current- induced torque and thermal effects, which encompass the Nernst and spin Seebeck effects \(^{36}\) . The electric field induced by the Nernst effect can be expressed as \(\pmb{E}_{\mathrm{NE}}\propto \nabla T\times \pmb{M}^{37}\) , which share the same symmetry as SSE in the longitudinal configuration. When a strong magnetic field is applied, the current- induced torque is suppressed \(^{36}\) , leaving only thermal effects in the second harmonic response \(R_{xy}^{2\omega}\) . Fig. 1d illustrates the angular dependence of \(R_{xy}^{2\omega}\) in the \(xz\) plane under different applied fields with the applied current of \(1\mathrm{mA}\) (peak value) and the ambient temperature of \(15\mathrm{K}\) . \(R_{xy}^{2\omega}\) reaches the maximum when the magnetic field is aligned with the \(x\) - axis and disappears when aligned with the \(z\) - axis (or \(c\) - axis), and the angular dependence data can be fitted well using the sine function following the Eq. (1). By applying an in- plane magnetic field, the Zeeman splitting lift the degeneracy of the two magnon eigenmodes, resulting in the spin current that induces the SSE signal (see discussion below and Fig. 3c). In the canted phase, the SEE increases with the strength of the applied magnetic field. This increase is generally attributed to the larger canted magnetization resulting from a strong magnetic field \(^{23,38}\) or the increased SSE coefficient in response to the magnetic field \(^{39}\) . This fundamentally differs from the SSE in ferromagnets, where an increased applied field would open the magnon gap, causing a decrease in SSE due to the reduction of the thermal magnon population \(^{4}\) . Additionally, the + +<--- Page Split ---> + +magnitude of \(R_{xy}^{2\omega}\) is proportional to the applied current as shown in Fig. 1e, demonstrating a thermoelectric nature similar to previous findings40. It is important to note that CrPS4 has a semiconducting characteristic with an energy gap of \(E_{a} = 0.166 \mathrm{eV}\) , and the resistivity \(\rho\) of CrPS4 is reported to be \(\sim 2 \times 10^{4} \Omega \mathrm{cm}\) at \(200 \mathrm{K}^{24}\) . By applying the Arrhenius equation41 \(\ln \rho = \ln \rho_{0} - E_{a} / k_{B} T\) , one could estimate that the resistivity of CrPS4 below \(50 \mathrm{K}\) is higher than \(1 \times 10^{12} \Omega \mathrm{cm}\) , allowing us to safely rule out the Nernst effect from conducting electrons in CrPS4. + +![](images/Figure_2.jpg) + +
Fig. 2 Temperature dependence of the SSE in CrPS4/Pt and CrPS4/Ta. a and d The schematics of spin Seebeck effect in CrPS4 in contact with Pt and Ta, the differing signs of the spin Hall angle result in a change in the sign of the SSE. b and e Field dependence \((\mu_{0}H_{x})\) of \(R_{xy}^{2\omega}\) at various temperatures for both CrPS4/Pt (5 nm) (applied current of 1 mA) and CrPS4/Ta (11 nm) (applied current of 0.6 mA). c Temperature dependence of the SSE effective resistance in CrPS4/Pt at 9 T, along with the magnetization as a function of temperature under a 50 mT applied field. The Néel temperature \((T_{\mathrm{N}})\) is identified as 36 K, however, the SSE signal continues to be present even above \(T_{\mathrm{N}}\) . f The field of the \(R_{xy}^{2\omega}\) peak decreases with increasing temperature (blue star and red square), which is similar to the temperature dependence of the spin-flip transition field (black circle).
+ +<--- Page Split ---> + +To better distinguish the SSE from other spurious effects, we utilize Pt and Ta in the two Hall bar devices (Fig. 2a and d). Due to the opposite spin Hall angles42, the thermally generated spin current should yield SSE signals with opposite polarities in Pt and Ta samples. In contrast, other magnetic thermoelectric effects, such as the Nernst effect arising from the proximity effect43, retain the same polarity in both Pt and Ta. As illustrated in Fig. 2b and e, the \(R_{xy}^{2\omega}\) shows the opposite polarities in Pt and Ta samples, suggesting that the phenomenon originates from the SSE. As the temperature increases, the strength of the SSE decreases, and the SSE remains present even at temperatures exceeding the \(T_{\mathrm{N}}\) of CrPS4. A more apparent trend is illustrated in Fig. 2c. Although the propagation of spin waves without magnetic interactions is not permitted in the paramagnetic phase, short- range magnetic interactions still facilitate short- wavelength magnetic excitations, resulting in the paramagnetic SSE16. In addition to the increase in \(R_{xy}^{2\omega}\) with the applied field, peaks of \(R_{xy}^{2\omega}\) are observed in both samples at varying temperatures. Similar effects are observed in the sample with a different Pt thickness (see Supplemental Material S3 for details). The magnetic field at which the \(R_{xy}^{2\omega}\) peak occurs aligns with the spin- flip field of CrPS4, as illustrated in Fig. 2f, suggesting a strong connection between the \(R_{xy}^{2\omega}\) peak and the magnetic phase transition induced by the magnetic field. The longitudinal resistances for CrPS4/Pt and CrPS4/Ta are \(\sim 600 \Omega\) and \(1560 \Omega\) respectively, with applied currents of 1 mA and 0.6 mA for the two samples. This results in a higher heating power in CrPS4/Pt, causing a larger temperature difference between the sample and the chamber. There is expected to be a shift in the spin- flip field for the samples with and without heating at the same chamber temperatures, and this discrepancy will become more pronounced at lower temperatures (see Fig. 2f). + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3 Origin of SSE peak at the spin-flip field. a Comparison of the field dependence of \(R_{xy}^{2\omega}\) in CrPS4/Pt (obtained at 15 K) and magnetic moment CrPS4 flake (measured at 20 K). b Angular dependence (in the \(xz\) plane) of \(R_{xy}^{2\omega}\) when the applied field approaches the spin-flip field at a temperature of 15 K and an applied current of 1 mA. c Magnon mode edges \((k = 0)\) as a function of the applied field perpendicular to the \(c\) axis. The inset shows the simulated magnetic moment as a function of the magnetic field. d The canted magnetization of \(\omega_{\alpha}\) mode precesses around the applied field, while that of \(\omega_{\beta}\) mode oscillates in the direction of the applied field.
+ +The origin of the SSE peak. The peak of \(R_{xy}^{2\omega}\) observed at the spin- flip field is intriguing as it is not associated with the static magnetic moment, which would not show an increased canted magnetization during the spin- flip transition, as illustrated in Fig. 3a. In Fig. 3b, the angular dependence (in the \(xz\) plane) of \(R_{xy}^{2\omega}\) is shown as the applied field approaches the spin- flip transition at a temperature of 15 K and an applied current of 1 mA in the CrPS4/Pt. The curves can be well- fitted with the sine function according to Eq.(1), with a maximum observed at 6.8 T, indicating that the peak originates from the SSE. Additionally, the SSE continues to be present above \(T_{\mathrm{N}}\) , while the peak of \(R_{xy}^{2\omega}\) disappears beyond \(T_{\mathrm{N}}\) (see Fig. 2b,e). Although the paramagnetic phase could exhibit a SSE, the loss of long- range + +<--- Page Split ---> + +ordering above the \(T_{\mathrm{N}}\) causes the spin- flip transition to vanish. This highlights the significant connection between the peak of SSE and the spin- flip transition. + +The SSE consists of three components: 1. The temperature gradient excites the magnetization dynamics, leading to a non- equilibrium magnon current. 2. This magnon current is transformed into a conduction- electron spin current through the \(s - d\) interaction, which travels across the interface connected to the metal. 3. Finally, the spin current is converted into a charge current via the ISHE. Notably, detecting the spin current is not crucial for the SSE peak, as both the CrPS \(_4\) /Pt and CrPS \(_4\) /Ta samples exhibit peaks (see Fig. 2b,e). The only remaining likely mechanism for the SSE peak is related to the pumped spin current \(J_{s}\) from the antiferromagnet into heavy metals which includes the effect of both thermal magnon excitation and interfacial spin mixing conductance. + +Considering the canted magnetic phase, the magnetic field dependence of magnon frequency can be obtained by diagonalizing the spin Hamiltonian \(^{44}\) with eigenfrequencies \(^{45}\) . Before the spin- flip field, \(\mu_{0}H \leq 2\mu_{0}H_{\mathrm{E}} + \mu_{0}H_{\mathrm{A}}\) , + +\[\omega_{\alpha} = \gamma \mu_{0}\sqrt{(2H_{\mathrm{E}}s i n^{2}\phi + H_{\mathrm{A}}c o s^{2}\phi)(2H_{\mathrm{E}} + H_{\mathrm{A}})}, \quad (2)\] + +\[\omega_{\beta} = \gamma \mu_{0}\sqrt{H_{\mathrm{A}}(2H_{\mathrm{E}} + H_{\mathrm{A}})c o s^{2}\phi}, \quad (3)\] + +After the spin- flip field, \(\mu_{0}H > 2\mu_{0}H_{\mathrm{E}} + \mu_{0}H_{\mathrm{A}}\) , + +\[\omega_{\alpha} = \gamma \mu_{0}\sqrt{H(H - H_{\mathrm{A}})}, \quad (4)\] + +\[\omega_{\beta} = \gamma \mu_{0}\sqrt{(H - 2H_{\mathrm{E}})(H - 2H_{\mathrm{E}} - H_{\mathrm{A}})}, \quad (5)\] + +where \(\mu_{0}H\) , \(\mu_{0}H_{\mathrm{E}}\) , and \(\mu_{0}H_{\mathrm{A}}\) represent the applied in- plane field, interlayer exchange field, and anisotropic field along the \(c\) - axis, respectively. The simplified model only considers the anisotropic field along the \(c\) - axis. \(\omega_{\alpha}\) and \(\omega_{\beta}\) are the two magnon modes. \(\gamma\) is the gyromagnetic ratio and \(\phi\) is the canted angle along the \(c\) - axis applied in the plane field, \(\phi = \arcsin \frac{\mu_{0}H}{2\mu_{0}H_{\mathrm{E}} + \mu_{0}H_{\mathrm{A}}}\) . + +<--- Page Split ---> + +The field dependence of the magnon mode frequency is plotted in Fig. 3c with parameters \(\mu_{0}H_{\mathrm{E}} = 3.5\mathrm{T}\) and \(\mu_{0}H_{\mathrm{A}} = 0.12\mathrm{T}^{35}\) . The \(\omega_{\alpha}\) mode has the potential to transport angular momentum due to the canted magnetization of the mode rotating around the applied magnetic field. This mode is similar to the quasi- ferromagnetic mode that emerges following a spin- flop transition when a magnetic field is applied along the \(c\) - axis14. Moreover, the SSE in CrPS4/Pt has the same sign as that in YIG/Pt (see Supplemental Material S4 for details), suggesting that right- handed magnons \((\omega_{\alpha}\) mode) are responsible for the SSE signal. In contrast, the \(\omega_{\beta}\) mode oscillates in the direction of the applied field (see Fig. 3d). + +We further calculate the spin current in the heavy metal following Ref. [23] using a minimal model where the CrPS4 sample is modeled as a one- dimensional antiferromagnetic chain with periodic boundary conditions. The model has an interfacial \(s\) - \(d\) coupling that couples the localized spins in the antiferromagnet with the itinerant electrons in the heavy metal. Using Fermi's Golden rule to calculate the transition probability for the spins to be pumped from the antiferromagnet into the heavy metal, the thermal spin current density polarized along the \(x\) - axis in the heavy metal is given by23 + +\[J_{s} = \Lambda \Delta T\sin \phi \sum_{k}\hbar \omega_{k,\alpha}\frac{\partial f_{B E}(\omega_{k,\alpha})}{\partial T} +\Delta^{2}\hbar \omega_{k,\beta}\frac{\partial f_{B E}(\omega_{k,\beta})}{\partial T}, \quad (6)\] + +where \(\Lambda\) is a constant depending on the interface and the density of states for the electrons in the heavy metal, \(\Delta T\) is the temperature difference across the interface, \(k\) is the wave vector of the one- dimensional chain, and \(\Delta\) parametrizes the degree of compensation at the interface; \(\Delta = 0\) corresponds to a compensated interface and \(\Delta = \pm 1\) corresponds to a fully uncompensated interface where only one of the two sublattices couple to the heavy metal. The \(\omega_{\beta}\) mode only contributes to the spin current for an uncompensated interface, reflecting the linearly polarized nature of the mode (see Supplemental Material S5 for the calculation of the spin current as a function of applied field). + +The effect of the in- plane magnetic field on the pumped spin current in the heavy metal is twofold: first, the magnetic field increases the canting angle \(\phi\) , causing a linear increase of the factor \(\sin \phi\) in Eq. (6) Physically, this can be interpreted by noting that each of the two + +<--- Page Split ---> + +sublattices pump a spin current that on average is polarized along the sublattice equilibrium direction, thus, the measured spin current is given as the projection on the \(x\) - axis, which is proportional to \(\sin \phi\) . Second, the magnetic field changes the magnon frequencies of both magnon modes. Above the spin- flip critical field, the energy of the \(\omega_{\alpha}\) mode and the \(\omega_{\beta}\) mode increases with the in- plane field. This causes a decrease in the terms inside the sum in the above equation. Importantly, the increase due to the change in canting angle is proportional to \(\sin \phi \sim H\) below the critical field and constant above the critical field since the canting angle has reached its maximum at this point. In total, these two effects explain the observed peaks and saturation in SSE of \(\mathrm{CrPS}_4 / \mathrm{Pt}\) at the spin- flip field. + +The gap closure of the \(\omega_{\beta}\) mode frequencies at the critical field could further increase the peak observed in the spin Seebeck effect at the critical field for systems with an uncompensated interface. However, to probe the low- frequency excitations, the temperature needs to be smaller than or comparable to the gap energy, which for \(\mathrm{CrPS}_4\) is \(0.4\mathrm{K}\) in units of temperature. Therefore, a sharper peak is expected for temperatures approaching this value (see Supplemental Material S5 for details). + +![](images/Figure_4.jpg) + +
Fig. 4 Nonlocal SSE measurement. a Schematics of nonlocal SSE measurement. b Field dependence of SSE at different angles at \(5\mathrm{K}\) with the applied current of \(1\mathrm{mA}\) . Inset shows the field dependence SSE when the applied field is slightly off the \(c\) -axis ( \(z\) -axis).
+ +Nonlocal SSE in \(\mathrm{CrPS}_4 / \mathrm{Pt}\) . The nonlocal configuration is further introduced to explore the SSE in \(\mathrm{CrPS}_4 / \mathrm{Pt}\) as shown in Fig. 4a (see method for details). An in- plane heat gradient is created by passing current through one of the \(\mathrm{Pt}\) strips, resulting in a nonequilibrium + +<--- Page Split ---> + +distribution of magnons. At the detection part, the magnon spin current is injected into Pt, which leads to the SSE. It is worth noting, in this configuration, that the temperature gradient \(\nabla T\) is oriented along the \(x\) - axis, while the spin current \(J_{s}\) flows along the \(z\) - axis, differing from the longitudinal SSE previously discussed. Fig. 4b shows the field dependence of SSE at different angles \((\theta)\) at \(5\mathrm{K}\) with the applied current of \(1\mathrm{mA}\) . By applying the in- plane field \((\theta = 0^{\circ})\) , the SSE as a function of the applied field is similar to the longitudinal configuration, and a peak of SSE is also observed at the spin- flip field. + +A weak SSE response occurs when the applied field is close to the \(z\) - axis, with nominal angles of \(\theta = 92^{\circ}\) and \(87^{\circ}\) . Typically, the SSE should not be present when the field is directed along the \(z\) - axis ( \(c\) - axis), as the parallel alignment of spin polarization \(\sigma\) and spin currents \(J_{s}\) does not generate a SSE voltage. However, a slight deviation from the \(z\) - axis in the direction of the applied field results in a finite value of \(J_{s} \times \sigma\) , since the spin polarization aligns with the canted magnetization. This accounts for the observed positive and negative SSE at strong positive fields when \(\theta = 87^{\circ}\) and \(92^{\circ}\) , respectively. The plateau in the SSE is observed before the spin- flop transition, as there is no \(x\) - component of the canted magnetization. In particular, one could also find a peak of SSE at the spin- flop field, which is attributed to the divergence of spin conductance as the magnon gap closes approaching the spin- flop transition46. Similar effects are also observed in the longitudinal SSE configuration (see Supplemental Material S6 for details). + +## Discussion + +We report evidence of the SSE in the weak interlayer exchange coupled van der Waals antiferromagnet \(\mathrm{CrPS_4}\) in contact with the heavy metal. We showed how the SSE is substantially enhanced by tuning the magnetic field. In particular, we observe a peak of SSE which shares the same temperature dependence as the spin- flip transition of \(\mathrm{CrPS_4}\) when applying magnetic field perpendicular to the \(c\) - axis. By considering the thermal spin current density into the heavy metal, we conclude that the SSE peak is related to the magnon mode edges as a function of the applied field across the spin- flip field. + +Field- induced peaks in SSE were also observed in \(\mathrm{Y_3Fe_5O_{12} / Pt^{47}}\) , \(\mathrm{Lu_2BiFe_4GaO_{12} / Pt^{48}}\) , \(\mathrm{Fe_3O_4 / Pt^{49}}\) and \(\mathrm{Cr_2O_3 / Pt^{50}}\) bilayers. These peaks in SSE arise when the magnetic field + +<--- Page Split ---> + +adjusts the magnon energy to the point of anticrossing between the magnon and phonon dispersion curves, creating magnon- polarons47. The combined magnetoelastic excitation couples the long- lasting acoustic phonons in single crystals with the short- lived magnons, increasing the magnon lifetime and the associated SSE48. The SSE peak in CrPS4/Pt (Ta) exhibits similar field- like behaviors, but it arises from a mechanism involving the magnon mode and spin conductance. Given that the SSE peak in CrPS4/Pt (Ta) is observed at low temperatures where the phonon population is frozen, we do not expect the magnon- polarons to dominate the signal our samples. + +The SSE is a sensitive tool for investigating the interfacial spin conductance and magnon population across various materials. Our findings indicate that the magnon spin transport in CrSP4/Pt(Ta) can be effectively modulated through adjustments in temperature and applied magnetic field, particularly at the spin- flip field. This approach paves the way for innovative magnonic devices that utilize weakly exchange- coupled van der Waals antiferromagnetic materials. + +## Method + +Sample Preparation and Characterization: The chemical vapor transport technique produced single crystal flakes of CrPS4. Chromium (Aladdin,99.99%), red phosphorus (Aladdin,99.999%), and sulfur (Aladdin,99.999%) powders were measured in a stoichiometric ratio of 1:1:4 and combined with 5% more sulfur as transport agents. The mixed powders were sealed in a quartz tube and placed in a two- zone furnace, where the temperatures at the source and sink ends were maintained at 923 K and 823 K for a duration of 7 days. The atomic structure was analyzed using X- ray diffraction (XRD) with Cu Kα radiation (λ = 1.54056 Å). The magnetic properties were measured using a Superconducting Quantum Interference Device (SQUID). The CrPS4 flakes were mechanically exfoliated from the single crystals using adhesive tape and transferred onto a SiO2/Si substrate. CrPS4/Pt(Ta) samples were prepared with the magnetron sputtering in a vacuum of approximately 6×10-8 torr. The thickness of the Pt layer is 5 nm, while the Ta layer is 15 nm; 5 nm of Ta will oxidize in air, leaving 10 nm of Ta to facilitate the inverse spin Hall effect for detecting spin current generation. The Hall bar with 10 μm in width + +<--- Page Split ---> + +and \(25 \mu \mathrm{m}\) in length was fabricated using photolithography followed by ion beam etching. The width of the heater and the detection Pt strips are designed to be \(1.4 \mu \mathrm{m}\) and \(2.3 \mu \mathrm{m}\) , the distance of the two stipes are \(1.6 \mu \mathrm{m}\) in the nonlocal device. An atomic force microscopy image of the samples is provided in supplementary S2, showing the thickness of the CrPS \(_4\) flakes to be \(74 \mathrm{nm}\) . + +Transport measurement. The SSE is measured at different temperatures by varying the magnetic field in the Physical Properties Measurement System (PPMS- 9T). An alternating current ranging from \(0.4\) to \(1 \mathrm{mA}\) at a frequency of \(13 \mathrm{Hz}\) was supplied to the Hall bar or nonlocal device using a Keithley 6221 instrument, while the transverse voltage was measured with a lock- in amplifier (SR830). + +## Data availability + +The data in the main figures are provided with this paper. Other data that support the findings of this study are available from the corresponding authors upon reasonable request. + +## Acknowledgements + +This work is supported by the National Key R&D Program of China (grant no. 2022YFA1203902, 2022YFA1200093), the National Natural Science Foundation of China (NSFC) (grant nos. 12241401, 12374108 and 12104052, 52373226, 52027801, 92263203), and the China- Germany Collaboration Project (M- 0199), the Strategic Special Project of Guangdong Province, the Fundamental Research Funds for the Central Universities, and the State Key Lab of Luminescent Materials and Devices, South China University of Technology. We acknowledge support by the German Research Foundation (CRC TRR 288 - 422213477 Project A12 and CRC TRR 173 - 268565370 Projects A01 and B02) and the Research Council of Norway through its Center of Excellence 262633 "QuSpin". + +## Author contributions + +S.D. and R.W. conceived the experiments. X.H. fabricated the devices. X.H., S.D., J.W. and R.W. carried out the transport and magnetic measurements. Z.C.L., Z.Y.L. and J.Y. made the single crystal samples and carried out basic characterizations. H.G.G. and A.B. + +<--- Page Split ---> + +contributed to the theoretical calculation. X.H., S.D., R.W., and M.K. contributed to data analysis. S.D. draft the manuscript and all authors contributed to the reviewing and revising of the manuscript. Y.H. and R.W. supervised the research and contributed to the acquisition of the financial support for the project leading to this work. + +## Reference + +1. Zoui, M. A., Bentouba, S., Stocholm, J. G. & Bourouis, M. A Review on Thermoelectric Generators: Progress and Applications. Energies 13, (2020). +2. Hirohata, A. et al. Review on spintronics: Principles and device applications. J. Magn. Magn. Mater. 509, 166711 (2020). +3. Adachi, H., Uchida, K., Saitoh, E. & Maekawa, S. Theory of the spin Seebeck effect. Reports Prog. Phys. 76, 36501 (2013). +4. 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Click to download. + +SupportingInformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__07d4a546b84977cbe671fd9636cce724b9118c33a913f81362f52341cbf8a751/preprint__07d4a546b84977cbe671fd9636cce724b9118c33a913f81362f52341cbf8a751_det.mmd b/preprint/preprint__07d4a546b84977cbe671fd9636cce724b9118c33a913f81362f52341cbf8a751/preprint__07d4a546b84977cbe671fd9636cce724b9118c33a913f81362f52341cbf8a751_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..2a3b914baa993504a5bb4d8b32c46ca48045e805 --- /dev/null +++ b/preprint/preprint__07d4a546b84977cbe671fd9636cce724b9118c33a913f81362f52341cbf8a751/preprint__07d4a546b84977cbe671fd9636cce724b9118c33a913f81362f52341cbf8a751_det.mmd @@ -0,0 +1,399 @@ +<|ref|>title<|/ref|><|det|>[[42, 106, 951, 175]]<|/det|> +# Spin Seebeck in the weak exchange coupled van der Waals antiferromagnet + +<|ref|>text<|/ref|><|det|>[[42, 195, 280, 240]]<|/det|> +Rui Wu ruiwu001@scut.edu.cn + +<|ref|>text<|/ref|><|det|>[[50, 268, 738, 288]]<|/det|> +South China University of Technology https://orcid.org/0000- 0003- 2010- 5961 + +<|ref|>text<|/ref|><|det|>[[42, 293, 384, 333]]<|/det|> +Xue He South China University of Technology + +<|ref|>text<|/ref|><|det|>[[42, 339, 400, 380]]<|/det|> +Shilei Ding https://orcid.org/0000- 0002- 5534- 6901 + +<|ref|>text<|/ref|><|det|>[[42, 385, 481, 426]]<|/det|> +Hans Gill Norwegian University of Science and Technology + +<|ref|>text<|/ref|><|det|>[[42, 431, 383, 472]]<|/det|> +Jicheng Wang South China University of Technology + +<|ref|>text<|/ref|><|det|>[[42, 478, 208, 519]]<|/det|> +Zhongchong Lin Peking University + +<|ref|>text<|/ref|><|det|>[[42, 525, 562, 565]]<|/det|> +Zhongyu Liang Peking university https://orcid.org/0000- 0002- 7872- 7571 + +<|ref|>text<|/ref|><|det|>[[42, 570, 911, 633]]<|/det|> +Jinbo Yang State Key Laboratory for Mesoscopic Physics, School of Physics, Peking University, Beijing 100871 https://orcid.org/0000- 0003- 3517- 9701 + +<|ref|>text<|/ref|><|det|>[[42, 639, 770, 680]]<|/det|> +Mathias Kläui Johannes Gutenberg University of Mainz https://orcid.org/0000- 0002- 4848- 2569 + +<|ref|>text<|/ref|><|det|>[[42, 685, 841, 727]]<|/det|> +Arne Brataas Norwegian University of Science and Technology https://orcid.org/0000- 0003- 0867- 6323 + +<|ref|>text<|/ref|><|det|>[[42, 732, 444, 774]]<|/det|> +Yanglong Hou Shenzhen Campus of Sun Yat- Sen University + +<|ref|>sub_title<|/ref|><|det|>[[42, 815, 103, 832]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[42, 853, 135, 871]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[42, 891, 328, 910]]<|/det|> +Posted Date: October 28th, 2024 + +<|ref|>text<|/ref|><|det|>[[42, 929, 473, 948]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 5308219/v1 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 916, 87]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 106, 535, 125]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 161, 930, 204]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on March 28th, 2025. See the published version at https://doi.org/10.1038/s41467-025-58306-3. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[210, 88, 786, 141]]<|/det|> +# Spin Seebeck in the weak exchange coupled van der Waals antiferromagnet + +<|ref|>text<|/ref|><|det|>[[146, 163, 852, 210]]<|/det|> +Xue He \(^{1}\) , Shilei Ding \(^{2*}\) , Hans Glockner Gill \(^{3}\) , Jicheng Wang \(^{1}\) , Zhongchong Lin \(^{4}\) , Zhongyu Liang \(^{4}\) , Jinbo Yang \(^{4*}\) , Mathias Klaui \(^{3,5}\) , Arne Brataas \(^{3}\) , Yanglong Hou \(^{6,7*}\) , Rui Wu \(^{1*}\) + +<|ref|>text<|/ref|><|det|>[[156, 232, 833, 333]]<|/det|> +1. Spin-X Institute, School of Physics and Optoelectronics, State Key Laboratory of Luminescent Materials and Devices, and Guangdong-Hong Kong-Macao Joint Laboratory of Optoelectronic and Magnetic Functional Materials, South China University of Technology, Guangzhou 511442, China + +<|ref|>text<|/ref|><|det|>[[230, 338, 765, 357]]<|/det|> +2. Department of Materials, ETH Zurich, 8093 Zurich, Switzerland + +<|ref|>text<|/ref|><|det|>[[156, 363, 840, 409]]<|/det|> +3. Center for Quantum Spintronics, Norwegian University of Science and Technology, Trondheim 7491, Norway + +<|ref|>text<|/ref|><|det|>[[155, 415, 840, 461]]<|/det|> +4. State Key Laboratory for Mesoscopic Physics, School of Physics, Peking University, Beijing 100871, P.R. China + +<|ref|>text<|/ref|><|det|>[[155, 467, 844, 513]]<|/det|> +5. Institute of Physics, Johannes Gutenberg-University Mainz, Staudingerweg 7, Mainz 55128, Germany + +<|ref|>text<|/ref|><|det|>[[153, 519, 844, 565]]<|/det|> +6. School of Materials, Shenzhen Campus of Sun Yat-Sen University, Shenzhen 518107, China + +<|ref|>text<|/ref|><|det|>[[170, 572, 825, 618]]<|/det|> +7. School of Materials Science and Engineering, Beijing Key Laboratory for Magnetoelectric Materials and Devices, Peking University, Beijing 100871, China + +<|ref|>text<|/ref|><|det|>[[146, 639, 852, 686]]<|/det|> +\*Corresponding author: shilei.ding@mat.ethz.ch, jbyang@pku.edu.cn, hou@sysu.edu.cn, and ruiwu001@scut.edu.cn. + +<|ref|>text<|/ref|><|det|>[[143, 716, 857, 895]]<|/det|> +Spin Seebeck effect (SSE) refers to the creation of spin currents due to a temperature gradient in the magnetic materials or across magnet- normal metal interfaces, which can be electrically detected through the inverse spin Hall effect (ISHE) when in contact with heavy metals. It offers fundamental insights into the magnetic properties of materials, including the magnetic phase transition, static magnetic order, and magnon excitations. However, the SSE in van der Waals antiferromagnet is still elusive, especially across the spin- flip transition. Here, we demonstrate the SSE in the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[143, 88, 856, 240]]<|/det|> +weak exchange coupled van der Waals antiferromagnet CrPS4. The SSE increases as the magnetic field increases before the spin- flip transition due to the enhancement of the thermal spin current as a function of the applied field. A peak of SSE is observed at the spin- flip field, which is related to the magnon mode edges across the spin- flip field. Our results extend SSE research to van der Waals antiferromagnets and demonstrate an enhancement of SSE at the spin- flip transition. + +<|ref|>text<|/ref|><|det|>[[143, 260, 856, 622]]<|/det|> +Thermoelectricity combines heat transfer and electric voltage in solid materials, presenting a promising option for green energy production by harnessing waste heat with a simple device design1. In particular, thermal spintronics effect utilize nonequilibrium magnon transport phenomena in the presence of a heat gradient, enabling magnetic insulators to serve as effective thermoelectric devices2. The spin Seebeck effect (SSE) has therefore drawn significant interest, where a temperature gradient ( \(\nabla T\) ) in magnetic materials leads to the generation of spin currents ( \(J_{\mathrm{s}}\) ). SSE can be subsequently detected via the inverse spin Hall effect (ISHE) in a heavy metal contact with strong spin- orbit coupling3- 24. The phenomenon was initially discovered in 20085, and various configurations have been suggested to explore the SSE, such as transverse SSE6, longitudinal SSE7, and nonlocal SSE8. Additionally, it has been examined in a range of magnetically ordered systems, including ferromagnets5,9, ferrimagnets6,10, antiferromagnets11- 15, paramagnets16, chiral helimagnets17, and quantum magnets18, where the magnon excitations play critical roles regardless of long- range or short- range magnetic interactions. + +<|ref|>text<|/ref|><|det|>[[143, 642, 856, 899]]<|/det|> +In ferromagnet/heavy metal bilayers, the SSE observed below the Curie temperature is associated with the spin current generated by thermally excited magnons that exhibit only right- handed chirality19. The SSE mechanism in antiferromagnetic heterostructures is more complex due to two magnetic sublattices, which result in different magnon modes20- 23. In a uniaxial antiferromagnet, there are two magnon branches with opposite chirality carrying opposite angular momentum. These modes are degenerate at zero magnetic field, meaning there is no net magnon current until a field is applied to lift this degeneracy. A change in the sign of the SSE was observed during the spin- flop transition14,15, which is attributed to the change in the chirality of the thermally excited magnon mode, which dominates. Additionally, the interfacial Néel coupling and spin conductance can influence the sign and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 87, 855, 188]]<|/det|> +magnitude of the SSE \(^{21,23}\) . Nonetheless, the spin Seebeck effect in van der Waals antiferromagnets requires further investigation \(^{25,26}\) , particularly in the van der Waals system with interlayer antiferromagnetic coupling, which typically suggests weak exchange coupling and a low spin- flip field. + +<|ref|>text<|/ref|><|det|>[[143, 208, 856, 570]]<|/det|> +CrPS \(_4\) is an antiferromagnetic van der Waals material constituted of chains of chromium octahedra interconnected through phosphorus \(^{27 - 33}\) as shown in Fig. 1a. Due to the chemical composition and multi- bonded crystal structures, CrPS \(_4\) is a comparably air- stable material that makes the device fabrication easier compared with other van der Waals materials \(^{34}\) . It shows a sizeable Néel temperature ( \(T_{\mathrm{N}} = 36 \mathrm{~K}\) ) and A- type antiferromagnetic ordering \(^{27}\) . Unlike the conventional bulk antiferromagnetic materials, CrPS \(_4\) with a layered structure exhibits extremely weak interlayer interactions between sublattice spins, where spins within each monolayer are aligned ferromagnetically out of the plane, subsequently leading to the weak spin- flop field (0.8 T at 5 K) and spin- flip field (7 T at 5 K) as shown in Fig. 1b. This characteristic also significantly lowers the frequency of antiferromagnetic magnons to the GHz range \(^{35}\) . As a result, it provides easier access to antiferromagnetic dynamics. Notably, it improves the efficiency of the thermal magnon population compared to traditional antiferromagnets with a large magnon gap, making CrPS \(_4\) an excellent candidate for investigating the mechanism of SSE in antiferromagnets. + +<|ref|>text<|/ref|><|det|>[[143, 590, 856, 741]]<|/det|> +Here, we demonstrate the SSE in CrPS \(_4\) in contact with a heavy metal. A vertical temperature gradient in CrPS \(_4\) drives the magnon current in the longitudinal SSE configuration. The SSE increases as a function of the applied field before the spin- flip transition. The enhancement of the canted magnetization leads to pronounced magnon pumping. At the spin- flip field, a peak and saturation of SSE is observed that further disappears above the Néel temperature. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[150, 100, 844, 390]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[142, 411, 856, 697]]<|/det|> +
Fig.1 Structure and magnetic properties of CrPS4 and the Hall bar device for spin Seebeck effect (SSE) measurement. a Crystal structure of CrPS4. The red and blue arrows indicate the direction of the magnetic moment. b The magnetic measurements at \(15\mathrm{K}\) are taken both along and perpendicular to the \(c\) -axis. The spin-flop and spin-flip transitions appear when the magnetic field is aligned with the \(c\) -axis. In contrast, only the spin-flip transition occurs when the field is applied perpendicularly to the \(c\) -axis, c Schematic of the Hall bar devices for the longitudinal spin Seebeck effect. The alternating current heats the sample, creating a vertical heat gradient and generating a spin current perpendicular to the sample plane. d Angular dependence (in the \(xz\) plane) of \(R_{xy}^{2\omega}\) at different fields at a temperature of \(15\mathrm{K}\) and an applied current of \(1\mathrm{mA}\) (peak value). e Applied current dependence of \(R_{xy}^{2\omega}\) (at \(9\mathrm{T}\) ) at \(15\mathrm{K}\) . The dashed-dot line is the linear fit.
+ +<|ref|>sub_title<|/ref|><|det|>[[144, 721, 208, 738]]<|/det|> +## Results + +<|ref|>text<|/ref|><|det|>[[143, 760, 856, 886]]<|/det|> +Longitudinal SSE in CrPS4/Pt(Ta). To obtain CrPS4/Pt heterostructures for the SSE measurements, we deposited \(5\mathrm{nm}\) Pt on top of exfoliated CrPS4 flakes and subsequently fabricated Hall bar devices (see methods for details and schematic in Fig. 1c). The structure and phase of the CrPS4 are characterized with X- ray Diffractometer and Raman spectroscopy (details see Supplemental Material S1). The microscopic picture of the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[142, 87, 856, 243]]<|/det|> +\(\mathrm{CrPS_4 / Pt}\) Hall bar device can be found in Supplemental Material S2, where one could obtain the thickness of the \(\mathrm{CrPS_4}\) flake to be \(75\mathrm{nm}\) . An alternating current \((\tilde{I})\) is applied to the Hall bar to generate vertical temperature gradient \(\nabla T\) , leading to the population of spin current \(\pmb{J}_{\mathrm{s}} = - \pmb {S}\nabla T\) . \(S\) is the SSE coefficient. By further applying a magnetic field, it is possible to observe the SSE detected via the inverse spin Hall effect. The resultant electric field \(\pmb{E}_{\mathrm{ISHE}}\) is given by \(^3\) + +<|ref|>equation<|/ref|><|det|>[[401, 264, 836, 285]]<|/det|> +\[\pmb{E}_{\mathrm{ISHE}}\propto \theta_{\mathrm{SH}}\pmb{J}_{\mathrm{s}}\times \pmb {\sigma}, \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[142, 304, 855, 408]]<|/det|> +Where \(\theta_{\mathrm{SH}}\) is the spin Hall angle. \(\sigma\) is the spin polarization direction, which is parallel to the equilibrium magnetization \(\pmb{M}\) . Since the temperature gradient results from the heating power of \(\mathrm{Pt}\) , which is proportional to \(\tilde{I}^2\) , it is expected that the thermal signal can be detected through the second harmonic response \(R_{xy}^{2\omega}\) . + +<|ref|>text<|/ref|><|det|>[[141, 427, 857, 907]]<|/det|> +In the magnetic material/Pt bilayer system, \(R_{xy}^{2\omega}\) typically involves different factors, including current- induced torque and thermal effects, which encompass the Nernst and spin Seebeck effects \(^{36}\) . The electric field induced by the Nernst effect can be expressed as \(\pmb{E}_{\mathrm{NE}}\propto \nabla T\times \pmb{M}^{37}\) , which share the same symmetry as SSE in the longitudinal configuration. When a strong magnetic field is applied, the current- induced torque is suppressed \(^{36}\) , leaving only thermal effects in the second harmonic response \(R_{xy}^{2\omega}\) . Fig. 1d illustrates the angular dependence of \(R_{xy}^{2\omega}\) in the \(xz\) plane under different applied fields with the applied current of \(1\mathrm{mA}\) (peak value) and the ambient temperature of \(15\mathrm{K}\) . \(R_{xy}^{2\omega}\) reaches the maximum when the magnetic field is aligned with the \(x\) - axis and disappears when aligned with the \(z\) - axis (or \(c\) - axis), and the angular dependence data can be fitted well using the sine function following the Eq. (1). By applying an in- plane magnetic field, the Zeeman splitting lift the degeneracy of the two magnon eigenmodes, resulting in the spin current that induces the SSE signal (see discussion below and Fig. 3c). In the canted phase, the SEE increases with the strength of the applied magnetic field. This increase is generally attributed to the larger canted magnetization resulting from a strong magnetic field \(^{23,38}\) or the increased SSE coefficient in response to the magnetic field \(^{39}\) . This fundamentally differs from the SSE in ferromagnets, where an increased applied field would open the magnon gap, causing a decrease in SSE due to the reduction of the thermal magnon population \(^{4}\) . Additionally, the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[142, 87, 856, 271]]<|/det|> +magnitude of \(R_{xy}^{2\omega}\) is proportional to the applied current as shown in Fig. 1e, demonstrating a thermoelectric nature similar to previous findings40. It is important to note that CrPS4 has a semiconducting characteristic with an energy gap of \(E_{a} = 0.166 \mathrm{eV}\) , and the resistivity \(\rho\) of CrPS4 is reported to be \(\sim 2 \times 10^{4} \Omega \mathrm{cm}\) at \(200 \mathrm{K}^{24}\) . By applying the Arrhenius equation41 \(\ln \rho = \ln \rho_{0} - E_{a} / k_{B} T\) , one could estimate that the resistivity of CrPS4 below \(50 \mathrm{K}\) is higher than \(1 \times 10^{12} \Omega \mathrm{cm}\) , allowing us to safely rule out the Nernst effect from conducting electrons in CrPS4. + +<|ref|>image<|/ref|><|det|>[[150, 293, 848, 565]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[142, 593, 857, 855]]<|/det|> +
Fig. 2 Temperature dependence of the SSE in CrPS4/Pt and CrPS4/Ta. a and d The schematics of spin Seebeck effect in CrPS4 in contact with Pt and Ta, the differing signs of the spin Hall angle result in a change in the sign of the SSE. b and e Field dependence \((\mu_{0}H_{x})\) of \(R_{xy}^{2\omega}\) at various temperatures for both CrPS4/Pt (5 nm) (applied current of 1 mA) and CrPS4/Ta (11 nm) (applied current of 0.6 mA). c Temperature dependence of the SSE effective resistance in CrPS4/Pt at 9 T, along with the magnetization as a function of temperature under a 50 mT applied field. The Néel temperature \((T_{\mathrm{N}})\) is identified as 36 K, however, the SSE signal continues to be present even above \(T_{\mathrm{N}}\) . f The field of the \(R_{xy}^{2\omega}\) peak decreases with increasing temperature (blue star and red square), which is similar to the temperature dependence of the spin-flip transition field (black circle).
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[142, 80, 857, 702]]<|/det|> +To better distinguish the SSE from other spurious effects, we utilize Pt and Ta in the two Hall bar devices (Fig. 2a and d). Due to the opposite spin Hall angles42, the thermally generated spin current should yield SSE signals with opposite polarities in Pt and Ta samples. In contrast, other magnetic thermoelectric effects, such as the Nernst effect arising from the proximity effect43, retain the same polarity in both Pt and Ta. As illustrated in Fig. 2b and e, the \(R_{xy}^{2\omega}\) shows the opposite polarities in Pt and Ta samples, suggesting that the phenomenon originates from the SSE. As the temperature increases, the strength of the SSE decreases, and the SSE remains present even at temperatures exceeding the \(T_{\mathrm{N}}\) of CrPS4. A more apparent trend is illustrated in Fig. 2c. Although the propagation of spin waves without magnetic interactions is not permitted in the paramagnetic phase, short- range magnetic interactions still facilitate short- wavelength magnetic excitations, resulting in the paramagnetic SSE16. In addition to the increase in \(R_{xy}^{2\omega}\) with the applied field, peaks of \(R_{xy}^{2\omega}\) are observed in both samples at varying temperatures. Similar effects are observed in the sample with a different Pt thickness (see Supplemental Material S3 for details). The magnetic field at which the \(R_{xy}^{2\omega}\) peak occurs aligns with the spin- flip field of CrPS4, as illustrated in Fig. 2f, suggesting a strong connection between the \(R_{xy}^{2\omega}\) peak and the magnetic phase transition induced by the magnetic field. The longitudinal resistances for CrPS4/Pt and CrPS4/Ta are \(\sim 600 \Omega\) and \(1560 \Omega\) respectively, with applied currents of 1 mA and 0.6 mA for the two samples. This results in a higher heating power in CrPS4/Pt, causing a larger temperature difference between the sample and the chamber. There is expected to be a shift in the spin- flip field for the samples with and without heating at the same chamber temperatures, and this discrepancy will become more pronounced at lower temperatures (see Fig. 2f). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[261, 98, 761, 373]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[142, 396, 856, 608]]<|/det|> +
Fig. 3 Origin of SSE peak at the spin-flip field. a Comparison of the field dependence of \(R_{xy}^{2\omega}\) in CrPS4/Pt (obtained at 15 K) and magnetic moment CrPS4 flake (measured at 20 K). b Angular dependence (in the \(xz\) plane) of \(R_{xy}^{2\omega}\) when the applied field approaches the spin-flip field at a temperature of 15 K and an applied current of 1 mA. c Magnon mode edges \((k = 0)\) as a function of the applied field perpendicular to the \(c\) axis. The inset shows the simulated magnetic moment as a function of the magnetic field. d The canted magnetization of \(\omega_{\alpha}\) mode precesses around the applied field, while that of \(\omega_{\beta}\) mode oscillates in the direction of the applied field.
+ +<|ref|>text<|/ref|><|det|>[[142, 628, 856, 867]]<|/det|> +The origin of the SSE peak. The peak of \(R_{xy}^{2\omega}\) observed at the spin- flip field is intriguing as it is not associated with the static magnetic moment, which would not show an increased canted magnetization during the spin- flip transition, as illustrated in Fig. 3a. In Fig. 3b, the angular dependence (in the \(xz\) plane) of \(R_{xy}^{2\omega}\) is shown as the applied field approaches the spin- flip transition at a temperature of 15 K and an applied current of 1 mA in the CrPS4/Pt. The curves can be well- fitted with the sine function according to Eq.(1), with a maximum observed at 6.8 T, indicating that the peak originates from the SSE. Additionally, the SSE continues to be present above \(T_{\mathrm{N}}\) , while the peak of \(R_{xy}^{2\omega}\) disappears beyond \(T_{\mathrm{N}}\) (see Fig. 2b,e). Although the paramagnetic phase could exhibit a SSE, the loss of long- range + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 854, 136]]<|/det|> +ordering above the \(T_{\mathrm{N}}\) causes the spin- flip transition to vanish. This highlights the significant connection between the peak of SSE and the spin- flip transition. + +<|ref|>text<|/ref|><|det|>[[143, 156, 856, 388]]<|/det|> +The SSE consists of three components: 1. The temperature gradient excites the magnetization dynamics, leading to a non- equilibrium magnon current. 2. This magnon current is transformed into a conduction- electron spin current through the \(s - d\) interaction, which travels across the interface connected to the metal. 3. Finally, the spin current is converted into a charge current via the ISHE. Notably, detecting the spin current is not crucial for the SSE peak, as both the CrPS \(_4\) /Pt and CrPS \(_4\) /Ta samples exhibit peaks (see Fig. 2b,e). The only remaining likely mechanism for the SSE peak is related to the pumped spin current \(J_{s}\) from the antiferromagnet into heavy metals which includes the effect of both thermal magnon excitation and interfacial spin mixing conductance. + +<|ref|>text<|/ref|><|det|>[[143, 407, 857, 481]]<|/det|> +Considering the canted magnetic phase, the magnetic field dependence of magnon frequency can be obtained by diagonalizing the spin Hamiltonian \(^{44}\) with eigenfrequencies \(^{45}\) . Before the spin- flip field, \(\mu_{0}H \leq 2\mu_{0}H_{\mathrm{E}} + \mu_{0}H_{\mathrm{A}}\) , + +<|ref|>equation<|/ref|><|det|>[[333, 500, 837, 525]]<|/det|> +\[\omega_{\alpha} = \gamma \mu_{0}\sqrt{(2H_{\mathrm{E}}s i n^{2}\phi + H_{\mathrm{A}}c o s^{2}\phi)(2H_{\mathrm{E}} + H_{\mathrm{A}})}, \quad (2)\] + +<|ref|>equation<|/ref|><|det|>[[392, 547, 837, 572]]<|/det|> +\[\omega_{\beta} = \gamma \mu_{0}\sqrt{H_{\mathrm{A}}(2H_{\mathrm{E}} + H_{\mathrm{A}})c o s^{2}\phi}, \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[143, 594, 530, 614]]<|/det|> +After the spin- flip field, \(\mu_{0}H > 2\mu_{0}H_{\mathrm{E}} + \mu_{0}H_{\mathrm{A}}\) , + +<|ref|>equation<|/ref|><|det|>[[392, 636, 837, 660]]<|/det|> +\[\omega_{\alpha} = \gamma \mu_{0}\sqrt{H(H - H_{\mathrm{A}})}, \quad (4)\] + +<|ref|>equation<|/ref|><|det|>[[338, 682, 837, 707]]<|/det|> +\[\omega_{\beta} = \gamma \mu_{0}\sqrt{(H - 2H_{\mathrm{E}})(H - 2H_{\mathrm{E}} - H_{\mathrm{A}})}, \quad (5)\] + +<|ref|>text<|/ref|><|det|>[[143, 728, 856, 870]]<|/det|> +where \(\mu_{0}H\) , \(\mu_{0}H_{\mathrm{E}}\) , and \(\mu_{0}H_{\mathrm{A}}\) represent the applied in- plane field, interlayer exchange field, and anisotropic field along the \(c\) - axis, respectively. The simplified model only considers the anisotropic field along the \(c\) - axis. \(\omega_{\alpha}\) and \(\omega_{\beta}\) are the two magnon modes. \(\gamma\) is the gyromagnetic ratio and \(\phi\) is the canted angle along the \(c\) - axis applied in the plane field, \(\phi = \arcsin \frac{\mu_{0}H}{2\mu_{0}H_{\mathrm{E}} + \mu_{0}H_{\mathrm{A}}}\) . + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[142, 87, 856, 321]]<|/det|> +The field dependence of the magnon mode frequency is plotted in Fig. 3c with parameters \(\mu_{0}H_{\mathrm{E}} = 3.5\mathrm{T}\) and \(\mu_{0}H_{\mathrm{A}} = 0.12\mathrm{T}^{35}\) . The \(\omega_{\alpha}\) mode has the potential to transport angular momentum due to the canted magnetization of the mode rotating around the applied magnetic field. This mode is similar to the quasi- ferromagnetic mode that emerges following a spin- flop transition when a magnetic field is applied along the \(c\) - axis14. Moreover, the SSE in CrPS4/Pt has the same sign as that in YIG/Pt (see Supplemental Material S4 for details), suggesting that right- handed magnons \((\omega_{\alpha}\) mode) are responsible for the SSE signal. In contrast, the \(\omega_{\beta}\) mode oscillates in the direction of the applied field (see Fig. 3d). + +<|ref|>text<|/ref|><|det|>[[142, 341, 856, 519]]<|/det|> +We further calculate the spin current in the heavy metal following Ref. [23] using a minimal model where the CrPS4 sample is modeled as a one- dimensional antiferromagnetic chain with periodic boundary conditions. The model has an interfacial \(s\) - \(d\) coupling that couples the localized spins in the antiferromagnet with the itinerant electrons in the heavy metal. Using Fermi's Golden rule to calculate the transition probability for the spins to be pumped from the antiferromagnet into the heavy metal, the thermal spin current density polarized along the \(x\) - axis in the heavy metal is given by23 + +<|ref|>equation<|/ref|><|det|>[[273, 540, 836, 572]]<|/det|> +\[J_{s} = \Lambda \Delta T\sin \phi \sum_{k}\hbar \omega_{k,\alpha}\frac{\partial f_{B E}(\omega_{k,\alpha})}{\partial T} +\Delta^{2}\hbar \omega_{k,\beta}\frac{\partial f_{B E}(\omega_{k,\beta})}{\partial T}, \quad (6)\] + +<|ref|>text<|/ref|><|det|>[[142, 591, 856, 799]]<|/det|> +where \(\Lambda\) is a constant depending on the interface and the density of states for the electrons in the heavy metal, \(\Delta T\) is the temperature difference across the interface, \(k\) is the wave vector of the one- dimensional chain, and \(\Delta\) parametrizes the degree of compensation at the interface; \(\Delta = 0\) corresponds to a compensated interface and \(\Delta = \pm 1\) corresponds to a fully uncompensated interface where only one of the two sublattices couple to the heavy metal. The \(\omega_{\beta}\) mode only contributes to the spin current for an uncompensated interface, reflecting the linearly polarized nature of the mode (see Supplemental Material S5 for the calculation of the spin current as a function of applied field). + +<|ref|>text<|/ref|><|det|>[[143, 819, 856, 893]]<|/det|> +The effect of the in- plane magnetic field on the pumped spin current in the heavy metal is twofold: first, the magnetic field increases the canting angle \(\phi\) , causing a linear increase of the factor \(\sin \phi\) in Eq. (6) Physically, this can be interpreted by noting that each of the two + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[142, 88, 856, 323]]<|/det|> +sublattices pump a spin current that on average is polarized along the sublattice equilibrium direction, thus, the measured spin current is given as the projection on the \(x\) - axis, which is proportional to \(\sin \phi\) . Second, the magnetic field changes the magnon frequencies of both magnon modes. Above the spin- flip critical field, the energy of the \(\omega_{\alpha}\) mode and the \(\omega_{\beta}\) mode increases with the in- plane field. This causes a decrease in the terms inside the sum in the above equation. Importantly, the increase due to the change in canting angle is proportional to \(\sin \phi \sim H\) below the critical field and constant above the critical field since the canting angle has reached its maximum at this point. In total, these two effects explain the observed peaks and saturation in SSE of \(\mathrm{CrPS}_4 / \mathrm{Pt}\) at the spin- flip field. + +<|ref|>text<|/ref|><|det|>[[142, 343, 856, 497]]<|/det|> +The gap closure of the \(\omega_{\beta}\) mode frequencies at the critical field could further increase the peak observed in the spin Seebeck effect at the critical field for systems with an uncompensated interface. However, to probe the low- frequency excitations, the temperature needs to be smaller than or comparable to the gap energy, which for \(\mathrm{CrPS}_4\) is \(0.4\mathrm{K}\) in units of temperature. Therefore, a sharper peak is expected for temperatures approaching this value (see Supplemental Material S5 for details). + +<|ref|>image<|/ref|><|det|>[[225, 523, 767, 703]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[142, 732, 854, 805]]<|/det|> +
Fig. 4 Nonlocal SSE measurement. a Schematics of nonlocal SSE measurement. b Field dependence of SSE at different angles at \(5\mathrm{K}\) with the applied current of \(1\mathrm{mA}\) . Inset shows the field dependence SSE when the applied field is slightly off the \(c\) -axis ( \(z\) -axis).
+ +<|ref|>text<|/ref|><|det|>[[143, 825, 855, 899]]<|/det|> +Nonlocal SSE in \(\mathrm{CrPS}_4 / \mathrm{Pt}\) . The nonlocal configuration is further introduced to explore the SSE in \(\mathrm{CrPS}_4 / \mathrm{Pt}\) as shown in Fig. 4a (see method for details). An in- plane heat gradient is created by passing current through one of the \(\mathrm{Pt}\) strips, resulting in a nonequilibrium + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[143, 87, 855, 268]]<|/det|> +distribution of magnons. At the detection part, the magnon spin current is injected into Pt, which leads to the SSE. It is worth noting, in this configuration, that the temperature gradient \(\nabla T\) is oriented along the \(x\) - axis, while the spin current \(J_{s}\) flows along the \(z\) - axis, differing from the longitudinal SSE previously discussed. Fig. 4b shows the field dependence of SSE at different angles \((\theta)\) at \(5\mathrm{K}\) with the applied current of \(1\mathrm{mA}\) . By applying the in- plane field \((\theta = 0^{\circ})\) , the SSE as a function of the applied field is similar to the longitudinal configuration, and a peak of SSE is also observed at the spin- flip field. + +<|ref|>text<|/ref|><|det|>[[142, 288, 856, 599]]<|/det|> +A weak SSE response occurs when the applied field is close to the \(z\) - axis, with nominal angles of \(\theta = 92^{\circ}\) and \(87^{\circ}\) . Typically, the SSE should not be present when the field is directed along the \(z\) - axis ( \(c\) - axis), as the parallel alignment of spin polarization \(\sigma\) and spin currents \(J_{s}\) does not generate a SSE voltage. However, a slight deviation from the \(z\) - axis in the direction of the applied field results in a finite value of \(J_{s} \times \sigma\) , since the spin polarization aligns with the canted magnetization. This accounts for the observed positive and negative SSE at strong positive fields when \(\theta = 87^{\circ}\) and \(92^{\circ}\) , respectively. The plateau in the SSE is observed before the spin- flop transition, as there is no \(x\) - component of the canted magnetization. In particular, one could also find a peak of SSE at the spin- flop field, which is attributed to the divergence of spin conductance as the magnon gap closes approaching the spin- flop transition46. Similar effects are also observed in the longitudinal SSE configuration (see Supplemental Material S6 for details). + +<|ref|>sub_title<|/ref|><|det|>[[144, 620, 235, 637]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[143, 660, 856, 837]]<|/det|> +We report evidence of the SSE in the weak interlayer exchange coupled van der Waals antiferromagnet \(\mathrm{CrPS_4}\) in contact with the heavy metal. We showed how the SSE is substantially enhanced by tuning the magnetic field. In particular, we observe a peak of SSE which shares the same temperature dependence as the spin- flip transition of \(\mathrm{CrPS_4}\) when applying magnetic field perpendicular to the \(c\) - axis. By considering the thermal spin current density into the heavy metal, we conclude that the SSE peak is related to the magnon mode edges as a function of the applied field across the spin- flip field. + +<|ref|>text<|/ref|><|det|>[[144, 857, 855, 904]]<|/det|> +Field- induced peaks in SSE were also observed in \(\mathrm{Y_3Fe_5O_{12} / Pt^{47}}\) , \(\mathrm{Lu_2BiFe_4GaO_{12} / Pt^{48}}\) , \(\mathrm{Fe_3O_4 / Pt^{49}}\) and \(\mathrm{Cr_2O_3 / Pt^{50}}\) bilayers. These peaks in SSE arise when the magnetic field + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[143, 88, 856, 291]]<|/det|> +adjusts the magnon energy to the point of anticrossing between the magnon and phonon dispersion curves, creating magnon- polarons47. The combined magnetoelastic excitation couples the long- lasting acoustic phonons in single crystals with the short- lived magnons, increasing the magnon lifetime and the associated SSE48. The SSE peak in CrPS4/Pt (Ta) exhibits similar field- like behaviors, but it arises from a mechanism involving the magnon mode and spin conductance. Given that the SSE peak in CrPS4/Pt (Ta) is observed at low temperatures where the phonon population is frozen, we do not expect the magnon- polarons to dominate the signal our samples. + +<|ref|>text<|/ref|><|det|>[[143, 313, 856, 463]]<|/det|> +The SSE is a sensitive tool for investigating the interfacial spin conductance and magnon population across various materials. Our findings indicate that the magnon spin transport in CrSP4/Pt(Ta) can be effectively modulated through adjustments in temperature and applied magnetic field, particularly at the spin- flip field. This approach paves the way for innovative magnonic devices that utilize weakly exchange- coupled van der Waals antiferromagnetic materials. + +<|ref|>sub_title<|/ref|><|det|>[[144, 486, 212, 503]]<|/det|> +## Method + +<|ref|>text<|/ref|><|det|>[[142, 525, 856, 886]]<|/det|> +Sample Preparation and Characterization: The chemical vapor transport technique produced single crystal flakes of CrPS4. Chromium (Aladdin,99.99%), red phosphorus (Aladdin,99.999%), and sulfur (Aladdin,99.999%) powders were measured in a stoichiometric ratio of 1:1:4 and combined with 5% more sulfur as transport agents. The mixed powders were sealed in a quartz tube and placed in a two- zone furnace, where the temperatures at the source and sink ends were maintained at 923 K and 823 K for a duration of 7 days. The atomic structure was analyzed using X- ray diffraction (XRD) with Cu Kα radiation (λ = 1.54056 Å). The magnetic properties were measured using a Superconducting Quantum Interference Device (SQUID). The CrPS4 flakes were mechanically exfoliated from the single crystals using adhesive tape and transferred onto a SiO2/Si substrate. CrPS4/Pt(Ta) samples were prepared with the magnetron sputtering in a vacuum of approximately 6×10-8 torr. The thickness of the Pt layer is 5 nm, while the Ta layer is 15 nm; 5 nm of Ta will oxidize in air, leaving 10 nm of Ta to facilitate the inverse spin Hall effect for detecting spin current generation. The Hall bar with 10 μm in width + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 88, 854, 212]]<|/det|> +and \(25 \mu \mathrm{m}\) in length was fabricated using photolithography followed by ion beam etching. The width of the heater and the detection Pt strips are designed to be \(1.4 \mu \mathrm{m}\) and \(2.3 \mu \mathrm{m}\) , the distance of the two stipes are \(1.6 \mu \mathrm{m}\) in the nonlocal device. An atomic force microscopy image of the samples is provided in supplementary S2, showing the thickness of the CrPS \(_4\) flakes to be \(74 \mathrm{nm}\) . + +<|ref|>text<|/ref|><|det|>[[144, 234, 855, 358]]<|/det|> +Transport measurement. The SSE is measured at different temperatures by varying the magnetic field in the Physical Properties Measurement System (PPMS- 9T). An alternating current ranging from \(0.4\) to \(1 \mathrm{mA}\) at a frequency of \(13 \mathrm{Hz}\) was supplied to the Hall bar or nonlocal device using a Keithley 6221 instrument, while the transverse voltage was measured with a lock- in amplifier (SR830). + +<|ref|>sub_title<|/ref|><|det|>[[144, 380, 285, 399]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[144, 422, 855, 468]]<|/det|> +The data in the main figures are provided with this paper. Other data that support the findings of this study are available from the corresponding authors upon reasonable request. + +<|ref|>sub_title<|/ref|><|det|>[[145, 490, 309, 508]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[143, 529, 856, 758]]<|/det|> +This work is supported by the National Key R&D Program of China (grant no. 2022YFA1203902, 2022YFA1200093), the National Natural Science Foundation of China (NSFC) (grant nos. 12241401, 12374108 and 12104052, 52373226, 52027801, 92263203), and the China- Germany Collaboration Project (M- 0199), the Strategic Special Project of Guangdong Province, the Fundamental Research Funds for the Central Universities, and the State Key Lab of Luminescent Materials and Devices, South China University of Technology. We acknowledge support by the German Research Foundation (CRC TRR 288 - 422213477 Project A12 and CRC TRR 173 - 268565370 Projects A01 and B02) and the Research Council of Norway through its Center of Excellence 262633 "QuSpin". + +<|ref|>sub_title<|/ref|><|det|>[[145, 781, 325, 799]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[144, 821, 854, 893]]<|/det|> +S.D. and R.W. conceived the experiments. X.H. fabricated the devices. X.H., S.D., J.W. and R.W. carried out the transport and magnetic measurements. Z.C.L., Z.Y.L. and J.Y. made the single crystal samples and carried out basic characterizations. H.G.G. and A.B. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[143, 88, 855, 188]]<|/det|> +contributed to the theoretical calculation. X.H., S.D., R.W., and M.K. contributed to data analysis. S.D. draft the manuscript and all authors contributed to the reviewing and revising of the manuscript. Y.H. and R.W. supervised the research and contributed to the acquisition of the financial support for the project leading to this work. + +<|ref|>sub_title<|/ref|><|det|>[[143, 209, 231, 227]]<|/det|> +## Reference + +<|ref|>text<|/ref|><|det|>[[135, 245, 856, 900]]<|/det|> +1. Zoui, M. A., Bentouba, S., Stocholm, J. G. & Bourouis, M. A Review on Thermoelectric Generators: Progress and Applications. Energies 13, (2020). +2. Hirohata, A. et al. Review on spintronics: Principles and device applications. J. Magn. Magn. Mater. 509, 166711 (2020). +3. Adachi, H., Uchida, K., Saitoh, E. & Maekawa, S. Theory of the spin Seebeck effect. Reports Prog. Phys. 76, 36501 (2013). +4. Kikkawa, T. & Saitoh, E. Spin Seebeck Effect: Sensitive Probe for Elementary Excitation, Spin Correlation, Transport, Magnetic Order, and Domains in Solids. Annu. Rev. Condens. Matter Phys. 14, 129–151 (2023). +5. Uchida, K. et al. Observation of the spin Seebeck effect. Nature 455, 778–781 (2008). +6. 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Enhanced Spin Conductance of a Thin-Film Insulating Antiferromagnet. Phys. Rev. Lett. 119, 56804 (2017).47. Kikkawa, T. et al. Magnon Polarons in the Spin Seebeck Effect. Phys. Rev. Lett. 117, 207203 (2016).48. Ramos, R. et al. Room temperature and low-field resonant enhancement of spin Seebeck effect in partially compensated magnets. Nat. Commun. 10, 5162 (2019).49. Xing, W. et al. Facet-dependent magnon-polarons in epitaxial ferrimagnetic Fe3O4thin films. Phys. Rev. B 102, 184416 (2020).50. Li, J. et al. Observation of Magnon Polarons in a Uniaxial Antiferromagnetic Insulator. Phys. Rev. Lett. 125, 217201 (2020). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 92, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 318, 150]]<|/det|> +SupportingInformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__07d848095b10cf7d1bd2dc1364b3f060d960ab7bbcd3b04a26e7fd0f64cb356a/images_list.json b/preprint/preprint__07d848095b10cf7d1bd2dc1364b3f060d960ab7bbcd3b04a26e7fd0f64cb356a/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..1e69a4b26ca7586708f7a948b84e459065512614 --- /dev/null +++ b/preprint/preprint__07d848095b10cf7d1bd2dc1364b3f060d960ab7bbcd3b04a26e7fd0f64cb356a/images_list.json @@ -0,0 +1,137 @@ +[ + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "Scheme 1 Design and synthesis of PI aerogels with covalent crosslinking, radially distributed cellular structure and diverse shaped bulk aerogels.", + "footnote": [], + "bbox": [ + [ + 150, + 149, + 844, + 737 + ] + ], + "page_idx": 23 + }, + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1 (a) Shearing viscosity cures of PI oligomers with different contents of TAB cross-linkers under a constant shearing rate. (b) Mechanism of the directional freeze-gelling process. (c) Optical images after thawing frozen gel of PI/TAB/DMSO. (d) DSC cures of DMSO solvent containing different content of PI/TAB. (e) Microscopy images of the pre-freezing and freeze-drying process from the microscopy system of vacuum freeze-drying. (f) Simulation of residual DMSO during freeze-drying process by COMSOL Multiphysics.", + "footnote": [], + "bbox": [ + [ + 148, + 84, + 848, + 455 + ] + ], + "page_idx": 24 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2 (a) DSC curves of PI aerogels. (b) Average shrinkage rates of PI aerogels from chemical imidization, thermal imidization and related references, 5 parallel tests were performed on each series of samples. (c) Optical images of the volume and weight of a typical PI aerogel. (d) SEM images of anisotropic PI aerogels. (e) Temperature distribution and growing direction of DMSO crystals at the bottom of mold. (f) overview of radial distributed morphology in PI aerogels. (g) Simulated NPR behavior of PI aerogels. (h) Sequential optical images showing NPR under compressive loading.", + "footnote": [], + "bbox": [ + [ + 148, + 90, + 845, + 691 + ] + ], + "page_idx": 25 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3 (a) Optical images of a PI aerogel’s recovery after \\(180^{\\circ}\\) bending and \\(99\\%\\) compression. (b) Compressive stress-strain curves with \\(70\\%\\) strain of PI aerogels with different crosslinking degrees. (c) Compressive stress-strain curves with \\(99\\%\\) strain and residual strain of PI aerogels with different crosslinking degrees. (d) Comparison of ultimate recoverable strains and densities of PI-10 aerogels with reported polymeric aerogels. (e) Simulated results of local stress distribution in cellular PI aerogels. (f) Insitu sequential SEM images of microstructure in PI-10 aerogels with different strain. (g) Fatigue test curves and SEM images of PI-10 aerogels before and after fatigue test of 5000 cycles.", + "footnote": [], + "bbox": [ + [ + 164, + 85, + 825, + 656 + ] + ], + "page_idx": 26 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4 (a) Optical images of PI-10 aerogels during elastic tests at different temperature from 4 K to 573 K. (b) Compressive stress-strain curves and compressive stress at \\(99\\%\\) strain of PI-10 aerogels after being treated under different temperatures for 3 min. (c) Optical images of PI-10 aerogel, PU foam and PVC foam before and after compression tests in liquid N₂. (d) Fatigue test curves of PI-10 aerogels after being treated in liquid helium (4K) for 24 h. (e) Models and stress-strain curves of linear PI and crosslinked PI by simulation of molecular dynamics. (f) Compressive stress-strain curves and SEM images of PI-10 aerogels before and after thermal shock test.", + "footnote": [], + "bbox": [ + [ + 147, + 85, + 843, + 435 + ] + ], + "page_idx": 27 + }, + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [ + [ + 42, + 90, + 950, + 620 + ] + ], + "page_idx": 28 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 40, + 45, + 828, + 789 + ] + ], + "page_idx": 29 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [ + [ + 42, + 90, + 830, + 830 + ] + ], + "page_idx": 30 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4", + "footnote": [], + "bbox": [ + [ + 62, + 155, + 743, + 530 + ] + ], + "page_idx": 31 + } +] \ No newline at end of file diff --git a/preprint/preprint__07d848095b10cf7d1bd2dc1364b3f060d960ab7bbcd3b04a26e7fd0f64cb356a/preprint__07d848095b10cf7d1bd2dc1364b3f060d960ab7bbcd3b04a26e7fd0f64cb356a.mmd b/preprint/preprint__07d848095b10cf7d1bd2dc1364b3f060d960ab7bbcd3b04a26e7fd0f64cb356a/preprint__07d848095b10cf7d1bd2dc1364b3f060d960ab7bbcd3b04a26e7fd0f64cb356a.mmd new file mode 100644 index 0000000000000000000000000000000000000000..b81f9cefa462b9c70dc1a40d2f7d6cbce762399c --- /dev/null +++ b/preprint/preprint__07d848095b10cf7d1bd2dc1364b3f060d960ab7bbcd3b04a26e7fd0f64cb356a/preprint__07d848095b10cf7d1bd2dc1364b3f060d960ab7bbcd3b04a26e7fd0f64cb356a.mmd @@ -0,0 +1,349 @@ + +# Super-elasticity at 4K of Covalently Crosslinked Polyimide Aerogels with Ultrahigh Negative Poisson's Ratio + +Yang Cheng Institute of special materials and technology, Fudan University, Shanghai + +Xiang Zhang Rice University https://orcid.org/0000- 0003- 4004- 5185 + +Yixiu Qin Fudan University + +Pei Dong Department of Mechanical Engineering, George Mason University, Virginia + +Wei Yao Institute of special materials and technology, Fudan University, Shanghai + +John Matz George Mason University + +Pulickel Ajayan Rice University https://orcid.org/0000- 0001- 8323- 7860 + +Jianfeng Shen ( jfshen@fudan.edu.cn) Fudan University + +Mingxin Ye Fudan University + +## Article + +Keywords: polymer chemistry, polymers synthesis + +Posted Date: January 27th, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 146724/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on July 2nd, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 24388- y. + +<--- Page Split ---> + +# 1. 1. 1. 1. 1. 1. 1. 2. 2. 2. 2. 2. 2. 2. + +<--- Page Split ---> + +# Super-elasticity at 4K of Covalently Crosslinked Polyimide Aerogels with Ultrahigh Negative Poisson's Ratio + +Yang Cheng \(^{1,2}\) , Xiang Zhang \(^{3}\) , Yixiu Qin \(^{4}\) , Pei Dong \(^{5}\) , Wei Yao \(^{1,2}\) , John Matz \(^{5}\) , Pulickel M. Ajayan \(^{3}\) , Jianfeng Shen \(^{1*}\) , Mingxin Ye \(^{1*}\) + +\(^{1}\) Institute of Special materials and Technology, Fudan University, Shanghai, P. R. China + +\(^{2}\) Department of Materials Science, Fudan University, Shanghai, P. R. China + +\(^{3}\) Department of Materials Science and Nanoengineering, Rice University, 6100 Main Street, Houston, TX 77005, USA + +\(^{4}\) State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, P. R. China + +\(^{5}\) Department of Mechanical Engineering, George Mason University, VA 22030, USA + +\*Corresponding Author. Email: mxye@fudan.edu.cn, jfshen@fudan.edu.cn + +## Abstract + +The deep cryogenic temperatures encountered in aerospace present significant challenges for the performance of elastic materials in spacecrafts and related apparatus. Reported elastic carbon or ceramic aerogels overcome the low- temperature brittleness in conventional elastic polymers. However, complicated fabrication process and high costs greatly limited their applications. In this work, super- elasticity at deep cryogenic temperature of covalently crosslinked polyimide (PI) aerogels is achieved based on scalable and low- cost directional dimethyl sulfoxide crystals assisted freeze- gelling and freeze- drying strategy. The covalently crosslinked chemical structure, cellular architecture, negative Poisson's ratio (- 0.2), extremely low volume shrinkage (3.1%) and ultralow density (6.1 mg/cm³) endow the PI aerogels with an elastic compressive strain up to 99% even in liquid helium (4K), almost zero loss of resilience after dramatic thermal shocks ( \(\Delta \mathrm{T} = 569 \mathrm{~K}\) ), and fatigue resistance over 5000 times compressive cycles. This work provides a new pathway for constructing polymer- based materials + +<--- Page Split ---> + +with super- elasticity at deep cryogenic temperature, demonstrating much promise for extensive applications in ongoing and near- future aerospace exploration. + +## Introduction + +In the field of aerospace exploration, spacecrafts and supporting apparatus often suffer from the impact of deep cryogenic environments. For instance, the lowest temperature on the surface of Mars is \(130 - 140\mathrm{K}^1\) , while the temperature is as low as \(50\mathrm{K}\) on the moon's poles2. The Space Shuttle Challenger event shocked the whole world as exploding within 73 seconds after its takeoff due to the elastic failure of rubber O- ring at low temperature, indicating the vitally essential role of elastic materials resistant to cryogenic environment for aerospace exploration. + +Unfortunately, most of the conventional intrinsic elastic materials, such as thermo plastic elastomers, natural and synthetic rubbers, generally tend to lose their intrinsic elasticity in deep cryogenic environments.3, 4 To address this problem, structurally elastic aerogels, mainly based on carbon5 and ceramics6, have captured researchers' attention due to their satisfactory elasticity from three- dimensional (3D) network architectures and excellent resistance to deep cryogenic conditions. For instance, graphene coated carbon nanotubes (CNTs) aerogels7- 10 and CNFs aerogels11 can bear compressive strain of \(50\%\) to \(90\%\) at \(173\mathrm{K}\) . Notably, Chen et al created graphene aerogels with satisfying recoverability under \(98\%\) compressive strain at \(77\mathrm{K}^{12}\) or resilience under \(90\%\) strain at the deep cryogenic temperature of \(4\mathrm{K}^{13}\) . Moreover, ceramic aerogels of BN nanoribbon and nanofibrous \(\mathrm{SiO_2}\) - based composites are also in possession of compressive super- elasticity at \(77\mathrm{K}^{14 - 17}\) . These newly emerged carbon and ceramic aerogels make significant promotion for the development of elastic materials in deep cryogenic environments, while their complex fabrication process and high cost still raise misgivings. + +In this regard, with easy processability and low- cost fabrication, it will be much more intriguing if special polymers can be synthesized and achieve super- elasticity at deep cryogenic environments. Wang et al recently demonstrated a polymeric aerogel composed of low- cost chitosan and melamine- formaldehyde resin, with super- elasticity + +<--- Page Split ---> + +at liquid nitrogen temperature (77 K), which opens up a new avenue for further development of elastic polymeric materials resistance to deep cryogenic temperature. \(^{18}\) Among polymeric materials, polyimide (PI) with remarkable resistance to extreme conditions (fire, radiation, chemical corrosion, low and high temperature, etc.) is considered to be potentially ideal candidates for elastic materials applied at deep cryogenic temperatures. \(^{19 - 22}\) Generally, freeze- casting techniques based on water- soluble PI precursors of poly (amic acid) ammonium salt (PAAS) are widely applied in the fabrication of elastic PI aerogels. \(^{23, 24}\) Based on this strategy, various elastic PI aerogels composited with carbon nanotubes \(^{25, 26}\) , graphene \(^{27, 28}\) , silica \(^{29}\) , MXene \(^{30, 31}\) and nanofibers \(^{32, 33}\) have been produced, endowing PI aerogels with such promising applications as electromagnetic shielding, oil- water separation, pressure sensors, etc. Unfortunately, the thermal imidization after freeze- drying in the above strategy inevitably causes large shrinkage up to \(40\%\) , greatly impairing the compressibility of elastic PI aerogels. \(^{34}\) Furthermore, the decomposition of PAAS in water cannot be completely avoided as a result of imperfect salinization of poly (amic acid) (PAA), leading to weak resilience of elastic PI aerogels because of low molecular weight. The recently emerged electro- spun nanofibrous PI aerogels provide an effective pathway to avoid the large shrinkage and decomposition of PAAS in water \(^{35 - 37}\) , but the incorporation of electro- spun process complicates the whole fabrication process and increases the costs. + +In this study, we proposed a novel directional dimethyl sulfoxide crystal assisted freeze- gelling and freeze- drying (DMSO- FGFD) strategy to construct covalently crosslinked PI aerogels with super- elasticity at deep cryogenic temperatures even down to \(4\mathrm{K}\) . Chemical imidization without water can be realized to transform PAA into PI oligomers at room temperature in this strategy, thus resulting in extremely low volume shrinkage of \(3.1\%\) and density of \(6.1\mathrm{mg / cm}^3\) , which are far superior to elastic PI aerogels from conventional thermal imidization. Meanwhile, innovative mold design and temperature adjustment endow the obtained PI aerogels with radially distributed cellular structure to realize negative Poisson's ratio (NPR) behavior. Thanks to the covalently crosslinked chemical structure, favorable NPR behavior, extremely low shrinkage and density, the + +<--- Page Split ---> + +prepared PI aerogels are endowed with fully reversible super- elastic behavior of up to \(90\%\) strain, satisfying stability of compressive cycles over 5000 times. Furthermore, the fantastic super- elasticity and fatigue resistance is proved to be temperature invariant over wide temperature range from \(4\mathrm{K}\) to \(573\mathrm{K}\) , and almost zero loss of resilience is observed even after dramatic thermal shocks ( \(\Delta \mathrm{T} = 569\mathrm{K}\) ). + +## Results + +## Fabrication of PI aerogels + +Fabrication of covalently crosslinked PI aerogels with super- elasticity was demonstrated in Scheme 1. Firstly, PI oligomers end- capped by anhydride were obtained through chemical imidization at room temperature by adding acetic anhydride and triethylamine into PAA precursors which were synthesized from \(4,4'\) - oxydianiline (ODA) and \(4,4'\) - oxydiphthalic anhydrides (ODPA) in DMSO solvent (Figure S1, supporting information). Subsequently, a directional freeze- gel process was carried out by adding the DMSO solution containing PI oligomers and 1,3,5- triaminophenoxybenzene (TAB) crosslinkers into a predesigned model subjected to a programmable temperature gradient. At the initial freeze- gelling stage, the DMSO crystals grew horizontally from the periphery to the center, resulting in radially distributed crystals due to a predesigned model and temperature adjustment. After that, the covalently crosslinked PI was formed between vertically grown DMSO crystals. Finally, 3D honeycombed PI aerogels with radially distributed cellular structure were obtained after freeze- drying to remove DMSO and a thermal treatment to transform residual PAA units into PI. The crosslinking degree of the obtained PI aerogels could be controlled by adjusting the molar ratio of ODPA, ODA and TAB, which is described detaillly in the section of materials and methods. The obtained crosslinked PI aerogel is marked as PI- 10, PI- 20, PI- 30, PI- 40 when the initial PI oligomers maintain the polymerization degrees of 10, 20, 30 and 40, while PI- L is corresponding to PI aerogels prepared without crosslinker. Theoretically, shorter polymer chains are easier to orient under shear stress due to fewer entanglements between them, resulting in a faster shear- thinning effect and lower shearing viscosity. As shown in Figure S2 (supporting + +<--- Page Split ---> + +information), the shearing viscosity curves of the PI oligomers solution demonstrated an increasingly rapid decrease of viscosity in the shearing thinning region from PI- L to PI- 10 as well as progressively lower constant shearing viscosity in the constant viscosity region, illustrating progressively shorter polymer chains from PI- L to PI- 10. More TAB crosslinkers are added into the solution containing shorter PI oligomers to ensure complete reaction of the anhydride groups terminated PI oligomers, resulting in higher crosslinking degrees in the final PI aerogels in accordance with the initial design. Benefiting from the facile process and low cost of raw materials, bulk PI aerogels with a volume of \(300~\mathrm{cm}^3\) and diverse shapes have been prepared, demonstrating the feasibility of large- scale fabrication based on this creative DMSO- FGFD strategy. + +## Investigation of DMSO-FGFD process + +The DMSO- FGFD process was further investigated in depth. To explore the process of gelling interaction between PI oligomers and TAB crosslinkers, the rheological behavior of PI/TAB/DMSO mixtures was observed on a rotational rheometer as shown in Figure 1a. Under a constant shearing rate, PI- L/DMSO without TAB crosslinkers shows a relatively high zero shear viscosity, but stays constant. In contrast, the viscosity of the PI/TAB/DMSO mixtures possesses a sharp increase at the initial stage demonstrating a high reactivity between PI oligomers and TAB to form covalently crosslinked PI. Besides, from PI- 40 to PI- 10, the viscosity rising rate and final viscosity tend to increase, which should be mainly attributed to a higher content of reactive groups in the solution with shorter oligomers. Figure 1b illustrates the mechanism of the freeze- gelling process. PI oligomers and TAB show a very low reaction rate and almost stayed uniform in a dilute solution. Upon freezing, phase separation took place, and PI oligomers with TAB were expelled to the boundaries of the DMSO crystals because of the volume exclusion effect, resulting in an increase in the localized concentration of PI/TAB around vertical DMSO crystals. With the continuing growth of DMSO crystals, PI oligomers and TAB diffused to diluted area driven by the concentration gradient, thus forming a high concentration area between DMSO crystals, which significantly promoted the reactivity between PI oligomers and TAB to form the crosslinked PI networks. As a result, an anisotropic frozen gel composed of covalently + +<--- Page Split ---> + +crosslinked PI and DMSO crystals was obtained. After thawing at \(35^{\circ}\mathrm{C}\) , the frozen gel with TAB transformed into an agglomerate wet gel (Figure 1c), verifying the formation of PI with covalent crosslinking in the freeze-gelling process, while the frozen gel without TAB returned to a flowing state in contrast (Figure S3, supporting information). + +Different from the process using water as the solvent, the freeze- drying process with DMSO has rarely been investigated previously. It is generally accepted that stabilization of frozen monoliths is crucial to obtain a satisfactory 3D architecture in a freeze- drying process. The Differential Scanning Calorimetry (DSC) curves demonstrated the melting temperature range of DMSO crystals containing different amounts of PI oligomers (Figure 1d). As the contents of PI oligomers in DMSO reduced from \(12 \mathrm{wt}\%\) to \(0.5 \mathrm{wt}\%\) , the melting points (peak value) tend to increase from \(9.4\) to \(16.9^{\circ}\mathrm{C}\) closing to the melting point of pure DMSO ( \(18.3^{\circ}\mathrm{C}\) ). The onset melting points of DMSO solution with different concentrations ranging from - 10 to \(0^{\circ}\mathrm{C}\) , which determines the upper limit of the freeze- drying temperature at the initial stage. Furthermore, PI/DMSO solution with a content of \(2.0 \mathrm{wt}\%\) was chosen to in- situ observe the formation of DMSO crystals in the pre- freeze and analyze temperature range of vacuum drying by the vacuum freeze- drying microscopy system. As shown in Figure 1e, when cooled down from \(25^{\circ}\mathrm{C}\) to - \(60^{\circ}\mathrm{C}\) at ambient pressure, parallel DMSO crystals come into being as templates to push PI chains to aggregate between them, demonstrating that the DMSO solvent was beneficial to prepare PI aerogels with well- organized directional frameworks. The dried area began to enlarge as the temperature rising from - 60 to - \(35^{\circ}\mathrm{C}\) at vacuum (5 Pa), and then crystal fusion was observed as the temperature reaching - \(2.0^{\circ}\mathrm{C}\) , which was in accord with the DSC results. It illustrated that temperature range of the vacuum freeze- drying process is - 35 to - \(2^{\circ}\mathrm{C}\) . As a high boiling solvent ( \(189^{\circ}\mathrm{C}\) ) with low saturated vapor pressure, DMSO is not easy to be dried. According to simulated results from COMSOL Multiphysics, 4 days are necessary to completely finish the vacuum drying process, which is in line with the experimental results (Figure 1f). + +## Structure and morphology + +<--- Page Split ---> + +The chemical structure of PI aerogels has been characterized by Fourier Transform Infrared Spectroscopy- Attenuated Total Reflection (FTIR- ATR). As seen in Figure S4 (supporting information), the spectra exhibit typical characteristic peaks of imide structure at \(1776\mathrm{cm^{- 1}}\) (imide \(\mathrm{C = O}\) asymmetric stretching), \(1714\mathrm{cm^{- 1}}\) (imide \(\mathrm{C = O}\) symmetric stretching), \(1371\mathrm{cm^{- 1}}\) (C- N stretching vibration), \(1014\mathrm{cm^{- 1}}\) and \(742\mathrm{cm^{- 1}}\) (C- N- C stretching vibration). DSC was further taken to analyze the crosslinking structure in PI aerogels. According to the research of Loshaeck \(^{39}\) , \(T_{g}\) of a polymer shows a positive correlation with its crosslinking degree as shown in the following equation. + +\[T_{g} = K_{x}\rho +T_{g}(\infty) - \frac{\kappa}{M} \quad (Equation (1))\] + +where \(T_{g}\) and \(T_{g}(\infty)\) are the glass transition temperatures of crosslinking polymer and linear polymer respectively, \(\rho\) is the crosslinking degree, \(M\) represents the molecular weight, \(K_{x}\) and \(K\) are constants. Obviously, the \(T_{g}\) of PI aerogels exhibits a remarkable upward trend from \(252^{\circ}\mathrm{C}\) of PI- L to \(285^{\circ}\mathrm{C}\) of PI- 10, clearly demonstrating the increase of crosslinking degrees (Figure 2a). + +PI aerogels produced by freeze- casting based on PAAS precursor usually suffer from severe volume shrinkage due to thermal stress shock and free volume reduction during the thermal imidization process at \(200 \sim 300^{\circ}\mathrm{C}\) , which greatly hinder its practical applications. In this work, benefiting from the good solubility of DMSO, the chemical imidization process and a covalently crosslinked structure were achieved simultaneously to fabricate PI aerogels, which synergistically mitigate the volume shrinkage of the obtained aerogels. As shown in Figure 2b, PI aerogels made by chemical imidization with DMSO display volume shrinkage less than \(7.3\%\) , which is much superior to those of \(19.5 \sim 25.3\%\) from thermal imidization. Additionally, with the increasing of crosslinking degrees, shrinkage tends to be inhibited gradually whether by chemical imidization or thermal imidization. Notably, the volume shrinkage of PI- 10 aerogels by chemical imidization can be as low as \(3.1\%\) , which is far superior to any reported PI aerogels. \(^{23, 32, 40}\) It can be attributed that chemical imidization can transform PAA into PI before thermal annealing in ahead, thus avoiding the decrease of free volume in thermal imidization (proved by molecular dynamic simulation in Figure S5, + +<--- Page Split ---> + +supporting information). Apart from that, the covalently crosslinked structure usually endows PI aerogels with much better thermal resistance and mechanical properties, which could inhibit the structural damage by thermal stress shock in thermal annealing at high temperatures. Shrinkage of elastic PI aerogels can vary with different chemical structures and constitutions in thermal imidization, while the chemical imidization based on DMSO- FGFD method should be universal for most of elastic PI aerogels to restraint shrinkage effectively. + +Little volume shrinkage is the premise for ultralow density, high porosity and well- organized structure in PI aerogels. As shown in Figure 2c, a \(3\mathrm{cm}\times 3\mathrm{cm}\) bulk aerogel of PI- 10 weighs only \(164.2\mathrm{mg}\) , indicating its density is as low as \(6.1\mathrm{mg / cm}^3\) , while the corresponding porosity is up to \(99.57\%\) . As demonstrated by scanning electron microscope (SEM) images (Figure 2d) of the cross section and longitudinal section, PI- 10 aerogel consists of parallel hollow tubes with a pore size of \(200\sim 300\mu \mathrm{m}\) and a wall thickness of \(\sim 2\mu \mathrm{m}\) , which is the result of evaporation of DMSO crystals in vacuum freeze- dryer. In contrast, the PI aerogel fabricated by freeze- caste based on \(1\mathrm{wt}\%\) PAAS aqueous solution exhibits the unregular porous microstructure (Figure S6, supporting information). Such ultralow density, high porosity and well- organized morphology mainly benefit from the minimal shrinkage and ordered DMSO crystals formed in the pre- freezing process. Apart from that, by fine- tuning the solid contents of PI/TAB/DMSO mixtures, the density of final PI aerogels can be tuned from 6.1 \(\mathrm{mg / cm}^3\) to \(52.5\mathrm{mg / cm}^3\) , corresponding to a porosity change from \(99.57\%\) to \(99.29\%\) (Figure S7, supporting information). Thus, with this pioneering DMSO- FGFD process, the density, porosity and wall thickness can be adjusted flexibly according to actual practical requirements. + +NPR behavior was observed in the obtained PI aerogels due to innovative mold design and temperature adjustment. \(^{41 - 43}\) With the help of finite element simulation based on COMSOL Multiphysics software, a radial temperature distribution (Figure 2e) was achieved at the initial stage of freeze- gelling through a design with a slightly sunken center on the outer bottom of the mold (Figure S8, supporting information). The contribvable temperature distribution was capable of controlling the growing direction + +<--- Page Split ---> + +of DMSO crystals from the periphery to the center on the inner bottom (Movie S1, supporting information), resulting in a radially distributed cellular structure of PI aerogels (Figure 2f). According to simulation results, the special structure network reveals a hyperbolic- patterned deformation under compression, depicting obvious NPR behavior (Figure 2g). Figure 2h presents the real longitudinal \((\epsilon_{y})\) and transverse \((\epsilon_{x})\) strain evolution of PI aerogels under loading, demonstrating hyperbolic- patterned shrinkage in the macroscopic configuration in a transverse direction under longitudinal compression. \(\epsilon_{y}\) decreases from \(0\%\) to \(- 41.7\%\) accompanied by \(\epsilon_{x}\) decreasing from \(0\%\) to \(- 8.3\%\) indicating significant NPR behavior from 0 to - 0.20 calculated by \(\nu = - \epsilon_{x} / \epsilon_{y}\) . The favorable NPR behavior is beneficial to the super- elasticity of PI aerogels due to a wide distribution of compressive strain and better dissipation of impact energy.41,44 + +## Evaluation of Super-elastic performance + +Benefitting from the ultra- low density, radially distributed cellular structure with NPR behavior and enhanced crosslinking networks of PI chains, the acquired PI aerogels display anisotropic mechanical performance, such as high stiffness along channel direction and ultra- high flexibility on vertical channel direction. Interestingly, the bulk aerogel is able to bear 2000 times of its own weight (Figure S9, supporting information) along channel direction, which clearly reveals its strong stiffness. Besides, as shown in Figure 3a, on vertical channel direction, they are capable to recover under \(180^{\circ}\) bending for several times (Movie S2, supporting information) and \(99\%\) compressive strain (Movie S3, supporting information), indicating amazing flexibility and super- elasticity. The effect of crosslinking degree on compressive stress of PI aerogels was further investigated. As shown in Figure 3b, all prepared PI aerogels can recover under \(70\%\) compressive strain, and exhibit a growing tendency of stress with increasing crosslinking degree. Covalently crosslinked structure endows PI- 10 aerogels with a compressive stress of \(6.5\mathrm{KPa}\) under \(70\%\) strain, which is 2.5 times that of PI- L without crosslinking. + +The compressibility and elasticity of PI aerogels have been further evaluated under an ultimate strain of \(99\%\) . As shown in Figure 3c, all PI aerogels with different + +<--- Page Split ---> + +crosslinking degrees can be compressed to \(99\%\) due to their extraordinary flexibility and ultra- low density. However, in contrast to complete recovery under \(70\%\) compressive strain, PI- L suffers from serious plastic deformation with a residual strain of \(37.9\%\) after \(99\%\) compression (Figure S10, supporting information). With the incorporation of a covalently crosslinked structure, elastic recovery has been improved, while the residual strain gradually decreases with the increasing of crosslinking degree. PI- 10 aerogels are capable of springing back to their original shape, revealing excellent super- elastic performance. When compared with reported PI aerogels and other polymeric aerogels, PI- 10 aerogels display much lower density but far superior elastic properties (Figure 3d).23, 25- 28, 31, 33, 45- 51 + +The highly recoverable compressibility of PI- 10 aerogels can be mainly attributed to the enhanced mechanical properties because of their covalently crosslinked structure and NPR behavior via of their radially distributed cellular structure. In order to deeply investigate the promotion of enhanced constituent PI to the super- elasticity of PI- 10 aerogels, structural variations in compression have been analyzed in detail. PI aerogels in this work are assembled with thin PI walls connected by cell nodes that are the main supporting parts of the framework. Figure 3e demonstrates the simulated results of stress distribution and deformation of the cellular structure under compression based on two contiguous honeycomb- configured models with a commonly used connectivity of three in single nodes13, 33, 52. While the cellular structure is bearing compressive stress, cell units are approaching each other gradually along the compressive direction, forcing the cell nodes to become stress- concentrated regions which determines the recoverability of aerogels under large compressive strains. To verify the above simulations, Figure S11 in supporting information and Figure 3f respectively present the overview and local structural evolution of PI- 10 aerogels during \(99\%\) compression- decompression by in- situ SEM observations. Obviously, the cell units undergo large pressing flat accompanied with a distinct angular variation of cell nodes under \(99\%\) compressive strain, which recovers its original shape without any structural damage after the release of stress due to the enhanced mechanical properties from the covalently crosslinked structure of PI aerogels. In contrast, damage to the cell nodes in PI- L with + +<--- Page Split ---> + +relatively lower strength is observed after \(99\%\) compression, resulting in obvious plastic deformation (Figure S12, supporting information). Apart from the enhanced constituent PI in aerogels, framework structures with NPR behavior endow PI- 10 aerogels with hyperbolic- patterned deformation under compression. The structural variations possess wide distributions of compressive strain and better dissipation of impact energy, mitigating structural damage to ensure a perfect recoverability during high compression. Under the synergistic effect of covalent crosslinking and NPR behavior, PI- 10 obtained much stronger mechanical properties and better dissipation of impact energy so that the cell nodes can stay intact even under \(99\%\) compressive strain. Fatigue resistance and adjustable mechanical properties are two vital factors for aerospace materials. A cyclic compression test was carried out to estimate the mechanical durability of PI- 10 aerogels with a sinusoidal frequency of \(1\mathrm{Hz}\) . Interestingly, there was no significant decrease in compressive stress or cracking failure in the cell structure, even after 5000 compression- decompression cycles, indicating excellent long- term stability of PI- 10 aerogels (Figure 3g). Additionally, by tailoring the solid content of PI/TAB/DMSO mixtures from 0.5 to \(3.0\mathrm{wt}\%\) , the wall thickness of PI- 10 aerogels varies from 2 to \(10\mu \mathrm{m}\) (Figure S13a, supporting information), corresponding to a stress variation of 6.7 to \(26.1\mathrm{KPa}\) at \(70\%\) compressive strain (Figure S13b, supporting information). It reveals that a higher PI concentration in the DMSO solution results in thicker walls and more robust mechanical properties, demonstrating manipulatable structural and mechanical performances. + +## Evaluation of Super-elasticity at deep cryogenic temperature + +The super- elasticity of PI- 10 aerogel was further evaluated in a gradually freezing environment from \(573\mathrm{K}\) to \(4\mathrm{K}\) (Figure 4a). PI aerogels exhibit rising thermal decomposition temperature and glass transition temperature (Figure 2b) with the increase of crosslinking degree, and the \(T_{d5}\) (temperature of \(95\%\) residual weight) of PI- 10 aerogels is up to \(539^{\circ}\mathrm{C}\) that is \(19^{\circ}\mathrm{C}\) higher than that of PI- L aerogels without crosslinking (Figure S14, supporting information). Benefiting from the enhanced thermal resistance, PI- 10 aerogel is able to completely recover to its original shape after suffering from large compressive deformation at \(298\mathrm{K}\) and \(573\mathrm{K}\) . Furthermore, the + +<--- Page Split ---> + +PI- 10 aerogel was soaked in liquid \(\mathrm{N}_2\) (77 K). Generally, most polymeric materials become hard and brittle under such circumstance. In contrast, PI- 10 aerogel could be circularly compressed with \(90\%\) strain several times and still perfectly recover without plastic deformation (Movie S3, supporting information). Moreover, the elastic behavior of PI- 10 aerogels was further investigated by a single uniaxial compress-release operation in liquid helium (4 K) using a customized apparatus (Figure S15, supporting information). Even at such a deep cryogenic temperature, PI- 10 aerogel still possesses excellent resilience after repeated compression up to \(90\%\) strain (Movie S4, supporting information). To the best of our knowledge, such remarkable and macroscopic temperature- invariant super- elasticity performances, even down to deep cryogenic temperature, have never been reported for any polymeric materials. Additionally, Figure 4b presents the stress- strain curves of PI- 10 aerogels, treated in different temperatures (573 K, 298 K, 77 K and 4 K) for 3 min and then taken out to compressive tests immediately. Note that the stress- strain curves of PI- 10 aerogels, which were treated at 4 K, 77 K and 573 K almost overlap with the curves of aerogels treated at room temperature (298 K), presenting a similar stress of \(37.2 \sim 40.1 \mathrm{KPa}\) at \(99\%\) compressive strain. Upon unloading, no residual strain has been observed, demonstrating the astonishing temperature- invariant super- elasticity of PI- 10 aerogel. As a comparison, the elastic performances of polyurethane (PU) foam and polyvinyl chloride (PVC) foams were also estimated with compression of large deformation in liquid \(\mathrm{N}_2\) . More than \(90\%\) plastic deformation was left in PU and PVC foam, while PI- 10 aerogel perfectly recovered to its original shape, revealing the great advantage of covalently crosslinked PI- 10 aerogels (Figure 4c). Additionally, A fatigue test of the compressive mechanical property of the PI- 10 aerogel treated in liquid helium (4 K) for 24 h also demonstrated that there was no significant deterioration of mechanical properties after long- term treatment at deep cryogenic temperatures (Figure 4d). At deep cryogenic temperatures, polymer chains, chain segments, and even the secondary structures (rotation and stretch of covalent bonds) in polymer chains are frozen almost completely, resulting in a sharp increase in modulus and Poisson's ratio of bulk polymeric materials. \(^{53}\) However, PU, PVC and many other polymeric materials are born + +<--- Page Split ---> + +with low compressive strength. Extremely high modulus and low strength in a deep cryogenic environment easily give rise to structural fracture under stress. In this work, slight increases in bulk modulus, shear modulus, Young's modulus and Poisson's ratio of constituent materials PI- 10 were also observed by molecular dynamic simulations (Figure S16, supporting information). Through the DMSO- FGFD process, chemical structure with covalent crosslinking and a framework structure with NPR behavior have been incorporated into PI aerogels, generating enhanced strength (Figure 4e) matched with increased modulus and remitted energy impact from compression, endowing PI aerogels with excellent super- elasticity at deep cryogenic temperatures. In terms of the application environment with temperature jumps in aerospace, a rapid thermal shock evaluation between 4 K and 573 K was also carried out on PI- 10 aerogels (Figure 4f and Figure S17, supporting information). Before and after thermal shocks with a temperature jump of 569 K, PI- 10 aerogel still maintains a similar compressibility up to 99% strain and perfect recoverability, with no obvious structural damage being observed. This excellent resistance to thermal shock is vitally important for practical application in extreme environments in aerospace. + +## Discussion + +In summary, we have reported a novel DMSO- FGFD strategy to design and synthesize covalently crosslinked PI aerogels with super- elasticity. Benefiting from an innovative chemical imidization process, this PI aerogel exhibits remarkably ultralow volume shrinkage of \(3.1\%\) and an ultralow density of \(6.1\mathrm{mg / cm}^3\) , which are superior to reported elastic PI- based aerogels. Innovative mold design and temperature adjustment endowed the obtained PI aerogels with a radially distributed cellular structure to realize NPR of - 0.2. Ultralow volume shrinkage and density, covalently crosslinked structure and NPR behavior endow the ultralight PI aerogels with the capacity to bear compressive strain up to 99% and perfectly recover its original shape. Surprisingly, obtained PI aerogels also exhibit marvelous super- elasticity at the deep cryogenic temperature of 4 K, which has never been achieved for any polymeric materials. Even after suffering from a thermal shock between 4 K and 573 K, PI aerogels still retain compressibility up to + +<--- Page Split ---> + +99% strain and perfect recoverability. To this end, these ultralight PI aerogels possess much promise for application as super- elastic materials resistant to deep cryogenic temperature in aerospace exploration. + +## Methods + +## Materials + +4,4'- oxydianiline (ODA) (99.5%) and 4,4'- oxydiphthalic anhydrides (ODPA) (99.5%) were purchased from Changzhou Sunlight Pharmaceutical Co. Ltd. 1,3,5- Triaminophenoxybenzene (TAB) (99.5%) was provided by Haorui Chemicals Co., Ltd. Dimethyl sulfoxide (DMSO) was purchased from Shanghai Taitan Technology Co., Ltd and dried with molecular sieves prior to use. Acetic anhydride (AR) and triethylamine (AR) were purchased from Sinopharm Chemical Reagent Co., Ltd. + +## Fabrication of covalently crosslinked PI aerogels + +Firstly, PI oligomers end- capped by anhydride were obtained through chemical imidization at room temperature by adding n1 mol acetic anhydride and n1 mol triethylamine into PAA precursors which were synthesized from n1 mol ODA and n2 mol ODPA in DMSO solvent. Subsequently, nTAB mol TAB was added into the DMSO solution containing PI oligomers to obtain a uniform mixture. In this work, to adjust the crosslinking degree, the relationship between n1, n2 and nTAB were designed as follows. + +\[\frac{n_1}{n_2} = \frac{n}{n + 1} \qquad \text{Equation (2)}\] + +\[n_{TAB} = \frac{2}{3} (n_1 - n_2) \qquad \text{Equation (3)}\] + +where n (n=10,20,30,40) is the polymerization degree of PI oligomers, and the corresponding PI aerogels were marked as PI- n. Particularly, n1 is equal to n2 in preparing linear PI (PI- L) without crosslinkers. As an example, the preparation of PI- 10 aerogels is described as follows: 133.5 g DMSO was added into a 250 mL three- necked flask equipped with a nitrogen inlet and a mechanical stirrer. 3.0036 g ODA (15 mmol) was added into the flask and dissolved completely, followed by adding 5.1186 g ODPA (16.5 mmol) into the solution. A PAA/DMSO solution with a solid content of 6 wt% was obtained after stirring for 12 h at room temperature. After that, PI oligomers + +<--- Page Split ---> + +were obtained by adding \(3.0627\mathrm{g}(30\mathrm{mmol})\) acetic anhydride and \(3.3393\mathrm{g}(30\mathrm{mmol})\) triethylamine into the PAA/DMSO solution and stirring for \(1\mathrm{h}\) . The PI oligomers/DMSO solution was diluted into \(0.5\mathrm{wt}\%\) by adding more DMSO solvent, followed by adding \(0.3994\mathrm{g}(1\mathrm{mmol})\) TAB to obtain a uniform mixture of PI /TAB/DMSO. A directional freeze- gelling process was carried out by adding abovementioned mixtures into the predesigned model on a freezing stage of \(- 60^{\circ}\mathrm{C}\) . After the solution was frozen entirely, the frozen gel was kept in the refrigerator for \(24\mathrm{h}\) . And then, the sample was freeze- dried for 4 days in a freeze dryer with temperatures of \(- 110^{\circ}\mathrm{C}\) in a cold trap and \(- 3^{\circ}\mathrm{C}\) in the sample chamber, and the pressure was kept at \(1\mathrm{Pa}\) . The dried samples were treated at \(250^{\circ}\mathrm{C}\) in a vacuum oven for \(3\mathrm{h}\) to obtain the final PI- 10 aerogels. + +## Characterizations + +In situ observations of the pre- freezing and freeze- drying process were carried out on LINKAM FDCS196 Microscopy System of Vacuum Freeze- drying. Briefly, \(2\mu \mathrm{L}\) solution was added on the testing stage which was frozen to \(- 60^{\circ}\mathrm{C}\) by \(5^{\circ}\mathrm{C / min}\) and then heated to \(- 2^{\circ}\mathrm{C}\) by \(1^{\circ}\mathrm{C / min}\) . The pressure was kept at \(101\mathrm{KPa}\) in the freezing process and \(5\mathrm{Pa}\) in the heating process. Attenuated total reflectance infrared spectroscopy (ATR- FTIR) was recorded on a Nicolet is10 spectroscope with the range of \(4000 - 600\mathrm{cm}^{- 1}\) by averaging 32 scans. The microstructure of the aerogels was observed on a scanning electron microscope (SEM) (TESCAN MAIA3) at an accelerating voltage of \(15\mathrm{KV}\) , and the wall thickness and pore size of the aerogels was analyzed using MAIA TC software. Differential scanning calorimetry (DSC) was performed on a Netzsch DSC 404F3 at a scan rate of \(10^{\circ}\mathrm{C / min}\) in flowing nitrogen. The thermal conversion process was analyzed by Netzsch TG 209 F1 Thermogravimetric Analyzer (TGA) at a heating rate of \(10^{\circ}\mathrm{C / min}\) in flowing nitrogen. Rheological behavior measurements were performed on a HAAKE MARS III Rotational Rheometer at \(25^{\circ}\mathrm{C}\) . The shearing rate was kept constant at \(10\mathrm{rad / s}\) for the crosslinking process analysis of PI oligomers and TAB. An increasing shearing rate from 0 to \(300\mathrm{rad / s}\) was taken to analyze the polymerization degree of PI oligomers at \(25^{\circ}\mathrm{C}\) . The compressive tests of PI aerogels were performed on an Instron 5966 material + +<--- Page Split ---> + +testing instrument, 5 parallel tests were performed on each series of samples. The strain ramp rate was kept at \(10\mathrm{mm / min}\) for all compressive tests, with the size of the samples of (L)30 mmx(W)30 mmx(H)30 mm. The fatigue tests were performed on a TA ElectroForce 3220 Mechanical Test Instrument with a compressive frequency of \(1\mathrm{Hz}\) and compressive strain of \(50\%\) , and the size of the samples was (L)8 mmx(W)8 mmx(H)8 mm. + +## Molecular dynamics (MD) simulations + +The MD simulations for Fraction of Free Volume (FFV) and mechanical properties were carried out based on Material Studio 2019 software. In the calculation of FFV, 5 polymeric chains with a polymerization degree of 20 were packed into a periodic cube for the construction of amorphous cell followed by a dynamic optimization in Forcite module with the force field of COMPASS. NPT (Number of atoms, pressure, and temperature are constant) and NVT (Number of atoms, volume and constant temperature are constant) ensembles were taken to deduce the final equilibrium model. And the temperature and pressure in the equilibrium process of amorphous cell were controlled by Nose thermostat and Berendsen barostat. \(\mathrm{FFV} = \frac{\mathrm{V}_{\mathrm{f}}}{\mathrm{V}_{\mathrm{sp}}}\) , where the free volume was calculated by \(\mathrm{V}_{\mathrm{f}} = \mathrm{V}_{\mathrm{sp}} - 1.3\mathrm{V}_{\mathrm{w}}\) , using geometric measures of the specific volume (Vsp), and van der Waals volume (Vw). In the calculation of stress- strain curves, 10 crosslinked PI chains were packed into a periodic cube for the construction of amorphous cell followed by a dynamic optimization in Forcite module with the force field of COMPASS. NPT and NVT ensembles were taken to deduce the final equilibrium model. And the temperature and pressure in the equilibrium process of amorphous cell were controlled by Andersen thermostat and Berendsen barostat. And then, a uniaxial tensile test was carried out on the constructed amorphous cell with a strain rate of \(2 \times 10^{8}\mathrm{s}^{- 1}\) on the z direction at \(300\mathrm{K}\) until the maximum strain of \(17\%\) in an NPT ensemble. In the calculation of modulus, 10 crosslinked PI chains were packed into a periodic cube for the construction of amorphous cell followed by a dynamic optimization in Forcite module with the force field of COMPASS. NPT and NVT ensembles were taken to deduce the final equilibrium model at \(4\mathrm{K}\) and \(298\mathrm{K}\) . And the + +<--- Page Split ---> + +temperature and pressure in the equilibrium process of amorphous cell were controlled by Nose thermostat and Berendsen barostat respectively. + +## Simulation of physical processes + +All the simulations of physical processes in this work are implemented by the finite element method (FEM) with the COMSOL Multiphysics software. In the simulation of the freeze- drying process, a 3D model with size of (L)30 mm \(\times\) (W)30 mm \(\times\) (H)30 mm was created, and the volume fraction of DMSO is \(99.5\%\) . The Physical field is Transport of Concentrated Species. In the simulation of temperature distribution on the bottom of the mold, a 2D model with (L)30 mm \(\times\) (W)30 mm was created, and the physical field is Solid Heat Transmission in which the initial temperatures of surrounding and center are \(- 60^{\circ}\mathrm{C}\) and \(25^{\circ}\mathrm{C}\) . A 2D model with (L)30 mm \(\times\) (W)30 mm and a 3D model with 2 hexagon frameworks next to each other with a wall thickness of \(2\mu \mathrm{m}\) was created to simulate the NPR behavior and the deformation process of cellular structure in PI- 10 aerogels, and the physical field is Solid Mechanics. + +## Acknowledgments + +Funding: This work was financially supported by National Natural Science Foundation of China (51972064). + +Author contributions: Y. C. performed most of the tests and analyses, and wrote the manuscript. X. Z and P. D. came up with constructive proposals and revised the manuscript. Y. Q performed mechanical properties characterization. W. Y. helped to modify the experiments. J. M. and P. M. A revised the manuscript and came up with significant suggestions. J. S. and M. Y. supervised all research phases. 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Scheme 1 Design and synthesis of PI aerogels with covalent crosslinking, radially distributed cellular structure and diverse shaped bulk aerogels.
+ +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1 (a) Shearing viscosity cures of PI oligomers with different contents of TAB cross-linkers under a constant shearing rate. (b) Mechanism of the directional freeze-gelling process. (c) Optical images after thawing frozen gel of PI/TAB/DMSO. (d) DSC cures of DMSO solvent containing different content of PI/TAB. (e) Microscopy images of the pre-freezing and freeze-drying process from the microscopy system of vacuum freeze-drying. (f) Simulation of residual DMSO during freeze-drying process by COMSOL Multiphysics.
+ +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2 (a) DSC curves of PI aerogels. (b) Average shrinkage rates of PI aerogels from chemical imidization, thermal imidization and related references, 5 parallel tests were performed on each series of samples. (c) Optical images of the volume and weight of a typical PI aerogel. (d) SEM images of anisotropic PI aerogels. (e) Temperature distribution and growing direction of DMSO crystals at the bottom of mold. (f) overview of radial distributed morphology in PI aerogels. (g) Simulated NPR behavior of PI aerogels. (h) Sequential optical images showing NPR under compressive loading.
+ +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3 (a) Optical images of a PI aerogel’s recovery after \(180^{\circ}\) bending and \(99\%\) compression. (b) Compressive stress-strain curves with \(70\%\) strain of PI aerogels with different crosslinking degrees. (c) Compressive stress-strain curves with \(99\%\) strain and residual strain of PI aerogels with different crosslinking degrees. (d) Comparison of ultimate recoverable strains and densities of PI-10 aerogels with reported polymeric aerogels. (e) Simulated results of local stress distribution in cellular PI aerogels. (f) Insitu sequential SEM images of microstructure in PI-10 aerogels with different strain. (g) Fatigue test curves and SEM images of PI-10 aerogels before and after fatigue test of 5000 cycles.
+ +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4 (a) Optical images of PI-10 aerogels during elastic tests at different temperature from 4 K to 573 K. (b) Compressive stress-strain curves and compressive stress at \(99\%\) strain of PI-10 aerogels after being treated under different temperatures for 3 min. (c) Optical images of PI-10 aerogel, PU foam and PVC foam before and after compression tests in liquid N₂. (d) Fatigue test curves of PI-10 aerogels after being treated in liquid helium (4K) for 24 h. (e) Models and stress-strain curves of linear PI and crosslinked PI by simulation of molecular dynamics. (f) Compressive stress-strain curves and SEM images of PI-10 aerogels before and after thermal shock test.
+ +<--- Page Split ---> + +## Figures + +![](images/Figure_1.jpg) + +
Figure 1
+ +(a) Shearing viscosity cures of PI oligomers with different contents of TAB cross-linkers under a constant shearing rate. (b) Mechanism of the directional freeze-gelling process. (c) Optical images after thawing frozen gel of PI/TAB/DMSO. (d) DSC cures of DMSO solvent containing different content of PI/TAB. (e) Microscopy images of the pre-freezing and freeze-drying process from the microscopy system of vacuum freeze-drying. (f) Simulation of residual DMSO during freeze-drying process by COMSOL Multiphysics. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2
+ +(a) DSC curves of PI aerogels. (b) Average shrinkage rates of PI aerogels from chemical imidization, thermal imidization and related references, 5 parallel tests were performed on each series of samples. (c) Optical images of the volume and weight of a typical PI aerogel. (d) SEM images of anisotropic PI aerogels. (e) Temperature distribution and growing direction of DMSO crystals at the bottom of mold. (f) + +<--- Page Split ---> + +overview of radial distributed morphology in PI aerogels. (g) Simulated NPR behavior of PI aerogels. (h) Sequential optical images showing NPR under compressive loading. + +![](images/Figure_3.jpg) + +
Figure 3
+ +(a) Optical images of a PI aerogel's recovery after \(180^{\circ}\) bending and \(99\%\) compression. (b) Compressive stress-strain curves with \(70\%\) strain of PI aerogels with different crosslinking degrees. (c) Compressive stress-strain curves with \(99\%\) strain and residual strain of PI aerogels with different crosslinking degrees. + +<--- Page Split ---> + +(d) Comparison of ultimate recoverable strains and densities of PI-10 aerogels with reported polymeric aerogels. (e) Simulated results of local stress distribution in cellular PI aerogels. (f) In-situ sequential SEM images of microstructure in PI-10 aerogels with different strain. (g) Fatigue test curves and SEM images of PI-10 aerogels before and after fatigue test of 5000 cycles. + +![](images/Figure_4.jpg) + +
Figure 4
+ +(a) Optical images of PI-10 aerogels during elastic tests at different temperature from 4 K to 573 K. (b) Compressive stress-strain curves and compressive stress at 99% strain of PI-10 aerogels after being treated under different temperatures for 3 min. (c) Optical images of PI-10 aerogel, PU foam and PVC foam before and after compression tests in liquid N2. (d) Fatigue test curves of PI-10 aerogels after being treated in liquid helium (4K) for 24 h. (e) Models and stress-strain curves of linear PI and crosslinked PI by simulation of molecular dynamics. (f) Compressive stress-strain curves and SEM images of PI-10 aerogels before and after thermal shock test. + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- MovieS1.mp4- MovieS2.mp4- MovieS3.mp4 + +<--- Page Split ---> + +- MovieS4.mp4- SupplementaryInformationsNC.docx- Scheme1.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__07d848095b10cf7d1bd2dc1364b3f060d960ab7bbcd3b04a26e7fd0f64cb356a/preprint__07d848095b10cf7d1bd2dc1364b3f060d960ab7bbcd3b04a26e7fd0f64cb356a_det.mmd b/preprint/preprint__07d848095b10cf7d1bd2dc1364b3f060d960ab7bbcd3b04a26e7fd0f64cb356a/preprint__07d848095b10cf7d1bd2dc1364b3f060d960ab7bbcd3b04a26e7fd0f64cb356a_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..ec52dbd170d0ef6913e324ad54cbe7fff58166f2 --- /dev/null +++ b/preprint/preprint__07d848095b10cf7d1bd2dc1364b3f060d960ab7bbcd3b04a26e7fd0f64cb356a/preprint__07d848095b10cf7d1bd2dc1364b3f060d960ab7bbcd3b04a26e7fd0f64cb356a_det.mmd @@ -0,0 +1,441 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 870, 208]]<|/det|> +# Super-elasticity at 4K of Covalently Crosslinked Polyimide Aerogels with Ultrahigh Negative Poisson's Ratio + +<|ref|>text<|/ref|><|det|>[[44, 230, 686, 272]]<|/det|> +Yang Cheng Institute of special materials and technology, Fudan University, Shanghai + +<|ref|>text<|/ref|><|det|>[[44, 277, 543, 317]]<|/det|> +Xiang Zhang Rice University https://orcid.org/0000- 0003- 4004- 5185 + +<|ref|>text<|/ref|><|det|>[[44, 323, 204, 363]]<|/det|> +Yixiu Qin Fudan University + +<|ref|>text<|/ref|><|det|>[[44, 370, 694, 412]]<|/det|> +Pei Dong Department of Mechanical Engineering, George Mason University, Virginia + +<|ref|>text<|/ref|><|det|>[[44, 416, 686, 457]]<|/det|> +Wei Yao Institute of special materials and technology, Fudan University, Shanghai + +<|ref|>text<|/ref|><|det|>[[44, 463, 275, 503]]<|/det|> +John Matz George Mason University + +<|ref|>text<|/ref|><|det|>[[44, 509, 543, 550]]<|/det|> +Pulickel Ajayan Rice University https://orcid.org/0000- 0001- 8323- 7860 + +<|ref|>text<|/ref|><|det|>[[44, 555, 416, 595]]<|/det|> +Jianfeng Shen ( jfshen@fudan.edu.cn) Fudan University + +<|ref|>text<|/ref|><|det|>[[44, 602, 204, 642]]<|/det|> +Mingxin Ye Fudan University + +<|ref|>sub_title<|/ref|><|det|>[[44, 685, 102, 702]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 722, 475, 741]]<|/det|> +Keywords: polymer chemistry, polymers synthesis + +<|ref|>text<|/ref|><|det|>[[44, 759, 328, 779]]<|/det|> +Posted Date: January 27th, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 797, 463, 817]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 146724/v1 + +<|ref|>text<|/ref|><|det|>[[44, 835, 909, 878]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 912, 951, 956]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on July 2nd, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 24388- y. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[0, 0, 997, 997]]<|/det|> +# 1. 1. 1. 1. 1. 1. 1. 2. 2. 2. 2. 2. 2. 2. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[147, 91, 850, 152]]<|/det|> +# Super-elasticity at 4K of Covalently Crosslinked Polyimide Aerogels with Ultrahigh Negative Poisson's Ratio + +<|ref|>text<|/ref|><|det|>[[147, 161, 850, 208]]<|/det|> +Yang Cheng \(^{1,2}\) , Xiang Zhang \(^{3}\) , Yixiu Qin \(^{4}\) , Pei Dong \(^{5}\) , Wei Yao \(^{1,2}\) , John Matz \(^{5}\) , Pulickel M. Ajayan \(^{3}\) , Jianfeng Shen \(^{1*}\) , Mingxin Ye \(^{1*}\) + +<|ref|>text<|/ref|><|det|>[[147, 217, 850, 263]]<|/det|> +\(^{1}\) Institute of Special materials and Technology, Fudan University, Shanghai, P. R. China + +<|ref|>text<|/ref|><|det|>[[147, 273, 764, 293]]<|/det|> +\(^{2}\) Department of Materials Science, Fudan University, Shanghai, P. R. China + +<|ref|>text<|/ref|><|det|>[[147, 302, 850, 348]]<|/det|> +\(^{3}\) Department of Materials Science and Nanoengineering, Rice University, 6100 Main Street, Houston, TX 77005, USA + +<|ref|>text<|/ref|><|det|>[[147, 357, 850, 403]]<|/det|> +\(^{4}\) State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, P. R. China + +<|ref|>text<|/ref|><|det|>[[147, 413, 843, 432]]<|/det|> +\(^{5}\) Department of Mechanical Engineering, George Mason University, VA 22030, USA + +<|ref|>text<|/ref|><|det|>[[147, 468, 759, 487]]<|/det|> +\*Corresponding Author. Email: mxye@fudan.edu.cn, jfshen@fudan.edu.cn + +<|ref|>sub_title<|/ref|><|det|>[[148, 521, 228, 538]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[147, 549, 852, 902]]<|/det|> +The deep cryogenic temperatures encountered in aerospace present significant challenges for the performance of elastic materials in spacecrafts and related apparatus. Reported elastic carbon or ceramic aerogels overcome the low- temperature brittleness in conventional elastic polymers. However, complicated fabrication process and high costs greatly limited their applications. In this work, super- elasticity at deep cryogenic temperature of covalently crosslinked polyimide (PI) aerogels is achieved based on scalable and low- cost directional dimethyl sulfoxide crystals assisted freeze- gelling and freeze- drying strategy. The covalently crosslinked chemical structure, cellular architecture, negative Poisson's ratio (- 0.2), extremely low volume shrinkage (3.1%) and ultralow density (6.1 mg/cm³) endow the PI aerogels with an elastic compressive strain up to 99% even in liquid helium (4K), almost zero loss of resilience after dramatic thermal shocks ( \(\Delta \mathrm{T} = 569 \mathrm{~K}\) ), and fatigue resistance over 5000 times compressive cycles. This work provides a new pathway for constructing polymer- based materials + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[148, 90, 850, 136]]<|/det|> +with super- elasticity at deep cryogenic temperature, demonstrating much promise for extensive applications in ongoing and near- future aerospace exploration. + +<|ref|>sub_title<|/ref|><|det|>[[148, 173, 261, 190]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[147, 199, 852, 386]]<|/det|> +In the field of aerospace exploration, spacecrafts and supporting apparatus often suffer from the impact of deep cryogenic environments. For instance, the lowest temperature on the surface of Mars is \(130 - 140\mathrm{K}^1\) , while the temperature is as low as \(50\mathrm{K}\) on the moon's poles2. The Space Shuttle Challenger event shocked the whole world as exploding within 73 seconds after its takeoff due to the elastic failure of rubber O- ring at low temperature, indicating the vitally essential role of elastic materials resistant to cryogenic environment for aerospace exploration. + +<|ref|>text<|/ref|><|det|>[[147, 394, 852, 803]]<|/det|> +Unfortunately, most of the conventional intrinsic elastic materials, such as thermo plastic elastomers, natural and synthetic rubbers, generally tend to lose their intrinsic elasticity in deep cryogenic environments.3, 4 To address this problem, structurally elastic aerogels, mainly based on carbon5 and ceramics6, have captured researchers' attention due to their satisfactory elasticity from three- dimensional (3D) network architectures and excellent resistance to deep cryogenic conditions. For instance, graphene coated carbon nanotubes (CNTs) aerogels7- 10 and CNFs aerogels11 can bear compressive strain of \(50\%\) to \(90\%\) at \(173\mathrm{K}\) . Notably, Chen et al created graphene aerogels with satisfying recoverability under \(98\%\) compressive strain at \(77\mathrm{K}^{12}\) or resilience under \(90\%\) strain at the deep cryogenic temperature of \(4\mathrm{K}^{13}\) . Moreover, ceramic aerogels of BN nanoribbon and nanofibrous \(\mathrm{SiO_2}\) - based composites are also in possession of compressive super- elasticity at \(77\mathrm{K}^{14 - 17}\) . These newly emerged carbon and ceramic aerogels make significant promotion for the development of elastic materials in deep cryogenic environments, while their complex fabrication process and high cost still raise misgivings. + +<|ref|>text<|/ref|><|det|>[[147, 811, 850, 913]]<|/det|> +In this regard, with easy processability and low- cost fabrication, it will be much more intriguing if special polymers can be synthesized and achieve super- elasticity at deep cryogenic environments. Wang et al recently demonstrated a polymeric aerogel composed of low- cost chitosan and melamine- formaldehyde resin, with super- elasticity + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 85, 853, 638]]<|/det|> +at liquid nitrogen temperature (77 K), which opens up a new avenue for further development of elastic polymeric materials resistance to deep cryogenic temperature. \(^{18}\) Among polymeric materials, polyimide (PI) with remarkable resistance to extreme conditions (fire, radiation, chemical corrosion, low and high temperature, etc.) is considered to be potentially ideal candidates for elastic materials applied at deep cryogenic temperatures. \(^{19 - 22}\) Generally, freeze- casting techniques based on water- soluble PI precursors of poly (amic acid) ammonium salt (PAAS) are widely applied in the fabrication of elastic PI aerogels. \(^{23, 24}\) Based on this strategy, various elastic PI aerogels composited with carbon nanotubes \(^{25, 26}\) , graphene \(^{27, 28}\) , silica \(^{29}\) , MXene \(^{30, 31}\) and nanofibers \(^{32, 33}\) have been produced, endowing PI aerogels with such promising applications as electromagnetic shielding, oil- water separation, pressure sensors, etc. Unfortunately, the thermal imidization after freeze- drying in the above strategy inevitably causes large shrinkage up to \(40\%\) , greatly impairing the compressibility of elastic PI aerogels. \(^{34}\) Furthermore, the decomposition of PAAS in water cannot be completely avoided as a result of imperfect salinization of poly (amic acid) (PAA), leading to weak resilience of elastic PI aerogels because of low molecular weight. The recently emerged electro- spun nanofibrous PI aerogels provide an effective pathway to avoid the large shrinkage and decomposition of PAAS in water \(^{35 - 37}\) , but the incorporation of electro- spun process complicates the whole fabrication process and increases the costs. + +<|ref|>text<|/ref|><|det|>[[147, 644, 853, 914]]<|/det|> +In this study, we proposed a novel directional dimethyl sulfoxide crystal assisted freeze- gelling and freeze- drying (DMSO- FGFD) strategy to construct covalently crosslinked PI aerogels with super- elasticity at deep cryogenic temperatures even down to \(4\mathrm{K}\) . Chemical imidization without water can be realized to transform PAA into PI oligomers at room temperature in this strategy, thus resulting in extremely low volume shrinkage of \(3.1\%\) and density of \(6.1\mathrm{mg / cm}^3\) , which are far superior to elastic PI aerogels from conventional thermal imidization. Meanwhile, innovative mold design and temperature adjustment endow the obtained PI aerogels with radially distributed cellular structure to realize negative Poisson's ratio (NPR) behavior. Thanks to the covalently crosslinked chemical structure, favorable NPR behavior, extremely low shrinkage and density, the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 852, 219]]<|/det|> +prepared PI aerogels are endowed with fully reversible super- elastic behavior of up to \(90\%\) strain, satisfying stability of compressive cycles over 5000 times. Furthermore, the fantastic super- elasticity and fatigue resistance is proved to be temperature invariant over wide temperature range from \(4\mathrm{K}\) to \(573\mathrm{K}\) , and almost zero loss of resilience is observed even after dramatic thermal shocks ( \(\Delta \mathrm{T} = 569\mathrm{K}\) ). + +<|ref|>sub_title<|/ref|><|det|>[[148, 256, 214, 273]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[149, 284, 375, 302]]<|/det|> +## Fabrication of PI aerogels + +<|ref|>text<|/ref|><|det|>[[147, 303, 852, 917]]<|/det|> +Fabrication of covalently crosslinked PI aerogels with super- elasticity was demonstrated in Scheme 1. Firstly, PI oligomers end- capped by anhydride were obtained through chemical imidization at room temperature by adding acetic anhydride and triethylamine into PAA precursors which were synthesized from \(4,4'\) - oxydianiline (ODA) and \(4,4'\) - oxydiphthalic anhydrides (ODPA) in DMSO solvent (Figure S1, supporting information). Subsequently, a directional freeze- gel process was carried out by adding the DMSO solution containing PI oligomers and 1,3,5- triaminophenoxybenzene (TAB) crosslinkers into a predesigned model subjected to a programmable temperature gradient. At the initial freeze- gelling stage, the DMSO crystals grew horizontally from the periphery to the center, resulting in radially distributed crystals due to a predesigned model and temperature adjustment. After that, the covalently crosslinked PI was formed between vertically grown DMSO crystals. Finally, 3D honeycombed PI aerogels with radially distributed cellular structure were obtained after freeze- drying to remove DMSO and a thermal treatment to transform residual PAA units into PI. The crosslinking degree of the obtained PI aerogels could be controlled by adjusting the molar ratio of ODPA, ODA and TAB, which is described detaillly in the section of materials and methods. The obtained crosslinked PI aerogel is marked as PI- 10, PI- 20, PI- 30, PI- 40 when the initial PI oligomers maintain the polymerization degrees of 10, 20, 30 and 40, while PI- L is corresponding to PI aerogels prepared without crosslinker. Theoretically, shorter polymer chains are easier to orient under shear stress due to fewer entanglements between them, resulting in a faster shear- thinning effect and lower shearing viscosity. As shown in Figure S2 (supporting + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 853, 358]]<|/det|> +information), the shearing viscosity curves of the PI oligomers solution demonstrated an increasingly rapid decrease of viscosity in the shearing thinning region from PI- L to PI- 10 as well as progressively lower constant shearing viscosity in the constant viscosity region, illustrating progressively shorter polymer chains from PI- L to PI- 10. More TAB crosslinkers are added into the solution containing shorter PI oligomers to ensure complete reaction of the anhydride groups terminated PI oligomers, resulting in higher crosslinking degrees in the final PI aerogels in accordance with the initial design. Benefiting from the facile process and low cost of raw materials, bulk PI aerogels with a volume of \(300~\mathrm{cm}^3\) and diverse shapes have been prepared, demonstrating the feasibility of large- scale fabrication based on this creative DMSO- FGFD strategy. + +<|ref|>sub_title<|/ref|><|det|>[[149, 367, 481, 385]]<|/det|> +## Investigation of DMSO-FGFD process + +<|ref|>text<|/ref|><|det|>[[147, 395, 853, 915]]<|/det|> +The DMSO- FGFD process was further investigated in depth. To explore the process of gelling interaction between PI oligomers and TAB crosslinkers, the rheological behavior of PI/TAB/DMSO mixtures was observed on a rotational rheometer as shown in Figure 1a. Under a constant shearing rate, PI- L/DMSO without TAB crosslinkers shows a relatively high zero shear viscosity, but stays constant. In contrast, the viscosity of the PI/TAB/DMSO mixtures possesses a sharp increase at the initial stage demonstrating a high reactivity between PI oligomers and TAB to form covalently crosslinked PI. Besides, from PI- 40 to PI- 10, the viscosity rising rate and final viscosity tend to increase, which should be mainly attributed to a higher content of reactive groups in the solution with shorter oligomers. Figure 1b illustrates the mechanism of the freeze- gelling process. PI oligomers and TAB show a very low reaction rate and almost stayed uniform in a dilute solution. Upon freezing, phase separation took place, and PI oligomers with TAB were expelled to the boundaries of the DMSO crystals because of the volume exclusion effect, resulting in an increase in the localized concentration of PI/TAB around vertical DMSO crystals. With the continuing growth of DMSO crystals, PI oligomers and TAB diffused to diluted area driven by the concentration gradient, thus forming a high concentration area between DMSO crystals, which significantly promoted the reactivity between PI oligomers and TAB to form the crosslinked PI networks. As a result, an anisotropic frozen gel composed of covalently + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 852, 219]]<|/det|> +crosslinked PI and DMSO crystals was obtained. After thawing at \(35^{\circ}\mathrm{C}\) , the frozen gel with TAB transformed into an agglomerate wet gel (Figure 1c), verifying the formation of PI with covalent crosslinking in the freeze-gelling process, while the frozen gel without TAB returned to a flowing state in contrast (Figure S3, supporting information). + +<|ref|>text<|/ref|><|det|>[[147, 225, 852, 888]]<|/det|> +Different from the process using water as the solvent, the freeze- drying process with DMSO has rarely been investigated previously. It is generally accepted that stabilization of frozen monoliths is crucial to obtain a satisfactory 3D architecture in a freeze- drying process. The Differential Scanning Calorimetry (DSC) curves demonstrated the melting temperature range of DMSO crystals containing different amounts of PI oligomers (Figure 1d). As the contents of PI oligomers in DMSO reduced from \(12 \mathrm{wt}\%\) to \(0.5 \mathrm{wt}\%\) , the melting points (peak value) tend to increase from \(9.4\) to \(16.9^{\circ}\mathrm{C}\) closing to the melting point of pure DMSO ( \(18.3^{\circ}\mathrm{C}\) ). The onset melting points of DMSO solution with different concentrations ranging from - 10 to \(0^{\circ}\mathrm{C}\) , which determines the upper limit of the freeze- drying temperature at the initial stage. Furthermore, PI/DMSO solution with a content of \(2.0 \mathrm{wt}\%\) was chosen to in- situ observe the formation of DMSO crystals in the pre- freeze and analyze temperature range of vacuum drying by the vacuum freeze- drying microscopy system. As shown in Figure 1e, when cooled down from \(25^{\circ}\mathrm{C}\) to - \(60^{\circ}\mathrm{C}\) at ambient pressure, parallel DMSO crystals come into being as templates to push PI chains to aggregate between them, demonstrating that the DMSO solvent was beneficial to prepare PI aerogels with well- organized directional frameworks. The dried area began to enlarge as the temperature rising from - 60 to - \(35^{\circ}\mathrm{C}\) at vacuum (5 Pa), and then crystal fusion was observed as the temperature reaching - \(2.0^{\circ}\mathrm{C}\) , which was in accord with the DSC results. It illustrated that temperature range of the vacuum freeze- drying process is - 35 to - \(2^{\circ}\mathrm{C}\) . As a high boiling solvent ( \(189^{\circ}\mathrm{C}\) ) with low saturated vapor pressure, DMSO is not easy to be dried. According to simulated results from COMSOL Multiphysics, 4 days are necessary to completely finish the vacuum drying process, which is in line with the experimental results (Figure 1f). + +<|ref|>sub_title<|/ref|><|det|>[[149, 896, 380, 913]]<|/det|> +## Structure and morphology + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[146, 88, 852, 303]]<|/det|> +The chemical structure of PI aerogels has been characterized by Fourier Transform Infrared Spectroscopy- Attenuated Total Reflection (FTIR- ATR). As seen in Figure S4 (supporting information), the spectra exhibit typical characteristic peaks of imide structure at \(1776\mathrm{cm^{- 1}}\) (imide \(\mathrm{C = O}\) asymmetric stretching), \(1714\mathrm{cm^{- 1}}\) (imide \(\mathrm{C = O}\) symmetric stretching), \(1371\mathrm{cm^{- 1}}\) (C- N stretching vibration), \(1014\mathrm{cm^{- 1}}\) and \(742\mathrm{cm^{- 1}}\) (C- N- C stretching vibration). DSC was further taken to analyze the crosslinking structure in PI aerogels. According to the research of Loshaeck \(^{39}\) , \(T_{g}\) of a polymer shows a positive correlation with its crosslinking degree as shown in the following equation. + +<|ref|>equation<|/ref|><|det|>[[147, 310, 627, 341]]<|/det|> +\[T_{g} = K_{x}\rho +T_{g}(\infty) - \frac{\kappa}{M} \quad (Equation (1))\] + +<|ref|>text<|/ref|><|det|>[[147, 348, 852, 479]]<|/det|> +where \(T_{g}\) and \(T_{g}(\infty)\) are the glass transition temperatures of crosslinking polymer and linear polymer respectively, \(\rho\) is the crosslinking degree, \(M\) represents the molecular weight, \(K_{x}\) and \(K\) are constants. Obviously, the \(T_{g}\) of PI aerogels exhibits a remarkable upward trend from \(252^{\circ}\mathrm{C}\) of PI- L to \(285^{\circ}\mathrm{C}\) of PI- 10, clearly demonstrating the increase of crosslinking degrees (Figure 2a). + +<|ref|>text<|/ref|><|det|>[[147, 487, 852, 896]]<|/det|> +PI aerogels produced by freeze- casting based on PAAS precursor usually suffer from severe volume shrinkage due to thermal stress shock and free volume reduction during the thermal imidization process at \(200 \sim 300^{\circ}\mathrm{C}\) , which greatly hinder its practical applications. In this work, benefiting from the good solubility of DMSO, the chemical imidization process and a covalently crosslinked structure were achieved simultaneously to fabricate PI aerogels, which synergistically mitigate the volume shrinkage of the obtained aerogels. As shown in Figure 2b, PI aerogels made by chemical imidization with DMSO display volume shrinkage less than \(7.3\%\) , which is much superior to those of \(19.5 \sim 25.3\%\) from thermal imidization. Additionally, with the increasing of crosslinking degrees, shrinkage tends to be inhibited gradually whether by chemical imidization or thermal imidization. Notably, the volume shrinkage of PI- 10 aerogels by chemical imidization can be as low as \(3.1\%\) , which is far superior to any reported PI aerogels. \(^{23, 32, 40}\) It can be attributed that chemical imidization can transform PAA into PI before thermal annealing in ahead, thus avoiding the decrease of free volume in thermal imidization (proved by molecular dynamic simulation in Figure S5, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 852, 275]]<|/det|> +supporting information). Apart from that, the covalently crosslinked structure usually endows PI aerogels with much better thermal resistance and mechanical properties, which could inhibit the structural damage by thermal stress shock in thermal annealing at high temperatures. Shrinkage of elastic PI aerogels can vary with different chemical structures and constitutions in thermal imidization, while the chemical imidization based on DMSO- FGFD method should be universal for most of elastic PI aerogels to restraint shrinkage effectively. + +<|ref|>text<|/ref|><|det|>[[147, 283, 852, 748]]<|/det|> +Little volume shrinkage is the premise for ultralow density, high porosity and well- organized structure in PI aerogels. As shown in Figure 2c, a \(3\mathrm{cm}\times 3\mathrm{cm}\) bulk aerogel of PI- 10 weighs only \(164.2\mathrm{mg}\) , indicating its density is as low as \(6.1\mathrm{mg / cm}^3\) , while the corresponding porosity is up to \(99.57\%\) . As demonstrated by scanning electron microscope (SEM) images (Figure 2d) of the cross section and longitudinal section, PI- 10 aerogel consists of parallel hollow tubes with a pore size of \(200\sim 300\mu \mathrm{m}\) and a wall thickness of \(\sim 2\mu \mathrm{m}\) , which is the result of evaporation of DMSO crystals in vacuum freeze- dryer. In contrast, the PI aerogel fabricated by freeze- caste based on \(1\mathrm{wt}\%\) PAAS aqueous solution exhibits the unregular porous microstructure (Figure S6, supporting information). Such ultralow density, high porosity and well- organized morphology mainly benefit from the minimal shrinkage and ordered DMSO crystals formed in the pre- freezing process. Apart from that, by fine- tuning the solid contents of PI/TAB/DMSO mixtures, the density of final PI aerogels can be tuned from 6.1 \(\mathrm{mg / cm}^3\) to \(52.5\mathrm{mg / cm}^3\) , corresponding to a porosity change from \(99.57\%\) to \(99.29\%\) (Figure S7, supporting information). Thus, with this pioneering DMSO- FGFD process, the density, porosity and wall thickness can be adjusted flexibly according to actual practical requirements. + +<|ref|>text<|/ref|><|det|>[[147, 755, 852, 913]]<|/det|> +NPR behavior was observed in the obtained PI aerogels due to innovative mold design and temperature adjustment. \(^{41 - 43}\) With the help of finite element simulation based on COMSOL Multiphysics software, a radial temperature distribution (Figure 2e) was achieved at the initial stage of freeze- gelling through a design with a slightly sunken center on the outer bottom of the mold (Figure S8, supporting information). The contribvable temperature distribution was capable of controlling the growing direction + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 852, 412]]<|/det|> +of DMSO crystals from the periphery to the center on the inner bottom (Movie S1, supporting information), resulting in a radially distributed cellular structure of PI aerogels (Figure 2f). According to simulation results, the special structure network reveals a hyperbolic- patterned deformation under compression, depicting obvious NPR behavior (Figure 2g). Figure 2h presents the real longitudinal \((\epsilon_{y})\) and transverse \((\epsilon_{x})\) strain evolution of PI aerogels under loading, demonstrating hyperbolic- patterned shrinkage in the macroscopic configuration in a transverse direction under longitudinal compression. \(\epsilon_{y}\) decreases from \(0\%\) to \(- 41.7\%\) accompanied by \(\epsilon_{x}\) decreasing from \(0\%\) to \(- 8.3\%\) indicating significant NPR behavior from 0 to - 0.20 calculated by \(\nu = - \epsilon_{x} / \epsilon_{y}\) . The favorable NPR behavior is beneficial to the super- elasticity of PI aerogels due to a wide distribution of compressive strain and better dissipation of impact energy.41,44 + +<|ref|>sub_title<|/ref|><|det|>[[148, 422, 500, 440]]<|/det|> +## Evaluation of Super-elastic performance + +<|ref|>text<|/ref|><|det|>[[147, 449, 852, 858]]<|/det|> +Benefitting from the ultra- low density, radially distributed cellular structure with NPR behavior and enhanced crosslinking networks of PI chains, the acquired PI aerogels display anisotropic mechanical performance, such as high stiffness along channel direction and ultra- high flexibility on vertical channel direction. Interestingly, the bulk aerogel is able to bear 2000 times of its own weight (Figure S9, supporting information) along channel direction, which clearly reveals its strong stiffness. Besides, as shown in Figure 3a, on vertical channel direction, they are capable to recover under \(180^{\circ}\) bending for several times (Movie S2, supporting information) and \(99\%\) compressive strain (Movie S3, supporting information), indicating amazing flexibility and super- elasticity. The effect of crosslinking degree on compressive stress of PI aerogels was further investigated. As shown in Figure 3b, all prepared PI aerogels can recover under \(70\%\) compressive strain, and exhibit a growing tendency of stress with increasing crosslinking degree. Covalently crosslinked structure endows PI- 10 aerogels with a compressive stress of \(6.5\mathrm{KPa}\) under \(70\%\) strain, which is 2.5 times that of PI- L without crosslinking. + +<|ref|>text<|/ref|><|det|>[[148, 867, 850, 912]]<|/det|> +The compressibility and elasticity of PI aerogels have been further evaluated under an ultimate strain of \(99\%\) . As shown in Figure 3c, all PI aerogels with different + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 852, 357]]<|/det|> +crosslinking degrees can be compressed to \(99\%\) due to their extraordinary flexibility and ultra- low density. However, in contrast to complete recovery under \(70\%\) compressive strain, PI- L suffers from serious plastic deformation with a residual strain of \(37.9\%\) after \(99\%\) compression (Figure S10, supporting information). With the incorporation of a covalently crosslinked structure, elastic recovery has been improved, while the residual strain gradually decreases with the increasing of crosslinking degree. PI- 10 aerogels are capable of springing back to their original shape, revealing excellent super- elastic performance. When compared with reported PI aerogels and other polymeric aerogels, PI- 10 aerogels display much lower density but far superior elastic properties (Figure 3d).23, 25- 28, 31, 33, 45- 51 + +<|ref|>text<|/ref|><|det|>[[147, 365, 852, 917]]<|/det|> +The highly recoverable compressibility of PI- 10 aerogels can be mainly attributed to the enhanced mechanical properties because of their covalently crosslinked structure and NPR behavior via of their radially distributed cellular structure. In order to deeply investigate the promotion of enhanced constituent PI to the super- elasticity of PI- 10 aerogels, structural variations in compression have been analyzed in detail. PI aerogels in this work are assembled with thin PI walls connected by cell nodes that are the main supporting parts of the framework. Figure 3e demonstrates the simulated results of stress distribution and deformation of the cellular structure under compression based on two contiguous honeycomb- configured models with a commonly used connectivity of three in single nodes13, 33, 52. While the cellular structure is bearing compressive stress, cell units are approaching each other gradually along the compressive direction, forcing the cell nodes to become stress- concentrated regions which determines the recoverability of aerogels under large compressive strains. To verify the above simulations, Figure S11 in supporting information and Figure 3f respectively present the overview and local structural evolution of PI- 10 aerogels during \(99\%\) compression- decompression by in- situ SEM observations. Obviously, the cell units undergo large pressing flat accompanied with a distinct angular variation of cell nodes under \(99\%\) compressive strain, which recovers its original shape without any structural damage after the release of stress due to the enhanced mechanical properties from the covalently crosslinked structure of PI aerogels. In contrast, damage to the cell nodes in PI- L with + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 80, 853, 667]]<|/det|> +relatively lower strength is observed after \(99\%\) compression, resulting in obvious plastic deformation (Figure S12, supporting information). Apart from the enhanced constituent PI in aerogels, framework structures with NPR behavior endow PI- 10 aerogels with hyperbolic- patterned deformation under compression. The structural variations possess wide distributions of compressive strain and better dissipation of impact energy, mitigating structural damage to ensure a perfect recoverability during high compression. Under the synergistic effect of covalent crosslinking and NPR behavior, PI- 10 obtained much stronger mechanical properties and better dissipation of impact energy so that the cell nodes can stay intact even under \(99\%\) compressive strain. Fatigue resistance and adjustable mechanical properties are two vital factors for aerospace materials. A cyclic compression test was carried out to estimate the mechanical durability of PI- 10 aerogels with a sinusoidal frequency of \(1\mathrm{Hz}\) . Interestingly, there was no significant decrease in compressive stress or cracking failure in the cell structure, even after 5000 compression- decompression cycles, indicating excellent long- term stability of PI- 10 aerogels (Figure 3g). Additionally, by tailoring the solid content of PI/TAB/DMSO mixtures from 0.5 to \(3.0\mathrm{wt}\%\) , the wall thickness of PI- 10 aerogels varies from 2 to \(10\mu \mathrm{m}\) (Figure S13a, supporting information), corresponding to a stress variation of 6.7 to \(26.1\mathrm{KPa}\) at \(70\%\) compressive strain (Figure S13b, supporting information). It reveals that a higher PI concentration in the DMSO solution results in thicker walls and more robust mechanical properties, demonstrating manipulatable structural and mechanical performances. + +<|ref|>sub_title<|/ref|><|det|>[[149, 674, 674, 692]]<|/det|> +## Evaluation of Super-elasticity at deep cryogenic temperature + +<|ref|>text<|/ref|><|det|>[[148, 701, 852, 914]]<|/det|> +The super- elasticity of PI- 10 aerogel was further evaluated in a gradually freezing environment from \(573\mathrm{K}\) to \(4\mathrm{K}\) (Figure 4a). PI aerogels exhibit rising thermal decomposition temperature and glass transition temperature (Figure 2b) with the increase of crosslinking degree, and the \(T_{d5}\) (temperature of \(95\%\) residual weight) of PI- 10 aerogels is up to \(539^{\circ}\mathrm{C}\) that is \(19^{\circ}\mathrm{C}\) higher than that of PI- L aerogels without crosslinking (Figure S14, supporting information). Benefiting from the enhanced thermal resistance, PI- 10 aerogel is able to completely recover to its original shape after suffering from large compressive deformation at \(298\mathrm{K}\) and \(573\mathrm{K}\) . Furthermore, the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 852, 917]]<|/det|> +PI- 10 aerogel was soaked in liquid \(\mathrm{N}_2\) (77 K). Generally, most polymeric materials become hard and brittle under such circumstance. In contrast, PI- 10 aerogel could be circularly compressed with \(90\%\) strain several times and still perfectly recover without plastic deformation (Movie S3, supporting information). Moreover, the elastic behavior of PI- 10 aerogels was further investigated by a single uniaxial compress-release operation in liquid helium (4 K) using a customized apparatus (Figure S15, supporting information). Even at such a deep cryogenic temperature, PI- 10 aerogel still possesses excellent resilience after repeated compression up to \(90\%\) strain (Movie S4, supporting information). To the best of our knowledge, such remarkable and macroscopic temperature- invariant super- elasticity performances, even down to deep cryogenic temperature, have never been reported for any polymeric materials. Additionally, Figure 4b presents the stress- strain curves of PI- 10 aerogels, treated in different temperatures (573 K, 298 K, 77 K and 4 K) for 3 min and then taken out to compressive tests immediately. Note that the stress- strain curves of PI- 10 aerogels, which were treated at 4 K, 77 K and 573 K almost overlap with the curves of aerogels treated at room temperature (298 K), presenting a similar stress of \(37.2 \sim 40.1 \mathrm{KPa}\) at \(99\%\) compressive strain. Upon unloading, no residual strain has been observed, demonstrating the astonishing temperature- invariant super- elasticity of PI- 10 aerogel. As a comparison, the elastic performances of polyurethane (PU) foam and polyvinyl chloride (PVC) foams were also estimated with compression of large deformation in liquid \(\mathrm{N}_2\) . More than \(90\%\) plastic deformation was left in PU and PVC foam, while PI- 10 aerogel perfectly recovered to its original shape, revealing the great advantage of covalently crosslinked PI- 10 aerogels (Figure 4c). Additionally, A fatigue test of the compressive mechanical property of the PI- 10 aerogel treated in liquid helium (4 K) for 24 h also demonstrated that there was no significant deterioration of mechanical properties after long- term treatment at deep cryogenic temperatures (Figure 4d). At deep cryogenic temperatures, polymer chains, chain segments, and even the secondary structures (rotation and stretch of covalent bonds) in polymer chains are frozen almost completely, resulting in a sharp increase in modulus and Poisson's ratio of bulk polymeric materials. \(^{53}\) However, PU, PVC and many other polymeric materials are born + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 853, 527]]<|/det|> +with low compressive strength. Extremely high modulus and low strength in a deep cryogenic environment easily give rise to structural fracture under stress. In this work, slight increases in bulk modulus, shear modulus, Young's modulus and Poisson's ratio of constituent materials PI- 10 were also observed by molecular dynamic simulations (Figure S16, supporting information). Through the DMSO- FGFD process, chemical structure with covalent crosslinking and a framework structure with NPR behavior have been incorporated into PI aerogels, generating enhanced strength (Figure 4e) matched with increased modulus and remitted energy impact from compression, endowing PI aerogels with excellent super- elasticity at deep cryogenic temperatures. In terms of the application environment with temperature jumps in aerospace, a rapid thermal shock evaluation between 4 K and 573 K was also carried out on PI- 10 aerogels (Figure 4f and Figure S17, supporting information). Before and after thermal shocks with a temperature jump of 569 K, PI- 10 aerogel still maintains a similar compressibility up to 99% strain and perfect recoverability, with no obvious structural damage being observed. This excellent resistance to thermal shock is vitally important for practical application in extreme environments in aerospace. + +<|ref|>sub_title<|/ref|><|det|>[[148, 564, 242, 580]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[147, 590, 853, 914]]<|/det|> +In summary, we have reported a novel DMSO- FGFD strategy to design and synthesize covalently crosslinked PI aerogels with super- elasticity. Benefiting from an innovative chemical imidization process, this PI aerogel exhibits remarkably ultralow volume shrinkage of \(3.1\%\) and an ultralow density of \(6.1\mathrm{mg / cm}^3\) , which are superior to reported elastic PI- based aerogels. Innovative mold design and temperature adjustment endowed the obtained PI aerogels with a radially distributed cellular structure to realize NPR of - 0.2. Ultralow volume shrinkage and density, covalently crosslinked structure and NPR behavior endow the ultralight PI aerogels with the capacity to bear compressive strain up to 99% and perfectly recover its original shape. Surprisingly, obtained PI aerogels also exhibit marvelous super- elasticity at the deep cryogenic temperature of 4 K, which has never been achieved for any polymeric materials. Even after suffering from a thermal shock between 4 K and 573 K, PI aerogels still retain compressibility up to + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 89, 850, 164]]<|/det|> +99% strain and perfect recoverability. To this end, these ultralight PI aerogels possess much promise for application as super- elastic materials resistant to deep cryogenic temperature in aerospace exploration. + +<|ref|>sub_title<|/ref|><|det|>[[148, 201, 226, 217]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[148, 229, 235, 245]]<|/det|> +## Materials + +<|ref|>text<|/ref|><|det|>[[147, 255, 852, 413]]<|/det|> +4,4'- oxydianiline (ODA) (99.5%) and 4,4'- oxydiphthalic anhydrides (ODPA) (99.5%) were purchased from Changzhou Sunlight Pharmaceutical Co. Ltd. 1,3,5- Triaminophenoxybenzene (TAB) (99.5%) was provided by Haorui Chemicals Co., Ltd. Dimethyl sulfoxide (DMSO) was purchased from Shanghai Taitan Technology Co., Ltd and dried with molecular sieves prior to use. Acetic anhydride (AR) and triethylamine (AR) were purchased from Sinopharm Chemical Reagent Co., Ltd. + +<|ref|>sub_title<|/ref|><|det|>[[148, 422, 570, 441]]<|/det|> +## Fabrication of covalently crosslinked PI aerogels + +<|ref|>text<|/ref|><|det|>[[147, 449, 852, 604]]<|/det|> +Firstly, PI oligomers end- capped by anhydride were obtained through chemical imidization at room temperature by adding n1 mol acetic anhydride and n1 mol triethylamine into PAA precursors which were synthesized from n1 mol ODA and n2 mol ODPA in DMSO solvent. Subsequently, nTAB mol TAB was added into the DMSO solution containing PI oligomers to obtain a uniform mixture. In this work, to adjust the crosslinking degree, the relationship between n1, n2 and nTAB were designed as follows. + +<|ref|>equation<|/ref|><|det|>[[191, 610, 656, 650]]<|/det|> +\[\frac{n_1}{n_2} = \frac{n}{n + 1} \qquad \text{Equation (2)}\] + +<|ref|>equation<|/ref|><|det|>[[186, 655, 654, 682]]<|/det|> +\[n_{TAB} = \frac{2}{3} (n_1 - n_2) \qquad \text{Equation (3)}\] + +<|ref|>text<|/ref|><|det|>[[147, 691, 852, 905]]<|/det|> +where n (n=10,20,30,40) is the polymerization degree of PI oligomers, and the corresponding PI aerogels were marked as PI- n. Particularly, n1 is equal to n2 in preparing linear PI (PI- L) without crosslinkers. As an example, the preparation of PI- 10 aerogels is described as follows: 133.5 g DMSO was added into a 250 mL three- necked flask equipped with a nitrogen inlet and a mechanical stirrer. 3.0036 g ODA (15 mmol) was added into the flask and dissolved completely, followed by adding 5.1186 g ODPA (16.5 mmol) into the solution. A PAA/DMSO solution with a solid content of 6 wt% was obtained after stirring for 12 h at room temperature. After that, PI oligomers + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 852, 386]]<|/det|> +were obtained by adding \(3.0627\mathrm{g}(30\mathrm{mmol})\) acetic anhydride and \(3.3393\mathrm{g}(30\mathrm{mmol})\) triethylamine into the PAA/DMSO solution and stirring for \(1\mathrm{h}\) . The PI oligomers/DMSO solution was diluted into \(0.5\mathrm{wt}\%\) by adding more DMSO solvent, followed by adding \(0.3994\mathrm{g}(1\mathrm{mmol})\) TAB to obtain a uniform mixture of PI /TAB/DMSO. A directional freeze- gelling process was carried out by adding abovementioned mixtures into the predesigned model on a freezing stage of \(- 60^{\circ}\mathrm{C}\) . After the solution was frozen entirely, the frozen gel was kept in the refrigerator for \(24\mathrm{h}\) . And then, the sample was freeze- dried for 4 days in a freeze dryer with temperatures of \(- 110^{\circ}\mathrm{C}\) in a cold trap and \(- 3^{\circ}\mathrm{C}\) in the sample chamber, and the pressure was kept at \(1\mathrm{Pa}\) . The dried samples were treated at \(250^{\circ}\mathrm{C}\) in a vacuum oven for \(3\mathrm{h}\) to obtain the final PI- 10 aerogels. + +<|ref|>sub_title<|/ref|><|det|>[[149, 396, 306, 413]]<|/det|> +## Characterizations + +<|ref|>text<|/ref|><|det|>[[147, 421, 852, 915]]<|/det|> +In situ observations of the pre- freezing and freeze- drying process were carried out on LINKAM FDCS196 Microscopy System of Vacuum Freeze- drying. Briefly, \(2\mu \mathrm{L}\) solution was added on the testing stage which was frozen to \(- 60^{\circ}\mathrm{C}\) by \(5^{\circ}\mathrm{C / min}\) and then heated to \(- 2^{\circ}\mathrm{C}\) by \(1^{\circ}\mathrm{C / min}\) . The pressure was kept at \(101\mathrm{KPa}\) in the freezing process and \(5\mathrm{Pa}\) in the heating process. Attenuated total reflectance infrared spectroscopy (ATR- FTIR) was recorded on a Nicolet is10 spectroscope with the range of \(4000 - 600\mathrm{cm}^{- 1}\) by averaging 32 scans. The microstructure of the aerogels was observed on a scanning electron microscope (SEM) (TESCAN MAIA3) at an accelerating voltage of \(15\mathrm{KV}\) , and the wall thickness and pore size of the aerogels was analyzed using MAIA TC software. Differential scanning calorimetry (DSC) was performed on a Netzsch DSC 404F3 at a scan rate of \(10^{\circ}\mathrm{C / min}\) in flowing nitrogen. The thermal conversion process was analyzed by Netzsch TG 209 F1 Thermogravimetric Analyzer (TGA) at a heating rate of \(10^{\circ}\mathrm{C / min}\) in flowing nitrogen. Rheological behavior measurements were performed on a HAAKE MARS III Rotational Rheometer at \(25^{\circ}\mathrm{C}\) . The shearing rate was kept constant at \(10\mathrm{rad / s}\) for the crosslinking process analysis of PI oligomers and TAB. An increasing shearing rate from 0 to \(300\mathrm{rad / s}\) was taken to analyze the polymerization degree of PI oligomers at \(25^{\circ}\mathrm{C}\) . The compressive tests of PI aerogels were performed on an Instron 5966 material + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 852, 247]]<|/det|> +testing instrument, 5 parallel tests were performed on each series of samples. The strain ramp rate was kept at \(10\mathrm{mm / min}\) for all compressive tests, with the size of the samples of (L)30 mmx(W)30 mmx(H)30 mm. The fatigue tests were performed on a TA ElectroForce 3220 Mechanical Test Instrument with a compressive frequency of \(1\mathrm{Hz}\) and compressive strain of \(50\%\) , and the size of the samples was (L)8 mmx(W)8 mmx(H)8 mm. + +<|ref|>sub_title<|/ref|><|det|>[[148, 256, 483, 274]]<|/det|> +## Molecular dynamics (MD) simulations + +<|ref|>text<|/ref|><|det|>[[147, 280, 852, 909]]<|/det|> +The MD simulations for Fraction of Free Volume (FFV) and mechanical properties were carried out based on Material Studio 2019 software. In the calculation of FFV, 5 polymeric chains with a polymerization degree of 20 were packed into a periodic cube for the construction of amorphous cell followed by a dynamic optimization in Forcite module with the force field of COMPASS. NPT (Number of atoms, pressure, and temperature are constant) and NVT (Number of atoms, volume and constant temperature are constant) ensembles were taken to deduce the final equilibrium model. And the temperature and pressure in the equilibrium process of amorphous cell were controlled by Nose thermostat and Berendsen barostat. \(\mathrm{FFV} = \frac{\mathrm{V}_{\mathrm{f}}}{\mathrm{V}_{\mathrm{sp}}}\) , where the free volume was calculated by \(\mathrm{V}_{\mathrm{f}} = \mathrm{V}_{\mathrm{sp}} - 1.3\mathrm{V}_{\mathrm{w}}\) , using geometric measures of the specific volume (Vsp), and van der Waals volume (Vw). In the calculation of stress- strain curves, 10 crosslinked PI chains were packed into a periodic cube for the construction of amorphous cell followed by a dynamic optimization in Forcite module with the force field of COMPASS. NPT and NVT ensembles were taken to deduce the final equilibrium model. And the temperature and pressure in the equilibrium process of amorphous cell were controlled by Andersen thermostat and Berendsen barostat. And then, a uniaxial tensile test was carried out on the constructed amorphous cell with a strain rate of \(2 \times 10^{8}\mathrm{s}^{- 1}\) on the z direction at \(300\mathrm{K}\) until the maximum strain of \(17\%\) in an NPT ensemble. In the calculation of modulus, 10 crosslinked PI chains were packed into a periodic cube for the construction of amorphous cell followed by a dynamic optimization in Forcite module with the force field of COMPASS. NPT and NVT ensembles were taken to deduce the final equilibrium model at \(4\mathrm{K}\) and \(298\mathrm{K}\) . And the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[148, 90, 850, 135]]<|/det|> +temperature and pressure in the equilibrium process of amorphous cell were controlled by Nose thermostat and Berendsen barostat respectively. + +<|ref|>sub_title<|/ref|><|det|>[[149, 145, 430, 163]]<|/det|> +## Simulation of physical processes + +<|ref|>text<|/ref|><|det|>[[147, 172, 853, 469]]<|/det|> +All the simulations of physical processes in this work are implemented by the finite element method (FEM) with the COMSOL Multiphysics software. In the simulation of the freeze- drying process, a 3D model with size of (L)30 mm \(\times\) (W)30 mm \(\times\) (H)30 mm was created, and the volume fraction of DMSO is \(99.5\%\) . The Physical field is Transport of Concentrated Species. In the simulation of temperature distribution on the bottom of the mold, a 2D model with (L)30 mm \(\times\) (W)30 mm was created, and the physical field is Solid Heat Transmission in which the initial temperatures of surrounding and center are \(- 60^{\circ}\mathrm{C}\) and \(25^{\circ}\mathrm{C}\) . A 2D model with (L)30 mm \(\times\) (W)30 mm and a 3D model with 2 hexagon frameworks next to each other with a wall thickness of \(2\mu \mathrm{m}\) was created to simulate the NPR behavior and the deformation process of cellular structure in PI- 10 aerogels, and the physical field is Solid Mechanics. + +<|ref|>sub_title<|/ref|><|det|>[[149, 507, 310, 523]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[148, 533, 850, 579]]<|/det|> +Funding: This work was financially supported by National Natural Science Foundation of China (51972064). + +<|ref|>text<|/ref|><|det|>[[147, 588, 852, 747]]<|/det|> +Author contributions: Y. C. performed most of the tests and analyses, and wrote the manuscript. X. Z and P. D. came up with constructive proposals and revised the manuscript. Y. Q performed mechanical properties characterization. W. Y. helped to modify the experiments. J. M. and P. M. A revised the manuscript and came up with significant suggestions. J. S. and M. Y. supervised all research phases. All authors discussed and commented on the manuscript. + +<|ref|>text<|/ref|><|det|>[[148, 756, 687, 774]]<|/det|> +Competing interests: The authors declare no competing interests. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[147, 90, 245, 107]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[144, 115, 852, 888]]<|/det|> +1. D. R. Williams, Planetary Fact Sheet-Metric, https://nssdc.gsfc.nasa.gov/planetary/factsheet/, 2019. +2. G. Hautaluoma, JPL Instrument Set for Lunar Orbiter Mission, https://www.nasa.gov/mission_pages/LRO/news/lro-20090617.html, 2009. +3. R. P. Reed, R. E. Schramm, A. F. 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Yu, W. Chen, H. Hu, H. Li, Adv. Mater. 2016, 28, 9223. + +<|ref|>text<|/ref|><|det|>[[147, 565, 848, 607]]<|/det|> +43. Q. Zhang, X. Xu, D. Lin, W. Chen, G. Xiong, Y. Yu, T. S. Fisher, H. Li, Adv. Mater. 2016, 28, 2229. + +<|ref|>text<|/ref|><|det|>[[147, 620, 737, 636]]<|/det|> +44. D. Li, X. Bu, Z. Xu, Y. Luo, H. Bai, Adv. Mater. 2020, 32, 2001222. + +<|ref|>text<|/ref|><|det|>[[147, 649, 848, 691]]<|/det|> +45. X. Zhao, W. Wang, Z. Wang, J. Wang, T. Huang, J. Dong, Q. Zhang, Chem. Eng. J. 2020, 395, 125115. + +<|ref|>text<|/ref|><|det|>[[147, 704, 848, 747]]<|/det|> +46. F. Zhang, Y. Feng, M. Qin, L. Gao, Z. Li, F. Zhao, Z. Zhang, F. Lv, W. Feng, Adv. Funct. Mater. 2019, 29, 1901383. + +<|ref|>text<|/ref|><|det|>[[147, 760, 848, 802]]<|/det|> +47. H. Wu, Y. Li, L. Zhao, S. Wang, Y. Tian, Y. Si, J. Yu, B. Ding, ACS Appl. Mater. Interfaces 2020, 12, 27562. + +<|ref|>text<|/ref|><|det|>[[147, 815, 848, 830]]<|/det|> +48. T. Pirzada, Z. Ashrafi, W. Xie, S. A. Khan, Adv. Funct. Mater. 2020, 30, 1907359. + +<|ref|>text<|/ref|><|det|>[[147, 843, 800, 859]]<|/det|> +49. C. Xie, L. He, Y. Shi, Z.-X. Guo, T. Qiu, X. Tuo, ACS Nano 2019, 13, 7811. + +<|ref|>text<|/ref|><|det|>[[147, 872, 848, 915]]<|/det|> +50. M. Peydayesh, M. K. Suter, S. Bolisetty, S. Boulos, S. Handschin, L. Nyström, R. Mezzenga, Adv. Mater. 2020, 32, 1907932. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[145, 88, 850, 194]]<|/det|> +51. Q. Zhang, H. Wang, L. Wang, Y. Zhuang, W. Li, Y. Zhou, S. Gu, X. Wang, H. Yang, W. Xu, ACS Appl. Mater. Interfaces 2018, 10, 41871.52. Y. Si, X. Wang, L. Dou, J. Yu, B. Ding, Sci. Adv. 2018, 4, eaas8925.53. S. Kalia, S.-Y. Fu, Polymers at Cryogenic Temperatures, Springer, 2013. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[150, 149, 844, 737]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 746, 848, 805]]<|/det|> +
Scheme 1 Design and synthesis of PI aerogels with covalent crosslinking, radially distributed cellular structure and diverse shaped bulk aerogels.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[148, 84, 848, 455]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 465, 852, 706]]<|/det|> +
Figure 1 (a) Shearing viscosity cures of PI oligomers with different contents of TAB cross-linkers under a constant shearing rate. (b) Mechanism of the directional freeze-gelling process. (c) Optical images after thawing frozen gel of PI/TAB/DMSO. (d) DSC cures of DMSO solvent containing different content of PI/TAB. (e) Microscopy images of the pre-freezing and freeze-drying process from the microscopy system of vacuum freeze-drying. (f) Simulation of residual DMSO during freeze-drying process by COMSOL Multiphysics.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[148, 90, 845, 691]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 702, 852, 888]]<|/det|> +
Figure 2 (a) DSC curves of PI aerogels. (b) Average shrinkage rates of PI aerogels from chemical imidization, thermal imidization and related references, 5 parallel tests were performed on each series of samples. (c) Optical images of the volume and weight of a typical PI aerogel. (d) SEM images of anisotropic PI aerogels. (e) Temperature distribution and growing direction of DMSO crystals at the bottom of mold. (f) overview of radial distributed morphology in PI aerogels. (g) Simulated NPR behavior of PI aerogels. (h) Sequential optical images showing NPR under compressive loading.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[164, 85, 825, 656]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 664, 852, 905]]<|/det|> +
Figure 3 (a) Optical images of a PI aerogel’s recovery after \(180^{\circ}\) bending and \(99\%\) compression. (b) Compressive stress-strain curves with \(70\%\) strain of PI aerogels with different crosslinking degrees. (c) Compressive stress-strain curves with \(99\%\) strain and residual strain of PI aerogels with different crosslinking degrees. (d) Comparison of ultimate recoverable strains and densities of PI-10 aerogels with reported polymeric aerogels. (e) Simulated results of local stress distribution in cellular PI aerogels. (f) Insitu sequential SEM images of microstructure in PI-10 aerogels with different strain. (g) Fatigue test curves and SEM images of PI-10 aerogels before and after fatigue test of 5000 cycles.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[147, 85, 843, 435]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 441, 852, 655]]<|/det|> +
Figure 4 (a) Optical images of PI-10 aerogels during elastic tests at different temperature from 4 K to 573 K. (b) Compressive stress-strain curves and compressive stress at \(99\%\) strain of PI-10 aerogels after being treated under different temperatures for 3 min. (c) Optical images of PI-10 aerogel, PU foam and PVC foam before and after compression tests in liquid N₂. (d) Fatigue test curves of PI-10 aerogels after being treated in liquid helium (4K) for 24 h. (e) Models and stress-strain curves of linear PI and crosslinked PI by simulation of molecular dynamics. (f) Compressive stress-strain curves and SEM images of PI-10 aerogels before and after thermal shock test.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 44, 144, 68]]<|/det|> +## Figures + +<|ref|>image<|/ref|><|det|>[[42, 90, 950, 620]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 641, 115, 660]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[42, 682, 953, 795]]<|/det|> +(a) Shearing viscosity cures of PI oligomers with different contents of TAB cross-linkers under a constant shearing rate. (b) Mechanism of the directional freeze-gelling process. (c) Optical images after thawing frozen gel of PI/TAB/DMSO. (d) DSC cures of DMSO solvent containing different content of PI/TAB. (e) Microscopy images of the pre-freezing and freeze-drying process from the microscopy system of vacuum freeze-drying. (f) Simulation of residual DMSO during freeze-drying process by COMSOL Multiphysics. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[40, 45, 828, 789]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 802, 117, 821]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[41, 842, 949, 932]]<|/det|> +(a) DSC curves of PI aerogels. (b) Average shrinkage rates of PI aerogels from chemical imidization, thermal imidization and related references, 5 parallel tests were performed on each series of samples. (c) Optical images of the volume and weight of a typical PI aerogel. (d) SEM images of anisotropic PI aerogels. (e) Temperature distribution and growing direction of DMSO crystals at the bottom of mold. (f) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 935, 88]]<|/det|> +overview of radial distributed morphology in PI aerogels. (g) Simulated NPR behavior of PI aerogels. (h) Sequential optical images showing NPR under compressive loading. + +<|ref|>image<|/ref|><|det|>[[42, 90, 830, 830]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 846, 116, 865]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[42, 888, 944, 954]]<|/det|> +(a) Optical images of a PI aerogel's recovery after \(180^{\circ}\) bending and \(99\%\) compression. (b) Compressive stress-strain curves with \(70\%\) strain of PI aerogels with different crosslinking degrees. (c) Compressive stress-strain curves with \(99\%\) strain and residual strain of PI aerogels with different crosslinking degrees. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 45, 955, 134]]<|/det|> +(d) Comparison of ultimate recoverable strains and densities of PI-10 aerogels with reported polymeric aerogels. (e) Simulated results of local stress distribution in cellular PI aerogels. (f) In-situ sequential SEM images of microstructure in PI-10 aerogels with different strain. (g) Fatigue test curves and SEM images of PI-10 aerogels before and after fatigue test of 5000 cycles. + +<|ref|>image<|/ref|><|det|>[[62, 155, 743, 530]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[43, 570, 117, 588]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[40, 609, 955, 769]]<|/det|> +(a) Optical images of PI-10 aerogels during elastic tests at different temperature from 4 K to 573 K. (b) Compressive stress-strain curves and compressive stress at 99% strain of PI-10 aerogels after being treated under different temperatures for 3 min. (c) Optical images of PI-10 aerogel, PU foam and PVC foam before and after compression tests in liquid N2. (d) Fatigue test curves of PI-10 aerogels after being treated in liquid helium (4K) for 24 h. (e) Models and stress-strain curves of linear PI and crosslinked PI by simulation of molecular dynamics. (f) Compressive stress-strain curves and SEM images of PI-10 aerogels before and after thermal shock test. + +<|ref|>sub_title<|/ref|><|det|>[[44, 791, 311, 818]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 841, 765, 861]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 880, 208, 950]]<|/det|> +- MovieS1.mp4- MovieS2.mp4- MovieS3.mp4 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 46, 402, 118]]<|/det|> +- MovieS4.mp4- SupplementaryInformationsNC.docx- Scheme1.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__07d8bd7526aa69b2ae52061d5d94ffccdef61e6e41b48b7510715547a86819c4/images_list.json b/preprint/preprint__07d8bd7526aa69b2ae52061d5d94ffccdef61e6e41b48b7510715547a86819c4/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..8d5d694b817fe498ece3ae71fdd7747107fc3ccd --- /dev/null +++ b/preprint/preprint__07d8bd7526aa69b2ae52061d5d94ffccdef61e6e41b48b7510715547a86819c4/images_list.json @@ -0,0 +1,93 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 | Thermal conductance of Al/Si and Al/GaN interfaces. (a) Thermal conductance of Al/Si Sample 1 (red spheres) and Sample 2 (blue spheres). Black and green dashed lines are interface thermal conductance calculated by phonon radiation limit and DMM, respectively. For comparison, we show previously measured Al/Si thermal conductance in open squares, triangles, and diamond by Minnich31, Wilson32, and Jiang33, respectively. Yellow solid triangles are measured thermal conductance of Al/Si with a native oxide layer, comparing with the results by Hopkins, shown in open circles14. (b) Thermal conductance of Al/GaN interface (red spheres). For comparison, calculated thermal conductance of phonon radiation limit (black dashed line) and DMM (green dashed line) is plotted. Previous measurement results by Donovan34 are shown in open circles, the Al film of which was deposited by e-beam evaporation. Phonon dispersion relations of Al/Si (c) and Al/GaN (d) are calculated from first-principles.", + "footnote": [], + "bbox": [ + [ + 160, + 90, + 840, + 585 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 | Interface structure of Al/Si Sample 1 and Sample 2. Cross-sectional TEM image of Al(111)/Si(111) Sample 1 (a) and Sample 2 (b). Scale bars are \\(2.5 \\mathrm{nm}\\) .", + "footnote": [], + "bbox": [ + [ + 234, + 90, + 755, + 450 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 | Phonon transport behavior across Al/Si interface computed by molecular dynamics. (a) Calculated thermal conductance of sharp (red dashed line) and rough (violet dashed line) Al/Si interfaces. (b) Phonon transmission coefficient for sharp (red) and rough (violet) interfaces (c) Schematic of temperature distributions near the sharp and rough interfaces. Here \\(\\mathrm{T}_{\\mathrm{p,h}}\\) and \\(\\mathrm{T}_{\\mathrm{p,l}}\\) represent temperatures of high- and low-energy phonons. \\(\\Delta \\mathrm{Ts}\\) and \\(\\Delta \\mathrm{T}_{\\mathrm{r}}\\) are temperatures drop across sharp and rough interfaces, respectively.", + "footnote": [], + "bbox": [ + [ + 150, + 174, + 833, + 350 + ] + ], + "page_idx": 9 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "Figures", + "footnote": [], + "bbox": [ + [ + 75, + 103, + 490, + 483 + ] + ], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [ + [ + 75, + 63, + 912, + 295 + ] + ], + "page_idx": 16 + } +] \ No newline at end of file diff --git a/preprint/preprint__07d8bd7526aa69b2ae52061d5d94ffccdef61e6e41b48b7510715547a86819c4/preprint__07d8bd7526aa69b2ae52061d5d94ffccdef61e6e41b48b7510715547a86819c4.mmd b/preprint/preprint__07d8bd7526aa69b2ae52061d5d94ffccdef61e6e41b48b7510715547a86819c4/preprint__07d8bd7526aa69b2ae52061d5d94ffccdef61e6e41b48b7510715547a86819c4.mmd new file mode 100644 index 0000000000000000000000000000000000000000..856637516c5b54d8cff93c1403a5e5e84832f5b4 --- /dev/null +++ b/preprint/preprint__07d8bd7526aa69b2ae52061d5d94ffccdef61e6e41b48b7510715547a86819c4/preprint__07d8bd7526aa69b2ae52061d5d94ffccdef61e6e41b48b7510715547a86819c4.mmd @@ -0,0 +1,290 @@ + +# Inelastic phonon transport across atomically sharp metal/semiconductor interfaces + +Houfu Song Tsinghua University + +Fang Liu Peking University + +Song Hu Shanghai Jiao Tong University + +Qinshu Li Tsinghua University + +Susu Yang Peking University + +Lu Zhao Tsinghua University + +Hailing Jiang Peking University + +Jinlin Wang Peking University + +Tao Wang Peking University + +Yee Kan Koh + +National University of Singapore https://orcid.org/0000- 0002- 4156- 6209 + +Feiyu Kang Tsinghua University + +Jungiao Wu University of California, Berkeley https://orcid.org/0000- 0002- 1498- 0148 + +xiaokun gu + +Institute of Engineering Thermophysics, School of Mechanical Engineering, Shanghai Jiao Tong University + +Bo Sun (sun.bo@sz.tsinghua.edu.cn) + +Tsinghua University https://orcid.org/0000- 0002- 2122- 6637 + +Xinqiang Wang + +Peking University https://orcid.org/0000- 0001- 5514- 8588 + +<--- Page Split ---> + +## Article + +Keywords: phonon transport, thermal transport, microelectronics + +Posted Date: September 8th, 2021 + +DOI: https://doi.org/10.21203/rs.3.rs- 828907/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on August 20th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 32600- w. + +<--- Page Split ---> + +# Inelastic phonon transport across atomically sharp metal/semiconductor interfaces + +Houfu Song \(^{1,*}\) , Fang Liu \(^{2,3,*}\) , Song Hu \(^{4,*}\) , Qinshu Li \(^{1,*}\) , Susu Yang \(^{2}\) , Lu Zhao \(^{1}\) , Hailing Jiang \(^{2}\) , Jinlin Wang \(^{2}\) , Tao Wang \(^{5}\) , Yee Kan Koh \(^{6}\) , Feiyu Kang \(^{1,7}\) , Junqiao Wu \(^{8,9}\) , Xiaokun Gu \(^{4,\dagger}\) , Bo Sun \(^{1,7,\dagger}\) and Xinqiang Wang \(^{2,3,\dagger}\) + +\(^{1}\) Tsinghua- Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China. \(^{2}\) State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano- optoelectronics, School of Physics, Peking University, Beijing 100871, China. \(^{3}\) Collaborative Innovation Center of Quantum Matter, Beijing 100871, China. \(^{4}\) Institute of Engineering Thermophysics, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. \(^{5}\) Electron Microscopy Laboratory, School of Physics, Peking University, Beijing 100871, China. \(^{6}\) Department of Mechanical Engineering and Center of Advanced 2D Materials, National University of Singapore, 117576 Singapore. \(^{7}\) Tsinghua Shenzhen International Graduate School and Guangdong Provincial Key Laboratory of Thermal Management Engineering & Materials, Shenzhen 518055, China. \(^{8}\) Department of Materials Science and Engineering, University of California, Berkeley, CA 94720, USA. \(^{9}\) Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. + +\*These authors contributed equally. \(^{1}\) To whom correspondence should be addressed: sun.bo@sz.tsinghua.edu.cn, wangshi@pku.edu.cn, xiaokun.gu@sjtu.edu.cn + +<--- Page Split ---> + +Understanding thermal transport across metal/semiconductor interfaces is crucial for heat dissipation of electronics The dominant heat carriers in non- metals, phonons, transport elastically across most interfaces, except for a few extreme cases where the two materials that formed the interface are highly dissimilar with a large difference in Debye temperature. In this work we show that even for two materials with similar Debye temperatures (Al/Si, Al/GaN), a substantial portion of phonons will transport inelastically across their interfaces at high temperatures, significantly enhancing interface thermal conductance. Moreover, we find that interface roughness strongly affects phonon transport process. For atomically sharp interfaces, phonons are allowed to transport inelastically and interface thermal conductance linearly increases at high temperatures. With increasing interface roughness, inelastic phonon transport rapidly diminishes. Our results provide new insights on phonon transport across interfaces and open up opportunities to engineering interface thermal conductance specifically for materials of relevance to microelectronics. + +In modern electronics, thermal resistance of interfaces (reciprocal of thermal conductance) is the main limiting factor for heat dissipation, especially for power electronics and high energy density applications \(^{1 - 5}\) . The scattering of heat carriers, predominately phonons, leads to interface thermal resistance. Over the past several decades, there were extensive studies on thermal conductance across metal/semiconductor interfaces, both experimentally and theoretically \(^{6 - 12}\) . However, the rare agreement between experiments and calculations as well as the scattered experimental results even for the same interface warrant study for much better understanding of thermal transport across interfaces \(^{6,13 - 15}\) . + +Theories have been developed to explain interface thermal conductance since 1950s, such as the widely used acoustic mismatch model (AMM) and diffuse mismatch model (DMM) \(^{16,17}\) . AMM is based on the assumption that phonon is reflected or transmitted specularly, while DMM assumes that phonon scattering is completely diffusive at the interface. Both DMM and AMM assume phonon transport across interface is elastic, which means the transmitted/reflected phonon has the same frequency as the incident phonon. The elastic transport assumption predicts that interface thermal conductance will reach a plateau at temperatures higher than the lower Debye temperature (TD) of the two materials that formed the interface, when all phonons in this side have been excited. In recent years, advanced calculation methods such as molecular dynamics (MD) and atomistic Green function (AGF) have been used to study phonon transport process across interfaces \(^{6 - 8,10,11,18}\) . Inelastic phonon transport process has been predicted to exist in interfaces between very dissimilar materials, where the transmitted phonons do not have the same frequency as the incident phonons \(^{7,11,19}\) , and anharmonicity was found to be of fundamental importance + +<--- Page Split ---> + +for the inelastic phonon transport across interfaces. Despite these advances, there are still controversies related to under what conditions inelastic process will occur in the first place. For example, Landry and McGaughey predicted that inelastic phonon transport will become dominant when temperature is higher than \(\sim 500 \mathrm{~K}\) , whereas Feng and Ruan computed that inelastic phonon transport contributes more than \(50\%\) to the total thermal conductance for Si/Ge interfaces even at room temperature \(^{7,11}\) . + +Most experimental results show that phonon transport across metal/semiconductor interface is an elastic process, as interface thermal conductance saturates at high temperatures for most interfaces under study \(^{6,15,20}\) . There are only a few exceptions to this trend \(^{12,13,21}\) , and all of these suggest a large Debye temperature difference across interface leads to the observation of non- saturated thermal conductance at high temperatures. The most notable one is highly dissimilar Bi/diamond interface (the TD ratio of diamond and Bi is \(\sim 19\) ), where Lyeo and Cahill observed a linear increase of thermal conductance with temperature even at high temperatures \(^{12}\) . This is attributed to the temperature- dependent inelastic phonon transport, which add an additional thermal transport channel across interfaces \(^{12,16}\) . However, it is still an open question how large a Debye temperature difference is required for inelastic phonon transport to occur, which needs to be examined in details. + +The lack of high- quality interface limited experimental study of phonon transport across interfaces. Extrinsic phonon scattering centers, such as atomic intermixing, interface roughness and contamination, would easily scatter phonons and bury the phonon's intrinsic elastic and inelastic transport processes across interfaces \(^{22}\) . Epitaxial metal/semiconductor interfaces are usually used to study the intrinsic interface phonon transport due to their importance and high quality \(^{6,22 - 24}\) . However, previous studies on epitaxial interfaces used typically lacked atomic- level structural details \(^{12,21}\) with interface roughness and atomic intermixing often ignored, thus only lead to qualitative analysis and limit our understanding of intrinsic phonon transport across interfaces. + +Here, we report the observation of inelastic phonon transport across metal/semiconductor interfaces, with a clear crossover from elastic- dominated to inelastic- dominated phonon transport following the rise of temperature and reduction of interface roughness. Our results show that, even in an interface formed with highly similar materials with Debye temperature ratio less than 1.5, inelastic phonon transport still exists and significantly enhances thermal conductance at high temperatures, suggesting that inelastic phonon transport is universal across interfaces even in acoustically similar materials. We also observed that inelastic phonon transport could only dominate the process when the interface is atomically sharp. Our MD simulations also confirmed that the interface roughness is crucial for inelastic phonon transport. + +We build high- quality metal/semiconductor interfaces by epitaxial growth of Al(111) on Si(111) and GaN (0001) using molecular beam epitaxy (MBE) (See Methods and Supplementary Information Note + +<--- Page Split ---> + +I). Before the growth of Al/Si interface, a Si wafer was cleaned by hydrofluoric acid to remove native oxide and then was heated in vacuum at \(300^{\circ}\mathrm{C}\) for 30 minutes to degas. The growth was in ultrahigh vacuum to ensure that the interface is free of oxide layer and adsorbates. To fabricate an Al/Si interface with controlled interface quality, Al growth was proceeded at different temperatures, \(100^{\circ}\mathrm{C}\) and \(300^{\circ}\mathrm{C}\) (denoted as Sample 1 and Sample 2, respectively), knowing that \(100^{\circ}\mathrm{C}\) is the optimum temperature for Al deposition in our prior work \(^{25}\) (see Supplementary Information Note I). For Al/GaN interface, GaN thin film was grown on sapphire substrate in MBE chamber first, and then the temperature was ramped down to \(150^{\circ}\mathrm{C}\) to grow Al layer. For comparison, an Al/SiO2/Si sample was also prepared by e-beam evaporation of Al on Si substrate in presence of native oxide. + +We measure the thermal conductance of Al/Si and Al/GaN interfaces over a wide range of temperatures (80- 700 K) by time- domain thermoreflectance (TDTR) \(^{26,27}\) . The details of our TDTR setup, data analysis and uncertainty estimation can be found in Methods and Supplementary Information Note IV. Note that only the Al/Si Sample 1 can reach 700 K for TDTR measurement. For Al/Si Sample 2 and Al/GaN, the Al films start to melt and large pores form when temperature is higher than 600 K, making TDTR measurement impossible. + +The measured thermal conductance, G, of Al/Si and Al/GaN interfaces as a function of temperature is plotted in Fig. 1a and Fig. 1b. At room temperature, Al/Si Sample 1 and Al/GaN show a record high thermal conductance of \(370\mathrm{MWm^{- 2}K^{- 1}}\) and \(410\mathrm{MWm^{- 2}K^{- 1}}\) , respectively. For both Al/Si Sample 1 and Al/GaN, our results can be clearly divided into two regimes. At temperatures lower than the Debye temperature of Al ( \(\mathrm{TD} = 428\mathrm{K}\) ), the thermal conductance of Al/Si Sample 1 and Al/GaN gradually saturate with the increase of temperature, which has the same trend as previous measured thermal conductance of Al/Si and Al/GaN interfaces. However, when temperature approaches 400 K (close to the Debye temperature of Al) and beyond, both Al/Si Sample 1 and Al/GaN show linear increase in thermal conductance with temperature instead of reaching a plateau. For Al/Si Sample 2, it has a thermal conductance of \(330\mathrm{MWm^{- 2}K^{- 1}}\) at room temperature. Throughout the low temperatures ( \(\mathrm{T}< \mathrm{TD}\) ), the thermal conductance of Al/Si Sample 2 is \(\sim 10\%\) lower than that of Al/Si Sample 1. However, unlike Al/Si Sample 1 and Al/GaN, Al/Si Sample 2 shows a saturated thermal conductance when \(\mathrm{T} > \mathrm{TD}\) . + +For metal/semiconductor interface, there are four thermal transport pathways, which are the phonon- phonon transport across interface including both elastic and inelastic phonon transport, as well as the electron- phonon coupling in the metal and across interfaces. The effect of electron- phonon coupling across interfaces is negligible \(^{13}\) . The electron- phonon coupling in Al adds an additional thermal resistance in series with the phonon- phonon interactions \(^{28}\) , which is driven by the thermal non- equilibrium between electrons and phonons near the interface. To calculate the phonon- phonon transport + +<--- Page Split ---> + +induced interface thermal conductance alone, we follow Majumdar and Reddy's treatment of electron- phonon coupling to use \(\mathrm{G} = \frac{G_{\mathrm{ep}}G_{\mathrm{pp}}}{G_{\mathrm{ep}} + G_{\mathrm{pp}}}\) , where G is the total thermal conductance, \(\mathrm{G_{ep}}\) and \(\mathrm{G_{pp}}\) are electron- phonon coupling and phonon- phonon transport induced thermal conductance, respectively28. The electron- phonon coupling induces the conductance \(\mathrm{G_{ep}} = \sqrt{\mathrm{g}\Lambda_{\mathrm{p}}}\) , where g is the electron cooling rate and \(\Lambda_{\mathrm{p}}\) is the lattice thermal conductivity of Al. We use experimental determined \(\mathrm{g}^{29}\) and first- principles calculated \(\Lambda_{\mathrm{p}}^{30}\) , which have been reported previously and are well accepted, to determine \(\mathrm{G_{ep}}\) . The calculated \(\mathrm{G_{pp}}\) for Al/Si Sample 1, Sample 2 and Al/GaN are shown in Fig. S3. It shows that \(\mathrm{G_{pp}}\) of Al/Si Sample 1 and Al/GaN interfaces has a stronger temperature dependence than G, while \(\mathrm{G_{pp}}\) of Al/Si Sample 2 changes slightly with temperature at high temperatures. + +To study the relationship between interface quality and thermal conductance and understand the difference of the two Al/Si interfaces, high- angle annular dark- field scanning transmission electron microscopy (HAADF- STEM) was used to study the cross- sectional interface structure, as shown in Fig. 2. The interface structure of Al/Si Sample 1 is shown in Fig. 2a, showing crystalline Al film epitaxially grown on Si substrate. The interplanar spacing between Al is 2.3 Å, in agreement with the lattice constant of Al(111)25. The enlarged area in yellow box demonstrates that the interface between Al and Si is atomically sharp, with only 1 - 2 distorted layers of interface atoms observed. The cross- sectional HAADF- STEM image of Al/Si Sample 2 is shown in Fig. 2b and Fig. S2a. Unlike the Al/Si Sample 1, it shows a higher interface roughness of around 1 - 2 nm, which roughly equals the thickness of 3- 6 atomic layers of Si. Further structure analysis in Supplementary Fig. S2 shows that the rough interface of Sample 2 results from the intermixing of Al and Si atoms, which is due to the high growth temperature that facilitates atomic diffusion across interfaces. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1 | Thermal conductance of Al/Si and Al/GaN interfaces. (a) Thermal conductance of Al/Si Sample 1 (red spheres) and Sample 2 (blue spheres). Black and green dashed lines are interface thermal conductance calculated by phonon radiation limit and DMM, respectively. For comparison, we show previously measured Al/Si thermal conductance in open squares, triangles, and diamond by Minnich31, Wilson32, and Jiang33, respectively. Yellow solid triangles are measured thermal conductance of Al/Si with a native oxide layer, comparing with the results by Hopkins, shown in open circles14. (b) Thermal conductance of Al/GaN interface (red spheres). For comparison, calculated thermal conductance of phonon radiation limit (black dashed line) and DMM (green dashed line) is plotted. Previous measurement results by Donovan34 are shown in open circles, the Al film of which was deposited by e-beam evaporation. Phonon dispersion relations of Al/Si (c) and Al/GaN (d) are calculated from first-principles.
+ +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2 | Interface structure of Al/Si Sample 1 and Sample 2. Cross-sectional TEM image of Al(111)/Si(111) Sample 1 (a) and Sample 2 (b). Scale bars are \(2.5 \mathrm{nm}\) .
+ +To understand the phonon transport mechanism, we use non- equilibrium molecular dynamics (NEMD) to compute interfacial thermal conductance of Al/Si interfaces with different roughness (see Methods and Supplementary Information Note VI). The Al/Si structure along the (111) orientation is established with sharp and rough interface, as shown in Fig. S5a and Fig. S5b. The sharp interface is composed of Si(111) \(3 \times 3\) unit cell and Al(111) \(4 \times 4\) unit cell. Before calculating the thermal interface conductance, the structure was fully relaxed to reduce the stress at the interface. A rough interface was attained by locally melting the interface at \(3000 \mathrm{K}\) and quenched at \(300 \mathrm{K}\) in the MD simulation, and the thickness of the rough interface is about \(1.1 \mathrm{nm}\) . The simulation results with a heat bath temperature difference of \(60 \mathrm{K}\) at the sharp interface of \(500 \mathrm{K}\) is shown in Supplementary Information Note VI, and the thermal conductance was calculated as \(738 \mathrm{MW} \mathrm{m}^{- 2} \mathrm{K}^{- 1}\) . The thermal conductance predicted by MD at high temperature is shown in Fig. 3a. The interface thermal conductance at the rough interface is lower than the value of sharp interface, which is consistent with the experiment results. As temperature rises, the increasing slope of thermal conductance as a function of temperature at the sharp interface is much higher than that of rough interface, demonstrating that the surface roughness is crucial for the observation of inelastic phonon scattering. Considering that MD simulation does not make assumptions on the + +<--- Page Split ---> + +phonon scattering mechanisms, the temperature dependence of thermal conductance at high temperatures is possibly universal, indicating that inelastic phonon scattering is always expected at high- quality interfaces. + +![](images/Figure_3.jpg) + +
Fig. 3 | Phonon transport behavior across Al/Si interface computed by molecular dynamics. (a) Calculated thermal conductance of sharp (red dashed line) and rough (violet dashed line) Al/Si interfaces. (b) Phonon transmission coefficient for sharp (red) and rough (violet) interfaces (c) Schematic of temperature distributions near the sharp and rough interfaces. Here \(\mathrm{T}_{\mathrm{p,h}}\) and \(\mathrm{T}_{\mathrm{p,l}}\) represent temperatures of high- and low-energy phonons. \(\Delta \mathrm{Ts}\) and \(\Delta \mathrm{T}_{\mathrm{r}}\) are temperatures drop across sharp and rough interfaces, respectively.
+ +To further explain the observed distinct temperature dependence of the thermal conductance, we calculate the spectral phonon transmissivity using atomistic Green's function, as shown in Fig. 3b. For the sharp interface, the transmissivity of low- energy phonons (here we define phonons within the energy overlap between Al and Si, i.e. lower than \(10\mathrm{THz}\) , as low- energy phonons) is higher than the high- energy phonons. This will result in a relatively large temperature difference between these phonons, as sketched in Fig. 3c. Such a thermal non- equilibrium between high- energy phonons and low- energy phonons leads to mode conversion and energy communication between them through phonon scatterings. Thus, the high- energy phonons with low transmission probability will convert to high transmissivity low- energy phonons before they undergo the transport process across interface, leading to inelastic phonon transport. + +For rough interfaces, the difference of transmissivity between the low- and high- energy phonons becomes smaller comparing with that of sharp interfaces, as the transmissivity for all phonons reduces. As a result, the temperature difference of different phonons is expected to be smaller, thus the phonon non- equilibrium is smaller for rough interfaces. The reduced phonon non- equilibrium leads to less energy communication between high and low energy phonons, and the inelastic phonon transport will diminish. + +Our results point out that large Debye temperature difference is not required for inelastic phonon + +<--- Page Split ---> + +transport process to occur, as the Debye temperature ratio of both Al/Si and Al/GaN is less than 1.5. This finding is in stark contrast to what previous experiments suggested, where inelastic transport can be only observed across interfaces formed with large Debye temperature difference12,13,21. This is also the first direct observation of the crossover from elastic- dominated to inelastic- dominated interface phonon transport processes over the wide temperature range far below to far above \(\mathrm{T_D}\) . Our work also suggest that optical phonons contribute significantly in the inelastic transport. As shown in Fig. 1d, the high- energy phonons are predominately optical phonons in GaN. For Al/Si, the thermal conductance does not saturate at \(700\mathrm{K}\) , which is higher than the Debye temperature of Si, suggesting that high frequency optical phonons excited at temperatures higher than \(700\mathrm{K}\) contribute to the thermal conductance. + +A recent work by Cheng et al. claimed that no inelastic phonon transport is observed in atomically sharp \(\mathrm{Al / Al_2O_3}\) interface grown by MBE6. We would like to point out that at least 5 monolayers of \(\mathrm{Al_2O_3}\) near the interface are distorted, as shown in their TEM image, which is similar to our Al/Si Sample 2 and not as sharp as our Al/Si Sample 1. Their work actually echoes with our finding, that significant inelastic thermal transport can only be observed at atomically sharp interfaces. + +In summary, we report the observation of inelastic phonon transport across high- quality Al/Si and Al/GaN interfaces grown by MBE. We observed a continuously increasing thermal conductance at high temperatures, which is attributed to inelastic phonon transport process across the interface. The inelastic phonon transport is expected to occur at atomically sharp interfaces where the strong phonon non- equilibrium exists, in contrast to rough interfaces. This work sheds light on increasing thermal conductance across interface at high temperatures and improving heat dissipation of electronic devices. + +<--- Page Split ---> + +## References + +1. Moore, A. L. & Shi, L. Emerging challenges and materials for thermal management of electronics. Mater. Today 17, 163–174 (2014). +2. Pop, E., Sinha, S. & Goodson, K. E. Heat Generation and Transport in Nanometer-Scale Transistors. Proc. IEEE 94, 1587–1601 (2006). +3. Cahill, D. G., Goodson, K. & Majumdar, A. Thermometry and Thermal Transport in Micro/Nanoscale Solid-State Devices and Structures. J. Heat Transf. 124, 223–241 (2001). +4. Cahill, D. G. et al. Nanoscale thermal transport. J. Appl. Phys. 93, 793–818 (2002). +5. Sun, B. et al. Dislocation-induced thermal transport anisotropy in single-crystal group-III nitride films. Nat. Mater. 18, 136–140 (2019). +6. Cheng, Z. et al. Thermal conductance across harmonic-matched epitaxial Al-sapphire heterointerfaces. Commun. Phys. 3, 1–8 (2020). +7. Landry, E. S. & McGaughey, A. J. H. Thermal boundary resistance predictions from molecular dynamics simulations and theoretical calculations. Phys. Rev. B 80, 165304 (2009). +8. Wu, X. & Luo, T. The importance of anharmonicity in thermal transport across solid-solid interfaces. J. Appl. Phys. 115, 014901 (2014). +9. Fang, J., Qian, X., Zhao, C. Y., Li, B. & Gu, X. Monitoring anharmonic phonon transport across interfaces in one-dimensional lattice chains. Phys. Rev. E 101, 022133 (2020). +10. Le, N. Q. et al. Effects of bulk and interfacial anharmonicity on thermal conductance at solid/solid interfaces. Phys. Rev. B 95, 245417 (2017). +11. Feng, T., Zhong, Y., Shi, J. & Ruan, X. Unexpected high inelastic phonon transport across solid-solid interface: Modal nonequilibrium molecular dynamics simulations and Landauer analysis. Phys. Rev. B 99, 045301 (2019). +12. Lyeo, H. K. & Cahill, D. G. Thermal conductance of interfaces between highly dissimilar materials. Phys. Rev. B 73, 144301 (2006). +13. Stoner, R. J. & Maris, H. J. Kapitza conductance and heat flow between solids at temperatures from 50 to 300 K. Phys. Rev. B 48, 16373–16387 (1993). +14. Hopkins, P. E. et al. Influence of anisotropy on thermal boundary conductance at solid interfaces. Phys. Rev. B 84, 125408 (2011). +15. Ye, N. et al. Thermal transport across metal silicide-silicon interfaces: An experimental comparison between epitaxial and nonepitaxial interfaces. Phys. Rev. B 95, 085430 (2017). +16. Swartz, E. T. & Pohl, R. O. Thermal boundary resistance. Rev. Mod. Phys. 61, 605–668 (1989). +17. Little, W. A. The transport of heat between dissimilar solids at low temperatures. Can. J. Phys. 37, + +<--- Page Split ---> + +334- 349 (1959). + +18. Yang, N. et al. Thermal Interface Conductance Between Aluminum and Silicon by Molecular Dynamics Simulations. J. Comput. Theor. Nanosci. 12, 168-174 (2015). + +19. Sääskilahti, K., Oksanen, J., Tulkki, J. & Volz, S. Role of anharmonic phonon scattering in the spectrally decomposed thermal conductance at planar interfaces. Phys. Rev. B 90, 134312 (2014). + +20. Koh, Y. R. et al. Thermal boundary conductance across epitaxial metal/sapphire interfaces. Phys. Rev. B 102, 205304 (2020). + +21. Hopkins, P. E., Norris, P. M. & Stevens, R. J. Influence of Inelastic Scattering at Metal-Dielectric Interfaces. J. Heat Transf. 130, (2008). + +22. Ye, N. et al. Thermal transport across metal silicide-silicon interfaces: An experimental comparison between epitaxial and nonepitaxial interfaces. Phys. Rev. B 95, 085430 (2017). + +23. Costescu, R. M., Wall, M. A. & Cahill, D. G. Thermal conductance of epitaxial interfaces. Phys. Rev. B 67, 054302 (2003). + +24. Ju, J. (Zi-J). et al. Thermoelectric properties of In-rich InGaN and InN/InGaN superlattices. AIP Adv. 6, 045216 (2016). + +25. Liu, S. et al. Molecular beam epitaxy of single-crystalline aluminum film for low threshold ultraviolet plasmonic nanolasers. Appl. Phys. Lett. 112, 231904 (2018). + +26. Cahill, D. G. Analysis of heat flow in layered structures for time-domain thermoreflectance. Rev. Sci. Instrum. 75, 5119-5122 (2004). + +27. Sun, B. & Koh, Y. K. Understanding and eliminating artifact signals from diffusely scattered pump beam in measurements of rough samples by time-domain thermoreflectance (TDTR). Rev. Sci. Instrum. 87, 064901 (2016). + +28. Majumdar, A. & Reddy, P. Role of electron-phonon coupling in thermal conductance of metal-nonmetal interfaces. Appl. Phys. Lett. 84, 4768-4770 (2004). + +29. Waldecker, L., Bertoni, R., Ernstorfer, R. & Vorberger, J. Electron-Phonon Coupling and Energy Flow in a Simple Metal beyond the Two-Temperature Approximation. Phys. Rev. X 6, 021003 (2016). + +30. Jain, A. & McGaughey, A. J. H. Thermal transport by phonons and electrons in aluminum, silver, and gold from first principles. Phys. Rev. B 93, 081206 (2016). + +31. Minnich, A. J. et al. Thermal Conductivity Spectroscopy Technique to Measure Phonon Mean Free Paths. Phys. Rev. Lett. 107, 095901 (2011). + +32. Wilson, R. B. & Cahill, D. G. Anisotropic failure of Fourier theory in time-domain thermoreflectance experiments. Nat. Commun. 5, 5075 (2014). + +33. Jiang, P., Lindsay, L., Huang, X. & Koh, Y. K. Interfacial phonon scattering and transmission loss + +<--- Page Split ---> + +in \(>1\) um thick silicon- on- insulator thin films. Phys. Rev. B 97, 195308 (2018). + +34. Donovan, B. F. et al. Thermal boundary conductance across metal-gallium nitride interfaces from 80 to 450 K. Appl. Phys. Lett. 105, 203502 (2014). + +## Methods + +Samples. The epitaxial growth of Al was carried out in a molecular beam epitaxy (MBE) system with a base pressure of \(2.7 \times 10^{- 7}\) Torr. A Si wafer was cleaned by hydrofluoric acid to remove the native oxide layer before loading into MBE chamber. The Si wafer was degassed at \(900^{\circ}\mathrm{C}\) for 90 minutes, and then cooled down for aluminum growth. To form an Al/Si interface with controlled quality, Al growth was proceeded at different temperature: \(100^{\circ}\mathrm{C}\) for Sample 1 and \(300^{\circ}\mathrm{C}\) for Sample 2. The deposition rate of Al is \(15 \mathrm{nm / min}\) . On GaN substrate, Al is grown at \(150^{\circ}\mathrm{C}\) . + +TDTR measurement. We measure interface thermal conductance of Al/Si at 80- 700 K by time- domain thermoreflectance (TDTR) in a Janis VPF- 800 cryostat \(^{26}\) . The measurement was performed with \(5\mathrm{x}\) objective lens with \(1 / \mathrm{e}^{2}\) radius of \(10.6 \mu \mathrm{m}\) and a modulation frequency of \(10.1 \mathrm{MHz}\) for pump beam. The laser power is in the range of \(100 - 200 \mathrm{mW}\) for low temperature measurements, with steady- state temperature rise less than \(2 \mathrm{K}\) . + +DMM and radiation limit calculation. Assuming a Debye approximation of phonon dispersion relation, the phonon transmission probability \(\alpha_{A}(\omega)\) can be written as \(^{16}\) , + +\[\alpha_{A}(\omega) = \frac{\sum_{j}\nu_{B}^{-2}}{\sum_{j}\nu_{A}^{-2} + \sum_{j}\nu_{B}^{-2}} \quad (1.)\] + +where \(\omega\) is phonon frequency, \(\nu\) is phonon group velocity, superscript \(A\) and \(B\) refers to materials at one and the other side of interface, respectively, \(j\) is the branch of phonons. In this way, interface thermal conductance \(G\) is given by \(^{16}\) , + +\[\mathrm{G} = \frac{1}{4}\sum_{j}\int_{0}^{\omega_{A,j}^{\mathrm{Debye}}}\mathrm{D}_{A,j}(\omega)\cdot \frac{\partial}{\partial\mathrm{T}}\mathrm{f}(\omega ,\mathrm{T})\cdot \hbar \omega \cdot \nu_{A,j}\cdot \alpha_{A}(\omega)\mathrm{d}\omega \quad (2.)\] + +where D is the phonon density of states, fis Bose- Einstein distribution, \(\nu\) is phonon group velocity, \(\omega_{A,j}^{\mathrm{Debye}}\) is the Debye frequency of phonon mode \(j\) in material A. + +The maximum interface thermal conductance based on elastic phonon scattering is considered as phonon radiation limit ( \(\mathrm{G}_{\mathrm{RL}}\) ). In this case, all the phonons in material B below the Debye frequency of material A are assumed to transmit across the interface with unit transmission probability ( \(\alpha_{B} = 1\) ). With this assumption, the thermal conductance can be written as, + +\[\mathrm{G}_{\mathrm{RL}} = \frac{1}{4}\sum_{j}\int_{0}^{\omega_{A,j}^{\mathrm{Debye}}}\mathrm{D}_{\mathrm{B},j}(\omega)\cdot \frac{\partial}{\partial\mathrm{T}}\mathrm{f}(\omega ,\mathrm{T})\cdot \hbar \omega \cdot \nu_{\mathrm{B},j}\mathrm{d}\omega \quad (3.)\] + +<--- Page Split ---> + +MD simulation. The cross- section area is \(19.89 \times 22.97 \times 10^{- 20} \mathrm{m}^2\) . The simulation is based on non- equilibrium molecular dynamics (NEMD) method, which applies heat bath at both ends of the structure and calculates the interface thermal conductance using Fourier's Law: + +\[\mathrm{G} = \frac{\mathrm{J}}{\mathrm{A}\Delta\mathrm{T}}\] + +where \(A\) is heat flux across interface at unit time (W), \(J\) is cross- section area \((\mathrm{m}^2)\) , and \(\Delta T\) is temperature drop across interface (K). The simulation is carried using LAMMPS by MEAN potential function with time step of 1fs. The system is under canonical ensemble (NVT) with a relaxation time of 500 ps. The temperature and heat flux are recorded under micro- canonical ensemble (NVE) by applying heat bath using Langevin method. The structure length L is set as \(44 \mathrm{nm}\) and \(49 \mathrm{nm}\) for comparison, and the structure along the other two dimensions are set as infinite. + +## Acknowledgements + +We acknowledge funding support from National Natural Science Foundation of China (No. 12004211), Shenzhen Science and Technology Program (No. RCYX20200714114643187), and Tsinghua Shenzhen International Graduate School (No. QD2021008N). X.W. acknowledge Beijing Outstanding Young Scientist Program (No. BJJWZYJH0120191000103) and the National Natural Science Foundation of China (Nos. 61734001 and 61521004). + +## Data availability + +Source data are available for this paper. All other data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request. + +## Code availability + +The analysis codes that support the findings of the study are available from the corresponding authors upon reasonable request. + +## Author contributions + +B.S. and X.W. conceived the project and supervised the research with J.W. and F.K. H.S., Q.L. and L.Z. carried out the TDTR measurement and the DMM calculations. S.Y., F.L., and X.W. grew and characterized the samples. T.W., H.J. and J.W. performed the TEM analysis. S.H. and X.G conducted the MD simulations. B.S. and X.G. figured out the heat transport mechanism with help from Y.K.K. All authors contributed to the discussions and manuscript preparation. + +## Competing interests + +<--- Page Split ---> + +The authors declare no competing interests. + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + +
Figures
+ +![](images/Figure_1.jpg) + + +![](images/Figure_2.jpg) + +
Figure 1
+ +![](images/Figure_3.jpg) + + +Thermal conductance of Al/Si and Al/GaN interfaces. (a) Thermal conductance of Al/Si Sample 1 (red spheres) and Sample 2 (blue spheres). Black and green dashed lines are interface thermal conductance calculated by phonon radiation limit and DMM, respectively. For comparison, we show previously measured Al/Si thermal conductance in open squares, triangles, and diamond by Minnich31, Wilson32, + +<--- Page Split ---> + +and Jiang33, respectively. Yellow solid triangles are measured thermal conductance of Al/Si with a native oxide layer, comparing with the results by Hopkins, shown in open circles14. (b) Thermal conductance of Al/GaN interface (red spheres). For comparison, calculated thermal conductance of phonon radiation limit (black dashed line) and DMM (green dashed line) is plotted. Previous measurement results by Donovan 34 are shown in open circles, the Al film of which was deposited by e-beam evaporation. Phonon dispersion relations of Al/ Si (c) and Al/GaN (d) are calculated from first-principles. + +![PLACEHOLDER_17_0] + +
Figure 2
+ +Interface structure of Al/Si Sample 1 and Sample 2. Cross- sectional TEM image of Al(111)/Si(111) Sample 1 (a) and Sample 2 (b). Scale bars are 2.5 nm. + +<--- Page Split ---> +![PLACEHOLDER_18_0] + +
Figure 3
+ +Phonon transport behavior across Al/Si interface computed by molecular dynamics. (a) Calculated thermal conductance of sharp (red dashed line) and rough (violet dashed line) Al/Si interfaces. (b) Phonon transmission coefficient for sharp (red) and rough (violet) interfaces (c) Schematic of temperature distributions near the sharp and rough interfaces. Here Tp,h and Tp,l represent temperatures of high- and low- energy phonons. \(\Delta \mathrm{TS}\) and \(\Delta \mathrm{Tr}\) are temperatures drop across sharp and rough interfaces, respectively. + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- SINC.docx + +<--- Page Split ---> diff --git a/preprint/preprint__07d8bd7526aa69b2ae52061d5d94ffccdef61e6e41b48b7510715547a86819c4/preprint__07d8bd7526aa69b2ae52061d5d94ffccdef61e6e41b48b7510715547a86819c4_det.mmd b/preprint/preprint__07d8bd7526aa69b2ae52061d5d94ffccdef61e6e41b48b7510715547a86819c4/preprint__07d8bd7526aa69b2ae52061d5d94ffccdef61e6e41b48b7510715547a86819c4_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..b08f7d1082d70df74b16d7539979f8f5e9cc4d5e --- /dev/null +++ b/preprint/preprint__07d8bd7526aa69b2ae52061d5d94ffccdef61e6e41b48b7510715547a86819c4/preprint__07d8bd7526aa69b2ae52061d5d94ffccdef61e6e41b48b7510715547a86819c4_det.mmd @@ -0,0 +1,376 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 933, 175]]<|/det|> +# Inelastic phonon transport across atomically sharp metal/semiconductor interfaces + +<|ref|>text<|/ref|><|det|>[[42, 195, 280, 240]]<|/det|> +Houfu Song Tsinghua University + +<|ref|>text<|/ref|><|det|>[[44, 245, 207, 285]]<|/det|> +Fang Liu Peking University + +<|ref|>text<|/ref|><|det|>[[44, 291, 323, 331]]<|/det|> +Song Hu Shanghai Jiao Tong University + +<|ref|>text<|/ref|><|det|>[[44, 337, 230, 377]]<|/det|> +Qinshu Li Tsinghua University + +<|ref|>text<|/ref|><|det|>[[44, 383, 207, 423]]<|/det|> +Susu Yang Peking University + +<|ref|>text<|/ref|><|det|>[[44, 429, 230, 469]]<|/det|> +Lu Zhao Tsinghua University + +<|ref|>text<|/ref|><|det|>[[44, 475, 207, 515]]<|/det|> +Hailing Jiang Peking University + +<|ref|>text<|/ref|><|det|>[[44, 521, 207, 560]]<|/det|> +Jinlin Wang Peking University + +<|ref|>text<|/ref|><|det|>[[44, 567, 207, 606]]<|/det|> +Tao Wang Peking University + +<|ref|>text<|/ref|><|det|>[[44, 612, 155, 631]]<|/det|> +Yee Kan Koh + +<|ref|>text<|/ref|><|det|>[[50, 635, 694, 655]]<|/det|> +National University of Singapore https://orcid.org/0000- 0002- 4156- 6209 + +<|ref|>text<|/ref|><|det|>[[44, 660, 230, 700]]<|/det|> +Feiyu Kang Tsinghua University + +<|ref|>text<|/ref|><|det|>[[44, 706, 694, 747]]<|/det|> +Jungiao Wu University of California, Berkeley https://orcid.org/0000- 0002- 1498- 0148 + +<|ref|>text<|/ref|><|det|>[[44, 752, 142, 770]]<|/det|> +xiaokun gu + +<|ref|>text<|/ref|><|det|>[[44, 774, 886, 815]]<|/det|> +Institute of Engineering Thermophysics, School of Mechanical Engineering, Shanghai Jiao Tong University + +<|ref|>text<|/ref|><|det|>[[44, 820, 406, 840]]<|/det|> +Bo Sun (sun.bo@sz.tsinghua.edu.cn) + +<|ref|>text<|/ref|><|det|>[[50, 843, 585, 862]]<|/det|> +Tsinghua University https://orcid.org/0000- 0002- 2122- 6637 + +<|ref|>text<|/ref|><|det|>[[44, 867, 177, 886]]<|/det|> +Xinqiang Wang + +<|ref|>text<|/ref|><|det|>[[50, 890, 564, 909]]<|/det|> +Peking University https://orcid.org/0000- 0001- 5514- 8588 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 45, 101, 63]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 84, 600, 103]]<|/det|> +Keywords: phonon transport, thermal transport, microelectronics + +<|ref|>text<|/ref|><|det|>[[44, 121, 339, 141]]<|/det|> +Posted Date: September 8th, 2021 + +<|ref|>text<|/ref|><|det|>[[44, 159, 463, 179]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 828907/v1 + +<|ref|>text<|/ref|><|det|>[[44, 196, 910, 240]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 275, 930, 317]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on August 20th, 2022. See the published version at https://doi.org/10.1038/s41467- 022- 32600- w. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[241, 101, 755, 146]]<|/det|> +# Inelastic phonon transport across atomically sharp metal/semiconductor interfaces + +<|ref|>text<|/ref|><|det|>[[147, 180, 854, 250]]<|/det|> +Houfu Song \(^{1,*}\) , Fang Liu \(^{2,3,*}\) , Song Hu \(^{4,*}\) , Qinshu Li \(^{1,*}\) , Susu Yang \(^{2}\) , Lu Zhao \(^{1}\) , Hailing Jiang \(^{2}\) , Jinlin Wang \(^{2}\) , Tao Wang \(^{5}\) , Yee Kan Koh \(^{6}\) , Feiyu Kang \(^{1,7}\) , Junqiao Wu \(^{8,9}\) , Xiaokun Gu \(^{4,\dagger}\) , Bo Sun \(^{1,7,\dagger}\) and Xinqiang Wang \(^{2,3,\dagger}\) + +<|ref|>text<|/ref|><|det|>[[147, 281, 852, 600]]<|/det|> +\(^{1}\) Tsinghua- Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518055, China. \(^{2}\) State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano- optoelectronics, School of Physics, Peking University, Beijing 100871, China. \(^{3}\) Collaborative Innovation Center of Quantum Matter, Beijing 100871, China. \(^{4}\) Institute of Engineering Thermophysics, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. \(^{5}\) Electron Microscopy Laboratory, School of Physics, Peking University, Beijing 100871, China. \(^{6}\) Department of Mechanical Engineering and Center of Advanced 2D Materials, National University of Singapore, 117576 Singapore. \(^{7}\) Tsinghua Shenzhen International Graduate School and Guangdong Provincial Key Laboratory of Thermal Management Engineering & Materials, Shenzhen 518055, China. \(^{8}\) Department of Materials Science and Engineering, University of California, Berkeley, CA 94720, USA. \(^{9}\) Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. + +<|ref|>text<|/ref|><|det|>[[148, 632, 850, 700]]<|/det|> +\*These authors contributed equally. \(^{1}\) To whom correspondence should be addressed: sun.bo@sz.tsinghua.edu.cn, wangshi@pku.edu.cn, xiaokun.gu@sjtu.edu.cn + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 852, 407]]<|/det|> +Understanding thermal transport across metal/semiconductor interfaces is crucial for heat dissipation of electronics The dominant heat carriers in non- metals, phonons, transport elastically across most interfaces, except for a few extreme cases where the two materials that formed the interface are highly dissimilar with a large difference in Debye temperature. In this work we show that even for two materials with similar Debye temperatures (Al/Si, Al/GaN), a substantial portion of phonons will transport inelastically across their interfaces at high temperatures, significantly enhancing interface thermal conductance. Moreover, we find that interface roughness strongly affects phonon transport process. For atomically sharp interfaces, phonons are allowed to transport inelastically and interface thermal conductance linearly increases at high temperatures. With increasing interface roughness, inelastic phonon transport rapidly diminishes. Our results provide new insights on phonon transport across interfaces and open up opportunities to engineering interface thermal conductance specifically for materials of relevance to microelectronics. + +<|ref|>text<|/ref|><|det|>[[147, 439, 852, 607]]<|/det|> +In modern electronics, thermal resistance of interfaces (reciprocal of thermal conductance) is the main limiting factor for heat dissipation, especially for power electronics and high energy density applications \(^{1 - 5}\) . The scattering of heat carriers, predominately phonons, leads to interface thermal resistance. Over the past several decades, there were extensive studies on thermal conductance across metal/semiconductor interfaces, both experimentally and theoretically \(^{6 - 12}\) . However, the rare agreement between experiments and calculations as well as the scattered experimental results even for the same interface warrant study for much better understanding of thermal transport across interfaces \(^{6,13 - 15}\) . + +<|ref|>text<|/ref|><|det|>[[147, 615, 852, 910]]<|/det|> +Theories have been developed to explain interface thermal conductance since 1950s, such as the widely used acoustic mismatch model (AMM) and diffuse mismatch model (DMM) \(^{16,17}\) . AMM is based on the assumption that phonon is reflected or transmitted specularly, while DMM assumes that phonon scattering is completely diffusive at the interface. Both DMM and AMM assume phonon transport across interface is elastic, which means the transmitted/reflected phonon has the same frequency as the incident phonon. The elastic transport assumption predicts that interface thermal conductance will reach a plateau at temperatures higher than the lower Debye temperature (TD) of the two materials that formed the interface, when all phonons in this side have been excited. In recent years, advanced calculation methods such as molecular dynamics (MD) and atomistic Green function (AGF) have been used to study phonon transport process across interfaces \(^{6 - 8,10,11,18}\) . Inelastic phonon transport process has been predicted to exist in interfaces between very dissimilar materials, where the transmitted phonons do not have the same frequency as the incident phonons \(^{7,11,19}\) , and anharmonicity was found to be of fundamental importance + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 852, 205]]<|/det|> +for the inelastic phonon transport across interfaces. Despite these advances, there are still controversies related to under what conditions inelastic process will occur in the first place. For example, Landry and McGaughey predicted that inelastic phonon transport will become dominant when temperature is higher than \(\sim 500 \mathrm{~K}\) , whereas Feng and Ruan computed that inelastic phonon transport contributes more than \(50\%\) to the total thermal conductance for Si/Ge interfaces even at room temperature \(^{7,11}\) . + +<|ref|>text<|/ref|><|det|>[[147, 214, 853, 457]]<|/det|> +Most experimental results show that phonon transport across metal/semiconductor interface is an elastic process, as interface thermal conductance saturates at high temperatures for most interfaces under study \(^{6,15,20}\) . There are only a few exceptions to this trend \(^{12,13,21}\) , and all of these suggest a large Debye temperature difference across interface leads to the observation of non- saturated thermal conductance at high temperatures. The most notable one is highly dissimilar Bi/diamond interface (the TD ratio of diamond and Bi is \(\sim 19\) ), where Lyeo and Cahill observed a linear increase of thermal conductance with temperature even at high temperatures \(^{12}\) . This is attributed to the temperature- dependent inelastic phonon transport, which add an additional thermal transport channel across interfaces \(^{12,16}\) . However, it is still an open question how large a Debye temperature difference is required for inelastic phonon transport to occur, which needs to be examined in details. + +<|ref|>text<|/ref|><|det|>[[147, 465, 852, 657]]<|/det|> +The lack of high- quality interface limited experimental study of phonon transport across interfaces. Extrinsic phonon scattering centers, such as atomic intermixing, interface roughness and contamination, would easily scatter phonons and bury the phonon's intrinsic elastic and inelastic transport processes across interfaces \(^{22}\) . Epitaxial metal/semiconductor interfaces are usually used to study the intrinsic interface phonon transport due to their importance and high quality \(^{6,22 - 24}\) . However, previous studies on epitaxial interfaces used typically lacked atomic- level structural details \(^{12,21}\) with interface roughness and atomic intermixing often ignored, thus only lead to qualitative analysis and limit our understanding of intrinsic phonon transport across interfaces. + +<|ref|>text<|/ref|><|det|>[[147, 666, 852, 858]]<|/det|> +Here, we report the observation of inelastic phonon transport across metal/semiconductor interfaces, with a clear crossover from elastic- dominated to inelastic- dominated phonon transport following the rise of temperature and reduction of interface roughness. Our results show that, even in an interface formed with highly similar materials with Debye temperature ratio less than 1.5, inelastic phonon transport still exists and significantly enhances thermal conductance at high temperatures, suggesting that inelastic phonon transport is universal across interfaces even in acoustically similar materials. We also observed that inelastic phonon transport could only dominate the process when the interface is atomically sharp. Our MD simulations also confirmed that the interface roughness is crucial for inelastic phonon transport. + +<|ref|>text<|/ref|><|det|>[[148, 867, 850, 909]]<|/det|> +We build high- quality metal/semiconductor interfaces by epitaxial growth of Al(111) on Si(111) and GaN (0001) using molecular beam epitaxy (MBE) (See Methods and Supplementary Information Note + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 852, 306]]<|/det|> +I). Before the growth of Al/Si interface, a Si wafer was cleaned by hydrofluoric acid to remove native oxide and then was heated in vacuum at \(300^{\circ}\mathrm{C}\) for 30 minutes to degas. The growth was in ultrahigh vacuum to ensure that the interface is free of oxide layer and adsorbates. To fabricate an Al/Si interface with controlled interface quality, Al growth was proceeded at different temperatures, \(100^{\circ}\mathrm{C}\) and \(300^{\circ}\mathrm{C}\) (denoted as Sample 1 and Sample 2, respectively), knowing that \(100^{\circ}\mathrm{C}\) is the optimum temperature for Al deposition in our prior work \(^{25}\) (see Supplementary Information Note I). For Al/GaN interface, GaN thin film was grown on sapphire substrate in MBE chamber first, and then the temperature was ramped down to \(150^{\circ}\mathrm{C}\) to grow Al layer. For comparison, an Al/SiO2/Si sample was also prepared by e-beam evaporation of Al on Si substrate in presence of native oxide. + +<|ref|>text<|/ref|><|det|>[[147, 314, 852, 456]]<|/det|> +We measure the thermal conductance of Al/Si and Al/GaN interfaces over a wide range of temperatures (80- 700 K) by time- domain thermoreflectance (TDTR) \(^{26,27}\) . The details of our TDTR setup, data analysis and uncertainty estimation can be found in Methods and Supplementary Information Note IV. Note that only the Al/Si Sample 1 can reach 700 K for TDTR measurement. For Al/Si Sample 2 and Al/GaN, the Al films start to melt and large pores form when temperature is higher than 600 K, making TDTR measurement impossible. + +<|ref|>text<|/ref|><|det|>[[147, 465, 852, 758]]<|/det|> +The measured thermal conductance, G, of Al/Si and Al/GaN interfaces as a function of temperature is plotted in Fig. 1a and Fig. 1b. At room temperature, Al/Si Sample 1 and Al/GaN show a record high thermal conductance of \(370\mathrm{MWm^{- 2}K^{- 1}}\) and \(410\mathrm{MWm^{- 2}K^{- 1}}\) , respectively. For both Al/Si Sample 1 and Al/GaN, our results can be clearly divided into two regimes. At temperatures lower than the Debye temperature of Al ( \(\mathrm{TD} = 428\mathrm{K}\) ), the thermal conductance of Al/Si Sample 1 and Al/GaN gradually saturate with the increase of temperature, which has the same trend as previous measured thermal conductance of Al/Si and Al/GaN interfaces. However, when temperature approaches 400 K (close to the Debye temperature of Al) and beyond, both Al/Si Sample 1 and Al/GaN show linear increase in thermal conductance with temperature instead of reaching a plateau. For Al/Si Sample 2, it has a thermal conductance of \(330\mathrm{MWm^{- 2}K^{- 1}}\) at room temperature. Throughout the low temperatures ( \(\mathrm{T}< \mathrm{TD}\) ), the thermal conductance of Al/Si Sample 2 is \(\sim 10\%\) lower than that of Al/Si Sample 1. However, unlike Al/Si Sample 1 and Al/GaN, Al/Si Sample 2 shows a saturated thermal conductance when \(\mathrm{T} > \mathrm{TD}\) . + +<|ref|>text<|/ref|><|det|>[[147, 766, 852, 909]]<|/det|> +For metal/semiconductor interface, there are four thermal transport pathways, which are the phonon- phonon transport across interface including both elastic and inelastic phonon transport, as well as the electron- phonon coupling in the metal and across interfaces. The effect of electron- phonon coupling across interfaces is negligible \(^{13}\) . The electron- phonon coupling in Al adds an additional thermal resistance in series with the phonon- phonon interactions \(^{28}\) , which is driven by the thermal non- equilibrium between electrons and phonons near the interface. To calculate the phonon- phonon transport + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 852, 306]]<|/det|> +induced interface thermal conductance alone, we follow Majumdar and Reddy's treatment of electron- phonon coupling to use \(\mathrm{G} = \frac{G_{\mathrm{ep}}G_{\mathrm{pp}}}{G_{\mathrm{ep}} + G_{\mathrm{pp}}}\) , where G is the total thermal conductance, \(\mathrm{G_{ep}}\) and \(\mathrm{G_{pp}}\) are electron- phonon coupling and phonon- phonon transport induced thermal conductance, respectively28. The electron- phonon coupling induces the conductance \(\mathrm{G_{ep}} = \sqrt{\mathrm{g}\Lambda_{\mathrm{p}}}\) , where g is the electron cooling rate and \(\Lambda_{\mathrm{p}}\) is the lattice thermal conductivity of Al. We use experimental determined \(\mathrm{g}^{29}\) and first- principles calculated \(\Lambda_{\mathrm{p}}^{30}\) , which have been reported previously and are well accepted, to determine \(\mathrm{G_{ep}}\) . The calculated \(\mathrm{G_{pp}}\) for Al/Si Sample 1, Sample 2 and Al/GaN are shown in Fig. S3. It shows that \(\mathrm{G_{pp}}\) of Al/Si Sample 1 and Al/GaN interfaces has a stronger temperature dependence than G, while \(\mathrm{G_{pp}}\) of Al/Si Sample 2 changes slightly with temperature at high temperatures. + +<|ref|>text<|/ref|><|det|>[[147, 314, 853, 606]]<|/det|> +To study the relationship between interface quality and thermal conductance and understand the difference of the two Al/Si interfaces, high- angle annular dark- field scanning transmission electron microscopy (HAADF- STEM) was used to study the cross- sectional interface structure, as shown in Fig. 2. The interface structure of Al/Si Sample 1 is shown in Fig. 2a, showing crystalline Al film epitaxially grown on Si substrate. The interplanar spacing between Al is 2.3 Å, in agreement with the lattice constant of Al(111)25. The enlarged area in yellow box demonstrates that the interface between Al and Si is atomically sharp, with only 1 - 2 distorted layers of interface atoms observed. The cross- sectional HAADF- STEM image of Al/Si Sample 2 is shown in Fig. 2b and Fig. S2a. Unlike the Al/Si Sample 1, it shows a higher interface roughness of around 1 - 2 nm, which roughly equals the thickness of 3- 6 atomic layers of Si. Further structure analysis in Supplementary Fig. S2 shows that the rough interface of Sample 2 results from the intermixing of Al and Si atoms, which is due to the high growth temperature that facilitates atomic diffusion across interfaces. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[160, 90, 840, 585]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 590, 852, 858]]<|/det|> +
Fig. 1 | Thermal conductance of Al/Si and Al/GaN interfaces. (a) Thermal conductance of Al/Si Sample 1 (red spheres) and Sample 2 (blue spheres). Black and green dashed lines are interface thermal conductance calculated by phonon radiation limit and DMM, respectively. For comparison, we show previously measured Al/Si thermal conductance in open squares, triangles, and diamond by Minnich31, Wilson32, and Jiang33, respectively. Yellow solid triangles are measured thermal conductance of Al/Si with a native oxide layer, comparing with the results by Hopkins, shown in open circles14. (b) Thermal conductance of Al/GaN interface (red spheres). For comparison, calculated thermal conductance of phonon radiation limit (black dashed line) and DMM (green dashed line) is plotted. Previous measurement results by Donovan34 are shown in open circles, the Al film of which was deposited by e-beam evaporation. Phonon dispersion relations of Al/Si (c) and Al/GaN (d) are calculated from first-principles.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[234, 90, 755, 450]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 465, 850, 507]]<|/det|> +
Fig. 2 | Interface structure of Al/Si Sample 1 and Sample 2. Cross-sectional TEM image of Al(111)/Si(111) Sample 1 (a) and Sample 2 (b). Scale bars are \(2.5 \mathrm{nm}\) .
+ +<|ref|>text<|/ref|><|det|>[[147, 540, 852, 909]]<|/det|> +To understand the phonon transport mechanism, we use non- equilibrium molecular dynamics (NEMD) to compute interfacial thermal conductance of Al/Si interfaces with different roughness (see Methods and Supplementary Information Note VI). The Al/Si structure along the (111) orientation is established with sharp and rough interface, as shown in Fig. S5a and Fig. S5b. The sharp interface is composed of Si(111) \(3 \times 3\) unit cell and Al(111) \(4 \times 4\) unit cell. Before calculating the thermal interface conductance, the structure was fully relaxed to reduce the stress at the interface. A rough interface was attained by locally melting the interface at \(3000 \mathrm{K}\) and quenched at \(300 \mathrm{K}\) in the MD simulation, and the thickness of the rough interface is about \(1.1 \mathrm{nm}\) . The simulation results with a heat bath temperature difference of \(60 \mathrm{K}\) at the sharp interface of \(500 \mathrm{K}\) is shown in Supplementary Information Note VI, and the thermal conductance was calculated as \(738 \mathrm{MW} \mathrm{m}^{- 2} \mathrm{K}^{- 1}\) . The thermal conductance predicted by MD at high temperature is shown in Fig. 3a. The interface thermal conductance at the rough interface is lower than the value of sharp interface, which is consistent with the experiment results. As temperature rises, the increasing slope of thermal conductance as a function of temperature at the sharp interface is much higher than that of rough interface, demonstrating that the surface roughness is crucial for the observation of inelastic phonon scattering. Considering that MD simulation does not make assumptions on the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 89, 850, 155]]<|/det|> +phonon scattering mechanisms, the temperature dependence of thermal conductance at high temperatures is possibly universal, indicating that inelastic phonon scattering is always expected at high- quality interfaces. + +<|ref|>image<|/ref|><|det|>[[150, 174, 833, 350]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[147, 365, 852, 507]]<|/det|> +
Fig. 3 | Phonon transport behavior across Al/Si interface computed by molecular dynamics. (a) Calculated thermal conductance of sharp (red dashed line) and rough (violet dashed line) Al/Si interfaces. (b) Phonon transmission coefficient for sharp (red) and rough (violet) interfaces (c) Schematic of temperature distributions near the sharp and rough interfaces. Here \(\mathrm{T}_{\mathrm{p,h}}\) and \(\mathrm{T}_{\mathrm{p,l}}\) represent temperatures of high- and low-energy phonons. \(\Delta \mathrm{Ts}\) and \(\Delta \mathrm{T}_{\mathrm{r}}\) are temperatures drop across sharp and rough interfaces, respectively.
+ +<|ref|>text<|/ref|><|det|>[[147, 540, 852, 758]]<|/det|> +To further explain the observed distinct temperature dependence of the thermal conductance, we calculate the spectral phonon transmissivity using atomistic Green's function, as shown in Fig. 3b. For the sharp interface, the transmissivity of low- energy phonons (here we define phonons within the energy overlap between Al and Si, i.e. lower than \(10\mathrm{THz}\) , as low- energy phonons) is higher than the high- energy phonons. This will result in a relatively large temperature difference between these phonons, as sketched in Fig. 3c. Such a thermal non- equilibrium between high- energy phonons and low- energy phonons leads to mode conversion and energy communication between them through phonon scatterings. Thus, the high- energy phonons with low transmission probability will convert to high transmissivity low- energy phonons before they undergo the transport process across interface, leading to inelastic phonon transport. + +<|ref|>text<|/ref|><|det|>[[147, 766, 852, 884]]<|/det|> +For rough interfaces, the difference of transmissivity between the low- and high- energy phonons becomes smaller comparing with that of sharp interfaces, as the transmissivity for all phonons reduces. As a result, the temperature difference of different phonons is expected to be smaller, thus the phonon non- equilibrium is smaller for rough interfaces. The reduced phonon non- equilibrium leads to less energy communication between high and low energy phonons, and the inelastic phonon transport will diminish. + +<|ref|>text<|/ref|><|det|>[[178, 892, 850, 909]]<|/det|> +Our results point out that large Debye temperature difference is not required for inelastic phonon + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 852, 306]]<|/det|> +transport process to occur, as the Debye temperature ratio of both Al/Si and Al/GaN is less than 1.5. This finding is in stark contrast to what previous experiments suggested, where inelastic transport can be only observed across interfaces formed with large Debye temperature difference12,13,21. This is also the first direct observation of the crossover from elastic- dominated to inelastic- dominated interface phonon transport processes over the wide temperature range far below to far above \(\mathrm{T_D}\) . Our work also suggest that optical phonons contribute significantly in the inelastic transport. As shown in Fig. 1d, the high- energy phonons are predominately optical phonons in GaN. For Al/Si, the thermal conductance does not saturate at \(700\mathrm{K}\) , which is higher than the Debye temperature of Si, suggesting that high frequency optical phonons excited at temperatures higher than \(700\mathrm{K}\) contribute to the thermal conductance. + +<|ref|>text<|/ref|><|det|>[[147, 314, 852, 431]]<|/det|> +A recent work by Cheng et al. claimed that no inelastic phonon transport is observed in atomically sharp \(\mathrm{Al / Al_2O_3}\) interface grown by MBE6. We would like to point out that at least 5 monolayers of \(\mathrm{Al_2O_3}\) near the interface are distorted, as shown in their TEM image, which is similar to our Al/Si Sample 2 and not as sharp as our Al/Si Sample 1. Their work actually echoes with our finding, that significant inelastic thermal transport can only be observed at atomically sharp interfaces. + +<|ref|>text<|/ref|><|det|>[[147, 438, 852, 583]]<|/det|> +In summary, we report the observation of inelastic phonon transport across high- quality Al/Si and Al/GaN interfaces grown by MBE. We observed a continuously increasing thermal conductance at high temperatures, which is attributed to inelastic phonon transport process across the interface. The inelastic phonon transport is expected to occur at atomically sharp interfaces where the strong phonon non- equilibrium exists, in contrast to rough interfaces. This work sheds light on increasing thermal conductance across interface at high temperatures and improving heat dissipation of electronic devices. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[148, 90, 230, 104]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[144, 113, 853, 910]]<|/det|> +1. Moore, A. L. & Shi, L. Emerging challenges and materials for thermal management of electronics. Mater. Today 17, 163–174 (2014). +2. Pop, E., Sinha, S. & Goodson, K. E. Heat Generation and Transport in Nanometer-Scale Transistors. Proc. IEEE 94, 1587–1601 (2006). +3. Cahill, D. G., Goodson, K. & Majumdar, A. Thermometry and Thermal Transport in Micro/Nanoscale Solid-State Devices and Structures. J. Heat Transf. 124, 223–241 (2001). +4. Cahill, D. G. et al. Nanoscale thermal transport. J. Appl. Phys. 93, 793–818 (2002). +5. Sun, B. et al. Dislocation-induced thermal transport anisotropy in single-crystal group-III nitride films. Nat. Mater. 18, 136–140 (2019). +6. Cheng, Z. et al. Thermal conductance across harmonic-matched epitaxial Al-sapphire heterointerfaces. Commun. Phys. 3, 1–8 (2020). +7. Landry, E. S. & McGaughey, A. J. H. Thermal boundary resistance predictions from molecular dynamics simulations and theoretical calculations. Phys. Rev. B 80, 165304 (2009). +8. Wu, X. & Luo, T. The importance of anharmonicity in thermal transport across solid-solid interfaces. J. Appl. Phys. 115, 014901 (2014). +9. Fang, J., Qian, X., Zhao, C. Y., Li, B. & Gu, X. Monitoring anharmonic phonon transport across interfaces in one-dimensional lattice chains. Phys. Rev. E 101, 022133 (2020). +10. Le, N. Q. et al. Effects of bulk and interfacial anharmonicity on thermal conductance at solid/solid interfaces. Phys. Rev. B 95, 245417 (2017). +11. Feng, T., Zhong, Y., Shi, J. & Ruan, X. Unexpected high inelastic phonon transport across solid-solid interface: Modal nonequilibrium molecular dynamics simulations and Landauer analysis. Phys. Rev. B 99, 045301 (2019). +12. Lyeo, H. K. & Cahill, D. G. Thermal conductance of interfaces between highly dissimilar materials. Phys. Rev. B 73, 144301 (2006). +13. Stoner, R. J. & Maris, H. J. Kapitza conductance and heat flow between solids at temperatures from 50 to 300 K. Phys. Rev. B 48, 16373–16387 (1993). +14. Hopkins, P. E. et al. Influence of anisotropy on thermal boundary conductance at solid interfaces. Phys. Rev. B 84, 125408 (2011). +15. Ye, N. et al. Thermal transport across metal silicide-silicon interfaces: An experimental comparison between epitaxial and nonepitaxial interfaces. Phys. Rev. B 95, 085430 (2017). +16. Swartz, E. T. & Pohl, R. O. Thermal boundary resistance. Rev. Mod. Phys. 61, 605–668 (1989). +17. Little, W. A. The transport of heat between dissimilar solids at low temperatures. Can. J. Phys. 37, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 90, 263, 104]]<|/det|> +334- 349 (1959). + +<|ref|>text<|/ref|><|det|>[[147, 114, 850, 155]]<|/det|> +18. Yang, N. et al. Thermal Interface Conductance Between Aluminum and Silicon by Molecular Dynamics Simulations. J. Comput. Theor. Nanosci. 12, 168-174 (2015). + +<|ref|>text<|/ref|><|det|>[[147, 164, 850, 205]]<|/det|> +19. Sääskilahti, K., Oksanen, J., Tulkki, J. & Volz, S. Role of anharmonic phonon scattering in the spectrally decomposed thermal conductance at planar interfaces. Phys. Rev. B 90, 134312 (2014). + +<|ref|>text<|/ref|><|det|>[[147, 214, 848, 255]]<|/det|> +20. Koh, Y. R. et al. Thermal boundary conductance across epitaxial metal/sapphire interfaces. Phys. Rev. B 102, 205304 (2020). + +<|ref|>text<|/ref|><|det|>[[147, 264, 850, 305]]<|/det|> +21. Hopkins, P. E., Norris, P. M. & Stevens, R. J. Influence of Inelastic Scattering at Metal-Dielectric Interfaces. J. Heat Transf. 130, (2008). + +<|ref|>text<|/ref|><|det|>[[147, 314, 850, 355]]<|/det|> +22. Ye, N. et al. Thermal transport across metal silicide-silicon interfaces: An experimental comparison between epitaxial and nonepitaxial interfaces. Phys. Rev. B 95, 085430 (2017). + +<|ref|>text<|/ref|><|det|>[[147, 364, 848, 405]]<|/det|> +23. Costescu, R. M., Wall, M. A. & Cahill, D. G. Thermal conductance of epitaxial interfaces. Phys. Rev. B 67, 054302 (2003). + +<|ref|>text<|/ref|><|det|>[[147, 414, 850, 455]]<|/det|> +24. Ju, J. (Zi-J). et al. Thermoelectric properties of In-rich InGaN and InN/InGaN superlattices. AIP Adv. 6, 045216 (2016). + +<|ref|>text<|/ref|><|det|>[[147, 464, 850, 506]]<|/det|> +25. Liu, S. et al. Molecular beam epitaxy of single-crystalline aluminum film for low threshold ultraviolet plasmonic nanolasers. Appl. Phys. Lett. 112, 231904 (2018). + +<|ref|>text<|/ref|><|det|>[[147, 514, 850, 555]]<|/det|> +26. Cahill, D. G. Analysis of heat flow in layered structures for time-domain thermoreflectance. Rev. Sci. Instrum. 75, 5119-5122 (2004). + +<|ref|>text<|/ref|><|det|>[[147, 564, 850, 630]]<|/det|> +27. Sun, B. & Koh, Y. K. Understanding and eliminating artifact signals from diffusely scattered pump beam in measurements of rough samples by time-domain thermoreflectance (TDTR). Rev. Sci. Instrum. 87, 064901 (2016). + +<|ref|>text<|/ref|><|det|>[[147, 640, 850, 681]]<|/det|> +28. Majumdar, A. & Reddy, P. Role of electron-phonon coupling in thermal conductance of metal-nonmetal interfaces. Appl. Phys. Lett. 84, 4768-4770 (2004). + +<|ref|>text<|/ref|><|det|>[[147, 690, 850, 731]]<|/det|> +29. Waldecker, L., Bertoni, R., Ernstorfer, R. & Vorberger, J. Electron-Phonon Coupling and Energy Flow in a Simple Metal beyond the Two-Temperature Approximation. Phys. Rev. X 6, 021003 (2016). + +<|ref|>text<|/ref|><|det|>[[147, 740, 850, 781]]<|/det|> +30. Jain, A. & McGaughey, A. J. H. Thermal transport by phonons and electrons in aluminum, silver, and gold from first principles. Phys. Rev. B 93, 081206 (2016). + +<|ref|>text<|/ref|><|det|>[[147, 790, 850, 831]]<|/det|> +31. Minnich, A. J. et al. Thermal Conductivity Spectroscopy Technique to Measure Phonon Mean Free Paths. Phys. Rev. Lett. 107, 095901 (2011). + +<|ref|>text<|/ref|><|det|>[[147, 840, 850, 881]]<|/det|> +32. Wilson, R. B. & Cahill, D. G. Anisotropic failure of Fourier theory in time-domain thermoreflectance experiments. Nat. Commun. 5, 5075 (2014). + +<|ref|>text<|/ref|><|det|>[[147, 891, 850, 907]]<|/det|> +33. Jiang, P., Lindsay, L., Huang, X. & Koh, Y. K. Interfacial phonon scattering and transmission loss + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 680, 105]]<|/det|> +in \(>1\) um thick silicon- on- insulator thin films. Phys. Rev. B 97, 195308 (2018). + +<|ref|>text<|/ref|><|det|>[[147, 114, 850, 156]]<|/det|> +34. Donovan, B. F. et al. Thermal boundary conductance across metal-gallium nitride interfaces from 80 to 450 K. Appl. Phys. Lett. 105, 203502 (2014). + +<|ref|>sub_title<|/ref|><|det|>[[148, 190, 214, 204]]<|/det|> +## Methods + +<|ref|>text<|/ref|><|det|>[[147, 214, 852, 356]]<|/det|> +Samples. The epitaxial growth of Al was carried out in a molecular beam epitaxy (MBE) system with a base pressure of \(2.7 \times 10^{- 7}\) Torr. A Si wafer was cleaned by hydrofluoric acid to remove the native oxide layer before loading into MBE chamber. The Si wafer was degassed at \(900^{\circ}\mathrm{C}\) for 90 minutes, and then cooled down for aluminum growth. To form an Al/Si interface with controlled quality, Al growth was proceeded at different temperature: \(100^{\circ}\mathrm{C}\) for Sample 1 and \(300^{\circ}\mathrm{C}\) for Sample 2. The deposition rate of Al is \(15 \mathrm{nm / min}\) . On GaN substrate, Al is grown at \(150^{\circ}\mathrm{C}\) . + +<|ref|>text<|/ref|><|det|>[[147, 365, 852, 483]]<|/det|> +TDTR measurement. We measure interface thermal conductance of Al/Si at 80- 700 K by time- domain thermoreflectance (TDTR) in a Janis VPF- 800 cryostat \(^{26}\) . The measurement was performed with \(5\mathrm{x}\) objective lens with \(1 / \mathrm{e}^{2}\) radius of \(10.6 \mu \mathrm{m}\) and a modulation frequency of \(10.1 \mathrm{MHz}\) for pump beam. The laser power is in the range of \(100 - 200 \mathrm{mW}\) for low temperature measurements, with steady- state temperature rise less than \(2 \mathrm{K}\) . + +<|ref|>text<|/ref|><|det|>[[147, 490, 850, 533]]<|/det|> +DMM and radiation limit calculation. Assuming a Debye approximation of phonon dispersion relation, the phonon transmission probability \(\alpha_{A}(\omega)\) can be written as \(^{16}\) , + +<|ref|>equation<|/ref|><|det|>[[404, 542, 848, 580]]<|/det|> +\[\alpha_{A}(\omega) = \frac{\sum_{j}\nu_{B}^{-2}}{\sum_{j}\nu_{A}^{-2} + \sum_{j}\nu_{B}^{-2}} \quad (1.)\] + +<|ref|>text<|/ref|><|det|>[[147, 590, 850, 657]]<|/det|> +where \(\omega\) is phonon frequency, \(\nu\) is phonon group velocity, superscript \(A\) and \(B\) refers to materials at one and the other side of interface, respectively, \(j\) is the branch of phonons. In this way, interface thermal conductance \(G\) is given by \(^{16}\) , + +<|ref|>equation<|/ref|><|det|>[[286, 661, 848, 705]]<|/det|> +\[\mathrm{G} = \frac{1}{4}\sum_{j}\int_{0}^{\omega_{A,j}^{\mathrm{Debye}}}\mathrm{D}_{A,j}(\omega)\cdot \frac{\partial}{\partial\mathrm{T}}\mathrm{f}(\omega ,\mathrm{T})\cdot \hbar \omega \cdot \nu_{A,j}\cdot \alpha_{A}(\omega)\mathrm{d}\omega \quad (2.)\] + +<|ref|>text<|/ref|><|det|>[[147, 715, 850, 759]]<|/det|> +where D is the phonon density of states, fis Bose- Einstein distribution, \(\nu\) is phonon group velocity, \(\omega_{A,j}^{\mathrm{Debye}}\) is the Debye frequency of phonon mode \(j\) in material A. + +<|ref|>text<|/ref|><|det|>[[147, 766, 852, 857]]<|/det|> +The maximum interface thermal conductance based on elastic phonon scattering is considered as phonon radiation limit ( \(\mathrm{G}_{\mathrm{RL}}\) ). In this case, all the phonons in material B below the Debye frequency of material A are assumed to transmit across the interface with unit transmission probability ( \(\alpha_{B} = 1\) ). With this assumption, the thermal conductance can be written as, + +<|ref|>equation<|/ref|><|det|>[[308, 863, 848, 907]]<|/det|> +\[\mathrm{G}_{\mathrm{RL}} = \frac{1}{4}\sum_{j}\int_{0}^{\omega_{A,j}^{\mathrm{Debye}}}\mathrm{D}_{\mathrm{B},j}(\omega)\cdot \frac{\partial}{\partial\mathrm{T}}\mathrm{f}(\omega ,\mathrm{T})\cdot \hbar \omega \cdot \nu_{\mathrm{B},j}\mathrm{d}\omega \quad (3.)\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[147, 88, 850, 156]]<|/det|> +MD simulation. The cross- section area is \(19.89 \times 22.97 \times 10^{- 20} \mathrm{m}^2\) . The simulation is based on non- equilibrium molecular dynamics (NEMD) method, which applies heat bath at both ends of the structure and calculates the interface thermal conductance using Fourier's Law: + +<|ref|>equation<|/ref|><|det|>[[464, 170, 533, 200]]<|/det|> +\[\mathrm{G} = \frac{\mathrm{J}}{\mathrm{A}\Delta\mathrm{T}}\] + +<|ref|>text<|/ref|><|det|>[[147, 213, 853, 357]]<|/det|> +where \(A\) is heat flux across interface at unit time (W), \(J\) is cross- section area \((\mathrm{m}^2)\) , and \(\Delta T\) is temperature drop across interface (K). The simulation is carried using LAMMPS by MEAN potential function with time step of 1fs. The system is under canonical ensemble (NVT) with a relaxation time of 500 ps. The temperature and heat flux are recorded under micro- canonical ensemble (NVE) by applying heat bath using Langevin method. The structure length L is set as \(44 \mathrm{nm}\) and \(49 \mathrm{nm}\) for comparison, and the structure along the other two dimensions are set as infinite. + +<|ref|>sub_title<|/ref|><|det|>[[149, 380, 290, 395]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[147, 404, 852, 521]]<|/det|> +We acknowledge funding support from National Natural Science Foundation of China (No. 12004211), Shenzhen Science and Technology Program (No. RCYX20200714114643187), and Tsinghua Shenzhen International Graduate School (No. QD2021008N). X.W. acknowledge Beijing Outstanding Young Scientist Program (No. BJJWZYJH0120191000103) and the National Natural Science Foundation of China (Nos. 61734001 and 61521004). + +<|ref|>sub_title<|/ref|><|det|>[[148, 544, 270, 559]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[148, 569, 850, 611]]<|/det|> +Source data are available for this paper. All other data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request. + +<|ref|>sub_title<|/ref|><|det|>[[148, 634, 272, 649]]<|/det|> +## Code availability + +<|ref|>text<|/ref|><|det|>[[148, 658, 850, 700]]<|/det|> +The analysis codes that support the findings of the study are available from the corresponding authors upon reasonable request. + +<|ref|>sub_title<|/ref|><|det|>[[149, 723, 303, 738]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[147, 747, 852, 864]]<|/det|> +B.S. and X.W. conceived the project and supervised the research with J.W. and F.K. H.S., Q.L. and L.Z. carried out the TDTR measurement and the DMM calculations. S.Y., F.L., and X.W. grew and characterized the samples. T.W., H.J. and J.W. performed the TEM analysis. S.H. and X.G conducted the MD simulations. B.S. and X.G. figured out the heat transport mechanism with help from Y.K.K. All authors contributed to the discussions and manuscript preparation. + +<|ref|>sub_title<|/ref|><|det|>[[148, 888, 295, 903]]<|/det|> +## Competing interests + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[148, 90, 444, 105]]<|/det|> +The authors declare no competing interests. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[75, 103, 490, 483]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 45, 144, 68]]<|/det|> +
Figures
+ +<|ref|>image<|/ref|><|det|>[[504, 105, 923, 483]]<|/det|> + +<|ref|>image<|/ref|><|det|>[[75, 488, 490, 789]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 816, 115, 835]]<|/det|> +
Figure 1
+ +<|ref|>image<|/ref|><|det|>[[504, 488, 920, 789]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[42, 856, 937, 947]]<|/det|> +Thermal conductance of Al/Si and Al/GaN interfaces. (a) Thermal conductance of Al/Si Sample 1 (red spheres) and Sample 2 (blue spheres). Black and green dashed lines are interface thermal conductance calculated by phonon radiation limit and DMM, respectively. For comparison, we show previously measured Al/Si thermal conductance in open squares, triangles, and diamond by Minnich31, Wilson32, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 45, 951, 180]]<|/det|> +and Jiang33, respectively. Yellow solid triangles are measured thermal conductance of Al/Si with a native oxide layer, comparing with the results by Hopkins, shown in open circles14. (b) Thermal conductance of Al/GaN interface (red spheres). For comparison, calculated thermal conductance of phonon radiation limit (black dashed line) and DMM (green dashed line) is plotted. Previous measurement results by Donovan 34 are shown in open circles, the Al film of which was deposited by e-beam evaporation. Phonon dispersion relations of Al/ Si (c) and Al/GaN (d) are calculated from first-principles. + +<|ref|>image<|/ref|><|det|>[[75, 206, 784, 740]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 774, 117, 793]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[42, 815, 898, 858]]<|/det|> +Interface structure of Al/Si Sample 1 and Sample 2. Cross- sectional TEM image of Al(111)/Si(111) Sample 1 (a) and Sample 2 (b). Scale bars are 2.5 nm. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[75, 63, 912, 295]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 335, 117, 354]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[41, 376, 949, 513]]<|/det|> +Phonon transport behavior across Al/Si interface computed by molecular dynamics. (a) Calculated thermal conductance of sharp (red dashed line) and rough (violet dashed line) Al/Si interfaces. (b) Phonon transmission coefficient for sharp (red) and rough (violet) interfaces (c) Schematic of temperature distributions near the sharp and rough interfaces. Here Tp,h and Tp,l represent temperatures of high- and low- energy phonons. \(\Delta \mathrm{TS}\) and \(\Delta \mathrm{Tr}\) are temperatures drop across sharp and rough interfaces, respectively. + +<|ref|>sub_title<|/ref|><|det|>[[44, 535, 310, 562]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 585, 764, 606]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 624, 175, 642]]<|/det|> +- SINC.docx + +<--- Page Split ---> diff --git a/preprint/preprint__07da031c5a170ed2448db8d4d493b3aefc0c8a156ebbd67b372a56dc6f8a0f87/images_list.json b/preprint/preprint__07da031c5a170ed2448db8d4d493b3aefc0c8a156ebbd67b372a56dc6f8a0f87/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..9b599457d39f1e503887806ebc921c88f18a33aa --- /dev/null +++ b/preprint/preprint__07da031c5a170ed2448db8d4d493b3aefc0c8a156ebbd67b372a56dc6f8a0f87/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 | Generation of acoustic spatiotemporal vortex pulses and their topological nature. a, Sketch of a meta-grating generating a spatiotemporal vortex pulse for airborne sound by breaking mirror symmetry, where the asymmetric structure is necessary to create the phase singularity in the spatiotemporal domain. b-d, Numerical simulations for the phase (above) and the amplitude (below) of the transmission spectrum function by breaking the mirror symmetry of the meta-grating. The synthetic parameter \\(\\eta\\) controls the deformation of symmetry breaking, and \\(\\eta = 0,0.5,0.7\\) correspond to b-d, respectively. The vortices with the winding numbers of \\(+1\\) and -1, are indicated by white and black circles, respectively. e, Nodal lines in the extended dimension with the asymmetry parameter \\(\\eta\\) , emerges at the critical values of \\(\\eta_c = 0.40\\) , for the vortices with the winding number as \\(+1\\) and -1 with the green and the purple lines, respectively.", + "footnote": [], + "bbox": [ + [ + 147, + 124, + 850, + 280 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2| Experimental measurement for the phase (a-c) and amplitude (d-f) of transmission spectrum function for the asymmetry parameters \\(\\eta = 0, 0.5, 0.7\\) , respectively. The vortices with the winding numbers of \\(+1\\) and \\(-1\\) , which are indicated by white and black circles, respectively.", + "footnote": [], + "bbox": [ + [ + 112, + 99, + 820, + 342 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 | Experimental measurement of STVP generations with the acoustic meta-grating. a, Sketch of the experimental setup with an incident Gaussian-profile pulse in both spatial and temporal domains. b, Experimental sample of the acoustic STVP generator with the asymmetry parameter \\(\\eta = 1.0\\) . c, Schematics of the perturbed case where fifteen particles of different shapes such as sphere, pyramid, cube and ring are randomly placed with additional small truncations. d-g, Simulation results of the transmitted pulse envelopes at the positions which are separated by \\(\\frac{\\lambda}{3}\\) from each other (\\(\\lambda\\) is the center wavelength of the pulse), respectively. h-k, Experimental measurements of the transmitted pulse envelopes at the corresponding positions of d-g, respectively. l-o, Experimental measurement of", + "footnote": [], + "bbox": [ + [ + 115, + 115, + 884, + 733 + ] + ], + "page_idx": 9 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4| Acoustic STVP propagation in real space. a-f, The pressure amplitude distribution (colormap) and the momentum density (arrows) of the transmitted wave are shown at the time of \\(-1.4, -0.5, 0.4, 1.4, 2.3, 3.2 \\text{ms}\\), respectively.", + "footnote": [], + "bbox": [ + [ + 113, + 84, + 800, + 395 + ] + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/preprint/preprint__07da031c5a170ed2448db8d4d493b3aefc0c8a156ebbd67b372a56dc6f8a0f87/preprint__07da031c5a170ed2448db8d4d493b3aefc0c8a156ebbd67b372a56dc6f8a0f87.mmd b/preprint/preprint__07da031c5a170ed2448db8d4d493b3aefc0c8a156ebbd67b372a56dc6f8a0f87/preprint__07da031c5a170ed2448db8d4d493b3aefc0c8a156ebbd67b372a56dc6f8a0f87.mmd new file mode 100644 index 0000000000000000000000000000000000000000..cabe3b6625e3080dbc33df500c16bfb42e27a9af --- /dev/null +++ b/preprint/preprint__07da031c5a170ed2448db8d4d493b3aefc0c8a156ebbd67b372a56dc6f8a0f87/preprint__07da031c5a170ed2448db8d4d493b3aefc0c8a156ebbd67b372a56dc6f8a0f87.mmd @@ -0,0 +1,170 @@ + +# Observation of acoustic spatiotemporal vortices + +Hongliang Zhang Zhejiang University + +Yeyang Sun Zhejiang University + +Junyi Huang Zhejiang University + +Bingjun Wu Zhejiang University + +Zhaoju Yang Zhejiang University https://orcid.org/0000- 0002- 9880- 2655 + +Konstantin Bliokh + +Konstantin BliokhTheoretical Quantum Physics Laboratory, Cluster for Pioneering Research, RIKENZhichao Ruan ( \(\square\) zhichao@zju.edu.cn)Zhejiang University https://orcid.org/0000- 0001- 8311- 6970 + +## Article + +Keywords: + +Posted Date: June 6th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 2978419/v1 + +License: \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: Yes there is potential Competing Interest. Z.R., Z.Y., H.Z., Y.S., J.H., and B.W. are named inventors on a number of patent applications related to this work. + +Version of Record: A version of this preprint was published at Nature Communications on October 6th, 2023. See the published version at https://doi.org/10.1038/s41467- 023- 41776- 8. + +<--- Page Split ---> + +# Observation of acoustic spatiotemporal vortices + +Hongliang Zhang \(^{1\#}\) , Yeyang Sun \(^{1\#}\) , Junyi Huang \(^{1\#}\) , Bingjun Wu, Zhaoju Yang \(^{1*}\) , Konstantin Y. Bliokh \(^{2,3,4}\) , and Zhichao Ruan \(^{1**}\) + +\(^{1}\) Interdisciplinary Center of Quantum Information, State Key Laboratory of Modern Optical Instrumentation, and Zhejiang Province Key Laboratory of Quantum Technology and Device, Department of Physics, Zhejiang University, Hangzhou 310027, China \(^{2}\) Theoretical Quantum Physics Laboratory, Cluster for Pioneering Research, RIKEN, Wakoshi, Saitama 351- 0198, Japan \(^{3}\) Centre of Excellence ENSEMBLE3 Sp. z o.o., 01- 919 Warsaw, Poland \(^{4}\) Donostia International Physics Center (DIPC), Donostia- San Sebastián 20018, Spain + +\* These authors equal contributions to this work \* zhaojiuyang@zju.edu.cn \*\* zhichao@zju.edu.cn + +## Abstract + +Vortices in fluids and gases have piqued the interest of human for centuries. Development of classical- wave physics and quantum mechanics highlighted wave vortices characterized by phase singularities and topological charges. In particular, vortex beams have found numerous applications in modern optics and other areas. Recently, optical spatiotemporal vortex states exhibiting the phase singularity both in space and time have been reported. Here, we report the first generation of acoustic spatiotemporal vortex pulses. We utilize an acoustic meta- grating with mirror- symmetry breaking as the spatiotemporal vortex generator. In the momentum- frequency domain, we unravel that the transmission spectrum functions exhibit a topological phase transition where the vortices with opposite topological charges are created or annihilated in pairs. Furthermore, with the topological textures of the nodal lines, these vortices are robust and exploited to generate spatiotemporal vortex pulse against structural perturbations and disorder. Our work paves the way for studies and applications of spatiotemporal structured waves in acoustics and other wave systems. + +## Introduction + +Wave vortices, i.e., structures with the wavefield intensity vanishing in the center and the phase winding around, are of enormous importance for various areas of physics. They are essential parts of almost any structured waves: atomic orbitals and superfluids in quantum mechanics, complex wave interference from ocean waves to nanophotonics and metamaterials, etc. Cylindrical vortex beams have been generated and found applications in electromagnetic [1- 8], sound [9- 17], elastic [18], electron [19- 21], neutron [22], and atom [23] waves. Such states contain on- axis vortex lines and carry intrinsic orbital angular momentum (OAM) along their propagation direction. + +<--- Page Split ---> + +Recently, there was a great rise of interest in spatiotemporal vortex pulses (STVPs), which are generalizations of usual 'spatial' vortex states to the space- time domain and the OAM tilted with respect to the propagation direction [24- 40]. This conforms with the rapidly growing field of space- time structured waves allowing manipulation in both spatial and temporal degrees of freedom [41,42]. In the simplest case, STVPs are flying doughnut- shaped pulses with the vortex line and OAM orthogonal to their propagation direction. Until now, such states have been generated only in optics, although theoretically these were also discussed for quantum matter and acoustic waves [33]. + +Here, we report the first generation of acoustic STVPs for sound waves in air. Our STVP generator is based on a meta- grating with spatial mirror symmetry breaking, which can be further controlled by a synthetic parameter. Through mapping the transmission spectra of the meta- grating as a function of the synthetic parameter, we show that there exist vortices in the momentum- frequency domains created and annihilated together in pairs at a critical point by mirror- symmetry breaking. In contrast to diffraction gratings with fork- like dislocations, which are used for the generation of spatial vortex beams in the first and higher orders of diffraction [1,3,4,19], this method uses the zeroth- order transmitted field and realizes simultaneous control in both spatial and temporal domains. Importantly, similar to the topological textures for electronic and optical systems [43,44], these vortices associated with the nodal lines are robust and exploited to generate STVPs with the topological protection against structural disorder. Our results open the avenue for spatiotemporal vortex generation and applications in acoustics and other areas of wave physics [7,11,32,45- 49]. + +## Results + +## Breaking spatial mirror symmetry for generating STVPs + +Our STVP generator is based on spatial mirror symmetry breaking [32]. Figure 1a schematically displays the spatial- symmetry analysis of the spatiotemporal vortex generation [the detailed geometry in Supplementary Material (SM) Sec. I]. Here, a spatiotemporal Gaussian pulse impinges on the structure along \(x = 0\) (indicated by the white dashed line). Without loss of generality, assume that a meta- grating exists mirror symmetry about the plane \(x = 0\) , the phase distribution of the transmitted pulse must also be symmetric about the mirror plane, and thus there is no phase singularity. Therefore, the necessary condition for generating STVPs with nonzero winding numbers is the mirror symmetry breaking, which provides an asymmetry modulation for sound in both spatial and temporal domains simultaneously. + +To realize the asymmetric spatiotemporal modulation, we design the meta- grating (Fig. 1a) with a unit cell consisting of four air blocks with different sizes as indicated by dashed boxes in SM Fig. S1. In the meta- grating, all cells are connected with a middle air channel (yellow areas in Fig. 1a). Initially, all the four air blocks are symmetric about the axis of \(x = 0\) . To break the spatial mirror symmetry, we introduce a synthetic parameter \(\eta\) , which describes the different \(x\) - directional shifts from the center \(\delta x_{i} = A_{i}\eta\) , where \(A_{i}\) is the shifting ratio given in Supplementary Table S1 for the \(i\) - th block and \(i = 1,2,3,4\) . Thus, \(\eta\) controls the degree of the mirror asymmetry of the grating. + +<--- Page Split ---> + +This asymmetric modulation stemming from mirror symmetry breaking can be illustrated by the transmission spectrum function \(T(k_{x},\omega)\) between the incident and transmitted waves, where \(\omega\) is the angular frequency of a plane wave, and \(k_{x}\) is the wavevector along the structure interface. Here only the zeroth order diffraction is considered in the operating- frequency range. For the mirror symmetric case of \(\eta = 0\) , as expected, the transmission spectrum function \(T(k_{x},\omega)\) exhibits symmetric spectrum about \(k_{x} = 0\) for both the phase and the amplitude distributions (Fig. 1b). On the other hand, by breaking the mirror symmetry, the case of \(\eta = 0.5\) shows that the transmission spectrum function \(T(k_{x},\omega)\) has two vortices with the winding numbers of \(l = +1\) (white circle) and \(l = -1\) (black circle), respectively (Fig. 1c). Correspondingly, such two phase singularities coincide with the zero- value transmission at the center of the vortices. Furthermore, for \(\eta = 0.7\) , the two vortices are further separated with a larger strength of mirror symmetry breaking (Fig. 1d). + +The vortices associated with the transmission spectrum function are analogous to the fundamental 'charges' in the \(k_{x} - \omega\) domain, because they are always created or annihilated together in pairs of opposite charges. To clearly illustrate the creation and annihilation, Fig. 1e shows the parameter \(\eta\) spectrum, which is in the form of nodal lines in the extended dimension with the asymmetry parameter \(\eta\) . When a plane with a fixed value of \(\eta\) has intersections with the nodal lines, there must be two vortices with opposite handedness appearing in the cross- section plane. Therefore, the total topological charge of the vortices is a conserved quantity of zero. Through numerical simulations, we find that the critical value of \(\eta\) is \(\eta_{c} = 0.40\) for our designed meta- grating. + +To experimentally demonstrate the topological phase transition, we fabricate three metagratings (SM Fig. S3) and measure the transmission spectrum function (Methods). Figure 2a- c show the measured phase distributions of the transmission spectrum function for \(\eta = 0, 0.5, 0.7\) , and the corresponding amplitude distributions are shown in Fig. 2d- f. For the mirror symmetric case (Fig. 2a and d), there is no phase singularity in the measured phase distribution of the transmission spectrum function. By breaking the mirror symmetry with \(\eta = 0.5\) , there are two vortices created with the winding numbers of \(+1\) and \(-1\) , marked by the white and black circles in Fig. 2b, and hence two zero- value transmissions occur at \(\omega /2\pi = 8.31 kHz\) and \(\omega /2\pi = 8.23 kHz\) in Fig. 2e, respectively. For the case of \(\eta = 0.7\) as shown in Fig. 2c and f, two vortices with opposite winding numbers are further separated at \(\omega /2\pi = 8.31 kHz\) and \(\omega /2\pi = 8.04 kHz\) . The measured results agree that the topological phase transition appears at the critical point of \(\eta_{c} = 0.40\) . The creation of the vortices confirms that the asymmetric modulation by breaking mirror symmetry indeed induces the topological charges in the \((k_{x},\omega)\) domain. + +## Topological protected generation of STVP + +Owing to the asymmetric modulation, the phase singularity of the transmission spectrum function located at \((\omega_{0}, k_{0x})\) in the \((k_{x},\omega)\) domain can be directly transferred into the spatiotemporal domain. Considering an incident Gaussian wave pulse with the central angular frequency \(\omega_{0}\) and transverse wavevector component \(k_{0x}\) , the transmitted wave packet can be determined to be a STVP with a nonzero winding number, but opposite to that of the vortex in + +<--- Page Split ---> + +the \((k_{x},\omega)\) domain (see the detailed derivation in SM Sec. II). Because of the topological robustness of the nodal lines, the corresponding vortices with the winding number \(l = \pm 1\) are stable. When the vortices are distanced from the critical points of the topological phase transition, small changes to the meta-grating geometry in the real space can be treated as perturbations. The strength of such topological protection for the vortex at \((\omega_{0},k_{0x})\) can be evaluated by the distance to its nearest vortex \(\Delta = \sqrt{(\omega_{n} - \omega_{0})^{2} + \nu^{2}(k_{nx} - k_{0x})^{2}}\) , where \(\omega_{n}\) and \(k_{nx}\) are the frequency and the wavevector component of the nearest vortex. + +We next experimentally demonstrate the topologically protected generation of acoustic STVPs, schematically displayed in Fig. 3a. Here, we choose the meta- grating with the asymmetry parameter \(\eta = 1.0\) , because such a meta- grating has a relatively strong strength of topological protection up to \(\Delta\) . We first experimentally investigate the transmission spectrum function (SM Sec. III and Fig. S4), which exhibits a vortex at \(\omega_{0} / 2\pi = 8.02kHz\) and \(k_{0x} = 0.01k_{0}\) \((k_{0}\) is the wavenumber in air). For \(k_{0x}\ll k_{0}\) , therefore, it can be used as for normally- incident pulses. Using an arc- like linear transducer array, with an oscillatory Gaussian envelopes electric signal at a central carrier frequency \(\omega_{0}\) , we produce a \(z\) - propagating Gaussian pulses, which has the full waist of \(145\mathrm{mm}\) and the durations of \(3.2\mathrm{ms}\) and the diffraction Rayleigh range about \(383\mathrm{mm}\) (the detailed experimental setup and the measurement found in Methods and Fig. S6). + +We first consider the unperturbed meta- grating as the experimental sample Fig. 3b. We numerically simulate the transmitted pulse envelope at different propagation distances of \(64.4\mathrm{mm}\) , \(78.8\mathrm{mm}\) , \(93.2\mathrm{mm}\) , and \(107.6\mathrm{mm}\) , which are separated by \(\frac{\lambda}{3}\) from each other ( \(\lambda\) is the center wavelength of the pulse) as indicated by red dash lines in Fig. 3a, respectively. Here the transmitted acoustic wave \(P(x,t) = s_{out}(x,t)e^{- i\omega_{0}t}\) , where \(s_{out}(x,t)\) is the transmitted pulse envelope. The simulation results are depicted in Fig. 3d- g, where the HSV color and the brightness represent the phase and amplitude of the pulse envelope, respectively. We then measure the transmitted pulse envelopes, as shown in Fig. 3h- k. The experimental measurements of the transmitted pulse envelope exhibit the vanishing amplitude and the whirl dislocated phase in the spatiotemporal domain corresponding to the winding number of \(l = - 1\) . Furthermore, the phase distributions at different distances exhibit that the phase of the STVP rotates around the center along with the pulse propagation, which agrees well with the central wavelength of the pulse. + +Using the experimental data and numerical \(z\) - propagation of the field, we calculate the real- time amplitude of the pulse and the momentum density at different instants of time, which shows the propagation evolution and diffraction of the generated STVP (Fig. 4). At the early stage of the pulse generation, Fig. 4a shows that the pressure amplitude increases as the pulse propagates along the \(z\) direction, and the directions of the momentum density indicate the compression and the decompression of air. However, the zero- value amplitude is shown up when \(t = - 0.4ms\) , which corresponds to the vortex center (Fig. 4b). Figs. 4c- d further exhibit the vortex rolls and diffract along with the propagation. Since there is an ongoing theoretical + +<--- Page Split ---> + +debate in community about the intrinsic OAM carried by a STVP [33,35,40], the calculation of the intrinsic OAM carried by the generated STVP is beyond the present studies, which needs more detailed theoretical and experimental investigation [SM Sec. IV]. + +To demonstrate the topologically robustness of STVP generation, we perturb the meta- grating structure by randomly placing fifteen photopolymer- resins particles of different shapes and sizes about \(0.8\mathrm{cm} - 1\mathrm{cm}\) , as shown in Fig. 3c. Moreover, as a regular perturbation to the grating, we also add one more block in the unit cell. The transmission spectrum function of the perturbed meta- grating still exhibited the same phase singularity with slightly shifted position at \(\omega_0 / 2\pi = 7.56kHz\) and \(k_{x0} = 0.02k_0\) (SM Fig. S5). Adjusting the incident pulse to the perturbed central frequency, we measured the transmitted pulse envelopes at the propagation distances of \(65.3\mathrm{mm}\) , \(80.6\mathrm{mm}\) , \(95.9\mathrm{mm}\) and \(111.2\mathrm{mm}\) , respectively. Fig. 31- o clearly show a STVP quite similar to that in the unperturbed case. + +## Discussion + +In summary, our experimental results demonstrate the topologically protected generation of acoustic STVPs in two spatial dimensions and one temporal dimension, using a 1D periodic meta- grating. On the one hand, acoustic STVPs open an avenue for acoustic spacetime- structured waves, so far mostly studied in optics [41,42]. On the other hand, our new method of the generation of STVPs opens can find applications in acoustics, optics, and other types of waves. One can expect that by designing 2D metasurfaces with an additional spatial dimension, one can synthesize full- dimensional \((3 + 1)\mathrm{D}\) spatiotemporal acoustic vortices, such as vortices with arbitrarily tilted OAM [30,31] or toroidal vortices. We also note that the nodal line in the space of \(\omega ,k_{x},\eta\) is mathematically analog to many topological textures in other nodal- line topological physical systems [43,44]. In general, due to drastic geometric and physical differences from conventional monochromatic vortex beams, the STVPs can bring novel functionalities to acoustic/optical manipulation of particles, information transfer, and other applications [7,11,32,45- 49]. + +Furthermore, similarly to the image processing of edge detection in the spatial domain [50- 63], our STVP generator, based on the phase singularity (vortex) in the momentum- frequency domain, operates as the first- order differentiator in both spatial and temporal domains. This allows efficient extraction of the spacetime boundary information in the incident sound wavepacket (SM Sec. V and Fig. S7), which can find useful applications in sonars and sensing. In our experiment, the frequency bandwidth with the near- linear dependence of the transmission amplitude near the vortex center, which provides the first- order differentiation, is about \(431.7\mathrm{Hz}\) . + +## Methods + +## Experimental setup and methods to measure the transmission spectrum function + +The experimental setup is shown in Fig. S2(a). A data acquisition (Bruel & Kjær 3160- A- 042- R) is used to collect the data of acoustic field and control the output waveform. Two + +<--- Page Split ---> + +microphones (Bruel & Kjær 4193- L- 004) are connected to the data acquisition and used to measure the acoustic field. We use a power amplifier (Bruel & Kjær 2735) to amplify the input signal. The displacement platform (LINBOU NFS03) and the data acquisition are integrated into a PC. The meta- grating and the sound absorber are placed between two glasses as shown in Fig. 3a. + +To measure the transmission spectrum function in the frequency domain (as shown in Fig. S2(b)), we use 10 transducers to form a rectangle source in the spatial domain, which ensures that the spatial spectrum of the incident field is sufficiently wide but don't overlap with the non- zero diffraction order. The incident(transmitted) acoustic wave \(P_{in(out)}(x,t) =\) \(s_{in(out)}(x,t)e^{- i\omega_0t}\) is collected by two microphones, where \(s_{in(out)}(x,t)\) is the pulse envelope of the input(transmitted) waves. One microphone is fixed in the acoustic field as a reference probe, and the other one probes the acoustic distribution through the displacement platform as a measurement probe. In order to obtain the transmission spectrum function of the signal, we obtain the spatial distribution of the sound field by sweeping the field, and using cross- power spectrum methods to process the data from the measurement probe and the reference probe. + +## Experimental setup and measurement principle of STVP + +We use a curved transducer array to simultaneously generate a series of pulses with spatiotemporal Gaussian envelope at the center frequency \(\omega_0 / 2\pi = 7.56 \times 10^3 Hz\) as shown in Fig. S6(a). The distribution of curved transducers array satisfies Gaussian function distribution \((z = \exp (x^2) - 1)\) , and the gap between the transducers in x direction is 1cm. We put the sample 50cm away from the transducers array. The distribution of \(s_{in}(x,t)\) is shown in Fig. S6(b). The height shows the amplitude and the color indicates the phase distribution, respectively. Figures S6(c, d) show the amplitude distribution of the envelope along (c)t = 0 and (d)x = 0. + +## Acknowledgement + +The authors acknowledge funding through the National Key Research and Development Program of China (Grant No. 2022YFA1405200, 2022YFA1404203), the National Natural Science Foundation of China (NSFC Grants Nos. 12174340, 12174339). Z. Y. acknowledges Zhejiang Provincial Natural Science Foundation of China under Grant No. LR23A040003, and the Excellent Youth Science Foundation Project (Overseas). + +## Author Contributions + +Z.R. initiated the idea and this project. H.Z., Y.S., and J.H. developed the meta- grating design, and preformed the experiments and measurements. J.H. and B.W. performed numerical calculation of angular momentum. Z.R., Z.Y., and K.Y.B. analyzed the experimental data and wrote the manuscript. Z.R. and Z.Y. supervised the project. + +## Competing Interests + +Z.R., Z.Y., H.Z., Y.S., J.H., and B.W. are named inventors on a number of patent applications related to this work. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1 | Generation of acoustic spatiotemporal vortex pulses and their topological nature. a, Sketch of a meta-grating generating a spatiotemporal vortex pulse for airborne sound by breaking mirror symmetry, where the asymmetric structure is necessary to create the phase singularity in the spatiotemporal domain. b-d, Numerical simulations for the phase (above) and the amplitude (below) of the transmission spectrum function by breaking the mirror symmetry of the meta-grating. The synthetic parameter \(\eta\) controls the deformation of symmetry breaking, and \(\eta = 0,0.5,0.7\) correspond to b-d, respectively. The vortices with the winding numbers of \(+1\) and -1, are indicated by white and black circles, respectively. e, Nodal lines in the extended dimension with the asymmetry parameter \(\eta\) , emerges at the critical values of \(\eta_c = 0.40\) , for the vortices with the winding number as \(+1\) and -1 with the green and the purple lines, respectively.
+ +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2| Experimental measurement for the phase (a-c) and amplitude (d-f) of transmission spectrum function for the asymmetry parameters \(\eta = 0, 0.5, 0.7\) , respectively. The vortices with the winding numbers of \(+1\) and \(-1\) , which are indicated by white and black circles, respectively.
+ +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3 | Experimental measurement of STVP generations with the acoustic meta-grating. a, Sketch of the experimental setup with an incident Gaussian-profile pulse in both spatial and temporal domains. b, Experimental sample of the acoustic STVP generator with the asymmetry parameter \(\eta = 1.0\) . c, Schematics of the perturbed case where fifteen particles of different shapes such as sphere, pyramid, cube and ring are randomly placed with additional small truncations. d-g, Simulation results of the transmitted pulse envelopes at the positions which are separated by \(\frac{\lambda}{3}\) from each other (\(\lambda\) is the center wavelength of the pulse), respectively. h-k, Experimental measurements of the transmitted pulse envelopes at the corresponding positions of d-g, respectively. l-o, Experimental measurement of
+ +<--- Page Split ---> + +topologically protected STVP generations with perturbation, at the corresponding positions away from the sample, respectively. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4| Acoustic STVP propagation in real space. a-f, The pressure amplitude distribution (colormap) and the momentum density (arrows) of the transmitted wave are shown at the time of \(-1.4, -0.5, 0.4, 1.4, 2.3, 3.2 \text{ms}\), respectively.
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Soifer, First- order optical spatial differentiator based on a guided- mode resonant grating, Opt. Express 26, 10997 (2018). + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +Supplementary.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__07da031c5a170ed2448db8d4d493b3aefc0c8a156ebbd67b372a56dc6f8a0f87/preprint__07da031c5a170ed2448db8d4d493b3aefc0c8a156ebbd67b372a56dc6f8a0f87_det.mmd b/preprint/preprint__07da031c5a170ed2448db8d4d493b3aefc0c8a156ebbd67b372a56dc6f8a0f87/preprint__07da031c5a170ed2448db8d4d493b3aefc0c8a156ebbd67b372a56dc6f8a0f87_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..763c6f74788d2df9c22680f42b867f3551592c1e --- /dev/null +++ b/preprint/preprint__07da031c5a170ed2448db8d4d493b3aefc0c8a156ebbd67b372a56dc6f8a0f87/preprint__07da031c5a170ed2448db8d4d493b3aefc0c8a156ebbd67b372a56dc6f8a0f87_det.mmd @@ -0,0 +1,221 @@ +<|ref|>title<|/ref|><|det|>[[45, 108, 886, 142]]<|/det|> +# Observation of acoustic spatiotemporal vortices + +<|ref|>text<|/ref|><|det|>[[42, 161, 225, 201]]<|/det|> +Hongliang Zhang Zhejiang University + +<|ref|>text<|/ref|><|det|>[[44, 208, 223, 247]]<|/det|> +Yeyang Sun Zhejiang University + +<|ref|>text<|/ref|><|det|>[[44, 254, 223, 293]]<|/det|> +Junyi Huang Zhejiang University + +<|ref|>text<|/ref|><|det|>[[44, 300, 223, 339]]<|/det|> +Bingjun Wu Zhejiang University + +<|ref|>text<|/ref|><|det|>[[44, 346, 582, 386]]<|/det|> +Zhaoju Yang Zhejiang University https://orcid.org/0000- 0002- 9880- 2655 + +<|ref|>text<|/ref|><|det|>[[44, 392, 201, 410]]<|/det|> +Konstantin Bliokh + +<|ref|>text<|/ref|><|det|>[[44, 415, 755, 480]]<|/det|> +Konstantin BliokhTheoretical Quantum Physics Laboratory, Cluster for Pioneering Research, RIKENZhichao Ruan ( \(\square\) zhichao@zju.edu.cn)Zhejiang University https://orcid.org/0000- 0001- 8311- 6970 + +<|ref|>sub_title<|/ref|><|det|>[[44, 522, 102, 540]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 560, 135, 578]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 597, 290, 616]]<|/det|> +Posted Date: June 6th, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 635, 473, 655]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 2978419/v1 + +<|ref|>text<|/ref|><|det|>[[42, 672, 911, 715]]<|/det|> +License: \(\circledcirc\) This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 733, 936, 777]]<|/det|> +Additional Declarations: Yes there is potential Competing Interest. Z.R., Z.Y., H.Z., Y.S., J.H., and B.W. are named inventors on a number of patent applications related to this work. + +<|ref|>text<|/ref|><|det|>[[42, 812, 928, 855]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on October 6th, 2023. See the published version at https://doi.org/10.1038/s41467- 023- 41776- 8. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[222, 106, 772, 129]]<|/det|> +# Observation of acoustic spatiotemporal vortices + +<|ref|>text<|/ref|><|det|>[[238, 149, 758, 187]]<|/det|> +Hongliang Zhang \(^{1\#}\) , Yeyang Sun \(^{1\#}\) , Junyi Huang \(^{1\#}\) , Bingjun Wu, Zhaoju Yang \(^{1*}\) , Konstantin Y. Bliokh \(^{2,3,4}\) , and Zhichao Ruan \(^{1**}\) + +<|ref|>text<|/ref|><|det|>[[120, 206, 875, 336]]<|/det|> +\(^{1}\) Interdisciplinary Center of Quantum Information, State Key Laboratory of Modern Optical Instrumentation, and Zhejiang Province Key Laboratory of Quantum Technology and Device, Department of Physics, Zhejiang University, Hangzhou 310027, China \(^{2}\) Theoretical Quantum Physics Laboratory, Cluster for Pioneering Research, RIKEN, Wakoshi, Saitama 351- 0198, Japan \(^{3}\) Centre of Excellence ENSEMBLE3 Sp. z o.o., 01- 919 Warsaw, Poland \(^{4}\) Donostia International Physics Center (DIPC), Donostia- San Sebastián 20018, Spain + +<|ref|>text<|/ref|><|det|>[[115, 355, 503, 409]]<|/det|> +\* These authors equal contributions to this work \* zhaojiuyang@zju.edu.cn \*\* zhichao@zju.edu.cn + +<|ref|>sub_title<|/ref|><|det|>[[459, 430, 536, 446]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[115, 448, 882, 687]]<|/det|> +Vortices in fluids and gases have piqued the interest of human for centuries. Development of classical- wave physics and quantum mechanics highlighted wave vortices characterized by phase singularities and topological charges. In particular, vortex beams have found numerous applications in modern optics and other areas. Recently, optical spatiotemporal vortex states exhibiting the phase singularity both in space and time have been reported. Here, we report the first generation of acoustic spatiotemporal vortex pulses. We utilize an acoustic meta- grating with mirror- symmetry breaking as the spatiotemporal vortex generator. In the momentum- frequency domain, we unravel that the transmission spectrum functions exhibit a topological phase transition where the vortices with opposite topological charges are created or annihilated in pairs. Furthermore, with the topological textures of the nodal lines, these vortices are robust and exploited to generate spatiotemporal vortex pulse against structural perturbations and disorder. Our work paves the way for studies and applications of spatiotemporal structured waves in acoustics and other wave systems. + +<|ref|>sub_title<|/ref|><|det|>[[116, 716, 247, 735]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[115, 744, 882, 891]]<|/det|> +Wave vortices, i.e., structures with the wavefield intensity vanishing in the center and the phase winding around, are of enormous importance for various areas of physics. They are essential parts of almost any structured waves: atomic orbitals and superfluids in quantum mechanics, complex wave interference from ocean waves to nanophotonics and metamaterials, etc. Cylindrical vortex beams have been generated and found applications in electromagnetic [1- 8], sound [9- 17], elastic [18], electron [19- 21], neutron [22], and atom [23] waves. Such states contain on- axis vortex lines and carry intrinsic orbital angular momentum (OAM) along their propagation direction. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 231]]<|/det|> +Recently, there was a great rise of interest in spatiotemporal vortex pulses (STVPs), which are generalizations of usual 'spatial' vortex states to the space- time domain and the OAM tilted with respect to the propagation direction [24- 40]. This conforms with the rapidly growing field of space- time structured waves allowing manipulation in both spatial and temporal degrees of freedom [41,42]. In the simplest case, STVPs are flying doughnut- shaped pulses with the vortex line and OAM orthogonal to their propagation direction. Until now, such states have been generated only in optics, although theoretically these were also discussed for quantum matter and acoustic waves [33]. + +<|ref|>text<|/ref|><|det|>[[115, 250, 881, 490]]<|/det|> +Here, we report the first generation of acoustic STVPs for sound waves in air. Our STVP generator is based on a meta- grating with spatial mirror symmetry breaking, which can be further controlled by a synthetic parameter. Through mapping the transmission spectra of the meta- grating as a function of the synthetic parameter, we show that there exist vortices in the momentum- frequency domains created and annihilated together in pairs at a critical point by mirror- symmetry breaking. In contrast to diffraction gratings with fork- like dislocations, which are used for the generation of spatial vortex beams in the first and higher orders of diffraction [1,3,4,19], this method uses the zeroth- order transmitted field and realizes simultaneous control in both spatial and temporal domains. Importantly, similar to the topological textures for electronic and optical systems [43,44], these vortices associated with the nodal lines are robust and exploited to generate STVPs with the topological protection against structural disorder. Our results open the avenue for spatiotemporal vortex generation and applications in acoustics and other areas of wave physics [7,11,32,45- 49]. + +<|ref|>sub_title<|/ref|><|det|>[[116, 518, 192, 536]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[116, 546, 586, 564]]<|/det|> +## Breaking spatial mirror symmetry for generating STVPs + +<|ref|>text<|/ref|><|det|>[[115, 565, 881, 731]]<|/det|> +Our STVP generator is based on spatial mirror symmetry breaking [32]. Figure 1a schematically displays the spatial- symmetry analysis of the spatiotemporal vortex generation [the detailed geometry in Supplementary Material (SM) Sec. I]. Here, a spatiotemporal Gaussian pulse impinges on the structure along \(x = 0\) (indicated by the white dashed line). Without loss of generality, assume that a meta- grating exists mirror symmetry about the plane \(x = 0\) , the phase distribution of the transmitted pulse must also be symmetric about the mirror plane, and thus there is no phase singularity. Therefore, the necessary condition for generating STVPs with nonzero winding numbers is the mirror symmetry breaking, which provides an asymmetry modulation for sound in both spatial and temporal domains simultaneously. + +<|ref|>text<|/ref|><|det|>[[115, 750, 881, 898]]<|/det|> +To realize the asymmetric spatiotemporal modulation, we design the meta- grating (Fig. 1a) with a unit cell consisting of four air blocks with different sizes as indicated by dashed boxes in SM Fig. S1. In the meta- grating, all cells are connected with a middle air channel (yellow areas in Fig. 1a). Initially, all the four air blocks are symmetric about the axis of \(x = 0\) . To break the spatial mirror symmetry, we introduce a synthetic parameter \(\eta\) , which describes the different \(x\) - directional shifts from the center \(\delta x_{i} = A_{i}\eta\) , where \(A_{i}\) is the shifting ratio given in Supplementary Table S1 for the \(i\) - th block and \(i = 1,2,3,4\) . Thus, \(\eta\) controls the degree of the mirror asymmetry of the grating. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 305]]<|/det|> +This asymmetric modulation stemming from mirror symmetry breaking can be illustrated by the transmission spectrum function \(T(k_{x},\omega)\) between the incident and transmitted waves, where \(\omega\) is the angular frequency of a plane wave, and \(k_{x}\) is the wavevector along the structure interface. Here only the zeroth order diffraction is considered in the operating- frequency range. For the mirror symmetric case of \(\eta = 0\) , as expected, the transmission spectrum function \(T(k_{x},\omega)\) exhibits symmetric spectrum about \(k_{x} = 0\) for both the phase and the amplitude distributions (Fig. 1b). On the other hand, by breaking the mirror symmetry, the case of \(\eta = 0.5\) shows that the transmission spectrum function \(T(k_{x},\omega)\) has two vortices with the winding numbers of \(l = +1\) (white circle) and \(l = -1\) (black circle), respectively (Fig. 1c). Correspondingly, such two phase singularities coincide with the zero- value transmission at the center of the vortices. Furthermore, for \(\eta = 0.7\) , the two vortices are further separated with a larger strength of mirror symmetry breaking (Fig. 1d). + +<|ref|>text<|/ref|><|det|>[[115, 325, 882, 490]]<|/det|> +The vortices associated with the transmission spectrum function are analogous to the fundamental 'charges' in the \(k_{x} - \omega\) domain, because they are always created or annihilated together in pairs of opposite charges. To clearly illustrate the creation and annihilation, Fig. 1e shows the parameter \(\eta\) spectrum, which is in the form of nodal lines in the extended dimension with the asymmetry parameter \(\eta\) . When a plane with a fixed value of \(\eta\) has intersections with the nodal lines, there must be two vortices with opposite handedness appearing in the cross- section plane. Therefore, the total topological charge of the vortices is a conserved quantity of zero. Through numerical simulations, we find that the critical value of \(\eta\) is \(\eta_{c} = 0.40\) for our designed meta- grating. + +<|ref|>text<|/ref|><|det|>[[115, 510, 882, 770]]<|/det|> +To experimentally demonstrate the topological phase transition, we fabricate three metagratings (SM Fig. S3) and measure the transmission spectrum function (Methods). Figure 2a- c show the measured phase distributions of the transmission spectrum function for \(\eta = 0, 0.5, 0.7\) , and the corresponding amplitude distributions are shown in Fig. 2d- f. For the mirror symmetric case (Fig. 2a and d), there is no phase singularity in the measured phase distribution of the transmission spectrum function. By breaking the mirror symmetry with \(\eta = 0.5\) , there are two vortices created with the winding numbers of \(+1\) and \(-1\) , marked by the white and black circles in Fig. 2b, and hence two zero- value transmissions occur at \(\omega /2\pi = 8.31 kHz\) and \(\omega /2\pi = 8.23 kHz\) in Fig. 2e, respectively. For the case of \(\eta = 0.7\) as shown in Fig. 2c and f, two vortices with opposite winding numbers are further separated at \(\omega /2\pi = 8.31 kHz\) and \(\omega /2\pi = 8.04 kHz\) . The measured results agree that the topological phase transition appears at the critical point of \(\eta_{c} = 0.40\) . The creation of the vortices confirms that the asymmetric modulation by breaking mirror symmetry indeed induces the topological charges in the \((k_{x},\omega)\) domain. + +<|ref|>sub_title<|/ref|><|det|>[[117, 789, 470, 806]]<|/det|> +## Topological protected generation of STVP + +<|ref|>text<|/ref|><|det|>[[116, 808, 881, 899]]<|/det|> +Owing to the asymmetric modulation, the phase singularity of the transmission spectrum function located at \((\omega_{0}, k_{0x})\) in the \((k_{x},\omega)\) domain can be directly transferred into the spatiotemporal domain. Considering an incident Gaussian wave pulse with the central angular frequency \(\omega_{0}\) and transverse wavevector component \(k_{0x}\) , the transmitted wave packet can be determined to be a STVP with a nonzero winding number, but opposite to that of the vortex in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 881, 233]]<|/det|> +the \((k_{x},\omega)\) domain (see the detailed derivation in SM Sec. II). Because of the topological robustness of the nodal lines, the corresponding vortices with the winding number \(l = \pm 1\) are stable. When the vortices are distanced from the critical points of the topological phase transition, small changes to the meta-grating geometry in the real space can be treated as perturbations. The strength of such topological protection for the vortex at \((\omega_{0},k_{0x})\) can be evaluated by the distance to its nearest vortex \(\Delta = \sqrt{(\omega_{n} - \omega_{0})^{2} + \nu^{2}(k_{nx} - k_{0x})^{2}}\) , where \(\omega_{n}\) and \(k_{nx}\) are the frequency and the wavevector component of the nearest vortex. + +<|ref|>text<|/ref|><|det|>[[115, 250, 882, 454]]<|/det|> +We next experimentally demonstrate the topologically protected generation of acoustic STVPs, schematically displayed in Fig. 3a. Here, we choose the meta- grating with the asymmetry parameter \(\eta = 1.0\) , because such a meta- grating has a relatively strong strength of topological protection up to \(\Delta\) . We first experimentally investigate the transmission spectrum function (SM Sec. III and Fig. S4), which exhibits a vortex at \(\omega_{0} / 2\pi = 8.02kHz\) and \(k_{0x} = 0.01k_{0}\) \((k_{0}\) is the wavenumber in air). For \(k_{0x}\ll k_{0}\) , therefore, it can be used as for normally- incident pulses. Using an arc- like linear transducer array, with an oscillatory Gaussian envelopes electric signal at a central carrier frequency \(\omega_{0}\) , we produce a \(z\) - propagating Gaussian pulses, which has the full waist of \(145\mathrm{mm}\) and the durations of \(3.2\mathrm{ms}\) and the diffraction Rayleigh range about \(383\mathrm{mm}\) (the detailed experimental setup and the measurement found in Methods and Fig. S6). + +<|ref|>text<|/ref|><|det|>[[115, 472, 882, 732]]<|/det|> +We first consider the unperturbed meta- grating as the experimental sample Fig. 3b. We numerically simulate the transmitted pulse envelope at different propagation distances of \(64.4\mathrm{mm}\) , \(78.8\mathrm{mm}\) , \(93.2\mathrm{mm}\) , and \(107.6\mathrm{mm}\) , which are separated by \(\frac{\lambda}{3}\) from each other ( \(\lambda\) is the center wavelength of the pulse) as indicated by red dash lines in Fig. 3a, respectively. Here the transmitted acoustic wave \(P(x,t) = s_{out}(x,t)e^{- i\omega_{0}t}\) , where \(s_{out}(x,t)\) is the transmitted pulse envelope. The simulation results are depicted in Fig. 3d- g, where the HSV color and the brightness represent the phase and amplitude of the pulse envelope, respectively. We then measure the transmitted pulse envelopes, as shown in Fig. 3h- k. The experimental measurements of the transmitted pulse envelope exhibit the vanishing amplitude and the whirl dislocated phase in the spatiotemporal domain corresponding to the winding number of \(l = - 1\) . Furthermore, the phase distributions at different distances exhibit that the phase of the STVP rotates around the center along with the pulse propagation, which agrees well with the central wavelength of the pulse. + +<|ref|>text<|/ref|><|det|>[[115, 750, 881, 899]]<|/det|> +Using the experimental data and numerical \(z\) - propagation of the field, we calculate the real- time amplitude of the pulse and the momentum density at different instants of time, which shows the propagation evolution and diffraction of the generated STVP (Fig. 4). At the early stage of the pulse generation, Fig. 4a shows that the pressure amplitude increases as the pulse propagates along the \(z\) direction, and the directions of the momentum density indicate the compression and the decompression of air. However, the zero- value amplitude is shown up when \(t = - 0.4ms\) , which corresponds to the vortex center (Fig. 4b). Figs. 4c- d further exhibit the vortex rolls and diffract along with the propagation. Since there is an ongoing theoretical + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 84, 881, 139]]<|/det|> +debate in community about the intrinsic OAM carried by a STVP [33,35,40], the calculation of the intrinsic OAM carried by the generated STVP is beyond the present studies, which needs more detailed theoretical and experimental investigation [SM Sec. IV]. + +<|ref|>text<|/ref|><|det|>[[116, 158, 882, 323]]<|/det|> +To demonstrate the topologically robustness of STVP generation, we perturb the meta- grating structure by randomly placing fifteen photopolymer- resins particles of different shapes and sizes about \(0.8\mathrm{cm} - 1\mathrm{cm}\) , as shown in Fig. 3c. Moreover, as a regular perturbation to the grating, we also add one more block in the unit cell. The transmission spectrum function of the perturbed meta- grating still exhibited the same phase singularity with slightly shifted position at \(\omega_0 / 2\pi = 7.56kHz\) and \(k_{x0} = 0.02k_0\) (SM Fig. S5). Adjusting the incident pulse to the perturbed central frequency, we measured the transmitted pulse envelopes at the propagation distances of \(65.3\mathrm{mm}\) , \(80.6\mathrm{mm}\) , \(95.9\mathrm{mm}\) and \(111.2\mathrm{mm}\) , respectively. Fig. 31- o clearly show a STVP quite similar to that in the unperturbed case. + +<|ref|>sub_title<|/ref|><|det|>[[116, 351, 224, 370]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[115, 380, 882, 620]]<|/det|> +In summary, our experimental results demonstrate the topologically protected generation of acoustic STVPs in two spatial dimensions and one temporal dimension, using a 1D periodic meta- grating. On the one hand, acoustic STVPs open an avenue for acoustic spacetime- structured waves, so far mostly studied in optics [41,42]. On the other hand, our new method of the generation of STVPs opens can find applications in acoustics, optics, and other types of waves. One can expect that by designing 2D metasurfaces with an additional spatial dimension, one can synthesize full- dimensional \((3 + 1)\mathrm{D}\) spatiotemporal acoustic vortices, such as vortices with arbitrarily tilted OAM [30,31] or toroidal vortices. We also note that the nodal line in the space of \(\omega ,k_{x},\eta\) is mathematically analog to many topological textures in other nodal- line topological physical systems [43,44]. In general, due to drastic geometric and physical differences from conventional monochromatic vortex beams, the STVPs can bring novel functionalities to acoustic/optical manipulation of particles, information transfer, and other applications [7,11,32,45- 49]. + +<|ref|>text<|/ref|><|det|>[[115, 639, 882, 786]]<|/det|> +Furthermore, similarly to the image processing of edge detection in the spatial domain [50- 63], our STVP generator, based on the phase singularity (vortex) in the momentum- frequency domain, operates as the first- order differentiator in both spatial and temporal domains. This allows efficient extraction of the spacetime boundary information in the incident sound wavepacket (SM Sec. V and Fig. S7), which can find useful applications in sonars and sensing. In our experiment, the frequency bandwidth with the near- linear dependence of the transmission amplitude near the vortex center, which provides the first- order differentiation, is about \(431.7\mathrm{Hz}\) . + +<|ref|>sub_title<|/ref|><|det|>[[116, 814, 206, 832]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[116, 843, 786, 860]]<|/det|> +## Experimental setup and methods to measure the transmission spectrum function + +<|ref|>text<|/ref|><|det|>[[115, 862, 881, 897]]<|/det|> +The experimental setup is shown in Fig. S2(a). A data acquisition (Bruel & Kjær 3160- A- 042- R) is used to collect the data of acoustic field and control the output waveform. Two + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 881, 175]]<|/det|> +microphones (Bruel & Kjær 4193- L- 004) are connected to the data acquisition and used to measure the acoustic field. We use a power amplifier (Bruel & Kjær 2735) to amplify the input signal. The displacement platform (LINBOU NFS03) and the data acquisition are integrated into a PC. The meta- grating and the sound absorber are placed between two glasses as shown in Fig. 3a. + +<|ref|>text<|/ref|><|det|>[[115, 177, 881, 396]]<|/det|> +To measure the transmission spectrum function in the frequency domain (as shown in Fig. S2(b)), we use 10 transducers to form a rectangle source in the spatial domain, which ensures that the spatial spectrum of the incident field is sufficiently wide but don't overlap with the non- zero diffraction order. The incident(transmitted) acoustic wave \(P_{in(out)}(x,t) =\) \(s_{in(out)}(x,t)e^{- i\omega_0t}\) is collected by two microphones, where \(s_{in(out)}(x,t)\) is the pulse envelope of the input(transmitted) waves. One microphone is fixed in the acoustic field as a reference probe, and the other one probes the acoustic distribution through the displacement platform as a measurement probe. In order to obtain the transmission spectrum function of the signal, we obtain the spatial distribution of the sound field by sweeping the field, and using cross- power spectrum methods to process the data from the measurement probe and the reference probe. + +<|ref|>sub_title<|/ref|><|det|>[[115, 416, 590, 434]]<|/det|> +## Experimental setup and measurement principle of STVP + +<|ref|>text<|/ref|><|det|>[[115, 435, 881, 582]]<|/det|> +We use a curved transducer array to simultaneously generate a series of pulses with spatiotemporal Gaussian envelope at the center frequency \(\omega_0 / 2\pi = 7.56 \times 10^3 Hz\) as shown in Fig. S6(a). The distribution of curved transducers array satisfies Gaussian function distribution \((z = \exp (x^2) - 1)\) , and the gap between the transducers in x direction is 1cm. We put the sample 50cm away from the transducers array. The distribution of \(s_{in}(x,t)\) is shown in Fig. S6(b). The height shows the amplitude and the color indicates the phase distribution, respectively. Figures S6(c, d) show the amplitude distribution of the envelope along (c)t = 0 and (d)x = 0. + +<|ref|>sub_title<|/ref|><|det|>[[117, 592, 304, 611]]<|/det|> +## Acknowledgement + +<|ref|>text<|/ref|><|det|>[[115, 620, 881, 712]]<|/det|> +The authors acknowledge funding through the National Key Research and Development Program of China (Grant No. 2022YFA1405200, 2022YFA1404203), the National Natural Science Foundation of China (NSFC Grants Nos. 12174340, 12174339). Z. Y. acknowledges Zhejiang Provincial Natural Science Foundation of China under Grant No. LR23A040003, and the Excellent Youth Science Foundation Project (Overseas). + +<|ref|>sub_title<|/ref|><|det|>[[117, 721, 339, 740]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[115, 750, 881, 822]]<|/det|> +Z.R. initiated the idea and this project. H.Z., Y.S., and J.H. developed the meta- grating design, and preformed the experiments and measurements. J.H. and B.W. performed numerical calculation of angular momentum. Z.R., Z.Y., and K.Y.B. analyzed the experimental data and wrote the manuscript. Z.R. and Z.Y. supervised the project. + +<|ref|>sub_title<|/ref|><|det|>[[117, 832, 323, 851]]<|/det|> +## Competing Interests + +<|ref|>text<|/ref|><|det|>[[115, 860, 881, 896]]<|/det|> +Z.R., Z.Y., H.Z., Y.S., J.H., and B.W. are named inventors on a number of patent applications related to this work. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[147, 124, 850, 280]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 288, 884, 454]]<|/det|> +
Fig. 1 | Generation of acoustic spatiotemporal vortex pulses and their topological nature. a, Sketch of a meta-grating generating a spatiotemporal vortex pulse for airborne sound by breaking mirror symmetry, where the asymmetric structure is necessary to create the phase singularity in the spatiotemporal domain. b-d, Numerical simulations for the phase (above) and the amplitude (below) of the transmission spectrum function by breaking the mirror symmetry of the meta-grating. The synthetic parameter \(\eta\) controls the deformation of symmetry breaking, and \(\eta = 0,0.5,0.7\) correspond to b-d, respectively. The vortices with the winding numbers of \(+1\) and -1, are indicated by white and black circles, respectively. e, Nodal lines in the extended dimension with the asymmetry parameter \(\eta\) , emerges at the critical values of \(\eta_c = 0.40\) , for the vortices with the winding number as \(+1\) and -1 with the green and the purple lines, respectively.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 99, 820, 342]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 344, 883, 398]]<|/det|> +
Fig. 2| Experimental measurement for the phase (a-c) and amplitude (d-f) of transmission spectrum function for the asymmetry parameters \(\eta = 0, 0.5, 0.7\) , respectively. The vortices with the winding numbers of \(+1\) and \(-1\) , which are indicated by white and black circles, respectively.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 115, 884, 733]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 732, 884, 900]]<|/det|> +
Fig. 3 | Experimental measurement of STVP generations with the acoustic meta-grating. a, Sketch of the experimental setup with an incident Gaussian-profile pulse in both spatial and temporal domains. b, Experimental sample of the acoustic STVP generator with the asymmetry parameter \(\eta = 1.0\) . c, Schematics of the perturbed case where fifteen particles of different shapes such as sphere, pyramid, cube and ring are randomly placed with additional small truncations. d-g, Simulation results of the transmitted pulse envelopes at the positions which are separated by \(\frac{\lambda}{3}\) from each other (\(\lambda\) is the center wavelength of the pulse), respectively. h-k, Experimental measurements of the transmitted pulse envelopes at the corresponding positions of d-g, respectively. l-o, Experimental measurement of
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 84, 881, 120]]<|/det|> +topologically protected STVP generations with perturbation, at the corresponding positions away from the sample, respectively. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[113, 84, 800, 395]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 400, 883, 453]]<|/det|> +
Fig. 4| Acoustic STVP propagation in real space. a-f, The pressure amplitude distribution (colormap) and the momentum density (arrows) of the transmitted wave are shown at the time of \(-1.4, -0.5, 0.4, 1.4, 2.3, 3.2 \text{ms}\), respectively.
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Soifer, First- order optical spatial differentiator based on a guided- mode resonant grating, Opt. Express 26, 10997 (2018). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 311, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 92, 765, 112]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 130, 250, 150]]<|/det|> +Supplementary.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__07e3c01bfb7c257f74c13b751d60027ccfb31a80dc7afca89f4153da3d24fa3d/images_list.json b/preprint/preprint__07e3c01bfb7c257f74c13b751d60027ccfb31a80dc7afca89f4153da3d24fa3d/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..6bedea8f8d009f2fa215a5bb95fa4b6fcdb9dea3 --- /dev/null +++ b/preprint/preprint__07e3c01bfb7c257f74c13b751d60027ccfb31a80dc7afca89f4153da3d24fa3d/images_list.json @@ -0,0 +1,122 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "FIG. 1. Device geometry and static characterization. a, Optical image of a photonic device consisting of two grating couplers (GC), a silicon optical waveguide (Si WG) and a hBN-HfO2-hBN based graphene EA modulator on top (see zoom-in optical and scanning electron microscope (SEM) images for details). The metal contacts are yellow/brown and the bottom and top graphene electrodes violet and light blue, respectively. The core of the waveguide is highlighted by the green dashed lines. b, Electrical connections and schematic cross-section of a EA modulator with a hBN-HfO2-hBN dielectric. The top and bottom graphene electrodes are fully encapsulated by hBN (in green) protecting both graphene electrodes from the out-of-plane dangling bonds typical of 3D oxide materials, e.g. HfO2 (in red). See inset for a molecular representation. c, Transmission curves as a function of the voltage between the bottom and top graphene electrodes (VBT-axis, bottom) and the Fermi energy at the graphene electrodes (EF-axis, top) for the EA modulator in panel a with a hBN-HfO2-hBN dielectric (see sketch). The 1550 nm excitation power was set to 0 dBm. The forward and backward voltage sweeps (black and blue, respectively) show no major hysteresis compared to a modulator with a hBN-HfO2 dielectric (see inset). The red line is a linear fit to the forward voltage sweep within a 0.5 V voltage span (extracted slope: 2.2 dB/V).", + "footnote": [], + "bbox": [ + [ + 84, + 60, + 920, + 301 + ] + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "FIG. 2. Dynamic characterization. a, \\(\\mathrm{f_{3dB}}\\) bandwidth as a function of the charge carrier-dependent mobility \\((\\mu)\\) and the contact resistivity \\((\\rho_{c})\\) calculated for a device with the same geometry and dielectric combination as the device in Fig. 1 (section XI in SI). The dashed lines indicate the expected \\(\\mathrm{f_{3dB}}\\sim 46\\mathrm{GHz}\\) at \\(\\mu \\sim 12,000\\mathrm{cm}^2 /\\mathrm{Vs}\\) (evaluated at \\(\\mathrm{V_{BT}} = 10.4\\mathrm{V}\\) , refer to panel b). b, Measured electro-optical \\(\\mathrm{S_{21}}\\) frequency response of the EA modulator at \\(\\mathrm{V_{BT}} = 10.4\\mathrm{V}\\) and \\(\\mathrm{V_{AC}} = 200\\mathrm{mV}\\) , without de-embedding, i.e. including the contributions of the setup and photodetector (section XII in SI). c, \\(2^{31} - 1\\) pseudo-random binary sequence non-return-to-zero eye diagram at \\(28\\mathrm{Gbps}\\) and \\(40\\mathrm{Gbps}\\) . The EA modulator is d.c. biased at \\(\\mathrm{V_{BT}} = 11\\mathrm{V}\\) and driven by a \\(\\mathrm{V_{AC}} = 3.5\\mathrm{V}\\) peak-to-peak RF signal. The eye diagram measured at \\(40\\mathrm{Gbps}\\) has a \\(5.2\\mathrm{dB}\\) ER and a \\(2.28\\mathrm{dB}\\) signal-to-noise ratio (SNR). The green arrows indicate the \\(0\\mathrm{W}\\) baseline.", + "footnote": [], + "bbox": [ + [ + 86, + 60, + 920, + 319 + ] + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "FIG. 3. Dielectric breakdown and Pauli blocking operation. a, Maximum Fermi energy, noted \\(\\mathrm{E}_{\\mathrm{F}}^{\\mathrm{max}}\\) , expected at the graphene electrodes of a graphene modulator with a dielectric's relative permittivity \\(\\epsilon_{\\mathrm{r}}\\) and dielectric strength \\(\\mathrm{E}_{\\mathrm{BD}}\\) . All points lying inside the blue-colored region represent a dielectric allowing for Pauli blocking operation \\(\\mathrm{E}_{\\mathrm{F}}^{\\mathrm{max}} > 0.5\\mathrm{eV}\\) , refer to section I in SI). The red-colored region indicates otherwise \\(\\mathrm{E}_{\\mathrm{F}}^{\\mathrm{max}}< 0.5\\mathrm{eV}\\) . The white band represents the Pauli blocking boundary condition, defined as \\(\\mathrm{E}_{\\mathrm{F}}^{\\mathrm{max}} = 0.5\\mathrm{eV}\\) . The expected \\(\\mathrm{E}_{\\mathrm{F}}^{\\mathrm{max}}\\) for \\(\\mathrm{HfO_2}\\) and hBN are represented by the red and green squares respectively, taking the values of \\(\\mathrm{E}_{\\mathrm{BD}}\\) and \\(\\epsilon_{\\mathrm{r}}\\) from literature \\(^{28,31 - 33}\\) (marked with dots) and our dielectric characterization (marked with stars, see sections V and X in SI). The black star represents the \\(\\mathrm{E}_{\\mathrm{F}}^{\\mathrm{max}} = 0.57\\mathrm{eV}\\) expected for the hBN-HfO \\(_2\\) -hBN modulator in Fig. 1c (section X in SI). b and c, Normalized transmission as a function of \\(\\mathrm{E}_{\\mathrm{F}}\\) and \\(\\mathrm{V}_{\\mathrm{BT}}\\) for modulators with hBN (b) and hBN-HfO \\(_2\\) -hBN (c) dielectric. The data points are measurements and the solid curves simulations (see sections I-III and X in SI). The vertical dashed lines indicate the \\(\\mathrm{E}_{\\mathrm{F}}^{\\mathrm{max}}\\) achieved at the dielectric breakdown. The orange-shaded regions show the full transparency range, i.e. Pauli blocking. The top \\(\\mathrm{V}_{\\mathrm{BT}}\\) axis in panel b is for the \\(42\\mu \\mathrm{m}\\) -long device only (see section VII in SI for the other hBN devices). The graphene Dirac cones in panel b show the absorption and Pauli blocking processes at low and high Fermi energies, respectively.", + "footnote": [], + "bbox": [ + [ + 84, + 60, + 916, + 298 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "FIG. 4. Comparison graph. The black and red data points and axis represent the static modulation efficiency as a function of the \\(\\mathrm{f}_{3\\mathrm{dB}}\\) bandwidth and the dynamic modulation efficiency (extracted from eye-diagrams) as a function of the modulation speed, respectively. The red, blue and green data clouds enclose single \\(^{11,12,34,38}\\) and double-layer \\(^{8 - 10,35}\\) graphene and silicon- \\(^{39 - 41}\\) state-of-the-art modulators operating at \\(\\lambda = 1.55\\mu \\mathrm{m}\\) . Refer to sections XV and XVI in SI for a more detailed comparison of graphene-based modulators.", + "footnote": [], + "bbox": [ + [ + 83, + 65, + 490, + 341 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [ + [ + 50, + 100, + 945, + 365 + ] + ], + "page_idx": 9 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 65, + 50, + 930, + 320 + ] + ], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [ + [ + 63, + 603, + 941, + 857 + ] + ], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4", + "footnote": [], + "bbox": [ + [ + 76, + 393, + 800, + 891 + ] + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/preprint/preprint__07e3c01bfb7c257f74c13b751d60027ccfb31a80dc7afca89f4153da3d24fa3d/preprint__07e3c01bfb7c257f74c13b751d60027ccfb31a80dc7afca89f4153da3d24fa3d.mmd b/preprint/preprint__07e3c01bfb7c257f74c13b751d60027ccfb31a80dc7afca89f4153da3d24fa3d/preprint__07e3c01bfb7c257f74c13b751d60027ccfb31a80dc7afca89f4153da3d24fa3d.mmd new file mode 100644 index 0000000000000000000000000000000000000000..f820f008a563e5f57f2953e210d295df55ee1a27 --- /dev/null +++ b/preprint/preprint__07e3c01bfb7c257f74c13b751d60027ccfb31a80dc7afca89f4153da3d24fa3d/preprint__07e3c01bfb7c257f74c13b751d60027ccfb31a80dc7afca89f4153da3d24fa3d.mmd @@ -0,0 +1,191 @@ + +# 2D-3D integration of hBN and a high-κ dielectric for ultrafast graphene-based electro-absorption modulators + +Hitesh AgarwalICFO – Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona) + +Bemat TerresInstitute of Photonic Sciences + +Lorenzo OrsiniICFO – Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona) + +Alberto MontanaroConsorzio Nazionale Interuniversitario Per Le Telecomunicazioni + +Vito SorianelloCNIT + +Marianna PantouvakiIMEC + +Kenji WatanabeNational Institute for Materials Science https://orcid.org/0000- 0003- 3701- 8119 + +Takashi TaniguchiNational Institute for Materials Science https://orcid.org/0000- 0002- 1467- 3105 + +Dries Van ThourhoutPhotonics Research Group, INTEC Department, Ghent University- imec https://orcid.org/0000- 0003- 0111- 431X + +Marco RomagnoliConsorzio Nazionale Interuniversitario Telecomunicazioni https://orcid.org/0000- 0002- 4274- 5620Frank Koppens ( frank.koppens@icfo.eu)ICFO - The Institute of Photonic Sciences https://orcid.org/0000- 0001- 9764- 6120 + +## Article + +Keywords: EA, hBN, HfO2, Electro- absorption + +Posted Date: November 9th, 2020 + +<--- Page Split ---> + +DOI: https://doi.org/10.21203/rs.3.rs- 57385/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Communications on February 16th, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 20926- w. + +<--- Page Split ---> + +# 2D-3D integration of hBN and a high- \(\kappa\) dielectric for ultrafast graphene-based electro-absorption modulators + +Hitesh Agarwal, \(^{1, *}\) Bernat Terres, \(^{1, *}\) , Lorenzo Orsini, \(^{1, 2}\) Alberto Montanaro, \(^{3}\) Vito Sorianello, \(^{3}\) Marianna Pantouvaki, \(^{4}\) Kenji Watanabe, \(^{5}\) Takashi Taniguchi, \(^{5}\) Dries Van Thourhout, \(^{4}\) Marco Romagnoli, \(^{3}\) and Frank H. L. Koppens \(^{1, 6, \dagger}\) \(^{1}\) ICFO — Institut de Ciències Fotoniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona), Spain \(^{2}\) Dipartimento di Fisica E. Fermi, Università di Pisa, Largo Bruno Pontecorvo 3, 56127 Pisa, Italy. \(^{3}\) Consorzio Nazionale per le Telecomunicazioni (CNIT), Photonic Networks and Technologies National Laboratory, via Moruzzi 1, 56124 Pisa, Italy \(^{4}\) Photonics Research Group, Department of Information Technology, Ghent University- IMEC, Sint- Pietersnieuwstraat 41, Gent, 9000 Belgium \(^{5}\) National Institute for Materials Science, 1- 1 Namiki, Tsukuba 305- 0044, Japan \(^{6}\) ICREA — Institució Catalana de Recerca i Estudis Avancats, Barcelona, Spain. (Dated: Tuesday \(3^{\mathrm{rd}}\) November, 2020 12:17 :: git HEAD ref not available) + +Electro- absorption (EA) waveguide- coupled modulators are essential building blocks for on- chip optical communications. Compared to state- of- the- art silicon (Si) devices, graphene- based EA modulators promise smaller footprints, larger temperature stability, cost- effective integration and high speeds. However, combining high speed and large modulation efficiencies in a single graphene- based device has remained elusive so far. In this work, we overcome this fundamental trade- off by demonstrating the first 2D- 3D dielectric integration in a high- quality encapsulated graphene device. We integrated hafnium oxide \(\mathrm{(HfO_2)}\) and two- dimensional hexagonal boron nitride (hBN) within the insulating section of a double- layer (DL) graphene EA modulator. This novel combination of materials allows for a high- quality modulator device with record high performances: a \(\sim 39\mathrm{GHz}\) bandwidth (BW) with a three- fold increase in modulation efficiency compared to previously reported high- speed modulators. This 2D- 3D dielectric integration paves the way to a plethora of electronic and opto- electronic devices with enhanced performance and stability, while expanding the freedom for new device designs. + +Broadband optical modulators with ultra- high speed, low- drive voltage and hysteresis- free operation are key devices for next- generation datacom transceivers \(^{1}\) . Although Si photonics is nowadays a prime candidate to fulfill these requirements \(^{2,3}\) , graphene is rapidly becoming a major contender in several optoelectronic applications, such as ultrafast modulators \(^{4,5}\) and silicon- integrated photodetectors \(^{6,7}\) . Graphene- based modulators have already proven broadband optical bandwidth \(^{1}\) , + +high- speed \(^{8,9}\) , relatively high modulation efficiencies \(^{10}\) and temperature stability \(^{8}\) . These devices are all based on CMOS compatible materials \(^{7,10 - 13}\) , where CMOS design and fabrication techniques can be further leveraged to decrease costs. However, graphene- based modulators have yet to demonstrate all operation requirements at once. More specifically, EA graphene modulators struggle to show high- speed and high modulation efficiencies simultaneously \(^{14}\) . This bottleneck is mostly due to the weak graphene/dielectric combination and the limited quality of the graphene. + +Unlike Si technology, where high- \(\kappa\) dielectrics lie at the core of its success, 2D dielectrics are hindering the development of graphene- and other 2D- based electronics and optoelectronic devices \(^{1,13,15}\) and are clearly outperformed by traditional 3D high- \(\kappa\) dielectrics. This under- performing 2D- dielectric/graphene combination deepens even further the fundamental trade- off between speed and modulation efficiency inherent to the DL modulators \(^{14}\) . In the DL architecture, the overlapped top and bottom graphene electrodes act as a capacitor (C). The larger the C, the higher the modulation efficiency. On the other hand, the speed of the modulator defined as \(\mathrm{f}_{3\mathrm{dB}} = 1 / (2\pi \mathrm{RC})\) is inversely proportional to C (R being the total resistance). In this framework, the quality of graphene appears as a valid turnaround to overcome this fundamental limitation. A high electron mobility is expected to minimize the overall resistance and reduce the insertion loss (IL) \(^{1,9}\) , thus increasing the bandwidth and the extinction ratio (ER). However, the quality of graphene is very sensitive to its environment, e.g. the dielectric to encapsulate it. Indeed, no graphene/dielectric combination has been able to ensure high charge carrier mobilities and low levels of residual doping in existing graphene waveguide- coupled modulators \(^{16}\) . The growth of non- layered (i.e. 3D) dielectrics, e.g. aluminum oxide (Al₂O₃), silicon nitride (SiN) or HfO₂ directly on top of graphene leads to low electronic mobility \(^{16 - 18}\) and/or inhomogeneous doping \(^{19}\) . + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
FIG. 1. Device geometry and static characterization. a, Optical image of a photonic device consisting of two grating couplers (GC), a silicon optical waveguide (Si WG) and a hBN-HfO2-hBN based graphene EA modulator on top (see zoom-in optical and scanning electron microscope (SEM) images for details). The metal contacts are yellow/brown and the bottom and top graphene electrodes violet and light blue, respectively. The core of the waveguide is highlighted by the green dashed lines. b, Electrical connections and schematic cross-section of a EA modulator with a hBN-HfO2-hBN dielectric. The top and bottom graphene electrodes are fully encapsulated by hBN (in green) protecting both graphene electrodes from the out-of-plane dangling bonds typical of 3D oxide materials, e.g. HfO2 (in red). See inset for a molecular representation. c, Transmission curves as a function of the voltage between the bottom and top graphene electrodes (VBT-axis, bottom) and the Fermi energy at the graphene electrodes (EF-axis, top) for the EA modulator in panel a with a hBN-HfO2-hBN dielectric (see sketch). The 1550 nm excitation power was set to 0 dBm. The forward and backward voltage sweeps (black and blue, respectively) show no major hysteresis compared to a modulator with a hBN-HfO2 dielectric (see inset). The red line is a linear fit to the forward voltage sweep within a 0.5 V voltage span (extracted slope: 2.2 dB/V).
+ +In this work, we demonstrate the 2D- 3D integration of hBN and HfO2 within the dielectric section of a DL graphene EA modulator. This dielectric combination enhances the capacitance of the EA modulators without compromising its robustness against high voltages and preserves the high mobility and low doping of intrinsic graphene. As a result, we achieved a static and dynamic (at 40 Gbps) modulation efficiency as high as \(2.2 \mathrm{dB / V}\) and \(1.49 \mathrm{dB / V}\) , respectively, a \(f_{3 \mathrm{dB}}\) bandwidth of \(\sim 39 \mathrm{GHz}\) and a device footprint of \(60 \mu \mathrm{m} \times 0.45 \mu \mathrm{m} \approx 27 \mu \mathrm{m}^2\) (neglecting the metal pads and graphene leads). Moreover, the hBN- HfO2- hBN based devices show a symmetric and nearly hysteresis- free operation. The larger breakdown voltage of this 2D- 3D dielectric, even beyond the full transparency regime (i.e. Pauli blocking), increases the ER and reduces the IL of the modulators. + +## I. RESULTS AND DISCUSSIONS + +The EA modulators were fabricated on top of a photonic structure \(^{20}\) formed by two gratings couplers \(^{21}\) feeding light in and out of an optical waveguide (Fig. 1a). The \(750 \mathrm{nm}\) - wide waveguide for the device in Fig. 1) was designed to support a single transverse- magnetic (TM) optical mode \(^{20}\) (see sections III in SI). The presented DL graphene modulators were built, for the very first time, with hBN- encapsulated graphene top and bottom + +electrodes (Fig. 1b). The hBN- graphene- hBN stacks have been fabricated following state- of- the- art fabrication techniques \(^{22,23}\) . This ensured low levels of doping and high charge carrier mobilities. We characterized the quality of the resulting modulators (sections II and VI in SI) and extracted a carrier density- independent mobility as high as \(30,000 \mathrm{cm}^2 /(\mathrm{Vs})\) at room temperature \(^{23}\) (section II in SI). + +Although hBN- encapsulated graphene devices have allowed for device designs with unprecedented functionalities \(^{24 - 26}\) and improved performance \(^{23}\) , such layered dielectric material typically contains impurities and/or crystal defects leading to low breakdown voltages \(^{27,28}\) . Moreover, the dielectric permittivity of hBN is rather low compared to existing high- \(\kappa\) dielectrics \(^{29}\) , with a value close to that of SiO2 ( \(\epsilon_{\mathrm{r}} \sim 4\) ). This low dielectric constant and reduced breakdown voltage (see section V in SI) compromises not only the power consumption and the ability to reach high modulation efficiencies at reasonably low drive voltages but also limits the IL and the ER of the modulators \(^{1,9}\) . We thus integrate HfO2, a high- \(\kappa\) dielectric material, within the hBN- encapsulated graphene electrodes (see the sketch in Fig. 1b). + +With such hBN- HfO2- hBN dielectric arrangement, graphene remains isolated from HfO2, shielded away from any possible out- of- plane dangling bonds of the 3D oxide material (see inset of Fig. 1b for the molecular represen + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
FIG. 2. Dynamic characterization. a, \(\mathrm{f_{3dB}}\) bandwidth as a function of the charge carrier-dependent mobility \((\mu)\) and the contact resistivity \((\rho_{c})\) calculated for a device with the same geometry and dielectric combination as the device in Fig. 1 (section XI in SI). The dashed lines indicate the expected \(\mathrm{f_{3dB}}\sim 46\mathrm{GHz}\) at \(\mu \sim 12,000\mathrm{cm}^2 /\mathrm{Vs}\) (evaluated at \(\mathrm{V_{BT}} = 10.4\mathrm{V}\) , refer to panel b). b, Measured electro-optical \(\mathrm{S_{21}}\) frequency response of the EA modulator at \(\mathrm{V_{BT}} = 10.4\mathrm{V}\) and \(\mathrm{V_{AC}} = 200\mathrm{mV}\) , without de-embedding, i.e. including the contributions of the setup and photodetector (section XII in SI). c, \(2^{31} - 1\) pseudo-random binary sequence non-return-to-zero eye diagram at \(28\mathrm{Gbps}\) and \(40\mathrm{Gbps}\) . The EA modulator is d.c. biased at \(\mathrm{V_{BT}} = 11\mathrm{V}\) and driven by a \(\mathrm{V_{AC}} = 3.5\mathrm{V}\) peak-to-peak RF signal. The eye diagram measured at \(40\mathrm{Gbps}\) has a \(5.2\mathrm{dB}\) ER and a \(2.28\mathrm{dB}\) signal-to-noise ratio (SNR). The green arrows indicate the \(0\mathrm{W}\) baseline.
+ +tation of the 2D- 3D dielectric interface). More importantly, the hBN- graphene interfaces remain atomically sharp and clean22,23,30. This nanoscale control of the interfaces brings further advantages to real- world EA graphene modulators, like a symmetric and hysteresis- free operation. This is directly visible in the transmission curves as a function of the applied voltage \(\mathrm{V_{BT}}\) or, alternatively, as a function of the Fermi energy \(\mathrm{E_F}\) at the graphene electrodes (see bottom and top axis in Fig. 1c and section IX in SI). Both forward and backward voltage sweeps (black and blue traces, respectively) show minor hysteresis and appear symmetric with respect to the charge neutrality point. For comparison, a device fabricated with a \(\mathrm{HfO_2}\) - hBN dielectric shows no overlap between the forward and backward sweeps (inset of Fig. 1c). This strong hysteresis is nonetheless expected for this \(\mathrm{HfO_2}\) - hBN modulator since, in that case, the top graphene electrode is in direct contact with \(\mathrm{HfO_2}\) . The hBN- \(\mathrm{HfO_2}\) - hBN modulator device exhibits a modulation efficiency as high as \(\sim 2.2\mathrm{dB / V}\) within a \(0.5\mathrm{V}\) voltage span (see red linear fit to the data in Fig. 1c). Considering the length of our modulator ( \(\sim 60\mu \mathrm{m}\) ), we obtain a normalized static modulation efficiency of \(\sim 0.037\mathrm{dB / V\mu m}\) , a three- fold increase compared to previously reported high- speed graphene EA modulators9. + +With such a high static modulation efficiency (Fig. 1), one might expect the device speed to be compromised14. However, the high mobility of the hBN- encapsulated graphene is expected to increase the bandwidth. This + +is visible in Fig. 2a, where we calculated the \(\mathrm{f_{3dB}}\) bandwidth as a function of the charge carrier- dependent mobility \((\mu)\) and contact resistivity \((\rho_{c})\) for a graphene modulator with the same geometry and dielectric combination as the device in Fig. 1 (section XI in SI). As observed, the graphene mobility and the contact resistivity have a major influence on the modulator speed. Considering the mobility \(\mu \approx 12,000\mathrm{cm}^2 /\mathrm{(Vs)}\) (evaluated at \(\mathrm{V_{BT}} = 10.4\mathrm{V}\) ) and the contact resistivity \(\rho_{c} \approx 800\Omega \cdot \mu \mathrm{m}\) achieved experimentally (sections IV and XI in SI), we expect a bandwidth of \(\mathrm{f_{3dB}} \sim 46\mathrm{GHz}\) (dashed lines in Fig. 2a). To confirm this value experimentally, we measured the electro- optical (EO) bandwidth of the device in Fig. 1 at a DC voltage \(\mathrm{V_{BT}} = 10.4\mathrm{V}\) and a peak- to peak voltage \(\mathrm{V_{AC}} = 200\mathrm{mV}\) (Fig. 2b). The bandwidth of the measured device attains \(\mathrm{f_{3dB}} \approx 39\mathrm{GHz}\) (without de- embedding, section XIII in SI). This value is close to the capabilities of our setup, limited to \(40\mathrm{GHz}\) by the vector network analyzer (VNA) and the RF probes (section XII in SI). Even tough the measured \(\mathrm{f_{3dB}}\) does not reach the expected \(\mathrm{f_{3dB}} \sim 46\mathrm{GHz}\) (Fig. 2a), possibly due to an increased contact resistivity of the measured device (section XI in SI), this is still the highest \(\mathrm{f_{3dB}}\) bandwidth among all graphene- based modulators reported so far8,9,11,12,34,35. + +The high- speed operation of our modulator device is also supported by non- return to zero (NRZ) eye diagram measurements. The data were obtained through an electrical pattern generator (PG) driving the modulator with a \(2^{31} - 1\) pseudo- random binary sequence (PRBS) + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
FIG. 3. Dielectric breakdown and Pauli blocking operation. a, Maximum Fermi energy, noted \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}}\) , expected at the graphene electrodes of a graphene modulator with a dielectric's relative permittivity \(\epsilon_{\mathrm{r}}\) and dielectric strength \(\mathrm{E}_{\mathrm{BD}}\) . All points lying inside the blue-colored region represent a dielectric allowing for Pauli blocking operation \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}} > 0.5\mathrm{eV}\) , refer to section I in SI). The red-colored region indicates otherwise \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}}< 0.5\mathrm{eV}\) . The white band represents the Pauli blocking boundary condition, defined as \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}} = 0.5\mathrm{eV}\) . The expected \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}}\) for \(\mathrm{HfO_2}\) and hBN are represented by the red and green squares respectively, taking the values of \(\mathrm{E}_{\mathrm{BD}}\) and \(\epsilon_{\mathrm{r}}\) from literature \(^{28,31 - 33}\) (marked with dots) and our dielectric characterization (marked with stars, see sections V and X in SI). The black star represents the \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}} = 0.57\mathrm{eV}\) expected for the hBN-HfO \(_2\) -hBN modulator in Fig. 1c (section X in SI). b and c, Normalized transmission as a function of \(\mathrm{E}_{\mathrm{F}}\) and \(\mathrm{V}_{\mathrm{BT}}\) for modulators with hBN (b) and hBN-HfO \(_2\) -hBN (c) dielectric. The data points are measurements and the solid curves simulations (see sections I-III and X in SI). The vertical dashed lines indicate the \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}}\) achieved at the dielectric breakdown. The orange-shaded regions show the full transparency range, i.e. Pauli blocking. The top \(\mathrm{V}_{\mathrm{BT}}\) axis in panel b is for the \(42\mu \mathrm{m}\) -long device only (see section VII in SI for the other hBN devices). The graphene Dirac cones in panel b show the absorption and Pauli blocking processes at low and high Fermi energies, respectively.
+ +at 28 and 40 Gbps bit- rate (section XII in SI). The signal was driven by a \(3.5\mathrm{V}\) peak- to- peak voltage while the DC bias was set to \(11\mathrm{V}\) . The device was terminated with a \(50\Omega\) load to avoid reflections due to the impedance mismatch between the PG electrical output and the modulator (when measured at 40 Gbps). Open eye- diagrams at 28 Gbps and 40 Gbps are shown in Fig. 2c, with an ER as high as \(5.2\mathrm{dB}\) and a signal- to- noise ratio (SNR) of \(2.28\mathrm{dB}\) for the latter (see section XIV in SI for an eye- diagram at 10 Gbps). These results confirm the large modulation efficiency of our hBN- HfO \(_2\) - hBN- based modulator device, even at high speeds, with a record- high dynamic modulation efficiency of \(1.49\mathrm{dB / V}\) at \(40\mathrm{Gbps}^{9}\) . + +Like the speed of the modulator, the power consumption, understood as the switching energy per bit, also benefits from the small footprint of the device. Ignoring the parasitic pad capacitance, we obtain for the modulator in Fig. 1 an energy per bit of \(\mathrm{C}(\mathrm{V}_{\mathrm{AC}})^2 /4\approx 160\mathrm{fJ / bit}\) , where \(\mathrm{C} = 52\mathrm{fF}\) is the capacitance between the top and bottom graphene electrodes and \(\mathrm{V}_{\mathrm{AC}} = 3.5\mathrm{V}\) the voltage swing \(^{12}\) . This value of energy per bit is on par with state- of- the- art SiGe technologies \(^{36,37}\) . + +To directly compare modulators with different dielectrics, it is more convenient to compare the transmission as a function of \(\mathrm{E}_{\mathrm{F}}\) (see the \(\mathrm{E}_{\mathrm{F}}\) - axis in Fig. 1c and + +Fig. 3b and c) since \(\mathrm{E}_{\mathrm{F}}\) already considers the thickness and the relative permittivity of the dielectric (section VII in SI). Operating the modulators at high \(\mathrm{E}_{\mathrm{F}}\) enhances both ER and IL, with the ER (IL) increasing (decreasing) as a function of \(\mathrm{E}_{\mathrm{F}}^{9}\) . In the full transparency regime (Pauli blocking, see section I in SI), the ER is maximized and the IL is expected to become nearly zero for high- quality graphene \(^{1,9}\) (section X in SI). It is thus crucial to determine which dielectric materials facilitate Pauli blocking operation. Fig. 3a illustrates the expected maximum \(\mathrm{E}_{\mathrm{F}}\) , + +\[\mathrm{E}_{\mathrm{F}}^{\mathrm{max}} = \mathrm{hv}_{\mathrm{F}}\sqrt{\pi\epsilon_{0}\epsilon_{\mathrm{r}}E_{\mathrm{BD}} / \mathrm{q}}, \quad (1)\] + +as a function of the relative permittivity \((\epsilon_{\mathrm{r}})\) and dielectric strength \((\mathrm{E}_{\mathrm{BD}})\) of any given dielectric. The square boxes in Fig. 3a enclose the expected \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}}\) for the \(\mathrm{HfO_2}\) and hBN- based modulators (in red and green, respectively) and the black star represents the \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}} = 0.57\mathrm{eV}\) expected for the hBN- HfO \(_2\) - hBN modulator of Fig. 1c (section X in SI). The boundaries of the boxes are taken from literature \(^{28,31 - 33}\) (marked with dots) and from our dielectric characterization (marked with stars, sections V and X in SI). All dielectric materials fulfilling \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}} > 0.5\mathrm{eV}\) (see white fringe in Fig. 3a) allow full transparency, i.e. Pauli blocking. The comparison in Fig. 3a highlights the advantages of the hBN- HfO \(_2\) - hBN dielectric (black star), achieving higher \(\mathrm{E}_{\mathrm{F}}\) values than the hBN + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
FIG. 4. Comparison graph. The black and red data points and axis represent the static modulation efficiency as a function of the \(\mathrm{f}_{3\mathrm{dB}}\) bandwidth and the dynamic modulation efficiency (extracted from eye-diagrams) as a function of the modulation speed, respectively. The red, blue and green data clouds enclose single \(^{11,12,34,38}\) and double-layer \(^{8 - 10,35}\) graphene and silicon- \(^{39 - 41}\) state-of-the-art modulators operating at \(\lambda = 1.55\mu \mathrm{m}\) . Refer to sections XV and XVI in SI for a more detailed comparison of graphene-based modulators.
+ +dielectric while equally preserving the intrinsic qualities of graphene. + +These results are confirmed by the transmission traces in Fig. 3b and c. None of the hBN- based modulators were able to withstand Pauli blocking operation (orange- shaded region Fig. 3b), all breaking their hBN dielectric at a similar \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}} \approx 0.4\mathrm{eV}\) (see vertical dashed lines in Fig. 3b and section VII and VIII in SI). Even though these hBN- based modulators were too fragile, we obtained modulation efficiencies as a high as 0.3, 1.3 and \(2\mathrm{dB / V}\) for device lengths \(\mathrm{L} = 12\) , 24 and \(42\mu \mathrm{m}\) , respectively. Once normalized by its length, we obtain 0.025, 0.054 and \(0.047\mathrm{dB / (V\mu m)}\) . These results exceed the state- of- the- art modulation efficiency of \(0.038\mathrm{dB / (V\mu m)^{10}}\) . Still, the premature hBN breakdown compromises the ER and the IL. Indeed, the measured \(\mathrm{ER} = 0.75\) , 2.3 and \(4.9\mathrm{dB}\) (data points in Fig. 3b) are far from the simulated \(\mathrm{ER} = 1.8\) , 4.4 and \(7.9\mathrm{dB}\) (solid traces in Fig. 3b) expected for the 12, 24 and \(42\mu \mathrm{m}\) - long modulators, respectively (for simulations, refer to sections I- III in SI). Likewise, the measured \(\mathrm{IL} = 1\) , 2.2 and \(3.4\mathrm{dB}\) are higher than the \(\mathrm{IL} \approx 0\mathrm{dB}\) expected for high mobility graphene modulators \(^{1}\) (see the minimum \(0\mathrm{dB}\) normalized transmission, i.e. neglecting the losses from grating couplers and Si waveguide, achieved by the simulation traces in Fig. 3b and section X in SI). + +On the other hand, the second hBN- HfO \(_2\) - hBN modulator device attains the Pauli blocking regime (Fig. 3c), + +In agreement with the dielectric characterization of hBN- HfO \(_2\) - hBN (Fig. 3a and sections V and X in SI), reaching a maximum Fermi energy of \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}} \approx 0.54\mathrm{eV}\) . The ER and IL improve accordingly, with an \(\mathrm{ER} = 7.8\mathrm{dB}\) almost twice the value obtained by the hBN- based modulator of comparable length (compare the black and red traces of Fig. 3c and b, respectively) and a IL reaching nearly zero ( \(\mathrm{IL} \approx 0.04\mathrm{dB}\) in Fig. 3c and section X in SI). However, being shorter ( \(\mathrm{L} = 44\mu \mathrm{m}\) ) than the device in Fig. 1c ( \(\mathrm{L} = 60\mu \mathrm{m}\) ), the modulation efficiency is lower ( \(1.3\mathrm{dB / V}\) in a \(0.5\mathrm{V}\) span, see Fig. 3c). We note that the hBN- HfO \(_2\) - hBN device of Fig. 1c has a relatively weak measured \(\mathrm{ER} \approx 4.4\mathrm{dB}\) and \(\mathrm{IL} \approx 7.8\mathrm{dB}\) (section X in SI) due to an over- cautious \(\mathrm{V}_{\mathrm{BT}} = 12.1\mathrm{V}\) applied voltage (or alternatively \(\mathrm{E}_{\mathrm{F}} = 0.41\mathrm{eV}\) ). Considering the breakdown capabilities of hBN- HfO \(_2\) - hBN dielectric (black star in Fig. 3a), we evaluated a potential \(\mathrm{ER} \approx 12\mathrm{dB}\) and \(\mathrm{IL} \approx 0.042\mathrm{dB}\) for this device (section X in SI). + +Although material platforms like Lithium Niobate \(^{42}\) (LiNbO \(_3\) ) or hybrid technologies like Si/Indium Phosphide \(^{43}\) (InP), Si/SiGe \(^{44}\) or InGaAlAs \(^{44}\) offer outstanding performances in modulator applications, those are either not scalable \(^{42,45}\) (LiNbO \(_3\) ) or their integration with a CMOS fabrication line remains challenging \(^{44,46}\) . Nowadays, Si and graphene are envisaged as the most scalable, cost- effective and CMOS compatible materials for amplitude modulator applications \(^{1}\) . To compare our results with state- of- the- art graphene and Si amplitude modulators, both EA and Mach- Zehnder interferometer configurations included, we summarize our results in Fig. 4 and in sections XV and XVI of SI. Fig. 4 shows the dynamic modulation efficiency (extracted from the eye- diagrams and normalized by the device length and drive voltage) as a function of the modulation speed (red axis and red data point in Fig. 4) and the static modulation efficiency (measured in DC and normalized by the device length), as a function of the \(\mathrm{f}_{3\mathrm{dB}}\) bandwidth (black axis and black data point in Fig. 4). To avoid discrepancies due to the different extraction methods, we determine the static modulation efficiency of the compared literature \(^{8 - 12}\) using the same method as in Fig. 1c, i.e. by applying a linear fit within a \(0.5\mathrm{V}\) voltage span. Results highlight the trade- offs between speed and modulation efficiency and stresses the advantages of a hBN- HfO \(_2\) - hBN dielectric to obtain large static and dynamic modulation efficiencies even at high speed. As observed, the modulation efficiency typically drops for devices with high speed \(^{8,9}\) , being our device the only modulator able to operate at high speed with a large static and dynamic modulation efficiency (Fig. 4). These results outperform state- of- the- art graphene and not yet commercial silicon- based electro- absorption modulators \(^{39 - 41}\) (see blue/red and green data clouds, respectively in Fig. 4) when considering the modulation efficiency normalized by the length (i.e. footprint). This figure- of- merit is rather an important one since for many envisaged applications (e.g. chip interconnects) multiple + +<--- Page Split ---> + +modulator devices are expected to coexist on the same chip. + +## II. CONCLUSION + +With this work, we demonstrated the advantages of integrating hBN with a 3D high- \(\kappa\) dielectric for high- quality graphene- based EA modulators. Compared to traditional oxide sputtering or ALD- growth on top of graphene, the integration of \(\mathrm{HfO_2}\) in between hBN prevented any damage of the underlying graphene and allowed clean graphene- hBN interfaces. These clean interfaces yielded a symmetric and nearly hysteresis- free + +operation. Moreover, this 2D- 3D integration enabled full transparency while maintaining the high mobility and low doping of intrinsic graphene. More importantly, the hBN- \(\mathrm{HfO_2}\) - hBN based EA modulators were able to reach high modulation speeds with strong modulation efficiencies, overcoming the fundamental limitations of the DL graphene configuration and outperforming state- of- the- art graphene and Si technologies. 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C., Ye, P. D. & Wilk, G. D. Leakage current and breakdown electric- field studies on ultrathin atomic- layer- deposited \(\mathrm{Al}_2\mathrm{O}_3\) on GaAs. Appl. Phys. Lett. 87, 182904 (2005). 33 Groner, M. D., Elam, J. W., Fabreguette, F. H. & George, S. M. Electrical characterization of thin \(\mathrm{Al}_2\mathrm{O}_3\) films grown by atomic layer deposition on silicon and various metal substrates. Thin Solid Films 413, 186- 197 (2002). 34 Sorianello, V. et al. Chirp management in silicon- graphene electro absorption modulators. Optics Express 25, 19371- 19381 (2017). 35 Mohsin, M. et al. Graphene based low insertion loss electro- absorption modulator on SOI waveguide. Optics Express 22, 15292- 15297 (2014). 36 Edwards, E. H. et al. Ge/SiGe asymmetric Fabry- Perot quantum well electroabsorption modulators. Optics Express 20, 29164- 29173 (2012). 37 Audet, R. M. et al. Surface- normal Ge/SiGe asymmetric Fabry- Perot optical modulators fabricated on silicon substrates. Journal of Lightwave Technology 31, 3995- 4003 (2013). 38 Sorianello, V. et al. Graphene- silicon phase modulators with gigahertz bandwidth. Nature Photonics 12, 40- 44 (2018). 39 Giewont, K. et al. 300- mm monolithic silicon photonics foundry technology. IEEE Journal of Selected Topics in Quantum Electronics 25, 1- 11 (2019). 40 Gill, D. M. et al. Demonstration of error- free 32- Gb/s operation from monolithic CMOS nanophotonic transmitters. IEEE Photonics Technology Letters 28, 1410- 1413 (2016). 41 Xiong, C. et al. Monolithic 56 Gb/s silicon photonic pulse- amplitude modulation transmitter. Optica 3, 1060- 1065 (2016). 42 Mercante, A. J. et al. Thin film lithium niobate electro- optic modulator with terahertz operating bandwidth. Optics Express 26, 14810- 14816 (2018). 43 Smit, M., Williams, K. & van der Tol, J. Past, present, and future of InP- based photonic integration. APL Photonics 4, 050901 (2019). 44 Liu, K., Ye, C. R., Khan, S. & Sorger, V. J. Review and perspective on ultrafast wavelength- size electro- optic modulators. Laser & Photonics Reviews 9, 172- 194 (2015). 45 Wang, C., Zhang, M., Stern, B., Lipson, M. & Loncar, M. Nanophotonic lithium niobate electro- optic modulators. Optics Express 26, 1547- 1555 (2018). 46 Tang, Y. et al. 50 Gb/s hybrid silicon traveling- wave electroabsorption modulator. Optics Express 19, 5811- 5816 (2011). + +## ACKNOWLEDGMENTS + +We thank S. Pradhan for his assistance in capacitance measurements and D. A. Iranzo for his inputs on the illustration in Fig. 1b. H.A. acknowledges funding from the European Unions Horizon 2020 research and innovation program under the Marie Sklodowska- Curie grant agreement No. 665884. + +## CONTRIBUTIONS + +B.T., H.A., and F.H.L.K. conceived the idea. H.A., and B.T. fabricated the devices. L.O., B.T. did the simulations. B.T., H.A. performed the measurements and data analysis. A.M., V.S. performed high frequency measurements under the supervision of M.R., M.P., and D.V.T. provided Si waveguides. K.W., and T.T. synthesized the h- BN crystals. F.H.L.K., and B.T., supervised the project. B.T., H.A., and F.H.L.K. wrote the manuscript with input from all authors. + +## DATA AVAILABILITY + +The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request. + +## COMPETING FINANCIAL INTERESTS + +The authors declare no competing financial interests. + +## METHODS + +The Si photonic waveguide with a core cross- section of \(750\mathrm{nm}\times 220\mathrm{nm}\) was prepared on the IMEC iSiPP25G silicon on insulator (SOI) platform. For the fabrication of the electro- absorption modulator (EAM), the graphene and hBN flakes were exfoliated from highly oriented pyrolytic graphite (HOPG) and hBN crystals, respectively. The bottom hBN- graphene- hBN stacks was prepared by the van der Waals assembly technique and transferred directly onto the Si waveguide separated by a \(10\mathrm{nm}\) spacer of high- quality thermal \(\mathrm{SiO}_2\) . The bottom hBN flake (separating the graphene and the \(\mathrm{SiO}_2\) layer) thickness of \(\sim 5\mathrm{nm}\) was chosen to enhanced the graphene absorption while isolating the graphene from the rough \(\mathrm{SiO}_2\) substrate. The top hBN has a thickness of \(\sim 10\mathrm{nm}\) . The stack has been etched by reactive ion etching (RIE) in an oxygen ( \(\mathrm{O}_2\) ) and Trifluoromethane ( \(\mathrm{CHF}_3\) ) (4:40 sccm) environment to expose the graphene edge. The bottom stack was then contacted by a \(3 / 15 / 30\mathrm{nm}\) Cr/Pd/Au metal combination. The \(10\mathrm{nm}\) hafnium oxide film has been deposited at \(250^{\circ}\mathrm{C}\) prior deposition of a \(2\mathrm{nm}\) sputtered \(\mathrm{SiO}_2\) seed layer by atomic layer deposition (ALD). Tetrakis- dimethylamido hafnium (TDMAM) (0.4 sec purge time) and water vapor (5 sec purge time) as precursors have been used in a Savannah G1 system from Cambridge Nanotech. The top hBN- graphene- hBN stack with a \(7\mathrm{nm}\) - and \(21\mathrm{nm}\) - thick bottom and top hBN layers has followed the same fabrication steps as the bottom stack. + +<--- Page Split ---> + +## Figures + +![](images/Figure_1.jpg) + +
Figure 1
+ +Device geometry and static characterization. a, Optical image of a photonic device consisting of two grating couplers (GC), a silicon optical waveguide (Si WG) and a hBN- HfO2- hBN based graphene EA modulator on top (see zoom- in optical and scanning electron microscope (SEM) images for details). The metal contacts are yellow/brown and the bottom and top graphene electrodes violet and light blue, respectively. The core of the waveguide is highlighted by the green dashed lines. b, Electrical connections and schematic cross- section of a EA modulator with a hBN- HfO2- hBN dielectric. The top and bottom graphene electrodes are fully encapsulated by hBN (in green) protecting both graphene electrodes from the out- of- plane dangling bonds typical of 3D oxide materials, e.g. HfO2 (in red). See inset for a molecular representation. c, Transmission curves as a function of the voltage between the bottom and top graphene electrodes (VBT- axis, bottom) and the Fermi energy at the graphene electrodes (EF- axis, top) for the EA modulator in panel a with a hBN- HfO2- hBN dielectric (see sketch). The 1550nm excitation power was set to 0 dBm. The forward and backward voltage sweeps (black and blue, respectively) show no major hysteresis compared to a modulator with a hBN- HfO2 dielectric (see inset). The red line is a linear fit to the forward voltage sweep within a 0.5V voltage span (extracted slope: 2.2 dB/V). + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2
+ +Dynamic characterization. a, f3dB bandwidth as a function of the charge carrier- dependent mobility \((\mu)\) and the contact resistivity (pc) calculated for a device with the same geometry and dielectric combination as the device in Fig. 1 (section XI in SI). The dashed lines indicate the expected f3dB \(\boxed {\mathbb{R}}46\) GHz at \(\mu \boxed {\mathbb{R}}12\) , 000 cm2/Vs (evaluated at VBT \(= 10.4\) V, refer to panel b). b, Measured electro- optical S21 frequency response of the EA modulator at VBT \(= 10.4\) V and VAC \(= 200\) mV, without de- embedding, i.e. including the contributions of the setup and photodetector (section XII in SI). c, 231 - 1 pseudo- random binary sequence non- return- to- zero eye diagram at 28 Gbps and 40 Gbps. The EA modulator is d.c. biased at VBT \(= 11\) V and driven by a VAC \(= 3.5\) V peak- to- peak RF signal. The eye diagram measured at 40 Gbps has a 5.2 dB ER and a 2.28 dB signal- to- noise ratio (SNR). The green arrows indicate the 0 W baseline. + +![](images/Figure_3.jpg) + +
Figure 3
+ +<--- Page Split ---> + +Dielectric breakdown and Pauli blocking operation. a, Maximum Fermi energy, noted Emax F, expected at the graphene electrodes of a graphene modulator with a dielectric's relative permittivity \(\mathbb{R}\) and dielectric strength EBD. All points lying inside the blue- colored region represent a dielectric allowing for Pauli blocking operation (Emax F > 0.5 eV, refer to section I in SI). The red- colored region indicates otherwise (Emax F < 0.5 eV). The white band represents the Pauli blocking boundary condition, defined as Emax F = 0.5 eV. The expected Emax F for HfO2 and hBN are represented by the red and green squares respectively, taking the values of EBD and \(\mathbb{R}\) from literature28,31- 33 (marked with dots) and our dielectric characterization (marked with stars, see sections V and X in SI). The black star represents the Emax F = 0.57 eV expected for the hBN- HfO2- hBN modulator in Fig. 1c (section X in SI). b and c, Normalized transmission as a function of EF and VBT for modulators with hBN (b) and hBN- HfO2- hBN (c) dielectric. The data points are measurements and the solid curves simulations (see sections I- III and X in SI). The vertical dashed lines indicate the Emax F achieved at the dielectric breakdown. The orange- shaded regions show the full transparency range, i.e Pauli blocking. The top VBT axis in panel b is for the 42 \(\mu\)m- long device only (see section VII in SI for the other hBN devices). The graphene Dirac cones in panel b show the absorption and Pauli blocking processes at low and high Fermi energies, respectively. + +![](images/Figure_4.jpg) + +
Figure 4
+ +<--- Page Split ---> + +Comparison graph. The black and red data points and axis represent the static modulation efficiency as a function of the f3dB bandwidth and the dynamic modulation efficiency (extracted from eye- diagrams) as a function of the modulation speed, respectively. The red, blue and green data clouds enclose single- 11,12,34,38 and double- layer 8–10, 35 graphene and silicon- 39–41 state- of- the- art modulators operating at \(\lambda = 1.55 \mu \mathrm{m}\) . Refer to sections XV and XVI in SI for a more detailed comparison of graphene- based modulators. + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- Supplinf.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__07e3c01bfb7c257f74c13b751d60027ccfb31a80dc7afca89f4153da3d24fa3d/preprint__07e3c01bfb7c257f74c13b751d60027ccfb31a80dc7afca89f4153da3d24fa3d_det.mmd b/preprint/preprint__07e3c01bfb7c257f74c13b751d60027ccfb31a80dc7afca89f4153da3d24fa3d/preprint__07e3c01bfb7c257f74c13b751d60027ccfb31a80dc7afca89f4153da3d24fa3d_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..2e74e560dec0f75357fae507b488633b093d8b30 --- /dev/null +++ b/preprint/preprint__07e3c01bfb7c257f74c13b751d60027ccfb31a80dc7afca89f4153da3d24fa3d/preprint__07e3c01bfb7c257f74c13b751d60027ccfb31a80dc7afca89f4153da3d24fa3d_det.mmd @@ -0,0 +1,252 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 933, 207]]<|/det|> +# 2D-3D integration of hBN and a high-κ dielectric for ultrafast graphene-based electro-absorption modulators + +<|ref|>text<|/ref|><|det|>[[44, 230, 900, 275]]<|/det|> +Hitesh AgarwalICFO – Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona) + +<|ref|>text<|/ref|><|det|>[[44, 301, 316, 339]]<|/det|> +Bemat TerresInstitute of Photonic Sciences + +<|ref|>text<|/ref|><|det|>[[44, 345, 900, 410]]<|/det|> +Lorenzo OrsiniICFO – Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona) + +<|ref|>text<|/ref|><|det|>[[44, 415, 615, 456]]<|/det|> +Alberto MontanaroConsorzio Nazionale Interuniversitario Per Le Telecomunicazioni + +<|ref|>text<|/ref|><|det|>[[44, 461, 125, 499]]<|/det|> +Vito SorianelloCNIT + +<|ref|>text<|/ref|><|det|>[[44, 507, 233, 546]]<|/det|> +Marianna PantouvakiIMEC + +<|ref|>text<|/ref|><|det|>[[44, 554, 752, 596]]<|/det|> +Kenji WatanabeNational Institute for Materials Science https://orcid.org/0000- 0003- 3701- 8119 + +<|ref|>text<|/ref|><|det|>[[44, 600, 752, 641]]<|/det|> +Takashi TaniguchiNational Institute for Materials Science https://orcid.org/0000- 0002- 1467- 3105 + +<|ref|>text<|/ref|><|det|>[[44, 646, 917, 710]]<|/det|> +Dries Van ThourhoutPhotonics Research Group, INTEC Department, Ghent University- imec https://orcid.org/0000- 0003- 0111- 431X + +<|ref|>text<|/ref|><|det|>[[44, 715, 914, 803]]<|/det|> +Marco RomagnoliConsorzio Nazionale Interuniversitario Telecomunicazioni https://orcid.org/0000- 0002- 4274- 5620Frank Koppens ( frank.koppens@icfo.eu)ICFO - The Institute of Photonic Sciences https://orcid.org/0000- 0001- 9764- 6120 + +<|ref|>sub_title<|/ref|><|det|>[[44, 844, 101, 861]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 881, 429, 901]]<|/det|> +Keywords: EA, hBN, HfO2, Electro- absorption + +<|ref|>text<|/ref|><|det|>[[44, 920, 336, 938]]<|/det|> +Posted Date: November 9th, 2020 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 451, 64]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 57385/v1 + +<|ref|>text<|/ref|><|det|>[[42, 82, 911, 125]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 161, 945, 205]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on February 16th, 2021. See the published version at https://doi.org/10.1038/s41467- 021- 20926- w. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[123, 62, 880, 99]]<|/det|> +# 2D-3D integration of hBN and a high- \(\kappa\) dielectric for ultrafast graphene-based electro-absorption modulators + +<|ref|>text<|/ref|><|det|>[[163, 110, 840, 295]]<|/det|> +Hitesh Agarwal, \(^{1, *}\) Bernat Terres, \(^{1, *}\) , Lorenzo Orsini, \(^{1, 2}\) Alberto Montanaro, \(^{3}\) Vito Sorianello, \(^{3}\) Marianna Pantouvaki, \(^{4}\) Kenji Watanabe, \(^{5}\) Takashi Taniguchi, \(^{5}\) Dries Van Thourhout, \(^{4}\) Marco Romagnoli, \(^{3}\) and Frank H. L. Koppens \(^{1, 6, \dagger}\) \(^{1}\) ICFO — Institut de Ciències Fotoniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona), Spain \(^{2}\) Dipartimento di Fisica E. Fermi, Università di Pisa, Largo Bruno Pontecorvo 3, 56127 Pisa, Italy. \(^{3}\) Consorzio Nazionale per le Telecomunicazioni (CNIT), Photonic Networks and Technologies National Laboratory, via Moruzzi 1, 56124 Pisa, Italy \(^{4}\) Photonics Research Group, Department of Information Technology, Ghent University- IMEC, Sint- Pietersnieuwstraat 41, Gent, 9000 Belgium \(^{5}\) National Institute for Materials Science, 1- 1 Namiki, Tsukuba 305- 0044, Japan \(^{6}\) ICREA — Institució Catalana de Recerca i Estudis Avancats, Barcelona, Spain. (Dated: Tuesday \(3^{\mathrm{rd}}\) November, 2020 12:17 :: git HEAD ref not available) + +<|ref|>text<|/ref|><|det|>[[87, 327, 487, 704]]<|/det|> +Electro- absorption (EA) waveguide- coupled modulators are essential building blocks for on- chip optical communications. Compared to state- of- the- art silicon (Si) devices, graphene- based EA modulators promise smaller footprints, larger temperature stability, cost- effective integration and high speeds. However, combining high speed and large modulation efficiencies in a single graphene- based device has remained elusive so far. In this work, we overcome this fundamental trade- off by demonstrating the first 2D- 3D dielectric integration in a high- quality encapsulated graphene device. We integrated hafnium oxide \(\mathrm{(HfO_2)}\) and two- dimensional hexagonal boron nitride (hBN) within the insulating section of a double- layer (DL) graphene EA modulator. This novel combination of materials allows for a high- quality modulator device with record high performances: a \(\sim 39\mathrm{GHz}\) bandwidth (BW) with a three- fold increase in modulation efficiency compared to previously reported high- speed modulators. This 2D- 3D dielectric integration paves the way to a plethora of electronic and opto- electronic devices with enhanced performance and stability, while expanding the freedom for new device designs. + +<|ref|>text<|/ref|><|det|>[[87, 718, 486, 847]]<|/det|> +Broadband optical modulators with ultra- high speed, low- drive voltage and hysteresis- free operation are key devices for next- generation datacom transceivers \(^{1}\) . Although Si photonics is nowadays a prime candidate to fulfill these requirements \(^{2,3}\) , graphene is rapidly becoming a major contender in several optoelectronic applications, such as ultrafast modulators \(^{4,5}\) and silicon- integrated photodetectors \(^{6,7}\) . Graphene- based modulators have already proven broadband optical bandwidth \(^{1}\) , + +<|ref|>text<|/ref|><|det|>[[515, 327, 916, 486]]<|/det|> +high- speed \(^{8,9}\) , relatively high modulation efficiencies \(^{10}\) and temperature stability \(^{8}\) . These devices are all based on CMOS compatible materials \(^{7,10 - 13}\) , where CMOS design and fabrication techniques can be further leveraged to decrease costs. However, graphene- based modulators have yet to demonstrate all operation requirements at once. More specifically, EA graphene modulators struggle to show high- speed and high modulation efficiencies simultaneously \(^{14}\) . This bottleneck is mostly due to the weak graphene/dielectric combination and the limited quality of the graphene. + +<|ref|>text<|/ref|><|det|>[[515, 495, 916, 900]]<|/det|> +Unlike Si technology, where high- \(\kappa\) dielectrics lie at the core of its success, 2D dielectrics are hindering the development of graphene- and other 2D- based electronics and optoelectronic devices \(^{1,13,15}\) and are clearly outperformed by traditional 3D high- \(\kappa\) dielectrics. This under- performing 2D- dielectric/graphene combination deepens even further the fundamental trade- off between speed and modulation efficiency inherent to the DL modulators \(^{14}\) . In the DL architecture, the overlapped top and bottom graphene electrodes act as a capacitor (C). The larger the C, the higher the modulation efficiency. On the other hand, the speed of the modulator defined as \(\mathrm{f}_{3\mathrm{dB}} = 1 / (2\pi \mathrm{RC})\) is inversely proportional to C (R being the total resistance). In this framework, the quality of graphene appears as a valid turnaround to overcome this fundamental limitation. A high electron mobility is expected to minimize the overall resistance and reduce the insertion loss (IL) \(^{1,9}\) , thus increasing the bandwidth and the extinction ratio (ER). However, the quality of graphene is very sensitive to its environment, e.g. the dielectric to encapsulate it. Indeed, no graphene/dielectric combination has been able to ensure high charge carrier mobilities and low levels of residual doping in existing graphene waveguide- coupled modulators \(^{16}\) . The growth of non- layered (i.e. 3D) dielectrics, e.g. aluminum oxide (Al₂O₃), silicon nitride (SiN) or HfO₂ directly on top of graphene leads to low electronic mobility \(^{16 - 18}\) and/or inhomogeneous doping \(^{19}\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[84, 60, 920, 301]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[85, 315, 919, 477]]<|/det|> +
FIG. 1. Device geometry and static characterization. a, Optical image of a photonic device consisting of two grating couplers (GC), a silicon optical waveguide (Si WG) and a hBN-HfO2-hBN based graphene EA modulator on top (see zoom-in optical and scanning electron microscope (SEM) images for details). The metal contacts are yellow/brown and the bottom and top graphene electrodes violet and light blue, respectively. The core of the waveguide is highlighted by the green dashed lines. b, Electrical connections and schematic cross-section of a EA modulator with a hBN-HfO2-hBN dielectric. The top and bottom graphene electrodes are fully encapsulated by hBN (in green) protecting both graphene electrodes from the out-of-plane dangling bonds typical of 3D oxide materials, e.g. HfO2 (in red). See inset for a molecular representation. c, Transmission curves as a function of the voltage between the bottom and top graphene electrodes (VBT-axis, bottom) and the Fermi energy at the graphene electrodes (EF-axis, top) for the EA modulator in panel a with a hBN-HfO2-hBN dielectric (see sketch). The 1550 nm excitation power was set to 0 dBm. The forward and backward voltage sweeps (black and blue, respectively) show no major hysteresis compared to a modulator with a hBN-HfO2 dielectric (see inset). The red line is a linear fit to the forward voltage sweep within a 0.5 V voltage span (extracted slope: 2.2 dB/V).
+ +<|ref|>text<|/ref|><|det|>[[86, 505, 487, 736]]<|/det|> +In this work, we demonstrate the 2D- 3D integration of hBN and HfO2 within the dielectric section of a DL graphene EA modulator. This dielectric combination enhances the capacitance of the EA modulators without compromising its robustness against high voltages and preserves the high mobility and low doping of intrinsic graphene. As a result, we achieved a static and dynamic (at 40 Gbps) modulation efficiency as high as \(2.2 \mathrm{dB / V}\) and \(1.49 \mathrm{dB / V}\) , respectively, a \(f_{3 \mathrm{dB}}\) bandwidth of \(\sim 39 \mathrm{GHz}\) and a device footprint of \(60 \mu \mathrm{m} \times 0.45 \mu \mathrm{m} \approx 27 \mu \mathrm{m}^2\) (neglecting the metal pads and graphene leads). Moreover, the hBN- HfO2- hBN based devices show a symmetric and nearly hysteresis- free operation. The larger breakdown voltage of this 2D- 3D dielectric, even beyond the full transparency regime (i.e. Pauli blocking), increases the ER and reduces the IL of the modulators. + +<|ref|>sub_title<|/ref|><|det|>[[149, 764, 424, 779]]<|/det|> +## I. RESULTS AND DISCUSSIONS + +<|ref|>text<|/ref|><|det|>[[87, 796, 488, 912]]<|/det|> +The EA modulators were fabricated on top of a photonic structure \(^{20}\) formed by two gratings couplers \(^{21}\) feeding light in and out of an optical waveguide (Fig. 1a). The \(750 \mathrm{nm}\) - wide waveguide for the device in Fig. 1) was designed to support a single transverse- magnetic (TM) optical mode \(^{20}\) (see sections III in SI). The presented DL graphene modulators were built, for the very first time, with hBN- encapsulated graphene top and bottom + +<|ref|>text<|/ref|><|det|>[[515, 505, 916, 620]]<|/det|> +electrodes (Fig. 1b). The hBN- graphene- hBN stacks have been fabricated following state- of- the- art fabrication techniques \(^{22,23}\) . This ensured low levels of doping and high charge carrier mobilities. We characterized the quality of the resulting modulators (sections II and VI in SI) and extracted a carrier density- independent mobility as high as \(30,000 \mathrm{cm}^2 /(\mathrm{Vs})\) at room temperature \(^{23}\) (section II in SI). + +<|ref|>text<|/ref|><|det|>[[515, 621, 916, 853]]<|/det|> +Although hBN- encapsulated graphene devices have allowed for device designs with unprecedented functionalities \(^{24 - 26}\) and improved performance \(^{23}\) , such layered dielectric material typically contains impurities and/or crystal defects leading to low breakdown voltages \(^{27,28}\) . Moreover, the dielectric permittivity of hBN is rather low compared to existing high- \(\kappa\) dielectrics \(^{29}\) , with a value close to that of SiO2 ( \(\epsilon_{\mathrm{r}} \sim 4\) ). This low dielectric constant and reduced breakdown voltage (see section V in SI) compromises not only the power consumption and the ability to reach high modulation efficiencies at reasonably low drive voltages but also limits the IL and the ER of the modulators \(^{1,9}\) . We thus integrate HfO2, a high- \(\kappa\) dielectric material, within the hBN- encapsulated graphene electrodes (see the sketch in Fig. 1b). + +<|ref|>text<|/ref|><|det|>[[515, 854, 916, 912]]<|/det|> +With such hBN- HfO2- hBN dielectric arrangement, graphene remains isolated from HfO2, shielded away from any possible out- of- plane dangling bonds of the 3D oxide material (see inset of Fig. 1b for the molecular represen + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[86, 60, 920, 319]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[85, 330, 919, 442]]<|/det|> +
FIG. 2. Dynamic characterization. a, \(\mathrm{f_{3dB}}\) bandwidth as a function of the charge carrier-dependent mobility \((\mu)\) and the contact resistivity \((\rho_{c})\) calculated for a device with the same geometry and dielectric combination as the device in Fig. 1 (section XI in SI). The dashed lines indicate the expected \(\mathrm{f_{3dB}}\sim 46\mathrm{GHz}\) at \(\mu \sim 12,000\mathrm{cm}^2 /\mathrm{Vs}\) (evaluated at \(\mathrm{V_{BT}} = 10.4\mathrm{V}\) , refer to panel b). b, Measured electro-optical \(\mathrm{S_{21}}\) frequency response of the EA modulator at \(\mathrm{V_{BT}} = 10.4\mathrm{V}\) and \(\mathrm{V_{AC}} = 200\mathrm{mV}\) , without de-embedding, i.e. including the contributions of the setup and photodetector (section XII in SI). c, \(2^{31} - 1\) pseudo-random binary sequence non-return-to-zero eye diagram at \(28\mathrm{Gbps}\) and \(40\mathrm{Gbps}\) . The EA modulator is d.c. biased at \(\mathrm{V_{BT}} = 11\mathrm{V}\) and driven by a \(\mathrm{V_{AC}} = 3.5\mathrm{V}\) peak-to-peak RF signal. The eye diagram measured at \(40\mathrm{Gbps}\) has a \(5.2\mathrm{dB}\) ER and a \(2.28\mathrm{dB}\) signal-to-noise ratio (SNR). The green arrows indicate the \(0\mathrm{W}\) baseline.
+ +<|ref|>text<|/ref|><|det|>[[86, 469, 488, 830]]<|/det|> +tation of the 2D- 3D dielectric interface). More importantly, the hBN- graphene interfaces remain atomically sharp and clean22,23,30. This nanoscale control of the interfaces brings further advantages to real- world EA graphene modulators, like a symmetric and hysteresis- free operation. This is directly visible in the transmission curves as a function of the applied voltage \(\mathrm{V_{BT}}\) or, alternatively, as a function of the Fermi energy \(\mathrm{E_F}\) at the graphene electrodes (see bottom and top axis in Fig. 1c and section IX in SI). Both forward and backward voltage sweeps (black and blue traces, respectively) show minor hysteresis and appear symmetric with respect to the charge neutrality point. For comparison, a device fabricated with a \(\mathrm{HfO_2}\) - hBN dielectric shows no overlap between the forward and backward sweeps (inset of Fig. 1c). This strong hysteresis is nonetheless expected for this \(\mathrm{HfO_2}\) - hBN modulator since, in that case, the top graphene electrode is in direct contact with \(\mathrm{HfO_2}\) . The hBN- \(\mathrm{HfO_2}\) - hBN modulator device exhibits a modulation efficiency as high as \(\sim 2.2\mathrm{dB / V}\) within a \(0.5\mathrm{V}\) voltage span (see red linear fit to the data in Fig. 1c). Considering the length of our modulator ( \(\sim 60\mu \mathrm{m}\) ), we obtain a normalized static modulation efficiency of \(\sim 0.037\mathrm{dB / V\mu m}\) , a three- fold increase compared to previously reported high- speed graphene EA modulators9. + +<|ref|>text<|/ref|><|det|>[[87, 854, 488, 911]]<|/det|> +With such a high static modulation efficiency (Fig. 1), one might expect the device speed to be compromised14. However, the high mobility of the hBN- encapsulated graphene is expected to increase the bandwidth. This + +<|ref|>text<|/ref|><|det|>[[515, 469, 917, 825]]<|/det|> +is visible in Fig. 2a, where we calculated the \(\mathrm{f_{3dB}}\) bandwidth as a function of the charge carrier- dependent mobility \((\mu)\) and contact resistivity \((\rho_{c})\) for a graphene modulator with the same geometry and dielectric combination as the device in Fig. 1 (section XI in SI). As observed, the graphene mobility and the contact resistivity have a major influence on the modulator speed. Considering the mobility \(\mu \approx 12,000\mathrm{cm}^2 /\mathrm{(Vs)}\) (evaluated at \(\mathrm{V_{BT}} = 10.4\mathrm{V}\) ) and the contact resistivity \(\rho_{c} \approx 800\Omega \cdot \mu \mathrm{m}\) achieved experimentally (sections IV and XI in SI), we expect a bandwidth of \(\mathrm{f_{3dB}} \sim 46\mathrm{GHz}\) (dashed lines in Fig. 2a). To confirm this value experimentally, we measured the electro- optical (EO) bandwidth of the device in Fig. 1 at a DC voltage \(\mathrm{V_{BT}} = 10.4\mathrm{V}\) and a peak- to peak voltage \(\mathrm{V_{AC}} = 200\mathrm{mV}\) (Fig. 2b). The bandwidth of the measured device attains \(\mathrm{f_{3dB}} \approx 39\mathrm{GHz}\) (without de- embedding, section XIII in SI). This value is close to the capabilities of our setup, limited to \(40\mathrm{GHz}\) by the vector network analyzer (VNA) and the RF probes (section XII in SI). Even tough the measured \(\mathrm{f_{3dB}}\) does not reach the expected \(\mathrm{f_{3dB}} \sim 46\mathrm{GHz}\) (Fig. 2a), possibly due to an increased contact resistivity of the measured device (section XI in SI), this is still the highest \(\mathrm{f_{3dB}}\) bandwidth among all graphene- based modulators reported so far8,9,11,12,34,35. + +<|ref|>text<|/ref|><|det|>[[515, 838, 916, 911]]<|/det|> +The high- speed operation of our modulator device is also supported by non- return to zero (NRZ) eye diagram measurements. The data were obtained through an electrical pattern generator (PG) driving the modulator with a \(2^{31} - 1\) pseudo- random binary sequence (PRBS) + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[84, 60, 916, 298]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[84, 310, 919, 488]]<|/det|> +
FIG. 3. Dielectric breakdown and Pauli blocking operation. a, Maximum Fermi energy, noted \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}}\) , expected at the graphene electrodes of a graphene modulator with a dielectric's relative permittivity \(\epsilon_{\mathrm{r}}\) and dielectric strength \(\mathrm{E}_{\mathrm{BD}}\) . All points lying inside the blue-colored region represent a dielectric allowing for Pauli blocking operation \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}} > 0.5\mathrm{eV}\) , refer to section I in SI). The red-colored region indicates otherwise \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}}< 0.5\mathrm{eV}\) . The white band represents the Pauli blocking boundary condition, defined as \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}} = 0.5\mathrm{eV}\) . The expected \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}}\) for \(\mathrm{HfO_2}\) and hBN are represented by the red and green squares respectively, taking the values of \(\mathrm{E}_{\mathrm{BD}}\) and \(\epsilon_{\mathrm{r}}\) from literature \(^{28,31 - 33}\) (marked with dots) and our dielectric characterization (marked with stars, see sections V and X in SI). The black star represents the \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}} = 0.57\mathrm{eV}\) expected for the hBN-HfO \(_2\) -hBN modulator in Fig. 1c (section X in SI). b and c, Normalized transmission as a function of \(\mathrm{E}_{\mathrm{F}}\) and \(\mathrm{V}_{\mathrm{BT}}\) for modulators with hBN (b) and hBN-HfO \(_2\) -hBN (c) dielectric. The data points are measurements and the solid curves simulations (see sections I-III and X in SI). The vertical dashed lines indicate the \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}}\) achieved at the dielectric breakdown. The orange-shaded regions show the full transparency range, i.e. Pauli blocking. The top \(\mathrm{V}_{\mathrm{BT}}\) axis in panel b is for the \(42\mu \mathrm{m}\) -long device only (see section VII in SI for the other hBN devices). The graphene Dirac cones in panel b show the absorption and Pauli blocking processes at low and high Fermi energies, respectively.
+ +<|ref|>text<|/ref|><|det|>[[86, 512, 488, 703]]<|/det|> +at 28 and 40 Gbps bit- rate (section XII in SI). The signal was driven by a \(3.5\mathrm{V}\) peak- to- peak voltage while the DC bias was set to \(11\mathrm{V}\) . The device was terminated with a \(50\Omega\) load to avoid reflections due to the impedance mismatch between the PG electrical output and the modulator (when measured at 40 Gbps). Open eye- diagrams at 28 Gbps and 40 Gbps are shown in Fig. 2c, with an ER as high as \(5.2\mathrm{dB}\) and a signal- to- noise ratio (SNR) of \(2.28\mathrm{dB}\) for the latter (see section XIV in SI for an eye- diagram at 10 Gbps). These results confirm the large modulation efficiency of our hBN- HfO \(_2\) - hBN- based modulator device, even at high speeds, with a record- high dynamic modulation efficiency of \(1.49\mathrm{dB / V}\) at \(40\mathrm{Gbps}^{9}\) . + +<|ref|>text<|/ref|><|det|>[[87, 707, 488, 850]]<|/det|> +Like the speed of the modulator, the power consumption, understood as the switching energy per bit, also benefits from the small footprint of the device. Ignoring the parasitic pad capacitance, we obtain for the modulator in Fig. 1 an energy per bit of \(\mathrm{C}(\mathrm{V}_{\mathrm{AC}})^2 /4\approx 160\mathrm{fJ / bit}\) , where \(\mathrm{C} = 52\mathrm{fF}\) is the capacitance between the top and bottom graphene electrodes and \(\mathrm{V}_{\mathrm{AC}} = 3.5\mathrm{V}\) the voltage swing \(^{12}\) . This value of energy per bit is on par with state- of- the- art SiGe technologies \(^{36,37}\) . + +<|ref|>text<|/ref|><|det|>[[86, 868, 488, 911]]<|/det|> +To directly compare modulators with different dielectrics, it is more convenient to compare the transmission as a function of \(\mathrm{E}_{\mathrm{F}}\) (see the \(\mathrm{E}_{\mathrm{F}}\) - axis in Fig. 1c and + +<|ref|>text<|/ref|><|det|>[[515, 512, 917, 672]]<|/det|> +Fig. 3b and c) since \(\mathrm{E}_{\mathrm{F}}\) already considers the thickness and the relative permittivity of the dielectric (section VII in SI). Operating the modulators at high \(\mathrm{E}_{\mathrm{F}}\) enhances both ER and IL, with the ER (IL) increasing (decreasing) as a function of \(\mathrm{E}_{\mathrm{F}}^{9}\) . In the full transparency regime (Pauli blocking, see section I in SI), the ER is maximized and the IL is expected to become nearly zero for high- quality graphene \(^{1,9}\) (section X in SI). It is thus crucial to determine which dielectric materials facilitate Pauli blocking operation. Fig. 3a illustrates the expected maximum \(\mathrm{E}_{\mathrm{F}}\) , + +<|ref|>equation<|/ref|><|det|>[[615, 681, 915, 700]]<|/det|> +\[\mathrm{E}_{\mathrm{F}}^{\mathrm{max}} = \mathrm{hv}_{\mathrm{F}}\sqrt{\pi\epsilon_{0}\epsilon_{\mathrm{r}}E_{\mathrm{BD}} / \mathrm{q}}, \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[515, 709, 917, 912]]<|/det|> +as a function of the relative permittivity \((\epsilon_{\mathrm{r}})\) and dielectric strength \((\mathrm{E}_{\mathrm{BD}})\) of any given dielectric. The square boxes in Fig. 3a enclose the expected \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}}\) for the \(\mathrm{HfO_2}\) and hBN- based modulators (in red and green, respectively) and the black star represents the \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}} = 0.57\mathrm{eV}\) expected for the hBN- HfO \(_2\) - hBN modulator of Fig. 1c (section X in SI). The boundaries of the boxes are taken from literature \(^{28,31 - 33}\) (marked with dots) and from our dielectric characterization (marked with stars, sections V and X in SI). All dielectric materials fulfilling \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}} > 0.5\mathrm{eV}\) (see white fringe in Fig. 3a) allow full transparency, i.e. Pauli blocking. The comparison in Fig. 3a highlights the advantages of the hBN- HfO \(_2\) - hBN dielectric (black star), achieving higher \(\mathrm{E}_{\mathrm{F}}\) values than the hBN + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[83, 65, 490, 341]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[85, 354, 488, 475]]<|/det|> +
FIG. 4. Comparison graph. The black and red data points and axis represent the static modulation efficiency as a function of the \(\mathrm{f}_{3\mathrm{dB}}\) bandwidth and the dynamic modulation efficiency (extracted from eye-diagrams) as a function of the modulation speed, respectively. The red, blue and green data clouds enclose single \(^{11,12,34,38}\) and double-layer \(^{8 - 10,35}\) graphene and silicon- \(^{39 - 41}\) state-of-the-art modulators operating at \(\lambda = 1.55\mu \mathrm{m}\) . Refer to sections XV and XVI in SI for a more detailed comparison of graphene-based modulators.
+ +<|ref|>text<|/ref|><|det|>[[86, 504, 487, 534]]<|/det|> +dielectric while equally preserving the intrinsic qualities of graphene. + +<|ref|>text<|/ref|><|det|>[[86, 535, 488, 881]]<|/det|> +These results are confirmed by the transmission traces in Fig. 3b and c. None of the hBN- based modulators were able to withstand Pauli blocking operation (orange- shaded region Fig. 3b), all breaking their hBN dielectric at a similar \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}} \approx 0.4\mathrm{eV}\) (see vertical dashed lines in Fig. 3b and section VII and VIII in SI). Even though these hBN- based modulators were too fragile, we obtained modulation efficiencies as a high as 0.3, 1.3 and \(2\mathrm{dB / V}\) for device lengths \(\mathrm{L} = 12\) , 24 and \(42\mu \mathrm{m}\) , respectively. Once normalized by its length, we obtain 0.025, 0.054 and \(0.047\mathrm{dB / (V\mu m)}\) . These results exceed the state- of- the- art modulation efficiency of \(0.038\mathrm{dB / (V\mu m)^{10}}\) . Still, the premature hBN breakdown compromises the ER and the IL. Indeed, the measured \(\mathrm{ER} = 0.75\) , 2.3 and \(4.9\mathrm{dB}\) (data points in Fig. 3b) are far from the simulated \(\mathrm{ER} = 1.8\) , 4.4 and \(7.9\mathrm{dB}\) (solid traces in Fig. 3b) expected for the 12, 24 and \(42\mu \mathrm{m}\) - long modulators, respectively (for simulations, refer to sections I- III in SI). Likewise, the measured \(\mathrm{IL} = 1\) , 2.2 and \(3.4\mathrm{dB}\) are higher than the \(\mathrm{IL} \approx 0\mathrm{dB}\) expected for high mobility graphene modulators \(^{1}\) (see the minimum \(0\mathrm{dB}\) normalized transmission, i.e. neglecting the losses from grating couplers and Si waveguide, achieved by the simulation traces in Fig. 3b and section X in SI). + +<|ref|>text<|/ref|><|det|>[[86, 883, 488, 911]]<|/det|> +On the other hand, the second hBN- HfO \(_2\) - hBN modulator device attains the Pauli blocking regime (Fig. 3c), + +<|ref|>text<|/ref|><|det|>[[516, 66, 918, 327]]<|/det|> +In agreement with the dielectric characterization of hBN- HfO \(_2\) - hBN (Fig. 3a and sections V and X in SI), reaching a maximum Fermi energy of \(\mathrm{E}_{\mathrm{F}}^{\mathrm{max}} \approx 0.54\mathrm{eV}\) . The ER and IL improve accordingly, with an \(\mathrm{ER} = 7.8\mathrm{dB}\) almost twice the value obtained by the hBN- based modulator of comparable length (compare the black and red traces of Fig. 3c and b, respectively) and a IL reaching nearly zero ( \(\mathrm{IL} \approx 0.04\mathrm{dB}\) in Fig. 3c and section X in SI). However, being shorter ( \(\mathrm{L} = 44\mu \mathrm{m}\) ) than the device in Fig. 1c ( \(\mathrm{L} = 60\mu \mathrm{m}\) ), the modulation efficiency is lower ( \(1.3\mathrm{dB / V}\) in a \(0.5\mathrm{V}\) span, see Fig. 3c). We note that the hBN- HfO \(_2\) - hBN device of Fig. 1c has a relatively weak measured \(\mathrm{ER} \approx 4.4\mathrm{dB}\) and \(\mathrm{IL} \approx 7.8\mathrm{dB}\) (section X in SI) due to an over- cautious \(\mathrm{V}_{\mathrm{BT}} = 12.1\mathrm{V}\) applied voltage (or alternatively \(\mathrm{E}_{\mathrm{F}} = 0.41\mathrm{eV}\) ). Considering the breakdown capabilities of hBN- HfO \(_2\) - hBN dielectric (black star in Fig. 3a), we evaluated a potential \(\mathrm{ER} \approx 12\mathrm{dB}\) and \(\mathrm{IL} \approx 0.042\mathrm{dB}\) for this device (section X in SI). + +<|ref|>text<|/ref|><|det|>[[516, 348, 918, 912]]<|/det|> +Although material platforms like Lithium Niobate \(^{42}\) (LiNbO \(_3\) ) or hybrid technologies like Si/Indium Phosphide \(^{43}\) (InP), Si/SiGe \(^{44}\) or InGaAlAs \(^{44}\) offer outstanding performances in modulator applications, those are either not scalable \(^{42,45}\) (LiNbO \(_3\) ) or their integration with a CMOS fabrication line remains challenging \(^{44,46}\) . Nowadays, Si and graphene are envisaged as the most scalable, cost- effective and CMOS compatible materials for amplitude modulator applications \(^{1}\) . To compare our results with state- of- the- art graphene and Si amplitude modulators, both EA and Mach- Zehnder interferometer configurations included, we summarize our results in Fig. 4 and in sections XV and XVI of SI. Fig. 4 shows the dynamic modulation efficiency (extracted from the eye- diagrams and normalized by the device length and drive voltage) as a function of the modulation speed (red axis and red data point in Fig. 4) and the static modulation efficiency (measured in DC and normalized by the device length), as a function of the \(\mathrm{f}_{3\mathrm{dB}}\) bandwidth (black axis and black data point in Fig. 4). To avoid discrepancies due to the different extraction methods, we determine the static modulation efficiency of the compared literature \(^{8 - 12}\) using the same method as in Fig. 1c, i.e. by applying a linear fit within a \(0.5\mathrm{V}\) voltage span. Results highlight the trade- offs between speed and modulation efficiency and stresses the advantages of a hBN- HfO \(_2\) - hBN dielectric to obtain large static and dynamic modulation efficiencies even at high speed. As observed, the modulation efficiency typically drops for devices with high speed \(^{8,9}\) , being our device the only modulator able to operate at high speed with a large static and dynamic modulation efficiency (Fig. 4). These results outperform state- of- the- art graphene and not yet commercial silicon- based electro- absorption modulators \(^{39 - 41}\) (see blue/red and green data clouds, respectively in Fig. 4) when considering the modulation efficiency normalized by the length (i.e. footprint). This figure- of- merit is rather an important one since for many envisaged applications (e.g. chip interconnects) multiple + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 66, 488, 95]]<|/det|> +modulator devices are expected to coexist on the same chip. + +<|ref|>sub_title<|/ref|><|det|>[[210, 121, 364, 135]]<|/det|> +## II. CONCLUSION + +<|ref|>text<|/ref|><|det|>[[86, 155, 488, 270]]<|/det|> +With this work, we demonstrated the advantages of integrating hBN with a 3D high- \(\kappa\) dielectric for high- quality graphene- based EA modulators. Compared to traditional oxide sputtering or ALD- growth on top of graphene, the integration of \(\mathrm{HfO_2}\) in between hBN prevented any damage of the underlying graphene and allowed clean graphene- hBN interfaces. These clean interfaces yielded a symmetric and nearly hysteresis- free + +<|ref|>text<|/ref|><|det|>[[513, 66, 916, 253]]<|/det|> +operation. Moreover, this 2D- 3D integration enabled full transparency while maintaining the high mobility and low doping of intrinsic graphene. More importantly, the hBN- \(\mathrm{HfO_2}\) - hBN based EA modulators were able to reach high modulation speeds with strong modulation efficiencies, overcoming the fundamental limitations of the DL graphene configuration and outperforming state- of- the- art graphene and Si technologies. The compatibility of this hBN- \(\mathrm{HfO_2}\) - hBN dielectric with Si and other 2D materials might allow for considerable scaling improvements and greater device functionality in a broad range of graphene- and 2D- based electronic and optoelectronic applications, even beyond graphene- based modulators. + +<|ref|>text<|/ref|><|det|>[[85, 320, 490, 910]]<|/det|> +1 Romagnoli, M. et al. Graphene- based integrated photonics for next- generation datacom and telecom. Nature Reviews Materials 3, 392 (2018). 2 Chaisakul, P. et al. 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Chirp management in silicon- graphene electro absorption modulators. Optics Express 25, 19371- 19381 (2017). 35 Mohsin, M. et al. Graphene based low insertion loss electro- absorption modulator on SOI waveguide. Optics Express 22, 15292- 15297 (2014). 36 Edwards, E. H. et al. Ge/SiGe asymmetric Fabry- Perot quantum well electroabsorption modulators. Optics Express 20, 29164- 29173 (2012). 37 Audet, R. M. et al. Surface- normal Ge/SiGe asymmetric Fabry- Perot optical modulators fabricated on silicon substrates. Journal of Lightwave Technology 31, 3995- 4003 (2013). 38 Sorianello, V. et al. Graphene- silicon phase modulators with gigahertz bandwidth. Nature Photonics 12, 40- 44 (2018). 39 Giewont, K. et al. 300- mm monolithic silicon photonics foundry technology. IEEE Journal of Selected Topics in Quantum Electronics 25, 1- 11 (2019). 40 Gill, D. M. et al. Demonstration of error- free 32- Gb/s operation from monolithic CMOS nanophotonic transmitters. IEEE Photonics Technology Letters 28, 1410- 1413 (2016). 41 Xiong, C. et al. Monolithic 56 Gb/s silicon photonic pulse- amplitude modulation transmitter. Optica 3, 1060- 1065 (2016). 42 Mercante, A. J. et al. Thin film lithium niobate electro- optic modulator with terahertz operating bandwidth. Optics Express 26, 14810- 14816 (2018). 43 Smit, M., Williams, K. & van der Tol, J. Past, present, and future of InP- based photonic integration. APL Photonics 4, 050901 (2019). 44 Liu, K., Ye, C. R., Khan, S. & Sorger, V. J. Review and perspective on ultrafast wavelength- size electro- optic modulators. Laser & Photonics Reviews 9, 172- 194 (2015). 45 Wang, C., Zhang, M., Stern, B., Lipson, M. & Loncar, M. Nanophotonic lithium niobate electro- optic modulators. Optics Express 26, 1547- 1555 (2018). 46 Tang, Y. et al. 50 Gb/s hybrid silicon traveling- wave electroabsorption modulator. Optics Express 19, 5811- 5816 (2011). + +<|ref|>sub_title<|/ref|><|det|>[[191, 763, 384, 777]]<|/det|> +## ACKNOWLEDGMENTS + +<|ref|>text<|/ref|><|det|>[[86, 795, 488, 862]]<|/det|> +We thank S. Pradhan for his assistance in capacitance measurements and D. A. Iranzo for his inputs on the illustration in Fig. 1b. H.A. acknowledges funding from the European Unions Horizon 2020 research and innovation program under the Marie Sklodowska- Curie grant agreement No. 665884. + +<|ref|>sub_title<|/ref|><|det|>[[640, 79, 792, 92]]<|/det|> +## CONTRIBUTIONS + +<|ref|>text<|/ref|><|det|>[[515, 112, 917, 228]]<|/det|> +B.T., H.A., and F.H.L.K. conceived the idea. H.A., and B.T. fabricated the devices. L.O., B.T. did the simulations. B.T., H.A. performed the measurements and data analysis. A.M., V.S. performed high frequency measurements under the supervision of M.R., M.P., and D.V.T. provided Si waveguides. K.W., and T.T. synthesized the h- BN crystals. F.H.L.K., and B.T., supervised the project. B.T., H.A., and F.H.L.K. wrote the manuscript with input from all authors. + +<|ref|>sub_title<|/ref|><|det|>[[625, 255, 806, 268]]<|/det|> +## DATA AVAILABILITY + +<|ref|>text<|/ref|><|det|>[[515, 286, 917, 327]]<|/det|> +The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request. + +<|ref|>sub_title<|/ref|><|det|>[[555, 379, 877, 393]]<|/det|> +## COMPETING FINANCIAL INTERESTS + +<|ref|>text<|/ref|><|det|>[[532, 412, 881, 425]]<|/det|> +The authors declare no competing financial interests. + +<|ref|>sub_title<|/ref|><|det|>[[671, 454, 761, 468]]<|/det|> +## METHODS + +<|ref|>text<|/ref|><|det|>[[515, 487, 917, 862]]<|/det|> +The Si photonic waveguide with a core cross- section of \(750\mathrm{nm}\times 220\mathrm{nm}\) was prepared on the IMEC iSiPP25G silicon on insulator (SOI) platform. For the fabrication of the electro- absorption modulator (EAM), the graphene and hBN flakes were exfoliated from highly oriented pyrolytic graphite (HOPG) and hBN crystals, respectively. The bottom hBN- graphene- hBN stacks was prepared by the van der Waals assembly technique and transferred directly onto the Si waveguide separated by a \(10\mathrm{nm}\) spacer of high- quality thermal \(\mathrm{SiO}_2\) . The bottom hBN flake (separating the graphene and the \(\mathrm{SiO}_2\) layer) thickness of \(\sim 5\mathrm{nm}\) was chosen to enhanced the graphene absorption while isolating the graphene from the rough \(\mathrm{SiO}_2\) substrate. The top hBN has a thickness of \(\sim 10\mathrm{nm}\) . The stack has been etched by reactive ion etching (RIE) in an oxygen ( \(\mathrm{O}_2\) ) and Trifluoromethane ( \(\mathrm{CHF}_3\) ) (4:40 sccm) environment to expose the graphene edge. The bottom stack was then contacted by a \(3 / 15 / 30\mathrm{nm}\) Cr/Pd/Au metal combination. The \(10\mathrm{nm}\) hafnium oxide film has been deposited at \(250^{\circ}\mathrm{C}\) prior deposition of a \(2\mathrm{nm}\) sputtered \(\mathrm{SiO}_2\) seed layer by atomic layer deposition (ALD). Tetrakis- dimethylamido hafnium (TDMAM) (0.4 sec purge time) and water vapor (5 sec purge time) as precursors have been used in a Savannah G1 system from Cambridge Nanotech. The top hBN- graphene- hBN stack with a \(7\mathrm{nm}\) - and \(21\mathrm{nm}\) - thick bottom and top hBN layers has followed the same fabrication steps as the bottom stack. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 43, 143, 70]]<|/det|> +## Figures + +<|ref|>image<|/ref|><|det|>[[50, 100, 945, 365]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 392, 115, 411]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[39, 433, 955, 750]]<|/det|> +Device geometry and static characterization. a, Optical image of a photonic device consisting of two grating couplers (GC), a silicon optical waveguide (Si WG) and a hBN- HfO2- hBN based graphene EA modulator on top (see zoom- in optical and scanning electron microscope (SEM) images for details). The metal contacts are yellow/brown and the bottom and top graphene electrodes violet and light blue, respectively. The core of the waveguide is highlighted by the green dashed lines. b, Electrical connections and schematic cross- section of a EA modulator with a hBN- HfO2- hBN dielectric. The top and bottom graphene electrodes are fully encapsulated by hBN (in green) protecting both graphene electrodes from the out- of- plane dangling bonds typical of 3D oxide materials, e.g. HfO2 (in red). See inset for a molecular representation. c, Transmission curves as a function of the voltage between the bottom and top graphene electrodes (VBT- axis, bottom) and the Fermi energy at the graphene electrodes (EF- axis, top) for the EA modulator in panel a with a hBN- HfO2- hBN dielectric (see sketch). The 1550nm excitation power was set to 0 dBm. The forward and backward voltage sweeps (black and blue, respectively) show no major hysteresis compared to a modulator with a hBN- HfO2 dielectric (see inset). The red line is a linear fit to the forward voltage sweep within a 0.5V voltage span (extracted slope: 2.2 dB/V). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[65, 50, 930, 320]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 347, 118, 367]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[39, 388, 955, 591]]<|/det|> +Dynamic characterization. a, f3dB bandwidth as a function of the charge carrier- dependent mobility \((\mu)\) and the contact resistivity (pc) calculated for a device with the same geometry and dielectric combination as the device in Fig. 1 (section XI in SI). The dashed lines indicate the expected f3dB \(\boxed {\mathbb{R}}46\) GHz at \(\mu \boxed {\mathbb{R}}12\) , 000 cm2/Vs (evaluated at VBT \(= 10.4\) V, refer to panel b). b, Measured electro- optical S21 frequency response of the EA modulator at VBT \(= 10.4\) V and VAC \(= 200\) mV, without de- embedding, i.e. including the contributions of the setup and photodetector (section XII in SI). c, 231 - 1 pseudo- random binary sequence non- return- to- zero eye diagram at 28 Gbps and 40 Gbps. The EA modulator is d.c. biased at VBT \(= 11\) V and driven by a VAC \(= 3.5\) V peak- to- peak RF signal. The eye diagram measured at 40 Gbps has a 5.2 dB ER and a 2.28 dB signal- to- noise ratio (SNR). The green arrows indicate the 0 W baseline. + +<|ref|>image<|/ref|><|det|>[[63, 603, 941, 857]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 881, 117, 900]]<|/det|> +
Figure 3
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 44, 955, 385]]<|/det|> +Dielectric breakdown and Pauli blocking operation. a, Maximum Fermi energy, noted Emax F, expected at the graphene electrodes of a graphene modulator with a dielectric's relative permittivity \(\mathbb{R}\) and dielectric strength EBD. All points lying inside the blue- colored region represent a dielectric allowing for Pauli blocking operation (Emax F > 0.5 eV, refer to section I in SI). The red- colored region indicates otherwise (Emax F < 0.5 eV). The white band represents the Pauli blocking boundary condition, defined as Emax F = 0.5 eV. The expected Emax F for HfO2 and hBN are represented by the red and green squares respectively, taking the values of EBD and \(\mathbb{R}\) from literature28,31- 33 (marked with dots) and our dielectric characterization (marked with stars, see sections V and X in SI). The black star represents the Emax F = 0.57 eV expected for the hBN- HfO2- hBN modulator in Fig. 1c (section X in SI). b and c, Normalized transmission as a function of EF and VBT for modulators with hBN (b) and hBN- HfO2- hBN (c) dielectric. The data points are measurements and the solid curves simulations (see sections I- III and X in SI). The vertical dashed lines indicate the Emax F achieved at the dielectric breakdown. The orange- shaded regions show the full transparency range, i.e Pauli blocking. The top VBT axis in panel b is for the 42 \(\mu\)m- long device only (see section VII in SI for the other hBN devices). The graphene Dirac cones in panel b show the absorption and Pauli blocking processes at low and high Fermi energies, respectively. + +<|ref|>image<|/ref|><|det|>[[76, 393, 800, 891]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 923, 118, 940]]<|/det|> +
Figure 4
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 45, 952, 179]]<|/det|> +Comparison graph. The black and red data points and axis represent the static modulation efficiency as a function of the f3dB bandwidth and the dynamic modulation efficiency (extracted from eye- diagrams) as a function of the modulation speed, respectively. The red, blue and green data clouds enclose single- 11,12,34,38 and double- layer 8–10, 35 graphene and silicon- 39–41 state- of- the- art modulators operating at \(\lambda = 1.55 \mu \mathrm{m}\) . Refer to sections XV and XVI in SI for a more detailed comparison of graphene- based modulators. + +<|ref|>sub_title<|/ref|><|det|>[[44, 203, 310, 230]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 252, 765, 273]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 291, 194, 310]]<|/det|> +- Supplinf.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__07e9239695f584ac300021ec424d256bf17aa5f9ae8aedc18032364d80132cfd/images_list.json b/preprint/preprint__07e9239695f584ac300021ec424d256bf17aa5f9ae8aedc18032364d80132cfd/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..97d2666210880a10248b707ba702ada23bf16d08 --- /dev/null +++ b/preprint/preprint__07e9239695f584ac300021ec424d256bf17aa5f9ae8aedc18032364d80132cfd/images_list.json @@ -0,0 +1,77 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [ + [ + 58, + 54, + 950, + 316 + ] + ], + "page_idx": 11 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 48, + 575, + 952, + 866 + ] + ], + "page_idx": 11 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [ + [ + 57, + 180, + 884, + 545 + ] + ], + "page_idx": 12 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "Change in soil C (%) with declining fire frequency", + "footnote": [], + "bbox": [ + [ + 70, + 60, + 710, + 300 + ] + ], + "page_idx": 13 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4", + "footnote": [], + "bbox": [ + [ + 70, + 352, + 710, + 775 + ] + ], + "page_idx": 13 + } +] \ No newline at end of file diff --git a/preprint/preprint__07e9239695f584ac300021ec424d256bf17aa5f9ae8aedc18032364d80132cfd/preprint__07e9239695f584ac300021ec424d256bf17aa5f9ae8aedc18032364d80132cfd.mmd b/preprint/preprint__07e9239695f584ac300021ec424d256bf17aa5f9ae8aedc18032364d80132cfd/preprint__07e9239695f584ac300021ec424d256bf17aa5f9ae8aedc18032364d80132cfd.mmd new file mode 100644 index 0000000000000000000000000000000000000000..c3905c44961a0e0b420276e6e757a9c8b7eba2fd --- /dev/null +++ b/preprint/preprint__07e9239695f584ac300021ec424d256bf17aa5f9ae8aedc18032364d80132cfd/preprint__07e9239695f584ac300021ec424d256bf17aa5f9ae8aedc18032364d80132cfd.mmd @@ -0,0 +1,344 @@ + +# Determinants of the capacity of dryland ecosystems to store soil carbon under altered fire regimes + +Adam Pellegrini (ap2188@cam.ac.uk) University of Cambridge https://orcid.org/0000- 0003- 0418- 4129 + +Peter B Reich University of Minnesota, St. Paul https://orcid.org/0000- 0003- 4424- 662X + +Sarah Hobbie https://orcid.org/0000- 0001- 5159- 031X + +Corli Coetsee South African National Parks + +Benjamin Wigley South African National Parks + +Edmund February University of Cape Town + +Katerina Georgiou Lawrence Livermore National Laboratory + +César Terrer Massachusetts Institute Of Technology https://orcid.org/0000- 0002- 5479- 3486 + +E.N. Brookshire Montana State University https://orcid.org/0000- 0002- 0412- 7696 + +Anders Ahlström Lund University https://orcid.org/0000- 0003- 1642- 0037 + +Lars Nieradzik Lund University + +Stephen Sitch University of Exeter https://orcid.org/0000- 0003- 1821- 8561 + +Joe Melton Environment and Climate Change Canada https://orcid.org/0000- 0002- 9414- 064X + +Matthew Forrest Senckenberg Biodiversity and Climate Research Centre (SBiK- F) https://orcid.org/0000- 0003- 1858- 3489 + +Fang Li Chinese Acad Sci, Int Ctr Climate & Environm Sci, Inst Atmosphere Phys, Beijing, Peoples R China https://orcid.org/0000- 0002- 3686- 2257 + +Stijn Hantson + +<--- Page Split ---> + +Universidad del Rosario https://orcid.org/0000- 0003- 4607- 9204 + +## Chantelle Burton + +Met Office Hadley Centre https://orcid.org/0000- 0003- 0201- 5727 + +## Chao Yue + +Institute of Soil and Water Conservation, Northwest A&F University https://orcid.org/0000- 0003- 0026- 237X + +## Philippe Ciais + +Laboratoire des Sciences du Climat et de l'Environnement https://orcid.org/0000- 0001- 8560- 4943 + +## Robert Jackson + +Stanford University https://orcid.org/0000- 0001- 8846- 7147 + +## Article + +## Keywords: + +Posted Date: February 27th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 2581535/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Climate Change on October 2nd, 2023. See the published version at https://doi.org/10.1038/s41558- 023- 01800- 7. + +<--- Page Split ---> + +## Abstract + +AbstractWidespread changes in the intensity and frequency of fires across the globe are altering the terrestrial carbon (C) sink \(^{1 - 4}\) . Although the changes in ecosystem C have been reasonably well quantified for plant biomass pools \(^{5 - 7}\) , an understanding of the determinants of fire- driven changes in soil organic C (SOC) across broad environmental gradients remains unclear, especially in global drylands \(^{3,4,7 - 9}\) . Here, we combined multiple datasets and original field sampling of fire manipulation experiments to evaluate where and why fire changes SOC the most, built a statistical model to estimate historical changes in SOC, and compared these estimates to simulations from ecosystem models. We found that drier ecosystems experienced larger relative changes in SOC than humid ecosystems—in some cases exceeding losses from plant biomass pools—primarily explained by high fire- driven declines in tree biomass inputs in dry ecosystems. Ecosystem models provided more mixed insight into potential SOC changes because many models underestimated the SOC changes in drier ecosystems. Upscaling our statistical model predicted that soils in 1.57 million km \(^{2}\) savanna- grassland regions experiencing declines in burned area over the past ca. two decades may have 23% more SOC, equating to 1.78 PgC in topsoils. Consequently, ongoing declines in fire frequencies have likely created an extensive carbon sink in the soils of global drylands that may have been underestimated by ecosystem models. + +## Full Text + +Fire- driven changes in SOC arising from altered fire frequencies are hypothesized to be predicted by how much fire directly combusts SOC \(^{4,10}\) , and indirectly alters plant biomass inputs to soils and decomposition of residual SOC post- fire \(^{11 - 17}\) . In drier and warmer ecosystems, which dominate global burned area, most SOC is in the mineral horizon where heat rapidly dissipates \(^{18}\) and little direct combustion of SOC occurs \(^{16,19,20}\) . In these drier sites, fire- driven shifts in plant biomass inputs, especially from trees \(^{21 - 23}\) , are thought to determine changes in SOC stored in the mineral horizon \(^{24 - 26}\) . Consequently, increases in fire frequency may drive large SOC losses in climates with low precipitation and/or seasonal rainfall, where water constrains tree growth and post- fire biomass recovery \(^{6,27 - 29}\) , relative to ecosystems in climates where biomass recovery is faster. In addition to climate, soil texture and mineralogy can modify post- fire decomposition rates \(^{30 - 32}\) and promote higher C losses in ecosystems with coarse textured soils \(^{12}\) . Thus, we hypothesize that water availability, temperature, and soil texture all act to modify the effect of repeated burning on SOC storage in the mineral horizon. + +Global data to evaluate these hypotheses are lacking because there have yet to be studies examining repeated burning effects on SOC and plant biomass in parallel across broad climatic and ecological gradients, despite comparisons within individual ecosystems \(^{4,24,33}\) . Thus, models used to simulate the effects of fire regime changes on ecosystems, such as fire- enabled Dynamic Global Vegetation Models \(^{34}\) (DGVMs), lack a clear benchmark for evaluating how well they simulate SOC responses across environmental gradients \(^{7}\) . Here, we examine the factors that determine the magnitude of SOC losses or gains in the mineral horizon when fire frequencies change, evaluate whether DGVMs capture spatial + +<--- Page Split ---> + +patterns in fire effects on SOC storage, and estimate the potential impact of recent regional changes in fire frequencies on SOC storage. + +To evaluate the determinants of fire effects on SOC, we conducted a meta- analysis to identify the environmental variables relating to how multi- decadal alterations of fire frequency impact SOC storage in the mineral horizon using data from experiments in 53 sites containing 434 replicate plots. Within these sites, we compared the effect of repeated burning at different frequencies relative to unburned plots or plots burned at lower frequencies over the same period (Table S1, Supplemental Information, S). We focused our analyses on ecosystems that account for the majority of both total burned area and recent changes in fire frequency (savannas, grasslands, as well as seasonal woodlands and forests) (Figure S1). + +Globally, our meta- analysis demonstrated that the most important climatic and edaphic variables explaining fire effects on SOC in the mineral horizon were aridity, precipitation seasonality, mean annual temperature, and silt content, with larger relative changes in SOC in drier and cooler environments on coarsely textured soils ( \(r^2 = 0.82\) , p<0.001, Table S2; S). Using model selection on a mixed- effects metaregression model, we identified the important environmental variables, and then fit the most parsimonious model to the data to illustrate the influence of these variables on fire effects (Table S2; S). Variables related to experimental design and overall ecosystem type were also incorporated to better isolate environmental variables (SI). Relative to unburned plots, SOC in burned plots was \(17 \pm 10\%\) lower in sites with average aridity and \(37 \pm 23\%\) lower in sites with high aridity (p<0.001, Figure 1a, Table S2; aridity defined as the ratio between precipitation/potential evapotranspiration, S). Annual precipitation seasonality was the second most important environmental variable in the statistical model, with twice as large fire- driven declines in high vs. average seasonality sites ( \(27 \pm 8\%\) lower vs. \(13 \pm 9\%\) lower, respectively, p<0.001, Figure 1b, Table S2). Relative SOC losses were also greater in sites with cooler temperatures and coarser textured soils (p<0.01 and p=0.068, respectively), although these environmental variables were less important than aridity and precipitation seasonality according to variable importance analyses (Table S2). These variables were identified when we controlled for differences arising from broad ecosystem classification and experimental duration and fire frequency (Table S2, Figures S2). Taken together, water availability was the most important environmental factor for explaining the relative change in SOC with altered fire frequencies. + +To test the hypothesis that fire- driven changes in SOC could be attributed to changes in tree biomass inputs across sites, we focused on savanna- grasslands and analysed 74 plots across seven sites in our meta- analysis with data on soil \(^{13}\mathrm{C}\) . We used \(^{13}\mathrm{C}\) as a proxy for tree biomass inputs in these sites because C3 tree biomass has a lower \(^{13}\mathrm{C}\) than C4 grass biomass. Thus, SOC \(^{13}\mathrm{C}\) is commonly used to quantify tree biomass inputs relative to C4 grass inputs in savannas \(^{24,25,35}\) . + +Fire- driven changes in \(^{13}\mathrm{C}\) illustrate that larger decreases in SOC in dry environments is linked with lower tree biomass inputs to soils. Comparing \(^{13}\mathrm{C}\) values across sites and in unburned vs. burned plots illustrated that frequent burning and higher aridity caused a shift from C3 tree- to C4 grass- derived + +<--- Page Split ---> + +biomass inputs. The proportion of SOC from C3 trees was, on average, \(55 \pm 11\%\) lower in frequently burned relative to unburned plots. The losses of C3- derived inputs positively correlated with the losses in SOC stocks across sites, pointing to changes in woody biomass inputs to soils driving changes in SOC storage but the relative magnitude of change varied across sites ( \(r^2 = 0.71\) , \(p = 0.01\) , Figure 2a, Table S3). Aridity explained the variability in \(^{13}\mathrm{C}\) changes across sites because the most arid sites experienced the strongest fire- driven declines in C3- derived inputs ( \(r^2 = 0.58\) , \(p = 0.029\) , Figure 2b), consistent with fire causing the largest relative changes in SOC in drier climates. Thus, the degree to which fire changes plant biomass inputs, specifically trees, into soils helps explains patterns in SOC responses to fire. + +To evaluate estimates of fire effects on SOC at the global scale, we analyzed whether an ensemble of seven fire- enabled DGVMs were able to recreate the biogeographical trends in fire effects found in our empirical findings (model details in refs. \(^{7,34}\) and SI, Figure S3 for global maps). We used model experiments that were similar in concept to our field experiments: comparing simulations of SOC in a world with fire vs. world without fire. Rather than compare model- based estimates of SOC fluxes with data at individual sites, we compare the within- model relationships between fire effects and water availability gradients with the predictions from our empirical model across the same gradients. Generally, the DGVMs predicted that areas experiencing the largest differences in burned area between the fire vs. no fire simulations also experienced the largest changes in SOC (Figure S4). However, the models were inconsistent in their simulated patterns of SOC sensitivity to fire across aridity and precipitation seasonality gradients (Figures 3,S5,S6). In savanna- grasslands, which account for \(70\%\) of global burned area, only two of the seven models (LPJ- GUESS- BLAZE and CTEM models) correctly recreated the empirically determined relationships between the sensitivity of SOC to fire and both precipitation seasonality and aridity (Figures 3,S5,S6). Although we cannot isolate the role of model- based differences in simulating burned area across climates, these results suggest a DGVM ensemble is likely biased towards underestimating fire- driven changes in SOC in drier regions. + +To estimate the potential area over which frequent burning could limit mineral SOC, we scaled up our statistical model of fire effects on SOC to savanna- grasslands. To estimate what areas may be either losing or gaining SOC, we extrapolated observed trends in burned area for ca. two decades \(^2\) to identify areas of increasing or decreasing fire frequency and used the environmental covariates and SOC content derived from other global maps (S) to estimate potential SOC changes. In 2.30 million \(\mathrm{km}^2\) where burned area is tending to decline, SOC has potentially risen by \(23\%\) (Figure 4a). In 1.38 million \(\mathrm{km}^2\) where burned area is tending to rise, SOC has potentially declined by \(25\%\) (Figure 4b). By multiplying these relative values with total SOC stocks, we estimate reductions in burning from 1998- 2015 resulted in a gain of 1.78 PgC while more frequent fires resulted in a loss of 1.14 PgC, for a net- change of 0.64 PgC, or a flux of 0.038 PgC yr \(^{- 1}\) . + +While previous research has highlighted the capacity of savanna- grasslands soils to serve as a C sink \(^{26}\) , our study identifies multiple mechanisms that explain the wide variability in SOC responses to fire across drylands as a whole. With this information, informed management choices can be made that implement + +<--- Page Split ---> + +nature- based climate solutions \(^{12}\) . We demonstrate the relative SOC potential is roughly double in drier savanna- grasslands, but potentially comes with a cost of high tree encroachment, which can reduce biodiversity—a key consideration that may be offset through management of browsing herbivores, which we did not consider here. + +We did not focus on systems with either intense crown wildfires such as spruce boreal forest or smoldering ground fires in peatlands. Although these ecosystems can store large amounts of SOC, they burn relatively rarely (albeit increasing in frequency) \(^{12}\) , and the factors that determine direct combustion of SOC are well characterised \(^{4}\) . Meanwhile, savannas have lower per- area SOC stocks, but given their large spatial extent and frequent burning, SOC stocks underlying savannas that burn equate to \(\sim 14.5\) \(\mathrm{PgC}^{12}\) , which could be relevant for future terrestrial C storage. Our sink estimate is small relative to the residual land C sink \((3.1 \pm 0.6 \mathrm{PgC} \mathrm{yr}^{- 1})^{36}\) , but given the lack of saturation observed over the duration of experiments in our dataset ( \(\sim 60\) years), SOC in drylands may be a persistent sink. + +## Declarations + +Acknowledgements: Funding was provided by the Gordon and Betty Moore Foundation and USDA National Institute of Food and Agriculture grant 2018- 67012- 28077. The Cedar Creek Long Term Ecological Research program was funded by National Science Foundation grants DEB- 0620652, DEB- 1234162, DEB- 1831944, and DBI- 2021898. Sampling in other sites were funded by the National Park Service and Sequoia Parks Conservancy, and South African National Parks. + +Data availability statement: Data will be made freely available on Figshare following publication. + +Code availability statement: Code will be made freely available on Figshare following publication. + +Competing Interests Statement: The authors declare no competing interests. + +## References + +1. Zheng, B. et al. Increasing forest fire emissions despite the decline in global burned area. Sci. Adv. 7, (2021). +2. Andela, N. et al. A human-driven decline in global burned area. Science (80-. ). 356, 1356-1362 (2017). +3. Pellegrini, A. F. A. et al. Fire frequency drives decadal changes in soil carbon and nitrogen and ecosystem productivity. Nature 553, 194-198 (2018). +4. Walker, X. J. et al. Increasing wildfires threaten historic carbon sink of boreal forest soils. Nature 572, 520-523 (2019). +5. Yin, Y. et al. 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Model Dev. 10, 1175-1197 (2017). + +## Figures + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1
+ +Water availability modifies the effect of fire on soil organic carbon (SOC). Results from meta- regression of the top model illustrating how environmental conditions influenced the percent difference in SOC concentrations in the burned versus unburned plots (lower values thus signify a fire- driven loss). a) Fire effects as a function of aridity (precipitation/potential evapotranspiration), with a low aridity index indicating dry conditions (S). b) Precipitation seasonality is the coefficient of variation of monthly precipitation within a year multiplied by 100. All dashed lines indicate 95% confidence intervals of the model fit. Importance of all variables in the model selection presented in Table S2. + +![](images/Figure_2.jpg) + +
Figure 2
+ +<--- Page Split ---> + +Fire effects on soil organic carbon (SOC) are predicted by changes in tree- based SOC which track the aridity gradient a) Difference in the total SOC between burned – unburned and the percent of SOC derived from tree biomass (stocks in 0- 20 cm of the mineral horizon). b) Aridity index (lower values are drier) and the difference in SOC from trees between burned – unburned. Lines are linear regressions with shaded area representing the standard error. These averages are based on \(n = 74\) plots distributed across the sites. + +![](images/Figure_3.jpg) + +
Figure 3
+ +Most models underpredict the stronger relative effects of fire on soil organic carbon (SOC) in drier environments. Comparison between model simulations and empirical data of fire- driven changes in SOC across aridity classes. Coloured lines represent means from different Dynamic Global Vegetation Models (DGVMs) and the black line represents the data with error bar illustrating the \(95\%\) confidence intervals. DGVMs calculate the percent difference by comparing the simulations with fire vs. a 'world without fire' (described in SI). Aridity index is calculated as the ratio between precipitation and potential evapotranspiration. + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + +
Change in soil C (%) with declining fire frequency
+ +![](images/Figure_4.jpg) + +
Figure 4
+ +Sensitivity of soil carbon to changes in fire frequencies across savanna- grasslands globally. Upscaling our analysis to savanna- grasslands worldwide across the distribution of the areas where our model predicted a change in soil organic carbon (C) under observed trends in burned area. a) Predicted gain in soil C in areas with declining fire frequency, with a positive % illustrating a gain in soil C under the new fire regime. b) Predicted change in soil C with higher fire frequency, with a lower % illustrating a loss in soil C + +<--- Page Split ---> + +in the frequently burned. We estimated changes only in areas within environmental conditions used in our training model. + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementalInformation.docx + +<--- Page Split ---> diff --git a/preprint/preprint__07e9239695f584ac300021ec424d256bf17aa5f9ae8aedc18032364d80132cfd/preprint__07e9239695f584ac300021ec424d256bf17aa5f9ae8aedc18032364d80132cfd_det.mmd b/preprint/preprint__07e9239695f584ac300021ec424d256bf17aa5f9ae8aedc18032364d80132cfd/preprint__07e9239695f584ac300021ec424d256bf17aa5f9ae8aedc18032364d80132cfd_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..65206a1000072623acbcfd62f89785888528d4b5 --- /dev/null +++ b/preprint/preprint__07e9239695f584ac300021ec424d256bf17aa5f9ae8aedc18032364d80132cfd/preprint__07e9239695f584ac300021ec424d256bf17aa5f9ae8aedc18032364d80132cfd_det.mmd @@ -0,0 +1,477 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 953, 175]]<|/det|> +# Determinants of the capacity of dryland ecosystems to store soil carbon under altered fire regimes + +<|ref|>text<|/ref|><|det|>[[44, 195, 625, 238]]<|/det|> +Adam Pellegrini (ap2188@cam.ac.uk) University of Cambridge https://orcid.org/0000- 0003- 0418- 4129 + +<|ref|>text<|/ref|><|det|>[[44, 243, 700, 285]]<|/det|> +Peter B Reich University of Minnesota, St. Paul https://orcid.org/0000- 0003- 4424- 662X + +<|ref|>text<|/ref|><|det|>[[44, 290, 400, 332]]<|/det|> +Sarah Hobbie https://orcid.org/0000- 0001- 5159- 031X + +<|ref|>text<|/ref|><|det|>[[44, 337, 312, 378]]<|/det|> +Corli Coetsee South African National Parks + +<|ref|>text<|/ref|><|det|>[[44, 383, 312, 424]]<|/det|> +Benjamin Wigley South African National Parks + +<|ref|>text<|/ref|><|det|>[[44, 430, 270, 471]]<|/det|> +Edmund February University of Cape Town + +<|ref|>text<|/ref|><|det|>[[44, 476, 410, 516]]<|/det|> +Katerina Georgiou Lawrence Livermore National Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 521, 755, 563]]<|/det|> +César Terrer Massachusetts Institute Of Technology https://orcid.org/0000- 0002- 5479- 3486 + +<|ref|>text<|/ref|><|det|>[[44, 568, 635, 609]]<|/det|> +E.N. Brookshire Montana State University https://orcid.org/0000- 0002- 0412- 7696 + +<|ref|>text<|/ref|><|det|>[[44, 614, 550, 655]]<|/det|> +Anders Ahlström Lund University https://orcid.org/0000- 0003- 1642- 0037 + +<|ref|>text<|/ref|><|det|>[[44, 660, 194, 701]]<|/det|> +Lars Nieradzik Lund University + +<|ref|>text<|/ref|><|det|>[[44, 707, 580, 748]]<|/det|> +Stephen Sitch University of Exeter https://orcid.org/0000- 0003- 1821- 8561 + +<|ref|>text<|/ref|><|det|>[[44, 754, 780, 795]]<|/det|> +Joe Melton Environment and Climate Change Canada https://orcid.org/0000- 0002- 9414- 064X + +<|ref|>text<|/ref|><|det|>[[44, 800, 920, 863]]<|/det|> +Matthew Forrest Senckenberg Biodiversity and Climate Research Centre (SBiK- F) https://orcid.org/0000- 0003- 1858- 3489 + +<|ref|>text<|/ref|><|det|>[[44, 869, 884, 932]]<|/det|> +Fang Li Chinese Acad Sci, Int Ctr Climate & Environm Sci, Inst Atmosphere Phys, Beijing, Peoples R China https://orcid.org/0000- 0002- 3686- 2257 + +<|ref|>text<|/ref|><|det|>[[44, 937, 164, 955]]<|/det|> +Stijn Hantson + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[52, 45, 620, 66]]<|/det|> +Universidad del Rosario https://orcid.org/0000- 0003- 4607- 9204 + +<|ref|>sub_title<|/ref|><|det|>[[44, 70, 191, 88]]<|/det|> +## Chantelle Burton + +<|ref|>text<|/ref|><|det|>[[52, 92, 628, 111]]<|/det|> +Met Office Hadley Centre https://orcid.org/0000- 0003- 0201- 5727 + +<|ref|>sub_title<|/ref|><|det|>[[44, 116, 131, 135]]<|/det|> +## Chao Yue + +<|ref|>text<|/ref|><|det|>[[44, 139, 941, 180]]<|/det|> +Institute of Soil and Water Conservation, Northwest A&F University https://orcid.org/0000- 0003- 0026- 237X + +<|ref|>sub_title<|/ref|><|det|>[[44, 186, 165, 205]]<|/det|> +## Philippe Ciais + +<|ref|>text<|/ref|><|det|>[[44, 208, 912, 228]]<|/det|> +Laboratoire des Sciences du Climat et de l'Environnement https://orcid.org/0000- 0001- 8560- 4943 + +<|ref|>sub_title<|/ref|><|det|>[[44, 233, 181, 251]]<|/det|> +## Robert Jackson + +<|ref|>text<|/ref|><|det|>[[52, 255, 580, 274]]<|/det|> +Stanford University https://orcid.org/0000- 0001- 8846- 7147 + +<|ref|>sub_title<|/ref|><|det|>[[44, 314, 102, 332]]<|/det|> +## Article + +<|ref|>sub_title<|/ref|><|det|>[[44, 352, 135, 371]]<|/det|> +## Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 389, 335, 409]]<|/det|> +Posted Date: February 27th, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 427, 474, 448]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 2581535/v1 + +<|ref|>text<|/ref|><|det|>[[42, 465, 911, 508]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 543, 920, 587]]<|/det|> +Version of Record: A version of this preprint was published at Nature Climate Change on October 2nd, 2023. See the published version at https://doi.org/10.1038/s41558- 023- 01800- 7. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 158, 68]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[40, 81, 952, 431]]<|/det|> +AbstractWidespread changes in the intensity and frequency of fires across the globe are altering the terrestrial carbon (C) sink \(^{1 - 4}\) . Although the changes in ecosystem C have been reasonably well quantified for plant biomass pools \(^{5 - 7}\) , an understanding of the determinants of fire- driven changes in soil organic C (SOC) across broad environmental gradients remains unclear, especially in global drylands \(^{3,4,7 - 9}\) . Here, we combined multiple datasets and original field sampling of fire manipulation experiments to evaluate where and why fire changes SOC the most, built a statistical model to estimate historical changes in SOC, and compared these estimates to simulations from ecosystem models. We found that drier ecosystems experienced larger relative changes in SOC than humid ecosystems—in some cases exceeding losses from plant biomass pools—primarily explained by high fire- driven declines in tree biomass inputs in dry ecosystems. Ecosystem models provided more mixed insight into potential SOC changes because many models underestimated the SOC changes in drier ecosystems. Upscaling our statistical model predicted that soils in 1.57 million km \(^{2}\) savanna- grassland regions experiencing declines in burned area over the past ca. two decades may have 23% more SOC, equating to 1.78 PgC in topsoils. Consequently, ongoing declines in fire frequencies have likely created an extensive carbon sink in the soils of global drylands that may have been underestimated by ecosystem models. + +<|ref|>sub_title<|/ref|><|det|>[[44, 452, 157, 478]]<|/det|> +## Full Text + +<|ref|>text<|/ref|><|det|>[[40, 491, 950, 781]]<|/det|> +Fire- driven changes in SOC arising from altered fire frequencies are hypothesized to be predicted by how much fire directly combusts SOC \(^{4,10}\) , and indirectly alters plant biomass inputs to soils and decomposition of residual SOC post- fire \(^{11 - 17}\) . In drier and warmer ecosystems, which dominate global burned area, most SOC is in the mineral horizon where heat rapidly dissipates \(^{18}\) and little direct combustion of SOC occurs \(^{16,19,20}\) . In these drier sites, fire- driven shifts in plant biomass inputs, especially from trees \(^{21 - 23}\) , are thought to determine changes in SOC stored in the mineral horizon \(^{24 - 26}\) . Consequently, increases in fire frequency may drive large SOC losses in climates with low precipitation and/or seasonal rainfall, where water constrains tree growth and post- fire biomass recovery \(^{6,27 - 29}\) , relative to ecosystems in climates where biomass recovery is faster. In addition to climate, soil texture and mineralogy can modify post- fire decomposition rates \(^{30 - 32}\) and promote higher C losses in ecosystems with coarse textured soils \(^{12}\) . Thus, we hypothesize that water availability, temperature, and soil texture all act to modify the effect of repeated burning on SOC storage in the mineral horizon. + +<|ref|>text<|/ref|><|det|>[[41, 795, 945, 959]]<|/det|> +Global data to evaluate these hypotheses are lacking because there have yet to be studies examining repeated burning effects on SOC and plant biomass in parallel across broad climatic and ecological gradients, despite comparisons within individual ecosystems \(^{4,24,33}\) . Thus, models used to simulate the effects of fire regime changes on ecosystems, such as fire- enabled Dynamic Global Vegetation Models \(^{34}\) (DGVMs), lack a clear benchmark for evaluating how well they simulate SOC responses across environmental gradients \(^{7}\) . Here, we examine the factors that determine the magnitude of SOC losses or gains in the mineral horizon when fire frequencies change, evaluate whether DGVMs capture spatial + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 931, 88]]<|/det|> +patterns in fire effects on SOC storage, and estimate the potential impact of recent regional changes in fire frequencies on SOC storage. + +<|ref|>text<|/ref|><|det|>[[41, 105, 950, 288]]<|/det|> +To evaluate the determinants of fire effects on SOC, we conducted a meta- analysis to identify the environmental variables relating to how multi- decadal alterations of fire frequency impact SOC storage in the mineral horizon using data from experiments in 53 sites containing 434 replicate plots. Within these sites, we compared the effect of repeated burning at different frequencies relative to unburned plots or plots burned at lower frequencies over the same period (Table S1, Supplemental Information, S). We focused our analyses on ecosystems that account for the majority of both total burned area and recent changes in fire frequency (savannas, grasslands, as well as seasonal woodlands and forests) (Figure S1). + +<|ref|>text<|/ref|><|det|>[[39, 304, 955, 739]]<|/det|> +Globally, our meta- analysis demonstrated that the most important climatic and edaphic variables explaining fire effects on SOC in the mineral horizon were aridity, precipitation seasonality, mean annual temperature, and silt content, with larger relative changes in SOC in drier and cooler environments on coarsely textured soils ( \(r^2 = 0.82\) , p<0.001, Table S2; S). Using model selection on a mixed- effects metaregression model, we identified the important environmental variables, and then fit the most parsimonious model to the data to illustrate the influence of these variables on fire effects (Table S2; S). Variables related to experimental design and overall ecosystem type were also incorporated to better isolate environmental variables (SI). Relative to unburned plots, SOC in burned plots was \(17 \pm 10\%\) lower in sites with average aridity and \(37 \pm 23\%\) lower in sites with high aridity (p<0.001, Figure 1a, Table S2; aridity defined as the ratio between precipitation/potential evapotranspiration, S). Annual precipitation seasonality was the second most important environmental variable in the statistical model, with twice as large fire- driven declines in high vs. average seasonality sites ( \(27 \pm 8\%\) lower vs. \(13 \pm 9\%\) lower, respectively, p<0.001, Figure 1b, Table S2). Relative SOC losses were also greater in sites with cooler temperatures and coarser textured soils (p<0.01 and p=0.068, respectively), although these environmental variables were less important than aridity and precipitation seasonality according to variable importance analyses (Table S2). These variables were identified when we controlled for differences arising from broad ecosystem classification and experimental duration and fire frequency (Table S2, Figures S2). Taken together, water availability was the most important environmental factor for explaining the relative change in SOC with altered fire frequencies. + +<|ref|>text<|/ref|><|det|>[[42, 755, 940, 872]]<|/det|> +To test the hypothesis that fire- driven changes in SOC could be attributed to changes in tree biomass inputs across sites, we focused on savanna- grasslands and analysed 74 plots across seven sites in our meta- analysis with data on soil \(^{13}\mathrm{C}\) . We used \(^{13}\mathrm{C}\) as a proxy for tree biomass inputs in these sites because C3 tree biomass has a lower \(^{13}\mathrm{C}\) than C4 grass biomass. Thus, SOC \(^{13}\mathrm{C}\) is commonly used to quantify tree biomass inputs relative to C4 grass inputs in savannas \(^{24,25,35}\) . + +<|ref|>text<|/ref|><|det|>[[42, 891, 943, 958]]<|/det|> +Fire- driven changes in \(^{13}\mathrm{C}\) illustrate that larger decreases in SOC in dry environments is linked with lower tree biomass inputs to soils. Comparing \(^{13}\mathrm{C}\) values across sites and in unburned vs. burned plots illustrated that frequent burning and higher aridity caused a shift from C3 tree- to C4 grass- derived + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 44, 950, 230]]<|/det|> +biomass inputs. The proportion of SOC from C3 trees was, on average, \(55 \pm 11\%\) lower in frequently burned relative to unburned plots. The losses of C3- derived inputs positively correlated with the losses in SOC stocks across sites, pointing to changes in woody biomass inputs to soils driving changes in SOC storage but the relative magnitude of change varied across sites ( \(r^2 = 0.71\) , \(p = 0.01\) , Figure 2a, Table S3). Aridity explained the variability in \(^{13}\mathrm{C}\) changes across sites because the most arid sites experienced the strongest fire- driven declines in C3- derived inputs ( \(r^2 = 0.58\) , \(p = 0.029\) , Figure 2b), consistent with fire causing the largest relative changes in SOC in drier climates. Thus, the degree to which fire changes plant biomass inputs, specifically trees, into soils helps explains patterns in SOC responses to fire. + +<|ref|>text<|/ref|><|det|>[[40, 245, 951, 611]]<|/det|> +To evaluate estimates of fire effects on SOC at the global scale, we analyzed whether an ensemble of seven fire- enabled DGVMs were able to recreate the biogeographical trends in fire effects found in our empirical findings (model details in refs. \(^{7,34}\) and SI, Figure S3 for global maps). We used model experiments that were similar in concept to our field experiments: comparing simulations of SOC in a world with fire vs. world without fire. Rather than compare model- based estimates of SOC fluxes with data at individual sites, we compare the within- model relationships between fire effects and water availability gradients with the predictions from our empirical model across the same gradients. Generally, the DGVMs predicted that areas experiencing the largest differences in burned area between the fire vs. no fire simulations also experienced the largest changes in SOC (Figure S4). However, the models were inconsistent in their simulated patterns of SOC sensitivity to fire across aridity and precipitation seasonality gradients (Figures 3,S5,S6). In savanna- grasslands, which account for \(70\%\) of global burned area, only two of the seven models (LPJ- GUESS- BLAZE and CTEM models) correctly recreated the empirically determined relationships between the sensitivity of SOC to fire and both precipitation seasonality and aridity (Figures 3,S5,S6). Although we cannot isolate the role of model- based differences in simulating burned area across climates, these results suggest a DGVM ensemble is likely biased towards underestimating fire- driven changes in SOC in drier regions. + +<|ref|>text<|/ref|><|det|>[[41, 626, 951, 860]]<|/det|> +To estimate the potential area over which frequent burning could limit mineral SOC, we scaled up our statistical model of fire effects on SOC to savanna- grasslands. To estimate what areas may be either losing or gaining SOC, we extrapolated observed trends in burned area for ca. two decades \(^2\) to identify areas of increasing or decreasing fire frequency and used the environmental covariates and SOC content derived from other global maps (S) to estimate potential SOC changes. In 2.30 million \(\mathrm{km}^2\) where burned area is tending to decline, SOC has potentially risen by \(23\%\) (Figure 4a). In 1.38 million \(\mathrm{km}^2\) where burned area is tending to rise, SOC has potentially declined by \(25\%\) (Figure 4b). By multiplying these relative values with total SOC stocks, we estimate reductions in burning from 1998- 2015 resulted in a gain of 1.78 PgC while more frequent fires resulted in a loss of 1.14 PgC, for a net- change of 0.64 PgC, or a flux of 0.038 PgC yr \(^{- 1}\) . + +<|ref|>text<|/ref|><|det|>[[42, 880, 949, 947]]<|/det|> +While previous research has highlighted the capacity of savanna- grasslands soils to serve as a C sink \(^{26}\) , our study identifies multiple mechanisms that explain the wide variability in SOC responses to fire across drylands as a whole. With this information, informed management choices can be made that implement + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 949, 136]]<|/det|> +nature- based climate solutions \(^{12}\) . We demonstrate the relative SOC potential is roughly double in drier savanna- grasslands, but potentially comes with a cost of high tree encroachment, which can reduce biodiversity—a key consideration that may be offset through management of browsing herbivores, which we did not consider here. + +<|ref|>text<|/ref|><|det|>[[41, 152, 940, 341]]<|/det|> +We did not focus on systems with either intense crown wildfires such as spruce boreal forest or smoldering ground fires in peatlands. Although these ecosystems can store large amounts of SOC, they burn relatively rarely (albeit increasing in frequency) \(^{12}\) , and the factors that determine direct combustion of SOC are well characterised \(^{4}\) . Meanwhile, savannas have lower per- area SOC stocks, but given their large spatial extent and frequent burning, SOC stocks underlying savannas that burn equate to \(\sim 14.5\) \(\mathrm{PgC}^{12}\) , which could be relevant for future terrestrial C storage. Our sink estimate is small relative to the residual land C sink \((3.1 \pm 0.6 \mathrm{PgC} \mathrm{yr}^{- 1})^{36}\) , but given the lack of saturation observed over the duration of experiments in our dataset ( \(\sim 60\) years), SOC in drylands may be a persistent sink. + +<|ref|>sub_title<|/ref|><|det|>[[45, 363, 213, 389]]<|/det|> +## Declarations + +<|ref|>text<|/ref|><|det|>[[42, 402, 920, 516]]<|/det|> +Acknowledgements: Funding was provided by the Gordon and Betty Moore Foundation and USDA National Institute of Food and Agriculture grant 2018- 67012- 28077. The Cedar Creek Long Term Ecological Research program was funded by National Science Foundation grants DEB- 0620652, DEB- 1234162, DEB- 1831944, and DBI- 2021898. Sampling in other sites were funded by the National Park Service and Sequoia Parks Conservancy, and South African National Parks. + +<|ref|>text<|/ref|><|det|>[[45, 532, 881, 555]]<|/det|> +Data availability statement: Data will be made freely available on Figshare following publication. + +<|ref|>text<|/ref|><|det|>[[45, 569, 884, 592]]<|/det|> +Code availability statement: Code will be made freely available on Figshare following publication. + +<|ref|>text<|/ref|><|det|>[[45, 608, 701, 629]]<|/det|> +Competing Interests Statement: The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[45, 652, 197, 677]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[57, 693, 945, 936]]<|/det|> +1. Zheng, B. et al. Increasing forest fire emissions despite the decline in global burned area. Sci. Adv. 7, (2021). +2. Andela, N. et al. A human-driven decline in global burned area. Science (80-. ). 356, 1356-1362 (2017). +3. Pellegrini, A. F. A. et al. 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Plant Soil 323, 235-247 (2009). + +<|ref|>text<|/ref|><|det|>[[47, 902, 951, 947]]<|/det|> +56. Turner, C. L., Blair, J. M., Schartz, R. J. & Neel, J. C. Soil N and plant responses to fire, topography, and supplemental N in tallgrass prairie. Ecology 78, 1832-1843 (1997). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[47, 44, 955, 94]]<|/det|> +57. Biggs, H. C. et al. Experimental burn plot trial in the Kruger National Park: history, experimental design and suggestions for data analysis. Koedoe 46, 1-15 (2003). + +<|ref|>text<|/ref|><|det|>[[47, 95, 911, 140]]<|/det|> +58. Burns, P. Y. Effect of fire on forest soils in the Pine Barren region of New Jersey. (Yale University, 1952). + +<|ref|>text<|/ref|><|det|>[[47, 144, 949, 211]]<|/det|> +59. Harris, W. N., Moretto, A. S., Distel, R. A., Boutton, T. W. & Boo, R. M. Fire and grazing in grasslands of the Argentine Caldenal: effects on plant and soil carbon and nitrogen. Acta Oecologica 32, 207-214 (2007). + +<|ref|>text<|/ref|><|det|>[[47, 216, 936, 283]]<|/det|> +60. Neill, C., Patterson, W. A. & Crary, D. W. Responses of soil carbon, nitrogen and cations to the frequency and seasonality of prescribed burning in a Cape Cod oak-pine forest. For. Ecol. Manage. 250, 234-243 (2007). + +<|ref|>text<|/ref|><|det|>[[47, 287, 950, 333]]<|/det|> +61. Furley, P. A., Rees, R. M., Ryan, C. M. & Saiz, G. Savanna burning and the assessment of long-term fire experiments with particular reference to Zimbabwe. Prog. Phys. Geogr. 32, 611-634 (2008). + +<|ref|>text<|/ref|><|det|>[[47, 336, 951, 359]]<|/det|> +62. McKee, W. H. Changes in soil fertility following prescribed burning on coastal plain pine sites. (1982). + +<|ref|>text<|/ref|><|det|>[[47, 363, 951, 430]]<|/det|> +63. Scharenbroch, B. C., Nix, B., Jacobs, K. A. & Bowles, M. L. Two decades of low-severity prescribed fire increases soil nutrient availability in a Midwestern, USA oak (Quercus) forest. Geoderma 183-184, 80-91 (2012). + +<|ref|>text<|/ref|><|det|>[[47, 433, 940, 480]]<|/det|> +64. Tongway, D. J. & Hodgkinson, K. C. The effects of fire on the soil in a degraded semi-arid woodland. III. Nutrient pool sizes, biological activity and herbage response. Soil Res. 30, 17-26 (1992). + +<|ref|>text<|/ref|><|det|>[[47, 483, 930, 552]]<|/det|> +65. Williams, R. J., Hallgren, S. W. & Wilson, G. W. T. Frequency of prescribed burning in an upland oak forest determines soil and litter properties and alters the soil microbial community. For. Ecol. Manage. 265, 241-247 (2012). + +<|ref|>text<|/ref|><|det|>[[47, 555, 950, 602]]<|/det|> +66. Bizzari, L. E., Collins, C. D., Brudvig, L. A. & Damschen, E. I. Historical agriculture and contemporary fire frequency alter soil properties in longleaf pine woodlands. For. Ecol. Manage. 349, 45-54 (2015). + +<|ref|>text<|/ref|><|det|>[[47, 605, 936, 674]]<|/det|> +67. Savadogo, P., Sawadogo, L. & Tiveau, D. Effects of grazing intensity and prescribed fire on soil physical and hydrological properties and pasture yield in the savanna woodlands of Burkina Faso. Agric. Ecosyst. Environ. 118, 80-92 (2007). + +<|ref|>text<|/ref|><|det|>[[47, 677, 940, 723]]<|/det|> +68. Butler, O. M., Lewis, T. & Chen, C. Prescribed fire alters foliar stoichiometry and nutrient resorption in the understorey of a subtropical eucalypt forest. Plant Soil 410, 181-191 (2017). + +<|ref|>text<|/ref|><|det|>[[47, 727, 904, 773]]<|/det|> +69. Fynn, R. W. S., Haynes, R. J. & O'Connor, T. G. Burning causes long-term changes in soil organic matter content of a South African grassland. Soil Biol. Biochem. 35, 677-687 (2003). + +<|ref|>text<|/ref|><|det|>[[47, 777, 951, 823]]<|/det|> +70. Eivazi, F. & Bayan, M. R. Effects of long-term prescribed burning on the activity of select soil enzymes in an oak-hickory forest. Can. J. For. Res. 26, 1799-1804 (1996). + +<|ref|>text<|/ref|><|det|>[[47, 826, 904, 873]]<|/det|> +71. Mayor, A. G. et al. Fire-induced pine woodland to shrubland transitions in Southern Europe may promote shifts in soil fertility. Sci. Total Environ. 573, (2016). + +<|ref|>text<|/ref|><|det|>[[47, 876, 950, 943]]<|/det|> +72. Close, D. C., Davidson, N. J. & Swanborough, P. W. Fire history and understorey vegetation: water and nutrient relations of Eucalyptus gomphocephala and E. delegatensis overstorey trees. For. Ecol. Manage. 262, 208-214 (2011). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[46, 44, 955, 90]]<|/det|> +73. Close, D. C., Davidson, N. J., Swanborough, P. W. & Corkrey, R. Does low-intensity surface fire increase water- and nutrient-availability to overstorey Eucalyptus gomphocephala? Plant Soil 349, 203-214 (2011). + +<|ref|>text<|/ref|><|det|>[[48, 95, 955, 161]]<|/det|> +74. Butler, O. M. et al. The stoichiometric legacy of fire regime regulates the roles of micro-organisms and invertebrates in decomposition. Ecology 100, (2019). + +<|ref|>text<|/ref|><|det|>[[48, 166, 944, 211]]<|/det|> +75. Olson, M. G. Tree regeneration in oak-pine stands with and without prescribed fire in the New Jersey Pine Barrens: management implications. North. J. Appl. For. 28, 47-49 (2011). + +<|ref|>text<|/ref|><|det|>[[48, 216, 920, 260]]<|/det|> +76. Pellegrini, A. F. A. et al. Frequent burning causes large losses of carbon from deep soil layers in a temperate savanna. J. Ecol. 108, 1426-1441 (2020). + +<|ref|>text<|/ref|><|det|>[[48, 265, 933, 332]]<|/det|> +77. Campbell, B. M., Frost, P., King, J. A., Mawanza, M. & Mhlanga, L. The influence of trees on soil fertility on two contrasting semi-arid soil types at Matopos, Zimbabwe. Agrofor. Syst. 28, 159-172 (1994). + +<|ref|>text<|/ref|><|det|>[[48, 336, 822, 381]]<|/det|> +78. U.S. Department of Agriculture, N. R. C. S. National soil survey handbook, title 430-VI. http://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/ref/?cid=nrcs142p2_054242. + +<|ref|>text<|/ref|><|det|>[[48, 385, 935, 430]]<|/det|> +79. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965-1978 (2005). + +<|ref|>text<|/ref|><|det|>[[48, 435, 950, 503]]<|/det|> +80. Zomer, R. J., Trabucco, A., Bossio, D. A. & Verchot, L. V. Climate change mitigation: A spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric. Ecosyst. Environ. 126, 67-80 (2008). + +<|ref|>text<|/ref|><|det|>[[48, 507, 952, 552]]<|/det|> +81. Hargreaves, G. L., Hargreaves, G. H. & Riley, J. P. Irrigation water requirements for Senegal River basin. J. Irrig. Drain. Eng. 111, 265-275 (1985). + +<|ref|>text<|/ref|><|det|>[[48, 557, 902, 602]]<|/det|> +82. Furukawa, T. A., Barbui, C., Cipriani, A., Brambilla, P. & Watanabe, N. Imputing missing standard deviations in meta-analyses can provide accurate results. J. Clin. Epidemiol. 59, 7-10 (2006). + +<|ref|>text<|/ref|><|det|>[[48, 606, 891, 629]]<|/det|> +83. R Development Core Team. R: A language and environment for statistical computing. (2010). + +<|ref|>text<|/ref|><|det|>[[48, 633, 916, 678]]<|/det|> +84. Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J Stat Softw 36, 1-48 (2010). + +<|ref|>text<|/ref|><|det|>[[48, 682, 940, 750]]<|/det|> +85. Roscoe, R., Buurman, P., Velthorst, E. J. & Pereira, J. A. A. Effects of fire on soil organic matter in a "cerrado sensu-stricto" from southeast Brazil as revealed by changes in δ 13 C. Geoderma 95, 141-160 (2000). + +<|ref|>text<|/ref|><|det|>[[48, 754, 950, 821]]<|/det|> +86. Pellegrini, A. F. A., Hedin, L. O., Staver, A. C. & Govender, N. Fire alters ecosystem carbon and nutrients but not plant nutrient stoichiometry or composition in tropical savanna. Ecology 96, 1275-1285 (2015). + +<|ref|>text<|/ref|><|det|>[[48, 825, 928, 871]]<|/det|> +87. Rabin, S. S. et al. The Fire Modeling Intercomparison Project (FireMIP), phase 1: Experimental and analytical protocols with detailed model descriptions. Geosci. Model Dev. 10, 1175-1197 (2017). + +<|ref|>sub_title<|/ref|><|det|>[[44, 892, 143, 918]]<|/det|> +## Figures + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[58, 54, 950, 316]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 350, 115, 369]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[42, 390, 950, 549]]<|/det|> +Water availability modifies the effect of fire on soil organic carbon (SOC). Results from meta- regression of the top model illustrating how environmental conditions influenced the percent difference in SOC concentrations in the burned versus unburned plots (lower values thus signify a fire- driven loss). a) Fire effects as a function of aridity (precipitation/potential evapotranspiration), with a low aridity index indicating dry conditions (S). b) Precipitation seasonality is the coefficient of variation of monthly precipitation within a year multiplied by 100. All dashed lines indicate 95% confidence intervals of the model fit. Importance of all variables in the model selection presented in Table S2. + +<|ref|>image<|/ref|><|det|>[[48, 575, 952, 866]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 898, 116, 916]]<|/det|> +
Figure 2
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[40, 44, 955, 157]]<|/det|> +Fire effects on soil organic carbon (SOC) are predicted by changes in tree- based SOC which track the aridity gradient a) Difference in the total SOC between burned – unburned and the percent of SOC derived from tree biomass (stocks in 0- 20 cm of the mineral horizon). b) Aridity index (lower values are drier) and the difference in SOC from trees between burned – unburned. Lines are linear regressions with shaded area representing the standard error. These averages are based on \(n = 74\) plots distributed across the sites. + +<|ref|>image<|/ref|><|det|>[[57, 180, 884, 545]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 571, 117, 590]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[40, 611, 950, 770]]<|/det|> +Most models underpredict the stronger relative effects of fire on soil organic carbon (SOC) in drier environments. Comparison between model simulations and empirical data of fire- driven changes in SOC across aridity classes. Coloured lines represent means from different Dynamic Global Vegetation Models (DGVMs) and the black line represents the data with error bar illustrating the \(95\%\) confidence intervals. DGVMs calculate the percent difference by comparing the simulations with fire vs. a 'world without fire' (described in SI). Aridity index is calculated as the ratio between precipitation and potential evapotranspiration. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[70, 60, 710, 300]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[169, 321, 625, 342]]<|/det|> +
Change in soil C (%) with declining fire frequency
+ +<|ref|>image<|/ref|><|det|>[[70, 352, 710, 775]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 800, 118, 820]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[42, 841, 955, 955]]<|/det|> +Sensitivity of soil carbon to changes in fire frequencies across savanna- grasslands globally. Upscaling our analysis to savanna- grasslands worldwide across the distribution of the areas where our model predicted a change in soil organic carbon (C) under observed trends in burned area. a) Predicted gain in soil C in areas with declining fire frequency, with a positive % illustrating a gain in soil C under the new fire regime. b) Predicted change in soil C with higher fire frequency, with a lower % illustrating a loss in soil C + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 955, 88]]<|/det|> +in the frequently burned. We estimated changes only in areas within environmental conditions used in our training model. + +<|ref|>sub_title<|/ref|><|det|>[[44, 110, 312, 138]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 161, 765, 181]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 200, 355, 218]]<|/det|> +SupplementalInformation.docx + +<--- Page Split ---> diff --git a/preprint/preprint__07ed9c6d9e6ca0cab5b9b0180c58f6b6054610c90c12780b9cc4a7e3a9a2f0e1/images_list.json b/preprint/preprint__07ed9c6d9e6ca0cab5b9b0180c58f6b6054610c90c12780b9cc4a7e3a9a2f0e1/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..c40f5fecb8d5552d0ca5c623905cc86d1912ecf9 --- /dev/null +++ b/preprint/preprint__07ed9c6d9e6ca0cab5b9b0180c58f6b6054610c90c12780b9cc4a7e3a9a2f0e1/images_list.json @@ -0,0 +1,77 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 | Synthetic engineering of the PA molecular structure with rationally designed", + "footnote": [], + "bbox": [ + [ + 112, + 99, + 880, + 660 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 | Morphological and structural features of PA membranes. a,d, Surface FESEM images.", + "footnote": [], + "bbox": [ + [ + 112, + 135, + 875, + 644 + ] + ], + "page_idx": 13 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 | Ultrafast and precision co-ion separation via a low-pressure NF. a, Pure water flux", + "footnote": [], + "bbox": [ + [ + 112, + 135, + 884, + 585 + ] + ], + "page_idx": 17 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 | DFT atomistic simulations of the mutual interactions between PA and cations within", + "footnote": [], + "bbox": [ + [ + 112, + 95, + 875, + 707 + ] + ], + "page_idx": 23 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "429 Fig. 5 | Mechanistic insights into facilitated water permeation in DP-M. a, Schematic diagram", + "footnote": [], + "bbox": [ + [ + 111, + 333, + 884, + 744 + ] + ], + "page_idx": 26 + } +] \ No newline at end of file diff --git a/preprint/preprint__07ed9c6d9e6ca0cab5b9b0180c58f6b6054610c90c12780b9cc4a7e3a9a2f0e1/preprint__07ed9c6d9e6ca0cab5b9b0180c58f6b6054610c90c12780b9cc4a7e3a9a2f0e1.mmd b/preprint/preprint__07ed9c6d9e6ca0cab5b9b0180c58f6b6054610c90c12780b9cc4a7e3a9a2f0e1/preprint__07ed9c6d9e6ca0cab5b9b0180c58f6b6054610c90c12780b9cc4a7e3a9a2f0e1.mmd new file mode 100644 index 0000000000000000000000000000000000000000..cc2412ab79cd9d067272b969556bc5281b4b47e5 --- /dev/null +++ b/preprint/preprint__07ed9c6d9e6ca0cab5b9b0180c58f6b6054610c90c12780b9cc4a7e3a9a2f0e1/preprint__07ed9c6d9e6ca0cab5b9b0180c58f6b6054610c90c12780b9cc4a7e3a9a2f0e1.mmd @@ -0,0 +1,456 @@ + +# Molecular Manipulation of Polyamide Nanostructures Reconciles the Permeance- Selectivity Threshold for Precise Ion Separation + +Gang Han hangang@nankai.edu.cn + +Nankai University https://orcid.org/0000- 0001- 8943- 569X Zhenxiang pan + +Nankai university https://orcid.org/0000- 0001- 6818- 553X + +Yalong Lei Nankai University + +Tiange Yan Nankai University + +Fuxin Zheng Nankai University + +Yu Liao Nankai University + +Jiang Zhan Nankai University + +Tong Zhang Nankai University + +LU SHAO + +Harbin Institute of Technology https://orcid.org/0000- 0002- 4161- 3861 + +## Article + +Keywords: Nanofiltration, Polyamide membrane, Interfacial polymerization, Ion separation, Permselectivity + +Posted Date: December 17th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 5431568/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> + +Version of Record: A version of this preprint was published at Nature Communications on August 4th, 2025. See the published version at https://doi.org/10.1038/s41467-025-62376-8. + +<--- Page Split ---> + +# Molecular Manipulation of Polyamide Nanostructures Reconciles the Permeance-Selectivity + +# Threshold for Precise Ion Separation + +Zhenxiang Pan a, Yalong Lei a, Tiange Yan a, Fuxin Zheng a, Yu Liao a, Jiang Zhan a, Tong Zhang + +a, Lu Shao b, \\*, Gang Han a, \\* + +5 + +a College of Environmental Science and Engineering, Tianjin Key Laboratory of Environmental + +Remediation and Pollution Control, Nankai University, 38 Tongyan Road, Tianjin, 300350, China + +b State Key Laboratory of Urban Water Resource and Environment, School of Chemistry and + +Chemical Engineering, Harbin Institute of Technology, Harbin, China + +10 + +\\* Corresponding author + +Tel: +86 022- 23501117. Email: hangang@nankai.edu.cn + +Tel: +86 13100870576. Email: shaolu@hit.edu.cn + +14 + +## Abstract + +Membrane nanofiltration (NF) has emerged as a prominent energy- efficient separation technology for widespread applications related to the water- energy nexus. However, state- of- the- art polyamide (PA) NF membranes are markedly constrained by a ubiquitous, pernicious tradeoff between water permeance and selectivity. Leveraging the prestigious structure- determining performance rationale, this work conceives a facile and robust molecular engineering approach + +<--- Page Split ---> + +that enables simultaneous improvements in water permeance and co- cation selectivity through synthetic molecular construction of a PA nanofilm with unique cationic triazolyl heterocyclic polyamide (CTHP) structures during scalable interfacial polymerization. Experimental data in conjunction with molecular simulations reveal that the CTHP structures instigate exquisite regulation of the PA subnanometer pore architecture and the specific binding affinity with water and ions, which not only affords precise ion sieving ability and advanced Donnan exclusion selectivity but also energetically facilitates the partitioning and transport of water molecules. The exemplified PA membrane exhibits unparalleled divalent cation rejections of over \(99\%\) , accompanied by a 9- fold increase in monovalent/divalent cation sieving selectivity, which is substantially greater than that of the pristine benchmark, a superior water permeation rate, and excellent chemical and operational stability, circumventing the permeance/selectivity threshold. We believe that the molecular engineering strategy implemented in this work holds broad prospects for the rational design and fabrication of semipermeable polymeric NF membranes for sustainable and precision separations. + +## Keywords + +Nanofiltration; Polyamide membrane; Interfacial polymerization; Ion separation; Permselectivity + +## Introduction + +Precision discrimination of target ions and molecules from complex aqueous mixtures of similar species remains a superior challenge in widespread applications such as water, clean energy, and + +<--- Page Split ---> + +resource reclamation \(^{1 - 3}\) . Membrane nanofiltration (NF), featuring phase- free conversion separation, has evolved into a premier tool for sustainable water separation because of its high energy efficiency, low carbon footprint, compact design, and manufacturing scalability \(^{4,5}\) . The rapid dissemination of NF technology relies on high- performance membranes that ideally have high values of both water permeance and selectivity to fully exploit the prominent process advantages, but such a combination is exceedingly difficult to achieve, particularly for polymeric membranes, as the material properties that affect solute transport would, in turn, affect water permeation \(^{6 - 8}\) . + +Polyamide (PA) thin- film composite membranes are state- of- the- art NF membranes that are particularly attractive for water filtration in practical modules across all scales \(^{9 - 12}\) . However, the deleterious tradeoff between water permeance and membrane selectivity consistently poses a stumbling block for further advancing their performance, where increasing water permeance is inevitably accompanied by a diminished ability to selectively reject solutes \(^{13,14}\) . According to the prevailing membrane separation mechanisms, effective strategies for rationally regulating mass transfer across PA membranes hinge on well- defined pore sizes with a narrow size distribution, properly tuned interactions between PA and the permeants of interest, and a thin PA selective layer \(^{15 - 17}\) . Innovative materials and fabrication methods that can precisely regulate PA chemistry and nanostructures have therefore become essential pursuits of academic research. + +<--- Page Split ---> + +A prevalent approach that has been widely adopted to increase the permselectivity of PA membranes toward charged species involves tuning the surface charges to strengthen electrostatic exclusion effects via in situ and/or post- synthetic modifications \(^{18, 19}\) . However, most of the approaches reported thus far have focused primarily on promoting solute rejection and selectivity rather than overcoming the permeance/selectivity tradeoff threshold \(^{20, 21}\) . Recently, superior size sieving ability and co- cation selectivity have been achieved by trailblazing studies that use interfacial modulators to narrow the pore size distribution of the PA layer \(^{22- 25}\) . Unfortunately, a significant decrease in water permeance is usually accompanied by a concomitant increase in water transport resistance \(^{23, 24}\) . Many studies have focused on exploring advanced membrane materials ranging from biological ion channels and aquaporins to emerging microporous materials of zeolites, metal- organic frameworks, covalent organic frameworks, macrocycles, and porous organic cages, some of which achieve superior permselectivity with unusual combinations of high permeance and selectivity \(^{26- 28}\) . Although the practicality of these intriguing materials is markedly restricted by many daunting limitations that vary from inherent low structural stability to inferior material availability and the feasibility of membrane fabrication on a large scale, their unique structural features underscore the importance of well- defined pore sizes and exquisitely regulated mutual interactions in achieving exceptional molecular sieving capabilities and water transport rates \(^{29- 31}\) . Hereupon, we believe that multifunctional monomers with synthetically engineered chemistry that enable the formation of a novel PA structure that not only imparts small pores with a narrow size distribution but also provides low resistance for water transport are likely to achieve disruptive + +<--- Page Split ---> + +improvements in both water permeance and solute selectivity, successfully overcoming the formidable tradeoff in PA membranes. Unfortunately, there is currently a lack of rational material design and feasible membrane fabrication strategies to accomplish this arduous undertaking. + +Herein, we demonstrate a facile and robust molecular engineering approach for precise regulation of the mass transfer behavior of PA membranes to circumvent the deleterious permeance/selectivity tradeoff in ion differentiation. Our strategy is contingent on molecular-level control over the nanoporous structure of the PA nanofilm and its interactions with water and ions via in situ construction of cationic triazolyl heterocyclic polyamide (CTHP) structures during interfacial polymerization (IP) via synthetic triamino quaternary triazole ammonium (DAT- NH2) isomers. Experimental data and molecular simulations revealed that the CTHP structures endow the PA layer with well- defined subnanometer pores with a narrow size distribution and abundant positive charge but low intrinsic water transport resistance, which synergistically enhances steric hindrance sieving and Donnan exclusion and facilitates the permeation of water. The advantages of this molecularly engineered PA structure were demonstrated by its superior performance within precise ion separation (Fig. 1a), achieving a 9- fold increase in monovalent/divalent cation selectivity with a tripled water flux relative to the benchmark, reconciling the tradeoff threshold. In light of the diverse array of monomer chemistries, the implemented molecular design strategy provides a gateway to advance the rational design and fabrication of PA membranes with superior permselectivity for precise ion separations toward clean water and renewable energy. + +<--- Page Split ---> + +## Results and Discussion + +## Synthesis of the DAT- \(\mathbf{NH}_2\) monomer and fabrication of PA membranes + +Fig. 1b shows the conceptual diagram of the ideal PA structure we intend to construct to circumvent the permeance/selectivity tradeoff threshold in precision nanofiltration (NF). Specifically, the PA layer is synthetically designed with well- defined permeate- PA binding affinity and profound steric sieving selectivity imparted by pore size confinement to efficiently manipulate the enthalpy and entropy barriers for water and solute transport \(^{32,33}\) . To realize this design strategy, we molecularly constructed a multifunctional structure with primary amine dangling and a highly polarized triazolyl heterocyclic core bearing quaternary ammonium (i.e., DAT- \(\mathbf{NH}_2\) , Fig. 1c). Our pursuit of this molecular structure was inspired by these trailblazing studies showing that the triazole derivative heterocyclic moieties may provide preferential water transport paths with a low energy barrier \(^{34}\) , whereas the amine groups concurrently provide highly reactive sites to crosslink with trimesoyl chloride (TMC) to form a PA nanofilm with superior hydrophilicity and interconnected positively charged subnanometer pores. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1 | Synthetic engineering of the PA molecular structure with rationally designed
+ +monomers. a, Working principle of precision co- ion separation via nanofiltration. b, Schematic diagram of the interconnected subnanometer- sized pores in a desired PA nanofilm with high selectivity and low water transport resistance, and three- dimensional view of an amorphous cell of the PA (cell size: \(65 \times 65 \times 65 \mathring{\mathrm{A}}^3\) ). c, Synthetic reaction formula of DAT- NH₂ isomers and the visualized conformation of their atomic electrostatic potential. d, \(^1\mathrm{H}\) NMR spectra and liquid + +<--- Page Split ---> + +chromatography (upper left inset) of DAT- NH2 isomers. e, Schematic illustration of the interfacial polymerization between DAT- NH2/PEI and TMC at the water- hexane interface to form a PA nanofilm. f, Molecular structure of the DP- M PA nanofilm (left) and its corresponding chain structure derived from an amorphous cell generated by molecular dynamic (MD) simulations. g, FT- IR spectra of the DP- M and P- M PA nanofilms. h, N 1s XPS spectra of the DP- M PA nanofilm. The N1s core level spectrum was deconvoluted into three components located at 399.7, 400.4, and 401.7 eV corresponding to N- (C=O)- , N(H)- C- , and N+- C- , respectively. + +The molecularly designed DAT- NH2 was synthesized via a one- pot quaternization reaction between 3,5- diamino- 1,2,4- triazole and 2- bromoethylamine in DMF and then purified by nonsolvent precipitation (Fig. 1c and Supplementary Fig. 1). Intriguingly, liquid chromatography- mass spectrometry (LC- MS) data reveals that isomers of 3,5- diamino- 4/1- (2- aminoethyl)- 1,2,4- triazole were formed during the reaction, where two distinct peaks with almost the same intensity and area ratio are observed in the LC chromatogram pattern (Fig. 1d), and two peaks at m/z = 143 show up in MS (Supplementary Fig. 2), which is in good agreement with the chemical structures of the DAT- NH2 isomers (C4N6H11+, Mw = 143). Proton nuclear magnetic resonance (1H NMR) spectra corroborate these results, where four 1H NMR peaks corresponding to the two types of protons in each isomer are spotted at 3.11 (labeled H I), 3.30 (labeled H I), 3.35 (labeled H II), and 3.99 (labeled H II) ppm. The area ratios of the two peaks (H I/H II) in the 1H NMR spectrum were measured to be 0.88 and 1.04 for the isomers, which is in close proximity to the theoretically + +<--- Page Split ---> + +expected values based on the DAT- NH₂ chemical structure. The visualized atomic electrostatic potential image of DAT- NH₂ intuitively proclaims the positive charge characteristics of the triazole ring, and quantitative analysis of the molecular van der Waals surface electrostatic potential (ESP) shows that the distribution area and intensity of the positive ESP of the DAT- NH₂ isomers are greater than the negative values (Fig. 1c and Supplementary Fig. 3). + +A self- sustaining PA thin film with good stability immediately formed when DAT- NH₂ was brought in contact with TMC at the water/hexane interface (Supplementary Fig. 4), indicating a high polymerization rate between DAT- NH₂ and TMC. DFT calculations further confirmed the high nucleophilic substitution reactivity of the amino groups on DAT- NH₂ toward acyl chloride (Supplementary Figs. 5 and 6). Comprehensive ESP and average local ionization energy (ALIE) analyses reveal that the backbone and chemical functional moieties of the PA structures formed by the two isomers with TMC are almost identical (Supplementary Fig. 7). Therefore, the DAT- NH₂ isomers were directly used for PA membrane preparation without further purification. A continuous PA selective layer can also be synthesized via a similar scalable interfacial polymerization (IP) procedure on top of a polyethersulfone (PES) substrate to prepare robust PA membranes for NF tests (Supplementary Fig. 8). Unfortunately, we observed that the obtained DAT- NH₂/TMC PA membrane experienced severe water swelling and thus relatively low permselectivity were obtained (Supplementary Fig. 9), likely owing to the superior hydrophilic nature of the cationic triazolyl heterocyclic structures. We thereby further modified the PA chemical structure by using + +<--- Page Split ---> + +PEI as a comonomer during IP to increase the membrane stability and separation performance (Fig. 1e,f). PEI is a benchmark monomer that is widely used for the synthesis of positively charged PA NF membranes (Supplementary Fig. 10). Synthesis condition optimization experiments confirmed that the DP- M membrane fabricated with a 0.06 wt% DAT- NH₂ shows an optimum combination of water permeance and ion selectivity and excellent robustness (Supplementary Figs. 11 and 12), substantially exceeding that of the PA membrane formed solely by PEI (denoted as the P- M benchmark). The FT- IR peaks observed at 1621 cm⁻¹, 1806 cm⁻¹, and 1705 cm⁻¹ are associated with the amide I band, which arises from the stretching vibration of C=O and the coupling with the bending of N- H in subtly different chemical environments (Fig. 1g and Supplementary Fig. 13), validating the formation of polyamide structure during IP. The quaternary ammonium peak at 401.8 eV in the XPS spectrum of DP- M (Fig. 1h and Supplementary Figs. 14 and 15) confirms the presence of DAT- NH₂ moieties in the PA layer. It is noteworthy that a significant decline in the O/N ratio was observed by DP- M compared with that of the P- M benchmark, where the O/N ratio decreases from \(\sim 1.50\) to 0.96 (Supplementary Table 1), suggesting a substantial increase in the PA crosslinking degree of DP- M. The elevated crosslinking degree constricts the space between the stacked polymer chains, thus diminishing the pore sizes and augmenting the mechanical strength of the PA film (Supplementary Fig. 12). According to the chemical characterization and molecular simulations, the DAT- NH₂ modulated PA nanofilm of DP- M has a semirigid 3D polyamide network with a large amount of intrinsic positive charges and smaller chain space compared to the P- M benchmark (Fig. 1f and Supplementary Figs. 16 and 17). + +<--- Page Split ---> + +## Morphological and structural analysis of the DP-M PA membrane + +Field emission scanning electron microscopy (FESEM) images corroborate the uniformity and integrity of the formed PA thin layer at the macroscopic scale (Fig. 2a,d and Supplementary Fig. 18). At a finer scale, the PA layer of DP- M appears a smooth and compact surface, whereas the counterpart of the P- M benchmark shows a crumpled surface with numerous ridged wrinkles unequivocally seen on top. This surface morphological discrepancy was further manifested by the atomic force microscopy (AFM) data, where the surface roughness of DP- M ( \(\mathrm{Rq} = 3.44 \mathrm{nm}\) ) is distinctly lower than that of P- M ( \(\mathrm{Rq} = 9.09 \mathrm{nm}\) ) (Fig. 2b,e and Supplementary Fig. 19). A smooth surface is conducive to alleviating the fouling tendency. Cross- sectional transmission electron microscopy (TEM) images showcase that DP- M has an extremely low PA thickness of \(59 \pm 2 \mathrm{nm}\) (Fig. 2c,f and Supplementary Fig. 20), which is much thinner than that of the P- M benchmark (i.e., \(88 \pm 2 \mathrm{nm}\) ). The significantly reduced PA thickness might be attributed to the rapid formation of a relatively dense nascent PA film mediated by DAT- \(\mathrm{NH}_2\) at an initial stage of IP (Supplementary Fig. 21), which stymies the diffusion of aqueous monomers at the interface and thus suppresses the subsequent growth of the PA layer \(^{35,36}\) . On the other hand, the positively charged structure of DAT- \(\mathrm{NH}_2\) may slow down the diffusion of PEI towards the interface via H- bonding interactions \(^{37- 41}\) . In the context of membrane filtration, a thinner PA selective layer spontaneously confers shorter transport pathways and lower water penetration resistance, which is favorable for achieving high water permeance. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2 | Morphological and structural features of PA membranes. a,d, Surface FESEM images.
+ +b,e, 2D and 3D AFM images. c,f, Cross-sectional TEM images. Top: P-M. Bottom: DP-M. g, Pore diameter distribution of PA membranes obtained by PEG rejection tests. h, Molecular dynamics (MD) simulations of the fractional free volume (FFV) of PA layers (left). The dark blue and gray colors represent the voids between the polymer chains and the space occupied by the polymer skeleton, respectively. Representative porous molecular structures of the DP-M and P-M PA + +<--- Page Split ---> + +networks (right). i, MD simulations of the pore diameter distribution of the PA nanofilms. j, Zeta potential as a function of pH. k, Summary of the MWCO, water contact angle (CA), root mean square roughness (Rq), FFV, polyamide layer thickness, and zeta potential (ZP) at \(\mathrm{pH} = 6\) for DP- M and P- M. + +The rejection tests with neutral solutes indicate that DP- M has a molecular weight cutoff (MWCO) of 245 Da, almost two times smaller than that of the P- M benchmark (MWCO = 479 Da, Supplementary Fig. 22). Correspondingly, a small effective mean pore diameter of 2.8 Å accompanied by a narrow size distribution is achieved by DP- M (Fig. 2g), whereas P- M shows a relatively larger mean pore size of 3.1 Å and broader pore size distribution, in accordance with our design strategy and the XPS results (Fig. 1b,h). MD simulations were performed to construct realistic structural models via simulated polymeric algorithms to glean molecular- level insights into the porous structure of the PA layer. As shown in Fig. 2h, the fractional free volumes (FFVs) of DP- M and P- M PA layers are approximately \(10.3\%\) and \(21.6\%\) , respectively. Moreover, the pore diameter analyses conducted via MD molecular simulations substantiate that most of the pores inside the DP- M PA layer are approximately \(2.75\) Å in length, which is significantly smaller than that of the P- M benchmark (i.e., \(3.38\) Å). Further analyses of the interior cavity diameters disclose a narrower range of pore sizes within DP- M (Fig. 2i and Supplementary Fig. 23), signifying the compact structure of the PA mediated by DAT- NH₂ (Fig. 1c). Notably, the microscopic pore features derived from molecular simulations coincide well with those experimentally obtained + +<--- Page Split ---> + +from neutral solute rejection tests (Fig. 2g,i). The tight nanostructure of DP- M constricts membrane pores to dimensions more favorable for size sieving, with precise ionic and molecular sieving capabilities and a threshold of 2.8 Å. Furthermore, aligning with the chemical features of the CTHP structures in DP- M (Fig. 1g), the cationic DAT- NH₂ moieties adequately elevate the membrane hydrophilicity and positive charge density, as manifested by its smaller surface water contact angle (CA) and higher zeta potential (ZP) than that of the P- M benchmark (Supplementary Fig. 23 and Fig. 2j). In the realm of NF applications, the enhanced hydrophilicity facilitates surface partitioning and interior diffusion of water molecules, whereas the ameliorated positive charge density reinforces the electrostatic repulsion selectivity. Collectively, the advanced membrane characteristics gained by DP- M resonate with our intended design strategy illustrated in Fig. 1b, which underpins the significance of synthetic molecular engineering in precisely regulating the nanoporous structure and chemical features of the PA selective layer (Fig. 2k). + +## Simultaneous improvement in water permeance and ion selectivity + +The well- defined subnanometer pores with a sharped size distribution and the inherent positive charges of the DP- M membrane would afford prominent molecular sieving and electrostatic repulsion selectivity in sustainable NF applications. We subsequently examined the mass transport behavior of a wide spectrum of inorganic salts through DP- M using a crossflow filtration system. In contrast to the acquiescent expectation that a decrease in the membrane pore size along with a downscaled FFV generally accompanied by a concomitant reduction in the water permeation rate, + +<--- Page Split ---> + +a substantial increase in the water permeance was achieved by DP- M, where the water permeation flux of DP- M is almost three times greater than that of the P- M benchmark at the same pressure (Fig. 3a), corresponding to an approximately 3- fold increase in the pure water permeance (PWP). The incongruence between the enhanced water permeance and the reduced pore sizes and FFVs likely stems from the molecularly constructed CTHP structures and the low thickness of the PA layer, which provide facilitated water transport pathways with low resistance. + +At the same time, DP- M shows a sharp size- exclusion cutoff of \(\sim 2.5 \mathrm{\AA}\) in the Stokes radius of cations (Fig. 3b), adhering to its densified catatonic PA molecular structure, which thereby facilitates the transport of smaller monovalent cations (i.e., \(\mathrm{Rb^{+}}\) , \(\mathrm{K^{+}}\) , \(\mathrm{Na^{+}}\) , and \(\mathrm{Li^{+}}\) ) while sufficiently blocking larger divalent cations (i.e., \(\mathrm{Ni^{2 + }}\) , \(\mathrm{Ca^{2 + }}\) , \(\mathrm{Mg^{2 + }}\) , and \(\mathrm{Zn^{2 + }}\) ), with rejection rates greater than \(98.0\%\) and high water flux of \(50 \mathrm{L} \mathrm{m}^{- 2} \mathrm{h}^{- 1}\) (LMH). Notably, judiciously high rejections of up to \(99.1\%\) towards \(\mathrm{MgCl_{2}}\) and \(\mathrm{MgSO_{4}}\) were specifically achieved by DP- M (Fig. 3c), exceeding most of the state- of- the- art NF membranes, and similar rejections and water permeance were maintained over a wide pressure range of 2–16 bar (Fig. 3d). In contrast, \(\mathrm{LiCl}\) rejection monolithically increases from \(45.7\%\) to \(81.2\%\) when the pressure is raised from 2 to 16 bar (Supplementary Fig. 24). As a result, an ideal \(\mathrm{Li^{+} / Mg^{2 + }}\) selectivity of \(35.8–42.9\) was obtained on the basis of single salt rejections (Supplementary Fig. 25), demonstrating its promising capability for precise cation screening. In the same vein, the P- M benchmark is inferior in terms of both salt rejection and cation differentiation selectivity (Fig. 3b and Supplementary Fig. 26). + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3 | Ultrafast and precision co-ion separation via a low-pressure NF. a, Pure water flux
+ +(PWF) of DP- M and P- M at different operation pressures. b, Water flux and ion rejections of DP- + +M for filtrating different cation solutions. (Feed salt concentration: 1000 ppm; test pressure: 6.0 + +bar). c, MgSO4 and MgCl2 rejections of P- M and DP- M (feed salt concentration: 1000 ppm, test + +pressure: 6.0 bar). d, Effect of operation pressure on the water permeance and MgCl2 rejection of + +DP- M (feed: 1000 ppm MgCl2). e, Effect of pH on the water flux and LiCl rejection of DP- M + +(feed: 1000 ppm LiCl, test pressure: 6.0 bar). f, Effect of operation time on the water flux and + +MgCl2 rejection of DP- M (feed: 1000 ppm MgCl2, test pressure: 6.0 bar). g, Effect of operation + +<--- Page Split ---> + +pressure on the water permeance and \(\mathrm{S_{Li + / Mg2 + }}\) of DP- M (feed: 2000 ppm binary mixture of \(\mathrm{MgCl_2}\) and \(\mathrm{LiCl}\) with a \(\mathrm{MgCl_2 / LiCl}\) mass ratio of 20). \(\mathbf{h}\) , Effects of the \(\mathrm{Mg^{2 + } / Li^{+}}\) ratio on the water flux and \(\mathrm{S_{Li + / Mg2 + }}\) ratio of DP- M (feed: 2000 ppm binary mixture of \(\mathrm{MgCl_2}\) and \(\mathrm{LiCl}\) with various \(\mathrm{MgCl_2 / LiCl}\) mass ratios; test pressure: 6.0 bar). i, Performance comparison of DP- M with other reported state- of- the- art PA NF membranes operated under cross- flow nanofiltration. The corresponding references for the data points in (i) are specified in Supplementary Table 2. + +The separation performances of conventional NF membranes are generally susceptible to the feed salt concentration and pH due to the electrostatic screening effects. Interestingly, DP- M consistently retains its water flux, salt rejection, and co- cation selectivity across a wide range of feed salt contents and pH values. As demonstrated by the cycling performance tests, DP- M maintains stable \(\mathrm{MgCl_2}\) rejections of over \(95.8\%\) and relatively low \(\mathrm{LiCl}\) retentions of less than \(59.8\%\) when the feed salt content spanning from 1000 to 7000 mg/L, and the rejections recover to the initial values of the test (Supplementary Fig. 27), substantiating its strong electrostatic shielding resistance toward high ionic strength. There were also no obvious deteriorations in salt rejections and water flux as the feed pH escalated from 1 to 13 (Fig. 3e), underscoring the ability of DP- M to maintain excellent separation performance in both acidic and alkaline environments. The superior pH and salinity stabilities of DP- M are consistent with its highly ionizable cationic PA structure and outstanding size- sieving ability imparted by the well- defined pore sizes. Moreover, DP- M shows excellent structural durability and stability throughout long- term filtration + +<--- Page Split ---> + +for \(120\mathrm{h}\) (Fig. 3f), where \(\mathrm{MgCl}_2\) rejection consistently surpasses \(99\%\) , with a stable water flux of \(\sim 51\) LMH being retained. + +The co- cation sieving ability of DP- M was further illuminated by binary salt filtration tests using a mixture of \(\mathrm{LiCl}\) and \(\mathrm{MgCl}_2\) as the probe feed. Similar to the single- salt tests (Supplementary Fig. 25), DP- M shows high \(\mathrm{Li^{+} / Mg^{2 + }}\) co- cation selectivity of greater than 39.0 \((\mathrm{S}_{\mathrm{Li^{+} / Mg2 + }})\) for binary mixtures at different operation pressures (Fig. 3g), which signifies a 9- fold greater magnitude than that achieved by the P- M benchmark \((\mathrm{S}_{\mathrm{Li^{+} / Mg2 + }} = 4.3)\) , accompanied by an approximately 2.5- fold increase in water permeance. The persistently high rejections toward divalent cations and co- cation selectivity are presumably ascribed to the advanced molecular sieving and electrostatic repulsion effects afforded by the exquisitely regulated subnanometer pores and inherent positive charges of DP- M. Furthermore, slight fluctuations in the co- cation selectivity are observed when the feed \(\mathrm{Mg^{2 + } / Li^{+}}\) mass ratio alters from 1–120, where the \(\mathrm{S}_{\mathrm{Li^{+} / Mg2 + }}\) oscillates between 33.5 and 44.3 and the water flux is consistently higher than 51.4 LMH (Fig. 3h). Compared with other reported PA NF membranes with similar chemical and structural properties, DM- P exhibits upper- level water permeance and co- cation selectivity (i.e., \(\mathrm{Li^{+} / Mg^{2 + }}\) ) (Fig. 3i). The successful breakthrough of the permeance/selectivity tradeoff underpins our membrane design strategy illustrated in Fig. 1b and exemplifies the great feasibility of synthetic molecular engineering in rational membrane design. The excelled water permeation rate and co- ion screening capability in tandem with the scalable fabrication bolster the remarkable potential of the implemented strategy for developing effective + +<--- Page Split ---> + +NF membranes for widespread applications pertaining to wastewater treatment, lithium extraction, recycling, and removal of heavy metal ions, in line with sustainable development. + +# Regulatory mechanisms of facilitated water permeation and superior co-cation selectivity + +Given an average pore diameter of \(\sim 2.75 \mathrm{\AA}\) with narrow size distribution and the strong intrinsic positive charge of DP- M (Fig. 2k), we speculate that its superior rejection of divalent cations and the co- cation sieving ability lean upon the on- demand tuning of PA chemistry and nanoporous structure according to the size and valence differences between cations, which instigates unusual differences in energy barriers that cations need to overcome for dissolution and diffusion. To gain fundamental insights into the underlying mechanisms responsible for the intriguing mass transport behavior of DP- M, dynamic molecular simulations were performed to correlate the separation performance with the membrane chemical structure. The simulations initiated with DFT calculations to illuminate the mutual interactions between PA and cations by performing configuration optimization and cation- PA binding energy calculations (Supplementary Table 3). As displayed in Fig. 4a, the negative binding energies of hexahydrated \(\mathrm{Mg}^{2 + }\) with the DP- M PA fragments (i.e., \(- 12.03\) and \(- 18.93 \mathrm{kcal / mol}\) ) are consistently lower than those with the P- M fragments ( \(- 22.17\) and \(- 26.01 \mathrm{kcal / mol}\) ), suggesting that the binding interactions between hexahydrated \(\mathrm{Mg}^{2 + }\) and P- M are relatively more stable. In other words, hexahydrated \(\mathrm{Mg}^{2 + }\) has a greater energy barrier towards DP- M than P- M, which signifies that it is more difficult for \(\mathrm{Mg}^{2 + }\) to pass through DP- M. On the contrary, the binding energy between hydrated \(\mathrm{Li}^{+}\) and the DP- M + +<--- Page Split ---> + +fragments (- 28.83 kcal/mol) is close to that between it and the P- M fragments (- 29.65 kcal/mol) (Fig. 4b), suggesting that the transport of hydrated \(\mathrm{Li}^{+}\) through DP- M and P- M nearly remains energetically unchanged even though the pore sizes of DP- M are substantially diminished and narrowed. However, the binding energy gaps between \(\mathrm{Li}^{+}\) and \(\mathrm{Mg}^{2 + }\) in the DP- M fragments are 16.80 and 9.90 kcal/mol, respectively, which are markedly larger than those in the P- M fragments (i.e., 2.82 and 6.66 kcal/mol) (Fig. 4b), implying that DP- M has an overwhelming advantage over P- M to differentiate \(\mathrm{Li}^{+}\) and \(\mathrm{Mg}^{2 + }\) from the perspective of energy barrier. + +The interaction region indicator (IRI) was subsequently applied to perform an in- depth analysis of the specific type of interactions between the PA fragments and hexahydrated \(\mathrm{Mg}^{2 + }\) . As a slight modification of the reduced density gradient (RDG), IRI (IRI(l) = \(|\nabla \rho (\mathrm{l})| / [\rho (\mathrm{l})^{\mathrm{a}}])\) can effectively manifest the chemical bonding and weak interaction regions \(^{42}\) . IRI visual representations of these two binding configurations and the PA molecular fragments of DP- M and P- M were constructed (Supplementary Fig. 28). The corresponding scatter plots reveal that the interaction forces involved are intricate and hard to distinguish (Supplementary Fig. 29). Therefore, the hydration layer of \(\mathrm{Mg}^{2 + }\) and the influence of TMC were shielded to better disclose the contributions of DAT- \(\mathrm{NH}_{2}\) moieties in the PA (Fig. 4c). Examining their respective IRI scatter plots, eminent peaks appear near \(\mathrm{sign}(\mathrm{l}_{2})\mathrm{r}\) values of \(- 0.05\) and 0.06 a.u. in the DP- M PA molecular fragments. From the electron density point of view, the peak at \(- 0.05\) a.u. corresponds to a weak interaction of higher strength, whereas the peak at 0.06 a.u. stems from a stronger spatial repulsion. Notably, the scatter + +<--- Page Split ---> + +plot of the weak interaction portion ascertains an anomalous peak at approximately 0.013 a.u. in DP- M (Supplementary Fig. 30), indicating that the CTHP structures derived from DAT- NH2 may generate proprietary steric hindrance at the molecular level. The two distinct peaks in the scatter plot obtained from interaction decomposition confirm that CTHP instigates both attractive and repulsive interactions (Fig. 4d). Further analysis of the isosurface of the IRI visualization reveals that the peak near \(- 0.05\) a.u. is attributed to intramolecular H- bonding interactions. These H- bonds account for the in situ formation of the cyclic conformations in the CTHP structures (Fig. 4d), which dictate additional steric hindrance at approximately 0.013 a.u. (the peak near 0.013 is retained in Supplementary Fig. 31). Moreover, the peak at approximately 0.06 a.u. is associated with the strong repulsion induced by the overlap of the triazole rings in CTHP driven by van der Waals surfaces. Other than the narrowed subnanometer pore sizes, these anomalous intramolecular H- bonding structures provide additional steric hindrance at the molecular level, further amplifying the energy barrier of permeation acting on divalent cations. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4 | DFT atomistic simulations of the mutual interactions between PA and cations within
+ +NF. a, Binding energies of hydrated \(\mathrm{Mg^{2 + }}\) to the PA molecular fragments of DP- M and P- M (for each membrane, two binding energies were calculated by positioning the hydrated \(\mathrm{Mg^{2 + }}\) at two representative binding sites of the PA fragments). b, The binding energies between the hydrated \(\mathrm{Li^{+} / Mg^{2 + }}\) and the PA molecular fragments of P- M/DP- M. The numbers represent the calculated + +<--- Page Split ---> + +energy gaps between \(\mathrm{Li^{+}}\) and \(\mathrm{Mg^{2 + }}\) in P- M and DP- M. c, Interaction region indicator analysis of PA fragments interacting with \(\mathrm{Mg^{2 + }}\) . The effects of the hydration layer and TMC were shielded (the unit a.u. here represents energy, and 1 a.u. is approximately 27.21 eV). d, Interaction region indicator analysis of the PA fragments derived from DAT- \(\mathrm{NH_2}\) and the visualized structure diagram. e, The independent gradient model based on Hirshfeld partition (IGMH) was used to analyze the binding configurations between P- M/DP- M molecular fragments and hydrated \(\mathrm{Mg^{2 + }}\) and \(\mathrm{Li^{+}}\) . f, Electrostatic potential analysis of the PA molecular fragments. g, van der Waals surface area ratio corresponding to various electrostatic potential intervals of the PA molecular fragments. + +The interactions of P- M and DP- M fragments with hydrated \(\mathrm{Li^{+}}\) and \(\mathrm{Mg^{2 + }}\) ions were also visualized using the independent gradient model based on Hirshfeld partitioning (IGMH) (Fig. 4e and Supplementary Fig. 32). Compared with the P- M fragments, IGMH analysis shows that weaker H- bonding and van der Waals interactions exist between the DP- M fragments and the water molecules in the hydration shell of hydrated \(\mathrm{Mg^{2 + }}\) ions, which are energetically unfavorable for stabilizing the configuration, thereby hindering the thermodynamic partitioning and kinetic diffusion of hydrated \(\mathrm{Mg^{2 + }}\) ions, resulting in high rejections. In addition, the lower intrinsic charge number of \(\mathrm{Li^{+}}\) relative to \(\mathrm{Mg^{2 + }}\) impairs its ability to effectively bind water molecules in the hydration shell (Supplementary Fig. 33), while the positively charged CTHP structures in DP- M further promote the escape of polar water molecules from the \(\mathrm{Li^{+}}\) hydration shell by forming extensive van der Waals interactions to counter the electrostatic confinement, as demonstrated in + +<--- Page Split ---> + +the DP- M/hydrated \(\mathrm{Li^{+}}\) configuration (Fig. 4e), aggrandizing the dehydration of the hydrated \(\mathrm{Li^{+}}\) ions during the permeation. According to DFT calculations, the system diameters of \(\mathrm{Mg^{2 + }}\) dodecahydrate and \(\mathrm{Li^{+}}\) hexahydrate are 11.49 and 8.42 A (Supplementary Fig. 34a), and their hydration energies are approximately \(- 1922.45\) and \(- 563.52 \mathrm{kJ / mol}\) (Supplementary Fig. 34b), respectively, implying that hydrated \(\mathrm{Mg^{2 + }}\) faces greater challenges than hydrated \(\mathrm{Li^{+}}\) in overcoming the dehydration energy threshold at the same transmembrane pressure. This energetically promoted dehydration of \(\mathrm{Li^{+}}\) expedites its transport through DP- M, playing an important role in enhancing \(\mathrm{Li^{+} / Mg^{2 + }}\) selectivity particularly when the membrane pore sizes are diminished and the size distribution is constricted. + +In addition to the non- Coulombic interactions, long- range Coulombic electrostatic forces (Donnan exclusion) also play an imperative role in the cation- PA interactions, particularly considering the strongly positively charged structure of DP- M. Qualitative and quantitative analyses of the electrostatic potential (ESP) are conducted, and Fig. 4f and Supplementary Fig. 35 illustrate the distribution of van der Waals surface ESP of the PA molecular fragments of DP- M and P- M. It was found that DP- M exhibits a superior positive potential and this observation is reaffirmed by the quantitative calculation of the ESP region proportions, where DP- M shows a large positive potential region proportion of \(97\%\) and an average ESP value of 55.55 kcal/mol, far exceeding the respective value of P- M (Fig. 4g). The pronounced electrostatic repulsion between DP- M and the positively charged hexahydrated cations is unequivocally conferred by the CTHP structures of the + +<--- Page Split ---> + +422 PA layer. The ESP of hydrated \(\mathrm{Mg^{2 + }}\) is nearly twice as high as that of hydrated \(\mathrm{Li^{+}}\) (193.29 vs. 423 98.53 kcal/mol, Supplementary Fig. 33), which inevitably invokes formidable Donnan exclusion 424 selectivity towards the positively charged DP- M (55.55 kcal/mol). Overall, the intriguing co- cation 425 screening ability of DP- M proceeds through a cooperative mechanism of steric hindrance and 426 electrostatic repulsion, as corroborated by the comprehensive IRI, IGMH, and ESP analyses. + +![](images/Figure_5.jpg) + +
429 Fig. 5 | Mechanistic insights into facilitated water permeation in DP-M. a, Schematic diagram
+ +430 of the advanced structure of an ultrapermeable PA membrane for precise differentiation of 431 monovalent/divalent cations. b, DFT atomistic calculation of polarity differences between DP- M 432 and P- M molecular fragments. c, ESP distributions of van der Waals surfaces of the water molecule + +<--- Page Split ---> + +obtained by DFT calculations. d, Three representative PA molecular fragments of DP- M and P- M (left) and schematic illustration of the distribution of water cluster in each fragment (right). e, Radial distribution functions between water and the three PA fragments. f, Binding energy (BE) and the number of water molecules around the three PA fragments. All the information and data described in (c- e) were obtained from MD simulations. g, Schematic illustration of the working principle of semipermeable DP- M for ultrafast co- cation separation (yellow and blue spheres represent monovalent and divalent cations, respectively). + +The permeation of water through the PA membrane is primarily governed by the chemical features and nanoporous structure of the PA selective layer, during which water molecules need to overcome certain energy barriers when dissolving and diffusing through. The mutual interactions of water molecules with the binding sites on PA networks thereby have substantial impacts on the water permeation rate (Fig. 5a). To gain a fundamental understanding of the mechanisms governing the facilitated permeation of water through the DP- M membrane, DFT atomistic calculations and MD simulations were performed. DFT was employed to specifically evaluate the molecular interactions between water and the cationic DP- M PA molecular fragments by calculating the surface free energy (SFE, Supplementary Table 4) and the molecular polarity index (MPI), which reflect membrane hydrophilicity from the perspectives of interfacial thermodynamics and quantum chemistry, respectively. As displayed in Fig. 5b, DP- M shows a higher MPI value than that of P- M (i.e., 56.0 vs. 19.7), which is consistent with the lower surface + +<--- Page Split ---> + +water contact angle of the former. Meanwhile, the SFE value of DP- M is greater than that of P- M (i.e., 50.5 vs. 41.4 kJ/m²), indicating a clear preference of polar water molecules for wetting and partitioning into the PA fragments of DP- M (Fig. 5c and Supplementary Table 4). To further elucidate the diffusion behavior of water molecules within the PA layer, MD simulations were subsequently conducted to acquire the binding affinities of water molecules to the PA fragments at the molecular level (Fig. 5d). The radial distribution functions (RDFs) plots (Fig. 5e), drawn with the respect to the PA fragments labeled in green (I), blue (II), and orange (III), respectively, reveal that the peak of I- H₂O in the first coordination layer is significantly higher than those of II- H₂O and III- H₂O, closely aligning with the DFT data. Furthermore, the water bonding (WB) capacity calculated by RDFs follows an order of \(\mathrm{WB_1 > WB_{II} > WB_{III}}\) , while the computed binding energies (BE) of fragments I, II, and III with water are in the order of \(\left|\mathrm{BE_I}\right| > \left|\mathrm{BE_{II}}\right| > \left|\mathrm{BE_{III}}\right|\) (Fig. 5f). Hereupon, the cationic triazolyl heterocyclic PA structures derived from DAT- NH₂ in DP- M have a relatively lower affinity to water clusters. Such energy metrics accentuate a thermodynamic inclination of DP- M to pronouncedly facilitate the transport of water by providing water binding sites with moderate resistance, aligned with our design strategy and the ideal PA nanostructure we intend to construct (Fig. 5g). + +## Conclusion + +Nanofiltration membranes with superior water permeance and precise ionic and molecular sieving capabilities offer promising solutions to address numerous challenges associated with water + +<--- Page Split ---> + +scarcity and renewable energy. In this study, a facile and robust molecular engineering strategy was demonstrated to reconcile the longstanding permeance- selectivity tradeoff threshold in state- of- the- art PA nanofiltration membranes. Our approach is contingent on the exquisite regulation of the porous nanostructure and the mutual interactions of the PA selective layer with water molecules and ions by the in situ creation of cationic triazolyl heterocyclic polyamide (CTHP) structures during interfacial polycondensations via synthetic DTA- NH2 isomers. The obtained PA membrane exhibited synchronously enhanced water permeance and subangular selectivity for cation separation, achieving unparalleled divalent cation rejections of over \(99\%\) , accompanied by a 9- fold increase in monovalent/divalent cation sieving selectivity and tripled water permeance in comparison with the pristine benchmark, as well as outstanding chemical and operational stability, circumventing the permeance/selectivity threshold. Experimental data in tandem with advanced molecular simulations confirm that the intriguing permselectivity springs from the intricacy of the porous and chemical structures of the CTHP- modulated PA layer, which not only investigates a substantial decrease in the effective mean pore size and narrows the size distribution but also affords a high positive charge density, significantly strengthening the size sieving and Donnan exclusion effects in nanofiltration. Coincidentally, the CTHP structures also provide preferential water binding sites with low energy barriers, energetically facilitating the accommodation and diffusion of water molecules in the PA layer, which eliminates the increased water transport resistance caused by pore size shrinkage. The developed synthetic engineering strategy sheds light on the rational design and fabrication of advanced polymer membranes for high- precision + +<--- Page Split ---> + +separations affiliated with water- energy nexuses. + +## Methods + +## Synthesis of DAT- \(\mathbf{NH}_2\) isomers + +3,5- Diamino- 4/1- (2- aminoethyl)- 1,2,4- triazole (DAT- \(\mathbf{NH}_2\) ) isomers were synthesized via a one- step quaternization reaction following the reaction path shown in Supplementary Fig. 1. In a typical synthesis, 4.24 g of 3,5- diamino- 1,2,4- triazole (DAT, 42.8 mmol) and 8.77 g of 2- bromoethylamine (42.8 mmol) were dissolved in 90 mL of N,N- dimethylformamide (DMF) in a 150 mL round- bottom flask. The flask with the reaction mixture was then heated to 45 °C in a water bath and reacted at this temperature for 24 h under vigorous stirring. A pale green solution rapidly formed with increasing reaction time. When the reaction was complete, the resulting mixture was immediately transferred into a 500 mL beaker, and 180 mL of acetonitrile was then added to obtain a milky white suspension. The obtained flocculent precipitates were subsequently redissolved in 10 mL of DMF and then precipitated with 180 mL of acetonitrile. The white solids were collected via high- speed centrifugation. The above dissolution and precipitation treatment was repeated three times. Finally, the as- synthesized DAT- \(\mathbf{NH}_2\) was vacuum dried at 40 °C overnight and then stored in a sealed container for subsequent characterization and membrane fabrication. Detailed information on chemicals can be found in the Supporting Information (Supplementary Texts 1). + +<--- Page Split ---> + +## Preparation of the PA NF membrane + +For the fabrication of the PA thin- film composite NF membrane, the polyether sulfone (PES) substrate was first immersed in an amine monomer aqueous solution with \(0.1 \mathrm{wt}\%\) sodium dodecyl sulfate (SDS) and \(0.1 \mathrm{wt}\% \mathrm{Na}_{2}\mathrm{CO}_{3}\) for \(5 \mathrm{min}\) . After the excess water on the top surface was removed via filter paper, the amine- monomer saturated PES substrate was sandwiched into a homemade frame with the top surface facing upward. Interfacial polymerization was initiated by carefully adding excessive \(0.3 \mathrm{wt}\% \mathrm{TMC}\) solution into the frame to cover the surface, which was allowed to react for \(1 \mathrm{min}\) . When the reaction was complete, the excess hexane solution was drained, and the resulting membrane was dried at \(60^{\circ}\mathrm{C}\) for \(30 \mathrm{min}\) . Specifically, DP- M represents a PA membrane that was prepared following the above synthesis procedure using a mixture of DAT- \(\mathrm{NH}_{2}\) ( \(0.06 \mathrm{wt}\%\) ) and PEI ( \(0.44 \mathrm{wt}\%\) ) as the amine monomer. The P- M benchmark membrane was fabricated solely by using PEI as the amine monomer. All the as- synthesized PA membranes were stored in deionized water at \(5^{\circ}\mathrm{C}\) for further characterization and performance tests. The detailed preparation process of the PA nanofilm is included in the Supporting Information (Supplementary Texts 2). + +## Characterization + +The successful synthesis of DAT- \(\mathrm{NH}_{2}\) isomers was confirmed by mass spectrometry (MS, MSQ Plus, USA) and high- performance liquid chromatography (HPLC, Ultimate 3000 RS, USA). The chemical structure of DAT- \(\mathrm{NH}_{2}\) was characterized by proton nuclear magnetic resonance ( \(^{1}\mathrm{H}\) NMR) + +<--- Page Split ---> + +spectroscopy (Bruker AVANCE AV400, USA). The chemical features of the PA membranes were also analyzed via Fourier transform infrared spectroscopy (FT- IR, Nicolet IN10, Thermo Fisher, USA) and X- ray photoelectron spectroscopy (XPS, Escalab 250Xi, Thermo Fisher, USA). The membrane surface morphology and roughness were examined via field emission scanning electron microscopy (FESEM, Quanta 250 FEG, FEI, USA) and atomic force microscopy (AFM, Nano Wizard 4, Bruker, Germany). The membrane cross- sectional morphology was identified via high- resolution transmission electron microscopy (TEM, FEI Tecnai G2 F30, FEI, USA). Surface hydrophilicity was assessed via water contact angle measurements on a contact angle goniometer (HARKE- SPCA, HARKE, China). The surface zeta potential was measured via a SurPASS electrokinetic analyzer (Anton Paar, GmbH, Austria). The molecular weight cutoff (MWCO) and pore size distribution of the membrane were obtained via solute retention tests using polyethylene glycol (PEG) probes with different molecular weights. The detailed procedures for each measurement are included in the Supporting Information (Supplementary Texts 3–4). + +## Nanofiltration performance tests + +The separation performance of the PA membranes was characterized in nanofiltration mode at \(23^{\circ}\mathrm{C}\) via a cross- flow filtration apparatus with an effective membrane filtration area of \(6.0 \mathrm{cm}^2\) . Before data collection, the membrane sample was conditioned at a pressure of 2 bar greater than the intended test pressure until the water flux stabilized. The pure water flux \((J_{\mathrm{w}}, \mathrm{L} \mathrm{m}^{- 2} \mathrm{h}^{- 1}\) , abbreviated as LMH) was measured using deionized water as the feed, and the water permeance + +<--- Page Split ---> + +553 (A, LMH/bar) was calculated via Eq. (1). + +\[\mathrm{A} = \frac{J_{w}}{\Delta P} = \frac{\Delta V}{\Delta t\times S\times\Delta P} \quad (1)\] + +555 where \(\Delta \mathrm{P}\) (bar) is the trans- membrane hydraulic pressure, \(\Delta V\) (L) is the volume of permeate water collected during a time interval of \(\Delta \mathrm{t}\) (h), and S ( \(\mathrm{m}^{2}\) ) is the effective membrane filtration area. + +559 The membrane ion sieving ability was examined via rejection tests that were conducted under various conditions using a wide spectrum of inorganic salts as solutes. Specifically, \(\mathrm{Na}_{2}\mathrm{SO}_{4}\) , \(\mathrm{Li}_{2}\mathrm{SO}_{4}\) , \(\mathrm{MgSO}_{4}\) , \(\mathrm{MgCl}_{2}\) , \(\mathrm{LiCl}\) , \(\mathrm{NaCl}\) , \(\mathrm{RbCl}\) , \(\mathrm{KCl}\) , \(\mathrm{NiCl}_{2}\) , \(\mathrm{CaCl}_{2}\) , and \(\mathrm{ZnCl}_{2}\) solutions with different concentrations and compositions were used as the feed. The single salt rejection (R, \(\%\) ) was calculated via Eq. (2). + +\[\mathrm{R} = \left(1 - \frac{C_{p}}{C_{f}}\right)\times 100\% \quad (2)\] + +565 where \(\mathrm{C}_{\mathrm{p}}\) and \(\mathrm{C}_{\mathrm{f}}\) are the salt contents of the permeate and feed, respectively. The salt concentration was determined via conductivity measurement via a SevenCompact™ S230 (Mettler Toledo) conductivity meter. Binary mixtures of \(\mathrm{MgCl}_{2}\) and \(\mathrm{LiCl}\) with different mass ratios were used to evaluate the membrane selectivity for coaction fractionation. The \(\mathrm{Li}^{+} / \mathrm{Mg}^{2 + }\) separation factor \((S_{Li^{+} / Mg^{2 + }})\) was calculated via Eq. (3). + +\[S_{Li^{+} / Mg^{2 + }} = \left(\frac{C_{f}M g^{2 + } / C_{f}L i^{+}}{C_{p}M g^{2 + } / C_{p}L i^{+}}\right) \quad (3)\] + +<--- Page Split ---> + +where \(C_{f Mg^{2 + }}\) and \(C_{f Li^{+}}\) and where \(C_{p Mg^{2 + }}\) and \(C_{p Li^{+}}\) represent the concentrations of \(\mathrm{Mg^{2 + }}\) and \(\mathrm{Li^{+}}\) in the feed and permeate, respectively. An inductively coupled plasma optical emission spectrometer (ICP- OES, iCAP 7000, Germany) was used to quantify the ion contents of the solution. Each data point was tested three times under the same conditions using randomly selected membrane samples, and the average value was reported. + +The long- term stability of the membrane was evaluated by monitoring the water flux and salt rejection for up to \(120\mathrm{h}\) at \(6\mathrm{bar}\) using \(1000\mathrm{ppm}\mathrm{MgCl}_2\) solution as the feed. The pH stability of the membrane was assessed by measuring the water flux and salt rejection in a feed pH range of \(1 - 13\) using \(1000\mathrm{ppm}\mathrm{LiCl}\) solution as the probe feed, during which the solution pH was adjusted via HCl and \(\mathrm{NaOH}\) . + +## Density functional theory (DFT) calculations and molecular dynamics (MD) simulations + +DFT atomistic calculations were conducted via ORCA quantum chemistry software (version 5.0.4) 43- 45. The binding energy and ion hydration energy were obtained via single- point energy calculations. Electrostatic potential (ESP) 46, 47, average local ionization energy (ALIE) 48, interaction region indicator (IRI) 42, independent gradient model based on Hirshfeld partition (IGMH) 49, and molecular polarity index (MPI) 50 analyses were performed with the Multiwfn software package to gain insights into molecular electronic properties and interaction patterns 51. LAMMPS and GROMACS were employed for MD simulations 52, 53, where LAMMPS was used + +<--- Page Split ---> + +to calculate the free volume fraction of the PA nanofilm, whereas GROMACS was applied to analyze the distribution and binding energy of water molecules around specific PA molecular segments. These simulations enabled a detailed exploration of water- PA interactions, which is crucial for understanding the hydration behavior and separation performance of the membrane. Comprehensive details of the computational methods and protocols are provided in Supplementary Text 5 and Text 6. + +## References + +1. Zhang, F., Fan, J.-b. & Wang, S. Interfacial Polymerization: From Chemistry to Functional Materials. Angewandte Chemie International Edition 59, 21840-21856 (2020). +2. Lu, X. & Elimelech, M. Fabrication of desalination membranes by interfacial polymerization: history, current efforts, and future directions. Chem. Soc. Rev. 50, 6290-6307 (2021). +3. 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Commun. 271, 108171 (2022). + +## Acknowledgments + +The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (22125603), Fundamental Research Funds for the Central Universities (nos. 040- 63243125 and 040- 63233061), and the National Key Research and Development Project (nos. 2023YFC3708003 and 2023YFC3708000). Special thanks are also made to the Han Gang Research Lab members for their helpful suggestions related to the characterization of materials. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupportinginformationNov112024. pdf + +<--- Page Split ---> diff --git a/preprint/preprint__07ed9c6d9e6ca0cab5b9b0180c58f6b6054610c90c12780b9cc4a7e3a9a2f0e1/preprint__07ed9c6d9e6ca0cab5b9b0180c58f6b6054610c90c12780b9cc4a7e3a9a2f0e1_det.mmd b/preprint/preprint__07ed9c6d9e6ca0cab5b9b0180c58f6b6054610c90c12780b9cc4a7e3a9a2f0e1/preprint__07ed9c6d9e6ca0cab5b9b0180c58f6b6054610c90c12780b9cc4a7e3a9a2f0e1_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..944050b38f840c3b59a9603b4f7272b2dfa558e3 --- /dev/null +++ b/preprint/preprint__07ed9c6d9e6ca0cab5b9b0180c58f6b6054610c90c12780b9cc4a7e3a9a2f0e1/preprint__07ed9c6d9e6ca0cab5b9b0180c58f6b6054610c90c12780b9cc4a7e3a9a2f0e1_det.mmd @@ -0,0 +1,592 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 866, 208]]<|/det|> +# Molecular Manipulation of Polyamide Nanostructures Reconciles the Permeance- Selectivity Threshold for Precise Ion Separation + +<|ref|>text<|/ref|><|det|>[[44, 230, 290, 276]]<|/det|> +Gang Han hangang@nankai.edu.cn + +<|ref|>text<|/ref|><|det|>[[44, 303, 570, 345]]<|/det|> +Nankai University https://orcid.org/0000- 0001- 8943- 569X Zhenxiang pan + +<|ref|>text<|/ref|><|det|>[[44, 349, 570, 368]]<|/det|> +Nankai university https://orcid.org/0000- 0001- 6818- 553X + +<|ref|>text<|/ref|><|det|>[[44, 373, 210, 412]]<|/det|> +Yalong Lei Nankai University + +<|ref|>text<|/ref|><|det|>[[44, 418, 210, 456]]<|/det|> +Tiange Yan Nankai University + +<|ref|>text<|/ref|><|det|>[[44, 463, 210, 501]]<|/det|> +Fuxin Zheng Nankai University + +<|ref|>text<|/ref|><|det|>[[44, 508, 210, 546]]<|/det|> +Yu Liao Nankai University + +<|ref|>text<|/ref|><|det|>[[44, 553, 210, 591]]<|/det|> +Jiang Zhan Nankai University + +<|ref|>text<|/ref|><|det|>[[44, 598, 210, 636]]<|/det|> +Tong Zhang Nankai University + +<|ref|>text<|/ref|><|det|>[[44, 643, 210, 661]]<|/det|> +LU SHAO + +<|ref|>text<|/ref|><|det|>[[50, 667, 678, 688]]<|/det|> +Harbin Institute of Technology https://orcid.org/0000- 0002- 4161- 3861 + +<|ref|>sub_title<|/ref|><|det|>[[44, 733, 103, 750]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 772, 827, 813]]<|/det|> +Keywords: Nanofiltration, Polyamide membrane, Interfacial polymerization, Ion separation, Permselectivity + +<|ref|>text<|/ref|><|det|>[[44, 832, 350, 850]]<|/det|> +Posted Date: December 17th, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 870, 475, 889]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 5431568/v1 + +<|ref|>text<|/ref|><|det|>[[44, 907, 914, 949]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 100, 926, 142]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on August 4th, 2025. See the published version at https://doi.org/10.1038/s41467-025-62376-8. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[70, 100, 883, 121]]<|/det|> +# Molecular Manipulation of Polyamide Nanostructures Reconciles the Permeance-Selectivity + +<|ref|>title<|/ref|><|det|>[[68, 142, 428, 162]]<|/det|> +# Threshold for Precise Ion Separation + +<|ref|>text<|/ref|><|det|>[[68, 180, 884, 202]]<|/det|> +Zhenxiang Pan a, Yalong Lei a, Tiange Yan a, Fuxin Zheng a, Yu Liao a, Jiang Zhan a, Tong Zhang + +<|ref|>text<|/ref|><|det|>[[68, 222, 345, 242]]<|/det|> +a, Lu Shao b, \\*, Gang Han a, \\* + +<|ref|>text<|/ref|><|det|>[[68, 264, 88, 279]]<|/det|> +5 + +<|ref|>text<|/ref|><|det|>[[68, 305, 884, 327]]<|/det|> +a College of Environmental Science and Engineering, Tianjin Key Laboratory of Environmental + +<|ref|>text<|/ref|><|det|>[[68, 346, 884, 368]]<|/det|> +Remediation and Pollution Control, Nankai University, 38 Tongyan Road, Tianjin, 300350, China + +<|ref|>text<|/ref|><|det|>[[68, 387, 884, 409]]<|/det|> +b State Key Laboratory of Urban Water Resource and Environment, School of Chemistry and + +<|ref|>text<|/ref|><|det|>[[68, 428, 666, 450]]<|/det|> +Chemical Engineering, Harbin Institute of Technology, Harbin, China + +<|ref|>text<|/ref|><|det|>[[68, 472, 88, 487]]<|/det|> +10 + +<|ref|>text<|/ref|><|det|>[[68, 513, 304, 533]]<|/det|> +\\* Corresponding author + +<|ref|>text<|/ref|><|det|>[[68, 553, 562, 574]]<|/det|> +Tel: +86 022- 23501117. Email: hangang@nankai.edu.cn + +<|ref|>text<|/ref|><|det|>[[68, 594, 510, 614]]<|/det|> +Tel: +86 13100870576. Email: shaolu@hit.edu.cn + +<|ref|>text<|/ref|><|det|>[[68, 636, 88, 651]]<|/det|> +14 + +<|ref|>sub_title<|/ref|><|det|>[[68, 677, 203, 697]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[66, 715, 884, 905]]<|/det|> +Membrane nanofiltration (NF) has emerged as a prominent energy- efficient separation technology for widespread applications related to the water- energy nexus. However, state- of- the- art polyamide (PA) NF membranes are markedly constrained by a ubiquitous, pernicious tradeoff between water permeance and selectivity. Leveraging the prestigious structure- determining performance rationale, this work conceives a facile and robust molecular engineering approach + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 98, 886, 660]]<|/det|> +that enables simultaneous improvements in water permeance and co- cation selectivity through synthetic molecular construction of a PA nanofilm with unique cationic triazolyl heterocyclic polyamide (CTHP) structures during scalable interfacial polymerization. Experimental data in conjunction with molecular simulations reveal that the CTHP structures instigate exquisite regulation of the PA subnanometer pore architecture and the specific binding affinity with water and ions, which not only affords precise ion sieving ability and advanced Donnan exclusion selectivity but also energetically facilitates the partitioning and transport of water molecules. The exemplified PA membrane exhibits unparalleled divalent cation rejections of over \(99\%\) , accompanied by a 9- fold increase in monovalent/divalent cation sieving selectivity, which is substantially greater than that of the pristine benchmark, a superior water permeation rate, and excellent chemical and operational stability, circumventing the permeance/selectivity threshold. We believe that the molecular engineering strategy implemented in this work holds broad prospects for the rational design and fabrication of semipermeable polymeric NF membranes for sustainable and precision separations. + +<|ref|>sub_title<|/ref|><|det|>[[115, 678, 217, 698]]<|/det|> +## Keywords + +<|ref|>text<|/ref|><|det|>[[112, 717, 880, 738]]<|/det|> +Nanofiltration; Polyamide membrane; Interfacial polymerization; Ion separation; Permselectivity + +<|ref|>sub_title<|/ref|><|det|>[[115, 802, 243, 821]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[112, 841, 883, 901]]<|/det|> +Precision discrimination of target ions and molecules from complex aqueous mixtures of similar species remains a superior challenge in widespread applications such as water, clean energy, and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 98, 886, 410]]<|/det|> +resource reclamation \(^{1 - 3}\) . Membrane nanofiltration (NF), featuring phase- free conversion separation, has evolved into a premier tool for sustainable water separation because of its high energy efficiency, low carbon footprint, compact design, and manufacturing scalability \(^{4,5}\) . The rapid dissemination of NF technology relies on high- performance membranes that ideally have high values of both water permeance and selectivity to fully exploit the prominent process advantages, but such a combination is exceedingly difficult to achieve, particularly for polymeric membranes, as the material properties that affect solute transport would, in turn, affect water permeation \(^{6 - 8}\) . + +<|ref|>text<|/ref|><|det|>[[110, 470, 886, 862]]<|/det|> +Polyamide (PA) thin- film composite membranes are state- of- the- art NF membranes that are particularly attractive for water filtration in practical modules across all scales \(^{9 - 12}\) . However, the deleterious tradeoff between water permeance and membrane selectivity consistently poses a stumbling block for further advancing their performance, where increasing water permeance is inevitably accompanied by a diminished ability to selectively reject solutes \(^{13,14}\) . According to the prevailing membrane separation mechanisms, effective strategies for rationally regulating mass transfer across PA membranes hinge on well- defined pore sizes with a narrow size distribution, properly tuned interactions between PA and the permeants of interest, and a thin PA selective layer \(^{15 - 17}\) . Innovative materials and fabrication methods that can precisely regulate PA chemistry and nanostructures have therefore become essential pursuits of academic research. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 100, 888, 904]]<|/det|> +A prevalent approach that has been widely adopted to increase the permselectivity of PA membranes toward charged species involves tuning the surface charges to strengthen electrostatic exclusion effects via in situ and/or post- synthetic modifications \(^{18, 19}\) . However, most of the approaches reported thus far have focused primarily on promoting solute rejection and selectivity rather than overcoming the permeance/selectivity tradeoff threshold \(^{20, 21}\) . Recently, superior size sieving ability and co- cation selectivity have been achieved by trailblazing studies that use interfacial modulators to narrow the pore size distribution of the PA layer \(^{22- 25}\) . Unfortunately, a significant decrease in water permeance is usually accompanied by a concomitant increase in water transport resistance \(^{23, 24}\) . Many studies have focused on exploring advanced membrane materials ranging from biological ion channels and aquaporins to emerging microporous materials of zeolites, metal- organic frameworks, covalent organic frameworks, macrocycles, and porous organic cages, some of which achieve superior permselectivity with unusual combinations of high permeance and selectivity \(^{26- 28}\) . Although the practicality of these intriguing materials is markedly restricted by many daunting limitations that vary from inherent low structural stability to inferior material availability and the feasibility of membrane fabrication on a large scale, their unique structural features underscore the importance of well- defined pore sizes and exquisitely regulated mutual interactions in achieving exceptional molecular sieving capabilities and water transport rates \(^{29- 31}\) . Hereupon, we believe that multifunctional monomers with synthetically engineered chemistry that enable the formation of a novel PA structure that not only imparts small pores with a narrow size distribution but also provides low resistance for water transport are likely to achieve disruptive + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 100, 884, 202]]<|/det|> +improvements in both water permeance and solute selectivity, successfully overcoming the formidable tradeoff in PA membranes. Unfortunately, there is currently a lack of rational material design and feasible membrane fabrication strategies to accomplish this arduous undertaking. + +<|ref|>text<|/ref|><|det|>[[110, 264, 884, 904]]<|/det|> +Herein, we demonstrate a facile and robust molecular engineering approach for precise regulation of the mass transfer behavior of PA membranes to circumvent the deleterious permeance/selectivity tradeoff in ion differentiation. Our strategy is contingent on molecular-level control over the nanoporous structure of the PA nanofilm and its interactions with water and ions via in situ construction of cationic triazolyl heterocyclic polyamide (CTHP) structures during interfacial polymerization (IP) via synthetic triamino quaternary triazole ammonium (DAT- NH2) isomers. Experimental data and molecular simulations revealed that the CTHP structures endow the PA layer with well- defined subnanometer pores with a narrow size distribution and abundant positive charge but low intrinsic water transport resistance, which synergistically enhances steric hindrance sieving and Donnan exclusion and facilitates the permeation of water. The advantages of this molecularly engineered PA structure were demonstrated by its superior performance within precise ion separation (Fig. 1a), achieving a 9- fold increase in monovalent/divalent cation selectivity with a tripled water flux relative to the benchmark, reconciling the tradeoff threshold. In light of the diverse array of monomer chemistries, the implemented molecular design strategy provides a gateway to advance the rational design and fabrication of PA membranes with superior permselectivity for precise ion separations toward clean water and renewable energy. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 142, 340, 163]]<|/det|> +## Results and Discussion + +<|ref|>sub_title<|/ref|><|det|>[[113, 183, 706, 203]]<|/det|> +## Synthesis of the DAT- \(\mathbf{NH}_2\) monomer and fabrication of PA membranes + +<|ref|>text<|/ref|><|det|>[[111, 222, 886, 700]]<|/det|> +Fig. 1b shows the conceptual diagram of the ideal PA structure we intend to construct to circumvent the permeance/selectivity tradeoff threshold in precision nanofiltration (NF). Specifically, the PA layer is synthetically designed with well- defined permeate- PA binding affinity and profound steric sieving selectivity imparted by pore size confinement to efficiently manipulate the enthalpy and entropy barriers for water and solute transport \(^{32,33}\) . To realize this design strategy, we molecularly constructed a multifunctional structure with primary amine dangling and a highly polarized triazolyl heterocyclic core bearing quaternary ammonium (i.e., DAT- \(\mathbf{NH}_2\) , Fig. 1c). Our pursuit of this molecular structure was inspired by these trailblazing studies showing that the triazole derivative heterocyclic moieties may provide preferential water transport paths with a low energy barrier \(^{34}\) , whereas the amine groups concurrently provide highly reactive sites to crosslink with trimesoyl chloride (TMC) to form a PA nanofilm with superior hydrophilicity and interconnected positively charged subnanometer pores. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 99, 880, 660]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 677, 884, 700]]<|/det|> +
Fig. 1 | Synthetic engineering of the PA molecular structure with rationally designed
+ +<|ref|>text<|/ref|><|det|>[[110, 718, 886, 905]]<|/det|> +monomers. a, Working principle of precision co- ion separation via nanofiltration. b, Schematic diagram of the interconnected subnanometer- sized pores in a desired PA nanofilm with high selectivity and low water transport resistance, and three- dimensional view of an amorphous cell of the PA (cell size: \(65 \times 65 \times 65 \mathring{\mathrm{A}}^3\) ). c, Synthetic reaction formula of DAT- NH₂ isomers and the visualized conformation of their atomic electrostatic potential. d, \(^1\mathrm{H}\) NMR spectra and liquid + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 100, 884, 369]]<|/det|> +chromatography (upper left inset) of DAT- NH2 isomers. e, Schematic illustration of the interfacial polymerization between DAT- NH2/PEI and TMC at the water- hexane interface to form a PA nanofilm. f, Molecular structure of the DP- M PA nanofilm (left) and its corresponding chain structure derived from an amorphous cell generated by molecular dynamic (MD) simulations. g, FT- IR spectra of the DP- M and P- M PA nanofilms. h, N 1s XPS spectra of the DP- M PA nanofilm. The N1s core level spectrum was deconvoluted into three components located at 399.7, 400.4, and 401.7 eV corresponding to N- (C=O)- , N(H)- C- , and N+- C- , respectively. + +<|ref|>text<|/ref|><|det|>[[111, 430, 884, 904]]<|/det|> +The molecularly designed DAT- NH2 was synthesized via a one- pot quaternization reaction between 3,5- diamino- 1,2,4- triazole and 2- bromoethylamine in DMF and then purified by nonsolvent precipitation (Fig. 1c and Supplementary Fig. 1). Intriguingly, liquid chromatography- mass spectrometry (LC- MS) data reveals that isomers of 3,5- diamino- 4/1- (2- aminoethyl)- 1,2,4- triazole were formed during the reaction, where two distinct peaks with almost the same intensity and area ratio are observed in the LC chromatogram pattern (Fig. 1d), and two peaks at m/z = 143 show up in MS (Supplementary Fig. 2), which is in good agreement with the chemical structures of the DAT- NH2 isomers (C4N6H11+, Mw = 143). Proton nuclear magnetic resonance (1H NMR) spectra corroborate these results, where four 1H NMR peaks corresponding to the two types of protons in each isomer are spotted at 3.11 (labeled H I), 3.30 (labeled H I), 3.35 (labeled H II), and 3.99 (labeled H II) ppm. The area ratios of the two peaks (H I/H II) in the 1H NMR spectrum were measured to be 0.88 and 1.04 for the isomers, which is in close proximity to the theoretically + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 100, 884, 286]]<|/det|> +expected values based on the DAT- NH₂ chemical structure. The visualized atomic electrostatic potential image of DAT- NH₂ intuitively proclaims the positive charge characteristics of the triazole ring, and quantitative analysis of the molecular van der Waals surface electrostatic potential (ESP) shows that the distribution area and intensity of the positive ESP of the DAT- NH₂ isomers are greater than the negative values (Fig. 1c and Supplementary Fig. 3). + +<|ref|>text<|/ref|><|det|>[[111, 346, 884, 904]]<|/det|> +A self- sustaining PA thin film with good stability immediately formed when DAT- NH₂ was brought in contact with TMC at the water/hexane interface (Supplementary Fig. 4), indicating a high polymerization rate between DAT- NH₂ and TMC. DFT calculations further confirmed the high nucleophilic substitution reactivity of the amino groups on DAT- NH₂ toward acyl chloride (Supplementary Figs. 5 and 6). Comprehensive ESP and average local ionization energy (ALIE) analyses reveal that the backbone and chemical functional moieties of the PA structures formed by the two isomers with TMC are almost identical (Supplementary Fig. 7). Therefore, the DAT- NH₂ isomers were directly used for PA membrane preparation without further purification. A continuous PA selective layer can also be synthesized via a similar scalable interfacial polymerization (IP) procedure on top of a polyethersulfone (PES) substrate to prepare robust PA membranes for NF tests (Supplementary Fig. 8). Unfortunately, we observed that the obtained DAT- NH₂/TMC PA membrane experienced severe water swelling and thus relatively low permselectivity were obtained (Supplementary Fig. 9), likely owing to the superior hydrophilic nature of the cationic triazolyl heterocyclic structures. We thereby further modified the PA chemical structure by using + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 100, 886, 905]]<|/det|> +PEI as a comonomer during IP to increase the membrane stability and separation performance (Fig. 1e,f). PEI is a benchmark monomer that is widely used for the synthesis of positively charged PA NF membranes (Supplementary Fig. 10). Synthesis condition optimization experiments confirmed that the DP- M membrane fabricated with a 0.06 wt% DAT- NH₂ shows an optimum combination of water permeance and ion selectivity and excellent robustness (Supplementary Figs. 11 and 12), substantially exceeding that of the PA membrane formed solely by PEI (denoted as the P- M benchmark). The FT- IR peaks observed at 1621 cm⁻¹, 1806 cm⁻¹, and 1705 cm⁻¹ are associated with the amide I band, which arises from the stretching vibration of C=O and the coupling with the bending of N- H in subtly different chemical environments (Fig. 1g and Supplementary Fig. 13), validating the formation of polyamide structure during IP. The quaternary ammonium peak at 401.8 eV in the XPS spectrum of DP- M (Fig. 1h and Supplementary Figs. 14 and 15) confirms the presence of DAT- NH₂ moieties in the PA layer. It is noteworthy that a significant decline in the O/N ratio was observed by DP- M compared with that of the P- M benchmark, where the O/N ratio decreases from \(\sim 1.50\) to 0.96 (Supplementary Table 1), suggesting a substantial increase in the PA crosslinking degree of DP- M. The elevated crosslinking degree constricts the space between the stacked polymer chains, thus diminishing the pore sizes and augmenting the mechanical strength of the PA film (Supplementary Fig. 12). According to the chemical characterization and molecular simulations, the DAT- NH₂ modulated PA nanofilm of DP- M has a semirigid 3D polyamide network with a large amount of intrinsic positive charges and smaller chain space compared to the P- M benchmark (Fig. 1f and Supplementary Figs. 16 and 17). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[112, 143, 670, 163]]<|/det|> +## Morphological and structural analysis of the DP-M PA membrane + +<|ref|>text<|/ref|><|det|>[[110, 179, 886, 900]]<|/det|> +Field emission scanning electron microscopy (FESEM) images corroborate the uniformity and integrity of the formed PA thin layer at the macroscopic scale (Fig. 2a,d and Supplementary Fig. 18). At a finer scale, the PA layer of DP- M appears a smooth and compact surface, whereas the counterpart of the P- M benchmark shows a crumpled surface with numerous ridged wrinkles unequivocally seen on top. This surface morphological discrepancy was further manifested by the atomic force microscopy (AFM) data, where the surface roughness of DP- M ( \(\mathrm{Rq} = 3.44 \mathrm{nm}\) ) is distinctly lower than that of P- M ( \(\mathrm{Rq} = 9.09 \mathrm{nm}\) ) (Fig. 2b,e and Supplementary Fig. 19). A smooth surface is conducive to alleviating the fouling tendency. Cross- sectional transmission electron microscopy (TEM) images showcase that DP- M has an extremely low PA thickness of \(59 \pm 2 \mathrm{nm}\) (Fig. 2c,f and Supplementary Fig. 20), which is much thinner than that of the P- M benchmark (i.e., \(88 \pm 2 \mathrm{nm}\) ). The significantly reduced PA thickness might be attributed to the rapid formation of a relatively dense nascent PA film mediated by DAT- \(\mathrm{NH}_2\) at an initial stage of IP (Supplementary Fig. 21), which stymies the diffusion of aqueous monomers at the interface and thus suppresses the subsequent growth of the PA layer \(^{35,36}\) . On the other hand, the positively charged structure of DAT- \(\mathrm{NH}_2\) may slow down the diffusion of PEI towards the interface via H- bonding interactions \(^{37- 41}\) . In the context of membrane filtration, a thinner PA selective layer spontaneously confers shorter transport pathways and lower water penetration resistance, which is favorable for achieving high water permeance. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 135, 875, 644]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 658, 883, 680]]<|/det|> +
Fig. 2 | Morphological and structural features of PA membranes. a,d, Surface FESEM images.
+ +<|ref|>text<|/ref|><|det|>[[111, 699, 884, 883]]<|/det|> +b,e, 2D and 3D AFM images. c,f, Cross-sectional TEM images. Top: P-M. Bottom: DP-M. g, Pore diameter distribution of PA membranes obtained by PEG rejection tests. h, Molecular dynamics (MD) simulations of the fractional free volume (FFV) of PA layers (left). The dark blue and gray colors represent the voids between the polymer chains and the space occupied by the polymer skeleton, respectively. Representative porous molecular structures of the DP-M and P-M PA + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 100, 884, 245]]<|/det|> +networks (right). i, MD simulations of the pore diameter distribution of the PA nanofilms. j, Zeta potential as a function of pH. k, Summary of the MWCO, water contact angle (CA), root mean square roughness (Rq), FFV, polyamide layer thickness, and zeta potential (ZP) at \(\mathrm{pH} = 6\) for DP- M and P- M. + +<|ref|>text<|/ref|><|det|>[[110, 303, 886, 910]]<|/det|> +The rejection tests with neutral solutes indicate that DP- M has a molecular weight cutoff (MWCO) of 245 Da, almost two times smaller than that of the P- M benchmark (MWCO = 479 Da, Supplementary Fig. 22). Correspondingly, a small effective mean pore diameter of 2.8 Å accompanied by a narrow size distribution is achieved by DP- M (Fig. 2g), whereas P- M shows a relatively larger mean pore size of 3.1 Å and broader pore size distribution, in accordance with our design strategy and the XPS results (Fig. 1b,h). MD simulations were performed to construct realistic structural models via simulated polymeric algorithms to glean molecular- level insights into the porous structure of the PA layer. As shown in Fig. 2h, the fractional free volumes (FFVs) of DP- M and P- M PA layers are approximately \(10.3\%\) and \(21.6\%\) , respectively. Moreover, the pore diameter analyses conducted via MD molecular simulations substantiate that most of the pores inside the DP- M PA layer are approximately \(2.75\) Å in length, which is significantly smaller than that of the P- M benchmark (i.e., \(3.38\) Å). Further analyses of the interior cavity diameters disclose a narrower range of pore sizes within DP- M (Fig. 2i and Supplementary Fig. 23), signifying the compact structure of the PA mediated by DAT- NH₂ (Fig. 1c). Notably, the microscopic pore features derived from molecular simulations coincide well with those experimentally obtained + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 98, 886, 579]]<|/det|> +from neutral solute rejection tests (Fig. 2g,i). The tight nanostructure of DP- M constricts membrane pores to dimensions more favorable for size sieving, with precise ionic and molecular sieving capabilities and a threshold of 2.8 Å. Furthermore, aligning with the chemical features of the CTHP structures in DP- M (Fig. 1g), the cationic DAT- NH₂ moieties adequately elevate the membrane hydrophilicity and positive charge density, as manifested by its smaller surface water contact angle (CA) and higher zeta potential (ZP) than that of the P- M benchmark (Supplementary Fig. 23 and Fig. 2j). In the realm of NF applications, the enhanced hydrophilicity facilitates surface partitioning and interior diffusion of water molecules, whereas the ameliorated positive charge density reinforces the electrostatic repulsion selectivity. Collectively, the advanced membrane characteristics gained by DP- M resonate with our intended design strategy illustrated in Fig. 1b, which underpins the significance of synthetic molecular engineering in precisely regulating the nanoporous structure and chemical features of the PA selective layer (Fig. 2k). + +<|ref|>sub_title<|/ref|><|det|>[[113, 636, 673, 656]]<|/det|> +## Simultaneous improvement in water permeance and ion selectivity + +<|ref|>text<|/ref|><|det|>[[111, 675, 886, 904]]<|/det|> +The well- defined subnanometer pores with a sharped size distribution and the inherent positive charges of the DP- M membrane would afford prominent molecular sieving and electrostatic repulsion selectivity in sustainable NF applications. We subsequently examined the mass transport behavior of a wide spectrum of inorganic salts through DP- M using a crossflow filtration system. In contrast to the acquiescent expectation that a decrease in the membrane pore size along with a downscaled FFV generally accompanied by a concomitant reduction in the water permeation rate, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 100, 884, 326]]<|/det|> +a substantial increase in the water permeance was achieved by DP- M, where the water permeation flux of DP- M is almost three times greater than that of the P- M benchmark at the same pressure (Fig. 3a), corresponding to an approximately 3- fold increase in the pure water permeance (PWP). The incongruence between the enhanced water permeance and the reduced pore sizes and FFVs likely stems from the molecularly constructed CTHP structures and the low thickness of the PA layer, which provide facilitated water transport pathways with low resistance. + +<|ref|>text<|/ref|><|det|>[[111, 388, 885, 905]]<|/det|> +At the same time, DP- M shows a sharp size- exclusion cutoff of \(\sim 2.5 \mathrm{\AA}\) in the Stokes radius of cations (Fig. 3b), adhering to its densified catatonic PA molecular structure, which thereby facilitates the transport of smaller monovalent cations (i.e., \(\mathrm{Rb^{+}}\) , \(\mathrm{K^{+}}\) , \(\mathrm{Na^{+}}\) , and \(\mathrm{Li^{+}}\) ) while sufficiently blocking larger divalent cations (i.e., \(\mathrm{Ni^{2 + }}\) , \(\mathrm{Ca^{2 + }}\) , \(\mathrm{Mg^{2 + }}\) , and \(\mathrm{Zn^{2 + }}\) ), with rejection rates greater than \(98.0\%\) and high water flux of \(50 \mathrm{L} \mathrm{m}^{- 2} \mathrm{h}^{- 1}\) (LMH). Notably, judiciously high rejections of up to \(99.1\%\) towards \(\mathrm{MgCl_{2}}\) and \(\mathrm{MgSO_{4}}\) were specifically achieved by DP- M (Fig. 3c), exceeding most of the state- of- the- art NF membranes, and similar rejections and water permeance were maintained over a wide pressure range of 2–16 bar (Fig. 3d). In contrast, \(\mathrm{LiCl}\) rejection monolithically increases from \(45.7\%\) to \(81.2\%\) when the pressure is raised from 2 to 16 bar (Supplementary Fig. 24). As a result, an ideal \(\mathrm{Li^{+} / Mg^{2 + }}\) selectivity of \(35.8–42.9\) was obtained on the basis of single salt rejections (Supplementary Fig. 25), demonstrating its promising capability for precise cation screening. In the same vein, the P- M benchmark is inferior in terms of both salt rejection and cation differentiation selectivity (Fig. 3b and Supplementary Fig. 26). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 135, 884, 585]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 595, 884, 616]]<|/det|> +
Fig. 3 | Ultrafast and precision co-ion separation via a low-pressure NF. a, Pure water flux
+ +<|ref|>text<|/ref|><|det|>[[111, 635, 884, 657]]<|/det|> +(PWF) of DP- M and P- M at different operation pressures. b, Water flux and ion rejections of DP- + +<|ref|>text<|/ref|><|det|>[[111, 677, 884, 698]]<|/det|> +M for filtrating different cation solutions. (Feed salt concentration: 1000 ppm; test pressure: 6.0 + +<|ref|>text<|/ref|><|det|>[[111, 718, 884, 739]]<|/det|> +bar). c, MgSO4 and MgCl2 rejections of P- M and DP- M (feed salt concentration: 1000 ppm, test + +<|ref|>text<|/ref|><|det|>[[111, 760, 884, 781]]<|/det|> +pressure: 6.0 bar). d, Effect of operation pressure on the water permeance and MgCl2 rejection of + +<|ref|>text<|/ref|><|det|>[[111, 801, 884, 822]]<|/det|> +DP- M (feed: 1000 ppm MgCl2). e, Effect of pH on the water flux and LiCl rejection of DP- M + +<|ref|>text<|/ref|><|det|>[[111, 842, 884, 863]]<|/det|> +(feed: 1000 ppm LiCl, test pressure: 6.0 bar). f, Effect of operation time on the water flux and + +<|ref|>text<|/ref|><|det|>[[111, 883, 884, 904]]<|/det|> +MgCl2 rejection of DP- M (feed: 1000 ppm MgCl2, test pressure: 6.0 bar). g, Effect of operation + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 100, 884, 328]]<|/det|> +pressure on the water permeance and \(\mathrm{S_{Li + / Mg2 + }}\) of DP- M (feed: 2000 ppm binary mixture of \(\mathrm{MgCl_2}\) and \(\mathrm{LiCl}\) with a \(\mathrm{MgCl_2 / LiCl}\) mass ratio of 20). \(\mathbf{h}\) , Effects of the \(\mathrm{Mg^{2 + } / Li^{+}}\) ratio on the water flux and \(\mathrm{S_{Li + / Mg2 + }}\) ratio of DP- M (feed: 2000 ppm binary mixture of \(\mathrm{MgCl_2}\) and \(\mathrm{LiCl}\) with various \(\mathrm{MgCl_2 / LiCl}\) mass ratios; test pressure: 6.0 bar). i, Performance comparison of DP- M with other reported state- of- the- art PA NF membranes operated under cross- flow nanofiltration. The corresponding references for the data points in (i) are specified in Supplementary Table 2. + +<|ref|>text<|/ref|><|det|>[[111, 390, 886, 905]]<|/det|> +The separation performances of conventional NF membranes are generally susceptible to the feed salt concentration and pH due to the electrostatic screening effects. Interestingly, DP- M consistently retains its water flux, salt rejection, and co- cation selectivity across a wide range of feed salt contents and pH values. As demonstrated by the cycling performance tests, DP- M maintains stable \(\mathrm{MgCl_2}\) rejections of over \(95.8\%\) and relatively low \(\mathrm{LiCl}\) retentions of less than \(59.8\%\) when the feed salt content spanning from 1000 to 7000 mg/L, and the rejections recover to the initial values of the test (Supplementary Fig. 27), substantiating its strong electrostatic shielding resistance toward high ionic strength. There were also no obvious deteriorations in salt rejections and water flux as the feed pH escalated from 1 to 13 (Fig. 3e), underscoring the ability of DP- M to maintain excellent separation performance in both acidic and alkaline environments. The superior pH and salinity stabilities of DP- M are consistent with its highly ionizable cationic PA structure and outstanding size- sieving ability imparted by the well- defined pore sizes. Moreover, DP- M shows excellent structural durability and stability throughout long- term filtration + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 100, 884, 160]]<|/det|> +for \(120\mathrm{h}\) (Fig. 3f), where \(\mathrm{MgCl}_2\) rejection consistently surpasses \(99\%\) , with a stable water flux of \(\sim 51\) LMH being retained. + +<|ref|>text<|/ref|><|det|>[[111, 220, 884, 920]]<|/det|> +The co- cation sieving ability of DP- M was further illuminated by binary salt filtration tests using a mixture of \(\mathrm{LiCl}\) and \(\mathrm{MgCl}_2\) as the probe feed. Similar to the single- salt tests (Supplementary Fig. 25), DP- M shows high \(\mathrm{Li^{+} / Mg^{2 + }}\) co- cation selectivity of greater than 39.0 \((\mathrm{S}_{\mathrm{Li^{+} / Mg2 + }})\) for binary mixtures at different operation pressures (Fig. 3g), which signifies a 9- fold greater magnitude than that achieved by the P- M benchmark \((\mathrm{S}_{\mathrm{Li^{+} / Mg2 + }} = 4.3)\) , accompanied by an approximately 2.5- fold increase in water permeance. The persistently high rejections toward divalent cations and co- cation selectivity are presumably ascribed to the advanced molecular sieving and electrostatic repulsion effects afforded by the exquisitely regulated subnanometer pores and inherent positive charges of DP- M. Furthermore, slight fluctuations in the co- cation selectivity are observed when the feed \(\mathrm{Mg^{2 + } / Li^{+}}\) mass ratio alters from 1–120, where the \(\mathrm{S}_{\mathrm{Li^{+} / Mg2 + }}\) oscillates between 33.5 and 44.3 and the water flux is consistently higher than 51.4 LMH (Fig. 3h). Compared with other reported PA NF membranes with similar chemical and structural properties, DM- P exhibits upper- level water permeance and co- cation selectivity (i.e., \(\mathrm{Li^{+} / Mg^{2 + }}\) ) (Fig. 3i). The successful breakthrough of the permeance/selectivity tradeoff underpins our membrane design strategy illustrated in Fig. 1b and exemplifies the great feasibility of synthetic molecular engineering in rational membrane design. The excelled water permeation rate and co- ion screening capability in tandem with the scalable fabrication bolster the remarkable potential of the implemented strategy for developing effective + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 100, 884, 161]]<|/det|> +NF membranes for widespread applications pertaining to wastewater treatment, lithium extraction, recycling, and removal of heavy metal ions, in line with sustainable development. + +<|ref|>title<|/ref|><|det|>[[112, 225, 864, 245]]<|/det|> +# Regulatory mechanisms of facilitated water permeation and superior co-cation selectivity + +<|ref|>text<|/ref|><|det|>[[111, 264, 886, 912]]<|/det|> +Given an average pore diameter of \(\sim 2.75 \mathrm{\AA}\) with narrow size distribution and the strong intrinsic positive charge of DP- M (Fig. 2k), we speculate that its superior rejection of divalent cations and the co- cation sieving ability lean upon the on- demand tuning of PA chemistry and nanoporous structure according to the size and valence differences between cations, which instigates unusual differences in energy barriers that cations need to overcome for dissolution and diffusion. To gain fundamental insights into the underlying mechanisms responsible for the intriguing mass transport behavior of DP- M, dynamic molecular simulations were performed to correlate the separation performance with the membrane chemical structure. The simulations initiated with DFT calculations to illuminate the mutual interactions between PA and cations by performing configuration optimization and cation- PA binding energy calculations (Supplementary Table 3). As displayed in Fig. 4a, the negative binding energies of hexahydrated \(\mathrm{Mg}^{2 + }\) with the DP- M PA fragments (i.e., \(- 12.03\) and \(- 18.93 \mathrm{kcal / mol}\) ) are consistently lower than those with the P- M fragments ( \(- 22.17\) and \(- 26.01 \mathrm{kcal / mol}\) ), suggesting that the binding interactions between hexahydrated \(\mathrm{Mg}^{2 + }\) and P- M are relatively more stable. In other words, hexahydrated \(\mathrm{Mg}^{2 + }\) has a greater energy barrier towards DP- M than P- M, which signifies that it is more difficult for \(\mathrm{Mg}^{2 + }\) to pass through DP- M. On the contrary, the binding energy between hydrated \(\mathrm{Li}^{+}\) and the DP- M + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 98, 884, 370]]<|/det|> +fragments (- 28.83 kcal/mol) is close to that between it and the P- M fragments (- 29.65 kcal/mol) (Fig. 4b), suggesting that the transport of hydrated \(\mathrm{Li}^{+}\) through DP- M and P- M nearly remains energetically unchanged even though the pore sizes of DP- M are substantially diminished and narrowed. However, the binding energy gaps between \(\mathrm{Li}^{+}\) and \(\mathrm{Mg}^{2 + }\) in the DP- M fragments are 16.80 and 9.90 kcal/mol, respectively, which are markedly larger than those in the P- M fragments (i.e., 2.82 and 6.66 kcal/mol) (Fig. 4b), implying that DP- M has an overwhelming advantage over P- M to differentiate \(\mathrm{Li}^{+}\) and \(\mathrm{Mg}^{2 + }\) from the perspective of energy barrier. + +<|ref|>text<|/ref|><|det|>[[111, 429, 886, 905]]<|/det|> +The interaction region indicator (IRI) was subsequently applied to perform an in- depth analysis of the specific type of interactions between the PA fragments and hexahydrated \(\mathrm{Mg}^{2 + }\) . As a slight modification of the reduced density gradient (RDG), IRI (IRI(l) = \(|\nabla \rho (\mathrm{l})| / [\rho (\mathrm{l})^{\mathrm{a}}])\) can effectively manifest the chemical bonding and weak interaction regions \(^{42}\) . IRI visual representations of these two binding configurations and the PA molecular fragments of DP- M and P- M were constructed (Supplementary Fig. 28). The corresponding scatter plots reveal that the interaction forces involved are intricate and hard to distinguish (Supplementary Fig. 29). Therefore, the hydration layer of \(\mathrm{Mg}^{2 + }\) and the influence of TMC were shielded to better disclose the contributions of DAT- \(\mathrm{NH}_{2}\) moieties in the PA (Fig. 4c). Examining their respective IRI scatter plots, eminent peaks appear near \(\mathrm{sign}(\mathrm{l}_{2})\mathrm{r}\) values of \(- 0.05\) and 0.06 a.u. in the DP- M PA molecular fragments. From the electron density point of view, the peak at \(- 0.05\) a.u. corresponds to a weak interaction of higher strength, whereas the peak at 0.06 a.u. stems from a stronger spatial repulsion. Notably, the scatter + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 100, 886, 617]]<|/det|> +plot of the weak interaction portion ascertains an anomalous peak at approximately 0.013 a.u. in DP- M (Supplementary Fig. 30), indicating that the CTHP structures derived from DAT- NH2 may generate proprietary steric hindrance at the molecular level. The two distinct peaks in the scatter plot obtained from interaction decomposition confirm that CTHP instigates both attractive and repulsive interactions (Fig. 4d). Further analysis of the isosurface of the IRI visualization reveals that the peak near \(- 0.05\) a.u. is attributed to intramolecular H- bonding interactions. These H- bonds account for the in situ formation of the cyclic conformations in the CTHP structures (Fig. 4d), which dictate additional steric hindrance at approximately 0.013 a.u. (the peak near 0.013 is retained in Supplementary Fig. 31). Moreover, the peak at approximately 0.06 a.u. is associated with the strong repulsion induced by the overlap of the triazole rings in CTHP driven by van der Waals surfaces. Other than the narrowed subnanometer pore sizes, these anomalous intramolecular H- bonding structures provide additional steric hindrance at the molecular level, further amplifying the energy barrier of permeation acting on divalent cations. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[112, 95, 875, 707]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[112, 718, 883, 738]]<|/det|> +
Fig. 4 | DFT atomistic simulations of the mutual interactions between PA and cations within
+ +<|ref|>text<|/ref|><|det|>[[110, 757, 884, 905]]<|/det|> +NF. a, Binding energies of hydrated \(\mathrm{Mg^{2 + }}\) to the PA molecular fragments of DP- M and P- M (for each membrane, two binding energies were calculated by positioning the hydrated \(\mathrm{Mg^{2 + }}\) at two representative binding sites of the PA fragments). b, The binding energies between the hydrated \(\mathrm{Li^{+} / Mg^{2 + }}\) and the PA molecular fragments of P- M/DP- M. The numbers represent the calculated + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 100, 886, 410]]<|/det|> +energy gaps between \(\mathrm{Li^{+}}\) and \(\mathrm{Mg^{2 + }}\) in P- M and DP- M. c, Interaction region indicator analysis of PA fragments interacting with \(\mathrm{Mg^{2 + }}\) . The effects of the hydration layer and TMC were shielded (the unit a.u. here represents energy, and 1 a.u. is approximately 27.21 eV). d, Interaction region indicator analysis of the PA fragments derived from DAT- \(\mathrm{NH_2}\) and the visualized structure diagram. e, The independent gradient model based on Hirshfeld partition (IGMH) was used to analyze the binding configurations between P- M/DP- M molecular fragments and hydrated \(\mathrm{Mg^{2 + }}\) and \(\mathrm{Li^{+}}\) . f, Electrostatic potential analysis of the PA molecular fragments. g, van der Waals surface area ratio corresponding to various electrostatic potential intervals of the PA molecular fragments. + +<|ref|>text<|/ref|><|det|>[[110, 469, 886, 904]]<|/det|> +The interactions of P- M and DP- M fragments with hydrated \(\mathrm{Li^{+}}\) and \(\mathrm{Mg^{2 + }}\) ions were also visualized using the independent gradient model based on Hirshfeld partitioning (IGMH) (Fig. 4e and Supplementary Fig. 32). Compared with the P- M fragments, IGMH analysis shows that weaker H- bonding and van der Waals interactions exist between the DP- M fragments and the water molecules in the hydration shell of hydrated \(\mathrm{Mg^{2 + }}\) ions, which are energetically unfavorable for stabilizing the configuration, thereby hindering the thermodynamic partitioning and kinetic diffusion of hydrated \(\mathrm{Mg^{2 + }}\) ions, resulting in high rejections. In addition, the lower intrinsic charge number of \(\mathrm{Li^{+}}\) relative to \(\mathrm{Mg^{2 + }}\) impairs its ability to effectively bind water molecules in the hydration shell (Supplementary Fig. 33), while the positively charged CTHP structures in DP- M further promote the escape of polar water molecules from the \(\mathrm{Li^{+}}\) hydration shell by forming extensive van der Waals interactions to counter the electrostatic confinement, as demonstrated in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 100, 885, 451]]<|/det|> +the DP- M/hydrated \(\mathrm{Li^{+}}\) configuration (Fig. 4e), aggrandizing the dehydration of the hydrated \(\mathrm{Li^{+}}\) ions during the permeation. According to DFT calculations, the system diameters of \(\mathrm{Mg^{2 + }}\) dodecahydrate and \(\mathrm{Li^{+}}\) hexahydrate are 11.49 and 8.42 A (Supplementary Fig. 34a), and their hydration energies are approximately \(- 1922.45\) and \(- 563.52 \mathrm{kJ / mol}\) (Supplementary Fig. 34b), respectively, implying that hydrated \(\mathrm{Mg^{2 + }}\) faces greater challenges than hydrated \(\mathrm{Li^{+}}\) in overcoming the dehydration energy threshold at the same transmembrane pressure. This energetically promoted dehydration of \(\mathrm{Li^{+}}\) expedites its transport through DP- M, playing an important role in enhancing \(\mathrm{Li^{+} / Mg^{2 + }}\) selectivity particularly when the membrane pore sizes are diminished and the size distribution is constricted. + +<|ref|>text<|/ref|><|det|>[[110, 512, 885, 905]]<|/det|> +In addition to the non- Coulombic interactions, long- range Coulombic electrostatic forces (Donnan exclusion) also play an imperative role in the cation- PA interactions, particularly considering the strongly positively charged structure of DP- M. Qualitative and quantitative analyses of the electrostatic potential (ESP) are conducted, and Fig. 4f and Supplementary Fig. 35 illustrate the distribution of van der Waals surface ESP of the PA molecular fragments of DP- M and P- M. It was found that DP- M exhibits a superior positive potential and this observation is reaffirmed by the quantitative calculation of the ESP region proportions, where DP- M shows a large positive potential region proportion of \(97\%\) and an average ESP value of 55.55 kcal/mol, far exceeding the respective value of P- M (Fig. 4g). The pronounced electrostatic repulsion between DP- M and the positively charged hexahydrated cations is unequivocally conferred by the CTHP structures of the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 100, 884, 285]]<|/det|> +422 PA layer. The ESP of hydrated \(\mathrm{Mg^{2 + }}\) is nearly twice as high as that of hydrated \(\mathrm{Li^{+}}\) (193.29 vs. 423 98.53 kcal/mol, Supplementary Fig. 33), which inevitably invokes formidable Donnan exclusion 424 selectivity towards the positively charged DP- M (55.55 kcal/mol). Overall, the intriguing co- cation 425 screening ability of DP- M proceeds through a cooperative mechanism of steric hindrance and 426 electrostatic repulsion, as corroborated by the comprehensive IRI, IGMH, and ESP analyses. + +<|ref|>image<|/ref|><|det|>[[111, 333, 884, 744]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[111, 760, 884, 781]]<|/det|> +
429 Fig. 5 | Mechanistic insights into facilitated water permeation in DP-M. a, Schematic diagram
+ +<|ref|>text<|/ref|><|det|>[[111, 800, 884, 900]]<|/det|> +430 of the advanced structure of an ultrapermeable PA membrane for precise differentiation of 431 monovalent/divalent cations. b, DFT atomistic calculation of polarity differences between DP- M 432 and P- M molecular fragments. c, ESP distributions of van der Waals surfaces of the water molecule + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 100, 884, 370]]<|/det|> +obtained by DFT calculations. d, Three representative PA molecular fragments of DP- M and P- M (left) and schematic illustration of the distribution of water cluster in each fragment (right). e, Radial distribution functions between water and the three PA fragments. f, Binding energy (BE) and the number of water molecules around the three PA fragments. All the information and data described in (c- e) were obtained from MD simulations. g, Schematic illustration of the working principle of semipermeable DP- M for ultrafast co- cation separation (yellow and blue spheres represent monovalent and divalent cations, respectively). + +<|ref|>text<|/ref|><|det|>[[110, 430, 884, 904]]<|/det|> +The permeation of water through the PA membrane is primarily governed by the chemical features and nanoporous structure of the PA selective layer, during which water molecules need to overcome certain energy barriers when dissolving and diffusing through. The mutual interactions of water molecules with the binding sites on PA networks thereby have substantial impacts on the water permeation rate (Fig. 5a). To gain a fundamental understanding of the mechanisms governing the facilitated permeation of water through the DP- M membrane, DFT atomistic calculations and MD simulations were performed. DFT was employed to specifically evaluate the molecular interactions between water and the cationic DP- M PA molecular fragments by calculating the surface free energy (SFE, Supplementary Table 4) and the molecular polarity index (MPI), which reflect membrane hydrophilicity from the perspectives of interfacial thermodynamics and quantum chemistry, respectively. As displayed in Fig. 5b, DP- M shows a higher MPI value than that of P- M (i.e., 56.0 vs. 19.7), which is consistent with the lower surface + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 100, 884, 744]]<|/det|> +water contact angle of the former. Meanwhile, the SFE value of DP- M is greater than that of P- M (i.e., 50.5 vs. 41.4 kJ/m²), indicating a clear preference of polar water molecules for wetting and partitioning into the PA fragments of DP- M (Fig. 5c and Supplementary Table 4). To further elucidate the diffusion behavior of water molecules within the PA layer, MD simulations were subsequently conducted to acquire the binding affinities of water molecules to the PA fragments at the molecular level (Fig. 5d). The radial distribution functions (RDFs) plots (Fig. 5e), drawn with the respect to the PA fragments labeled in green (I), blue (II), and orange (III), respectively, reveal that the peak of I- H₂O in the first coordination layer is significantly higher than those of II- H₂O and III- H₂O, closely aligning with the DFT data. Furthermore, the water bonding (WB) capacity calculated by RDFs follows an order of \(\mathrm{WB_1 > WB_{II} > WB_{III}}\) , while the computed binding energies (BE) of fragments I, II, and III with water are in the order of \(\left|\mathrm{BE_I}\right| > \left|\mathrm{BE_{II}}\right| > \left|\mathrm{BE_{III}}\right|\) (Fig. 5f). Hereupon, the cationic triazolyl heterocyclic PA structures derived from DAT- NH₂ in DP- M have a relatively lower affinity to water clusters. Such energy metrics accentuate a thermodynamic inclination of DP- M to pronouncedly facilitate the transport of water by providing water binding sites with moderate resistance, aligned with our design strategy and the ideal PA nanostructure we intend to construct (Fig. 5g). + +<|ref|>sub_title<|/ref|><|det|>[[115, 802, 228, 821]]<|/det|> +## Conclusion + +<|ref|>text<|/ref|><|det|>[[112, 841, 884, 901]]<|/det|> +Nanofiltration membranes with superior water permeance and precise ionic and molecular sieving capabilities offer promising solutions to address numerous challenges associated with water + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 100, 886, 911]]<|/det|> +scarcity and renewable energy. In this study, a facile and robust molecular engineering strategy was demonstrated to reconcile the longstanding permeance- selectivity tradeoff threshold in state- of- the- art PA nanofiltration membranes. Our approach is contingent on the exquisite regulation of the porous nanostructure and the mutual interactions of the PA selective layer with water molecules and ions by the in situ creation of cationic triazolyl heterocyclic polyamide (CTHP) structures during interfacial polycondensations via synthetic DTA- NH2 isomers. The obtained PA membrane exhibited synchronously enhanced water permeance and subangular selectivity for cation separation, achieving unparalleled divalent cation rejections of over \(99\%\) , accompanied by a 9- fold increase in monovalent/divalent cation sieving selectivity and tripled water permeance in comparison with the pristine benchmark, as well as outstanding chemical and operational stability, circumventing the permeance/selectivity threshold. Experimental data in tandem with advanced molecular simulations confirm that the intriguing permselectivity springs from the intricacy of the porous and chemical structures of the CTHP- modulated PA layer, which not only investigates a substantial decrease in the effective mean pore size and narrows the size distribution but also affords a high positive charge density, significantly strengthening the size sieving and Donnan exclusion effects in nanofiltration. Coincidentally, the CTHP structures also provide preferential water binding sites with low energy barriers, energetically facilitating the accommodation and diffusion of water molecules in the PA layer, which eliminates the increased water transport resistance caused by pore size shrinkage. The developed synthetic engineering strategy sheds light on the rational design and fabrication of advanced polymer membranes for high- precision + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 101, 504, 120]]<|/det|> +separations affiliated with water- energy nexuses. + +<|ref|>sub_title<|/ref|><|det|>[[113, 184, 204, 203]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[113, 224, 372, 243]]<|/det|> +## Synthesis of DAT- \(\mathbf{NH}_2\) isomers + +<|ref|>text<|/ref|><|det|>[[111, 260, 886, 867]]<|/det|> +3,5- Diamino- 4/1- (2- aminoethyl)- 1,2,4- triazole (DAT- \(\mathbf{NH}_2\) ) isomers were synthesized via a one- step quaternization reaction following the reaction path shown in Supplementary Fig. 1. In a typical synthesis, 4.24 g of 3,5- diamino- 1,2,4- triazole (DAT, 42.8 mmol) and 8.77 g of 2- bromoethylamine (42.8 mmol) were dissolved in 90 mL of N,N- dimethylformamide (DMF) in a 150 mL round- bottom flask. The flask with the reaction mixture was then heated to 45 °C in a water bath and reacted at this temperature for 24 h under vigorous stirring. A pale green solution rapidly formed with increasing reaction time. When the reaction was complete, the resulting mixture was immediately transferred into a 500 mL beaker, and 180 mL of acetonitrile was then added to obtain a milky white suspension. The obtained flocculent precipitates were subsequently redissolved in 10 mL of DMF and then precipitated with 180 mL of acetonitrile. The white solids were collected via high- speed centrifugation. The above dissolution and precipitation treatment was repeated three times. Finally, the as- synthesized DAT- \(\mathbf{NH}_2\) was vacuum dried at 40 °C overnight and then stored in a sealed container for subsequent characterization and membrane fabrication. Detailed information on chemicals can be found in the Supporting Information (Supplementary Texts 1). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[113, 100, 426, 120]]<|/det|> +## Preparation of the PA NF membrane + +<|ref|>text<|/ref|><|det|>[[111, 140, 886, 700]]<|/det|> +For the fabrication of the PA thin- film composite NF membrane, the polyether sulfone (PES) substrate was first immersed in an amine monomer aqueous solution with \(0.1 \mathrm{wt}\%\) sodium dodecyl sulfate (SDS) and \(0.1 \mathrm{wt}\% \mathrm{Na}_{2}\mathrm{CO}_{3}\) for \(5 \mathrm{min}\) . After the excess water on the top surface was removed via filter paper, the amine- monomer saturated PES substrate was sandwiched into a homemade frame with the top surface facing upward. Interfacial polymerization was initiated by carefully adding excessive \(0.3 \mathrm{wt}\% \mathrm{TMC}\) solution into the frame to cover the surface, which was allowed to react for \(1 \mathrm{min}\) . When the reaction was complete, the excess hexane solution was drained, and the resulting membrane was dried at \(60^{\circ}\mathrm{C}\) for \(30 \mathrm{min}\) . Specifically, DP- M represents a PA membrane that was prepared following the above synthesis procedure using a mixture of DAT- \(\mathrm{NH}_{2}\) ( \(0.06 \mathrm{wt}\%\) ) and PEI ( \(0.44 \mathrm{wt}\%\) ) as the amine monomer. The P- M benchmark membrane was fabricated solely by using PEI as the amine monomer. All the as- synthesized PA membranes were stored in deionized water at \(5^{\circ}\mathrm{C}\) for further characterization and performance tests. The detailed preparation process of the PA nanofilm is included in the Supporting Information (Supplementary Texts 2). + +<|ref|>sub_title<|/ref|><|det|>[[115, 761, 260, 780]]<|/det|> +## Characterization + +<|ref|>text<|/ref|><|det|>[[111, 800, 888, 904]]<|/det|> +The successful synthesis of DAT- \(\mathrm{NH}_{2}\) isomers was confirmed by mass spectrometry (MS, MSQ Plus, USA) and high- performance liquid chromatography (HPLC, Ultimate 3000 RS, USA). The chemical structure of DAT- \(\mathrm{NH}_{2}\) was characterized by proton nuclear magnetic resonance ( \(^{1}\mathrm{H}\) NMR) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 98, 884, 620]]<|/det|> +spectroscopy (Bruker AVANCE AV400, USA). The chemical features of the PA membranes were also analyzed via Fourier transform infrared spectroscopy (FT- IR, Nicolet IN10, Thermo Fisher, USA) and X- ray photoelectron spectroscopy (XPS, Escalab 250Xi, Thermo Fisher, USA). The membrane surface morphology and roughness were examined via field emission scanning electron microscopy (FESEM, Quanta 250 FEG, FEI, USA) and atomic force microscopy (AFM, Nano Wizard 4, Bruker, Germany). The membrane cross- sectional morphology was identified via high- resolution transmission electron microscopy (TEM, FEI Tecnai G2 F30, FEI, USA). Surface hydrophilicity was assessed via water contact angle measurements on a contact angle goniometer (HARKE- SPCA, HARKE, China). The surface zeta potential was measured via a SurPASS electrokinetic analyzer (Anton Paar, GmbH, Austria). The molecular weight cutoff (MWCO) and pore size distribution of the membrane were obtained via solute retention tests using polyethylene glycol (PEG) probes with different molecular weights. The detailed procedures for each measurement are included in the Supporting Information (Supplementary Texts 3–4). + +<|ref|>sub_title<|/ref|><|det|>[[115, 679, 393, 698]]<|/det|> +## Nanofiltration performance tests + +<|ref|>text<|/ref|><|det|>[[111, 716, 884, 903]]<|/det|> +The separation performance of the PA membranes was characterized in nanofiltration mode at \(23^{\circ}\mathrm{C}\) via a cross- flow filtration apparatus with an effective membrane filtration area of \(6.0 \mathrm{cm}^2\) . Before data collection, the membrane sample was conditioned at a pressure of 2 bar greater than the intended test pressure until the water flux stabilized. The pure water flux \((J_{\mathrm{w}}, \mathrm{L} \mathrm{m}^{- 2} \mathrm{h}^{- 1}\) , abbreviated as LMH) was measured using deionized water as the feed, and the water permeance + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[62, 100, 443, 121]]<|/det|> +553 (A, LMH/bar) was calculated via Eq. (1). + +<|ref|>equation<|/ref|><|det|>[[400, 142, 881, 184]]<|/det|> +\[\mathrm{A} = \frac{J_{w}}{\Delta P} = \frac{\Delta V}{\Delta t\times S\times\Delta P} \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[62, 203, 884, 304]]<|/det|> +555 where \(\Delta \mathrm{P}\) (bar) is the trans- membrane hydraulic pressure, \(\Delta V\) (L) is the volume of permeate water collected during a time interval of \(\Delta \mathrm{t}\) (h), and S ( \(\mathrm{m}^{2}\) ) is the effective membrane filtration area. + +<|ref|>text<|/ref|><|det|>[[62, 360, 884, 555]]<|/det|> +559 The membrane ion sieving ability was examined via rejection tests that were conducted under various conditions using a wide spectrum of inorganic salts as solutes. Specifically, \(\mathrm{Na}_{2}\mathrm{SO}_{4}\) , \(\mathrm{Li}_{2}\mathrm{SO}_{4}\) , \(\mathrm{MgSO}_{4}\) , \(\mathrm{MgCl}_{2}\) , \(\mathrm{LiCl}\) , \(\mathrm{NaCl}\) , \(\mathrm{RbCl}\) , \(\mathrm{KCl}\) , \(\mathrm{NiCl}_{2}\) , \(\mathrm{CaCl}_{2}\) , and \(\mathrm{ZnCl}_{2}\) solutions with different concentrations and compositions were used as the feed. The single salt rejection (R, \(\%\) ) was calculated via Eq. (2). + +<|ref|>equation<|/ref|><|det|>[[400, 571, 881, 618]]<|/det|> +\[\mathrm{R} = \left(1 - \frac{C_{p}}{C_{f}}\right)\times 100\% \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[62, 635, 884, 821]]<|/det|> +565 where \(\mathrm{C}_{\mathrm{p}}\) and \(\mathrm{C}_{\mathrm{f}}\) are the salt contents of the permeate and feed, respectively. The salt concentration was determined via conductivity measurement via a SevenCompact™ S230 (Mettler Toledo) conductivity meter. Binary mixtures of \(\mathrm{MgCl}_{2}\) and \(\mathrm{LiCl}\) with different mass ratios were used to evaluate the membrane selectivity for coaction fractionation. The \(\mathrm{Li}^{+} / \mathrm{Mg}^{2 + }\) separation factor \((S_{Li^{+} / Mg^{2 + }})\) was calculated via Eq. (3). + +<|ref|>equation<|/ref|><|det|>[[368, 838, 881, 884]]<|/det|> +\[S_{Li^{+} / Mg^{2 + }} = \left(\frac{C_{f}M g^{2 + } / C_{f}L i^{+}}{C_{p}M g^{2 + } / C_{p}L i^{+}}\right) \quad (3)\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 97, 884, 285]]<|/det|> +where \(C_{f Mg^{2 + }}\) and \(C_{f Li^{+}}\) and where \(C_{p Mg^{2 + }}\) and \(C_{p Li^{+}}\) represent the concentrations of \(\mathrm{Mg^{2 + }}\) and \(\mathrm{Li^{+}}\) in the feed and permeate, respectively. An inductively coupled plasma optical emission spectrometer (ICP- OES, iCAP 7000, Germany) was used to quantify the ion contents of the solution. Each data point was tested three times under the same conditions using randomly selected membrane samples, and the average value was reported. + +<|ref|>text<|/ref|><|det|>[[110, 346, 886, 533]]<|/det|> +The long- term stability of the membrane was evaluated by monitoring the water flux and salt rejection for up to \(120\mathrm{h}\) at \(6\mathrm{bar}\) using \(1000\mathrm{ppm}\mathrm{MgCl}_2\) solution as the feed. The pH stability of the membrane was assessed by measuring the water flux and salt rejection in a feed pH range of \(1 - 13\) using \(1000\mathrm{ppm}\mathrm{LiCl}\) solution as the probe feed, during which the solution pH was adjusted via HCl and \(\mathrm{NaOH}\) . + +<|ref|>sub_title<|/ref|><|det|>[[112, 595, 853, 617]]<|/det|> +## Density functional theory (DFT) calculations and molecular dynamics (MD) simulations + +<|ref|>text<|/ref|><|det|>[[110, 634, 886, 905]]<|/det|> +DFT atomistic calculations were conducted via ORCA quantum chemistry software (version 5.0.4) 43- 45. The binding energy and ion hydration energy were obtained via single- point energy calculations. Electrostatic potential (ESP) 46, 47, average local ionization energy (ALIE) 48, interaction region indicator (IRI) 42, independent gradient model based on Hirshfeld partition (IGMH) 49, and molecular polarity index (MPI) 50 analyses were performed with the Multiwfn software package to gain insights into molecular electronic properties and interaction patterns 51. LAMMPS and GROMACS were employed for MD simulations 52, 53, where LAMMPS was used + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 100, 884, 325]]<|/det|> +to calculate the free volume fraction of the PA nanofilm, whereas GROMACS was applied to analyze the distribution and binding energy of water molecules around specific PA molecular segments. These simulations enabled a detailed exploration of water- PA interactions, which is crucial for understanding the hydration behavior and separation performance of the membrane. Comprehensive details of the computational methods and protocols are provided in Supplementary Text 5 and Text 6. + +<|ref|>sub_title<|/ref|><|det|>[[113, 389, 225, 409]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[110, 428, 886, 900]]<|/det|> +1. Zhang, F., Fan, J.-b. & Wang, S. Interfacial Polymerization: From Chemistry to Functional Materials. Angewandte Chemie International Edition 59, 21840-21856 (2020). +2. Lu, X. & Elimelech, M. Fabrication of desalination membranes by interfacial polymerization: history, current efforts, and future directions. Chem. Soc. Rev. 50, 6290-6307 (2021). +3. Epsztein, R., DuChanois, R.M., Ritt, C.L., Noy, A. & Elimelech, M. Towards single-species selectivity of membranes with subnanometre pores. Nat. Nanotechnol. 15, 426-436 (2020). +4. Wang, K. et al. Tailored design of nanofiltration membranes for water treatment based on synthesis–property–performance relationships. Chem. Soc. Rev. 51, 672-719 (2022). +5. Sengupta, B. et al. 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Sci. 657, 120673 (2022). + +<|ref|>text<|/ref|><|det|>[[170, 595, 884, 655]]<|/det|> +Karan, S., Jiang, Z. & Livingston, A.G. Sub- 10 nm polyamide nanofilms with ultrafast solvent transport for molecular separation. Science 348, 1347- 1351 (2015). + +<|ref|>text<|/ref|><|det|>[[170, 676, 884, 736]]<|/det|> +Košutić, K., Dolar, D., Ašperger, D. & Kunst, B. Removal of antibiotics from a model wastewater by RO/NF membranes. Sep. Purif. Technol. 53, 244- 249 (2007). + +<|ref|>text<|/ref|><|det|>[[170, 758, 884, 863]]<|/det|> +Sarkar, P., Ray, S., Sutariya, B., Chaudhari, J.C. & Karan, S. Precise separation of small neutral solutes with mixed- diamine- based nanofiltration membranes and the impact of solvent activation. Sep. Purif. Technol. 279, 119692 (2021). + +<|ref|>text<|/ref|><|det|>[[170, 884, 884, 904]]<|/det|> +Lu, T. & Chen, Q. Interaction Region Indicator: A Simple Real Space Function Clearly + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[170, 100, 884, 160]]<|/det|> +Revealing Both Chemical Bonds and Weak Interactions\*\*. Chemistry–Methods 1, 231- 239 (2021). + +<|ref|>text<|/ref|><|det|>[[170, 182, 884, 245]]<|/det|> +Neese, F., Wennmohs, F., Becker, U. & Riplinger, C. The ORCA quantum chemistry program package. The Journal of Chemical Physics 152, 224108 (2020). + +<|ref|>text<|/ref|><|det|>[[170, 265, 884, 327]]<|/det|> +Stoychev, G.L., Auer, A.A. & Neese, F. Automatic Generation of Auxiliary Basis Sets. J. Chem. Theory Comput. 13, 554- 562 (2017). + +<|ref|>text<|/ref|><|det|>[[170, 348, 884, 410]]<|/det|> +Neese, F. Software update: The ORCA program system—Version 5.0. WIREs Computational Molecular Science 12, e1606 (2022). + +<|ref|>text<|/ref|><|det|>[[170, 430, 884, 492]]<|/det|> +Manzetti, S. & Lu, T. The geometry and electronic structure of Aristolochic acid: possible implications for a frozen resonance. J. Phys. Org. Chem. 26, 473- 483 (2013). + +<|ref|>text<|/ref|><|det|>[[170, 512, 884, 574]]<|/det|> +Lu, T. & Manzetti, S. Wavefunction and reactivity study of benzo[a]pyrene diol epoxide and its enantiomeric forms. Struct. Chem. 25, 1521- 1533 (2014). + +<|ref|>text<|/ref|><|det|>[[170, 594, 884, 656]]<|/det|> +Zhang, J. & Lu, T. Efficient evaluation of electrostatic potential with computerized optimized code. Phys. Chem. Chem. Phys. 23, 20323- 20328 (2021). + +<|ref|>text<|/ref|><|det|>[[170, 677, 884, 739]]<|/det|> +Lu, T. & Chen, Q. Independent gradient model based on Hirshfeld partition: A new method for visual study of interactions in chemical systems. J. Comput. Chem. 43, 539- 555 (2022). + +<|ref|>text<|/ref|><|det|>[[170, 760, 884, 863]]<|/det|> +Liu, Z., Lu, T. & Chen, Q. Intermolecular interaction characteristics of the all- carboatomic ring, cyclo[18]carbon: Focusing on molecular adsorption and stacking. Carbon 171, 514- 523 (2021). + +<|ref|>text<|/ref|><|det|>[[170, 884, 884, 905]]<|/det|> +Lu, T. & Chen, F. Multiwfn: A multifunctional wavefunction analyzer. J. Comput. Chem. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[170, 100, 334, 120]]<|/det|> +33, 580- 592 (2012). + +<|ref|>text<|/ref|><|det|>[[170, 141, 884, 245]]<|/det|> +Hess, B., Kutzner, C., van der Spoel, D. & Lindahl, E. GROMACS 4: Algorithms for Highly Efficient, Load- Balanced, and Scalable Molecular Simulation. J. Chem. Theory Comput. 4, 435- 447 (2008). + +<|ref|>text<|/ref|><|det|>[[170, 265, 884, 368]]<|/det|> +Thompson, A.P. et al. LAMMPS - a flexible simulation tool for particle- based materials modeling at the atomic, meso, and continuum scales. Comput. Phys. Commun. 271, 108171 (2022). + +<|ref|>sub_title<|/ref|><|det|>[[115, 429, 298, 451]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[110, 470, 884, 670]]<|/det|> +The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (22125603), Fundamental Research Funds for the Central Universities (nos. 040- 63243125 and 040- 63233061), and the National Key Research and Development Project (nos. 2023YFC3708003 and 2023YFC3708000). Special thanks are also made to the Han Gang Research Lab members for their helpful suggestions related to the characterization of materials. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 418, 150]]<|/det|> +SupportinginformationNov112024. pdf + +<--- Page Split ---> diff --git a/preprint/preprint__07f28e497a966d2241ff131830d461c8058880986d6873f21a89c91e352b254e/images_list.json b/preprint/preprint__07f28e497a966d2241ff131830d461c8058880986d6873f21a89c91e352b254e/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..8b3b5a63342ab20147535c7ebf58022126420ac1 --- /dev/null +++ b/preprint/preprint__07f28e497a966d2241ff131830d461c8058880986d6873f21a89c91e352b254e/images_list.json @@ -0,0 +1,47 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [ + [ + 55, + 60, + 930, + 722 + ] + ], + "page_idx": 13 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 66, + 95, + 936, + 744 + ] + ], + "page_idx": 14 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [ + [ + 101, + 60, + 860, + 710 + ] + ], + "page_idx": 15 + } +] \ No newline at end of file diff --git a/preprint/preprint__07f28e497a966d2241ff131830d461c8058880986d6873f21a89c91e352b254e/preprint__07f28e497a966d2241ff131830d461c8058880986d6873f21a89c91e352b254e.mmd b/preprint/preprint__07f28e497a966d2241ff131830d461c8058880986d6873f21a89c91e352b254e/preprint__07f28e497a966d2241ff131830d461c8058880986d6873f21a89c91e352b254e.mmd new file mode 100644 index 0000000000000000000000000000000000000000..4f59ca2c9571488600c1bf17b2872590693b732f --- /dev/null +++ b/preprint/preprint__07f28e497a966d2241ff131830d461c8058880986d6873f21a89c91e352b254e/preprint__07f28e497a966d2241ff131830d461c8058880986d6873f21a89c91e352b254e.mmd @@ -0,0 +1,264 @@ + +# The Dynamics of Plasmon-Induced Hot Carrier Creation in Colloidal Gold + +Jacinto Sa jacinto.sa@kemi.uu.se + +Uppsala University https://orcid.org/0000- 0003- 2124- 9510 + +Anna Wach SOLARIS National Synchrotron Radiation Centre, Jagiellonian University + +Jakub Szlachetko SOLARIS National Synchrotron Radiation Centre, Jagiellonian University + +Alexey Maximenko SOLARIS National Synchrotron Radiation Centre, Jagiellonian University + +Tomasz Sobol SOLARIS National Synchrotron Radiation Centre, Jagiellonian University https://orcid.org/0000- 0002- 9661- 0932 + +Ewa Partyka- Jankowska SOLARIS National Synchrotron Radiation Centre, Jagiellonian University + +Camila Bacellar Paul Scherrer Institute https://orcid.org/0000- 0003- 2166- 241X + +Claudio Cirelli Paul Scherrer Institute https://orcid.org/0000- 0003- 4576- 3805 + +Philip Johnson Paul Scherrer Institut https://orcid.org/0000- 0002- 7251- 4815 + +Rebeca Gomez Castillo École Polytechnique Fédérale de Lausanne + +Vitor R. Silveira Uppsala University + +Peter Broqvist Uppsala University + +Jolla Kullgren Uppsala University + +Naomi Halas Rice University https://orcid.org/0000- 0002- 8461- 8494 + +Peter Nordlander + +<--- Page Split ---> + +## Physical Sciences - Article + +# Keywords: + +Posted Date: April 8th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 3799527/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on March 7th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 57657- 1. + +<--- Page Split ---> + +## Abstract + +There is an increasing interest in nonequilibrium "hot" carrier generation, created by the decay of collective electronic oscillations on metals known as surface plasmons. Despite extensive efforts, direct observation of the mechanism responsible for generating hot carriers due to plasmon decay has proven challenging. Here, the dynamics of hot carrier generation on gold nanoparticles (Au NPs) are followed with unparalleled detail through ultrafast X- ray absorption spectroscopy (XAS) at the X- ray free- electron laser (XFEL). In Au NPs, the plasmon dephases after 25 fs and the hot carrier population peaks within 105 fs, reaching thermal equilibrium within 1.5 ps. The nonequilibrium carriers display an energy dispersion governed by the density of states of the metal, with some carriers possessing energies surpassing that of a single photon, consistent with the involvement of an Auger heating mechanism distinct from the expected impact excitation that dominates the carrier multiplication step. The most energetic carriers exhibit relatively shorter lifespans, a property that may be critical for exploiting them in applications. This study substantiates hot carrier formation through nonradiative decay as the main decay channel of plasmon resonance. The proposed methodology provides a straightforward approach for real- time tracking of plasmon- induced hot carrier dynamics. + +## Introduction + +Surface plasmons, the collective oscillations of conduction electrons in metallic nanostructures, have emerged as an essential elementary excitation in condensed matter, giving rise to multiple practical applications. They can capture distant radiation and focus it within subwavelength regions, defying diffraction limits,1,2 resulting in potent near- fields and profound field amplifications.3 These attributes have propelled innovative applications of plasmonics, such as highly sensitive biosensing,4 photothermal therapy for cancer,5 photovoltaics,6,7 and photocatalysis.8 + +Surface plasmons exhibit finite lifetimes, decaying either by photon emission (radiatively) or the creation of electron- hole pairs (nonradiatively). Over the past decade, the radiative decay pathway has been researched extensively, yielding the development of efficient nanoantennas that amplify and steer emissions from individual emitters.9,10 Recent research has focused on leveraging nonradiative decay for applications.11 Hot carriers can initiate chemical reactions in adjacent molecules, even those that demand high energy under conventional thermal conditions.12,13 Moreover, plasmon- induced hot carriers offer a potent means to transform light into electrical currents,14 fostering novel solar energy converters15 and circumventing the bandgap limitations of traditional photodetectors.16 + +While the direct excitation of hot carriers on metal surfaces using high- intensity laser pulses has been a longstanding practice in surface femtochemistry, exploiting surface plasmon decay to amplify hot carrier generation is a recent development. This significant advance stems from the remarkably boosted light harvesting ability of collective plasmon excitations, combined with the substantial enhancement of the plasmon- induced field when metals are nano- confined. Comprehending the underlying physical + +<--- Page Split ---> + +mechanisms driving plasmon- induced hot carrier generation is essential to leverage these benefits fully. Although theoretical frameworks elucidating this phenomenon exist, \(^{17 - 18}\) [i] 19 [ii] 20 [iii] 21 [iv] 22 a suitable experimental methodology to validate these models still needs to be developed. + +X- ray absorption spectroscopy (XAS) provides a way to investigate the interplay between X- ray photons and matter, simultaneously unveiling unparalleled insights into a material's electronic and chemical characteristics. When X- ray photons are directed toward a material, they can be absorbed by core electrons, resulting in these electrons shifting to higher energy states. The precise energy at which this absorption occurs depends on the specific element's electronic structure and its local environment. Hot carriers emerge from the interaction between external electric fields and valence electrons, creating electrons and holes with energies above and below the Fermi level (EF). + +Transient XAS (aka time- resolved XAS (TR- XAS)) probes empty states around the Fermi energy and, in the case of \(d^{10}\) metals with the L3- edge transition, provides direct information about the amount of carrier participation and their nonequilibrium energy distributions.23 At synchrotrons, such dynamical measurements are typically hampered by limited temporal resolution ( \(\sim 5\) ps) and photon density,24 impeding real- time observations of the hot carrier generation process.8 However, this limitation has been surpassed by the advent of hard X- ray free electron lasers (XFELs),25 capable of delivering intense and ultrashort hard X- ray pulses (up to 30 keV at the European XFEL26 and 12 keV at SwissFEL (used in this study) 27) of less than 50 fs in duration.27,28 With this unique combination of high photon energies and ultrashort pulses, time- resolved XAS has become an exceptionally valuable experimental probe of dynamical processes. Typical time- resolved measurements are implemented in a pump- probe scheme, where an optical- frequency pump laser triggers electron dynamics, and the X- ray probe captures the evolving nonequilibrium electron distribution. Over the past few years, femtosecond TR- XAS studies have been used to probe photoinduced electronic and structural changes in photoexcited transition metal oxides29 and complexes.30 In this study, TR- XAS was used to observe the generation and relaxation of plasmon- induced hot carriers in gold nanoparticles directly.31,32 + +The widely accepted understanding of how localised surface plasmon resonance (LSPR) excitation leads to hot carrier formation and subsequent thermalisation, including the hypothesised timescales for each process, is summarised in Figure 1A.8,22 Briefly, the light electric field induces a coherent excitation of Au valence electrons. The excited electrons' coherence dephases due to Landau damping after the light excitation elapses. The process is expected to take 10- 100 fs, resulting in a non- Fermi- Dirac distribution of hot carriers. The carriers undergo multiplication, eventually reaching a Fermi- Dirac distribution and thermal relaxation after \(\sim 1\) ps. This description of hot carrier formation has been deduced from physical models that underpin our understanding. Still, it has never been validated experimentally due to the lack of element- specific techniques with sufficient temporal resolution. However, the attempts from Bigot et al.33 and Lehmann et al.34 with femtosecond optical pump- probe investigations with ionising probe pulses, which provided earlier evidence for hot electrons and their dynamics, should be mentioned. Nevertheless, no information could be extracted about the hot holes. + +<--- Page Split ---> + +Figure 1B illustrates the TR- XAS approach for tracking the density of states (DOS) changes induced by LSPR excitation. More specifically, the study focuses on the X- ray absorption near edge structure (XANES) part of the XAS spectrum, which contains the electronic changes in the element, i.e., information on LSPR- induced hot carrier formation. The transient data was collected using the classic pump- probe methodology for optical spectroscopy. The technique involves "pumping" a sample with an initial laser pulse and then "probing" it with a delayed pulse to observe the changes induced by the pump pulse. In the present case, the probe is an fs X- ray pulse from the XFEL. To prevent the excitation of damaged Au NPs induced by intense XFEL pulses, a liquid jet was employed to circulate the Au NPs and the solution was refreshed every four hours. + +Since nanoparticle measurements at XFELs are uncommon, it was essential to validate that the XANES spectra collected with this radiation represent the sample. Figure S1 shows the steady- state XANES spectra of Au foil and nanoparticles measured at the Au \(L_{3}\) - edge transition \((2p_{3 / 2}\lambda 5d)\) at the synchrotron (Solaris synchrotron, Poland). Au has a \([Xe]4f^{14}5d^{10}6s^{1}\) electronic structure, i.e., with a filled \(d\) - shell, which results in a slight absorption edge only visible due to some level of \(s\) - \(d\) shell hybridisation. For comparison purposes, the signal was plotted against Pt \(([Xe]4f^{14}5d^{9}6s^{1})\) (Fig. S2), revealing the method sensitivity to empty states within the metal \(5d\) shell and, to some extent, the \(s\) - shell due to this hybridisation. In this study, Au NPs with an average particle size of \(8\pm 2 \mathrm{nm}\) were used, as confirmed by atomic force microscopy (AFM) and dynamic light scattering (DLS) (Figs. S3 and S4). The Au NPs have a LSPR centered at nominally \(520 \mathrm{nm}\) (2.38 eV) according to UV- vis spectroscopy (Fig. S5). + +The steady- state XANES analysis established that the Au NPs exhibit an electronic structure resembling bulk gold, as reported elsewhere. \(^{22,35}\) This agreement is further corroborated by our theoretical calculations, showing the evolution of the DOS as function of particle size (Fig. S6). The unexcited XANES spectrum of the Au NPs, measured at XFEL (SwissFEL, Switzerland) and the synchrotron, displayed a consistent shape. This consistency supports the applied methodology's ability to capture the transient alterations in the electronic structure of gold before the sample gets damaged, i.e., probe- before destruction concept. \(^{36,37}\) XFELs have only recently provided access to hard X- ray energies, allowing one for the first time to probe the Au \(L_{3}\) - edge. + +Ultrafast time- resolved XANES data were acquired with the XFEL source as a probe, following the excitation of \(5 \mathrm{mM}\) Au NPs at \(532 \mathrm{nm}\) ( \(\sim 2.33 \mathrm{eV}\) ), utilising a \(15 \mathrm{nm}\) full width at half maximum (FHWM) bandwidth, a pulse duration of approximately \(75 \mathrm{fs}\) , and a power density of \(98 \mathrm{mJ / cm^2}\) (equivalent to 4 \(\mu \mathrm{J}\) within a \(60 \times 60 \mu \mathrm{m}^2\) spot). The choice of this precise plasmon excitation energy was to induce LSPR through intra- band \(s\) - to \(s\) - shell excitation while minimising interband excitation ( \(d\) - to \(s\) - shell excitation). The centre of the Au \(d\) - shell is located at \(2.5 - 2.58 \mathrm{eV}\) ( \(\sim 496 - 480 \mathrm{nm}\) ) from the metal Fermi level ( \(E_{\mathrm{F}}\) ), \(^{38,39}\) meaning that the laser pulse with \(2.33 \pm 0.13 \mathrm{eV}\) ( \(15 \mathrm{nm}\) FHWM) photon energy can only excite the low energy tail of the \(d\) - shell at best. Figure 1C compares the XANES spectra of unexcited (unpumped spectrum) and excited (pumped spectrum) recorded at \(\mathrm{Dt} = 100 \mathrm{fs}\) time delay after excitation at \(532 \mathrm{nm}\) . Optical excitation induced a spectral downshift in energy and decreased XANES whiteline intensity, + +<--- Page Split ---> + +corroborating that it induced changes in the gold electronic structure around its Fermi- level energy, and the TR- XAS can track the changes. + +To better illustrate the results, the XANES difference spectrum (pumped- unpumped XANES spectra) is also shown in Figure 1C. The difference spectrum is dominated by the positive signal below and a negative signal above the Au \(\mathsf{E}_{\mathsf{F}}\) . Transient \(\mathsf{L}_{3}\) - edge XANES readily capture changes in the density of unoccupied states, particularly those induced in the \(d\) - shell, either directly or through processes like hybridisation with the \(s\) - shell. Accordingly, a positive signal correlates with an increase in density of states (DOS); conversely, a negative signal (i.e., a bleached signal) indicates a decrease in empty states. Therefore, the positive signal below the Au \(\mathsf{E}_{\mathsf{F}}\) is ascribed to the formation of a hot hole population induced by the plasmon optical excitation. In contrast, hot electrons give rise to the negative signal above the Au \(\mathsf{E}_{\mathsf{F}}\) , consistent with empty states filling. The transient signal directly demonstrates the generation of hot carriers through LSPR decoherence via Landau damping (the non- radiative pathway dominant in small nanoparticles). \(^{21,24,40}\) Most notably, the hot hole and electron signals are neither symmetric nor have the same integrated magnitude. This is related to the XANES higher sensitivity to empty states formation and the \(\mathsf{L}_{3}\) - edge transition changes in the \(d\) - shell that is part of the valence, where the hot holes are formed. + +To establish the time scales for plasmon damping (g) and the average lifetime of carriers (t), kinetic traces were extracted at the maximum of the hot hole intensity (11916 eV, 2.5 eV below Au \(\mathsf{E}_{\mathsf{F}}\) (Figure 2B)) and the excited electron intensity (11922 eV, 3.0 eV above Au \(\mathsf{E}_{\mathsf{F}}\) (Figure 2C)) populations, as depicted in (Figure 2A). The kinetic data from the time scans were fitted by a model published elsewhere, \(^{41}\) described in SI equations S1 and S2. In brief, the data collected at 11916 and 11922 eV were fitted with a convolution of temporal instrument response function (Gaussian) with a monoexponential decay (with a time constant). The resulting fit is the solid green in Figure 2B. Due to the low signal- to- noise ratio for the hot electron data, the error bars are relatively large. However, qualitatively, it is possible to see that the signal has dynamics similar to the hot holes. + +The \(\gamma\) time can be extracted from the transient signal onset time because it is the point at which the Au DOS starts to change, i.e., the fingerprint for hot carrier formation. In this particular case, it was estimated to be \(24.6 \pm 6\) fs, corroborating that plasmon decoherence occurs between 10- 100 fs. \(^{24}\) Following plasmon damping, the hot carriers undergo a carrier multiplication reaching a maximum at \(105 \pm 8\) fs, estimated from rising edge analysis. The lifetimes of the hot carriers were determined from a single exponential decay to be \(498 \pm 35\) fs with complete carrier thermalisation occurring within 1.5 ps. These time constants align with previous postulations \(^{24}\) but are here substantiated through direct measurement. The confirmed ultrafast hot carrier dynamics in plasmonic nanoparticles are the primary bottleneck in plasmonic applications. + +To estimate the number of electrons engaged when exciting 5 mM Au NPs at 532 nm, utilising a 15 nm full width at half maximum (FHWM) bandwidth, a pulse duration of approximately 75 fs, and a power + +<--- Page Split ---> + +density of \(98~\mathrm{mJ / cm^2}\) , the positive signal variance at 0 and 100 fs were integrated. This integrated signal was then juxtaposed with the signal difference between the Au and Pt \(\mathsf{L}_3\) - edges (Fig. S2). Note that the signal difference between Au and Pt relates to \(1e^{- }\) less in Pt valence states, i.e., the integrated positive signal of the difference between Pt and Au corresponds to the equivalent of having \(1e^{- }\) from each Au atom participating in the resonance. Employing this simple methodology, we estimated that each gold atom contributed with \(0.19e^{- }\) at the start of the resonance, which underwent multiplication until 105 fs, reaching a maximum of \(0.46e^{- }\) from each Au atom contributing to hot carrier formation at this excitation power. + +Assuming an excitation volume of \(60\times 60\times 100\mu \mathrm{m}^3\) and considering the Au solution concentration (5 mM), one can expect \(1.5\times 10^{8}\) nanoparticles in the excited volume. An \(8\mathrm{nm}\) Au NP has \(>12000\) atoms, equating to about \(1.8\times 10^{12}\) Au atoms in the excited volume. The photon density in the optical pulses is about \(10^{13}\) , from which \(20\%\) is absorbed according to UV- Vis, implying that the excited volume absorbs around \(2\times 10^{12}\) photons. This suggests an excitation of about \(1e^{- }\) per atom of Au, from which \(19\%\) are converted into hot carriers at the onset, multiplying to about \(46\%\) within 100 fs. The observation suggests that hot carrier generation is a prime decay channel of Au LSPR and undoubtedly the most significant mechanism in nonradiative decay. + +After verifying the generation of hot carriers, the next step is the investigation of the dynamics of their energy distribution - a significant yet elusive aspect in the realm of plasmonic hot carriers, particularly when it comes to holes. Our understanding is derived mainly from theoretical studies \(^{20,4243}\) and indirect techniques. \(^{22,33,34,44}\) For example, internal quantum efficiency measurements have inherent limitations as they solely quantify carriers injected into an acceptor layer, like a semiconductor, failing to provide insights into the dynamic behaviour of the carriers in the metal. While the hot electrons can only populate the empty states within the sp- shells, the holes can be in sp- and d- shells, confirmed by valence band - X- ray photoelectron spectroscopy (VB- XPS) shown in Figure 3A. It is evident when the VB- XPS is overlapped with the transient XANES spectrum (recorded at time zero) that the generated holes are indeed located throughout the entire valence, including the d- shell, despite the optical pulse energy allowing primarily sp- shell excitation. + +Figure 3B shows the energy distribution and population of the carriers at different time delays after excitation. As expected, the plasmonic excitation depopulates and populates states below and above the Fermi energy. The ultrafast carrier- carrier interactions during dephasing and multiplication determine their energy and respective population. The hot carrier energy distribution goes beyond single photon energy for hot electrons and holes. Moreover, it is noticeable that both carrier populations and the width of their energy distributions increase until about 100 fs, decreasing asymptotically after that. A slight asymmetry exists between hot electron and hot hole populations, which cannot be fully explored here due to the probe's lower sensitivity to hot electrons. + +<--- Page Split ---> + +The rapid depopulation of electrons in the \(d\) - shell is expected due to the broad energy overlap between the \(d\) and \(sp\) - band, which provides a high density of \(d\) - electrons that couples with the plasmonic resonance and dissipates its energy. \(^{43}\) However, this does not explain the observation of carriers having energies above the photon energy, even considering that the Au core- hole lifetime broadening is 5.41 eV at the \(L_{3}\) - edge, \(^{45}\) which inevitably broadens the energy scale. Achieving precise energy distributions of carriers requires high- resolution measurements, \(^{46}\) which implies extended acquisition times rarely offered at XFEL facilities. Nonetheless, hot holes are distributed across the entire valence electronic structure, and their energy distribution increases up to 250 fs (Figure 3C) before relaxing. These two observations indicate the involvement of carrier multiplication mechanisms that can increase the carrier population and their energy distribution, an effect that has yet to be reported. \(^{43}\) Note that the low optical laser fluency and short pulse duration used in this experiment make it highly unlikely that multiphoton excitation of single electrons occurs. + +Regarding carrier multiplication, there are two scattering mechanisms: impact excitation and Auger heating, \(^{47,48}\) The predominant mechanism in carrier multiplication is impact excitation, where an excited electron (hole) undergoes Coulomb scattering, losing energy and momentum and giving rise to an additional electron- hole pair. The distinctive feature of impact excitation is a rise in the number of carriers and a simultaneous reduction in their energy. Conversely, Auger heating characterises the non- radiative recombination of an electron with a hole, where the energy and momentum are transferred to an electron (hole) within the same shell. The hallmark of Auger heating is a decline in the number of carriers and an increase in their energy. + +To enhance the visualisation and comprehension of the hot hole multiplication process, the shape of different spectra regarding charge width and charge energy was analysed (see Figure 3C). The details of the data analysis procedure are outlined in the SI. Commencing with the average hot carrier distribution energy, it remained constant until 100 fs before exhibiting a subsequent decrease. This implies the LSPR dephasing process extends to 100 fs, increasing the nonequilibrium hot carrier population through the impact excitation scattering mechanism. However, examining the hot carrier distribution width, reflecting the energy distribution of the hot holes unveils a relative surge in the energy distribution beyond the time when the hole population is at its maximum (approximately 100 fs), i.e., the energy distribution of hot holes increases up to 250 fs. This observation is noteworthy, especially considering this process competes with hole thermalisation, occurring within tens of femtoseconds in metals. The broadening induced by the core hole relaxation cannot account for the increase in distribution width, as it should have smeared the energy resolution from the outset, preventing the difference signal from accurately reflecting the valence state of gold. This suggests the involvement of a mechanism that generates carriers with higher energy than the dephasing process produces - specifically, the participation of Auger heating. This mechanism has yet to be considered in plasmon relaxation dynamics, altering the current understanding of hot carrier formation, multiplication, and relaxation in plasmonic materials. + +<--- Page Split ---> + +In this work, we presented the results from an ultrafast X- ray absorption experiment conducted at the XFEL involving citrate- capped gold nanoparticles excited at their LSPR with minimum intraband excitation. This experiment enabled the real- time observation of the generation and subsequent relaxation of hot carriers. The plasmon damping was determined to be 25 fs, with a maximum hot carrier population of \(0.46e^{- }\) from each Au atom detected at 105 fs after excitation. The lifetimes of the hot carriers were estimated to be 498 fs, with complete carrier thermalisation occurring within 1.5 ps. Energy scans conducted at varying delay times revealed that the energies of these carriers conform to the density of states of the metal, with some carriers possessing energies that exceed the photon energy, consistent with an Auger heating scattering mechanism. The observation impacts hot carrier applications, particularly those that are based on the energy of the hot carriers, such as photocatalysis and photovoltaics. For instance, without the Auger process, chemical reactions with redox windows larger than photon energy could not be catalysed. Similarly, the open circuit voltage of photovoltaic devices could not exceed the voltage offered by a single photon. The novel insight into plasmon induced hot carrier generation and dynamics provided here is likely to significantly impact applications for years to come. + +[i]. Kornbluth, M., Nitzan, A., Seideman, T. Light- Induced Electronic Non- Equilibrium in Plasmonic Particles. J. Chem. Phys. 138, 174707 (2013). [ii]. Govorov, A. O., Zhang, H., Demir, H. V., Gun'ko, Y. K. Photogeneration of Hot Plasmonic Electrons with Metal Nanocrystals: Quantum Description and Potential Applications. Nano Today 9, 85- 101 (2014). [iii]. Manjavacas, A., Liu, J. G., Kulkarni, V., Nordlander, P. Plasmon- Induced Hot Carriers in Metallic Nanoparticles. ACS Nano 8, 7630- 7638 (2014). [iv]. Rossi, T. P., Erhart, P., Kuisma, M. Hot- Carrier Generation in Plasmonic Nanoparticles: The Importance of Atomic Structure. ACS Nano 14, 9963- 9971 (2020). + +## Declarations + +## Acknowledgements + +We acknowledge the Paul Scherrer Institut, Villigen, Switzerland, for providing beamtime at the Alvra beamline of the SwissFEL facility. We also acknowledge SOLARIS National Synchrotron Radiation Centre, Krakow, Poland, for the access to the ASTRA and PHELIX beamline. The simulations were performed using computational resources provided by the Swedish National Infrastructure for Computing (SNIC) at UPPMAX and NSC, for which we want to thank. + +Funding: J.Sa acknowledges funding from Olle Engkvists Stiftelse (grant no. 210- 0007), Knut & Alice Wallenberg Foundation (Grant No. 2019- 0071) and Swedish Research Council (grant no. 2019- 03597). A.W. acknowledges funding from the European Union's Horizon 2020 research and innovation program under Marie Sklodowska- Curie grant agreement no. 884104 (PSI- FELLOW- III- 3i). N.J.H. and P.N. acknowledge support from the Robert A. Welch Foundation under grants C- 1220 and C- 1222 and the Air Force Office of Scientific Research via the Department of Defense Multidisciplinary University Research + +<--- Page Split ---> + +Initiative under AFOSR Award No. FA9550- 15- 1- 0022. The work is partially supported under the Polish Ministry and Higher Education project: "Support for research and development with the use of research infrastructure of the National Synchrotron Radiation Centre SOLARIS" under contract nr 1/SOL/2021/2. The work is partially funded by the National Science Centre in Poland under grant number 2020/37/B/ST3/00555. + +Author contributions: Conceptualization and methodology: A.W., J. Sz. and J.Sa; formal data analysis: A.W., J. Sz. and J.Sa; experimental investigations: A.W, C.B., C.C., P.J.M.J., R.G.C., V.R.S., P-B., J.K., A.M., T.S., E.P.- J. and J.Sa; data visualisation concepts: A.W., J. Sa, J.Sz and N.J.H., draft preparation: A.W., N.J.H., J. Sz. and J.Sa; writing- review and editing: all the authors. All authors have read and agreed to the published version of the manuscript. + +Competing interests: The authors declare that they have no competing interests. + +Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors. + +## References + +1. Novotny, L., Hecht, BPrinciples of Nano-OpticsCambridge University Press: New York, (2006). +2. Maier, SAPlasmonics: Fundamentals and ApplicationsSpringer: New York (2007). +3. Halas, NJ., Lal, S., Chang, W., Link, S., Nordlander, PPlasmons in Strongly Coupled Metallic NanostructuresChemRev111, 3913-3961 (2011). +4. Xu, H., Bjerneld, EJ., Kall, M., Borjesson, LSpectroscopy of Single Hemoglobin Molecules by Surface Enhanced Raman ScatteringPhysRevLett83, 4357-4360 (1999). +5. O'Neal, DP., Hirsch, LR., Halas, NJ., Payne, JD., West, JLPhto-Thermal Tumor Ablation in Mice Using Near Infrared-Absorbing NanoparticlesCancer Lett209, 171-176 (2004). +6. Atwater, HA., Polman, APlasmonics for Improved Photovoltaic DevicesNatMater9, 205-213 (2010). +7. Geng, X., Abdellah, M., Vadell, RB., Folkenant, M., Edvinsson, T., SaJDirect Plasmonic Solar cell Efficiency Dependence on Spiro-OMeTAD Li-TFSI ContentNanomaterials 11, 3329 (2021). +8. Brongersma, ML., Halas, NJ., Nordlander, PPlasmon-induced hot carrier science and technologyNatNanotechnol10, 25-34 (2015). +9. Curto, AG., Volpe, G., Taminiau, TH., Kreuzer, MP., Quidant, R., van Hulst, NFUnidirectional Emission of a Quantum Dot Coupled to a NanoantennaScience 329, 930-933 (2010). +10. Novotny, L., Van Hulst, NAntennas for LightNatPhotonics 5, 83-90 (2011). +11. Clavero, CPlasmon-Induced Hot-Electron Generation at Nanoparticle/Metal-Oxide Interfaces for Photovoltaic and Photocatalytic DevicesNatPhotonics 8, 95-103 (2014). + +<--- Page Split ---> + +12. Mubeen, S., Lee, J., Singh, N., Kramer, S., Stucky, GD., Moskovits, MAn Autonomous Photosynthetic Device in Which All Charge Carriers Derive from Surface PlasmonsNatNanotechnol8, 247-251 (2013). + +13. Shi, X., Ueno, K., Oshikiri, T., SunQ., Sasaki, K., Misawa, HEnhanced water splitting under modal strong coupling conditionsNatNanotechnol13, 953-958 (2018). + +14. Garcia de Arquer, FP., Mihi, A., Kufer, D., Konstantatos, GPhotoelectric Energy Conversion of Plasmon-Generated Hot Carriers in Metal-Insulator-Semiconductor StructuresACS Nano 7, 3581-3588 (2013). + +15. Schwede, JW., Bargatin, I., Riley, DC., Hardin, BE., Rosenthal, SJ., Sun, Y., Schmitt, F.; Pianetta, P., Howe, RT., Shen, Z.-X.; et alPhoton-Enhanced Thermionic Emission for Solar Concentrator SystemsNatMater9, 762-767 (2011). + +16. Knight, MW., Sobhani, H., Nordlander, P., Halas, NJPhotodetection with Active Optical AntennasScience 332, 702-704 (2011). + +17. White, TP., Catchpole, KRPlasmon-Enhanced Internal Photoemission for Photovoltaics: Theoretical Efficiency LimitsApplPhysLett101, 073905 (2012). + +18. Kornbluth, M., Nitzan, A., Seideman, TLight-Induced Electronic Non-Equilibrium in Plasmonic ParticlesJChemPhys138, 174707 (2013). + +19. Govorov, AO., Zhang, H., Demir, HV., Gun'ko, YKPhotogeneration of Hot Plasmonic Electrons with Metal Nanocrystals: Quantum Description and Potential ApplicationsNano Today 9, 85-101 (2014). + +20. Manjavacas, A., Liu, JG., Kulkarni, V., Nordlander, PPlasmon-Induced Hot Carriers in Metallic NanoparticlesACS Nano 8, 7630-7638 (2014). + +21. Rossi, TP., Erhart, P., Kuisma, MHot-Carrier Generation in Plasmonic Nanoparticles: The Importance of Atomic StructureACS Nano 14, 9963-9971 (2020) + +22. Khurgin, JBFundamental limits of hot carrier injection from metal in nanoplasmonicsNanophotonics 9, 453-471 (2020). + +23. Sa, J., Tagliabue, G., Friedli, P., Szlachetko, J., Rittmann-Frank, MH., Santomauro, FG., Milne, CJ., Sigg, HDirect observation of charge separation on Au localized surface plasmonEnergy EnvironSci6, 3584-3588 (2013). + +24. He, J., Liu, M., Yin, C., Liu, Z., Dong, X., Zhang, Z., Wang, JExperimental studies on the X-ray single-pulse jitter at the SSRFNuclear InstMethPhysRes1025, 166038 (2022). + +25. Pellegrini, CThe history of X-ray free-electron lasersEurPhysJH 37, 659-708 (2012). + +26. https://www.xfel.eu/news_and_events/news/index_eng.html?openDirectAnchor=1772 (Accessed on 2023/11/28). + +27. https://www.psi.ch/en/swissfel/accelerator (Accessed on 2023/11/28). + +28. Alonso-Mori, R., Sokaras, D., Cammarata, M., Ding, Y., Feng, Y., Fritz, D., Gaffney, KJ., Hastings, J., Kao, C.-C., Lemke, HT., et alFemtosecond electronic structure response to high intensity XFEL pulses probed by iron X-ray emission spectroscopySciRep10, 16837 (2020). + +<--- Page Split ---> + +29. Park, SH., Katoch, A., Chae, KH., Gautam, S., Miedema, P., Cho, SW., Kim, M., Wang, R.-P., Lazemi, M., de Groot, F., Kwon, SDirect and real-time observation of hole transport dynamics in anatase TiO2 using X-ray free-electron laserNatCommun13, 2531 (2022). + +30. Jay, RM., Banerjee, A., Leitner, T., Wang, R.-P., Harich, J., Stefanuik, R., Wikmark, H., Coates, MR., Beale, EV., Kabanova, V., et alTracking C-H activation with orbital resolutionScience 380, 955 (2023). + +31. Narang, P., Sundararaman, R., Atwater, HAPlasmonic hot carrier dynamics in solid-state and chemical systems for energy conversionNanophotonics 5, 96-111 (2016). + +32. Brown, AM., Sundararaman, R., Narang, P., Schwartzberg, AM., Goddard III, W., Atwater, HAExperimental and Ab Initio Ultrafast Carrier Dynamics in Plasmonic NanoparticlesPhysRevLett118, 087401 (2017). + +33. Bigot, J.-Y., Merle, J.-C., Cregut, O., Daunois, AElectron Dynamics in Copper Metallic Nanoparticles Probed with Femtosecond Optical PulsesPhysRevLett75, 4702-4705 (1995). + +34. Lehmann, J., Merschdorf, M., Pfeiffer, W., Thon, A., Voll, S., Gerber, GSurface Plasmon Dynamics in Silver Nanoparticles Studied by Femtosecond Time-Resolved Photoemission PhysRevLett85, 2921-2924 (2000). + +35. Zamponi, F., Penfold, TJ., Nachtegaal, M., Lubcke, A, Rittmann, J., Milne, CJ., Chergui, M., van Bokhoven, JAProbing the dynamics of plasmon.excited hexanethiol-capped gold nanoparticles by picosecond X-ray absorption spectroscopyPhysChemChemPhys16, 23157-23163 (2014). + +36. Neutze, R., Wouts, R., van der Spoel, D., Weckert, E., Hajdu, JPotential for biomolecular imaging with femtosecond X-ray pulsesNature 406, 752-757 (2000). + +37. Kern, Jet alSimultaneous femtosecond X-ray spectroscopy and diffraction of photosystem II at room temperatureScience 340, 491-495 (2013). + +38. Johnson, PB., Christy, RWOptical constants of the noble metalsPhysRevB 6, 4370-4379 (1972). + +39. Dreesen, L., Humbert, C., Celebi, M, Lemaire, JJ., Mani, AA., Thiry, PA., Peremans, Ainfuence of the metal electronic properties on the sum-frequency generation spectra of dodecanethiol self-assemble monolayers on Pt (111), Ag (111) and Au(111) single crystalApplPhysB 74, 621-625 (2002). + +40. Escande, DF., Doveil, F., Elskens, YBody description of Debye shielding and Landau dampingPlasma PhysControlled Fusion 58, 014040 (2016). + +41. Bacellar, C., Kinschel, D., Mancini, GF., Ingle, RA., Rouxel, J., Cannelli, O., Cirelli, C., Knopp, G., Szlachetko, J., Lima, FAet alSpin cascade and doming in ferric hemes: Femtosecond X-ray absorption and X-ray emission studiesProcNatlAcadSciUSA 117, 21914-21920 (2020). + +42. Liu, JG., Zhang, H., Links., Nordlander, PRelaxation of Plasmon-Induced Hot CarriersACS Photon5, 2584-2595 (2018). + +43. Douglas-Gallardo, OA., Berdakin, M., Frauenheim, T., Sanchez, CGPlasmon-induced hot carrier generation diffrences in gold and silver nanoclustersNanoscale 11, 8604-8615 (2019). + +<--- Page Split ---> + +44. Tagliabue, G., Jermyn, AS., Sundararaman, R., Welch, AJ., DuChene, JS., Pala, R., Davoyan, AR., Narang, P., Atwater, HAQuantifying the role of surface plasmon excitation and hot carrier transport in plasmonic devicesNatCommun9, 3394 (2018). +45. Krause, MO., Oliver, JHNatural widths of atomic K and L levels, K(X-ray lines and seberal KLL Auger linesJPhysChemRefData 8, 329-338 (1979). +46. Hamalainen, K., Siddons, DP., Hastings, JB., Berman, LEElimination of the Inner-Shell Lifetime Broadening in X-ray-Absorption SpectroscopyPhysRevLett67, 2850-2853 (1991). +47. Gierz, I, Calegari, F., Aeschlimann, S., Chavez Cervantes, M., Cacho, C., Chapman, RT., Springate, E., Link, S., Starke, U., Ast, CR., Cavalleri, ATracking primary thermalization events in graphene with photoemission at extreme time scalesPhysRevLett115, 086803 (2015). +48. Sidiropoulos, TPH., Di Palo, N., Rivas, DE., Severino, S., Reduzzi, M., Bauerhenne, B., Krylow, S., Vasileiades, T., Danz, T., Elliot, P., Sharma, S., et alProbing the energy conversion pathways between light, carriers, and lattice in real time with attosecond core-level spectroscopyPhysRevX 11, 041060 (2021). + +## Figures + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1
+ +X- ray absorption signatures of gold nanoparticles. (A) Illustration depicting the temporal progression of the plasmonic resonance decay mechanism, including hypothesized time constants for associated processes. (B) The generation of hot carriers was investigated using the concept of ultrafast transient XANES, presented schematically. (C) Superimposed \(\mathsf{L}_3\) - edge spectra of steady- state (black trace) and excited- state (red trace) Au nanoparticles with excited spectrum recorded at \(\Delta t = 100\) fs time delay after excitation at \(532 \text{nm}\) . The transient XAS spectrum (blue trace) is the difference between excited + +<--- Page Split ---> + +(pumped) and stead-state (unpumped) spectra. A positive signal in the difference spectrum equates to an increase in empty states (holes) and vice- versa. + +![](images/Figure_2.jpg) + +
Figure 2
+ +Temporal evolution of the generated hot carriers. (A) The difference spectrum (pumped- unpumped signal) shows kinetic traces of energy extraction points. (B) & (C) Time traces (intensities vs time delay) extracted at 11916 eV (i.e. hot holes, green trace) and 11922 eV (i.e. hot electrons, orange trace) X- ray photon energies, respectively. The solid line in the figures (B) and (C) refers to the fitting using the methodology presented elsewhere \(^{41}\) and described in SI. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3
+ +Ultrafast energies distribution dynamics of excited state evolution of gold nanoparticles. (A) Comparison of the valence band photoelectron spectrum (VB- XPS) with the Au L3- edge transient XANES spectrum collected at time zero. The relevant energy scale is given to the Fermi level. (B) The transient XANES measured at the Au L3- edge absorption spectra collected at different pump- probe time delays (0 fs corresponds to the best possible overlap between pump and probe). (C) Relative changes in hot holes mean energy (red trace) and width (blue trace) distributions. + +## Supplementary Files + +<--- Page Split ---> + +This is a list of supplementary files associated with this preprint. Click to download. + +- Supportinginformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__07f28e497a966d2241ff131830d461c8058880986d6873f21a89c91e352b254e/preprint__07f28e497a966d2241ff131830d461c8058880986d6873f21a89c91e352b254e_det.mmd b/preprint/preprint__07f28e497a966d2241ff131830d461c8058880986d6873f21a89c91e352b254e/preprint__07f28e497a966d2241ff131830d461c8058880986d6873f21a89c91e352b254e_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..359e592fee730a1ec818647a4309000924596026 --- /dev/null +++ b/preprint/preprint__07f28e497a966d2241ff131830d461c8058880986d6873f21a89c91e352b254e/preprint__07f28e497a966d2241ff131830d461c8058880986d6873f21a89c91e352b254e_det.mmd @@ -0,0 +1,352 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 857, 175]]<|/det|> +# The Dynamics of Plasmon-Induced Hot Carrier Creation in Colloidal Gold + +<|ref|>text<|/ref|><|det|>[[44, 196, 290, 240]]<|/det|> +Jacinto Sa jacinto.sa@kemi.uu.se + +<|ref|>text<|/ref|><|det|>[[52, 268, 580, 288]]<|/det|> +Uppsala University https://orcid.org/0000- 0003- 2124- 9510 + +<|ref|>text<|/ref|><|det|>[[44, 293, 680, 335]]<|/det|> +Anna Wach SOLARIS National Synchrotron Radiation Centre, Jagiellonian University + +<|ref|>text<|/ref|><|det|>[[44, 340, 680, 383]]<|/det|> +Jakub Szlachetko SOLARIS National Synchrotron Radiation Centre, Jagiellonian University + +<|ref|>text<|/ref|><|det|>[[44, 387, 680, 429]]<|/det|> +Alexey Maximenko SOLARIS National Synchrotron Radiation Centre, Jagiellonian University + +<|ref|>text<|/ref|><|det|>[[44, 433, 944, 497]]<|/det|> +Tomasz Sobol SOLARIS National Synchrotron Radiation Centre, Jagiellonian University https://orcid.org/0000- 0002- 9661- 0932 + +<|ref|>text<|/ref|><|det|>[[44, 502, 680, 544]]<|/det|> +Ewa Partyka- Jankowska SOLARIS National Synchrotron Radiation Centre, Jagiellonian University + +<|ref|>text<|/ref|><|det|>[[44, 549, 620, 590]]<|/det|> +Camila Bacellar Paul Scherrer Institute https://orcid.org/0000- 0003- 2166- 241X + +<|ref|>text<|/ref|><|det|>[[44, 595, 610, 636]]<|/det|> +Claudio Cirelli Paul Scherrer Institute https://orcid.org/0000- 0003- 4576- 3805 + +<|ref|>text<|/ref|><|det|>[[44, 641, 601, 682]]<|/det|> +Philip Johnson Paul Scherrer Institut https://orcid.org/0000- 0002- 7251- 4815 + +<|ref|>text<|/ref|><|det|>[[44, 687, 430, 728]]<|/det|> +Rebeca Gomez Castillo École Polytechnique Fédérale de Lausanne + +<|ref|>text<|/ref|><|det|>[[44, 733, 220, 775]]<|/det|> +Vitor R. Silveira Uppsala University + +<|ref|>text<|/ref|><|det|>[[44, 780, 220, 822]]<|/det|> +Peter Broqvist Uppsala University + +<|ref|>text<|/ref|><|det|>[[44, 827, 220, 868]]<|/det|> +Jolla Kullgren Uppsala University + +<|ref|>text<|/ref|><|det|>[[44, 873, 545, 914]]<|/det|> +Naomi Halas Rice University https://orcid.org/0000- 0002- 8461- 8494 + +<|ref|>text<|/ref|><|det|>[[44, 919, 195, 937]]<|/det|> +Peter Nordlander + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 105, 275, 125]]<|/det|> +## Physical Sciences - Article + +<|ref|>title<|/ref|><|det|>[[44, 144, 136, 163]]<|/det|> +# Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 181, 290, 201]]<|/det|> +Posted Date: April 8th, 2024 + +<|ref|>text<|/ref|><|det|>[[43, 220, 475, 239]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3799527/v1 + +<|ref|>text<|/ref|><|det|>[[42, 256, 915, 300]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 317, 533, 337]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 372, 919, 416]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on March 7th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 57657- 1. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 158, 68]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[40, 82, 951, 400]]<|/det|> +There is an increasing interest in nonequilibrium "hot" carrier generation, created by the decay of collective electronic oscillations on metals known as surface plasmons. Despite extensive efforts, direct observation of the mechanism responsible for generating hot carriers due to plasmon decay has proven challenging. Here, the dynamics of hot carrier generation on gold nanoparticles (Au NPs) are followed with unparalleled detail through ultrafast X- ray absorption spectroscopy (XAS) at the X- ray free- electron laser (XFEL). In Au NPs, the plasmon dephases after 25 fs and the hot carrier population peaks within 105 fs, reaching thermal equilibrium within 1.5 ps. The nonequilibrium carriers display an energy dispersion governed by the density of states of the metal, with some carriers possessing energies surpassing that of a single photon, consistent with the involvement of an Auger heating mechanism distinct from the expected impact excitation that dominates the carrier multiplication step. The most energetic carriers exhibit relatively shorter lifespans, a property that may be critical for exploiting them in applications. This study substantiates hot carrier formation through nonradiative decay as the main decay channel of plasmon resonance. The proposed methodology provides a straightforward approach for real- time tracking of plasmon- induced hot carrier dynamics. + +<|ref|>sub_title<|/ref|><|det|>[[44, 422, 204, 449]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[41, 461, 955, 604]]<|/det|> +Surface plasmons, the collective oscillations of conduction electrons in metallic nanostructures, have emerged as an essential elementary excitation in condensed matter, giving rise to multiple practical applications. They can capture distant radiation and focus it within subwavelength regions, defying diffraction limits,1,2 resulting in potent near- fields and profound field amplifications.3 These attributes have propelled innovative applications of plasmonics, such as highly sensitive biosensing,4 photothermal therapy for cancer,5 photovoltaics,6,7 and photocatalysis.8 + +<|ref|>text<|/ref|><|det|>[[41, 619, 955, 813]]<|/det|> +Surface plasmons exhibit finite lifetimes, decaying either by photon emission (radiatively) or the creation of electron- hole pairs (nonradiatively). Over the past decade, the radiative decay pathway has been researched extensively, yielding the development of efficient nanoantennas that amplify and steer emissions from individual emitters.9,10 Recent research has focused on leveraging nonradiative decay for applications.11 Hot carriers can initiate chemical reactions in adjacent molecules, even those that demand high energy under conventional thermal conditions.12,13 Moreover, plasmon- induced hot carriers offer a potent means to transform light into electrical currents,14 fostering novel solar energy converters15 and circumventing the bandgap limitations of traditional photodetectors.16 + +<|ref|>text<|/ref|><|det|>[[41, 828, 955, 940]]<|/det|> +While the direct excitation of hot carriers on metal surfaces using high- intensity laser pulses has been a longstanding practice in surface femtochemistry, exploiting surface plasmon decay to amplify hot carrier generation is a recent development. This significant advance stems from the remarkably boosted light harvesting ability of collective plasmon excitations, combined with the substantial enhancement of the plasmon- induced field when metals are nano- confined. Comprehending the underlying physical + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 944, 112]]<|/det|> +mechanisms driving plasmon- induced hot carrier generation is essential to leverage these benefits fully. Although theoretical frameworks elucidating this phenomenon exist, \(^{17 - 18}\) [i] 19 [ii] 20 [iii] 21 [iv] 22 a suitable experimental methodology to validate these models still needs to be developed. + +<|ref|>text<|/ref|><|det|>[[41, 129, 945, 288]]<|/det|> +X- ray absorption spectroscopy (XAS) provides a way to investigate the interplay between X- ray photons and matter, simultaneously unveiling unparalleled insights into a material's electronic and chemical characteristics. When X- ray photons are directed toward a material, they can be absorbed by core electrons, resulting in these electrons shifting to higher energy states. The precise energy at which this absorption occurs depends on the specific element's electronic structure and its local environment. Hot carriers emerge from the interaction between external electric fields and valence electrons, creating electrons and holes with energies above and below the Fermi level (EF). + +<|ref|>text<|/ref|><|det|>[[39, 304, 955, 667]]<|/det|> +Transient XAS (aka time- resolved XAS (TR- XAS)) probes empty states around the Fermi energy and, in the case of \(d^{10}\) metals with the L3- edge transition, provides direct information about the amount of carrier participation and their nonequilibrium energy distributions.23 At synchrotrons, such dynamical measurements are typically hampered by limited temporal resolution ( \(\sim 5\) ps) and photon density,24 impeding real- time observations of the hot carrier generation process.8 However, this limitation has been surpassed by the advent of hard X- ray free electron lasers (XFELs),25 capable of delivering intense and ultrashort hard X- ray pulses (up to 30 keV at the European XFEL26 and 12 keV at SwissFEL (used in this study) 27) of less than 50 fs in duration.27,28 With this unique combination of high photon energies and ultrashort pulses, time- resolved XAS has become an exceptionally valuable experimental probe of dynamical processes. Typical time- resolved measurements are implemented in a pump- probe scheme, where an optical- frequency pump laser triggers electron dynamics, and the X- ray probe captures the evolving nonequilibrium electron distribution. Over the past few years, femtosecond TR- XAS studies have been used to probe photoinduced electronic and structural changes in photoexcited transition metal oxides29 and complexes.30 In this study, TR- XAS was used to observe the generation and relaxation of plasmon- induced hot carriers in gold nanoparticles directly.31,32 + +<|ref|>text<|/ref|><|det|>[[39, 683, 950, 960]]<|/det|> +The widely accepted understanding of how localised surface plasmon resonance (LSPR) excitation leads to hot carrier formation and subsequent thermalisation, including the hypothesised timescales for each process, is summarised in Figure 1A.8,22 Briefly, the light electric field induces a coherent excitation of Au valence electrons. The excited electrons' coherence dephases due to Landau damping after the light excitation elapses. The process is expected to take 10- 100 fs, resulting in a non- Fermi- Dirac distribution of hot carriers. The carriers undergo multiplication, eventually reaching a Fermi- Dirac distribution and thermal relaxation after \(\sim 1\) ps. This description of hot carrier formation has been deduced from physical models that underpin our understanding. Still, it has never been validated experimentally due to the lack of element- specific techniques with sufficient temporal resolution. However, the attempts from Bigot et al.33 and Lehmann et al.34 with femtosecond optical pump- probe investigations with ionising probe pulses, which provided earlier evidence for hot electrons and their dynamics, should be mentioned. Nevertheless, no information could be extracted about the hot holes. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[40, 45, 950, 248]]<|/det|> +Figure 1B illustrates the TR- XAS approach for tracking the density of states (DOS) changes induced by LSPR excitation. More specifically, the study focuses on the X- ray absorption near edge structure (XANES) part of the XAS spectrum, which contains the electronic changes in the element, i.e., information on LSPR- induced hot carrier formation. The transient data was collected using the classic pump- probe methodology for optical spectroscopy. The technique involves "pumping" a sample with an initial laser pulse and then "probing" it with a delayed pulse to observe the changes induced by the pump pulse. In the present case, the probe is an fs X- ray pulse from the XFEL. To prevent the excitation of damaged Au NPs induced by intense XFEL pulses, a liquid jet was employed to circulate the Au NPs and the solution was refreshed every four hours. + +<|ref|>text<|/ref|><|det|>[[40, 264, 954, 496]]<|/det|> +Since nanoparticle measurements at XFELs are uncommon, it was essential to validate that the XANES spectra collected with this radiation represent the sample. Figure S1 shows the steady- state XANES spectra of Au foil and nanoparticles measured at the Au \(L_{3}\) - edge transition \((2p_{3 / 2}\lambda 5d)\) at the synchrotron (Solaris synchrotron, Poland). Au has a \([Xe]4f^{14}5d^{10}6s^{1}\) electronic structure, i.e., with a filled \(d\) - shell, which results in a slight absorption edge only visible due to some level of \(s\) - \(d\) shell hybridisation. For comparison purposes, the signal was plotted against Pt \(([Xe]4f^{14}5d^{9}6s^{1})\) (Fig. S2), revealing the method sensitivity to empty states within the metal \(5d\) shell and, to some extent, the \(s\) - shell due to this hybridisation. In this study, Au NPs with an average particle size of \(8\pm 2 \mathrm{nm}\) were used, as confirmed by atomic force microscopy (AFM) and dynamic light scattering (DLS) (Figs. S3 and S4). The Au NPs have a LSPR centered at nominally \(520 \mathrm{nm}\) (2.38 eV) according to UV- vis spectroscopy (Fig. S5). + +<|ref|>text<|/ref|><|det|>[[41, 512, 954, 699]]<|/det|> +The steady- state XANES analysis established that the Au NPs exhibit an electronic structure resembling bulk gold, as reported elsewhere. \(^{22,35}\) This agreement is further corroborated by our theoretical calculations, showing the evolution of the DOS as function of particle size (Fig. S6). The unexcited XANES spectrum of the Au NPs, measured at XFEL (SwissFEL, Switzerland) and the synchrotron, displayed a consistent shape. This consistency supports the applied methodology's ability to capture the transient alterations in the electronic structure of gold before the sample gets damaged, i.e., probe- before destruction concept. \(^{36,37}\) XFELs have only recently provided access to hard X- ray energies, allowing one for the first time to probe the Au \(L_{3}\) - edge. + +<|ref|>text<|/ref|><|det|>[[41, 716, 950, 950]]<|/det|> +Ultrafast time- resolved XANES data were acquired with the XFEL source as a probe, following the excitation of \(5 \mathrm{mM}\) Au NPs at \(532 \mathrm{nm}\) ( \(\sim 2.33 \mathrm{eV}\) ), utilising a \(15 \mathrm{nm}\) full width at half maximum (FHWM) bandwidth, a pulse duration of approximately \(75 \mathrm{fs}\) , and a power density of \(98 \mathrm{mJ / cm^2}\) (equivalent to 4 \(\mu \mathrm{J}\) within a \(60 \times 60 \mu \mathrm{m}^2\) spot). The choice of this precise plasmon excitation energy was to induce LSPR through intra- band \(s\) - to \(s\) - shell excitation while minimising interband excitation ( \(d\) - to \(s\) - shell excitation). The centre of the Au \(d\) - shell is located at \(2.5 - 2.58 \mathrm{eV}\) ( \(\sim 496 - 480 \mathrm{nm}\) ) from the metal Fermi level ( \(E_{\mathrm{F}}\) ), \(^{38,39}\) meaning that the laser pulse with \(2.33 \pm 0.13 \mathrm{eV}\) ( \(15 \mathrm{nm}\) FHWM) photon energy can only excite the low energy tail of the \(d\) - shell at best. Figure 1C compares the XANES spectra of unexcited (unpumped spectrum) and excited (pumped spectrum) recorded at \(\mathrm{Dt} = 100 \mathrm{fs}\) time delay after excitation at \(532 \mathrm{nm}\) . Optical excitation induced a spectral downshift in energy and decreased XANES whiteline intensity, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 936, 88]]<|/det|> +corroborating that it induced changes in the gold electronic structure around its Fermi- level energy, and the TR- XAS can track the changes. + +<|ref|>text<|/ref|><|det|>[[40, 105, 940, 431]]<|/det|> +To better illustrate the results, the XANES difference spectrum (pumped- unpumped XANES spectra) is also shown in Figure 1C. The difference spectrum is dominated by the positive signal below and a negative signal above the Au \(\mathsf{E}_{\mathsf{F}}\) . Transient \(\mathsf{L}_{3}\) - edge XANES readily capture changes in the density of unoccupied states, particularly those induced in the \(d\) - shell, either directly or through processes like hybridisation with the \(s\) - shell. Accordingly, a positive signal correlates with an increase in density of states (DOS); conversely, a negative signal (i.e., a bleached signal) indicates a decrease in empty states. Therefore, the positive signal below the Au \(\mathsf{E}_{\mathsf{F}}\) is ascribed to the formation of a hot hole population induced by the plasmon optical excitation. In contrast, hot electrons give rise to the negative signal above the Au \(\mathsf{E}_{\mathsf{F}}\) , consistent with empty states filling. The transient signal directly demonstrates the generation of hot carriers through LSPR decoherence via Landau damping (the non- radiative pathway dominant in small nanoparticles). \(^{21,24,40}\) Most notably, the hot hole and electron signals are neither symmetric nor have the same integrated magnitude. This is related to the XANES higher sensitivity to empty states formation and the \(\mathsf{L}_{3}\) - edge transition changes in the \(d\) - shell that is part of the valence, where the hot holes are formed. + +<|ref|>text<|/ref|><|det|>[[40, 447, 927, 658]]<|/det|> +To establish the time scales for plasmon damping (g) and the average lifetime of carriers (t), kinetic traces were extracted at the maximum of the hot hole intensity (11916 eV, 2.5 eV below Au \(\mathsf{E}_{\mathsf{F}}\) (Figure 2B)) and the excited electron intensity (11922 eV, 3.0 eV above Au \(\mathsf{E}_{\mathsf{F}}\) (Figure 2C)) populations, as depicted in (Figure 2A). The kinetic data from the time scans were fitted by a model published elsewhere, \(^{41}\) described in SI equations S1 and S2. In brief, the data collected at 11916 and 11922 eV were fitted with a convolution of temporal instrument response function (Gaussian) with a monoexponential decay (with a time constant). The resulting fit is the solid green in Figure 2B. Due to the low signal- to- noise ratio for the hot electron data, the error bars are relatively large. However, qualitatively, it is possible to see that the signal has dynamics similar to the hot holes. + +<|ref|>text<|/ref|><|det|>[[40, 675, 949, 880]]<|/det|> +The \(\gamma\) time can be extracted from the transient signal onset time because it is the point at which the Au DOS starts to change, i.e., the fingerprint for hot carrier formation. In this particular case, it was estimated to be \(24.6 \pm 6\) fs, corroborating that plasmon decoherence occurs between 10- 100 fs. \(^{24}\) Following plasmon damping, the hot carriers undergo a carrier multiplication reaching a maximum at \(105 \pm 8\) fs, estimated from rising edge analysis. The lifetimes of the hot carriers were determined from a single exponential decay to be \(498 \pm 35\) fs with complete carrier thermalisation occurring within 1.5 ps. These time constants align with previous postulations \(^{24}\) but are here substantiated through direct measurement. The confirmed ultrafast hot carrier dynamics in plasmonic nanoparticles are the primary bottleneck in plasmonic applications. + +<|ref|>text<|/ref|><|det|>[[42, 899, 937, 942]]<|/det|> +To estimate the number of electrons engaged when exciting 5 mM Au NPs at 532 nm, utilising a 15 nm full width at half maximum (FHWM) bandwidth, a pulse duration of approximately 75 fs, and a power + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[40, 46, 950, 239]]<|/det|> +density of \(98~\mathrm{mJ / cm^2}\) , the positive signal variance at 0 and 100 fs were integrated. This integrated signal was then juxtaposed with the signal difference between the Au and Pt \(\mathsf{L}_3\) - edges (Fig. S2). Note that the signal difference between Au and Pt relates to \(1e^{- }\) less in Pt valence states, i.e., the integrated positive signal of the difference between Pt and Au corresponds to the equivalent of having \(1e^{- }\) from each Au atom participating in the resonance. Employing this simple methodology, we estimated that each gold atom contributed with \(0.19e^{- }\) at the start of the resonance, which underwent multiplication until 105 fs, reaching a maximum of \(0.46e^{- }\) from each Au atom contributing to hot carrier formation at this excitation power. + +<|ref|>text<|/ref|><|det|>[[40, 256, 941, 444]]<|/det|> +Assuming an excitation volume of \(60\times 60\times 100\mu \mathrm{m}^3\) and considering the Au solution concentration (5 mM), one can expect \(1.5\times 10^{8}\) nanoparticles in the excited volume. An \(8\mathrm{nm}\) Au NP has \(>12000\) atoms, equating to about \(1.8\times 10^{12}\) Au atoms in the excited volume. The photon density in the optical pulses is about \(10^{13}\) , from which \(20\%\) is absorbed according to UV- Vis, implying that the excited volume absorbs around \(2\times 10^{12}\) photons. This suggests an excitation of about \(1e^{- }\) per atom of Au, from which \(19\%\) are converted into hot carriers at the onset, multiplying to about \(46\%\) within 100 fs. The observation suggests that hot carrier generation is a prime decay channel of Au LSPR and undoubtedly the most significant mechanism in nonradiative decay. + +<|ref|>text<|/ref|><|det|>[[40, 460, 950, 715]]<|/det|> +After verifying the generation of hot carriers, the next step is the investigation of the dynamics of their energy distribution - a significant yet elusive aspect in the realm of plasmonic hot carriers, particularly when it comes to holes. Our understanding is derived mainly from theoretical studies \(^{20,4243}\) and indirect techniques. \(^{22,33,34,44}\) For example, internal quantum efficiency measurements have inherent limitations as they solely quantify carriers injected into an acceptor layer, like a semiconductor, failing to provide insights into the dynamic behaviour of the carriers in the metal. While the hot electrons can only populate the empty states within the sp- shells, the holes can be in sp- and d- shells, confirmed by valence band - X- ray photoelectron spectroscopy (VB- XPS) shown in Figure 3A. It is evident when the VB- XPS is overlapped with the transient XANES spectrum (recorded at time zero) that the generated holes are indeed located throughout the entire valence, including the d- shell, despite the optical pulse energy allowing primarily sp- shell excitation. + +<|ref|>text<|/ref|><|det|>[[40, 731, 950, 911]]<|/det|> +Figure 3B shows the energy distribution and population of the carriers at different time delays after excitation. As expected, the plasmonic excitation depopulates and populates states below and above the Fermi energy. The ultrafast carrier- carrier interactions during dephasing and multiplication determine their energy and respective population. The hot carrier energy distribution goes beyond single photon energy for hot electrons and holes. Moreover, it is noticeable that both carrier populations and the width of their energy distributions increase until about 100 fs, decreasing asymptotically after that. A slight asymmetry exists between hot electron and hot hole populations, which cannot be fully explored here due to the probe's lower sensitivity to hot electrons. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 43, 949, 327]]<|/det|> +The rapid depopulation of electrons in the \(d\) - shell is expected due to the broad energy overlap between the \(d\) and \(sp\) - band, which provides a high density of \(d\) - electrons that couples with the plasmonic resonance and dissipates its energy. \(^{43}\) However, this does not explain the observation of carriers having energies above the photon energy, even considering that the Au core- hole lifetime broadening is 5.41 eV at the \(L_{3}\) - edge, \(^{45}\) which inevitably broadens the energy scale. Achieving precise energy distributions of carriers requires high- resolution measurements, \(^{46}\) which implies extended acquisition times rarely offered at XFEL facilities. Nonetheless, hot holes are distributed across the entire valence electronic structure, and their energy distribution increases up to 250 fs (Figure 3C) before relaxing. These two observations indicate the involvement of carrier multiplication mechanisms that can increase the carrier population and their energy distribution, an effect that has yet to be reported. \(^{43}\) Note that the low optical laser fluency and short pulse duration used in this experiment make it highly unlikely that multiphoton excitation of single electrons occurs. + +<|ref|>text<|/ref|><|det|>[[40, 343, 947, 525]]<|/det|> +Regarding carrier multiplication, there are two scattering mechanisms: impact excitation and Auger heating, \(^{47,48}\) The predominant mechanism in carrier multiplication is impact excitation, where an excited electron (hole) undergoes Coulomb scattering, losing energy and momentum and giving rise to an additional electron- hole pair. The distinctive feature of impact excitation is a rise in the number of carriers and a simultaneous reduction in their energy. Conversely, Auger heating characterises the non- radiative recombination of an electron with a hole, where the energy and momentum are transferred to an electron (hole) within the same shell. The hallmark of Auger heating is a decline in the number of carriers and an increase in their energy. + +<|ref|>text<|/ref|><|det|>[[39, 541, 955, 906]]<|/det|> +To enhance the visualisation and comprehension of the hot hole multiplication process, the shape of different spectra regarding charge width and charge energy was analysed (see Figure 3C). The details of the data analysis procedure are outlined in the SI. Commencing with the average hot carrier distribution energy, it remained constant until 100 fs before exhibiting a subsequent decrease. This implies the LSPR dephasing process extends to 100 fs, increasing the nonequilibrium hot carrier population through the impact excitation scattering mechanism. However, examining the hot carrier distribution width, reflecting the energy distribution of the hot holes unveils a relative surge in the energy distribution beyond the time when the hole population is at its maximum (approximately 100 fs), i.e., the energy distribution of hot holes increases up to 250 fs. This observation is noteworthy, especially considering this process competes with hole thermalisation, occurring within tens of femtoseconds in metals. The broadening induced by the core hole relaxation cannot account for the increase in distribution width, as it should have smeared the energy resolution from the outset, preventing the difference signal from accurately reflecting the valence state of gold. This suggests the involvement of a mechanism that generates carriers with higher energy than the dephasing process produces - specifically, the participation of Auger heating. This mechanism has yet to be considered in plasmon relaxation dynamics, altering the current understanding of hot carrier formation, multiplication, and relaxation in plasmonic materials. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 45, 956, 386]]<|/det|> +In this work, we presented the results from an ultrafast X- ray absorption experiment conducted at the XFEL involving citrate- capped gold nanoparticles excited at their LSPR with minimum intraband excitation. This experiment enabled the real- time observation of the generation and subsequent relaxation of hot carriers. The plasmon damping was determined to be 25 fs, with a maximum hot carrier population of \(0.46e^{- }\) from each Au atom detected at 105 fs after excitation. The lifetimes of the hot carriers were estimated to be 498 fs, with complete carrier thermalisation occurring within 1.5 ps. Energy scans conducted at varying delay times revealed that the energies of these carriers conform to the density of states of the metal, with some carriers possessing energies that exceed the photon energy, consistent with an Auger heating scattering mechanism. The observation impacts hot carrier applications, particularly those that are based on the energy of the hot carriers, such as photocatalysis and photovoltaics. For instance, without the Auger process, chemical reactions with redox windows larger than photon energy could not be catalysed. Similarly, the open circuit voltage of photovoltaic devices could not exceed the voltage offered by a single photon. The novel insight into plasmon induced hot carrier generation and dynamics provided here is likely to significantly impact applications for years to come. + +<|ref|>text<|/ref|><|det|>[[39, 401, 953, 582]]<|/det|> +[i]. Kornbluth, M., Nitzan, A., Seideman, T. Light- Induced Electronic Non- Equilibrium in Plasmonic Particles. J. Chem. Phys. 138, 174707 (2013). [ii]. Govorov, A. O., Zhang, H., Demir, H. V., Gun'ko, Y. K. Photogeneration of Hot Plasmonic Electrons with Metal Nanocrystals: Quantum Description and Potential Applications. Nano Today 9, 85- 101 (2014). [iii]. Manjavacas, A., Liu, J. G., Kulkarni, V., Nordlander, P. Plasmon- Induced Hot Carriers in Metallic Nanoparticles. ACS Nano 8, 7630- 7638 (2014). [iv]. Rossi, T. P., Erhart, P., Kuisma, M. Hot- Carrier Generation in Plasmonic Nanoparticles: The Importance of Atomic Structure. ACS Nano 14, 9963- 9971 (2020). + +<|ref|>sub_title<|/ref|><|det|>[[44, 604, 210, 630]]<|/det|> +## Declarations + +<|ref|>sub_title<|/ref|><|det|>[[44, 646, 218, 665]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[42, 682, 955, 795]]<|/det|> +We acknowledge the Paul Scherrer Institut, Villigen, Switzerland, for providing beamtime at the Alvra beamline of the SwissFEL facility. We also acknowledge SOLARIS National Synchrotron Radiation Centre, Krakow, Poland, for the access to the ASTRA and PHELIX beamline. The simulations were performed using computational resources provided by the Swedish National Infrastructure for Computing (SNIC) at UPPMAX and NSC, for which we want to thank. + +<|ref|>text<|/ref|><|det|>[[41, 811, 945, 947]]<|/det|> +Funding: J.Sa acknowledges funding from Olle Engkvists Stiftelse (grant no. 210- 0007), Knut & Alice Wallenberg Foundation (Grant No. 2019- 0071) and Swedish Research Council (grant no. 2019- 03597). A.W. acknowledges funding from the European Union's Horizon 2020 research and innovation program under Marie Sklodowska- Curie grant agreement no. 884104 (PSI- FELLOW- III- 3i). N.J.H. and P.N. acknowledge support from the Robert A. Welch Foundation under grants C- 1220 and C- 1222 and the Air Force Office of Scientific Research via the Department of Defense Multidisciplinary University Research + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 44, 940, 156]]<|/det|> +Initiative under AFOSR Award No. FA9550- 15- 1- 0022. The work is partially supported under the Polish Ministry and Higher Education project: "Support for research and development with the use of research infrastructure of the National Synchrotron Radiation Centre SOLARIS" under contract nr 1/SOL/2021/2. The work is partially funded by the National Science Centre in Poland under grant number 2020/37/B/ST3/00555. + +<|ref|>text<|/ref|><|det|>[[41, 173, 950, 285]]<|/det|> +Author contributions: Conceptualization and methodology: A.W., J. Sz. and J.Sa; formal data analysis: A.W., J. Sz. and J.Sa; experimental investigations: A.W, C.B., C.C., P.J.M.J., R.G.C., V.R.S., P-B., J.K., A.M., T.S., E.P.- J. and J.Sa; data visualisation concepts: A.W., J. Sa, J.Sz and N.J.H., draft preparation: A.W., N.J.H., J. Sz. and J.Sa; writing- review and editing: all the authors. All authors have read and agreed to the published version of the manuscript. + +<|ref|>text<|/ref|><|det|>[[44, 302, 740, 322]]<|/det|> +Competing interests: The authors declare that they have no competing interests. + +<|ref|>text<|/ref|><|det|>[[42, 339, 930, 405]]<|/det|> +Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors. + +<|ref|>sub_title<|/ref|><|det|>[[44, 428, 193, 454]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[52, 469, 944, 919]]<|/det|> +1. Novotny, L., Hecht, BPrinciples of Nano-OpticsCambridge University Press: New York, (2006). +2. Maier, SAPlasmonics: Fundamentals and ApplicationsSpringer: New York (2007). +3. Halas, NJ., Lal, S., Chang, W., Link, S., Nordlander, PPlasmons in Strongly Coupled Metallic NanostructuresChemRev111, 3913-3961 (2011). +4. Xu, H., Bjerneld, EJ., Kall, M., Borjesson, LSpectroscopy of Single Hemoglobin Molecules by Surface Enhanced Raman ScatteringPhysRevLett83, 4357-4360 (1999). +5. O'Neal, DP., Hirsch, LR., Halas, NJ., Payne, JD., West, JLPhto-Thermal Tumor Ablation in Mice Using Near Infrared-Absorbing NanoparticlesCancer Lett209, 171-176 (2004). +6. Atwater, HA., Polman, APlasmonics for Improved Photovoltaic DevicesNatMater9, 205-213 (2010). +7. 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Zamponi, F., Penfold, TJ., Nachtegaal, M., Lubcke, A, Rittmann, J., Milne, CJ., Chergui, M., van Bokhoven, JAProbing the dynamics of plasmon.excited hexanethiol-capped gold nanoparticles by picosecond X-ray absorption spectroscopyPhysChemChemPhys16, 23157-23163 (2014). + +<|ref|>text<|/ref|><|det|>[[45, 480, 945, 526]]<|/det|> +36. Neutze, R., Wouts, R., van der Spoel, D., Weckert, E., Hajdu, JPotential for biomolecular imaging with femtosecond X-ray pulsesNature 406, 752-757 (2000). + +<|ref|>text<|/ref|><|det|>[[45, 530, 953, 576]]<|/det|> +37. Kern, Jet alSimultaneous femtosecond X-ray spectroscopy and diffraction of photosystem II at room temperatureScience 340, 491-495 (2013). + +<|ref|>text<|/ref|><|det|>[[45, 580, 920, 600]]<|/det|> +38. Johnson, PB., Christy, RWOptical constants of the noble metalsPhysRevB 6, 4370-4379 (1972). + +<|ref|>text<|/ref|><|det|>[[45, 604, 933, 695]]<|/det|> +39. Dreesen, L., Humbert, C., Celebi, M, Lemaire, JJ., Mani, AA., Thiry, PA., Peremans, Ainfuence of the metal electronic properties on the sum-frequency generation spectra of dodecanethiol self-assemble monolayers on Pt (111), Ag (111) and Au(111) single crystalApplPhysB 74, 621-625 (2002). + +<|ref|>text<|/ref|><|det|>[[45, 699, 940, 745]]<|/det|> +40. Escande, DF., Doveil, F., Elskens, YBody description of Debye shielding and Landau dampingPlasma PhysControlled Fusion 58, 014040 (2016). + +<|ref|>text<|/ref|><|det|>[[45, 749, 872, 817]]<|/det|> +41. Bacellar, C., Kinschel, D., Mancini, GF., Ingle, RA., Rouxel, J., Cannelli, O., Cirelli, C., Knopp, G., Szlachetko, J., Lima, FAet alSpin cascade and doming in ferric hemes: Femtosecond X-ray absorption and X-ray emission studiesProcNatlAcadSciUSA 117, 21914-21920 (2020). + +<|ref|>text<|/ref|><|det|>[[45, 821, 930, 866]]<|/det|> +42. Liu, JG., Zhang, H., Links., Nordlander, PRelaxation of Plasmon-Induced Hot CarriersACS Photon5, 2584-2595 (2018). + +<|ref|>text<|/ref|><|det|>[[45, 871, 890, 916]]<|/det|> +43. Douglas-Gallardo, OA., Berdakin, M., Frauenheim, T., Sanchez, CGPlasmon-induced hot carrier generation diffrences in gold and silver nanoclustersNanoscale 11, 8604-8615 (2019). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[46, 44, 952, 377]]<|/det|> +44. Tagliabue, G., Jermyn, AS., Sundararaman, R., Welch, AJ., DuChene, JS., Pala, R., Davoyan, AR., Narang, P., Atwater, HAQuantifying the role of surface plasmon excitation and hot carrier transport in plasmonic devicesNatCommun9, 3394 (2018). +45. Krause, MO., Oliver, JHNatural widths of atomic K and L levels, K(X-ray lines and seberal KLL Auger linesJPhysChemRefData 8, 329-338 (1979). +46. Hamalainen, K., Siddons, DP., Hastings, JB., Berman, LEElimination of the Inner-Shell Lifetime Broadening in X-ray-Absorption SpectroscopyPhysRevLett67, 2850-2853 (1991). +47. Gierz, I, Calegari, F., Aeschlimann, S., Chavez Cervantes, M., Cacho, C., Chapman, RT., Springate, E., Link, S., Starke, U., Ast, CR., Cavalleri, ATracking primary thermalization events in graphene with photoemission at extreme time scalesPhysRevLett115, 086803 (2015). +48. Sidiropoulos, TPH., Di Palo, N., Rivas, DE., Severino, S., Reduzzi, M., Bauerhenne, B., Krylow, S., Vasileiades, T., Danz, T., Elliot, P., Sharma, S., et alProbing the energy conversion pathways between light, carriers, and lattice in real time with attosecond core-level spectroscopyPhysRevX 11, 041060 (2021). + +<|ref|>sub_title<|/ref|><|det|>[[44, 399, 143, 425]]<|/det|> +## Figures + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[55, 60, 930, 722]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 748, 115, 768]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[40, 789, 949, 927]]<|/det|> +X- ray absorption signatures of gold nanoparticles. (A) Illustration depicting the temporal progression of the plasmonic resonance decay mechanism, including hypothesized time constants for associated processes. (B) The generation of hot carriers was investigated using the concept of ultrafast transient XANES, presented schematically. (C) Superimposed \(\mathsf{L}_3\) - edge spectra of steady- state (black trace) and excited- state (red trace) Au nanoparticles with excited spectrum recorded at \(\Delta t = 100\) fs time delay after excitation at \(532 \text{nm}\) . The transient XAS spectrum (blue trace) is the difference between excited + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 936, 88]]<|/det|> +(pumped) and stead-state (unpumped) spectra. A positive signal in the difference spectrum equates to an increase in empty states (holes) and vice- versa. + +<|ref|>image<|/ref|><|det|>[[66, 95, 936, 744]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 776, 117, 796]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[41, 816, 944, 932]]<|/det|> +Temporal evolution of the generated hot carriers. (A) The difference spectrum (pumped- unpumped signal) shows kinetic traces of energy extraction points. (B) & (C) Time traces (intensities vs time delay) extracted at 11916 eV (i.e. hot holes, green trace) and 11922 eV (i.e. hot electrons, orange trace) X- ray photon energies, respectively. The solid line in the figures (B) and (C) refers to the fitting using the methodology presented elsewhere \(^{41}\) and described in SI. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[101, 60, 860, 710]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 730, 117, 750]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[42, 770, 949, 911]]<|/det|> +Ultrafast energies distribution dynamics of excited state evolution of gold nanoparticles. (A) Comparison of the valence band photoelectron spectrum (VB- XPS) with the Au L3- edge transient XANES spectrum collected at time zero. The relevant energy scale is given to the Fermi level. (B) The transient XANES measured at the Au L3- edge absorption spectra collected at different pump- probe time delays (0 fs corresponds to the best possible overlap between pump and probe). (C) Relative changes in hot holes mean energy (red trace) and width (blue trace) distributions. + +<|ref|>sub_title<|/ref|><|det|>[[44, 933, 312, 960]]<|/det|> +## Supplementary Files + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 768, 64]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 83, 317, 101]]<|/det|> +- Supportinginformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__07fc271c3e70c1e56898e60a1febbc2db8cea3186f51b0a6b8ad23d104d455a0/images_list.json b/preprint/preprint__07fc271c3e70c1e56898e60a1febbc2db8cea3186f51b0a6b8ad23d104d455a0/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..431bec1a26c00f69a2a30af876b98a48dcfa664d --- /dev/null +++ b/preprint/preprint__07fc271c3e70c1e56898e60a1febbc2db8cea3186f51b0a6b8ad23d104d455a0/images_list.json @@ -0,0 +1,47 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1. Esyt1 overexpression in SOD1G93A;En1cre mice. a) Expression of Esyt1 transcript in the lumbar spinal cord of a WT mouse visualized by RNascope. b) 40X magnification microphotograph showing Esyt1 expression in the ventral horn of the spinal cord in a WT mouse. c) Esyt1 is downregulated in neurons during early ALS progression in the ventral horn of the spinal cord. Esyt1 in orange, DAPI in light blue. d) Quantifications performed in WT and SOD1G93A mice at postnatal day 45 and 63 show downregulation of Esyt1 transcript starting at P63 (Oneway ANOVA and Dunnett's post hoc, P45 P=0.0714, P63 P=0.0191, N=3 per timepoint, per genotype). e) Cartoon depicting methodological approach in all experiments. Intraspinal injections delivering AAV vectors were performed in WT, En1cre, SOD1G93A and SOD1G93A;En1cre mice throughout the study, all genotypes were injected in L1-L3 spinal segments. f) Schematic of cre-dependent constructs designed to overexpress hEsyt1 and mCherry. g) Longitudinal section of the spinal cord of an En1cre mouse upon overexpression of the AAV8-hSyn-DIO-mCherry virus validates successful cre-dependent expression in the ventral/medial areas of the cord. AAV8-hSyn-DIO-hEsyt1-W3SL driven overexpression was analyzed by RNascope utilizing a probe recognizing the inverted viral construct upon cre-recombination. h) Expression of viral-hEsyt1 in En1cre mice. i) Close-up microphotograph showing a neuron overexpressing hEsyt1. j) The same neuron is also positive for En1 transcript. k) hEsyt1 was not detected WT injected mice. I-m) En1 positive neuron negative for hEsyt1. n) Quantification of Esyt1 positive neurons in the lumbar spinal cord of En1cre and WT mice, 20% of En1+ neurons are positive for the hEsyt1 (t test, P=0.0057, En1cre N=4, WT N=3). Scale bar in a) = 100 μm, in b) = 50 μm and in h) = 100 μm. All graphs show mean values ± SEM.", + "footnote": [], + "bbox": [ + [ + 24, + 20, + 944, + 472 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2. Esyt1 overexpression increases motor neuron survival and synaptic density onto spared motor neurons. Quantifications of inhibitory synapses on spared motor neurons at postnatal day 112 upon AAV8-hSyn-DIO-hEsyt1 injections. Synaptic densities are normalized by motor neuron area. Masks in a) and b) show synaptic density in WT and En1cre mice respectively. c) and d) show differences in synaptic densities between SOD1G93A and SOD1G93A;En1cre mice. Microphotographs show examples of quantified motor neurons in the different genotypes. VGAT in red, ChAT in green, DAPI in blue. Scale bar in g) = 50 μm. e) Synaptic density in SOD1G93A;En1cre is significantly increased compared to SOD1G93A and comparable to the synaptic densities of WT and En1cre mice (One-way ANOVA and Dunnett's post hoc WT P = 0.0060, En1cre P = 0.0097, SOD1G93A;En1cre P = 0.0149, N = 4). Motor neuron quantifications performed at postnatal day 112 in f) WT, g) En1cre, h) SOD1G93A and i) SOD1G93A;En1cre mice. Fluoro-Nissl in red. Scale bar in i) = 50 μm. l) SOD1G93A;En1cre mice show increased motor neuron survival upon Esyt1 overexpression when compared to the SOD1G93A mice (One-way ANOVA and Dunnett's post hoc SOD1G93A; En1cre P = 0.0080, WT P = 0.0023, N = 5). A minimum of 170 motor neurons per condition was quantified. En1cre mice overexpressing hEsyt1 show a trend in lower number of large motor neurons in the lumbar spinal cord (One-way ANOVA and Dunnett's post hoc, En1cre P = 0.2732, N = 5). m) Comparison of motor neuron size in En1cre non-injected and En1cre injected mice with AAV8-hSyn-DIO-hEsyt1 shows shrinkage of motor neurons upon hEsyt1 overexpression (t test, P = 0.0073, En1cre non-injected N = 4, En1cre injected N = 5). All graphs show mean values ± SEM.", + "footnote": [], + "bbox": [ + [ + 17, + 0, + 970, + 377 + ] + ], + "page_idx": 9 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3. Amelioration of locomotor phenotype in SOD1G93A mice upon hEyt1 overexpression. a) Cartoon depicting treadmill paradigm. b) Average speed analyzed between day 49 and 112 shows amelioration in SOD1G93A;En1cre mice from P63 (Two-way ANOVA and Dunnett's post hoc, \\(\\mathsf{P} = 0.0257\\) ), c) step frequency from P70 (Two-way ANOVA and Dunnett's post hoc, \\(\\mathsf{P} = 0.0318\\) ), and d) stride length from P84 (Two-way ANOVA and Dunnett's post hoc, \\(\\mathsf{P} = 0.0096\\) ). e) Peak acceleration does not change upon Esyt1 overexpression (Two-way ANOVA and Dunnett's post hoc, \\(\\mathsf{N} = 6 - 8\\) mice per condition; all quantifications were performed in triplicates). At P112 timepoint f) average speed is higher in SOD1G93A;En1cre compared to SOD1G93A mice (One-way ANOVA and Dunnett's post hoc, SOD1G93A;En1cre vs SOD1G93A \\(\\mathsf{P} = 1.8e - 07\\) ), as well as g) step frequency (One-way ANOVA and Dunnett's post hoc, \\(\\mathsf{P} = 3.1e - 06\\) ) and h) stride length (One-way ANOVA and Dunnett's post hoc, \\(\\mathsf{P} = 0.0032\\) ), however i) peak acceleration remains unchanged (One-way ANOVA and Dunnett's post hoc, \\(\\mathsf{P} = 0.7406\\) ) (WT \\(\\mathsf{N} = 6\\) ; SOD1G93A \\(\\mathsf{N} = 8\\) , En1cre \\(\\mathsf{N} = 6\\) , SOD1G93A;En1cre \\(\\mathsf{N} = 6\\) , all quantifications were performed in triplicates). j) Stick figures depict intralimb coordination in SOD1G93A and SOD1G93A;En1cre mice. Two full cycles are visualized for SOD1G93A and SOD1G93A;En1cre mice upon AAV8-hSyn-DIO-hEsyt1 injections. Stance phase in blue and swing phase in green. Changes in joint angles were analyzed for k) hip angle, l) knee angle, m) ankle angle and n) foot angle. Significant changes were found for the foot angle (One-way ANOVA and Dunnett's post hoc, foot angle \\(\\mathsf{P} = 4.1e - 05\\) ) and the ankle angle (One-way ANOVA and Dunnett's post hoc, foot angle \\(\\mathsf{P} = 2.8e - 05\\) ) upon hEsyt1 overexpression (WT \\(= 5\\) , SOD1G93A \\(\\mathsf{N} = 8\\) , En1cre \\(\\mathsf{N} = 6\\) , SOD1G93A;En1cre \\(\\mathsf{N} = 6\\) , all quantifications were performed in triplicates). All graphs show mean values \\(\\pm\\) SEM, averages values in f-l and k-n are shown in black, technical triplicates are shown in gray.", + "footnote": [], + "bbox": [ + [ + 20, + 25, + 950, + 420 + ] + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/preprint/preprint__07fc271c3e70c1e56898e60a1febbc2db8cea3186f51b0a6b8ad23d104d455a0/preprint__07fc271c3e70c1e56898e60a1febbc2db8cea3186f51b0a6b8ad23d104d455a0.mmd b/preprint/preprint__07fc271c3e70c1e56898e60a1febbc2db8cea3186f51b0a6b8ad23d104d455a0/preprint__07fc271c3e70c1e56898e60a1febbc2db8cea3186f51b0a6b8ad23d104d455a0.mmd new file mode 100644 index 0000000000000000000000000000000000000000..afd73b9508e7341897eda4b1b288334d24b5bdc4 --- /dev/null +++ b/preprint/preprint__07fc271c3e70c1e56898e60a1febbc2db8cea3186f51b0a6b8ad23d104d455a0/preprint__07fc271c3e70c1e56898e60a1febbc2db8cea3186f51b0a6b8ad23d104d455a0.mmd @@ -0,0 +1,122 @@ + +# Stabilization of V1 interneuron-motor neuron connectivity ameliorates motor phenotype in a mouse model of ALS + +Santiago Mora University of Copenhagen Rasmus von Huth Friis University of Copenhagen Anna Stuckert University of Copenhagen Gith Noes- Holt University of Copenhagen Roser Montañana- Rosell University of Copenhagen https://orcid.org/0000- 0003- 4268- 4765 Andreas Sørensen Massachusetts Institute of Technology https://orcid.org/0000- 0001- 5887- 9247 Raghavendra Selvan University of Copenhagen https://orcid.org/0000- 0003- 4302- 0207 Ilary Allodi iallodi@sund.ku.dk + +University of Copenhagen https://orcid.org/0000- 0003- 4361- 163X + +## Article + +Keywords: + +Posted Date: March 7th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 2613071/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> + +Version of Record: A version of this preprint was published at Nature Communications on June 7th, 2024. See the published version at https://doi.org/10.1038/s41467-024-48925-7. + +<--- Page Split ---> + +1 Stabilization of V1 interneuron- motor neuron connectivity ameliorates motor phenotype in a mouse model of ALS + +3 Santiago Mora \(^{1*}\) , Rasmus von Huth Friis \(^{1*}\) , Anna Stuckert \(^{1}\) , Gith Noes- Holt \(^{1}\) , Roser Montañana- Rosell \(^{1}\) , Andreas Toft Sørensen \(^{1}\) , Raghavendra Selvan \(^{2}\) , Ilary Allodi \(^{1*}\) + +6 1 Department of Neuroscience, University of Copenhagen, Denmark 8 2 Department of Computer Science, University of Copenhagen, Denmark 9 \* These authors equally contributed to the work 10 \* Corresponding author + +## Abstract + +Loss of connectivity between spinal V1 inhibitory interneurons and motor neurons is found early in disease in the SOD1 \(^{G93A}\) ALS mice. Such changes in premotor inputs can contribute to homeostatic imbalance of vulnerable motor neurons. Here, we show, for the first time, that stabilization of V1 synapses by overexpression of the Extended Synaptotagmin 1 presynaptic organizer increases motor neuron survival and ameliorates motor phenotypes, demonstrating that interneurons can be a potential target to attenuate ALS symptoms. + +## Main + +Somatic motor neurons are the ultimate output of the brain since they control movements by directly connecting to muscles. Their synchronized activation is regulated by a complex network of inhibitory and excitatory spinal interneurons \(^{1}\) . Hence, functional connectivity between motor neurons and their premotor circuits is a prerequisite for maintenance of inhibitory- excitatory balance and execution of movements. In the fatal disease Amyotrophic Lateral Sclerosis (ALS), somatic motor neurons degenerate, and subjects progressively lose the ability to perform movements. In our previous work \(^{2}\) , we showed that the spinal V1 inhibitory interneurons, positive for Engrailed- 1 (En1) marker, lose their synapses onto the vulnerable fast- twitch fatigable motor neurons early in disease in the SOD1 \(^{G93A}\) ALS mouse model \(^{3}\) . This preferential loss of inhibitory inputs onto fast- twitch fatigable motor neurons might contribute to their unbalanced excitability \(^{4}\) , leading to excitotoxicity and ultimately to their vulnerability to disease. Moreover, V1 inhibitory interneurons are known to control the speed of locomotion in vertebrates \(^{5}\) , and the loss of connectivity observed in the SOD1 \(^{G93A}\) mice led to an onset of locomotor phenotype. Such phenotype is characterized by a reduction of speed and acceleration, a decrease in stride length and step frequency, and a hyperflexion of the hindlimbs \(^{2}\) . These symptoms were observed at a timepoint preceding motor neuron death and muscle denervation, and could be directly associated to loss of V1 inputs \(^{2, 5, 6}\) . Further evidence of alterations in inhibitory synapses has been reported also in a Fused in sarcoma (FUS) mouse showing Amyotrophic Lateral Sclerosis and Frontotemporal Dementia (FTD)- like phenotypes \(^{7- 8}\) . Thus, inhibitory synaptopathy is not restricted to the SOD1 \(^{G93A}\) mouse model. Interestingly, synaptic proteomics performed on postmortem tissue of + +<--- Page Split ---> + +C9ORF72 patients identified \(\sim 500\) proteins with altered expression levels also within inhibitory synapses 9. Moreover, two recent studies showed that misprocessing of UNC13A mRNA strongly associates with ALS- FTD pathology caused by TDP43 downregulation 10, 11. The UNC13A gene plays a pivotal role in neurosecretion and is a fundamental component of neuron- to- neuron communication 12, 13, suggesting general synaptic dysregulations in the disease. Thus, the potential development of strategies directed to promote neurosecretion and synapse stabilization might be beneficial in the attempt to overcome such synaptopathy. + +In the present study, we investigated if by stabilizing connectivity between spinal V1 inhibitory interneurons and motor neurons we could modify disease progression in the SOD1G93A mice. To this aim, we overexpressed the human presynaptic protein Extended synaptotagmin 1 (Esyt1) specifically in V1 interneurons. Esyt1 was previously shown to promote synaptic growth and stabilization 14, and is preferentially expressed in neurons resistant to ALS 15. Moreover, Esyt1 transcript is downregulated in neurons within the ventral horn of the spinal cord of SOD1G93A mice at postnatal day 63 (P63) (Figure 1a- d), the same timepoint at which we found decreased levels of the En1 transcript 2. To achieve V1 restricted overexpression, an adeno associated (AAV) serotype 8 virus was generated to overexpress Esyt1 upon cre- dependent recombination. The AAV8- hSyn- DIO- hEsyt1- W3SL virus was injected intraspinally in SOD1G93A mice crossed with En1cre mice 16 (Figure 1e- f). Phenotype and genotype of the SOD1G93A;En1cre mice, including copy number of the mutated gene, was evaluated and did not differ from mice expressing SOD1G93A alone (Supplementary figure 1a- c). All four genotypes resulting from the crossing – WT, En1cre, SOD1G93A and SOD1G93A;En1cre – received bilateral injections in each of the L1- L3 lumbar segments (six in total) of 100 nl each at postnatal day 30. Virus was used at a final titer of 4x1012 vg/mL. L1- L3 segments were targeted since they are the first affected in SOD1G93A mice 17, and responsible for the onset of locomotor phenotype 2. An AAV8- hSyn- DIO- mCherry- WPRE virus was also generated and injected as described above in the lumbar segment of the spinal cord of En1cre and SOD1G93A;En1cre mice to validate V1- restricted transduction (Figure 1e, g). Due to the large size of the Esyt1 insert ( \(\sim 3.3\mathrm{kb}\) ), a fluorescent tag could not be added to the AAV8- hSyn- DIO- hEsyt1- W3SL and Esyt1 overexpression was analyzed utilizing an RNAscope probe recognizing the inverted vector sequence upon cre- recombination (Figure 1h). As expected, overexpression was specific to neurons within the ventral/medial areas of the spinal cord and restricted to cre mice (Figure 1h- j). Fluorescence was not detected in the WT injected mice (Figure 1k- m). Quantification of hEsyt1 revealed overexpression in 20% of En1+ neurons in the analyzed lumbar segments (Figure 1n). + +Upon AAV8- hSyn- DIO- hEsyt1- W3SL administration in all four genotypes, changes in inhibitory synaptic density on motor neurons were investigated at postnatal day 112 (P112) (Figure 2a- e). This timepoint was chosen since significant motor neuron loss can be observed at P112 in the SOD1G93A mice 18, and we previously showed a decrease in inhibitory synaptic inputs from P45 2. Inhibitory synaptic inputs were visualized utilizing a VGAT antibody, while motor + +<--- Page Split ---> + +neurons were identified by ventral localization, size, and Chat staining (Figure 2a- d). VGAT synaptic density on motor neurons was reconstructed and corrected by motor neuron area (Figure 2a- d). Injected SOD1G93A; En1cre mice exhibited significant increase in synaptic density when compared to injected SOD1G93A, similar to injected WT and En1cre conditions (Figure 2e). Motor neurons were also quantified at the same timepoint in all four AAV8- hSyn- DIO- hEsyt1- W3SL injected genotypes (Figure 2f- l). Here, only neurons over \(28 \mu m\) in diameter within the ventral horn of the spinal cord (putative of fast motor neurons) were quantified. SOD1G93A; En1cre mice showed an increased number of spared motor neurons compared to SOD1G93A mice after AAV8- hEsyt1 overexpression. A trend in reduction of larger motor neurons in En1cre control mice upon hEsyt1 overexpression was observed. Analysis of motor neuron areas of control En1cre mice with and without hEsyt1 overexpression demonstrated a shrinkage of motor neurons at P112 (Figure 2m). + +Finally, we analyzed motor phenotypes after hEsyt1 overexpression in all four genotypes by placing the mice on a treadmill at a speed of \(20cm / s\) , equivalent to a fast walk \(^{19}\) . Videos were recorded from ventral and lateral views (Figure 3a;j). Our previously published data showed that \(\sim 40\%\) of SOD1G93A mice cannot cope with such speed by P63. Hence, we investigated if the increased synaptic connectivity upon hEsyt1 overexpression could ameliorate SOD1G93A motor phenotype. Following a brief training period, mice were assessed once a week from postnatal day P49 until P112. Videos were analyzed using the DeepLabCut marker- less pose estimation tool \(^{20}\) as previously described \(^{2}\) . SOD1G93A mice showed a significant reduction in locomotor performance and, consistently with our previous data, \(37.5\%\) showed an onset of locomotor phenotype by P63 (Supplementary Figure 2e). However, upon hEsyt1 overexpression only \(16.7\%\) of the SOD1G93A; En1cre mice had an onset of locomotor phenotype by P63, and \(50\%\) of them could perform the task by P112 (SOD1G93A median = P70, SOD1G93A; En1cre median = P105). Here, average speed, step frequency, stride length and peak acceleration were analyzed longitudinally (Figure 3b- e). SOD1G93A; En1cre mice showed an amelioration in average speed from P63 (Figure 3b), step frequency from P70 (Figure 3c), and stride length from P84 (Figure 3d). However, we did not observe changes in peak acceleration (Figure 3e). When assessing the final timepoint (P112) SOD1G93A; En1cre mice generally outperformed SOD1G93A mice, and half of them showed preserved average speed (Figure 3f). Amelioration was also observed for step frequency (Figure 3g), and stride length (Figure 3h), while peak acceleration remained unchanged (Figure 3i). Intralimb kinematics was performed by analyzing changes in degree amplitudes of four angle joints: hip, knee, ankle, and foot (Supplementary Figure 3 and Supplementary video 1 and 2). 15 individual steps per trial were analyzed and the mean amplitude for each mouse was calculated. Hindlimb hyperflexion, a key element of the phenotype resulting from the loss of V1 synaptic inputs (Supplementary Figure 3), was improved in the SOD1G93A; En1cre mice upon hEsyt1 overexpression for the foot and the ankle angles, but not for the hip and the knee (Figure 3k- n). Moreover, comparisons of lateral view recordings between SOD1G93A and SOD1G93A; En1cre mice revealed that the latter could better support their body weight as they maintained their bodies at a greater distance + +<--- Page Split ---> + +from the belt of the treadmill (Supplementary video 1 - kinematics of SOD1G93A and Supplementary video 2 - kinematics of SOD1G93A, En1cre). + +Altogether, these results indicate that motor impairment in SOD1G93A mice can be attenuated by stabilization of synaptic inputs between V1 interneurons and motor neurons. By overexpression of Esyt1 presynaptic protein in V1 interneurons, we were able to increase motor neuron survival and to slow down disease progression. It has been previously hypothesized that therapies targeting specific microcircuit dysfunctions might help slowing down the course of the disease21. The present work supports this hypothesis and indicates that the V1 interneurons- motor neuron circuit can be a potential target for treatment in ALS. Electrophysiological studies investigating recurrent inhibition support general inhibition dysregulation in ALS patients, and reduced inhibition in patients showing initial motor weakness in the lower limbs22. Thus, further effort will be required to identify a potential therapeutic window for targeting premotor circuits at the appropriate timepoint to slow down the disease. Moreover, while specific intraspinal delivery within the lumbar segments of the spinal cord was found beneficial in this study, it remains to be investigated if systemic overexpression of hEsyt1 in En1 positive cells could further improve disease phenotype and survival. The surprising maladaptive changes observed in healthy En1cre mice upon Esyt1 overexpression, leading to motor neuron shrinkage, suggest the need for targeted administration. En1 is expressed in neuronal populations outside the spinal cord (e.g., dopaminergic neurons) that could be affected by hEsyt1 overexpression. Hence, the use of specific enhancers23 or synthetic synaptic organizers24 could be viable alternatives for selective targeting of affected circuits. + +Author contribution + +Conceptualization I.A., Methodology I.A., S.M., R.F., A.S., R.M.R, R.S., Viral vector production A.T.S, G.N.H., I.A., R.F. Data Analysis S.M., R.F., A.S., Supervision and Funding acquisition I.A. + +Acknowledgements + +We thank Prof. Ole Kiehn, Department of Neuroscience - University of Copenhagen, for the access to the surgery room and the use of the DigiGait treadmill. We acknowledge the Core Facility for Integrated Microscopy, at the Faculty of Health and Medical Science of University of Copenhagen and the Department of Experimental Medicine, especially Dr. Pablo Hernandez- Varas (CFIM) and Alex Soelberg Laugesen (AEM). This work was supported by the Lundbeck Foundation (I.A.), the Louis- Hansen Foundation (I.A. and R.M.R.), the Danish Society for ALS (I.A.), the Laage Sofus Carl Emil Friis og hustru Olga Doris Friis' foundation (I.A.) and the Danish Society for Neuroscience (R.F. and A.S.). + +Data availability + +<--- Page Split ---> + +164 The data that support the findings of this study are available from the corresponding authors upon reasonable request. + +Code availability + +The code used to analyze data, produce figure content and videos is available at: https://github.com/Allodi-Lab/hEsyt1_spinal_injection_gait_analysis + +References + +1. Kiehn, O. Locomotor circuits in the mammalian spinal cord. Annu Rev Neurosci 29, 279-306 (2006). +2. Allodi, I., Montanana-Rosell, R., Selvan, R., Low, P. & Kiehn, O. Locomotor deficits in a mouse model of ALS are paralleled by loss of V1-interneuron connections onto fast motor neurons. Nat Commun 12, 3251 (2021). +3. Gurney, M.E., et al. Motor neuron degeneration in mice that express a human Cu,Zn superoxide dismutase mutation. Science 264, 1772-1775 (1994). +4. Jensen, D.B., Kadlecova, M., Allodi, I. & Meehan, C.F. Spinal motoneurones are intrinsically more responsive in the adult G93A SOD1 mouse model of amyotrophic lateral sclerosis. J Physiol 598, 4385-4403 (2020). +5. Gosgnach, S., et al. V1 spinal neurons regulate the speed of vertebrate locomotor outputs. Nature 440, 215-219 (2006). +6. Britz, O., et al. A genetically defined asymmetry underlies the inhibitory control of flexor-extensor locomotor movements. Elife 4(2015). +7. Sahadevan, S., et al. Synaptic FUS accumulation triggers early misregulation of synaptic RNAs in a mouse model of ALS. Nat Commun 12, 3027 (2021). +8. Scekic-Zahirovic, J., et al. Cytoplasmic FUS triggers early behavioral alterations linked to cortical neuronal hyperactivity and inhibitory synaptic defects. Nat Commun 12, 3028 (2021). +9. Laszlo Z.I., H.N., Sanchez Avila A., Kline R.A., Eaton S.L., & Lamont D.J., S.C., Spires-Jones T.L., Wishart T.M., Henstridge C.M. Synaptic proteomics reveal distinct molecular signatures of cognitive change and C9ORF72 repeat expansion in the human ALS cortex. Acta Neuropathologica Communications 10:156(2022). +10. Brown, A.L., et al. TDP-43 loss and ALS-risk SNPs drive mis-splicing and depletion of UNC13A. Nature 603, 131-137 (2022). +11. Ma, X.R., et al. TDP-43 represses cryptic exon inclusion in the FTD-ALS gene UNC13A. Nature 603, 124-130 (2022). +12. Augustin, I., Rosenmund, C., Sudhof, T.C. & Brose, N. Munc13-1 is essential for fusion competence of glutamatergic synaptic vesicles. Nature 400, 457-461 (1999). +13. Lipstein, N., et al. Munc13-1 is a Ca(2+)-phospholipid-dependent vesicle priming hub that shapes synaptic short-term plasticity and enables sustained neurotransmission. Neuron 109, 3980-4000 e3987 (2021). +14. Kikuma, K., Li, X., Kim, D., Sutter, D. & Dickman, D.K. Extended Synaptotagmin Localizes to Presynaptic ER and Promotes Neurotransmission and Synaptic Growth in Drosophila. Genetics 207, 993-1006 (2017). + +<--- Page Split ---> + +209 15. Allodi, I., et al. Modeling Motor Neuron Resilience in ALS Using Stem Cells. Stem Cell Reports 12, 1329- 1341 (2019). 211 16. Kimmel, R.A., et al. Two lineage boundaries coordinate vertebrate apical ectodermal ridge formation. Genes Dev 14, 1377- 1389 (2000). 213 17. Hegedus, J., Putman, C.T. & Gordon, T. Time course of preferential motor unit loss in the SOD1 G93A mouse model of amyotrophic lateral sclerosis. Neurobiol Dis 28, 154- 164 (2007). 216 18. Comley, L., et al. Motor neurons with differential vulnerability to degeneration show distinct protein signatures in health and ALS. Neuroscience 291, 216- 229 (2015). 217 19. Bellardita, C. & Kiehn, O. Phenotypic characterization of speed- associated gait changes in mice reveals modular organization of locomotor networks. Curr Biol 25, 1426- 1436 (2015). 221 20. Mathis, A., et al. DeepLabCut: markerless pose estimation of user- defined body parts with deep learning. Nat Neurosci 21, 1281- 1289 (2018). 223 21. Brownstone, R.M. & Lancelin, C. Escape from homeostasis: spinal microcircuits and progression of amyotrophic lateral sclerosis. J Neurophysiol 119, 1782- 1794 (2018). 225 22. Sangari S., P.I., Lackmy- Vallée A., Bayen E., Pradat P.F., Marchand- Pauvert V. Transient increase in recurrent inhibition in amyotrophic lateral sclerosis as a putative protection from neurodegeneration. Acta Physiologica 234, e13758 (2022). 228 23. Hoshino, C., et al. GABAergic neuron- specific whole- brain transduction by AAV- PHP.B incorporated with a new GAD65 promoter. Mol Brain 14, 33 (2021). 230 24. Suzuki, K., et al. A synthetic synaptic organizer protein restores glutamatergic neuronal circuits. Science 369(2020). 232 25. Bankhead, P., et al. QuPath: Open source software for digital pathology image analysis. Sci Rep 7, 16878 (2017). + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1. Esyt1 overexpression in SOD1G93A;En1cre mice. a) Expression of Esyt1 transcript in the lumbar spinal cord of a WT mouse visualized by RNascope. b) 40X magnification microphotograph showing Esyt1 expression in the ventral horn of the spinal cord in a WT mouse. c) Esyt1 is downregulated in neurons during early ALS progression in the ventral horn of the spinal cord. Esyt1 in orange, DAPI in light blue. d) Quantifications performed in WT and SOD1G93A mice at postnatal day 45 and 63 show downregulation of Esyt1 transcript starting at P63 (Oneway ANOVA and Dunnett's post hoc, P45 P=0.0714, P63 P=0.0191, N=3 per timepoint, per genotype). e) Cartoon depicting methodological approach in all experiments. Intraspinal injections delivering AAV vectors were performed in WT, En1cre, SOD1G93A and SOD1G93A;En1cre mice throughout the study, all genotypes were injected in L1-L3 spinal segments. f) Schematic of cre-dependent constructs designed to overexpress hEsyt1 and mCherry. g) Longitudinal section of the spinal cord of an En1cre mouse upon overexpression of the AAV8-hSyn-DIO-mCherry virus validates successful cre-dependent expression in the ventral/medial areas of the cord. AAV8-hSyn-DIO-hEsyt1-W3SL driven overexpression was analyzed by RNascope utilizing a probe recognizing the inverted viral construct upon cre-recombination. h) Expression of viral-hEsyt1 in En1cre mice. i) Close-up microphotograph showing a neuron overexpressing hEsyt1. j) The same neuron is also positive for En1 transcript. k) hEsyt1 was not detected WT injected mice. I-m) En1 positive neuron negative for hEsyt1. n) Quantification of Esyt1 positive neurons in the lumbar spinal cord of En1cre and WT mice, 20% of En1+ neurons are positive for the hEsyt1 (t test, P=0.0057, En1cre N=4, WT N=3). Scale bar in a) = 100 μm, in b) = 50 μm and in h) = 100 μm. All graphs show mean values ± SEM.
+ +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2. Esyt1 overexpression increases motor neuron survival and synaptic density onto spared motor neurons. Quantifications of inhibitory synapses on spared motor neurons at postnatal day 112 upon AAV8-hSyn-DIO-hEsyt1 injections. Synaptic densities are normalized by motor neuron area. Masks in a) and b) show synaptic density in WT and En1cre mice respectively. c) and d) show differences in synaptic densities between SOD1G93A and SOD1G93A;En1cre mice. Microphotographs show examples of quantified motor neurons in the different genotypes. VGAT in red, ChAT in green, DAPI in blue. Scale bar in g) = 50 μm. e) Synaptic density in SOD1G93A;En1cre is significantly increased compared to SOD1G93A and comparable to the synaptic densities of WT and En1cre mice (One-way ANOVA and Dunnett's post hoc WT P = 0.0060, En1cre P = 0.0097, SOD1G93A;En1cre P = 0.0149, N = 4). Motor neuron quantifications performed at postnatal day 112 in f) WT, g) En1cre, h) SOD1G93A and i) SOD1G93A;En1cre mice. Fluoro-Nissl in red. Scale bar in i) = 50 μm. l) SOD1G93A;En1cre mice show increased motor neuron survival upon Esyt1 overexpression when compared to the SOD1G93A mice (One-way ANOVA and Dunnett's post hoc SOD1G93A; En1cre P = 0.0080, WT P = 0.0023, N = 5). A minimum of 170 motor neurons per condition was quantified. En1cre mice overexpressing hEsyt1 show a trend in lower number of large motor neurons in the lumbar spinal cord (One-way ANOVA and Dunnett's post hoc, En1cre P = 0.2732, N = 5). m) Comparison of motor neuron size in En1cre non-injected and En1cre injected mice with AAV8-hSyn-DIO-hEsyt1 shows shrinkage of motor neurons upon hEsyt1 overexpression (t test, P = 0.0073, En1cre non-injected N = 4, En1cre injected N = 5). All graphs show mean values ± SEM.
+ +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3. Amelioration of locomotor phenotype in SOD1G93A mice upon hEyt1 overexpression. a) Cartoon depicting treadmill paradigm. b) Average speed analyzed between day 49 and 112 shows amelioration in SOD1G93A;En1cre mice from P63 (Two-way ANOVA and Dunnett's post hoc, \(\mathsf{P} = 0.0257\) ), c) step frequency from P70 (Two-way ANOVA and Dunnett's post hoc, \(\mathsf{P} = 0.0318\) ), and d) stride length from P84 (Two-way ANOVA and Dunnett's post hoc, \(\mathsf{P} = 0.0096\) ). e) Peak acceleration does not change upon Esyt1 overexpression (Two-way ANOVA and Dunnett's post hoc, \(\mathsf{N} = 6 - 8\) mice per condition; all quantifications were performed in triplicates). At P112 timepoint f) average speed is higher in SOD1G93A;En1cre compared to SOD1G93A mice (One-way ANOVA and Dunnett's post hoc, SOD1G93A;En1cre vs SOD1G93A \(\mathsf{P} = 1.8e - 07\) ), as well as g) step frequency (One-way ANOVA and Dunnett's post hoc, \(\mathsf{P} = 3.1e - 06\) ) and h) stride length (One-way ANOVA and Dunnett's post hoc, \(\mathsf{P} = 0.0032\) ), however i) peak acceleration remains unchanged (One-way ANOVA and Dunnett's post hoc, \(\mathsf{P} = 0.7406\) ) (WT \(\mathsf{N} = 6\) ; SOD1G93A \(\mathsf{N} = 8\) , En1cre \(\mathsf{N} = 6\) , SOD1G93A;En1cre \(\mathsf{N} = 6\) , all quantifications were performed in triplicates). j) Stick figures depict intralimb coordination in SOD1G93A and SOD1G93A;En1cre mice. Two full cycles are visualized for SOD1G93A and SOD1G93A;En1cre mice upon AAV8-hSyn-DIO-hEsyt1 injections. Stance phase in blue and swing phase in green. Changes in joint angles were analyzed for k) hip angle, l) knee angle, m) ankle angle and n) foot angle. Significant changes were found for the foot angle (One-way ANOVA and Dunnett's post hoc, foot angle \(\mathsf{P} = 4.1e - 05\) ) and the ankle angle (One-way ANOVA and Dunnett's post hoc, foot angle \(\mathsf{P} = 2.8e - 05\) ) upon hEsyt1 overexpression (WT \(= 5\) , SOD1G93A \(\mathsf{N} = 8\) , En1cre \(\mathsf{N} = 6\) , SOD1G93A;En1cre \(\mathsf{N} = 6\) , all quantifications were performed in triplicates). All graphs show mean values \(\pm\) SEM, averages values in f-l and k-n are shown in black, technical triplicates are shown in gray.
+ +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- MoraetalSupplementaryinformation.pdf- p112SOD110cms.mp4- p112SOD1En115cms.mp4 + +<--- Page Split ---> diff --git a/preprint/preprint__07fc271c3e70c1e56898e60a1febbc2db8cea3186f51b0a6b8ad23d104d455a0/preprint__07fc271c3e70c1e56898e60a1febbc2db8cea3186f51b0a6b8ad23d104d455a0_det.mmd b/preprint/preprint__07fc271c3e70c1e56898e60a1febbc2db8cea3186f51b0a6b8ad23d104d455a0/preprint__07fc271c3e70c1e56898e60a1febbc2db8cea3186f51b0a6b8ad23d104d455a0_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..f84f94810fa7d9e549055a7294fc1c3a7536b152 --- /dev/null +++ b/preprint/preprint__07fc271c3e70c1e56898e60a1febbc2db8cea3186f51b0a6b8ad23d104d455a0/preprint__07fc271c3e70c1e56898e60a1febbc2db8cea3186f51b0a6b8ad23d104d455a0_det.mmd @@ -0,0 +1,151 @@ +<|ref|>title<|/ref|><|det|>[[43, 108, 850, 207]]<|/det|> +# Stabilization of V1 interneuron-motor neuron connectivity ameliorates motor phenotype in a mouse model of ALS + +<|ref|>text<|/ref|><|det|>[[42, 230, 757, 644]]<|/det|> +Santiago Mora University of Copenhagen Rasmus von Huth Friis University of Copenhagen Anna Stuckert University of Copenhagen Gith Noes- Holt University of Copenhagen Roser Montañana- Rosell University of Copenhagen https://orcid.org/0000- 0003- 4268- 4765 Andreas Sørensen Massachusetts Institute of Technology https://orcid.org/0000- 0001- 5887- 9247 Raghavendra Selvan University of Copenhagen https://orcid.org/0000- 0003- 4302- 0207 Ilary Allodi iallodi@sund.ku.dk + +<|ref|>text<|/ref|><|det|>[[55, 626, 642, 647]]<|/det|> +University of Copenhagen https://orcid.org/0000- 0003- 4361- 163X + +<|ref|>sub_title<|/ref|><|det|>[[44, 688, 103, 705]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 725, 137, 744]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 764, 303, 783]]<|/det|> +Posted Date: March 7th, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 802, 474, 821]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 2613071/v1 + +<|ref|>text<|/ref|><|det|>[[42, 839, 914, 881]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 900, 535, 920]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 45, 904, 88]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on June 7th, 2024. See the published version at https://doi.org/10.1038/s41467-024-48925-7. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[70, 85, 884, 123]]<|/det|> +1 Stabilization of V1 interneuron- motor neuron connectivity ameliorates motor phenotype in a mouse model of ALS + +<|ref|>text<|/ref|><|det|>[[70, 127, 884, 166]]<|/det|> +3 Santiago Mora \(^{1*}\) , Rasmus von Huth Friis \(^{1*}\) , Anna Stuckert \(^{1}\) , Gith Noes- Holt \(^{1}\) , Roser Montañana- Rosell \(^{1}\) , Andreas Toft Sørensen \(^{1}\) , Raghavendra Selvan \(^{2}\) , Ilary Allodi \(^{1*}\) + +<|ref|>text<|/ref|><|det|>[[70, 170, 713, 287]]<|/det|> +6 1 Department of Neuroscience, University of Copenhagen, Denmark 8 2 Department of Computer Science, University of Copenhagen, Denmark 9 \* These authors equally contributed to the work 10 \* Corresponding author + +<|ref|>sub_title<|/ref|><|det|>[[119, 308, 193, 323]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[118, 326, 883, 444]]<|/det|> +Loss of connectivity between spinal V1 inhibitory interneurons and motor neurons is found early in disease in the SOD1 \(^{G93A}\) ALS mice. Such changes in premotor inputs can contribute to homeostatic imbalance of vulnerable motor neurons. Here, we show, for the first time, that stabilization of V1 synapses by overexpression of the Extended Synaptotagmin 1 presynaptic organizer increases motor neuron survival and ameliorates motor phenotypes, demonstrating that interneurons can be a potential target to attenuate ALS symptoms. + +<|ref|>sub_title<|/ref|><|det|>[[119, 468, 166, 482]]<|/det|> +## Main + +<|ref|>text<|/ref|><|det|>[[115, 485, 883, 905]]<|/det|> +Somatic motor neurons are the ultimate output of the brain since they control movements by directly connecting to muscles. Their synchronized activation is regulated by a complex network of inhibitory and excitatory spinal interneurons \(^{1}\) . Hence, functional connectivity between motor neurons and their premotor circuits is a prerequisite for maintenance of inhibitory- excitatory balance and execution of movements. In the fatal disease Amyotrophic Lateral Sclerosis (ALS), somatic motor neurons degenerate, and subjects progressively lose the ability to perform movements. In our previous work \(^{2}\) , we showed that the spinal V1 inhibitory interneurons, positive for Engrailed- 1 (En1) marker, lose their synapses onto the vulnerable fast- twitch fatigable motor neurons early in disease in the SOD1 \(^{G93A}\) ALS mouse model \(^{3}\) . This preferential loss of inhibitory inputs onto fast- twitch fatigable motor neurons might contribute to their unbalanced excitability \(^{4}\) , leading to excitotoxicity and ultimately to their vulnerability to disease. Moreover, V1 inhibitory interneurons are known to control the speed of locomotion in vertebrates \(^{5}\) , and the loss of connectivity observed in the SOD1 \(^{G93A}\) mice led to an onset of locomotor phenotype. Such phenotype is characterized by a reduction of speed and acceleration, a decrease in stride length and step frequency, and a hyperflexion of the hindlimbs \(^{2}\) . These symptoms were observed at a timepoint preceding motor neuron death and muscle denervation, and could be directly associated to loss of V1 inputs \(^{2, 5, 6}\) . Further evidence of alterations in inhibitory synapses has been reported also in a Fused in sarcoma (FUS) mouse showing Amyotrophic Lateral Sclerosis and Frontotemporal Dementia (FTD)- like phenotypes \(^{7- 8}\) . Thus, inhibitory synaptopathy is not restricted to the SOD1 \(^{G93A}\) mouse model. Interestingly, synaptic proteomics performed on postmortem tissue of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 85, 883, 225]]<|/det|> +C9ORF72 patients identified \(\sim 500\) proteins with altered expression levels also within inhibitory synapses 9. Moreover, two recent studies showed that misprocessing of UNC13A mRNA strongly associates with ALS- FTD pathology caused by TDP43 downregulation 10, 11. The UNC13A gene plays a pivotal role in neurosecretion and is a fundamental component of neuron- to- neuron communication 12, 13, suggesting general synaptic dysregulations in the disease. Thus, the potential development of strategies directed to promote neurosecretion and synapse stabilization might be beneficial in the attempt to overcome such synaptopathy. + +<|ref|>text<|/ref|><|det|>[[115, 245, 883, 787]]<|/det|> +In the present study, we investigated if by stabilizing connectivity between spinal V1 inhibitory interneurons and motor neurons we could modify disease progression in the SOD1G93A mice. To this aim, we overexpressed the human presynaptic protein Extended synaptotagmin 1 (Esyt1) specifically in V1 interneurons. Esyt1 was previously shown to promote synaptic growth and stabilization 14, and is preferentially expressed in neurons resistant to ALS 15. Moreover, Esyt1 transcript is downregulated in neurons within the ventral horn of the spinal cord of SOD1G93A mice at postnatal day 63 (P63) (Figure 1a- d), the same timepoint at which we found decreased levels of the En1 transcript 2. To achieve V1 restricted overexpression, an adeno associated (AAV) serotype 8 virus was generated to overexpress Esyt1 upon cre- dependent recombination. The AAV8- hSyn- DIO- hEsyt1- W3SL virus was injected intraspinally in SOD1G93A mice crossed with En1cre mice 16 (Figure 1e- f). Phenotype and genotype of the SOD1G93A;En1cre mice, including copy number of the mutated gene, was evaluated and did not differ from mice expressing SOD1G93A alone (Supplementary figure 1a- c). All four genotypes resulting from the crossing – WT, En1cre, SOD1G93A and SOD1G93A;En1cre – received bilateral injections in each of the L1- L3 lumbar segments (six in total) of 100 nl each at postnatal day 30. Virus was used at a final titer of 4x1012 vg/mL. L1- L3 segments were targeted since they are the first affected in SOD1G93A mice 17, and responsible for the onset of locomotor phenotype 2. An AAV8- hSyn- DIO- mCherry- WPRE virus was also generated and injected as described above in the lumbar segment of the spinal cord of En1cre and SOD1G93A;En1cre mice to validate V1- restricted transduction (Figure 1e, g). Due to the large size of the Esyt1 insert ( \(\sim 3.3\mathrm{kb}\) ), a fluorescent tag could not be added to the AAV8- hSyn- DIO- hEsyt1- W3SL and Esyt1 overexpression was analyzed utilizing an RNAscope probe recognizing the inverted vector sequence upon cre- recombination (Figure 1h). As expected, overexpression was specific to neurons within the ventral/medial areas of the spinal cord and restricted to cre mice (Figure 1h- j). Fluorescence was not detected in the WT injected mice (Figure 1k- m). Quantification of hEsyt1 revealed overexpression in 20% of En1+ neurons in the analyzed lumbar segments (Figure 1n). + +<|ref|>text<|/ref|><|det|>[[117, 806, 882, 905]]<|/det|> +Upon AAV8- hSyn- DIO- hEsyt1- W3SL administration in all four genotypes, changes in inhibitory synaptic density on motor neurons were investigated at postnatal day 112 (P112) (Figure 2a- e). This timepoint was chosen since significant motor neuron loss can be observed at P112 in the SOD1G93A mice 18, and we previously showed a decrease in inhibitory synaptic inputs from P45 2. Inhibitory synaptic inputs were visualized utilizing a VGAT antibody, while motor + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[67, 85, 884, 326]]<|/det|> +neurons were identified by ventral localization, size, and Chat staining (Figure 2a- d). VGAT synaptic density on motor neurons was reconstructed and corrected by motor neuron area (Figure 2a- d). Injected SOD1G93A; En1cre mice exhibited significant increase in synaptic density when compared to injected SOD1G93A, similar to injected WT and En1cre conditions (Figure 2e). Motor neurons were also quantified at the same timepoint in all four AAV8- hSyn- DIO- hEsyt1- W3SL injected genotypes (Figure 2f- l). Here, only neurons over \(28 \mu m\) in diameter within the ventral horn of the spinal cord (putative of fast motor neurons) were quantified. SOD1G93A; En1cre mice showed an increased number of spared motor neurons compared to SOD1G93A mice after AAV8- hEsyt1 overexpression. A trend in reduction of larger motor neurons in En1cre control mice upon hEsyt1 overexpression was observed. Analysis of motor neuron areas of control En1cre mice with and without hEsyt1 overexpression demonstrated a shrinkage of motor neurons at P112 (Figure 2m). + +<|ref|>text<|/ref|><|det|>[[66, 344, 884, 907]]<|/det|> +Finally, we analyzed motor phenotypes after hEsyt1 overexpression in all four genotypes by placing the mice on a treadmill at a speed of \(20cm / s\) , equivalent to a fast walk \(^{19}\) . Videos were recorded from ventral and lateral views (Figure 3a;j). Our previously published data showed that \(\sim 40\%\) of SOD1G93A mice cannot cope with such speed by P63. Hence, we investigated if the increased synaptic connectivity upon hEsyt1 overexpression could ameliorate SOD1G93A motor phenotype. Following a brief training period, mice were assessed once a week from postnatal day P49 until P112. Videos were analyzed using the DeepLabCut marker- less pose estimation tool \(^{20}\) as previously described \(^{2}\) . SOD1G93A mice showed a significant reduction in locomotor performance and, consistently with our previous data, \(37.5\%\) showed an onset of locomotor phenotype by P63 (Supplementary Figure 2e). However, upon hEsyt1 overexpression only \(16.7\%\) of the SOD1G93A; En1cre mice had an onset of locomotor phenotype by P63, and \(50\%\) of them could perform the task by P112 (SOD1G93A median = P70, SOD1G93A; En1cre median = P105). Here, average speed, step frequency, stride length and peak acceleration were analyzed longitudinally (Figure 3b- e). SOD1G93A; En1cre mice showed an amelioration in average speed from P63 (Figure 3b), step frequency from P70 (Figure 3c), and stride length from P84 (Figure 3d). However, we did not observe changes in peak acceleration (Figure 3e). When assessing the final timepoint (P112) SOD1G93A; En1cre mice generally outperformed SOD1G93A mice, and half of them showed preserved average speed (Figure 3f). Amelioration was also observed for step frequency (Figure 3g), and stride length (Figure 3h), while peak acceleration remained unchanged (Figure 3i). Intralimb kinematics was performed by analyzing changes in degree amplitudes of four angle joints: hip, knee, ankle, and foot (Supplementary Figure 3 and Supplementary video 1 and 2). 15 individual steps per trial were analyzed and the mean amplitude for each mouse was calculated. Hindlimb hyperflexion, a key element of the phenotype resulting from the loss of V1 synaptic inputs (Supplementary Figure 3), was improved in the SOD1G93A; En1cre mice upon hEsyt1 overexpression for the foot and the ankle angles, but not for the hip and the knee (Figure 3k- n). Moreover, comparisons of lateral view recordings between SOD1G93A and SOD1G93A; En1cre mice revealed that the latter could better support their body weight as they maintained their bodies at a greater distance + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 85, 881, 124]]<|/det|> +from the belt of the treadmill (Supplementary video 1 - kinematics of SOD1G93A and Supplementary video 2 - kinematics of SOD1G93A, En1cre). + +<|ref|>text<|/ref|><|det|>[[117, 144, 883, 544]]<|/det|> +Altogether, these results indicate that motor impairment in SOD1G93A mice can be attenuated by stabilization of synaptic inputs between V1 interneurons and motor neurons. By overexpression of Esyt1 presynaptic protein in V1 interneurons, we were able to increase motor neuron survival and to slow down disease progression. It has been previously hypothesized that therapies targeting specific microcircuit dysfunctions might help slowing down the course of the disease21. The present work supports this hypothesis and indicates that the V1 interneurons- motor neuron circuit can be a potential target for treatment in ALS. Electrophysiological studies investigating recurrent inhibition support general inhibition dysregulation in ALS patients, and reduced inhibition in patients showing initial motor weakness in the lower limbs22. Thus, further effort will be required to identify a potential therapeutic window for targeting premotor circuits at the appropriate timepoint to slow down the disease. Moreover, while specific intraspinal delivery within the lumbar segments of the spinal cord was found beneficial in this study, it remains to be investigated if systemic overexpression of hEsyt1 in En1 positive cells could further improve disease phenotype and survival. The surprising maladaptive changes observed in healthy En1cre mice upon Esyt1 overexpression, leading to motor neuron shrinkage, suggest the need for targeted administration. En1 is expressed in neuronal populations outside the spinal cord (e.g., dopaminergic neurons) that could be affected by hEsyt1 overexpression. Hence, the use of specific enhancers23 or synthetic synaptic organizers24 could be viable alternatives for selective targeting of affected circuits. + +<|ref|>text<|/ref|><|det|>[[120, 585, 285, 600]]<|/det|> +Author contribution + +<|ref|>text<|/ref|><|det|>[[119, 604, 881, 642]]<|/det|> +Conceptualization I.A., Methodology I.A., S.M., R.F., A.S., R.M.R, R.S., Viral vector production A.T.S, G.N.H., I.A., R.F. Data Analysis S.M., R.F., A.S., Supervision and Funding acquisition I.A. + +<|ref|>text<|/ref|><|det|>[[120, 664, 285, 680]]<|/det|> +Acknowledgements + +<|ref|>text<|/ref|><|det|>[[118, 684, 883, 840]]<|/det|> +We thank Prof. Ole Kiehn, Department of Neuroscience - University of Copenhagen, for the access to the surgery room and the use of the DigiGait treadmill. We acknowledge the Core Facility for Integrated Microscopy, at the Faculty of Health and Medical Science of University of Copenhagen and the Department of Experimental Medicine, especially Dr. Pablo Hernandez- Varas (CFIM) and Alex Soelberg Laugesen (AEM). This work was supported by the Lundbeck Foundation (I.A.), the Louis- Hansen Foundation (I.A. and R.M.R.), the Danish Society for ALS (I.A.), the Laage Sofus Carl Emil Friis og hustru Olga Doris Friis' foundation (I.A.) and the Danish Society for Neuroscience (R.F. and A.S.). + +<|ref|>text<|/ref|><|det|>[[120, 863, 253, 878]]<|/det|> +Data availability + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[67, 85, 884, 125]]<|/det|> +164 The data that support the findings of this study are available from the corresponding authors upon reasonable request. + +<|ref|>text<|/ref|><|det|>[[119, 145, 258, 161]]<|/det|> +Code availability + +<|ref|>text<|/ref|><|det|>[[119, 165, 881, 202]]<|/det|> +The code used to analyze data, produce figure content and videos is available at: https://github.com/Allodi-Lab/hEsyt1_spinal_injection_gait_analysis + +<|ref|>text<|/ref|><|det|>[[119, 245, 219, 262]]<|/det|> +References + +<|ref|>text<|/ref|><|det|>[[115, 280, 884, 899]]<|/det|> +1. Kiehn, O. Locomotor circuits in the mammalian spinal cord. Annu Rev Neurosci 29, 279-306 (2006). +2. Allodi, I., Montanana-Rosell, R., Selvan, R., Low, P. & Kiehn, O. Locomotor deficits in a mouse model of ALS are paralleled by loss of V1-interneuron connections onto fast motor neurons. Nat Commun 12, 3251 (2021). +3. Gurney, M.E., et al. Motor neuron degeneration in mice that express a human Cu,Zn superoxide dismutase mutation. Science 264, 1772-1775 (1994). +4. Jensen, D.B., Kadlecova, M., Allodi, I. & Meehan, C.F. Spinal motoneurones are intrinsically more responsive in the adult G93A SOD1 mouse model of amyotrophic lateral sclerosis. J Physiol 598, 4385-4403 (2020). +5. Gosgnach, S., et al. V1 spinal neurons regulate the speed of vertebrate locomotor outputs. Nature 440, 215-219 (2006). +6. Britz, O., et al. A genetically defined asymmetry underlies the inhibitory control of flexor-extensor locomotor movements. Elife 4(2015). +7. Sahadevan, S., et al. Synaptic FUS accumulation triggers early misregulation of synaptic RNAs in a mouse model of ALS. Nat Commun 12, 3027 (2021). +8. Scekic-Zahirovic, J., et al. Cytoplasmic FUS triggers early behavioral alterations linked to cortical neuronal hyperactivity and inhibitory synaptic defects. Nat Commun 12, 3028 (2021). +9. Laszlo Z.I., H.N., Sanchez Avila A., Kline R.A., Eaton S.L., & Lamont D.J., S.C., Spires-Jones T.L., Wishart T.M., Henstridge C.M. Synaptic proteomics reveal distinct molecular signatures of cognitive change and C9ORF72 repeat expansion in the human ALS cortex. Acta Neuropathologica Communications 10:156(2022). +10. Brown, A.L., et al. TDP-43 loss and ALS-risk SNPs drive mis-splicing and depletion of UNC13A. Nature 603, 131-137 (2022). +11. Ma, X.R., et al. TDP-43 represses cryptic exon inclusion in the FTD-ALS gene UNC13A. Nature 603, 124-130 (2022). +12. Augustin, I., Rosenmund, C., Sudhof, T.C. & Brose, N. Munc13-1 is essential for fusion competence of glutamatergic synaptic vesicles. Nature 400, 457-461 (1999). +13. Lipstein, N., et al. Munc13-1 is a Ca(2+)-phospholipid-dependent vesicle priming hub that shapes synaptic short-term plasticity and enables sustained neurotransmission. Neuron 109, 3980-4000 e3987 (2021). +14. Kikuma, K., Li, X., Kim, D., Sutter, D. & Dickman, D.K. Extended Synaptotagmin Localizes to Presynaptic ER and Promotes Neurotransmission and Synaptic Growth in Drosophila. Genetics 207, 993-1006 (2017). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 85, 884, 530]]<|/det|> +209 15. Allodi, I., et al. Modeling Motor Neuron Resilience in ALS Using Stem Cells. Stem Cell Reports 12, 1329- 1341 (2019). 211 16. Kimmel, R.A., et al. Two lineage boundaries coordinate vertebrate apical ectodermal ridge formation. Genes Dev 14, 1377- 1389 (2000). 213 17. Hegedus, J., Putman, C.T. & Gordon, T. Time course of preferential motor unit loss in the SOD1 G93A mouse model of amyotrophic lateral sclerosis. Neurobiol Dis 28, 154- 164 (2007). 216 18. Comley, L., et al. Motor neurons with differential vulnerability to degeneration show distinct protein signatures in health and ALS. Neuroscience 291, 216- 229 (2015). 217 19. Bellardita, C. & Kiehn, O. Phenotypic characterization of speed- associated gait changes in mice reveals modular organization of locomotor networks. Curr Biol 25, 1426- 1436 (2015). 221 20. Mathis, A., et al. DeepLabCut: markerless pose estimation of user- defined body parts with deep learning. Nat Neurosci 21, 1281- 1289 (2018). 223 21. Brownstone, R.M. & Lancelin, C. Escape from homeostasis: spinal microcircuits and progression of amyotrophic lateral sclerosis. J Neurophysiol 119, 1782- 1794 (2018). 225 22. Sangari S., P.I., Lackmy- Vallée A., Bayen E., Pradat P.F., Marchand- Pauvert V. Transient increase in recurrent inhibition in amyotrophic lateral sclerosis as a putative protection from neurodegeneration. Acta Physiologica 234, e13758 (2022). 228 23. Hoshino, C., et al. GABAergic neuron- specific whole- brain transduction by AAV- PHP.B incorporated with a new GAD65 promoter. Mol Brain 14, 33 (2021). 230 24. Suzuki, K., et al. A synthetic synaptic organizer protein restores glutamatergic neuronal circuits. Science 369(2020). 232 25. Bankhead, P., et al. QuPath: Open source software for digital pathology image analysis. Sci Rep 7, 16878 (2017). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[24, 20, 944, 472]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[21, 492, 966, 780]]<|/det|> +
Figure 1. Esyt1 overexpression in SOD1G93A;En1cre mice. a) Expression of Esyt1 transcript in the lumbar spinal cord of a WT mouse visualized by RNascope. b) 40X magnification microphotograph showing Esyt1 expression in the ventral horn of the spinal cord in a WT mouse. c) Esyt1 is downregulated in neurons during early ALS progression in the ventral horn of the spinal cord. Esyt1 in orange, DAPI in light blue. d) Quantifications performed in WT and SOD1G93A mice at postnatal day 45 and 63 show downregulation of Esyt1 transcript starting at P63 (Oneway ANOVA and Dunnett's post hoc, P45 P=0.0714, P63 P=0.0191, N=3 per timepoint, per genotype). e) Cartoon depicting methodological approach in all experiments. Intraspinal injections delivering AAV vectors were performed in WT, En1cre, SOD1G93A and SOD1G93A;En1cre mice throughout the study, all genotypes were injected in L1-L3 spinal segments. f) Schematic of cre-dependent constructs designed to overexpress hEsyt1 and mCherry. g) Longitudinal section of the spinal cord of an En1cre mouse upon overexpression of the AAV8-hSyn-DIO-mCherry virus validates successful cre-dependent expression in the ventral/medial areas of the cord. AAV8-hSyn-DIO-hEsyt1-W3SL driven overexpression was analyzed by RNascope utilizing a probe recognizing the inverted viral construct upon cre-recombination. h) Expression of viral-hEsyt1 in En1cre mice. i) Close-up microphotograph showing a neuron overexpressing hEsyt1. j) The same neuron is also positive for En1 transcript. k) hEsyt1 was not detected WT injected mice. I-m) En1 positive neuron negative for hEsyt1. n) Quantification of Esyt1 positive neurons in the lumbar spinal cord of En1cre and WT mice, 20% of En1+ neurons are positive for the hEsyt1 (t test, P=0.0057, En1cre N=4, WT N=3). Scale bar in a) = 100 μm, in b) = 50 μm and in h) = 100 μm. All graphs show mean values ± SEM.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[17, 0, 970, 377]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[19, 393, 970, 664]]<|/det|> +
Figure 2. Esyt1 overexpression increases motor neuron survival and synaptic density onto spared motor neurons. Quantifications of inhibitory synapses on spared motor neurons at postnatal day 112 upon AAV8-hSyn-DIO-hEsyt1 injections. Synaptic densities are normalized by motor neuron area. Masks in a) and b) show synaptic density in WT and En1cre mice respectively. c) and d) show differences in synaptic densities between SOD1G93A and SOD1G93A;En1cre mice. Microphotographs show examples of quantified motor neurons in the different genotypes. VGAT in red, ChAT in green, DAPI in blue. Scale bar in g) = 50 μm. e) Synaptic density in SOD1G93A;En1cre is significantly increased compared to SOD1G93A and comparable to the synaptic densities of WT and En1cre mice (One-way ANOVA and Dunnett's post hoc WT P = 0.0060, En1cre P = 0.0097, SOD1G93A;En1cre P = 0.0149, N = 4). Motor neuron quantifications performed at postnatal day 112 in f) WT, g) En1cre, h) SOD1G93A and i) SOD1G93A;En1cre mice. Fluoro-Nissl in red. Scale bar in i) = 50 μm. l) SOD1G93A;En1cre mice show increased motor neuron survival upon Esyt1 overexpression when compared to the SOD1G93A mice (One-way ANOVA and Dunnett's post hoc SOD1G93A; En1cre P = 0.0080, WT P = 0.0023, N = 5). A minimum of 170 motor neurons per condition was quantified. En1cre mice overexpressing hEsyt1 show a trend in lower number of large motor neurons in the lumbar spinal cord (One-way ANOVA and Dunnett's post hoc, En1cre P = 0.2732, N = 5). m) Comparison of motor neuron size in En1cre non-injected and En1cre injected mice with AAV8-hSyn-DIO-hEsyt1 shows shrinkage of motor neurons upon hEsyt1 overexpression (t test, P = 0.0073, En1cre non-injected N = 4, En1cre injected N = 5). All graphs show mean values ± SEM.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[20, 25, 950, 420]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[25, 433, 960, 793]]<|/det|> +
Figure 3. Amelioration of locomotor phenotype in SOD1G93A mice upon hEyt1 overexpression. a) Cartoon depicting treadmill paradigm. b) Average speed analyzed between day 49 and 112 shows amelioration in SOD1G93A;En1cre mice from P63 (Two-way ANOVA and Dunnett's post hoc, \(\mathsf{P} = 0.0257\) ), c) step frequency from P70 (Two-way ANOVA and Dunnett's post hoc, \(\mathsf{P} = 0.0318\) ), and d) stride length from P84 (Two-way ANOVA and Dunnett's post hoc, \(\mathsf{P} = 0.0096\) ). e) Peak acceleration does not change upon Esyt1 overexpression (Two-way ANOVA and Dunnett's post hoc, \(\mathsf{N} = 6 - 8\) mice per condition; all quantifications were performed in triplicates). At P112 timepoint f) average speed is higher in SOD1G93A;En1cre compared to SOD1G93A mice (One-way ANOVA and Dunnett's post hoc, SOD1G93A;En1cre vs SOD1G93A \(\mathsf{P} = 1.8e - 07\) ), as well as g) step frequency (One-way ANOVA and Dunnett's post hoc, \(\mathsf{P} = 3.1e - 06\) ) and h) stride length (One-way ANOVA and Dunnett's post hoc, \(\mathsf{P} = 0.0032\) ), however i) peak acceleration remains unchanged (One-way ANOVA and Dunnett's post hoc, \(\mathsf{P} = 0.7406\) ) (WT \(\mathsf{N} = 6\) ; SOD1G93A \(\mathsf{N} = 8\) , En1cre \(\mathsf{N} = 6\) , SOD1G93A;En1cre \(\mathsf{N} = 6\) , all quantifications were performed in triplicates). j) Stick figures depict intralimb coordination in SOD1G93A and SOD1G93A;En1cre mice. Two full cycles are visualized for SOD1G93A and SOD1G93A;En1cre mice upon AAV8-hSyn-DIO-hEsyt1 injections. Stance phase in blue and swing phase in green. Changes in joint angles were analyzed for k) hip angle, l) knee angle, m) ankle angle and n) foot angle. Significant changes were found for the foot angle (One-way ANOVA and Dunnett's post hoc, foot angle \(\mathsf{P} = 4.1e - 05\) ) and the ankle angle (One-way ANOVA and Dunnett's post hoc, foot angle \(\mathsf{P} = 2.8e - 05\) ) upon hEsyt1 overexpression (WT \(= 5\) , SOD1G93A \(\mathsf{N} = 8\) , En1cre \(\mathsf{N} = 6\) , SOD1G93A;En1cre \(\mathsf{N} = 6\) , all quantifications were performed in triplicates). All graphs show mean values \(\pm\) SEM, averages values in f-l and k-n are shown in black, technical triplicates are shown in gray.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[42, 43, 312, 71]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[42, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 430, 202]]<|/det|> +- MoraetalSupplementaryinformation.pdf- p112SOD110cms.mp4- p112SOD1En115cms.mp4 + +<--- Page Split ---> diff --git a/preprint/preprint__0807f99e4afb929d1e093bb7dc645550d50e8be613458192678ecc0c97d6a34f/images_list.json b/preprint/preprint__0807f99e4afb929d1e093bb7dc645550d50e8be613458192678ecc0c97d6a34f/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..44f8ec27d31d2f5caec35af49715c5ed0ff56a32 --- /dev/null +++ b/preprint/preprint__0807f99e4afb929d1e093bb7dc645550d50e8be613458192678ecc0c97d6a34f/images_list.json @@ -0,0 +1,47 @@ +[ + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "Numbered assemblages: (1) act gl o g bi ta law ru chl; (2) mu o ky ru chl g ta law; (3) mu o ky ru g ta law; (4) gl(hb) mu o ky ru g ta; (5) mu o zo ky ru g ta chl; (6) act gl o g mu ta law ru chl; (7) act gl q mu law ru chl g; (8) act gl g mu ta ru chleplaw; (9) ep gl g mu ky ru zo chl; (10) ep hb(gl) g mu ky ru zo; (11) hb(gl) mu o ky ru g zo; (12) hb(gl) o mu g ky ru; (13) ep gl g mu ta ky ru zo chl; (14) act gl g mu ta law ru; (15) act gl q mu law ru chl di; (16) act gl q mu law sph chl di; (17) act gl chl ep mu sph q law; (18) act gl chl ep mu ru q law; (19) act gl chl g ep mu ru q law; (20) gl(hb) chl g ep mu ta ru zo q; (21) hb(gl) g mu chl ru q zo ky; (22) hb(gl) bi ky ru q g zo; (23) hb bi chl ky ru q zo g; (24) hb bi chl ky ru q; (25) ep hb g mu chl ru q zo; (26) hb bi ky q ru g; (27) hb bichl ky ru q g; (28) hb g mu chl ru q zo; (29) gl o bi ta law ru chl; (30) ep hb chl ru q zo mu pa; (31) hb bichl ru q zo mu; (32) hb mu chl ru q zo; (33) ep hb mu chl ru q zo; (34) hb bi chl ru q zo pa; (35) act ep bi chl ru pa; (36) act chl ep bi sph pa q; (37) act gl chl ep bi sph pa q; (38) act gl chl ep mu sph pa q; (39) act mu chl sph q lep; (40) hb bi chl sph q lep; (41) ep hb bi chl sph q zo pl; (42) ep hb bi chl ru q zo pl; (43) hb bi chl ru q zo pl", + "footnote": [], + "bbox": [ + [ + 84, + 103, + 770, + 630 + ] + ], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 45, + 170, + 945, + 750 + ] + ], + "page_idx": 17 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [ + [ + 88, + 94, + 928, + 540 + ] + ], + "page_idx": 18 + } +] \ No newline at end of file diff --git a/preprint/preprint__0807f99e4afb929d1e093bb7dc645550d50e8be613458192678ecc0c97d6a34f/preprint__0807f99e4afb929d1e093bb7dc645550d50e8be613458192678ecc0c97d6a34f.mmd b/preprint/preprint__0807f99e4afb929d1e093bb7dc645550d50e8be613458192678ecc0c97d6a34f/preprint__0807f99e4afb929d1e093bb7dc645550d50e8be613458192678ecc0c97d6a34f.mmd new file mode 100644 index 0000000000000000000000000000000000000000..1f26e61bc939e1ce260243815d00c9d80016254b --- /dev/null +++ b/preprint/preprint__0807f99e4afb929d1e093bb7dc645550d50e8be613458192678ecc0c97d6a34f/preprint__0807f99e4afb929d1e093bb7dc645550d50e8be613458192678ecc0c97d6a34f.mmd @@ -0,0 +1,232 @@ + +# Venus' light slab hinders its development of planetary-scale subduction and habitability + +Junxing Chen (junxing.chen@mail.utoronto.ca) + +University of Toronto + +Hehe Jiang University of Toronto + +Ming Tang Peking University + +Jihua Hao University of Science and Technology of China + +Meng Tian University of Bern + +Chu Xu https://orcid.org/0000- 0002- 6816- 1076 + +Article + +Keywords: + +Posted Date: January 7th, 2022 + +DOI: https://doi.org/10.21203/rs.3.rs- 1104964/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> + +1 VENUS' LIGHT SLAB HINDERS ITS DEVELOPMENT OF PLANETARY-SCALE2 SUBDUCTION AND HABITABILITY3 4 Junxing Chen \(^{1*}\) , Hehe Jiang \(^{1}\) , Ming Tang \(^{2}\) , Jihua Hao \(^{3}\) , Meng Tian \(^{4}\) , Xu Chu \(^{1}\) 5 6 \(^{1}\) Department of Earth Science, University of Toronto, Toronto, Ontario M5S 3B1, Canada 7 \(^{2}\) Key Laboratory of Orogenic Belt and Crustal Evolution, MOE; School of Earth and Space Science, Peking 8 University, Beijing 100871, China. 9 \(^{3}\) CAS Key Laboratory of Crust-Mantle Materials and Environments, School of Earth and Space Sciences, 10 University of Science and Technology of China, Hefei 230026, China. 11 \(^{4}\) University of Bern, Center for Space and Habitability, Gesellschaftsstrasse 6, 3012 Bern, Switzerland 12 13 \(*\) junxing.chen@mail.utoronto.ca 14 + +<--- Page Split ---> + +## Abstract + +Terrestrial planets Venus and Earth have similar sizes, masses, and bulk compositions, but only Earth developed planetary- scale plate tectonics. Plate tectonics generates weatherable fresh rocks and transfers surface carbon back to Earth's interior, which provides a long- term climate feedback, serving as a thermostat to keep Earth a habitable planet. Yet Venus shares a few common features with early Earth, such as stagnant- lid tectonics and the possible early development of a liquid ocean. Given all these similarities with early Earth, why would Venus fail to develop global- scale plate tectonics? In this study, we explore solutions to this problem by examining Venus' slab densities under hypothesized subduction- zone conditions. Our petrologic simulations show that eclogite facies may be reached at greater depths on Venus than on Earth, and Venus' slab densities are consistently lower than Earth's. We suggest that the lack of sufficient density contrast between the high- pressure metamorphosed slab and mantle rocks may have impeded self- sustaining subduction. Although plume- induced crustal downwelling exists on Venus, the dipping of Venus' crustal rocks to mantle depth fails to transition into subduction tectonics. As a consequence, the supply of fresh silicate rocks to the surface has been limited. This missing carbon sink eventually diverged the evolution of Venus' surface environment from that of Earth. + +## Introduction + +Venus is regarded as a "twin planet" of Earth, having a slightly smaller radius (6052 km) and lower gravity \(\mathrm{(g = 8.87 m / s^2)}\) . Despite these similarities, Venus' \(\mathrm{CO_2}\) - rich \((>90\%)\) atmosphere \((4.8\times 10^{20}\mathrm{kg})^{2}\) is \(\sim 100\) times denser than Earth's \((5.15\times 10^{18}\mathrm{kg})^{3}\) , resulting in a runaway greenhouse effect and extremely high surface temperature of \(460^{\circ}\mathrm{C}\) . The carbon budget of a planet's surface reservoirs is primarily governed by endogenic degassing and carbon sequestration processes. Magmatism and metamorphism in tectonically active regions release carbon into the atmosphere \(^{4,5,6}\) , whereas newly formed silicate rocks are subject to weathering that sequestrates atmospheric \(\mathrm{CO_2}\) . The size of this carbon sink depends primarily on the supply rate of fresh silicate reactants or the weathering reaction kinetics \(^{7,8}\) . Both the input and output fluxes are strongly associated with the tectonic regime of a planet. + +<--- Page Split ---> + +The twin planets Venus and Earth show contrasting tectonic characteristics. Planetary- scale subduction does not seem to operate on Venus, and therefore it is widely accepted that Venus is in a stagnant- lid regime9. A similar tectonic pattern is inferred to have been prevalent on early Earth, before primitive plate tectonics emerged10,11. The origin of plate tectonics on Earth is an ongoing fundamental debate in geosciences12,13. Some researchers suggest that plume upwelling and magma loading broke the lithosphere and initiated proto- subduction14,15. This lithospheric damage accumulated and formed plate boundaries16; self- sustaining subduction further drove plate tectonics on Earth17. Plumed- induced subduction sites are also identified around corona structures on Venus18,19. The question comes as to why the local subduction failed to trigger plate tectonics on Venus. + +Here, we explore the mechanisms for the lack of large- scale subduction on Venus from a petrologic perspective and further discuss the consequences. As a crustal slab sinks into the mantle, the increasing pressure produces dense metamorphic assemblages, which, if denser than mantle rocks, would drag the slab further into the mantle and promote plate convergence. Such a process dating back to as early as the Neoarchean on Earth20 is favored in cold geothermal conditions16,21. In nucleating and sustaining subduction, water plays a vital role as hydration weakens shear zones in the lithosphere22, and lubricates the slab interface in the subduction zone23. Modern Venus clearly meets neither necessity. However, studies suggest that early Venus might have had a similar liquid ocean and surface temperature to the Archean Earth24. Assuming such a favorable environment existed, we explore whether metamorphism on Venus would have resulted in dense slabs. Specifically, we perform forward phase equilibria simulation to model the mineral assemblages, densities of Venus’ slabs along typical subduction- zone geotherms. From the modeling results, we discuss how the crustal compositions and densities diverge the tectonic and environmental evolutions of Venus from that of Earth. + +## Modeling slab densities and tectonic development + +<--- Page Split ---> + +There are three measured bulk compositions of Venus' crust from the USSR landing probes in the 1980s: Venera \(13^{25}\) , Venera \(14^{25}\) and Vega \(2^{26}\) . These data were acquired by X- Ray Fluorescence (XRF), and reflect a broadly basaltic crust of Venus. Light elements (e.g., Mg) had large uncertainties. In particular, Na concentrations were unavailable from XRF but were estimated using K, Mg and Fe oxides concentration \(^{25,26}\) . The Venus' crust appears to have higher Al contents and lower contents of divalent cations than that of early Earth \(^{27}\) . Isostatic compensation models are not able to identify major heterogeneity within Venus' crust by fitting topography and gravity data \(^{28}\) ; without further constraints, we use these three data points to represent the crustal composition of Venus. We model the equilibrium phase relations in the pressure- temperature (P- T) ranges of 0.5- 3.5 GPa, 300- 900 °C (Fig. 1, S1), using similar approaches reported in ref. 27 (See Methods). The predicted mineral assemblages and mineral compositions enable us to estimate the rock densities. We then test the sensitivity of our modeled slab densities to Na, Mg concentrations to account for the large uncertainties associated with Venus' crust. + +The pressure- temperature (P- T) phase diagrams for the three Venus samples are topologically alike—the eclogite- facies assemblages consisting of dense minerals (garnet and amphacite/diopside) are only stable at \(> \sim 1.5\) GPa (conservatively \(\sim 60\) km) and \(> 530\) °C (green- shaded in Fig. 1, S1). These Venus' eclogite fields are smaller than those of the Archean- Proterozoic terrestrial samples \(^{27}\) . The similarities of the moments of inertia and the average densities adjusted to the sizes between Venus and Earth allows us to presume that the mantle density of Venus approaches the Preliminary Reference Earth Model (PREM), and accordingly, these two planets have similar internal and thermal structures \(^{29}\) . Similar to ref. 27, we consider two representative geotherms on Venus—11.3 °C/km (warm) and 8.1 °C/km (cold). The densities of Venus' slabs are generally lower than Earth's Proterozoic slabs along both geotherms, by up to 0.1- 0.2 g/cm \(^3\) (Fig. 2a, b). Venus slabs' higher Al contents result in more abundant low- density minerals like chlorite and lower proportions of high- density minerals garnet and pyroxene. The depth that reaches eclogite- facies condition is 5- 10 km greater on Venus than Earth. The Venus' slab densities are about 3.2 g/cm \(^3\) at 55 km along a warm geotherm (Fig. 2a), or 70 km along a cold geotherm (Fig. 2b). At such depths, the Proterozoic Earth's + +<--- Page Split ---> + +slab densities \(^{27}\) exceed \(3.3 \mathrm{g / cm^3}\) and approach mantle density ( \(\sim 3.35 \mathrm{g / cm^3}\) ). Along both geotherms, the Venus' slab densities shoot up and reach \(\sim 3.4 \mathrm{g / cm^3}\) in the next 10- 15 km due to eclogitization (Fig. 2b, S3a), remaining at \(3.35 - 3.55 \mathrm{g / cm^3}\) at greater depths (Fig. S3). The slab densities exceed that of the upper mantle at \(\sim 65 \mathrm{km}\) along the warm geotherm (Fig. S3a), and \(\sim 75 \mathrm{km}\) along the cold geotherm (Fig. 2b). The density of metamorphosed Venera 14 approaches that of the Paleoproterozoic high- MgO basalt on Earth \(^{27}\) . We note that the Venera 14 composition is distinctively Mg- poor, and the effect of compositional variations on the slab density is discussed below. + +We now explore how the uncertainties in concentrations may influence the equilibrium mineral assemblages and densities. To do this, we first propagate the analytical uncertainties and calculate the density and its likely range by assuming that the three XRF measurements sampled a uniform Venus' crust (Table S2; see Method). As an example, at \(525^{\circ}\mathrm{C}\) along the warm geotherm, the distribution of the sampled densities peak at \(3.07 \mathrm{g / cm^3}\) , with \(1\sigma\) standard deviation of \(\pm 0.03 \mathrm{g / cm^3}\) (Fig. 2a inset), so the density of Venus' average slab is significantly lower than Earth's. We then run phase equilibrium simulations and slab density calculations at \(650^{\circ}\mathrm{C}\) with \(\pm 30\%\) variations in bulk rock Na and Mg contents (Fig. S5, S6, Fig. 2c, d). The Na content, or the Na/Ca ratio, of the crustal protolith, influences the relative stabilities of glaucophane, pyroxene, and garnet \(^{30}\) (Fig. S5). However, the density variation due to the uncertainty associated with the Na content is minor ( \(< 0.05 \mathrm{g / cm^3}\) , Fig. 2c). Mg and \(\mathrm{Fe}^{2 + }\) substitute in mafic minerals, such as garnet and pyroxene. Mg- endmembers are less dense, resulting in lower rock density. Mg numbers \((\mathrm{Mg}\# = \mathrm{molar} \mathrm{Mg} / [\mathrm{Mg} + \mathrm{Fe}])\) of the three samples range from 0.62 to 0.72. The densities drop significantly as the Mg contents increase, by up to \(0.1 \mathrm{g / cm^3}\) along the cold geotherm and \(0.15 \mathrm{g / cm^3}\) along warm geotherm, respectively (Fig. 2d). If the bulk \(\mathrm{Mg}\#\) of Venera 14 were comparable to the others, it would have a similarly low density. Venus has been resurfaced in the past \(\sim 500 \mathrm{Myr}^{31,32}\) , and therefore its surface rocks represent a relatively modern crust. In the early history of a planet, the melting of a hotter mantle would have produced crustal rocks with higher Mg contents \(^{33}\) . The Mg- rich protolith composition results + +<--- Page Split ---> + +in less dense metamorphic rocks, especially along a warm geotherm (Fig. 2d). Thereby, our modeling results could be regarded as an upper limit for the slab density on early Venus. + +On Earth, prototype subduction submerged a slab to the mantle depth, triggered self- sustaining subduction, and initiated plate tectonics10,12,34. High- pressure metamorphism creates a dense slab and facilitates slab subduction to greater depths35 (Fig. 3b) where the cold core of the subducting slab further contributes negative thermal buoyancy36 (Fig. 3c). The negative buoyancy induced by phase transformation might play a particularly important role in the Archean when the basaltic crust was thicker than present37. The recycling of slab gives rise to new crustal materials generated at convergent and divergent boundaries (Fig. 3c). On Venus, the modeled slab's densities are consistently lower than Earth's by \(>0.1 \mathrm{g / cm^3}\) , and bulk density averaged from shallow depths to \(80 \mathrm{km}\) is \(3.18 - 3.30 \mathrm{g / cm^3}\) along the warm geotherm, or \(3.14 - 3.25 \mathrm{g / cm^3}\) along the cold geotherm (Fig. S4). The average densities of slabs hardly exceed the mantle density ( \(\sim 3.35 \mathrm{g / cm^3}\) ). At these depths, the mantle lithospheres of the subsiding and overriding plates are of similar temperatures (Fig. 3b), so the negative thermal buoyancy of slab is minor. Given that \(80 \mathrm{km}\) is the overestimation for crustal thickness38 and the depth of incipient subduction18, spontaneous subduction is unlikely triggered by local and temporal dipping of crust (Fig. 3b). The root of thickened orogen could also delaminate (Fig. 3c), an alternative recycling pathway that might prevail in the Archean. The ecologized lower crust still needs to be dense enough to introduce gravitational instability39, which is unlikely archived on Venus, either. We speculate that light slabs diverged Venus' tectonic regime from Earth in the early stage (Fig. 3d). The difference in tectonic regime eventually leads to contrasting surface carbon budget, environments, and habitability on these two planets. + +## Earth and Venus' environmental evolution + +Both Earth and Venus have considerable amounts of magmatic \(\mathrm{CO_2}\) degassing (Tmol/yr), despite of their different tectonic regimes. Combining with the metamorphic decarbonation40 and clay formation by reversed weathering41, the total decarbonation rate is 4 to 29 Tmol/yr on Earth. As for Venus, the long- term + +<--- Page Split ---> + +average volcanic eruption rate is \(\sim 0.37 \mathrm{km}^{3} / \mathrm{yr}^{32}\) ; given \(>4 \mathrm{wt.} \% \mathrm{CO}_{2}^{42}\) and a density \(\sim 2.5 \mathrm{g} / \mathrm{cm}^{3}\) of the basaltic magma, this endogenic carbon flux on Venus reaches 1 Tmol/yr, half- to- one orders of magnitude smaller than on Earth. + +Silicate weathering is the primary mechanism of \(\mathrm{CO}_{2}\) removal from the atmosphere43,44, of which the rate is limited by either the reaction kinetics or the supply rate of reactants7,8. The difference in the tectonic regime on Earth (plate tectonics) and Venus (stagnant- lid) lead to a large discrepancy in the tectonic resurfacing rate. On Earth, the large amount of fresh rock produced at active plate boundaries ( \(\sim 30 \mathrm{km}^{3} / \mathrm{yr}\) , mostly from mid- ocean ridges45 and arcs46) and a proper land fraction result in a high \(\mathrm{CO}_{2}\) - consumption rate of 16 to 27 Tmol/yr47,48. The subsequent carbonate deposition ( \(\sim 3.6 \mathrm{Tmol} / \mathrm{yr}^{49}\) ), followed by subduction or crustal delamination, provides a path for recycling carbonate back to the mantle and establishes a planetary thermostat50 (Fig. 3c). When both the surface temperature and weathering rate are primarily controlled by the greenhouse gas content in the atmosphere, negative feedback and thus planetary thermostat could be established50. By contrast, subduction on Venus came to a premature end (Fig. 3b, d). Without sufficient new crustal material generated at active plate boundaries, fresh silicate rocks are mainly produced by plume- related volcanism at a rate of \(\sim 0.37 \mathrm{km}^{3} / \mathrm{yr}^{32}\) , about \(\sim 1 / 100\) the rate of that in Earth ( \(\sim 30 \mathrm{km}^{3} / \mathrm{yr}\) ). Thus, weathering is ‘supply limited’51, is less sensitive to temperature, and proceeds at a slow rate on Venus52. + +Efficient resurfacing on Earth, which is brought by active plate tectonics, sustains a long- term negative feedback that prevents Earth’s carbon from being accumulated in the atmosphere or being stored entirely in the lithosphere. Even in a supposedly sluggish tectonic regime in the Neoarchean53, resurfacing brought by active plate tectonics would still be enough to balance carbon degassing54. On the stagnant- lid Venus the \(\mathrm{CO}_{2}\) consumption is unlikely to catch up with the endogenic carbon emission, resulting in a long- term \(\mathrm{CO}_{2}\) accumulation in Venus’ atmosphere, runaway greenhouse climate, and eventual ocean evaporation + +<--- Page Split ---> + +within 1 b.y. \(^{55}\) . In return, the warming surface and lithosphere, together with an anhydrous crust, prevented self- sustaining subduction and locked Venus in a stagnant- lid regime. + +## Acknowledgments + +This work was supported by the NSERC Discovery Grant (RGPIN- 2018- 03925) to X.C. + +## Author Contributions + +X.C. initiated the idea, J.C. performed simulation, and J.C. and X.C. wrote the first manuscript. All authors contributed to data interpretation and manuscript writing. + +## Competing Interests + +The authors declare no competing interests. + +## References + +1. Fegley, B. Treatise on Geochemistry Ch. 1.19 (Elsevier Science, 2003) + +2. Basilevsky, A. T., Head, J. W. 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T., et al. Evolution of the global carbon cycle and climate regulation on earth. Global Biogeochemical Cycles 34(2) (2020). +44. Hilton, R. G., West, A. J. Mountains, erosion and the carbon cycle. Nature Reviews Earth & Environment 1(6), 284-299 (2020). +45. Bird, P. An updated digital model of plate boundaries. Geochemistry, Geophysics, Geosystems 4(3), (2003). +46. Jicha, B. R., Jagoutz, O. Magma production rates for intraoceanic arcs. Elements 11(2), 105-111 (2015). +47. Coogan, L. A., Gillis, K. M. Temperature dependence of chemical exchange during seafloor weathering: Insights from the Troodos ophiolite. Geochimica et Cosmochimica Acta 243, 24-41 (2018). +48. Wallmann, K., Aloisi, G. The global carbon cycle: geological processes. Fundamentals of Geobiology 20-35 (2012). +49. Sleep, N. H., Zahnle, K. Carbon dioxide cycling and implications for climate on ancient Earth. Journal of Geophysical Research: Planets 106(E1), 1373-1399 (2001). +50. Kasting, J. F. The Goldilocks planet? How silicate weathering maintains Earth "just right". Elements: An International Magazine of Mineralogy, Geochemistry, and Petrology 15(4), 235-240 (2019). +51. Foley, B. J. The role of plate tectonic-climate coupling and exposed land area in the development of habitable climates on rocky planets. The Astrophysical Journal 812(1), 36 (2015). +52. Hakim, K., et al. Lithologic controls on silicate weathering regimes of temperate planets. The Planetary Science Journal 2(2), 49.2 (2021). +53. Korenaga, J. Archean geodynamics and the thermal evolution of Earth. Geophysical Monograph-American Geophysical Union 164, 7 (2006). +54. Driscoll, P., Bercovici, D. Divergent evolution of Earth and Venus: influence of degassing, tectonics, and magnetic fields. Icarus 226(2), 1447-1464 (2013). +55. Honing, D., Baumeister, P., Grenfell, J. L., Tosi, N., Way, M. J. Early Habitability and Crustal Decarbonation of a Stagnant - Lid Venus. Journal of Geophysical Research: Planets 126(10), (2021). +56. Holland, T. J. B., Powell, R. An internally consistent thermodynamic data set for phases of petrological interest. Journal of Metamorphic Geology 16 309-343 (1998). + +<--- Page Split ---> + +57. Carroll, M. R., Rutherford, M. J. Sulfide and sulfate saturation in hydrous silicate melts. Journal of Geophysical Research: Solid Earth 90(S02), C601-C612 (1985). +58. Hacker, B. R., Abers, G. A., Peacock, S. M. Subduction factory 1. Theoretical mineralogy, densities, seismic wave speeds, and \(\mathrm{H}_2\mathrm{O}\) contents. Journal of Geophysical Research: Solid Earth 108(B1), (2003). + +## Figure Captions + +Figure 1. P-T phase diagram in the range 300-650 °C, 0.5-2.5 GPa calculated for Vega 2 composition (Table S2). The phase diagram is labeled with mineral assembles and stability boundaries of important phases (garnet, pyroxene, amphiboles). The cold (8.1 °C/km) and warm (11.3 °C/km) geotherm are shown as yellow dashed lines. The eclogite-facies assemblages are highlighted in light green. The phase diagrams for the other two compositions are presented in Fig. S1, and the phase relations at higher pressures and temperatures are presented in Figure S2. + +Figure 2. (a,b) The Earth \(^{27}\) (blue) and Venus (red) slab densities along warm and cold geotherms from 300 to \(650^{\circ}\mathrm{C}\) . The inset diagram in Figure 2a shows the statistical distribution of Venus' slab density at \(525^{\circ}\mathrm{C}\) along warm geotherm (see Method). The slab densities at higher pressures and temperatures are shown in Fig. S3 and the bulk densities of slabs are shown in Fig. S4. (c,d) The slab densities as functions of the variations of bulk Mg and Na concentrations at \(650^{\circ}\mathrm{C}\) . The phase diagrams are presented in Figures S5 and S6. + +Figure 3. Skematic cartoons illustrating the tectonic evolutions on Earth and Venus, and their diverging carbon cycles. (a) Plume upwelling breaks the lithosphere. (b) Incipient slab dipping on Venus and Earth; ecologization might trigger self- sustaining subduction. (c, d) Difference in Earth and Venus tectonic and environmental evolution as results of different slab densities and subduction self- sustainability (see text for discussions). + +<--- Page Split ---> + +Through thermodynamic and mass balance equations, the minerals + fluid equilibria and their compositions are calculable for a fixed rock bulk composition (X) at specific pressure (P)- temperature (T) conditions. The equilibrium phase diagrams, also known as "pseudosection", depict the phase relations and outline the areas of stable phase assemblages in P- T, T- X and P- X spaces. In this study, we use the bulk compositions of Venus' crust to plot P- T, P- XMg, P- XNa equilibrium phase diagrams. Assuming that the metamorphism in a hypothesized subduction zone reaches equilibrium, the plotted P- T equilibrium phase diagrams indicate the slabs' mineral assemblages along subduction geotherms. The P- XMg, P- XNa diagrams reflect the influences of Na, Mg concentration variances on the mineral assemblages and their compositions. The equilibrium phase diagrams are calculated by using THERMOCALC version 3.33 (https://hpxeosandthermocalc.org/), thermodynamic dataset of ds55 (ref. 56) and compatible activity models (Table S3). To estimate the bulk density of a slab averaged from shallow to mantle depths, we model the phase equilibria in a larger pressure and temperature range (up to 3.5 GPa, 900 °C; Fig. S2). We do not include silicate melts due to a lack of compatible activity models. Incipient melting would lower the bulk density and strength of a slab, impeding slab subduction. The effect of partial melting is further discussed in Supplementary Information. + +We use the NCKFMASHT0 \(\mathrm{(Na_{2}O - CaO - K_{2}O - FeO - MgO - Al_{2}O_{3} - SiO_{2} - H_{2}O - TiO_{2} - O_{2})}\) model system. The crust compositions of Venus are represented by three available X- ray fluorescence data (Table S1,2) from USSR landing probes Venera \(13^{25}\) , Venera \(14^{25}\) and Vega \(2^{26}\) . The Venera 13 composition is an alkaline basalt likely consisting of weathered olivine leucitite, nephelinite. Venera 14 and Vega 2 are both weathered N- MORB- like basaltic tholeiite. We assume that ocean existed on the early Venus like the Earth, the basaltic crust could have been hydrated. Progressive dehydration reactions during metamorphism keeps \(\mathrm{H}_{2}\mathrm{O}\) saturation. The crustal \(\mathrm{Fe}^{3 + } / (\mathrm{Fe}^{2 + } + \mathrm{Fe}^{3 + })\) ratios are mainly controlled by the mantle redox condition and partial melting process. Assuming similar settings of the early Earth and Venus, we use the same ratio + +<--- Page Split ---> + +as the early Earth \((\mathrm{Fe}^{3 + } / (\mathrm{Fe}^{2 + } + \mathrm{Fe}^{3 + }) = 0.1\) in mole, ref. 27). The three Venus data all contain a fair amount of sulfur, which might reflect anhydrite \((\mathrm{CaSO_4})\) or sulfate-bearing scapolite \(^{57}\) , the products of chemical weathering by an extreme atmospheric \(\mathrm{SO}_2\) content. If Venus had liquid ocean at its early stage, the sulfur content would not have been as significant. Thus, we ignore the S contents in our model. We do not remove corresponding CaO from the bulk composition because sulfur weathering does not introduce additional CaO. The MnO contents are low, have large uncertainties, and barely affect the phase relations, so we omit MnO in the model system. The other elements' mass percentage are transferred into mole fraction for the calculation. + +Phase equilibria modeling predicts quantitative mineral proportions and compositions. The rock densities \((\rho)\) are calculated on basis of the information from phase relations and the minerals' thermoelastic parameters, using a similar method as ref. 58: + +\[\rho = \sum_{i = 1}^{n}\rho_{i}\nu_{i}\] + +where + +\(\nu_{i}\) is the volume fraction of a mineral (mode), provided by the phase equilibria modeling results; + +\(\rho_{i}\) is the density of a mineral, calculated using: + +\[\rho_{i} = \frac{\sum_{j = 1}^{m}M_{j}x_{j}}{\sum_{j = 1}^{m}V_{j}x_{j}}\] + +where + +\(M_{i}\) is the molar weight of an endmember, calculated from its formula; + +\(x_{j}\) is the mole fractionation of one end member, provided by the phase equilibria modeling results; + +\(V_{j}\) is the molar volume of an endmember, calculated from the equation of state in ref. 56: + +\[V_{j} = V_{T}\left(1 - \frac{4P}{K_{T} + 4P}\right)^{1 / 4}\] + +where + +<--- Page Split ---> + +\(V_{T}\) is the molar volume at 1bar, \(T(\mathrm{Kelvin}\) in unit), \(V_{T} = V_{1,298}[1 + \alpha_{0}(T - 298) - 2\alpha_{0}\alpha_{1}(\sqrt{T} -\) \(\sqrt{298})]\) , in which \(\alpha_{0},\alpha_{1}\) : the thermal expansion parameter; + +\(K_{T}\) : the end- member's bulk modulus at \(T(\mathrm{Kelvin}\) in unit), \(K_{T} = K_{298}(1 - 1.5\times 10^{- 4}(T - 298))\) , in which \(K_{298}\) is the bulk modulus at 298 Kelvin, and \(V_{1,298}\) is the molar volume at 1 bar, 298 Kelvin (standard condition). + +In addition to phase relations corresponding to these three individual bulk compositions, we propagate the uncertainties of concentrations, and calculate Venus' slab density and its standard deviation, to explore how likely the slab density of Venus overlap those of Earth. This exercise is time consuming, so we only calculate at \(525^{\circ}\mathrm{C}\) , along the warm geotherm, where the calculated slab densities of two Venus bulk compositions (Venera 13 and 14) overlap the densities of Earth's Proterozoic slabs (Fig. 2a). The assumption is that these three XRF data all represent one hypothetical uniform crustal composition of Venus. The means and standard variations of Venus' crustal bulk compositions are shown in Tables S1 and S2. At each step, we randomly sample one element's abundance within it range (Gibbs sampler), model the phase relations using the new bulk composition, and calculate the density of the assemblage. We perform 300 such sampling (Table S4), and then fit the series of calculated densities into a normal distribution for the mean and standard deviation of the density estimate. + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + + + +
Numbered assemblages: (1) act gl o g bi ta law ru chl; (2) mu o ky ru chl g ta law; (3) mu o ky ru g ta law; (4) gl(hb) mu o ky ru g ta; (5) mu o zo ky ru g ta chl; (6) act gl o g mu ta law ru chl; (7) act gl q mu law ru chl g; (8) act gl g mu ta ru chleplaw; (9) ep gl g mu ky ru zo chl; (10) ep hb(gl) g mu ky ru zo; (11) hb(gl) mu o ky ru g zo; (12) hb(gl) o mu g ky ru; (13) ep gl g mu ta ky ru zo chl; (14) act gl g mu ta law ru; (15) act gl q mu law ru chl di; (16) act gl q mu law sph chl di; (17) act gl chl ep mu sph q law; (18) act gl chl ep mu ru q law; (19) act gl chl g ep mu ru q law; (20) gl(hb) chl g ep mu ta ru zo q; (21) hb(gl) g mu chl ru q zo ky; (22) hb(gl) bi ky ru q g zo; (23) hb bi chl ky ru q zo g; (24) hb bi chl ky ru q; (25) ep hb g mu chl ru q zo; (26) hb bi ky q ru g; (27) hb bichl ky ru q g; (28) hb g mu chl ru q zo; (29) gl o bi ta law ru chl; (30) ep hb chl ru q zo mu pa; (31) hb bichl ru q zo mu; (32) hb mu chl ru q zo; (33) ep hb mu chl ru q zo; (34) hb bi chl ru q zo pa; (35) act ep bi chl ru pa; (36) act chl ep bi sph pa q; (37) act gl chl ep bi sph pa q; (38) act gl chl ep mu sph pa q; (39) act mu chl sph q lep; (40) hb bi chl sph q lep; (41) ep hb bi chl sph q zo pl; (42) ep hb bi chl ru q zo pl; (43) hb bi chl ru q zo pl
+ +
Figure 1
+ +P-T phase diagram in the range 300-650 °C, 0.5-2.5 GPa calculated for Vega 2 composition (Table S2). The phase diagram is labeled with mineral assemblies and stability boundaries of important phases (garnet, pyroxene, amphiboles). The cold (8.1 °C/km) and warm (11.3 °C/km) geotherm are shown as yellow + +<--- Page Split ---> + +dashed lines. The ecogite-facies assemblages are highlighted in light green. The phase diagrams for the other two compositions are presented in Fig. S1, and the phase relations at higher pressures and temperatures are presented in Figure S2. + +![](images/Figure_2.jpg) + +
Figure 2
+ +(a,b) The Earth27 (blue) and Venus (red) slab densities along warm and cold geotherms from 300 to 650 °C. The inset diagram in Figure 2a shows the statistical distribution of Venus' slab density at 525 °C along warm geotherm (see Method). The slab densities at higher pressures and temperatures are shown in Fig. S3 and the bulk densities of slabs are shown in Fig. S4. (c,d) The slab densities as functions of the variations of bulk Mg and Na concentrations at 650 °C. The phase diagrams are presented in Figures S5 and S6. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3
+ +Skematic cartoons illustrating the tectonic evolutions on Earth and Venus, and their diverging carbon cycles. (a) Plume upwelling breaks the lithosphere. (b) Incipient slab dipping on Venus and Earth; ecogitization might trigger self- sustaining subduction. (c, d) Difference in Earth and Venus tectonic and environmental evolution as results of different slab densities and subduction self- sustainability (see text for discussions). + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +Supplementary Table4. xlsx Supplementary.docx + +<--- Page Split ---> diff --git a/preprint/preprint__0807f99e4afb929d1e093bb7dc645550d50e8be613458192678ecc0c97d6a34f/preprint__0807f99e4afb929d1e093bb7dc645550d50e8be613458192678ecc0c97d6a34f_det.mmd b/preprint/preprint__0807f99e4afb929d1e093bb7dc645550d50e8be613458192678ecc0c97d6a34f/preprint__0807f99e4afb929d1e093bb7dc645550d50e8be613458192678ecc0c97d6a34f_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..4a8d954815467377526e6221094c068cd597995b --- /dev/null +++ b/preprint/preprint__0807f99e4afb929d1e093bb7dc645550d50e8be613458192678ecc0c97d6a34f/preprint__0807f99e4afb929d1e093bb7dc645550d50e8be613458192678ecc0c97d6a34f_det.mmd @@ -0,0 +1,298 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 810, 177]]<|/det|> +# Venus' light slab hinders its development of planetary-scale subduction and habitability + +<|ref|>text<|/ref|><|det|>[[44, 195, 494, 216]]<|/det|> +Junxing Chen (junxing.chen@mail.utoronto.ca) + +<|ref|>text<|/ref|><|det|>[[55, 219, 240, 237]]<|/det|> +University of Toronto + +<|ref|>text<|/ref|><|det|>[[44, 243, 240, 283]]<|/det|> +Hehe Jiang University of Toronto + +<|ref|>text<|/ref|><|det|>[[44, 290, 207, 330]]<|/det|> +Ming Tang Peking University + +<|ref|>text<|/ref|><|det|>[[44, 336, 462, 376]]<|/det|> +Jihua Hao University of Science and Technology of China + +<|ref|>text<|/ref|><|det|>[[44, 381, 210, 421]]<|/det|> +Meng Tian University of Bern + +<|ref|>text<|/ref|><|det|>[[44, 428, 400, 468]]<|/det|> +Chu Xu https://orcid.org/0000- 0002- 6816- 1076 + +<|ref|>text<|/ref|><|det|>[[44, 510, 102, 527]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 548, 136, 566]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 586, 320, 605]]<|/det|> +Posted Date: January 7th, 2022 + +<|ref|>text<|/ref|><|det|>[[44, 624, 474, 644]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 1104964/v1 + +<|ref|>text<|/ref|><|det|>[[44, 661, 910, 704]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[70, 88, 886, 508]]<|/det|> +1 VENUS' LIGHT SLAB HINDERS ITS DEVELOPMENT OF PLANETARY-SCALE2 SUBDUCTION AND HABITABILITY3 4 Junxing Chen \(^{1*}\) , Hehe Jiang \(^{1}\) , Ming Tang \(^{2}\) , Jihua Hao \(^{3}\) , Meng Tian \(^{4}\) , Xu Chu \(^{1}\) 5 6 \(^{1}\) Department of Earth Science, University of Toronto, Toronto, Ontario M5S 3B1, Canada 7 \(^{2}\) Key Laboratory of Orogenic Belt and Crustal Evolution, MOE; School of Earth and Space Science, Peking 8 University, Beijing 100871, China. 9 \(^{3}\) CAS Key Laboratory of Crust-Mantle Materials and Environments, School of Earth and Space Sciences, 10 University of Science and Technology of China, Hefei 230026, China. 11 \(^{4}\) University of Bern, Center for Space and Habitability, Gesellschaftsstrasse 6, 3012 Bern, Switzerland 12 13 \(*\) junxing.chen@mail.utoronto.ca 14 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 92, 186, 107]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[111, 118, 886, 558]]<|/det|> +Terrestrial planets Venus and Earth have similar sizes, masses, and bulk compositions, but only Earth developed planetary- scale plate tectonics. Plate tectonics generates weatherable fresh rocks and transfers surface carbon back to Earth's interior, which provides a long- term climate feedback, serving as a thermostat to keep Earth a habitable planet. Yet Venus shares a few common features with early Earth, such as stagnant- lid tectonics and the possible early development of a liquid ocean. Given all these similarities with early Earth, why would Venus fail to develop global- scale plate tectonics? In this study, we explore solutions to this problem by examining Venus' slab densities under hypothesized subduction- zone conditions. Our petrologic simulations show that eclogite facies may be reached at greater depths on Venus than on Earth, and Venus' slab densities are consistently lower than Earth's. We suggest that the lack of sufficient density contrast between the high- pressure metamorphosed slab and mantle rocks may have impeded self- sustaining subduction. Although plume- induced crustal downwelling exists on Venus, the dipping of Venus' crustal rocks to mantle depth fails to transition into subduction tectonics. As a consequence, the supply of fresh silicate rocks to the surface has been limited. This missing carbon sink eventually diverged the evolution of Venus' surface environment from that of Earth. + +<|ref|>sub_title<|/ref|><|det|>[[115, 601, 216, 617]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[111, 630, 886, 905]]<|/det|> +Venus is regarded as a "twin planet" of Earth, having a slightly smaller radius (6052 km) and lower gravity \(\mathrm{(g = 8.87 m / s^2)}\) . Despite these similarities, Venus' \(\mathrm{CO_2}\) - rich \((>90\%)\) atmosphere \((4.8\times 10^{20}\mathrm{kg})^{2}\) is \(\sim 100\) times denser than Earth's \((5.15\times 10^{18}\mathrm{kg})^{3}\) , resulting in a runaway greenhouse effect and extremely high surface temperature of \(460^{\circ}\mathrm{C}\) . The carbon budget of a planet's surface reservoirs is primarily governed by endogenic degassing and carbon sequestration processes. Magmatism and metamorphism in tectonically active regions release carbon into the atmosphere \(^{4,5,6}\) , whereas newly formed silicate rocks are subject to weathering that sequestrates atmospheric \(\mathrm{CO_2}\) . The size of this carbon sink depends primarily on the supply rate of fresh silicate reactants or the weathering reaction kinetics \(^{7,8}\) . Both the input and output fluxes are strongly associated with the tectonic regime of a planet. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 120, 886, 396]]<|/det|> +The twin planets Venus and Earth show contrasting tectonic characteristics. Planetary- scale subduction does not seem to operate on Venus, and therefore it is widely accepted that Venus is in a stagnant- lid regime9. A similar tectonic pattern is inferred to have been prevalent on early Earth, before primitive plate tectonics emerged10,11. The origin of plate tectonics on Earth is an ongoing fundamental debate in geosciences12,13. Some researchers suggest that plume upwelling and magma loading broke the lithosphere and initiated proto- subduction14,15. This lithospheric damage accumulated and formed plate boundaries16; self- sustaining subduction further drove plate tectonics on Earth17. Plumed- induced subduction sites are also identified around corona structures on Venus18,19. The question comes as to why the local subduction failed to trigger plate tectonics on Venus. + +<|ref|>text<|/ref|><|det|>[[111, 412, 886, 844]]<|/det|> +Here, we explore the mechanisms for the lack of large- scale subduction on Venus from a petrologic perspective and further discuss the consequences. As a crustal slab sinks into the mantle, the increasing pressure produces dense metamorphic assemblages, which, if denser than mantle rocks, would drag the slab further into the mantle and promote plate convergence. Such a process dating back to as early as the Neoarchean on Earth20 is favored in cold geothermal conditions16,21. In nucleating and sustaining subduction, water plays a vital role as hydration weakens shear zones in the lithosphere22, and lubricates the slab interface in the subduction zone23. Modern Venus clearly meets neither necessity. However, studies suggest that early Venus might have had a similar liquid ocean and surface temperature to the Archean Earth24. Assuming such a favorable environment existed, we explore whether metamorphism on Venus would have resulted in dense slabs. Specifically, we perform forward phase equilibria simulation to model the mineral assemblages, densities of Venus’ slabs along typical subduction- zone geotherms. From the modeling results, we discuss how the crustal compositions and densities diverge the tectonic and environmental evolutions of Venus from that of Earth. + +<|ref|>sub_title<|/ref|><|det|>[[113, 888, 497, 905]]<|/det|> +## Modeling slab densities and tectonic development + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 886, 460]]<|/det|> +There are three measured bulk compositions of Venus' crust from the USSR landing probes in the 1980s: Venera \(13^{25}\) , Venera \(14^{25}\) and Vega \(2^{26}\) . These data were acquired by X- Ray Fluorescence (XRF), and reflect a broadly basaltic crust of Venus. Light elements (e.g., Mg) had large uncertainties. In particular, Na concentrations were unavailable from XRF but were estimated using K, Mg and Fe oxides concentration \(^{25,26}\) . The Venus' crust appears to have higher Al contents and lower contents of divalent cations than that of early Earth \(^{27}\) . Isostatic compensation models are not able to identify major heterogeneity within Venus' crust by fitting topography and gravity data \(^{28}\) ; without further constraints, we use these three data points to represent the crustal composition of Venus. We model the equilibrium phase relations in the pressure- temperature (P- T) ranges of 0.5- 3.5 GPa, 300- 900 °C (Fig. 1, S1), using similar approaches reported in ref. 27 (See Methods). The predicted mineral assemblages and mineral compositions enable us to estimate the rock densities. We then test the sensitivity of our modeled slab densities to Na, Mg concentrations to account for the large uncertainties associated with Venus' crust. + +<|ref|>text<|/ref|><|det|>[[111, 501, 886, 907]]<|/det|> +The pressure- temperature (P- T) phase diagrams for the three Venus samples are topologically alike—the eclogite- facies assemblages consisting of dense minerals (garnet and amphacite/diopside) are only stable at \(> \sim 1.5\) GPa (conservatively \(\sim 60\) km) and \(> 530\) °C (green- shaded in Fig. 1, S1). These Venus' eclogite fields are smaller than those of the Archean- Proterozoic terrestrial samples \(^{27}\) . The similarities of the moments of inertia and the average densities adjusted to the sizes between Venus and Earth allows us to presume that the mantle density of Venus approaches the Preliminary Reference Earth Model (PREM), and accordingly, these two planets have similar internal and thermal structures \(^{29}\) . Similar to ref. 27, we consider two representative geotherms on Venus—11.3 °C/km (warm) and 8.1 °C/km (cold). The densities of Venus' slabs are generally lower than Earth's Proterozoic slabs along both geotherms, by up to 0.1- 0.2 g/cm \(^3\) (Fig. 2a, b). Venus slabs' higher Al contents result in more abundant low- density minerals like chlorite and lower proportions of high- density minerals garnet and pyroxene. The depth that reaches eclogite- facies condition is 5- 10 km greater on Venus than Earth. The Venus' slab densities are about 3.2 g/cm \(^3\) at 55 km along a warm geotherm (Fig. 2a), or 70 km along a cold geotherm (Fig. 2b). At such depths, the Proterozoic Earth's + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 87, 886, 300]]<|/det|> +slab densities \(^{27}\) exceed \(3.3 \mathrm{g / cm^3}\) and approach mantle density ( \(\sim 3.35 \mathrm{g / cm^3}\) ). Along both geotherms, the Venus' slab densities shoot up and reach \(\sim 3.4 \mathrm{g / cm^3}\) in the next 10- 15 km due to eclogitization (Fig. 2b, S3a), remaining at \(3.35 - 3.55 \mathrm{g / cm^3}\) at greater depths (Fig. S3). The slab densities exceed that of the upper mantle at \(\sim 65 \mathrm{km}\) along the warm geotherm (Fig. S3a), and \(\sim 75 \mathrm{km}\) along the cold geotherm (Fig. 2b). The density of metamorphosed Venera 14 approaches that of the Paleoproterozoic high- MgO basalt on Earth \(^{27}\) . We note that the Venera 14 composition is distinctively Mg- poor, and the effect of compositional variations on the slab density is discussed below. + +<|ref|>text<|/ref|><|det|>[[111, 345, 886, 877]]<|/det|> +We now explore how the uncertainties in concentrations may influence the equilibrium mineral assemblages and densities. To do this, we first propagate the analytical uncertainties and calculate the density and its likely range by assuming that the three XRF measurements sampled a uniform Venus' crust (Table S2; see Method). As an example, at \(525^{\circ}\mathrm{C}\) along the warm geotherm, the distribution of the sampled densities peak at \(3.07 \mathrm{g / cm^3}\) , with \(1\sigma\) standard deviation of \(\pm 0.03 \mathrm{g / cm^3}\) (Fig. 2a inset), so the density of Venus' average slab is significantly lower than Earth's. We then run phase equilibrium simulations and slab density calculations at \(650^{\circ}\mathrm{C}\) with \(\pm 30\%\) variations in bulk rock Na and Mg contents (Fig. S5, S6, Fig. 2c, d). The Na content, or the Na/Ca ratio, of the crustal protolith, influences the relative stabilities of glaucophane, pyroxene, and garnet \(^{30}\) (Fig. S5). However, the density variation due to the uncertainty associated with the Na content is minor ( \(< 0.05 \mathrm{g / cm^3}\) , Fig. 2c). Mg and \(\mathrm{Fe}^{2 + }\) substitute in mafic minerals, such as garnet and pyroxene. Mg- endmembers are less dense, resulting in lower rock density. Mg numbers \((\mathrm{Mg}\# = \mathrm{molar} \mathrm{Mg} / [\mathrm{Mg} + \mathrm{Fe}])\) of the three samples range from 0.62 to 0.72. The densities drop significantly as the Mg contents increase, by up to \(0.1 \mathrm{g / cm^3}\) along the cold geotherm and \(0.15 \mathrm{g / cm^3}\) along warm geotherm, respectively (Fig. 2d). If the bulk \(\mathrm{Mg}\#\) of Venera 14 were comparable to the others, it would have a similarly low density. Venus has been resurfaced in the past \(\sim 500 \mathrm{Myr}^{31,32}\) , and therefore its surface rocks represent a relatively modern crust. In the early history of a planet, the melting of a hotter mantle would have produced crustal rocks with higher Mg contents \(^{33}\) . The Mg- rich protolith composition results + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 140]]<|/det|> +in less dense metamorphic rocks, especially along a warm geotherm (Fig. 2d). Thereby, our modeling results could be regarded as an upper limit for the slab density on early Venus. + +<|ref|>text<|/ref|><|det|>[[111, 183, 886, 748]]<|/det|> +On Earth, prototype subduction submerged a slab to the mantle depth, triggered self- sustaining subduction, and initiated plate tectonics10,12,34. High- pressure metamorphism creates a dense slab and facilitates slab subduction to greater depths35 (Fig. 3b) where the cold core of the subducting slab further contributes negative thermal buoyancy36 (Fig. 3c). The negative buoyancy induced by phase transformation might play a particularly important role in the Archean when the basaltic crust was thicker than present37. The recycling of slab gives rise to new crustal materials generated at convergent and divergent boundaries (Fig. 3c). On Venus, the modeled slab's densities are consistently lower than Earth's by \(>0.1 \mathrm{g / cm^3}\) , and bulk density averaged from shallow depths to \(80 \mathrm{km}\) is \(3.18 - 3.30 \mathrm{g / cm^3}\) along the warm geotherm, or \(3.14 - 3.25 \mathrm{g / cm^3}\) along the cold geotherm (Fig. S4). The average densities of slabs hardly exceed the mantle density ( \(\sim 3.35 \mathrm{g / cm^3}\) ). At these depths, the mantle lithospheres of the subsiding and overriding plates are of similar temperatures (Fig. 3b), so the negative thermal buoyancy of slab is minor. Given that \(80 \mathrm{km}\) is the overestimation for crustal thickness38 and the depth of incipient subduction18, spontaneous subduction is unlikely triggered by local and temporal dipping of crust (Fig. 3b). The root of thickened orogen could also delaminate (Fig. 3c), an alternative recycling pathway that might prevail in the Archean. The ecologized lower crust still needs to be dense enough to introduce gravitational instability39, which is unlikely archived on Venus, either. We speculate that light slabs diverged Venus' tectonic regime from Earth in the early stage (Fig. 3d). The difference in tectonic regime eventually leads to contrasting surface carbon budget, environments, and habitability on these two planets. + +<|ref|>sub_title<|/ref|><|det|>[[115, 793, 446, 810]]<|/det|> +## Earth and Venus' environmental evolution + +<|ref|>text<|/ref|><|det|>[[115, 822, 884, 905]]<|/det|> +Both Earth and Venus have considerable amounts of magmatic \(\mathrm{CO_2}\) degassing (Tmol/yr), despite of their different tectonic regimes. Combining with the metamorphic decarbonation40 and clay formation by reversed weathering41, the total decarbonation rate is 4 to 29 Tmol/yr on Earth. As for Venus, the long- term + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 171]]<|/det|> +average volcanic eruption rate is \(\sim 0.37 \mathrm{km}^{3} / \mathrm{yr}^{32}\) ; given \(>4 \mathrm{wt.} \% \mathrm{CO}_{2}^{42}\) and a density \(\sim 2.5 \mathrm{g} / \mathrm{cm}^{3}\) of the basaltic magma, this endogenic carbon flux on Venus reaches 1 Tmol/yr, half- to- one orders of magnitude smaller than on Earth. + +<|ref|>text<|/ref|><|det|>[[111, 216, 884, 650]]<|/det|> +Silicate weathering is the primary mechanism of \(\mathrm{CO}_{2}\) removal from the atmosphere43,44, of which the rate is limited by either the reaction kinetics or the supply rate of reactants7,8. The difference in the tectonic regime on Earth (plate tectonics) and Venus (stagnant- lid) lead to a large discrepancy in the tectonic resurfacing rate. On Earth, the large amount of fresh rock produced at active plate boundaries ( \(\sim 30 \mathrm{km}^{3} / \mathrm{yr}\) , mostly from mid- ocean ridges45 and arcs46) and a proper land fraction result in a high \(\mathrm{CO}_{2}\) - consumption rate of 16 to 27 Tmol/yr47,48. The subsequent carbonate deposition ( \(\sim 3.6 \mathrm{Tmol} / \mathrm{yr}^{49}\) ), followed by subduction or crustal delamination, provides a path for recycling carbonate back to the mantle and establishes a planetary thermostat50 (Fig. 3c). When both the surface temperature and weathering rate are primarily controlled by the greenhouse gas content in the atmosphere, negative feedback and thus planetary thermostat could be established50. By contrast, subduction on Venus came to a premature end (Fig. 3b, d). Without sufficient new crustal material generated at active plate boundaries, fresh silicate rocks are mainly produced by plume- related volcanism at a rate of \(\sim 0.37 \mathrm{km}^{3} / \mathrm{yr}^{32}\) , about \(\sim 1 / 100\) the rate of that in Earth ( \(\sim 30 \mathrm{km}^{3} / \mathrm{yr}\) ). Thus, weathering is ‘supply limited’51, is less sensitive to temperature, and proceeds at a slow rate on Venus52. + +<|ref|>text<|/ref|><|det|>[[111, 696, 884, 876]]<|/det|> +Efficient resurfacing on Earth, which is brought by active plate tectonics, sustains a long- term negative feedback that prevents Earth’s carbon from being accumulated in the atmosphere or being stored entirely in the lithosphere. Even in a supposedly sluggish tectonic regime in the Neoarchean53, resurfacing brought by active plate tectonics would still be enough to balance carbon degassing54. On the stagnant- lid Venus the \(\mathrm{CO}_{2}\) consumption is unlikely to catch up with the endogenic carbon emission, resulting in a long- term \(\mathrm{CO}_{2}\) accumulation in Venus’ atmosphere, runaway greenhouse climate, and eventual ocean evaporation + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 884, 140]]<|/det|> +within 1 b.y. \(^{55}\) . In return, the warming surface and lithosphere, together with an anhydrous crust, prevented self- sustaining subduction and locked Venus in a stagnant- lid regime. + +<|ref|>sub_title<|/ref|><|det|>[[115, 185, 259, 202]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[112, 216, 754, 235]]<|/det|> +This work was supported by the NSERC Discovery Grant (RGPIN- 2018- 03925) to X.C. + +<|ref|>sub_title<|/ref|><|det|>[[115, 249, 286, 266]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[112, 280, 884, 331]]<|/det|> +X.C. initiated the idea, J.C. performed simulation, and J.C. and X.C. wrote the first manuscript. All authors contributed to data interpretation and manuscript writing. + +<|ref|>sub_title<|/ref|><|det|>[[115, 345, 274, 362]]<|/det|> +## Competing Interests + +<|ref|>text<|/ref|><|det|>[[115, 377, 430, 395]]<|/det|> +The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[115, 440, 202, 456]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[111, 470, 870, 490]]<|/det|> +1. Fegley, B. Treatise on Geochemistry Ch. 1.19 (Elsevier Science, 2003) + +<|ref|>text<|/ref|><|det|>[[112, 494, 859, 536]]<|/det|> +2. Basilevsky, A. T., Head, J. W. The surface of Venus. Reports on Progress in Physics 66(10), 1699 (2003). + +<|ref|>text<|/ref|><|det|>[[112, 541, 868, 583]]<|/det|> +3. Trenberth, K. E., Smith, L. The mass of the atmosphere: A constraint on global analyses. Journal of Climate 18(6), 864-875 (2005). + +<|ref|>text<|/ref|><|det|>[[112, 588, 797, 630]]<|/det|> +4. Marty, B., Tolstikhin, I. 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L., Morein, G., Roberts, D., Malamud, B. D. Catastrophic resurfacing and episodic subduction on Venus. Icarus 139(1), 49-54 (1999).32. Bullock, M. A., Grinspoon, D. H., Head III, J. W. Venus resurfacing rates: Constraints provided by 3-D Monte Carlo simulations. Geophysical research letters 20(19), 2147-2150 (1993).33. Lee, C. T. A., Luffi, P., Plank, T., Dalton, H., Leeman, W. P. Constraints on the depths and temperatures of basaltic magma generation on Earth and other terrestrial planets using new thermobarometers for mafic magmas. Earth and Planetary Science Letters 279(1-2), 20-33(2009).34. Cawood, P. A., et al. Geological archive of the onset of plate tectonics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376(2132), 20170405. (2018).35. Doin, M.P., Henry, P. Subduction initiation and continental crust recycling: the roles of rheology and eclogitization. Tectonophysics 342(1-2), 163-191(2001).36. Billen, M.I. Modeling the dynamics of subducting slabs. Annu. Rev. Earth Planet. Sci. 36, 325-356(2008).37. Sizova, E., Gerya, T., Brown, M., Perchuk, L.L. Subduction styles in the Precambrian: Insight from numerical experiments. Lithos 116(3-4), 209-229(2010).38. Gudkova, T. V., Zharkov, V. N. Models of the internal structure of the Earth-like Venus. Solar System Research 54(1), 20-27 (2020).39. Johnson, T., Brown, M., Kaus, B., Vantongeren, J. A. Delamination and recycling of Archaean crust caused by gravitational instabilities. Nature Geosci 7, 47-52 (2014).40. Becker, J. A., Bickle, M. J., Galy, A., Holland, T. J. B. Himalayan metamorphic CO₂ fluxes: Quantitative constraints from hydrothermal springs. Earth and Planetary Science Letters 265(3-4), 616-629 (2008). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[58, 88, 884, 901]]<|/det|> +41. Rahman, S., Aller, R. C., Cochran, J. K. Cosmogenic \(^{32}\mathrm{Si}\) as a tracer of biogenic silica burial and diagenesis: Major deltaic sinks in the silica cycle. Geophysical Research Letters 43(13), 7124-7132 (2016). +42. Head, J. W., Wilson, L. Volcanic Processes on Venus. Lunar and planetary science xiii, 312-313 (1982) +43. Isson, T. T., et al. Evolution of the global carbon cycle and climate regulation on earth. Global Biogeochemical Cycles 34(2) (2020). +44. Hilton, R. G., West, A. J. Mountains, erosion and the carbon cycle. Nature Reviews Earth & Environment 1(6), 284-299 (2020). +45. Bird, P. An updated digital model of plate boundaries. Geochemistry, Geophysics, Geosystems 4(3), (2003). +46. Jicha, B. R., Jagoutz, O. Magma production rates for intraoceanic arcs. Elements 11(2), 105-111 (2015). +47. Coogan, L. A., Gillis, K. M. Temperature dependence of chemical exchange during seafloor weathering: Insights from the Troodos ophiolite. Geochimica et Cosmochimica Acta 243, 24-41 (2018). +48. Wallmann, K., Aloisi, G. The global carbon cycle: geological processes. Fundamentals of Geobiology 20-35 (2012). +49. Sleep, N. H., Zahnle, K. Carbon dioxide cycling and implications for climate on ancient Earth. Journal of Geophysical Research: Planets 106(E1), 1373-1399 (2001). +50. Kasting, J. F. The Goldilocks planet? How silicate weathering maintains Earth "just right". Elements: An International Magazine of Mineralogy, Geochemistry, and Petrology 15(4), 235-240 (2019). +51. Foley, B. J. The role of plate tectonic-climate coupling and exposed land area in the development of habitable climates on rocky planets. The Astrophysical Journal 812(1), 36 (2015). +52. Hakim, K., et al. Lithologic controls on silicate weathering regimes of temperate planets. The Planetary Science Journal 2(2), 49.2 (2021). +53. Korenaga, J. Archean geodynamics and the thermal evolution of Earth. Geophysical Monograph-American Geophysical Union 164, 7 (2006). +54. Driscoll, P., Bercovici, D. Divergent evolution of Earth and Venus: influence of degassing, tectonics, and magnetic fields. Icarus 226(2), 1447-1464 (2013). +55. Honing, D., Baumeister, P., Grenfell, J. L., Tosi, N., Way, M. J. Early Habitability and Crustal Decarbonation of a Stagnant - Lid Venus. Journal of Geophysical Research: Planets 126(10), (2021). +56. Holland, T. J. B., Powell, R. An internally consistent thermodynamic data set for phases of petrological interest. Journal of Metamorphic Geology 16 309-343 (1998). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 886, 177]]<|/det|> +57. Carroll, M. R., Rutherford, M. J. Sulfide and sulfate saturation in hydrous silicate melts. Journal of Geophysical Research: Solid Earth 90(S02), C601-C612 (1985). +58. Hacker, B. R., Abers, G. A., Peacock, S. M. Subduction factory 1. Theoretical mineralogy, densities, seismic wave speeds, and \(\mathrm{H}_2\mathrm{O}\) contents. Journal of Geophysical Research: Solid Earth 108(B1), (2003). + +<|ref|>sub_title<|/ref|><|det|>[[115, 218, 243, 235]]<|/det|> +## Figure Captions + +<|ref|>text<|/ref|><|det|>[[113, 248, 884, 388]]<|/det|> +Figure 1. P-T phase diagram in the range 300-650 °C, 0.5-2.5 GPa calculated for Vega 2 composition (Table S2). The phase diagram is labeled with mineral assembles and stability boundaries of important phases (garnet, pyroxene, amphiboles). The cold (8.1 °C/km) and warm (11.3 °C/km) geotherm are shown as yellow dashed lines. The eclogite-facies assemblages are highlighted in light green. The phase diagrams for the other two compositions are presented in Fig. S1, and the phase relations at higher pressures and temperatures are presented in Figure S2. + +<|ref|>text<|/ref|><|det|>[[112, 414, 884, 555]]<|/det|> +Figure 2. (a,b) The Earth \(^{27}\) (blue) and Venus (red) slab densities along warm and cold geotherms from 300 to \(650^{\circ}\mathrm{C}\) . The inset diagram in Figure 2a shows the statistical distribution of Venus' slab density at \(525^{\circ}\mathrm{C}\) along warm geotherm (see Method). The slab densities at higher pressures and temperatures are shown in Fig. S3 and the bulk densities of slabs are shown in Fig. S4. (c,d) The slab densities as functions of the variations of bulk Mg and Na concentrations at \(650^{\circ}\mathrm{C}\) . The phase diagrams are presented in Figures S5 and S6. + +<|ref|>text<|/ref|><|det|>[[112, 584, 884, 700]]<|/det|> +Figure 3. Skematic cartoons illustrating the tectonic evolutions on Earth and Venus, and their diverging carbon cycles. (a) Plume upwelling breaks the lithosphere. (b) Incipient slab dipping on Venus and Earth; ecologization might trigger self- sustaining subduction. (c, d) Difference in Earth and Venus tectonic and environmental evolution as results of different slab densities and subduction self- sustainability (see text for discussions). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 152, 886, 620]]<|/det|> +Through thermodynamic and mass balance equations, the minerals + fluid equilibria and their compositions are calculable for a fixed rock bulk composition (X) at specific pressure (P)- temperature (T) conditions. The equilibrium phase diagrams, also known as "pseudosection", depict the phase relations and outline the areas of stable phase assemblages in P- T, T- X and P- X spaces. In this study, we use the bulk compositions of Venus' crust to plot P- T, P- XMg, P- XNa equilibrium phase diagrams. Assuming that the metamorphism in a hypothesized subduction zone reaches equilibrium, the plotted P- T equilibrium phase diagrams indicate the slabs' mineral assemblages along subduction geotherms. The P- XMg, P- XNa diagrams reflect the influences of Na, Mg concentration variances on the mineral assemblages and their compositions. The equilibrium phase diagrams are calculated by using THERMOCALC version 3.33 (https://hpxeosandthermocalc.org/), thermodynamic dataset of ds55 (ref. 56) and compatible activity models (Table S3). To estimate the bulk density of a slab averaged from shallow to mantle depths, we model the phase equilibria in a larger pressure and temperature range (up to 3.5 GPa, 900 °C; Fig. S2). We do not include silicate melts due to a lack of compatible activity models. Incipient melting would lower the bulk density and strength of a slab, impeding slab subduction. The effect of partial melting is further discussed in Supplementary Information. + +<|ref|>text<|/ref|><|det|>[[111, 664, 886, 905]]<|/det|> +We use the NCKFMASHT0 \(\mathrm{(Na_{2}O - CaO - K_{2}O - FeO - MgO - Al_{2}O_{3} - SiO_{2} - H_{2}O - TiO_{2} - O_{2})}\) model system. The crust compositions of Venus are represented by three available X- ray fluorescence data (Table S1,2) from USSR landing probes Venera \(13^{25}\) , Venera \(14^{25}\) and Vega \(2^{26}\) . The Venera 13 composition is an alkaline basalt likely consisting of weathered olivine leucitite, nephelinite. Venera 14 and Vega 2 are both weathered N- MORB- like basaltic tholeiite. We assume that ocean existed on the early Venus like the Earth, the basaltic crust could have been hydrated. Progressive dehydration reactions during metamorphism keeps \(\mathrm{H}_{2}\mathrm{O}\) saturation. The crustal \(\mathrm{Fe}^{3 + } / (\mathrm{Fe}^{2 + } + \mathrm{Fe}^{3 + })\) ratios are mainly controlled by the mantle redox condition and partial melting process. Assuming similar settings of the early Earth and Venus, we use the same ratio + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 88, 884, 330]]<|/det|> +as the early Earth \((\mathrm{Fe}^{3 + } / (\mathrm{Fe}^{2 + } + \mathrm{Fe}^{3 + }) = 0.1\) in mole, ref. 27). The three Venus data all contain a fair amount of sulfur, which might reflect anhydrite \((\mathrm{CaSO_4})\) or sulfate-bearing scapolite \(^{57}\) , the products of chemical weathering by an extreme atmospheric \(\mathrm{SO}_2\) content. If Venus had liquid ocean at its early stage, the sulfur content would not have been as significant. Thus, we ignore the S contents in our model. We do not remove corresponding CaO from the bulk composition because sulfur weathering does not introduce additional CaO. The MnO contents are low, have large uncertainties, and barely affect the phase relations, so we omit MnO in the model system. The other elements' mass percentage are transferred into mole fraction for the calculation. + +<|ref|>text<|/ref|><|det|>[[112, 376, 884, 459]]<|/det|> +Phase equilibria modeling predicts quantitative mineral proportions and compositions. The rock densities \((\rho)\) are calculated on basis of the information from phase relations and the minerals' thermoelastic parameters, using a similar method as ref. 58: + +<|ref|>equation<|/ref|><|det|>[[448, 470, 549, 520]]<|/det|> +\[\rho = \sum_{i = 1}^{n}\rho_{i}\nu_{i}\] + +<|ref|>text<|/ref|><|det|>[[112, 535, 163, 551]]<|/det|> +where + +<|ref|>text<|/ref|><|det|>[[142, 565, 842, 586]]<|/det|> +\(\nu_{i}\) is the volume fraction of a mineral (mode), provided by the phase equilibria modeling results; + +<|ref|>text<|/ref|><|det|>[[142, 599, 483, 617]]<|/det|> +\(\rho_{i}\) is the density of a mineral, calculated using: + +<|ref|>equation<|/ref|><|det|>[[440, 630, 560, 675]]<|/det|> +\[\rho_{i} = \frac{\sum_{j = 1}^{m}M_{j}x_{j}}{\sum_{j = 1}^{m}V_{j}x_{j}}\] + +<|ref|>text<|/ref|><|det|>[[112, 688, 163, 704]]<|/det|> +where + +<|ref|>text<|/ref|><|det|>[[142, 719, 651, 738]]<|/det|> +\(M_{i}\) is the molar weight of an endmember, calculated from its formula; + +<|ref|>text<|/ref|><|det|>[[142, 751, 857, 771]]<|/det|> +\(x_{j}\) is the mole fractionation of one end member, provided by the phase equilibria modeling results; + +<|ref|>text<|/ref|><|det|>[[142, 785, 787, 804]]<|/det|> +\(V_{j}\) is the molar volume of an endmember, calculated from the equation of state in ref. 56: + +<|ref|>equation<|/ref|><|det|>[[397, 820, 600, 860]]<|/det|> +\[V_{j} = V_{T}\left(1 - \frac{4P}{K_{T} + 4P}\right)^{1 / 4}\] + +<|ref|>text<|/ref|><|det|>[[112, 875, 163, 891]]<|/det|> +where + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 149]]<|/det|> +\(V_{T}\) is the molar volume at 1bar, \(T(\mathrm{Kelvin}\) in unit), \(V_{T} = V_{1,298}[1 + \alpha_{0}(T - 298) - 2\alpha_{0}\alpha_{1}(\sqrt{T} -\) \(\sqrt{298})]\) , in which \(\alpha_{0},\alpha_{1}\) : the thermal expansion parameter; + +<|ref|>text<|/ref|><|det|>[[113, 161, 884, 248]]<|/det|> +\(K_{T}\) : the end- member's bulk modulus at \(T(\mathrm{Kelvin}\) in unit), \(K_{T} = K_{298}(1 - 1.5\times 10^{- 4}(T - 298))\) , in which \(K_{298}\) is the bulk modulus at 298 Kelvin, and \(V_{1,298}\) is the molar volume at 1 bar, 298 Kelvin (standard condition). + +<|ref|>text<|/ref|><|det|>[[112, 292, 884, 633]]<|/det|> +In addition to phase relations corresponding to these three individual bulk compositions, we propagate the uncertainties of concentrations, and calculate Venus' slab density and its standard deviation, to explore how likely the slab density of Venus overlap those of Earth. This exercise is time consuming, so we only calculate at \(525^{\circ}\mathrm{C}\) , along the warm geotherm, where the calculated slab densities of two Venus bulk compositions (Venera 13 and 14) overlap the densities of Earth's Proterozoic slabs (Fig. 2a). The assumption is that these three XRF data all represent one hypothetical uniform crustal composition of Venus. The means and standard variations of Venus' crustal bulk compositions are shown in Tables S1 and S2. At each step, we randomly sample one element's abundance within it range (Gibbs sampler), model the phase relations using the new bulk composition, and calculate the density of the assemblage. We perform 300 such sampling (Table S4), and then fit the series of calculated densities into a normal distribution for the mean and standard deviation of the density estimate. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[84, 103, 770, 630]]<|/det|> + + +<|ref|>image_caption<|/ref|><|det|>[[150, 640, 764, 816]]<|/det|> +
Numbered assemblages: (1) act gl o g bi ta law ru chl; (2) mu o ky ru chl g ta law; (3) mu o ky ru g ta law; (4) gl(hb) mu o ky ru g ta; (5) mu o zo ky ru g ta chl; (6) act gl o g mu ta law ru chl; (7) act gl q mu law ru chl g; (8) act gl g mu ta ru chleplaw; (9) ep gl g mu ky ru zo chl; (10) ep hb(gl) g mu ky ru zo; (11) hb(gl) mu o ky ru g zo; (12) hb(gl) o mu g ky ru; (13) ep gl g mu ta ky ru zo chl; (14) act gl g mu ta law ru; (15) act gl q mu law ru chl di; (16) act gl q mu law sph chl di; (17) act gl chl ep mu sph q law; (18) act gl chl ep mu ru q law; (19) act gl chl g ep mu ru q law; (20) gl(hb) chl g ep mu ta ru zo q; (21) hb(gl) g mu chl ru q zo ky; (22) hb(gl) bi ky ru q g zo; (23) hb bi chl ky ru q zo g; (24) hb bi chl ky ru q; (25) ep hb g mu chl ru q zo; (26) hb bi ky q ru g; (27) hb bichl ky ru q g; (28) hb g mu chl ru q zo; (29) gl o bi ta law ru chl; (30) ep hb chl ru q zo mu pa; (31) hb bichl ru q zo mu; (32) hb mu chl ru q zo; (33) ep hb mu chl ru q zo; (34) hb bi chl ru q zo pa; (35) act ep bi chl ru pa; (36) act chl ep bi sph pa q; (37) act gl chl ep bi sph pa q; (38) act gl chl ep mu sph pa q; (39) act mu chl sph q lep; (40) hb bi chl sph q lep; (41) ep hb bi chl sph q zo pl; (42) ep hb bi chl ru q zo pl; (43) hb bi chl ru q zo pl
+ +<|ref|>image_caption<|/ref|><|det|>[[42, 852, 111, 871]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[42, 894, 951, 959]]<|/det|> +P-T phase diagram in the range 300-650 °C, 0.5-2.5 GPa calculated for Vega 2 composition (Table S2). The phase diagram is labeled with mineral assemblies and stability boundaries of important phases (garnet, pyroxene, amphiboles). The cold (8.1 °C/km) and warm (11.3 °C/km) geotherm are shown as yellow + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 949, 110]]<|/det|> +dashed lines. The ecogite-facies assemblages are highlighted in light green. The phase diagrams for the other two compositions are presented in Fig. S1, and the phase relations at higher pressures and temperatures are presented in Figure S2. + +<|ref|>image<|/ref|><|det|>[[45, 170, 945, 750]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 777, 117, 796]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[41, 819, 944, 955]]<|/det|> +(a,b) The Earth27 (blue) and Venus (red) slab densities along warm and cold geotherms from 300 to 650 °C. The inset diagram in Figure 2a shows the statistical distribution of Venus' slab density at 525 °C along warm geotherm (see Method). The slab densities at higher pressures and temperatures are shown in Fig. S3 and the bulk densities of slabs are shown in Fig. S4. (c,d) The slab densities as functions of the variations of bulk Mg and Na concentrations at 650 °C. The phase diagrams are presented in Figures S5 and S6. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[88, 94, 928, 540]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 583, 116, 601]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[41, 623, 936, 736]]<|/det|> +Skematic cartoons illustrating the tectonic evolutions on Earth and Venus, and their diverging carbon cycles. (a) Plume upwelling breaks the lithosphere. (b) Incipient slab dipping on Venus and Earth; ecogitization might trigger self- sustaining subduction. (c, d) Difference in Earth and Venus tectonic and environmental evolution as results of different slab densities and subduction self- sustainability (see text for discussions). + +<|ref|>sub_title<|/ref|><|det|>[[44, 797, 310, 824]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 847, 765, 867]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 886, 315, 931]]<|/det|> +Supplementary Table4. xlsx Supplementary.docx + +<--- Page Split ---> diff --git a/preprint/preprint__081a787feb042eb392b973be710c939c45b17f8fc6f5e177ef5f2e56bb16567b/images_list.json b/preprint/preprint__081a787feb042eb392b973be710c939c45b17f8fc6f5e177ef5f2e56bb16567b/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..b69f0c9e3ca226806084352ecb661730fc079d68 --- /dev/null +++ b/preprint/preprint__081a787feb042eb392b973be710c939c45b17f8fc6f5e177ef5f2e56bb16567b/images_list.json @@ -0,0 +1,100 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1: HOTAIR regulates site-specific gene expression in synovial fibroblasts", + "footnote": [], + "bbox": [], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2 HOTAIR is expressed in synovial tissues of lower extremity joints with higher expression in OA than in RA.", + "footnote": [], + "bbox": [ + [ + 144, + 138, + 777, + 550 + ] + ], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3. HOTAIR is downregulated by inflammatory cytokines and shapes the epigenetic landscape of SF.", + "footnote": [], + "bbox": [ + [ + 130, + 123, + 790, + 417 + ] + ], + "page_idx": 12 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4. HOTAIR modulates arthritis relevant pathways.", + "footnote": [], + "bbox": [ + [ + 90, + 198, + 465, + 700 + ] + ], + "page_idx": 14 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5. Changes in HOTAIR expression modulate SF subtypes.", + "footnote": [], + "bbox": [ + [ + 120, + 126, + 733, + 775 + ] + ], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6. HOTAIR silencing induces functional changes in SF.", + "footnote": [], + "bbox": [ + [ + 122, + 113, + 748, + 777 + ] + ], + "page_idx": 18 + }, + { + "type": "image", + "img_path": "images/Figure_7.jpg", + "caption": "Figure 7. HOTAIR silencing increases lymphocyte chemotaxis.", + "footnote": [], + "bbox": [ + [ + 156, + 234, + 680, + 812 + ] + ], + "page_idx": 20 + } +] \ No newline at end of file diff --git a/preprint/preprint__081a787feb042eb392b973be710c939c45b17f8fc6f5e177ef5f2e56bb16567b/preprint__081a787feb042eb392b973be710c939c45b17f8fc6f5e177ef5f2e56bb16567b.mmd b/preprint/preprint__081a787feb042eb392b973be710c939c45b17f8fc6f5e177ef5f2e56bb16567b/preprint__081a787feb042eb392b973be710c939c45b17f8fc6f5e177ef5f2e56bb16567b.mmd new file mode 100644 index 0000000000000000000000000000000000000000..8f5bb4db3b7ec858592c47068bc6e7365326393b --- /dev/null +++ b/preprint/preprint__081a787feb042eb392b973be710c939c45b17f8fc6f5e177ef5f2e56bb16567b/preprint__081a787feb042eb392b973be710c939c45b17f8fc6f5e177ef5f2e56bb16567b.mmd @@ -0,0 +1,601 @@ + +# The long-non coding RNA HOTAIR as site specific regulator of inflammation in chronic arthritis + +Muriel elhai UniversitätsSpital Zürich Zentrum für Experimentelle Rheumatologie https://orcid.org/0000- 0001- 8627- 5758 + +Raphael Micheroli University Hospital Zurich + +Miranda Houtman University Hospital Zurich + +Masoumeh Mirrahimi Center of Experimental Rheumatology, Department of Rheumatology, University Hospital of Zurich, University of Zurich + +Larissa Moser Center of Experimental Rheumatology, Department of Rheumatology, University Hospital of Zurich, University of Zurich + +Chantal Pauli University Hospital Zürich and the University of Zurich + +Kristina Bürki Center of Experimental Rheumatology, Department of Rheumatology, University Hospital of Zurich, University of Zurich + +Andrea Laimbacher Center of Experimental Rheumatology, Department of Rheumatology, University Hospital of Zurich, University of Zurich + +Gabriela Kania Center of Experimental Rheumatology, Department of Rheumatology, University Hospital Zürich + +Kerstin Klein Department of Rheumatology and Immunology, University Hospital Bern + +Philipp Schätzle Cytometry Facility, University of Zurich + +Mojca Frank Bertoncelj Center of Experimental Rheumatology, Department of Rheumatology, University Hospital of Zurich, University of Zurich + +Sam Edalat University Hospital Zurich + +Maria Sakkou + +<--- Page Split ---> + +Biomedical Sciences Research Center Alexander Fleming + +George Kollias B.S.R.C. Alexander Fleming https://orcid.org/0000- 0003- 1867- 3150 + +Marietta Armaka + +Biomedical Sciences Research Center Alexander Fleming https://orcid.org/0000- 0003- 0985- 9076 + +Oliver Distler + +Oliver DistlerCenter of Experimental Rheumatology, Department of Rheumatology, University Hospital Zürich + +Caroline Ospelt ( caroline.ospelt@usz.ch) + +Caroline Ospelt ( caroline.ospelt@usz.ch)Department of Rheumatology, Center of Experimental Rheumatology, University Hospital Zurich, University of Zurich https://orcid.org/0000- 0002- 9151- 4650 + +## Article + +Keywords: HOTAIR, synovial fibroblast, epigenetic, arthritis + +Posted Date: February 10th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 2543547/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on December 9th, 2023. See the published version at https://doi.org/10.1038/s41467- 023- 44053-w. + +<--- Page Split ---> + +# The long-non coding RNA HOTAIR as site specific regulator of inflammation in chronic arthritis + +Muriel Elhai \(^{1}\) , Raphael Micheroli \(^{1}\) , Miranda Houtman \(^{1}\) , Masoumeh Mirrahimi \(^{1}\) , Larissa Moser \(^{1}\) , Chantal Pauli \(^{2}\) , Kristina Bürki \(^{1}\) , Andrea Laimbacher \(^{1}\) , Gabriela Kania \(^{1}\) , Kerstin Klein \(^{1,3,4}\) , Philipp Schätzle \(^{5}\) , Mojca Frank Bertoncelj \(^{1}\) , Sam G. Edalat \(^{1}\) , Maria Sakkou \(^{6,7}\) , George Kollias \(^{6,7}\) , Marietta Armaka \(^{8}\) , Oliver Distler \(^{1}\) , Caroline Ospelt \(^{1*}\) \(^{1}\) Center of Experimental Rheumatology, Department of Rheumatology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland \(^{2}\) Institute for Pathology and Molecular Pathology, University Hospital Zurich, Zurich 8091, Switzerland \(^{3}\) Department of BioMedical Research, University of Bern, Bern, Switzerland \(^{4}\) Department of Rheumatology and Immunology, University Hospital Bern, Bern, Switzerland \(^{5}\) Cytometry Facility, University of Zurich, Zurich Switzerland \(^{6}\) Institute for Bioinnovation, Biomedical Sciences Research Center (BSRC) \(^{7}\) Alexander Fleming', Vari, Greece \(^{7}\) Department of Physiology, Medical School, National and Kapodistrian University of Athens, Athens, Greece \(^{8}\) Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece. + +<--- Page Split ---> + +# CORRESPONDING AUTHOR + +CORRESPONDING AUTHORCaroline Ospelt, Center of Experimental Rheumatology, Departement of Rheumatology, University Hospital of Zurich, University of Zurich, Rämistrasse 100, CH- 8091, Zurich, Switzerland + +31 + +32 + +33 + +34 + +35 + +36 + +37 + +38 + +39 + +40 + +41 + +42 + +43 + +44 + +45 + +46 + +47 + +48 + +49 + +50 + +51 + +52 + +53 + +<--- Page Split ---> + +## ABSTRACT + +Most forms of arthritis, have a distinctive topographical pattern of joint involvement. Beyond these differences among diseases, there are also differences in phenotype and response to treatment between joints of the same type of arthritis, suggesting that molecular mechanisms may differ depending on joint location. Here we show that there are joint- specific molecular and tissue changes in the synovium and in local stromal cells (synovial fibroblasts; SF). The long non- coding RNA HOTAIR, expressed only in lower extremities SF, regulates much of this site- specific gene expression in SF. Downregulation of HOTAIR after TNF stimulation regulated relevant inflammatory pathways by epigenetic and transcriptional mechanisms and modified the migratory function of SF, decreased SF- mediated osteoclastogenesis, and increased the attraction of B cells by SF. Since site- specific expression of HOTAIR was also measured in the skin, spine and gastrointestinal tract, we propose HOTAIR as important epigenetic factor that modulates site- specific phenotypes of chronic inflammation. + +## TEASER + +HOTAIR as important epigenetic factor that modulates site- specific phenotypes of chronic inflammation. + +Keywords: HOTAIR, synovial fibroblast, epigenetic, arthritis + +<--- Page Split ---> +![](images/Figure_1.jpg) + + +<--- Page Split ---> + +## INTRODUCTION + +Chronic arthritis is a major public health problem, which has a substantial influence on health and quality of life(1). Most forms of arthritis, including rheumatoid arthritis (RA), osteoarthritis (OA) and spondyloarthritis, have a distinctive topographical pattern of joint involvement(2). Among them, RA is the most frequent autoimmune arthritis, affecting \(1\%\) of the population(3). Despite the advances made in the management of RA in the last decades, 6 - \(17\%\) of the patients remain refractory to immunosuppressive treatment(4). + +Although patients with untreated RA typically exhibit a symmetrical polyarthritis, individuals with refractory disease might develop a less extensive pattern of polyarthritis, an oligoarticular or even a monoarticular disease, suggesting that immunosuppressive therapy might be effective in some joints and not in others(5). Thus, beyond differences between diseases, there are also differences in phenotype and response to treatment depending on the joints within the same type of arthritis, suggesting that molecular mechanisms may differ according to joint location. Deciphering the heterogeneity of synovium at both the cellular and molecular levels has revolutionized the understanding of the pathogen(6- 9). In particular, refractory RA has been associated with a pauci- immune, fibroid pathotype of the synovium and a molecular signature suggestive of activated fibroblasts(10). Activation of synovial fibroblasts (SF) has long been known to play a critical role in joint inflammation and destruction(11, 12), but has attracted considerable attention more recently due to the discovery of pathogenic subpopulations of SF in the synovium through single- cell analysis(10, 13- 16). Changes in the epigenetic landscape have been shown to be central to the permanent activation and aggressiveness of SF in RA(12). + +<--- Page Split ---> + +We have previously demonstrated the existence of transcriptomic, epigenetic and functional changes of SF depending on their joint location(17). This specific stromal signature in particular concerned genes involved in the embryonic development of the respective joint regions (i.e. HOX genes), suggesting an embryonically imprinted joint specific stromal signature. In particular, the HOX transcript antisense intergenic RNA (HOTAIR), which is an important regulator of the epigenetic landscape(18), was exclusively expressed in joints of the lower extremity in human and in mice(17). HOX genes encode a family of transcriptional regulators, which are involved in distinct developmental programmes along the head- tail axis of vertebrates(19). Moreover, they remain site- specifically expressed in several differentiated tissues, e.g. in cartilage, the skin, the vasculature and gastrointestinal tract(17, 18, 20- 24). It remains to be determined whether differences between the phenotype of arthritis at the tissue and molecular levels depend on its location, and if so whether the site- specific expression of HOX genes is involved in these changes. + +Here, we showed that there are joint- specific molecular and tissue changes in RA and that the long non- coding RNA (lncRNA) HOTAIR (HOX transcript antisense RNA) is a master regulator of joint- specific gene expression in arthritic SF. Down- regulation of HOTAIR in an inflammatory environment led to activation of specific arthritis relevant pathways and changes in SF function, that might modulate the arthritis phenotype in lower extremity joints. + +<--- Page Split ---> + +## RESULTS + +## Joint-specific histological and molecular differences in RA + +We first analysed joint- specific differences in the synovium of RA patients using a multi- level approach including histological and molecular analysis. Comparison of the histological grade of synovitis(25) between hand and knee RA showed a trend towards a higher synovitis score in RA hands (mean: \(5.25 \pm 1.78\) ) compared to RA knees (mean: \(4.31 \pm 1.78\) ; difference \(- 0.94 \pm 0.52\) , p=0.076), as previously seen(17) (Figure 1A). Accordingly, vascular density of the synovium was higher in RA hand synovium, with a larger percentage of patients having highly vascularised synovium in the hands than in the knees (54% vs. 37% p=0.031) (Figure 1B). Assessment of synovial pathotypes as defined by Humby et al.(9) in hand and knee RA showed a predominance of the lymphoid pathotype in hand RA, whereas the pathotypes were balanced in the knees (Figure 1C). The lymphoid pathotype presents with strong infiltration of T and B cells in the synovium, the diffuse- myeloid pathotype shows predominant influx of myeloid cells and the pauci- immune/fibroid pathotype is characterised by scanty immune cells and prevalent stromal cells(9). Consequently, analysis of synovial cell proportions by single cell RNA sequencing (scRNAseq) showed substantial expansion of T- and B- cell compartments in wrist compared to knee RA synovium (Figure 1D and Table 1). In summary, all these data pointed towards higher inflammatory activity in hand versus knee RA synovium. + +We then used the scRNAseq data to assess whether these site- specific tissue changes were associated with molecular changes in SF. In total, 1,966 genes were differentially expressed in hand and knee SF, 1,026 genes were overexpressed in hand SF and 940 in knee SF. We confirmed the joint- specific expression of HOX genes in SF from RA patients in this dataset (Figure 1E). In addition, various pathways that we had + +<--- Page Split ---> + +previously found to be differentially enriched in cultured hand and knee SF in vitro(17), were also joint- specifically activated in vivo, such as cell adhesion, extracellular matrix (ECM) interaction and bone remodeling/osteoclast differentiation pathways (Supplementary Tables S1/S2 and Figure 1F). Several enriched pathways were previously implicated to be relevant in RA such as MAPK, Wnt and PI- Akt signaling(26). Additionally, knee SF showed increased levels of HLA and CD74 genes ('Antigen processing and presentation' and 'Rheumatoid arthritis' pathways). Thus, these results confirmed joint specific gene expression in SF in terms of developmental as well as inflammatory pathways. + +We then sought to understand in how far the joint specific expression of HOX transcripts was involved in these site- specific gene expression changes. To this end, we silenced SF for HOXD10, HOXD11, HOXD13, and the long non- coding RNAs HOTAIR and HOTTIP, respectively. These HOX transcripts were the most discriminating transcripts in cultured SF and synovial tissues between the hand and knee in our previous in vitro analysis(17). Due to the lower sequence depths in scRNAseq, the less expressed transcripts HOXD13, HOTTIP and HOTAIR were not detectable in the scRNAseq dataset (Figure 1E). The differential gene expression by HOX gene silencing in vitro corresponded to \(70.9\%\) of the differential gene expression between the hand and knee in vivo using scRNAseq (Figure 1G). Among the different HOX genes, HOTAIR alone regulated almost \(49.3\%\) of this joint- dependent gene expression. This suggested that joint- specific expressed HOX transcription factors and non- coding RNAs drive most of the joint- specific transcriptome in RA SF. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 1: HOTAIR regulates site-specific gene expression in synovial fibroblasts
+ +A) Krenn synovitis score in hand (n = 36) and knee (n = 27) synovium from RA patients. Unpaired t test. Mean +/- standard deviation is shown. B) Vascularization as assessed by CD31staining in synovium from RA patients (35 hands and 24 knees). Vessels were counted in 5 fields (20x magnification). Chi squared test. C) Synovial pathotype in 36 hand and 27 knee RA synovium, Chi squared test. D) Cell proportions between hand (n = 8) and knee (n = 4) synovium using single cell RNA sequencing. SF: synovial fibroblasts, SMC: smooth muscle cells, EC: endothelial cells, MastC: mast cells, PC: plasma cells, BC: B cells, TC: T cells, NKC: NK cells, nGC: neutrophilic granulocytes, MC: myeloid cells, ProIC: proliferating cells. E) HOX gene expression in hand (n = 8) and knee (n = 4) synovial fibroblasts from RA patients in single cell RNA sequencing analysis. F) Scatter dot plot of pathway enrichment analysis of genes significantly enriched in hand SF (n = 8) and in knee SF (n = 4) (FDR < 0.05; Log fold change +/-1). Blue dots: significantly enriched pathways, darker color corresponds to lower p-values. Grey dots represent pathways with p > 0.05. G) Overlap between genes regulated by HOX genes (HOXD10, HOXD11, HOXD13, HOTTIP, HOTAIR) and genes differentially expressed between hand (n = 8) and knee (n = 4) SF in single cell RNA sequencing. Intersection was assessed using Venn diagram. SF: synovial fibroblasts. + +<--- Page Split ---> + + +Table 1: Patient's characteristics single cell RNA sequencing of RA patients + +
Wrist/metacarpophalangeal joint (n=8)Knee (n=4)
Age (yrs)59.6 ± 12.058.5 ± 19.2
Female (n)64
RF or anti-CCP positivity (n)52
(n=1 with missing information)
Previous biological treatment (n)73
(n=1 with missing information)
Disease modifying treatment at the time of the biopsy (n)30
(n=1 with missing information)
+ +Yrs: years, RF: rheumatoid factor, RA. Rheumatoid arthritis + +## Joint and disease specific expression of HOTAIR + +We previously showed that HOTAIR is exclusively expressed in lower limb SF and joints in both mice and human(17). By in situ hybridisation (ISH), we confirmed that HOTAIR is expressed in synovial tissues from knees (Figure 2A), but not from hands (Figure 2B). In the synovium, HOTAIR was expressed mainly in SF, in both the lining and the sublining synovium (Figure 2C). Analysis of HOTAIR expression in knee joints of OA and RA patients showed that HOTAIR was significantly more abundant in OA than in RA knees (Figure 2D/E). The difference in HOTAIR expression between OA and RA was lost in cultured knee SF (p=0.133) (Figure 2F), suggesting that the lower expression of HOTAIR in RA joints was modulated by local factors in vivo. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 2 HOTAIR is expressed in synovial tissues of lower extremity joints with higher expression in OA than in RA.
+ +A) Representative pictures of synovial tissues from knee joints of OA (left, \(\mathrm{n} = 6\) ) and RA (right, \(\mathrm{n} = 5\) ) patients stained for HOTAIR by in-situ hybridization (ISH). Magnification 100x. Inset shows staining with the anti-sense probe (negative control). +B) Representative picture of HOTAIR in-situ hybridization in synovial tissues of OA and RA hand joints (n = 3). Magnification 100x. C) Double staining of HOTAIR (in blue) and vimentin (in red, left panel) or CD68 (in red, right panel) to assess HOTAIR expression in synovial fibroblasts and macrophages, respectively. Magnification 400x. D) Relative quantification of HOTAIR ISH in OA and RA synovial tissue using ImageJ. Unpaired t test. E) Expression of HOTAIR measured by quantitative PCR in OA and RA synovial tissues. Unpaired t test. F) Expression of HOTAIR measured by quantitative PCR in cultured OA and RA SF. Unpaired t test. SF: synovial fibroblasts, OA: osteoarthritis, RA: rheumatoid arthritis. dct: cycle of threshold target - cycle of threshold housekeeping gene. Mean +/- standard deviation is shown. + +To determine which local factors could influence the expression of HOTAIR in arthritis, we assessed HOTAIR expression in SF upon stimulation by various cytokines and Toll- like receptor (TLR) ligands. Stimulation with most of the inflammatory cytokines decreased HOTAIR expression in SF (Figure 3A). Furthermore, active promoter and enhancer sites at the HOTAIR locus were closed after TNF stimulation (Figure 3B). Consistently, HOTAIR expression inversely correlated with TNF + +<--- Page Split ---> + +expression in arthritic synovium (Figure 3C). SF isolated from arthritic, TNF transgenic mice (TG197) expressed lower levels of Hotair than SF from healthy wild- type mice (Figure 3D), showing that the down- regulation of HOTAIR in inflammatory conditions is conserved across species. + +## HOTAIR regulates arthritis relevant pathways + +We next examined the effect of downregulation of HOTAIR in inflammatory states in SF. Since HOTAIR represses gene expression by placing repressive H3K27me3 marks(19), we first analysed the effect of HOTAIR downregulation in SF on H3K27me3. A total of 2,376 genomic sites with differential presence of H3K27me3 marks were identified between control SF and SF silenced for HOTAIR. The frequency of repressive H3K27me3 marks was decreased in promoters near transcription start sites in HOTAIR- silenced SF, showing a clear impact of HOTAIR downregulation on the epigenetic landscape of SF (Figure 3E). + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 3. HOTAIR is downregulated by inflammatory cytokines and shapes the epigenetic landscape of SF.
+ +A) SF were left untreated or stimulated with TNF, LPS, poly I:C (PIC), TGFβ, IL1β, or bacterial lipoprotein (bLP) for 24 hours. Expression of HOTAIR was measured by qPCR. P < 0.001 for TNF, LPS, PIC and TGFβ. IL1β and bLP p > 0.05. Unstimulated samples were set to 1. B) CAGE analysis of enhancers/promoters at the HOTAIR locus in SF from knees (n = 2) in basal conditions and after stimulation with TNF. After TNF stimulation several active promoters (light blue bars) and the enhancer (pink bar) disappear in knee SF. C) Correlation between TNF and HOTAIR expression measured by qPCR in RA synovial tissues and analysed by Spearman correlation. D) Expression of Hotair was measured by qPCR in ankle SF isolated from wildtype (WT) and TNF transgenic (Tg197) C57BL/6 mice and analysed by unpaired t test. E) ChIP sequencing of H3K27me3 marks in SF silenced for HOTAIR (n = 3) and control SF (n = 3) was performed 48h after transfection. TSS: Transcription start site. SF: synovial fibroblasts, RA: rheumatoid arthritis, OA: osteoarthritis, dct: cycle of threshold target - cycle of threshold housekeeping gene. Mean +/- standard deviation is shown. + +We then investigated the transcriptional changes associated with HOTAIR silencing in SF, mimicking an inflammatory arthritis environment. A total of 7,885 genes were differentially expressed between control and HOTAIR- silenced SF (FDR < 0.05), with enrichment of site- specific signaling pathways such as MAPK, Wnt and PI- Akt signaling (Figure 4A, Supplementary Table S3). RNA and ChIP sequencing suggested a HOTAIR- dependent regulation of several collagen transcripts, including COL1A1, COL1A2 and COL3A1, which could, however, not be confirmed by quantitative PCR (Figure 4B). In contrast, there was a trend towards increased procollagen release into supernatants from HOTAIR- silenced SF (Figure 4C). Assuming that prolonged 3D culture systems provide a more natural environment for + +<--- Page Split ---> + +the production of extracellular matrix proteins from fibroblasts, we cultured control and HOTAIR- silenced SF in 3D micromass organ systems. After 3 weeks in culture, HOTAIR- silenced SF had deposited significantly more collagen- 1 in micromasses compared to control SF (Figure 4D). Consistent with an increase in extracellular matrix remodelling in HOTAIR- silenced micromasses, several transcripts of the fibroblast growth factor (FGF) family, known to play a key role in extracellular matrix remodelling (27), were regulated by HOTAIR, albeit most of them were downregulated. However, a particular strong upregulation of FGF8 after HOTAIR silencing was observed (Figure 4E). FGF were previously shown to signal via the PI- Akt and the Wnt signaling pathway and to play a crucial role in limb development(28, 29). Measurements of the expression of AKT and its active phoporylated form showed that HOTAIR silencing did not influence AKT levels, but resulted in increased AKT phosphorylation (Figures 4F). Furthermore, we confirmed that silencing of HOTAIR regulated several transcripts in the Wnt signaling pathway (Figure 4G) and repressed the activation of the canonical Wnt pathway in SF (Figure 4H). Finally, we confirmed that HOTAIR regulated several cytokines and transcription factors which were previously implicated in SF activation in RA (IL- 12(30), IL- 6(31), CXCL12(32), PTEN(33), FOXO1(34), RUNX1(35)) (Figure 4I). IL- 12 and IL- 6 secretion by SF increased after the silencing of HOTAIR, while CXCL12 secretion decreased (Figure 4J). Increased IL- 6 levels were also released by micromasses formed with HOTAIR silenced SF compared to control SF (Figure 4K). Taken together, these data clearly show that HOTAIR can regulate pathways relevant to joint inflammation and tissue remodelling in SF. Inhibition of protein translation did not influence the effect of HOTAIR silencing on CTNNB1, FGFR2 and LGR5 (Supplementary Figure S1A), but suppressed the effect of HOTAIR silencing on GSK3B and FGF7 (Supplementary + +<--- Page Split ---> + +Figure S1B), suggesting direct as well as indirect mechanisms (e.g. mediated by an intermediate protein) of this regulation. + +![](images/Figure_5.jpg) + +
Figure 4. HOTAIR modulates arthritis relevant pathways.
+ +A) Scatter dot plot of pathway enrichment analysis of genes significantly changed after HOTAIR silencing (FDR < 0.05; Log fold change +/-1). Blue dots: significantly enriched pathways, darker color corresponds to lower p-values. Grey dots represent pathways with p > 0.05. B) Expression of COL1A1 and COL1A2 measured by quantitative PCR between SF transfected with control or HOTAIR targeting GAPmeR after 48h. Control transfected cells were set to 1. C) Pro-Collagen was measured in supernatants of SF transfected with control or HOTAIR targeting GAPmeR after 72h by ELISA. Paired t test. D) Collagen I was stained by immunohistochemistry in 3D micromasses formed with control or HOTAIR silenced SF. Right panel: representative pictures, 25x magnification; left panel: quantification with ImageJ and analysis with paired t test. E) Expression of FGF2, FGF7, FGF8 and FGFR2 measured by quantitative PCR between SF transfected with control or HOTAIR targeting GAPmeR after 48h. Control transfected cells were set to 1. F) Expression of AKT and phosphorylated AKT in SF transfected with control or HOTAIR targeting GAPmeR after 72h. Right panel: representative examples; left panel: densitometric analysis. Paired t test. G) Expression of CTNNB1, LRP6, LGR5 and GSK3B measured by quantitative PCR between SF transfected with control or HOTAIR targeting GAPmeR after 48h. Control transfected cells were set to 1. H) Activation of the canonical Wnt pathway was assessed by luciferase assay with a Wnt reporter gene (Top) or a mutated Wnt reported gene (Fop) as control in SF transfected with control or HOTAIR targeting GAPmeR after 48h. One sample t test. I) Expression of IL12A, IL6, CXCL12, PTEN, FOXO1 and RUNX1 measured by quantitative PCR between SF transfected with control or HOTAIR targeting GAPmeR after 48h. Control transfected cells were set to 1. J) Selected proteins were measured in supernatants of SF transfected with control or HOTAIR targeting GAPmeR after 48h (CXL12) or 72h (IL-6, IL-12-p35) by ELISA. Paired t test. K) IL-6 was measured in supernatants of micromasses after 3 weeks by ELISA. Paired t test. Data are representative for at-least 2 experiments. Mean +/- standard deviation is shown. + +## Changes in HOTAIR expression modulate SF subtypes + +Since several marker genes for recently described SF subpopulations(15, 36- 38) were affected by HOTAIR silencing, we wondered whether the observed transcriptional changes after HOTAIR silencing might be connected to changes in the proportion and the formation of SF subtypes. Four subpopulations of SF have been described by scRNAseq (15, 36- 38): PRG4+ SF are considered as lining SF, CXCL12+ SF are + +<--- Page Split ---> + +characterised by increased expression of CXCL12, CD74 and IL- 6, POSTN+ SF show high production of extracellular matrix proteins such as collagens and periostin, and CXCL14+ SF are CD34+ SF. We integrated scRNAseq data from control and HOTAIR- silenced SF cultures with our previous meta- analysis of synovial tissue scRNAseq data from five different datasets(38) (Figure 5). In line with published data, PRG4+ SF and CXCL12+ SF subtypes were partially lost during culture (Figure 5B)(14). The main SF subpopulations in culture were POSTN+ SF, CXCL14+ SF, and a mixed- marker cell population consisting of proliferating cells (proLSF) (Figures 5A- E). Silencing of HOTAIR indeed resulted in a shift in the distribution of SF subpopulations with an enrichment in PRG4+ SF and a decrease in POSTN+ SF (Figure 5C). COL1A1 and COL1A2 expression was mainly increased in PRG4+ SF, with no change or a decrease in POSTN+ SF (Figure 5F): This might reflect a transcriptional switch from POSTN+ SF to collagen producing PRG4+ SF and might explain the inconsistent results seen in the bulk analysis of COL transcripts (Figure 4B). CXCL12 downregulation was found in CXCL14+ SF, which produce CXCL12, but to a lesser extent than CXCL12+ SF (Figure 5D/E/F). IL- 6 upregulation was observed across all SF subtypes (Figure 5F). From this analysis, we concluded that HOTAIR played a role in the formation of SF subtypes, for example, by regulating differential collagen production between the SF subtypes. However, there were also regulatory mechanisms of HOTAIR that were evident in all SF subtypes. + +<--- Page Split ---> +![](images/Figure_6.jpg) + +
Figure 5. Changes in HOTAIR expression modulate SF subtypes.
+ +A) Single cell RNA sequencing (scRNAseq) data from synovial tissues were integrated with scRNAseq data from cultured SF transfected with control or HOTAIR targeting GapmeR (n = 3). UMAP representation of the different SF subtypes is shown. B) UMAP representation of the distribution of cells in control and HOTAIR silenced SF (cultured cells only). C) Proportions of the different SF subtypes in control and HOTAIR silenced SF. D) Selected marker gene expression in the different SF subtypes. E) Heatmap with the top 5 marker genes for each subtype. F) Change of expression of selected genes within the different SF subtypes. + +<--- Page Split ---> + +## HOTAIR downregulation alters key functions in SF and surrounding cells + +Next we aimed to decipher the functional changes of SF induced by HOTAIR downregulation upon inflammation. Real- time analysis of attachment and growth of + +SF in vitro did not show any changes in adhesion (Figure 6A) and proliferation (Figure + +6C), but a decrease in spreading of HOTAIR silenced SF (Figure 6B). Accordingly, + +silencing of HOTAIR resulted in decreased migration of SF (Figure 6D and Movie 1). + +These data are in line with the findings that HOTAIR regulated genes were enriched + +in the pathways "actin cytoskeleton" and "Wnt signaling" (Table S3 and Figure 4G/H), + +which were previously linked to tissue remodelling and cell migration(39, 40). + +Furthermore, HOTAIR silencing increased Fas- induced apoptosis in SF (Figure 6E), + +as also indicated by the pathway ('apoptosis') in the pathway analysis (Table S3). + +Since osteoclastogenesis is of major importance in RA and was differentially + +expressed between hand and knee SF (Table S2), we assessed the effect of silencing + +HOTAIR in SF on osteoclastogenesis and osteoclast function. Co- culture of + +differentiating monocytes with HOTAIR silenced SF, but not the addition of + +supernatants from HOTAIR silenced SF, decreased osteoclast formation (Figure 6F), + +HOTAIR in SF on osteoclastogenesis and osteoclast function. Co- culture of + +differentiating monocytes with HOTAIR silenced SF, but not the addition of + +supernatants from HOTAIR silenced SF, decreased osteoclast formation (Figure 6F), + +suggesting that cell- cell contact was needed to inhibit osteoclastogenesis. In contrast, + +osteoclasts in co- culture with HOTAIR silenced SF, as well as incubated with + +supernatants showed decreased osteoclast activity, which could be an effect of + +decreased secretion of CXCL12 by HOTAIR silenced SF(41) (Figure 6G). + +<--- Page Split ---> +![](images/Figure_7.jpg) + +
Figure 6. HOTAIR silencing induces functional changes in SF.
+ +Real- time measurements of SF A) adhesion (0- 16 h), B) spreading (17- 127 h) and C) proliferation (132- 245 h) in HOTAIR silenced and control SF \((n = 3)\) . Two- way ANOVA. D) Measurement of open area covered over time by control SF or HOTAIR silenced SF \((n = 6)\) in scratch assay. Two- way ANOVA. E) Caspase 3/7 activation in untransfected, control and HOTAIR GapmeR transfected SF after 48h of transfection. RLU = relative luminescence units after background subtraction. One- way ANOVA with Bonferroni's correction. F) Left panel: representative pictures of tartrate- resistant acid phosphatase (TRAP) staining of osteoclasts differentiated from monocytes by co- culture (upper panel) or incubation with supernatants (SN) of control or HOTAIR transfected SF (lower panel). Magnification 400 x. Right panel: Quantification of TRAP+ cells in the described conditions. Paired t test with Bonferroni correction. G) Left panel: representative pictures of resorption areas after incubation of bone slices with osteoclasts differentiated by co- culture (upper panel) or incubation with supernatants of control or HOTAIR transfected SF (lower panel). Right panel: Quantification of resorption areas in the described conditions. Paired t test with Bonferroni correction. The results are representative for at- least 2 experiments. Mean +/- standard deviation is shown. + +<--- Page Split ---> + +Since the levels of several chemokines and cytokines were affected by HOTAIR silencing (Supplementary Table S3), we compared the chemotactic activity of supernatants derived from control or HOTAIR silenced SF. Despite a similar amount of healthy peripheral blood mononuclear cells (PBMCs) migrating towards conditioned supernatants from controls and HOTAIR silenced SF (Figure 7A), we observed a shift in the cellular composition of the migrated PBMCs with an increased number of CD19+ B cells (Figure 7B) and a decreased number of CD14+ monocytes (Figure 7C) using supernatants from HOTAIR silenced SF. A slight increase was also seen for the chemotaxis of CD3+ T cells (Figure 7D). In line with these results, synovia of RA and OA patients with low HOTAIR expression were characterized by higher CD20+ B cell (Figure 7E) and, in particular, CD138+ plasma cell infiltration (Figure 7F) and a lymphoid pathotype (Figure 7G). Consistently, in the early RA cohort (PEAC) (PEAC (qmul.ac.uk))(42), a low synovial HOTAIR expression in synovium was associated with a trend towards a lymphoid pathotype (Figure 7H) and HOTAIR expression was negatively correlated with CD138+ plasma cell infiltrates (r: 0.27, padj: 0.023) (Figure 7I). In summary, these data support the notion that expression levels of HOTAIR in SF can shape the influx of immune cells in the synovium in arthritis and vice-versa. + +## HOTAIR site-specific expression may shape inflammatory response in other organs + +Since HOX gene expression is site specifically expressed in stromal cells of several organs and tissues, we wondered whether HOTAIR might shape the inflammatory response in other tissues than joints. Site- specific expression of HOTAIR was already shown in human skin where it also follows the upper vs lower body part pattern(18). In addition, we measured site- specific expression of Hotair in mouse spine and found + +<--- Page Split ---> + +increased expression in lumbal compared to cervical spine \((\mathrm{p} = 0.009)\) (Figure 7J). Furthermore, in the different anatomic compartments of the gastrointestinal tract, Hotair showed site-specific expression with higher expression in the distal parts of the intestines compared to stomach and the small intestines (Figure 7K). + +![PLACEHOLDER_22_0] + +
Figure 7. HOTAIR silencing increases lymphocyte chemotaxis.
+ +A) The amounts of PBMCs migrating through a transwell system towards conditioned supernatants from SF transfected with control GapmeR or HOTAIR GapmeR. Paired t test, \(\mathrm{p} = 0.19\) . B-D) The percentage of B) CD19, C) CD14 and D) CD3 positive cells was measured in PBMCs that had migrated towards supernatants of control or HOTAIR silenced SF. Paired t test. E-G) Expression levels of HOTAIR were measured by qPCR in synovial tissues from rheumatoid arthritis (RA) and osteoarthritis (OA) with E) high ( \(\mathrm{CD20 + }\) score \(\geq 2\) ) or low amounts of \(\mathrm{CD20 + }\) B cells (unpaired t test, \(\mathrm{p} = 0.10\) ), F) with high ( \(\mathrm{CD138 + }\) score \(\geq 2\) ) or low amounts of \(\mathrm{CD138 + }\) plasma cells (unpaired t test), G) with pauci-immune, myeloid or lymphoid pathotypes (one-way ANOVA, \(\mathrm{p} = 0.11\) ). dct: cycle of threshold target - cycle of threshold housekeeping gene. H-I) Expression levels of HOTAIR in + +<--- Page Split ---> + +RNA sequencing in synovial tissues from the PEAC cohort according to H) the synovial pathotype and to I) CD138 infiltrates. J- K) Expression of Hotair was measured by qPCR in C57/BL6 mice \((n = 3)\) J) in cervical, thoracal and lumbar spine and in K) different parts of the gut (stomach, small intestine, caecum, colon and rectum). Heatmap of the dct of HOTAIR expression according to the localisation are presented. Mean \(+ / -\) standard deviation is shown. + +## DISCUSSION + +Here, we show that HOTAIR is a major regulator of site- specific gene expression in SF and modulates a series of highly relevant signalling pathways and SF functions in arthritis. HOTAIR- modulated changes in SF gene expression and function were associated with changes between hand and knee arthritis in RA, suggesting that HOTAIR may shape the phenotype of arthritis in lower extremity joints. By showing that an embryonically imprinted, site- specific factor can regulate inflammation- related signalling pathways, our data support the concept that anatomically defined features of the local stroma can influence the susceptibility and manifestation of inflammation. Here, we showed that HOTAIR expression in SF is decreased after stimulation by local inflammatory factors, which could explain this decreased expression in RA SF. Other recent studies have shown that HOTAIR expression could be regulated by various factors in the local microenvironment, such as hypoxia(43), hormones(24) or inflammatory factors(44). + +The ability of external factors to influence HOTAIR expression supports the idea that embryonic site- specific expression of HOTAIR is used to trigger a locally distinct and anatomically restricted stress response. Interestingly, it has been suggested that HOTAIR may also be mechanoresponsive in response to stretch(45). Given the increased expression of HOTAIR in the lower limbs and lumbar spine, load and mechanosensing could be additional factors that regulate site- specific response pathways via HOTAIR. + +In our study, inflammation- induced downregulation of HOTAIR modulated several inflammatory response pathways in SF, such as the MAPK, PI- Akt and canonical Wnt + +<--- Page Split ---> + +pathways. Consistent with our results, HOTAIR silencing inhibited the canonical Wnt pathway in gastric and pancreatic cancer cells(46, 47) and in OA chondrocytes(48, 49). In OA chondrocytes(49), HOTAIR has been shown to act directly on Wnt inhibitory factor 1 (WIF- 1) by increasing histone H3K27 trimethylation in the WIF- 1 promoter, leading to WIF- 1 repression that promotes activation of the Wnt/β- catenin pathway. Consistent with this study, our own results based on the adjunction of cycloheximide suggest an indirect mechanism underlying the HOTAIR- mediated regulation of the Wnt pathway. + +Activation of the Wnt pathway is a characteristic of the pauci- immune subtype of synovitis in RA, whereas more inflammatory pathways such as PI- Akt have been found to be activated in lymphoid/myeloid synovial pathologies(6, 8, 9). Consistently, lower levels of HOTAIR in synovial tissue were associated with a lymphoid pathotype in our study. Furthermore, HOTAIR silencing exerted a chemotactic effect on lymphocytes in vitro. Thus, it can be speculated that HOTAIR acts as a stromal regulator of the inflammatory tissue response, whose downregulation under conditions with high levels of TNF might promote the development of a lymphocyte- dominated inflammatory response. + +Studies analysing the differences between RA in different joints are rare. Our results suggest that hand RA is more likely than knee RA to show the classic signs of synovitis described for RA, whereas the fibroid pathotype of synovitis was more common in knee RA than in hand RA. Refractory, difficult- to- treat arthritis has been associated with this non- inflammatory fibroid pathotype (10), however, up to now it is not known whether lower limb synovitis is more often resistant to immunosuppressive therapy. In depth analysis of the characteristics of knee RA has suggested severe cartilage destruction but less bone erosion compared to what is known from RA of the hand(50). + +<--- Page Split ---> + +Similarly, OA of the hand tends to be more erosive than OA of the knee(51). The less destructive phenotype of RA and OA in knees may be due to protective mechanisms exerted by the joint- specific stroma. Both, our comparison of knee and hand RA as well as our analysis of HOTAIR function pointed towards decreased osteoclastogenesis in knees compared to hands. Co- culture of differentiated monocytes with HOTAIR- silenced SF, but not the addition of HOTAIR- silenced SF supernatants, decreased osteoclast formation, suggesting that cell- cell contact was necessary to inhibit osteoclastogenesis. Thus, the decrease in osteoclastogenesis after HOTAIR silencing could be related to the reduced migratory function of SF silenced for HOTAIR, which was also observed in another study(44). Another explanation is that the decrease in osteoclastogenesis is mediated by the decreased CXCL12 secretion following HOTAIR silencing(52), as osteoclasts co- cultured with HOTAIR silenced SF but also incubated with supernatants showed decreased osteoclast activity(41). One limitation of our study is that with the available data we cannot directly show how down- regulation of HOTAIR during inflammation affects the inflammatory response in a joint- specific manner. In vivo confirmation of our hypotheses is hampered by the fact that Hotair does not have the same function in morphological development in mice and humans(53), and it is therefore questionable whether the function in regulating inflammatory pathways is conserved. These differences between the species might be explained by biomechanical and anatomical differences between quadrupedal walking in mice versus bipedal walking in humans(45, 54). If our hypothesis of an influence of site- specific embryonic factors on inflammatory responses is valid, species- specific differences in limb formation, anatomy and biomechanics may underlie the limited translation of preclinical studies in mice to human arthritis. + +<--- Page Split ---> + +Beyond the joint, HOTAIR appears to be involved in the site- specific regulation of inflammation in other organs. Thus, it has recently been identified as a major regulator of region- specific development of adipose tissue, which is associated with site- specific metabolic complications, with exclusive expression in gluteofemoral subcutaneous adipose tissue (24, 55). In addition, HOTAIR is mostly expressed in distal dermal fibroblasts(18). Consistently, we observed site- specific expression of HOTAIR in the spine and intestine with increased expression in the lumbar spine and distal parts of the intestines. In spondyloarthritis, it has been suggested that involvement of the lumbar spine is more frequent(56) and more severe with more bone bridges compared to cervical involvement(57). Furthermore, Wnt signaling plays a crucial role in the formation of bone bridges in spondyloarthritis(58). Similarly, inflammatory bowel disease encompasses two types of idiopathic intestinal disease that are differentiated by their location and the depth of involvement in the bowel wall(59). Ulcerative colitis most commonly affects the rectum, whereas Crohn's disease most often affects the terminal ileum and colon. Interestingly, Wnt signaling has been identified as a key regulatory pathway in the intestinal mucosa(60). Thus, important future work will be to elucidate whether the site- specific expression of HOTAIR underlies the development of site- specific phenotypes of these inflammatory diseases. + +In conclusion, we suggest that the phenotype and severity of inflammation are modulated by the embryonic imprinted local stromal gene signature. Further investigation into joint specific factors favouring and shaping the development of arthritis will improve understanding of the pathogenesis of arthritis and could lead to the development of specific therapies targeting joint specific signaling pathways. + +<--- Page Split ---> + +## METHODS + +### Patients + +Synovial tissues were obtained from OA and RA patients undergoing joint replacement surgery at the Schulthess Clinic Zurich, Switzerland or from ultra-sound guided joint biopsies. RA patients fulfilled the 2010 ACR/EULAR (American College of Rheumatology/European League Against Rheumatism) criteria for the classification of RA(61) whereas OA was considered in cases of chronic pain and manifest radiographic signs of OA(62) without any underlining inflammatory rheumatic disease. Patients' characteristics are given in Table 2. + +Table 2: Patient's characteristics + +
In-situ hybridizationOA (n=6)RA (N=5)
Age (yrs)65.0 ± 6.968.0 ± 8.1
Female (n)45
Disease duration (yrs)23.0 ± 10.6
Knee, hip, feet6, 0, 05, 0, 0
Positive RF60%
CRP (mg/L)2.0 ± 1.922.0 ± 30.3
RA treatment2 TNF-blocker, 2 steroids, 1 none
qPCR in synovial tissuesOA (n=12)RA (N=14)
Age (yrs)69.0 ± 11.569.0 ± 12.2
Female (n)812
Disease duration (yrs)19.0 ± 12.9
Knee, hip, feet9, 3, 010, 1, 3
Positive RF100%
CRP (mg/L)2.4 ± 2.611.0 ± 14.7
RA treatment5 TNF-blocker, 7 csDMARDS, 2 unknown
qPCR in cultured SFOA (n=7)RA (N=8)
+ +<--- Page Split ---> + +
Age (yrs)64 ± 8.270 ± 10.5
Female (n)35
Disease duration (yrs)15 ± 16.3
Knee, hip, feet4, 3, 04, 3, 1
Positive RF83%
CRP (mg/L)2.6 ± 8.02.8 ± 6.4
RA treatment1 TNF-blocker, 5 csDMARDS, 1 steroid, 1 unknown
+ +OA: osteoarthritis, RA: rheumatoid arthritis, RF: rheumatoid factor, csDMARDS. Conventional disease modifying drugs + +# Culture of SF + +## Human SF + +Synovial tissues were digested with dispase (37 °C, 1 h) and SF were cultured in Dulbecco's modified Eagle's medium (DMEM; Life Technologies) supplemented with 10% fetal calf serum (FCS), 50 U ml-1 penicillin/streptomycin, 2 mM L-glutamine, 10 mM HEPES and 0.2% amphotericin B (all from Life Technologies). For functional assays, vitamin C (50 μg/ml) was added to the culture medium. Purity of SF cultures was confirmed by flow cytometry showing the presence of the fibroblast surface marker CD90 (Thy-1) and the absence of leukocytes (CD45), macrophages (CD14; CD68), T lymphocytes (CD3), B lymphocytes (CD19) and endothelial cells (CD31). Cell cultures were negative for mycoplasma contamination as assessed by MycoAlert mycoplasma detection kit (Lonza). SF from passages 5 to 8 were used. + +## Murine SF + +Primary mouse SF were isolated and cultured for four passages, as previously described (63). + +## Histological analysis + +<--- Page Split ---> + +Formalin- fixed, paraffin- embedded synovial tissues of RA or OA patients were cut, put on slides and stained with hematoxylin/eosin. 36 synovial tissues from hands (22 from joint replacement and 14 from synovial biopsies) and 27 from knee (18 from joint replacement and 9 from synovial biopsies) were assessed. Synovitis score was assessed by evaluation of the thickness of the lining cell layer, the cellular density of synovial stroma and leukocyte infiltration as described by Krenn et al.(25). Vascularization was assessed by counting the amount of CD31+ cells on 5 consecutive pictures on 20x objective. Synovial tissue were stained with CD3 (ab16669; Abcam), CD4 (ab183685; Abcam), CD8 (ab22378; Abcam), CD31 (ab28364; Abcam), CD20 (M075501- 2, Agilent), CD138 (Clone MI15, Agilent) and CD68 (M081401; Dako), to stratify them into lymphoid, myeloid and fibroid pathotypes according to previously published histological features(7, 9, 25). + +## Gene silencing + +Hand SF were transfected with \(50\mathrm{nM}\) antisense LNA HOXD10 (Qiagen, Sequence: 5'- TGT CTG CGC TAG GTG G- 3'), HOXD11 (Qiagen, Sequence: 5'- TGC TAG CGA AGT CAG A- 3'), HOXD13 (Qiagen, Sequence: 5'- CAT CAG GAG ACA GTA T- 3') or HOTTIP (HOTTIP1 Qiagen, Sequence: 5'- TCG GAA AAG TAA GAG T- 3' and HOTTIP2 Qiagen, Sequence: 5'- TAC CTA AGT GTG CGA A- 3') GapmeR. Knee SF were transfected with \(50\mathrm{nM}\) antisense LNA HOTAIR GapmeR (Qiagen, Sequence: 5'- AGG CTT CTA AAT CCG T- 3'). Transfections were performed using Lipofectamine 2000 (Invitrogen) according to the manufacturer's instructions. Antisense LNA GapmeR Negative Control A (Cat No 300610) was used as transfection control. + +## Single-cell RNA sequencing + +scRNAseq of hand and knee synovial tissues + +<--- Page Split ---> + +ScRNAseq sequencing was performed on ultrasound- guided joint biopsies of hand (n = 8) or knee (n = 4) joints of RA patients (Patient's characteristics in Table 1). Tissues were processed as previously described(38). In brief, tissues were mechanically minced and enzymatically digested using Liberase TL (Roche). 10'000 unsorted synovial cells (viability >80%) per patient were prepared for single cell analysis using the Chromium Single Cell 3' GEM, Library and Gel Bead Kit v3.1, the Chromium Chip G Single Cell Kit and the Chromium controller (all 10x Genomic). Libraries were sequenced on the Illumina NovaSeq6000 instrument to a sequence depth of 20,000 to 70,000 reads per cell. CellRanger (v2.0.2) from 10x Genomics was used to demultiplex, align the reads to Ensembl reference build GRCh38.p13 and collapse unique molecular identifiers (UMIs). The standard Seurat protocol(64) was used for further analysis. Gene expression between hand and knee was compared. Pathway enrichment analyses of genes differentially expressed between SF from hands and knees were performed using Enrichr (all genes with FDR < 0.05, log fold change +/- 1) (65). The scatter dot plots were created with the Enrichr Appyter. + +scRNAseq of control SF and SF silenced for HOTAIR + +scRNAseq in cultured SF transfected with either control or HOTAIR targeting GapmeR (n = 3) (Patient's characteristics in Table 3) was performed. SF were washed and counted on a LUNA automated cell counter (Logos Biosystems). 15'000 unsorted SF (viability 60- 88%) per patient were prepared for single cell analysis using the Chromium Single Cell 3' GEM, Library and Gel Bead Kit v3.1, the Chromium Chip G Single Cell Kit and the Chromium controller (all 10x Genomic). Libraries were sequenced on the Illumina NovaSeq6000 instrument to a sequence depth of 20,000 to 70,000 reads per cell. CellRanger (v2.0.2) from 10x Genomics was used to demultiplex, align the reads to Ensembl reference build GRCh38.p13 and collapse + +<--- Page Split ---> + +unique molecular identifiers (UMIs). scRNAseq for control SF and SF silenced for HOTAIR were integrated with SF of publicly available datasets (15, 36, 37, 66) and two in-house datasets(38). The standard Seurat (version 4.0) protocol(64) was followed for analysis. Quality control included the exclusion of cells with \(>25\%\) mitochondrial reads and \(<5\%\) ribosomal reads; exclusion of mitochondrial, ribosomal and hemoglobin genes and inclusion of only cells with at least 200 detected genes. For the integration of all datasets, we applied harmony(67) within the standard Seurat workflow and performed the analysis as previously described(38). Cell clustering was computed with 30 principal components and resolution of 0.1. Marker genes were identified by Wilcoxon Rank Sum test and log fold change of 0.25. Differential gene expression analysis for selected genes (COL1A1, COL1A2, CXCL12 and IL6) between control and HOTAIR targeting GapmeR transfected SF were performed within each SF cluster applying Wilcoxon Rank Sum test and FDR p-value adjustment for multiple testing. + +Table 3: Patient's characteristics (scRNAseq of control SF and SF silenced for HOTAIR) \((n = 3)\) + +
Age (yrs)72.7 ± 10.2
Female (n)3
Osteoarthritis3
Knee3
CRP (mg/L)1.0 ± 1.0
TreatmentNo
+ +<--- Page Split ---> + +## In Situ Hybridization (ISH) + +Construction of probes + +PCR on cDNA generated from total RNA of knee SF was used to produce an amplicon of 267 base pairs of HOTAIR (see Table S4). Amplicons were cloned into pPCR- Script Amp SK (+) plasmids using the PCR- Script Amp cloning kit (Agilent Technologies). Plasmids were amplified and purified with the PureLink MiniPrep kit (Thermo Fisher Scientific). Plasmids were linearized using restriction enzymes (EcoRI or NotI; both New England Biolabs) and purified with the QIAquick PCR Purification Kit (Qiagen). DIG- labeled HOTAIR probes were prepared by in vitro transcription with RNA polymerases and plasmid vectors containing target transcript sequences. Linearized plasmid DNA (10 \(\mu\) l) was used as a template, and RNA probes were synthesized with T7 or T3 RNA polymerase (Roche) and DIG Labeling Mix (Roche) for 100 min at \(37^{\circ}\mathrm{C}\) . + +## In-situ hybridization + +HOTAIR expression was examined by ISH in paraffin- embedded synovial tissue from OA and RA patients (for patients' characteristics see Table 2). All steps prior to and during hybridization were conducted under RNase- free conditions. Sections were deparaffinized, and incubated with HCl (20 min) and PFA \(4\%\) (10 min). Then sections were treated with trypsin (1 mg/ml) at \(37^{\circ}\mathrm{C}\) for 30 min. The slides were incubated with 2X SSC 5 min and washed twice with triethanolamine- HCl solution. The sections were acetylated for 20 min with \(0.25\%\) acetic anhydride in 0.1 M triethanolamine (pH 8.0) and washed twice in triethanolamine- HCl. Following incubation with hybridization buffer for 1 h at RT, the sections were incubated with hybridization solution which contained 1:10 diluted DIG- labelled probes in hybridization buffer (50% deionised formamide, 40% dextran sulfate/SSC solution, 50x Denhardt's solution, + +<--- Page Split ---> + +\(5\%\) preheated herring sperm DNA and \(250\mu \mathrm{g / ml}\) tRNA). Slides were then incubated in a humidified chamber at \(50^{\circ}\mathrm{C}\) overnight. After hybridization, slides were washed with 5X SSC at \(50^{\circ}\mathrm{C}\) (20 min), with \(50\%\) formamide in 2X SSC at \(50^{\circ}\mathrm{C}\) (30 min) and two times with STE buffer (500 mM NaCl, 1mM EDTA, 20mM TRIS- HCL pH 7.5). Sections were treated with RNase A (40 \(\mu \mathrm{g / ml}\) ) in STE buffer at \(37^{\circ}\mathrm{C}\) for \(1\mathrm{h}\) , followed by successive washing with STE buffer (RT), 2X SSC (RT), buffer 1 ( \(0.2\%\) SDS in 1X SSC, \(50^{\circ}\mathrm{C}\) ), buffer 2 ( \(0.2\%\) SDS in \(0.5\mathrm{X}\) SSC; \(50^{\circ}\mathrm{C}\) )) and buffer 3 ( \(0.2\%\) SDS in \(0.1\mathrm{X}\) SSC; \(50^{\circ}\mathrm{C}\) ). Slides were incubated with \(2\%\) horse serum at RT for \(30\mathrm{min}\) . Then the slides were incubated in a humidified chamber at RT for \(1\mathrm{h}\) with sheep anti- digoxigenin- AP- Fab (Roche), diluted to 1:250 in TBS- T containing \(1\%\) blocking reagent. Slides were washed with TBS- T and stained with nitro blue tetrazolium/5- bromo- 4- chloro- 3- indolylphosphate (Roche) in the dark. The intensity of staining was quantified with ImageJ software (http://rsbweb.nih.gov/ij/docs/examples/stained- sections/index.html). Negative controls were conducted by the substitution of sense for anti- sense probes or by the omission of anti- sense probes in the hybridization solution. + +## Immunohistochemistry + +For double staining of ISH slides, tissue sections were pre- treated with proteinase K 10 min at \(37^{\circ}\mathrm{C}\) . Endogenous peroxidase activity was disrupted with \(3\% \mathrm{H}_2\mathrm{O}_2\) . Slides were permeabilized with \(0.1\%\) Triton in PBS. Nonspecific protein binding was blocked with \(10\%\) goat serum in antibody diluent (DakoCytomation) for \(1\mathrm{h}\) . Mouse anti- human CD68 (clone KP1; DakoCytomation) antibodies, mouse anti- human vimentin (EPR3779; Abcam) antibodies or mouse IgG1 were applied over night at \(4^{\circ}\mathrm{C}\) . Slides were washed in PBS- T ( \(0.05\%\) Tween 20 in PBS) and incubated with + +<--- Page Split ---> + +biotinylated goat anti- mouse antibodies (Jackson ImmunoResearch). The signal was amplified with ABC reagent and detected with AEC (Vector laboratories). Staining was imaged on a Zeiss Imager.Z1 (25x magnification) and quantified with ImageJ using brightness values. + +## Quantitative Real-time polymerase chain reaction (qPCR) + +Snap frozen synovial tissues were minced and total RNA was isolated using the miRNAsey Mini kit (Qiagen) including on- column DNaseI digestion. From cultured cells, total RNA was isolated using the Quick- RNA MicroPrep Kit (Zymo) including on- column DNaseI digestion. + +Total RNA was reversed transcribed and qPCR was performed using SYBR green (Life Technologies) or TaqMan probes for the detection of HOTAIR. Primer sequences are available in Tables S4 (human) and S5 (mouse). Changes after cycloheximide adjunction (10ug/ml 6 h and 24 h) were evaluated (n = 3). No template control samples, dissociation curves and samples containing the untranscribed RNA were measured in parallel as controls. Data were analyzed with the comparative \(\mathrm{C_T}\) method and presented as \(\Delta \mathrm{CT}\) or \(2^{-\Delta \Delta \mathrm{CT}}\) as described(68). Constitutively expressed HPRT was measured for internal standard sample normalization in humans and beta2- microglobulin in mouse SF. + +## SF stimulation + +SF were stimulated with human recombinant TNF (10 ng/ml; R&D Systems), human recombinant IL1β (1 ng/ml; R&D Systems), lipopolysaccaride (LPS) from Escherichia coli J5 (100 ng/ml; List Biological Laboratories), polyI- C (PIC) (10 μg/ml; InvivoGen), bacterial lipopeptide (bLP) palmitoyl- 3- cysteine- serine- lysine- 4 + +<--- Page Split ---> + +(300ng/ml; InvivoGen) or human recombinant TGFβ (10 ng/ml; R&D Systems) for 24 h. + +## Cap Analysis Gene Expression (CAGE) + +CAGE data from control or TNF stimulated (10 ng/ml; 24 h) RA SF from 2 knees were obtained from GSE163548. Mapping and identification of CAGE transcription start sites (CTSSs) were performed by DNAFORM (Yokohama, Kanagawa, Japan). In brief, the sequenced CAGE tags were mapped to hg19 using BWA software and HISAT2 after discarding ribosomal RNAs. Identification of CTSSs was performed with the Bioconductor package CAGEr (version 1.16.0)(69). Promoter and enhancer candidate identification and quantification were performed with the Bioconductor package CAGEfightR(70) (version 1.6.0) with default settings. Clusters were kept when present in at least one sample. + +## ChIP DNA sequencing + +SF pellets from OA knees transfected with GapmeR HOTAIR and GapmeR Control (n = 3 each) were prepared using the iDeal ChIP seq kit for Histones (Diagenode) with a shearing of 12 cycles (30"ON 30"OFF, Bioruptor Pico). The shearing efficiency was analyzed using an automated capillary electrophoresis system Fragment Analyser (High sensitivity NGS fragment kit) after RNase treatment, reversion of crosslinking and purification of DNA. ChIP assays were performed using 1 million cells per IP and H3K27me3 (1 \(\mu \mathrm{g}\) , C15410195, Diagenode). A control library was processed in parallel using the same amount of control Diagenode ChIP'd DNA. After the IP, the ChIP'd DNA was analyzed by qPCR to evaluate the specificity of the reaction. The promoter of GAPDH (GAPDH-TSS) was used as negative control region, Myelin Transcription Factor 1 gene (MYT1) was used as a positive control region. The ratios + +<--- Page Split ---> + +of the recovery for the positive regions over the background, i.e. the specificity of the signal, were substantially smaller in two of the samples (one GapmeR HOTAIR and one GapmeR control) (ratio MYT1/GAPDH-TSS 74 and 25, respectively compared to a mean of \(122 \pm 33\) in the other samples). These samples also did not cluster with the other samples in unsupervised principal component analysis. Therefore, these 2 samples were excluded for the analysis. Libraries were prepared from 1 ng of IP and input DNA using the MicroPLEX v2 protocol, quantified by BioAnalyzer, purified (AMPure beads) and eluted in TE. Purified libraries were quantified (Qubit ds DNA HS kit), analysed for size (Fragment Analyzer) and diluted to \(20 \mathrm{nM}\) concentration. Libraries were sequenced on an Illumina HiSeq 2500 (50 bp, single end). + +The quality of sequencing reads was assessed using FastQC. Reads were aligned to the reference genome (hg19) using BWA v. 0.7.5a(71). Samples were filtered for regions blacklisted by the ENCODE project (72, 73). Subsequently samples were deduplicated using SAMtools version 1.3.1(74). Alignment coordinates were converted to BED format using BEDTools v.2.17(74). Peaks were annotated on gene and transcript level using "ChIPpeakAnno", "ChIPQC", and "ChIPseeker" packages of R. "DiffBind" package was used for differential binding. + +## RNA sequencing + +Total RNA was isolated with the miRNeasy Mini kit (Qiagen) including on- column DNaseI digestion from SF silenced for HOTAIR and control SF (n = 3 for each) 48 h after the transfection. RNA quantity and quality were evaluated using the Agilent RNA 6000 Nano kit with the Agilent 2100 Bioanalyzer instrument (Agilent Technologies, Inc.). The Illumina TruSeq Stranded total RNA protocol with the TruSeq Stranded total RNA Sample Preparation Kit was used to produce RNA- seq libraries. The quality and quantity of the generated libraries were determined by Agilent Technologies 2100 + +<--- Page Split ---> + +Bioanalyzer with DNA- specific chip and quantitative PCR (qPCR) using Illumina adapter- specific primers using the Roche LightCycler system (Roche Diagnostics), respectively. Diluted indexed long RNA- seq (10 nM) libraries were pooled in equal volumes, used for cluster generation (TruSeq SR Cluster Kit v3- cBot- HS reagents, according to the manufacturer's recommendations) and sequenced (TruSeq SBS Kit v3- HS reagents, Illumina HiSeq4000). Sequencing data reads were quality- checked with FastQC. Reads were trimmed with Trimmomatic and aligned to the reference genome and transcriptome (FASTA and GTF files, respectively, Ensembl GRCh37) with STAR(75). Gene expression was quantified using the R/Bioconductor package Rsubread(76) version 1.22. Differentially expressed genes between conditions were identified using the R/Bioconductor packages DESeq2(77). + +Pathway enrichment analyses of genes differentially expressed between SF invalidated for HOTAIR and controls were performed using Enrichr (all genes with FDR \(< 0.05\) , log fold change \(+ / - 1\) )(65). The scatter dot plot was created with the Enrichr Appyter. + +## ELISA + +The human CXCL12 DuoSet ELISA kit (R&D Systems), the human IL12- p35 ELISA kit (Elabscience), the human IL6 ELISA DuoSet kit (R&D Systems), and the human procollagen \(1\alpha\) ELISA Set (BD Biosciences), respectively was used with cell culture supernatants. + +## SF organ micromasses + +3D micromasses were generated as previously described(78). In brief, SF transfected with control or HOTAIR GapmeR were mixed with Matrigel (LDEV- free, Corning) \((3\times 10^{6}\) SF/ml Matrigel) and \(30\mu \mathrm{l}\) droplets added to 12- well plates coated with poly 2- + +<--- Page Split ---> + +hydroxyethylmethacrylate (Sigma). Micromasses were left in culture for 3 weeks in Dulbecco's modified Eagle's medium (DMEM; Life Technologies) supplemented with \(10\%\) fetal calf serum (FCS), \(1\%\) penicillin/streptomycin, \(1\%\) minimum Essential medium non- Essential Amino Acids (Giboc), \(1\%\) ITS + premix (BD) und \(17.6 \mu \mathrm{g} / \mathrm{ml}\) vitamin C. After 3 weeks, micromasses were fixed with \(2\%\) paraformaldehyde. After \(24 \mathrm{~h}\) , paraformaldehyde was replaced by \(70\%\) ethanol and micromasses were embedded in paraffin and sectioned for IHC. Spheroids were stained with anti- human collagen I antibodies (EPR7785, Abcam) on a BOND- MAX autostainer (Leica). Staining was imaged on a Zeiss Imager.Z1 (25x magnification) and quantified with ImageJ using brightness values. + +## Western blotting + +Cells were lysed in Laemmli buffer (62.5 mM TrisHCl, \(2\%\) SDS, \(10\%\) Glycerol, \(0.1\%\) Bromphenolblue, \(5 \mathrm{mM} \beta\) mercaptoethanol). Whole cell lysates were separated on \(10\%\) SDS polyacrylamide gels and electroblotted onto nitrocellulose membranes (Whatman). Membranes were blocked for \(1 \mathrm{~h}\) in \(5\%\) (w/v) non- fat milk in TBS- T (20 mM Tris base, \(137 \mathrm{mM}\) sodium chloride, \(0.1\%\) Tween- 20, pH 7.6) or in \(5\%\) (w/v) BSA in TBS- T BSA for phosphorylated proteins. After blocking, the membranes were probed with antibodies against p- AKT (4060, Cell Signaling), AKT (4685, Cell Signaling) \(\alpha\) - tubulin (ab7291, abcam) overnight at \(4^{\circ} \mathrm{C}\) . As secondary antibodies, horseradish peroxidase- conjugated goat anti- rabbit (111- 036- 047, Jackson ImmunoResearch) or goat anti- mouse antibodies (115- 036- 062, Jackson ImmunoResearch) were used. Signals were detected using the ECL Western blotting detection reagents (GE Healthcare) and the Alpha Imager Software system (Alpha Innotech). + +<--- Page Split ---> + +## Luciferase activity assay + +To measure the effect of HOTAIR silencing on the canonical Wnt pathway, SF were transfected by electroporation (BTX) with the beta- catenin reporter M50 Super 8x TOPFlash (Addgene plasmid #12456) or M51 Super 8x FOPFlash, which contains mutated binding sites upstream of the luciferase reporter (Addgene plasmid #12457)(79). Both plasmids were a gift from Randall Moon. For normalization, pRenilla Luciferase Control Reporter Vectors (Promega) were co- transfected. 24 h after transfection, cells were transfected with GapmeR for HOTAIR or control as mentioned above. Luciferase activity was measured with a dual luciferase reporter assay system (Promega), and the results were normalized to the activity of Renilla luciferase. + +## Real-time cell analysis (RTCA) + +For RTCA of cell adhesion and proliferation of SF, the xCELLigence RTCA DP Instrument (ACEA Biosciences, Inc.) was used. 16- well E- plates were equilibrated with \(100\mu \mathrm{l}\) of DMEM, \(10\%\) FCS for \(30\mathrm{min}\) at RT. The impedance, expressed as arbitrary Cell Index (CI) units, of the wells with media alone (background impedance- Rb) was measured before adding the cells. SF were detached with accurate (Merck), resuspended in DMEM, and seeded at a cell density of 25,000 cells per well. Cell adhesion and spreading, measured as changes in impedance, was monitored every \(5\mathrm{min}\) for a period of first \(12\mathrm{h}\) and every \(15\mathrm{min}\) after that for the next \(12\mathrm{h}\) . The CI at each time point is defined as \((\mathrm{Rn} - \mathrm{Rb}) / 15\) , where Rn is the cell- electrode impedance of the well when it contains cells and Rb is the background impedance. Each condition was analysed in quadruplicates. Impedance changes were recorded every \(15\mathrm{min}\) (0- \(24\mathrm{h}\) ) and every \(30\mathrm{min}\) (24- \(245\mathrm{h}\) ). Adhesion was analysed over the first \(16\mathrm{h}\) of the + +<--- Page Split ---> + +experiment, spreading was analysed between 17 and 127 h and proliferation during the exponential phase of the slopes (120- 245). + +## Scratch assay + +To assess real time migration of SF, we performed a scratch assay(80) using Ibidi culture inserts (Ibidi, Germany) with \(70 \mu \mathrm{l}\) cell suspension \((1.2 \times 10^{5}\) cells/ml). Cells were incubated at \(37^{\circ} \mathrm{C}\) and \(5\%\) \(\mathrm{CO}_{2}\) for \(24 \mathrm{~h}\) to obtain a confluent cell layer. Next, cells were transfected with GampR HOTAIR or controls and incubated for additional \(24 \mathrm{~h}\) . Culture inserts were removed and cell layers were washed with PBS. Culture medium (with vitamin C) was added and time lapse images were recorded every 30 minutes for 48 hours using a widefield Zeiss AxioObserver equipped with a stage incubator to maintain temperature and \(\mathrm{CO}_{2}\) conditions. Each assay was performed in triplicate and repeated three times. Open area was calculated over \(24 \mathrm{~h}\) to \(48 \mathrm{~h}\) according to experiments(80). + +## Apoptosis assay + +Activity of the key effector caspases 3 and 7 was measured using the Caspase- Glo 3/7 assay (Promega, Madison, WI). SF were seeded at a density of 4000 cells/well in 96- well white- walled plates. The next day, SF were transfected with GampR for HOTAIR or GampR control or left untransfected. Cleaved caspase 3/7 activity was assessed \(48 \mathrm{~h}\) after transfection. FAS- ligand \((2 \mu \mathrm{g} / \mathrm{ml})\) was added \(18 \mathrm{~h}\) before the assay to stimulate apoptosis. Luminescence signals were measured using a Synergy HT microplate reader (Bio Tek). Each assay was performed in triplicate and repeated two times. + +## Osteoclastogenesis assay + +<--- Page Split ---> + +Human osteoclasts precursors were isolated from blood donations of healthy volunteers ( \(n = 4\) , Red Cross, Schlieren, Switzerland). In brief, CD14+ cells were isolated by positive selection using magnetic separation (Milteny Biotec) after a Ficoll gradient (GE Healthcare) separation. Isolated monocytes (5 x \(10^{4}\) ) were cultured in chamber slides in \(\alpha\) MEM supplemented with \(10\%\) FBS (GE Healthcare), \(2\mathrm{mM}\) 1- glutamine and antibiotics in the presence of \(25\mathrm{ng / mL}\) macrophage colony-stimulating factor (M- CSF) (PeproTech) for 3 days. SF were transfected with HOTAIR or control GapmeR and were added to differentiating monocytes (5 x \(10^{5}\) SF/well). Alternatively, supernatants from control or HOTAIR silenced SF were added. Cells were cultured in the presence of \(25\mathrm{ng / mL}\) M- CSF, \(50\mathrm{ng / mL}\) RANKL (PeproTech) and vitamin C. The media was replaced every 2 days. After 6 days of culture, the cells were fixed by \(4\%\) paraformaldehyde and were stained by TRAP. Control experiments included untransfected SF, SF alone (without precursors of osteoclasts) and precursors without SF or supernatants. Each assay was performed in duplicate and repeated three times. To assess bone resorption, osteoclasts precursors ( \(10^{5}\) /well) were co- cultured with SF (1.5 x \(10^{4}\) /well) or supernatants from SF in 96 wells on bovine bone slices (Jelling, Denmark) for two weeks in the presence of \(25\mathrm{ng / mL}\) M- CSF, \(50\mathrm{ng / mL}\) RANKL and vitamin C. The osteoclastogenic medium was replaced every 2- 3 days. SF were re- transfected every 5 days with HOTAIR or control GapmeRs. Analyses were done after 14 days of differentiation. Cells on bone slices were subsequently incubated with a 0.1 m NaOH solution, ultrasonicated for 2 min, rinsed with water to remove cells from the slices, and placed in a \(1\%\) aqueous toluidine blue solution containing \(1\%\) sodium borate for 5 min. Photomicrographs of resorption pits were taken using a light microscope. Resorption area was measured using Image J. + +## Chemotaxis assay + +<--- Page Split ---> + +PBMCs from one healthy donor were isolated using CPT™ tubes (BD Biosciences) and \(10^{6}\) PBMCs were seeded in the upper well of a 96- well transwell migration chamber with \(5 \mu \mathrm{m}\) pore size (Corning). The lower chamber was filled with supernatants collected from control or HOTAIR silenced SF 48h after transfection (triplicates). Unconditioned medium or PBS were used as controls. Cells were collected from the lower chamber after 18h using ice- cold 20mM EDTA/0.5% FCS in PBS as detachment solution. Collected cells were counted in a cell counter (Casy, OLS) and stained with anti- human CD14- PE, anti- human CD19- PE or anti- human CD3- FITC antibodies (all Miltenyi) for 1 h at \(4^{\circ}\mathrm{C}\) . FITC- and PE- labelled IgG were used as negative control antibodies. Percentage of positive cells was assessed on a FACSCalibur™ flow cytometry platform (BD Biosciences). + +## RNA sequencing from the early arthritis cohort + +RNA- sequencing data from the Pathobiology of Early Arthritis Cohort, which is available on the PEAC (qmul.ac.uk) was studied. + +## Isolation of mouse tissue from lung, spine and gut + +Wild- type C57BL/6 mice, 6 weeks old, were purchased from Jackson and dissected. The spine and gut were isolated and separated into different parts (spine: cervical, thoracic and lumbar) and gut (stomach, small intestine, caecum, colon and rectum). Total RNA was isolated using the miRNAseasy Mini kit (Qiagen) including on- column DNaseI digestion. Total RNA was reversed transcribed and qPCR was performed using SYBR green (Life Technologies). Constitutively expressed beta2- microglobulin was measured for internal standard sample normalization. In cases of undetectable expression of Hotair in mouse tissues the ct was set arbitrarily to 45 in order to calculate the dct. + +<--- Page Split ---> + +## Statistical analysis + +Data were analysed with GraphPad Prism version 6.0 or higher and IBM SPSS Statistics software. Two groups were compared with two- tailed unpaired or paired t test, as appropriate. Multiple group comparisons were performed by adjustments for multiple comparisons using Bonferroni correction or two- way ANOVA. Correlations were tested using Spearman's correlation coefficient. \(P\) values of \(< 0.05\) were considered statistically significant. + +## Study approval + +The collection and experimental usage of the human samples was approved by the ethical commission of the Kanton Zurich (swissethics number: 2019- 00674, PB- 2016- 02014 and 2019- 00115). Informed consent was obtained from all patients. All experiments have been performed in accordance with the institutional guidelines. Mouse experiments were approved by the Institutional Committee of Protocol Evaluation in conjunction with the Veterinary Service Management of the Hellenic Republic Prefecture of Attika according to all current European and national legislation and were performed in accordance with relevant guidelines and regulations, under the relevant animal protocol licenses with number 2199- 11/4/2017. + +## Data availability + +The RNA seq, ChIP seq of H3K27me3 and the scRNA seq data with control GapmeR and HOTAIR GapmeR transfected SF are uploaded to the GEO repository accession GSE185440. + +<--- Page Split ---> + +## AUTHOR CONTRIBUTIONS + +CO, ME and RM designed, analyzed and interpreted experiments. ME, CO, RM and MH wrote the manuscript. ME, MH, MaMi, LM, CP, KB, KK, PS, MFB and SGE performed experiments. RM and MH did the computational analysis. ME, AL, GK, MS, GeKo, MA and CO performed the mouse experiments. RM, KB and OD organized ethic approval, recruited patients and collected samples. All authors critically reviewed the manuscript. + +## ACKNOWLEDGMENTS + +We thank Maria Comazzi and Peter Künzler (Center of Experimental Rheumatology, University Hospital Zurich, Switzerland) for excellent technical assistance. We thank Miriam Marks for providing the samples from joint replacement. + +ME was supported by Société Française de Rhumatologie, Fondation pour la Recherche Médicale, Assistance publique - Hôpitaux de Paris (APHP) and EULAR. CO was supported by the Hartmann- Müller Foundation, the Foundation for Research in Science and the Humanities at the University of Zurich, the EMDO Foundation and the Iten- Kohaut Foundation. This work was supported by the IMI project BTCure (GA no. 115142- 2). We also acknowledge support by the InfrafrontierGR infrastructure (MIS 5002135), co- funded by Greece and the European Union [European Regional Development Fund] under NSRF 2014- 2020, which provided mouse hosting and phenotyping facilities. The graphical abstract was created with BioRender.com. + +## CONFLICTS OF INTEREST + +Muriel Elhai, Raphael Micheroli, Miranda Houtman, Masoumeh Mirrahimi, Larissa Moser, Chantal Pauli, Kristina Bürki, Andrea Laimbacher, Gabriela Kania, Kerstin Klein, Philipp Schätzle, Mojca Frank Bertoncelj, Sam G. Edalat, Maria Sakkou, George Kollias, Marietta Armaka, and Caroline Ospelt: none + +<--- Page Split ---> + +1030 Oliver Distler has/had relationships with the following companies in the area of potential treatments for systemic sclerosis and its complications in the last three calendar years: 1032 Speaker fee: Bayer, Boehringer Ingelheim, Janssen, Medscape 1034 Consultancy fee: 4P- Pharma, Abbvie, Acceleron, Alcimed, Altavant Siences, 1035 Amgen, AnaMar, Arxx, AstraZeneca, Baecon, Blade, Bayer, Boehringer Ingelheim, 1036 Corbus, CSL Behring, Galapagos, Glenmark, Horizon, Inventiva, Kymera, Lupin, 1037 Miltenyi Biotec, Mitsubishi Tanabe, MSD, Novartis, Prometheus, Redxpharma, 1038 Roivant, Sanofi and Topadur 1039 Research Grants: Kymera, Mitsubishi Tanabe, Boehringer Ingelheim 1040 Oliver Distler has/had relationships with the following companies in the area of potential treatments for dermatomyositis and its complications in the last three calendar years: 1043 Consultancy fee for rheumatology topic: Pfizer (2021) 1044 Oliver Distler has/had relationships with the following companies in the area of potential treatments for arthritis in the last three 1046 Consultancy fee: Abbvie + +1047 + +1048 + +1049 + +1050 + +1051 + +1052 + +1053 + +1054 + +<--- Page Split ---> + +1056 1057 1. 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Cell adhesion & 1328 migration 8, 440- 451 (2014). 1329 + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- Suppl.FiguresHOTAIR20221107v20.pdf- SupplementaryTables20221222ME.pdf- Movie.mp4 + +<--- Page Split ---> diff --git a/preprint/preprint__081a787feb042eb392b973be710c939c45b17f8fc6f5e177ef5f2e56bb16567b/preprint__081a787feb042eb392b973be710c939c45b17f8fc6f5e177ef5f2e56bb16567b_det.mmd b/preprint/preprint__081a787feb042eb392b973be710c939c45b17f8fc6f5e177ef5f2e56bb16567b/preprint__081a787feb042eb392b973be710c939c45b17f8fc6f5e177ef5f2e56bb16567b_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..9531c5db22fdd7e1ef6c2028624032a436175b11 --- /dev/null +++ b/preprint/preprint__081a787feb042eb392b973be710c939c45b17f8fc6f5e177ef5f2e56bb16567b/preprint__081a787feb042eb392b973be710c939c45b17f8fc6f5e177ef5f2e56bb16567b_det.mmd @@ -0,0 +1,788 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 914, 175]]<|/det|> +# The long-non coding RNA HOTAIR as site specific regulator of inflammation in chronic arthritis + +<|ref|>text<|/ref|><|det|>[[44, 195, 911, 260]]<|/det|> +Muriel elhai UniversitätsSpital Zürich Zentrum für Experimentelle Rheumatologie https://orcid.org/0000- 0001- 8627- 5758 + +<|ref|>text<|/ref|><|det|>[[44, 264, 281, 306]]<|/det|> +Raphael Micheroli University Hospital Zurich + +<|ref|>text<|/ref|><|det|>[[44, 312, 281, 353]]<|/det|> +Miranda Houtman University Hospital Zurich + +<|ref|>text<|/ref|><|det|>[[44, 358, 919, 424]]<|/det|> +Masoumeh Mirrahimi Center of Experimental Rheumatology, Department of Rheumatology, University Hospital of Zurich, University of Zurich + +<|ref|>text<|/ref|><|det|>[[44, 428, 909, 492]]<|/det|> +Larissa Moser Center of Experimental Rheumatology, Department of Rheumatology, University Hospital of Zurich, University of Zurich + +<|ref|>text<|/ref|><|det|>[[44, 496, 525, 538]]<|/det|> +Chantal Pauli University Hospital Zürich and the University of Zurich + +<|ref|>text<|/ref|><|det|>[[44, 543, 909, 606]]<|/det|> +Kristina Bürki Center of Experimental Rheumatology, Department of Rheumatology, University Hospital of Zurich, University of Zurich + +<|ref|>text<|/ref|><|det|>[[44, 611, 909, 676]]<|/det|> +Andrea Laimbacher Center of Experimental Rheumatology, Department of Rheumatology, University Hospital of Zurich, University of Zurich + +<|ref|>text<|/ref|><|det|>[[44, 680, 880, 724]]<|/det|> +Gabriela Kania Center of Experimental Rheumatology, Department of Rheumatology, University Hospital Zürich + +<|ref|>text<|/ref|><|det|>[[44, 728, 680, 770]]<|/det|> +Kerstin Klein Department of Rheumatology and Immunology, University Hospital Bern + +<|ref|>text<|/ref|><|det|>[[44, 775, 390, 816]]<|/det|> +Philipp Schätzle Cytometry Facility, University of Zurich + +<|ref|>text<|/ref|><|det|>[[44, 820, 909, 885]]<|/det|> +Mojca Frank Bertoncelj Center of Experimental Rheumatology, Department of Rheumatology, University Hospital of Zurich, University of Zurich + +<|ref|>text<|/ref|><|det|>[[44, 890, 281, 931]]<|/det|> +Sam Edalat University Hospital Zurich + +<|ref|>text<|/ref|><|det|>[[44, 936, 167, 954]]<|/det|> +Maria Sakkou + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[52, 45, 550, 65]]<|/det|> +Biomedical Sciences Research Center Alexander Fleming + +<|ref|>text<|/ref|><|det|>[[44, 70, 650, 112]]<|/det|> +George Kollias B.S.R.C. Alexander Fleming https://orcid.org/0000- 0003- 1867- 3150 + +<|ref|>text<|/ref|><|det|>[[44, 117, 193, 136]]<|/det|> +Marietta Armaka + +<|ref|>text<|/ref|><|det|>[[44, 139, 905, 160]]<|/det|> +Biomedical Sciences Research Center Alexander Fleming https://orcid.org/0000- 0003- 0985- 9076 + +<|ref|>text<|/ref|><|det|>[[44, 164, 156, 182]]<|/det|> +Oliver Distler + +<|ref|>text<|/ref|><|det|>[[44, 185, 883, 206]]<|/det|> +Oliver DistlerCenter of Experimental Rheumatology, Department of Rheumatology, University Hospital Zürich + +<|ref|>text<|/ref|><|det|>[[44, 210, 435, 230]]<|/det|> +Caroline Ospelt ( caroline.ospelt@usz.ch) + +<|ref|>text<|/ref|><|det|>[[44, 232, 884, 273]]<|/det|> +Caroline Ospelt ( caroline.ospelt@usz.ch)Department of Rheumatology, Center of Experimental Rheumatology, University Hospital Zurich, University of Zurich https://orcid.org/0000- 0002- 9151- 4650 + +<|ref|>sub_title<|/ref|><|det|>[[44, 315, 102, 333]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 352, 548, 372]]<|/det|> +Keywords: HOTAIR, synovial fibroblast, epigenetic, arthritis + +<|ref|>text<|/ref|><|det|>[[44, 390, 336, 409]]<|/det|> +Posted Date: February 10th, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 428, 474, 448]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 2543547/v1 + +<|ref|>text<|/ref|><|det|>[[44, 465, 910, 508]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 526, 531, 547]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 582, 945, 625]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on December 9th, 2023. See the published version at https://doi.org/10.1038/s41467- 023- 44053-w. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[70, 83, 808, 120]]<|/det|> +# The long-non coding RNA HOTAIR as site specific regulator of inflammation in chronic arthritis + +<|ref|>text<|/ref|><|det|>[[67, 160, 810, 800]]<|/det|> +Muriel Elhai \(^{1}\) , Raphael Micheroli \(^{1}\) , Miranda Houtman \(^{1}\) , Masoumeh Mirrahimi \(^{1}\) , Larissa Moser \(^{1}\) , Chantal Pauli \(^{2}\) , Kristina Bürki \(^{1}\) , Andrea Laimbacher \(^{1}\) , Gabriela Kania \(^{1}\) , Kerstin Klein \(^{1,3,4}\) , Philipp Schätzle \(^{5}\) , Mojca Frank Bertoncelj \(^{1}\) , Sam G. Edalat \(^{1}\) , Maria Sakkou \(^{6,7}\) , George Kollias \(^{6,7}\) , Marietta Armaka \(^{8}\) , Oliver Distler \(^{1}\) , Caroline Ospelt \(^{1*}\) \(^{1}\) Center of Experimental Rheumatology, Department of Rheumatology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland \(^{2}\) Institute for Pathology and Molecular Pathology, University Hospital Zurich, Zurich 8091, Switzerland \(^{3}\) Department of BioMedical Research, University of Bern, Bern, Switzerland \(^{4}\) Department of Rheumatology and Immunology, University Hospital Bern, Bern, Switzerland \(^{5}\) Cytometry Facility, University of Zurich, Zurich Switzerland \(^{6}\) Institute for Bioinnovation, Biomedical Sciences Research Center (BSRC) \(^{7}\) Alexander Fleming', Vari, Greece \(^{7}\) Department of Physiology, Medical School, National and Kapodistrian University of Athens, Athens, Greece \(^{8}\) Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[67, 84, 395, 101]]<|/det|> +# CORRESPONDING AUTHOR + +<|ref|>text<|/ref|><|det|>[[67, 115, 790, 260]]<|/det|> +CORRESPONDING AUTHORCaroline Ospelt, Center of Experimental Rheumatology, Departement of Rheumatology, University Hospital of Zurich, University of Zurich, Rämistrasse 100, CH- 8091, Zurich, Switzerland + +<|ref|>text<|/ref|><|det|>[[67, 270, 95, 285]]<|/det|> +31 + +<|ref|>text<|/ref|><|det|>[[67, 300, 95, 315]]<|/det|> +32 + +<|ref|>text<|/ref|><|det|>[[67, 330, 95, 344]]<|/det|> +33 + +<|ref|>text<|/ref|><|det|>[[67, 360, 95, 374]]<|/det|> +34 + +<|ref|>text<|/ref|><|det|>[[67, 389, 95, 403]]<|/det|> +35 + +<|ref|>text<|/ref|><|det|>[[67, 419, 95, 433]]<|/det|> +36 + +<|ref|>text<|/ref|><|det|>[[67, 448, 95, 462]]<|/det|> +37 + +<|ref|>text<|/ref|><|det|>[[67, 477, 95, 491]]<|/det|> +38 + +<|ref|>text<|/ref|><|det|>[[67, 506, 95, 520]]<|/det|> +39 + +<|ref|>text<|/ref|><|det|>[[67, 535, 95, 549]]<|/det|> +40 + +<|ref|>text<|/ref|><|det|>[[67, 564, 95, 578]]<|/det|> +41 + +<|ref|>text<|/ref|><|det|>[[67, 593, 95, 607]]<|/det|> +42 + +<|ref|>text<|/ref|><|det|>[[67, 622, 95, 636]]<|/det|> +43 + +<|ref|>text<|/ref|><|det|>[[67, 651, 95, 665]]<|/det|> +44 + +<|ref|>text<|/ref|><|det|>[[67, 680, 95, 694]]<|/det|> +45 + +<|ref|>text<|/ref|><|det|>[[67, 709, 95, 723]]<|/det|> +46 + +<|ref|>text<|/ref|><|det|>[[67, 738, 95, 752]]<|/det|> +47 + +<|ref|>text<|/ref|><|det|>[[67, 767, 95, 781]]<|/det|> +48 + +<|ref|>text<|/ref|><|det|>[[67, 796, 95, 810]]<|/det|> +49 + +<|ref|>text<|/ref|><|det|>[[67, 825, 95, 839]]<|/det|> +50 + +<|ref|>text<|/ref|><|det|>[[67, 854, 95, 868]]<|/det|> +51 + +<|ref|>text<|/ref|><|det|>[[67, 883, 95, 897]]<|/det|> +52 + +<|ref|>text<|/ref|><|det|>[[67, 912, 95, 926]]<|/det|> +53 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 84, 231, 101]]<|/det|> +## ABSTRACT + +<|ref|>text<|/ref|><|det|>[[115, 115, 813, 561]]<|/det|> +Most forms of arthritis, have a distinctive topographical pattern of joint involvement. Beyond these differences among diseases, there are also differences in phenotype and response to treatment between joints of the same type of arthritis, suggesting that molecular mechanisms may differ depending on joint location. Here we show that there are joint- specific molecular and tissue changes in the synovium and in local stromal cells (synovial fibroblasts; SF). The long non- coding RNA HOTAIR, expressed only in lower extremities SF, regulates much of this site- specific gene expression in SF. Downregulation of HOTAIR after TNF stimulation regulated relevant inflammatory pathways by epigenetic and transcriptional mechanisms and modified the migratory function of SF, decreased SF- mediated osteoclastogenesis, and increased the attraction of B cells by SF. Since site- specific expression of HOTAIR was also measured in the skin, spine and gastrointestinal tract, we propose HOTAIR as important epigenetic factor that modulates site- specific phenotypes of chronic inflammation. + +<|ref|>sub_title<|/ref|><|det|>[[118, 652, 202, 668]]<|/det|> +## TEASER + +<|ref|>text<|/ref|><|det|>[[118, 683, 811, 733]]<|/det|> +HOTAIR as important epigenetic factor that modulates site- specific phenotypes of chronic inflammation. + +<|ref|>text<|/ref|><|det|>[[118, 880, 617, 898]]<|/det|> +Keywords: HOTAIR, synovial fibroblast, epigenetic, arthritis + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[117, 115, 808, 395]]<|/det|> + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[117, 84, 283, 101]]<|/det|> +## INTRODUCTION + +<|ref|>text<|/ref|><|det|>[[113, 115, 813, 330]]<|/det|> +Chronic arthritis is a major public health problem, which has a substantial influence on health and quality of life(1). Most forms of arthritis, including rheumatoid arthritis (RA), osteoarthritis (OA) and spondyloarthritis, have a distinctive topographical pattern of joint involvement(2). Among them, RA is the most frequent autoimmune arthritis, affecting \(1\%\) of the population(3). Despite the advances made in the management of RA in the last decades, 6 - \(17\%\) of the patients remain refractory to immunosuppressive treatment(4). + +<|ref|>text<|/ref|><|det|>[[112, 345, 813, 860]]<|/det|> +Although patients with untreated RA typically exhibit a symmetrical polyarthritis, individuals with refractory disease might develop a less extensive pattern of polyarthritis, an oligoarticular or even a monoarticular disease, suggesting that immunosuppressive therapy might be effective in some joints and not in others(5). Thus, beyond differences between diseases, there are also differences in phenotype and response to treatment depending on the joints within the same type of arthritis, suggesting that molecular mechanisms may differ according to joint location. Deciphering the heterogeneity of synovium at both the cellular and molecular levels has revolutionized the understanding of the pathogen(6- 9). In particular, refractory RA has been associated with a pauci- immune, fibroid pathotype of the synovium and a molecular signature suggestive of activated fibroblasts(10). Activation of synovial fibroblasts (SF) has long been known to play a critical role in joint inflammation and destruction(11, 12), but has attracted considerable attention more recently due to the discovery of pathogenic subpopulations of SF in the synovium through single- cell analysis(10, 13- 16). Changes in the epigenetic landscape have been shown to be central to the permanent activation and aggressiveness of SF in RA(12). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 80, 814, 550]]<|/det|> +We have previously demonstrated the existence of transcriptomic, epigenetic and functional changes of SF depending on their joint location(17). This specific stromal signature in particular concerned genes involved in the embryonic development of the respective joint regions (i.e. HOX genes), suggesting an embryonically imprinted joint specific stromal signature. In particular, the HOX transcript antisense intergenic RNA (HOTAIR), which is an important regulator of the epigenetic landscape(18), was exclusively expressed in joints of the lower extremity in human and in mice(17). HOX genes encode a family of transcriptional regulators, which are involved in distinct developmental programmes along the head- tail axis of vertebrates(19). Moreover, they remain site- specifically expressed in several differentiated tissues, e.g. in cartilage, the skin, the vasculature and gastrointestinal tract(17, 18, 20- 24). It remains to be determined whether differences between the phenotype of arthritis at the tissue and molecular levels depend on its location, and if so whether the site- specific expression of HOX genes is involved in these changes. + +<|ref|>text<|/ref|><|det|>[[115, 540, 813, 725]]<|/det|> +Here, we showed that there are joint- specific molecular and tissue changes in RA and that the long non- coding RNA (lncRNA) HOTAIR (HOX transcript antisense RNA) is a master regulator of joint- specific gene expression in arthritic SF. Down- regulation of HOTAIR in an inflammatory environment led to activation of specific arthritis relevant pathways and changes in SF function, that might modulate the arthritis phenotype in lower extremity joints. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 84, 214, 101]]<|/det|> +## RESULTS + +<|ref|>sub_title<|/ref|><|det|>[[118, 117, 621, 136]]<|/det|> +## Joint-specific histological and molecular differences in RA + +<|ref|>text<|/ref|><|det|>[[115, 144, 813, 732]]<|/det|> +We first analysed joint- specific differences in the synovium of RA patients using a multi- level approach including histological and molecular analysis. Comparison of the histological grade of synovitis(25) between hand and knee RA showed a trend towards a higher synovitis score in RA hands (mean: \(5.25 \pm 1.78\) ) compared to RA knees (mean: \(4.31 \pm 1.78\) ; difference \(- 0.94 \pm 0.52\) , p=0.076), as previously seen(17) (Figure 1A). Accordingly, vascular density of the synovium was higher in RA hand synovium, with a larger percentage of patients having highly vascularised synovium in the hands than in the knees (54% vs. 37% p=0.031) (Figure 1B). Assessment of synovial pathotypes as defined by Humby et al.(9) in hand and knee RA showed a predominance of the lymphoid pathotype in hand RA, whereas the pathotypes were balanced in the knees (Figure 1C). The lymphoid pathotype presents with strong infiltration of T and B cells in the synovium, the diffuse- myeloid pathotype shows predominant influx of myeloid cells and the pauci- immune/fibroid pathotype is characterised by scanty immune cells and prevalent stromal cells(9). Consequently, analysis of synovial cell proportions by single cell RNA sequencing (scRNAseq) showed substantial expansion of T- and B- cell compartments in wrist compared to knee RA synovium (Figure 1D and Table 1). In summary, all these data pointed towards higher inflammatory activity in hand versus knee RA synovium. + +<|ref|>text<|/ref|><|det|>[[117, 739, 813, 888]]<|/det|> +We then used the scRNAseq data to assess whether these site- specific tissue changes were associated with molecular changes in SF. In total, 1,966 genes were differentially expressed in hand and knee SF, 1,026 genes were overexpressed in hand SF and 940 in knee SF. We confirmed the joint- specific expression of HOX genes in SF from RA patients in this dataset (Figure 1E). In addition, various pathways that we had + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 812, 365]]<|/det|> +previously found to be differentially enriched in cultured hand and knee SF in vitro(17), were also joint- specifically activated in vivo, such as cell adhesion, extracellular matrix (ECM) interaction and bone remodeling/osteoclast differentiation pathways (Supplementary Tables S1/S2 and Figure 1F). Several enriched pathways were previously implicated to be relevant in RA such as MAPK, Wnt and PI- Akt signaling(26). Additionally, knee SF showed increased levels of HLA and CD74 genes ('Antigen processing and presentation' and 'Rheumatoid arthritis' pathways). Thus, these results confirmed joint specific gene expression in SF in terms of developmental as well as inflammatory pathways. + +<|ref|>text<|/ref|><|det|>[[115, 378, 812, 790]]<|/det|> +We then sought to understand in how far the joint specific expression of HOX transcripts was involved in these site- specific gene expression changes. To this end, we silenced SF for HOXD10, HOXD11, HOXD13, and the long non- coding RNAs HOTAIR and HOTTIP, respectively. These HOX transcripts were the most discriminating transcripts in cultured SF and synovial tissues between the hand and knee in our previous in vitro analysis(17). Due to the lower sequence depths in scRNAseq, the less expressed transcripts HOXD13, HOTTIP and HOTAIR were not detectable in the scRNAseq dataset (Figure 1E). The differential gene expression by HOX gene silencing in vitro corresponded to \(70.9\%\) of the differential gene expression between the hand and knee in vivo using scRNAseq (Figure 1G). Among the different HOX genes, HOTAIR alone regulated almost \(49.3\%\) of this joint- dependent gene expression. This suggested that joint- specific expressed HOX transcription factors and non- coding RNAs drive most of the joint- specific transcriptome in RA SF. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 106, 790, 728]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[120, 85, 802, 102]]<|/det|> +
Figure 1: HOTAIR regulates site-specific gene expression in synovial fibroblasts
+ +<|ref|>text<|/ref|><|det|>[[117, 730, 810, 866]]<|/det|> +A) Krenn synovitis score in hand (n = 36) and knee (n = 27) synovium from RA patients. Unpaired t test. Mean +/- standard deviation is shown. B) Vascularization as assessed by CD31staining in synovium from RA patients (35 hands and 24 knees). Vessels were counted in 5 fields (20x magnification). Chi squared test. C) Synovial pathotype in 36 hand and 27 knee RA synovium, Chi squared test. D) Cell proportions between hand (n = 8) and knee (n = 4) synovium using single cell RNA sequencing. SF: synovial fibroblasts, SMC: smooth muscle cells, EC: endothelial cells, MastC: mast cells, PC: plasma cells, BC: B cells, TC: T cells, NKC: NK cells, nGC: neutrophilic granulocytes, MC: myeloid cells, ProIC: proliferating cells. E) HOX gene expression in hand (n = 8) and knee (n = 4) synovial fibroblasts from RA patients in single cell RNA sequencing analysis. F) Scatter dot plot of pathway enrichment analysis of genes significantly enriched in hand SF (n = 8) and in knee SF (n = 4) (FDR < 0.05; Log fold change +/-1). Blue dots: significantly enriched pathways, darker color corresponds to lower p-values. Grey dots represent pathways with p > 0.05. G) Overlap between genes regulated by HOX genes (HOXD10, HOXD11, HOXD13, HOTTIP, HOTAIR) and genes differentially expressed between hand (n = 8) and knee (n = 4) SF in single cell RNA sequencing. Intersection was assessed using Venn diagram. SF: synovial fibroblasts. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[117, 115, 716, 421]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[67, 85, 737, 101]]<|/det|> +Table 1: Patient's characteristics single cell RNA sequencing of RA patients + +
Wrist/metacarpophalangeal joint (n=8)Knee (n=4)
Age (yrs)59.6 ± 12.058.5 ± 19.2
Female (n)64
RF or anti-CCP positivity (n)52
(n=1 with missing information)
Previous biological treatment (n)73
(n=1 with missing information)
Disease modifying treatment at the time of the biopsy (n)30
(n=1 with missing information)
+ +<|ref|>table_footnote<|/ref|><|det|>[[120, 421, 530, 436]]<|/det|> +Yrs: years, RF: rheumatoid factor, RA. Rheumatoid arthritis + +<|ref|>sub_title<|/ref|><|det|>[[117, 449, 536, 467]]<|/det|> +## Joint and disease specific expression of HOTAIR + +<|ref|>text<|/ref|><|det|>[[115, 480, 813, 762]]<|/det|> +We previously showed that HOTAIR is exclusively expressed in lower limb SF and joints in both mice and human(17). By in situ hybridisation (ISH), we confirmed that HOTAIR is expressed in synovial tissues from knees (Figure 2A), but not from hands (Figure 2B). In the synovium, HOTAIR was expressed mainly in SF, in both the lining and the sublining synovium (Figure 2C). Analysis of HOTAIR expression in knee joints of OA and RA patients showed that HOTAIR was significantly more abundant in OA than in RA knees (Figure 2D/E). The difference in HOTAIR expression between OA and RA was lost in cultured knee SF (p=0.133) (Figure 2F), suggesting that the lower expression of HOTAIR in RA joints was modulated by local factors in vivo. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[144, 138, 777, 550]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 85, 808, 118]]<|/det|> +
Figure 2 HOTAIR is expressed in synovial tissues of lower extremity joints with higher expression in OA than in RA.
+ +<|ref|>text<|/ref|><|det|>[[115, 560, 810, 664]]<|/det|> +A) Representative pictures of synovial tissues from knee joints of OA (left, \(\mathrm{n} = 6\) ) and RA (right, \(\mathrm{n} = 5\) ) patients stained for HOTAIR by in-situ hybridization (ISH). Magnification 100x. Inset shows staining with the anti-sense probe (negative control). +B) Representative picture of HOTAIR in-situ hybridization in synovial tissues of OA and RA hand joints (n = 3). Magnification 100x. C) Double staining of HOTAIR (in blue) and vimentin (in red, left panel) or CD68 (in red, right panel) to assess HOTAIR expression in synovial fibroblasts and macrophages, respectively. Magnification 400x. D) Relative quantification of HOTAIR ISH in OA and RA synovial tissue using ImageJ. Unpaired t test. E) Expression of HOTAIR measured by quantitative PCR in OA and RA synovial tissues. Unpaired t test. F) Expression of HOTAIR measured by quantitative PCR in cultured OA and RA SF. Unpaired t test. SF: synovial fibroblasts, OA: osteoarthritis, RA: rheumatoid arthritis. dct: cycle of threshold target - cycle of threshold housekeeping gene. Mean +/- standard deviation is shown. + +<|ref|>text<|/ref|><|det|>[[115, 713, 810, 895]]<|/det|> +To determine which local factors could influence the expression of HOTAIR in arthritis, we assessed HOTAIR expression in SF upon stimulation by various cytokines and Toll- like receptor (TLR) ligands. Stimulation with most of the inflammatory cytokines decreased HOTAIR expression in SF (Figure 3A). Furthermore, active promoter and enhancer sites at the HOTAIR locus were closed after TNF stimulation (Figure 3B). Consistently, HOTAIR expression inversely correlated with TNF + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 811, 201]]<|/det|> +expression in arthritic synovium (Figure 3C). SF isolated from arthritic, TNF transgenic mice (TG197) expressed lower levels of Hotair than SF from healthy wild- type mice (Figure 3D), showing that the down- regulation of HOTAIR in inflammatory conditions is conserved across species. + +<|ref|>sub_title<|/ref|><|det|>[[118, 216, 523, 234]]<|/det|> +## HOTAIR regulates arthritis relevant pathways + +<|ref|>text<|/ref|><|det|>[[115, 248, 812, 496]]<|/det|> +We next examined the effect of downregulation of HOTAIR in inflammatory states in SF. Since HOTAIR represses gene expression by placing repressive H3K27me3 marks(19), we first analysed the effect of HOTAIR downregulation in SF on H3K27me3. A total of 2,376 genomic sites with differential presence of H3K27me3 marks were identified between control SF and SF silenced for HOTAIR. The frequency of repressive H3K27me3 marks was decreased in promoters near transcription start sites in HOTAIR- silenced SF, showing a clear impact of HOTAIR downregulation on the epigenetic landscape of SF (Figure 3E). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[130, 123, 790, 417]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[66, 85, 810, 120]]<|/det|> +
Figure 3. HOTAIR is downregulated by inflammatory cytokines and shapes the epigenetic landscape of SF.
+ +<|ref|>text<|/ref|><|det|>[[117, 424, 800, 540]]<|/det|> +A) SF were left untreated or stimulated with TNF, LPS, poly I:C (PIC), TGFβ, IL1β, or bacterial lipoprotein (bLP) for 24 hours. Expression of HOTAIR was measured by qPCR. P < 0.001 for TNF, LPS, PIC and TGFβ. IL1β and bLP p > 0.05. Unstimulated samples were set to 1. B) CAGE analysis of enhancers/promoters at the HOTAIR locus in SF from knees (n = 2) in basal conditions and after stimulation with TNF. After TNF stimulation several active promoters (light blue bars) and the enhancer (pink bar) disappear in knee SF. C) Correlation between TNF and HOTAIR expression measured by qPCR in RA synovial tissues and analysed by Spearman correlation. D) Expression of Hotair was measured by qPCR in ankle SF isolated from wildtype (WT) and TNF transgenic (Tg197) C57BL/6 mice and analysed by unpaired t test. E) ChIP sequencing of H3K27me3 marks in SF silenced for HOTAIR (n = 3) and control SF (n = 3) was performed 48h after transfection. TSS: Transcription start site. SF: synovial fibroblasts, RA: rheumatoid arthritis, OA: osteoarthritis, dct: cycle of threshold target - cycle of threshold housekeeping gene. Mean +/- standard deviation is shown. + +<|ref|>text<|/ref|><|det|>[[116, 570, 811, 882]]<|/det|> +We then investigated the transcriptional changes associated with HOTAIR silencing in SF, mimicking an inflammatory arthritis environment. A total of 7,885 genes were differentially expressed between control and HOTAIR- silenced SF (FDR < 0.05), with enrichment of site- specific signaling pathways such as MAPK, Wnt and PI- Akt signaling (Figure 4A, Supplementary Table S3). RNA and ChIP sequencing suggested a HOTAIR- dependent regulation of several collagen transcripts, including COL1A1, COL1A2 and COL3A1, which could, however, not be confirmed by quantitative PCR (Figure 4B). In contrast, there was a trend towards increased procollagen release into supernatants from HOTAIR- silenced SF (Figure 4C). Assuming that prolonged 3D culture systems provide a more natural environment for + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 78, 812, 895]]<|/det|> +the production of extracellular matrix proteins from fibroblasts, we cultured control and HOTAIR- silenced SF in 3D micromass organ systems. After 3 weeks in culture, HOTAIR- silenced SF had deposited significantly more collagen- 1 in micromasses compared to control SF (Figure 4D). Consistent with an increase in extracellular matrix remodelling in HOTAIR- silenced micromasses, several transcripts of the fibroblast growth factor (FGF) family, known to play a key role in extracellular matrix remodelling (27), were regulated by HOTAIR, albeit most of them were downregulated. However, a particular strong upregulation of FGF8 after HOTAIR silencing was observed (Figure 4E). FGF were previously shown to signal via the PI- Akt and the Wnt signaling pathway and to play a crucial role in limb development(28, 29). Measurements of the expression of AKT and its active phoporylated form showed that HOTAIR silencing did not influence AKT levels, but resulted in increased AKT phosphorylation (Figures 4F). Furthermore, we confirmed that silencing of HOTAIR regulated several transcripts in the Wnt signaling pathway (Figure 4G) and repressed the activation of the canonical Wnt pathway in SF (Figure 4H). Finally, we confirmed that HOTAIR regulated several cytokines and transcription factors which were previously implicated in SF activation in RA (IL- 12(30), IL- 6(31), CXCL12(32), PTEN(33), FOXO1(34), RUNX1(35)) (Figure 4I). IL- 12 and IL- 6 secretion by SF increased after the silencing of HOTAIR, while CXCL12 secretion decreased (Figure 4J). Increased IL- 6 levels were also released by micromasses formed with HOTAIR silenced SF compared to control SF (Figure 4K). Taken together, these data clearly show that HOTAIR can regulate pathways relevant to joint inflammation and tissue remodelling in SF. Inhibition of protein translation did not influence the effect of HOTAIR silencing on CTNNB1, FGFR2 and LGR5 (Supplementary Figure S1A), but suppressed the effect of HOTAIR silencing on GSK3B and FGF7 (Supplementary + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 84, 808, 135]]<|/det|> +Figure S1B), suggesting direct as well as indirect mechanisms (e.g. mediated by an intermediate protein) of this regulation. + +<|ref|>image<|/ref|><|det|>[[90, 198, 465, 700]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[116, 150, 615, 167]]<|/det|> +
Figure 4. HOTAIR modulates arthritis relevant pathways.
+ +<|ref|>text<|/ref|><|det|>[[488, 199, 810, 658]]<|/det|> +A) Scatter dot plot of pathway enrichment analysis of genes significantly changed after HOTAIR silencing (FDR < 0.05; Log fold change +/-1). Blue dots: significantly enriched pathways, darker color corresponds to lower p-values. Grey dots represent pathways with p > 0.05. B) Expression of COL1A1 and COL1A2 measured by quantitative PCR between SF transfected with control or HOTAIR targeting GAPmeR after 48h. Control transfected cells were set to 1. C) Pro-Collagen was measured in supernatants of SF transfected with control or HOTAIR targeting GAPmeR after 72h by ELISA. Paired t test. D) Collagen I was stained by immunohistochemistry in 3D micromasses formed with control or HOTAIR silenced SF. Right panel: representative pictures, 25x magnification; left panel: quantification with ImageJ and analysis with paired t test. E) Expression of FGF2, FGF7, FGF8 and FGFR2 measured by quantitative PCR between SF transfected with control or HOTAIR targeting GAPmeR after 48h. Control transfected cells were set to 1. F) Expression of AKT and phosphorylated AKT in SF transfected with control or HOTAIR targeting GAPmeR after 72h. Right panel: representative examples; left panel: densitometric analysis. Paired t test. G) Expression of CTNNB1, LRP6, LGR5 and GSK3B measured by quantitative PCR between SF transfected with control or HOTAIR targeting GAPmeR after 48h. Control transfected cells were set to 1. H) Activation of the canonical Wnt pathway was assessed by luciferase assay with a Wnt reporter gene (Top) or a mutated Wnt reported gene (Fop) as control in SF transfected with control or HOTAIR targeting GAPmeR after 48h. One sample t test. I) Expression of IL12A, IL6, CXCL12, PTEN, FOXO1 and RUNX1 measured by quantitative PCR between SF transfected with control or HOTAIR targeting GAPmeR after 48h. Control transfected cells were set to 1. J) Selected proteins were measured in supernatants of SF transfected with control or HOTAIR targeting GAPmeR after 48h (CXL12) or 72h (IL-6, IL-12-p35) by ELISA. Paired t test. K) IL-6 was measured in supernatants of micromasses after 3 weeks by ELISA. Paired t test. Data are representative for at-least 2 experiments. Mean +/- standard deviation is shown. + +<|ref|>sub_title<|/ref|><|det|>[[118, 723, 589, 741]]<|/det|> +## Changes in HOTAIR expression modulate SF subtypes + +<|ref|>text<|/ref|><|det|>[[116, 755, 810, 900]]<|/det|> +Since several marker genes for recently described SF subpopulations(15, 36- 38) were affected by HOTAIR silencing, we wondered whether the observed transcriptional changes after HOTAIR silencing might be connected to changes in the proportion and the formation of SF subtypes. Four subpopulations of SF have been described by scRNAseq (15, 36- 38): PRG4+ SF are considered as lining SF, CXCL12+ SF are + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 80, 813, 730]]<|/det|> +characterised by increased expression of CXCL12, CD74 and IL- 6, POSTN+ SF show high production of extracellular matrix proteins such as collagens and periostin, and CXCL14+ SF are CD34+ SF. We integrated scRNAseq data from control and HOTAIR- silenced SF cultures with our previous meta- analysis of synovial tissue scRNAseq data from five different datasets(38) (Figure 5). In line with published data, PRG4+ SF and CXCL12+ SF subtypes were partially lost during culture (Figure 5B)(14). The main SF subpopulations in culture were POSTN+ SF, CXCL14+ SF, and a mixed- marker cell population consisting of proliferating cells (proLSF) (Figures 5A- E). Silencing of HOTAIR indeed resulted in a shift in the distribution of SF subpopulations with an enrichment in PRG4+ SF and a decrease in POSTN+ SF (Figure 5C). COL1A1 and COL1A2 expression was mainly increased in PRG4+ SF, with no change or a decrease in POSTN+ SF (Figure 5F): This might reflect a transcriptional switch from POSTN+ SF to collagen producing PRG4+ SF and might explain the inconsistent results seen in the bulk analysis of COL transcripts (Figure 4B). CXCL12 downregulation was found in CXCL14+ SF, which produce CXCL12, but to a lesser extent than CXCL12+ SF (Figure 5D/E/F). IL- 6 upregulation was observed across all SF subtypes (Figure 5F). From this analysis, we concluded that HOTAIR played a role in the formation of SF subtypes, for example, by regulating differential collagen production between the SF subtypes. However, there were also regulatory mechanisms of HOTAIR that were evident in all SF subtypes. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 126, 733, 775]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[120, 85, 671, 101]]<|/det|> +
Figure 5. Changes in HOTAIR expression modulate SF subtypes.
+ +<|ref|>text<|/ref|><|det|>[[117, 787, 810, 856]]<|/det|> +A) Single cell RNA sequencing (scRNAseq) data from synovial tissues were integrated with scRNAseq data from cultured SF transfected with control or HOTAIR targeting GapmeR (n = 3). UMAP representation of the different SF subtypes is shown. B) UMAP representation of the distribution of cells in control and HOTAIR silenced SF (cultured cells only). C) Proportions of the different SF subtypes in control and HOTAIR silenced SF. D) Selected marker gene expression in the different SF subtypes. E) Heatmap with the top 5 marker genes for each subtype. F) Change of expression of selected genes within the different SF subtypes. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 83, 755, 102]]<|/det|> +## HOTAIR downregulation alters key functions in SF and surrounding cells + +<|ref|>text<|/ref|><|det|>[[115, 113, 812, 150]]<|/det|> +Next we aimed to decipher the functional changes of SF induced by HOTAIR downregulation upon inflammation. Real- time analysis of attachment and growth of + +<|ref|>text<|/ref|><|det|>[[115, 160, 812, 179]]<|/det|> +SF in vitro did not show any changes in adhesion (Figure 6A) and proliferation (Figure + +<|ref|>text<|/ref|><|det|>[[115, 190, 812, 208]]<|/det|> +6C), but a decrease in spreading of HOTAIR silenced SF (Figure 6B). Accordingly, + +<|ref|>text<|/ref|><|det|>[[115, 219, 812, 237]]<|/det|> +silencing of HOTAIR resulted in decreased migration of SF (Figure 6D and Movie 1). + +<|ref|>text<|/ref|><|det|>[[115, 248, 812, 266]]<|/det|> +These data are in line with the findings that HOTAIR regulated genes were enriched + +<|ref|>text<|/ref|><|det|>[[115, 277, 812, 295]]<|/det|> +in the pathways "actin cytoskeleton" and "Wnt signaling" (Table S3 and Figure 4G/H), + +<|ref|>text<|/ref|><|det|>[[115, 305, 812, 323]]<|/det|> +which were previously linked to tissue remodelling and cell migration(39, 40). + +<|ref|>text<|/ref|><|det|>[[115, 334, 812, 352]]<|/det|> +Furthermore, HOTAIR silencing increased Fas- induced apoptosis in SF (Figure 6E), + +<|ref|>text<|/ref|><|det|>[[115, 363, 780, 382]]<|/det|> +as also indicated by the pathway ('apoptosis') in the pathway analysis (Table S3). + +<|ref|>text<|/ref|><|det|>[[115, 392, 812, 410]]<|/det|> +Since osteoclastogenesis is of major importance in RA and was differentially + +<|ref|>text<|/ref|><|det|>[[115, 421, 812, 439]]<|/det|> +expressed between hand and knee SF (Table S2), we assessed the effect of silencing + +<|ref|>text<|/ref|><|det|>[[115, 450, 812, 468]]<|/det|> +HOTAIR in SF on osteoclastogenesis and osteoclast function. Co- culture of + +<|ref|>text<|/ref|><|det|>[[115, 479, 812, 497]]<|/det|> +differentiating monocytes with HOTAIR silenced SF, but not the addition of + +<|ref|>text<|/ref|><|det|>[[115, 508, 812, 526]]<|/det|> +supernatants from HOTAIR silenced SF, decreased osteoclast formation (Figure 6F), + +<|ref|>text<|/ref|><|det|>[[115, 537, 812, 555]]<|/det|> +HOTAIR in SF on osteoclastogenesis and osteoclast function. Co- culture of + +<|ref|>text<|/ref|><|det|>[[115, 566, 812, 584]]<|/det|> +differentiating monocytes with HOTAIR silenced SF, but not the addition of + +<|ref|>text<|/ref|><|det|>[[115, 595, 812, 613]]<|/det|> +supernatants from HOTAIR silenced SF, decreased osteoclast formation (Figure 6F), + +<|ref|>text<|/ref|><|det|>[[115, 624, 812, 642]]<|/det|> +suggesting that cell- cell contact was needed to inhibit osteoclastogenesis. In contrast, + +<|ref|>text<|/ref|><|det|>[[115, 653, 812, 671]]<|/det|> +osteoclasts in co- culture with HOTAIR silenced SF, as well as incubated with + +<|ref|>text<|/ref|><|det|>[[115, 682, 812, 700]]<|/det|> +supernatants showed decreased osteoclast activity, which could be an effect of + +<|ref|>text<|/ref|><|det|>[[115, 710, 718, 728]]<|/det|> +decreased secretion of CXCL12 by HOTAIR silenced SF(41) (Figure 6G). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[122, 113, 748, 777]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[120, 85, 650, 101]]<|/det|> +
Figure 6. HOTAIR silencing induces functional changes in SF.
+ +<|ref|>text<|/ref|><|det|>[[117, 784, 810, 907]]<|/det|> +Real- time measurements of SF A) adhesion (0- 16 h), B) spreading (17- 127 h) and C) proliferation (132- 245 h) in HOTAIR silenced and control SF \((n = 3)\) . Two- way ANOVA. D) Measurement of open area covered over time by control SF or HOTAIR silenced SF \((n = 6)\) in scratch assay. Two- way ANOVA. E) Caspase 3/7 activation in untransfected, control and HOTAIR GapmeR transfected SF after 48h of transfection. RLU = relative luminescence units after background subtraction. One- way ANOVA with Bonferroni's correction. F) Left panel: representative pictures of tartrate- resistant acid phosphatase (TRAP) staining of osteoclasts differentiated from monocytes by co- culture (upper panel) or incubation with supernatants (SN) of control or HOTAIR transfected SF (lower panel). Magnification 400 x. Right panel: Quantification of TRAP+ cells in the described conditions. Paired t test with Bonferroni correction. G) Left panel: representative pictures of resorption areas after incubation of bone slices with osteoclasts differentiated by co- culture (upper panel) or incubation with supernatants of control or HOTAIR transfected SF (lower panel). Right panel: Quantification of resorption areas in the described conditions. Paired t test with Bonferroni correction. The results are representative for at- least 2 experiments. Mean +/- standard deviation is shown. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 78, 813, 667]]<|/det|> +Since the levels of several chemokines and cytokines were affected by HOTAIR silencing (Supplementary Table S3), we compared the chemotactic activity of supernatants derived from control or HOTAIR silenced SF. Despite a similar amount of healthy peripheral blood mononuclear cells (PBMCs) migrating towards conditioned supernatants from controls and HOTAIR silenced SF (Figure 7A), we observed a shift in the cellular composition of the migrated PBMCs with an increased number of CD19+ B cells (Figure 7B) and a decreased number of CD14+ monocytes (Figure 7C) using supernatants from HOTAIR silenced SF. A slight increase was also seen for the chemotaxis of CD3+ T cells (Figure 7D). In line with these results, synovia of RA and OA patients with low HOTAIR expression were characterized by higher CD20+ B cell (Figure 7E) and, in particular, CD138+ plasma cell infiltration (Figure 7F) and a lymphoid pathotype (Figure 7G). Consistently, in the early RA cohort (PEAC) (PEAC (qmul.ac.uk))(42), a low synovial HOTAIR expression in synovium was associated with a trend towards a lymphoid pathotype (Figure 7H) and HOTAIR expression was negatively correlated with CD138+ plasma cell infiltrates (r: 0.27, padj: 0.023) (Figure 7I). In summary, these data support the notion that expression levels of HOTAIR in SF can shape the influx of immune cells in the synovium in arthritis and vice-versa. + +<|ref|>sub_title<|/ref|><|det|>[[115, 679, 810, 730]]<|/det|> +## HOTAIR site-specific expression may shape inflammatory response in other organs + +<|ref|>text<|/ref|><|det|>[[115, 742, 812, 893]]<|/det|> +Since HOX gene expression is site specifically expressed in stromal cells of several organs and tissues, we wondered whether HOTAIR might shape the inflammatory response in other tissues than joints. Site- specific expression of HOTAIR was already shown in human skin where it also follows the upper vs lower body part pattern(18). In addition, we measured site- specific expression of Hotair in mouse spine and found + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[66, 83, 810, 202]]<|/det|> +increased expression in lumbal compared to cervical spine \((\mathrm{p} = 0.009)\) (Figure 7J). Furthermore, in the different anatomic compartments of the gastrointestinal tract, Hotair showed site-specific expression with higher expression in the distal parts of the intestines compared to stomach and the small intestines (Figure 7K). + +<|ref|>image<|/ref|><|det|>[[156, 234, 680, 812]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[67, 216, 653, 233]]<|/det|> +
Figure 7. HOTAIR silencing increases lymphocyte chemotaxis.
+ +<|ref|>text<|/ref|><|det|>[[67, 826, 810, 907]]<|/det|> +A) The amounts of PBMCs migrating through a transwell system towards conditioned supernatants from SF transfected with control GapmeR or HOTAIR GapmeR. Paired t test, \(\mathrm{p} = 0.19\) . B-D) The percentage of B) CD19, C) CD14 and D) CD3 positive cells was measured in PBMCs that had migrated towards supernatants of control or HOTAIR silenced SF. Paired t test. E-G) Expression levels of HOTAIR were measured by qPCR in synovial tissues from rheumatoid arthritis (RA) and osteoarthritis (OA) with E) high ( \(\mathrm{CD20 + }\) score \(\geq 2\) ) or low amounts of \(\mathrm{CD20 + }\) B cells (unpaired t test, \(\mathrm{p} = 0.10\) ), F) with high ( \(\mathrm{CD138 + }\) score \(\geq 2\) ) or low amounts of \(\mathrm{CD138 + }\) plasma cells (unpaired t test), G) with pauci-immune, myeloid or lymphoid pathotypes (one-way ANOVA, \(\mathrm{p} = 0.11\) ). dct: cycle of threshold target - cycle of threshold housekeeping gene. H-I) Expression levels of HOTAIR in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 84, 810, 128]]<|/det|> +RNA sequencing in synovial tissues from the PEAC cohort according to H) the synovial pathotype and to I) CD138 infiltrates. J- K) Expression of Hotair was measured by qPCR in C57/BL6 mice \((n = 3)\) J) in cervical, thoracal and lumbar spine and in K) different parts of the gut (stomach, small intestine, caecum, colon and rectum). Heatmap of the dct of HOTAIR expression according to the localisation are presented. Mean \(+ / -\) standard deviation is shown. + +<|ref|>sub_title<|/ref|><|det|>[[118, 160, 245, 177]]<|/det|> +## DISCUSSION + +<|ref|>text<|/ref|><|det|>[[113, 192, 812, 610]]<|/det|> +Here, we show that HOTAIR is a major regulator of site- specific gene expression in SF and modulates a series of highly relevant signalling pathways and SF functions in arthritis. HOTAIR- modulated changes in SF gene expression and function were associated with changes between hand and knee arthritis in RA, suggesting that HOTAIR may shape the phenotype of arthritis in lower extremity joints. By showing that an embryonically imprinted, site- specific factor can regulate inflammation- related signalling pathways, our data support the concept that anatomically defined features of the local stroma can influence the susceptibility and manifestation of inflammation. Here, we showed that HOTAIR expression in SF is decreased after stimulation by local inflammatory factors, which could explain this decreased expression in RA SF. Other recent studies have shown that HOTAIR expression could be regulated by various factors in the local microenvironment, such as hypoxia(43), hormones(24) or inflammatory factors(44). + +<|ref|>text<|/ref|><|det|>[[115, 619, 812, 835]]<|/det|> +The ability of external factors to influence HOTAIR expression supports the idea that embryonic site- specific expression of HOTAIR is used to trigger a locally distinct and anatomically restricted stress response. Interestingly, it has been suggested that HOTAIR may also be mechanoresponsive in response to stretch(45). Given the increased expression of HOTAIR in the lower limbs and lumbar spine, load and mechanosensing could be additional factors that regulate site- specific response pathways via HOTAIR. + +<|ref|>text<|/ref|><|det|>[[115, 848, 810, 900]]<|/det|> +In our study, inflammation- induced downregulation of HOTAIR modulated several inflammatory response pathways in SF, such as the MAPK, PI- Akt and canonical Wnt + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 812, 333]]<|/det|> +pathways. Consistent with our results, HOTAIR silencing inhibited the canonical Wnt pathway in gastric and pancreatic cancer cells(46, 47) and in OA chondrocytes(48, 49). In OA chondrocytes(49), HOTAIR has been shown to act directly on Wnt inhibitory factor 1 (WIF- 1) by increasing histone H3K27 trimethylation in the WIF- 1 promoter, leading to WIF- 1 repression that promotes activation of the Wnt/β- catenin pathway. Consistent with this study, our own results based on the adjunction of cycloheximide suggest an indirect mechanism underlying the HOTAIR- mediated regulation of the Wnt pathway. + +<|ref|>text<|/ref|><|det|>[[115, 345, 813, 625]]<|/det|> +Activation of the Wnt pathway is a characteristic of the pauci- immune subtype of synovitis in RA, whereas more inflammatory pathways such as PI- Akt have been found to be activated in lymphoid/myeloid synovial pathologies(6, 8, 9). Consistently, lower levels of HOTAIR in synovial tissue were associated with a lymphoid pathotype in our study. Furthermore, HOTAIR silencing exerted a chemotactic effect on lymphocytes in vitro. Thus, it can be speculated that HOTAIR acts as a stromal regulator of the inflammatory tissue response, whose downregulation under conditions with high levels of TNF might promote the development of a lymphocyte- dominated inflammatory response. + +<|ref|>text<|/ref|><|det|>[[115, 639, 813, 889]]<|/det|> +Studies analysing the differences between RA in different joints are rare. Our results suggest that hand RA is more likely than knee RA to show the classic signs of synovitis described for RA, whereas the fibroid pathotype of synovitis was more common in knee RA than in hand RA. Refractory, difficult- to- treat arthritis has been associated with this non- inflammatory fibroid pathotype (10), however, up to now it is not known whether lower limb synovitis is more often resistant to immunosuppressive therapy. In depth analysis of the characteristics of knee RA has suggested severe cartilage destruction but less bone erosion compared to what is known from RA of the hand(50). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 78, 813, 895]]<|/det|> +Similarly, OA of the hand tends to be more erosive than OA of the knee(51). The less destructive phenotype of RA and OA in knees may be due to protective mechanisms exerted by the joint- specific stroma. Both, our comparison of knee and hand RA as well as our analysis of HOTAIR function pointed towards decreased osteoclastogenesis in knees compared to hands. Co- culture of differentiated monocytes with HOTAIR- silenced SF, but not the addition of HOTAIR- silenced SF supernatants, decreased osteoclast formation, suggesting that cell- cell contact was necessary to inhibit osteoclastogenesis. Thus, the decrease in osteoclastogenesis after HOTAIR silencing could be related to the reduced migratory function of SF silenced for HOTAIR, which was also observed in another study(44). Another explanation is that the decrease in osteoclastogenesis is mediated by the decreased CXCL12 secretion following HOTAIR silencing(52), as osteoclasts co- cultured with HOTAIR silenced SF but also incubated with supernatants showed decreased osteoclast activity(41). One limitation of our study is that with the available data we cannot directly show how down- regulation of HOTAIR during inflammation affects the inflammatory response in a joint- specific manner. In vivo confirmation of our hypotheses is hampered by the fact that Hotair does not have the same function in morphological development in mice and humans(53), and it is therefore questionable whether the function in regulating inflammatory pathways is conserved. These differences between the species might be explained by biomechanical and anatomical differences between quadrupedal walking in mice versus bipedal walking in humans(45, 54). If our hypothesis of an influence of site- specific embryonic factors on inflammatory responses is valid, species- specific differences in limb formation, anatomy and biomechanics may underlie the limited translation of preclinical studies in mice to human arthritis. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 80, 813, 666]]<|/det|> +Beyond the joint, HOTAIR appears to be involved in the site- specific regulation of inflammation in other organs. Thus, it has recently been identified as a major regulator of region- specific development of adipose tissue, which is associated with site- specific metabolic complications, with exclusive expression in gluteofemoral subcutaneous adipose tissue (24, 55). In addition, HOTAIR is mostly expressed in distal dermal fibroblasts(18). Consistently, we observed site- specific expression of HOTAIR in the spine and intestine with increased expression in the lumbar spine and distal parts of the intestines. In spondyloarthritis, it has been suggested that involvement of the lumbar spine is more frequent(56) and more severe with more bone bridges compared to cervical involvement(57). Furthermore, Wnt signaling plays a crucial role in the formation of bone bridges in spondyloarthritis(58). Similarly, inflammatory bowel disease encompasses two types of idiopathic intestinal disease that are differentiated by their location and the depth of involvement in the bowel wall(59). Ulcerative colitis most commonly affects the rectum, whereas Crohn's disease most often affects the terminal ileum and colon. Interestingly, Wnt signaling has been identified as a key regulatory pathway in the intestinal mucosa(60). Thus, important future work will be to elucidate whether the site- specific expression of HOTAIR underlies the development of site- specific phenotypes of these inflammatory diseases. + +<|ref|>text<|/ref|><|det|>[[115, 675, 813, 825]]<|/det|> +In conclusion, we suggest that the phenotype and severity of inflammation are modulated by the embryonic imprinted local stromal gene signature. Further investigation into joint specific factors favouring and shaping the development of arthritis will improve understanding of the pathogenesis of arthritis and could lead to the development of specific therapies targeting joint specific signaling pathways. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[117, 86, 226, 101]]<|/det|> +## METHODS + +<|ref|>sub_title<|/ref|><|det|>[[117, 135, 194, 150]]<|/det|> +### Patients + +<|ref|>text<|/ref|><|det|>[[117, 167, 812, 383]]<|/det|> +Synovial tissues were obtained from OA and RA patients undergoing joint replacement surgery at the Schulthess Clinic Zurich, Switzerland or from ultra-sound guided joint biopsies. RA patients fulfilled the 2010 ACR/EULAR (American College of Rheumatology/European League Against Rheumatism) criteria for the classification of RA(61) whereas OA was considered in cases of chronic pain and manifest radiographic signs of OA(62) without any underlining inflammatory rheumatic disease. Patients' characteristics are given in Table 2. + +<|ref|>table_caption<|/ref|><|det|>[[117, 398, 387, 414]]<|/det|> +Table 2: Patient's characteristics + +<|ref|>table<|/ref|><|det|>[[117, 427, 810, 907]]<|/det|> +
In-situ hybridizationOA (n=6)RA (N=5)
Age (yrs)65.0 ± 6.968.0 ± 8.1
Female (n)45
Disease duration (yrs)23.0 ± 10.6
Knee, hip, feet6, 0, 05, 0, 0
Positive RF60%
CRP (mg/L)2.0 ± 1.922.0 ± 30.3
RA treatment2 TNF-blocker, 2 steroids, 1 none
qPCR in synovial tissuesOA (n=12)RA (N=14)
Age (yrs)69.0 ± 11.569.0 ± 12.2
Female (n)812
Disease duration (yrs)19.0 ± 12.9
Knee, hip, feet9, 3, 010, 1, 3
Positive RF100%
CRP (mg/L)2.4 ± 2.611.0 ± 14.7
RA treatment5 TNF-blocker, 7 csDMARDS, 2 unknown
qPCR in cultured SFOA (n=7)RA (N=8)
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[117, 81, 810, 295]]<|/det|> +
Age (yrs)64 ± 8.270 ± 10.5
Female (n)35
Disease duration (yrs)15 ± 16.3
Knee, hip, feet4, 3, 04, 3, 1
Positive RF83%
CRP (mg/L)2.6 ± 8.02.8 ± 6.4
RA treatment1 TNF-blocker, 5 csDMARDS, 1 steroid, 1 unknown
+ +<|ref|>text<|/ref|><|det|>[[117, 294, 772, 306]]<|/det|> +OA: osteoarthritis, RA: rheumatoid arthritis, RF: rheumatoid factor, csDMARDS. Conventional disease modifying drugs + +<|ref|>title<|/ref|><|det|>[[117, 355, 241, 370]]<|/det|> +# Culture of SF + +<|ref|>sub_title<|/ref|><|det|>[[117, 389, 211, 403]]<|/det|> +## Human SF + +<|ref|>text<|/ref|><|det|>[[117, 421, 810, 730]]<|/det|> +Synovial tissues were digested with dispase (37 °C, 1 h) and SF were cultured in Dulbecco's modified Eagle's medium (DMEM; Life Technologies) supplemented with 10% fetal calf serum (FCS), 50 U ml-1 penicillin/streptomycin, 2 mM L-glutamine, 10 mM HEPES and 0.2% amphotericin B (all from Life Technologies). For functional assays, vitamin C (50 μg/ml) was added to the culture medium. Purity of SF cultures was confirmed by flow cytometry showing the presence of the fibroblast surface marker CD90 (Thy-1) and the absence of leukocytes (CD45), macrophages (CD14; CD68), T lymphocytes (CD3), B lymphocytes (CD19) and endothelial cells (CD31). Cell cultures were negative for mycoplasma contamination as assessed by MycoAlert mycoplasma detection kit (Lonza). SF from passages 5 to 8 were used. + +<|ref|>sub_title<|/ref|><|det|>[[117, 767, 211, 780]]<|/det|> +## Murine SF + +<|ref|>text<|/ref|><|det|>[[117, 799, 810, 844]]<|/det|> +Primary mouse SF were isolated and cultured for four passages, as previously described (63). + +<|ref|>sub_title<|/ref|><|det|>[[117, 882, 299, 896]]<|/det|> +## Histological analysis + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 812, 465]]<|/det|> +Formalin- fixed, paraffin- embedded synovial tissues of RA or OA patients were cut, put on slides and stained with hematoxylin/eosin. 36 synovial tissues from hands (22 from joint replacement and 14 from synovial biopsies) and 27 from knee (18 from joint replacement and 9 from synovial biopsies) were assessed. Synovitis score was assessed by evaluation of the thickness of the lining cell layer, the cellular density of synovial stroma and leukocyte infiltration as described by Krenn et al.(25). Vascularization was assessed by counting the amount of CD31+ cells on 5 consecutive pictures on 20x objective. Synovial tissue were stained with CD3 (ab16669; Abcam), CD4 (ab183685; Abcam), CD8 (ab22378; Abcam), CD31 (ab28364; Abcam), CD20 (M075501- 2, Agilent), CD138 (Clone MI15, Agilent) and CD68 (M081401; Dako), to stratify them into lymphoid, myeloid and fibroid pathotypes according to previously published histological features(7, 9, 25). + +<|ref|>sub_title<|/ref|><|det|>[[118, 479, 247, 496]]<|/det|> +## Gene silencing + +<|ref|>text<|/ref|><|det|>[[115, 508, 812, 823]]<|/det|> +Hand SF were transfected with \(50\mathrm{nM}\) antisense LNA HOXD10 (Qiagen, Sequence: 5'- TGT CTG CGC TAG GTG G- 3'), HOXD11 (Qiagen, Sequence: 5'- TGC TAG CGA AGT CAG A- 3'), HOXD13 (Qiagen, Sequence: 5'- CAT CAG GAG ACA GTA T- 3') or HOTTIP (HOTTIP1 Qiagen, Sequence: 5'- TCG GAA AAG TAA GAG T- 3' and HOTTIP2 Qiagen, Sequence: 5'- TAC CTA AGT GTG CGA A- 3') GapmeR. Knee SF were transfected with \(50\mathrm{nM}\) antisense LNA HOTAIR GapmeR (Qiagen, Sequence: 5'- AGG CTT CTA AAT CCG T- 3'). Transfections were performed using Lipofectamine 2000 (Invitrogen) according to the manufacturer's instructions. Antisense LNA GapmeR Negative Control A (Cat No 300610) was used as transfection control. + +<|ref|>sub_title<|/ref|><|det|>[[118, 855, 359, 873]]<|/det|> +## Single-cell RNA sequencing + +<|ref|>text<|/ref|><|det|>[[118, 888, 477, 905]]<|/det|> +scRNAseq of hand and knee synovial tissues + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 812, 565]]<|/det|> +ScRNAseq sequencing was performed on ultrasound- guided joint biopsies of hand (n = 8) or knee (n = 4) joints of RA patients (Patient's characteristics in Table 1). Tissues were processed as previously described(38). In brief, tissues were mechanically minced and enzymatically digested using Liberase TL (Roche). 10'000 unsorted synovial cells (viability >80%) per patient were prepared for single cell analysis using the Chromium Single Cell 3' GEM, Library and Gel Bead Kit v3.1, the Chromium Chip G Single Cell Kit and the Chromium controller (all 10x Genomic). Libraries were sequenced on the Illumina NovaSeq6000 instrument to a sequence depth of 20,000 to 70,000 reads per cell. CellRanger (v2.0.2) from 10x Genomics was used to demultiplex, align the reads to Ensembl reference build GRCh38.p13 and collapse unique molecular identifiers (UMIs). The standard Seurat protocol(64) was used for further analysis. Gene expression between hand and knee was compared. Pathway enrichment analyses of genes differentially expressed between SF from hands and knees were performed using Enrichr (all genes with FDR < 0.05, log fold change +/- 1) (65). The scatter dot plots were created with the Enrichr Appyter. + +<|ref|>text<|/ref|><|det|>[[115, 592, 556, 610]]<|/det|> +scRNAseq of control SF and SF silenced for HOTAIR + +<|ref|>text<|/ref|><|det|>[[115, 623, 812, 907]]<|/det|> +scRNAseq in cultured SF transfected with either control or HOTAIR targeting GapmeR (n = 3) (Patient's characteristics in Table 3) was performed. SF were washed and counted on a LUNA automated cell counter (Logos Biosystems). 15'000 unsorted SF (viability 60- 88%) per patient were prepared for single cell analysis using the Chromium Single Cell 3' GEM, Library and Gel Bead Kit v3.1, the Chromium Chip G Single Cell Kit and the Chromium controller (all 10x Genomic). Libraries were sequenced on the Illumina NovaSeq6000 instrument to a sequence depth of 20,000 to 70,000 reads per cell. CellRanger (v2.0.2) from 10x Genomics was used to demultiplex, align the reads to Ensembl reference build GRCh38.p13 and collapse + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 812, 530]]<|/det|> +unique molecular identifiers (UMIs). scRNAseq for control SF and SF silenced for HOTAIR were integrated with SF of publicly available datasets (15, 36, 37, 66) and two in-house datasets(38). The standard Seurat (version 4.0) protocol(64) was followed for analysis. Quality control included the exclusion of cells with \(>25\%\) mitochondrial reads and \(<5\%\) ribosomal reads; exclusion of mitochondrial, ribosomal and hemoglobin genes and inclusion of only cells with at least 200 detected genes. For the integration of all datasets, we applied harmony(67) within the standard Seurat workflow and performed the analysis as previously described(38). Cell clustering was computed with 30 principal components and resolution of 0.1. Marker genes were identified by Wilcoxon Rank Sum test and log fold change of 0.25. Differential gene expression analysis for selected genes (COL1A1, COL1A2, CXCL12 and IL6) between control and HOTAIR targeting GapmeR transfected SF were performed within each SF cluster applying Wilcoxon Rank Sum test and FDR p-value adjustment for multiple testing. + +<|ref|>table<|/ref|><|det|>[[232, 622, 694, 792]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[115, 559, 810, 595]]<|/det|> +Table 3: Patient's characteristics (scRNAseq of control SF and SF silenced for HOTAIR) \((n = 3)\) + +
Age (yrs)72.7 ± 10.2
Female (n)3
Osteoarthritis3
Knee3
CRP (mg/L)1.0 ± 1.0
TreatmentNo
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[117, 84, 360, 102]]<|/det|> +## In Situ Hybridization (ISH) + +<|ref|>text<|/ref|><|det|>[[118, 117, 308, 134]]<|/det|> +Construction of probes + +<|ref|>text<|/ref|><|det|>[[115, 149, 812, 494]]<|/det|> +PCR on cDNA generated from total RNA of knee SF was used to produce an amplicon of 267 base pairs of HOTAIR (see Table S4). Amplicons were cloned into pPCR- Script Amp SK (+) plasmids using the PCR- Script Amp cloning kit (Agilent Technologies). Plasmids were amplified and purified with the PureLink MiniPrep kit (Thermo Fisher Scientific). Plasmids were linearized using restriction enzymes (EcoRI or NotI; both New England Biolabs) and purified with the QIAquick PCR Purification Kit (Qiagen). DIG- labeled HOTAIR probes were prepared by in vitro transcription with RNA polymerases and plasmid vectors containing target transcript sequences. Linearized plasmid DNA (10 \(\mu\) l) was used as a template, and RNA probes were synthesized with T7 or T3 RNA polymerase (Roche) and DIG Labeling Mix (Roche) for 100 min at \(37^{\circ}\mathrm{C}\) . + +<|ref|>sub_title<|/ref|><|det|>[[118, 527, 286, 544]]<|/det|> +## In-situ hybridization + +<|ref|>text<|/ref|><|det|>[[115, 558, 812, 905]]<|/det|> +HOTAIR expression was examined by ISH in paraffin- embedded synovial tissue from OA and RA patients (for patients' characteristics see Table 2). All steps prior to and during hybridization were conducted under RNase- free conditions. Sections were deparaffinized, and incubated with HCl (20 min) and PFA \(4\%\) (10 min). Then sections were treated with trypsin (1 mg/ml) at \(37^{\circ}\mathrm{C}\) for 30 min. The slides were incubated with 2X SSC 5 min and washed twice with triethanolamine- HCl solution. The sections were acetylated for 20 min with \(0.25\%\) acetic anhydride in 0.1 M triethanolamine (pH 8.0) and washed twice in triethanolamine- HCl. Following incubation with hybridization buffer for 1 h at RT, the sections were incubated with hybridization solution which contained 1:10 diluted DIG- labelled probes in hybridization buffer (50% deionised formamide, 40% dextran sulfate/SSC solution, 50x Denhardt's solution, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 81, 813, 595]]<|/det|> +\(5\%\) preheated herring sperm DNA and \(250\mu \mathrm{g / ml}\) tRNA). Slides were then incubated in a humidified chamber at \(50^{\circ}\mathrm{C}\) overnight. After hybridization, slides were washed with 5X SSC at \(50^{\circ}\mathrm{C}\) (20 min), with \(50\%\) formamide in 2X SSC at \(50^{\circ}\mathrm{C}\) (30 min) and two times with STE buffer (500 mM NaCl, 1mM EDTA, 20mM TRIS- HCL pH 7.5). Sections were treated with RNase A (40 \(\mu \mathrm{g / ml}\) ) in STE buffer at \(37^{\circ}\mathrm{C}\) for \(1\mathrm{h}\) , followed by successive washing with STE buffer (RT), 2X SSC (RT), buffer 1 ( \(0.2\%\) SDS in 1X SSC, \(50^{\circ}\mathrm{C}\) ), buffer 2 ( \(0.2\%\) SDS in \(0.5\mathrm{X}\) SSC; \(50^{\circ}\mathrm{C}\) )) and buffer 3 ( \(0.2\%\) SDS in \(0.1\mathrm{X}\) SSC; \(50^{\circ}\mathrm{C}\) ). Slides were incubated with \(2\%\) horse serum at RT for \(30\mathrm{min}\) . Then the slides were incubated in a humidified chamber at RT for \(1\mathrm{h}\) with sheep anti- digoxigenin- AP- Fab (Roche), diluted to 1:250 in TBS- T containing \(1\%\) blocking reagent. Slides were washed with TBS- T and stained with nitro blue tetrazolium/5- bromo- 4- chloro- 3- indolylphosphate (Roche) in the dark. The intensity of staining was quantified with ImageJ software (http://rsbweb.nih.gov/ij/docs/examples/stained- sections/index.html). Negative controls were conducted by the substitution of sense for anti- sense probes or by the omission of anti- sense probes in the hybridization solution. + +<|ref|>sub_title<|/ref|><|det|>[[118, 622, 321, 640]]<|/det|> +## Immunohistochemistry + +<|ref|>text<|/ref|><|det|>[[115, 653, 813, 871]]<|/det|> +For double staining of ISH slides, tissue sections were pre- treated with proteinase K 10 min at \(37^{\circ}\mathrm{C}\) . Endogenous peroxidase activity was disrupted with \(3\% \mathrm{H}_2\mathrm{O}_2\) . Slides were permeabilized with \(0.1\%\) Triton in PBS. Nonspecific protein binding was blocked with \(10\%\) goat serum in antibody diluent (DakoCytomation) for \(1\mathrm{h}\) . Mouse anti- human CD68 (clone KP1; DakoCytomation) antibodies, mouse anti- human vimentin (EPR3779; Abcam) antibodies or mouse IgG1 were applied over night at \(4^{\circ}\mathrm{C}\) . Slides were washed in PBS- T ( \(0.05\%\) Tween 20 in PBS) and incubated with + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 811, 201]]<|/det|> +biotinylated goat anti- mouse antibodies (Jackson ImmunoResearch). The signal was amplified with ABC reagent and detected with AEC (Vector laboratories). Staining was imaged on a Zeiss Imager.Z1 (25x magnification) and quantified with ImageJ using brightness values. + +<|ref|>sub_title<|/ref|><|det|>[[118, 231, 620, 250]]<|/det|> +## Quantitative Real-time polymerase chain reaction (qPCR) + +<|ref|>text<|/ref|><|det|>[[115, 262, 812, 381]]<|/det|> +Snap frozen synovial tissues were minced and total RNA was isolated using the miRNAsey Mini kit (Qiagen) including on- column DNaseI digestion. From cultured cells, total RNA was isolated using the Quick- RNA MicroPrep Kit (Zymo) including on- column DNaseI digestion. + +<|ref|>text<|/ref|><|det|>[[115, 394, 812, 675]]<|/det|> +Total RNA was reversed transcribed and qPCR was performed using SYBR green (Life Technologies) or TaqMan probes for the detection of HOTAIR. Primer sequences are available in Tables S4 (human) and S5 (mouse). Changes after cycloheximide adjunction (10ug/ml 6 h and 24 h) were evaluated (n = 3). No template control samples, dissociation curves and samples containing the untranscribed RNA were measured in parallel as controls. Data were analyzed with the comparative \(\mathrm{C_T}\) method and presented as \(\Delta \mathrm{CT}\) or \(2^{-\Delta \Delta \mathrm{CT}}\) as described(68). Constitutively expressed HPRT was measured for internal standard sample normalization in humans and beta2- microglobulin in mouse SF. + +<|ref|>sub_title<|/ref|><|det|>[[118, 707, 247, 724]]<|/det|> +## SF stimulation + +<|ref|>text<|/ref|><|det|>[[115, 738, 812, 857]]<|/det|> +SF were stimulated with human recombinant TNF (10 ng/ml; R&D Systems), human recombinant IL1β (1 ng/ml; R&D Systems), lipopolysaccaride (LPS) from Escherichia coli J5 (100 ng/ml; List Biological Laboratories), polyI- C (PIC) (10 μg/ml; InvivoGen), bacterial lipopeptide (bLP) palmitoyl- 3- cysteine- serine- lysine- 4 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 84, 810, 133]]<|/det|> +(300ng/ml; InvivoGen) or human recombinant TGFβ (10 ng/ml; R&D Systems) for 24 h. + +<|ref|>sub_title<|/ref|><|det|>[[118, 164, 462, 183]]<|/det|> +## Cap Analysis Gene Expression (CAGE) + +<|ref|>text<|/ref|><|det|>[[115, 195, 812, 477]]<|/det|> +CAGE data from control or TNF stimulated (10 ng/ml; 24 h) RA SF from 2 knees were obtained from GSE163548. Mapping and identification of CAGE transcription start sites (CTSSs) were performed by DNAFORM (Yokohama, Kanagawa, Japan). In brief, the sequenced CAGE tags were mapped to hg19 using BWA software and HISAT2 after discarding ribosomal RNAs. Identification of CTSSs was performed with the Bioconductor package CAGEr (version 1.16.0)(69). Promoter and enhancer candidate identification and quantification were performed with the Bioconductor package CAGEfightR(70) (version 1.6.0) with default settings. Clusters were kept when present in at least one sample. + +<|ref|>sub_title<|/ref|><|det|>[[118, 508, 317, 526]]<|/det|> +## ChIP DNA sequencing + +<|ref|>text<|/ref|><|det|>[[115, 539, 812, 888]]<|/det|> +SF pellets from OA knees transfected with GapmeR HOTAIR and GapmeR Control (n = 3 each) were prepared using the iDeal ChIP seq kit for Histones (Diagenode) with a shearing of 12 cycles (30"ON 30"OFF, Bioruptor Pico). The shearing efficiency was analyzed using an automated capillary electrophoresis system Fragment Analyser (High sensitivity NGS fragment kit) after RNase treatment, reversion of crosslinking and purification of DNA. ChIP assays were performed using 1 million cells per IP and H3K27me3 (1 \(\mu \mathrm{g}\) , C15410195, Diagenode). A control library was processed in parallel using the same amount of control Diagenode ChIP'd DNA. After the IP, the ChIP'd DNA was analyzed by qPCR to evaluate the specificity of the reaction. The promoter of GAPDH (GAPDH-TSS) was used as negative control region, Myelin Transcription Factor 1 gene (MYT1) was used as a positive control region. The ratios + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 813, 404]]<|/det|> +of the recovery for the positive regions over the background, i.e. the specificity of the signal, were substantially smaller in two of the samples (one GapmeR HOTAIR and one GapmeR control) (ratio MYT1/GAPDH-TSS 74 and 25, respectively compared to a mean of \(122 \pm 33\) in the other samples). These samples also did not cluster with the other samples in unsupervised principal component analysis. Therefore, these 2 samples were excluded for the analysis. Libraries were prepared from 1 ng of IP and input DNA using the MicroPLEX v2 protocol, quantified by BioAnalyzer, purified (AMPure beads) and eluted in TE. Purified libraries were quantified (Qubit ds DNA HS kit), analysed for size (Fragment Analyzer) and diluted to \(20 \mathrm{nM}\) concentration. Libraries were sequenced on an Illumina HiSeq 2500 (50 bp, single end). + +<|ref|>text<|/ref|><|det|>[[115, 412, 813, 626]]<|/det|> +The quality of sequencing reads was assessed using FastQC. Reads were aligned to the reference genome (hg19) using BWA v. 0.7.5a(71). Samples were filtered for regions blacklisted by the ENCODE project (72, 73). Subsequently samples were deduplicated using SAMtools version 1.3.1(74). Alignment coordinates were converted to BED format using BEDTools v.2.17(74). Peaks were annotated on gene and transcript level using "ChIPpeakAnno", "ChIPQC", and "ChIPseeker" packages of R. "DiffBind" package was used for differential binding. + +<|ref|>sub_title<|/ref|><|det|>[[117, 656, 266, 673]]<|/det|> +## RNA sequencing + +<|ref|>text<|/ref|><|det|>[[115, 686, 813, 903]]<|/det|> +Total RNA was isolated with the miRNeasy Mini kit (Qiagen) including on- column DNaseI digestion from SF silenced for HOTAIR and control SF (n = 3 for each) 48 h after the transfection. RNA quantity and quality were evaluated using the Agilent RNA 6000 Nano kit with the Agilent 2100 Bioanalyzer instrument (Agilent Technologies, Inc.). The Illumina TruSeq Stranded total RNA protocol with the TruSeq Stranded total RNA Sample Preparation Kit was used to produce RNA- seq libraries. The quality and quantity of the generated libraries were determined by Agilent Technologies 2100 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 813, 530]]<|/det|> +Bioanalyzer with DNA- specific chip and quantitative PCR (qPCR) using Illumina adapter- specific primers using the Roche LightCycler system (Roche Diagnostics), respectively. Diluted indexed long RNA- seq (10 nM) libraries were pooled in equal volumes, used for cluster generation (TruSeq SR Cluster Kit v3- cBot- HS reagents, according to the manufacturer's recommendations) and sequenced (TruSeq SBS Kit v3- HS reagents, Illumina HiSeq4000). Sequencing data reads were quality- checked with FastQC. Reads were trimmed with Trimmomatic and aligned to the reference genome and transcriptome (FASTA and GTF files, respectively, Ensembl GRCh37) with STAR(75). Gene expression was quantified using the R/Bioconductor package Rsubread(76) version 1.22. Differentially expressed genes between conditions were identified using the R/Bioconductor packages DESeq2(77). + +<|ref|>text<|/ref|><|det|>[[115, 450, 812, 561]]<|/det|> +Pathway enrichment analyses of genes differentially expressed between SF invalidated for HOTAIR and controls were performed using Enrichr (all genes with FDR \(< 0.05\) , log fold change \(+ / - 1\) )(65). The scatter dot plot was created with the Enrichr Appyter. + +<|ref|>sub_title<|/ref|><|det|>[[115, 592, 182, 608]]<|/det|> +## ELISA + +<|ref|>text<|/ref|><|det|>[[115, 624, 812, 740]]<|/det|> +The human CXCL12 DuoSet ELISA kit (R&D Systems), the human IL12- p35 ELISA kit (Elabscience), the human IL6 ELISA DuoSet kit (R&D Systems), and the human procollagen \(1\alpha\) ELISA Set (BD Biosciences), respectively was used with cell culture supernatants. + +<|ref|>sub_title<|/ref|><|det|>[[115, 772, 315, 790]]<|/det|> +## SF organ micromasses + +<|ref|>text<|/ref|><|det|>[[115, 805, 812, 889]]<|/det|> +3D micromasses were generated as previously described(78). In brief, SF transfected with control or HOTAIR GapmeR were mixed with Matrigel (LDEV- free, Corning) \((3\times 10^{6}\) SF/ml Matrigel) and \(30\mu \mathrm{l}\) droplets added to 12- well plates coated with poly 2- + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 813, 398]]<|/det|> +hydroxyethylmethacrylate (Sigma). Micromasses were left in culture for 3 weeks in Dulbecco's modified Eagle's medium (DMEM; Life Technologies) supplemented with \(10\%\) fetal calf serum (FCS), \(1\%\) penicillin/streptomycin, \(1\%\) minimum Essential medium non- Essential Amino Acids (Giboc), \(1\%\) ITS + premix (BD) und \(17.6 \mu \mathrm{g} / \mathrm{ml}\) vitamin C. After 3 weeks, micromasses were fixed with \(2\%\) paraformaldehyde. After \(24 \mathrm{~h}\) , paraformaldehyde was replaced by \(70\%\) ethanol and micromasses were embedded in paraffin and sectioned for IHC. Spheroids were stained with anti- human collagen I antibodies (EPR7785, Abcam) on a BOND- MAX autostainer (Leica). Staining was imaged on a Zeiss Imager.Z1 (25x magnification) and quantified with ImageJ using brightness values. + +<|ref|>sub_title<|/ref|><|det|>[[118, 429, 266, 447]]<|/det|> +## Western blotting + +<|ref|>text<|/ref|><|det|>[[115, 459, 813, 874]]<|/det|> +Cells were lysed in Laemmli buffer (62.5 mM TrisHCl, \(2\%\) SDS, \(10\%\) Glycerol, \(0.1\%\) Bromphenolblue, \(5 \mathrm{mM} \beta\) mercaptoethanol). Whole cell lysates were separated on \(10\%\) SDS polyacrylamide gels and electroblotted onto nitrocellulose membranes (Whatman). Membranes were blocked for \(1 \mathrm{~h}\) in \(5\%\) (w/v) non- fat milk in TBS- T (20 mM Tris base, \(137 \mathrm{mM}\) sodium chloride, \(0.1\%\) Tween- 20, pH 7.6) or in \(5\%\) (w/v) BSA in TBS- T BSA for phosphorylated proteins. After blocking, the membranes were probed with antibodies against p- AKT (4060, Cell Signaling), AKT (4685, Cell Signaling) \(\alpha\) - tubulin (ab7291, abcam) overnight at \(4^{\circ} \mathrm{C}\) . As secondary antibodies, horseradish peroxidase- conjugated goat anti- rabbit (111- 036- 047, Jackson ImmunoResearch) or goat anti- mouse antibodies (115- 036- 062, Jackson ImmunoResearch) were used. Signals were detected using the ECL Western blotting detection reagents (GE Healthcare) and the Alpha Imager Software system (Alpha Innotech). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[117, 85, 331, 102]]<|/det|> +## Luciferase activity assay + +<|ref|>text<|/ref|><|det|>[[115, 115, 812, 430]]<|/det|> +To measure the effect of HOTAIR silencing on the canonical Wnt pathway, SF were transfected by electroporation (BTX) with the beta- catenin reporter M50 Super 8x TOPFlash (Addgene plasmid #12456) or M51 Super 8x FOPFlash, which contains mutated binding sites upstream of the luciferase reporter (Addgene plasmid #12457)(79). Both plasmids were a gift from Randall Moon. For normalization, pRenilla Luciferase Control Reporter Vectors (Promega) were co- transfected. 24 h after transfection, cells were transfected with GapmeR for HOTAIR or control as mentioned above. Luciferase activity was measured with a dual luciferase reporter assay system (Promega), and the results were normalized to the activity of Renilla luciferase. + +<|ref|>sub_title<|/ref|><|det|>[[118, 444, 388, 462]]<|/det|> +## Real-time cell analysis (RTCA) + +<|ref|>text<|/ref|><|det|>[[115, 475, 812, 858]]<|/det|> +For RTCA of cell adhesion and proliferation of SF, the xCELLigence RTCA DP Instrument (ACEA Biosciences, Inc.) was used. 16- well E- plates were equilibrated with \(100\mu \mathrm{l}\) of DMEM, \(10\%\) FCS for \(30\mathrm{min}\) at RT. The impedance, expressed as arbitrary Cell Index (CI) units, of the wells with media alone (background impedance- Rb) was measured before adding the cells. SF were detached with accurate (Merck), resuspended in DMEM, and seeded at a cell density of 25,000 cells per well. Cell adhesion and spreading, measured as changes in impedance, was monitored every \(5\mathrm{min}\) for a period of first \(12\mathrm{h}\) and every \(15\mathrm{min}\) after that for the next \(12\mathrm{h}\) . The CI at each time point is defined as \((\mathrm{Rn} - \mathrm{Rb}) / 15\) , where Rn is the cell- electrode impedance of the well when it contains cells and Rb is the background impedance. Each condition was analysed in quadruplicates. Impedance changes were recorded every \(15\mathrm{min}\) (0- \(24\mathrm{h}\) ) and every \(30\mathrm{min}\) (24- \(245\mathrm{h}\) ). Adhesion was analysed over the first \(16\mathrm{h}\) of the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 84, 810, 135]]<|/det|> +experiment, spreading was analysed between 17 and 127 h and proliferation during the exponential phase of the slopes (120- 245). + +<|ref|>sub_title<|/ref|><|det|>[[115, 167, 238, 184]]<|/det|> +## Scratch assay + +<|ref|>text<|/ref|><|det|>[[115, 199, 812, 512]]<|/det|> +To assess real time migration of SF, we performed a scratch assay(80) using Ibidi culture inserts (Ibidi, Germany) with \(70 \mu \mathrm{l}\) cell suspension \((1.2 \times 10^{5}\) cells/ml). Cells were incubated at \(37^{\circ} \mathrm{C}\) and \(5\%\) \(\mathrm{CO}_{2}\) for \(24 \mathrm{~h}\) to obtain a confluent cell layer. Next, cells were transfected with GampR HOTAIR or controls and incubated for additional \(24 \mathrm{~h}\) . Culture inserts were removed and cell layers were washed with PBS. Culture medium (with vitamin C) was added and time lapse images were recorded every 30 minutes for 48 hours using a widefield Zeiss AxioObserver equipped with a stage incubator to maintain temperature and \(\mathrm{CO}_{2}\) conditions. Each assay was performed in triplicate and repeated three times. Open area was calculated over \(24 \mathrm{~h}\) to \(48 \mathrm{~h}\) according to experiments(80). + +<|ref|>sub_title<|/ref|><|det|>[[117, 527, 257, 544]]<|/det|> +## Apoptosis assay + +<|ref|>text<|/ref|><|det|>[[115, 559, 812, 806]]<|/det|> +Activity of the key effector caspases 3 and 7 was measured using the Caspase- Glo 3/7 assay (Promega, Madison, WI). SF were seeded at a density of 4000 cells/well in 96- well white- walled plates. The next day, SF were transfected with GampR for HOTAIR or GampR control or left untransfected. Cleaved caspase 3/7 activity was assessed \(48 \mathrm{~h}\) after transfection. FAS- ligand \((2 \mu \mathrm{g} / \mathrm{ml})\) was added \(18 \mathrm{~h}\) before the assay to stimulate apoptosis. Luminescence signals were measured using a Synergy HT microplate reader (Bio Tek). Each assay was performed in triplicate and repeated two times. + +<|ref|>sub_title<|/ref|><|det|>[[117, 839, 330, 856]]<|/det|> +## Osteoclastogenesis assay + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 78, 813, 860]]<|/det|> +Human osteoclasts precursors were isolated from blood donations of healthy volunteers ( \(n = 4\) , Red Cross, Schlieren, Switzerland). In brief, CD14+ cells were isolated by positive selection using magnetic separation (Milteny Biotec) after a Ficoll gradient (GE Healthcare) separation. Isolated monocytes (5 x \(10^{4}\) ) were cultured in chamber slides in \(\alpha\) MEM supplemented with \(10\%\) FBS (GE Healthcare), \(2\mathrm{mM}\) 1- glutamine and antibiotics in the presence of \(25\mathrm{ng / mL}\) macrophage colony-stimulating factor (M- CSF) (PeproTech) for 3 days. SF were transfected with HOTAIR or control GapmeR and were added to differentiating monocytes (5 x \(10^{5}\) SF/well). Alternatively, supernatants from control or HOTAIR silenced SF were added. Cells were cultured in the presence of \(25\mathrm{ng / mL}\) M- CSF, \(50\mathrm{ng / mL}\) RANKL (PeproTech) and vitamin C. The media was replaced every 2 days. After 6 days of culture, the cells were fixed by \(4\%\) paraformaldehyde and were stained by TRAP. Control experiments included untransfected SF, SF alone (without precursors of osteoclasts) and precursors without SF or supernatants. Each assay was performed in duplicate and repeated three times. To assess bone resorption, osteoclasts precursors ( \(10^{5}\) /well) were co- cultured with SF (1.5 x \(10^{4}\) /well) or supernatants from SF in 96 wells on bovine bone slices (Jelling, Denmark) for two weeks in the presence of \(25\mathrm{ng / mL}\) M- CSF, \(50\mathrm{ng / mL}\) RANKL and vitamin C. The osteoclastogenic medium was replaced every 2- 3 days. SF were re- transfected every 5 days with HOTAIR or control GapmeRs. Analyses were done after 14 days of differentiation. Cells on bone slices were subsequently incubated with a 0.1 m NaOH solution, ultrasonicated for 2 min, rinsed with water to remove cells from the slices, and placed in a \(1\%\) aqueous toluidine blue solution containing \(1\%\) sodium borate for 5 min. Photomicrographs of resorption pits were taken using a light microscope. Resorption area was measured using Image J. + +<|ref|>sub_title<|/ref|><|det|>[[118, 888, 272, 904]]<|/det|> +## Chemotaxis assay + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 83, 812, 430]]<|/det|> +PBMCs from one healthy donor were isolated using CPT™ tubes (BD Biosciences) and \(10^{6}\) PBMCs were seeded in the upper well of a 96- well transwell migration chamber with \(5 \mu \mathrm{m}\) pore size (Corning). The lower chamber was filled with supernatants collected from control or HOTAIR silenced SF 48h after transfection (triplicates). Unconditioned medium or PBS were used as controls. Cells were collected from the lower chamber after 18h using ice- cold 20mM EDTA/0.5% FCS in PBS as detachment solution. Collected cells were counted in a cell counter (Casy, OLS) and stained with anti- human CD14- PE, anti- human CD19- PE or anti- human CD3- FITC antibodies (all Miltenyi) for 1 h at \(4^{\circ}\mathrm{C}\) . FITC- and PE- labelled IgG were used as negative control antibodies. Percentage of positive cells was assessed on a FACSCalibur™ flow cytometry platform (BD Biosciences). + +<|ref|>sub_title<|/ref|><|det|>[[117, 460, 531, 479]]<|/det|> +## RNA sequencing from the early arthritis cohort + +<|ref|>text<|/ref|><|det|>[[117, 493, 810, 545]]<|/det|> +RNA- sequencing data from the Pathobiology of Early Arthritis Cohort, which is available on the PEAC (qmul.ac.uk) was studied. + +<|ref|>sub_title<|/ref|><|det|>[[117, 575, 548, 594]]<|/det|> +## Isolation of mouse tissue from lung, spine and gut + +<|ref|>text<|/ref|><|det|>[[115, 607, 812, 888]]<|/det|> +Wild- type C57BL/6 mice, 6 weeks old, were purchased from Jackson and dissected. The spine and gut were isolated and separated into different parts (spine: cervical, thoracic and lumbar) and gut (stomach, small intestine, caecum, colon and rectum). Total RNA was isolated using the miRNAseasy Mini kit (Qiagen) including on- column DNaseI digestion. Total RNA was reversed transcribed and qPCR was performed using SYBR green (Life Technologies). Constitutively expressed beta2- microglobulin was measured for internal standard sample normalization. In cases of undetectable expression of Hotair in mouse tissues the ct was set arbitrarily to 45 in order to calculate the dct. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 84, 280, 101]]<|/det|> +## Statistical analysis + +<|ref|>text<|/ref|><|det|>[[117, 115, 812, 300]]<|/det|> +Data were analysed with GraphPad Prism version 6.0 or higher and IBM SPSS Statistics software. Two groups were compared with two- tailed unpaired or paired t test, as appropriate. Multiple group comparisons were performed by adjustments for multiple comparisons using Bonferroni correction or two- way ANOVA. Correlations were tested using Spearman's correlation coefficient. \(P\) values of \(< 0.05\) were considered statistically significant. + +<|ref|>sub_title<|/ref|><|det|>[[118, 328, 254, 346]]<|/det|> +## Study approval + +<|ref|>text<|/ref|><|det|>[[117, 359, 812, 640]]<|/det|> +The collection and experimental usage of the human samples was approved by the ethical commission of the Kanton Zurich (swissethics number: 2019- 00674, PB- 2016- 02014 and 2019- 00115). Informed consent was obtained from all patients. All experiments have been performed in accordance with the institutional guidelines. Mouse experiments were approved by the Institutional Committee of Protocol Evaluation in conjunction with the Veterinary Service Management of the Hellenic Republic Prefecture of Attika according to all current European and national legislation and were performed in accordance with relevant guidelines and regulations, under the relevant animal protocol licenses with number 2199- 11/4/2017. + +<|ref|>sub_title<|/ref|><|det|>[[118, 669, 262, 686]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[117, 701, 810, 785]]<|/det|> +The RNA seq, ChIP seq of H3K27me3 and the scRNA seq data with control GapmeR and HOTAIR GapmeR transfected SF are uploaded to the GEO repository accession GSE185440. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 84, 386, 101]]<|/det|> +## AUTHOR CONTRIBUTIONS + +<|ref|>text<|/ref|><|det|>[[115, 115, 813, 262]]<|/det|> +CO, ME and RM designed, analyzed and interpreted experiments. ME, CO, RM and MH wrote the manuscript. ME, MH, MaMi, LM, CP, KB, KK, PS, MFB and SGE performed experiments. RM and MH did the computational analysis. ME, AL, GK, MS, GeKo, MA and CO performed the mouse experiments. RM, KB and OD organized ethic approval, recruited patients and collected samples. All authors critically reviewed the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[118, 301, 345, 318]]<|/det|> +## ACKNOWLEDGMENTS + +<|ref|>text<|/ref|><|det|>[[115, 333, 813, 415]]<|/det|> +We thank Maria Comazzi and Peter Künzler (Center of Experimental Rheumatology, University Hospital Zurich, Switzerland) for excellent technical assistance. We thank Miriam Marks for providing the samples from joint replacement. + +<|ref|>text<|/ref|><|det|>[[115, 430, 813, 708]]<|/det|> +ME was supported by Société Française de Rhumatologie, Fondation pour la Recherche Médicale, Assistance publique - Hôpitaux de Paris (APHP) and EULAR. CO was supported by the Hartmann- Müller Foundation, the Foundation for Research in Science and the Humanities at the University of Zurich, the EMDO Foundation and the Iten- Kohaut Foundation. This work was supported by the IMI project BTCure (GA no. 115142- 2). We also acknowledge support by the InfrafrontierGR infrastructure (MIS 5002135), co- funded by Greece and the European Union [European Regional Development Fund] under NSRF 2014- 2020, which provided mouse hosting and phenotyping facilities. The graphical abstract was created with BioRender.com. + +<|ref|>sub_title<|/ref|><|det|>[[118, 727, 378, 744]]<|/det|> +## CONFLICTS OF INTEREST + +<|ref|>text<|/ref|><|det|>[[115, 758, 799, 876]]<|/det|> +Muriel Elhai, Raphael Micheroli, Miranda Houtman, Masoumeh Mirrahimi, Larissa Moser, Chantal Pauli, Kristina Bürki, Andrea Laimbacher, Gabriela Kania, Kerstin Klein, Philipp Schätzle, Mojca Frank Bertoncelj, Sam G. Edalat, Maria Sakkou, George Kollias, Marietta Armaka, and Caroline Ospelt: none + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[60, 83, 800, 633]]<|/det|> +1030 Oliver Distler has/had relationships with the following companies in the area of potential treatments for systemic sclerosis and its complications in the last three calendar years: 1032 Speaker fee: Bayer, Boehringer Ingelheim, Janssen, Medscape 1034 Consultancy fee: 4P- Pharma, Abbvie, Acceleron, Alcimed, Altavant Siences, 1035 Amgen, AnaMar, Arxx, AstraZeneca, Baecon, Blade, Bayer, Boehringer Ingelheim, 1036 Corbus, CSL Behring, Galapagos, Glenmark, Horizon, Inventiva, Kymera, Lupin, 1037 Miltenyi Biotec, Mitsubishi Tanabe, MSD, Novartis, Prometheus, Redxpharma, 1038 Roivant, Sanofi and Topadur 1039 Research Grants: Kymera, Mitsubishi Tanabe, Boehringer Ingelheim 1040 Oliver Distler has/had relationships with the following companies in the area of potential treatments for dermatomyositis and its complications in the last three calendar years: 1043 Consultancy fee for rheumatology topic: Pfizer (2021) 1044 Oliver Distler has/had relationships with the following companies in the area of potential treatments for arthritis in the last three 1046 Consultancy fee: Abbvie + +<|ref|>text<|/ref|><|det|>[[55, 641, 92, 656]]<|/det|> +1047 + +<|ref|>text<|/ref|><|det|>[[55, 676, 92, 690]]<|/det|> +1048 + +<|ref|>text<|/ref|><|det|>[[55, 710, 92, 724]]<|/det|> +1049 + +<|ref|>text<|/ref|><|det|>[[55, 744, 92, 757]]<|/det|> +1050 + +<|ref|>text<|/ref|><|det|>[[55, 778, 92, 791]]<|/det|> +1051 + +<|ref|>text<|/ref|><|det|>[[55, 812, 92, 825]]<|/det|> +1052 + +<|ref|>text<|/ref|><|det|>[[55, 846, 92, 859]]<|/det|> +1053 + +<|ref|>text<|/ref|><|det|>[[55, 880, 92, 893]]<|/det|> +1054 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[55, 120, 812, 911]]<|/det|> +1056 1057 1. 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Cell adhesion & 1328 migration 8, 440- 451 (2014). 1329 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 43, 311, 71]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 428, 203]]<|/det|> +- Suppl.FiguresHOTAIR20221107v20.pdf- SupplementaryTables20221222ME.pdf- Movie.mp4 + +<--- Page Split ---> diff --git a/preprint/preprint__083e5f5a0f713c3af3e82feefebfcb5cf2bc07b59bedb610fe0e85b5e07a23b3/images_list.json b/preprint/preprint__083e5f5a0f713c3af3e82feefebfcb5cf2bc07b59bedb610fe0e85b5e07a23b3/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..d88197638ac19ef7f2cefef276d0e8df4a230c42 --- /dev/null +++ b/preprint/preprint__083e5f5a0f713c3af3e82feefebfcb5cf2bc07b59bedb610fe0e85b5e07a23b3/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1: Time series (1958-2018) of the global oceanic oxygen inventory in the hindcast and sensitivity experiments, and comparable observational data. (a,c,d) Globally integrated (full water column) time series of (a) oxygen inventory anomalies, (c) \\(\\mathrm{O}_{2}^{\\mathrm{at}}\\) anomalies, and (d) the residual between (a) and (c) for the HIND (black), WIND (rose), and HEAT-FW (purple) experiments (see Methods). (b) Upper 1,000 m oceanic oxygen inventory anomalies simulated by ORCA025-MOPS HIND (black), and observational data from Ito-17 [35] (dark green) and GOBAI-O2 [36] (light green). For the model data, averages over the two sets of experiments (see Methods) are shown, with shading indicating the range between minimum and maximum estimate. No uncertainty was computed for the \\(\\mathrm{O}_{2}^{\\mathrm{at}}\\) anomalies in (c), as each pair of experiments share the same physics and do not differ in their \\(\\mathrm{O}_{2}^{\\mathrm{at}}\\) estimates. All data are mean centred using the long-term mean calculated over the full time span of each dataset, except for GOBAI-O2 where the mean was calculated only over the plotted time span (2004-2018). Red dashed lines delineate the four periods of the oxygen inventory trajectory described in Section 2.1. The percentages at the bottom of (c) and (d) refer to the share of the total \\(\\mathrm{O}_{2}\\) change explained by the respective process in the respective time interval in HIND. Note the different y-axis boundaries between panels.", + "footnote": [], + "bbox": [ + [ + 120, + 208, + 877, + 560 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2: Trends in ocean oxygen during the deoxygenation period 1967-2018 by depth and latitude. The upper panels show linear trends in oxygen content as a function of (a) depth and (b-d) as a function of depth and latitude. The panels below show the same as (a-d), but decompose the oxygen change into its solubility-driven ( \\(\\mathrm{O2^{sat}}\\) ; centre row panels) and non-solubility-driven components (derived by subtracting the solubility component from the total oxygen trend; bottom row panels). Trends are shown for the hindcast (HIND), the sensitivity experiments WIND and HEAT-FW (see Methods), and for (a) only, the observational data product Ito-17 [35]. The shading in the line graphs indicates the standard error of the estimated linear least-squares regression slopes and black contour lines show the the neutral density surfaces \\(\\gamma^{n} = 26.75\\) , 27.45, and 28.05 kg m \\(^{-3}\\) .", + "footnote": [], + "bbox": [ + [ + 120, + 150, + 860, + 696 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3: Correlation analysis between the oxygen inventory time series (1958-2018) in the hindcast and those in the sensitivity experiments. The top two rows show the coefficient of determination ( \\(\\mathrm{R}^2\\) ) between the oxygen inventory time series in HIND and those in (a-c) HEAT-FW and (d-f) WIND for the three depth horizons 0-300 m, 300-1,000 m, and below 1,000 m (columns). Regions where the Pearson correlation coefficient is non-significant ( \\(\\mathrm{p} > 0.05\\) ) are shown in white, denoting non-significance. Based on the \\(\\mathrm{R}^2\\) values, panels (g-i) show, for each grid point, whether the oxygen in HIND aligns more closely with that in WIND or HEAT-FW. Regions where HIND is not significantly correlated with either sensitivity experiment are shown in white. This analysis captures the relationship between the experiments on time scales from interannual to multi-decadal.", + "footnote": [], + "bbox": [ + [ + 120, + 222, + 875, + 626 + ] + ], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4: Annual time series of upper \\(700\\mathrm{m}\\) (a) ocean oxygen and (b) ocean heat content (OHC) anomalies for different observational and model-derived datasets. The model-based estimates are partitioned into estimates from ocean hindcast models (datasets associated with the RECCAP2 effort [60]), shown in blue, and Earth system models from CMIP6, shown in red. For each set, the multi-model mean is shown as a bold solid line and the associated standard deviation is shaded. Thin coloured lines show individual model estimates and the dashed blue line shows the ORCA025-MOPS model estimate (hindcast). Observation-based data are shown in black, with ocean oxygen data sourced from Ito et al. [35] and OHC data from the National Oceanic and Atmospheric Administration (NOAA, data updated from Levitus et al. [58] and available at: https://www.ncei.noaa.gov/access/) and IAPv4 [59] (available at: http://www.ocean.iap.ac.cn). Anomalies are calculated with respect to 1980.", + "footnote": [], + "bbox": [ + [ + 120, + 180, + 872, + 662 + ] + ], + "page_idx": 13 + } +] \ No newline at end of file diff --git a/preprint/preprint__083e5f5a0f713c3af3e82feefebfcb5cf2bc07b59bedb610fe0e85b5e07a23b3/preprint__083e5f5a0f713c3af3e82feefebfcb5cf2bc07b59bedb610fe0e85b5e07a23b3.mmd b/preprint/preprint__083e5f5a0f713c3af3e82feefebfcb5cf2bc07b59bedb610fe0e85b5e07a23b3/preprint__083e5f5a0f713c3af3e82feefebfcb5cf2bc07b59bedb610fe0e85b5e07a23b3.mmd new file mode 100644 index 0000000000000000000000000000000000000000..354913215d6565866d28e3a6c497f8f176672162 --- /dev/null +++ b/preprint/preprint__083e5f5a0f713c3af3e82feefebfcb5cf2bc07b59bedb610fe0e85b5e07a23b3/preprint__083e5f5a0f713c3af3e82feefebfcb5cf2bc07b59bedb610fe0e85b5e07a23b3.mmd @@ -0,0 +1,426 @@ + +# Competing effects of wind and buoyancy forcing on recent ocean oxygen trends + +Helene Hollitzer + +helenehollitzer@web.de + +University of Bern https://orcid.org/0009- 0005- 7842- 5126 + +Lavinia Patara + +GEOMAR Helmholtz Centre for Ocean Research Kiel https://orcid.org/0000- 0003- 4093- 3609 + +Jens Terhaar + +University of Bern https://orcid.org/0000- 0001- 9377- 415X + +Andreas Oschlies + +GEOMAR Helmholtz Centre for Ocean Research Kiel https://orcid.org/0000- 0002- 8295- 4013 + +## Article + +Keywords: + +Posted Date: March 22nd, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4094246/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on October 26th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 53557- y. + +<--- Page Split ---> + +# Competing effects of wind and buoyancy forcing on recent ocean oxygen trends + +Helene A. L. Hollitzer\*1,2,3, Lavinia Patara1, Jens Terhaar2,3, and Andreas Oschlies1,4 + +1GEOMAR Helmholtz Centre for Ocean Research Kiel, 24105 Kiel, Germany 2Climate and Environmental Physics, Physics Institute, University of Bern, 3012 Bern, Switzerland 3Oeschger Centre for Climate Change Research, University of Bern, 3012 Bern, Switzerland 4Kiel University, 24118 Kiel, Germany + +Corresponding author (\*): Helene A. L. Hollitzer (helene.hollitzer@unibe.ch) + +<--- Page Split ---> + +## 1 Abstract + +2 Ocean deoxygenation is becoming a major stressor for marine ecosystems. Climate change affects ocean oxygen by altering wind fields and air- sea heat and freshwater fluxes. However, the quantitative contribution of these drivers to ocean deoxygenation remains uncertain. Here, we use a global ocean biogeochemistry model run under historical atmospheric forcing to show that deoxygenation since the late 1960s has been driven mainly by changing air- sea heat and freshwater fluxes and associated changes in solubility and ocean circulation. However, \(\sim 60\%\) of this deoxygenation was offset by a wind- driven increase in ventilation and interior oxygen supply, mainly in the Southern Ocean. In the coming decades, the projected slowdown in wind stress intensification, combined with continued ocean warming, could greatly accelerate ocean deoxygenation. While ocean biogeochemistry models under historical atmospheric forcing struggle to reproduce the observed deoxygenation after 2000, fully coupled Earth system models capture the trend, indicating systematic problems in hindcast simulations. + +## 1 Introduction + +Oxygen ( \(\mathrm{O_2}\) ) is critical for sustaining marine life. Also, \(\mathrm{O_2}\) regulates important elemental cycles in the ocean, such as those of nitrogen, phosphorus, and iron, through its on effect redox- sensitive source and sink processes [1]. Observations now indicate that the global ocean dissolved ( \(\mathrm{O_2}\) ) inventory, currently estimated at \(227.4\pm\) 1.1 Pmol [2], has decreased by more than \(1\%\) between 1960 and 2010 [3], mainly in response to anthropogenic climate change. This deoxygenation is projected to continue in the coming decades, even under the low- emission, high- mitigation Shared Socioeconomic Pathways 1- 2.6 [4, 5] (SSP1- 2.6) or a complete cessation of carbon dioxide emissions [6]. However, while there is a clear downward trend in the global oceanic \(\mathrm{O_2}\) inventory, regional \(\mathrm{O_2}\) responses to climate change vary widely across ocean basins and depths, implying spatial and temporal variability in the underlying drivers [2, 7]. + +Ocean oxygen distribution and changes are the result of a complex interplay of driving forces that either enhance or deplete \(\mathrm{O_2}\) . At the sea surface, ocean \(\mathrm{O_2}\) is in direct contact with the atmosphere through air- sea gas exchange, so that the waters within the mixed layer are, to first order, in equilibrium with the \(\mathrm{O_2}\) partial pressure of the atmosphere. The equilibrium oxygen concentration depends on the solubility of \(\mathrm{O_2}\) in seawater and therefore primarily on sea surface temperature (SST). Secondly, oxygen is produced by photosynthesis in the sunlit near- surface zone below the sea surface. Beneath the surface and near- surface zone there are no significant sources of oxygen, and \(\mathrm{O_2}\) can only be supplied by ventilation, defined as the physical processes by which ( \(\mathrm{O_2}\) - rich) waters are transferred from the surface mixed layer into the ocean + +<--- Page Split ---> + +interior. In the interior ocean, these water masses remain isolated from the atmosphere over long timescales set by the interior transport patterns [8]. Alongside these \(\mathrm{O_2}\) - supply processes, \(\mathrm{O_2}\) is consumed by the respiration of organic matter at all depths. As the respiration of organic matter is usually limited by the availability of organic substrates, total respiration depends strongly on the biological productivity in the sunlit near- surface ocean above, which is largely determined by ambient nutrient concentrations [9]. + +In the ocean interior, the large- scale pattern of oxygen concentrations is directly related to ocean ventilation and the associated supply of oxygen to the interior of the ocean. This supply is not homogeneous across the global ocean, but is largely concentrated in specific locations [8, 10], such as the North Atlantic [11] or the Southern Ocean [12]. Ventilation is the result of a suite of interacting processes [13], with the main atmospheric drivers being wind stress (i.e. the shear stress exerted on the ocean surface by wind) and air- sea heat and freshwater fluxes. Wind stress is a major driver of the large- scale ocean circulation, and its divergence and convergence patterns force the gyre transports and the meridional overturning circulation [14, 15] (MOC). Air- sea heat and freshwater fluxes regulate the transformation of surface water masses and ocean mixing [16]. Prominent regions of ocean interior oxygenation include the subpolar North Atlantic, where strong surface buoyancy loss triggers open- ocean convection [17], the coastal regions around Antarctica where Antarctic Bottom Water is formed [18, 19], and the regions of mode and intermediate water formation at mid- latitudes [20, 21]. These intensely ventilated regions can all be traced as oxygen maxima throughout the ocean interior [22]. As opposed to these well- ventilated oxygen maximum zones, poorly ventilated sites often result in oxygen minimum zones (OMZs), mostly located in the eastern part of the tropical oceans [23, 24]. + +Anthropogenic climate change has had far- reaching effects on ocean oxygen concentrations in recent decades, altering oxygen dynamics both directly and indirectly. Ocean warming directly depletes the oceanic oxygen inventory by reducing the gas solubility in warming surface waters. However, solubility- related changes associated with anthropogenic warming and the continuous ocean heat uptake [25] are estimated to account for only about half of the \(\mathrm{O_2}\) loss in the upper 1,000 m of the water column, and their contribution to changes integrated over the entire water column reaches only about \(15\%\) [2, 26]. Consequently, the dominant fraction of global oxygen loss must be mediated by other mechanisms, explained either by changes in biological consumption or by changes in ocean ventilation. + +Apart from the direct effect on solubility, anthropogenic warming also intensifies near- surface stratification and modifies wind fields. Increased stratification and reduced winds can decouple \(\mathrm{O_2}\) - saturated, nutrient- poor surface waters from \(\mathrm{O_2}\) - undersaturated, nutrient- rich subsurface waters. Stronger stratification and reduced winds may thus reduce ocean oxygenation by reducing the transport of \(\mathrm{O_2}\) - rich surface waters into the permanent thermocline. At the same time, enhanced stratification may mitigate deoxygenation by + +<--- Page Split ---> + +reducing the upwelling of nutrient- rich deeper waters, thereby limiting biological production in the euphotic zone and the subsequent export and oxygen- consuming respiration of organic matter [4, 27]. Examples of these counteracting effects are found in the Pacific and Southern Ocean. The strengthening of the Pacific trade winds since the 1990s [28, 29] has led to intensified wind- driven nutrient upwelling, greater biological activity, and consequently increased \(\mathrm{O_2}\) consumption below the surface ocean [30]. Conversely, the continued strengthening of the Southern Ocean westerlies [31, 32] contributes to increased formation rates of oxygen- rich deep and intermediate water masses, and, according to models, eventually increases global oxygen supply [33]. + +Despite recent advances in understanding the drivers of regional \(\mathrm{O_2}\) changes, our present understanding of the spatial distribution of \(\mathrm{O_2}\) changes and their causes remains limited, partly due to the superposition of a number of forcings and mechanisms that complicate clear attribution [34]. While climate change is known to affect ocean oxygen by modifying wind fields and air- sea heat and freshwater fluxes [9], the quantitative contribution of these factors to ocean deoxygenation remains poorly constrained. One way to quantify the contribution of each driver, and to deconstruct the superimposed mechanisms, is the use of global ocean biogeochemical models run under historical atmospheric forcing. + +In this study, we investigate the long- term changes (1958- 2018) and interannual variability of \(\mathrm{O_2}\) using a global ocean biogeochemical model at \(0.25^{\circ}\) horizontal resolution, run under historical atmospheric forcing. To isolate the effects of changing wind stress and air- sea heat and freshwater fluxes on ocean \(\mathrm{O_2}\) , we perform a set of hindcast experiments with different biogeochemical parameter settings and additional sensitivity experiments run under different atmospheric forcing (see Methods). By decomposing oceanic \(\mathrm{O_2}\) trends into their drivers and analysing global and regional changes in \(\mathrm{O_2}\) , we aim at improving our mechanistic understanding of \(\mathrm{O_2}\) changes in the ocean and thereby our ability to understand and predict future changes in dissolved oxygen. + +## 2 Results + +### 2.1 Trends in the global ocean oxygen inventory + +The simulated trajectory of the global oceanic oxygen inventory change from 1958 to 2018 can be separated into four periods (Fig. 1a, Supplementary Table 1). In the first period, from 1958 to 1967, the global \(\mathrm{O_2}\) inventory increases at a rate of 258.1 (± 26.9, standard error of the estimated slope) Tmol \(\mathrm{O_2}\) per decade (hereafter Tmol dec \(^{- 1}\) ). The second period from 1967 to 1994 is characterised by a gradual decrease of - 46.4 ± 5.0 Tmol dec \(^{- 1}\) , mostly confined to the upper 1,000 m of the water column (Fig. 1a,b, Supplementary + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1: Time series (1958-2018) of the global oceanic oxygen inventory in the hindcast and sensitivity experiments, and comparable observational data. (a,c,d) Globally integrated (full water column) time series of (a) oxygen inventory anomalies, (c) \(\mathrm{O}_{2}^{\mathrm{at}}\) anomalies, and (d) the residual between (a) and (c) for the HIND (black), WIND (rose), and HEAT-FW (purple) experiments (see Methods). (b) Upper 1,000 m oceanic oxygen inventory anomalies simulated by ORCA025-MOPS HIND (black), and observational data from Ito-17 [35] (dark green) and GOBAI-O2 [36] (light green). For the model data, averages over the two sets of experiments (see Methods) are shown, with shading indicating the range between minimum and maximum estimate. No uncertainty was computed for the \(\mathrm{O}_{2}^{\mathrm{at}}\) anomalies in (c), as each pair of experiments share the same physics and do not differ in their \(\mathrm{O}_{2}^{\mathrm{at}}\) estimates. All data are mean centred using the long-term mean calculated over the full time span of each dataset, except for GOBAI-O2 where the mean was calculated only over the plotted time span (2004-2018). Red dashed lines delineate the four periods of the oxygen inventory trajectory described in Section 2.1. The percentages at the bottom of (c) and (d) refer to the share of the total \(\mathrm{O}_{2}\) change explained by the respective process in the respective time interval in HIND. Note the different y-axis boundaries between panels.
+ +<--- Page Split ---> + +Fig. 1). In the third period, from 1994 to 2002, the simulated trend in the \(\mathrm{O_2}\) inventory departs from the long- term declining trend, first falling anomalously fast until 1998 and then recovering rapidly until 2002, also dominated by changes in the upper 1,000 m. From 2002 to the end of the simulation period, the simulated global oceanic oxygen inventory decreases continuously at an accelerated rate of \(- 116.8 \pm 6.6\) Tmol dec \(^{- 1}\) , and is increasingly influenced by changes below 1,000 m compared to earlier periods (Fig. 1a,b, Supplementary Fig. 2). + +The temporal evolution of the global \(\mathrm{O_2}\) inventory from the surface ocean to 1,000 m depth is similar to the observation- based estimate by Ito et al. [35] (hereafter Ito- 17) from 1958 to 2002, but differs substantially from 2002 to 2018 (Fig. 1b, Supplementary Table 2). After 2002, Ito- 17 and the observation- based data product GOBAI- \(\mathrm{O_2}\) [36, 37] show a strong decrease in oceanic \(\mathrm{O_2}\) , far exceeding the rate of decrease observed between 1967 and 1994. In contrast, the model simulates a nearly stagnant global oceanic oxygen inventory for the upper 1,000 m after 2002 (Fig. 1b). + +### 2.2 Drivers underlying ocean oxygen trends and variability + +#### 2.2.1 Global drivers + +We decompose \(\mathrm{O_2}\) changes into those driven by changes in solubility and those driven by changes in ventilation or remineralisation, termed non- solubility- driven changes (see Methods). Globally, solubility and non- solubility- driven changes tend to co- evolve to first order (Fig. 1), as any perturbation in SST alters both \(\mathrm{O_2}\) solubility and upper ocean stratification. However, the relative contributions of solubility and non- solubility- driven changes to the total oxygen change differ in time (Fig. 1), regionally, and with depth (Fig. 2). Since the late 1990s, the model shows that solubility has driven about half of the global oxygen decrease. However, from the late 1960s to the late 1990s, non- solubility effects accounted for virtually all of the deoxygenation trend (Fig. 1c,d). Consistent with past studies [2, 35], solubility- driven deoxygenation is mostly confined to the upper 200 m of the water column (Fig. 2e), while non- solubility- driven changes become dominant below the thermocline (Fig. 2i). + +The simulated non- solubility- driven decline in global \(\mathrm{O_2}\) is mainly attributed to changes in ocean stratification and ventilation rather than changes in remineralisation. In our simulations, remineralisation rates gradually decrease throughout the simulation period (Supplementary Fig. 3), indicating a slight reduction in respiratory oxygen consumption rather than an increase. The overriding importance of ventilation changes in the non- solubility- driven part of global deoxygenation is consistent with projections from an Earth system model (1990s- 2090s, RCP8.5), which show that a decrease in subduction contributes to a deoxygenation trend that outweighs the mitigating effect of reduced respiration [38]. Therefore, the terms "non- solubility- driven + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2: Trends in ocean oxygen during the deoxygenation period 1967-2018 by depth and latitude. The upper panels show linear trends in oxygen content as a function of (a) depth and (b-d) as a function of depth and latitude. The panels below show the same as (a-d), but decompose the oxygen change into its solubility-driven ( \(\mathrm{O2^{sat}}\) ; centre row panels) and non-solubility-driven components (derived by subtracting the solubility component from the total oxygen trend; bottom row panels). Trends are shown for the hindcast (HIND), the sensitivity experiments WIND and HEAT-FW (see Methods), and for (a) only, the observational data product Ito-17 [35]. The shading in the line graphs indicates the standard error of the estimated linear least-squares regression slopes and black contour lines show the the neutral density surfaces \(\gamma^{n} = 26.75\) , 27.45, and 28.05 kg m \(^{-3}\) .
+ +<--- Page Split ---> + +changes" and "ventilation- driven changes" are used synonymously hereinafter. + +The global deoxygenation since the 1970s has been driven by changes in air- sea heat and freshwater fluxes (hereafter buoyancy fluxes), while changes in wind stress have mitigated the oxygen loss (Fig. 1). Globally, air- sea buoyancy fluxes have led to an average decrease in the global oxygen inventory of \(- 94 \pm 3\) Tmol dec \(^{- 1}\) since the onset of global oxygen loss in 1967 (Fig. 1a). \(35\%\) of this oxygen loss is attributed to reduced solubility and the remaining \(65\%\) to reduced ventilation. The global decrease in ocean oxygen has been partly counteracted by a steady increase in wind stress (Supplementary Fig. 4), which has increased the global ocean \(\mathrm{O}_2\) inventory by about \(64 \pm 2\) Tmol dec \(^{- 1}\) since 1967 (Fig. 1a), through its effect on both oxygen solubility (Fig. 1c) and ocean ventilation (Fig. 1d). + +The acceleration of deoxygenation over the last two decades (Fig. 1) has been caused simultaneously by a smaller increase in wind stress and associated wind- driven \(\mathrm{O}_2\) increases, and an increase in \(\mathrm{O}_2\) losses driven by changing air- sea heat and freshwater fluxes (Fig. 1a). Oxygen depletion imposed by changes in air- sea buoyancy fluxes nearly doubled between 2002 and 2018 relative to the period between 1967 and 1994, coinciding with an increase in ocean heat uptake around 2000 [39]. At the same time, the increase in oxygen due to increased wind stress decreased by about \(60\%\) (Supplementary Table 1). + +#### 2.2.2 Regional drivers + +The wind stress- driven oxygenation trend found at a global scale can be predominantly traced back to a ventilation- driven oxygenation at intermediate depths in the Southern Ocean (Fig. 2a- b,i- j). A steady strengthening of the Southern Hemisphere westerly winds in past decades [31, 40] is thought to have strengthened the upper cell of the MOC [41] and to have increased the ventilation of Subantarctic Mode Water and Antarctic Intermediate Water [42- 44]. In our model results, the upper cell of the Southern Ocean MOC also shows a long- term strengthening (Supplementary Fig. 5), much of which is attributed to changes in wind stress (Supplementary Fig. 6). Wind stress- driven oxygenation at intermediate levels has partly been counteracted by concomitant changes in air- sea heat and freshwater fluxes that have reduced ventilation [44], leading to ventilation- driven deoxygenation at intermediate depths of the Southern Ocean (Fig. 2l). + +The equatorial regions show an overall deoxygenation that is strongest in the 100- 400 m range, consistent with the observed expansion of the tropical OMZs [24]. We find that most of the deoxygenation is driven by a wind stress- induced reduction in ventilation (Fig. 2k), largely originating in the Pacific Ocean (Supplementary Fig. 7). This is consistent with the concomitant weakening and shoaling of the OMZ- ventilating subtropical cells in the model (Supplementary Fig. 5) and observations [45]. The importance of wind stress in tropical regions is in line with previous studies showing that variations in the strength of tropical trade winds strongly influence oxygen concentrations locally [30, 46]. + +<--- Page Split ---> + +Fluctuations in the wind field over the equatorial Pacific can also have a large effect on the variability of the global oxygen inventory. During the strong El Niño conditions of 1997- 98 [47], global oxygen levels fell, as simulated by the model and shown by observations (Fig. 1). This oxygen low is dominated by non- solubility- driven changes due to wind stress in the model (Fig. 1), and may be driven by changes in wind fields that modulate the source depth and rate of equatorial upwelling [46]: During El Niño, shallower and less intense upwelling may reduce the upward transport of low- \(O_{2}\) waters, thus decreasing the \(O_{2}\) inflow along the eastern and central equatorial Pacific [46], and eventually lowering the oxygen inventory. + +In the Northern Hemisphere, oxygen trends in the upper 1,000 m (Fig. 2b- d) are dominated by changes in the North Pacific (Supplementary Fig. 7). This ocean region is known for its large regional and temporal variations in dissolved oxygen, which are strongly related to the Pacific Decadal Oscillation [48]. Substantial shifts in oxygen injection into the thermocline waters have been shown to result from variations in surface outcrops of different mode water masses, located primarily in the western North Pacific and affecting water masses lighter than about \(\gamma^{n} = 26.6 \mathrm{kg} \mathrm{m}^{- 3}\) [49]. For waters lighter than \(\gamma^{n} = 26.6 \mathrm{kg} \mathrm{m}^{- - 3}\) , our model shows a pronounced oxygen decline, consistent with an observation- based analysis [50]. In addition, the simulated oxygen increase in heavier water masses at densities between \(\gamma^{n} = 26.6 \mathrm{kg} \mathrm{m}^{- 3}\) and about \(\gamma^{n} = 27.6 \mathrm{kg} \mathrm{m}^{- 3}\) is consistent with the observation- based analysis of Mecking and Drushka [51]. A similar pattern is simulated in the North Atlantic basin, where deoxygenation in the upper ocean is superimposed on an oxygenation trend in the intermediate and deep layers (Supplementary Fig. 7), driven mainly by changes in ventilation. This pattern is consistent with observation- based data [52] showing a deoxygenation trend between 1960- 2009 in the mode and upper intermediate waters driven by increased stratification, and an oxygenation trend in the lower intermediate waters and Labrador Sea water driven by the strong subpolar convection in the 1990s in response to a positive phase of the North Atlantic Oscillation [53]. + +While changes in air- sea buoyancy fluxes are the main driver of the global long- term deoxygenation trend, wind stress is the dominant driver of the interannual variability of \(O_{2}\) in most ocean regions (Fig. 3). Only in areas of water mass formation, such as upper ocean mode waters in the subtropical gyres (Fig. 3g), mode and intermediate waters in the mid- latitude Southern Ocean (Fig. 3g- i), and deep and bottom waters in the North Atlantic and close to Antarctica (Fig. 3i), air- sea buoyancy fluxes are the main drivers of oxygen dynamics and explain a larger part of the interannual variability. The decadal variability, however, is mostly driven by buoyancy fluxes (Supplementary Figs. 8- 9), consistent with the hypothesis that the global MOC is driven predominantly by wind stress on interannual time scales, but thermohaline processes dominate on decadal time scales [54, 55]. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3: Correlation analysis between the oxygen inventory time series (1958-2018) in the hindcast and those in the sensitivity experiments. The top two rows show the coefficient of determination ( \(\mathrm{R}^2\) ) between the oxygen inventory time series in HIND and those in (a-c) HEAT-FW and (d-f) WIND for the three depth horizons 0-300 m, 300-1,000 m, and below 1,000 m (columns). Regions where the Pearson correlation coefficient is non-significant ( \(\mathrm{p} > 0.05\) ) are shown in white, denoting non-significance. Based on the \(\mathrm{R}^2\) values, panels (g-i) show, for each grid point, whether the oxygen in HIND aligns more closely with that in WIND or HEAT-FW. Regions where HIND is not significantly correlated with either sensitivity experiment are shown in white. This analysis captures the relationship between the experiments on time scales from interannual to multi-decadal.
+ +<--- Page Split ---> + +## 3 Discussion and Conclusion + +Our simulations allowed us to identify the underlying drivers of a persistent negative trend in the global oceanic oxygen inventory that began in the early 1970s. We find that the overall oceanic deoxygenation was driven by changes in air- sea heat and freshwater fluxes, and was only partially counteracted by a concomitant wind stress- driven increase in ocean ventilation. + +While the model is generally consistent with observed oxygen trends until the early 2000s, the severity of deoxygenation from 2002 to 2018 is underestimated by a factor of about ten compared to both Ito- 17 and GOBAI- \(O_{2}\) . This misrepresentation of present- day deoxygenation is a common deficiency in state- of- the- art ocean models [9, 34]. Indeed, we find that the insufficient deoxygenation ( \(< 700 \mathrm{m}\) ) in the model analysed here (ORCA025- MOPS) is also simulated by global ocean biogeochemistry models (GOBMs) participating in RECCAP2 [56] (Fig. 4a), which largely contribute to the Global Ocean Carbon Budget [57]. + +Possible reasons for the underestimation of deoxygenation by GOBMs might be (1) deficiencies in simulating critical biogeochemical processes, such as erroneous model stoichiometry or the misrepresentation or neglect of critical biogeochemical feedback mechanisms, (2) biases in ocean circulation and mixing, or (3) biases in the air- sea buoyancy fluxes. Specifically, an insufficient ocean heat content (OHC) increase in response to global warming (Fig. 4b) could be a key factor contributing to the model- observation mismatch, given the strong negative correlation between OHC and ocean oxygen [4, 35]. We find that ORCA025- MOPS underestimates the recent rise in global OHC ( \(< 700 \mathrm{m}\) ) compared to observations [58, 59], and that this underestimation is of similar magnitude to that found in other GOBMs [56] (Fig. 4b). The similarity of the trends across models of different physical and biogeochemical complexity supports the hypothesis that the underestimated OHC trend is not model specific, but common to all of them. + +By contrast, coupled Earth system models (ESMs) from CMIP6, which often have the same ocean components as those used in GOBMs, simulate upper \(700 \mathrm{m}\) deoxygenation and ocean warming trends that are consistent with observation- based estimates within uncertainties, but at the lower end (Fig. 4). Considering the whole water column, the CMIP6 ESMs simulate an oxygen loss of \(0.42 \pm 0.16\%\) (standard deviation over the model ensemble) from 1970 to 2010, which is smaller than the IPCC estimate of \(1.15 \pm 0.88\%\) (90% confidence interval) [3], but still within the large uncertainties arising from extrapolation from spatially and temporally sparse observations, especially in the deep ocean. A similar underestimation over the whole water column has been shown previously for the CMIP5 ESMs [9]. In contrast to the ESMs and observation- based estimates, the GOBMs (RECCAP2) simulate on average a minor oxygen increase over the entire water column of \(0.08 \pm 0.30\%\) (standard deviation over the model ensemble). Since ESMs are able to approximate past deoxygenation in the upper \(700 \mathrm{m}\) and perform better than GOBMs when considering + +<--- Page Split ---> + +the whole water column, it is unlikely that the underestimation of deoxygenation in atmospherically forced hindcast simulations by GOBMs is caused by a common bias in the biogeochemical component of GOBMs and ESMs. + +Thus, possible explanations for the underestimation of OHC change and deoxygenation in hindcast models could be either a bias in the atmospheric forcing from reanalysis data or a more general problem with hindcast simulations. While the JRA55- do dataset, which provides high quality and realistic spatial wind patterns, is one of the most widely used reanalysis datasets for hindcast modelling, regional- scale inconsistencies with observations have been found, especially in coastal, equatorial, and subpolar regions [61]. Taboada et al. [61] thus caution that the significant mismatches in high latitudes may affect the rate of deep water formation and upwelling, and in coastal regions may impede the correct reproduction of transport patterns, leading to biases in the model's heat and nutrient fluxes. In addition, forcing a dynamical ocean with a prescribed atmosphere may lead to erroneous results, especially in ventilation, as the interior ocean circulation cannot dynamically feedback on the atmospheric circulation as it does in ESMs. The underestimation of the strong decline in oxygen and rise in OHC over the last 18 years by GOBMs raises the question of whether the GOBM- derived trends in the ocean carbon sink, as reported by the Global Carbon Budget [57], are similarly biased by systematic biases due to the atmospheric forcing or the hindcast approach in general. + +While acknowledging these limitations, the strength of this study is to isolate the mechanisms underlying recent ocean oxygen trends and variability, which are well represented on the regional scale (Section 2.2.2). Our results highlight the importance of regional processes in driving the global response of ocean oxygen to atmospheric change. In our simulations, a key region for global oxygen trends is the Southern Ocean, where the formation of mode and intermediate waters is a key conduit for oxygen, nutrients, and anthropogenic carbon and heat into the interior ocean [62, 63]. In this region, the recent strengthening of westerly winds, caused by increasing greenhouse gas emissions and stratospheric ozone depletion [31], has counteracted the deoxygenation caused by widespread surface warming and mid- depth freshening [64, 65]. The poleward shift and implicit movement of the sinking branch of intermediate water masses into denser water may have further accelerated intermediate water mass formation [41], while increasing oxygen solubility in subducted waters. By modulating the strength of the Southern Ocean upper circulation cell [41] and setting the formation rates and regions of oxygen- rich intermediate water masses [43, 44], the Southern Hemisphere westerly winds are critical for global oxygen supply [33]. + +Recent studies suggest that the current intensification of westerlies in the Southern Hemisphere may slow or cease altogether in the coming century under low to moderate emissions scenarios that align with or approach the temperature targets of the Paris Agreement [32, 66]. In these scenarios, the slowdown in wind stress intensification and the concomitant vanishing of wind- driven oxygen enrichment are attributed to the + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4: Annual time series of upper \(700\mathrm{m}\) (a) ocean oxygen and (b) ocean heat content (OHC) anomalies for different observational and model-derived datasets. The model-based estimates are partitioned into estimates from ocean hindcast models (datasets associated with the RECCAP2 effort [60]), shown in blue, and Earth system models from CMIP6, shown in red. For each set, the multi-model mean is shown as a bold solid line and the associated standard deviation is shaded. Thin coloured lines show individual model estimates and the dashed blue line shows the ORCA025-MOPS model estimate (hindcast). Observation-based data are shown in black, with ocean oxygen data sourced from Ito et al. [35] and OHC data from the National Oceanic and Atmospheric Administration (NOAA, data updated from Levitus et al. [58] and available at: https://www.ncei.noaa.gov/access/) and IAPv4 [59] (available at: http://www.ocean.iap.ac.cn). Anomalies are calculated with respect to 1980.
+ +<--- Page Split ---> + +recovery of stratospheric ozone by mid- century [67]. The reduction in ventilation- driven oxygen supply under these low emissions scenarios is likely to coincide with additional losses caused by ocean warming, even if \(\mathrm{CO_2}\) emissions are stopped [68- 70]. The ocean will continue to warm to equilibrate with the warmer atmosphere and thus continue to lose oxygen [6, 71, 72]. Our study indicates that the stabilisation or reversal of the past intensification of wind stress, together with continued ocean warming, is likely to accelerate oxygen loss in the future, particularly in the Southern Hemisphere intermediate waters and in regions fed by these water masses, such as the equatorial OMZs [73]. + +Our analysis draws attention to the complex interplay between the counteracting forces of wind stress and air- sea heat and freshwater fluxes, and emphasises their importance in determining changes in oceanic oxygen. Given the centrality of atmospheric drivers in determining trends in oceanic oxygen, we stress the importance of the accurate representation of wind stress and air- sea heat and freshwater fluxes in reanalysis data for assessing past deoxygenation with GOBMs, which do not capture trends in \(\mathrm{O_2}\) and OHC since 2002. In addition, a robust projection of wind stress and air- sea heat and freshwater fluxes in coupled ESMs, which better capture past trends in \(\mathrm{O_2}\) and OHC, is also essential for a robust prediction of future changes in \(\mathrm{O_2}\) and OHC. By identifying and quantifying the drivers of deoxygenation and their regional patterns, this analysis contributes to a much- needed improved mechanistic understanding of \(\mathrm{O_2}\) changes and emphasises the complexity of our changing oceans. Understanding this complexity will ultimately help to better project and anticipate future global and regional \(\mathrm{O_2}\) inventory changes and their potential consequences for marine ecosystems. + +## 4 Methods + +### 4.1 ORCA025-MOPS model + +We used a global configuration of the ocean- sea ice model NEMO- LIM2 [74] with a horizontal resolution of \(0.25^{\circ}\) (ORCA025) and 46 unequally spaced vertical levels that increase with depth [75]. The model resolution is sufficient to capture much of the mesoscale eddy spectrum [76], so no eddy parameterisation was used. The ocean- sea ice model was coupled to the marine biogeochemical model MOPS [77, 78], which simulates the lower trophic levels of the ecosystem and the associated nutrient cycling using nine tracers: phosphate (P), nitrate (N), \(\mathrm{O_2}\) (O), dissolved inorganic carbon, alkalinity, and fixed- stoichiometry representations of phytoplankton, zooplankton, detritus, and dissolved organic matter. We find that the choice of biogeochemical parameters, and in particular the stoichiometric \(\mathrm{O_2:P}\) ratio, can influence the long- term trends and adjustment times of ocean oxygen [77, 79]. To evaluate the uncertainty associated with the choice of the + +<--- Page Split ---> + +\(\mathrm{O}_{2}\) :P ratio, we employ two different model configurations with different values of the parameter ( \(\mathrm{O}_{2}\) :P ratio of either 150 or 162). + +ORCA025- MOPS was initialised by a spin- up performed with the \(0.5^{\circ}\) resolution model ORCA05- MOPS. ORCA05- MOPS was initialised with climatological temperature and salinity from Levitus98 [80], with World Ocean Atlas 2013 conditions [81, 82] for \(\mathrm{PO}_{4}\) , \(\mathrm{NO}_{3}\) , and \(\mathrm{O}_{2}\) , and with GLODAPv2 conditions [83] for alkalinity and \(\mathrm{C}_{\mathrm{nat}}\) . The model was forced by the JRA55- do runoff dataset (version 1.1, at \(0.25^{\circ}\) horizontal resolution) and JRA55- do atmospheric forcing dataset (version 1.4, at \(0.25^{\circ}\) horizontal and 3- hourly temporal resolution) from 1958 to 2018 [84]. ORCA05- MOPS was run under three cycles of JRA55- do atmospheric forcing, amounting to a total spin- up of 183 years. The end of the third cycle of ORCA05- MOPS provided the biogeochemical initial conditions for a spin- up with the \(0.25^{\circ}\) resolution model ORCA025- MOPS, run under one cycle of JRA55- do atmospheric forcing. For technical reasons, the physics had to be restarted from Levitus98. The end of the fourth cycle provided the initial conditions for the experiments analysed in this study. + +Errors in the representation of biogeochemical processes in the model may bias the results, particularly in the low- oxygen zones along the eastern margins where biogeochemical processes are of high importance in altering the oxygen demand [85]. It has been suggested that the temperature dependence of remineralisation may impact on \(\mathrm{O}_{2}\) concentrations through shoaling of remineralisation profiles [86]. In MOPS, remineralisation is temperature- independent, so these effects are neglected. In addition, biases due to biogeochemical processes may arise especially in iron- limited regions such as the Southern Ocean, but also in equatorial Pacific upwelling regions [87, 88], because ORCA025- MOPS neglects iron limitation of primary productivity. While the calibrated parameters of ORCA025- MOPS may mitigate this deficiency somewhat [79], this omission may still lead to an overestimation of primary production and concomitant remineralisation and oxygen loss below the sea surface. + +### 4.2 Simulations + +For both model configurations ( \(\mathrm{O}_{2}\) :P ratio of 150 or 162), four experiments were run. A hindcast (HIND) experiment, performed under interannual forcing of JRA55- do, simulates changes in the \(\mathrm{O}_{2}\) inventory due to climate change and climate variability. However, HIND may also contain \(\mathrm{O}_{2}\) changes introduced by a spurious model drift, as the model is not expected to be in equilibrium after the relatively short spin- up time compared to deep ocean equilibration timescales. To quantify this model drift, we performed a repeated- year- forcing (RYF) experiment, which was integrated by repeating the JRA55- do forcing of a single year (May 1, 1990 to April 30, 1991), most neutral in terms of the major climate modes [89]. The RYF experiment does not include + +<--- Page Split ---> + +changes in the \(\mathrm{O_2}\) inventory due to climate change nor due to climate variability, so any ongoing changes are caused by the model drift. To correct for such model drift, the \(\mathrm{O_2}\) inventories in RYF were removed (gridpoint- wise and for each year) from those in HIND, under the assumption that the drifts in HIND and RYF are the same. As RYF simulates neither climate change nor climate variability, the difference between the two experiments results in \(\mathrm{O_2}\) inventory anomalies caused by climate change and variability only. This procedure is commonly used to differentiate between steady- state and non- steady- state components of ocean biogeochemical tracers [56, 57], including \(\mathrm{O_2}\) [90]. Depending on the selected value of the \(\mathrm{O_2}\) :P ratio, the drifts in the two RYF experiments are \(+0.23\) and \(- 0.13\) Pmol \(\mathrm{O_2}\) per decade (Supplementary Fig. 10a). The choice of the \(\mathrm{O_2}\) :P ratio does not, however, substantially influence the HIND minus RYF \(\mathrm{O_2}\) anomalies. The \(\mathrm{O_2}\) anomalies are nearly identical between the two experiment twins (Supplementary Fig. 10b), suggesting that the biogeochemical parameterisation does not substantially influence the decadal climate- driven \(\mathrm{O_2}\) trends and variability that are the focus of this paper. + +Following the strategy used in Patara et al. [44], two complementary sensitivity experiments were performed to isolate the effects of changing air- sea heat and freshwater fluxes and wind stress on oxygen dynamics. In the air- sea heat and freshwater fluxes experiment (HEAT- FW), the interannual variability of the wind stress was suppressed, while the interannual variability of all variables needed to compute the air- sea fluxes of heat, freshwater, and oxygen was preserved. The opposite is true for the wind stress experiment (WIND), where only the interannual variability of wind stress was retained. The atmospheric variables used to compute the air- sea fluxes of heat, freshwater and oxygen are wind speed, air temperature, humidity, incoming solar radiation, outgoing longwave radiation, and precipitation. Similarly, RYF was subtracted from HEAT- FW and WIND to isolate the effects of interannual variability and climate change in air- sea heat and freshwater fluxes (for HEAT- FW) and in wind stress (for WIND) on oceanic \(\mathrm{O_2}\) . In the text, HIND, HEAT- FW, and WIND refer to the average of the drift- corrected results from each of the two model configurations. + +Changes in oceanic dissolved oxygen were further decomposed into two fractions for analysis of the underlying mechanisms: (1) solubility- driven changes and (2) non- solubility- driven changes (i.e. oxygen changes driven by changes in respiration or ventilation). \(\mathrm{O_2}\) solubility in seawater was approximated by \(\mathrm{O_2^{sat}}\) , calculated from potential temperature and salinity [91], and represents the \(\mathrm{O_2}\) concentration that a water mass has reached when in equilibrium with the \(\mathrm{O_2}\) partial pressure of the overlying atmosphere. Non- solubility- driven oxygen changes were calculated by subtracting the solubility component from the total oxygen anomaly [35], thereby isolating the fraction of oxygen changes that cannot be explained by solubility changes, and corresponds to the opposite of the commonly- used apparent oxygen utilization (AOU) diagnostic. + +<--- Page Split ---> + +### 4.3 Observational data sets + +We compare the simulated oxygen trends with observation- based estimates. Specifically, we use the observationbased data products GOBAI- \(\mathrm{O_2}\) [36, 37] (2004- 2022), and oxygen concentration anomalies developed by Ito et al. [35], referred to as Ito- 17 (1950- 2015). Due to the relatively low sampling density in Ito- 17 before 1960 and after 2010 [35], only data from 1960- 2010 were used. As Ito- 17 and our simulations provide \(\mathrm{O_2}\) anomalies, GOBAI- \(\mathrm{O_2}\) inventories were also converted to anomalies by subtracting the long- term mean. Additionally, in Supplementary Text 1 and Supplementary Figs. 11- 13, we compare observational climatological distributions of dissolved oxygen and \(\mathrm{O_2^{sat}}\) against the corresponding model outputs. + +Additionally, we compare the simulated trend in global OHC with observation- based estimates. Specifically, we use the observation- based data products IAPv4 [59], and OHC data from the National Oceanic and Atmospheric Administration (NOAA, updated from Levitus et al. [58]). + +### 4.4 RECCAP2 models + +To compare the ORCA025- MOPS simulations with other hindcast simulations, we used 9 GOBMs (CESMETHZ, CNRM- ESM2- 1, EC- Earth3, FESOM- REcoM- LR, MOM6- Princeton, MRI- ESM2- 1, NorESM- OC1.2, ORCA025- GEOMAR, and ORCA1- LIM3- PISCES) from RECCAP2 [56, 60]. Only models evaluated in the model evaluation chapter of RECCAP2 were used, as the remaining models either had drift problems when branching from another simulation with coarser model resolution, had too large salinity biases, or had incomparable historical and control simulations due to their setup [92]. Consistent with the analysis of ORCA025- MOPS, we calculated anomalies in OHC and \(\mathrm{O_2}\) by subtracting the simulation without climate change and variability from the historical simulation with climate change and variability. + +### 4.5 CMIP6 Earth system models + +Additionally, we have analysed all 7 ESMs from CMIP6 to compare hindcast simulations with fully coupled simulations. The ESMs used here are ACCESS- ESM1- 5 (ensemble member rli1p1f1) [93], CanESM5 (rli1p1f1) and CanESM5- CanOE (rli1p2f1) [94], CNRM- ESM2- 1 (rli1p1f2) [95], GFDL- CM4 (rli1p1f1) and GFDL- ESM4 (rli1p1f1) [96- 98], and MPI- ESM1- 2- LR (rli1p1f1) [99, 100]. We used all ESM outputs for which \(\mathrm{O_2}\) and potential temperature were available for the historical simulation, the Shared Socioeconomic Pathways 5- 8.5 (SSP5- 8.5) simulation [101], and the pre- industrial control simulation. The SSP5- 8.5 simulations were used for the years 2015 to 2018 because the historical simulation in CMIP6 stops after 2014. From 2015 to 2018, SSP5- 8.5 was chosen because it is the pathway for which most ESMs provide results. Although it is a high- emission pathway, the radiative forcing in these years is almost identical for + +<--- Page Split ---> + +all SSPs in the first year, and the marginal differences in radiative forcing are too small to distinguish the climate in the first years between SSPs. Here the anomaly has been calculated as the difference between the pre- industrial control run and the historical run. As the pre- industrial run still simulates internal climate variability, this difference only quantifies the effect of climate change and externally forced variability, such as volcanoes, and not the effect of internal climate variability. The remaining internal climate variability in the anomaly is a superposition of the internal climate variability from the historical simulation and that from the pre- industrial control simulation, and has no scientific relevance. + +## References + +1. Breitburg, D. et al. Declining oxygen in the global ocean and coastal waters. Science 359, eaam7240 (2018). +2. Schmidtko, S., Stramma, L. & Visbeck, M. Decline in global oceanic oxygen content during the past five decades. Nature 542, 335-339 (2017). +3. Bindoff, N. L. et al. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H. O. et al.) 447-587 (Cambridge University Press, Cambridge, UK and New York, NY, USA, 2019). +4. Bopp, L. et al. 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Ocean Biogeochemistry in GFDL's Earth System Model 4.1 and its response to increasing atmospheric \(\mathrm{CO_2}\) . Journal of Advances in Modeling Earth Systems 12, e2019MS002043 (2020). + +99. Giorgetta, M. A. et al. Climate and carbon cycle changes from 1850 to 2100 in MPI-ESM simulations for the Coupled Model Intercomparison Project phase 5. Journal of Advances in Modeling Earth Systems 5, 572-597 (2013). + +100. Mauritsen, T. et al. Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response to increasing \(\mathrm{CO_2}\) . Journal of Advances in Modeling Earth Systems 11, 998-1038 (2019). + +101. Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global environmental change 42, 153-168 (2017). + +## 4.6 Acknowledgements + +The ocean model simulations were integrated at the North German Supercomputing Alliance (HLRN). L. Patara acknowledges funding from the German Research Foundation under grant PA 3075/2-1. J. Terhaar and H. A. L. Hollitzer acknowledge funding from the Swiss National Science Foundation under grant #PZ00P2_209044. The authors thank Linus Vogt for providing the \(\mathrm{O_2}\) and OHC time series from the ESMs. + +## 4.7 Data availability + +A subset of the output data used for this study will be made freely available on the GEOMAR OPeNDAP Server after acceptance. The Earth system model output used in this study is available via the Earth System Grid Federation at esgf-node.ipsl.upmc.fr/projects/esgf-ipsl. The output from RECCAP2 model output is + +<--- Page Split ---> + +openly accessible at zenodo.org/records/7990823 [60]. All observational data used for model evaluation are openly available to the public at the following links: Oxygen data from Ito et al. [35] are available at o2.eas.gatech.edu/data.html and from GOBAI- O2 [36] at doi.org/10.25921/z72m-yz67. The World Ocean Atlas 2018 (WOA18) is accessible at www.ncei.noaa.gov/access/world-ocean-atlas-2018. OHC data from NOAA are available at www.ncei.noaa.gov/access and from IAPv4 [59] at www.ocean.iap.ac.cn. + +## Author contributions + +H. A. L. Hollitzer conceived and led the study, produced the figures, and wrote the initial manuscript under the supervision of L. Patara, A. Oschlies, and J. Terhaar. L. Patara performed the experiments with ORCA025-MOPS. All authors contributed to the analysis and interpretation of the results and to the editing of the manuscript into its final draft. + +## Competing interests + +The authors declare no competing interests. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +supportinginformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__083e5f5a0f713c3af3e82feefebfcb5cf2bc07b59bedb610fe0e85b5e07a23b3/preprint__083e5f5a0f713c3af3e82feefebfcb5cf2bc07b59bedb610fe0e85b5e07a23b3_det.mmd b/preprint/preprint__083e5f5a0f713c3af3e82feefebfcb5cf2bc07b59bedb610fe0e85b5e07a23b3/preprint__083e5f5a0f713c3af3e82feefebfcb5cf2bc07b59bedb610fe0e85b5e07a23b3_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..dd7c854cda7c54a949f78e48f88b9b8aeb173e55 --- /dev/null +++ b/preprint/preprint__083e5f5a0f713c3af3e82feefebfcb5cf2bc07b59bedb610fe0e85b5e07a23b3/preprint__083e5f5a0f713c3af3e82feefebfcb5cf2bc07b59bedb610fe0e85b5e07a23b3_det.mmd @@ -0,0 +1,577 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 944, 175]]<|/det|> +# Competing effects of wind and buoyancy forcing on recent ocean oxygen trends + +<|ref|>text<|/ref|><|det|>[[44, 196, 185, 214]]<|/det|> +Helene Hollitzer + +<|ref|>text<|/ref|><|det|>[[55, 223, 300, 240]]<|/det|> +helenehollitzer@web.de + +<|ref|>text<|/ref|><|det|>[[50, 269, 570, 289]]<|/det|> +University of Bern https://orcid.org/0009- 0005- 7842- 5126 + +<|ref|>text<|/ref|><|det|>[[44, 295, 170, 312]]<|/det|> +Lavinia Patara + +<|ref|>text<|/ref|><|det|>[[50, 315, 868, 335]]<|/det|> +GEOMAR Helmholtz Centre for Ocean Research Kiel https://orcid.org/0000- 0003- 4093- 3609 + +<|ref|>text<|/ref|><|det|>[[44, 340, 160, 357]]<|/det|> +Jens Terhaar + +<|ref|>text<|/ref|><|det|>[[50, 361, 571, 380]]<|/det|> +University of Bern https://orcid.org/0000- 0001- 9377- 415X + +<|ref|>text<|/ref|><|det|>[[44, 386, 198, 403]]<|/det|> +Andreas Oschlies + +<|ref|>text<|/ref|><|det|>[[50, 407, 866, 427]]<|/det|> +GEOMAR Helmholtz Centre for Ocean Research Kiel https://orcid.org/0000- 0002- 8295- 4013 + +<|ref|>sub_title<|/ref|><|det|>[[44, 469, 103, 487]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 507, 136, 524]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 544, 320, 563]]<|/det|> +Posted Date: March 22nd, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 583, 475, 602]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4094246/v1 + +<|ref|>text<|/ref|><|det|>[[42, 619, 914, 662]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 680, 534, 700]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 736, 941, 780]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on October 26th, 2024. See the published version at https://doi.org/10.1038/s41467- 024- 53557- y. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[115, 85, 880, 157]]<|/det|> +# Competing effects of wind and buoyancy forcing on recent ocean oxygen trends + +<|ref|>text<|/ref|><|det|>[[115, 198, 732, 216]]<|/det|> +Helene A. L. Hollitzer\*1,2,3, Lavinia Patara1, Jens Terhaar2,3, and Andreas Oschlies1,4 + +<|ref|>text<|/ref|><|det|>[[137, 242, 853, 335]]<|/det|> +1GEOMAR Helmholtz Centre for Ocean Research Kiel, 24105 Kiel, Germany 2Climate and Environmental Physics, Physics Institute, University of Bern, 3012 Bern, Switzerland 3Oeschger Centre for Climate Change Research, University of Bern, 3012 Bern, Switzerland 4Kiel University, 24118 Kiel, Germany + +<|ref|>text<|/ref|><|det|>[[115, 361, 694, 379]]<|/det|> +Corresponding author (\*): Helene A. L. Hollitzer (helene.hollitzer@unibe.ch) + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[94, 86, 218, 106]]<|/det|> +## 1 Abstract + +<|ref|>text<|/ref|><|det|>[[90, 125, 886, 396]]<|/det|> +2 Ocean deoxygenation is becoming a major stressor for marine ecosystems. Climate change affects ocean oxygen by altering wind fields and air- sea heat and freshwater fluxes. However, the quantitative contribution of these drivers to ocean deoxygenation remains uncertain. Here, we use a global ocean biogeochemistry model run under historical atmospheric forcing to show that deoxygenation since the late 1960s has been driven mainly by changing air- sea heat and freshwater fluxes and associated changes in solubility and ocean circulation. However, \(\sim 60\%\) of this deoxygenation was offset by a wind- driven increase in ventilation and interior oxygen supply, mainly in the Southern Ocean. In the coming decades, the projected slowdown in wind stress intensification, combined with continued ocean warming, could greatly accelerate ocean deoxygenation. While ocean biogeochemistry models under historical atmospheric forcing struggle to reproduce the observed deoxygenation after 2000, fully coupled Earth system models capture the trend, indicating systematic problems in hindcast simulations. + +<|ref|>sub_title<|/ref|><|det|>[[92, 430, 302, 452]]<|/det|> +## 1 Introduction + +<|ref|>text<|/ref|><|det|>[[88, 470, 886, 691]]<|/det|> +Oxygen ( \(\mathrm{O_2}\) ) is critical for sustaining marine life. Also, \(\mathrm{O_2}\) regulates important elemental cycles in the ocean, such as those of nitrogen, phosphorus, and iron, through its on effect redox- sensitive source and sink processes [1]. Observations now indicate that the global ocean dissolved ( \(\mathrm{O_2}\) ) inventory, currently estimated at \(227.4\pm\) 1.1 Pmol [2], has decreased by more than \(1\%\) between 1960 and 2010 [3], mainly in response to anthropogenic climate change. This deoxygenation is projected to continue in the coming decades, even under the low- emission, high- mitigation Shared Socioeconomic Pathways 1- 2.6 [4, 5] (SSP1- 2.6) or a complete cessation of carbon dioxide emissions [6]. However, while there is a clear downward trend in the global oceanic \(\mathrm{O_2}\) inventory, regional \(\mathrm{O_2}\) responses to climate change vary widely across ocean basins and depths, implying spatial and temporal variability in the underlying drivers [2, 7]. + +<|ref|>text<|/ref|><|det|>[[88, 697, 886, 892]]<|/det|> +Ocean oxygen distribution and changes are the result of a complex interplay of driving forces that either enhance or deplete \(\mathrm{O_2}\) . At the sea surface, ocean \(\mathrm{O_2}\) is in direct contact with the atmosphere through air- sea gas exchange, so that the waters within the mixed layer are, to first order, in equilibrium with the \(\mathrm{O_2}\) partial pressure of the atmosphere. The equilibrium oxygen concentration depends on the solubility of \(\mathrm{O_2}\) in seawater and therefore primarily on sea surface temperature (SST). Secondly, oxygen is produced by photosynthesis in the sunlit near- surface zone below the sea surface. Beneath the surface and near- surface zone there are no significant sources of oxygen, and \(\mathrm{O_2}\) can only be supplied by ventilation, defined as the physical processes by which ( \(\mathrm{O_2}\) - rich) waters are transferred from the surface mixed layer into the ocean + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 88, 884, 207]]<|/det|> +interior. In the interior ocean, these water masses remain isolated from the atmosphere over long timescales set by the interior transport patterns [8]. Alongside these \(\mathrm{O_2}\) - supply processes, \(\mathrm{O_2}\) is consumed by the respiration of organic matter at all depths. As the respiration of organic matter is usually limited by the availability of organic substrates, total respiration depends strongly on the biological productivity in the sunlit near- surface ocean above, which is largely determined by ambient nutrient concentrations [9]. + +<|ref|>text<|/ref|><|det|>[[88, 214, 885, 585]]<|/det|> +In the ocean interior, the large- scale pattern of oxygen concentrations is directly related to ocean ventilation and the associated supply of oxygen to the interior of the ocean. This supply is not homogeneous across the global ocean, but is largely concentrated in specific locations [8, 10], such as the North Atlantic [11] or the Southern Ocean [12]. Ventilation is the result of a suite of interacting processes [13], with the main atmospheric drivers being wind stress (i.e. the shear stress exerted on the ocean surface by wind) and air- sea heat and freshwater fluxes. Wind stress is a major driver of the large- scale ocean circulation, and its divergence and convergence patterns force the gyre transports and the meridional overturning circulation [14, 15] (MOC). Air- sea heat and freshwater fluxes regulate the transformation of surface water masses and ocean mixing [16]. Prominent regions of ocean interior oxygenation include the subpolar North Atlantic, where strong surface buoyancy loss triggers open- ocean convection [17], the coastal regions around Antarctica where Antarctic Bottom Water is formed [18, 19], and the regions of mode and intermediate water formation at mid- latitudes [20, 21]. These intensely ventilated regions can all be traced as oxygen maxima throughout the ocean interior [22]. As opposed to these well- ventilated oxygen maximum zones, poorly ventilated sites often result in oxygen minimum zones (OMZs), mostly located in the eastern part of the tropical oceans [23, 24]. + +<|ref|>text<|/ref|><|det|>[[88, 592, 885, 787]]<|/det|> +Anthropogenic climate change has had far- reaching effects on ocean oxygen concentrations in recent decades, altering oxygen dynamics both directly and indirectly. Ocean warming directly depletes the oceanic oxygen inventory by reducing the gas solubility in warming surface waters. However, solubility- related changes associated with anthropogenic warming and the continuous ocean heat uptake [25] are estimated to account for only about half of the \(\mathrm{O_2}\) loss in the upper 1,000 m of the water column, and their contribution to changes integrated over the entire water column reaches only about \(15\%\) [2, 26]. Consequently, the dominant fraction of global oxygen loss must be mediated by other mechanisms, explained either by changes in biological consumption or by changes in ocean ventilation. + +<|ref|>text<|/ref|><|det|>[[88, 794, 884, 911]]<|/det|> +Apart from the direct effect on solubility, anthropogenic warming also intensifies near- surface stratification and modifies wind fields. Increased stratification and reduced winds can decouple \(\mathrm{O_2}\) - saturated, nutrient- poor surface waters from \(\mathrm{O_2}\) - undersaturated, nutrient- rich subsurface waters. Stronger stratification and reduced winds may thus reduce ocean oxygenation by reducing the transport of \(\mathrm{O_2}\) - rich surface waters into the permanent thermocline. At the same time, enhanced stratification may mitigate deoxygenation by + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[87, 88, 885, 284]]<|/det|> +reducing the upwelling of nutrient- rich deeper waters, thereby limiting biological production in the euphotic zone and the subsequent export and oxygen- consuming respiration of organic matter [4, 27]. Examples of these counteracting effects are found in the Pacific and Southern Ocean. The strengthening of the Pacific trade winds since the 1990s [28, 29] has led to intensified wind- driven nutrient upwelling, greater biological activity, and consequently increased \(\mathrm{O_2}\) consumption below the surface ocean [30]. Conversely, the continued strengthening of the Southern Ocean westerlies [31, 32] contributes to increased formation rates of oxygen- rich deep and intermediate water masses, and, according to models, eventually increases global oxygen supply [33]. + +<|ref|>text<|/ref|><|det|>[[88, 290, 885, 459]]<|/det|> +Despite recent advances in understanding the drivers of regional \(\mathrm{O_2}\) changes, our present understanding of the spatial distribution of \(\mathrm{O_2}\) changes and their causes remains limited, partly due to the superposition of a number of forcings and mechanisms that complicate clear attribution [34]. While climate change is known to affect ocean oxygen by modifying wind fields and air- sea heat and freshwater fluxes [9], the quantitative contribution of these factors to ocean deoxygenation remains poorly constrained. One way to quantify the contribution of each driver, and to deconstruct the superimposed mechanisms, is the use of global ocean biogeochemical models run under historical atmospheric forcing. + +<|ref|>text<|/ref|><|det|>[[88, 465, 885, 660]]<|/det|> +In this study, we investigate the long- term changes (1958- 2018) and interannual variability of \(\mathrm{O_2}\) using a global ocean biogeochemical model at \(0.25^{\circ}\) horizontal resolution, run under historical atmospheric forcing. To isolate the effects of changing wind stress and air- sea heat and freshwater fluxes on ocean \(\mathrm{O_2}\) , we perform a set of hindcast experiments with different biogeochemical parameter settings and additional sensitivity experiments run under different atmospheric forcing (see Methods). By decomposing oceanic \(\mathrm{O_2}\) trends into their drivers and analysing global and regional changes in \(\mathrm{O_2}\) , we aim at improving our mechanistic understanding of \(\mathrm{O_2}\) changes in the ocean and thereby our ability to understand and predict future changes in dissolved oxygen. + +<|ref|>sub_title<|/ref|><|det|>[[89, 696, 242, 716]]<|/det|> +## 2 Results + +<|ref|>sub_title<|/ref|><|det|>[[89, 739, 594, 758]]<|/det|> +### 2.1 Trends in the global ocean oxygen inventory + +<|ref|>text<|/ref|><|det|>[[88, 773, 885, 893]]<|/det|> +The simulated trajectory of the global oceanic oxygen inventory change from 1958 to 2018 can be separated into four periods (Fig. 1a, Supplementary Table 1). In the first period, from 1958 to 1967, the global \(\mathrm{O_2}\) inventory increases at a rate of 258.1 (± 26.9, standard error of the estimated slope) Tmol \(\mathrm{O_2}\) per decade (hereafter Tmol dec \(^{- 1}\) ). The second period from 1967 to 1994 is characterised by a gradual decrease of - 46.4 ± 5.0 Tmol dec \(^{- 1}\) , mostly confined to the upper 1,000 m of the water column (Fig. 1a,b, Supplementary + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 208, 877, 560]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 579, 882, 790]]<|/det|> +
Figure 1: Time series (1958-2018) of the global oceanic oxygen inventory in the hindcast and sensitivity experiments, and comparable observational data. (a,c,d) Globally integrated (full water column) time series of (a) oxygen inventory anomalies, (c) \(\mathrm{O}_{2}^{\mathrm{at}}\) anomalies, and (d) the residual between (a) and (c) for the HIND (black), WIND (rose), and HEAT-FW (purple) experiments (see Methods). (b) Upper 1,000 m oceanic oxygen inventory anomalies simulated by ORCA025-MOPS HIND (black), and observational data from Ito-17 [35] (dark green) and GOBAI-O2 [36] (light green). For the model data, averages over the two sets of experiments (see Methods) are shown, with shading indicating the range between minimum and maximum estimate. No uncertainty was computed for the \(\mathrm{O}_{2}^{\mathrm{at}}\) anomalies in (c), as each pair of experiments share the same physics and do not differ in their \(\mathrm{O}_{2}^{\mathrm{at}}\) estimates. All data are mean centred using the long-term mean calculated over the full time span of each dataset, except for GOBAI-O2 where the mean was calculated only over the plotted time span (2004-2018). Red dashed lines delineate the four periods of the oxygen inventory trajectory described in Section 2.1. The percentages at the bottom of (c) and (d) refer to the share of the total \(\mathrm{O}_{2}\) change explained by the respective process in the respective time interval in HIND. Note the different y-axis boundaries between panels.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[87, 88, 884, 231]]<|/det|> +Fig. 1). In the third period, from 1994 to 2002, the simulated trend in the \(\mathrm{O_2}\) inventory departs from the long- term declining trend, first falling anomalously fast until 1998 and then recovering rapidly until 2002, also dominated by changes in the upper 1,000 m. From 2002 to the end of the simulation period, the simulated global oceanic oxygen inventory decreases continuously at an accelerated rate of \(- 116.8 \pm 6.6\) Tmol dec \(^{- 1}\) , and is increasingly influenced by changes below 1,000 m compared to earlier periods (Fig. 1a,b, Supplementary Fig. 2). + +<|ref|>text<|/ref|><|det|>[[87, 240, 884, 384]]<|/det|> +The temporal evolution of the global \(\mathrm{O_2}\) inventory from the surface ocean to 1,000 m depth is similar to the observation- based estimate by Ito et al. [35] (hereafter Ito- 17) from 1958 to 2002, but differs substantially from 2002 to 2018 (Fig. 1b, Supplementary Table 2). After 2002, Ito- 17 and the observation- based data product GOBAI- \(\mathrm{O_2}\) [36, 37] show a strong decrease in oceanic \(\mathrm{O_2}\) , far exceeding the rate of decrease observed between 1967 and 1994. In contrast, the model simulates a nearly stagnant global oceanic oxygen inventory for the upper 1,000 m after 2002 (Fig. 1b). + +<|ref|>sub_title<|/ref|><|det|>[[87, 411, 697, 431]]<|/det|> +### 2.2 Drivers underlying ocean oxygen trends and variability + +<|ref|>title<|/ref|><|det|>[[114, 446, 291, 462]]<|/det|> +#### 2.2.1 Global drivers + +<|ref|>text<|/ref|><|det|>[[87, 478, 884, 724]]<|/det|> +We decompose \(\mathrm{O_2}\) changes into those driven by changes in solubility and those driven by changes in ventilation or remineralisation, termed non- solubility- driven changes (see Methods). Globally, solubility and non- solubility- driven changes tend to co- evolve to first order (Fig. 1), as any perturbation in SST alters both \(\mathrm{O_2}\) solubility and upper ocean stratification. However, the relative contributions of solubility and non- solubility- driven changes to the total oxygen change differ in time (Fig. 1), regionally, and with depth (Fig. 2). Since the late 1990s, the model shows that solubility has driven about half of the global oxygen decrease. However, from the late 1960s to the late 1990s, non- solubility effects accounted for virtually all of the deoxygenation trend (Fig. 1c,d). Consistent with past studies [2, 35], solubility- driven deoxygenation is mostly confined to the upper 200 m of the water column (Fig. 2e), while non- solubility- driven changes become dominant below the thermocline (Fig. 2i). + +<|ref|>text<|/ref|><|det|>[[87, 732, 884, 899]]<|/det|> +The simulated non- solubility- driven decline in global \(\mathrm{O_2}\) is mainly attributed to changes in ocean stratification and ventilation rather than changes in remineralisation. In our simulations, remineralisation rates gradually decrease throughout the simulation period (Supplementary Fig. 3), indicating a slight reduction in respiratory oxygen consumption rather than an increase. The overriding importance of ventilation changes in the non- solubility- driven part of global deoxygenation is consistent with projections from an Earth system model (1990s- 2090s, RCP8.5), which show that a decrease in subduction contributes to a deoxygenation trend that outweighs the mitigating effect of reduced respiration [38]. Therefore, the terms "non- solubility- driven + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 150, 860, 696]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 710, 883, 847]]<|/det|> +
Figure 2: Trends in ocean oxygen during the deoxygenation period 1967-2018 by depth and latitude. The upper panels show linear trends in oxygen content as a function of (a) depth and (b-d) as a function of depth and latitude. The panels below show the same as (a-d), but decompose the oxygen change into its solubility-driven ( \(\mathrm{O2^{sat}}\) ; centre row panels) and non-solubility-driven components (derived by subtracting the solubility component from the total oxygen trend; bottom row panels). Trends are shown for the hindcast (HIND), the sensitivity experiments WIND and HEAT-FW (see Methods), and for (a) only, the observational data product Ito-17 [35]. The shading in the line graphs indicates the standard error of the estimated linear least-squares regression slopes and black contour lines show the the neutral density surfaces \(\gamma^{n} = 26.75\) , 27.45, and 28.05 kg m \(^{-3}\) .
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[87, 88, 675, 106]]<|/det|> +changes" and "ventilation- driven changes" are used synonymously hereinafter. + +<|ref|>text<|/ref|><|det|>[[115, 113, 884, 310]]<|/det|> +The global deoxygenation since the 1970s has been driven by changes in air- sea heat and freshwater fluxes (hereafter buoyancy fluxes), while changes in wind stress have mitigated the oxygen loss (Fig. 1). Globally, air- sea buoyancy fluxes have led to an average decrease in the global oxygen inventory of \(- 94 \pm 3\) Tmol dec \(^{- 1}\) since the onset of global oxygen loss in 1967 (Fig. 1a). \(35\%\) of this oxygen loss is attributed to reduced solubility and the remaining \(65\%\) to reduced ventilation. The global decrease in ocean oxygen has been partly counteracted by a steady increase in wind stress (Supplementary Fig. 4), which has increased the global ocean \(\mathrm{O}_2\) inventory by about \(64 \pm 2\) Tmol dec \(^{- 1}\) since 1967 (Fig. 1a), through its effect on both oxygen solubility (Fig. 1c) and ocean ventilation (Fig. 1d). + +<|ref|>text<|/ref|><|det|>[[115, 316, 884, 459]]<|/det|> +The acceleration of deoxygenation over the last two decades (Fig. 1) has been caused simultaneously by a smaller increase in wind stress and associated wind- driven \(\mathrm{O}_2\) increases, and an increase in \(\mathrm{O}_2\) losses driven by changing air- sea heat and freshwater fluxes (Fig. 1a). Oxygen depletion imposed by changes in air- sea buoyancy fluxes nearly doubled between 2002 and 2018 relative to the period between 1967 and 1994, coinciding with an increase in ocean heat uptake around 2000 [39]. At the same time, the increase in oxygen due to increased wind stress decreased by about \(60\%\) (Supplementary Table 1). + +<|ref|>title<|/ref|><|det|>[[116, 484, 309, 500]]<|/det|> +#### 2.2.2 Regional drivers + +<|ref|>text<|/ref|><|det|>[[115, 516, 884, 737]]<|/det|> +The wind stress- driven oxygenation trend found at a global scale can be predominantly traced back to a ventilation- driven oxygenation at intermediate depths in the Southern Ocean (Fig. 2a- b,i- j). A steady strengthening of the Southern Hemisphere westerly winds in past decades [31, 40] is thought to have strengthened the upper cell of the MOC [41] and to have increased the ventilation of Subantarctic Mode Water and Antarctic Intermediate Water [42- 44]. In our model results, the upper cell of the Southern Ocean MOC also shows a long- term strengthening (Supplementary Fig. 5), much of which is attributed to changes in wind stress (Supplementary Fig. 6). Wind stress- driven oxygenation at intermediate levels has partly been counteracted by concomitant changes in air- sea heat and freshwater fluxes that have reduced ventilation [44], leading to ventilation- driven deoxygenation at intermediate depths of the Southern Ocean (Fig. 2l). + +<|ref|>text<|/ref|><|det|>[[115, 744, 884, 913]]<|/det|> +The equatorial regions show an overall deoxygenation that is strongest in the 100- 400 m range, consistent with the observed expansion of the tropical OMZs [24]. We find that most of the deoxygenation is driven by a wind stress- induced reduction in ventilation (Fig. 2k), largely originating in the Pacific Ocean (Supplementary Fig. 7). This is consistent with the concomitant weakening and shoaling of the OMZ- ventilating subtropical cells in the model (Supplementary Fig. 5) and observations [45]. The importance of wind stress in tropical regions is in line with previous studies showing that variations in the strength of tropical trade winds strongly influence oxygen concentrations locally [30, 46]. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 884, 258]]<|/det|> +Fluctuations in the wind field over the equatorial Pacific can also have a large effect on the variability of the global oxygen inventory. During the strong El Niño conditions of 1997- 98 [47], global oxygen levels fell, as simulated by the model and shown by observations (Fig. 1). This oxygen low is dominated by non- solubility- driven changes due to wind stress in the model (Fig. 1), and may be driven by changes in wind fields that modulate the source depth and rate of equatorial upwelling [46]: During El Niño, shallower and less intense upwelling may reduce the upward transport of low- \(O_{2}\) waters, thus decreasing the \(O_{2}\) inflow along the eastern and central equatorial Pacific [46], and eventually lowering the oxygen inventory. + +<|ref|>text<|/ref|><|det|>[[111, 264, 886, 636]]<|/det|> +In the Northern Hemisphere, oxygen trends in the upper 1,000 m (Fig. 2b- d) are dominated by changes in the North Pacific (Supplementary Fig. 7). This ocean region is known for its large regional and temporal variations in dissolved oxygen, which are strongly related to the Pacific Decadal Oscillation [48]. Substantial shifts in oxygen injection into the thermocline waters have been shown to result from variations in surface outcrops of different mode water masses, located primarily in the western North Pacific and affecting water masses lighter than about \(\gamma^{n} = 26.6 \mathrm{kg} \mathrm{m}^{- 3}\) [49]. For waters lighter than \(\gamma^{n} = 26.6 \mathrm{kg} \mathrm{m}^{- - 3}\) , our model shows a pronounced oxygen decline, consistent with an observation- based analysis [50]. In addition, the simulated oxygen increase in heavier water masses at densities between \(\gamma^{n} = 26.6 \mathrm{kg} \mathrm{m}^{- 3}\) and about \(\gamma^{n} = 27.6 \mathrm{kg} \mathrm{m}^{- 3}\) is consistent with the observation- based analysis of Mecking and Drushka [51]. A similar pattern is simulated in the North Atlantic basin, where deoxygenation in the upper ocean is superimposed on an oxygenation trend in the intermediate and deep layers (Supplementary Fig. 7), driven mainly by changes in ventilation. This pattern is consistent with observation- based data [52] showing a deoxygenation trend between 1960- 2009 in the mode and upper intermediate waters driven by increased stratification, and an oxygenation trend in the lower intermediate waters and Labrador Sea water driven by the strong subpolar convection in the 1990s in response to a positive phase of the North Atlantic Oscillation [53]. + +<|ref|>text<|/ref|><|det|>[[111, 642, 885, 862]]<|/det|> +While changes in air- sea buoyancy fluxes are the main driver of the global long- term deoxygenation trend, wind stress is the dominant driver of the interannual variability of \(O_{2}\) in most ocean regions (Fig. 3). Only in areas of water mass formation, such as upper ocean mode waters in the subtropical gyres (Fig. 3g), mode and intermediate waters in the mid- latitude Southern Ocean (Fig. 3g- i), and deep and bottom waters in the North Atlantic and close to Antarctica (Fig. 3i), air- sea buoyancy fluxes are the main drivers of oxygen dynamics and explain a larger part of the interannual variability. The decadal variability, however, is mostly driven by buoyancy fluxes (Supplementary Figs. 8- 9), consistent with the hypothesis that the global MOC is driven predominantly by wind stress on interannual time scales, but thermohaline processes dominate on decadal time scales [54, 55]. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 222, 875, 626]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 644, 883, 782]]<|/det|> +
Figure 3: Correlation analysis between the oxygen inventory time series (1958-2018) in the hindcast and those in the sensitivity experiments. The top two rows show the coefficient of determination ( \(\mathrm{R}^2\) ) between the oxygen inventory time series in HIND and those in (a-c) HEAT-FW and (d-f) WIND for the three depth horizons 0-300 m, 300-1,000 m, and below 1,000 m (columns). Regions where the Pearson correlation coefficient is non-significant ( \(\mathrm{p} > 0.05\) ) are shown in white, denoting non-significance. Based on the \(\mathrm{R}^2\) values, panels (g-i) show, for each grid point, whether the oxygen in HIND aligns more closely with that in WIND or HEAT-FW. Regions where HIND is not significantly correlated with either sensitivity experiment are shown in white. This analysis captures the relationship between the experiments on time scales from interannual to multi-decadal.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[85, 85, 464, 108]]<|/det|> +## 3 Discussion and Conclusion + +<|ref|>text<|/ref|><|det|>[[85, 126, 884, 220]]<|/det|> +Our simulations allowed us to identify the underlying drivers of a persistent negative trend in the global oceanic oxygen inventory that began in the early 1970s. We find that the overall oceanic deoxygenation was driven by changes in air- sea heat and freshwater fluxes, and was only partially counteracted by a concomitant wind stress- driven increase in ocean ventilation. + +<|ref|>text<|/ref|><|det|>[[85, 226, 884, 371]]<|/det|> +While the model is generally consistent with observed oxygen trends until the early 2000s, the severity of deoxygenation from 2002 to 2018 is underestimated by a factor of about ten compared to both Ito- 17 and GOBAI- \(O_{2}\) . This misrepresentation of present- day deoxygenation is a common deficiency in state- of- the- art ocean models [9, 34]. Indeed, we find that the insufficient deoxygenation ( \(< 700 \mathrm{m}\) ) in the model analysed here (ORCA025- MOPS) is also simulated by global ocean biogeochemistry models (GOBMs) participating in RECCAP2 [56] (Fig. 4a), which largely contribute to the Global Ocean Carbon Budget [57]. + +<|ref|>text<|/ref|><|det|>[[85, 377, 884, 622]]<|/det|> +Possible reasons for the underestimation of deoxygenation by GOBMs might be (1) deficiencies in simulating critical biogeochemical processes, such as erroneous model stoichiometry or the misrepresentation or neglect of critical biogeochemical feedback mechanisms, (2) biases in ocean circulation and mixing, or (3) biases in the air- sea buoyancy fluxes. Specifically, an insufficient ocean heat content (OHC) increase in response to global warming (Fig. 4b) could be a key factor contributing to the model- observation mismatch, given the strong negative correlation between OHC and ocean oxygen [4, 35]. We find that ORCA025- MOPS underestimates the recent rise in global OHC ( \(< 700 \mathrm{m}\) ) compared to observations [58, 59], and that this underestimation is of similar magnitude to that found in other GOBMs [56] (Fig. 4b). The similarity of the trends across models of different physical and biogeochemical complexity supports the hypothesis that the underestimated OHC trend is not model specific, but common to all of them. + +<|ref|>text<|/ref|><|det|>[[85, 629, 884, 899]]<|/det|> +By contrast, coupled Earth system models (ESMs) from CMIP6, which often have the same ocean components as those used in GOBMs, simulate upper \(700 \mathrm{m}\) deoxygenation and ocean warming trends that are consistent with observation- based estimates within uncertainties, but at the lower end (Fig. 4). Considering the whole water column, the CMIP6 ESMs simulate an oxygen loss of \(0.42 \pm 0.16\%\) (standard deviation over the model ensemble) from 1970 to 2010, which is smaller than the IPCC estimate of \(1.15 \pm 0.88\%\) (90% confidence interval) [3], but still within the large uncertainties arising from extrapolation from spatially and temporally sparse observations, especially in the deep ocean. A similar underestimation over the whole water column has been shown previously for the CMIP5 ESMs [9]. In contrast to the ESMs and observation- based estimates, the GOBMs (RECCAP2) simulate on average a minor oxygen increase over the entire water column of \(0.08 \pm 0.30\%\) (standard deviation over the model ensemble). Since ESMs are able to approximate past deoxygenation in the upper \(700 \mathrm{m}\) and perform better than GOBMs when considering + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 88, 884, 157]]<|/det|> +the whole water column, it is unlikely that the underestimation of deoxygenation in atmospherically forced hindcast simulations by GOBMs is caused by a common bias in the biogeochemical component of GOBMs and ESMs. + +<|ref|>text<|/ref|><|det|>[[112, 163, 884, 485]]<|/det|> +Thus, possible explanations for the underestimation of OHC change and deoxygenation in hindcast models could be either a bias in the atmospheric forcing from reanalysis data or a more general problem with hindcast simulations. While the JRA55- do dataset, which provides high quality and realistic spatial wind patterns, is one of the most widely used reanalysis datasets for hindcast modelling, regional- scale inconsistencies with observations have been found, especially in coastal, equatorial, and subpolar regions [61]. Taboada et al. [61] thus caution that the significant mismatches in high latitudes may affect the rate of deep water formation and upwelling, and in coastal regions may impede the correct reproduction of transport patterns, leading to biases in the model's heat and nutrient fluxes. In addition, forcing a dynamical ocean with a prescribed atmosphere may lead to erroneous results, especially in ventilation, as the interior ocean circulation cannot dynamically feedback on the atmospheric circulation as it does in ESMs. The underestimation of the strong decline in oxygen and rise in OHC over the last 18 years by GOBMs raises the question of whether the GOBM- derived trends in the ocean carbon sink, as reported by the Global Carbon Budget [57], are similarly biased by systematic biases due to the atmospheric forcing or the hindcast approach in general. + +<|ref|>text<|/ref|><|det|>[[112, 491, 884, 812]]<|/det|> +While acknowledging these limitations, the strength of this study is to isolate the mechanisms underlying recent ocean oxygen trends and variability, which are well represented on the regional scale (Section 2.2.2). Our results highlight the importance of regional processes in driving the global response of ocean oxygen to atmospheric change. In our simulations, a key region for global oxygen trends is the Southern Ocean, where the formation of mode and intermediate waters is a key conduit for oxygen, nutrients, and anthropogenic carbon and heat into the interior ocean [62, 63]. In this region, the recent strengthening of westerly winds, caused by increasing greenhouse gas emissions and stratospheric ozone depletion [31], has counteracted the deoxygenation caused by widespread surface warming and mid- depth freshening [64, 65]. The poleward shift and implicit movement of the sinking branch of intermediate water masses into denser water may have further accelerated intermediate water mass formation [41], while increasing oxygen solubility in subducted waters. By modulating the strength of the Southern Ocean upper circulation cell [41] and setting the formation rates and regions of oxygen- rich intermediate water masses [43, 44], the Southern Hemisphere westerly winds are critical for global oxygen supply [33]. + +<|ref|>text<|/ref|><|det|>[[85, 818, 884, 911]]<|/det|> +Recent studies suggest that the current intensification of westerlies in the Southern Hemisphere may slow or cease altogether in the coming century under low to moderate emissions scenarios that align with or approach the temperature targets of the Paris Agreement [32, 66]. In these scenarios, the slowdown in wind stress intensification and the concomitant vanishing of wind- driven oxygen enrichment are attributed to the + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 180, 872, 662]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 679, 883, 833]]<|/det|> +
Figure 4: Annual time series of upper \(700\mathrm{m}\) (a) ocean oxygen and (b) ocean heat content (OHC) anomalies for different observational and model-derived datasets. The model-based estimates are partitioned into estimates from ocean hindcast models (datasets associated with the RECCAP2 effort [60]), shown in blue, and Earth system models from CMIP6, shown in red. For each set, the multi-model mean is shown as a bold solid line and the associated standard deviation is shaded. Thin coloured lines show individual model estimates and the dashed blue line shows the ORCA025-MOPS model estimate (hindcast). Observation-based data are shown in black, with ocean oxygen data sourced from Ito et al. [35] and OHC data from the National Oceanic and Atmospheric Administration (NOAA, data updated from Levitus et al. [58] and available at: https://www.ncei.noaa.gov/access/) and IAPv4 [59] (available at: http://www.ocean.iap.ac.cn). Anomalies are calculated with respect to 1980.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 884, 258]]<|/det|> +recovery of stratospheric ozone by mid- century [67]. The reduction in ventilation- driven oxygen supply under these low emissions scenarios is likely to coincide with additional losses caused by ocean warming, even if \(\mathrm{CO_2}\) emissions are stopped [68- 70]. The ocean will continue to warm to equilibrate with the warmer atmosphere and thus continue to lose oxygen [6, 71, 72]. Our study indicates that the stabilisation or reversal of the past intensification of wind stress, together with continued ocean warming, is likely to accelerate oxygen loss in the future, particularly in the Southern Hemisphere intermediate waters and in regions fed by these water masses, such as the equatorial OMZs [73]. + +<|ref|>text<|/ref|><|det|>[[111, 265, 885, 560]]<|/det|> +Our analysis draws attention to the complex interplay between the counteracting forces of wind stress and air- sea heat and freshwater fluxes, and emphasises their importance in determining changes in oceanic oxygen. Given the centrality of atmospheric drivers in determining trends in oceanic oxygen, we stress the importance of the accurate representation of wind stress and air- sea heat and freshwater fluxes in reanalysis data for assessing past deoxygenation with GOBMs, which do not capture trends in \(\mathrm{O_2}\) and OHC since 2002. In addition, a robust projection of wind stress and air- sea heat and freshwater fluxes in coupled ESMs, which better capture past trends in \(\mathrm{O_2}\) and OHC, is also essential for a robust prediction of future changes in \(\mathrm{O_2}\) and OHC. By identifying and quantifying the drivers of deoxygenation and their regional patterns, this analysis contributes to a much- needed improved mechanistic understanding of \(\mathrm{O_2}\) changes and emphasises the complexity of our changing oceans. Understanding this complexity will ultimately help to better project and anticipate future global and regional \(\mathrm{O_2}\) inventory changes and their potential consequences for marine ecosystems. + +<|ref|>sub_title<|/ref|><|det|>[[86, 595, 258, 616]]<|/det|> +## 4 Methods + +<|ref|>sub_title<|/ref|><|det|>[[115, 639, 403, 657]]<|/det|> +### 4.1 ORCA025-MOPS model + +<|ref|>text<|/ref|><|det|>[[111, 672, 885, 893]]<|/det|> +We used a global configuration of the ocean- sea ice model NEMO- LIM2 [74] with a horizontal resolution of \(0.25^{\circ}\) (ORCA025) and 46 unequally spaced vertical levels that increase with depth [75]. The model resolution is sufficient to capture much of the mesoscale eddy spectrum [76], so no eddy parameterisation was used. The ocean- sea ice model was coupled to the marine biogeochemical model MOPS [77, 78], which simulates the lower trophic levels of the ecosystem and the associated nutrient cycling using nine tracers: phosphate (P), nitrate (N), \(\mathrm{O_2}\) (O), dissolved inorganic carbon, alkalinity, and fixed- stoichiometry representations of phytoplankton, zooplankton, detritus, and dissolved organic matter. We find that the choice of biogeochemical parameters, and in particular the stoichiometric \(\mathrm{O_2:P}\) ratio, can influence the long- term trends and adjustment times of ocean oxygen [77, 79]. To evaluate the uncertainty associated with the choice of the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 89, 883, 131]]<|/det|> +\(\mathrm{O}_{2}\) :P ratio, we employ two different model configurations with different values of the parameter ( \(\mathrm{O}_{2}\) :P ratio of either 150 or 162). + +<|ref|>text<|/ref|><|det|>[[115, 139, 884, 409]]<|/det|> +ORCA025- MOPS was initialised by a spin- up performed with the \(0.5^{\circ}\) resolution model ORCA05- MOPS. ORCA05- MOPS was initialised with climatological temperature and salinity from Levitus98 [80], with World Ocean Atlas 2013 conditions [81, 82] for \(\mathrm{PO}_{4}\) , \(\mathrm{NO}_{3}\) , and \(\mathrm{O}_{2}\) , and with GLODAPv2 conditions [83] for alkalinity and \(\mathrm{C}_{\mathrm{nat}}\) . The model was forced by the JRA55- do runoff dataset (version 1.1, at \(0.25^{\circ}\) horizontal resolution) and JRA55- do atmospheric forcing dataset (version 1.4, at \(0.25^{\circ}\) horizontal and 3- hourly temporal resolution) from 1958 to 2018 [84]. ORCA05- MOPS was run under three cycles of JRA55- do atmospheric forcing, amounting to a total spin- up of 183 years. The end of the third cycle of ORCA05- MOPS provided the biogeochemical initial conditions for a spin- up with the \(0.25^{\circ}\) resolution model ORCA025- MOPS, run under one cycle of JRA55- do atmospheric forcing. For technical reasons, the physics had to be restarted from Levitus98. The end of the fourth cycle provided the initial conditions for the experiments analysed in this study. + +<|ref|>text<|/ref|><|det|>[[115, 416, 884, 659]]<|/det|> +Errors in the representation of biogeochemical processes in the model may bias the results, particularly in the low- oxygen zones along the eastern margins where biogeochemical processes are of high importance in altering the oxygen demand [85]. It has been suggested that the temperature dependence of remineralisation may impact on \(\mathrm{O}_{2}\) concentrations through shoaling of remineralisation profiles [86]. In MOPS, remineralisation is temperature- independent, so these effects are neglected. In addition, biases due to biogeochemical processes may arise especially in iron- limited regions such as the Southern Ocean, but also in equatorial Pacific upwelling regions [87, 88], because ORCA025- MOPS neglects iron limitation of primary productivity. While the calibrated parameters of ORCA025- MOPS may mitigate this deficiency somewhat [79], this omission may still lead to an overestimation of primary production and concomitant remineralisation and oxygen loss below the sea surface. + +<|ref|>sub_title<|/ref|><|det|>[[115, 688, 280, 705]]<|/det|> +### 4.2 Simulations + +<|ref|>text<|/ref|><|det|>[[115, 722, 884, 891]]<|/det|> +For both model configurations ( \(\mathrm{O}_{2}\) :P ratio of 150 or 162), four experiments were run. A hindcast (HIND) experiment, performed under interannual forcing of JRA55- do, simulates changes in the \(\mathrm{O}_{2}\) inventory due to climate change and climate variability. However, HIND may also contain \(\mathrm{O}_{2}\) changes introduced by a spurious model drift, as the model is not expected to be in equilibrium after the relatively short spin- up time compared to deep ocean equilibration timescales. To quantify this model drift, we performed a repeated- year- forcing (RYF) experiment, which was integrated by repeating the JRA55- do forcing of a single year (May 1, 1990 to April 30, 1991), most neutral in terms of the major climate modes [89]. The RYF experiment does not include + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 88, 885, 384]]<|/det|> +changes in the \(\mathrm{O_2}\) inventory due to climate change nor due to climate variability, so any ongoing changes are caused by the model drift. To correct for such model drift, the \(\mathrm{O_2}\) inventories in RYF were removed (gridpoint- wise and for each year) from those in HIND, under the assumption that the drifts in HIND and RYF are the same. As RYF simulates neither climate change nor climate variability, the difference between the two experiments results in \(\mathrm{O_2}\) inventory anomalies caused by climate change and variability only. This procedure is commonly used to differentiate between steady- state and non- steady- state components of ocean biogeochemical tracers [56, 57], including \(\mathrm{O_2}\) [90]. Depending on the selected value of the \(\mathrm{O_2}\) :P ratio, the drifts in the two RYF experiments are \(+0.23\) and \(- 0.13\) Pmol \(\mathrm{O_2}\) per decade (Supplementary Fig. 10a). The choice of the \(\mathrm{O_2}\) :P ratio does not, however, substantially influence the HIND minus RYF \(\mathrm{O_2}\) anomalies. The \(\mathrm{O_2}\) anomalies are nearly identical between the two experiment twins (Supplementary Fig. 10b), suggesting that the biogeochemical parameterisation does not substantially influence the decadal climate- driven \(\mathrm{O_2}\) trends and variability that are the focus of this paper. + +<|ref|>text<|/ref|><|det|>[[87, 391, 885, 684]]<|/det|> +Following the strategy used in Patara et al. [44], two complementary sensitivity experiments were performed to isolate the effects of changing air- sea heat and freshwater fluxes and wind stress on oxygen dynamics. In the air- sea heat and freshwater fluxes experiment (HEAT- FW), the interannual variability of the wind stress was suppressed, while the interannual variability of all variables needed to compute the air- sea fluxes of heat, freshwater, and oxygen was preserved. The opposite is true for the wind stress experiment (WIND), where only the interannual variability of wind stress was retained. The atmospheric variables used to compute the air- sea fluxes of heat, freshwater and oxygen are wind speed, air temperature, humidity, incoming solar radiation, outgoing longwave radiation, and precipitation. Similarly, RYF was subtracted from HEAT- FW and WIND to isolate the effects of interannual variability and climate change in air- sea heat and freshwater fluxes (for HEAT- FW) and in wind stress (for WIND) on oceanic \(\mathrm{O_2}\) . In the text, HIND, HEAT- FW, and WIND refer to the average of the drift- corrected results from each of the two model configurations. + +<|ref|>text<|/ref|><|det|>[[87, 692, 885, 911]]<|/det|> +Changes in oceanic dissolved oxygen were further decomposed into two fractions for analysis of the underlying mechanisms: (1) solubility- driven changes and (2) non- solubility- driven changes (i.e. oxygen changes driven by changes in respiration or ventilation). \(\mathrm{O_2}\) solubility in seawater was approximated by \(\mathrm{O_2^{sat}}\) , calculated from potential temperature and salinity [91], and represents the \(\mathrm{O_2}\) concentration that a water mass has reached when in equilibrium with the \(\mathrm{O_2}\) partial pressure of the overlying atmosphere. Non- solubility- driven oxygen changes were calculated by subtracting the solubility component from the total oxygen anomaly [35], thereby isolating the fraction of oxygen changes that cannot be explained by solubility changes, and corresponds to the opposite of the commonly- used apparent oxygen utilization (AOU) diagnostic. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[85, 87, 393, 106]]<|/det|> +### 4.3 Observational data sets + +<|ref|>text<|/ref|><|det|>[[85, 121, 896, 293]]<|/det|> +We compare the simulated oxygen trends with observation- based estimates. Specifically, we use the observationbased data products GOBAI- \(\mathrm{O_2}\) [36, 37] (2004- 2022), and oxygen concentration anomalies developed by Ito et al. [35], referred to as Ito- 17 (1950- 2015). Due to the relatively low sampling density in Ito- 17 before 1960 and after 2010 [35], only data from 1960- 2010 were used. As Ito- 17 and our simulations provide \(\mathrm{O_2}\) anomalies, GOBAI- \(\mathrm{O_2}\) inventories were also converted to anomalies by subtracting the long- term mean. Additionally, in Supplementary Text 1 and Supplementary Figs. 11- 13, we compare observational climatological distributions of dissolved oxygen and \(\mathrm{O_2^{sat}}\) against the corresponding model outputs. + +<|ref|>text<|/ref|><|det|>[[85, 298, 884, 366]]<|/det|> +Additionally, we compare the simulated trend in global OHC with observation- based estimates. Specifically, we use the observation- based data products IAPv4 [59], and OHC data from the National Oceanic and Atmospheric Administration (NOAA, updated from Levitus et al. [58]). + +<|ref|>sub_title<|/ref|><|det|>[[85, 394, 348, 412]]<|/det|> +### 4.4 RECCAP2 models + +<|ref|>text<|/ref|><|det|>[[85, 427, 886, 623]]<|/det|> +To compare the ORCA025- MOPS simulations with other hindcast simulations, we used 9 GOBMs (CESMETHZ, CNRM- ESM2- 1, EC- Earth3, FESOM- REcoM- LR, MOM6- Princeton, MRI- ESM2- 1, NorESM- OC1.2, ORCA025- GEOMAR, and ORCA1- LIM3- PISCES) from RECCAP2 [56, 60]. Only models evaluated in the model evaluation chapter of RECCAP2 were used, as the remaining models either had drift problems when branching from another simulation with coarser model resolution, had too large salinity biases, or had incomparable historical and control simulations due to their setup [92]. Consistent with the analysis of ORCA025- MOPS, we calculated anomalies in OHC and \(\mathrm{O_2}\) by subtracting the simulation without climate change and variability from the historical simulation with climate change and variability. + +<|ref|>sub_title<|/ref|><|det|>[[85, 650, 449, 670]]<|/det|> +### 4.5 CMIP6 Earth system models + +<|ref|>text<|/ref|><|det|>[[85, 684, 884, 905]]<|/det|> +Additionally, we have analysed all 7 ESMs from CMIP6 to compare hindcast simulations with fully coupled simulations. The ESMs used here are ACCESS- ESM1- 5 (ensemble member rli1p1f1) [93], CanESM5 (rli1p1f1) and CanESM5- CanOE (rli1p2f1) [94], CNRM- ESM2- 1 (rli1p1f2) [95], GFDL- CM4 (rli1p1f1) and GFDL- ESM4 (rli1p1f1) [96- 98], and MPI- ESM1- 2- LR (rli1p1f1) [99, 100]. We used all ESM outputs for which \(\mathrm{O_2}\) and potential temperature were available for the historical simulation, the Shared Socioeconomic Pathways 5- 8.5 (SSP5- 8.5) simulation [101], and the pre- industrial control simulation. The SSP5- 8.5 simulations were used for the years 2015 to 2018 because the historical simulation in CMIP6 stops after 2014. From 2015 to 2018, SSP5- 8.5 was chosen because it is the pathway for which most ESMs provide results. Although it is a high- emission pathway, the radiative forcing in these years is almost identical for + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[83, 88, 884, 258]]<|/det|> +all SSPs in the first year, and the marginal differences in radiative forcing are too small to distinguish the climate in the first years between SSPs. Here the anomaly has been calculated as the difference between the pre- industrial control run and the historical run. As the pre- industrial run still simulates internal climate variability, this difference only quantifies the effect of climate change and externally forced variability, such as volcanoes, and not the effect of internal climate variability. The remaining internal climate variability in the anomaly is a superposition of the internal climate variability from the historical simulation and that from the pre- industrial control simulation, and has no scientific relevance. + +<|ref|>sub_title<|/ref|><|det|>[[85, 294, 241, 315]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[82, 333, 886, 850]]<|/det|> +1. Breitburg, D. et al. Declining oxygen in the global ocean and coastal waters. Science 359, eaam7240 (2018). +2. Schmidtko, S., Stramma, L. & Visbeck, M. Decline in global oceanic oxygen content during the past five decades. Nature 542, 335-339 (2017). +3. Bindoff, N. L. et al. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (eds Pörtner, H. 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Mishonov. 27 pp. (2014). + +<|ref|>text<|/ref|><|det|>[[78, 198, 886, 242]]<|/det|> +82. Garcia, H. E. et al. World Ocean Atlas 2013, Volume 4: Dissolved inorganic nutrients (phosphate, nitrate, silicate). NOAA Atlas NESDIS 76. Ed. by S. Levitus and A. Mishonov. 25 pp. (2014). + +<|ref|>text<|/ref|><|det|>[[78, 253, 886, 297]]<|/det|> +83. Lauvset, S. K. et al. A new global interior ocean mapped climatology: the \(1^{\circ} \times 1^{\circ}\) GLODAP version 2. Earth System Science Data 8, 325-340 (2016). + +<|ref|>text<|/ref|><|det|>[[78, 308, 886, 352]]<|/det|> +84. Tsujino, H. et al. JRA-55 based surface dataset for driving ocean-sea-ice models (JRA55-do). Ocean Modelling 130, 79-139 (2018). + +<|ref|>text<|/ref|><|det|>[[78, 363, 886, 408]]<|/det|> +85. Buchanan, P. J. & Tagliabue, A. The Regional Importance of Oxygen Demand and Supply for Historical Ocean Oxygen Trends. 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Ocean Modelling 147. 101557 (2020). + +<|ref|>text<|/ref|><|det|>[[78, 691, 886, 735]]<|/det|> +90. Mayot, N. et al. Climate-driven variability of the Southern Ocean CO₂ sink. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 381, 20220055 (2023). + +<|ref|>text<|/ref|><|det|>[[78, 746, 886, 790]]<|/det|> +91. Garcia, H. E. & Gordon, L. I. Oxygen solubility in seawater: Better fitting equations. Limnology and Oceanography 37, 1307-1312 (1992). + +<|ref|>text<|/ref|><|det|>[[78, 802, 886, 846]]<|/det|> +92. Terhaar, J. et al. Assessment of global ocean biogeochemistry models for ocean carbon sink estimates in RECCAP2 and recommendations for future studies. ESS Open Archive. preprint (2023). + +<|ref|>text<|/ref|><|det|>[[78, 857, 886, 901]]<|/det|> +93. Ziehn, T. et al. The Australian earth system model: ACCESS-ESM1. 5. 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Journal of Advances in Modeling Earth Systems 11, 998-1038 (2019). + +<|ref|>text<|/ref|><|det|>[[78, 576, 886, 621]]<|/det|> +101. Riahi, K. et al. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview. Global environmental change 42, 153-168 (2017). + +<|ref|>sub_title<|/ref|><|det|>[[78, 648, 350, 667]]<|/det|> +## 4.6 Acknowledgements + +<|ref|>text<|/ref|><|det|>[[78, 682, 886, 777]]<|/det|> +The ocean model simulations were integrated at the North German Supercomputing Alliance (HLRN). L. Patara acknowledges funding from the German Research Foundation under grant PA 3075/2-1. J. Terhaar and H. A. L. Hollitzer acknowledge funding from the Swiss National Science Foundation under grant #PZ00P2_209044. The authors thank Linus Vogt for providing the \(\mathrm{O_2}\) and OHC time series from the ESMs. + +<|ref|>sub_title<|/ref|><|det|>[[78, 804, 325, 823]]<|/det|> +## 4.7 Data availability + +<|ref|>text<|/ref|><|det|>[[78, 838, 886, 908]]<|/det|> +A subset of the output data used for this study will be made freely available on the GEOMAR OPeNDAP Server after acceptance. The Earth system model output used in this study is available via the Earth System Grid Federation at esgf-node.ipsl.upmc.fr/projects/esgf-ipsl. The output from RECCAP2 model output is + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[83, 88, 884, 208]]<|/det|> +openly accessible at zenodo.org/records/7990823 [60]. All observational data used for model evaluation are openly available to the public at the following links: Oxygen data from Ito et al. [35] are available at o2.eas.gatech.edu/data.html and from GOBAI- O2 [36] at doi.org/10.25921/z72m-yz67. The World Ocean Atlas 2018 (WOA18) is accessible at www.ncei.noaa.gov/access/world-ocean-atlas-2018. OHC data from NOAA are available at www.ncei.noaa.gov/access and from IAPv4 [59] at www.ocean.iap.ac.cn. + +<|ref|>sub_title<|/ref|><|det|>[[83, 236, 321, 254]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[83, 270, 884, 363]]<|/det|> +H. A. L. Hollitzer conceived and led the study, produced the figures, and wrote the initial manuscript under the supervision of L. Patara, A. Oschlies, and J. Terhaar. L. Patara performed the experiments with ORCA025-MOPS. All authors contributed to the analysis and interpretation of the results and to the editing of the manuscript into its final draft. + +<|ref|>sub_title<|/ref|><|det|>[[83, 392, 312, 410]]<|/det|> +## Competing interests + +<|ref|>text<|/ref|><|det|>[[83, 427, 431, 443]]<|/det|> +The authors declare no competing interests. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 92, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 316, 150]]<|/det|> +supportinginformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0841883062c0d00625a3f29dfb0823965bfb4e68b1cc4e1fdb984e0c335f4fdf/images_list.json b/preprint/preprint__0841883062c0d00625a3f29dfb0823965bfb4e68b1cc4e1fdb984e0c335f4fdf/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..6019a952bf686c908cf0862095e7a913b20ae4a5 --- /dev/null +++ b/preprint/preprint__0841883062c0d00625a3f29dfb0823965bfb4e68b1cc4e1fdb984e0c335f4fdf/images_list.json @@ -0,0 +1,40 @@ +[ + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "FIG. 2. Temperature dependent pump-probe spectra taken at different pressures. (a) 0 GPa, (b) 4.2 GPa, (c) 8.2 GPa, (d) 13.3 GPa, (e) 16.7 GPa, (f) 19.7 GPa, (g) 26 GPa and (h) 34.2 GPa. It is clear that the negative component exists at low temperatures, and vanishes at temperatures higher than \\(T_{\\mathrm{DW}}\\) for all pressures. The scatters in each panel are the extracted \\(\\tau_{\\mathrm{s}}\\) . PB effect are observed except for 26 GPa, indicating the suppression of the DW gap.", + "footnote": [], + "bbox": [], + "page_idx": 15 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "FIG. 3. Pump-probe spectra at 20 K and extracted parameters. (a) Pump-probe spectra at various pressures at 20 K. The dashed line demonstrates the existence of \\(\\mathrm{A}_{\\mathrm{s}}\\) for 26 GPa. (b) The extracted amplitude \\(\\mathrm{A}_{\\mathrm{s}}\\) and decay time \\(\\tau_{\\mathrm{s}}\\) as function of pressure. \\(\\mathrm{A}_{\\mathrm{s}}\\) decreases with increasing pressure to 26 GPa, then increases with increasing pressure. On the other hand, \\(\\tau_{\\mathrm{s}}\\) shows a quasi-divergent character near 26 GPa, indicating the suppression of the DW gap before 26 GPa.", + "footnote": [], + "bbox": [ + [ + 115, + 88, + 880, + 348 + ] + ], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "FIG. 4. Temperature-Pressure phase diagram of the \\(\\mathrm{La}_{3}\\mathrm{Ni}_{2}\\mathrm{O}_{7}\\) based on the pump-probe spectroscopy measurements. The upper panel shows the evolution of the extracted gap \\(\\Delta_{\\mathrm{DW}}\\) as a function of pressure. \\(\\Delta_{\\mathrm{DW}}\\) first shows a decreasing trend as pressure increases to 13.3 GPa, then decreases slightly before elevating above 26 GPa. The points in the bottom panel are \\(T_{\\mathrm{DW}}\\) , which keeps decreasing to around 85 K at 16.7 GPa, then decreases slightly with pressure up to 19.7 GPa. After 29.4 GPa, \\(T_{\\mathrm{DW}}\\) rises again. The absence of data point at 26 GPa in the upper panel is due to the lack of PB effect and hence the temperature at which the negative component disappears is labeled as a hollow point in the phase diagram.", + "footnote": [], + "bbox": [ + [ + 304, + 90, + 686, + 435 + ] + ], + "page_idx": 17 + } +] \ No newline at end of file diff --git a/preprint/preprint__0841883062c0d00625a3f29dfb0823965bfb4e68b1cc4e1fdb984e0c335f4fdf/preprint__0841883062c0d00625a3f29dfb0823965bfb4e68b1cc4e1fdb984e0c335f4fdf.mmd b/preprint/preprint__0841883062c0d00625a3f29dfb0823965bfb4e68b1cc4e1fdb984e0c335f4fdf/preprint__0841883062c0d00625a3f29dfb0823965bfb4e68b1cc4e1fdb984e0c335f4fdf.mmd new file mode 100644 index 0000000000000000000000000000000000000000..3813f47b2b823f3ecaa294f4d09bc1092dcd351a --- /dev/null +++ b/preprint/preprint__0841883062c0d00625a3f29dfb0823965bfb4e68b1cc4e1fdb984e0c335f4fdf/preprint__0841883062c0d00625a3f29dfb0823965bfb4e68b1cc4e1fdb984e0c335f4fdf.mmd @@ -0,0 +1,307 @@ + +# Density-wave-like gap evolution in La3Ni2O7 under high pressure revealed by ultrafast optical spectroscopy + +Xiao Hui Yu yuxh@iphy.ac.cn + +Institute of Physics, Chinese Academy of Sciences https://orcid.org/0000- 0001- 8880- 2304 + +Yanghao Meng Institute of Physics, Chinese Academy of Sciences + +Yi Yang Shandong University + +Hualei Sun School of Physics, Sun Yat- Sen University + +Sasa Zhang Shandong University https://orcid.org/0000- 0002- 0568- 3803 + +Jianlin Luo Chinese Academy of Sciences + +Liucheng Chen Institute of Physics, Chinese Academy of Sciences + +Xiaoli Ma Chinese Academy of Sciences https://orcid.org/0000- 0002- 8835- 4684 + +Meng Wang School of Physics, Sun Yat- Sen University https://orcid.org/0000- 0002- 8232- 2331 + +Fang Hong Institute of Physics, Chinese Academy of Sciences + +Xinbo Wang Institute of Physics Chinese Academy of Sciences + +## Article + +Keywords: + +Posted Date: June 27th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4592948/v1 + +<--- Page Split ---> + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on November 29th, 2024. See the published version at https://doi.org/10.1038/s41467-024-54518-1. + +<--- Page Split ---> + +# Density-wave-like gap evolution in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) under high pressure revealed by ultrafast optical spectroscopy + +Yanghao Meng \(^{1,2,*}\) , Yi Yang \(^{1,3,*}\) , Hualei Sun \(^{4,*}\) , Sasa Zhang \(^{3,5}\) , Jianlin Luo \(^{1}\) , Liucheng Chen \(^{1,2,6}\) , Xiaoli Ma \(^{1,2,6}\) , Meng Wang \(^{4,*}\) , Fang Hong \(^{1,2,6,*}\) , Xinbo Wang \(^{1,*}\) , and Xiaohui Yu \(^{1,2,6,*}\) \(^{1}\) Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China \(^{2}\) School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China \(^{3}\) Key Laboratory of Education Ministry for Laser and Infrared System Integration Technology, Shandong University, Qingdao 266237, China \(^{4}\) Center for Neutron Science and Technology, Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics, Sun Yat- Sen University, Guangzhou, China \(^{5}\) School of Information Science and Engineering, Shandong University, Qingdao 266237, China \(^{6}\) Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China \(*\) These people contribute equally to the present work. \(^{**}\) Corresponding authors: wangmeng5@mail.sysu.edu.cn, hongfang@iphy.ac.cn, xinbowang@iphy.ac.cn, yuxh@iphy.ac.cn. + +(Dated: June 17, 2024) + +<--- Page Split ---> + +Density- wave- like (DW) order is believed to be correlated with superconductivity in the recently discovered high- temperature superconductor \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) . However, experimental investigations of its evolution under high pressure are still lacking. Here, we investigate the quasiparticle dynamics in bilayer nickelate \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) single crystals using ultrafast optical pump- probe spectroscopy under high pressures up to 34.2 GPa. Near ambient pressure, the temperature- dependent relaxation dynamics demonstrate a phonon bottleneck effect due to the opening of a DW gap at 151 K, with an energy scale of 66 meV as determined by the Rothwarf- Taylor model. With increasing pressure, this phonon bottleneck effect is gradually suppressed and completely disappears around 26 GPa. Remarkably, at pressures above 29.4 GPa, we observe the emergence of a new DW order with a transition temperature of approximately 130 K. Our study provides the first experimental evidence of the evolution of the DW gap under high pressure, offering critical insights into the correlation between DW order and superconductivity in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) . These findings highlight the complex electronic phase transitions in this material and underscore the role of high pressure in tuning its superconducting and DW properties. + +## Introduction + +Nickel- based superconductors have attracted significant interest in physical communities since the first member \(\mathrm{Nd}_{0.8}\mathrm{Sr}_{0.2}\mathrm{NiO}_{2}\) was discovered[1- 4]. They have similar \(d\) electron configurations resembling cuprates, suggesting that nickel- based superconductors could potentially exhibit high- temperature superconductivity as well. Recent studies have confirmed this hypothesis, \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) single crystal was found to show a superconducting transition temperature of \(T_{c} \approx 80 \mathrm{~K}\) at pressures above 14.0 GPa [5]. Many experiments are reporting its superconducting properties at high pressures [6- 9]. However, the mechanism of its superconductivity is still unclear and under debate.[10- 21]. + +In high- temperature superconductors, the interplay between DW order and superconductivity has been a widely investigated topic, since they are strongly related [22]. In \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) , at ambient pressure, two DW transitions were indicated in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) when the temperature is decreased [8, 9, 23- 32]. Nuclear magnetic resonance (NMR) [24, 27- 29], neutron scat + +<--- Page Split ---> + +tering [32], resonant inelastic X- ray scattering (RIXS) [29] and muon spin rotation ( \(\mu\) - SR) experiments [23, 30] showed possible SDW transition around 150 K, accompanied with a striped AFM ground state. Another charge density wave transition was also observed insignificantly through resistance measurements and thermodynamic characterizations, with \(T_{c}\) varying from 110 K [5, 26] to 130 K [9, 28] as indicated by a very small hump in resistance and heat capacity. These transitions were believed to be the result of the correlation of \(d_{x^{2} - y^{2}}\) and \(d_{z^{2}}\) orbitals [11- 13, 16, 33- 36], which was suspected to induce superconductivity and affect paring symmetry under high pressure. Thus, a clear understanding of the evolution of the DW transition at high pressure is required. + +Studies of the DW order in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) at high pressure are currently insufficient. \(\mu\) - SR experiment [30] indicates the DW order can persist up to 2.3 GPa without suppression, but the data at pressure larger than 2.3 GPa is lacking. Some experiments attempt to track the DW evolution under pressure through the presence of a small hump in the resistance- temperature curves, and report DW order vanishes quickly upon compression around 3.0 GPa [6, 9, 25, 26], beyond which the hump in resistance spreads broadly and become insignificant. These resistance measurements face the challenges that the wide spread of the turning point and the potential influence of non- hydrostatic effects under high pressure make it hard to determine the onset of the DW order. On the other hand, the inhomogeneous nature of \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) has been confirmed by X- ray diffraction (XRD) [8, 37, 38], scanning transmission electron microscopy (STEM) [8, 39], and magnetic susceptibility measurements [7]. Neutron scattering experiments have found magnetic excitations without an observable magnetic structure transition [40], indicating that the DW regions are too small for resistance measurements, which averages the results of a whole sample, making the results ambiguous. The evolution of the DW order in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) under high pressure remains unclear and warrants further investigation. + +Here, we report the evolution of DW in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) under high pressure using ultrafast optical spectroscopy. Time- resolved optical pump- probe spectroscopy has been widely employed to study nonequilibrium quasiparticle dynamics in various materials with superconductivity and density wave since it is extremely sensitive to the presence of energy gap [41, 42]. However, performing pump- probe experiments under high pressure and low temperature is challenging due to the technical difficulties in combining high- pressure equipment with cryogenic systems while maintaining optical access for ultrafast laser pulses. Despite these + +<--- Page Split ---> + +challenges, such experiments provide valuable insights into the behavior of materials under extreme conditions [43- 45]. In this work, we observed DW gap opening near ambient pressure below a transition temperature \(T_{\mathrm{DW}}\) , as indicated by the phonon bottleneck (PB) effect of a slow decay component which disappears above \(T_{\mathrm{DW}}\) . The gap fitted by Rothwarf- Taylor (RT) model is \(\Delta_{\mathrm{DW}} = 66 \mathrm{meV}\) . From 0 GPa to 19.7 GPa, low- pressure DW order is suppressed with \(T_{\mathrm{DW}}\) decreasing linearly along with increasing pressure. PB effect disappears at 26 GPa. Above 29.4 GPa, a new DW phase appears, as indicated by the re- emergence of the PB effect and a drastic increase in the transition temperature. The critical rule of interlayer AFM coupling in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) is revealed through the evolution of the extracted \(\Delta_{\mathrm{DW}}\) and the emergence of the DW II order. + +## Results and Discussion + +## DW gap opening near ambient pressure + +Fig. 1(a) shows the time- resolved reflectivity change \(\Delta R / R\) in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) at several selected temperatures near ambient pressure. At high temperatures, photoexcitation leads to a quick rise in the reflectivity, followed by a fast decay into a constant offset with a relaxation time change slightly as temperature is increased to 250 K. Below 151 K, an additional long- lived component with negative amplitude appears [46], which relaxes faster with increases in amplitude along further decreasing temperature. This results in the initial positive change of \(\Delta R / R\) turns to negative. The transition near 151 K corresponds to the DW transition as observed in NMR, and \(\mu\) - SR experiments [23, 24, 27- 30] at ambient pressure. Therefore, we ascribe the fast decay signal to the electron- phonon thermalization and the slow- decay component to the recombination across the DW gap as will be discussed in detail below. Accordingly, we fit the data using a single- component exponential function, \(\Delta R / R = \mathrm{A}_{\mathrm{f}}e^{- t / \tau_{\mathrm{f}}} + C\) above \(T_{\mathrm{DW}}\) , and two- component decay function, \(\Delta R / R = \mathrm{A}_{\mathrm{f}}e^{- t / \tau_{\mathrm{f}}} - \mathrm{A}_{\mathrm{s}}e^{- t / \tau_{\mathrm{s}}} + C\) at low temperature, where A and \(\tau\) represent the relaxation amplitude and decay time, respectively. The subscripts (f and s) denote the fast and slow relaxation processes, respectively. \(C\) is a constant offset. The experimental data can be fitted quite well as shown in Fig. 1(a). The extracted \(\mathrm{A}_{\mathrm{s}}\) and \(\tau_{\mathrm{s}}\) as a function of temperature are plotted in Fig. 1(b). Below \(T_{\mathrm{DW}}\) , \(\mathrm{A}_{\mathrm{s}}\) increases sharply from zero, while \(\tau_{\mathrm{s}}\) shows a continuous divergence. The anomalous behavior can be explained by a relaxation bottleneck + +<--- Page Split ---> + +associated with the opening of a DW gap. + +Here, we employ the RT model to explain the slow relaxation process in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) [47]. It is a phenomenological model that was initially proposed to describe the relaxation of photoexcited superconductors, where the formation of a gap in the electronic density of states creates a relaxation bottleneck of the photoexcited quasiparticles. The recombination is dominated by the emission and reabsorption of the high- frequency phonons whose decay determines the recovery of photoexcited quasiparticles back to the equilibrium states. The RT model has also been demonstrated to be applicable for other systems with gap opening in the density of states, such as change/spin density wave, and heavy fermion [41, 48, 49]. Based on this model, the thermally quasiparticle density \(n_{T}\) is related to the transient reflectivity amplitude \(A\) via \(n_{T} \propto [A(T) / A(T \to 0)]^{- 1} - 1\) . Combining the relationship of \(n_{T} \propto \sqrt{\Delta(T)T} \exp [-\Delta(T) / T]\) , we obtain [50]: + +\[A(T) \propto \frac{\Phi / (\Delta(T) + k_{B}T / 2)}{1 + \gamma \sqrt{2k_{B}T / \Delta(T)} \exp [-\Delta(T) / k_{B}T]} \quad (1)\] + +where the \(\Phi\) is the pump fluence, \(\Delta (T)\) is the temperature dependence of gap energy, \(k_{B}\) is the Boltzmann constant, and \(\gamma\) is a fitting parameter. In the RT model, the relaxation time near transition temperature is dominated by phonons with frequency \(\omega > 2\Delta\) transferring their energy to lower frequency phonons with \(\omega < 2\Delta\) , so the excitation of the condensed quasiparticles would stop. The relaxation time \(\tau\) near transition temperature is given by [50]: + +\[\tau^{-1}(T) \propto \Delta (T), \quad (2)\] + +Assuming that \(\Delta (T)\) obeys BCS temperature dependence \(\Delta (T) \approx \Delta (0) \tanh \left(1.74 \sqrt{\frac{T_{c}}{T}} - 1\right)\) , we fit the \(\mathrm{A}_{\mathrm{s}}\) and \(\tau_{\mathrm{s}}\) using Eq.(1) and (2). The results are shown as the solid lines in Fig. 1(b). From the fit we obtain the transition temperature \(T_{\mathrm{DW}} \sim 151 \mathrm{~K}\) and the gap energy \(\Delta (0) \sim 66 \mathrm{meV}\) , which is in good agreement with the values previously reported by optical conductivity and NMR spectroscopy [28, 51]. The excellent fit strongly supports our assumption of the formation of a gap in the electric density of states due to the development of DW long- range order below \(T_{\mathrm{DW}}\) . We notice a similar work in ref.[52] where no PB effect was observed at ambient pressure which is probably due to the inhomogeneous nature of \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) [7, 8, 37–40], as evidenced in Supplementary Information [46]. + +<--- Page Split ---> + +## DW order evolution at high pressures + +To further track the evolution of DW order in \(\mathrm{La_{3}Ni_{2}O_{7}}\) as a function of pressure, we perform ultrafast pump- probe measurements under high pressure up to 34.2 GPa in DAC. Fig. 2 displays the temperature dependent transient reflectivity data at several selected pressures. The slow relaxation component with negative amplitude that exists at a temperature below \(T_{\mathrm{DW}}\) survives for all pressures. We employ similar fitting procedures described above to the experimental data under various pressures and plot the fitting parameter \(\tau_{\mathrm{s}}\) as scatters in Fig. 2. It is obvious that \(\tau_{\mathrm{s}}\) diverges around \(T_{\mathrm{DW}}\) for all pressures except 26 GPa. Above 29.4 GPa, the relaxation time \(\tau_{\mathrm{s}}\) decreases slightly with increasing temperature and then increase sharply, manifesting a quasi- divergent behavior at \(T_{\mathrm{DW}}\sim 130\mathrm{K}\) . Such a temperature dependence of \(\tau_{\mathrm{s}}\) is similar to that near ambient pressure strongly suggesting the re- opening of an energy gap under pressure above 29.4 GPa. + +In order to obtain detailed information on the gap evolution, the \(\Delta R / R\) signals as a function of pressure at 20 K are plotted in Fig. 3(a). The negative amplitude monotonically reduces with increasing pressure and becomes indistinguishable at 26 GPa above which the negative signal appears again. Fig. 3(b) displays the fitting parameters \(\mathrm{A_{s}}\) and \(\tau_{\mathrm{s}}\) as a function of pressure at 20 K. As the pressure increases up to 2.2 GPa, \(\mathrm{A_{s}}\) drops dramatically, accompanied by a slight decrease of \(\tau_{\mathrm{s}}\) . Upon further compression, \(\mathrm{A_{s}}\) decreases gradually towards zero while \(\tau_{\mathrm{s}}\) exhibits a quasi- divergence around 26 GPa. According to Eq.(2), the relaxation time increases with the decrease of \(\Delta\) at fixed temperature and vice versa. Therefore, we can infer that the increase of \(\tau_{\mathrm{s}}\) with increasing pressure is due to the progressive suppression of the DW gap in this pressure range. Above 29.4 GPa, the increase of \(\mathrm{A_{s}}\) and decrease of \(\tau_{\mathrm{s}}\) indicate the DW gap gets promoted again, which is in line with the phenomenon shown in Fig. 2(h) that PB effect appears again with a higher \(T_{\mathrm{DW}}\) + +The identical RT analysis was applied to the temperature dependence of the slow relaxation \(\tau_{\mathrm{s}}\) and \(\mathrm{A_{s}}\) at high pressures [46]. The extracted \(T_{\mathrm{DW}}\) values are summarized in the Temperature- Pressure phase diagram in Fig. 4. Based on the above high pressure results, the diagram can be divided into two major regions with a critical pressure of 26 GPa, DW I, and DW II. In the low- pressure region, the DW transition is gradually suppressed from \(T_{\mathrm{DW}}\approx 151\mathrm{K}\) near ambient pressure to \(T_{\mathrm{DW}}\approx 110\mathrm{K}\) at 13.3 GPa. \(T_{\mathrm{DW}}\) rapidly decreases to around 85 K at 16.7 GPa and then decreases very slightly with pressure up to 19.7 GPa. + +<--- Page Split ---> + +Since no PB effect is observed at 26 GPa, a value of 95 K, at which the negative decay component disappears, is added to Fig. 4 as a hollow circle for the sake of comparison. Upon further increasing pressure, divergent behavior of \(\tau_{\mathrm{s}}\) appears again near 135 K, suggesting the presence of another energy gap in the density of states. The transition temperature increases slightly with further increasing pressure. + +## Discussion + +The present work gives an unambiguous fact that there is a gap opening in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) . Upon compression, the DW order is gradually suppressed, leading to the decrease of the gap amplitude from \(\sim 66 \mathrm{meV}\) near ambient pressure to \(\sim 20 \mathrm{meV}\) at 13.3 GPa. As the pressure continues to increase, the gap amplitude decreases slightly before vanishing at 26 GPa. In this pressure range, short- range density wave order may exist after the long range order is suppressed and induce the opening of a small gap in the density of states below \(T_{\mathrm{DW}}\) [9]. Although superconductivity with a transition temperature of \(80 \mathrm{K}\) under pressure above 14 GPa has been reported, we do not observe any signature of superconductivity in the transient reflectivity data probably due to the low superconducting volume of \(1\%\) in this nickelate [7]. The coincidence of the critical pressure point between the suppression of DW order and the emergence of superconductivity suggest that the DW order competes with the superconductivity in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) , which is reminiscent of the cuprates and iron- based superconductors [49]. In addition, the emergence of a new DW order under pressure beyond 29.4 GPa is probably related to the charge density wave order as proposed by the theoretical results [20]. The extracted energy gap of \(20 \mathrm{meV}\) , which is comparable to the value obtained in the pressure range from 13.3 to 19.7 GPa, increases slightly with further increased pressure. + +Our results to some extent support the scenario that the interlayer AFM coupling plays a critical role in the correlation between DW orders and superconductivity in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) under pressure. A bilayer \(t - J - J_{\perp}\) model suggests that DW instability increases with decreasing interlayer AFM coupling, whereas SC instability shows the opposite trend[36]. The suppression of the long- range DW order near 13.3 GPa coincides with SC transitions [5, 7–10]. Near 13.3 GPa, a structural transition occurs from the Amam to the Fmmm phase [5, 53], and the Ni- O- Ni angle turns to \(180^{\circ}\) [5, 8], enhancing the interlayer AFM coupling significantly [20, 36]. This suppresses the DW order and leads to strong spin fluctuations that + +<--- Page Split ---> + +facilitate copper pairing. Conversely, the linear decrease in superconducting temperature with increasing pressure beyond 20 GPa indicates that interlayer AFM coupling diminishes with increasing pressure[54], which may explain the emergence of the DW- II order. On the other hand, the observed DW- II order around 29.4 GPa and its increasing trend with increasing pressure coincide with the prediction in ref. [20], which is a consequence of the nesting of a partially flat band under the AFM ground state. The necessity of an AFM ground state for the formation of DW order under high pressure [20] underscores the crucial role played by interlayer AFM coupling in the interplay between DW and superconducting order in this system. + +In summary, we have performed ultrafast pump- probe measurements on the recently discovered nickelate superconductor \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) crystal under pressure up to 34.2 GPa. Near ambient pressure, the temperature dependence of relaxation indicates the appearance of PB effect due to the opening of DW- like gap at 151 K. By analyzing the data with RT model, the energy scale of the gap is identified to be 66 meV, consistent with the previous report. The relaxation bottleneck effect is suppressed gradually by the pressure and disappears around 26 GPa. At high pressure above 29.4 GPa, we discover a new DW order with a transition temperature of \(\sim 130 \mathrm{K}\) . Our results not only provide the first experimental evidence of the DW gap evolution under high pressure but also offer insight into the underlying correlation between the DW order and superconductivity in pressured \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) . + +## Methods + +## High pressure sample loading + +Single- crystalline \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) samples were grown using optical- image floating zone method as described in [5]. Typical sample size is \(1 \mathrm{mm}\) . We smashed the sample into small pieces, then picked up pieces with flat surface under metallographic microscope. High pressure was generated by screw- pressure- type diamond anvil cell (DAC) with a \(500 \mu \mathrm{m}\) culet. The sample chamber with a diameter of \(300 \mu \mathrm{m}\) was made in a Rhenium gasket. A small piece of \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) crystal was loaded in the center of the chamber and a ruby ball was placed aside the sample. Fine KBr powders were employed as the pressure transmitting medium. + +## Low temperature environment and pressure calibration + +The DAC was loaded in a cryostat (Janis ST500) with an optical window for the temperature dependent measurements. An additional thermal sensor was mounted on the force + +<--- Page Split ---> + +plate of the DAC for more precise measurement of sample temperature. The pressure was calibrated using the ruby fluorescence shift at low temperatures for all experiments. + +## Optical measurement + +The pump- probe setup was described in ref. [44], where \(400 \mathrm{nm}\) pump and \(800 \mathrm{nm}\) probe pulses with \(60 \mathrm{fs}\) pulse width and \(50 \mathrm{kHz}\) repetition rate were used. Both beams were focused onto the sample surface using a \(5 \times\) objective lens, giving pump and probe fluences of \(45 \mu \mathrm{J} / \mathrm{cm}^{- 2}\) and \(9 \mu \mathrm{J} / \mathrm{cm}^{- 2}\) , respectively. + +## DATA AVAILABILITY + +Data presented in this paper and the Supplementary Information are available from the corresponding author upon request. + +## References + +[1] Li, D. et al. Superconductivity in an infinite- layer nickelate. Nature 572, 624- 627 (2019). + +[2] Zeng, S. et al. Superconductivity in infinite- layer nickelate \(\mathrm{La}_{1 - x}\mathrm{Ca}_{x}\mathrm{NiO}_{2}\) thin films. Sci. Adv. 8, eabl9927 (2022). + +[3] Osada, M., Wang, B. Y., Lee, K., Li, D. & Hwang, H. Y. Phase diagram of infinite layer praseodymium nickelate \(\mathrm{Pr}_{1 - x}\mathrm{Sr}_{x}\mathrm{NiO}_{2}\) thin films. Phys. Rev. Mater. 4, 121801 (2020). + +[4] Osada, M. et al. 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Spin- density- wave transition in double- layer nickelate \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) . arXiv preprint arXiv:2402.03952 (2024). + +[25] Wu, G., Neumeier, J. & Hundley, M. Magnetic susceptibility, heat capacity, and pressure dependence of the electrical resistivity of \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) and \(\mathrm{La}_{4}\mathrm{Ni}_{3}\mathrm{O}_{10}\) . Phy. Rev. B 63, 245120 (2001). + +[26] Liu, Z. et al. Evidence for charge and spin order in single crystals of \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) and \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{6}\) . Sci. China Phys. Mech. Astron. 66, 217411 (2023). + +[27] Fukamachi, T., Kobayashi, Y., Miyashita, T. & Sato, M. \({}^{139}\mathrm{La}\) NMR studies of layered perovskite systems \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7 - \delta}\) and \(\mathrm{La}_{4}\mathrm{Ni}_{3}\mathrm{O}_{10}\) . J. Phys. Chem. Solids. 62, 195- 198 (2001). + +[28] Kakoi, M. et al. Multiband metallic ground state in multilayered nickelates \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7 - \delta}\) and \(\mathrm{La}_{4}\mathrm{Ni}_{3}\mathrm{O}_{10}\) revealed by \({}^{139}\mathrm{La}\) - NMR at ambient pressure. J. Phys. Soc. Jpn. 93, 053702 (2024). + +[29] Zhou, K.- J. et al. Electronic and magnetic excitations in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) . Preprinted on researchsquare (2024). + +[30] Khasanov, R. et al. Pressure- induced split of the density wave transitions in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7 - \delta}\) . arXiv preprint arXiv:2402.10485 (2024). + +[31] Cui, T. et al. Strain- mediated phase crossover in ruddlesden- popper nickelates. Commun. Mater. 5, 32 (2024). + +[32] Ling, C. D., Argyriou, D. N., Wu, G. & Neumeier, J. Neutron diffraction study of \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) : Structural relationships among \(\mathrm{n} = 1\) , 2, and 3 phases \(\mathrm{La}_{\mathrm{n} + 1}\mathrm{Ni}_{\mathrm{n}}\mathrm{O}_{3\mathrm{n} + 1}\) . J. Solid State Chem. 152, 517- 525 (2000). + +[33] Lu, C., Pan, Z., Yang, F. & Wu, C. Interlayer- coupling- driven high- temperature superconductivity in \(\mathrm{la}_{3}\mathrm{ni}_{2}\mathrm{o}_{7}\) under pressure. Phys. Rev. Lett. 132, 146002 (2024). + +[34] Yang, Q.- G., Wang, D. & Wang, Q.- H. Possible \(s_{\pm}\) - wave superconductivity in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) . Phys. Rev. B 108, L140505 (2023). + +[35] Liu, Y.- B., Mei, J.- W., Ye, F., Chen, W.- Q. & Yang, F. \(s^{\pm}\) - wave pairing and the destructive role of apical- oxygen deficiencies in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) under pressure. Phys. Rev. Lett. 131, 236002 (2023). + +[36] Qu, X.- Z. et al. Bilayer \(t - J - J_{\perp}\) model and magnetically mediated pairing in the pressurized + +<--- Page Split ---> + +nickelate \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) . Phys. Rev. Lett. 132, 036502 (2024). + +[37] Chen, X. et al. Polymorphism in the ruddlesden- popper nickelate \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) : Discovery of a hidden phase with distinctive layer stacking. J. Am. Chem. Soc. (2024). + +[38] Wang, H., Chen, L., Rutherford, A., Zhou, H. & Xie, W. Long- range structural order in a hidden phase of ruddlesden- popper bilayer nickelate \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) . Inorganic Chemistry 63, 5020- 5026 (2024). + +[39] Dong, Z. et al. Visualization of oxygen vacancies and self- doped ligand holes in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7 - \delta}\) . Nature (2024). + +[40] Xie, T. et al. Neutron scattering studies on the high- \(t_{c}\) superconductor \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7 - \delta}\) at ambient pressure. arXiv preprint arXiv:2401.12635 (2024). + +[41] Chia, E. E. M. et al. Ultrafast pump- probe study of phase separation and competing orders in the underdoped \((\mathrm{Ba},\mathrm{K})\mathrm{Fe}_{2}\mathrm{As}_{2}\) superconductor. Phys. Rev. Lett. 104, 027003 (2010). + +[42] Chia, E. E. M. et al. Observation of competing order in a high- \(T_{c}\) superconductor using femtosecond optical pulses. Phys. Rev. Lett. 99, 147008 (2007). + +[43] Ni, K. et al. Stronger interlayer interactions contribute to faster hot carrier cooling of bilayer graphene under pressure. Phys. Rev. Lett. 126, 027402 (2021). + +[44] Yang, Y. et al. Ultrafast carrier and phonon dynamics in \(\mathrm{Bi}_{2}\mathrm{Se}_{3}\) under high pressure. Phys. Rev. B 109, 064307 (2024). + +[45] Fotev, I. et al. Ultrafast relaxation dynamics of spin density wave order in \(\mathrm{BaFe}_{2}\mathrm{As}_{2}\) under high pressures. Phys. Rev. B 108, 035101 (2023). + +[46] See Supplementary Information for the pump- probe spectra of \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) sample at different positions near ambient pressure, Raman spectra at different positions for illustration of the inhomogeneous nature, and RT model fitted results. + +[47] Rothwarf, A. & Taylor, B. N. Measurement of recombination lifetimes in superconductors. Phys. Rev. Lett. 19, 27- 30 (1967). + +[48] Demsar, J., Podobnik, B., Kabanov, V. V., Wolf, T. & Mihailovic, D. Superconducting gap \(\Delta_{c}\) , the pseudogap \(\Delta_{p}\) , and pair fluctuations above \(T_{c}\) in overdoped \(\mathrm{Y}_{1 - x}\mathrm{Ca}_{x}\mathrm{Ba}_{2}\mathrm{Cu}_{3}\mathrm{O}_{7 - \delta}\) from femtosecond time- domain spectroscopy. Phys. Rev. Lett. 82, 4918- 4921 (1999). + +[49] Giannetti, C. et al. Ultrafast optical spectroscopy of strongly correlated materials and high- temperature superconductors: a non- equilibrium approach. Adv. Phys. 65, 58- 238 (2016). + +[50] Kabanov, V. V., Demsar, J., Podobnik, B. & Mihailovic, D. Quasiparticle relaxation dynamics + +<--- Page Split ---> + +in superconductors with different gap structures: Theory and experiments on \(\mathrm{YBa_2Cu_4O_7 - \delta}\) . + +Phys. Rev. B 59, 1497- 1506 (1999). + +[51] Liu, Z. et al. Electronic correlations and energy gap in the bilayer nickelate \(\mathrm{La_3Ni_2O_7}\) . arXiv preprint arXiv:2307.02950 (2023). + +[52] Li, Y. et al. Ultrafast dynamics of bilayer and trilayer nickelate superconductors. arXiv preprint arXiv:2403.05012 (2024). + +[53] Wang, L. et al. Structure responsible for the superconducting state in \(\mathrm{La_3Ni_2O_7}\) at low temperature and high pressure conditions. arXiv preprint arXiv:2311.09186 (2023). + +[54] Li, J. et al. Pressure-driven right-triangle shape superconductivity in bilayer nickelate \(\mathrm{La_3Ni_2O_7}\) . arXiv preprint arXiv:2404.11369 (2024). + +## ACKNOWLEDGMENTS + +This work was supported by the National Natural Science Foundation of China (Grants Nos. 11974414, No. 12374050) and National Key R&D Program of China (Grants No. 2021YFA1400300, No. 2021YFA0718700, No. 2023YFA1608901). This work was supported by Synergic Extreme Condition User Facility (SECUF). + +## AUTHOR CONTRIBUTIONS + +Yanghao Meng, Yi Yang, Xinbo Wang, Fang Hong and Xiaohui Yu. designed the experiment which is performed by Yanghao Meng and Yi Yang. Samples studied in this work were grown by Hualei Sun. and Meng Wang. Data analysis and draft preparation was performed by Yanghao Meng, Yi Yang and Xinbo Wang. All authors contribute to reviewing and editing. The project was supervised by Xiaohui Yu. + +## COMPETING INTERESTS + +The authors declare no competing interests. + +<--- Page Split ---> +![](images/Figure_2.jpg) + + +FIG. 1. Pump- probe spectra and extracted parameters near ambient pressure. (a) \(\Delta R / R\) signals at several selected temperatures near ambient pressure. The solid lines are the fitting curves. Below 151 K, the spectra can be well fitted by two exponential decay, while above 151 K, the spectra can be well fitted by one exponential decay (b) Temperature dependent amplitude \(\mathrm{A}_{\mathrm{s}}\) and relaxation time \(\tau_{\mathrm{s}}\) . \(\mathrm{A}_{\mathrm{s}}\) decreases to nearly 0 at 151 K, where \(\tau_{\mathrm{s}}\) shows a clear divergence. The solid lines are fitting results according to RT model. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
FIG. 2. Temperature dependent pump-probe spectra taken at different pressures. (a) 0 GPa, (b) 4.2 GPa, (c) 8.2 GPa, (d) 13.3 GPa, (e) 16.7 GPa, (f) 19.7 GPa, (g) 26 GPa and (h) 34.2 GPa. It is clear that the negative component exists at low temperatures, and vanishes at temperatures higher than \(T_{\mathrm{DW}}\) for all pressures. The scatters in each panel are the extracted \(\tau_{\mathrm{s}}\) . PB effect are observed except for 26 GPa, indicating the suppression of the DW gap.
+ +<--- Page Split ---> +![](images/Figure_4.jpg) + +
FIG. 3. Pump-probe spectra at 20 K and extracted parameters. (a) Pump-probe spectra at various pressures at 20 K. The dashed line demonstrates the existence of \(\mathrm{A}_{\mathrm{s}}\) for 26 GPa. (b) The extracted amplitude \(\mathrm{A}_{\mathrm{s}}\) and decay time \(\tau_{\mathrm{s}}\) as function of pressure. \(\mathrm{A}_{\mathrm{s}}\) decreases with increasing pressure to 26 GPa, then increases with increasing pressure. On the other hand, \(\tau_{\mathrm{s}}\) shows a quasi-divergent character near 26 GPa, indicating the suppression of the DW gap before 26 GPa.
+ +<--- Page Split ---> +![PLACEHOLDER_18_0] + +
FIG. 4. Temperature-Pressure phase diagram of the \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) based on the pump-probe spectroscopy measurements. The upper panel shows the evolution of the extracted gap \(\Delta_{\mathrm{DW}}\) as a function of pressure. \(\Delta_{\mathrm{DW}}\) first shows a decreasing trend as pressure increases to 13.3 GPa, then decreases slightly before elevating above 26 GPa. The points in the bottom panel are \(T_{\mathrm{DW}}\) , which keeps decreasing to around 85 K at 16.7 GPa, then decreases slightly with pressure up to 19.7 GPa. After 29.4 GPa, \(T_{\mathrm{DW}}\) rises again. The absence of data point at 26 GPa in the upper panel is due to the lack of PB effect and hence the temperature at which the negative component disappears is labeled as a hollow point in the phase diagram.
+ +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- supplementaryinformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0841883062c0d00625a3f29dfb0823965bfb4e68b1cc4e1fdb984e0c335f4fdf/preprint__0841883062c0d00625a3f29dfb0823965bfb4e68b1cc4e1fdb984e0c335f4fdf_det.mmd b/preprint/preprint__0841883062c0d00625a3f29dfb0823965bfb4e68b1cc4e1fdb984e0c335f4fdf/preprint__0841883062c0d00625a3f29dfb0823965bfb4e68b1cc4e1fdb984e0c335f4fdf_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..6974c690982bb391fc6457469650fe50efe70d23 --- /dev/null +++ b/preprint/preprint__0841883062c0d00625a3f29dfb0823965bfb4e68b1cc4e1fdb984e0c335f4fdf/preprint__0841883062c0d00625a3f29dfb0823965bfb4e68b1cc4e1fdb984e0c335f4fdf_det.mmd @@ -0,0 +1,418 @@ +<|ref|>title<|/ref|><|det|>[[42, 106, 923, 210]]<|/det|> +# Density-wave-like gap evolution in La3Ni2O7 under high pressure revealed by ultrafast optical spectroscopy + +<|ref|>text<|/ref|><|det|>[[42, 229, 225, 277]]<|/det|> +Xiao Hui Yu yuxh@iphy.ac.cn + +<|ref|>text<|/ref|><|det|>[[45, 301, 857, 323]]<|/det|> +Institute of Physics, Chinese Academy of Sciences https://orcid.org/0000- 0001- 8880- 2304 + +<|ref|>text<|/ref|><|det|>[[42, 327, 499, 370]]<|/det|> +Yanghao Meng Institute of Physics, Chinese Academy of Sciences + +<|ref|>text<|/ref|><|det|>[[42, 375, 238, 416]]<|/det|> +Yi Yang Shandong University + +<|ref|>text<|/ref|><|det|>[[42, 421, 418, 463]]<|/det|> +Hualei Sun School of Physics, Sun Yat- Sen University + +<|ref|>text<|/ref|><|det|>[[42, 468, 595, 510]]<|/det|> +Sasa Zhang Shandong University https://orcid.org/0000- 0002- 0568- 3803 + +<|ref|>text<|/ref|><|det|>[[42, 514, 322, 555]]<|/det|> +Jianlin Luo Chinese Academy of Sciences + +<|ref|>text<|/ref|><|det|>[[42, 560, 499, 602]]<|/det|> +Liucheng Chen Institute of Physics, Chinese Academy of Sciences + +<|ref|>text<|/ref|><|det|>[[42, 607, 680, 649]]<|/det|> +Xiaoli Ma Chinese Academy of Sciences https://orcid.org/0000- 0002- 8835- 4684 + +<|ref|>text<|/ref|><|det|>[[42, 653, 775, 696]]<|/det|> +Meng Wang School of Physics, Sun Yat- Sen University https://orcid.org/0000- 0002- 8232- 2331 + +<|ref|>text<|/ref|><|det|>[[42, 700, 499, 741]]<|/det|> +Fang Hong Institute of Physics, Chinese Academy of Sciences + +<|ref|>text<|/ref|><|det|>[[42, 745, 499, 787]]<|/det|> +Xinbo Wang Institute of Physics Chinese Academy of Sciences + +<|ref|>sub_title<|/ref|><|det|>[[42, 827, 103, 845]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[42, 865, 137, 884]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[42, 903, 303, 922]]<|/det|> +Posted Date: June 27th, 2024 + +<|ref|>text<|/ref|><|det|>[[42, 941, 475, 960]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4592948/v1 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 914, 87]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 106, 535, 125]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 161, 915, 204]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on November 29th, 2024. See the published version at https://doi.org/10.1038/s41467-024-54518-1. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[122, 84, 872, 142]]<|/det|> +# Density-wave-like gap evolution in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) under high pressure revealed by ultrafast optical spectroscopy + +<|ref|>text<|/ref|><|det|>[[163, 160, 825, 710]]<|/det|> +Yanghao Meng \(^{1,2,*}\) , Yi Yang \(^{1,3,*}\) , Hualei Sun \(^{4,*}\) , Sasa Zhang \(^{3,5}\) , Jianlin Luo \(^{1}\) , Liucheng Chen \(^{1,2,6}\) , Xiaoli Ma \(^{1,2,6}\) , Meng Wang \(^{4,*}\) , Fang Hong \(^{1,2,6,*}\) , Xinbo Wang \(^{1,*}\) , and Xiaohui Yu \(^{1,2,6,*}\) \(^{1}\) Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China \(^{2}\) School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China \(^{3}\) Key Laboratory of Education Ministry for Laser and Infrared System Integration Technology, Shandong University, Qingdao 266237, China \(^{4}\) Center for Neutron Science and Technology, Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices, School of Physics, Sun Yat- Sen University, Guangzhou, China \(^{5}\) School of Information Science and Engineering, Shandong University, Qingdao 266237, China \(^{6}\) Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China \(*\) These people contribute equally to the present work. \(^{**}\) Corresponding authors: wangmeng5@mail.sysu.edu.cn, hongfang@iphy.ac.cn, xinbowang@iphy.ac.cn, yuxh@iphy.ac.cn. + +<|ref|>text<|/ref|><|det|>[[405, 913, 589, 930]]<|/det|> +(Dated: June 17, 2024) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 85, 882, 532]]<|/det|> +Density- wave- like (DW) order is believed to be correlated with superconductivity in the recently discovered high- temperature superconductor \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) . However, experimental investigations of its evolution under high pressure are still lacking. Here, we investigate the quasiparticle dynamics in bilayer nickelate \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) single crystals using ultrafast optical pump- probe spectroscopy under high pressures up to 34.2 GPa. Near ambient pressure, the temperature- dependent relaxation dynamics demonstrate a phonon bottleneck effect due to the opening of a DW gap at 151 K, with an energy scale of 66 meV as determined by the Rothwarf- Taylor model. With increasing pressure, this phonon bottleneck effect is gradually suppressed and completely disappears around 26 GPa. Remarkably, at pressures above 29.4 GPa, we observe the emergence of a new DW order with a transition temperature of approximately 130 K. Our study provides the first experimental evidence of the evolution of the DW gap under high pressure, offering critical insights into the correlation between DW order and superconductivity in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) . These findings highlight the complex electronic phase transitions in this material and underscore the role of high pressure in tuning its superconducting and DW properties. + +<|ref|>sub_title<|/ref|><|det|>[[115, 563, 262, 584]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[114, 618, 882, 825]]<|/det|> +Nickel- based superconductors have attracted significant interest in physical communities since the first member \(\mathrm{Nd}_{0.8}\mathrm{Sr}_{0.2}\mathrm{NiO}_{2}\) was discovered[1- 4]. They have similar \(d\) electron configurations resembling cuprates, suggesting that nickel- based superconductors could potentially exhibit high- temperature superconductivity as well. Recent studies have confirmed this hypothesis, \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) single crystal was found to show a superconducting transition temperature of \(T_{c} \approx 80 \mathrm{~K}\) at pressures above 14.0 GPa [5]. Many experiments are reporting its superconducting properties at high pressures [6- 9]. However, the mechanism of its superconductivity is still unclear and under debate.[10- 21]. + +<|ref|>text<|/ref|><|det|>[[114, 832, 881, 932]]<|/det|> +In high- temperature superconductors, the interplay between DW order and superconductivity has been a widely investigated topic, since they are strongly related [22]. In \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) , at ambient pressure, two DW transitions were indicated in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) when the temperature is decreased [8, 9, 23- 32]. Nuclear magnetic resonance (NMR) [24, 27- 29], neutron scat + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 85, 881, 319]]<|/det|> +tering [32], resonant inelastic X- ray scattering (RIXS) [29] and muon spin rotation ( \(\mu\) - SR) experiments [23, 30] showed possible SDW transition around 150 K, accompanied with a striped AFM ground state. Another charge density wave transition was also observed insignificantly through resistance measurements and thermodynamic characterizations, with \(T_{c}\) varying from 110 K [5, 26] to 130 K [9, 28] as indicated by a very small hump in resistance and heat capacity. These transitions were believed to be the result of the correlation of \(d_{x^{2} - y^{2}}\) and \(d_{z^{2}}\) orbitals [11- 13, 16, 33- 36], which was suspected to induce superconductivity and affect paring symmetry under high pressure. Thus, a clear understanding of the evolution of the DW transition at high pressure is required. + +<|ref|>text<|/ref|><|det|>[[113, 327, 882, 744]]<|/det|> +Studies of the DW order in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) at high pressure are currently insufficient. \(\mu\) - SR experiment [30] indicates the DW order can persist up to 2.3 GPa without suppression, but the data at pressure larger than 2.3 GPa is lacking. Some experiments attempt to track the DW evolution under pressure through the presence of a small hump in the resistance- temperature curves, and report DW order vanishes quickly upon compression around 3.0 GPa [6, 9, 25, 26], beyond which the hump in resistance spreads broadly and become insignificant. These resistance measurements face the challenges that the wide spread of the turning point and the potential influence of non- hydrostatic effects under high pressure make it hard to determine the onset of the DW order. On the other hand, the inhomogeneous nature of \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) has been confirmed by X- ray diffraction (XRD) [8, 37, 38], scanning transmission electron microscopy (STEM) [8, 39], and magnetic susceptibility measurements [7]. Neutron scattering experiments have found magnetic excitations without an observable magnetic structure transition [40], indicating that the DW regions are too small for resistance measurements, which averages the results of a whole sample, making the results ambiguous. The evolution of the DW order in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) under high pressure remains unclear and warrants further investigation. + +<|ref|>text<|/ref|><|det|>[[113, 752, 881, 932]]<|/det|> +Here, we report the evolution of DW in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) under high pressure using ultrafast optical spectroscopy. Time- resolved optical pump- probe spectroscopy has been widely employed to study nonequilibrium quasiparticle dynamics in various materials with superconductivity and density wave since it is extremely sensitive to the presence of energy gap [41, 42]. However, performing pump- probe experiments under high pressure and low temperature is challenging due to the technical difficulties in combining high- pressure equipment with cryogenic systems while maintaining optical access for ultrafast laser pulses. Despite these + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 85, 881, 345]]<|/det|> +challenges, such experiments provide valuable insights into the behavior of materials under extreme conditions [43- 45]. In this work, we observed DW gap opening near ambient pressure below a transition temperature \(T_{\mathrm{DW}}\) , as indicated by the phonon bottleneck (PB) effect of a slow decay component which disappears above \(T_{\mathrm{DW}}\) . The gap fitted by Rothwarf- Taylor (RT) model is \(\Delta_{\mathrm{DW}} = 66 \mathrm{meV}\) . From 0 GPa to 19.7 GPa, low- pressure DW order is suppressed with \(T_{\mathrm{DW}}\) decreasing linearly along with increasing pressure. PB effect disappears at 26 GPa. Above 29.4 GPa, a new DW phase appears, as indicated by the re- emergence of the PB effect and a drastic increase in the transition temperature. The critical rule of interlayer AFM coupling in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) is revealed through the evolution of the extracted \(\Delta_{\mathrm{DW}}\) and the emergence of the DW II order. + +<|ref|>sub_title<|/ref|><|det|>[[115, 377, 382, 399]]<|/det|> +## Results and Discussion + +<|ref|>sub_title<|/ref|><|det|>[[115, 434, 501, 454]]<|/det|> +## DW gap opening near ambient pressure + +<|ref|>text<|/ref|><|det|>[[113, 461, 882, 932]]<|/det|> +Fig. 1(a) shows the time- resolved reflectivity change \(\Delta R / R\) in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) at several selected temperatures near ambient pressure. At high temperatures, photoexcitation leads to a quick rise in the reflectivity, followed by a fast decay into a constant offset with a relaxation time change slightly as temperature is increased to 250 K. Below 151 K, an additional long- lived component with negative amplitude appears [46], which relaxes faster with increases in amplitude along further decreasing temperature. This results in the initial positive change of \(\Delta R / R\) turns to negative. The transition near 151 K corresponds to the DW transition as observed in NMR, and \(\mu\) - SR experiments [23, 24, 27- 30] at ambient pressure. Therefore, we ascribe the fast decay signal to the electron- phonon thermalization and the slow- decay component to the recombination across the DW gap as will be discussed in detail below. Accordingly, we fit the data using a single- component exponential function, \(\Delta R / R = \mathrm{A}_{\mathrm{f}}e^{- t / \tau_{\mathrm{f}}} + C\) above \(T_{\mathrm{DW}}\) , and two- component decay function, \(\Delta R / R = \mathrm{A}_{\mathrm{f}}e^{- t / \tau_{\mathrm{f}}} - \mathrm{A}_{\mathrm{s}}e^{- t / \tau_{\mathrm{s}}} + C\) at low temperature, where A and \(\tau\) represent the relaxation amplitude and decay time, respectively. The subscripts (f and s) denote the fast and slow relaxation processes, respectively. \(C\) is a constant offset. The experimental data can be fitted quite well as shown in Fig. 1(a). The extracted \(\mathrm{A}_{\mathrm{s}}\) and \(\tau_{\mathrm{s}}\) as a function of temperature are plotted in Fig. 1(b). Below \(T_{\mathrm{DW}}\) , \(\mathrm{A}_{\mathrm{s}}\) increases sharply from zero, while \(\tau_{\mathrm{s}}\) shows a continuous divergence. The anomalous behavior can be explained by a relaxation bottleneck + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 87, 470, 107]]<|/det|> +associated with the opening of a DW gap. + +<|ref|>text<|/ref|><|det|>[[112, 112, 883, 401]]<|/det|> +Here, we employ the RT model to explain the slow relaxation process in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) [47]. It is a phenomenological model that was initially proposed to describe the relaxation of photoexcited superconductors, where the formation of a gap in the electronic density of states creates a relaxation bottleneck of the photoexcited quasiparticles. The recombination is dominated by the emission and reabsorption of the high- frequency phonons whose decay determines the recovery of photoexcited quasiparticles back to the equilibrium states. The RT model has also been demonstrated to be applicable for other systems with gap opening in the density of states, such as change/spin density wave, and heavy fermion [41, 48, 49]. Based on this model, the thermally quasiparticle density \(n_{T}\) is related to the transient reflectivity amplitude \(A\) via \(n_{T} \propto [A(T) / A(T \to 0)]^{- 1} - 1\) . Combining the relationship of \(n_{T} \propto \sqrt{\Delta(T)T} \exp [-\Delta(T) / T]\) , we obtain [50]: + +<|ref|>equation<|/ref|><|det|>[[300, 421, 878, 467]]<|/det|> +\[A(T) \propto \frac{\Phi / (\Delta(T) + k_{B}T / 2)}{1 + \gamma \sqrt{2k_{B}T / \Delta(T)} \exp [-\Delta(T) / k_{B}T]} \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[113, 473, 883, 628]]<|/det|> +where the \(\Phi\) is the pump fluence, \(\Delta (T)\) is the temperature dependence of gap energy, \(k_{B}\) is the Boltzmann constant, and \(\gamma\) is a fitting parameter. In the RT model, the relaxation time near transition temperature is dominated by phonons with frequency \(\omega > 2\Delta\) transferring their energy to lower frequency phonons with \(\omega < 2\Delta\) , so the excitation of the condensed quasiparticles would stop. The relaxation time \(\tau\) near transition temperature is given by [50]: + +<|ref|>equation<|/ref|><|det|>[[427, 660, 878, 682]]<|/det|> +\[\tau^{-1}(T) \propto \Delta (T), \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[113, 692, 884, 933]]<|/det|> +Assuming that \(\Delta (T)\) obeys BCS temperature dependence \(\Delta (T) \approx \Delta (0) \tanh \left(1.74 \sqrt{\frac{T_{c}}{T}} - 1\right)\) , we fit the \(\mathrm{A}_{\mathrm{s}}\) and \(\tau_{\mathrm{s}}\) using Eq.(1) and (2). The results are shown as the solid lines in Fig. 1(b). From the fit we obtain the transition temperature \(T_{\mathrm{DW}} \sim 151 \mathrm{~K}\) and the gap energy \(\Delta (0) \sim 66 \mathrm{meV}\) , which is in good agreement with the values previously reported by optical conductivity and NMR spectroscopy [28, 51]. The excellent fit strongly supports our assumption of the formation of a gap in the electric density of states due to the development of DW long- range order below \(T_{\mathrm{DW}}\) . We notice a similar work in ref.[52] where no PB effect was observed at ambient pressure which is probably due to the inhomogeneous nature of \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) [7, 8, 37–40], as evidenced in Supplementary Information [46]. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[114, 114, 483, 134]]<|/det|> +## DW order evolution at high pressures + +<|ref|>text<|/ref|><|det|>[[113, 141, 882, 427]]<|/det|> +To further track the evolution of DW order in \(\mathrm{La_{3}Ni_{2}O_{7}}\) as a function of pressure, we perform ultrafast pump- probe measurements under high pressure up to 34.2 GPa in DAC. Fig. 2 displays the temperature dependent transient reflectivity data at several selected pressures. The slow relaxation component with negative amplitude that exists at a temperature below \(T_{\mathrm{DW}}\) survives for all pressures. We employ similar fitting procedures described above to the experimental data under various pressures and plot the fitting parameter \(\tau_{\mathrm{s}}\) as scatters in Fig. 2. It is obvious that \(\tau_{\mathrm{s}}\) diverges around \(T_{\mathrm{DW}}\) for all pressures except 26 GPa. Above 29.4 GPa, the relaxation time \(\tau_{\mathrm{s}}\) decreases slightly with increasing temperature and then increase sharply, manifesting a quasi- divergent behavior at \(T_{\mathrm{DW}}\sim 130\mathrm{K}\) . Such a temperature dependence of \(\tau_{\mathrm{s}}\) is similar to that near ambient pressure strongly suggesting the re- opening of an energy gap under pressure above 29.4 GPa. + +<|ref|>text<|/ref|><|det|>[[113, 433, 882, 746]]<|/det|> +In order to obtain detailed information on the gap evolution, the \(\Delta R / R\) signals as a function of pressure at 20 K are plotted in Fig. 3(a). The negative amplitude monotonically reduces with increasing pressure and becomes indistinguishable at 26 GPa above which the negative signal appears again. Fig. 3(b) displays the fitting parameters \(\mathrm{A_{s}}\) and \(\tau_{\mathrm{s}}\) as a function of pressure at 20 K. As the pressure increases up to 2.2 GPa, \(\mathrm{A_{s}}\) drops dramatically, accompanied by a slight decrease of \(\tau_{\mathrm{s}}\) . Upon further compression, \(\mathrm{A_{s}}\) decreases gradually towards zero while \(\tau_{\mathrm{s}}\) exhibits a quasi- divergence around 26 GPa. According to Eq.(2), the relaxation time increases with the decrease of \(\Delta\) at fixed temperature and vice versa. Therefore, we can infer that the increase of \(\tau_{\mathrm{s}}\) with increasing pressure is due to the progressive suppression of the DW gap in this pressure range. Above 29.4 GPa, the increase of \(\mathrm{A_{s}}\) and decrease of \(\tau_{\mathrm{s}}\) indicate the DW gap gets promoted again, which is in line with the phenomenon shown in Fig. 2(h) that PB effect appears again with a higher \(T_{\mathrm{DW}}\) + +<|ref|>text<|/ref|><|det|>[[113, 752, 882, 931]]<|/det|> +The identical RT analysis was applied to the temperature dependence of the slow relaxation \(\tau_{\mathrm{s}}\) and \(\mathrm{A_{s}}\) at high pressures [46]. The extracted \(T_{\mathrm{DW}}\) values are summarized in the Temperature- Pressure phase diagram in Fig. 4. Based on the above high pressure results, the diagram can be divided into two major regions with a critical pressure of 26 GPa, DW I, and DW II. In the low- pressure region, the DW transition is gradually suppressed from \(T_{\mathrm{DW}}\approx 151\mathrm{K}\) near ambient pressure to \(T_{\mathrm{DW}}\approx 110\mathrm{K}\) at 13.3 GPa. \(T_{\mathrm{DW}}\) rapidly decreases to around 85 K at 16.7 GPa and then decreases very slightly with pressure up to 19.7 GPa. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 87, 881, 213]]<|/det|> +Since no PB effect is observed at 26 GPa, a value of 95 K, at which the negative decay component disappears, is added to Fig. 4 as a hollow circle for the sake of comparison. Upon further increasing pressure, divergent behavior of \(\tau_{\mathrm{s}}\) appears again near 135 K, suggesting the presence of another energy gap in the density of states. The transition temperature increases slightly with further increasing pressure. + +<|ref|>sub_title<|/ref|><|det|>[[114, 248, 218, 266]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[113, 275, 883, 718]]<|/det|> +The present work gives an unambiguous fact that there is a gap opening in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) . Upon compression, the DW order is gradually suppressed, leading to the decrease of the gap amplitude from \(\sim 66 \mathrm{meV}\) near ambient pressure to \(\sim 20 \mathrm{meV}\) at 13.3 GPa. As the pressure continues to increase, the gap amplitude decreases slightly before vanishing at 26 GPa. In this pressure range, short- range density wave order may exist after the long range order is suppressed and induce the opening of a small gap in the density of states below \(T_{\mathrm{DW}}\) [9]. Although superconductivity with a transition temperature of \(80 \mathrm{K}\) under pressure above 14 GPa has been reported, we do not observe any signature of superconductivity in the transient reflectivity data probably due to the low superconducting volume of \(1\%\) in this nickelate [7]. The coincidence of the critical pressure point between the suppression of DW order and the emergence of superconductivity suggest that the DW order competes with the superconductivity in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) , which is reminiscent of the cuprates and iron- based superconductors [49]. In addition, the emergence of a new DW order under pressure beyond 29.4 GPa is probably related to the charge density wave order as proposed by the theoretical results [20]. The extracted energy gap of \(20 \mathrm{meV}\) , which is comparable to the value obtained in the pressure range from 13.3 to 19.7 GPa, increases slightly with further increased pressure. + +<|ref|>text<|/ref|><|det|>[[113, 726, 882, 931]]<|/det|> +Our results to some extent support the scenario that the interlayer AFM coupling plays a critical role in the correlation between DW orders and superconductivity in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) under pressure. A bilayer \(t - J - J_{\perp}\) model suggests that DW instability increases with decreasing interlayer AFM coupling, whereas SC instability shows the opposite trend[36]. The suppression of the long- range DW order near 13.3 GPa coincides with SC transitions [5, 7–10]. Near 13.3 GPa, a structural transition occurs from the Amam to the Fmmm phase [5, 53], and the Ni- O- Ni angle turns to \(180^{\circ}\) [5, 8], enhancing the interlayer AFM coupling significantly [20, 36]. This suppresses the DW order and leads to strong spin fluctuations that + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 85, 882, 319]]<|/det|> +facilitate copper pairing. Conversely, the linear decrease in superconducting temperature with increasing pressure beyond 20 GPa indicates that interlayer AFM coupling diminishes with increasing pressure[54], which may explain the emergence of the DW- II order. On the other hand, the observed DW- II order around 29.4 GPa and its increasing trend with increasing pressure coincide with the prediction in ref. [20], which is a consequence of the nesting of a partially flat band under the AFM ground state. The necessity of an AFM ground state for the formation of DW order under high pressure [20] underscores the crucial role played by interlayer AFM coupling in the interplay between DW and superconducting order in this system. + +<|ref|>text<|/ref|><|det|>[[113, 326, 882, 585]]<|/det|> +In summary, we have performed ultrafast pump- probe measurements on the recently discovered nickelate superconductor \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) crystal under pressure up to 34.2 GPa. Near ambient pressure, the temperature dependence of relaxation indicates the appearance of PB effect due to the opening of DW- like gap at 151 K. By analyzing the data with RT model, the energy scale of the gap is identified to be 66 meV, consistent with the previous report. The relaxation bottleneck effect is suppressed gradually by the pressure and disappears around 26 GPa. At high pressure above 29.4 GPa, we discover a new DW order with a transition temperature of \(\sim 130 \mathrm{K}\) . Our results not only provide the first experimental evidence of the DW gap evolution under high pressure but also offer insight into the underlying correlation between the DW order and superconductivity in pressured \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 615, 219, 635]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[115, 644, 402, 664]]<|/det|> +## High pressure sample loading + +<|ref|>text<|/ref|><|det|>[[113, 670, 882, 850]]<|/det|> +Single- crystalline \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) samples were grown using optical- image floating zone method as described in [5]. Typical sample size is \(1 \mathrm{mm}\) . We smashed the sample into small pieces, then picked up pieces with flat surface under metallographic microscope. High pressure was generated by screw- pressure- type diamond anvil cell (DAC) with a \(500 \mu \mathrm{m}\) culet. The sample chamber with a diameter of \(300 \mu \mathrm{m}\) was made in a Rhenium gasket. A small piece of \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) crystal was loaded in the center of the chamber and a ruby ball was placed aside the sample. Fine KBr powders were employed as the pressure transmitting medium. + +<|ref|>sub_title<|/ref|><|det|>[[115, 858, 650, 877]]<|/det|> +## Low temperature environment and pressure calibration + +<|ref|>text<|/ref|><|det|>[[115, 885, 880, 931]]<|/det|> +The DAC was loaded in a cryostat (Janis ST500) with an optical window for the temperature dependent measurements. An additional thermal sensor was mounted on the force + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 87, 880, 133]]<|/det|> +plate of the DAC for more precise measurement of sample temperature. The pressure was calibrated using the ruby fluorescence shift at low temperatures for all experiments. + +<|ref|>sub_title<|/ref|><|det|>[[115, 142, 322, 161]]<|/det|> +## Optical measurement + +<|ref|>text<|/ref|><|det|>[[114, 169, 881, 270]]<|/det|> +The pump- probe setup was described in ref. [44], where \(400 \mathrm{nm}\) pump and \(800 \mathrm{nm}\) probe pulses with \(60 \mathrm{fs}\) pulse width and \(50 \mathrm{kHz}\) repetition rate were used. Both beams were focused onto the sample surface using a \(5 \times\) objective lens, giving pump and probe fluences of \(45 \mu \mathrm{J} / \mathrm{cm}^{- 2}\) and \(9 \mu \mathrm{J} / \mathrm{cm}^{- 2}\) , respectively. + +<|ref|>sub_title<|/ref|><|det|>[[139, 316, 340, 334]]<|/det|> +## DATA AVAILABILITY + +<|ref|>text<|/ref|><|det|>[[115, 361, 880, 408]]<|/det|> +Data presented in this paper and the Supplementary Information are available from the corresponding author upon request. + +<|ref|>sub_title<|/ref|><|det|>[[115, 441, 242, 461]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[121, 525, 860, 548]]<|/det|> +[1] Li, D. et al. Superconductivity in an infinite- layer nickelate. Nature 572, 624- 627 (2019). + +<|ref|>text<|/ref|><|det|>[[121, 555, 880, 602]]<|/det|> +[2] Zeng, S. et al. Superconductivity in infinite- layer nickelate \(\mathrm{La}_{1 - x}\mathrm{Ca}_{x}\mathrm{NiO}_{2}\) thin films. Sci. 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N., Wu, G. & Neumeier, J. Neutron diffraction study of \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) : Structural relationships among \(\mathrm{n} = 1\) , 2, and 3 phases \(\mathrm{La}_{\mathrm{n} + 1}\mathrm{Ni}_{\mathrm{n}}\mathrm{O}_{3\mathrm{n} + 1}\) . J. Solid State Chem. 152, 517- 525 (2000). + +<|ref|>text<|/ref|><|det|>[[113, 715, 880, 762]]<|/det|> +[33] Lu, C., Pan, Z., Yang, F. & Wu, C. Interlayer- coupling- driven high- temperature superconductivity in \(\mathrm{la}_{3}\mathrm{ni}_{2}\mathrm{o}_{7}\) under pressure. Phys. Rev. Lett. 132, 146002 (2024). + +<|ref|>text<|/ref|><|det|>[[113, 770, 880, 817]]<|/det|> +[34] Yang, Q.- G., Wang, D. & Wang, Q.- H. Possible \(s_{\pm}\) - wave superconductivity in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) . Phys. Rev. B 108, L140505 (2023). + +<|ref|>text<|/ref|><|det|>[[113, 824, 881, 900]]<|/det|> +[35] Liu, Y.- B., Mei, J.- W., Ye, F., Chen, W.- Q. & Yang, F. \(s^{\pm}\) - wave pairing and the destructive role of apical- oxygen deficiencies in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) under pressure. Phys. Rev. Lett. 131, 236002 (2023). + +<|ref|>text<|/ref|><|det|>[[112, 907, 880, 927]]<|/det|> +[36] Qu, X.- Z. et al. Bilayer \(t - J - J_{\perp}\) model and magnetically mediated pairing in the pressurized + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[150, 88, 606, 107]]<|/det|> +nickelate \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) . Phys. Rev. Lett. 132, 036502 (2024). + +<|ref|>text<|/ref|><|det|>[[115, 115, 881, 163]]<|/det|> +[37] Chen, X. et al. Polymorphism in the ruddlesden- popper nickelate \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) : Discovery of a hidden phase with distinctive layer stacking. J. Am. Chem. Soc. (2024). + +<|ref|>text<|/ref|><|det|>[[115, 170, 881, 245]]<|/det|> +[38] Wang, H., Chen, L., Rutherford, A., Zhou, H. & Xie, W. Long- range structural order in a hidden phase of ruddlesden- popper bilayer nickelate \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) . Inorganic Chemistry 63, 5020- 5026 (2024). + +<|ref|>text<|/ref|><|det|>[[115, 252, 880, 299]]<|/det|> +[39] Dong, Z. et al. Visualization of oxygen vacancies and self- doped ligand holes in \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7 - \delta}\) . Nature (2024). + +<|ref|>text<|/ref|><|det|>[[115, 307, 880, 355]]<|/det|> +[40] Xie, T. et al. Neutron scattering studies on the high- \(t_{c}\) superconductor \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7 - \delta}\) at ambient pressure. arXiv preprint arXiv:2401.12635 (2024). + +<|ref|>text<|/ref|><|det|>[[115, 361, 880, 410]]<|/det|> +[41] Chia, E. E. M. et al. Ultrafast pump- probe study of phase separation and competing orders in the underdoped \((\mathrm{Ba},\mathrm{K})\mathrm{Fe}_{2}\mathrm{As}_{2}\) superconductor. Phys. Rev. Lett. 104, 027003 (2010). + +<|ref|>text<|/ref|><|det|>[[115, 416, 880, 464]]<|/det|> +[42] Chia, E. E. M. et al. Observation of competing order in a high- \(T_{c}\) superconductor using femtosecond optical pulses. Phys. Rev. Lett. 99, 147008 (2007). + +<|ref|>text<|/ref|><|det|>[[115, 470, 880, 519]]<|/det|> +[43] Ni, K. et al. Stronger interlayer interactions contribute to faster hot carrier cooling of bilayer graphene under pressure. Phys. Rev. Lett. 126, 027402 (2021). + +<|ref|>text<|/ref|><|det|>[[115, 525, 880, 573]]<|/det|> +[44] Yang, Y. et al. Ultrafast carrier and phonon dynamics in \(\mathrm{Bi}_{2}\mathrm{Se}_{3}\) under high pressure. Phys. Rev. B 109, 064307 (2024). + +<|ref|>text<|/ref|><|det|>[[115, 580, 880, 628]]<|/det|> +[45] Fotev, I. et al. Ultrafast relaxation dynamics of spin density wave order in \(\mathrm{BaFe}_{2}\mathrm{As}_{2}\) under high pressures. Phys. Rev. B 108, 035101 (2023). + +<|ref|>text<|/ref|><|det|>[[115, 635, 880, 711]]<|/det|> +[46] See Supplementary Information for the pump- probe spectra of \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) sample at different positions near ambient pressure, Raman spectra at different positions for illustration of the inhomogeneous nature, and RT model fitted results. + +<|ref|>text<|/ref|><|det|>[[115, 717, 880, 765]]<|/det|> +[47] Rothwarf, A. & Taylor, B. N. Measurement of recombination lifetimes in superconductors. Phys. Rev. Lett. 19, 27- 30 (1967). + +<|ref|>text<|/ref|><|det|>[[115, 772, 880, 848]]<|/det|> +[48] Demsar, J., Podobnik, B., Kabanov, V. V., Wolf, T. & Mihailovic, D. Superconducting gap \(\Delta_{c}\) , the pseudogap \(\Delta_{p}\) , and pair fluctuations above \(T_{c}\) in overdoped \(\mathrm{Y}_{1 - x}\mathrm{Ca}_{x}\mathrm{Ba}_{2}\mathrm{Cu}_{3}\mathrm{O}_{7 - \delta}\) from femtosecond time- domain spectroscopy. Phys. Rev. Lett. 82, 4918- 4921 (1999). + +<|ref|>text<|/ref|><|det|>[[115, 854, 880, 902]]<|/det|> +[49] Giannetti, C. et al. Ultrafast optical spectroscopy of strongly correlated materials and high- temperature superconductors: a non- equilibrium approach. Adv. Phys. 65, 58- 238 (2016). + +<|ref|>text<|/ref|><|det|>[[111, 908, 880, 928]]<|/det|> +[50] Kabanov, V. V., Demsar, J., Podobnik, B. & Mihailovic, D. Quasiparticle relaxation dynamics + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[150, 89, 879, 109]]<|/det|> +in superconductors with different gap structures: Theory and experiments on \(\mathrm{YBa_2Cu_4O_7 - \delta}\) . + +<|ref|>text<|/ref|><|det|>[[152, 116, 436, 135]]<|/det|> +Phys. Rev. B 59, 1497- 1506 (1999). + +<|ref|>text<|/ref|><|det|>[[115, 143, 880, 191]]<|/det|> +[51] Liu, Z. et al. Electronic correlations and energy gap in the bilayer nickelate \(\mathrm{La_3Ni_2O_7}\) . arXiv preprint arXiv:2307.02950 (2023). + +<|ref|>text<|/ref|><|det|>[[115, 198, 880, 245]]<|/det|> +[52] Li, Y. et al. Ultrafast dynamics of bilayer and trilayer nickelate superconductors. arXiv preprint arXiv:2403.05012 (2024). + +<|ref|>text<|/ref|><|det|>[[115, 252, 880, 300]]<|/det|> +[53] Wang, L. et al. Structure responsible for the superconducting state in \(\mathrm{La_3Ni_2O_7}\) at low temperature and high pressure conditions. arXiv preprint arXiv:2311.09186 (2023). + +<|ref|>text<|/ref|><|det|>[[115, 307, 880, 354]]<|/det|> +[54] Li, J. et al. Pressure-driven right-triangle shape superconductivity in bilayer nickelate \(\mathrm{La_3Ni_2O_7}\) . arXiv preprint arXiv:2404.11369 (2024). + +<|ref|>sub_title<|/ref|><|det|>[[140, 394, 373, 413]]<|/det|> +## ACKNOWLEDGMENTS + +<|ref|>text<|/ref|><|det|>[[113, 437, 881, 538]]<|/det|> +This work was supported by the National Natural Science Foundation of China (Grants Nos. 11974414, No. 12374050) and National Key R&D Program of China (Grants No. 2021YFA1400300, No. 2021YFA0718700, No. 2023YFA1608901). This work was supported by Synergic Extreme Condition User Facility (SECUF). + +<|ref|>sub_title<|/ref|><|det|>[[140, 574, 420, 593]]<|/det|> +## AUTHOR CONTRIBUTIONS + +<|ref|>text<|/ref|><|det|>[[113, 616, 881, 743]]<|/det|> +Yanghao Meng, Yi Yang, Xinbo Wang, Fang Hong and Xiaohui Yu. designed the experiment which is performed by Yanghao Meng and Yi Yang. Samples studied in this work were grown by Hualei Sun. and Meng Wang. Data analysis and draft preparation was performed by Yanghao Meng, Yi Yang and Xinbo Wang. All authors contribute to reviewing and editing. The project was supervised by Xiaohui Yu. + +<|ref|>sub_title<|/ref|><|det|>[[140, 779, 397, 797]]<|/det|> +## COMPETING INTERESTS + +<|ref|>text<|/ref|><|det|>[[140, 823, 510, 842]]<|/det|> +The authors declare no competing interests. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[118, 88, 876, 333]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[113, 382, 883, 540]]<|/det|> +FIG. 1. Pump- probe spectra and extracted parameters near ambient pressure. (a) \(\Delta R / R\) signals at several selected temperatures near ambient pressure. The solid lines are the fitting curves. Below 151 K, the spectra can be well fitted by two exponential decay, while above 151 K, the spectra can be well fitted by one exponential decay (b) Temperature dependent amplitude \(\mathrm{A}_{\mathrm{s}}\) and relaxation time \(\tau_{\mathrm{s}}\) . \(\mathrm{A}_{\mathrm{s}}\) decreases to nearly 0 at 151 K, where \(\tau_{\mathrm{s}}\) shows a clear divergence. The solid lines are fitting results according to RT model. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[113, 93, 879, 349]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 372, 881, 500]]<|/det|> +
FIG. 2. Temperature dependent pump-probe spectra taken at different pressures. (a) 0 GPa, (b) 4.2 GPa, (c) 8.2 GPa, (d) 13.3 GPa, (e) 16.7 GPa, (f) 19.7 GPa, (g) 26 GPa and (h) 34.2 GPa. It is clear that the negative component exists at low temperatures, and vanishes at temperatures higher than \(T_{\mathrm{DW}}\) for all pressures. The scatters in each panel are the extracted \(\tau_{\mathrm{s}}\) . PB effect are observed except for 26 GPa, indicating the suppression of the DW gap.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 88, 880, 348]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 397, 881, 528]]<|/det|> +
FIG. 3. Pump-probe spectra at 20 K and extracted parameters. (a) Pump-probe spectra at various pressures at 20 K. The dashed line demonstrates the existence of \(\mathrm{A}_{\mathrm{s}}\) for 26 GPa. (b) The extracted amplitude \(\mathrm{A}_{\mathrm{s}}\) and decay time \(\tau_{\mathrm{s}}\) as function of pressure. \(\mathrm{A}_{\mathrm{s}}\) decreases with increasing pressure to 26 GPa, then increases with increasing pressure. On the other hand, \(\tau_{\mathrm{s}}\) shows a quasi-divergent character near 26 GPa, indicating the suppression of the DW gap before 26 GPa.
+ +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[304, 90, 686, 435]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 460, 882, 672]]<|/det|> +
FIG. 4. Temperature-Pressure phase diagram of the \(\mathrm{La}_{3}\mathrm{Ni}_{2}\mathrm{O}_{7}\) based on the pump-probe spectroscopy measurements. The upper panel shows the evolution of the extracted gap \(\Delta_{\mathrm{DW}}\) as a function of pressure. \(\Delta_{\mathrm{DW}}\) first shows a decreasing trend as pressure increases to 13.3 GPa, then decreases slightly before elevating above 26 GPa. The points in the bottom panel are \(T_{\mathrm{DW}}\) , which keeps decreasing to around 85 K at 16.7 GPa, then decreases slightly with pressure up to 19.7 GPa. After 29.4 GPa, \(T_{\mathrm{DW}}\) rises again. The absence of data point at 26 GPa in the upper panel is due to the lack of PB effect and hence the temperature at which the negative component disappears is labeled as a hollow point in the phase diagram.
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[42, 92, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 351, 150]]<|/det|> +- supplementaryinformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0843de24eb7cd42d0826bcc04a37651f74c3bae20b743963c695c5dc561236e4/images_list.json b/preprint/preprint__0843de24eb7cd42d0826bcc04a37651f74c3bae20b743963c695c5dc561236e4/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..90dc5ebcbc51127abaa3d4e417ceeec0853af788 --- /dev/null +++ b/preprint/preprint__0843de24eb7cd42d0826bcc04a37651f74c3bae20b743963c695c5dc561236e4/images_list.json @@ -0,0 +1,63 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 42, + 95, + 951, + 644 + ] + ], + "page_idx": 14 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [ + [ + 45, + 45, + 550, + 495 + ] + ], + "page_idx": 15 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4", + "footnote": [], + "bbox": [], + "page_idx": 16 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5", + "footnote": [], + "bbox": [ + [ + 42, + 90, + 899, + 835 + ] + ], + "page_idx": 16 + } +] \ No newline at end of file diff --git a/preprint/preprint__0843de24eb7cd42d0826bcc04a37651f74c3bae20b743963c695c5dc561236e4/preprint__0843de24eb7cd42d0826bcc04a37651f74c3bae20b743963c695c5dc561236e4.mmd b/preprint/preprint__0843de24eb7cd42d0826bcc04a37651f74c3bae20b743963c695c5dc561236e4/preprint__0843de24eb7cd42d0826bcc04a37651f74c3bae20b743963c695c5dc561236e4.mmd new file mode 100644 index 0000000000000000000000000000000000000000..eb5e5ab4de92dc8bd7217b7b26150b1062de0d9c --- /dev/null +++ b/preprint/preprint__0843de24eb7cd42d0826bcc04a37651f74c3bae20b743963c695c5dc561236e4/preprint__0843de24eb7cd42d0826bcc04a37651f74c3bae20b743963c695c5dc561236e4.mmd @@ -0,0 +1,311 @@ + +# Catalyzing Green Lubricants into Graphitic Carbon Layers by Iron Single Atoms to Reduce Friction and Wear + +Jinjin Li li.jinjin@mail.tsinghua.edu.cn + +Tsinghua University https://orcid.org/0000- 0002- 9835- 168X + +Wei Song wsong@illinois.edu + +Chongyang Zeng Imperial College London + +Janet S. S. Wong Imperial College London + +Chuke Ouyang Tsinghua University + +Ali Erdemir Texas A&M University + +Shouyi Sun Tsinghua University + +Seungjoo Lee Texas A&M University + +Weiwei Zhang Tiangong University + +Jianbin Luo Tsinghua University + +Xing Chen Tianjin University https://orcid.org/0000- 0002- 1223- 298X + +## Article + +Keywords: + +Posted Date: August 28th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4413576/v1 + +<--- Page Split ---> + +License: © ① This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on March 25th, 2025. See the published version at https://doi.org/10.1038/s41467-025-58292-6. + +<--- Page Split ---> + +## Abstract + +Reducing friction and wear in moving mechanical systems is essential for their intended functionality. This is currently accomplished using a large variety of anti- friction and anti- wear additives, that usually contain sulfur and phosphorous both of which cause harmful emission. Here, we introduce a series of diesters, typically dioctyl malate (DOM), as green and effective anti- friction and anti- wear additives which reduce wear by factors of 5- 7 and friction by over \(50\%\) compared to conventional additives when tested under extreme pressures (up to 2.78 GPa). Surface studies show that these impressive properties are primarily due to the formation of a \(30 \text{nm}\) graphitic tribofilm that protects rubbing surfaces against wear and hence provides low shear stress at nanoscale. This graphitic tribofilm is prone to form from diesters derived from short- chain carboxylic acid due to their lone pair effect, which stabilizes the carbon free radicals. Furthermore, the formation of this tribofilm was catalyzed by nascent iron single atoms, which were in- situ generated due to the mechanochemical effects during sliding contact. Computational simulations provided additional insights into the steps involved in the catalytic decomposition of DOM by iron and the formation of a graphitic carbon tribofilm. Due to its superior anti- friction and wear properties, DOM holds promise to replace conventional additives, and thus provide a green and more effective alternative for next- generation lubricant formulations. + +## Introduction + +Friction and wear result in significant energy and material losses in moving mechanical systems \(^{1,2}\) . This is commonly alleviated using liquid lubricants \(^{3}\) blended with anti- friction and anti- wear additives \(^{4}\) . Usually, lubricant additives play a crucial role in governing tribological performance, especially during boundary lubrication where the asperities can collide directly and very frequently \(^{4}\) . In recent decades, zinc dialkyl dithiophosphate (ZDDP), a typical lubricant additive, has been widely used in engine oils as an anti- wear agent. Its superior performance can be attributed to the in- situ formation of a patchy tribofilm on rubbing surfaces, preventing a direct metal to metal contact during the rubbing process \(^{5 - 7}\) and hence reducing wear. However, the sulfur and phosphorus in ZDDP are poisonous to the pollutant- reducing catalytic converters in all motored vehicles \(^{8,9}\) . In addition, ZDDP usually cannot provide a satisfactory friction- reducing performance; hence, it must be combined with additional friction modifiers, such as MoDTC. In addition to ZDDP, other nanomaterials have been extensively studied as lubricant additives in past decades. For example, typical nano- carbon nanomaterials, such as graphene \(^{10,11}\) , carbon nanotubes \(^{12}\) , and carbon quantum dots \(^{13,14}\) exhibits excellent friction- reducing and anti- wear performance. However, their application is limited because they cannot be well- dispersed in lubricants, as they agglomerate or separate from the base oil over time \(^{15,16}\) , as a result, they cannot enter the contact area and adhere to surfaces because of their larger size and chemical inertness \(^{4}\) . Therefore, developing environment- friendly additives that are readily soluble in lubricants and provide excellent tribological performance will be essential for achieving long- range efficiency and reliability goals of motored vehicles. + +<--- Page Split ---> + +Generally, a desirable lubricant additive is expected to adsorb strongly on the tribopair surfaces via physisorption or electrostatic interactions, hydrogen bonding or coordination bonding \(^{17,18}\) , and hence it can effectively protect the solid- solid contacts at the initial stage of sliding. Additionally, an ideal lubricant additive should be chemically reactive such that it can be further converted into a low- friction and highly protective tribofilm via the tribochemical reaction during the rubbing process \(^{19,20}\) . Due to strong chemical bonding, such a tribofilm can provide long- lasting reductions for friction and wear and this process can continue as the lost film is very quickly repaired or replenished through the robust tribochemical reaction. + +Amphiphilic molecules, which have been extensively studied by tribologists, are composed of polar head groups and nonpolar tail groups and are widely used as organic friction modifiers in commercial lubricants \(^{21,22}\) . Typical amphiphilic molecules, including fatty alcohols, fatty acids and glycerol monooleate (GMO), usually have no sulfur and phosphorus components, and have been used as green lubricant additives to circumvent harmful effect of ZDDP. Meanwhile, these additives can effectively reduce friction and wear by up to \(45\%\) and \(83\%\) , owing to the formation of a tribofilm, such as a carboxylate on the steel surface \(^{22}\) . Among these amphiphilic molecules, fatty alcohols were found to exhibit higher friction and wear than fatty acids owing to their lower adsorption strength on metal surfaces \(^{23}\) . Accordingly, we hypothesize that if the fatty alcohol is chemically modified into a molecule that can readily adsorb and strongly bond to the rubbing surface, the tribological performance of the modified fatty alcohols will be substantially improved. Here we selected a fatty alcohol molecule (i.e., octanol, see Fig. 1a) and chemically grafted it with malic acid to form the dioctyl malate (DOM in Fig. 1a). As malic acid contains multiple chemically active carboxylic acid and alcohol groups, it is anticipated to enhance the adsorption of DOM on a sliding surface thus leading to a superior anti- friction and wear performance \(^{24,25}\) . DOM was used as an additive in an ultra- low viscosity oil (i.e., polyalphaolefin (PAO2), referred as PAO hereafter) and tested in the boundary lubrication regime where frequent metal- to- metal contact and hence high- friction and - wear were anticipated. + +## The macrotribological performance + +Tribological tests on PAO and DOM addicted PAO (Fig. 1a) were carried out using a steel ball on steel disc tribometer (Optimol Instruments, SRV4) in a reciprocating mode (Fig. 1b). The addition of DOM into PAO substantially reduced friction coefficient (Fig. 1c, from \(\sim 0.25\) to \(\sim 0.11\) ), wear track depth (Fig. 1d, from \(\sim 1.5 \mu \mathrm{m}\) to \(\sim 0.2 \mu \mathrm{m}\) ), width (Fig. 1e, from \(\sim 420 \mu \mathrm{m}\) to \(\sim 224 \mu \mathrm{m}\) ), and wear volume (Fig. 1f, from \(\sim 27 \times 10^{- 5} \mathrm{mm}^3\) to \(\sim 3.7 \times 10^{- 5} \mathrm{mm}^3\) ). Note that the low friction and wear of 5 wt% DOM containing PAO persist even under a heavy load of 176 N (Fig. 1g, Fig. S1b), corresponding to a maximum Hertz contact pressure of 2.78 GPa. In comparison, neat PAO exhibited a steeply increasing friction coefficient (i.e., 0.82) accompanied by severe wear losses (Fig. S1a) once the load reached 76 N in about 10 min, indicating lubrication failure. PAO with other polar additives (i.e., octanol, and oleic acid (OA)) also showed some reduction in friction and wear under a 36 N test (Fig. 1c, f, Figs. S2- S6), but they were still much higher than those observed on DOM with PAO oil. Figs. S2, S3 and S7 in supplementary + +<--- Page Split ---> + +information shows that the DOM additive can reduce the friction coefficient and wear track width by up to \(45.0\%\) and \(21.4\%\) , respectively, compared to other industrial additives (i.e., GMO and ZDDP with friction coefficient of 0.20 and 0.14, and with wear track of \(278 \mu \mathrm{m}\) and \(228 \mu \mathrm{m}\) , respectively). This suggests that DOM is not only a good anti- friction but also an excellent anti- wear additive, and thus holds great promise to replace conventional lubricant additives ZDDP/GMO. + +From Figs. 1e and S5, S6, it is clear that deep scratches and wear debris are present within and around wear tracks (especially toward the end of strokes) formed during tests in PAO, \(5 \mathrm{wt}\%\) octanol, and \(5 \mathrm{wt}\%\) OA; however, these were noticeably absent with \(5 \mathrm{wt}\%\) DOM in PAO oil, further suggesting a much superior anti- wear property provided by DOM additive. + +After the tribological test, the wear track was rinsed with hexane to eliminate lubricant residue on the surface. Note that the rinsing will not affect the surface chemistry/mechanical properties of the wear track (Fig. S8). Interestingly, while neat PAO (Fig. S9), \(5 \mathrm{wt}\%\) OA (Fig. S10), and \(5 \mathrm{wt}\%\) octanol (Fig. S11) exhibited similar wear track appearances before and after rinsing, whereas \(5 \mathrm{wt}\%\) DOM in PAO showed a grey, patchy structure in the wear track after rinsing (Fig. S12b, Fig. 2a). Notably, this grey patchy area is higher than its surrounding area in the wear track (Fig. S12c), suggesting a raised island- like structure formation. Interestingly, the surface coverage of these islands in the wear track gradually increased from \(30 \mathrm{s}\) to \(5 \mathrm{min}\) (Fig. S13), which coincided with the decreasing tendency of friction coefficient (Fig. 1h). This suggests that the formation of these island- like structures may contribute to a superior anti- friction- and- wear performance of DOM. + +## The nanomechanical property of the tribofilm + +Tribological tests suggest patchy island- like film formation on the rubbing surface by \(5 \mathrm{wt}\%\) DOM, has minimised direct steel- steel contacts during rubbing. The mechanical properties of these islands were examined using nanoindentation tests on the three highlighted areas (with circles) shown in Fig. 2a. The penetration depth was fixed at \(40 \mathrm{nm}\) . The maximum nanoindentation load applied to the island area reaches \(220 \mu \mathrm{N}\) (Fig. 2b) which is significantly lower than those measured in the unrubbed or no- island- like areas inside the wear track. Our results suggest the island film has a considerably lower Young's modulus and hardness than its surroundings (Fig. S14). Additionally, the load- time curve on the island- like areas exhibits a viscoelastic behavior (Fig. S15a), suggesting that it might be composed of a polymer- like material \(^{26,27}\) . + +The AFM topography images located at the boundary between the unrubbed and island- like film areas show the typical patchy structure of this film (Fig. S16). The friction property of the film was studied in a \(10 \mu \mathrm{m} \times 10 \mu \mathrm{m}\) area via repeated AFM rubbing tests under a high load of \(60 \mathrm{nN}\) , corresponding to a maximum contact pressure of \(2.41 \mathrm{GPa}\) . Interestingly, while the topography of the island- like film remained the same from the 2nd to 110th rubbing cycles (Fig. S17a,b), the average friction force decreased during the initial 20 rubbing scans (Fig. 2c), after which it reached a plateau. This suggests that the film is strong against wear by the AFM tip and can provide low friction even at nano- scales. + +<--- Page Split ---> + +Meanwhile, the force- distance curves show that the island area exhibited the lowest slope (Fig. 2d) and pull- off force (Fig. 2d, e) than those of the other two areas \(^{28 - 30}\) . This shows that the island film has low adhesion and Young's modulus, which may result in low friction. In contrast, the wear track surface formed by 5 wt% octanol in PAO is easily modified under repeated AFM tip rubbing (Fig. S18). This suggests that octanol cannot form a robust tribofilm with low friction, and is consistent with its high friction and wear at microtubiological tests. + +## The surface chemistry of the tribofilm + +To unravel the chemical composition of the tribofilm, a variety of surface analytical techniques was used on wear tracks. Raman spectra of wear tracks formed in 5 wt% OA and 5 wt% octanol exhibited a distinctive \(\mathrm{Fe_3O_4}\) peak at \(669 \mathrm{cm}^{- 1}\) (Fig. 2f) \(^{31,32}\) , indicating that these surfaces experienced severe oxidation during sliding. In contrast, with the use of 5 wt% DOM in PAO, \(\mathrm{Fe_3O_4}\) peak intensity decreased substantially, and the D and G bands that are typical of graphitic carbon material emerged \(^{33 - 35}\) , especially in the island- like areas. The Raman G band intensity mapping in Fig. 2h showed a distribution that resembles that of the island area in Fig. 2g, further proving the presence of some graphitic carbon material within the island- like tribofilm leading to low friction and wear. These films may have also been responsible for protection against oxidation. Further, Fourier- transform infrared (FTIR) spectra show that very little or no traces of C- H and \(\mathrm{C} = \mathrm{O}\) stretching vibrations from the original PAO and DOM are observed in the wear track formed by 5 wt% DOM (Fig. 2i and Fig. S19), suggesting the dehydrogenation process and complete conversion of carbon backbone of DOM molecules into the carbon tribofilm. Typical \(\mathrm{sp}^2 \mathrm{C} = \mathrm{C}\) stretching originating from the aromatic ring compound \(^{36}\) can be found within the wear track formed by 5 wt% DOM containing PAO (Fig. 2i), which further suggests the formation of graphitic carbon. + +From Fig. 3a, the surface chemistry of the wear track determined by X- ray photoelectron spectroscopy (XPS) using the layer- by- layer sputtering method shows that 5 wt% DOM exhibits a considerably higher carbon concentration (beyond the top contaminant layer) at depths to 26 nm than those observed in the neat PAO, 5 wt% octanol, and unrebbed area. Note that the carbon in wear track of 5 wt% DOM showed \(\mathrm{sp}^2\) hybridization from 2 to 10 nm depth (Fig. 3f) \(^{37}\) , which differs from the \(\mathrm{sp}^3\) hybridized carbon \(^{37}\) in wear track of neat PAO (Fig. 3e) and 5 wt% octanol (Fig. S20), and the C- Fe phase (originate from \(\mathrm{Fe_3C}\) in steel) from the unrebbed area (Fig. 3d) \(^{38,39}\) . Meanwhile, 5 wt% DOM shows lower oxygen (Fig. 3b) and higher iron (Fig. 3c) concentrations (beyond the top native oxide layer) than those in neat PAO and 5 wt% octanol, and its iron exists in the form of iron (0) from 4 to 36 nm (Fig. 3f), which is similar to the unrebbed area (Fig. 3d) \(^{40}\) . In contrast, the wear track of neat PAO and 5 wt% octanol exhibit a distinctive iron oxide from depths of 0 to 36 nm (Fig. 3e, Fig. S20) \(^{41 - 43}\) . These results indicate that 5 wt% DOM forms a carbon tribofilm without an oxidation layer on top of the steel substrate. This tribofilm is approximately 30 nm thick and effectively reduces friction and wear. Conversely, 5 wt% octanol and neat PAO form a thick iron oxide film during rubbing action (Fig. S21), which cannot reduce friction or wear. This finding is consistent with the Raman and FTIR spectra. Note that the absence of oxidation in the + +<--- Page Split ---> + +case of DOM enables nascent iron atoms in steel to catalyze DOM molecules more effectively and hence ensure more effective carbon- based tribofilm formation. + +The cross- sectional view of the tribofilm formed by \(5 \text{wt}\%\) DOM was observed using transmission electron microscopy (TEM). A typical tribofilm with a thickness of \(30 \text{nm}\) is observed between the Cr top- coating and steel substrate (Fig. 4a), and the thickness matches well with the XPS carbon concentration depth profile in Fig. 3a. Notably, some layered material is observed inside the tribofilm (Fig. 4b), and its lattice fringe spacing distance is approximately \(0.39 \text{nm}\) , which is close to the theoretical value of graphene (which is \(0.34 \text{nm}^{44}\) ). EDS elemental mapping images and line scanning show that this tribofilm is rich in carbon (Fig. 4e,f), and its carbon K- edge electron energy loss spectroscopy exhibits typical \(\sigma^*\) and \(\pi^*\) state (Fig. 4c), corresponding to \(\text{sp}^3\) and \(\text{sp}^2\) bonding \(^{45,46}\) . Thereby, combined with the Raman and FTIR results, it can be inferred that this tribofilm consists of graphitic carbon. Additionally, the STEM image and corresponding EDS spectrum show that some iron are also atomically distributed inside the tribofilm (red circles in Fig. 4i), suggesting the iron single atoms formation \(^{47}\) . The extended X- ray absorption fine structure (EXAFS, Fig. 4k, l, Fig. S22, S23) further reveal that the iron single atoms bond with surrounding atoms in the form of - C- O- Fe, which favors their stabilization in the carbon tribofilm. Note that the iron single atoms formation under mechanochemistry effect has been reported in a ball- milling experiment before \(^{48}\) , but to the best of our knowledge, this is the first time to observe their formation from a bulk metal during a macroscopic tribological test. The iron single atoms probably arise from the wear debris generated during rubbing and are formed under the mechanochemistry \(^{48}\) . Here, we propose that iron single atoms were largely responsible for the continuous catalysis of DOM molecules leading to graphitic carbon formation, which will be further discussed in the subsequent section. + +## The effect of molecular structure on mechanochemistry + +The above experiments demonstrate a better tribological performance for DOM than other additives tested. This is ascribed to the much stronger adsorption of DOM on steel surfaces compared to other additives such as octanol, as well as the increased chemical reactivity of DOM to steel surfaces. These attributes can be demonstrated further by the quartz crystal microbalance with dissipation monitoring (QCM- D, Fig. S24, S25), and the energy gap between the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) calculated by first principles (Fig. S26). + +Usually, the tribochemical reaction of graphitic carbon formation can be initiated by the free radicals \(^{49,50}\) , which were easily generated during the rubbing process \(^{51,52}\) . Therefore, this sheds light on the main question raised by this work, i.e., which bond(s) in DOM dissociate first, consequently leading to the formation of the free radicals that initiate the graphitic carbon film formation process? In the case of DOM, considering the lower dissociative energy of the C- C bond compared to the C- H bond, the C- C bond might be more readily dissociated, thus forming free radicals that initiate the tribochemical reactions. Meanwhile, the dissociative energy of one chemical bond can also be influenced by the adjacent groups since these groups affect the stability of generated free radicals through the lone pair effect and/or + +<--- Page Split ---> + +hyperconjugation effect \(^{53}\) . Therefore, it is conceivable that the C- C bond between the hydroxyl- bonded carbon and ester group carbon is the easiest to dissociate, leading to the formation of free radicals as follows, + +![](images/Figure_1.jpg) + + +It is noted that the oxygen from both the hydroxyl side and ester side (the above red atoms) have two lone pairs of electrons which can donate electron density to the half- empty p orbital of carbon radicals. Since carbon radicals are electron deficient species, the electron- donating effect from lone pair electrons help to stabilize them, known as the lone pair effect \(^{53}\) . As a result, the stabilized free radicals can further propagate the tribochemical reactions to form the graphitic carbon \(^{49}\) . To verify this point further, the tribological properties of diester with different chain lengths (Table 1) but similar structure to DOM have been investigated, as shown in Fig. 5. The tribological tests of diesters inluclding dioctyl malonate (DOT), dioctyl fumarate (DOF), dioctyl adipate (DOA), dioctyl sebacate (DOS), didodecyl succinate (DOSN) were all carried out under 36 N at 30 °C. + +Table 1. The molecular structure of diesters derived from different length of carboxylic acids \((n_{\mathrm{c}})\) and different length of alcohols \((n_{\mathrm{a}})\) + +The diester derived from short- chain carboxylic acid (DOT, DOF in Fig. 5a- b, Fig. S27 \(\sim 28\) ) exhibits lower friction \((0.12 \sim 0.13)\) and wear \((210 \mu \mathrm{m})\) compared to that from long- chain carboxylic acid (DOA, DOS in Fig. 5a- b, Fig. S27 \(\sim 28\) ). Moreover, a distinctive grey island tribofilm, composed of graphitic carbon with a low Young's modulus, is observed on wear track formed by diesters derived from short- chain carboxylic acid (DOT, DOF, Fig. 5d \(\sim g\) , Fig. S29 \(\sim 33\) ), a feature not present on the diesters derived from long- chain carboxylic acid (DOA, DOS). Note that the low friction coefficient and wear track width are usually accompanied with the formation of grey island tribofilm with low Young's modulus (Fig. 5c, Fig. S29 \(\sim 31\) ), further implying that this carbon film contributing to the superior tribological performance at macro scale. In addition, the diester derived from short- chain carboxylic acid but long- chain alcohol can also provide low friction, wear, and graphitic carbon formation (DOSN in Fig. 5). Therefore, it can be proposed that for diester derived from short- chain carboxylic acids, the free radicals generated on both sides after C- C bond dissociation can be stabilized by the oxygen from the each side of the disassociated bond through lone pair effect (indicated by red oxygen atoms in DOT, Fig. 5j). This stabilization facilitates the initiation of tribochemical reactions. In contrast, for diesters derived from long- chain carboxylic + +<--- Page Split ---> + +acids, only one side of the generated free radicals can be stabilized by the oxygen from the ester group (DOS, Fig. 5j). The other side is unable to achieve stabilization due to the significant distance between free radicals and oxygen atom, resulting in a high dissociation energy that impedes the effective initiation of reactions. Hence, it can be concluded that the free radicals stabilized by lone pair effect contribute to lower bond dissociation energy, thereby triggering tribochemical reactions that lead to the formation of graphitic carbon and the reduction of friction and wear. + +## The effect of tribopairs on mechanochemistry + +The chemistry of rubbing surfaces can have a strong influence on the formation of tribofilms. When both the top balls and bottom discs were made of \(\mathrm{Si}_3\mathrm{N}_4\) (Fig. S34a,b), sapphire (Fig. S34c,d), glass (Fig. S34e) or Diamond- like Carbon coated steel (DLC) (Fig. S34f), 5 wt% DOM in PAO could not reduce friction. Nevertheless, once the bottom disc specimen is replaced by steel, 5 wt% DOM showed considerably lower friction and wear (Fig. 6a, b), and a graphitic carbon tribofilm was formed once again on the steel surface (Raman in Fig. 6a, b). This suggests that steel (or iron in steel) is an essential requirement for graphitic carbon formation, which might be attributed to its catalytic effects \(^{54}\) . Considering that the catalysis process is an interfacial phenomenon that occurs at the solid- liquid interface, once a tribofilm with a thickness of a few nanometers covers the steel surface, it prevents the interaction between the metallic tribopair surface and liquid lubricant (Fig. 6c); hence, it cannot grow beyond to a thickness of about \(30 \mathrm{nm}\) . Nevertheless, in this study, the iron single atoms observed in TEM (red circles in Fig. 4i) were in- situ generated during the rubbing process and were then incorporated into the graphitic carbon tribofilm (Fig. 6d). Consequently, the iron single- atom catalyst found in tribofilm can maintain the solid- liquid interaction with DOM molecules and therefore continuously catalyze them to produce graphitic carbon tribofilm to a thickness of \(30 \mathrm{nm}\) (Fig. 4a, Fig. 6e). + +To elucidate the atomistic mechanism governing the tribofilm formation on various surfaces, ReaxFF reactive molecular dynamics simulations were conducted with initial configurations comprising DOM molecules sandwiched between sliding tribological interfaces (see Fig. S35). Notably, the release of hydrogen atoms from the DOM molecules on the Fe (110) surface was observed at the beginning of the simulation (Fig. 7b). Remarkably, more than \(80\%\) of the C- H bonds dissociate within 1200 ps, as depicted in Fig. 7d. Consequently, most of the released hydrogen atoms recombine to form \(\mathrm{H}_2\) molecules, as illustrated in Fig. 7e. As a result, multi- membered carbon rings gradually form on the iron surface (Fig. 7c), eventually leading to the reconstruction of a large- scale defective or disordered graphene- like structures \(^{55,56}\) . In contrast, the dehydrogenation of DOM on the \(\mathrm{SiO}_2\) (001) surface occurs at a significantly lower rate ( \(< 15\%\) within 1.25 ns, Fig. 7d), resulting in no/little \(\mathrm{H}_2\) and multi- membered carbon rings formation (Fig. 7c, Fig. S36), which is consistent with the experimental results on glass discs surfaces (Fig. S34e). Thus, the atoms on the steel surface, as catalysts, expedite the dehydrogenation process of DOM, thus facilitating the formation of graphitic carbon. + +## Conclusion + +<--- Page Split ---> + +Containing no heavy metal or sulfur and phosphorus, the diesers in this work, typically DOM, are green and highly effective anti- friction and anti- wear additives under extreme loading conditions and in ultra- low viscosity oils, like PAO2. Compared to other additives (i.e., octanol, oleic acid, GMO, and ZDDP), DOM lowers friction by as much as \(50\%\) and reduces wear by up to \(80\%\) compared to common additives that have been used by industry for many decades. Its superior tribological performance is attributed to the in- situ formation of a graphitic carbon tribofilm on rubbing steel surfaces. This tribofilm, with a low Young's modulus and shear strength, prevents direct metal- to- metal contact hence reducing friction and wear. This film tends to form from diesers derived from short- chain carboxylic acid due to their lone pair effect, which stabilizes the carbon free radicals. Furthermore, this graphitic tribofilm can only form on steel surfaces, suggesting that the catalytic reactivity of the iron single atoms is essential for its continuous generation during sliding. This concept shown here provides a novel strategy for the design of effective and sustainable anti- friction and anti- wear additives (like DOM) for future tribological systems in industrial applications. + +## Declarations + +## Competing interests + +The authors declare no competing interests + +## Additional information + +Supplementary information. The online version contains supplementary material available at XXX + +Correspondence and requests for materials should be addressed to Jinjin Li, or Weiwei Zhang + +## Author contributions + +Wei Song, Janet S. S. Wong and Jinjin Li conceived the idea of the work. Wei Song and Chongyang Zeng performed tribological tests, AFM test and surface analysis. Wei Song, Chuke Ouyang, and Shouyi Sun performed the formal data analysis. Jinjin Li, Janet S. S. Wong, and Jianbin Luo supervised this work and made the writing review & editing. Weiwei Zhang and Xing Chen performed the simulation work. All authors discussed the results and assisted with paper preparation. + +## Acknowledgements + +The authors greatly appreciate Ms Zhiying Cheng for her invaluable help on single atoms analysis through STEM, and also appreciate Miss Yimai Liang, Ms Rong Wang and Ms Weiqi Wang for their tremendous experimental assistance and data interpretation on white light interferometry, FIB and AFM + +<--- Page Split ---> + +experiments. This work was supported by National Key R&D Program of China (grant numbers: 2020YFA0711003), National Natural Science Foundation of China (grant numbers: 52175174 and 52205202) and Postdoctoral Science Foundation of China (No. 2022TQ0177). + +## Data availability + +The data that support the finding of this study are available within the paper and its Supplementary Information, or are available from the figshare data repository (XXXXX) under XXXXX. + +## References + +1. 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Chem Mater 32 (19):8306–8317 +56. Xu X, Xu Z, Sun J, Tang G, Su F (2020) In situ Synthesizing Carbon-Based Film by Tribo-Induced Catalytic Degradation of Poly-α-Olefin Oil for Reducing Friction and Wear. Langmuir 36 (35):10555–10564 + +## Figures + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 1
+ +Tribological performance of PAO with various additives. (a) Molecular structure of the additives. (b) Schematic of a ball- on- disc tribometer with the reciprocating mode. The test details are in the tribological test section in Supplementary Information. (c) Friction coefficient of neat PAO and PAO containing various additives, including 5 wt% octanol, 5 wt% DOM, and 5 wt% OA. The test was conducted under 36 N at 30 °C, and the stroke length was 1 mm with a frequency of 10 Hz. The break in Y axis is from 0.005 to 0.09. (d) Height profiles (position marked in Fig. S5), and (e) optical images of wear tracks on discs lubricated by neat PAO, 5 wt% octanol in PAO, and 5 wt% DOM in PAO. (f) Wear volume of the balls lubricated by different lubricants. The calculation of wear volume calculation is in the tribological test section in Supplementary Information. (g) The friction coefficient of neat PAO and 5 wt% DOM in PAO under a load increasing stepwise from 36 to 176 N. The load increased by 20 N per 5 min, + +<--- Page Split ---> + +as shown in the blue curve. (h) Friction coefficient of 5 wt% DOM and its surface coverage of islands versus time. The calculation of surface coverage can be found in Fig. S13c. + +![](images/Figure_3.jpg) + +
Figure 2
+ +Nano- mechanical and surface chemical analysis on the wear track formed by 5 wt% DOM. (a) Optical microscopy image of wear track. The three marked areas are denoted as on island area inside wear, no- island area inside wear, and unrubbed area, where indentation and AFM tests were performed. (b) Load- depth curves during the indentation tests on three areas in (a). (c) AFM frictional force on a \(10 \mu \mathrm{m} \times 10 \mu \mathrm{m}\) island area during a rubbing test repeated 110 scan number. The insets are the AFM friction images at \(1 \mathrm{st}\) and \(110^{\mathrm{th}}\) scanning. (d) Force- distance curves and (e) histogram of the pull- off force when the AFM tip retracted from the three areas in (a). (f) Raman and (i) FTIR spectra on wear tracks compared to those of other additives. (g) Optical microscopy images of wear track and (h) the corresponding Raman G band intensity mapping images. + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 3
+ +![](images/Figure_5.jpg) + + +Surface topography and chemical analysis on wear track. (a) Carbon, (b) oxygen, and (c) iron atomic concentration at different depth, obtained by XPS sputter test on wear track of different additives. High- resolution XPS spectra of C1s in wear track formed by (d) un rubbed area, (e) neat PAO, and (f) 5 wt% DOM in PAO after normalization. (g) Cross- sectional TEM image of the wear track formed by 5 wt% DOM. (h) HRTEM and (i) high- angle annular dark field STEM (HAADF- STEM) image of the tribofilm area in (a). The inset in (h) is the lattice fringe spacing obtained from the red row. (j) Electron energy loss spectroscopy (EELS) spectrum, (k) Fourier transformation (FT) of k2- weighted extended X- ray absorption fine structure (EXAFS) spectra of Fe foil, \(\mathrm{Fe}_2\mathrm{O}_3\) , \(\mathrm{Fe}_3\mathrm{O}_4\) and the tribofilm formed by 5 wt% DOM. (l) Wavelet transforms (WTs) for the k2- weighted EXAFS signals from the tribofilm formed by 5 wt% DOM. + +<--- Page Split ---> +![PLACEHOLDER_17_0] + +
Figure 4
+ +Tribological performance and surface analysis on wear track of additives with different chain lengths. (a) Molecular structure of dioctyl malonate (DOT), dioctyl fumarate (DOF), dioctyl adipate (DOA), dioctyl sebacate (DOS), didodecyl succinate (DOSN), (b) friction coefficient, (c) optical image of wear track before rinsing, (d) magnified optical image of wear track after rinsing, (e) Raman spectra on wear track, (f) Raman G band mapping image, (g) load- depth curves during indentation tests on wear track formed + +<--- Page Split ---> + +by additives with different chain lengths. (h) The proposed free radical generation mechanism of DOT and DOS during friction. The scale bars in (d)(f) are \(25 \mu \mathrm{m}\) . + +![PLACEHOLDER_18_0] + +
Figure 5
+ +Schematic of the structural evolution of the DOM molecules confined between two Fe (100) surfaces. (a) at 0 ps; (b) at 600 ps; (c) at 1200 ps. The number of (d) C- H bonds, (e) H- H bonds and (f) carbon rings, + +<--- Page Split ---> + +throughout the simulation where the DOM molecules are confined between two Fe (110) and \(\mathrm{SiO_2}\) (001) surfaces. + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- Sl.docx + +<--- Page Split ---> diff --git a/preprint/preprint__0843de24eb7cd42d0826bcc04a37651f74c3bae20b743963c695c5dc561236e4/preprint__0843de24eb7cd42d0826bcc04a37651f74c3bae20b743963c695c5dc561236e4_det.mmd b/preprint/preprint__0843de24eb7cd42d0826bcc04a37651f74c3bae20b743963c695c5dc561236e4/preprint__0843de24eb7cd42d0826bcc04a37651f74c3bae20b743963c695c5dc561236e4_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..6b2b49d95f78456caea96ecb20ca641d56c1d40d --- /dev/null +++ b/preprint/preprint__0843de24eb7cd42d0826bcc04a37651f74c3bae20b743963c695c5dc561236e4/preprint__0843de24eb7cd42d0826bcc04a37651f74c3bae20b743963c695c5dc561236e4_det.mmd @@ -0,0 +1,405 @@ +<|ref|>title<|/ref|><|det|>[[43, 106, 930, 208]]<|/det|> +# Catalyzing Green Lubricants into Graphitic Carbon Layers by Iron Single Atoms to Reduce Friction and Wear + +<|ref|>text<|/ref|><|det|>[[43, 230, 378, 277]]<|/det|> +Jinjin Li li.jinjin@mail.tsinghua.edu.cn + +<|ref|>text<|/ref|><|det|>[[43, 302, 590, 322]]<|/det|> +Tsinghua University https://orcid.org/0000- 0002- 9835- 168X + +<|ref|>text<|/ref|><|det|>[[43, 327, 228, 368]]<|/det|> +Wei Song wsong@illinois.edu + +<|ref|>text<|/ref|><|det|>[[43, 374, 270, 415]]<|/det|> +Chongyang Zeng Imperial College London + +<|ref|>text<|/ref|><|det|>[[43, 420, 270, 460]]<|/det|> +Janet S. S. Wong Imperial College London + +<|ref|>text<|/ref|><|det|>[[43, 466, 230, 507]]<|/det|> +Chuke Ouyang Tsinghua University + +<|ref|>text<|/ref|><|det|>[[43, 512, 247, 552]]<|/det|> +Ali Erdemir Texas A&M University + +<|ref|>text<|/ref|><|det|>[[43, 558, 230, 599]]<|/det|> +Shouyi Sun Tsinghua University + +<|ref|>text<|/ref|><|det|>[[43, 604, 247, 645]]<|/det|> +Seungjoo Lee Texas A&M University + +<|ref|>text<|/ref|><|det|>[[43, 650, 230, 691]]<|/det|> +Weiwei Zhang Tiangong University + +<|ref|>text<|/ref|><|det|>[[43, 696, 230, 737]]<|/det|> +Jianbin Luo Tsinghua University + +<|ref|>text<|/ref|><|det|>[[43, 742, 584, 784]]<|/det|> +Xing Chen Tianjin University https://orcid.org/0000- 0002- 1223- 298X + +<|ref|>sub_title<|/ref|><|det|>[[43, 826, 103, 844]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[43, 864, 135, 882]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[43, 901, 321, 920]]<|/det|> +Posted Date: August 28th, 2024 + +<|ref|>text<|/ref|><|det|>[[43, 939, 475, 959]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4413576/v1 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 916, 87]]<|/det|> +License: © ① This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 105, 535, 125]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 161, 930, 205]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on March 25th, 2025. See the published version at https://doi.org/10.1038/s41467-025-58292-6. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 157, 68]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[39, 82, 955, 423]]<|/det|> +Reducing friction and wear in moving mechanical systems is essential for their intended functionality. This is currently accomplished using a large variety of anti- friction and anti- wear additives, that usually contain sulfur and phosphorous both of which cause harmful emission. Here, we introduce a series of diesters, typically dioctyl malate (DOM), as green and effective anti- friction and anti- wear additives which reduce wear by factors of 5- 7 and friction by over \(50\%\) compared to conventional additives when tested under extreme pressures (up to 2.78 GPa). Surface studies show that these impressive properties are primarily due to the formation of a \(30 \text{nm}\) graphitic tribofilm that protects rubbing surfaces against wear and hence provides low shear stress at nanoscale. This graphitic tribofilm is prone to form from diesters derived from short- chain carboxylic acid due to their lone pair effect, which stabilizes the carbon free radicals. Furthermore, the formation of this tribofilm was catalyzed by nascent iron single atoms, which were in- situ generated due to the mechanochemical effects during sliding contact. Computational simulations provided additional insights into the steps involved in the catalytic decomposition of DOM by iron and the formation of a graphitic carbon tribofilm. Due to its superior anti- friction and wear properties, DOM holds promise to replace conventional additives, and thus provide a green and more effective alternative for next- generation lubricant formulations. + +<|ref|>sub_title<|/ref|><|det|>[[44, 445, 204, 471]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[39, 485, 951, 934]]<|/det|> +Friction and wear result in significant energy and material losses in moving mechanical systems \(^{1,2}\) . This is commonly alleviated using liquid lubricants \(^{3}\) blended with anti- friction and anti- wear additives \(^{4}\) . Usually, lubricant additives play a crucial role in governing tribological performance, especially during boundary lubrication where the asperities can collide directly and very frequently \(^{4}\) . In recent decades, zinc dialkyl dithiophosphate (ZDDP), a typical lubricant additive, has been widely used in engine oils as an anti- wear agent. Its superior performance can be attributed to the in- situ formation of a patchy tribofilm on rubbing surfaces, preventing a direct metal to metal contact during the rubbing process \(^{5 - 7}\) and hence reducing wear. However, the sulfur and phosphorus in ZDDP are poisonous to the pollutant- reducing catalytic converters in all motored vehicles \(^{8,9}\) . In addition, ZDDP usually cannot provide a satisfactory friction- reducing performance; hence, it must be combined with additional friction modifiers, such as MoDTC. In addition to ZDDP, other nanomaterials have been extensively studied as lubricant additives in past decades. For example, typical nano- carbon nanomaterials, such as graphene \(^{10,11}\) , carbon nanotubes \(^{12}\) , and carbon quantum dots \(^{13,14}\) exhibits excellent friction- reducing and anti- wear performance. However, their application is limited because they cannot be well- dispersed in lubricants, as they agglomerate or separate from the base oil over time \(^{15,16}\) , as a result, they cannot enter the contact area and adhere to surfaces because of their larger size and chemical inertness \(^{4}\) . Therefore, developing environment- friendly additives that are readily soluble in lubricants and provide excellent tribological performance will be essential for achieving long- range efficiency and reliability goals of motored vehicles. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 45, 940, 228]]<|/det|> +Generally, a desirable lubricant additive is expected to adsorb strongly on the tribopair surfaces via physisorption or electrostatic interactions, hydrogen bonding or coordination bonding \(^{17,18}\) , and hence it can effectively protect the solid- solid contacts at the initial stage of sliding. Additionally, an ideal lubricant additive should be chemically reactive such that it can be further converted into a low- friction and highly protective tribofilm via the tribochemical reaction during the rubbing process \(^{19,20}\) . Due to strong chemical bonding, such a tribofilm can provide long- lasting reductions for friction and wear and this process can continue as the lost film is very quickly repaired or replenished through the robust tribochemical reaction. + +<|ref|>text<|/ref|><|det|>[[39, 245, 950, 640]]<|/det|> +Amphiphilic molecules, which have been extensively studied by tribologists, are composed of polar head groups and nonpolar tail groups and are widely used as organic friction modifiers in commercial lubricants \(^{21,22}\) . Typical amphiphilic molecules, including fatty alcohols, fatty acids and glycerol monooleate (GMO), usually have no sulfur and phosphorus components, and have been used as green lubricant additives to circumvent harmful effect of ZDDP. Meanwhile, these additives can effectively reduce friction and wear by up to \(45\%\) and \(83\%\) , owing to the formation of a tribofilm, such as a carboxylate on the steel surface \(^{22}\) . Among these amphiphilic molecules, fatty alcohols were found to exhibit higher friction and wear than fatty acids owing to their lower adsorption strength on metal surfaces \(^{23}\) . Accordingly, we hypothesize that if the fatty alcohol is chemically modified into a molecule that can readily adsorb and strongly bond to the rubbing surface, the tribological performance of the modified fatty alcohols will be substantially improved. Here we selected a fatty alcohol molecule (i.e., octanol, see Fig. 1a) and chemically grafted it with malic acid to form the dioctyl malate (DOM in Fig. 1a). As malic acid contains multiple chemically active carboxylic acid and alcohol groups, it is anticipated to enhance the adsorption of DOM on a sliding surface thus leading to a superior anti- friction and wear performance \(^{24,25}\) . DOM was used as an additive in an ultra- low viscosity oil (i.e., polyalphaolefin (PAO2), referred as PAO hereafter) and tested in the boundary lubrication regime where frequent metal- to- metal contact and hence high- friction and - wear were anticipated. + +<|ref|>sub_title<|/ref|><|det|>[[44, 660, 510, 688]]<|/det|> +## The macrotribological performance + +<|ref|>text<|/ref|><|det|>[[41, 702, 950, 954]]<|/det|> +Tribological tests on PAO and DOM addicted PAO (Fig. 1a) were carried out using a steel ball on steel disc tribometer (Optimol Instruments, SRV4) in a reciprocating mode (Fig. 1b). The addition of DOM into PAO substantially reduced friction coefficient (Fig. 1c, from \(\sim 0.25\) to \(\sim 0.11\) ), wear track depth (Fig. 1d, from \(\sim 1.5 \mu \mathrm{m}\) to \(\sim 0.2 \mu \mathrm{m}\) ), width (Fig. 1e, from \(\sim 420 \mu \mathrm{m}\) to \(\sim 224 \mu \mathrm{m}\) ), and wear volume (Fig. 1f, from \(\sim 27 \times 10^{- 5} \mathrm{mm}^3\) to \(\sim 3.7 \times 10^{- 5} \mathrm{mm}^3\) ). Note that the low friction and wear of 5 wt% DOM containing PAO persist even under a heavy load of 176 N (Fig. 1g, Fig. S1b), corresponding to a maximum Hertz contact pressure of 2.78 GPa. In comparison, neat PAO exhibited a steeply increasing friction coefficient (i.e., 0.82) accompanied by severe wear losses (Fig. S1a) once the load reached 76 N in about 10 min, indicating lubrication failure. PAO with other polar additives (i.e., octanol, and oleic acid (OA)) also showed some reduction in friction and wear under a 36 N test (Fig. 1c, f, Figs. S2- S6), but they were still much higher than those observed on DOM with PAO oil. Figs. S2, S3 and S7 in supplementary + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 44, 936, 156]]<|/det|> +information shows that the DOM additive can reduce the friction coefficient and wear track width by up to \(45.0\%\) and \(21.4\%\) , respectively, compared to other industrial additives (i.e., GMO and ZDDP with friction coefficient of 0.20 and 0.14, and with wear track of \(278 \mu \mathrm{m}\) and \(228 \mu \mathrm{m}\) , respectively). This suggests that DOM is not only a good anti- friction but also an excellent anti- wear additive, and thus holds great promise to replace conventional lubricant additives ZDDP/GMO. + +<|ref|>text<|/ref|><|det|>[[41, 173, 944, 263]]<|/det|> +From Figs. 1e and S5, S6, it is clear that deep scratches and wear debris are present within and around wear tracks (especially toward the end of strokes) formed during tests in PAO, \(5 \mathrm{wt}\%\) octanol, and \(5 \mathrm{wt}\%\) OA; however, these were noticeably absent with \(5 \mathrm{wt}\%\) DOM in PAO oil, further suggesting a much superior anti- wear property provided by DOM additive. + +<|ref|>text<|/ref|><|det|>[[40, 280, 951, 506]]<|/det|> +After the tribological test, the wear track was rinsed with hexane to eliminate lubricant residue on the surface. Note that the rinsing will not affect the surface chemistry/mechanical properties of the wear track (Fig. S8). Interestingly, while neat PAO (Fig. S9), \(5 \mathrm{wt}\%\) OA (Fig. S10), and \(5 \mathrm{wt}\%\) octanol (Fig. S11) exhibited similar wear track appearances before and after rinsing, whereas \(5 \mathrm{wt}\%\) DOM in PAO showed a grey, patchy structure in the wear track after rinsing (Fig. S12b, Fig. 2a). Notably, this grey patchy area is higher than its surrounding area in the wear track (Fig. S12c), suggesting a raised island- like structure formation. Interestingly, the surface coverage of these islands in the wear track gradually increased from \(30 \mathrm{s}\) to \(5 \mathrm{min}\) (Fig. S13), which coincided with the decreasing tendency of friction coefficient (Fig. 1h). This suggests that the formation of these island- like structures may contribute to a superior anti- friction- and- wear performance of DOM. + +<|ref|>sub_title<|/ref|><|det|>[[45, 527, 635, 556]]<|/det|> +## The nanomechanical property of the tribofilm + +<|ref|>text<|/ref|><|det|>[[41, 569, 945, 775]]<|/det|> +Tribological tests suggest patchy island- like film formation on the rubbing surface by \(5 \mathrm{wt}\%\) DOM, has minimised direct steel- steel contacts during rubbing. The mechanical properties of these islands were examined using nanoindentation tests on the three highlighted areas (with circles) shown in Fig. 2a. The penetration depth was fixed at \(40 \mathrm{nm}\) . The maximum nanoindentation load applied to the island area reaches \(220 \mu \mathrm{N}\) (Fig. 2b) which is significantly lower than those measured in the unrubbed or no- island- like areas inside the wear track. Our results suggest the island film has a considerably lower Young's modulus and hardness than its surroundings (Fig. S14). Additionally, the load- time curve on the island- like areas exhibits a viscoelastic behavior (Fig. S15a), suggesting that it might be composed of a polymer- like material \(^{26,27}\) . + +<|ref|>text<|/ref|><|det|>[[41, 791, 944, 950]]<|/det|> +The AFM topography images located at the boundary between the unrubbed and island- like film areas show the typical patchy structure of this film (Fig. S16). The friction property of the film was studied in a \(10 \mu \mathrm{m} \times 10 \mu \mathrm{m}\) area via repeated AFM rubbing tests under a high load of \(60 \mathrm{nN}\) , corresponding to a maximum contact pressure of \(2.41 \mathrm{GPa}\) . Interestingly, while the topography of the island- like film remained the same from the 2nd to 110th rubbing cycles (Fig. S17a,b), the average friction force decreased during the initial 20 rubbing scans (Fig. 2c), after which it reached a plateau. This suggests that the film is strong against wear by the AFM tip and can provide low friction even at nano- scales. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 44, 944, 180]]<|/det|> +Meanwhile, the force- distance curves show that the island area exhibited the lowest slope (Fig. 2d) and pull- off force (Fig. 2d, e) than those of the other two areas \(^{28 - 30}\) . This shows that the island film has low adhesion and Young's modulus, which may result in low friction. In contrast, the wear track surface formed by 5 wt% octanol in PAO is easily modified under repeated AFM tip rubbing (Fig. S18). This suggests that octanol cannot form a robust tribofilm with low friction, and is consistent with its high friction and wear at microtubiological tests. + +<|ref|>sub_title<|/ref|><|det|>[[44, 203, 537, 231]]<|/det|> +## The surface chemistry of the tribofilm + +<|ref|>text<|/ref|><|det|>[[39, 243, 951, 594]]<|/det|> +To unravel the chemical composition of the tribofilm, a variety of surface analytical techniques was used on wear tracks. Raman spectra of wear tracks formed in 5 wt% OA and 5 wt% octanol exhibited a distinctive \(\mathrm{Fe_3O_4}\) peak at \(669 \mathrm{cm}^{- 1}\) (Fig. 2f) \(^{31,32}\) , indicating that these surfaces experienced severe oxidation during sliding. In contrast, with the use of 5 wt% DOM in PAO, \(\mathrm{Fe_3O_4}\) peak intensity decreased substantially, and the D and G bands that are typical of graphitic carbon material emerged \(^{33 - 35}\) , especially in the island- like areas. The Raman G band intensity mapping in Fig. 2h showed a distribution that resembles that of the island area in Fig. 2g, further proving the presence of some graphitic carbon material within the island- like tribofilm leading to low friction and wear. These films may have also been responsible for protection against oxidation. Further, Fourier- transform infrared (FTIR) spectra show that very little or no traces of C- H and \(\mathrm{C} = \mathrm{O}\) stretching vibrations from the original PAO and DOM are observed in the wear track formed by 5 wt% DOM (Fig. 2i and Fig. S19), suggesting the dehydrogenation process and complete conversion of carbon backbone of DOM molecules into the carbon tribofilm. Typical \(\mathrm{sp}^2 \mathrm{C} = \mathrm{C}\) stretching originating from the aromatic ring compound \(^{36}\) can be found within the wear track formed by 5 wt% DOM containing PAO (Fig. 2i), which further suggests the formation of graphitic carbon. + +<|ref|>text<|/ref|><|det|>[[39, 609, 953, 960]]<|/det|> +From Fig. 3a, the surface chemistry of the wear track determined by X- ray photoelectron spectroscopy (XPS) using the layer- by- layer sputtering method shows that 5 wt% DOM exhibits a considerably higher carbon concentration (beyond the top contaminant layer) at depths to 26 nm than those observed in the neat PAO, 5 wt% octanol, and unrebbed area. Note that the carbon in wear track of 5 wt% DOM showed \(\mathrm{sp}^2\) hybridization from 2 to 10 nm depth (Fig. 3f) \(^{37}\) , which differs from the \(\mathrm{sp}^3\) hybridized carbon \(^{37}\) in wear track of neat PAO (Fig. 3e) and 5 wt% octanol (Fig. S20), and the C- Fe phase (originate from \(\mathrm{Fe_3C}\) in steel) from the unrebbed area (Fig. 3d) \(^{38,39}\) . Meanwhile, 5 wt% DOM shows lower oxygen (Fig. 3b) and higher iron (Fig. 3c) concentrations (beyond the top native oxide layer) than those in neat PAO and 5 wt% octanol, and its iron exists in the form of iron (0) from 4 to 36 nm (Fig. 3f), which is similar to the unrebbed area (Fig. 3d) \(^{40}\) . In contrast, the wear track of neat PAO and 5 wt% octanol exhibit a distinctive iron oxide from depths of 0 to 36 nm (Fig. 3e, Fig. S20) \(^{41 - 43}\) . These results indicate that 5 wt% DOM forms a carbon tribofilm without an oxidation layer on top of the steel substrate. This tribofilm is approximately 30 nm thick and effectively reduces friction and wear. Conversely, 5 wt% octanol and neat PAO form a thick iron oxide film during rubbing action (Fig. S21), which cannot reduce friction or wear. This finding is consistent with the Raman and FTIR spectra. Note that the absence of oxidation in the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 953, 88]]<|/det|> +case of DOM enables nascent iron atoms in steel to catalyze DOM molecules more effectively and hence ensure more effective carbon- based tribofilm formation. + +<|ref|>text<|/ref|><|det|>[[39, 103, 953, 548]]<|/det|> +The cross- sectional view of the tribofilm formed by \(5 \text{wt}\%\) DOM was observed using transmission electron microscopy (TEM). A typical tribofilm with a thickness of \(30 \text{nm}\) is observed between the Cr top- coating and steel substrate (Fig. 4a), and the thickness matches well with the XPS carbon concentration depth profile in Fig. 3a. Notably, some layered material is observed inside the tribofilm (Fig. 4b), and its lattice fringe spacing distance is approximately \(0.39 \text{nm}\) , which is close to the theoretical value of graphene (which is \(0.34 \text{nm}^{44}\) ). EDS elemental mapping images and line scanning show that this tribofilm is rich in carbon (Fig. 4e,f), and its carbon K- edge electron energy loss spectroscopy exhibits typical \(\sigma^*\) and \(\pi^*\) state (Fig. 4c), corresponding to \(\text{sp}^3\) and \(\text{sp}^2\) bonding \(^{45,46}\) . Thereby, combined with the Raman and FTIR results, it can be inferred that this tribofilm consists of graphitic carbon. Additionally, the STEM image and corresponding EDS spectrum show that some iron are also atomically distributed inside the tribofilm (red circles in Fig. 4i), suggesting the iron single atoms formation \(^{47}\) . The extended X- ray absorption fine structure (EXAFS, Fig. 4k, l, Fig. S22, S23) further reveal that the iron single atoms bond with surrounding atoms in the form of - C- O- Fe, which favors their stabilization in the carbon tribofilm. Note that the iron single atoms formation under mechanochemistry effect has been reported in a ball- milling experiment before \(^{48}\) , but to the best of our knowledge, this is the first time to observe their formation from a bulk metal during a macroscopic tribological test. The iron single atoms probably arise from the wear debris generated during rubbing and are formed under the mechanochemistry \(^{48}\) . Here, we propose that iron single atoms were largely responsible for the continuous catalysis of DOM molecules leading to graphitic carbon formation, which will be further discussed in the subsequent section. + +<|ref|>sub_title<|/ref|><|det|>[[45, 568, 775, 596]]<|/det|> +## The effect of molecular structure on mechanochemistry + +<|ref|>text<|/ref|><|det|>[[42, 608, 950, 744]]<|/det|> +The above experiments demonstrate a better tribological performance for DOM than other additives tested. This is ascribed to the much stronger adsorption of DOM on steel surfaces compared to other additives such as octanol, as well as the increased chemical reactivity of DOM to steel surfaces. These attributes can be demonstrated further by the quartz crystal microbalance with dissipation monitoring (QCM- D, Fig. S24, S25), and the energy gap between the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) calculated by first principles (Fig. S26). + +<|ref|>text<|/ref|><|det|>[[41, 760, 954, 944]]<|/det|> +Usually, the tribochemical reaction of graphitic carbon formation can be initiated by the free radicals \(^{49,50}\) , which were easily generated during the rubbing process \(^{51,52}\) . Therefore, this sheds light on the main question raised by this work, i.e., which bond(s) in DOM dissociate first, consequently leading to the formation of the free radicals that initiate the graphitic carbon film formation process? In the case of DOM, considering the lower dissociative energy of the C- C bond compared to the C- H bond, the C- C bond might be more readily dissociated, thus forming free radicals that initiate the tribochemical reactions. Meanwhile, the dissociative energy of one chemical bond can also be influenced by the adjacent groups since these groups affect the stability of generated free radicals through the lone pair effect and/or + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[40, 45, 933, 113]]<|/det|> +hyperconjugation effect \(^{53}\) . Therefore, it is conceivable that the C- C bond between the hydroxyl- bonded carbon and ester group carbon is the easiest to dissociate, leading to the formation of free radicals as follows, + +<|ref|>image<|/ref|><|det|>[[75, 175, 820, 260]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[39, 350, 955, 558]]<|/det|> +It is noted that the oxygen from both the hydroxyl side and ester side (the above red atoms) have two lone pairs of electrons which can donate electron density to the half- empty p orbital of carbon radicals. Since carbon radicals are electron deficient species, the electron- donating effect from lone pair electrons help to stabilize them, known as the lone pair effect \(^{53}\) . As a result, the stabilized free radicals can further propagate the tribochemical reactions to form the graphitic carbon \(^{49}\) . To verify this point further, the tribological properties of diester with different chain lengths (Table 1) but similar structure to DOM have been investigated, as shown in Fig. 5. The tribological tests of diesters inluclding dioctyl malonate (DOT), dioctyl fumarate (DOF), dioctyl adipate (DOA), dioctyl sebacate (DOS), didodecyl succinate (DOSN) were all carried out under 36 N at 30 °C. + +<|ref|>text<|/ref|><|det|>[[42, 574, 921, 620]]<|/det|> +Table 1. The molecular structure of diesters derived from different length of carboxylic acids \((n_{\mathrm{c}})\) and different length of alcohols \((n_{\mathrm{a}})\) + +<|ref|>text<|/ref|><|det|>[[39, 639, 950, 956]]<|/det|> +The diester derived from short- chain carboxylic acid (DOT, DOF in Fig. 5a- b, Fig. S27 \(\sim 28\) ) exhibits lower friction \((0.12 \sim 0.13)\) and wear \((210 \mu \mathrm{m})\) compared to that from long- chain carboxylic acid (DOA, DOS in Fig. 5a- b, Fig. S27 \(\sim 28\) ). Moreover, a distinctive grey island tribofilm, composed of graphitic carbon with a low Young's modulus, is observed on wear track formed by diesters derived from short- chain carboxylic acid (DOT, DOF, Fig. 5d \(\sim g\) , Fig. S29 \(\sim 33\) ), a feature not present on the diesters derived from long- chain carboxylic acid (DOA, DOS). Note that the low friction coefficient and wear track width are usually accompanied with the formation of grey island tribofilm with low Young's modulus (Fig. 5c, Fig. S29 \(\sim 31\) ), further implying that this carbon film contributing to the superior tribological performance at macro scale. In addition, the diester derived from short- chain carboxylic acid but long- chain alcohol can also provide low friction, wear, and graphitic carbon formation (DOSN in Fig. 5). Therefore, it can be proposed that for diester derived from short- chain carboxylic acids, the free radicals generated on both sides after C- C bond dissociation can be stabilized by the oxygen from the each side of the disassociated bond through lone pair effect (indicated by red oxygen atoms in DOT, Fig. 5j). This stabilization facilitates the initiation of tribochemical reactions. In contrast, for diesters derived from long- chain carboxylic + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 45, 950, 179]]<|/det|> +acids, only one side of the generated free radicals can be stabilized by the oxygen from the ester group (DOS, Fig. 5j). The other side is unable to achieve stabilization due to the significant distance between free radicals and oxygen atom, resulting in a high dissociation energy that impedes the effective initiation of reactions. Hence, it can be concluded that the free radicals stabilized by lone pair effect contribute to lower bond dissociation energy, thereby triggering tribochemical reactions that lead to the formation of graphitic carbon and the reduction of friction and wear. + +<|ref|>sub_title<|/ref|><|det|>[[44, 202, 644, 229]]<|/det|> +## The effect of tribopairs on mechanochemistry + +<|ref|>text<|/ref|><|det|>[[40, 243, 945, 588]]<|/det|> +The chemistry of rubbing surfaces can have a strong influence on the formation of tribofilms. When both the top balls and bottom discs were made of \(\mathrm{Si}_3\mathrm{N}_4\) (Fig. S34a,b), sapphire (Fig. S34c,d), glass (Fig. S34e) or Diamond- like Carbon coated steel (DLC) (Fig. S34f), 5 wt% DOM in PAO could not reduce friction. Nevertheless, once the bottom disc specimen is replaced by steel, 5 wt% DOM showed considerably lower friction and wear (Fig. 6a, b), and a graphitic carbon tribofilm was formed once again on the steel surface (Raman in Fig. 6a, b). This suggests that steel (or iron in steel) is an essential requirement for graphitic carbon formation, which might be attributed to its catalytic effects \(^{54}\) . Considering that the catalysis process is an interfacial phenomenon that occurs at the solid- liquid interface, once a tribofilm with a thickness of a few nanometers covers the steel surface, it prevents the interaction between the metallic tribopair surface and liquid lubricant (Fig. 6c); hence, it cannot grow beyond to a thickness of about \(30 \mathrm{nm}\) . Nevertheless, in this study, the iron single atoms observed in TEM (red circles in Fig. 4i) were in- situ generated during the rubbing process and were then incorporated into the graphitic carbon tribofilm (Fig. 6d). Consequently, the iron single- atom catalyst found in tribofilm can maintain the solid- liquid interaction with DOM molecules and therefore continuously catalyze them to produce graphitic carbon tribofilm to a thickness of \(30 \mathrm{nm}\) (Fig. 4a, Fig. 6e). + +<|ref|>text<|/ref|><|det|>[[40, 604, 950, 906]]<|/det|> +To elucidate the atomistic mechanism governing the tribofilm formation on various surfaces, ReaxFF reactive molecular dynamics simulations were conducted with initial configurations comprising DOM molecules sandwiched between sliding tribological interfaces (see Fig. S35). Notably, the release of hydrogen atoms from the DOM molecules on the Fe (110) surface was observed at the beginning of the simulation (Fig. 7b). Remarkably, more than \(80\%\) of the C- H bonds dissociate within 1200 ps, as depicted in Fig. 7d. Consequently, most of the released hydrogen atoms recombine to form \(\mathrm{H}_2\) molecules, as illustrated in Fig. 7e. As a result, multi- membered carbon rings gradually form on the iron surface (Fig. 7c), eventually leading to the reconstruction of a large- scale defective or disordered graphene- like structures \(^{55,56}\) . In contrast, the dehydrogenation of DOM on the \(\mathrm{SiO}_2\) (001) surface occurs at a significantly lower rate ( \(< 15\%\) within 1.25 ns, Fig. 7d), resulting in no/little \(\mathrm{H}_2\) and multi- membered carbon rings formation (Fig. 7c, Fig. S36), which is consistent with the experimental results on glass discs surfaces (Fig. S34e). Thus, the atoms on the steel surface, as catalysts, expedite the dehydrogenation process of DOM, thus facilitating the formation of graphitic carbon. + +<|ref|>sub_title<|/ref|><|det|>[[44, 929, 192, 954]]<|/det|> +## Conclusion + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[39, 42, 952, 339]]<|/det|> +Containing no heavy metal or sulfur and phosphorus, the diesers in this work, typically DOM, are green and highly effective anti- friction and anti- wear additives under extreme loading conditions and in ultra- low viscosity oils, like PAO2. Compared to other additives (i.e., octanol, oleic acid, GMO, and ZDDP), DOM lowers friction by as much as \(50\%\) and reduces wear by up to \(80\%\) compared to common additives that have been used by industry for many decades. Its superior tribological performance is attributed to the in- situ formation of a graphitic carbon tribofilm on rubbing steel surfaces. This tribofilm, with a low Young's modulus and shear strength, prevents direct metal- to- metal contact hence reducing friction and wear. This film tends to form from diesers derived from short- chain carboxylic acid due to their lone pair effect, which stabilizes the carbon free radicals. Furthermore, this graphitic tribofilm can only form on steel surfaces, suggesting that the catalytic reactivity of the iron single atoms is essential for its continuous generation during sliding. This concept shown here provides a novel strategy for the design of effective and sustainable anti- friction and anti- wear additives (like DOM) for future tribological systems in industrial applications. + +<|ref|>sub_title<|/ref|><|det|>[[44, 360, 210, 385]]<|/det|> +## Declarations + +<|ref|>sub_title<|/ref|><|det|>[[44, 417, 355, 448]]<|/det|> +## Competing interests + +<|ref|>text<|/ref|><|det|>[[44, 464, 425, 483]]<|/det|> +The authors declare no competing interests + +<|ref|>sub_title<|/ref|><|det|>[[44, 514, 387, 544]]<|/det|> +## Additional information + +<|ref|>text<|/ref|><|det|>[[44, 561, 888, 582]]<|/det|> +Supplementary information. The online version contains supplementary material available at XXX + +<|ref|>text<|/ref|><|det|>[[44, 599, 860, 620]]<|/det|> +Correspondence and requests for materials should be addressed to Jinjin Li, or Weiwei Zhang + +<|ref|>sub_title<|/ref|><|det|>[[44, 650, 360, 680]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[42, 695, 953, 808]]<|/det|> +Wei Song, Janet S. S. Wong and Jinjin Li conceived the idea of the work. Wei Song and Chongyang Zeng performed tribological tests, AFM test and surface analysis. Wei Song, Chuke Ouyang, and Shouyi Sun performed the formal data analysis. Jinjin Li, Janet S. S. Wong, and Jianbin Luo supervised this work and made the writing review & editing. Weiwei Zhang and Xing Chen performed the simulation work. All authors discussed the results and assisted with paper preparation. + +<|ref|>sub_title<|/ref|><|det|>[[44, 839, 348, 870]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[42, 886, 940, 952]]<|/det|> +The authors greatly appreciate Ms Zhiying Cheng for her invaluable help on single atoms analysis through STEM, and also appreciate Miss Yimai Liang, Ms Rong Wang and Ms Weiqi Wang for their tremendous experimental assistance and data interpretation on white light interferometry, FIB and AFM + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 888, 111]]<|/det|> +experiments. This work was supported by National Key R&D Program of China (grant numbers: 2020YFA0711003), National Natural Science Foundation of China (grant numbers: 52175174 and 52205202) and Postdoctoral Science Foundation of China (No. 2022TQ0177). + +<|ref|>sub_title<|/ref|><|det|>[[44, 141, 253, 169]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[44, 184, 901, 228]]<|/det|> +The data that support the finding of this study are available within the paper and its Supplementary Information, or are available from the figshare data repository (XXXXX) under XXXXX. + +<|ref|>sub_title<|/ref|><|det|>[[44, 250, 193, 276]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[50, 290, 940, 930]]<|/det|> +1. Holmberg K, Erdemir AJFT (2015) Global impact of friction on energy consumption, economy and environment. FME Trans 43:181-185 +2. Holmberg K, Erdemir A (2019) The impact of tribology on energy use and CO2 emission globally and in combustion engine and electric cars. 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Langmuir 36 (35):10555–10564 + +<|ref|>sub_title<|/ref|><|det|>[[44, 740, 143, 765]]<|/det|> +## Figures + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[52, 54, 970, 666]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 688, 115, 707]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[41, 727, 955, 952]]<|/det|> +Tribological performance of PAO with various additives. (a) Molecular structure of the additives. (b) Schematic of a ball- on- disc tribometer with the reciprocating mode. The test details are in the tribological test section in Supplementary Information. (c) Friction coefficient of neat PAO and PAO containing various additives, including 5 wt% octanol, 5 wt% DOM, and 5 wt% OA. The test was conducted under 36 N at 30 °C, and the stroke length was 1 mm with a frequency of 10 Hz. The break in Y axis is from 0.005 to 0.09. (d) Height profiles (position marked in Fig. S5), and (e) optical images of wear tracks on discs lubricated by neat PAO, 5 wt% octanol in PAO, and 5 wt% DOM in PAO. (f) Wear volume of the balls lubricated by different lubricants. The calculation of wear volume calculation is in the tribological test section in Supplementary Information. (g) The friction coefficient of neat PAO and 5 wt% DOM in PAO under a load increasing stepwise from 36 to 176 N. The load increased by 20 N per 5 min, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 914, 88]]<|/det|> +as shown in the blue curve. (h) Friction coefficient of 5 wt% DOM and its surface coverage of islands versus time. The calculation of surface coverage can be found in Fig. S13c. + +<|ref|>image<|/ref|><|det|>[[42, 95, 951, 644]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 664, 117, 683]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[40, 704, 949, 910]]<|/det|> +Nano- mechanical and surface chemical analysis on the wear track formed by 5 wt% DOM. (a) Optical microscopy image of wear track. The three marked areas are denoted as on island area inside wear, no- island area inside wear, and unrubbed area, where indentation and AFM tests were performed. (b) Load- depth curves during the indentation tests on three areas in (a). (c) AFM frictional force on a \(10 \mu \mathrm{m} \times 10 \mu \mathrm{m}\) island area during a rubbing test repeated 110 scan number. The insets are the AFM friction images at \(1 \mathrm{st}\) and \(110^{\mathrm{th}}\) scanning. (d) Force- distance curves and (e) histogram of the pull- off force when the AFM tip retracted from the three areas in (a). (f) Raman and (i) FTIR spectra on wear tracks compared to those of other additives. (g) Optical microscopy images of wear track and (h) the corresponding Raman G band intensity mapping images. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[45, 45, 550, 495]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 523, 117, 542]]<|/det|> +
Figure 3
+ +<|ref|>image<|/ref|><|det|>[[560, 45, 950, 500]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[40, 564, 955, 770]]<|/det|> +Surface topography and chemical analysis on wear track. (a) Carbon, (b) oxygen, and (c) iron atomic concentration at different depth, obtained by XPS sputter test on wear track of different additives. High- resolution XPS spectra of C1s in wear track formed by (d) un rubbed area, (e) neat PAO, and (f) 5 wt% DOM in PAO after normalization. (g) Cross- sectional TEM image of the wear track formed by 5 wt% DOM. (h) HRTEM and (i) high- angle annular dark field STEM (HAADF- STEM) image of the tribofilm area in (a). The inset in (h) is the lattice fringe spacing obtained from the red row. (j) Electron energy loss spectroscopy (EELS) spectrum, (k) Fourier transformation (FT) of k2- weighted extended X- ray absorption fine structure (EXAFS) spectra of Fe foil, \(\mathrm{Fe}_2\mathrm{O}_3\) , \(\mathrm{Fe}_3\mathrm{O}_4\) and the tribofilm formed by 5 wt% DOM. (l) Wavelet transforms (WTs) for the k2- weighted EXAFS signals from the tribofilm formed by 5 wt% DOM. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[42, 50, 857, 789]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 802, 118, 821]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[42, 843, 951, 956]]<|/det|> +Tribological performance and surface analysis on wear track of additives with different chain lengths. (a) Molecular structure of dioctyl malonate (DOT), dioctyl fumarate (DOF), dioctyl adipate (DOA), dioctyl sebacate (DOS), didodecyl succinate (DOSN), (b) friction coefficient, (c) optical image of wear track before rinsing, (d) magnified optical image of wear track after rinsing, (e) Raman spectra on wear track, (f) Raman G band mapping image, (g) load- depth curves during indentation tests on wear track formed + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 921, 87]]<|/det|> +by additives with different chain lengths. (h) The proposed free radical generation mechanism of DOT and DOS during friction. The scale bars in (d)(f) are \(25 \mu \mathrm{m}\) . + +<|ref|>image<|/ref|><|det|>[[42, 90, 899, 835]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 848, 118, 867]]<|/det|> +
Figure 5
+ +<|ref|>text<|/ref|><|det|>[[42, 890, 953, 933]]<|/det|> +Schematic of the structural evolution of the DOM molecules confined between two Fe (100) surfaces. (a) at 0 ps; (b) at 600 ps; (c) at 1200 ps. The number of (d) C- H bonds, (e) H- H bonds and (f) carbon rings, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 942, 90]]<|/det|> +throughout the simulation where the DOM molecules are confined between two Fe (110) and \(\mathrm{SiO_2}\) (001) surfaces. + +<|ref|>sub_title<|/ref|><|det|>[[44, 112, 312, 140]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 163, 768, 184]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[61, 202, 150, 220]]<|/det|> +- Sl.docx + +<--- Page Split ---> diff --git a/preprint/preprint__0845528f7ce8151c643f7cfb0afbbb91c8fd501364586b8b087110447c3361d9/images_list.json b/preprint/preprint__0845528f7ce8151c643f7cfb0afbbb91c8fd501364586b8b087110447c3361d9/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..324f3b68706d5ec8c0cea647c86c6130625de9a3 --- /dev/null +++ b/preprint/preprint__0845528f7ce8151c643f7cfb0afbbb91c8fd501364586b8b087110447c3361d9/images_list.json @@ -0,0 +1,107 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 Biological inspiration for robotic navigation. An ant in the foreground symbolizes nature's efficient navigational strategies, while the Antcar robot in the background integrates these principles into a neuromorphic system. The blurred image captures only the large masses of the environment, similar to the low-pass spatial filter in the ant's visual system, which retains these large features even when objects obstruct the view between the robot and the building. ©Tifenn Ripoll - VOST Collectif / Institut Carnot STAR.", + "footnote": [], + "bbox": [ + [ + 541, + 538, + 904, + 707 + ] + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 Overview of the Lateralized Route Memories model implemented in the Antcar robot. This figure illustrates the process from image encoding to navigation control in both learning and exploitation phases. a The Antcar robot: a compact car-like platform equipped with an omnidirectional camera and a (Global Positioning System - Real-Time Kinematic) GPS-RTK system for ground truth data. b The image encoding process mimics ant's visual processing. Panoramic images \\((I)\\) are captured, blurred, subsampled, and edge-filtered to create a low-resolution \\(32\\times 32\\) pixels panorama \\((IS)\\) . The \\(IS\\) is then transformed into Projection Neurons (PN), which are expanded into Excitatory Post-Synaptic Projections (EP) and reduced into Action Potentials (AP) via a \\(\\kappa\\) -WTA function, forming the Kenyon Cells (KC). c During learning, the robot follows a path \\((C)\\) from a start point \\((N)\\) with an oscillatory movement to simulate angular deviations \\((\\theta_{e})\\) . Synaptic updates occur in the Mushroom Body Output Neurons (MBNs) through the modulation by Dopaminergic-like Neurons (DAN), associating visual inputs with route memories in a self-supervised manner, dependent on the sign of \\(\\theta_{e}\\) . An internal oscillator adjusts the image to simulate different angular errors, while joystick inputs control learning dynamics. d During exploitation, the robot aims to minimize the lateral \\((d)\\) and angular \\((\\theta_{e})\\) errors relative to the route. The encoded image activates the MBNs according to the learned synaptic weights, allowing the robot to determine the position of the route and adjust its steering angle and speed. Familiarity indexes \\((\\lambda)\\) of MBNs work in an opponent valence process to guide navigation: steering adjustments are based on differentiated familiarities, while the maximum familiarity modulates the speed. Specific MBNs related to start and end points alter motivational states to adjust route polarity or stop movement.", + "footnote": [], + "bbox": [ + [ + 95, + 48, + 920, + 343 + ] + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 Offine familiarity mapping for learning of indoor and outdoor routes. This figure illustrates the differentiation and maximum familiarity of route Mushroom Body Output Neurons (MBNs) during offline analysis of panoramic images and positional data from indoor (Mediterranean Flight Arena) and outdoor (Luminy Campus, Marseille, France) environments. The mapping was performed using an oscillation amplitude \\(A\\) of \\(45^{\\circ}\\) . a,d Familiarity difference index \\((\\lambda_{diff})\\) and b,e familiarity maximum index \\((\\lambda_{max})\\) are mapped in the route's frame of reference, showing variations with both lateral and angular errors \\((d\\) and \\(\\theta_{e})\\) . The defined operating area is highlighted in pink. c Overview of the indoor (top) and f outdoor (bottom) environments with the learned route highlighted in red. g Cross-sectional view of the familiarity difference index \\((\\lambda_{diff})\\) and h familiarity maximum index \\((\\lambda_{max})\\) against the angular error \\((\\theta_{e})\\) when the lateral error \\((d)\\) is null. Plotted for indoor (solid line) and outdoor (dotted line) conditions. i Pearson correlation coefficient illustrating the linear relationship between familiarity difference index \\((\\lambda_{diff})\\) and angular error \\((\\theta_{e})\\) as a function of oscillation amplitude \\(A\\) . This evolution of the correlation coefficient also illustrates the learning time required for a single oscillation cycle for each image captured on board the robot.", + "footnote": [], + "bbox": [ + [ + 75, + 48, + 900, + 555 + ] + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 Real world experiments of indoor route following in different conditions. The learned route in red is approximately 8m long. These experiments used two route MBONs. Environmental configurations and specific familiarity data are provided in the Supplementary Fig. S4 and video. From a to f, Route following results using the proposed self-supervised one-shot learning approach in different environmental conditions. From g to k, The visual environments in which the robot evolved during the experiments.", + "footnote": [], + "bbox": [ + [ + 95, + 42, + 928, + 412 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5 Real word experiments of outdoor route-following with shared memories. a First day experiments, learning and autonomous route with several cars along the road. b Second day experiments, autonomous routes using the memories from day one in an altered environment (without cars).", + "footnote": [], + "bbox": [ + [ + 88, + 48, + 875, + 240 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Fig. 6 Real word experiments of outdoor homing and indoor shuttling. a Homing experiments using two route MBONs and one motivation MBON for a \\(53\\mathrm{m}\\) L-shaped route, in an outdoor cloudy environment. Autonomous route headed in the opposite direction. b Familiarity nest index \\((\\lambda_{N})\\) over traveled distance with the fixed stopping condition \\((p = 0.2)\\) . c Shuttling experiments using two route MBONs and two place MBONs in an indoor environment with artificial visual cues. Autonomous routes swing back (blue) and forth (black). d Familiarity nest \\((\\lambda_{N})\\) and feeder \\((\\lambda_{F})\\) index over the traveled distance, zoomed in to illustrate backward and forward movement.", + "footnote": [], + "bbox": [ + [ + 100, + 48, + 930, + 336 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_7.jpg", + "caption": "Fig. 7 Performance during route following overview a Detailed errors for each experiment. b Weighted bi-variate distribution for lateral \\((d)\\) and angular errors \\((\\theta_{e})\\) across 11 different experimental configurations.", + "footnote": [], + "bbox": [ + [ + 75, + 48, + 890, + 247 + ] + ], + "page_idx": 8 + } +] \ No newline at end of file diff --git a/preprint/preprint__0845528f7ce8151c643f7cfb0afbbb91c8fd501364586b8b087110447c3361d9/preprint__0845528f7ce8151c643f7cfb0afbbb91c8fd501364586b8b087110447c3361d9.mmd b/preprint/preprint__0845528f7ce8151c643f7cfb0afbbb91c8fd501364586b8b087110447c3361d9/preprint__0845528f7ce8151c643f7cfb0afbbb91c8fd501364586b8b087110447c3361d9.mmd new file mode 100644 index 0000000000000000000000000000000000000000..65674d05cbfc6f4bc5ac60adcd1db27e6aac75eb --- /dev/null +++ b/preprint/preprint__0845528f7ce8151c643f7cfb0afbbb91c8fd501364586b8b087110447c3361d9/preprint__0845528f7ce8151c643f7cfb0afbbb91c8fd501364586b8b087110447c3361d9.mmd @@ -0,0 +1,354 @@ + +# Continuous Visual Navigation with Ant-Inspired Memories + +Gabriel Gattaux + +gabriel.gattaux@univ- amu.fr + +Aix- Marseille University - Institute of Movement Science https://orcid.org/0000- 0002- 9424- 7543 + +Antoine Wystrach + +CNRS - Université Paul Sabatier https://orcid.org/0000- 0002- 3273- 7483 + +Julien Serres + +Aix Marseille University https://orcid.org/0000- 0002- 2840- 7932 + +Franck Ruffier + +Aix- Marseille Univ, CNRS https://orcid.org/0000- 0002- 7854- 1275 + +## Article + +Keywords: + +Posted Date: December 5th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 5505975/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on September 24th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 62327- 3. + +<--- Page Split ---> + +# Continuous Visual Navigation with Ant-Inspired Memories + +Gabriel Gattaux \(^{1*}\) , Antoine Wystrach \(^{2}\) , Julien R. Serres \(^{1,3}\) , Franck Ruffier \(^{1}\) + +\(^{1*}\) Aix Marseille Univ, CNRS, ISM, Marseille, France. \(^{2}\) Univ Toulouse, CRCA, CBI, UMR CNRS- UPS 5169, Toulouse, France. \(^{3}\) Institut Universitaire de France, IUF, Paris, France. + +\*Corresponding author(s). E- mail(s): gabriel.gattaux@univ- amu.fr; + +## Abstract + +Solitary foraging ants excel in following long visual routes in complex environments with limited sensory and neural resources—an ability that remains challenging for robots with minimal computational power. Here, we introduce a self- supervised, insect- inspired neural network that enables robust route- following on the compact, low- cost Antcar robot. The robot leverages key aspects of ant brain and behavior: (i) continuous, one- shot visual route learning using panoramic encoding in a mushroom body- inspired network, (ii) categorization of low- resolution egocentric panoramas via oscillatory movements, (iii) opponent- process control of angular and forward velocities based on visual familiarity, (iv) recognition of places of interest along routes, and (v) motivation- based memory modulation. Antcar autonomously followed routes between indoor or outdoor destinations, forward or backward, while remaining stable in both theoretical analysis and real- world testing despite occlusions and visual changes. Across 1.3 km of autonomous travel, Antcar achieved challenging route- following with sub- 20 cm lateral error at speeds up to 150 cm/s, requiring only 148 kilobits of memory and processing panoramas every 62 ms. This efficient, brain- inspired architecture stands out from more sensor- intensive and computationally demanding methods, presenting a neuromorphic approach with valuable insights into insect navigation and practical robotic applications. + +## 1 Introduction + +Insect navigation has long intrigued researchers across various fields, from biology to robotics, driving the development of cutting- edge technologies for autonomous mobile robots [1- 3]. Autonomous navigation remains a demanding and interdisciplinary challenge with applications ranging from space exploration to last miles delivery [4, 5], especially in scenarios where robots cannot rely on satellite systems [6]. Simultaneously, robots serve as valuable tools for studying insects navigation and brain structure, advancing neuromorphic engineering [7- 11]. + +In Robotics, visual teach- and- repeat methods combined with dead- reckoning techniques have gained in popularity [12- 15]. However, experienced solitary foraging ants navigate along familiar routes using only visual memories, without relying on dead reckoning (so- called path integration in the insect literature) [16- 18]. This behavior has inspired various robotic models, although current implementations are generally limited to short- range experiments of about ten meters, with modest computational efficiency, precision, and accuracy [19- 23]. While ant- inspired models achieve results comparable to conventional computer vision approaches [13, 24], they struggle in dynamic environments where computational efficiency must be balanced with resource use. + +![](images/Figure_1.jpg) + +
Fig. 1 Biological inspiration for robotic navigation. An ant in the foreground symbolizes nature's efficient navigational strategies, while the Antcar robot in the background integrates these principles into a neuromorphic system. The blurred image captures only the large masses of the environment, similar to the low-pass spatial filter in the ant's visual system, which retains these large features even when objects obstruct the view between the robot and the building. ©Tifenn Ripoll - VOST Collectif / Institut Carnot STAR.
+ +These challenges are partly due to early navigation models that emphasized hymenopteran behavior rather than underlying brain processes. Early models, referred to as perfect memory models, stored periodic snapshots at specific waypoints [25, 26]. Then, during autonomous route following (or exploitation), forced scanning movements compared acquired views to an image bank, using + +<--- Page Split ---> + +rotational image differences to establish the most familiar image and desired heading - a process known as the visual compass [27- 32]. However, these approaches has revealed two main limitations when applied in robotics. + +The first limitation involves the cumulative storage of snapshots, which significantly increases memory and computational demands as the route lengthens, making it unsuitable for long- distance navigation. This issue was partially addressed by a neural network using the Infomax algorithm [33], which enables efficient encoding of increasing numbers of images without a corresponding rise in memory load [20, 31, 34]. However, Infomax requires substantial adjustments to synaptic weights for each input through a non- local learning mechanism, limiting its biological plausibility. + +In parallel, research on the Mushroom Body (MB), a key part of the insect brain, has highlighted its essential role in olfactory and visual learning [35, 36]. In the MB, learning occurs through synaptic depression between thousands of Kenyon Cells (KCs) - intrinsic neurons that sparsely encode sensory input - and a few Mushroom Body Output Neurons (MBONs), which modulate behavioral responses based on learned associations. These processed signals are then transmitted to downstream neural circuits, influencing decisionmaking [37]. The first MB model simulating visual route following used a Spiking Neural Network with 20,000 KCs and one MBON to compute familiarity [38]. Despite this advancement, a second limitation remains: a forced systematic scanning during navigation slows robotic movement [21]. Also, this limitation does not reflect natural behavior, where scanning occurs only occasionally [39- 41]. + +To address the second limitation, an early robotic implementation combined a klinokinesis model with perfect memory, enhancing short- distance routefollowing by replacing cumbersome scanning with alternating, ballistic left and right turns where familiarity adjusted turn amplitude [19] (later also observed in ants [42]). + +To move beyond the random, undirected movement of kinesis, a taxis model was proposed, simulating directed movement toward a stimulus. In this model, KC firing activity was categorized into two distinct MBONs based on left or right orientation relative to the goal [43, 44]. This approach mirrors how insects, through continuous lateral body oscillations, sample multiple directions based on their nest position [42, 45]. Subsequent robotic models for route following attempted to integrate this lateralized approach by splitting the visual field into separate left and right memories, but these implementations showed limited efficiency in real- world tasks [22, 46]. In ants, however, the entire field of view is sent to the MB, and memories are fundamentally binocular [47]. + +Here, we propose the lateralized route memories model, an MB- inspired design with four MBONs: two dedicated to route following and two for recognizing route extremities (Fig. 2). During a one- shot outbound learning route, ant- like body oscillations are simulated through continuous in- silico rotation of the panoramic image, mimicking head movement. This simulated head orientation, relative to the dynamic local orientation of + +the route, categorizes views into left or right memory based on the polarity of the angular value, leading to a self- supervised model for route learning. This design also mimics dopaminergic feedback from motor centers, modulating MBON synapses based on the currently active KCs and the integration of left and right stimuli [44]. + +In addition, our model incorporates key aspects of ant navigation not previously applied in MB models, such as adjusting forward speed by accelerating on familiar routes and slowing down in unfamiliar areas [39]. Our model also enables bi- directional route learning, allowing to retrace a route while moving backward or forward, recognizing visual memories from the outbound journey [48- 51]. Embedded in the compact Antcar robot (Figs. 1 and 2a), the model was tested across 99 autonomous trajectories, covering 1.3 km indoors and outdoors, achieving median lateral and angular errors of 20 cm and \(3^{\circ}\) , respectively, with refresh rates of 16 Hz during exploitation and 38 Hz during learning. Our MB model showed strong robustness to visual changes, including light fluctuations and pedestrian interference. This performance demonstrates the potential of our MB model for efficient, adaptable visual navigation in complex environments with accessible hardware and minimal computing requirements. + +## Results + +Our proposed MB model emulates ant visual processing by encoding panoramic images as ultra- low resolution neural representations, enabling efficient learning and route recognition with minimal computational demands (see Methods for details, Fig. 2b). The model operates in two main phases: learning (Fig. 2c) and exploitation (Fig. 2d). During the learning phase, our self- supervised model encodes the route using two MBONs and stores place- specific memories for the Nest and Feeder as route extremities (see Methods, Fig. 2c). In the exploitation phase, the robot processes each view through both memory pathways, yielding two familiarity values (left and right MBON activities). The lateralized difference of familiarities \((\lambda_{diff})\) directs steering, while the maximum familiarity value modulates forward speed. Additionally, a motivational control modulates motor gain, allowing the robot to stop or reverse based on a familiarity thresholds set by place- specific MBONs (see Methods, Fig. 2d). + +This study begins with an offline analysis of the proposed self- supervised MB model using two route MBONs to assess stability, followed by experimental route- following tasks in challenging indoor and outdoor environments. Next, a homing task is described, in which the robot follows a long outdoor route in reverse toward the starting area, designated as the Nest (N), and stops nearby, utilizing three MBONs. Finally, a shuttling task is introduced, where the robot, after a single learning trial with two route MBONs and two extremities MBONs for the Nest and Feeder, autonomously shuttles to and fro between these two locations, driving both forward and backward. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2 Overview of the Lateralized Route Memories model implemented in the Antcar robot. This figure illustrates the process from image encoding to navigation control in both learning and exploitation phases. a The Antcar robot: a compact car-like platform equipped with an omnidirectional camera and a (Global Positioning System - Real-Time Kinematic) GPS-RTK system for ground truth data. b The image encoding process mimics ant's visual processing. Panoramic images \((I)\) are captured, blurred, subsampled, and edge-filtered to create a low-resolution \(32\times 32\) pixels panorama \((IS)\) . The \(IS\) is then transformed into Projection Neurons (PN), which are expanded into Excitatory Post-Synaptic Projections (EP) and reduced into Action Potentials (AP) via a \(\kappa\) -WTA function, forming the Kenyon Cells (KC). c During learning, the robot follows a path \((C)\) from a start point \((N)\) with an oscillatory movement to simulate angular deviations \((\theta_{e})\) . Synaptic updates occur in the Mushroom Body Output Neurons (MBNs) through the modulation by Dopaminergic-like Neurons (DAN), associating visual inputs with route memories in a self-supervised manner, dependent on the sign of \(\theta_{e}\) . An internal oscillator adjusts the image to simulate different angular errors, while joystick inputs control learning dynamics. d During exploitation, the robot aims to minimize the lateral \((d)\) and angular \((\theta_{e})\) errors relative to the route. The encoded image activates the MBNs according to the learned synaptic weights, allowing the robot to determine the position of the route and adjust its steering angle and speed. Familiarity indexes \((\lambda)\) of MBNs work in an opponent valence process to guide navigation: steering adjustments are based on differentiated familiarities, while the maximum familiarity modulates the speed. Specific MBNs related to start and end points alter motivational states to adjust route polarity or stop movement.
+ +## Self-supervised lateralized route memories model + +We first evaluated the self- supervised model for route learning (using only two MBNs) with a dataset of indoor and outdoor parallel routes (Figs. 3c,f). Results demonstrated that, with a controlled oscillation amplitude during learning, the model accurately estimated its heading error based on the differential familiarity \(\lambda_{diff}\) , handling angular deviations up to \(135^{\circ}\) indoors and \(90^{\circ}\) outdoors (Fig. 3a,d,g). Furthermore, the maximum familiarity index \(\lambda_{max}\) , used as feedback for speed control, increased proportionally with heading error, enabling the robot to slow down when misaligned with the route. This behavior was consistent even when the robot was moved laterally off- route (Fig. 3a,b). Outdoors, these gradients were steeper (Fig. 3a,b,d, and e), indicating a higher visual contrast with larger landmarks. + +The model's ability to identify heading error accurately across training oscillation amplitudes up to \(135^{\circ}\) (Fig. 3i, see also Supplementary note 1 and Fig. S1) suggests that this parameter may not require further tuning below this threshold. However, larger oscillation amplitudes increased computation time, especially on the Raspberry Pi platform (0.4s for \(\pm 45^{\circ}\) , Fig. 3i). Notably, the familiarity difference index (Fig. 3g) closely matched the spatial derivative of the maximum familiarity index, + +corresponding to the catchment area and turn rate amplitude observed in ants (Fig. 3h, Supplementary note 1, 2, Fig. S1 and S2 [43]). + +This analysis helped establish the operational limits of our MB model, maintaining stable behavior within a lateral error \((d)\) of 2 meters and an angular error \((\theta_{e})\) within the learning oscillation amplitude, set here at \(45^{\circ}\) . For asymptotic stability (i.e., the system's ability to return to equilibrium), we assumed a proportional relationship between \(\lambda_{diff}\) and \(\theta_{e}\) , supported by the Pearson correlation coefficient being close to 1 (Fig. 3i) and expressed as \(K_{diff} \cdot \lambda_{diff} = -\theta_{e}\) , where \(K_{diff}\) is a tuned negative gain. Integrating this relationship into the robot's motion equations, we applied a Lyapunov function for stability analysis. Results confirmed that the system converged to equilibrium points at \(d^{e} = 0\) and \(\theta_{e}^{e} = 0\) , effectively correcting small deviations and enabling the robot to remain aligned with the learned route. The full derivation of these equations and Lyapunov stability proof are provided in the Methods (section 6) and Supplementary note 3,4 and Fig. S3. + +## Route-following: robustness to visual changes + +The proposed self- supervised approach for route learning was validated through a series of indoor and outdoor route- following tasks in fully autonomous mode, with + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3 Offine familiarity mapping for learning of indoor and outdoor routes. This figure illustrates the differentiation and maximum familiarity of route Mushroom Body Output Neurons (MBNs) during offline analysis of panoramic images and positional data from indoor (Mediterranean Flight Arena) and outdoor (Luminy Campus, Marseille, France) environments. The mapping was performed using an oscillation amplitude \(A\) of \(45^{\circ}\) . a,d Familiarity difference index \((\lambda_{diff})\) and b,e familiarity maximum index \((\lambda_{max})\) are mapped in the route's frame of reference, showing variations with both lateral and angular errors \((d\) and \(\theta_{e})\) . The defined operating area is highlighted in pink. c Overview of the indoor (top) and f outdoor (bottom) environments with the learned route highlighted in red. g Cross-sectional view of the familiarity difference index \((\lambda_{diff})\) and h familiarity maximum index \((\lambda_{max})\) against the angular error \((\theta_{e})\) when the lateral error \((d)\) is null. Plotted for indoor (solid line) and outdoor (dotted line) conditions. i Pearson correlation coefficient illustrating the linear relationship between familiarity difference index \((\lambda_{diff})\) and angular error \((\theta_{e})\) as a function of oscillation amplitude \(A\) . This evolution of the correlation coefficient also illustrates the learning time required for a single oscillation cycle for each image captured on board the robot.
+ +only two MBNs. After a first outbound route with online learning, where images were captured continuously to update synaptic weights in real- time, the robot demonstrated robust route- following in various configurations (Figs. 4 and 5). First, the Antcar robot successfully navigated convex and concave routes in a cluttered indoor environment of approximately 8 meters (median lateral error \(\pm\) median absolute deviation \((\mathrm{MAD}) = 0.21 \pm 0.09 \mathrm{m}\) , angular error \(\pm \mathrm{MAD} = 3.4 \pm 6.2^{\circ}\) , Fig. 4a,g and Fig. 7a). Moreover, the robot showed resilience in a kidnapped robot scenario, realigning with the learned route after being displaced (lateral error \(\pm \mathrm{MAD} = 0.26 \pm 0.14 \mathrm{m}\) , angular error \(\pm \mathrm{MAD} = 6.45 \pm 4.19^{\circ}\) , Fig. 4b and Fig. 7a). Only one crash occurred when the robot exceeded theoretical angular limits (see Supplementary Fig. S5). + +Further tests assessed the robot's adaptability to high and low light conditions (Figs. 4c,h and Figs. 4d,i). Despite a single learning trial under standard lighting (815 Lux), the robot accurately followed its route in high (1,340 Lux) and low (81 Lux) lighting, with similar lateral and angular errors across tests (Fig. 7). This indicates that the MB- based control system is robust to significant changes in illumination. + +In dynamic conditions with pedestrians and camera occlusions (Figs. 4e,f), the robot maintained reliable route- following when encountering pedestrians (lateral error \(\pm \mathrm{MAD} = 0.27 \pm 0.15 \mathrm{m}\) , angular error \(\pm \mathrm{MAD} = 4 \pm 2.8^{\circ}\) , Fig. 4e and Fig. 7a) and with dynamic occlusions (lateral error \(\pm \mathrm{MAD} = 0.22 \pm 0.13 \mathrm{m}\) , angular error \(\pm \mathrm{MAD} = 4.7 \pm 3.3^{\circ}\) , Fig. 4f and Fig. 7a). The presence of pedestrians and occlusions was reflected by the + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4 Real world experiments of indoor route following in different conditions. The learned route in red is approximately 8m long. These experiments used two route MBONs. Environmental configurations and specific familiarity data are provided in the Supplementary Fig. S4 and video. From a to f, Route following results using the proposed self-supervised one-shot learning approach in different environmental conditions. From g to k, The visual environments in which the robot evolved during the experiments.
+ +loss of maximum familiarity and led to speed reductions and increased emerging oscillatory motion ( \(15\%\) slower than in the previous experiments, Figs. 4e,f and supplementary video), which was also observed near obstacles. These results underscore the system's resilience under challenging conditions. + +Outdoor experiments demonstrated the model's ability to maintain stable performance even over a long, 53- meter route and under altered environmental conditions. A route was learned and accurately recapitulated on a sunny day (lateral error \(\pm \mathrm{MAD} = 0.39 \pm 0.13\) m, angular error \(\pm \mathrm{MAD} = 5.8 \pm 2.8^{\circ}\) , Fig. 5a & 7a) and then retested the following day with parked cars removed (lateral error \(\pm \mathrm{MAD} = 1.3 \pm 0.5\) m, angular error \(\pm \mathrm{MAD} = 6.2 \pm 3.2^{\circ}\) , Fig. 5b & 7a). While the robot's error margins were slightly broader on the second day, it remained well within acceptable limits over the entire route. To test Antcar's maximum speed, a higher speed gain was applied during the second test (Fig. 5b), resulting in a cruising speed of \(1.5 \mathrm{m / s}\) compared to \(1 \mathrm{m / s}\) on the first day (see Supplementary Information note 5, Fig. S4 and Table S7). + +## Homing: homeward route and stop + +Building on the validated route- following strategy, further tests refined the robot's behavior, focusing on ant- like homing. Homing, by definition, is the ability to return to a specific location after displacement. To test this, we evaluated the robot's ability to follow a \(50 \mathrm{m}\) outdoor route in reverse, stopping at a designated Nest area (point N in Fig. 6a). During learning, a \(180^{\circ}\) shift in the visual oscillation pattern simulated the "turn back and look" behavior observed in ants and led to homeward route following. + +The robot successfully followed the \(50 \mathrm{m}\) route in reverse under cloudy outdoor conditions (lateral error \(\pm \mathrm{MAD} = 0.9 \pm 0.5 \mathrm{m}\) , angular error \(\pm \mathrm{MAD} = 6.3 \pm 4.2^{\circ}\) , Fig. 6a and Fig. 7a). Although maximum familiarity was higher than in previous outdoor experiments (see Supplementary note 5, Fig. S4 and Table S7), overall accuracy remained stable and emerging oscillatory movements was demonstrated (see Supplementary Video). + +To enable autonomous stopping at the Nest, a placespecific MBON was used to learn 'nest- views' at the starting point of the route. Subsequent 'recognition' in this MBON, based on a familiarity threshold, acted as a motivational cues to halt route- following behavior and reducing the robot's linear velocity. This mechanisms was sufficient for the robot to successfully reach and stop at the Nest area in 4 out of 5 trials, with a median stopping distance of \(1.4 \mathrm{m}\) (Fig. 6c, see also Supplementary Fig. S6b for detailed familiarities values over distance). + +## Shuttling: foodward and homeward routes + +Reverse route- following is also commonly observed in ants and was successfully replicated on board Antcar. Homing ants can pull food items backward when it is too large to carry forward, maintaining body alignment with the outbound route learned forward, and + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Fig. 5 Real word experiments of outdoor route-following with shared memories. a First day experiments, learning and autonomous route with several cars along the road. b Second day experiments, autonomous routes using the memories from day one in an altered environment (without cars).
+ +using outbound memories with an opposite valance [50]. Shuttling tests show the robot's ability to switch movement direction and drive backward while maintaining alignment with the outbound route (Fig. 6b). + +This foraging behavior was made possible by incorporating two additional place MBONs, which learned a series of panoramic views defining each endpoint of the route (Feeder and Nest). During shuttling, the model triggered a switch in motor gain polarity upon recognizing these panoramic views corresponding to the Feeder or Nest areas. In a cluttered indoor environment along a 6- meter learned route, the robot autonomously shuttled to and fro between the Feeder and the Nest, covering a total distance of 160 meters without interruption. Using a similar familiarity threshold on the two route- extremity MBONs, the robot detected the endpoints 22 times, achieving a median stopping distance of 0.31 m (Fig. 6d) (See Supplementary Fig. S6a for detailed familiarities values over distance). + +This continuous shuttling revealed distinct differences in error profiles between forward and backward movement (Fig. 6b). During forward motion, the robot maintained stable control with minimal deviations (lateral error \(\pm \mathrm{MAD} = 0.1\pm 0.03\mathrm{m}\) , angular error \(\pm \mathrm{MAD}\) \(= 1.26\pm 0.83^{\circ}\) , Fig. 6b). However, during backward motion, the traction- driven setup amplified steering effects, resulting in slightly larger deviations from both accuracy and precision, though overall performance remained acceptable (lateral error \(\pm \mathrm{MAD} = 0.19\pm 0.08\) m, angular error \(\pm \mathrm{MAD} = 2.7\pm 2.1^{\circ}\) , Fig. 6b & 7a). The increased 'motor' variability led to lower visual recognition signal and thus usefully affected speed, which decreased by \(14\%\) compared to forward motion (see Supplementary note 5, Fig. S4 and Table S7). Nonetheless, the robot consistently realigned with the correct path after such minor deviations. These results highlight the model's versatility across different driving dynamics, capability to implement inverted steering, and adaptability to variations in motor kinematics and propulsion. + +## Performance summary + +Across all experiments, including both indoor and outdoor route- following, homing and shuttling tasks, the model demonstrated robust and stable navigation performance, completing 99 autonomous trajectories with a total of 1.3 km traveled. The theoretical limits of the system were validated, with convergence toward equilibrium points consistently achieved under various environmental conditions, even in the presence of noise (lateral error \(\pm \mathrm{MAD} = 0.22\pm 0.10\mathrm{m}\) , angular error \(\pm \mathrm{MAD} = 3.8\pm 2.4^{\circ}\) , Fig. 7b). Lateral errors were within acceptable margins for both indoor and outdoor contexts, aligning within the standard widths of roads in France (5m) and typical indoor corridor (1.5m). + +Additionally, statistical analysis showed no significant differences in the lateral or angular errors across the eleven test scenarios (Kruskal- Wallis test, \(H = 1.20\) for lateral error, p value \(\approx 1\) ; \(H = 0.97\) for angular error, p value \(\approx 1\) ), underscoring the system's reliability across diverse conditions (see Statistical Information). These results highlight the robustness and adaptability of the MB model in both structured and dynamic environments, confirming its potential applicability in a variety of navigation contexts. + +## Discussion + +Our study presents a robust, embedded, and biologically inspired Mushroom Body (MB) model capable of long- distance navigation in the real world with minimal sensor acuity and computational resources. Using fewer than a thousand pixels, the Antcar robot successfully followed routes at speeds up to \(1.5\mathrm{m / s}\) —approximately eight times its body length—achieving continuous online learning in just \(20\mathrm{ms}\) per image, with exploitation times of 75 ms and an extrapolated memory footprint of only \(0.3\mathrm{Mo}\) per kilometer. By integrating ant- inspired lateralized memory with self- supervised panoramic learning through oscillations, our model sustained high navigational accuracy across dynamic lighting, cluttered, and altered environments, with a positional accuracy of approximately \(20\mathrm{cm}\) . Offline analysis confirmed the model's stability and alignment with defined limits, predicting robust real- time performance by reliably maintaining route alignment within learning oscillation bounds. + +The angular error between the agent's head direction and the dynamic local route orientation (defined + +<--- Page Split ---> +![](images/Figure_6.jpg) + +
Fig. 6 Real word experiments of outdoor homing and indoor shuttling. a Homing experiments using two route MBONs and one motivation MBON for a \(53\mathrm{m}\) L-shaped route, in an outdoor cloudy environment. Autonomous route headed in the opposite direction. b Familiarity nest index \((\lambda_{N})\) over traveled distance with the fixed stopping condition \((p = 0.2)\) . c Shuttling experiments using two route MBONs and two place MBONs in an indoor environment with artificial visual cues. Autonomous routes swing back (blue) and forth (black). d Familiarity nest \((\lambda_{N})\) and feeder \((\lambda_{F})\) index over the traveled distance, zoomed in to illustrate backward and forward movement.
+ +in the Methods as the Frenet frame [52]) emerged as both a challenge during exploitation- - where the system minimizes this error- - and a cue during learning, where the categorization process depends on its polarity. Our model demonstrated homing behavior using either a \(180^{\circ}\) shift in visual oscillation or by inverting motor gains, thus enabling forward and backward movements with only a single foodward learning route. Additionally, visual place memories stored in supplementary MBONs, paired with a motivational control system, allowed the robot to recognize route endpoints and modulate motor gain, halting movement or reversing foraging motivation. With a single learning pass in one direction, the agent could follow the route forward, backward, and in reverse, controlled by oscillation parameters and motivational cues. Only motivational rules required adjustment to switch between route following, homing, and shuttling, underscoring the model's flexibility. + +Our results surpass earlier ant- inspired familiarityonly models robots, which were generally limited to short indoor routes, slower linear speed (stop and scan), and lower efficiency [19- 23]. Our model also markedly outperforms state- of- the- art visual teach- andrepeat methods, which report memory footprints of 3 Mo per kilometer and processing times around \(400~\mathrm{ms}\) [13]. Our model also achieves competitive results against teach- and- repeat systems incorporating odometry [14, 15]. + +This lateralized MB model distinguishes itself through reduced time and space complexity for route direction processing compared to perfect memory, snapshot, and visual compass approaches [43]. Whereas time and space complexity increase with the number of images in perfect memory or snapshot models, our MB model maintains constant space complexity, relying only on the synaptic matrix size KCToMBON. Additionally, in contrast to visual compass approaches, where computational complexity scales with in- silico scan range and resolution during exploitation \((\mathcal{O}(n))\) , our MB model maintains a constant factor \((\mathcal{O}(1))\) since in- silico scanning is only required during learning. For instance, while a visual compass scanning a \(\pm 45^{\circ}\) range at \(1^{\circ}\) resolution requires 90 comparisons per image, our model requires only two comparisons, eliminating the need for angular scanning in exploitation. Notably, our model produced commands five times faster than the visual compass approach on the same robot platform [21]. + +Our contribution also aligns well with current biological observations, particularly highlighting the effectiveness of latent learning [53], where continuous learning bypass the need to control "when to learn" [31, 44]. The opposed event- triggered and snapshot- based learning models producing place learning [15, 54] where used here only to recognise place of interests such as the nest and the feeder to switch motivation, but were not engaged for route guidance. Also, our MB model prioritized body orientation within the local frame rather than divided the visual field [22, 46], aligning with biological observations in ants with unilateral visual impairment, showing that these insects store and recognise fundamentally binocular views [47]. Interestingly, the linear relationship observed between familiarity measures (and thus motor output) and angular error during exploitation closely mirrors experimental findings in ants [43]. This relationship enabled us to demonstrate the asymptotical stability of the system within a defined domain, ensuring the consistent and predictable behavior essential for a robotic navigation model [55]. + +<--- Page Split ---> +![](images/Figure_7.jpg) + +
Fig. 7 Performance during route following overview a Detailed errors for each experiment. b Weighted bi-variate distribution for lateral \((d)\) and angular errors \((\theta_{e})\) across 11 different experimental configurations.
+ +Furthermore, oscillatory learning behavior mirrors ant behavior, where initial routes involve slow, rotational movements, transitioning to direct paths on subsequent journeys [39]. These oscillations typically fall within \(\pm 100^{\circ}\) , with peaks around \(\pm 45^{\circ}\) in unfamiliar terrain [40, 42]. The robot's ability to slow down and produce emerging mechanical scanning upon entering unfamiliar areas (see Supplementary Video) are consistent with such naturalistic behaviors. Finally, Antcar's homing capability was maintained even when navigating backward, closely mirroring ant behavior while dragging food [48- 50, 56]. Overall, our attempt to integrate multiple MBNs, oscillations, "turn back and look" behavior, and motivational control mechanisms echoes insect mechanisms [2, 57], and the resulting expression when implemented in the robot echoes insect behaviours. + +This study addresses several core needs identified in research on embodied neuromorphic intelligence [6, 8], such as robustness to visual changes, adaptability to real- world environments, and support for extended route learning. Our algorithm's efficiency allows computational power for additional tasks, making it valuable in GPS- compromised or SLAM- disrupted scenarios (SLAM stands for Simultaneous Localization And Mapping). The robot's low- resolution, wide- angle vision proves resilient against moving objects that often disrupt SLAM. Our model is well- suited for dynamic environments or situations where odometry (e.g., visual, inertial, step- counting, or wheel- rotation) is unreliable. + +Interestingly, the semi- random encoding process, specifically the PNotKC synaptic projections, introduces a "fail- secure" memory- sharing mechanism. If synaptic weights for encoding differ, memory sharing becomes inaccessible, an advantageous feature for swarm robotics or cross- robot memory sharing. + +Future research could enhance this approach. Transitioning this model to a spiking neural network on neuromorphic hardware could further enhance computational efficiency and biological fidelity [11]. Additionally, incorporating obstacle avoidance [58], would improve performance in dynamic environments. + +In addition, a reduction of the visual field could correspond to more general cases, rendering in silico scanning impossible. In such scenarios, it would be necessary to estimate the angular error between the road frame and the agent. This could be achieved using a local angular path integration system (or odometry) during learning. As demonstrated by Collett et al. [59], showing that ants could utilize route segment odometry for navigation. + +Our approach does not cover beeline homing post- foraging or search behaviors near points of interest, although these could be added by adding path integration mechanisms [60] or using the current visual mechanism but adding "learning walk" behaviors around place of interest [44]. Additionally, fixed neural parameters across all experiments suggest an opportunity for further exploration by adjusting Kenyon Cell numbers or connectivity, or testing different MB learning mechanisms [61]. Expanding the number of MBNs, akin to the 34 in Drosophila [37], could enable more complex motivational states, multi- branch memory storage [53], and broader navigational abilities [62]. + +Overall, inspired by the neuroethology of ants, our MB model provides an effective bridge between theoretical insights and practical applications in insect- inspired autonomous robotic navigation. This egocentric model confirms the neuromorphic architecture's promise for autonomous systems, suggesting a scalable solution for both robotics and biological research applications. + +## Methods + +This section describes the methodology used in the present study, focusing on the Encoding, Learning, and Exploitation processes of the proposed MB model (Figs. 2b- d). We also provide details on the hardware setup, control architecture, and stability analysis (See Supplementary Fig. S7 for the detailed route following neural network). + +## Image Encoding + +Inspired by the visual system of ants [63], the model encoded real- world images into sparse, binary neural representations to efficiently handle visual input. + +<--- Page Split ---> + +The encoding function (Fig. 2b) processed panoramic images from a camera with a \(220^{\circ}\) vertical and \(360^{\circ}\) horizontal field of view. This wide field of view enabled the camera to capture from slightly below the horizon to nearly directly below itself. To enhance natural contrast, the green channel of each image was selected [63], followed by Gaussian smoothing ( \(\sigma = 3\) pixels) to reduce noise. The image was then downsampled to an ultra-low- resolution \(32 \times 32\) pixel thumbnail (0.145 pixel per degree), approximating the visual resolution of ants at \(7.1^{\circ}\) between adjacent photoreceptors. + +Next, a Sobel filter extracted edges, mimicking lateral inhibition as seen in insect optical lobes [64]. These processed images were flattened into 800 Visual Projection Neurons (PNs), comparable to the number of ommatidia in ants. The PNs were further expanded into Kenyon Cells (KCs) using a fixed, sparse pseudorandom synaptic matrix (PNtoKC). Each KC received input from four PNs, enhancing the visual encoding's discriminative power within the Mushroom Body (MB) [65], forming an Excitatory Post Synaptic Projection (EP) vector of size \(u\) . + +The EP vector size was set to \(u = 15,000\) for the route MBONs ( \(MBON_{R}\) and \(MBON_{L}\) ), while for place- specific MBONs ( \(MBON_{N}\) and \(MBON_{F}\) ), which required fewer images, \(u\) was set to 5,000. A \(\kappa\) - Winner- Take- All (WTA) mechanism was applied to capture the highest contrasts, creating a high- dimensional, sparsified binary vector. This vector, referred to as the Action Potential (AP), consequently activated only \(1\%\) of KCs ( \(\kappa = 0.01\) ), giving \(\overline{u} = u * \kappa\) active neurons. This final binary representation served as the encoded visual input. + +All parameters were predefined by literature and experimental tests, but not further optimized. + +## Routes and places learning + +The learning process is governed by synaptic depression through anti- Hebbian learning. + +\[K C t o M B O N_{i} = \left\{ \begin{array}{ll}0, & \mathrm{if~}A P_{i} = 1\\ K C t o M B O N_{i}, & \mathrm{otherwise} \end{array} \right. \quad (1)\] + +For each MBONs, their synaptic weight matrix ( \(K C t o M B O N\) ) dynamically adjusted their weight based on input from the \(A P\) layer described in equation 1 and from the mimicked dopaminergic feedback. Here, \(i\) represents the \(i^{t h}\) neuron in the specified vector, with \(K C t o M B O N_{i}\) and \(A P_{i}\) in \(\{0,1\}\) + +The simulated oscillatory movements during learning were obtained by rotating each captured image in steps, creating a sweep of rotations ( \(\theta_{c}\) ) described by the following function: + +\[\theta_{c}(n) = A\cdot \sin (n\cdot \Delta \theta +\phi)\quad \mathrm{for~}n = 0,1,2,\ldots ,\frac{2A}{\Delta\theta} \quad (2)\] + +where \(A\) represents the oscillation amplitude, \(\Delta \theta\) the step size, and \(\phi\) the phase shift. The step size was fixed at \(\Delta \theta = 5^{\circ}\) , with \(A = 45^{\circ}\) for route MBONs and \(A =\) + +\(30^{\circ}\) for place MBONs. The phase shift was \(\phi = 180^{\circ}\) only for the homing task (Fig. 6). + +For route learning, the model assumed the robot perfectly aligned to the route being learned. The body rotation was estimated as \(\hat{\theta}_{e} = \theta_{e} + \theta_{c}\) , where therefore \(\theta_{e} = 0\) during learning. The encoded binary image was categorized based on the polarity of \(\hat{\theta}_{e}\) , such that: + +\[\left\{ \begin{array}{ll}L e a r n(A P,K C t o M B O N_{R}), & \mathrm{if~}\hat{\theta}_{e}\leq 0\\ L e a r n(A P,K C t o M B O N_{L}), & \mathrm{if~}\hat{\theta}_{e}\geq 0 \end{array} \right. \quad (3)\] + +Here, the function \(L e a r n()\) follows equation 1. Synaptic weights (KCtoMBON) were stored in CSR format, achieving significant data compression to 148 kilo- bits independently of the route length, reducing memory requirements by \(99.97\%\) from cumulative image storage. This self- supervised model continuously learned visual input at high throughput without memory overload, as only novel views (i.e., newly recruited KCs) modulated synapses. Several panoramic views were learned to define the start and finish areas in their respective MBONs, serving as motivational cues. + +## Exploitation process and control architecture + +During exploitation, the model calculated familiarity scores ( \(\lambda\) ) by comparing the current input ( \(A P\) ) with each MBON's synaptic weight matrix ( \(K C t o M B O N\) ): + +\[\lambda = \frac{1}{\overline{u}}\sum_{i = 1}^{u}A P_{i}\cdot K C t o M B O N_{i} \quad (4)\] + +This familiarity score, ranging from 0 (unfamiliar) to 1 (familiar), was used to assess route alignment. The lateralized difference in familiarities between the left and right MBONs ( \(\lambda_{diff} = \lambda_{L} - \lambda_{R}\) ), which indicates whether the current view is more oriented to the left or right of the route, guided the robot's steering angle ( \(\phi\) ). Meanwhile, the maximum familiarity ( \(\lambda_{max} = \max (\lambda_{L}, \lambda_{R})\) ), representing how familiar the current view is, modulated its speed ( \(v\) ). + +Thus, the control input \(U\) was defined as: + +\[U = \left[ \begin{array}{l}v\\ \phi \end{array} \right] = \left[ \begin{array}{l}M\cdot K_{v}\cdot \mathrm{sat}(1 - \lambda_{max})\\ M\cdot K_{\phi}\cdot \lambda_{diff} \end{array} \right] \quad (1)\] + +Here, \(K_{v}\) and \(K_{\phi}\) are proportional gains that control linear and angular velocities, while the saturation function ( \(sat()\) ) establishes a minimum throttle level, ensuring minimum speed even at low familiarity levels. The motivational state ( \(M\) ) regulated transitions between behaviors based on a familiarity thresholds within place- specific MBONs. During route following, \(M\) was consistently set to 1. In homing experiments, where the objective was to stop at the nest, \(M\) initially started at 1 and switched to 0 once the familiarity of the nest- specific MBON ( \(\lambda_{N}\) ) fell below a fixed threshold ( \(p = 0.2\) ), signaling arrival at the nest. For shuttling tasks, \(M\) alternated between values of 1 and \(- 1\) as the + +<--- Page Split ---> + +robot reached each route extremity, driven by a familiarity thresholds of the two place- specific MBONs ( \(\lambda_{N}\) and \(\lambda_{F}\) ). + +## Theoretical analysis of the robot stability + +Stability in mobile agents, biological or robotic, is essential for reliable, predictable behavior. In control theory, an agent's motion is generally modeled as \(\dot{x} = f(x,U)\) where \(x\) is the state vector (e.g., position or velocity), \(U\) is the control input, and \(f\) describes system dynamics. A desired equilibrium point \(x_{e}\) is achieved by defining a control input \(U_{e}\) such that \(f(x_{e},U_{e}) = 0\) , allowing the system to maintain stability and return to equilibrium after disturbances. Stability is typically assessed using a Lyapunov function [55], which ensures the system converges to a stable state over time. + +In contrast to conventional control approach, we applied a neuroethologically inspired control input derived from ant behavior, assessing stability via an a posteriori Lyapunov analysis. The robot's motion was modeled in a Frenet frame, a moving reference frame coincident with the nearest point on the route, to minimize lateral and angular errors, defined by \(x = [d,\theta_{e}]\) . Empirical data for stability assessment was collected in indoor and outdoor environments (paths of approximately 6 meters with 855 learned images each), providing distinct visual contexts (Figs. 2, 3). The robot's equations of motion from a global to the Frenet frame are [66]: + +\[\left[ \begin{array}{c}\dot{s\] \[\dot{d\] \[\dot{\theta}_e} \end{array} \right] = \left[ \begin{array}{c}v(\cos \theta_e - \tan \phi \sin \theta_e)\] \[v(\sin \theta_e + \tan \phi \cos \theta_e)\] \[v\frac{\tan \phi}{L} \end{array} \right], \quad (5)\] + +where \(s\) is the arc length along the route, \(d\) is the lateral error, and \(\theta_{e}\) is the angular error. + +This kinematic model, along with by empirical observations (Fig. 3), enabled us to establish an asymptotically stable domain for lateral and angular errors ( \(d\) and \(\theta_{e}\) ), ensuring reliable route- following performance even with minor disturbances. The full theoretical stability proof and derivations of the model in the frenet frame are provided in the Supplementary note 3 and 4. + +## Antcar robot and ground truth system + +The experiments were conducted using Antcar (Fig. 1 and Fig. 2a), a PiRacer AI- branded car- like robot. Antcar features four wheels, with two rear drive wheels powered by 37- 520 DC motors (12V, 1:10 reduction rate) and a front steering mechanism controlled by an MG996R servomotor (9kg/cm torque, 4.8V). The robot's chassis measures \(13 \times 24 \times 19.6 \mathrm{cm}\) and is powered by three rechargeable 18650 batteries (2600mAh, 12.6V output). Antcar's primary sensor is a \(220^{\circ}\) Entaniya fisheye camera, mounted upward to capture panoramic images at \(160 \times 160px \times 3\) resolution and 30 Hz, processed using OpenCV on a Raspberry Pi 4 Model B (Quad- core Cortex- A72, 1.8GHz, 4GB RAM), running Ubuntu 20.04. Note that there was no closed- loop control on the wheel rotation speed. Raspberry Pi manages real- time performance and controls the motors through a custom ROS architecture. + +Real- time communication is facilitated by ROS Noetic, either via Wi- Fi (indoor) or a 4G dongle (outdoor). The robot can be controlled manually using a keyboard, joystick or with GPS waypoint, but in autonomous visual- only mode, it follows its own internal control law. Control inputs—steering angle \((\phi)\) and throttle \((v)\) are processed using the PyGame library. Real- time data visualization and post- experiment monitoring are achieved via Foxglove. + +Antcar has a maximum velocity of \(1.5 \mathrm{m / s}\) and a maximum steering angle of 1 rad, with a wheelbase of \(0.15 \mathrm{m}\) . The robot's configuration states \(q = (x, y, \theta)\) were tracked using different systems. Indoor experiments utilized eighteen Vicon™ motion capture cameras, with infrared markers on Antcar providing precise tracking at 50 Hz with 1 mm accuracy. Outdoor experiments employed a GPS- RTK system with a SparkFun GPS- RTK Surveyor, providing 14 mm accuracy at 2 Hz (GPS- RTK stands for Global Positioning System - Real- Time Kinematic). Ground speed and angular speed were calculated through position differentiation. The base station used for GPS corrections was a Centipede LLENX station located at 24 km (Aeroport Marseille Provence) from the experiment site in Marseille. Note that the ground truth acquisition system was run on the Raspberry Pi along with the mushroom body model. + +Lateral error was calculated by finding the nearest point on the learning route using the Euclidean distance, with the shortest distance representing the absolute lateral error. Angular error was defined as the absolute difference in heading between the nearest learning route point and the current position. The euclidean distance between the agent and the Nest or Feeder areas was calculated to estimate the distance when the robot switched behavior (i.e familiarity dropped below the threshold). + +## Statistical informations + +The errors used for statistics were recorded at each command decision timing. Due to non- normality in error values (with outliers retained), Box- Cox transformations were applied to stabilize variance across experiments, reducing the impact of outliers caused by indoor obstacles that hid the robot from the motion capture system or by GPS- RTK inaccuracies outdoors. The groups was compared using the Kruskal- Wallis test [67], and median values are reported with median absolute deviation (MAD), as median \(\pm\) MAD. The package python SciPy [68] was used for the statistics. The overall medians and bivariate distribution plots were weighted by the number of measurements per experiment for the Fig. 7. + +## Acknowledgments + +The authors thank David Wood for revising the English in this study, Guillaume Caron for providing the camera reference, and Thomas Gaillard, Clément Serrasse, and Hamidou Diallo for their assistance during the robotic tests. + +<--- Page Split ---> + +## Declarations + +- Funding: G.G. was supported by a doctoral fellowship grant from Aix Marseille University and the French Ministry of Defense (AID - Agence Innovation Defense, agreement #A01D22020549 ARM/DGA /AID). G.G., J.R.S. and F.R. were also supported by Aix Marseille University and the CNRS (Life Science, Information Science, and Engineering and Science & technology Institutes). The facilities for the experimental tests has been mainly provided by ROBOTEX 2.0 (Grants ROBOTEX ANR-10-EQPX-44-01 and TIRREX ANR-21-ESRE-0015).- Conflict of interest: the authors declare no competing interests.- Data availability: Upon publication- Code availability: Upon publication- Supplementary Video : https://youtu.be/Osu5Jyv6dF4- Author contribution: G.G., A.W., J.R.S., and F.R. designed this research work; G.G, A.W., J.R.S., and F.R. got funding for this study; G.G. performed experiments, collected and visualized the data; G.G., A.W., J.R.S., and F.R. analyzed data; G.G. wrote the first full draft. All authors reviewed the results and approved the final version of the manuscript. + +## References + +[1] Franceschini, N. Small Brains, Smart Machines: From Fly Vision to Robot Vision and Back Again. Proceedings of the IEEE 102, 751- 781 (2014).[2] Webb, B. & Wystrach, A. Neural mechanisms of insect navigation. 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The complete connectome of a learning and memory centre in an insect brain. Nature 548, 175- 182 (2017).[37] Aso, Y. et al. Mushroom body output neurons encode valence and guide memory- based action selection in Drosophila. eLife 3, e04580 (2014).[38] Ardin, P., Peng, F., Mangan, M., Lagogiannis, K. & Webb, B. Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments. PLOS Computational Biology 12, e1004683 (2016).[39] Haalck, L. et al. CATER: Combined Animal Tracking & Environment Reconstruction. SCIENCE ADVANCES (2023).[40] Deeti, S., Cheng, K., Graham, P. & Wyrstach, A. Scanning behaviour in ants: An interplay between random- rate processes and oscillators. Journal of Comparative Physiology A (2023).[41] Wyrstach, A., Philippides, A., Aurejac, A., Cheng, K. & Graham, P. Visual scanning behaviours and their role in the navigation of the australian desert ant melophorus bagoti. Journal of Comparative Physiology A 200, 615- 626 (2014).[42] Clement, L., Schwarz, S. & Wyrstach, A. An intrinsic oscillator underlies visual navigation in ants. Current Biology 33, 411- 422 (2023).[43] Wyrstach, A., Le Moël, F., Clement, L. & Schwarz, S. A lateralised design for the interaction of visual memories and heading representations in navigating ants. Preprint at https://www.biorxiv.org/content/10.1101/2020.08.13.249193v1 (2020).[44] Wyrstach, A. Neurons from pre- motor areas to the Mushroom bodies can orchestrate latent visual learning in navigating insects. Preprint at https://www.biorxiv.org/content/10.1101/2023.03.09.531867v1 (2023).[45] Stürzl, W., Zeil, J., Boeddeker, N. & Hemmi, J. M. How Wasps Acquire and Use Views for Homing. Current Biology 26, 470- 482 (2016).[46] Steinbeck, F. et al. Familiarity- taxis: A bilateral approach to view- based snapshot navigation. 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Click to download. + +- ContinuousvisualnavigationSupplementaryInformation.pdf- ContinuousvisualroutefollowingVF.mp4 + +<--- Page Split ---> diff --git a/preprint/preprint__0845528f7ce8151c643f7cfb0afbbb91c8fd501364586b8b087110447c3361d9/preprint__0845528f7ce8151c643f7cfb0afbbb91c8fd501364586b8b087110447c3361d9_det.mmd b/preprint/preprint__0845528f7ce8151c643f7cfb0afbbb91c8fd501364586b8b087110447c3361d9/preprint__0845528f7ce8151c643f7cfb0afbbb91c8fd501364586b8b087110447c3361d9_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..0d3d267bb7ab05b31834f2f4ca45f103b280c88a --- /dev/null +++ b/preprint/preprint__0845528f7ce8151c643f7cfb0afbbb91c8fd501364586b8b087110447c3361d9/preprint__0845528f7ce8151c643f7cfb0afbbb91c8fd501364586b8b087110447c3361d9_det.mmd @@ -0,0 +1,496 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 864, 172]]<|/det|> +# Continuous Visual Navigation with Ant-Inspired Memories + +<|ref|>text<|/ref|><|det|>[[44, 196, 155, 213]]<|/det|> +Gabriel Gattaux + +<|ref|>text<|/ref|><|det|>[[55, 222, 355, 240]]<|/det|> +gabriel.gattaux@univ- amu.fr + +<|ref|>text<|/ref|><|det|>[[44, 269, 901, 288]]<|/det|> +Aix- Marseille University - Institute of Movement Science https://orcid.org/0000- 0002- 9424- 7543 + +<|ref|>text<|/ref|><|det|>[[44, 294, 200, 311]]<|/det|> +Antoine Wystrach + +<|ref|>text<|/ref|><|det|>[[50, 315, 690, 334]]<|/det|> +CNRS - Université Paul Sabatier https://orcid.org/0000- 0002- 3273- 7483 + +<|ref|>text<|/ref|><|det|>[[44, 340, 160, 357]]<|/det|> +Julien Serres + +<|ref|>text<|/ref|><|det|>[[50, 362, 620, 380]]<|/det|> +Aix Marseille University https://orcid.org/0000- 0002- 2840- 7932 + +<|ref|>text<|/ref|><|det|>[[44, 386, 168, 403]]<|/det|> +Franck Ruffier + +<|ref|>text<|/ref|><|det|>[[50, 408, 630, 426]]<|/det|> +Aix- Marseille Univ, CNRS https://orcid.org/0000- 0002- 7854- 1275 + +<|ref|>sub_title<|/ref|><|det|>[[44, 470, 103, 487]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 507, 137, 524]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 544, 340, 562]]<|/det|> +Posted Date: December 5th, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 583, 474, 601]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 5505975/v1 + +<|ref|>text<|/ref|><|det|>[[42, 619, 914, 661]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 680, 535, 699]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 736, 920, 779]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on September 24th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 62327- 3. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[170, 108, 856, 131]]<|/det|> +# Continuous Visual Navigation with Ant-Inspired Memories + +<|ref|>text<|/ref|><|det|>[[180, 152, 844, 170]]<|/det|> +Gabriel Gattaux \(^{1*}\) , Antoine Wystrach \(^{2}\) , Julien R. Serres \(^{1,3}\) , Franck Ruffier \(^{1}\) + +<|ref|>text<|/ref|><|det|>[[220, 177, 803, 226]]<|/det|> +\(^{1*}\) Aix Marseille Univ, CNRS, ISM, Marseille, France. \(^{2}\) Univ Toulouse, CRCA, CBI, UMR CNRS- UPS 5169, Toulouse, France. \(^{3}\) Institut Universitaire de France, IUF, Paris, France. + +<|ref|>text<|/ref|><|det|>[[242, 254, 781, 270]]<|/det|> +\*Corresponding author(s). E- mail(s): gabriel.gattaux@univ- amu.fr; + +<|ref|>sub_title<|/ref|><|det|>[[478, 300, 546, 312]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[140, 316, 889, 499]]<|/det|> +Solitary foraging ants excel in following long visual routes in complex environments with limited sensory and neural resources—an ability that remains challenging for robots with minimal computational power. Here, we introduce a self- supervised, insect- inspired neural network that enables robust route- following on the compact, low- cost Antcar robot. The robot leverages key aspects of ant brain and behavior: (i) continuous, one- shot visual route learning using panoramic encoding in a mushroom body- inspired network, (ii) categorization of low- resolution egocentric panoramas via oscillatory movements, (iii) opponent- process control of angular and forward velocities based on visual familiarity, (iv) recognition of places of interest along routes, and (v) motivation- based memory modulation. Antcar autonomously followed routes between indoor or outdoor destinations, forward or backward, while remaining stable in both theoretical analysis and real- world testing despite occlusions and visual changes. Across 1.3 km of autonomous travel, Antcar achieved challenging route- following with sub- 20 cm lateral error at speeds up to 150 cm/s, requiring only 148 kilobits of memory and processing panoramas every 62 ms. This efficient, brain- inspired architecture stands out from more sensor- intensive and computationally demanding methods, presenting a neuromorphic approach with valuable insights into insect navigation and practical robotic applications. + +<|ref|>sub_title<|/ref|><|det|>[[80, 537, 248, 555]]<|/det|> +## 1 Introduction + +<|ref|>text<|/ref|><|det|>[[77, 565, 501, 723]]<|/det|> +Insect navigation has long intrigued researchers across various fields, from biology to robotics, driving the development of cutting- edge technologies for autonomous mobile robots [1- 3]. Autonomous navigation remains a demanding and interdisciplinary challenge with applications ranging from space exploration to last miles delivery [4, 5], especially in scenarios where robots cannot rely on satellite systems [6]. Simultaneously, robots serve as valuable tools for studying insects navigation and brain structure, advancing neuromorphic engineering [7- 11]. + +<|ref|>text<|/ref|><|det|>[[77, 723, 501, 936]]<|/det|> +In Robotics, visual teach- and- repeat methods combined with dead- reckoning techniques have gained in popularity [12- 15]. However, experienced solitary foraging ants navigate along familiar routes using only visual memories, without relying on dead reckoning (so- called path integration in the insect literature) [16- 18]. This behavior has inspired various robotic models, although current implementations are generally limited to short- range experiments of about ten meters, with modest computational efficiency, precision, and accuracy [19- 23]. While ant- inspired models achieve results comparable to conventional computer vision approaches [13, 24], they struggle in dynamic environments where computational efficiency must be balanced with resource use. + +<|ref|>image<|/ref|><|det|>[[541, 538, 904, 707]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[523, 715, 928, 818]]<|/det|> +
Fig. 1 Biological inspiration for robotic navigation. An ant in the foreground symbolizes nature's efficient navigational strategies, while the Antcar robot in the background integrates these principles into a neuromorphic system. The blurred image captures only the large masses of the environment, similar to the low-pass spatial filter in the ant's visual system, which retains these large features even when objects obstruct the view between the robot and the building. ©Tifenn Ripoll - VOST Collectif / Institut Carnot STAR.
+ +<|ref|>text<|/ref|><|det|>[[523, 834, 928, 933]]<|/det|> +These challenges are partly due to early navigation models that emphasized hymenopteran behavior rather than underlying brain processes. Early models, referred to as perfect memory models, stored periodic snapshots at specific waypoints [25, 26]. Then, during autonomous route following (or exploitation), forced scanning movements compared acquired views to an image bank, using + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[45, 50, 472, 108]]<|/det|> +rotational image differences to establish the most familiar image and desired heading - a process known as the visual compass [27- 32]. However, these approaches has revealed two main limitations when applied in robotics. + +<|ref|>text<|/ref|><|det|>[[45, 108, 472, 263]]<|/det|> +The first limitation involves the cumulative storage of snapshots, which significantly increases memory and computational demands as the route lengthens, making it unsuitable for long- distance navigation. This issue was partially addressed by a neural network using the Infomax algorithm [33], which enables efficient encoding of increasing numbers of images without a corresponding rise in memory load [20, 31, 34]. However, Infomax requires substantial adjustments to synaptic weights for each input through a non- local learning mechanism, limiting its biological plausibility. + +<|ref|>text<|/ref|><|det|>[[45, 264, 472, 520]]<|/det|> +In parallel, research on the Mushroom Body (MB), a key part of the insect brain, has highlighted its essential role in olfactory and visual learning [35, 36]. In the MB, learning occurs through synaptic depression between thousands of Kenyon Cells (KCs) - intrinsic neurons that sparsely encode sensory input - and a few Mushroom Body Output Neurons (MBONs), which modulate behavioral responses based on learned associations. These processed signals are then transmitted to downstream neural circuits, influencing decisionmaking [37]. The first MB model simulating visual route following used a Spiking Neural Network with 20,000 KCs and one MBON to compute familiarity [38]. Despite this advancement, a second limitation remains: a forced systematic scanning during navigation slows robotic movement [21]. Also, this limitation does not reflect natural behavior, where scanning occurs only occasionally [39- 41]. + +<|ref|>text<|/ref|><|det|>[[45, 521, 472, 620]]<|/det|> +To address the second limitation, an early robotic implementation combined a klinokinesis model with perfect memory, enhancing short- distance routefollowing by replacing cumbersome scanning with alternating, ballistic left and right turns where familiarity adjusted turn amplitude [19] (later also observed in ants [42]). + +<|ref|>text<|/ref|><|det|>[[45, 621, 472, 833]]<|/det|> +To move beyond the random, undirected movement of kinesis, a taxis model was proposed, simulating directed movement toward a stimulus. In this model, KC firing activity was categorized into two distinct MBONs based on left or right orientation relative to the goal [43, 44]. This approach mirrors how insects, through continuous lateral body oscillations, sample multiple directions based on their nest position [42, 45]. Subsequent robotic models for route following attempted to integrate this lateralized approach by splitting the visual field into separate left and right memories, but these implementations showed limited efficiency in real- world tasks [22, 46]. In ants, however, the entire field of view is sent to the MB, and memories are fundamentally binocular [47]. + +<|ref|>text<|/ref|><|det|>[[45, 834, 472, 946]]<|/det|> +Here, we propose the lateralized route memories model, an MB- inspired design with four MBONs: two dedicated to route following and two for recognizing route extremities (Fig. 2). During a one- shot outbound learning route, ant- like body oscillations are simulated through continuous in- silico rotation of the panoramic image, mimicking head movement. This simulated head orientation, relative to the dynamic local orientation of + +<|ref|>text<|/ref|><|det|>[[497, 50, 900, 149]]<|/det|> +the route, categorizes views into left or right memory based on the polarity of the angular value, leading to a self- supervised model for route learning. This design also mimics dopaminergic feedback from motor centers, modulating MBON synapses based on the currently active KCs and the integration of left and right stimuli [44]. + +<|ref|>text<|/ref|><|det|>[[497, 150, 900, 420]]<|/det|> +In addition, our model incorporates key aspects of ant navigation not previously applied in MB models, such as adjusting forward speed by accelerating on familiar routes and slowing down in unfamiliar areas [39]. Our model also enables bi- directional route learning, allowing to retrace a route while moving backward or forward, recognizing visual memories from the outbound journey [48- 51]. Embedded in the compact Antcar robot (Figs. 1 and 2a), the model was tested across 99 autonomous trajectories, covering 1.3 km indoors and outdoors, achieving median lateral and angular errors of 20 cm and \(3^{\circ}\) , respectively, with refresh rates of 16 Hz during exploitation and 38 Hz during learning. Our MB model showed strong robustness to visual changes, including light fluctuations and pedestrian interference. This performance demonstrates the potential of our MB model for efficient, adaptable visual navigation in complex environments with accessible hardware and minimal computing requirements. + +<|ref|>sub_title<|/ref|><|det|>[[497, 436, 583, 454]]<|/det|> +## Results + +<|ref|>text<|/ref|><|det|>[[497, 464, 900, 735]]<|/det|> +Our proposed MB model emulates ant visual processing by encoding panoramic images as ultra- low resolution neural representations, enabling efficient learning and route recognition with minimal computational demands (see Methods for details, Fig. 2b). The model operates in two main phases: learning (Fig. 2c) and exploitation (Fig. 2d). During the learning phase, our self- supervised model encodes the route using two MBONs and stores place- specific memories for the Nest and Feeder as route extremities (see Methods, Fig. 2c). In the exploitation phase, the robot processes each view through both memory pathways, yielding two familiarity values (left and right MBON activities). The lateralized difference of familiarities \((\lambda_{diff})\) directs steering, while the maximum familiarity value modulates forward speed. Additionally, a motivational control modulates motor gain, allowing the robot to stop or reverse based on a familiarity thresholds set by place- specific MBONs (see Methods, Fig. 2d). + +<|ref|>text<|/ref|><|det|>[[497, 736, 900, 920]]<|/det|> +This study begins with an offline analysis of the proposed self- supervised MB model using two route MBONs to assess stability, followed by experimental route- following tasks in challenging indoor and outdoor environments. Next, a homing task is described, in which the robot follows a long outdoor route in reverse toward the starting area, designated as the Nest (N), and stops nearby, utilizing three MBONs. Finally, a shuttling task is introduced, where the robot, after a single learning trial with two route MBONs and two extremities MBONs for the Nest and Feeder, autonomously shuttles to and fro between these two locations, driving both forward and backward. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[95, 48, 920, 343]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[96, 358, 928, 528]]<|/det|> +
Fig. 2 Overview of the Lateralized Route Memories model implemented in the Antcar robot. This figure illustrates the process from image encoding to navigation control in both learning and exploitation phases. a The Antcar robot: a compact car-like platform equipped with an omnidirectional camera and a (Global Positioning System - Real-Time Kinematic) GPS-RTK system for ground truth data. b The image encoding process mimics ant's visual processing. Panoramic images \((I)\) are captured, blurred, subsampled, and edge-filtered to create a low-resolution \(32\times 32\) pixels panorama \((IS)\) . The \(IS\) is then transformed into Projection Neurons (PN), which are expanded into Excitatory Post-Synaptic Projections (EP) and reduced into Action Potentials (AP) via a \(\kappa\) -WTA function, forming the Kenyon Cells (KC). c During learning, the robot follows a path \((C)\) from a start point \((N)\) with an oscillatory movement to simulate angular deviations \((\theta_{e})\) . Synaptic updates occur in the Mushroom Body Output Neurons (MBNs) through the modulation by Dopaminergic-like Neurons (DAN), associating visual inputs with route memories in a self-supervised manner, dependent on the sign of \(\theta_{e}\) . An internal oscillator adjusts the image to simulate different angular errors, while joystick inputs control learning dynamics. d During exploitation, the robot aims to minimize the lateral \((d)\) and angular \((\theta_{e})\) errors relative to the route. The encoded image activates the MBNs according to the learned synaptic weights, allowing the robot to determine the position of the route and adjust its steering angle and speed. Familiarity indexes \((\lambda)\) of MBNs work in an opponent valence process to guide navigation: steering adjustments are based on differentiated familiarities, while the maximum familiarity modulates the speed. Specific MBNs related to start and end points alter motivational states to adjust route polarity or stop movement.
+ +<|ref|>sub_title<|/ref|><|det|>[[97, 543, 416, 575]]<|/det|> +## Self-supervised lateralized route memories model + +<|ref|>text<|/ref|><|det|>[[97, 583, 504, 811]]<|/det|> +We first evaluated the self- supervised model for route learning (using only two MBNs) with a dataset of indoor and outdoor parallel routes (Figs. 3c,f). Results demonstrated that, with a controlled oscillation amplitude during learning, the model accurately estimated its heading error based on the differential familiarity \(\lambda_{diff}\) , handling angular deviations up to \(135^{\circ}\) indoors and \(90^{\circ}\) outdoors (Fig. 3a,d,g). Furthermore, the maximum familiarity index \(\lambda_{max}\) , used as feedback for speed control, increased proportionally with heading error, enabling the robot to slow down when misaligned with the route. This behavior was consistent even when the robot was moved laterally off- route (Fig. 3a,b). Outdoors, these gradients were steeper (Fig. 3a,b,d, and e), indicating a higher visual contrast with larger landmarks. + +<|ref|>text<|/ref|><|det|>[[98, 812, 504, 940]]<|/det|> +The model's ability to identify heading error accurately across training oscillation amplitudes up to \(135^{\circ}\) (Fig. 3i, see also Supplementary note 1 and Fig. S1) suggests that this parameter may not require further tuning below this threshold. However, larger oscillation amplitudes increased computation time, especially on the Raspberry Pi platform (0.4s for \(\pm 45^{\circ}\) , Fig. 3i). Notably, the familiarity difference index (Fig. 3g) closely matched the spatial derivative of the maximum familiarity index, + +<|ref|>text<|/ref|><|det|>[[525, 544, 928, 588]]<|/det|> +corresponding to the catchment area and turn rate amplitude observed in ants (Fig. 3h, Supplementary note 1, 2, Fig. S1 and S2 [43]). + +<|ref|>text<|/ref|><|det|>[[525, 588, 928, 844]]<|/det|> +This analysis helped establish the operational limits of our MB model, maintaining stable behavior within a lateral error \((d)\) of 2 meters and an angular error \((\theta_{e})\) within the learning oscillation amplitude, set here at \(45^{\circ}\) . For asymptotic stability (i.e., the system's ability to return to equilibrium), we assumed a proportional relationship between \(\lambda_{diff}\) and \(\theta_{e}\) , supported by the Pearson correlation coefficient being close to 1 (Fig. 3i) and expressed as \(K_{diff} \cdot \lambda_{diff} = -\theta_{e}\) , where \(K_{diff}\) is a tuned negative gain. Integrating this relationship into the robot's motion equations, we applied a Lyapunov function for stability analysis. Results confirmed that the system converged to equilibrium points at \(d^{e} = 0\) and \(\theta_{e}^{e} = 0\) , effectively correcting small deviations and enabling the robot to remain aligned with the learned route. The full derivation of these equations and Lyapunov stability proof are provided in the Methods (section 6) and Supplementary note 3,4 and Fig. S3. + +<|ref|>sub_title<|/ref|><|det|>[[525, 860, 890, 891]]<|/det|> +## Route-following: robustness to visual changes + +<|ref|>text<|/ref|><|det|>[[525, 899, 928, 942]]<|/det|> +The proposed self- supervised approach for route learning was validated through a series of indoor and outdoor route- following tasks in fully autonomous mode, with + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[75, 48, 900, 555]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[68, 571, 900, 696]]<|/det|> +
Fig. 3 Offine familiarity mapping for learning of indoor and outdoor routes. This figure illustrates the differentiation and maximum familiarity of route Mushroom Body Output Neurons (MBNs) during offline analysis of panoramic images and positional data from indoor (Mediterranean Flight Arena) and outdoor (Luminy Campus, Marseille, France) environments. The mapping was performed using an oscillation amplitude \(A\) of \(45^{\circ}\) . a,d Familiarity difference index \((\lambda_{diff})\) and b,e familiarity maximum index \((\lambda_{max})\) are mapped in the route's frame of reference, showing variations with both lateral and angular errors \((d\) and \(\theta_{e})\) . The defined operating area is highlighted in pink. c Overview of the indoor (top) and f outdoor (bottom) environments with the learned route highlighted in red. g Cross-sectional view of the familiarity difference index \((\lambda_{diff})\) and h familiarity maximum index \((\lambda_{max})\) against the angular error \((\theta_{e})\) when the lateral error \((d)\) is null. Plotted for indoor (solid line) and outdoor (dotted line) conditions. i Pearson correlation coefficient illustrating the linear relationship between familiarity difference index \((\lambda_{diff})\) and angular error \((\theta_{e})\) as a function of oscillation amplitude \(A\) . This evolution of the correlation coefficient also illustrates the learning time required for a single oscillation cycle for each image captured on board the robot.
+ +<|ref|>text<|/ref|><|det|>[[68, 715, 472, 942]]<|/det|> +only two MBNs. After a first outbound route with online learning, where images were captured continuously to update synaptic weights in real- time, the robot demonstrated robust route- following in various configurations (Figs. 4 and 5). First, the Antcar robot successfully navigated convex and concave routes in a cluttered indoor environment of approximately 8 meters (median lateral error \(\pm\) median absolute deviation \((\mathrm{MAD}) = 0.21 \pm 0.09 \mathrm{m}\) , angular error \(\pm \mathrm{MAD} = 3.4 \pm 6.2^{\circ}\) , Fig. 4a,g and Fig. 7a). Moreover, the robot showed resilience in a kidnapped robot scenario, realigning with the learned route after being displaced (lateral error \(\pm \mathrm{MAD} = 0.26 \pm 0.14 \mathrm{m}\) , angular error \(\pm \mathrm{MAD} = 6.45 \pm 4.19^{\circ}\) , Fig. 4b and Fig. 7a). Only one crash occurred when the robot exceeded theoretical angular limits (see Supplementary Fig. S5). + +<|ref|>text<|/ref|><|det|>[[496, 715, 899, 828]]<|/det|> +Further tests assessed the robot's adaptability to high and low light conditions (Figs. 4c,h and Figs. 4d,i). Despite a single learning trial under standard lighting (815 Lux), the robot accurately followed its route in high (1,340 Lux) and low (81 Lux) lighting, with similar lateral and angular errors across tests (Fig. 7). This indicates that the MB- based control system is robust to significant changes in illumination. + +<|ref|>text<|/ref|><|det|>[[496, 829, 899, 942]]<|/det|> +In dynamic conditions with pedestrians and camera occlusions (Figs. 4e,f), the robot maintained reliable route- following when encountering pedestrians (lateral error \(\pm \mathrm{MAD} = 0.27 \pm 0.15 \mathrm{m}\) , angular error \(\pm \mathrm{MAD} = 4 \pm 2.8^{\circ}\) , Fig. 4e and Fig. 7a) and with dynamic occlusions (lateral error \(\pm \mathrm{MAD} = 0.22 \pm 0.13 \mathrm{m}\) , angular error \(\pm \mathrm{MAD} = 4.7 \pm 3.3^{\circ}\) , Fig. 4f and Fig. 7a). The presence of pedestrians and occlusions was reflected by the + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[95, 42, 928, 412]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[97, 430, 928, 476]]<|/det|> +
Fig. 4 Real world experiments of indoor route following in different conditions. The learned route in red is approximately 8m long. These experiments used two route MBONs. Environmental configurations and specific familiarity data are provided in the Supplementary Fig. S4 and video. From a to f, Route following results using the proposed self-supervised one-shot learning approach in different environmental conditions. From g to k, The visual environments in which the robot evolved during the experiments.
+ +<|ref|>text<|/ref|><|det|>[[70, 494, 501, 580]]<|/det|> +loss of maximum familiarity and led to speed reductions and increased emerging oscillatory motion ( \(15\%\) slower than in the previous experiments, Figs. 4e,f and supplementary video), which was also observed near obstacles. These results underscore the system's resilience under challenging conditions. + +<|ref|>text<|/ref|><|det|>[[70, 581, 501, 809]]<|/det|> +Outdoor experiments demonstrated the model's ability to maintain stable performance even over a long, 53- meter route and under altered environmental conditions. A route was learned and accurately recapitulated on a sunny day (lateral error \(\pm \mathrm{MAD} = 0.39 \pm 0.13\) m, angular error \(\pm \mathrm{MAD} = 5.8 \pm 2.8^{\circ}\) , Fig. 5a & 7a) and then retested the following day with parked cars removed (lateral error \(\pm \mathrm{MAD} = 1.3 \pm 0.5\) m, angular error \(\pm \mathrm{MAD} = 6.2 \pm 3.2^{\circ}\) , Fig. 5b & 7a). While the robot's error margins were slightly broader on the second day, it remained well within acceptable limits over the entire route. To test Antcar's maximum speed, a higher speed gain was applied during the second test (Fig. 5b), resulting in a cruising speed of \(1.5 \mathrm{m / s}\) compared to \(1 \mathrm{m / s}\) on the first day (see Supplementary Information note 5, Fig. S4 and Table S7). + +<|ref|>sub_title<|/ref|><|det|>[[70, 824, 445, 841]]<|/det|> +## Homing: homeward route and stop + +<|ref|>text<|/ref|><|det|>[[70, 847, 501, 946], [523, 494, 927, 537]]<|/det|> +Building on the validated route- following strategy, further tests refined the robot's behavior, focusing on ant- like homing. Homing, by definition, is the ability to return to a specific location after displacement. To test this, we evaluated the robot's ability to follow a \(50 \mathrm{m}\) outdoor route in reverse, stopping at a designated Nest area (point N in Fig. 6a). During learning, a \(180^{\circ}\) shift in the visual oscillation pattern simulated the "turn back and look" behavior observed in ants and led to homeward route following. + +<|ref|>text<|/ref|><|det|>[[523, 538, 927, 666]]<|/det|> +The robot successfully followed the \(50 \mathrm{m}\) route in reverse under cloudy outdoor conditions (lateral error \(\pm \mathrm{MAD} = 0.9 \pm 0.5 \mathrm{m}\) , angular error \(\pm \mathrm{MAD} = 6.3 \pm 4.2^{\circ}\) , Fig. 6a and Fig. 7a). Although maximum familiarity was higher than in previous outdoor experiments (see Supplementary note 5, Fig. S4 and Table S7), overall accuracy remained stable and emerging oscillatory movements was demonstrated (see Supplementary Video). + +<|ref|>text<|/ref|><|det|>[[523, 666, 927, 823]]<|/det|> +To enable autonomous stopping at the Nest, a placespecific MBON was used to learn 'nest- views' at the starting point of the route. Subsequent 'recognition' in this MBON, based on a familiarity threshold, acted as a motivational cues to halt route- following behavior and reducing the robot's linear velocity. This mechanisms was sufficient for the robot to successfully reach and stop at the Nest area in 4 out of 5 trials, with a median stopping distance of \(1.4 \mathrm{m}\) (Fig. 6c, see also Supplementary Fig. S6b for detailed familiarities values over distance). + +<|ref|>sub_title<|/ref|><|det|>[[523, 837, 875, 870]]<|/det|> +## Shuttling: foodward and homeward routes + +<|ref|>text<|/ref|><|det|>[[523, 877, 927, 947]]<|/det|> +Reverse route- following is also commonly observed in ants and was successfully replicated on board Antcar. Homing ants can pull food items backward when it is too large to carry forward, maintaining body alignment with the outbound route learned forward, and + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[88, 48, 875, 240]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[66, 245, 900, 280]]<|/det|> +
Fig. 5 Real word experiments of outdoor route-following with shared memories. a First day experiments, learning and autonomous route with several cars along the road. b Second day experiments, autonomous routes using the memories from day one in an altered environment (without cars).
+ +<|ref|>text<|/ref|><|det|>[[67, 298, 472, 355]]<|/det|> +using outbound memories with an opposite valance [50]. Shuttling tests show the robot's ability to switch movement direction and drive backward while maintaining alignment with the outbound route (Fig. 6b). + +<|ref|>text<|/ref|><|det|>[[66, 356, 472, 568]]<|/det|> +This foraging behavior was made possible by incorporating two additional place MBONs, which learned a series of panoramic views defining each endpoint of the route (Feeder and Nest). During shuttling, the model triggered a switch in motor gain polarity upon recognizing these panoramic views corresponding to the Feeder or Nest areas. In a cluttered indoor environment along a 6- meter learned route, the robot autonomously shuttled to and fro between the Feeder and the Nest, covering a total distance of 160 meters without interruption. Using a similar familiarity threshold on the two route- extremity MBONs, the robot detected the endpoints 22 times, achieving a median stopping distance of 0.31 m (Fig. 6d) (See Supplementary Fig. S6a for detailed familiarities values over distance). + +<|ref|>text<|/ref|><|det|>[[66, 569, 472, 866]]<|/det|> +This continuous shuttling revealed distinct differences in error profiles between forward and backward movement (Fig. 6b). During forward motion, the robot maintained stable control with minimal deviations (lateral error \(\pm \mathrm{MAD} = 0.1\pm 0.03\mathrm{m}\) , angular error \(\pm \mathrm{MAD}\) \(= 1.26\pm 0.83^{\circ}\) , Fig. 6b). However, during backward motion, the traction- driven setup amplified steering effects, resulting in slightly larger deviations from both accuracy and precision, though overall performance remained acceptable (lateral error \(\pm \mathrm{MAD} = 0.19\pm 0.08\) m, angular error \(\pm \mathrm{MAD} = 2.7\pm 2.1^{\circ}\) , Fig. 6b & 7a). The increased 'motor' variability led to lower visual recognition signal and thus usefully affected speed, which decreased by \(14\%\) compared to forward motion (see Supplementary note 5, Fig. S4 and Table S7). Nonetheless, the robot consistently realigned with the correct path after such minor deviations. These results highlight the model's versatility across different driving dynamics, capability to implement inverted steering, and adaptability to variations in motor kinematics and propulsion. + +<|ref|>sub_title<|/ref|><|det|>[[66, 883, 293, 898]]<|/det|> +## Performance summary + +<|ref|>text<|/ref|><|det|>[[65, 905, 471, 933], [496, 298, 902, 455]]<|/det|> +Across all experiments, including both indoor and outdoor route- following, homing and shuttling tasks, the model demonstrated robust and stable navigation performance, completing 99 autonomous trajectories with a total of 1.3 km traveled. The theoretical limits of the system were validated, with convergence toward equilibrium points consistently achieved under various environmental conditions, even in the presence of noise (lateral error \(\pm \mathrm{MAD} = 0.22\pm 0.10\mathrm{m}\) , angular error \(\pm \mathrm{MAD} = 3.8\pm 2.4^{\circ}\) , Fig. 7b). Lateral errors were within acceptable margins for both indoor and outdoor contexts, aligning within the standard widths of roads in France (5m) and typical indoor corridor (1.5m). + +<|ref|>text<|/ref|><|det|>[[496, 456, 902, 596]]<|/det|> +Additionally, statistical analysis showed no significant differences in the lateral or angular errors across the eleven test scenarios (Kruskal- Wallis test, \(H = 1.20\) for lateral error, p value \(\approx 1\) ; \(H = 0.97\) for angular error, p value \(\approx 1\) ), underscoring the system's reliability across diverse conditions (see Statistical Information). These results highlight the robustness and adaptability of the MB model in both structured and dynamic environments, confirming its potential applicability in a variety of navigation contexts. + +<|ref|>sub_title<|/ref|><|det|>[[496, 612, 618, 629]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[496, 640, 901, 910]]<|/det|> +Our study presents a robust, embedded, and biologically inspired Mushroom Body (MB) model capable of long- distance navigation in the real world with minimal sensor acuity and computational resources. Using fewer than a thousand pixels, the Antcar robot successfully followed routes at speeds up to \(1.5\mathrm{m / s}\) —approximately eight times its body length—achieving continuous online learning in just \(20\mathrm{ms}\) per image, with exploitation times of 75 ms and an extrapolated memory footprint of only \(0.3\mathrm{Mo}\) per kilometer. By integrating ant- inspired lateralized memory with self- supervised panoramic learning through oscillations, our model sustained high navigational accuracy across dynamic lighting, cluttered, and altered environments, with a positional accuracy of approximately \(20\mathrm{cm}\) . Offline analysis confirmed the model's stability and alignment with defined limits, predicting robust real- time performance by reliably maintaining route alignment within learning oscillation bounds. + +<|ref|>text<|/ref|><|det|>[[496, 911, 901, 938]]<|/det|> +The angular error between the agent's head direction and the dynamic local route orientation (defined + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[100, 48, 930, 336]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[96, 351, 930, 419]]<|/det|> +
Fig. 6 Real word experiments of outdoor homing and indoor shuttling. a Homing experiments using two route MBONs and one motivation MBON for a \(53\mathrm{m}\) L-shaped route, in an outdoor cloudy environment. Autonomous route headed in the opposite direction. b Familiarity nest index \((\lambda_{N})\) over traveled distance with the fixed stopping condition \((p = 0.2)\) . c Shuttling experiments using two route MBONs and two place MBONs in an indoor environment with artificial visual cues. Autonomous routes swing back (blue) and forth (black). d Familiarity nest \((\lambda_{N})\) and feeder \((\lambda_{F})\) index over the traveled distance, zoomed in to illustrate backward and forward movement.
+ +<|ref|>text<|/ref|><|det|>[[95, 434, 500, 705]]<|/det|> +in the Methods as the Frenet frame [52]) emerged as both a challenge during exploitation- - where the system minimizes this error- - and a cue during learning, where the categorization process depends on its polarity. Our model demonstrated homing behavior using either a \(180^{\circ}\) shift in visual oscillation or by inverting motor gains, thus enabling forward and backward movements with only a single foodward learning route. Additionally, visual place memories stored in supplementary MBONs, paired with a motivational control system, allowed the robot to recognize route endpoints and modulate motor gain, halting movement or reversing foraging motivation. With a single learning pass in one direction, the agent could follow the route forward, backward, and in reverse, controlled by oscillation parameters and motivational cues. Only motivational rules required adjustment to switch between route following, homing, and shuttling, underscoring the model's flexibility. + +<|ref|>text<|/ref|><|det|>[[94, 707, 499, 848]]<|/det|> +Our results surpass earlier ant- inspired familiarityonly models robots, which were generally limited to short indoor routes, slower linear speed (stop and scan), and lower efficiency [19- 23]. Our model also markedly outperforms state- of- the- art visual teach- andrepeat methods, which report memory footprints of 3 Mo per kilometer and processing times around \(400~\mathrm{ms}\) [13]. Our model also achieves competitive results against teach- and- repeat systems incorporating odometry [14, 15]. + +<|ref|>text<|/ref|><|det|>[[93, 849, 498, 948], [525, 435, 929, 620]]<|/det|> +This lateralized MB model distinguishes itself through reduced time and space complexity for route direction processing compared to perfect memory, snapshot, and visual compass approaches [43]. Whereas time and space complexity increase with the number of images in perfect memory or snapshot models, our MB model maintains constant space complexity, relying only on the synaptic matrix size KCToMBON. Additionally, in contrast to visual compass approaches, where computational complexity scales with in- silico scan range and resolution during exploitation \((\mathcal{O}(n))\) , our MB model maintains a constant factor \((\mathcal{O}(1))\) since in- silico scanning is only required during learning. For instance, while a visual compass scanning a \(\pm 45^{\circ}\) range at \(1^{\circ}\) resolution requires 90 comparisons per image, our model requires only two comparisons, eliminating the need for angular scanning in exploitation. Notably, our model produced commands five times faster than the visual compass approach on the same robot platform [21]. + +<|ref|>text<|/ref|><|det|>[[523, 620, 928, 920]]<|/det|> +Our contribution also aligns well with current biological observations, particularly highlighting the effectiveness of latent learning [53], where continuous learning bypass the need to control "when to learn" [31, 44]. The opposed event- triggered and snapshot- based learning models producing place learning [15, 54] where used here only to recognise place of interests such as the nest and the feeder to switch motivation, but were not engaged for route guidance. Also, our MB model prioritized body orientation within the local frame rather than divided the visual field [22, 46], aligning with biological observations in ants with unilateral visual impairment, showing that these insects store and recognise fundamentally binocular views [47]. Interestingly, the linear relationship observed between familiarity measures (and thus motor output) and angular error during exploitation closely mirrors experimental findings in ants [43]. This relationship enabled us to demonstrate the asymptotical stability of the system within a defined domain, ensuring the consistent and predictable behavior essential for a robotic navigation model [55]. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[75, 48, 890, 247]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[66, 268, 900, 293]]<|/det|> +
Fig. 7 Performance during route following overview a Detailed errors for each experiment. b Weighted bi-variate distribution for lateral \((d)\) and angular errors \((\theta_{e})\) across 11 different experimental configurations.
+ +<|ref|>text<|/ref|><|det|>[[66, 310, 485, 550]]<|/det|> +Furthermore, oscillatory learning behavior mirrors ant behavior, where initial routes involve slow, rotational movements, transitioning to direct paths on subsequent journeys [39]. These oscillations typically fall within \(\pm 100^{\circ}\) , with peaks around \(\pm 45^{\circ}\) in unfamiliar terrain [40, 42]. The robot's ability to slow down and produce emerging mechanical scanning upon entering unfamiliar areas (see Supplementary Video) are consistent with such naturalistic behaviors. Finally, Antcar's homing capability was maintained even when navigating backward, closely mirroring ant behavior while dragging food [48- 50, 56]. Overall, our attempt to integrate multiple MBNs, oscillations, "turn back and look" behavior, and motivational control mechanisms echoes insect mechanisms [2, 57], and the resulting expression when implemented in the robot echoes insect behaviours. + +<|ref|>text<|/ref|><|det|>[[66, 552, 485, 737]]<|/det|> +This study addresses several core needs identified in research on embodied neuromorphic intelligence [6, 8], such as robustness to visual changes, adaptability to real- world environments, and support for extended route learning. Our algorithm's efficiency allows computational power for additional tasks, making it valuable in GPS- compromised or SLAM- disrupted scenarios (SLAM stands for Simultaneous Localization And Mapping). The robot's low- resolution, wide- angle vision proves resilient against moving objects that often disrupt SLAM. Our model is well- suited for dynamic environments or situations where odometry (e.g., visual, inertial, step- counting, or wheel- rotation) is unreliable. + +<|ref|>text<|/ref|><|det|>[[66, 738, 485, 823]]<|/det|> +Interestingly, the semi- random encoding process, specifically the PNotKC synaptic projections, introduces a "fail- secure" memory- sharing mechanism. If synaptic weights for encoding differ, memory sharing becomes inaccessible, an advantageous feature for swarm robotics or cross- robot memory sharing. + +<|ref|>text<|/ref|><|det|>[[66, 824, 485, 908]]<|/det|> +Future research could enhance this approach. Transitioning this model to a spiking neural network on neuromorphic hardware could further enhance computational efficiency and biological fidelity [11]. Additionally, incorporating obstacle avoidance [58], would improve performance in dynamic environments. + +<|ref|>text<|/ref|><|det|>[[66, 909, 485, 936], [503, 310, 922, 408]]<|/det|> +In addition, a reduction of the visual field could correspond to more general cases, rendering in silico scanning impossible. In such scenarios, it would be necessary to estimate the angular error between the road frame and the agent. This could be achieved using a local angular path integration system (or odometry) during learning. As demonstrated by Collett et al. [59], showing that ants could utilize route segment odometry for navigation. + +<|ref|>text<|/ref|><|det|>[[503, 409, 922, 594]]<|/det|> +Our approach does not cover beeline homing post- foraging or search behaviors near points of interest, although these could be added by adding path integration mechanisms [60] or using the current visual mechanism but adding "learning walk" behaviors around place of interest [44]. Additionally, fixed neural parameters across all experiments suggest an opportunity for further exploration by adjusting Kenyon Cell numbers or connectivity, or testing different MB learning mechanisms [61]. Expanding the number of MBNs, akin to the 34 in Drosophila [37], could enable more complex motivational states, multi- branch memory storage [53], and broader navigational abilities [62]. + +<|ref|>text<|/ref|><|det|>[[503, 595, 922, 693]]<|/det|> +Overall, inspired by the neuroethology of ants, our MB model provides an effective bridge between theoretical insights and practical applications in insect- inspired autonomous robotic navigation. This egocentric model confirms the neuromorphic architecture's promise for autonomous systems, suggesting a scalable solution for both robotics and biological research applications. + +<|ref|>sub_title<|/ref|><|det|>[[503, 709, 600, 727]]<|/det|> +## Methods + +<|ref|>text<|/ref|><|det|>[[503, 739, 922, 838]]<|/det|> +This section describes the methodology used in the present study, focusing on the Encoding, Learning, and Exploitation processes of the proposed MB model (Figs. 2b- d). We also provide details on the hardware setup, control architecture, and stability analysis (See Supplementary Fig. S7 for the detailed route following neural network). + +<|ref|>sub_title<|/ref|><|det|>[[503, 854, 656, 870]]<|/det|> +## Image Encoding + +<|ref|>text<|/ref|><|det|>[[503, 876, 922, 920]]<|/det|> +Inspired by the visual system of ants [63], the model encoded real- world images into sparse, binary neural representations to efficiently handle visual input. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[97, 50, 503, 220]]<|/det|> +The encoding function (Fig. 2b) processed panoramic images from a camera with a \(220^{\circ}\) vertical and \(360^{\circ}\) horizontal field of view. This wide field of view enabled the camera to capture from slightly below the horizon to nearly directly below itself. To enhance natural contrast, the green channel of each image was selected [63], followed by Gaussian smoothing ( \(\sigma = 3\) pixels) to reduce noise. The image was then downsampled to an ultra-low- resolution \(32 \times 32\) pixel thumbnail (0.145 pixel per degree), approximating the visual resolution of ants at \(7.1^{\circ}\) between adjacent photoreceptors. + +<|ref|>text<|/ref|><|det|>[[97, 221, 503, 377]]<|/det|> +Next, a Sobel filter extracted edges, mimicking lateral inhibition as seen in insect optical lobes [64]. These processed images were flattened into 800 Visual Projection Neurons (PNs), comparable to the number of ommatidia in ants. The PNs were further expanded into Kenyon Cells (KCs) using a fixed, sparse pseudorandom synaptic matrix (PNtoKC). Each KC received input from four PNs, enhancing the visual encoding's discriminative power within the Mushroom Body (MB) [65], forming an Excitatory Post Synaptic Projection (EP) vector of size \(u\) . + +<|ref|>text<|/ref|><|det|>[[97, 378, 503, 536]]<|/det|> +The EP vector size was set to \(u = 15,000\) for the route MBONs ( \(MBON_{R}\) and \(MBON_{L}\) ), while for place- specific MBONs ( \(MBON_{N}\) and \(MBON_{F}\) ), which required fewer images, \(u\) was set to 5,000. A \(\kappa\) - Winner- Take- All (WTA) mechanism was applied to capture the highest contrasts, creating a high- dimensional, sparsified binary vector. This vector, referred to as the Action Potential (AP), consequently activated only \(1\%\) of KCs ( \(\kappa = 0.01\) ), giving \(\overline{u} = u * \kappa\) active neurons. This final binary representation served as the encoded visual input. + +<|ref|>text<|/ref|><|det|>[[97, 536, 503, 564]]<|/det|> +All parameters were predefined by literature and experimental tests, but not further optimized. + +<|ref|>sub_title<|/ref|><|det|>[[98, 577, 366, 593]]<|/det|> +## Routes and places learning + +<|ref|>text<|/ref|><|det|>[[97, 600, 503, 628]]<|/det|> +The learning process is governed by synaptic depression through anti- Hebbian learning. + +<|ref|>equation<|/ref|><|det|>[[115, 650, 503, 688]]<|/det|> +\[K C t o M B O N_{i} = \left\{ \begin{array}{ll}0, & \mathrm{if~}A P_{i} = 1\\ K C t o M B O N_{i}, & \mathrm{otherwise} \end{array} \right. \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[97, 700, 503, 787]]<|/det|> +For each MBONs, their synaptic weight matrix ( \(K C t o M B O N\) ) dynamically adjusted their weight based on input from the \(A P\) layer described in equation 1 and from the mimicked dopaminergic feedback. Here, \(i\) represents the \(i^{t h}\) neuron in the specified vector, with \(K C t o M B O N_{i}\) and \(A P_{i}\) in \(\{0,1\}\) + +<|ref|>text<|/ref|><|det|>[[97, 788, 503, 843]]<|/det|> +The simulated oscillatory movements during learning were obtained by rotating each captured image in steps, creating a sweep of rotations ( \(\theta_{c}\) ) described by the following function: + +<|ref|>equation<|/ref|><|det|>[[109, 864, 503, 900]]<|/det|> +\[\theta_{c}(n) = A\cdot \sin (n\cdot \Delta \theta +\phi)\quad \mathrm{for~}n = 0,1,2,\ldots ,\frac{2A}{\Delta\theta} \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[97, 905, 503, 948]]<|/det|> +where \(A\) represents the oscillation amplitude, \(\Delta \theta\) the step size, and \(\phi\) the phase shift. The step size was fixed at \(\Delta \theta = 5^{\circ}\) , with \(A = 45^{\circ}\) for route MBONs and \(A =\) + +<|ref|>text<|/ref|><|det|>[[525, 50, 927, 79]]<|/det|> +\(30^{\circ}\) for place MBONs. The phase shift was \(\phi = 180^{\circ}\) only for the homing task (Fig. 6). + +<|ref|>text<|/ref|><|det|>[[525, 80, 927, 151]]<|/det|> +For route learning, the model assumed the robot perfectly aligned to the route being learned. The body rotation was estimated as \(\hat{\theta}_{e} = \theta_{e} + \theta_{c}\) , where therefore \(\theta_{e} = 0\) during learning. The encoded binary image was categorized based on the polarity of \(\hat{\theta}_{e}\) , such that: + +<|ref|>equation<|/ref|><|det|>[[572, 175, 927, 210]]<|/det|> +\[\left\{ \begin{array}{ll}L e a r n(A P,K C t o M B O N_{R}), & \mathrm{if~}\hat{\theta}_{e}\leq 0\\ L e a r n(A P,K C t o M B O N_{L}), & \mathrm{if~}\hat{\theta}_{e}\geq 0 \end{array} \right. \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[525, 225, 927, 382]]<|/det|> +Here, the function \(L e a r n()\) follows equation 1. Synaptic weights (KCtoMBON) were stored in CSR format, achieving significant data compression to 148 kilo- bits independently of the route length, reducing memory requirements by \(99.97\%\) from cumulative image storage. This self- supervised model continuously learned visual input at high throughput without memory overload, as only novel views (i.e., newly recruited KCs) modulated synapses. Several panoramic views were learned to define the start and finish areas in their respective MBONs, serving as motivational cues. + +<|ref|>sub_title<|/ref|><|det|>[[525, 398, 846, 430]]<|/det|> +## Exploitation process and control architecture + +<|ref|>text<|/ref|><|det|>[[525, 437, 927, 479]]<|/det|> +During exploitation, the model calculated familiarity scores ( \(\lambda\) ) by comparing the current input ( \(A P\) ) with each MBON's synaptic weight matrix ( \(K C t o M B O N\) ): + +<|ref|>equation<|/ref|><|det|>[[610, 489, 927, 526]]<|/det|> +\[\lambda = \frac{1}{\overline{u}}\sum_{i = 1}^{u}A P_{i}\cdot K C t o M B O N_{i} \quad (4)\] + +<|ref|>text<|/ref|><|det|>[[525, 529, 927, 656]]<|/det|> +This familiarity score, ranging from 0 (unfamiliar) to 1 (familiar), was used to assess route alignment. The lateralized difference in familiarities between the left and right MBONs ( \(\lambda_{diff} = \lambda_{L} - \lambda_{R}\) ), which indicates whether the current view is more oriented to the left or right of the route, guided the robot's steering angle ( \(\phi\) ). Meanwhile, the maximum familiarity ( \(\lambda_{max} = \max (\lambda_{L}, \lambda_{R})\) ), representing how familiar the current view is, modulated its speed ( \(v\) ). + +<|ref|>text<|/ref|><|det|>[[550, 657, 860, 671]]<|/det|> +Thus, the control input \(U\) was defined as: + +<|ref|>equation<|/ref|><|det|>[[587, 690, 927, 727]]<|/det|> +\[U = \left[ \begin{array}{l}v\\ \phi \end{array} \right] = \left[ \begin{array}{l}M\cdot K_{v}\cdot \mathrm{sat}(1 - \lambda_{max})\\ M\cdot K_{\phi}\cdot \lambda_{diff} \end{array} \right] \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[525, 738, 927, 923]]<|/det|> +Here, \(K_{v}\) and \(K_{\phi}\) are proportional gains that control linear and angular velocities, while the saturation function ( \(sat()\) ) establishes a minimum throttle level, ensuring minimum speed even at low familiarity levels. The motivational state ( \(M\) ) regulated transitions between behaviors based on a familiarity thresholds within place- specific MBONs. During route following, \(M\) was consistently set to 1. In homing experiments, where the objective was to stop at the nest, \(M\) initially started at 1 and switched to 0 once the familiarity of the nest- specific MBON ( \(\lambda_{N}\) ) fell below a fixed threshold ( \(p = 0.2\) ), signaling arrival at the nest. For shuttling tasks, \(M\) alternated between values of 1 and \(- 1\) as the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[40, 50, 471, 92]]<|/det|> +robot reached each route extremity, driven by a familiarity thresholds of the two place- specific MBONs ( \(\lambda_{N}\) and \(\lambda_{F}\) ). + +<|ref|>sub_title<|/ref|><|det|>[[40, 108, 471, 125]]<|/det|> +## Theoretical analysis of the robot stability + +<|ref|>text<|/ref|><|det|>[[40, 130, 471, 287]]<|/det|> +Stability in mobile agents, biological or robotic, is essential for reliable, predictable behavior. In control theory, an agent's motion is generally modeled as \(\dot{x} = f(x,U)\) where \(x\) is the state vector (e.g., position or velocity), \(U\) is the control input, and \(f\) describes system dynamics. A desired equilibrium point \(x_{e}\) is achieved by defining a control input \(U_{e}\) such that \(f(x_{e},U_{e}) = 0\) , allowing the system to maintain stability and return to equilibrium after disturbances. Stability is typically assessed using a Lyapunov function [55], which ensures the system converges to a stable state over time. + +<|ref|>text<|/ref|><|det|>[[40, 288, 471, 472]]<|/det|> +In contrast to conventional control approach, we applied a neuroethologically inspired control input derived from ant behavior, assessing stability via an a posteriori Lyapunov analysis. The robot's motion was modeled in a Frenet frame, a moving reference frame coincident with the nearest point on the route, to minimize lateral and angular errors, defined by \(x = [d,\theta_{e}]\) . Empirical data for stability assessment was collected in indoor and outdoor environments (paths of approximately 6 meters with 855 learned images each), providing distinct visual contexts (Figs. 2, 3). The robot's equations of motion from a global to the Frenet frame are [66]: + +<|ref|>equation<|/ref|><|det|>[[144, 479, 470, 530]]<|/det|> +\[\left[ \begin{array}{c}\dot{s\] \[\dot{d\] \[\dot{\theta}_e} \end{array} \right] = \left[ \begin{array}{c}v(\cos \theta_e - \tan \phi \sin \theta_e)\] \[v(\sin \theta_e + \tan \phi \cos \theta_e)\] \[v\frac{\tan \phi}{L} \end{array} \right], \quad (5)\] + +<|ref|>text<|/ref|><|det|>[[40, 530, 471, 558]]<|/det|> +where \(s\) is the arc length along the route, \(d\) is the lateral error, and \(\theta_{e}\) is the angular error. + +<|ref|>text<|/ref|><|det|>[[40, 559, 471, 660]]<|/det|> +This kinematic model, along with by empirical observations (Fig. 3), enabled us to establish an asymptotically stable domain for lateral and angular errors ( \(d\) and \(\theta_{e}\) ), ensuring reliable route- following performance even with minor disturbances. The full theoretical stability proof and derivations of the model in the frenet frame are provided in the Supplementary note 3 and 4. + +<|ref|>sub_title<|/ref|><|det|>[[40, 675, 455, 692]]<|/det|> +## Antcar robot and ground truth system + +<|ref|>text<|/ref|><|det|>[[40, 698, 471, 939]]<|/det|> +The experiments were conducted using Antcar (Fig. 1 and Fig. 2a), a PiRacer AI- branded car- like robot. Antcar features four wheels, with two rear drive wheels powered by 37- 520 DC motors (12V, 1:10 reduction rate) and a front steering mechanism controlled by an MG996R servomotor (9kg/cm torque, 4.8V). The robot's chassis measures \(13 \times 24 \times 19.6 \mathrm{cm}\) and is powered by three rechargeable 18650 batteries (2600mAh, 12.6V output). Antcar's primary sensor is a \(220^{\circ}\) Entaniya fisheye camera, mounted upward to capture panoramic images at \(160 \times 160px \times 3\) resolution and 30 Hz, processed using OpenCV on a Raspberry Pi 4 Model B (Quad- core Cortex- A72, 1.8GHz, 4GB RAM), running Ubuntu 20.04. Note that there was no closed- loop control on the wheel rotation speed. Raspberry Pi manages real- time performance and controls the motors through a custom ROS architecture. + +<|ref|>text<|/ref|><|det|>[[494, 50, 900, 178]]<|/det|> +Real- time communication is facilitated by ROS Noetic, either via Wi- Fi (indoor) or a 4G dongle (outdoor). The robot can be controlled manually using a keyboard, joystick or with GPS waypoint, but in autonomous visual- only mode, it follows its own internal control law. Control inputs—steering angle \((\phi)\) and throttle \((v)\) are processed using the PyGame library. Real- time data visualization and post- experiment monitoring are achieved via Foxglove. + +<|ref|>text<|/ref|><|det|>[[494, 179, 900, 434]]<|/det|> +Antcar has a maximum velocity of \(1.5 \mathrm{m / s}\) and a maximum steering angle of 1 rad, with a wheelbase of \(0.15 \mathrm{m}\) . The robot's configuration states \(q = (x, y, \theta)\) were tracked using different systems. Indoor experiments utilized eighteen Vicon™ motion capture cameras, with infrared markers on Antcar providing precise tracking at 50 Hz with 1 mm accuracy. Outdoor experiments employed a GPS- RTK system with a SparkFun GPS- RTK Surveyor, providing 14 mm accuracy at 2 Hz (GPS- RTK stands for Global Positioning System - Real- Time Kinematic). Ground speed and angular speed were calculated through position differentiation. The base station used for GPS corrections was a Centipede LLENX station located at 24 km (Aeroport Marseille Provence) from the experiment site in Marseille. Note that the ground truth acquisition system was run on the Raspberry Pi along with the mushroom body model. + +<|ref|>text<|/ref|><|det|>[[494, 435, 900, 576]]<|/det|> +Lateral error was calculated by finding the nearest point on the learning route using the Euclidean distance, with the shortest distance representing the absolute lateral error. Angular error was defined as the absolute difference in heading between the nearest learning route point and the current position. The euclidean distance between the agent and the Nest or Feeder areas was calculated to estimate the distance when the robot switched behavior (i.e familiarity dropped below the threshold). + +<|ref|>sub_title<|/ref|><|det|>[[494, 592, 772, 610]]<|/det|> +## Statistical informations + +<|ref|>text<|/ref|><|det|>[[494, 620, 900, 820]]<|/det|> +The errors used for statistics were recorded at each command decision timing. Due to non- normality in error values (with outliers retained), Box- Cox transformations were applied to stabilize variance across experiments, reducing the impact of outliers caused by indoor obstacles that hid the robot from the motion capture system or by GPS- RTK inaccuracies outdoors. The groups was compared using the Kruskal- Wallis test [67], and median values are reported with median absolute deviation (MAD), as median \(\pm\) MAD. The package python SciPy [68] was used for the statistics. The overall medians and bivariate distribution plots were weighted by the number of measurements per experiment for the Fig. 7. + +<|ref|>sub_title<|/ref|><|det|>[[494, 835, 708, 854]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[494, 863, 900, 934]]<|/det|> +The authors thank David Wood for revising the English in this study, Guillaume Caron for providing the camera reference, and Thomas Gaillard, Clément Serrasse, and Hamidou Diallo for their assistance during the robotic tests. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[98, 46, 247, 64]]<|/det|> +## Declarations + +<|ref|>text<|/ref|><|det|>[[95, 75, 504, 416]]<|/det|> +- Funding: G.G. was supported by a doctoral fellowship grant from Aix Marseille University and the French Ministry of Defense (AID - Agence Innovation Defense, agreement #A01D22020549 ARM/DGA /AID). G.G., J.R.S. and F.R. were also supported by Aix Marseille University and the CNRS (Life Science, Information Science, and Engineering and Science & technology Institutes). The facilities for the experimental tests has been mainly provided by ROBOTEX 2.0 (Grants ROBOTEX ANR-10-EQPX-44-01 and TIRREX ANR-21-ESRE-0015).- Conflict of interest: the authors declare no competing interests.- Data availability: Upon publication- Code availability: Upon publication- Supplementary Video : https://youtu.be/Osu5Jyv6dF4- Author contribution: G.G., A.W., J.R.S., and F.R. designed this research work; G.G, A.W., J.R.S., and F.R. got funding for this study; G.G. performed experiments, collected and visualized the data; G.G., A.W., J.R.S., and F.R. analyzed data; G.G. wrote the first full draft. 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Latent learning without map- like representation of space in navigating ants. Preprint at https://www.biorxiv.org/content/10.1101/2024.08.29.610243v1 (2024). + +<|ref|>text<|/ref|><|det|>[[80, 532, 504, 576]]<|/det|> +[54] Möller, R. & Vardy, A. Local visual homing by matched- filter descent in image distances. Biological Cybernetics 95, 413- 430 (2006). + +<|ref|>text<|/ref|><|det|>[[80, 586, 504, 630]]<|/det|> +[55] Lyapunov, A. M. The general problem of the stability of motion. International journal of control 55, 531- 534 (1992). + +<|ref|>text<|/ref|><|det|>[[80, 640, 504, 670]]<|/det|> +[56] Webb, B. The internal maps of insects. Journal of Experimental Biology 222, jeb188094 (2019). + +<|ref|>text<|/ref|><|det|>[[80, 681, 504, 725]]<|/det|> +[57] Aso, Y. et al. Mushroom body output neurons encode valence and guide memory- based action selection in drosophila. elife 3, e04580 (2014). + +<|ref|>text<|/ref|><|det|>[[80, 736, 504, 780]]<|/det|> +[58] Schoepe, T. et al. Finding the gap: Neuromorphic motion- vision in dense environments. Nature Communications 15, 817 (2024). + +<|ref|>text<|/ref|><|det|>[[80, 791, 504, 848]]<|/det|> +[59] Collett, T. S. & Collett, M. Route- segment odometry and its interactions with global path- integration. Journal of Comparative Physiology A 201, 617- 630 (2015). + +<|ref|>text<|/ref|><|det|>[[80, 859, 504, 903]]<|/det|> +[60] Stone, T. et al. An anatomically constrained model for path integration in the bee brain. Current Biology 27, 3069- 3085 (2017). + +<|ref|>text<|/ref|><|det|>[[80, 914, 504, 942]]<|/det|> +[61] Webb, B. Beyond prediction error: 25 years of modeling the associations formed in the insect + +<|ref|>text<|/ref|><|det|>[[555, 49, 963, 78]]<|/det|> +mushroom body. Learning & Memory 31, a053824 (2024). + +<|ref|>text<|/ref|><|det|>[[525, 88, 963, 147]]<|/det|> +[62] Sommer, S., Von Beeren, C. & Wehner, R. Multiroute memories in desert ants. Proceedings of the National Academy of Sciences 105, 317- 322 (2008). + +<|ref|>text<|/ref|><|det|>[[525, 158, 963, 187]]<|/det|> +[63] Aksoy, V. & Camlipepe, Y. Spectral sensitivities of ants- a review. Animal Biology 68, 55- 73 (2018). + +<|ref|>text<|/ref|><|det|>[[525, 198, 963, 283]]<|/det|> +[64] Wystrach, A., Dewar, A., Philippides, A. & Graham, P. How do field of view and resolution affect the information content of panoramic scenes for visual navigation? A computational investigation. Journal of Comparative Physiology A 202, 87- 95 (2016). + +<|ref|>text<|/ref|><|det|>[[525, 295, 963, 354]]<|/det|> +[65] Le Moël, F. & Wystrach, A. Vision is not olfaction: impact on the insect mushroom bodies connectivity. Preprint at https://www.biorxiv.org/content/10.1101/2024.08.31.610627v1 (2024). + +<|ref|>text<|/ref|><|det|>[[525, 365, 963, 424]]<|/det|> +[66] Applonié, R. & Jin, Y.- F. A novel steering control for car- like robots based on lyapunov stability. 2019 American Control Conference (ACC) 2396- 2401 (2019). + +<|ref|>text<|/ref|><|det|>[[525, 434, 963, 492]]<|/det|> +[67] Kruskal, W. H. & Wallis, W. A. Use of ranks in one- criterion variance analysis. Journal of the American Statistical Association 47, 583- 621 (1952). + +<|ref|>text<|/ref|><|det|>[[525, 503, 963, 547]]<|/det|> +[68] Virtanen, P. et al. Scipy 1.0: fundamental algorithms for scientific computing in python. Nature methods 17, 261- 272 (2020). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[42, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[42, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[59, 130, 595, 177]]<|/det|> +- ContinuousvisualnavigationSupplementaryInformation.pdf- ContinuousvisualroutefollowingVF.mp4 + +<--- Page Split ---> diff --git a/preprint/preprint__084645ecaf0633670ce86e9b62f37cf8c911eec46c0e61646e18f05eb546a1be/images_list.json b/preprint/preprint__084645ecaf0633670ce86e9b62f37cf8c911eec46c0e61646e18f05eb546a1be/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..526d9f7798d7ab597788d85def98aa6455c29f5f --- /dev/null +++ b/preprint/preprint__084645ecaf0633670ce86e9b62f37cf8c911eec46c0e61646e18f05eb546a1be/images_list.json @@ -0,0 +1,92 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1 | The overall architecture of PGMG. (a) The construction of pharmacophore networks. We use the shortest paths on the molecular graph to approximate the Euclidean distances between two pharmacophore features and construct a fully connected graph to represent a pharmacophore model. (b) The preprocessing of SMILES. We randomize a given canonical SMILES and corrupt it using the infilling scheme. (c) Model structure and pipelines for training and inferencing. \\(c\\) represents the embedding vector sequences for the given pharmacophore model; \\(x\\) represents the embedding sequence of input SMILES; \\(z\\) represents the latent variables for a molecule. Transformer encoder and decoder blocks are stacked with N layers. \\(\\oplus\\) denotes concatenation of two vectors and \\(\\otimes\\) denotes matrix multiplication. The overlap between the training and inferencing process is highlighted in the right panel.", + "footnote": [], + "bbox": [ + [ + 55, + 45, + 936, + 540 + ] + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2 | Performance of PGMG on the unconditional molecule generation task. (a) Performance of PGMG and SMILES-based models on ChEMBL. (b) Results of the ablation study. (c) Distribution of chemical properties for the ChEMBL training set and the molecules generated by PGMG. The scientific notation at the upper left of the figure indicates the scaling of the vertical coordinates.", + "footnote": [], + "bbox": [ + [ + 80, + 303, + 956, + 654 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3 | Pharmacophore matching test results and the distribution of four target docking scores. (a) The match score of random selected molecules and PGMG generated molecules. (b) The distributions of the predicted affinity of top 1000 molecules generated by PGMG over VEGFR2 (PDB: 1YWN), CDK6 (PDB: 2EUF), TGFβ 1 (PDB: 6B8Y),", + "footnote": [], + "bbox": [ + [ + 66, + 188, + 933, + 895 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4 | A display of the binding sites of the molecules generated by PGMG in structure-based drug design.", + "footnote": [], + "bbox": [ + [ + 63, + 174, + 928, + 641 + ] + ], + "page_idx": 9 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5 Alignment diagrams of terbinafine, pharmacophore model, and molecules generated by PGMG. The different colored spheres represent different pharmacophore features. Aromatic center is red, the positive charge center is yellow, and hydrophobic centers are green. The grey molecules represent terbinafine, and the green molecules represent the molecules generated by PGMG.", + "footnote": [], + "bbox": [ + [ + 62, + 492, + 930, + 825 + ] + ], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6 | Display diagram of the molecule generated by PGMG with known inhibitors in the case of Lavendustin A optimization. Molecules generated by PGMG are shown inside the circle and their closest active nearest neighbors are shown outside the circle. The colors indicate the pharmacophore features extracted from Lavendustin A. Red corresponds to the aromatic center, blue represent the hydrogen-bonded acceptor, and green represent the hydrophobic center.", + "footnote": [], + "bbox": [ + [ + 92, + 68, + 916, + 410 + ] + ], + "page_idx": 12 + } +] \ No newline at end of file diff --git a/preprint/preprint__084645ecaf0633670ce86e9b62f37cf8c911eec46c0e61646e18f05eb546a1be/preprint__084645ecaf0633670ce86e9b62f37cf8c911eec46c0e61646e18f05eb546a1be.mmd b/preprint/preprint__084645ecaf0633670ce86e9b62f37cf8c911eec46c0e61646e18f05eb546a1be/preprint__084645ecaf0633670ce86e9b62f37cf8c911eec46c0e61646e18f05eb546a1be.mmd new file mode 100644 index 0000000000000000000000000000000000000000..a24d2eebd035c78d96a23cffdbcf8cda3617a67a --- /dev/null +++ b/preprint/preprint__084645ecaf0633670ce86e9b62f37cf8c911eec46c0e61646e18f05eb546a1be/preprint__084645ecaf0633670ce86e9b62f37cf8c911eec46c0e61646e18f05eb546a1be.mmd @@ -0,0 +1,381 @@ + +# PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecular Generation + +Min Li ( limin@mail.csu.edu.cn ) Central South University https://orcid.org/0000- 0002- 0188- 1394 + +Huimin Zhu Central South University + +Renyi Zhou Central South University + +Jing Tang Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland https://orcid.org/0000- 0001- 7480- 7710 + +## Article + +# Keywords: + +Posted Date: September 15th, 2022 + +DOI: https://doi.org/10.21203/rs.3.rs- 1749921/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> + +# PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecular Generation + +Huimin Zhu \(^{1,\dagger}\) , Renyi Zhou \(^{1,\dagger}\) , Jing Tang \(^{2}\) and Min Li \(^{1,\ast}\) + +\(^{1}\) School of Computer Science and Engineering, Central South University, Changsha 410083, China \(^{2}\) Faculty of Medicine, University of Helsinki, Helsinki, 00290, Finland + +\(^{\dagger}\) These two authors contribute equally to the work. + +\* Corresponding author, limin@mail.csu.edu.cn + +## Abstract + +The rational design of novel molecules with desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. Here, we propose PGMG, a pharmacophore- guided deep learning approach for bioactive molecule generation. Through the guidance of pharmacophore, PGMG provides a flexible strategy to generate bioactive molecules matching given pharmacophore models. PGMG uses a graph neural network to encode pharmacophore features and spatial information and a transformer decoder to generate molecules. A latent variable is introduced to solve the many- to- many mapping between pharmacophores and molecules and improve the diversity of generated molecules. In addition, these generated molecules are of high validity, uniqueness, and novelty. In the case studies, we demonstrate using PGMG in ligand- based and structure- based drug de novo design, as well as in lead optimization scenarios. Overall, the flexibility and effectiveness make PGMG a useful tool to accelerate drug discovery process. + +## Introduction + +The acquisition of biologically active compounds is a vital but challenging step in drug discovery. It has been estimated that the drug- like chemical space is as large as \(10^{60}\) obeying Lipinski's "Rule of Five" \(^{1,2}\) . Hence, it is an extremely difficult task to search for desired molecules in such a huge space. Traditionally, hit compounds which exhibit initial activity on a specific target can be obtained from natural products, designed by medicinal chemists, or acquired by high- throughput screening (HTS) \(^{3}\) . These methods consume a lot of human and financial resources, which makes the acquisition of hit compounds inefficient and costly. Recently, some deep generative models have been proposed for the rational design of novel molecules with desired properties, which provide a new perspective for this task. + +Among the popular architectures and models for generating molecules from deep neural networks, the variational autoencoders (VAEs) \(^{4,5}\) , reinforcement learning (RL) \(^{6,7}\) , generative adversarial networks \(^{8 - 10}\) and auto- regressive models \(^{11,12}\) were successful to design desired molecules at a specified precondition. Regardless what framework were used, most above methods aim at generating molecules with given physicochemical properties, such as the Wildman- Crippen partition coefficient (LogP), synthetic accessibility (SA), molecular weight (molWt), quantitative estimate of drug likeness (QED), and others. + +<--- Page Split ---> + +However, the relationship between molecular and some physicochemical properties such as LogP, QED values are easy to achieve. In drug discovery, the most difficult and the real intrinsic objective is to design molecules that satisfy properties which require wet experimental to measure or require extensive calculations to approximate, such as the activity of the molecule13. These models are less suitable for generating bioactive molecules. For a specified target, these models require a large dataset of known active molecules to fine- tune and thus cannot be applied to a novel target or targets with few active compounds. + +Designing molecules using deep generative models with biological activity remains challenging14- 16. As mentioned above, one of the main obstacles is the limited data on target- specific molecules, which makes it difficult for models to learn the structure- activity relationship. For a novel target family, the paucity of activity data is even more severe. Besides, the scarcity of activity data affects the strategy of drug design. For example, the choice of ligand- based drug design or structure- based drug design depends on what information can be used, which narrows down the application scenarios of many methods. It is clear that incorporating expert knowledge in the generation process is beneficial to the full utilization of bioactivity data information17. Therefore, combining deep generative models with knowledge in biochemistry to efficiently use the scarce data to design biologically active molecules is a crucial project. + +Up to now, some methods that generate bioactive molecules by combining prior knowledge from biochemistry into molecule generation models have been proposed. For example, conditioned GAN can be used to design active- like molecules for desired gene expression signatures18, which provides a new perspective for molecule generation. However, the structure- activity relationship between the biological activity and the molecules generated by such methods is ambiguous. DeLinker19 and SyntaLinker20 adopt fragment- based drug design and retain active fragments while updating linkers to generate active molecules, and DEVELOP17 combines DeLinker with chemical features as constraints to improve the quality of the generated molecules. The fragment- based approaches require explicit knowledge of the active fragments, which lead to a limited chemical space for the model. DeepLigBuilder21 and 3D- Generative- SBDD22 utilize the structure- based drug design strategy and generate molecules based on the binding sites between molecules and proteins in the 3D Euclidean Space. However, these methods are limited when the binding site or the target structure is unknown. There are also some methods that use electronic features in molecule generation. For example, Reduced Graph23 simplifies a SMILES to an acyclic graph of functional group as its input to generation. Shape- based method proposed by Skalic et al24 generate molecules from a 3D representation using a seed ligand with a conditional chemical features. These methods require seed compounds to collect the input electronic features. The above generative models may perform well on specific types of activity data, but their usages are limited because of their assumptions on the data types. + +Here, we propose PGMG, a pharmacophore- guided molecule generation approach based on deep learning. PGMG uses pharmacophore models as a bridge to connect different types of activity data and can design bioactive molecules for newly discovered targets when there is no sufficient activity data. A pharmacophore is a set of chemical features and its spatial information that are necessary for a drug to bind to a target and can be constructed by superimposing a small number of active compounds25 or observing the structure of a given target26. Traditional drug design based on pharmacophores has many successful applications27, 28, but + +<--- Page Split ---> + +its potential in deep generative models has not been exploited. There are some works \(^{23, 24}\) that use pharmacophore- like information in molecule generation. However, the pharmacophore- like features used in these methods are incomplete and can only be extracted from seed compounds, making it difficult for domain knowledge to be leveraged. In PGMG, we use a complete graph to fully represent a pharmacophore with each node corresponding to a pharmacophore feature and the spatial information encoded as the distance between each node pair. Given the graph as the sole input, PGMG can generate molecules matching the corresponding pharmacophore. This gives PGMG the capability to utilize different types of activity data in a uniform representation and a biologically meaningful way to control the process of the bioactivity molecule design. + +Since pharmacophores and molecules have a many- to- many relationship, PGMG introduces latent variables to model such a relationship and boost the variety of generated molecules. Besides, the transformer structure is employed as the backbone to learn implicit rules of SMILES strings to map between latent variables and molecules. We evaluate the PGMG performance comprehensively in molecule generation with goal- directed metrics and drug- like metrics. The results show that PGMG can generate molecules satisfying given pharmacophore models and pharmacokinetic requirements, while maintaining a high level of validity, uniqueness, and novelty. The case studies further demonstrate that PGMG provides an effective strategy for both ligand- based and structure- based drug de novo designs and lead optimization. + +## Results + +## Overview of PGMG + +Our proposed PGMG is a pharmacophore- guided molecular generation approach based on deep learning. The overall architecture of PGMG is illustrated in Figure 1. + +Given a target pharmacophore, the goal of PGMG is to generate molecules which matches the pharmacophore. Here, we introduce a set of latent variables \(z\) to deal with the many- to- many mapping between pharmacophores and molecules. Thus, a molecule \(x\) can be represented as a unique combination of two complementary encodings including \(c\) representing the given pharmacophore and \(z\) corresponding to how chemical groups are placed within the molecule. From another perspective, the latent variables \(z\) grant PGMG to model multiple modes in the conditional distribution + +\[P(x|c) = \int_{z\sim P(z|c)}P(x|c,z)P(z|c)dz \quad (1)\] + +We train two neural networks, an encoder network \(P_{\phi}(z|c,x)\) to approximate \(P(z|c)\) indirectly and a decoder network \(P_{\theta}(x|c,z)\) to approximate \(P(x|c,z)\) . We embed molecules in SMILES format into dense feature vectors and use Gated GCN \(^{29}\) to embed pharmacophore models. The transformer structure proposed by Vaswani et al. \(^{30}\) is used as the backbone of our model to learn the mapping between pharmacophore and molecular structures. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1 | The overall architecture of PGMG. (a) The construction of pharmacophore networks. We use the shortest paths on the molecular graph to approximate the Euclidean distances between two pharmacophore features and construct a fully connected graph to represent a pharmacophore model. (b) The preprocessing of SMILES. We randomize a given canonical SMILES and corrupt it using the infilling scheme. (c) Model structure and pipelines for training and inferencing. \(c\) represents the embedding vector sequences for the given pharmacophore model; \(x\) represents the embedding sequence of input SMILES; \(z\) represents the latent variables for a molecule. Transformer encoder and decoder blocks are stacked with N layers. \(\oplus\) denotes concatenation of two vectors and \(\otimes\) denotes matrix multiplication. The overlap between the training and inferencing process is highlighted in the right panel.
+ +To train PGMG, we need only a number of SMILES strings with no additional information attached. A training sample can be constructed using the SMILES representation of a molecule. First, the chemical features of a molecule are identified using RDKit31 and we randomly select some of them to build a pharmacophore network \(G_p\) . As shown in Figure 1a, we approximate the Euclidean distance in the three- dimensional Euclidean space in a pharmacophore using the length of the shortest path between two pharmacophore features on the molecular graph. The analysis of the two distances can be found in Figure S1. Next, a molecule is represented as a randomized SMILES string and then segmented into a token sequence \(s\) . We then corrupt \(s\) to get the encoder input \(s'\) by using the infilling scheme32 and obtain a training sample \((G_p, s, s')\) . Since we avoid the use of target- specific active data in the training stage, PGMG bypasses the problem of data scarcity on active molecules. + +<--- Page Split ---> + +When using the trained model to generate molecules, a pharmacophore model is required. The generation process is as follows. Given a pharmacophore model \(c\) , a set of latent variables \(z\) is sampled from the prior distribution \(p(z|c)\) , which in our case is the standard Gaussian distribution \(N(0, I)\) , and molecules are then generated from the conditional distribution \(p(x|z, c)\) . There are multiple ways to construct a pharmacophore model using various active data types and this is where the flexibility of the PGMG approach comes in. We employ both ligand- based and structure- based approaches to build pharmacophores and use them to generate active molecules for de novo drug design. + +## Performance of PGMG on the unconditional molecule generation task. + +We evaluate our model's performance on the unconditional molecule generation task by comparing it with other SMILES- based methods including ORGAN9, VAE4, SMILES LSTM33, and Syntalinker20. We train PGMG and other SMILES- based models on the ChEMBL dataset34 based on the train- test split used in the GuacaMol benchmark35. Since PGMG is a conditional model, we approximate the unconditional distribution by generating molecules based on randomly sampled pharmacophore features. The molecule generation performance is evaluated by four metrics including validity, novelty, uniqueness, and ratio of available molecules (see Methods for the definition of the metrics). The comparison results of PGMG and four other SMILES- based methods on the four metrics are shown in Figure 2a. The results of ORGAN, VAE, SMILES LSTM on validity, novelty and are taken from the GuacaMol benchmark directly. + +As shows in Figure 2a, PGMG performs better in novelty and the ratio of available molecules, while keeping the same level of validity and uniqueness as the top models. The ratio of available molecules is the ratio of unique novel valid molecules to all generated molecules, and equals product of the previous three metrics, as a composite metric to assess the performance of the model to generate novel molecules. PGMG achieves the highest the ratio of available molecules. Comparing to the second- best method, PGMG improves the ratio of available molecules by \(6.3\%\) . Among the SMILES- based methods, SMILES LSTM33 performs the best in uniqueness, while Syntalinker20 performs the best in validity. + +In the ablation study, we remove features of PGMG and see how that affects performance. All models are trained using the ZINC36 dataset with the same parameters. Validity, uniqueness, and novelty are evaluated by generating 10 molecules for 1 pharmacophore extracted from each molecule in the test dataset. The match score is evaluated by generating \(512 \times 512\) molecules for 512 SMILES randomly sampled from the test dataset. The result of our ablation study can be found in Figure 2b. + +We find when using canonical SMILES to train PGMG (canonical_SMILES), the uniqueness increases from 0.98 to 0.99, but the match score decreases from 0.91 to 0.94. A similar result can be found when we change the Gaussian distribution of the latent variable \(z\) to a Dirac delta distribution, denoted as PGMG (remove_z). remove_z exhibits a huge decrease on the uniqueness (from 0.98 to 0.81) and a certain degree of increase on the match score (from 0.94 to 0.97). If we use random sampling during generation, we can make the uniqueness increase, but it cannot make up for the drop of both the validity and the match score. As we see here, there seems to be a trade- off between the uniqueness and match score. + +<--- Page Split ---> + +We also test PGMG's performance when replacing the distance between chemical features with a constant number PGMG (remove_dis). The results show a large decrease in both uniqueness (from 0.98 to 0.82) and match score (from 0.94 to 0.60) as expected, which shows that PGMG makes a good use of the spatial information of pharmacophores. + +To test whether PGMG catches the distribution of training dataset, we further examine the physicochemical properties of the generated molecules. The distribution of physicochemical properties of the generated molecules and the molecules in the training dataset are compared in Figure 2c. We find that the physicochemical properties distribution such as the topological polar surface area (TPSA), SA, QED, and LogP are close to the training set distribution. This demonstrates that PGMG captures the distribution of molecules in the training dataset well. + +![](images/Figure_2.jpg) + +
Figure 2 | Performance of PGMG on the unconditional molecule generation task. (a) Performance of PGMG and SMILES-based models on ChEMBL. (b) Results of the ablation study. (c) Distribution of chemical properties for the ChEMBL training set and the molecules generated by PGMG. The scientific notation at the upper left of the figure indicates the scaling of the vertical coordinates.
+ +## PGMG can generate bioactive molecules satisfying given pharmacophores. + +We evaluate the extent to which the generated molecules fit the given pharmacophore models and predict binding affinity between protein receptors and molecules generated using PGMG through the molecular docking tool vina37. We use a match score to estimate the matching degree between each molecule- pharmacophore pair (see calculation of match score section of the Supplementary Information for details). + +We extract a random pharmacophore model from each molecule in the test dataset. About 220,000 molecules in total are generated from those random pharmacophore models and the match score is calculated between each pair. For comparison, we also calculate the match score between 220,000 random + +<--- Page Split ---> + +molecules from the ChEMBL dataset34 and the selected pharmacophores. The result is shown in Figure 3a. As can be seen from Figure 3a, 86.3% of the generated molecules have matching scores concentrated in the range of 0.8- 1.0, with 77.9% having a matching score of 1.0. Meanwhile, the matching degrees for the random molecules are centered at 0.45, with only 4.8% having a matching score of 1.0. This result demonstrates PGMG's ability to generate molecules satisfying the given pharmacophore models. + +![](images/Figure_3.jpg) + +
Figure 3 | Pharmacophore matching test results and the distribution of four target docking scores. (a) The match score of random selected molecules and PGMG generated molecules. (b) The distributions of the predicted affinity of top 1000 molecules generated by PGMG over VEGFR2 (PDB: 1YWN), CDK6 (PDB: 2EUF), TGFβ 1 (PDB: 6B8Y),
+ +<--- Page Split ---> + +BRD4 (PDB: 3MXF), and the affinity for the known bioactivity molecules corresponding to these targets. (c) Distributions of ADMET properties are calculated using ADMETlab 2.038 of top 1000 molecules generated by PGMG. The threshold of each property according to ADMETlab 2.0 is given as the dashed line. "↑" indicates that the distribution greater than the threshold satisfies the expected property, while "↓" indicates that the part of lower than the threshold satisfies the expected property. TPSA represents the topological polar surface area, optimal: 0\~140 (Å2); MW denotes Molecular Weight, Optimal:100\~600; nHA represents the number of hydrogen bond donors, optimal: 0\~7; nHD represents the number of hydrogen bond acceptors, optimal: 0\~12; SAscore represents the synthetic accessibility score, optimal: 0\~6; Madin-Darby Canine Kidney cells (MDCK) measure the uptake efficiency of a drug into the body, optimal: >2 x 10-6 (cm/s); BBB measures the ability of a drug to cross the blood-brain barrier to its molecular targets, qualified value: 0- 0.7; F(20%) denotes human oral bioavailability 20% which assess the efficiency of the drug delivery to the systemic circulation, optimal: 0- 0.3; CYP2C9 assess drug metabolism reactions, the closer to 1, the more likely it is to be an inhibitor; T12 represents the half-life of the drug, qualified value: 0- 0.7 and hERG evaluates whether the molecule is toxic to the heart, qualified value: 0- 0.7; ROA measures acute toxicity in mammals, qualified value: 0- 0.7. Where a molecule with a property in the optimal range means that the property is optimal, and a molecule with a property in the qualified range means that there is no obvious evidence that the property of the molecule is defective. The scientific notation at the upper left of the figure indicates the scaling of the vertical coordinates. + +To further examine the binding activity of molecules generated by PGMG through the guidance of pharmacophores, we obtain pharmacophore models with known target structure from the literature39- 42. These targets include VEGFR2, CDK6, TFGβ 1, BRD4. For each pharmacophore model, 10,000 molecules are generated by PGMG. Autodock vina37 is used to calculate the binding affinities of generated molecules. And then, we select the top 1000 molecules with the strongest binding affinity. For comparison, we acquire the known bioactivity molecules for the four targets from CHEMBL, including 13299, 1648, 1885 and 4786 bioactivity molecules, respectively. In Figure 3b, we show the affinity distributions of the top 1000 molecules generated by PGMG and the affinities for the known bioactivity molecules from CHEMBL. The average affinity of the top 1000 molecules generated by PGMG is - 10.0 kcal/mol (1YWN), - 11.1 kcal/mol (2EUF), - 11.0 kcal/mol (6B8Y) and - 8.8 kcal/mol (3MXF), and the average affinity of the known bioactivity molecules is - 8.0 kcal/mol (1YWN), - 9.6 kcal/mol (2EUF), - 9.2 kcal/mol (6B8Y) and - 7.0 kcal/mol (3MXF) respectively. The distribution of affinities suggests that PGMG can generate desired bioactive molecules. + +To evaluate if PGMG is capable of generating drug- like molecules, we further calculate the pharmacokinetics properties (absorption, distribution, metabolism, excretion) and toxicity (ADMET) of the top 1000 molecules. The ADMET distributions of the top 1000 molecules are illustrated in Figure 3c. Most of the molecules generated by PGMG satisfy the pharmacokinetic properties and toxicity constraint for drug candidate according to the standard proposed by ADMETlab 2.038. And the majority of the generated molecules are predicted with no obvious toxicity to the heart. + +## Structure-based drug design + +Structure- based drug design is a powerful drug design strategy to generate the desired bioactivity molecules using the structure of specific target43. We use four targets (VEGFR2, CDK6, TFGβ 1, BRD4) from the above section with pharmacophore models which are built using ligand- receptor complex as examples to further demonstrate the performance of PGMG in structure- based drug design. It should be noted that the construction of pharmacophore models does not necessarily need any ligand. We choose these + +<--- Page Split ---> + +pharmacophore models for the convenience of the following analyses. We compare several top affinity conformations of the generated molecules with the top affinity conformation of the reference ligand in the crystal complex. Figure 4 shows the binding sites of the four receptors with corresponding molecules. Most of the generated molecules share the same amino acid residues as the reference ligand, which indicates that those generated molecules are capable of fitting into the binding site as well as the reference one. + +![](images/Figure_4.jpg) + +
Figure 4 | A display of the binding sites of the molecules generated by PGMG in structure-based drug design.
+ +In Figure 4(a- c), the generated molecules of 1YWM have a similar structure with the reference. As for 2EUF and 6B8Y, despite the structural differences between the generated molecules and the reference molecule, the generated molecules (Figure 4 (e- g, i- k)) share some common important functional groups as the reference ligands (Figure 4h, Figure 4l). And interestingly, we find that the structures of the generated molecules may differ from the reference ligand (Figure 4p) in a good way. For example, the molecules generated by PGMG for 3MXF (Figure 4 (m- o)) can bind to D88, P86, and P82 amino acid residues other than N140 (Figure 4p). This finding suggests that PGMG may have the potential in exploring new binding sites. Besides, we exam the drug- likeness using SA and hERG. SA is designed to estimate the ease of synthesis of drug- like molecules, and it's easy to synthesize when SA is less than 6. The hERG is a toxicity + +<--- Page Split ---> + +metric. Abnormal hERG values for a drug may lead to palpitations, syncope, and even sudden death. This metric measures the probability that a molecule will be toxic. Empirically, over 0.7, the molecule is considered toxic. These generated molecules perform well on SA and hERG. The above results show that PGMG can design molecules that not only fit well into the binding site but also exhibit drug- like quality in the structure- based drug design. + +## Ligand-based drug design + +Although structure- based drug design is a successful and highly attractive strategy, there are some prerequisites to use this strategy, including a certain target, a high- resolution crystal structure of the target, and some identified interaction sites. However, it is not easy to reach the above prerequisites. Ligand- based drug design is capable of designing drug molecules based on the conformational superposition of known active molecules when the target is unknown or the binding site is unclear. And it has been widely used in drug discovery, such as the search for new drugs for drug resistance. Squalene oxidase is the target for ringworm, superficial skin fungal infections, and other diseases. Butenafine and terbinafine are typical inhibitors for squalene oxidase44. However, these inhibitors are prone to drug resistance and side effects including skin erythema, burning, and itching. Therefore, it is urgent to design novel squalene oxidase inhibitors. Here, we generate 200 molecules using a pharmacophore model constructed from squalene oxidase inhibitors. + +![](images/Figure_5.jpg) + +
Figure 5 Alignment diagrams of terbinafine, pharmacophore model, and molecules generated by PGMG. The different colored spheres represent different pharmacophore features. Aromatic center is red, the positive charge center is yellow, and hydrophobic centers are green. The grey molecules represent terbinafine, and the green molecules represent the molecules generated by PGMG.
+ +As shows in Figure 5, the generated molecules align well to the active conformation of terbinafine which + +<--- Page Split ---> + +is obtained from drugbank \(^{45}\) . The listed molecules match well with the desired pharmacophore features, including two hydrophobic centers, a positive charge center, and an aromatic ring center. Here, we notice that PGMG has a good grasp of the equivalence of different substructures under the same pharmacophore feature. It matches the aromatic center with pyrrole, thiophene, and pyrimidine, and the hydrophobic center with aliphatic, cycloalkane, and benzene. This result shows that PGMG can generate diverse molecules while maintaining the important properties of the substructures the same as the known inhibitor. + +To further assess the pharmacokinetics and toxicity of the generated molecules, we calculate the TSPA, SA, and hERG of the generated molecules. See the previous section for a detailed SA and hERG description. TSPA is a molecular descriptor measuring drug transport properties such as intestinal absorption and blood- brain barrier (BBB) penetration. The TPSA in the range of 0- 140 means optimal. Of the six molecules generated by PGMG, their TSPA, SA, and hERG values are within the rational range. From Figure 5, we can see that PGMG is able to generate molecules that match the pharmacophore model and meet the overall criteria for TSPA, SA, and hERG. + +## Lead compound optimization + +Lead optimization refers to the improvement of one or more properties of a hit compound by chemical modification. The optimization objectives include adjusting the molecular flexibility ratio, improving the pharmacokinetic properties, or enhancing the bioavailability. Here we show how PGMG can help with lead compound optimization using Lavendustin A as a case study. Lavendustin A is an inhibitor of epidermal growth factor receptor (EGFR), while the lipophilicity of Lavendustin A is too poor to cross the cell membrane. It has been shown that improving the lipophilicity of Lavendustin A can lead to nanomolar levels of IC50 inhibition activity at the cellular level \(^{46}\) . In this case, we construct Lavendustin A's pharmacophore using Pharao \(^{47}\) , then we modify the polar pharmacophore features, and finally use PGMG to generate molecules for the modified pharmacophore to improve the lipophilicity of the generated molecules. + +We filter the generated molecules with lipophilicity (LogP > 3.41) to obtain 400 molecules with a higher lipophilicity than Lavendustin A. We calculate Tanimoto similarity using MACCSkeys Fingerprints with RDKit \(^{11}\) between the obtained molecules and the 1500 EGFR inhibitors acquired from the ExCAPE database \(^{48}\) . Figure 6 shows some examples of the generated molecules with their closest EGFR inhibitors obtained from the ExCAPE database and their respective Tanimoto similarities. We find that the generated molecules have high similarity to the EGFR target active molecules in the ExCAPE database, which are not included in the training set. And they all own the three pharmacophore features of the aromatic ring, hydrogen- bonded acceptor, and hydrophobic center. Based on the assumption that structurally similar molecules have similar properties, the similarity result demonstrates that molecules generated by PGMG have a probability of inhibiting EGFR. To some extent, the generated molecules gain structural diversity while maintaining the consistency of the pharmacophore. Overall, PGMG can optimize certain properties and maintain the bioactivity of a given lead compound. + +<--- Page Split ---> +![](images/Figure_6.jpg) + +
Figure 6 | Display diagram of the molecule generated by PGMG with known inhibitors in the case of Lavendustin A optimization. Molecules generated by PGMG are shown inside the circle and their closest active nearest neighbors are shown outside the circle. The colors indicate the pharmacophore features extracted from Lavendustin A. Red corresponds to the aromatic center, blue represent the hydrogen-bonded acceptor, and green represent the hydrophobic center.
+ +## Discussion + +In this work, we develop a pharmacophore- guided deep learning approach for bioactive molecule generation called PGMG. We manage to use pharmacophores as the only constraint during the generation process by (1) encoding both pharmacophore features and spatial information of a given pharmacophore into a complete graph with node and edge attributes and (2) introduce latent variables so that a molecule can be uniquely characterized by a pharmacophore and a set of latent variables to handle the many- to- many relationship of pharmacophores and molecules. Our approach offers some advantages over current molecule generation methods. Firstly, PGMG provides a way to utilize different types of activity data in a uniform representation, allowing it to overcome the problem of data scarcity and be used in various situations. Secondly, pharmacophores are biologically meaningful and can incorporate biochemists' knowledge, which provides a strong prior and certain interpretability into the generation process. Lastly, after training, PGMG can be directly applied to different targets without further fine- tuning. Besides, it is also worth mentioning that the training scheme itself does not require any activity data to proceed. This training scheme may be useful for other generative methods. + +PGMG makes solid progress on the challenging problem of generating desired bioactive molecules in various scenarios when known active data is scarce. When a target structure is available, PGMG is competent to design a large number of molecules that bind affinity better than the specific- target active + +<--- Page Split ---> + +molecules obtained from the ChEMBL database. Given the pharmacophore for certain targets, the PGMG can also be utilized to design dual or multi- target molecules. Besides, we expect that PGMG can be adopted to prepare chemical libraries to replace those used in HTS campaigns to improve virtual screen efficiency, as it can provide a sufficient number of candidate drug- like molecules for a specified target. Then, this method performs well in ligand- based drug design, which has wide use in drug design when the target structure is absent. The ligand- based case shows that PGMG is able to generate high- quality bioactivity molecules that match the pharmacophore model with structural diversity. This result implies that PGMG can be applied to multiple drug design scenarios such as researching alternative medicine, drug resistance, and scaffold hopping. Finally, the case of lead optimization demonstrates that PGMG can optimize the molecule's properties while maintaining the bioactivity and scaffold diversity of the generated molecules. The results demonstrate that PGMG is a promising approach for structure- based drug design, high- throughput screening, ligand based- drug design, and lead optimization in a real drug discovery setting. + +We believe that the de novo drug design is a complicated and situation- specific problem, and computational methods should try to get more assistance from chemists' experience and judgements. PGMG benefits from this idea a lot. Some limitations of PGMG should be acknowledged. PGMG currently does not support exclusion volume in pharmacophore models and as we focus on the task of generating molecules with desired activity, PGMG does not explicitly constrain the properties of the generated molecules. A future direction of our work is to include the exclusion volume and other features into PGMG and make the generated molecules to be more controllable and malleable. And furthermore, designing multi- conditional generation models to generate active molecules with specified properties is the ultimate goal of drug design, we will continue to work towards this objective. + +## Methods + +## Datasets + +We use the ChEMBL 24 dataset containing more than 1.25 million molecules to train PGMG. ChEMBL is a collection of bioactivity data for various targets and compounds from the literature. It contains 13 types of atoms \(\mathrm{(T = 13)}\) : H, B, C, N, O, F, Si, P, S, Cl, Se, Br, and I. Each bond is either a no- bond, single, double, triple or aromatic bond \(\mathrm{(R = 5)}\) . + +We also use the ZINC36 molecule dataset from JTVAE49 for our ablation study. It contains 220,000 molecules in the training data, 11 types of atoms \(\mathrm{(T = 11)}\) : H, B, C, N, O, F, P, S, Cl, Br, and I. Each bond is either a no- bond, single, double, triple or aromatic bond \(\mathrm{(R = 5)}\) . + +The structure of four targets VEGFR2, CDK6, TFGB 1, BRD4 are downloaded from PDB50 database. + +## Representation of Pharmacophores and Molecules + +A pharmacophore model consists of several chemical features and their spatial descriptions and are represented by a fully connected graph with chemical feature types as node attributes and distances as edge weights (a detailed description of the pharmacophore graph and the preparation of the pharmacophore graph is included in SI). We apply a state- of- the- art graph neural network, Gated- GCN29, to embed the graph with consideration of node attributes and edge attributes. + +<--- Page Split ---> + +Molecules are represented in SMILES format. Symbols of stereochemistry like \(^{\prime \prime}\mathrm{d}^{\prime \prime}\) ' /' are removed because stereochemistry information does not exist in the graph representation of a pharmacophore and it is not difficult to list all stereoisomers of a molecule. Then the SMILES string is separated into a sequence of tokens corresponding to heavy atoms and structural punctuation marks. For example, the SMILES string \(C(C[N H2 - ])O C(= O)C l\) will be split to \(C\) ( \(C[N H2 - ]\) ) \(O C\) ( \(= O\) ) \(C l\) , where each token will be embedded into a vector. + +## Encoder and Decoder + +An illustration of the encoder and decoder networks can be found in Figure.1. The encoder and decoder network are adapted from the standard transformer \(^{30}\) architecture with each consisting of several layers of stacked transformer encoder block and transformer decoder block. The difference between the transformer encoder and decoder blocks is that the encoder block uses only self- attention modules and the decoder block uses cross- attention modules to incorporate context in the generation process. Some modifications are made to handle our inputs and to better suit the variational autoencoder structure of PGMG. + +We first calculate the latent variables \(z\) of molecule \(x\) given pharmacophore \(c\) by the encoder network. The encoder input is a concatenation of molecule and pharmacophore features. Following BART \(^{32}\) , positional and segment encoding is added to the input sequence: + +\[\begin{array}{r}I n p u t_{e n c o d e r} = (E_{p}^{\prime};E_{m}^{\prime})\\ E_{m_{i}}^{\prime} = E_{m_{i}} + S E_{m} + P E_{i}\\ E_{p_{j}}^{\prime} = E_{p_{j}} + S E_{p} \end{array} \quad (3)\] + +where \(I n p u t_{e n c o d e r}\) is the input representation, \(E_{p_{j}}\) is the j- th pharmacophore feature vector, \(E_{m_{i}}\) is the i- th token embedding of molecule features, \(S E_{m}\) and \(S E_{p}\) are two segment embedding vectors for molecule features and pharmacophore features, and \(P E_{i}\) is the positional embedding for i- th token. After several layers of transformer encoder block, the molecule features are averaged by an attention pooling layer to obtain the final molecule representation \(h_{x}\) . \(h_{x}\) is then fed into two separate sub- networks to compute the mean \(\mu\) and log variance \(\log \Sigma\) of the posterior variational approximation. Latent variables \(z\) are then sampled from the Normal distribution \(N(\mu ,\Sigma)\) . + +The decoder network takes the latent variables \(z\) and pharmacophore features as input: + +\[\begin{array}{r}i n p u t_{d e c o d e r} = (E_{p}^{\prime};E_{z}^{\prime})\\ E_{z_{i}}^{\prime} = z_{i} + S E_{z} + P E_{i} \end{array} \quad (5)\] + +where \(E_{p}^{\prime}\) is the same as described above, \(S E_{z}\) is the segment embedding for latent variables, and \(P E_{i}\) is the positional embedding for i- th token. The decoder then uses \(i n p u t_{d e c o d e r}\) to generate target SMILES autoregressively. Each token is determined on the basis of previously generated tokens and context: + +\[o_{i} = (a r g m a x)_{o_{i}}P(o_{i}|o_{< i},c,z) \quad (7)\] + +where \(o_{i}\) is i- th generated token. + +## Loss Function + +PGMG's model is trained in an end- to- end manner. The Loss function consists of three different terms, KL Loss, LM Loss, and the mapping loss. The first two terms are the negative evidence lower bound (ELBO) of + +<--- Page Split ---> + +the log likelihood \(\log P_{\theta}(x|c)\) : + +\[\begin{array}{rl} & {\log P_{\theta}(x|c_{p}) = log\int P_{\theta}(x|c,z)P_{\phi}(z|c)dz}\\ & {\geq -KL(P_{\phi}(z|x,c)||P_{\theta}(z|c)) + E_{P_{\phi}(z|x,c)}[\log P_{\theta}(x|z,c)]}\\ & {\approx -KL(P_{\phi}(z|x,c)||P_{\theta}(z|c)) + \log P_{\theta}(x|z,c)} \end{array} \quad (A)\] + +where \(KL\) denotes the Kullback- Leibler divergence and we assume \(P_{\theta}(z|c)\) the prior distribution of \(z\) to be a standard gaussian \(N(0,I)\) . We call the left part of (A) KL Loss and it serves as a regulation term to mitigate the gap between the true prior distribution of \(z\) and the posterior distribution and to make the latent space of \(z\) smoother. The expectation term on the right part of (A) is estimated through sampling, and it is optimized using Monte Carlo estimation with one data point for each sample \(^{51}\) . This gives us the right part of (B). Since \(m\) takes form of the SMILES string, we call it the language modeling loss (LM Loss). + +The third part of PGMG's loss function is the mapping loss. It evaluates the model's performance in predicting the mapping between heavy atoms and pharmacophore elements. We use the mapping loss as a regulation term to help alleviate the problem of posterior collapse. The mapping score of the \(i^{\mathrm{th}}\) pharmacophore \(p_{i}\) and the \(j^{\mathrm{th}}\) output token \(o_{j}\) is calculated as + +\[mapping_{g_{score}_{ij}} = \sigma \left(g(W_{p}E_{p_i})\odot g\left(W_{o}E_{o_j}\right)\right) \quad (9)\] + +where \(E_{p_i}\) and \(E_{o_j}\) are the embedding vectors of \(p_i\) and \(o_j\) respectively, \(W_{p}\) and \(W_{o}\) are two learnable matrices to project two different embeddings into the same space, \(\odot\) is the dot product, \(\sigma\) is the sigmoid function, and \(g\) is the ReLU function. The calculation of mapping scores can be vectorized as + +\[mapping_{g_{score}} = \sigma \left(g(W_{p}E_{p})g(W_{o}E_{o})\right) \quad (10)\] + +Since SMILES format contains tokens other than atom symbols, we mask them when calculating the mapping loss. The mapping loss is then calculated as the cross- entropy of the masked scores and labels. An illustration of the masked mapping score and label is given in Supplementary Figure S3. + +## Training details and model parameter settings + +During training, we inject noise into the input to make training more robust by using the infilling scheme. Some random subsequences in every input sequence are replaced with a single [mask] token. Teacher forcing technique is applied to the generation process during training, by which we replace the previously generated tokens with the ground truth to produce the next token. Aside from the mapping loss introduced before, another approach we use to alleviate posterior collapse is KL annealing \(^{52}\) , where an increasing coefficient is used to control the size of KL Loss. + +We use the same model parameters in both ChEMBL and ZINC datasets. The hidden dimension is 384. The transformer encoder blocks and transformer decoder blocks are stacked 8 times. We use an 8- head attention and the feed- forward dimension is 1024. We use Adam optimizer to train the model with a 3e- 4 learning rate and a 1e- 6 weight decay rate. Cosine learning rate annealing is applied with a cycle length of 4 epochs. We use the gradient clipping technique and set the maximum gradient to be 5. Since the ChEMBL dataset contains a lot more molecules compared to the ZINC dataset, it requires less training epochs to reach + +<--- Page Split ---> + +a similar validation performance. Thus, the number of training epochs for the former is 32 and 48 for the latter. + +## Evaluation + +Firstly, four different metrics on 2D level, validity, uniqueness, novelty, and ratio of available molecules are employed to evaluate the ability of the PGMG to generate novel molecules. Validity is the percentage of chemically valid molecules with generated SMILES. Uniqueness measures how many valid molecules are non- repetitive. Novelty refers to the percentage of generated chemically valid molecules not present in the training set. And the ratio of available molecules is the proportion of novel molecules in all generated results. These metrics are calculated as follows: + +\[\text{validity} = \frac{\# \text{Number of chemically valid SMILES}}{\# \text{of generated SMILES}} \quad (11)\] + +\[\text{uniqueness} = \frac{\# \text{of non-duplicate, valid SMILES}}{\# \text{of valid SMILES}} \quad (12)\] + +\[\text{novelty} = \frac{\# \text{of novelty molecules not in training set}}{\# \text{of unique molecules}} \quad (13)\] + +\[\text{ratio of available molecules} = \frac{\# \text{of novel molecules}}{\# \text{of generated SMILES}} \quad (14)\] + +Secondly, goal- directed metrics are evaluated by the match score, which indicates the match degree of the generated molecules to the specified pharmacophore (see calculation of match score section of the Supplementary Information for details). We further evaluate the binding activity of the generated molecules to the target using affinity calculated by Autodock vina37. Finally, we use ADMETlab 2.038 to predict the ADMET properties of the generated molecules and to assess the drug- like potential of the generated molecules. + +## Acknowledgements + +This work is financially supported by the National Natural Science Foundation of China under Grants (No. 61832019 to M.L.), Hunan Provincial Science and Technology Program (2019CB1007) [M.L.], and European Research Council (No. 716063 to J.T.) + +## Author Contributions + +M.L. and J.T. guided the research and provided the experimental platform. H.Z., R.Z., and M.L conceived the initial idea and started the project. H.Z collected and preprocessed the data and R.Z designed the model. R.Z performed the generation experiments and H.Z performed the case studies. H.Z, R.Z, J.T., and M.L wrote the paper. + +## Declaration of Interests + +The authors declare no competing interests. + +## Data availability + +<--- Page Split ---> + +The data that support the findings of this study are available at https://github.com/CSUBioGroup/PGMG. + +## Code availability + +The code used to generate results shown in this study is available at https://github.com/CSUBioGroup/PGMG. + +## reference + +1. Lipinski, C.A., Lombardo, F., Dominy, B.W. & Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced drug delivery reviews 23, 3-25 (1997). +2. Bohacek, R.S., McMartin, C. & Guida, W.C. The art and practice of structure - based drug design: a molecular modeling perspective. Medicinal research reviews 16, 3-50 (1996). +3. Goodnow Jr, R.A. Hit and lead identification: Integrated technology-based approaches. Drug Discovery Today: Technologies 3, 367-375 (2006). +4. 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PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic acids research 45, W356-W360 (2017). + +27. Ma, Z. et al. Pharmacophore hybridisation and nanoscale assembly to discover self-delivering lysosomotropic new-chemical entities for cancer therapy. Nature communications 11, 1-12 (2020). + +28. Meslamani, J. et al. Protein-ligand-based pharmacophores: generation and utility assessment in computational ligand profiling. Journal of chemical information and modeling 52, 943-955 (2012). + +29. Bresson, X. & Laurent, T. Residual Gated Graph ConvNets. Preprint at https://arxiv.org/abs/1711.07553 (2017). + +30. Vaswani, A. et al. Attention is all you need. Advances in neural information processing systems 30 (2017). + +31. Landrum, G. A. RDKit: Open-source cheminformatics. http://www.rdkit.org. + +32. Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M. & Zettlemoyer, L. BART: Denoising Sequence-to-Sequence Pretraining for Natural Language Generation, Translation, and Comprehension. Preprint at http://arxiv.org/abs/1910.13461 (2019). + +33. Segler, M.H., Kogej, T., Tyrchan, C. & Waller, M.P. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS central science 4, 120-131 (2018). + +34. Mendez, D. et al. ChEMBL: towards direct deposition of bioassay data. Nucleic acids research 47, D930-D940 (2019). + +35. Brown, N., Fiscato, M., Segler, M.H. & Vaucher, A.C. GuacaMol: benchmarking models for de novo molecular design. Journal of chemical information and modeling 59, 1096-1108 (2019). + +36. Sterling, T. & Irwin, J.J. ZINC 15-ligand discovery for everyone. Journal of chemical information and modeling 55, 2324-2337 (2015). + +37. Trott, O. & Olson, A.J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry 31, 455-461 (2010). + +38. Xiong, G. et al. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Research 49, W5-W14 (2021). + +39. Lee, K. et al. Pharmacophore modeling and virtual screening studies for new VEGFR-2 kinase inhibitors. European journal of medicinal chemistry 45, 5420-5427 (2010). + +40. Shawky, A.M., Ibrahim, N.A., Abourehab, M.A., Abdalla, A.N. & Gouda, A.M. Pharmacophore-based virtual screening, synthesis, biological evaluation, and molecular docking study of novel pyrrolizines bearing urea/thiourea moieties with potential cytotoxicity and CDK inhibitory activities. Journal of enzyme inhibition and medicinal chemistry 36, 15-33 (2021). + +41. Jiang, J., Zhou, H., Jiang, Q., Sun, L. & Deng, P. Novel transforming growth factor-beta receptor 1 antagonists through a pharmacophore-based virtual screening approach. Molecules 23, 2824 (2018). + +42. Yan, G. et al. Pharmacophore - based virtual screening, molecular docking, molecular dynamics simulation, and biological evaluation for the discovery of novel BRD 4 inhibitors. Chemical Biology & Drug Design 91, 478-490 (2018). + +<--- Page Split ---> + +43. Pei, J., Yin, N., Ma, X. & Lai, L. Systems biology brings new dimensions for structure-based drug design. Journal of the American Chemical Society 136, 11556-11565 (2014). +44. Kermani, F. et al. In vitro activities of antifungal drugs against a large collection of Trichophyton tonsurans isolated from wrestlers. Mycoses 63, 1321-1330 (2020). +45. Wishart, D.S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic acids research 46, D1074-D1082 (2018). +46. Nussbaumer, P. et al. Novel antiproliferative agents derived from lavendustin A. Journal of medicinal chemistry 37, 4079-4084 (1994). +47. Taminau, J., Thijs, G. & De Winter, H. Pharao: pharmacophore alignment and optimization. Journal of Molecular Graphics and Modelling 27, 161-169 (2008). +48. Sun, J. et al. ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics. Journal of cheminformatics 9, 1-9 (2017). +49. Jin W, Barzilay R, Jaakkola T. Junction Tree Variational Autoencoder for Molecular Graph Generation. Proceedings of the 35th International Conference on Machine Learning. PMLR, 2323-2332 (2018). +50. Burley, S.K. et al. Protein Data Bank (PDB): the single global macromolecular structure archive. Protein Crystallography 1607, 627-641 (2017). +51. Kingma, D.P. & Welling, M. Auto-Encoding Variational Bayes. Preprint at http://arxiv.org/abs/1312.6114 (2014). +52. Bowman, S.R. et al. Generating sentences from a continuous space. Preprint at http://arxiv.org/abs/1511.06349 (2015). + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementaryinformationPGMGAPharmacophoreGuidedDeepLearningApproachforBioactiveMolecularGeneration.docx + +<--- Page Split ---> diff --git a/preprint/preprint__084645ecaf0633670ce86e9b62f37cf8c911eec46c0e61646e18f05eb546a1be/preprint__084645ecaf0633670ce86e9b62f37cf8c911eec46c0e61646e18f05eb546a1be_det.mmd b/preprint/preprint__084645ecaf0633670ce86e9b62f37cf8c911eec46c0e61646e18f05eb546a1be/preprint__084645ecaf0633670ce86e9b62f37cf8c911eec46c0e61646e18f05eb546a1be_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..16e31235db744cb1e551626b41bb0a19cfad32f5 --- /dev/null +++ b/preprint/preprint__084645ecaf0633670ce86e9b62f37cf8c911eec46c0e61646e18f05eb546a1be/preprint__084645ecaf0633670ce86e9b62f37cf8c911eec46c0e61646e18f05eb546a1be_det.mmd @@ -0,0 +1,507 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 890, 177]]<|/det|> +# PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecular Generation + +<|ref|>text<|/ref|><|det|>[[44, 195, 625, 238]]<|/det|> +Min Li ( limin@mail.csu.edu.cn ) Central South University https://orcid.org/0000- 0002- 0188- 1394 + +<|ref|>text<|/ref|><|det|>[[44, 243, 266, 283]]<|/det|> +Huimin Zhu Central South University + +<|ref|>text<|/ref|><|det|>[[44, 290, 266, 330]]<|/det|> +Renyi Zhou Central South University + +<|ref|>text<|/ref|><|det|>[[44, 336, 926, 399]]<|/det|> +Jing Tang Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland https://orcid.org/0000- 0001- 7480- 7710 + +<|ref|>sub_title<|/ref|><|det|>[[44, 440, 102, 457]]<|/det|> +## Article + +<|ref|>title<|/ref|><|det|>[[44, 478, 135, 496]]<|/det|> +# Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 516, 352, 535]]<|/det|> +Posted Date: September 15th, 2022 + +<|ref|>text<|/ref|><|det|>[[44, 554, 474, 573]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 1749921/v1 + +<|ref|>text<|/ref|><|det|>[[42, 591, 910, 634]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[57, 62, 900, 81]]<|/det|> +# PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecular Generation + +<|ref|>text<|/ref|><|det|>[[271, 98, 723, 117]]<|/det|> +Huimin Zhu \(^{1,\dagger}\) , Renyi Zhou \(^{1,\dagger}\) , Jing Tang \(^{2}\) and Min Li \(^{1,\ast}\) + +<|ref|>text<|/ref|><|det|>[[57, 133, 867, 170]]<|/det|> +\(^{1}\) School of Computer Science and Engineering, Central South University, Changsha 410083, China \(^{2}\) Faculty of Medicine, University of Helsinki, Helsinki, 00290, Finland + +<|ref|>text<|/ref|><|det|>[[57, 180, 470, 197]]<|/det|> +\(^{\dagger}\) These two authors contribute equally to the work. + +<|ref|>text<|/ref|><|det|>[[57, 200, 452, 215]]<|/det|> +\* Corresponding author, limin@mail.csu.edu.cn + +<|ref|>sub_title<|/ref|><|det|>[[75, 238, 155, 254]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[56, 270, 943, 522]]<|/det|> +The rational design of novel molecules with desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. Here, we propose PGMG, a pharmacophore- guided deep learning approach for bioactive molecule generation. Through the guidance of pharmacophore, PGMG provides a flexible strategy to generate bioactive molecules matching given pharmacophore models. PGMG uses a graph neural network to encode pharmacophore features and spatial information and a transformer decoder to generate molecules. A latent variable is introduced to solve the many- to- many mapping between pharmacophores and molecules and improve the diversity of generated molecules. In addition, these generated molecules are of high validity, uniqueness, and novelty. In the case studies, we demonstrate using PGMG in ligand- based and structure- based drug de novo design, as well as in lead optimization scenarios. Overall, the flexibility and effectiveness make PGMG a useful tool to accelerate drug discovery process. + +<|ref|>sub_title<|/ref|><|det|>[[75, 570, 189, 586]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[56, 606, 943, 787]]<|/det|> +The acquisition of biologically active compounds is a vital but challenging step in drug discovery. It has been estimated that the drug- like chemical space is as large as \(10^{60}\) obeying Lipinski's "Rule of Five" \(^{1,2}\) . Hence, it is an extremely difficult task to search for desired molecules in such a huge space. Traditionally, hit compounds which exhibit initial activity on a specific target can be obtained from natural products, designed by medicinal chemists, or acquired by high- throughput screening (HTS) \(^{3}\) . These methods consume a lot of human and financial resources, which makes the acquisition of hit compounds inefficient and costly. Recently, some deep generative models have been proposed for the rational design of novel molecules with desired properties, which provide a new perspective for this task. + +<|ref|>text<|/ref|><|det|>[[56, 802, 943, 937]]<|/det|> +Among the popular architectures and models for generating molecules from deep neural networks, the variational autoencoders (VAEs) \(^{4,5}\) , reinforcement learning (RL) \(^{6,7}\) , generative adversarial networks \(^{8 - 10}\) and auto- regressive models \(^{11,12}\) were successful to design desired molecules at a specified precondition. Regardless what framework were used, most above methods aim at generating molecules with given physicochemical properties, such as the Wildman- Crippen partition coefficient (LogP), synthetic accessibility (SA), molecular weight (molWt), quantitative estimate of drug likeness (QED), and others. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 44, 942, 180]]<|/det|> +However, the relationship between molecular and some physicochemical properties such as LogP, QED values are easy to achieve. In drug discovery, the most difficult and the real intrinsic objective is to design molecules that satisfy properties which require wet experimental to measure or require extensive calculations to approximate, such as the activity of the molecule13. These models are less suitable for generating bioactive molecules. For a specified target, these models require a large dataset of known active molecules to fine- tune and thus cannot be applied to a novel target or targets with few active compounds. + +<|ref|>text<|/ref|><|det|>[[57, 193, 943, 397]]<|/det|> +Designing molecules using deep generative models with biological activity remains challenging14- 16. As mentioned above, one of the main obstacles is the limited data on target- specific molecules, which makes it difficult for models to learn the structure- activity relationship. For a novel target family, the paucity of activity data is even more severe. Besides, the scarcity of activity data affects the strategy of drug design. For example, the choice of ligand- based drug design or structure- based drug design depends on what information can be used, which narrows down the application scenarios of many methods. It is clear that incorporating expert knowledge in the generation process is beneficial to the full utilization of bioactivity data information17. Therefore, combining deep generative models with knowledge in biochemistry to efficiently use the scarce data to design biologically active molecules is a crucial project. + +<|ref|>text<|/ref|><|det|>[[57, 410, 943, 802]]<|/det|> +Up to now, some methods that generate bioactive molecules by combining prior knowledge from biochemistry into molecule generation models have been proposed. For example, conditioned GAN can be used to design active- like molecules for desired gene expression signatures18, which provides a new perspective for molecule generation. However, the structure- activity relationship between the biological activity and the molecules generated by such methods is ambiguous. DeLinker19 and SyntaLinker20 adopt fragment- based drug design and retain active fragments while updating linkers to generate active molecules, and DEVELOP17 combines DeLinker with chemical features as constraints to improve the quality of the generated molecules. The fragment- based approaches require explicit knowledge of the active fragments, which lead to a limited chemical space for the model. DeepLigBuilder21 and 3D- Generative- SBDD22 utilize the structure- based drug design strategy and generate molecules based on the binding sites between molecules and proteins in the 3D Euclidean Space. However, these methods are limited when the binding site or the target structure is unknown. There are also some methods that use electronic features in molecule generation. For example, Reduced Graph23 simplifies a SMILES to an acyclic graph of functional group as its input to generation. Shape- based method proposed by Skalic et al24 generate molecules from a 3D representation using a seed ligand with a conditional chemical features. These methods require seed compounds to collect the input electronic features. The above generative models may perform well on specific types of activity data, but their usages are limited because of their assumptions on the data types. + +<|ref|>text<|/ref|><|det|>[[57, 814, 943, 949]]<|/det|> +Here, we propose PGMG, a pharmacophore- guided molecule generation approach based on deep learning. PGMG uses pharmacophore models as a bridge to connect different types of activity data and can design bioactive molecules for newly discovered targets when there is no sufficient activity data. A pharmacophore is a set of chemical features and its spatial information that are necessary for a drug to bind to a target and can be constructed by superimposing a small number of active compounds25 or observing the structure of a given target26. Traditional drug design based on pharmacophores has many successful applications27, 28, but + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 44, 943, 249]]<|/det|> +its potential in deep generative models has not been exploited. There are some works \(^{23, 24}\) that use pharmacophore- like information in molecule generation. However, the pharmacophore- like features used in these methods are incomplete and can only be extracted from seed compounds, making it difficult for domain knowledge to be leveraged. In PGMG, we use a complete graph to fully represent a pharmacophore with each node corresponding to a pharmacophore feature and the spatial information encoded as the distance between each node pair. Given the graph as the sole input, PGMG can generate molecules matching the corresponding pharmacophore. This gives PGMG the capability to utilize different types of activity data in a uniform representation and a biologically meaningful way to control the process of the bioactivity molecule design. + +<|ref|>text<|/ref|><|det|>[[57, 253, 943, 435]]<|/det|> +Since pharmacophores and molecules have a many- to- many relationship, PGMG introduces latent variables to model such a relationship and boost the variety of generated molecules. Besides, the transformer structure is employed as the backbone to learn implicit rules of SMILES strings to map between latent variables and molecules. We evaluate the PGMG performance comprehensively in molecule generation with goal- directed metrics and drug- like metrics. The results show that PGMG can generate molecules satisfying given pharmacophore models and pharmacokinetic requirements, while maintaining a high level of validity, uniqueness, and novelty. The case studies further demonstrate that PGMG provides an effective strategy for both ligand- based and structure- based drug de novo designs and lead optimization. + +<|ref|>sub_title<|/ref|><|det|>[[57, 459, 125, 475]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[57, 493, 234, 510]]<|/det|> +## Overview of PGMG + +<|ref|>text<|/ref|><|det|>[[57, 516, 939, 558]]<|/det|> +Our proposed PGMG is a pharmacophore- guided molecular generation approach based on deep learning. The overall architecture of PGMG is illustrated in Figure 1. + +<|ref|>text<|/ref|><|det|>[[57, 572, 943, 707]]<|/det|> +Given a target pharmacophore, the goal of PGMG is to generate molecules which matches the pharmacophore. Here, we introduce a set of latent variables \(z\) to deal with the many- to- many mapping between pharmacophores and molecules. Thus, a molecule \(x\) can be represented as a unique combination of two complementary encodings including \(c\) representing the given pharmacophore and \(z\) corresponding to how chemical groups are placed within the molecule. From another perspective, the latent variables \(z\) grant PGMG to model multiple modes in the conditional distribution + +<|ref|>equation<|/ref|><|det|>[[354, 726, 939, 750]]<|/det|> +\[P(x|c) = \int_{z\sim P(z|c)}P(x|c,z)P(z|c)dz \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[57, 763, 943, 882]]<|/det|> +We train two neural networks, an encoder network \(P_{\phi}(z|c,x)\) to approximate \(P(z|c)\) indirectly and a decoder network \(P_{\theta}(x|c,z)\) to approximate \(P(x|c,z)\) . We embed molecules in SMILES format into dense feature vectors and use Gated GCN \(^{29}\) to embed pharmacophore models. The transformer structure proposed by Vaswani et al. \(^{30}\) is used as the backbone of our model to learn the mapping between pharmacophore and molecular structures. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[55, 45, 936, 540]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[56, 543, 941, 690]]<|/det|> +
Figure 1 | The overall architecture of PGMG. (a) The construction of pharmacophore networks. We use the shortest paths on the molecular graph to approximate the Euclidean distances between two pharmacophore features and construct a fully connected graph to represent a pharmacophore model. (b) The preprocessing of SMILES. We randomize a given canonical SMILES and corrupt it using the infilling scheme. (c) Model structure and pipelines for training and inferencing. \(c\) represents the embedding vector sequences for the given pharmacophore model; \(x\) represents the embedding sequence of input SMILES; \(z\) represents the latent variables for a molecule. Transformer encoder and decoder blocks are stacked with N layers. \(\oplus\) denotes concatenation of two vectors and \(\otimes\) denotes matrix multiplication. The overlap between the training and inferencing process is highlighted in the right panel.
+ +<|ref|>text<|/ref|><|det|>[[56, 701, 942, 930]]<|/det|> +To train PGMG, we need only a number of SMILES strings with no additional information attached. A training sample can be constructed using the SMILES representation of a molecule. First, the chemical features of a molecule are identified using RDKit31 and we randomly select some of them to build a pharmacophore network \(G_p\) . As shown in Figure 1a, we approximate the Euclidean distance in the three- dimensional Euclidean space in a pharmacophore using the length of the shortest path between two pharmacophore features on the molecular graph. The analysis of the two distances can be found in Figure S1. Next, a molecule is represented as a randomized SMILES string and then segmented into a token sequence \(s\) . We then corrupt \(s\) to get the encoder input \(s'\) by using the infilling scheme32 and obtain a training sample \((G_p, s, s')\) . Since we avoid the use of target- specific active data in the training stage, PGMG bypasses the problem of data scarcity on active molecules. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 44, 943, 202]]<|/det|> +When using the trained model to generate molecules, a pharmacophore model is required. The generation process is as follows. Given a pharmacophore model \(c\) , a set of latent variables \(z\) is sampled from the prior distribution \(p(z|c)\) , which in our case is the standard Gaussian distribution \(N(0, I)\) , and molecules are then generated from the conditional distribution \(p(x|z, c)\) . There are multiple ways to construct a pharmacophore model using various active data types and this is where the flexibility of the PGMG approach comes in. We employ both ligand- based and structure- based approaches to build pharmacophores and use them to generate active molecules for de novo drug design. + +<|ref|>sub_title<|/ref|><|det|>[[58, 217, 668, 236]]<|/det|> +## Performance of PGMG on the unconditional molecule generation task. + +<|ref|>text<|/ref|><|det|>[[56, 243, 943, 448]]<|/det|> +We evaluate our model's performance on the unconditional molecule generation task by comparing it with other SMILES- based methods including ORGAN9, VAE4, SMILES LSTM33, and Syntalinker20. We train PGMG and other SMILES- based models on the ChEMBL dataset34 based on the train- test split used in the GuacaMol benchmark35. Since PGMG is a conditional model, we approximate the unconditional distribution by generating molecules based on randomly sampled pharmacophore features. The molecule generation performance is evaluated by four metrics including validity, novelty, uniqueness, and ratio of available molecules (see Methods for the definition of the metrics). The comparison results of PGMG and four other SMILES- based methods on the four metrics are shown in Figure 2a. The results of ORGAN, VAE, SMILES LSTM on validity, novelty and are taken from the GuacaMol benchmark directly. + +<|ref|>text<|/ref|><|det|>[[56, 461, 943, 620]]<|/det|> +As shows in Figure 2a, PGMG performs better in novelty and the ratio of available molecules, while keeping the same level of validity and uniqueness as the top models. The ratio of available molecules is the ratio of unique novel valid molecules to all generated molecules, and equals product of the previous three metrics, as a composite metric to assess the performance of the model to generate novel molecules. PGMG achieves the highest the ratio of available molecules. Comparing to the second- best method, PGMG improves the ratio of available molecules by \(6.3\%\) . Among the SMILES- based methods, SMILES LSTM33 performs the best in uniqueness, while Syntalinker20 performs the best in validity. + +<|ref|>text<|/ref|><|det|>[[56, 633, 942, 744]]<|/det|> +In the ablation study, we remove features of PGMG and see how that affects performance. All models are trained using the ZINC36 dataset with the same parameters. Validity, uniqueness, and novelty are evaluated by generating 10 molecules for 1 pharmacophore extracted from each molecule in the test dataset. The match score is evaluated by generating \(512 \times 512\) molecules for 512 SMILES randomly sampled from the test dataset. The result of our ablation study can be found in Figure 2b. + +<|ref|>text<|/ref|><|det|>[[56, 758, 943, 916]]<|/det|> +We find when using canonical SMILES to train PGMG (canonical_SMILES), the uniqueness increases from 0.98 to 0.99, but the match score decreases from 0.91 to 0.94. A similar result can be found when we change the Gaussian distribution of the latent variable \(z\) to a Dirac delta distribution, denoted as PGMG (remove_z). remove_z exhibits a huge decrease on the uniqueness (from 0.98 to 0.81) and a certain degree of increase on the match score (from 0.94 to 0.97). If we use random sampling during generation, we can make the uniqueness increase, but it cannot make up for the drop of both the validity and the match score. As we see here, there seems to be a trade- off between the uniqueness and match score. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 45, 942, 132]]<|/det|> +We also test PGMG's performance when replacing the distance between chemical features with a constant number PGMG (remove_dis). The results show a large decrease in both uniqueness (from 0.98 to 0.82) and match score (from 0.94 to 0.60) as expected, which shows that PGMG makes a good use of the spatial information of pharmacophores. + +<|ref|>text<|/ref|><|det|>[[57, 147, 942, 280]]<|/det|> +To test whether PGMG catches the distribution of training dataset, we further examine the physicochemical properties of the generated molecules. The distribution of physicochemical properties of the generated molecules and the molecules in the training dataset are compared in Figure 2c. We find that the physicochemical properties distribution such as the topological polar surface area (TPSA), SA, QED, and LogP are close to the training set distribution. This demonstrates that PGMG captures the distribution of molecules in the training dataset well. + +<|ref|>image<|/ref|><|det|>[[80, 303, 956, 654]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[57, 664, 942, 737]]<|/det|> +
Figure 2 | Performance of PGMG on the unconditional molecule generation task. (a) Performance of PGMG and SMILES-based models on ChEMBL. (b) Results of the ablation study. (c) Distribution of chemical properties for the ChEMBL training set and the molecules generated by PGMG. The scientific notation at the upper left of the figure indicates the scaling of the vertical coordinates.
+ +<|ref|>sub_title<|/ref|><|det|>[[58, 752, 702, 770]]<|/det|> +## PGMG can generate bioactive molecules satisfying given pharmacophores. + +<|ref|>text<|/ref|><|det|>[[57, 777, 942, 864]]<|/det|> +We evaluate the extent to which the generated molecules fit the given pharmacophore models and predict binding affinity between protein receptors and molecules generated using PGMG through the molecular docking tool vina37. We use a match score to estimate the matching degree between each molecule- pharmacophore pair (see calculation of match score section of the Supplementary Information for details). + +<|ref|>text<|/ref|><|det|>[[57, 879, 942, 943]]<|/det|> +We extract a random pharmacophore model from each molecule in the test dataset. About 220,000 molecules in total are generated from those random pharmacophore models and the match score is calculated between each pair. For comparison, we also calculate the match score between 220,000 random + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 45, 926, 166]]<|/det|> +molecules from the ChEMBL dataset34 and the selected pharmacophores. The result is shown in Figure 3a. As can be seen from Figure 3a, 86.3% of the generated molecules have matching scores concentrated in the range of 0.8- 1.0, with 77.9% having a matching score of 1.0. Meanwhile, the matching degrees for the random molecules are centered at 0.45, with only 4.8% having a matching score of 1.0. This result demonstrates PGMG's ability to generate molecules satisfying the given pharmacophore models. + +<|ref|>image<|/ref|><|det|>[[66, 188, 933, 895]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[57, 900, 943, 955]]<|/det|> +
Figure 3 | Pharmacophore matching test results and the distribution of four target docking scores. (a) The match score of random selected molecules and PGMG generated molecules. (b) The distributions of the predicted affinity of top 1000 molecules generated by PGMG over VEGFR2 (PDB: 1YWN), CDK6 (PDB: 2EUF), TGFβ 1 (PDB: 6B8Y),
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[56, 42, 941, 357]]<|/det|> +BRD4 (PDB: 3MXF), and the affinity for the known bioactivity molecules corresponding to these targets. (c) Distributions of ADMET properties are calculated using ADMETlab 2.038 of top 1000 molecules generated by PGMG. The threshold of each property according to ADMETlab 2.0 is given as the dashed line. "↑" indicates that the distribution greater than the threshold satisfies the expected property, while "↓" indicates that the part of lower than the threshold satisfies the expected property. TPSA represents the topological polar surface area, optimal: 0\~140 (Å2); MW denotes Molecular Weight, Optimal:100\~600; nHA represents the number of hydrogen bond donors, optimal: 0\~7; nHD represents the number of hydrogen bond acceptors, optimal: 0\~12; SAscore represents the synthetic accessibility score, optimal: 0\~6; Madin-Darby Canine Kidney cells (MDCK) measure the uptake efficiency of a drug into the body, optimal: >2 x 10-6 (cm/s); BBB measures the ability of a drug to cross the blood-brain barrier to its molecular targets, qualified value: 0- 0.7; F(20%) denotes human oral bioavailability 20% which assess the efficiency of the drug delivery to the systemic circulation, optimal: 0- 0.3; CYP2C9 assess drug metabolism reactions, the closer to 1, the more likely it is to be an inhibitor; T12 represents the half-life of the drug, qualified value: 0- 0.7 and hERG evaluates whether the molecule is toxic to the heart, qualified value: 0- 0.7; ROA measures acute toxicity in mammals, qualified value: 0- 0.7. Where a molecule with a property in the optimal range means that the property is optimal, and a molecule with a property in the qualified range means that there is no obvious evidence that the property of the molecule is defective. The scientific notation at the upper left of the figure indicates the scaling of the vertical coordinates. + +<|ref|>text<|/ref|><|det|>[[56, 368, 942, 642]]<|/det|> +To further examine the binding activity of molecules generated by PGMG through the guidance of pharmacophores, we obtain pharmacophore models with known target structure from the literature39- 42. These targets include VEGFR2, CDK6, TFGβ 1, BRD4. For each pharmacophore model, 10,000 molecules are generated by PGMG. Autodock vina37 is used to calculate the binding affinities of generated molecules. And then, we select the top 1000 molecules with the strongest binding affinity. For comparison, we acquire the known bioactivity molecules for the four targets from CHEMBL, including 13299, 1648, 1885 and 4786 bioactivity molecules, respectively. In Figure 3b, we show the affinity distributions of the top 1000 molecules generated by PGMG and the affinities for the known bioactivity molecules from CHEMBL. The average affinity of the top 1000 molecules generated by PGMG is - 10.0 kcal/mol (1YWN), - 11.1 kcal/mol (2EUF), - 11.0 kcal/mol (6B8Y) and - 8.8 kcal/mol (3MXF), and the average affinity of the known bioactivity molecules is - 8.0 kcal/mol (1YWN), - 9.6 kcal/mol (2EUF), - 9.2 kcal/mol (6B8Y) and - 7.0 kcal/mol (3MXF) respectively. The distribution of affinities suggests that PGMG can generate desired bioactive molecules. + +<|ref|>text<|/ref|><|det|>[[57, 655, 941, 790]]<|/det|> +To evaluate if PGMG is capable of generating drug- like molecules, we further calculate the pharmacokinetics properties (absorption, distribution, metabolism, excretion) and toxicity (ADMET) of the top 1000 molecules. The ADMET distributions of the top 1000 molecules are illustrated in Figure 3c. Most of the molecules generated by PGMG satisfy the pharmacokinetic properties and toxicity constraint for drug candidate according to the standard proposed by ADMETlab 2.038. And the majority of the generated molecules are predicted with no obvious toxicity to the heart. + +<|ref|>sub_title<|/ref|><|det|>[[58, 816, 305, 834]]<|/det|> +## Structure-based drug design + +<|ref|>text<|/ref|><|det|>[[57, 841, 941, 952]]<|/det|> +Structure- based drug design is a powerful drug design strategy to generate the desired bioactivity molecules using the structure of specific target43. We use four targets (VEGFR2, CDK6, TFGβ 1, BRD4) from the above section with pharmacophore models which are built using ligand- receptor complex as examples to further demonstrate the performance of PGMG in structure- based drug design. It should be noted that the construction of pharmacophore models does not necessarily need any ligand. We choose these + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 45, 943, 157]]<|/det|> +pharmacophore models for the convenience of the following analyses. We compare several top affinity conformations of the generated molecules with the top affinity conformation of the reference ligand in the crystal complex. Figure 4 shows the binding sites of the four receptors with corresponding molecules. Most of the generated molecules share the same amino acid residues as the reference ligand, which indicates that those generated molecules are capable of fitting into the binding site as well as the reference one. + +<|ref|>image<|/ref|><|det|>[[63, 174, 928, 641]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[60, 654, 916, 673]]<|/det|> +
Figure 4 | A display of the binding sites of the molecules generated by PGMG in structure-based drug design.
+ +<|ref|>text<|/ref|><|det|>[[56, 690, 944, 933]]<|/det|> +In Figure 4(a- c), the generated molecules of 1YWM have a similar structure with the reference. As for 2EUF and 6B8Y, despite the structural differences between the generated molecules and the reference molecule, the generated molecules (Figure 4 (e- g, i- k)) share some common important functional groups as the reference ligands (Figure 4h, Figure 4l). And interestingly, we find that the structures of the generated molecules may differ from the reference ligand (Figure 4p) in a good way. For example, the molecules generated by PGMG for 3MXF (Figure 4 (m- o)) can bind to D88, P86, and P82 amino acid residues other than N140 (Figure 4p). This finding suggests that PGMG may have the potential in exploring new binding sites. Besides, we exam the drug- likeness using SA and hERG. SA is designed to estimate the ease of synthesis of drug- like molecules, and it's easy to synthesize when SA is less than 6. The hERG is a toxicity + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 46, 942, 177]]<|/det|> +metric. Abnormal hERG values for a drug may lead to palpitations, syncope, and even sudden death. This metric measures the probability that a molecule will be toxic. Empirically, over 0.7, the molecule is considered toxic. These generated molecules perform well on SA and hERG. The above results show that PGMG can design molecules that not only fit well into the binding site but also exhibit drug- like quality in the structure- based drug design. + +<|ref|>sub_title<|/ref|><|det|>[[58, 196, 285, 214]]<|/det|> +## Ligand-based drug design + +<|ref|>text<|/ref|><|det|>[[56, 221, 943, 471]]<|/det|> +Although structure- based drug design is a successful and highly attractive strategy, there are some prerequisites to use this strategy, including a certain target, a high- resolution crystal structure of the target, and some identified interaction sites. However, it is not easy to reach the above prerequisites. Ligand- based drug design is capable of designing drug molecules based on the conformational superposition of known active molecules when the target is unknown or the binding site is unclear. And it has been widely used in drug discovery, such as the search for new drugs for drug resistance. Squalene oxidase is the target for ringworm, superficial skin fungal infections, and other diseases. Butenafine and terbinafine are typical inhibitors for squalene oxidase44. However, these inhibitors are prone to drug resistance and side effects including skin erythema, burning, and itching. Therefore, it is urgent to design novel squalene oxidase inhibitors. Here, we generate 200 molecules using a pharmacophore model constructed from squalene oxidase inhibitors. + +<|ref|>image<|/ref|><|det|>[[62, 492, 930, 825]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[56, 834, 942, 908]]<|/det|> +
Figure 5 Alignment diagrams of terbinafine, pharmacophore model, and molecules generated by PGMG. The different colored spheres represent different pharmacophore features. Aromatic center is red, the positive charge center is yellow, and hydrophobic centers are green. The grey molecules represent terbinafine, and the green molecules represent the molecules generated by PGMG.
+ +<|ref|>text<|/ref|><|det|>[[75, 920, 940, 940]]<|/det|> +As shows in Figure 5, the generated molecules align well to the active conformation of terbinafine which + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 43, 942, 179]]<|/det|> +is obtained from drugbank \(^{45}\) . The listed molecules match well with the desired pharmacophore features, including two hydrophobic centers, a positive charge center, and an aromatic ring center. Here, we notice that PGMG has a good grasp of the equivalence of different substructures under the same pharmacophore feature. It matches the aromatic center with pyrrole, thiophene, and pyrimidine, and the hydrophobic center with aliphatic, cycloalkane, and benzene. This result shows that PGMG can generate diverse molecules while maintaining the important properties of the substructures the same as the known inhibitor. + +<|ref|>text<|/ref|><|det|>[[57, 193, 942, 351]]<|/det|> +To further assess the pharmacokinetics and toxicity of the generated molecules, we calculate the TSPA, SA, and hERG of the generated molecules. See the previous section for a detailed SA and hERG description. TSPA is a molecular descriptor measuring drug transport properties such as intestinal absorption and blood- brain barrier (BBB) penetration. The TPSA in the range of 0- 140 means optimal. Of the six molecules generated by PGMG, their TSPA, SA, and hERG values are within the rational range. From Figure 5, we can see that PGMG is able to generate molecules that match the pharmacophore model and meet the overall criteria for TSPA, SA, and hERG. + +<|ref|>sub_title<|/ref|><|det|>[[58, 366, 315, 384]]<|/det|> +## Lead compound optimization + +<|ref|>text<|/ref|><|det|>[[57, 392, 943, 597]]<|/det|> +Lead optimization refers to the improvement of one or more properties of a hit compound by chemical modification. The optimization objectives include adjusting the molecular flexibility ratio, improving the pharmacokinetic properties, or enhancing the bioavailability. Here we show how PGMG can help with lead compound optimization using Lavendustin A as a case study. Lavendustin A is an inhibitor of epidermal growth factor receptor (EGFR), while the lipophilicity of Lavendustin A is too poor to cross the cell membrane. It has been shown that improving the lipophilicity of Lavendustin A can lead to nanomolar levels of IC50 inhibition activity at the cellular level \(^{46}\) . In this case, we construct Lavendustin A's pharmacophore using Pharao \(^{47}\) , then we modify the polar pharmacophore features, and finally use PGMG to generate molecules for the modified pharmacophore to improve the lipophilicity of the generated molecules. + +<|ref|>text<|/ref|><|det|>[[57, 610, 943, 883]]<|/det|> +We filter the generated molecules with lipophilicity (LogP > 3.41) to obtain 400 molecules with a higher lipophilicity than Lavendustin A. We calculate Tanimoto similarity using MACCSkeys Fingerprints with RDKit \(^{11}\) between the obtained molecules and the 1500 EGFR inhibitors acquired from the ExCAPE database \(^{48}\) . Figure 6 shows some examples of the generated molecules with their closest EGFR inhibitors obtained from the ExCAPE database and their respective Tanimoto similarities. We find that the generated molecules have high similarity to the EGFR target active molecules in the ExCAPE database, which are not included in the training set. And they all own the three pharmacophore features of the aromatic ring, hydrogen- bonded acceptor, and hydrophobic center. Based on the assumption that structurally similar molecules have similar properties, the similarity result demonstrates that molecules generated by PGMG have a probability of inhibiting EGFR. To some extent, the generated molecules gain structural diversity while maintaining the consistency of the pharmacophore. Overall, PGMG can optimize certain properties and maintain the bioactivity of a given lead compound. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[92, 68, 916, 410]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[56, 432, 943, 523]]<|/det|> +
Figure 6 | Display diagram of the molecule generated by PGMG with known inhibitors in the case of Lavendustin A optimization. Molecules generated by PGMG are shown inside the circle and their closest active nearest neighbors are shown outside the circle. The colors indicate the pharmacophore features extracted from Lavendustin A. Red corresponds to the aromatic center, blue represent the hydrogen-bonded acceptor, and green represent the hydrophobic center.
+ +<|ref|>sub_title<|/ref|><|det|>[[57, 538, 152, 555]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[56, 562, 945, 860]]<|/det|> +In this work, we develop a pharmacophore- guided deep learning approach for bioactive molecule generation called PGMG. We manage to use pharmacophores as the only constraint during the generation process by (1) encoding both pharmacophore features and spatial information of a given pharmacophore into a complete graph with node and edge attributes and (2) introduce latent variables so that a molecule can be uniquely characterized by a pharmacophore and a set of latent variables to handle the many- to- many relationship of pharmacophores and molecules. Our approach offers some advantages over current molecule generation methods. Firstly, PGMG provides a way to utilize different types of activity data in a uniform representation, allowing it to overcome the problem of data scarcity and be used in various situations. Secondly, pharmacophores are biologically meaningful and can incorporate biochemists' knowledge, which provides a strong prior and certain interpretability into the generation process. Lastly, after training, PGMG can be directly applied to different targets without further fine- tuning. Besides, it is also worth mentioning that the training scheme itself does not require any activity data to proceed. This training scheme may be useful for other generative methods. + +<|ref|>text<|/ref|><|det|>[[57, 873, 941, 940]]<|/det|> +PGMG makes solid progress on the challenging problem of generating desired bioactive molecules in various scenarios when known active data is scarce. When a target structure is available, PGMG is competent to design a large number of molecules that bind affinity better than the specific- target active + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[56, 44, 944, 319]]<|/det|> +molecules obtained from the ChEMBL database. Given the pharmacophore for certain targets, the PGMG can also be utilized to design dual or multi- target molecules. Besides, we expect that PGMG can be adopted to prepare chemical libraries to replace those used in HTS campaigns to improve virtual screen efficiency, as it can provide a sufficient number of candidate drug- like molecules for a specified target. Then, this method performs well in ligand- based drug design, which has wide use in drug design when the target structure is absent. The ligand- based case shows that PGMG is able to generate high- quality bioactivity molecules that match the pharmacophore model with structural diversity. This result implies that PGMG can be applied to multiple drug design scenarios such as researching alternative medicine, drug resistance, and scaffold hopping. Finally, the case of lead optimization demonstrates that PGMG can optimize the molecule's properties while maintaining the bioactivity and scaffold diversity of the generated molecules. The results demonstrate that PGMG is a promising approach for structure- based drug design, high- throughput screening, ligand based- drug design, and lead optimization in a real drug discovery setting. + +<|ref|>text<|/ref|><|det|>[[56, 332, 944, 536]]<|/det|> +We believe that the de novo drug design is a complicated and situation- specific problem, and computational methods should try to get more assistance from chemists' experience and judgements. PGMG benefits from this idea a lot. Some limitations of PGMG should be acknowledged. PGMG currently does not support exclusion volume in pharmacophore models and as we focus on the task of generating molecules with desired activity, PGMG does not explicitly constrain the properties of the generated molecules. A future direction of our work is to include the exclusion volume and other features into PGMG and make the generated molecules to be more controllable and malleable. And furthermore, designing multi- conditional generation models to generate active molecules with specified properties is the ultimate goal of drug design, we will continue to work towards this objective. + +<|ref|>sub_title<|/ref|><|det|>[[57, 552, 137, 568]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[57, 580, 135, 596]]<|/det|> +## Datasets + +<|ref|>text<|/ref|><|det|>[[57, 604, 942, 693]]<|/det|> +We use the ChEMBL 24 dataset containing more than 1.25 million molecules to train PGMG. ChEMBL is a collection of bioactivity data for various targets and compounds from the literature. It contains 13 types of atoms \(\mathrm{(T = 13)}\) : H, B, C, N, O, F, Si, P, S, Cl, Se, Br, and I. Each bond is either a no- bond, single, double, triple or aromatic bond \(\mathrm{(R = 5)}\) . + +<|ref|>text<|/ref|><|det|>[[57, 706, 941, 773]]<|/det|> +We also use the ZINC36 molecule dataset from JTVAE49 for our ablation study. It contains 220,000 molecules in the training data, 11 types of atoms \(\mathrm{(T = 11)}\) : H, B, C, N, O, F, P, S, Cl, Br, and I. Each bond is either a no- bond, single, double, triple or aromatic bond \(\mathrm{(R = 5)}\) . + +<|ref|>text<|/ref|><|det|>[[72, 785, 900, 805]]<|/det|> +The structure of four targets VEGFR2, CDK6, TFGB 1, BRD4 are downloaded from PDB50 database. + +<|ref|>sub_title<|/ref|><|det|>[[57, 818, 496, 837]]<|/det|> +## Representation of Pharmacophores and Molecules + +<|ref|>text<|/ref|><|det|>[[57, 841, 942, 954]]<|/det|> +A pharmacophore model consists of several chemical features and their spatial descriptions and are represented by a fully connected graph with chemical feature types as node attributes and distances as edge weights (a detailed description of the pharmacophore graph and the preparation of the pharmacophore graph is included in SI). We apply a state- of- the- art graph neural network, Gated- GCN29, to embed the graph with consideration of node attributes and edge attributes. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[56, 44, 942, 179]]<|/det|> +Molecules are represented in SMILES format. Symbols of stereochemistry like \(^{\prime \prime}\mathrm{d}^{\prime \prime}\) ' /' are removed because stereochemistry information does not exist in the graph representation of a pharmacophore and it is not difficult to list all stereoisomers of a molecule. Then the SMILES string is separated into a sequence of tokens corresponding to heavy atoms and structural punctuation marks. For example, the SMILES string \(C(C[N H2 - ])O C(= O)C l\) will be split to \(C\) ( \(C[N H2 - ]\) ) \(O C\) ( \(= O\) ) \(C l\) , where each token will be embedded into a vector. + +<|ref|>sub_title<|/ref|><|det|>[[57, 194, 247, 211]]<|/det|> +## Encoder and Decoder + +<|ref|>text<|/ref|><|det|>[[56, 216, 942, 351]]<|/det|> +An illustration of the encoder and decoder networks can be found in Figure.1. The encoder and decoder network are adapted from the standard transformer \(^{30}\) architecture with each consisting of several layers of stacked transformer encoder block and transformer decoder block. The difference between the transformer encoder and decoder blocks is that the encoder block uses only self- attention modules and the decoder block uses cross- attention modules to incorporate context in the generation process. Some modifications are made to handle our inputs and to better suit the variational autoencoder structure of PGMG. + +<|ref|>text<|/ref|><|det|>[[57, 364, 941, 429]]<|/det|> +We first calculate the latent variables \(z\) of molecule \(x\) given pharmacophore \(c\) by the encoder network. The encoder input is a concatenation of molecule and pharmacophore features. Following BART \(^{32}\) , positional and segment encoding is added to the input sequence: + +<|ref|>equation<|/ref|><|det|>[[411, 437, 940, 497]]<|/det|> +\[\begin{array}{r}I n p u t_{e n c o d e r} = (E_{p}^{\prime};E_{m}^{\prime})\\ E_{m_{i}}^{\prime} = E_{m_{i}} + S E_{m} + P E_{i}\\ E_{p_{j}}^{\prime} = E_{p_{j}} + S E_{p} \end{array} \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[56, 503, 942, 670]]<|/det|> +where \(I n p u t_{e n c o d e r}\) is the input representation, \(E_{p_{j}}\) is the j- th pharmacophore feature vector, \(E_{m_{i}}\) is the i- th token embedding of molecule features, \(S E_{m}\) and \(S E_{p}\) are two segment embedding vectors for molecule features and pharmacophore features, and \(P E_{i}\) is the positional embedding for i- th token. After several layers of transformer encoder block, the molecule features are averaged by an attention pooling layer to obtain the final molecule representation \(h_{x}\) . \(h_{x}\) is then fed into two separate sub- networks to compute the mean \(\mu\) and log variance \(\log \Sigma\) of the posterior variational approximation. Latent variables \(z\) are then sampled from the Normal distribution \(N(\mu ,\Sigma)\) . + +<|ref|>text<|/ref|><|det|>[[75, 684, 779, 703]]<|/det|> +The decoder network takes the latent variables \(z\) and pharmacophore features as input: + +<|ref|>equation<|/ref|><|det|>[[404, 710, 940, 750]]<|/det|> +\[\begin{array}{r}i n p u t_{d e c o d e r} = (E_{p}^{\prime};E_{z}^{\prime})\\ E_{z_{i}}^{\prime} = z_{i} + S E_{z} + P E_{i} \end{array} \quad (5)\] + +<|ref|>text<|/ref|><|det|>[[57, 752, 941, 817]]<|/det|> +where \(E_{p}^{\prime}\) is the same as described above, \(S E_{z}\) is the segment embedding for latent variables, and \(P E_{i}\) is the positional embedding for i- th token. The decoder then uses \(i n p u t_{d e c o d e r}\) to generate target SMILES autoregressively. Each token is determined on the basis of previously generated tokens and context: + +<|ref|>equation<|/ref|><|det|>[[399, 828, 940, 848]]<|/det|> +\[o_{i} = (a r g m a x)_{o_{i}}P(o_{i}|o_{< i},c,z) \quad (7)\] + +<|ref|>text<|/ref|><|det|>[[58, 854, 321, 871]]<|/det|> +where \(o_{i}\) is i- th generated token. + +<|ref|>sub_title<|/ref|><|det|>[[57, 889, 183, 905]]<|/det|> +## Loss Function + +<|ref|>text<|/ref|><|det|>[[57, 911, 941, 953]]<|/det|> +PGMG's model is trained in an end- to- end manner. The Loss function consists of three different terms, KL Loss, LM Loss, and the mapping loss. The first two terms are the negative evidence lower bound (ELBO) of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 45, 308, 63]]<|/det|> +the log likelihood \(\log P_{\theta}(x|c)\) : + +<|ref|>equation<|/ref|><|det|>[[241, 77, 939, 149]]<|/det|> +\[\begin{array}{rl} & {\log P_{\theta}(x|c_{p}) = log\int P_{\theta}(x|c,z)P_{\phi}(z|c)dz}\\ & {\geq -KL(P_{\phi}(z|x,c)||P_{\theta}(z|c)) + E_{P_{\phi}(z|x,c)}[\log P_{\theta}(x|z,c)]}\\ & {\approx -KL(P_{\phi}(z|x,c)||P_{\theta}(z|c)) + \log P_{\theta}(x|z,c)} \end{array} \quad (A)\] + +<|ref|>text<|/ref|><|det|>[[56, 150, 942, 287]]<|/det|> +where \(KL\) denotes the Kullback- Leibler divergence and we assume \(P_{\theta}(z|c)\) the prior distribution of \(z\) to be a standard gaussian \(N(0,I)\) . We call the left part of (A) KL Loss and it serves as a regulation term to mitigate the gap between the true prior distribution of \(z\) and the posterior distribution and to make the latent space of \(z\) smoother. The expectation term on the right part of (A) is estimated through sampling, and it is optimized using Monte Carlo estimation with one data point for each sample \(^{51}\) . This gives us the right part of (B). Since \(m\) takes form of the SMILES string, we call it the language modeling loss (LM Loss). + +<|ref|>text<|/ref|><|det|>[[56, 299, 941, 388]]<|/det|> +The third part of PGMG's loss function is the mapping loss. It evaluates the model's performance in predicting the mapping between heavy atoms and pharmacophore elements. We use the mapping loss as a regulation term to help alleviate the problem of posterior collapse. The mapping score of the \(i^{\mathrm{th}}\) pharmacophore \(p_{i}\) and the \(j^{\mathrm{th}}\) output token \(o_{j}\) is calculated as + +<|ref|>equation<|/ref|><|det|>[[325, 402, 939, 431]]<|/det|> +\[mapping_{g_{score}_{ij}} = \sigma \left(g(W_{p}E_{p_i})\odot g\left(W_{o}E_{o_j}\right)\right) \quad (9)\] + +<|ref|>text<|/ref|><|det|>[[56, 442, 941, 519]]<|/det|> +where \(E_{p_i}\) and \(E_{o_j}\) are the embedding vectors of \(p_i\) and \(o_j\) respectively, \(W_{p}\) and \(W_{o}\) are two learnable matrices to project two different embeddings into the same space, \(\odot\) is the dot product, \(\sigma\) is the sigmoid function, and \(g\) is the ReLU function. The calculation of mapping scores can be vectorized as + +<|ref|>equation<|/ref|><|det|>[[330, 533, 939, 561]]<|/det|> +\[mapping_{g_{score}} = \sigma \left(g(W_{p}E_{p})g(W_{o}E_{o})\right) \quad (10)\] + +<|ref|>text<|/ref|><|det|>[[56, 568, 941, 633]]<|/det|> +Since SMILES format contains tokens other than atom symbols, we mask them when calculating the mapping loss. The mapping loss is then calculated as the cross- entropy of the masked scores and labels. An illustration of the masked mapping score and label is given in Supplementary Figure S3. + +<|ref|>sub_title<|/ref|><|det|>[[57, 648, 459, 666]]<|/det|> +## Training details and model parameter settings + +<|ref|>text<|/ref|><|det|>[[56, 670, 941, 805]]<|/det|> +During training, we inject noise into the input to make training more robust by using the infilling scheme. Some random subsequences in every input sequence are replaced with a single [mask] token. Teacher forcing technique is applied to the generation process during training, by which we replace the previously generated tokens with the ground truth to produce the next token. Aside from the mapping loss introduced before, another approach we use to alleviate posterior collapse is KL annealing \(^{52}\) , where an increasing coefficient is used to control the size of KL Loss. + +<|ref|>text<|/ref|><|det|>[[56, 818, 941, 953]]<|/det|> +We use the same model parameters in both ChEMBL and ZINC datasets. The hidden dimension is 384. The transformer encoder blocks and transformer decoder blocks are stacked 8 times. We use an 8- head attention and the feed- forward dimension is 1024. We use Adam optimizer to train the model with a 3e- 4 learning rate and a 1e- 6 weight decay rate. Cosine learning rate annealing is applied with a cycle length of 4 epochs. We use the gradient clipping technique and set the maximum gradient to be 5. Since the ChEMBL dataset contains a lot more molecules compared to the ZINC dataset, it requires less training epochs to reach + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 45, 940, 85]]<|/det|> +a similar validation performance. Thus, the number of training epochs for the former is 32 and 48 for the latter. + +<|ref|>sub_title<|/ref|><|det|>[[57, 102, 156, 118]]<|/det|> +## Evaluation + +<|ref|>text<|/ref|><|det|>[[57, 123, 944, 258]]<|/det|> +Firstly, four different metrics on 2D level, validity, uniqueness, novelty, and ratio of available molecules are employed to evaluate the ability of the PGMG to generate novel molecules. Validity is the percentage of chemically valid molecules with generated SMILES. Uniqueness measures how many valid molecules are non- repetitive. Novelty refers to the percentage of generated chemically valid molecules not present in the training set. And the ratio of available molecules is the proportion of novel molecules in all generated results. These metrics are calculated as follows: + +<|ref|>equation<|/ref|><|det|>[[339, 278, 940, 307]]<|/det|> +\[\text{validity} = \frac{\# \text{Number of chemically valid SMILES}}{\# \text{of generated SMILES}} \quad (11)\] + +<|ref|>equation<|/ref|><|det|>[[346, 330, 940, 360]]<|/det|> +\[\text{uniqueness} = \frac{\# \text{of non-duplicate, valid SMILES}}{\# \text{of valid SMILES}} \quad (12)\] + +<|ref|>equation<|/ref|><|det|>[[346, 383, 940, 412]]<|/det|> +\[\text{novelty} = \frac{\# \text{of novelty molecules not in training set}}{\# \text{of unique molecules}} \quad (13)\] + +<|ref|>equation<|/ref|><|det|>[[329, 435, 940, 465]]<|/det|> +\[\text{ratio of available molecules} = \frac{\# \text{of novel molecules}}{\# \text{of generated SMILES}} \quad (14)\] + +<|ref|>text<|/ref|><|det|>[[57, 488, 942, 622]]<|/det|> +Secondly, goal- directed metrics are evaluated by the match score, which indicates the match degree of the generated molecules to the specified pharmacophore (see calculation of match score section of the Supplementary Information for details). We further evaluate the binding activity of the generated molecules to the target using affinity calculated by Autodock vina37. Finally, we use ADMETlab 2.038 to predict the ADMET properties of the generated molecules and to assess the drug- like potential of the generated molecules. + +<|ref|>sub_title<|/ref|><|det|>[[58, 640, 228, 657]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[57, 664, 942, 728]]<|/det|> +This work is financially supported by the National Natural Science Foundation of China under Grants (No. 61832019 to M.L.), Hunan Provincial Science and Technology Program (2019CB1007) [M.L.], and European Research Council (No. 716063 to J.T.) + +<|ref|>sub_title<|/ref|><|det|>[[58, 745, 250, 762]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[57, 770, 942, 858]]<|/det|> +M.L. and J.T. guided the research and provided the experimental platform. H.Z., R.Z., and M.L conceived the initial idea and started the project. H.Z collected and preprocessed the data and R.Z designed the model. R.Z performed the generation experiments and H.Z performed the case studies. H.Z, R.Z, J.T., and M.L wrote the paper. + +<|ref|>sub_title<|/ref|><|det|>[[58, 875, 264, 892]]<|/det|> +## Declaration of Interests + +<|ref|>text<|/ref|><|det|>[[58, 903, 413, 920]]<|/det|> +The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[58, 930, 205, 947]]<|/det|> +## Data availability + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[58, 46, 911, 65]]<|/det|> +The data that support the findings of this study are available at https://github.com/CSUBioGroup/PGMG. + +<|ref|>sub_title<|/ref|><|det|>[[58, 85, 209, 102]]<|/det|> +## Code availability + +<|ref|>text<|/ref|><|det|>[[57, 110, 941, 152]]<|/det|> +The code used to generate results shown in this study is available at https://github.com/CSUBioGroup/PGMG. + +<|ref|>sub_title<|/ref|><|det|>[[58, 177, 180, 198]]<|/det|> +## reference + +<|ref|>text<|/ref|><|det|>[[55, 216, 944, 945]]<|/det|> +1. Lipinski, C.A., Lombardo, F., Dominy, B.W. & Feeney, P.J. 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Advances in neural information processing systems 30 (2017). + +<|ref|>text<|/ref|><|det|>[[57, 451, 688, 468]]<|/det|> +31. Landrum, G. A. RDKit: Open-source cheminformatics. http://www.rdkit.org. + +<|ref|>text<|/ref|><|det|>[[57, 469, 940, 523]]<|/det|> +32. Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M. & Zettlemoyer, L. BART: Denoising Sequence-to-Sequence Pretraining for Natural Language Generation, Translation, and Comprehension. Preprint at http://arxiv.org/abs/1910.13461 (2019). + +<|ref|>text<|/ref|><|det|>[[57, 524, 940, 560]]<|/det|> +33. Segler, M.H., Kogej, T., Tyrchan, C. & Waller, M.P. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS central science 4, 120-131 (2018). + +<|ref|>text<|/ref|><|det|>[[57, 561, 940, 596]]<|/det|> +34. Mendez, D. et al. ChEMBL: towards direct deposition of bioassay data. Nucleic acids research 47, D930-D940 (2019). + +<|ref|>text<|/ref|><|det|>[[57, 598, 940, 634]]<|/det|> +35. Brown, N., Fiscato, M., Segler, M.H. & Vaucher, A.C. GuacaMol: benchmarking models for de novo molecular design. Journal of chemical information and modeling 59, 1096-1108 (2019). + +<|ref|>text<|/ref|><|det|>[[57, 635, 940, 671]]<|/det|> +36. Sterling, T. & Irwin, J.J. ZINC 15-ligand discovery for everyone. Journal of chemical information and modeling 55, 2324-2337 (2015). + +<|ref|>text<|/ref|><|det|>[[57, 672, 940, 708]]<|/det|> +37. Trott, O. & Olson, A.J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry 31, 455-461 (2010). + +<|ref|>text<|/ref|><|det|>[[57, 709, 940, 745]]<|/det|> +38. Xiong, G. et al. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Research 49, W5-W14 (2021). + +<|ref|>text<|/ref|><|det|>[[57, 746, 940, 782]]<|/det|> +39. Lee, K. et al. Pharmacophore modeling and virtual screening studies for new VEGFR-2 kinase inhibitors. European journal of medicinal chemistry 45, 5420-5427 (2010). + +<|ref|>text<|/ref|><|det|>[[57, 783, 940, 856]]<|/det|> +40. Shawky, A.M., Ibrahim, N.A., Abourehab, M.A., Abdalla, A.N. & Gouda, A.M. Pharmacophore-based virtual screening, synthesis, biological evaluation, and molecular docking study of novel pyrrolizines bearing urea/thiourea moieties with potential cytotoxicity and CDK inhibitory activities. Journal of enzyme inhibition and medicinal chemistry 36, 15-33 (2021). + +<|ref|>text<|/ref|><|det|>[[57, 858, 940, 894]]<|/det|> +41. Jiang, J., Zhou, H., Jiang, Q., Sun, L. & Deng, P. Novel transforming growth factor-beta receptor 1 antagonists through a pharmacophore-based virtual screening approach. Molecules 23, 2824 (2018). + +<|ref|>text<|/ref|><|det|>[[57, 895, 940, 949]]<|/det|> +42. Yan, G. et al. Pharmacophore - based virtual screening, molecular docking, molecular dynamics simulation, and biological evaluation for the discovery of novel BRD 4 inhibitors. Chemical Biology & Drug Design 91, 478-490 (2018). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[56, 42, 944, 415]]<|/det|> +43. Pei, J., Yin, N., Ma, X. & Lai, L. Systems biology brings new dimensions for structure-based drug design. Journal of the American Chemical Society 136, 11556-11565 (2014). +44. Kermani, F. et al. In vitro activities of antifungal drugs against a large collection of Trichophyton tonsurans isolated from wrestlers. Mycoses 63, 1321-1330 (2020). +45. Wishart, D.S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic acids research 46, D1074-D1082 (2018). +46. Nussbaumer, P. et al. Novel antiproliferative agents derived from lavendustin A. Journal of medicinal chemistry 37, 4079-4084 (1994). +47. Taminau, J., Thijs, G. & De Winter, H. Pharao: pharmacophore alignment and optimization. Journal of Molecular Graphics and Modelling 27, 161-169 (2008). +48. Sun, J. et al. ExCAPE-DB: an integrated large scale dataset facilitating Big Data analysis in chemogenomics. Journal of cheminformatics 9, 1-9 (2017). +49. Jin W, Barzilay R, Jaakkola T. Junction Tree Variational Autoencoder for Molecular Graph Generation. Proceedings of the 35th International Conference on Machine Learning. PMLR, 2323-2332 (2018). +50. Burley, S.K. et al. Protein Data Bank (PDB): the single global macromolecular structure archive. Protein Crystallography 1607, 627-641 (2017). +51. Kingma, D.P. & Welling, M. Auto-Encoding Variational Bayes. Preprint at http://arxiv.org/abs/1312.6114 (2014). +52. Bowman, S.R. et al. Generating sentences from a continuous space. Preprint at http://arxiv.org/abs/1511.06349 (2015). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 270, 66]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 85, 654, 102]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[55, 116, 955, 135]]<|/det|> +SupplementaryinformationPGMGAPharmacophoreGuidedDeepLearningApproachforBioactiveMolecularGeneration.docx + +<--- Page Split ---> diff --git a/preprint/preprint__0877cda3cf7745c3f9fe5760c2c8af30a0bf2b4fff0901f70dac9108eb425e22/images_list.json b/preprint/preprint__0877cda3cf7745c3f9fe5760c2c8af30a0bf2b4fff0901f70dac9108eb425e22/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..0637a088a01e8ddab3bf3fa98dbe804cbde1a0dc --- /dev/null +++ b/preprint/preprint__0877cda3cf7745c3f9fe5760c2c8af30a0bf2b4fff0901f70dac9108eb425e22/images_list.json @@ -0,0 +1 @@ +[] \ No newline at end of file diff --git a/preprint/preprint__0877cda3cf7745c3f9fe5760c2c8af30a0bf2b4fff0901f70dac9108eb425e22/preprint__0877cda3cf7745c3f9fe5760c2c8af30a0bf2b4fff0901f70dac9108eb425e22.mmd b/preprint/preprint__0877cda3cf7745c3f9fe5760c2c8af30a0bf2b4fff0901f70dac9108eb425e22/preprint__0877cda3cf7745c3f9fe5760c2c8af30a0bf2b4fff0901f70dac9108eb425e22.mmd new file mode 100644 index 0000000000000000000000000000000000000000..5f5660188d3d88c7964ad5d6659901a851b6a441 --- /dev/null +++ b/preprint/preprint__0877cda3cf7745c3f9fe5760c2c8af30a0bf2b4fff0901f70dac9108eb425e22/preprint__0877cda3cf7745c3f9fe5760c2c8af30a0bf2b4fff0901f70dac9108eb425e22.mmd @@ -0,0 +1,320 @@ + +# The structure of SpoT reveals evolutionary tuning of enzymatic output through constraint of the conformational landscape + +Hedvig Tamman Université Libre de Bruxelles https://orcid.org/0000- 0003- 4453- 7814 + +Karin Emits Lund University + +Mohammad Roghanian Lund University + +Andres Ainelo Université Libre de Bruxelles + +Christina Julius Umea University + +Anthony Perrier University of Namur https://orcid.org/0000- 0002- 4473- 3711 + +Ariel Talavera Vrije Universiteit Brussel, Vlaams Instituut voor Biotechnologie https://orcid.org/0000- 0002- 1865- 5959 + +Hanna Ainelo Université Libre de Bruxelles + +Rémy Duguquier Université https://orcid.org/0000- 0002- 9662- 8905 + +Safia Zedek Université Libre de Bruxelles + +Aurélien Thureau Swing Beamline, Synchrotron SOLEIL https://orcid.org/0000- 0001- 5666- 260X + +Javier Perez Synchrotron SOLEIL https://orcid.org/0000- 0003- 3083- 4754 + +Gipsi Lima- Mendez University of Namur + +Regis Hallez University of Namur https://orcid.org/0000- 0003- 1175- 8565 + +Gemma Atkinson Umeå University https://orcid.org/0000- 0002- 4861- 4584 + +Vasili Hauryliuk + +<--- Page Split ---> + +Lund University https://orcid.org/0000- 0003- 2389- 5057Abel Garcia- Pino ( \(\square\) agarciap@ulb.ac.be)Université Libre de Bruxelles https://orcid.org/0000- 0002- 0634- 0300 + +## Article + +Keywords: (p)ppGpp, stringent response, SpoT, RelA, Rel, allostery, intrinsically disordered 46 proteins, energetic frustration, metabolic hubs, conformational switches + +Posted Date: February 11th, 2022 + +DOI: https://doi.org/10.21203/rs.3.rs- 1293174/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> + +# The structure of SpoT reveals evolutionary tuning of enzymatic output through constraint of the conformational landscape + +4 Hedvig Tamman \(^{1,\dagger ,*}\) , Karin Ernits \(^{2,3,4,\dagger}\) , Mohammad Roghanian \(^{2,4,5,\dagger}\) , Andres Ainelo \(^{1}\) , Christina Julius \(^{4}\) , Anthony Perrier \(^{6,7}\) , Ariel Talavera \(^{1}\) , Hanna Ainelo \(^{1}\) , Rémy Duguquier \(^{1,5}\) , Safia Zedek \(^{1}\) , Aurelien Thureau \(^{8}\) , Javier Pérez \(^{8}\) , Gipsi Lima- Mendez \(^{6}\) , Regis Hallez \(^{6,7,9}\) , Gemma C. Atkinson \(^{2,4}\) , Vasili Hauryliuk \(^{2,4,10,*}\) , Abel Garcia- Pino \(^{1,9,*}\) + +9 1Cellular and Molecular Microbiology, Faculté des Sciences, Université libre de Bruxelles 10 (ULB), Boulevard du Triomphe, Building BC, (1C4 203), 1050 Brussels, Belgium 11 2Department of Experimental Medicine, University of Lund, 221 84 Lund, Sweden 12 3Department of Chemistry, Umeå University, 901 87 Umeå, Sweden 13 4Department of Molecular Biology, Umeå University, 901 87 Umeå, Sweden 14 5Departement of Clinical Microbiology, Rigshospitalet, 2200 Copenhagen, Denmark 15 6Biology of Microorganisms Research Unit (URBM), Namur Research Institute for Life 16 Science (NARILIS), University of Namur, 61 Rue de Bruxelles, 5000 Namur 17 7Bacterial Cell cycle & Development (BCcD), Biology of Microorganisms Research Unit 18 (URBM), Namur Research Institute for Life Science (NARILIS), University of Namur, 61 Rue 19 de Bruxelles, 5000 Namur 20 8Synchrotron SOLEIL, Saint- Aubin - BP 48, 91192 Gif sur Yvette Cedex, France 21 9WELBIO, Avenue Hippocrate 75, 1200 Brussels, Belgium 22 10University of Tartu, Institute of Technology, 50411 Tartu, Estonia + +\* to whom correspondence should be addressed: + +26 Hedvig Tamman: hedvig.tamman@ulb.be, +372 737 6038 27 Vasili Hauryliuk: vasili.hauryliuk@med.lu.se, +46 70 60 90 493 28 Abel Garcia- Pino: abel.garcia.pino@ulb.be, +32 2 650 53 77 29 †These authors contributed equally to the paper as first authors. + +## 31 Abstract: + +32 Stringent factors orchestrate bacterial cell reprogramming through increasing the level of the 33 alarmones (p)ppGpp. In Beta- and Gammaproteobacteria, SpoT hydrolyses (p)ppGpp to 34 counteract the synthetase activity of RelA. However, structural information about how SpoT 35 controls the levels of (p)ppGpp is missing. Here we present the crystal structure of the 36 hydrolase- only SpoT from Acinetobacter baumannii and uncover the mechanism of intra- 37 molecular regulation of "long"- RSH factors. In contrast to ribosome- associated Rel/RelA that 38 adopt an elongated structure, SpoT assumes a compact \(\tau\) - shaped structure in which the 39 regulatory domains wrap around a Core subdomain that controls the conformational state of the 40 enzyme. The Core is key to the specialisation of long- RSHs towards either synthesis or 41 hydrolysis: while the short and structured Core of SpoT stabilises the \(\tau\) - state priming the HD 42 domain for (p)ppGpp hydrolysis, the longer, more dynamic Core of RelA destabilises the \(\tau\) - 43 state precluding (p)ppGpp hydrolysis and priming RelA for synthesis. + +45 Keywords: (p)ppGpp, stringent response, SpoT, RelA, Rel, allostery, intrinsically disordered 46 proteins, energetic frustration, metabolic hubs, conformational switches + +<--- Page Split ---> + +## Introduction + +RelA- SpoT Homolog (RSH) stringent factors regulate virtually all aspects of bacterial physiology by controlling the levels of the signalling nucleotide alarrones guanosine pentaphosphate and tetraphosphate, collectively referred to as (p)ppGpp 1- 6. The ribosome- associated "long" multidomain RSH RelA is a dedicated amino acid starvation sensor with a strong (p)ppGpp synthesis activity (SYNTH) that is induced upon detection of deacylated tRNA in the ribosomal A site 7,8, and no detectable hydrolase activity 9. In the cell the SYNTH activity of RelA is balanced by SpoT RSH, a bifunctional enzyme with a strong, strictly \(\mathrm{Mn}^{2 + }\) dependent hydrolase (HD) activity 10,11 and weak SYNTH activity 12. The RelA- SpoT pair is a product of gene duplication of an ancestral factor - the ribosome- associated bifunctional RSH Rel - and the pair is limited in its taxonomic distribution to Beta- and Gammaproteobacteria 1,13. + +Subfunctionalisation - the partitioning of functions between two paralogues that arose through gene duplication - appears to have happened at least twice in Gammaproteobacteria (Fig. 1a). First, relatively soon after the duplication that gave rise to RelA and SpoT, RelA lost its capacity for alarrome hydrolysis, evolving into a monofunctional, SYNTH- only RSH. Secondly, as evidenced by a lack of sequence conservation in sites that are critical for nucleotide pyrophosphorylation, during the evolution of the Moraxellaceae lineage of Protobacteria, SpoT has likely lost its synthetase function (Fig. 1a- b) 1. This resulted in further specialization into mono- functional (p)ppGpp hydrolase, SpoT[Hs] (The uppercase "H" stands for hydrolase- competent, while the lowercase "s" indicates "synthetase- incompetent"), as opposed to the bifunctional HD- and SYNTH- competent SpoT[HS] found in other Beta- and Gammaproteobacteria. Recent studies of the Moraxellaceae representative A. baumannii - the "A" in the ESKAPE group of human pathogens of particular concern - indicate a lack (p)ppGpp in the \(\Delta relA\) strain, both with and without acute amino acid starvation induced by serine hydroxylamate (SHX) 14,15. These observations are consistent with the hypothesis that RelA is, indeed, the sole source of the alarrome in this bacterium. Furthermore, consistent with the key role of (p)ppGpp- mediated signalling in bacterial virulence and antibiotic tolerance 16,17, the likely ppGpp \(^0 A\) . baumannii \(\Delta relA\) strain displays increased sensitivity to multiple antibiotics 14,15, decreased virulence in a Galleria mellonella wax moth model and deficiency in switching from the virulent opaque colony variant to the avirulent translucent colony variant 15. + +Rel, RelA and SpoT all share the same conserved domain composition, indicative of a common architecture of the underlying intra- molecular allosteric regulation in long RSHs 1. When recruited to starved ribosomes, both Rel and RelA adopt a highly extended elongated + +<--- Page Split ---> + +conformation. In these complexes the regulatory C- terminal domain region (CTD: TGS, HEL, ZFD and RRM domains) is highly structured, while the N- terminal catalytic region (NTD: HD and SYNTH domains) and the interdomain linker regions are highly dynamic and unresolved in some structures \(^{18 - 21}\) . Off the ribosome, our structural understanding of long RSHs relies on the structures of isolated NTDs of several Rel representatives \(^{21 - 24}\) . While the physiological role of SpoT as a key virulence and stress tolerance factor is well established \(^{25,26}\) , structural insights into SpoT are lacking altogether. This limits our ability to interpret the physiological and microbiological studies on the molecular level. Obtaining full- length structures of Rel/RelA/SpoT is essential for understating how the auto- regulation signal transferred from the CTD to the NTD. + +The structural and biochemical data presented here provide the long- missing structural insight into the molecular mechanism of SpoT. We show that \(A\) . baumannii SpoT (SpoT \(_{Ab}\) ) is indeed, a monofunctional (p)ppGpp hydrolase and uncover how its CTD is an allosteric activator of the HD hydrolase function. The structures of the full- length HD- active SpoT \(_{Ab}\) complexed with the ppGpp substrate reveal a compact monomeric conformation in which all the regulatory domains wrap around a Core subdomain that connects the pseudoSYNTH and TGS domains. The Core is one of the intrinsically disordered regions (IDR) present in Rel and RelA when in the active synthetase state. In SpoT \(_{Ab}\) , Core and TGS cooperate to align and activate the hydrolase domain active site while translating allosteric feedback from the other regulatory domains to modulate the HD output. Finally, we propose a unifying conceptual framework that rationalises the relative balance between HD vs SYNTH activities of long RSHs Rel, RelA and SpoT, fine- tuned through the entropic force produced by intrinsically disordered regions that function as conformational gatekeepers of the enzyme. + +<--- Page Split ---> + +## Results + +## A. baumannii SpoTAb is a monofunctional hydrolase long RSH + +A. baumannii SpoTAb is a monofunctional hydrolase long RSHLack of conservation of active site residues critical for SYNTH activity suggest that Moraxallaceae SpoT enzymes have – like RelA – undergone subfunctionalisation to become monofunctional long RSHs (Fig. 1a and b). Like RelA’s pseudo-HD domain, the SYNTH domain region has been retained in Moraxallaceae as a presumably non-catalytic pseudo-SYNTH domain, suggesting it retains some function in stabilisation or allosteric regulation of the HD domain. To probe the hydrolysis function of A. baumannii SpoT (SpoTAb) in live cells, we leveraged SpoT’s hydrolytic activity being crucial for controlling the cellular levels of (p)ppGpp produced by RelA, which makes spoT conditionally essential in the relA+ Escherichia coli 12. We co-transformed a ppGpp (ΔrelA ΔspoT) E. coli strain with i) a pMG25-based plasmid driving the IPTG-inducible expression of spoTAb under the control of PA1/04/03 promoter and ii) a pMR33 derivative for arabinose-inducible expression of relAEC under the control of PBAD. While expression of the (p)ppGpp synthetase RelAEC strongly inhibited the growth of ppGpp0 E. coli, the growth was completely restored upon the ectopic co-expression of SpoTAb (Fig. 1c and Supplemental Data), demonstrating that SpoTAb is HD-active in the surrogate E. coli host. + +Next, we used our dual plasmid co- expression system to probe the (p)ppGpp synthetase activity of SpoT RSHs. ppGpp \(^0\) E. coli is auxotrophic for eleven amino acids, and (p)ppGpp synthetase activity of SpoT \(E_{C}\) is essential for growth of \(\Delta relA\) E. coli on minimal medium 12. Unlike the SYNTH- active SpoT \(E_{C}\) , SpoT \(Ab\) failed to promote the growth of ppGpp \(^0\) E. coli on M9 minimal medium (Fig. 1d), confirming that SpoT \(Ab\) is SYNTH- inactive. Taken together, these results demonstrate that SpoT \(Ab\) is a specialised monofunctional long RSH that lacks the ability to synthesise (p)ppGpp. + +## Full-length SpoTAb has a compact mushroom-like \(\tau\) -shaped structure + +To gain insight into the molecular workings of SpoT, we solved an X- ray structure of full- length catalytically- active SpoT \(Ab\) in a ppGpp- bound state at \(2.9\mathrm{\AA}\) resolution. The structure revealed a multi- domain architecture strikingly different to that observed earlier for ribosome- bound long RSHs Rel and RelA 18- 21 (Fig. 2a- c and Supplementary Table 1). The HD, SYNTH, TGS, HEL, ZFD and RRM domains of SpoT \(Ab\) form a mushroom- like tau \((\tau)\) - shaped quaternary structure (Fig. 2a- c). In this arrangement, pseudo- SYNTH, TGS, HEL, ZFD and RRM domains all lie in a single plane and form a compact disc- like structure that forms the "cap" of the "mushroom" (Fig. 2b). A helix- turn- helix sub- domain (residues 334 to 379) that + +<--- Page Split ---> + +provides the transition between the NTD and CTD regions, lies at the "Core" of the "cap" and seemingly mediates interactions among all domains of the enzyme. Such an arrangement suggests that the Core – which is disordered in Rel/RelA structures – stabilises the disc-like "cap" of SpoT (Fig. 2c). Moreover, the Core provides the HD domain with a physical link to each domain of SpoTAb. Finally, the HD protrudes from the plane of the "cap" in the opposite direction of the C-terminal RRM domain, forming the "stem" of the protein structure (Fig. 2b-c). + +The \(\tau\) - shaped structure of SpoTAb suggests a possible structural mechanism for the autoinhibition of SYNTH activity by the regulatory CTD both in Rel27,28 and RelA29,30. While the SYNTH and TGS domains are sequestered in the "cap", the HD hydrolase stands out unconfined and primed for (p)ppGpp hydrolysis. The TGS domain, which in the case of amino acid starvation sensors Rel and RelA specifically engages the deacylated tRNA CCA- 3' end at the A site19- 21,31, in the case of SpoTAb is partially trapped between the HD, HEL and ZFD domains. While we do detect a mild inhibitory effect of tRNA on SpoTAb hydrolysis activity, the effect is insensitive to tRNA aminoacylation status, i.e. non- specific (Fig. 2d). This is in contrast to the HD activity of bifunctional E. coli SpoT (SpoTEc), which was specifically inhibited by deacylated, but not aminoacylated tRNA32. + +Our structure reveals that the sites from the ZFD and RRM domains that mediate rRNA recognition in Rel/RelA18- 21,31 are held in by the Core subdomain, suggesting that in the \(\tau\) - shaped conformation the hydrolytically active (HDON) SpoTAb is incompatible with ribosome binding. In good agreement with this structural prediction, while the ribosome strongly suppresses the HD activity of Bacillus subtilis Rel (RelBs)28, the addition of E. coli 70S has no effect on the hydrolysis activity of SpoTAb (Fig. 2d). Thus, our biochemical results suggest that SpoTAb is a ribosome- independent enzyme. + +## Shorter intrinsically disordered regions (IDRs) in monofunctional SpoT are associated with specialisation for hydrolysis + +The presence of intrinsically disordered regions (IDR) located at the \(\alpha 6 - \alpha 7\) loop, the Core subdomain and the linker between HEL/ZFD domains in long RSHs RelA and Rel (Supplementary Fig. 1a) has posed an experimental challenge for structural studies18- 21. The molecular function of these flexible regions, unresolved in the structures, is unknown. Comparison between the well- structured SpoTAb in \(\tau\) - state and partially unstructured ribosome- bound RelA/Rel suggests that the unfolding of Core and HEL domains constitutes part of the + +<--- Page Split ---> + +conformational switch that positions TGS, ZFD and RRM domains to stimulate the synthesis activity of Rel/RelA upon recruitment to the ribosome (Supplementary Fig. 1b). + +The length of these disordered or flexible regions is on average shorter in monofunctional SpoT and much longer in the monofunctional RelA. Bifunctional Rels have interdomain IDRs of sizes between both monofunctional enzymes (Supplementary Fig. 1c). The \(\alpha 6 - \alpha 7\) loop of the HD domain of SpoT[Hs] in particular is a third of the size of that of RelA, which, in turn, is twice longer than that of bifunctional Rel (Supplementary Fig. 1c). The same pattern is observed for the other two IDRs: the Core subdomain and the region connecting HEL and ZFD domains. This is consistent with the significantly lower disordered propensity of the Core of SpoT \(_{Ab}\) compared to RelA \(_{Ab}\) (Supplementary Fig. 1d- e). We speculate that these IDRs have evolved to stabilise either \(\tau\) - (shorter IDRs) or elongated (longer IDRs) states of monofunctional SpoT[Hs] or RelA[hS], respectively, to tune the HD vs SYNTH output ratio. + +## SpoT \(_{Ab}\) is a monomer + +It was shown earlier that both Rel and RelA are prone to dimerization via the CTD, which would potentially serve to regulate their enzymatic activity \(^{21,33 - 35}\) . This idea is a subject of debate, with both genetic \(^{30}\) and mass photometry \(^{28}\) experiments suggesting that the dimerization is unlikely to take place at physiologically relevant concentrations. Therefore, we used small- angle X- ray scattering (SAXS) coupled to size exclusion chromatography (SEC) to probe the conformation and oligomeric state of SpoT \(_{Ab}\) in solution (Fig. 2e- f). + +The SAXS data revealed that in solution SpoT \(_{Ab}\) has an oblate shape compatible with the structure determined by X- ray. Both SAXS and SEC consistently support the monomeric nature of SpoT \(_{Ab}\) , even at concentrations as high as \(8\mathrm{mg / mL}\) . Both the molecular weight of \(\approx 90\) kDa by SEC as well the estimates of Mw of \(\approx 85\mathrm{kDa}\) and \(Rg\) of \(34.9\mathrm{\AA}\) by SAXS (Fig. 2e- f) agree with the \(80\mathrm{kDa}\) theoretical molecular weight of monomeric SpoT \(_{Ab}\) . Furthermore, the analysis of the normalised Kratky plot derived from the scattering curve lends further support for a compact monomeric structure of SpoT \(_{Ab}\) in solution (Fig. 2f), and the ab initio envelope calculated from the experimental SAXS data (Fig. 2g) is compatible with the \(\tau\) - shaped structure of SpoT \(_{Ab}\) determined by X- ray. Collectively these results demonstrate that in solution the monomeric SpoT \(_{Ab}\) adopts a conformation that is very similar to the \(\tau\) - shaped conformation observed in the crystal with the HD domain protruding from the disc- shaped enzyme. + +The enzymatically- inactive pseudo- SYNTH of SpoT \(_{Ab}\) is a regulatory domain + +<--- Page Split ---> + +In the monofunctional stringent factor RelA, the enzymatically inactive pseudo- HD domain has evolved into a regulatory domain controlling catalysis via an intra- NTD allosteric regulatory mechanism \(^{36,37}\) . This is also the case with the specialisation of SpoT \(_{Ab}\) as a monofunctional hydrolase where the pseudo- SYNTH domain has evolved into a strictly regulatory/structural domain. Superposition of the SYNTH domain from Rel \(_{Tt}\) onto the pseudo- SYNTH domain of SpoT \(_{Ab}\) reveals extensive reorganisation of the vestigial catalytic domain in SpoT \(_{Ab}\) , consistent with differential conservation patterns in the G- loop and the ATP recognition motif (Supplementary Fig. 2a). These involve the residues that coordinate adenosine and guanosine (R249 to N241, R277 to E267 and Y329 to N304) and the majority of phosphate- coordinating groups. Crucially, the catalytic residues D272 and Q347 are substituted for S263 and T321, respectively. These substitutions essentially impede the deprotonation and activation of the 3'- OH of GD(T)P, and \(\mathrm{Mg^{2 + }}\) binding, precluding the nucleophilic attack on the \(\beta\) - phosphate of ATP. We directly probed GDP binding by SpoT \(_{Ab}^{\mathrm{NTD}}\) and RelA \(_{Ab}^{\mathrm{NTD}}\) by ITC. As expected, while SpoT \(_{Ab}\) does not bind GDP, RelA \(_{Ab}\) binds GDP with an affinity of 62 \(\mu \mathrm{M}\) , which is similar to our earlier estimates for RelA \(_{E_c}^{\mathrm{NTD}}\) and Rel \(_{Ab}^{\mathrm{NTD}}\) \(^{28,36}\) (Supplementary Fig. 2b- c). + +## SpoT \(_{Ab}\) is not allosterically regulated by the alarome pppGpp + +The enzymatic activity of long RSHs is regulated via strong allosteric coupling between the HD and SYNTH domains that results in antagonistic conformational states \(^{22,23,36}\) . While in Rel/RelA (p)ppGpp bind the hinge region connecting the SYNTH and HD/pseudo- HD domains to stimulate the SYNTH activity, this regulation is lost in SpoT \(_{Ec}\) \(^{36}\) . Our structure of SpoT \(_{Ab}\) provides a mechanistic interpretation. In the \(\tau\) - state the highly structured Core subdomain makes numerous contacts with SYNTH providing further scaffolding to the already more stable version of the HD:SYNTH hinge of SpoT \(_{Ab}\) . Additionally, there are several important substitutions in the (p)ppGpp binding site that would be expected to compromise (p)ppGpp binding and alarome- mediated regulation, specifically in Q203 (a residue involved in ribose coordination and strictly conserved as A in RelA \(^{36}\) ) and in T209 (a residue involved in phosphate coordination, typically K or R in RelA \(^{36}\) ). + +To directly validate the lack of pppGpp- mediated regulation in SpoT \(_{Ab}\) , we characterised the interaction between pppGpp and SpoT \(_{Ab}^{\mathrm{NTD}}\) by ITC. As expected, SpoT \(_{Ab}^{\mathrm{NTD}}\) does not bind pppGpp allosterically (Supplementary Fig. 2d- e). Following the experimental approach used earlier for SpoT \(_{Ec}\) \(^{36}\) , we next grafted the allosteric site of A. baumannii RelA ( \(^{236}\) RelA \(^{246}\) ) onto SpoT \(_{Ab}^{\mathrm{NTD}}\) (replacing \(^{201}\) SpoT \(_{Ab}^{211}\) ). Just as in the case of SpoT \(_{Ec}\) , this resulted in a RelA- like affinity to pppGpp of the chimera RSH ( \(K_D = 5.6 \mu \mathrm{M}\) ). Collectively, these results support the + +<--- Page Split ---> + +generality of alarMone- mediated control being lost in SpoT and only present in SYNTH- active Rel/RelA stringent factors that mediate acute stringent response upon amino acid starvation. + +## The dipolar architecture of the HD active site is conserved between Rel and SpoT + +Inspection of the electron density map of the SpoT \(_{Ab}\) - ppGpp complex reveals that the alarMone is bound in high occupancy in each of the four SpoT \(_{Ab}\) molecules present in the asymmetric unit of the crystal (Supplementary Fig. 2f), with the coordination of the guanine base of ppGpp (Fig. 3a- c) resembling that observed in Rel \(_{T^{N T D}}\) - ppGpp \(^{23}\) and Rel \(_{T^{N T D}}\) - ppGpp complexes \(^{24}\) . We probed enzymatically the role of each residue involved in guanine coordination via systematic Ala- substitutions. While substitution of R45 (stacking the guanine) abrogated hydrolysis, removing Van der Waals contacts to L154 decreased the activity approximately two- fold; interactions with K46 were redundant (Fig. 3d). Disruption of the hydrogen bond of the guanine to T150 had only a minor effect. The additional hydrogen bond formed between the carbonyl group of the guanine and the enzyme's backbone likely accounts for the guanine specificity of SpoT over adenosine. + +As observed earlier for Rel \(_{T^{N T D}}\) \(^{23}\) , the hydrolase active site of SpoT \(_{Ab}\) displays a dipolar charge distribution with a highly basic half mediating the stabilization of the \(5'\) - and \(3'\) - polyphosphate groups of the substrate and the other highly acidic half mediating the \(3'\) - pyrophosphate hydrolysis (Fig. 3a- b). Closer inspection of the complex reveals the crucial role of Y51 and the \(^{82}\mathrm{ED}^{83}\) active site motifs as they work together with the \(\mathrm{Mn}^{2 + }\) cofactor to coordinate and stabilise a network of water molecules near the sugar- phosphate moiety during hydrolysis (Fig. 3b- c). Indeed, substitutions of Y51, E82, D83 or N147 render SpoT \(_{Ab}\) HD- inactive in our enzymatic assays (Fig. 3d). At the positively charged side active site the \(5'\) - polyphosphate is loosely coordinated and exposed to the bulk solvent. By contrast K140 and R144 hold the \(3'\) - pyrophosphate in place during hydrolysis and Ala substitutions of these residues decrease the activity of the enzyme between 5- and 10- fold suggesting these are key residues that orient the scissile bond. + +## \(\mathbf{Mn}^{2 + }\) ion organizes the HD active site of SpoT \(_{Ab}\) + +The essential role of the divalent manganese ion \(\mathrm{Mn}^{2 + }\) in (p)ppGpp pyrophosphate hydrolysis is well documented for both Rel \(^{22,28,38,39}\) and SpoT \(_{Ec}\) \(^{40}\) . Our isothermal titration calorimetry (ITC) measurements demonstrate that unliganded, metal- free SpoT \(_{Ab}\) NTD binds \(\mathrm{Mn}^{2 + }\) with a \(K_{\mathrm{D}}\) of \(35.3 \mu \mathrm{M}\) (Fig. 3e). Furthermore, while metal- free full- length SpoT \(_{Ab}\) is completely HD- inactive, the HD activity is readily restored upon addition of \(\mathrm{Mn}^{2 + }\) (Fig. 3f). + +<--- Page Split ---> + +To directly reveal the structural role of \(\mathrm{Mn}^{2 + }\) we determined the X- ray structure of \(\mathrm{SpoT}_{Ab}\mathrm{NTD}\) in the metal- free state (Fig. 3g and Supplementary Table 1). Comparison with the structure of the \(\mathrm{SpoT}_{Ab}\) - ppGpp complex provides a structural explanation for the essentiality of \(\mathrm{Mn}^{2 + }\) for catalysis: in addition to its role in hydrolysis, by connecting \(\alpha 3\) , \(\alpha 4\) and \(\alpha 8\) , \(\mathrm{Mn}^{2 + }\) coordination brings together the two halves of the HD domain and provides structural support to the active site (Fig. 3g- h). While the overall topology of the \(\mathrm{SpoT}_{Ab}\) HD domain is similar to that of \(\mathrm{Mn}^{2 + }\) - liganded \(\mathrm{Rel}_{Tl}\mathrm{NTD}^{23}\) , the removal of the metal ion has a profound effect on the local conformation of the active site of \(\mathrm{SpoT}_{Ab}\mathrm{NTD}\) . The catalytic \(^{78}\mathrm{HD}^{79}\) and \(^{82}\mathrm{ED}^{83}\) motifs are largely misaligned, loops S110- Y117 and A153- K158 that are involved in the \(3'\) - and \(5'\) - phosphate coordination are disordered, and the guanine- coordinating loop T44- Y51 assumes a conformation incompatible with the base coordination (Fig. 3h). Importantly, all of these changes do not result in the opening of the enzyme's NTD that was observed in \(\mathrm{Rel}_{Tl}\) upon removal of \(\mathrm{Mn}^{2 + }\) \(^{23}\) . These observations suggest that with the evolution as a monofunctional enzyme, \(\mathrm{SpoT}_{Ab}\) shed the allosteric conformational control between the HD and pseudo- SYNTH domains. + +## The CTD allosterically stimulates the hydrolysis activity of the SpoT NTD + +Until now, our understanding of the function of the CTD region of long RSHs was based exclusively on studies of Rel and ReIA. This has established a role of the CTD in the association of the stringent factors with starved ribosomes resulting in the activation of the SYNTH activity and the auto- inhibition of the factor's SYNTH activity off the ribosome \(^{18 - 21,27,28}\) . Weak hydrolase activity of the CTD- truncated Rel has also indicated a possible HD- stimulatory role of the CTD through an intra- molecular regulation of the hydrolase function \(^{28,41,42}\) , suggesting that a similar mechanism could also be at play in the case of SpoT. + +To probe this hypothesis, we characterised the HD activity – both in vitro and in vivo – of a set of progressively C- terminally truncated variants of \(\mathrm{SpoT}_{Ab}\) lacking i) RRM ( \(\mathrm{SpoT}_{Ab}^{1 - }\) \(^{614}\) , amino acids 1–614), ii) RRM and ZFD ( \(\mathrm{SpoT}_{Ab}^{1 - 560}\) ), iii) RRM, ZFD and HEL ( \(\mathrm{SpoT}_{Ab}^{1 - }\) \(^{454}\) ), iv) CTD altogether, i.e. RRM, ZFD, HEL and TGS ( \(\mathrm{SpoT}_{Ab}^{1 - 385}\) ), v) CTD as well as the Core domain ( \(\mathrm{SpoT}_{Ab}^{1 - 339}\) ), and finally, a variant that consisted of just the HD domain ( \(\mathrm{SpoT}_{Ab}^{1 - }\) \(^{195}\) ). These truncated variants were all generated at the endogenous spoT locus in a \(\Delta \mathrm{relA}\) Ptac::relA A. baumannii strain, and the ability to grow on complex media supplemented with IPTG was evaluated as a proxy of the (p)ppGpp hydrolase activity of \(\mathrm{SpoT}_{Ab}\) in vivo. + +While \(\mathrm{SpoT}_{Ab}\) variants lacking the RRM or the RRM and ZFD domains retained wild- type ability to sustain the bacterial growth grow – i.e. could efficiently degrade (p)ppGpp + +<--- Page Split ---> + +synthesised by RelA – further C-terminal truncations compromised the in vivo HD functionality, as evidenced from pronounced growth defects (Fig. 4a). Biochemical assays are in agreement with the in vivo data (Fig. 4b). Truncation of the RRM and ZFD decreases the HD activity 5-fold. Further deletion of the TGS- HEL domains leads to a dramatic 42- fold decrease in activity. Truncations beyond the TGS compromised the activity by 70- fold or more and isolated HD domain was nearly inactive. Collectively, our results suggest that the CTD region functions as an allosteric activator of the hydrolase function of SpoTAb. Next, we set out to dissect the molecular mechanism of the CTD- mediated NTD control and assign the molecular functions to individual CTD domains. + +## The Core domain is a linchpin that controls the \(\tau\) -state + +Both the overall structural arrangement of SpoTAb and our sequential domain truncation experiments (Fig. 4a- b) suggest that the Core- mediated allosteric crosstalk between the HD and rest of the domains of the enzyme is essential for enzyme's functionality. To specifically assess the role of the individual interdomain interactions we introduced single point substitutions at each of the interfaces of the Core with regulatory CTD domains (Fig. 4c) and measured the hydrolase activity of the SpoTAb variants (Fig. 4d). An intact HD:Core:TGS interface – the structure involved in scaffolding the HD active site – is crucial for HD activity, as the Y375G substitution at the HD:Core:TGS resulted in a 5- fold decrease in activity compared to the wild type. While substitutions at the ZFD (L373G / D374G) and RRM (A351K) domain interfaces also resulted in a pronounced defect (19 and 3- fold decrease, respectively), perturbations at the Core:pseudo- SYNTH domain interface (A348R) had only a minor effect on hydrolysis. Finally, decoupling the contacts of HD from the \(\tau\) - cap via the L356D substitution, located at the interface between Core domain and \(\alpha 6- \alpha 7\) motif of HD 23, has a dramatic 35- fold decrease in HD activity, suggestive of an allosteric signal transduction path between the cap and stem regions of the enzyme. When we monitored the thermodynamic stability of these Core variants of SpoTAb we observed they all have lower stability and loss of structure compared to the wild type (Supplementary Fig. 3a- e). This suggests that an increase in the configurational entropy of the Core has a global effect in the dynamics and compactness of the enzyme. The existence of an allosteric relay mediating a CTD- dependent activation of HD via the Core is further supported by the consistent decrease in hydrolysis associated with the aforementioned C- terminal truncations that affect the feedback of the Core to the HD (Fig. 4a- b), as well as by the observation that the deletion of domains HEL and TGS results in a 50- + +<--- Page Split ---> + +fold decrease in activity despite the presence of the other regulatory domains (pseudo- SYNTH, ZFD and RRM) (Fig. 4d). + +We next used SEC- SAXS to directly probe the role of each contact at the interface of the Core with the different domains of SpoT \(_{Ab}\) on stabilisation of the \(\tau\) - sate. The L356D substitution (SpoT \(_{Ab}\) L356D, Fig. 4c and Supplementary Table 2) results in the segregation of the population into two conformational states with major differences in \(R_{G}\) (radius of gyration) and particle dimensions \((D_{MAX})\) . In SpoT \(_{Ab}\) L356D one state is the compact \(\tau\) - shape observed in the crystal structure (Fig. 4e), while the other state is more relaxed \((R_{G} = 41\mathrm{\AA}, D_{MAX} = 130\mathrm{\AA})\) with dimensions reminiscent of that of the less compacted Rel and RelA – but not quite as elongated as in the ribosome- bound state (Fig. 4f). In this relaxed state the Core and HEL domains appear to have transitioned to a more disordered state that is consistent with the conformational states of these regions in the fully elongated state observed in Rel/RelA (Fig. 4g- h); the other domains retain their structural integrity. Prompted by this analogy, we next probed A. baumannii monofunctional synthetase RelA and B. subtilis bifunctional RSH Rel \(_{Bs}\) with SAXS. The dimensions of RelA \(_{Ab}\) \((R_{G} = 42\mathrm{\AA}, D_{MAX} = 130\mathrm{\AA}, \mathrm{Mw} = 88\mathrm{kDa})\) are consistent with that of the relaxed state of SpoT \(_{Ab}\) L356D, whereas Rel \(_{Bs}\) is populated by both the relaxed and \(\tau\) - states (Fig. 4i- k and Supplementary Table 2). + +Collectively, our results suggest that the Core domain functions as an allosteric relay that conveys signals from the CTD to the HD. At the structural level the composition of the Core is the key to the conformational state of the enzyme as defined by the three major conformations observed in SpoT, Rel and RelA (Fig. 4l). The correlation of the decrease in HD activity with entropy- increasing substitutions such as A351K, L356D, L371G / D374G, and Y375G supports the notion that the increase in structural disorder or flexibility of the Core domain (or the other IDRs) likely drives the conformational equilibrium of the enzyme away from the \(\tau\) - state. This decrease in activity observed by the disruption of the \(\tau\) - shape is also consistent with the lack of hydrolysis in Rel homologues that underwent an order- to- disordered transition while accommodating in ribosomal A site 28. In this context the aforementioned relaxed state is likely the idle resting state of long RSH enzymes in which the CTD precludes the function of SYNTH while not activating HD. + +## The TGS domain acts as a scaffold for the HD active site + +The \(\alpha 6 - \alpha 7\) element plays a crucial role in the allosteric regulation of the opposing activities of bifunctional Rel \(_{T_{I}}\) 23. In Rel \(_{T_{I}}\) , \(\alpha 6 - \alpha 7\) of projects away from the HD catalytic centre to accommodate the \(3'\) and \(5'\) polyphosphate groups as well as allowing the catalytic \(^{82}\mathrm{ED}^{83}\) motif + +<--- Page Split ---> + +to get in position, close to the 3' phosphates, priming the enzyme for hydrolysis. In SpoT \(_{Ab}\) the outward- pointing conformation of \(\alpha 6 - \alpha 7\) is further stabilised by the N- terminal region of the TGS and the Core domains which function as a clamp to keep \(\alpha 6 - \alpha 7\) in the HD- compatible position, with the HEL domain providing an additional support via the Core (Fig. 5a). The dramatic drop in the activity of the SpoT \(_{Ab}\) variant lacking the TGS and HEL domains (Fig. 4d) substantiates the functional importance of this stabilising effect. + +At the HD:TGS interface the \(\beta\) - hairpin of the TGS – the very element which is involved in tRNA recognition in Rel \(^{21,28,42}\) and RelA \(^{19,20,31}\) – is buried and stacking directly the \(\alpha 6 - \alpha 7\) element via a small hydrophobic interface formed by W382, Y384, L390 and the R124-E392 salt bridge (Fig. 5a). Disrupting this interface with the E379K / W382K substitutions (SpoT \(_{Ab}\) E379K/W382K) led to a 17- fold decrease in the hydrolase activity of the enzyme (Fig. 4d) suggesting that the HD:TGS interface constitutes as an important allosteric signal transduction pathway. This scaffolding role is complemented by the Core that wraps tightly around \(\alpha 7\) thus preventing the recoil of \(\alpha 6 - \alpha 7\) away of the HD active site, which, as we observed earlier in Rel \(_{Th}\) \(^{23}\) , induces the opening of the NTD. Indeed, substitutions at the Core: \(\alpha 6 - \alpha 7\) interface such as the aforementioned Y375G also affected hydrolysis (Fig. 4c- d). Interestingly, despite the strongly attenuated HD activity of SpoT \(_{Ab}\) E379K / W382K, SAXS showed SpoT \(_{Ab}\) E379K / W382K remains in the \(\tau\) - state \((R_{G} = 35\mathrm{\AA}, D_{MAX} = 104\mathrm{\AA})\) , suggesting an allosteric communication via the HD:Core:TGS axis (Fig. 5b and Supplementary Table 2). + +Given that SpoT \(_{Ab}\) is SYNTH- inactive and is not specifically regulated by tRNA or ribosomes (Fig. 2d), it is not surprising that TGS residues involved in tRNA recognition – such as the crucial His residue involved in the recognition of the 3' CCA end by Rel \(^{21,28,42}\) and RelA \(^{19,31}\) (S407 in SpoT \(_{Ab}\) ) – are lost in the monofunctional SpoT \(_{Ab}\) (but are present in bifunctional SpoT \(_{Ec}\) \(^{1}\) ). Moreover, the \(\tau\) - state is sterically incompatible with the potential recognition of tRNA by TGS due to sequestration the \(\beta\) - hairpin and \(\alpha\) - helical elements. All these observations suggest that in SpoT \(_{Ab}\) the TGS has been repurposed as a scaffolding domain crucial to sustain hydrolysis, with both TGS and Core cooperating to lock the \(\alpha 6 - \alpha 7\) in place, stabilising the HD active site. This contrasts with its crucial function of recognition of uncharged tRNA in Rel/RelA \(^{19 - 21,28,31}\) . + +## The ZFD and RRM domains finetune the hydrolytic activity of SpoT \(_{Ab}\) + +With ZFD and RRM positioned close to the disc- shaped cap and connecting with the pseudo- SYNTH domain, the resulting inter- domain interfaces are likely to play a role in the stability the \(\tau\) - state as well as to allosterically control of HD via the HD:pseudo- SYNTH relay. In + +<--- Page Split ---> + +agreement with this hypothesis, disruptive substitutions at the Core:HD (L356D), Core:pseudo- SYNTH:RRM (A351K) and Core:ZFD (L373G / D374G) that decreased the stability of the \(\tau\) - state (Supplementary Fig. 3b- e) also decreased the HD activity of the enzyme by 35- , 3- and 22- fold, respectively (Fig. 4d). Therefore, we reasoned that substitutions stabilising the Core:pseudo- SYNTH:RRM and Core:ZFD interfaces would, conversely, trigger an allosteric activation of hydrolysis. + +To probe this hypothesis, we introduced substitutions that would increase the contacts of RRM with pseudo- SYNTH via hydrogen bonds, I637D / R641D, and the Core with the ZDF, D374R (Fig. 5c). Denaturation experiments showed \(\mathrm{SpoT}_{Ab}\mathrm{D}^{374\mathrm{R}}\) and \(\mathrm{SpoT}_{Ab}^{1637\mathrm{D}} / \mathrm{R}641\mathrm{D}\) have higher stability and compactness than the WT (Supplementary Fig 4a- c) and SAXS measurements on \(\mathrm{SpoT}_{Ab}^{1637\mathrm{D}} / \mathrm{R}641\mathrm{D}\) confirmed this variant retained the \(\tau\) - state (Fig. 5d and Supplementary Table 2). As expected, the HD turnover of both enzyme variants increased (by 2.1- and 1.6- fold, respectively, Fig. 4d), and both behave like wild- type \(\mathrm{SpoT}_{Ab}\) in vivo (Fig. 5e). + +Collectively, our results establish that HD activity is coupled to the stability of the \(\tau\) - state, with the Core domain working as an allosteric transducer that allows the catalytic HD to communicate with all the regulatory domains. Substitutions or interactions that stabilise the \(\tau\) - state increase hydrolysis, whereas \(\tau\) - state- destabilising substitutions lower the HD activity. + +## An intact \(\tau\) -shaped \(\mathrm{SpoT}_{Ab}\) is required for virulence of \(A\) . baumannii + +Functional (p)ppGpp- mediated signalling plays a crucial role in antibiotic tolerance and virulence of \(A\) . baumannii \(^{15,43}\) . We used the wax moth \(G\) . mellonella larvae infection model to assess the functionality of mutant \(spo_{Ab}\) variants in supporting virulence of \(A\) . baumannii AB5075 (Fig. 5f). Only the strain with wild type- like virulence was the one expressing \(\mathrm{SpoT}_{Ab}\) D374R variant with a HD activity slightly higher than that of the WT \(\mathrm{SpoT}\) . The \(spo_{Ab}^{D374\mathrm{R}}\) strain has rapidly killed \(100\%\) of the larvae within the first two days whereas \(60\%\) of the larvae survived 6 days of infection with the (p)ppGpp \(^{0}\) \(\Delta relA\) strain. Infection with \(A\) . baumannii expressing the \(\Delta\) RRM- truncated enzyme \(\mathrm{SpoT}_{Ab}^{1 - 614}\) resulted in \(25\%\) survival rate of larvae after 6 days. Notably, the RRM- truncated \(\mathrm{SpoT}_{Ab}^{1 - 614}\) had 6- fold lower hydrolase activity as compared to wild type (Fig. 4d), and the strain displays no growth defects when grown on LB plates (Fig. 4a). The defect in virulence becomes more prominent with truncations beyond the TGS domain: \(\mathrm{SpoT}_{Ab}^{1 - 454}\) and \(\mathrm{SpoT}_{Ab}^{1 - 339}\) . The strong decrease in HD activity associated with the \(A\) . baumannii strains expressing these \(\mathrm{SpoT}\) variants results in \(100\%\) survival of the infected larvae (Fig. 5f). Collectively our results suggest that while a basal level of the HD hydrolase + +<--- Page Split ---> + +activity is sufficient to sustain bacterial growth in non- stressed conditions (e.g. on a plate and in liquid culture), the pathogen requires fully functional CTD- and Core- mediated control of SpoTAb to tune the HD activity and efficiently establish a successful infection. + +## Discussion + +This study reveals the unexpected \(\tau\) - shaped architecture of full- length monofunctional SpoTAb, which enables auto- stimulation of the hydrolase activity of the enzyme by its CTD. With the loss of the synthetase function, the pseudo- SYNTH domain of SpoTAb becomes a regulatory and structure stabilising domain. Together with TGS, HEL, ZFD and RRM, pseudo- SYNTH defines the interaction network that transmits the allosteric signal from the CTD to the HD active site via the Core of the enzyme, to regulate (p)ppGpp hydrolysis. The Core element, together with the TGS and \(\mathrm{Mn}^{2 + }\) , aligns the active site residues of the HD in the correct position for catalysis. Compromising the functionality of either of these elements through substitutions of key residues results in major defects in hydrolysis activity. By contrast, pseudo- SYNTH, ZFD and RRM all subtly tune the HD activity of SpoTAb up or down by modulating its interactions with the Core. Interestingly, the ribosome- associated Rel/RelA (p)ppGpp synthetases, lacking the Core are non- functional in vivo and SYNTH- inactive, with the minimal enzyme version with SYNTH activity consisting of HD/pseudo- HD, SYNTH and Core domains22,28,36,41. Therefore, the presence of the Core and its crosstalk with the HD/pseudo- HD domain likely constitutes universal structural requirement for the efficient stabilization of the active states of long RSH enzymes. + +We propose a unifying scheme that rationalises the evolution of the enzymatic output of long RSHs through fine- tuning of the conformational equilibrium of the \(\tau\) , relaxed and ribosome- bound states of these enzyme (Fig. 6). The very presence of catalytically- competent synthetase and hydrolase domains in bifunctional Rel[HS] and SpoT[HS] requires both the \(\tau\) and relaxed states as part of the conformational spectrum of these enzymes (Fig. 6a- b). While the \(\tau\) - state primes Rel/SpoT for efficient (p)ppGpp hydrolysis, the more elongated relaxed state sets the enzyme for low- efficiency (p)ppGpp synthesis. To fully activate its SYNTH activity, the enzyme needs to be further stimulated by starved ribosomes to attain the highly elongated ribosome- bound state; this transition is possible for amino acid starvation sensor Rel[HS], but not for SpoT, which is not under allosteric control by starved ribosomes and ppGpp36 (Fig. 6a). In the further subfunctionalised enzymes (dedicated hydrolase Moraxellaceae SpoT[HS] and dedicated synthetase RelA[hS]) the intrinsic structural equilibrium is limited to a subset of the conformations accessible to ancestral bifunctional Rel[HS] (Fig. 6c- d). Compared to + +<--- Page Split ---> + +SpoT[HS], in SpoT[Hs] the equilibrium is further shifted towards the HD- active \(\tau\) - state required for hydrolysis (Fig. 6c). In contrast, in RelA[hS] the \(\tau\) - state becomes inaccessible, and the enzyme is primed for ribosomal recruitment upon which it is stabilised in the highly elongated ribosome- bound SYNTH- active state (Fig. 6d). + +Expansion/contraction of the disordered regions is the likely molecular driver of the fine- tuning of the enzymatic output in long RSHs through the restriction of the conformational space (Supplementary Fig. 1c- e). Longer IDRs favour the relaxed state in RelA[hS] and increase the frustration of the enzyme, whereas the shorter IDRs favour the compact HD- active \(\tau\) - state in SpoT[Hs]. This genetic finetuning of a catalytic function, based on the optimization of the length and the forces generated by intrinsically disordered regions, is reminiscent of the evolution of human glucocorticoid receptor isoforms \(^{44}\) or the UDP- \(\alpha\) - d- glucose- 6- dehydrogenase \(^{45}\) . Such mechanisms seem to have evolved as a solution for conformationally heterogenous proteins with partially active resting states, that are under strong energetic and functional frustration. + +The unifying scheme presented here provides a framework that can be used to rationalise the "hub" nature of SpoT and how binding partners such as the Acyl Carrier Protein (ACP) and the Regulator of RpoD - \(\sigma^{70}\) - (Rsd) could modulate its output \(^{46,47}\) or in the case of Rel/RelA how the ribosome prevents hydrolysis by exploiting this extensive allosteric network. Other protein partners of Rel such \(\mathrm{EIIA}^{\mathrm{NTR}}\) and \(\mathrm{Darb}^{41,48}\) could also modulate the intramolecular allosteric communication of the regulatory domains with HD by favouring of the \(\tau\) - or relaxed states, thus conditioning the catalytic output of the enzyme. + +## Acknowledgments + +This work was supported by the Fonds National de Recherche Scientifique (FRFS- WELBIO CR- 2017S- 03, FNRS CDR J.0068.19, FNRS- EQP UN.025.19 and FNRS- PDR T.0066.18 to AGP); ERC (CoG DiStRes, \(\mathrm{n}^{\circ}\) 864311 to AGP) and Joint Programming Initiative on Antimicrobial Resistance, (JPIAMR) JPI- EC- AMR- R.8004.18 to AGP; the Program Actions de Recherche Concerté 2016- 2021, Fonds d'Encouragement à la Recherche of ULB (AGP); Fonds Jean Brachet and the Fondation Van Buuren (AGP); Chargé de Recherches fellowship from the FNRS \(\mathrm{n}^{\circ}\) CR/DM- 392 (HeT); the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska- Curie grant agreement \(\mathrm{N}^{\circ}\) 801505 (IF@ULB postdoctoral grant to AA) and (FRFS- WELBIO- CR- 2019S- 05 to RH). AP is an F.R.S. – FNRS Postdoctoral Researcher and RH is an F.R.S. – FNRS Research Associate. We are also grateful the Protein Expertise Platform at Umeå University for constructing plasmids. + +<--- Page Split ---> + +This work was supported by the European Regional Development Fund through the Centre of Excellence for Molecular Cell Technology (VH); project grant from the Knut and Alice Wallenberg Foundation (2020- 0037 to GCA); Ragnar Söderberg foundation (VH); Swedish Research council (2019- 01085 to GCA, 2017- 03783 and 2021- 01146 to VH, and 2018- 00956 to VH under the framework of Joint Programming Initiative on Antimicrobial Resistance, JPIAMR); the MIMS Excellence by Choice Postdoctoral Fellowship Programme grant 2018 (MR). The authors acknowledge the use of beamtimes PROXIMA 1 and 2A and SWING at the Soleil synchrotron (Gif- sur- Yvette, France). + +## Author contributions + +VH and AGP drafted the manuscript with contributions from all authors. AGP and VH coordinated the study. VH, AGP, RH, HeT and MR designed experiments and analysed the data. MR, HiT, SZ, JP, AT, HeT, PK, AT, AP, HA, AA performed experiments. RD, GL and GCA performed bioinformatic analyses. + +## Declaration of interests + +The authors declare no competing interests + +<--- Page Split ---> + +529 1. Atkinson, G.C., Tenson, T. & Hauryliuk, V. The RelA/SpoT homolog (RSH) superfamily: distribution and functional evolution of ppGpp synthetases and hydrolases across the tree of life. PLoS One 6, e23479 (2011). 532 2. Hauryliuk, V., Atkinson, G.C., Murakami, K.S., Tenson, T. & Gerdes, K. Recent functional insights into the role of (p)ppGpp in bacterial physiology. Nat Rev Microbiol 13, 298- 309 (2015). 535 3. Irving, S.E. & Corrigan, R.M. Triggering the stringent response: signals responsible for activating (p)ppGpp synthesis in bacteria. Microbiology 164, 268- 276 (2018). 536 4. Ronneau, S. & Hallez, R. 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Hogg, T., Mechold, U., Malke, H., Cashel, M. & Hilgenfeld, R. Conformational antagonism between opposing active sites in a bifunctional RelA/SpoT homolog + +<--- Page Split ---> + +578 modulates (p)ppGpp metabolism during the stringent response [corrected]. Cell 117, 579 57- 68 (2004). 580 23. Tamman, H. et al. A nucleotide- switch mechanism mediates opposing catalytic 581 activities of Rel enzymes. Nat Chem Biol 16, 834- 840 (2020). 582 24. Mojr, V. et al. Nonhydrolysable Analogues of (p)ppGpp and (p)ppApp Alamone 583 Nucleotides as Novel Molecular Tools. ACS Chem Biol (2021). 584 25. Fitzsimmons, L.F. et al. SpoT Induces Intracellular Salmonella Virulence Programs in 585 the Phagosome. mBio 11(2020). 586 26. Vogt, S.L. et al. The stringent response is essential for Pseudomonas aeruginosa 587 virulence in the rat lung agar bead and Drosophila melanogaster feeding models of 588 infection. Infect Immun 79, 4094- 104 (2011). 589 27. Mechold, U., Murphy, H., Brown, L. & Cashel, M. Intramolecular regulation of the 590 opposing (p)ppGpp catalytic activities of Rel(Seq), the Rel/Spo enzyme from 591 Streptococcus equisimilis. J Bacteriol 184, 2878- 88 (2002). 592 28. Takada, H. et al. Ribosome association primes the stringent factor Rel for tRNA- 593 dependent locking in the A- site and activation of (p)ppGpp synthesis. Nucleic Acids Res 594 49, 444- 457 (2021). 595 29. Svitil, A.L., Cashel, M. & Zyskind, J.W. Guanosine tetraphosphate inhibits protein 596 synthesis in vivo. A possible protective mechanism for starvation stress in Escherichia 597 coli. J Biol Chem 268, 2307- 11 (1993). 598 30. Turnbull, K.J., Dzhygry, I., Lindemose, S., Hauryliuk, V. & Roghanian, M. 599 Intramolecular Interactions Dominate the Autoregulation of Escherichia coli Stringent 600 Factor RelA. Front Microbiol 10, 1966 (2019). 601 31. Winther, K.S., Roghanian, M. & Gerdes, K. Activation of the Stringent Response by 602 Loading of RelA-tRNA Complexes at the Ribosomal A- Site. 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Commun Biol 4, 434 (2021). 617 38. Avarbock, D., Avarbock, A. & Rubin, H. Differential regulation of opposing RelMtb 618 activities by the aminoacylation state of a tRNA.ribosome.mRNA.RelMtb complex. 619 Biochemistry 39, 11640- 8 (2000). 620 39. Van Nerom, K., Tamman, H., Takada, H., Hauryliuk, V. & Garcia- Pino, A. The Rel 621 stringent factor from Thermus thermophilus: crystallization and X- ray analysis. Acta 622 Crystallogr F Struct Biol Commun 75, 561- 569 (2019). 623 40. Heinemeyer, E.A., Geis, M. & Richter, D. Degradation of guanosine 3'-diphosphate 5'- 624 diphosphate in vitro by the spoT gene product of Escherichia coli. Eur J Biochem 89, 625 125- 31 (1978). 626 41. Ronneau, S. et al. Regulation of (p)ppGpp hydrolysis by a conserved archetypal 627 regulatory domain. Nucleic Acids Res 47, 843- 854 (2019). + +<--- Page Split ---> + +628 42. Takada, H. et al. 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Kruger, L. et al. A meet-up of two second messengers: the c-di-AMP receptor DarB controls (p)ppGpp synthesis in Bacillus subtilis. Nat Commun 12, 1210 (2021). + +<--- Page Split ---> + +## Main Figures and legends + +Fig. 1. A. baumannii SpoT is a monofunctional alarTone hydrolase. (a) Evolution of long RSHs in Proteobacteria. Duplication of the ancestral bifunctional RSH Rel in Beta- and Gammaproteobacterial lineage gave rise to RelA and SpoT, leading to subfunctionalisation of RelA as monofunctional SYNTH- only alarTone synthetase and SpoT as a predominantly- HD RSH. In the Moraxellaceae family of Gammaproteobacteria, SpoT has undergone further subfunctionalisation, evolving into a monofunctional HD- only alarTone hydrolase. (b) Alignment of SYNTH- critical regions in long RSHs highlights the sequence divergence in Moraxellaceae SpoTs. (c) Co- expression of SpoT \(_{Ab}\) counteracts the growth defect in ppGpp \(^{0}\) \((\Delta relA \Delta spoT)\) E. coli caused by RelA expression. This demonstrates that SpoT \(_{Ab}\) is HD- active in the E. coli host. (d) While the SYNTH activity of ectopically expressed SpoT \(_{Ec}\) is essential and sufficient for promoting the growth of ppGpp \(^{0}\) E. coli on M9 minimal medium, SpoT \(_{Ab}\) fails to promote the growth of \(\Delta relA \Delta spoT\) E. coli on M9. This demonstrates that, unlike SpoT \(_{Ec}\) , which is SYNTH- active, SpoT \(_{Ab}\) is SYNTH- inactive. + +Fig. 2. Full- length monomeric A. baumannii SpoT adopts a compact "mushroom"- shaped HD- active \(\tau\) - state. (a) Structure of "mushroom"- shaped SpoT \(_{Ab}\) - ppGpp complex in the \(\tau\) - state. The domain organization, from N to C terminus: NTD domains hydrolase (HD), pseudo- synthetase (pseudo- SYNTH) and Core, and CTD domains, TGS, Helical (HEL), Zn- finger (ZFD) and RNA recognition motif (RRM). The ppGpp alarTone is in red. (b) Cartoon representation the SpoT \(_{Ab}\) . The "stem" of the mushroom is formed by the enzymatic HD domain and the "cap" by the regulatory domains: NTD pseudo- SYNTH domain and the CTD domains. (c) Ribbon representation of the SpoT \(_{Ab}\) - ppGpp complex. The \(\alpha 6 / \alpha 7\) motif is held in the hydrolysis- compatible position by the folded Core domain and the TGS \(\beta\) - hairpin, with the Core domain communicating allosteric signals to HD from the regulatory domains. (d) The HD activity of SpoT \(_{Ab}\) is insensitive to the addition of E. coli 70S ribosomes, and non- specifically weakly inhibited by both aminoacylated and deacylated E. coli tRNA \(^{\mathrm{Val}}\) . (e) Analytical size exclusion chromatography (SEC) of SpoT \(_{Ab}\) supports its monomeric nature in solution. (f) Experimental X- ray scattering (SAXS) analysis of SpoT \(_{Ab}\) at 8 mg/mL further confirms the monomeric nature of SpoT \(_{Ab}\) . The analysis of the normalised Kratky plot (insert) of the SAXS curve reveals folded globular shape of SpoT \(_{Ab}\) . (g) Ab initio envelope of SpoT \(_{Ab}\) reconstructed from the experimental SAXS data superimposed on the crystal structure. Comparison of both models shows that in solution the enzyme adopts the same conformation as observed in the crystal. + +<--- Page Split ---> + +Fig. 3. A. baumannii SpoT is a \(\mathbf{Mn}^{2 + }\) - dependent (p)ppGpp hydrolase. (a) Surface representation of SpoT \(_{Ab}\) in the \(\tau\) - state. The active site cavity in the HD domain is boxed in dashed lines. (b) Zoom into the HD active site of the SpoT \(_{Ab}\) - ppGpp complex. The acidic half of the interface (residues K140, E82, D83, Y51 and R45) and the \(\mathrm{Mn}^{2 + }\) ion activate the water molecule for nucleophilic attack of the pyrophosphate bond pf ppGpp, while the basic half of the interface (K46, K158 and R161) stabilises the \(3^{\prime}\) and \(5^{\prime}\) phosphates of the alarMone substrate. (c) Ribbon representation of the active site of SpoT \(_{Ab}\) revealing the residues involved in coordination of ppGpp. (d) Effects of Ala-substitutions in the ppGpp binding site on the HD activity of SpoT \(_{Ab}\) . The residues for substitution were selected as per (c). (e) ITC titration of \(\mathrm{Mn}^{2 + }\) into unliganded apo- SpoT \(_{Ab}\) . (f) Hydrolase activity of unliganded apo- SpoT \(_{Ab}\) as a function of increasing concentrations of \(\mathrm{Mn}^{2 + }\) . (g) Structure of the \(\mathrm{Mn}^{2 + }\) - free N-terminal region of SpoT \(_{Ab}\) , \(\mathrm{SpoT}_{Ab}^{\mathrm{NTD}}\) . The HD domain is in purple and the pseudo- SYNTH is in yellow. The disordered active site is labeled. (h) Superposition of the HD domain of SpoT \(_{Ab}\) complexed with ppGpp (in light blue) onto \(\mathrm{Mn}^{2 + }\) - free SpoT \(_{Ab}\) (in purple). The key conformation differences in catalytically- crucial active site residues and the structural elements \(\alpha 3\) , \(\alpha 4\) and \(\alpha 8\) are highlighted as dashed arrows and shown in bold, respectively. + +Fig. 4. The CTD controls the hydrolysis activity of SpoT by controlling the equilibrium between HD- active \(\tau\) - state and HD- inactive relaxed conformations. (a) The HD functionality test of truncated versions of SpoT \(_{Ab}\) . SpoT \(_{Ab}\) variants were co- expressed expressed with RelA \(_{Ab}\) in \(\Delta relA\Delta spoT\) Ptac::relA A. baumannii (AB5075). The ability of SpoT \(_{Ab}\) to promote the growth is reflective of its HD competence. (b) Hydrolase activity of SpoT \(_{Ab}\) and the C- terminally truncated SpoT \(_{Ab}\) variants. Tumovers corresponding to each protein variant are coloured as per the domain colour code on (a). (c) Cartoon representation of the allosteric network defined by the Core domain connecting the domains of the enzyme in the \(\tau\) - state. The key interface residues are shown as sticks and labeled. (d) Hydrolase activity of crucial Core residues involved in interactions with other domains of SpoT \(_{Ab}\) (A384R contacting SYNTH, A351K contacting RRM, L356D contacting HD, L373G/D374G contacting ZFD, Y375G contacting the TGS). The TGS:HD interface is also probed with the E379K/W382K point mutant and \(\Delta\) TGS- HEL versions. The \(\tau\) - state stabilising substitutions D374R and I637D/R641D increase the HD activity. (e, f) SAXS curves of L356D in the \(\tau\) - state (e) or relaxed state (f). (g) Pseudo- atomic model of the relaxed state of SpoT \(_{Ab}\) calculated with Dadimodo (Evrard et al., 2011) using the experimental SAXS data from (f). (h) Comparison of + +<--- Page Split ---> + +the experimental SAXS data from the relaxed state of L356D (in grey) with the theoretical scattering curve of the relaxed state (solid line) obtained from the Dadimodo model. (i) SAXS curve of RelAAb is consistent with the dimensions of the relaxed state. (j, k) SAXS curves of RelBs in the τ-state (j) or relaxed state (k). (l) Cartoon representation of experimentally observed conformational states as well as particle dimensions of long RSH enzymes. + +Fig. 5. The Core domain of SpoT transduces the allosteric signal from the regulatory CTD and pseudo- SNTH to the enzymatic HD domain. (a, b) Cartoon representation of the interactions stabilising the α6- α7 motif of the HD active site (A). While the Core wraps around α7, the TGS β- hairpin forms a small hydrophobic patch that stabilises α6. These interactions preclude the movement of α6- α7 and maintain SpoTAb in a constitutive hydrolase- primed state. Key interface residues are shown as sticks and labelled. (b) The experimental SAXS curve of SpoTAbE379K/W382K is consistent with the dimensions of the τ- state. Cartoon representation of the HD:Core:RRM signal transduction axis. (c) The architecture of the τ- state suggests that the RRM is locked in place via the Core and supporting interaction provided by pseudo- SYNTH, suggesting that additional tethering of RRM to pseudo- SYNTH could further stabilise the τ- conformation. (d) SAXS curve of the SpoTAb1637D/R641D variant in which substitutions I637D/R641D and I637A/R641A promote H- bonding and stabilise the α- helical structure, respectively, is consistent with the dimensions of the τ- state. (e) In vivo HD functionality tests of SpoTAb variants D374G and I637D/R641D expressed from the inducible Ptac promoter in a ΔrelAΔspoT background of A. baumannii (AB5075) expressing relA from a replicative plasmid (pPrelA::relA). The stabilising substitutions D374R and I637D/R641D phenocopy the WT. (f) Virulence assays in the G. mellonella infection model demonstrate the essentiality of intact allosteric regulation of SpoTAb for virulence. G. mellonella larvae were injected with ≈2x10 CFU of A. baumannii (AB5075) strains (10 μl at ≈2x107 CFU/mL), eight larvae were inoculated per strain, and incubated at 37 °C in the dark. The viability of the larvae was scored every 24 h. + +Fig. 6. The enzymatic output of subfunctionalised RelA and SpoT RSH enzymes is evolutionarily tuned through constrains of the conformational landscape. (a) Control of the enzymatic output of the ancestral bifunctional Rel[HS]. Upon amino acid starvation Rel is recruited to starved ribosomal complexes. The ribosome- bound Rel assumes an extended conformation in which the auto- inhibitory effect of the CTD region on the SYNTH activity is released. The full activation of SYNTH activity is achieved upon binding of (p)ppGpp to an + +<--- Page Split ---> + +allosteric site within the NTD and release of the SYNTH inhibition by the HD domain. Conversely, off the ribosome the enzyme assumes the \(\tau\) - state. In this conformation locking of the \(\alpha 6 - \alpha 7\) motif by the CTD organises the HD active site residues to promote the HD activity. This, in turn, strongly inhibits the SYNTH activity via inter- NTD regulation. The full activation of either SYNTH or HD requires allosteric signalling from CTD to NTD enzymatic domains. (b, c) Evolution of SpoT as a predominantly dedicated hydrolase involved the loss of the allosteric control of the NTD by (p)ppGpp as well as by the ribosome. In bifunctional SpoT[HS] present in the majority of Gamma- and Betaproteobacteria, while the equilibrium is strongly shifted towards the HD- active \(\tau\) - state, the enzyme is capable of inefficient (p)ppGpp synthesis in the relaxed state (B). Subfunctionalisation of SpoT in Moraxellaceae has resulted in the monofunctional hydrolase SpoT[Hs], which naturally populates only the compact \(\tau\) - state and is SYNTH- inactive. (d) Subfunctionalisation of Gamma- and Betaproteobacterial RelA[hS] constitutes the other extreme case of evolutionary restriction of the conformational dynamics of the ancestral Rel[HS]. While losing its HD activity, RelA retains all the allosteric regulatory elements of Rel. Being a dedicated (p)ppGpp synthetase enzyme, off the ribosome RelA does not assume the \(\tau\) - state. Instead, it predominantly populates the functionally frustrated resting state equivalent to the relaxed state of Rel, primed to assume the elongated ribosome- associated state triggered by the 70S ribosome, uncharged tRNA and alarmones during stringency. Red circles represent inhibited catalytic centres, green circles represent fully activated catalytic centres, and dashed green circles represent idling catalytic centres. + +<--- Page Split ---> + +## Figures + +## Figure 1 + +A. baumannii SpoT is a monofunctional alarome hydrolase. (a) Evolution of long RSHs in Proteobacteria. Duplication of the ancestral bifunctional RSH Rel in Beta- and Gammaproteobacterial lineage gave rise to RelA and SpoT, leading to subfunctionalisation of RelA as monofunctional SYNTH-only alarome synthetase and SpoT as a predominantly-HD RSH. In the Moraxellaceae family of Gammaproteobacteria, SpoT has undergone further subfunctionalisation, evolving into a monofunctional HD-only alarome hydrolase. (b) Alignment of SYNTH-critical regions in long RSHs highlights the sequence divergence in Moraxellaceae SpoTs. (c) Co-expression of SpoTAb counteracts the growth defect in ppGpp0 (DrelA DspoT) E. coli caused by RelA expression. This demonstrates that SpoTAb is HD-active in the E. coli host. (d) While the SYNTH activity of ectopically expressed SpoTEc is essential and sufficient for promoting the growth of ppGpp0 E. coli on M9 minimal medium, SpoTAb fails to promote the growth of DrelA DspoT E. coli on M9. This demonstrates that, unlike SpoTEc, which is SYNTH-active, SpoTAb is SYNTH-inactive. + +## Figure 2 + +Full- length monomeric A. baumannii SpoT adopts a compact "mushroom"- shaped HD- active \(\tau\) - state. (a) Structure of "mushroom"- shaped SpoTAb- ppGpp complex in the \(\tau\) - state. The domain organization, from N to C terminus: NTD domains hydrolase (HD), pseudo synthetase (pseudo- SYNTH) and Core, and CTD domains, TGS, Helical (HEL), Zn- finger (ZFD) and RNA recognition motif (RRM). The ppGpp alarome is in red. (b) Cartoon representation the SpoTAb. The "stem" of the mushroom is formed by the enzymatic HD domain and the "cap" by the regulatory domains: NTD pseudo- SYNTH domain and the CTD domains. (c) Ribbon representation of the SpoTAb- ppGpp complex. The a6/a7 motif is held in the hydrolysis- compatible position by the folded Core domain and the TGS \(\beta\) - hairpin, with the Core domain communicating allosteric signals to HD from the regulatory domains. (d) The HD activity of SpoTAb is insensitive to the addition of E. coli 70S ribosomes, and non- specifically weakly inhibited by both aminoacylated and deacetylated E. coli tRNAVal. (e) Analytical size exclusion chromatography (SEC) of SpoTAb supports its monomeric nature in solution. (f) Experimental X- ray scattering (SAXS) analysis of SpoTAb at 8 mg/mL further confirms the monomeric nature of SpoTAb. The analysis of the normalised Kratky plot (insert) of the SAXS curve reveals folded globular shape of SpoTAb. (g) Ab initio envelope of SpoTAb reconstructed from the experimental SAXS data superimposed on the crystal structure. Comparison of both models shows that in solution the enzyme adopts the same conformation as observed in the crystal. + +<--- Page Split ---> + +## Figure 3 + +A. baumannii SpoT is a Mn2+-dependent (p)ppGpp hydrolase. (a) Surface representation of SpoTAb in the \(\tau\) -state. The active site cavity in the HD domain is boxed in dashed lines. (b) Zoom into the HD active site of the SpoTAb-ppGpp complex. The acidic half of the interface (residues K140, E82, D83, Y51 and R45) and the Mn2+ ion activate the water molecule for nucleophilic attack of the pyrophosphate bond pf ppGpp, while the basic half of the interface (K46, K158 and R161) stabilises the 3' and 5' phosphates of the alarome substrate. (c) Ribbon representation of the active site of SpoTAb revealing the residues involved in coordination of ppGpp. (d) Effects of Ala-substitutions in the ppGpp binding site on the HD activity of SpoTAb. The residues for substitution were selected as per (c). (e) ITC titration of Mn2+ into unliganded apo-SpoTAb. (f) Hydrolase activity of unliganded apo-SpoTAb as a function of increasing concentrations of Mn2+. (g) Structure of the Mn2+-free N-terminal region of SpoTAb, SpoTAbNTD. The HD domain is in purple and the pseudo-SYNTH is in yellow. The disordered active site is labeled. (h) Superposition of the HD domain of SpoTAb complexed with ppGpp (in light blue) onto Mn2+-free SpoTAb (in purple). The key conformation differences in catalytically-crucial active site residues and the structural elements a3, a4 and a8 are highlighted as dashed arrows and shown in bold, respectively. + +## Figure 4 + +The CTD controls the hydrolysis activity of SpoT by controlling the equilibrium between HD- active \(\tau\) - state and HD- inactive relaxed conformations. (a) The HD functionality test of truncated versions of SpoTAb. SpoTAb variants were co- expressed expressed with RelAAb in \(\Delta\) relA \(\Delta\) spoT Ptac::relA A. baumannii (AB5075). The ability of SpoTAb to promote the growth is reflective of its HD competence. (b) Hydrolase activity of SpoTAb and the C- terminally truncated SpoTAb variants. Turnovers corresponding to each protein variant are coloured as per the domain colour code on (a). (c) Cartoon representation of the allosteric network defined by the Core domain connecting the domains of the enzyme in the \(\tau\) - state. The key interface residues are shown as sticks and labeled. (d) Hydrolase activity of crucial Core residues involved in interactions with other domains of SpoTAb (A384R contacting SYNTH, A351K contacting RRM, L356D contacting HD, L373G/D374G contacting ZFD, Y375G contacting the TGS). The TGS:HD interface is also probed with the E379K/W382K point mutant and \(\Delta\) TGS- HEL versions. The \(\tau\) - state stabilising substitutions D374R and I637D/R641D increase the HD activity. (e, f) SAXS curves of L356D in the \(\tau\) - state (e) or relaxed state (f). (g) Pseudo-atomic model of the relaxed state of SpoTAb calculated with Dadimodo (Evrard et al., 2011) using the experimental SAXS data from (f). (h) Comparison of 22 the experimental SAXS data from the relaxed state of L356D (in grey) with the theoretical scattering curve of the relaxed state (solid line) obtained from the Dadimodo model. (i) SAXS curve of RelAAb is consistent with the dimensions of the relaxed state. (j, k) SAXS curves of RelBs in the \(\tau\) - state (j) or relaxed state (k). + +<--- Page Split ---> + +(I) Cartoon representation of experimentally observed conformational states as well as particle dimensions of long RSH enzymes. + +## Figure 5 + +The Core domain of SpoT transduces the allosteric signal from the regulatory CTD and pseudo- SNTH to the enzymatic HD domain. (a, b) Cartoon representation of the interactions stabilising the a6- a7 motif of the HD active site (A). While the Core wraps around a7, the TGS \(\beta\) - hairpin forms a small hydrophobic patch that stabilises a6. These interactions preclude the movement of a6- a7 and maintain SpoTAb in a constitutive hydrolase- primed state. Key interface residues are shown as sticks and labelled. (b) The experimental SAXS curve of SpoTAbE379K/W382K is consistent with the dimensions of the \(\tau\) - state. Cartoon representation of the HD:Core:RRM signal transduction axis. (c) The architecture of the \(\tau\) - state suggests that the RRM is locked in place via the Core and supporting interaction provided by pseudo- SYNTH, suggesting that additional tethering of RRM to pseudo- SYNTH could further stabilise the \(\tau\) conformation. (d) SAXS curve of the SpoTAbI637D/R641D variant in which substitutions I637D/R641D and I637A/R641A promote H- bonding and stabilise the \(\alpha\) - helical structure, respectively, is consistent with the dimensions of the \(\tau\) - state. (e) In vivo HD functionality tests of SpoTAb variants D374G and I637D/R641D expressed from the inducible Ptac promoter in a \(\Delta\) relA\(\Delta\) spoT background of A. baumannii (AB5075) expressing relA from a replicative plasmid (pPrelA::relA). The stabilising substitutions D374R and I637D/R641D phenocopy the WT. (f) Virulence assays in the G. mellonella infection model demonstrate the essentiality of intact allosteric regulation of SpoTAb for virulence. G. mellonella larvae were injected with \(\approx 2 \times 10\) CFU of A. baumannii (AB5075) strains (10 \(\mu\) l at \(\approx 2 \times 10\)7 CFU/mL), eight larvae were inoculated per strain, and incubated at 37 °C in the dark. The viability of the larvae was scored every 24 h. + +## Figure 6 + +The enzymatic output of subfunctionalised RelA and SpoT RSH enzymes is evolutionarily tuned through constrains of the conformational landscape. (a) Control of the enzymatic output of the ancestral bifunctional Rel[HS]. Upon amino acid starvation Rel is recruited to starved ribosomal complexes. The ribosome- bound Rel assumes an extended conformation in which the auto- inhibitory effect of the CTD region on the SYNTH activity is released. The full activation of SYNTH activity is achieved upon binding of (p)ppGpp to an 23 allosteric site within the NTD and release of the SYNTH inhibition by the HD domain. Conversely, off the ribosome the enzyme assumes the \(\tau\) - state. In this conformation locking of the a6- a7 motif by the CTD organises the HD active site residues to promote the HD activity. This, in turn, strongly inhibits the SYNTH activity via inter- NTD regulation. The full activation of either SYNTH or HD requires allosteric signalling from CTD to NTD enzymatic domains. (b, c) Evolution of SpoT as a predominantly dedicated hydrolase involved the loss of the allosteric control of the NTD by (p)ppGpp as + +<--- Page Split ---> + +well as by the ribosome. In bifunctional SpoT[HS] present in the majority of Gamma- and Betaproteobacteria, while the equilibrium is strongly shifted towards the HD- active t- state, the enzyme is capable of inefficient (p)ppGpp synthesis in the relaxed state (B). Subfunctionalisation of SpoT in Moraxellaceae has resulted in the monofunctional hydrolase SpoT[Hs], which naturally populates only the compact t- state and is SYNTH- inactive. (d) Subfunctionalisation of Gamma- and Betaproteobacterial RelA[hS] constitutes the other extreme case of evolutionary restriction of the conformational dynamics of the ancestral Rel[HS]. While losing its HD activity, RelA retains all the allosteric regulatory elements of Rel. Being a dedicated (p)ppGpp synthetase enzyme, off the ribosome RelA does not assume the t- state. Instead, it predominantly populates the functionally frustrated resting state equivalent to the relaxed state of Rel, primed to assume the elongated ribosome- associated state triggered by the 70S ribosome, uncharged tRNA and alarmones during stringency. Red circles represent inhibited catalytic centres, green circles represent fully activated catalytic centres, and dashed green circles represent idling catalytic centres. + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- SupplementalDataDec.xlsx- D1292120009valreportfullP1.pdf- Tammanv26Onlinemethods.pdf- Tammanv26SupplementalFigures.pdf- Tammanv26SupplementalTables.pdf- abuSpoTppGppfinalaltgood.pdf- nreditorialpolicychecklistNCHEMBA220114337.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0877cda3cf7745c3f9fe5760c2c8af30a0bf2b4fff0901f70dac9108eb425e22/preprint__0877cda3cf7745c3f9fe5760c2c8af30a0bf2b4fff0901f70dac9108eb425e22_det.mmd b/preprint/preprint__0877cda3cf7745c3f9fe5760c2c8af30a0bf2b4fff0901f70dac9108eb425e22/preprint__0877cda3cf7745c3f9fe5760c2c8af30a0bf2b4fff0901f70dac9108eb425e22_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..970d51825e25d118826daf8898556cbd0b8a7aac --- /dev/null +++ b/preprint/preprint__0877cda3cf7745c3f9fe5760c2c8af30a0bf2b4fff0901f70dac9108eb425e22/preprint__0877cda3cf7745c3f9fe5760c2c8af30a0bf2b4fff0901f70dac9108eb425e22_det.mmd @@ -0,0 +1,422 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 952, 208]]<|/det|> +# The structure of SpoT reveals evolutionary tuning of enzymatic output through constraint of the conformational landscape + +<|ref|>text<|/ref|><|det|>[[44, 230, 657, 270]]<|/det|> +Hedvig Tamman Université Libre de Bruxelles https://orcid.org/0000- 0003- 4453- 7814 + +<|ref|>text<|/ref|><|det|>[[44, 276, 195, 315]]<|/det|> +Karin Emits Lund University + +<|ref|>text<|/ref|><|det|>[[44, 322, 253, 362]]<|/det|> +Mohammad Roghanian Lund University + +<|ref|>text<|/ref|><|det|>[[44, 369, 301, 409]]<|/det|> +Andres Ainelo Université Libre de Bruxelles + +<|ref|>text<|/ref|><|det|>[[44, 415, 198, 454]]<|/det|> +Christina Julius Umea University + +<|ref|>text<|/ref|><|det|>[[44, 461, 586, 502]]<|/det|> +Anthony Perrier University of Namur https://orcid.org/0000- 0002- 4473- 3711 + +<|ref|>text<|/ref|><|det|>[[44, 508, 951, 549]]<|/det|> +Ariel Talavera Vrije Universiteit Brussel, Vlaams Instituut voor Biotechnologie https://orcid.org/0000- 0002- 1865- 5959 + +<|ref|>text<|/ref|><|det|>[[44, 555, 301, 594]]<|/det|> +Hanna Ainelo Université Libre de Bruxelles + +<|ref|>text<|/ref|><|det|>[[44, 601, 496, 641]]<|/det|> +Rémy Duguquier Université https://orcid.org/0000- 0002- 9662- 8905 + +<|ref|>text<|/ref|><|det|>[[44, 647, 301, 686]]<|/det|> +Safia Zedek Université Libre de Bruxelles + +<|ref|>text<|/ref|><|det|>[[44, 693, 740, 733]]<|/det|> +Aurélien Thureau Swing Beamline, Synchrotron SOLEIL https://orcid.org/0000- 0001- 5666- 260X + +<|ref|>text<|/ref|><|det|>[[44, 739, 590, 780]]<|/det|> +Javier Perez Synchrotron SOLEIL https://orcid.org/0000- 0003- 3083- 4754 + +<|ref|>text<|/ref|><|det|>[[44, 786, 230, 825]]<|/det|> +Gipsi Lima- Mendez University of Namur + +<|ref|>text<|/ref|><|det|>[[44, 832, 586, 872]]<|/det|> +Regis Hallez University of Namur https://orcid.org/0000- 0003- 1175- 8565 + +<|ref|>text<|/ref|><|det|>[[44, 878, 556, 919]]<|/det|> +Gemma Atkinson Umeå University https://orcid.org/0000- 0002- 4861- 4584 + +<|ref|>text<|/ref|><|det|>[[44, 924, 183, 942]]<|/det|> +Vasili Hauryliuk + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[47, 46, 657, 111]]<|/det|> +Lund University https://orcid.org/0000- 0003- 2389- 5057Abel Garcia- Pino ( \(\square\) agarciap@ulb.ac.be)Université Libre de Bruxelles https://orcid.org/0000- 0002- 0634- 0300 + +<|ref|>sub_title<|/ref|><|det|>[[45, 152, 102, 170]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 190, 920, 233]]<|/det|> +Keywords: (p)ppGpp, stringent response, SpoT, RelA, Rel, allostery, intrinsically disordered 46 proteins, energetic frustration, metabolic hubs, conformational switches + +<|ref|>text<|/ref|><|det|>[[45, 251, 336, 270]]<|/det|> +Posted Date: February 11th, 2022 + +<|ref|>text<|/ref|><|det|>[[44, 289, 474, 308]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 1293174/v1 + +<|ref|>text<|/ref|><|det|>[[44, 326, 910, 368]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[70, 83, 880, 131]]<|/det|> +# The structure of SpoT reveals evolutionary tuning of enzymatic output through constraint of the conformational landscape + +<|ref|>text<|/ref|><|det|>[[67, 163, 881, 230]]<|/det|> +4 Hedvig Tamman \(^{1,\dagger ,*}\) , Karin Ernits \(^{2,3,4,\dagger}\) , Mohammad Roghanian \(^{2,4,5,\dagger}\) , Andres Ainelo \(^{1}\) , Christina Julius \(^{4}\) , Anthony Perrier \(^{6,7}\) , Ariel Talavera \(^{1}\) , Hanna Ainelo \(^{1}\) , Rémy Duguquier \(^{1,5}\) , Safia Zedek \(^{1}\) , Aurelien Thureau \(^{8}\) , Javier Pérez \(^{8}\) , Gipsi Lima- Mendez \(^{6}\) , Regis Hallez \(^{6,7,9}\) , Gemma C. Atkinson \(^{2,4}\) , Vasili Hauryliuk \(^{2,4,10,*}\) , Abel Garcia- Pino \(^{1,9,*}\) + +<|ref|>text<|/ref|><|det|>[[66, 245, 883, 470]]<|/det|> +9 1Cellular and Molecular Microbiology, Faculté des Sciences, Université libre de Bruxelles 10 (ULB), Boulevard du Triomphe, Building BC, (1C4 203), 1050 Brussels, Belgium 11 2Department of Experimental Medicine, University of Lund, 221 84 Lund, Sweden 12 3Department of Chemistry, Umeå University, 901 87 Umeå, Sweden 13 4Department of Molecular Biology, Umeå University, 901 87 Umeå, Sweden 14 5Departement of Clinical Microbiology, Rigshospitalet, 2200 Copenhagen, Denmark 15 6Biology of Microorganisms Research Unit (URBM), Namur Research Institute for Life 16 Science (NARILIS), University of Namur, 61 Rue de Bruxelles, 5000 Namur 17 7Bacterial Cell cycle & Development (BCcD), Biology of Microorganisms Research Unit 18 (URBM), Namur Research Institute for Life Science (NARILIS), University of Namur, 61 Rue 19 de Bruxelles, 5000 Namur 20 8Synchrotron SOLEIL, Saint- Aubin - BP 48, 91192 Gif sur Yvette Cedex, France 21 9WELBIO, Avenue Hippocrate 75, 1200 Brussels, Belgium 22 10University of Tartu, Institute of Technology, 50411 Tartu, Estonia + +<|ref|>text<|/ref|><|det|>[[66, 500, 504, 516]]<|/det|> +\* to whom correspondence should be addressed: + +<|ref|>text<|/ref|><|det|>[[66, 532, 640, 600]]<|/det|> +26 Hedvig Tamman: hedvig.tamman@ulb.be, +372 737 6038 27 Vasili Hauryliuk: vasili.hauryliuk@med.lu.se, +46 70 60 90 493 28 Abel Garcia- Pino: abel.garcia.pino@ulb.be, +32 2 650 53 77 29 †These authors contributed equally to the paper as first authors. + +<|ref|>sub_title<|/ref|><|det|>[[67, 624, 200, 639]]<|/det|> +## 31 Abstract: + +<|ref|>text<|/ref|><|det|>[[66, 647, 881, 844]]<|/det|> +32 Stringent factors orchestrate bacterial cell reprogramming through increasing the level of the 33 alarmones (p)ppGpp. In Beta- and Gammaproteobacteria, SpoT hydrolyses (p)ppGpp to 34 counteract the synthetase activity of RelA. However, structural information about how SpoT 35 controls the levels of (p)ppGpp is missing. Here we present the crystal structure of the 36 hydrolase- only SpoT from Acinetobacter baumannii and uncover the mechanism of intra- 37 molecular regulation of "long"- RSH factors. In contrast to ribosome- associated Rel/RelA that 38 adopt an elongated structure, SpoT assumes a compact \(\tau\) - shaped structure in which the 39 regulatory domains wrap around a Core subdomain that controls the conformational state of the 40 enzyme. The Core is key to the specialisation of long- RSHs towards either synthesis or 41 hydrolysis: while the short and structured Core of SpoT stabilises the \(\tau\) - state priming the HD 42 domain for (p)ppGpp hydrolysis, the longer, more dynamic Core of RelA destabilises the \(\tau\) - 43 state precluding (p)ppGpp hydrolysis and priming RelA for synthesis. + +<|ref|>text<|/ref|><|det|>[[66, 860, 880, 910]]<|/det|> +45 Keywords: (p)ppGpp, stringent response, SpoT, RelA, Rel, allostery, intrinsically disordered 46 proteins, energetic frustration, metabolic hubs, conformational switches + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 83, 230, 99]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[113, 106, 884, 370]]<|/det|> +RelA- SpoT Homolog (RSH) stringent factors regulate virtually all aspects of bacterial physiology by controlling the levels of the signalling nucleotide alarrones guanosine pentaphosphate and tetraphosphate, collectively referred to as (p)ppGpp 1- 6. The ribosome- associated "long" multidomain RSH RelA is a dedicated amino acid starvation sensor with a strong (p)ppGpp synthesis activity (SYNTH) that is induced upon detection of deacylated tRNA in the ribosomal A site 7,8, and no detectable hydrolase activity 9. In the cell the SYNTH activity of RelA is balanced by SpoT RSH, a bifunctional enzyme with a strong, strictly \(\mathrm{Mn}^{2 + }\) dependent hydrolase (HD) activity 10,11 and weak SYNTH activity 12. The RelA- SpoT pair is a product of gene duplication of an ancestral factor - the ribosome- associated bifunctional RSH Rel - and the pair is limited in its taxonomic distribution to Beta- and Gammaproteobacteria 1,13. + +<|ref|>text<|/ref|><|det|>[[112, 375, 884, 844]]<|/det|> +Subfunctionalisation - the partitioning of functions between two paralogues that arose through gene duplication - appears to have happened at least twice in Gammaproteobacteria (Fig. 1a). First, relatively soon after the duplication that gave rise to RelA and SpoT, RelA lost its capacity for alarrome hydrolysis, evolving into a monofunctional, SYNTH- only RSH. Secondly, as evidenced by a lack of sequence conservation in sites that are critical for nucleotide pyrophosphorylation, during the evolution of the Moraxellaceae lineage of Protobacteria, SpoT has likely lost its synthetase function (Fig. 1a- b) 1. This resulted in further specialization into mono- functional (p)ppGpp hydrolase, SpoT[Hs] (The uppercase "H" stands for hydrolase- competent, while the lowercase "s" indicates "synthetase- incompetent"), as opposed to the bifunctional HD- and SYNTH- competent SpoT[HS] found in other Beta- and Gammaproteobacteria. Recent studies of the Moraxellaceae representative A. baumannii - the "A" in the ESKAPE group of human pathogens of particular concern - indicate a lack (p)ppGpp in the \(\Delta relA\) strain, both with and without acute amino acid starvation induced by serine hydroxylamate (SHX) 14,15. These observations are consistent with the hypothesis that RelA is, indeed, the sole source of the alarrome in this bacterium. Furthermore, consistent with the key role of (p)ppGpp- mediated signalling in bacterial virulence and antibiotic tolerance 16,17, the likely ppGpp \(^0 A\) . baumannii \(\Delta relA\) strain displays increased sensitivity to multiple antibiotics 14,15, decreased virulence in a Galleria mellonella wax moth model and deficiency in switching from the virulent opaque colony variant to the avirulent translucent colony variant 15. + +<|ref|>text<|/ref|><|det|>[[115, 848, 882, 916]]<|/det|> +Rel, RelA and SpoT all share the same conserved domain composition, indicative of a common architecture of the underlying intra- molecular allosteric regulation in long RSHs 1. When recruited to starved ribosomes, both Rel and RelA adopt a highly extended elongated + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 80, 885, 323]]<|/det|> +conformation. In these complexes the regulatory C- terminal domain region (CTD: TGS, HEL, ZFD and RRM domains) is highly structured, while the N- terminal catalytic region (NTD: HD and SYNTH domains) and the interdomain linker regions are highly dynamic and unresolved in some structures \(^{18 - 21}\) . Off the ribosome, our structural understanding of long RSHs relies on the structures of isolated NTDs of several Rel representatives \(^{21 - 24}\) . While the physiological role of SpoT as a key virulence and stress tolerance factor is well established \(^{25,26}\) , structural insights into SpoT are lacking altogether. This limits our ability to interpret the physiological and microbiological studies on the molecular level. Obtaining full- length structures of Rel/RelA/SpoT is essential for understating how the auto- regulation signal transferred from the CTD to the NTD. + +<|ref|>text<|/ref|><|det|>[[111, 328, 885, 642]]<|/det|> +The structural and biochemical data presented here provide the long- missing structural insight into the molecular mechanism of SpoT. We show that \(A\) . baumannii SpoT (SpoT \(_{Ab}\) ) is indeed, a monofunctional (p)ppGpp hydrolase and uncover how its CTD is an allosteric activator of the HD hydrolase function. The structures of the full- length HD- active SpoT \(_{Ab}\) complexed with the ppGpp substrate reveal a compact monomeric conformation in which all the regulatory domains wrap around a Core subdomain that connects the pseudoSYNTH and TGS domains. The Core is one of the intrinsically disordered regions (IDR) present in Rel and RelA when in the active synthetase state. In SpoT \(_{Ab}\) , Core and TGS cooperate to align and activate the hydrolase domain active site while translating allosteric feedback from the other regulatory domains to modulate the HD output. Finally, we propose a unifying conceptual framework that rationalises the relative balance between HD vs SYNTH activities of long RSHs Rel, RelA and SpoT, fine- tuned through the entropic force produced by intrinsically disordered regions that function as conformational gatekeepers of the enzyme. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 83, 183, 99]]<|/det|> +## Results + +<|ref|>sub_title<|/ref|><|det|>[[115, 107, 653, 126]]<|/det|> +## A. baumannii SpoTAb is a monofunctional hydrolase long RSH + +<|ref|>text<|/ref|><|det|>[[113, 131, 884, 496]]<|/det|> +A. baumannii SpoTAb is a monofunctional hydrolase long RSHLack of conservation of active site residues critical for SYNTH activity suggest that Moraxallaceae SpoT enzymes have – like RelA – undergone subfunctionalisation to become monofunctional long RSHs (Fig. 1a and b). Like RelA’s pseudo-HD domain, the SYNTH domain region has been retained in Moraxallaceae as a presumably non-catalytic pseudo-SYNTH domain, suggesting it retains some function in stabilisation or allosteric regulation of the HD domain. To probe the hydrolysis function of A. baumannii SpoT (SpoTAb) in live cells, we leveraged SpoT’s hydrolytic activity being crucial for controlling the cellular levels of (p)ppGpp produced by RelA, which makes spoT conditionally essential in the relA+ Escherichia coli 12. We co-transformed a ppGpp (ΔrelA ΔspoT) E. coli strain with i) a pMG25-based plasmid driving the IPTG-inducible expression of spoTAb under the control of PA1/04/03 promoter and ii) a pMR33 derivative for arabinose-inducible expression of relAEC under the control of PBAD. While expression of the (p)ppGpp synthetase RelAEC strongly inhibited the growth of ppGpp0 E. coli, the growth was completely restored upon the ectopic co-expression of SpoTAb (Fig. 1c and Supplemental Data), demonstrating that SpoTAb is HD-active in the surrogate E. coli host. + +<|ref|>text<|/ref|><|det|>[[113, 502, 884, 670]]<|/det|> +Next, we used our dual plasmid co- expression system to probe the (p)ppGpp synthetase activity of SpoT RSHs. ppGpp \(^0\) E. coli is auxotrophic for eleven amino acids, and (p)ppGpp synthetase activity of SpoT \(E_{C}\) is essential for growth of \(\Delta relA\) E. coli on minimal medium 12. Unlike the SYNTH- active SpoT \(E_{C}\) , SpoT \(Ab\) failed to promote the growth of ppGpp \(^0\) E. coli on M9 minimal medium (Fig. 1d), confirming that SpoT \(Ab\) is SYNTH- inactive. Taken together, these results demonstrate that SpoT \(Ab\) is a specialised monofunctional long RSH that lacks the ability to synthesise (p)ppGpp. + +<|ref|>sub_title<|/ref|><|det|>[[115, 701, 710, 719]]<|/det|> +## Full-length SpoTAb has a compact mushroom-like \(\tau\) -shaped structure + +<|ref|>text<|/ref|><|det|>[[113, 724, 884, 916]]<|/det|> +To gain insight into the molecular workings of SpoT, we solved an X- ray structure of full- length catalytically- active SpoT \(Ab\) in a ppGpp- bound state at \(2.9\mathrm{\AA}\) resolution. The structure revealed a multi- domain architecture strikingly different to that observed earlier for ribosome- bound long RSHs Rel and RelA 18- 21 (Fig. 2a- c and Supplementary Table 1). The HD, SYNTH, TGS, HEL, ZFD and RRM domains of SpoT \(Ab\) form a mushroom- like tau \((\tau)\) - shaped quaternary structure (Fig. 2a- c). In this arrangement, pseudo- SYNTH, TGS, HEL, ZFD and RRM domains all lie in a single plane and form a compact disc- like structure that forms the "cap" of the "mushroom" (Fig. 2b). A helix- turn- helix sub- domain (residues 334 to 379) that + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 883, 248]]<|/det|> +provides the transition between the NTD and CTD regions, lies at the "Core" of the "cap" and seemingly mediates interactions among all domains of the enzyme. Such an arrangement suggests that the Core – which is disordered in Rel/RelA structures – stabilises the disc-like "cap" of SpoT (Fig. 2c). Moreover, the Core provides the HD domain with a physical link to each domain of SpoTAb. Finally, the HD protrudes from the plane of the "cap" in the opposite direction of the C-terminal RRM domain, forming the "stem" of the protein structure (Fig. 2b-c). + +<|ref|>text<|/ref|><|det|>[[113, 255, 884, 495]]<|/det|> +The \(\tau\) - shaped structure of SpoTAb suggests a possible structural mechanism for the autoinhibition of SYNTH activity by the regulatory CTD both in Rel27,28 and RelA29,30. While the SYNTH and TGS domains are sequestered in the "cap", the HD hydrolase stands out unconfined and primed for (p)ppGpp hydrolysis. The TGS domain, which in the case of amino acid starvation sensors Rel and RelA specifically engages the deacylated tRNA CCA- 3' end at the A site19- 21,31, in the case of SpoTAb is partially trapped between the HD, HEL and ZFD domains. While we do detect a mild inhibitory effect of tRNA on SpoTAb hydrolysis activity, the effect is insensitive to tRNA aminoacylation status, i.e. non- specific (Fig. 2d). This is in contrast to the HD activity of bifunctional E. coli SpoT (SpoTEc), which was specifically inhibited by deacylated, but not aminoacylated tRNA32. + +<|ref|>text<|/ref|><|det|>[[113, 500, 883, 666]]<|/det|> +Our structure reveals that the sites from the ZFD and RRM domains that mediate rRNA recognition in Rel/RelA18- 21,31 are held in by the Core subdomain, suggesting that in the \(\tau\) - shaped conformation the hydrolytically active (HDON) SpoTAb is incompatible with ribosome binding. In good agreement with this structural prediction, while the ribosome strongly suppresses the HD activity of Bacillus subtilis Rel (RelBs)28, the addition of E. coli 70S has no effect on the hydrolysis activity of SpoTAb (Fig. 2d). Thus, our biochemical results suggest that SpoTAb is a ribosome- independent enzyme. + +<|ref|>sub_title<|/ref|><|det|>[[115, 697, 881, 740]]<|/det|> +## Shorter intrinsically disordered regions (IDRs) in monofunctional SpoT are associated with specialisation for hydrolysis + +<|ref|>text<|/ref|><|det|>[[115, 745, 883, 888]]<|/det|> +The presence of intrinsically disordered regions (IDR) located at the \(\alpha 6 - \alpha 7\) loop, the Core subdomain and the linker between HEL/ZFD domains in long RSHs RelA and Rel (Supplementary Fig. 1a) has posed an experimental challenge for structural studies18- 21. The molecular function of these flexible regions, unresolved in the structures, is unknown. Comparison between the well- structured SpoTAb in \(\tau\) - state and partially unstructured ribosome- bound RelA/Rel suggests that the unfolding of Core and HEL domains constitutes part of the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 883, 126]]<|/det|> +conformational switch that positions TGS, ZFD and RRM domains to stimulate the synthesis activity of Rel/RelA upon recruitment to the ribosome (Supplementary Fig. 1b). + +<|ref|>text<|/ref|><|det|>[[114, 131, 884, 396]]<|/det|> +The length of these disordered or flexible regions is on average shorter in monofunctional SpoT and much longer in the monofunctional RelA. Bifunctional Rels have interdomain IDRs of sizes between both monofunctional enzymes (Supplementary Fig. 1c). The \(\alpha 6 - \alpha 7\) loop of the HD domain of SpoT[Hs] in particular is a third of the size of that of RelA, which, in turn, is twice longer than that of bifunctional Rel (Supplementary Fig. 1c). The same pattern is observed for the other two IDRs: the Core subdomain and the region connecting HEL and ZFD domains. This is consistent with the significantly lower disordered propensity of the Core of SpoT \(_{Ab}\) compared to RelA \(_{Ab}\) (Supplementary Fig. 1d- e). We speculate that these IDRs have evolved to stabilise either \(\tau\) - (shorter IDRs) or elongated (longer IDRs) states of monofunctional SpoT[Hs] or RelA[hS], respectively, to tune the HD vs SYNTH output ratio. + +<|ref|>sub_title<|/ref|><|det|>[[115, 427, 301, 444]]<|/det|> +## SpoT \(_{Ab}\) is a monomer + +<|ref|>text<|/ref|><|det|>[[115, 450, 884, 592]]<|/det|> +It was shown earlier that both Rel and RelA are prone to dimerization via the CTD, which would potentially serve to regulate their enzymatic activity \(^{21,33 - 35}\) . This idea is a subject of debate, with both genetic \(^{30}\) and mass photometry \(^{28}\) experiments suggesting that the dimerization is unlikely to take place at physiologically relevant concentrations. Therefore, we used small- angle X- ray scattering (SAXS) coupled to size exclusion chromatography (SEC) to probe the conformation and oligomeric state of SpoT \(_{Ab}\) in solution (Fig. 2e- f). + +<|ref|>text<|/ref|><|det|>[[114, 597, 884, 863]]<|/det|> +The SAXS data revealed that in solution SpoT \(_{Ab}\) has an oblate shape compatible with the structure determined by X- ray. Both SAXS and SEC consistently support the monomeric nature of SpoT \(_{Ab}\) , even at concentrations as high as \(8\mathrm{mg / mL}\) . Both the molecular weight of \(\approx 90\) kDa by SEC as well the estimates of Mw of \(\approx 85\mathrm{kDa}\) and \(Rg\) of \(34.9\mathrm{\AA}\) by SAXS (Fig. 2e- f) agree with the \(80\mathrm{kDa}\) theoretical molecular weight of monomeric SpoT \(_{Ab}\) . Furthermore, the analysis of the normalised Kratky plot derived from the scattering curve lends further support for a compact monomeric structure of SpoT \(_{Ab}\) in solution (Fig. 2f), and the ab initio envelope calculated from the experimental SAXS data (Fig. 2g) is compatible with the \(\tau\) - shaped structure of SpoT \(_{Ab}\) determined by X- ray. Collectively these results demonstrate that in solution the monomeric SpoT \(_{Ab}\) adopts a conformation that is very similar to the \(\tau\) - shaped conformation observed in the crystal with the HD domain protruding from the disc- shaped enzyme. + +<|ref|>text<|/ref|><|det|>[[115, 894, 780, 912]]<|/det|> +The enzymatically- inactive pseudo- SYNTH of SpoT \(_{Ab}\) is a regulatory domain + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 81, 885, 450]]<|/det|> +In the monofunctional stringent factor RelA, the enzymatically inactive pseudo- HD domain has evolved into a regulatory domain controlling catalysis via an intra- NTD allosteric regulatory mechanism \(^{36,37}\) . This is also the case with the specialisation of SpoT \(_{Ab}\) as a monofunctional hydrolase where the pseudo- SYNTH domain has evolved into a strictly regulatory/structural domain. Superposition of the SYNTH domain from Rel \(_{Tt}\) onto the pseudo- SYNTH domain of SpoT \(_{Ab}\) reveals extensive reorganisation of the vestigial catalytic domain in SpoT \(_{Ab}\) , consistent with differential conservation patterns in the G- loop and the ATP recognition motif (Supplementary Fig. 2a). These involve the residues that coordinate adenosine and guanosine (R249 to N241, R277 to E267 and Y329 to N304) and the majority of phosphate- coordinating groups. Crucially, the catalytic residues D272 and Q347 are substituted for S263 and T321, respectively. These substitutions essentially impede the deprotonation and activation of the 3'- OH of GD(T)P, and \(\mathrm{Mg^{2 + }}\) binding, precluding the nucleophilic attack on the \(\beta\) - phosphate of ATP. We directly probed GDP binding by SpoT \(_{Ab}^{\mathrm{NTD}}\) and RelA \(_{Ab}^{\mathrm{NTD}}\) by ITC. As expected, while SpoT \(_{Ab}\) does not bind GDP, RelA \(_{Ab}\) binds GDP with an affinity of 62 \(\mu \mathrm{M}\) , which is similar to our earlier estimates for RelA \(_{E_c}^{\mathrm{NTD}}\) and Rel \(_{Ab}^{\mathrm{NTD}}\) \(^{28,36}\) (Supplementary Fig. 2b- c). + +<|ref|>sub_title<|/ref|><|det|>[[115, 475, 656, 495]]<|/det|> +## SpoT \(_{Ab}\) is not allosterically regulated by the alarome pppGpp + +<|ref|>text<|/ref|><|det|>[[113, 499, 885, 767]]<|/det|> +The enzymatic activity of long RSHs is regulated via strong allosteric coupling between the HD and SYNTH domains that results in antagonistic conformational states \(^{22,23,36}\) . While in Rel/RelA (p)ppGpp bind the hinge region connecting the SYNTH and HD/pseudo- HD domains to stimulate the SYNTH activity, this regulation is lost in SpoT \(_{Ec}\) \(^{36}\) . Our structure of SpoT \(_{Ab}\) provides a mechanistic interpretation. In the \(\tau\) - state the highly structured Core subdomain makes numerous contacts with SYNTH providing further scaffolding to the already more stable version of the HD:SYNTH hinge of SpoT \(_{Ab}\) . Additionally, there are several important substitutions in the (p)ppGpp binding site that would be expected to compromise (p)ppGpp binding and alarome- mediated regulation, specifically in Q203 (a residue involved in ribose coordination and strictly conserved as A in RelA \(^{36}\) ) and in T209 (a residue involved in phosphate coordination, typically K or R in RelA \(^{36}\) ). + +<|ref|>text<|/ref|><|det|>[[113, 770, 884, 914]]<|/det|> +To directly validate the lack of pppGpp- mediated regulation in SpoT \(_{Ab}\) , we characterised the interaction between pppGpp and SpoT \(_{Ab}^{\mathrm{NTD}}\) by ITC. As expected, SpoT \(_{Ab}^{\mathrm{NTD}}\) does not bind pppGpp allosterically (Supplementary Fig. 2d- e). Following the experimental approach used earlier for SpoT \(_{Ec}\) \(^{36}\) , we next grafted the allosteric site of A. baumannii RelA ( \(^{236}\) RelA \(^{246}\) ) onto SpoT \(_{Ab}^{\mathrm{NTD}}\) (replacing \(^{201}\) SpoT \(_{Ab}^{211}\) ). Just as in the case of SpoT \(_{Ec}\) , this resulted in a RelA- like affinity to pppGpp of the chimera RSH ( \(K_D = 5.6 \mu \mathrm{M}\) ). Collectively, these results support the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 883, 125]]<|/det|> +generality of alarMone- mediated control being lost in SpoT and only present in SYNTH- active Rel/RelA stringent factors that mediate acute stringent response upon amino acid starvation. + +<|ref|>sub_title<|/ref|><|det|>[[115, 156, 819, 175]]<|/det|> +## The dipolar architecture of the HD active site is conserved between Rel and SpoT + +<|ref|>text<|/ref|><|det|>[[113, 180, 884, 445]]<|/det|> +Inspection of the electron density map of the SpoT \(_{Ab}\) - ppGpp complex reveals that the alarMone is bound in high occupancy in each of the four SpoT \(_{Ab}\) molecules present in the asymmetric unit of the crystal (Supplementary Fig. 2f), with the coordination of the guanine base of ppGpp (Fig. 3a- c) resembling that observed in Rel \(_{T^{N T D}}\) - ppGpp \(^{23}\) and Rel \(_{T^{N T D}}\) - ppGpp complexes \(^{24}\) . We probed enzymatically the role of each residue involved in guanine coordination via systematic Ala- substitutions. While substitution of R45 (stacking the guanine) abrogated hydrolysis, removing Van der Waals contacts to L154 decreased the activity approximately two- fold; interactions with K46 were redundant (Fig. 3d). Disruption of the hydrogen bond of the guanine to T150 had only a minor effect. The additional hydrogen bond formed between the carbonyl group of the guanine and the enzyme's backbone likely accounts for the guanine specificity of SpoT over adenosine. + +<|ref|>text<|/ref|><|det|>[[113, 450, 884, 740]]<|/det|> +As observed earlier for Rel \(_{T^{N T D}}\) \(^{23}\) , the hydrolase active site of SpoT \(_{Ab}\) displays a dipolar charge distribution with a highly basic half mediating the stabilization of the \(5'\) - and \(3'\) - polyphosphate groups of the substrate and the other highly acidic half mediating the \(3'\) - pyrophosphate hydrolysis (Fig. 3a- b). Closer inspection of the complex reveals the crucial role of Y51 and the \(^{82}\mathrm{ED}^{83}\) active site motifs as they work together with the \(\mathrm{Mn}^{2 + }\) cofactor to coordinate and stabilise a network of water molecules near the sugar- phosphate moiety during hydrolysis (Fig. 3b- c). Indeed, substitutions of Y51, E82, D83 or N147 render SpoT \(_{Ab}\) HD- inactive in our enzymatic assays (Fig. 3d). At the positively charged side active site the \(5'\) - polyphosphate is loosely coordinated and exposed to the bulk solvent. By contrast K140 and R144 hold the \(3'\) - pyrophosphate in place during hydrolysis and Ala substitutions of these residues decrease the activity of the enzyme between 5- and 10- fold suggesting these are key residues that orient the scissile bond. + +<|ref|>sub_title<|/ref|><|det|>[[115, 772, 529, 790]]<|/det|> +## \(\mathbf{Mn}^{2 + }\) ion organizes the HD active site of SpoT \(_{Ab}\) + +<|ref|>text<|/ref|><|det|>[[115, 795, 883, 912]]<|/det|> +The essential role of the divalent manganese ion \(\mathrm{Mn}^{2 + }\) in (p)ppGpp pyrophosphate hydrolysis is well documented for both Rel \(^{22,28,38,39}\) and SpoT \(_{Ec}\) \(^{40}\) . Our isothermal titration calorimetry (ITC) measurements demonstrate that unliganded, metal- free SpoT \(_{Ab}\) NTD binds \(\mathrm{Mn}^{2 + }\) with a \(K_{\mathrm{D}}\) of \(35.3 \mu \mathrm{M}\) (Fig. 3e). Furthermore, while metal- free full- length SpoT \(_{Ab}\) is completely HD- inactive, the HD activity is readily restored upon addition of \(\mathrm{Mn}^{2 + }\) (Fig. 3f). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 81, 884, 446]]<|/det|> +To directly reveal the structural role of \(\mathrm{Mn}^{2 + }\) we determined the X- ray structure of \(\mathrm{SpoT}_{Ab}\mathrm{NTD}\) in the metal- free state (Fig. 3g and Supplementary Table 1). Comparison with the structure of the \(\mathrm{SpoT}_{Ab}\) - ppGpp complex provides a structural explanation for the essentiality of \(\mathrm{Mn}^{2 + }\) for catalysis: in addition to its role in hydrolysis, by connecting \(\alpha 3\) , \(\alpha 4\) and \(\alpha 8\) , \(\mathrm{Mn}^{2 + }\) coordination brings together the two halves of the HD domain and provides structural support to the active site (Fig. 3g- h). While the overall topology of the \(\mathrm{SpoT}_{Ab}\) HD domain is similar to that of \(\mathrm{Mn}^{2 + }\) - liganded \(\mathrm{Rel}_{Tl}\mathrm{NTD}^{23}\) , the removal of the metal ion has a profound effect on the local conformation of the active site of \(\mathrm{SpoT}_{Ab}\mathrm{NTD}\) . The catalytic \(^{78}\mathrm{HD}^{79}\) and \(^{82}\mathrm{ED}^{83}\) motifs are largely misaligned, loops S110- Y117 and A153- K158 that are involved in the \(3'\) - and \(5'\) - phosphate coordination are disordered, and the guanine- coordinating loop T44- Y51 assumes a conformation incompatible with the base coordination (Fig. 3h). Importantly, all of these changes do not result in the opening of the enzyme's NTD that was observed in \(\mathrm{Rel}_{Tl}\) upon removal of \(\mathrm{Mn}^{2 + }\) \(^{23}\) . These observations suggest that with the evolution as a monofunctional enzyme, \(\mathrm{SpoT}_{Ab}\) shed the allosteric conformational control between the HD and pseudo- SYNTH domains. + +<|ref|>sub_title<|/ref|><|det|>[[115, 477, 755, 496]]<|/det|> +## The CTD allosterically stimulates the hydrolysis activity of the SpoT NTD + +<|ref|>text<|/ref|><|det|>[[114, 500, 884, 666]]<|/det|> +Until now, our understanding of the function of the CTD region of long RSHs was based exclusively on studies of Rel and ReIA. This has established a role of the CTD in the association of the stringent factors with starved ribosomes resulting in the activation of the SYNTH activity and the auto- inhibition of the factor's SYNTH activity off the ribosome \(^{18 - 21,27,28}\) . Weak hydrolase activity of the CTD- truncated Rel has also indicated a possible HD- stimulatory role of the CTD through an intra- molecular regulation of the hydrolase function \(^{28,41,42}\) , suggesting that a similar mechanism could also be at play in the case of SpoT. + +<|ref|>text<|/ref|><|det|>[[114, 672, 884, 864]]<|/det|> +To probe this hypothesis, we characterised the HD activity – both in vitro and in vivo – of a set of progressively C- terminally truncated variants of \(\mathrm{SpoT}_{Ab}\) lacking i) RRM ( \(\mathrm{SpoT}_{Ab}^{1 - }\) \(^{614}\) , amino acids 1–614), ii) RRM and ZFD ( \(\mathrm{SpoT}_{Ab}^{1 - 560}\) ), iii) RRM, ZFD and HEL ( \(\mathrm{SpoT}_{Ab}^{1 - }\) \(^{454}\) ), iv) CTD altogether, i.e. RRM, ZFD, HEL and TGS ( \(\mathrm{SpoT}_{Ab}^{1 - 385}\) ), v) CTD as well as the Core domain ( \(\mathrm{SpoT}_{Ab}^{1 - 339}\) ), and finally, a variant that consisted of just the HD domain ( \(\mathrm{SpoT}_{Ab}^{1 - }\) \(^{195}\) ). These truncated variants were all generated at the endogenous spoT locus in a \(\Delta \mathrm{relA}\) Ptac::relA A. baumannii strain, and the ability to grow on complex media supplemented with IPTG was evaluated as a proxy of the (p)ppGpp hydrolase activity of \(\mathrm{SpoT}_{Ab}\) in vivo. + +<|ref|>text<|/ref|><|det|>[[115, 869, 882, 912]]<|/det|> +While \(\mathrm{SpoT}_{Ab}\) variants lacking the RRM or the RRM and ZFD domains retained wild- type ability to sustain the bacterial growth grow – i.e. could efficiently degrade (p)ppGpp + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 884, 298]]<|/det|> +synthesised by RelA – further C-terminal truncations compromised the in vivo HD functionality, as evidenced from pronounced growth defects (Fig. 4a). Biochemical assays are in agreement with the in vivo data (Fig. 4b). Truncation of the RRM and ZFD decreases the HD activity 5-fold. Further deletion of the TGS- HEL domains leads to a dramatic 42- fold decrease in activity. Truncations beyond the TGS compromised the activity by 70- fold or more and isolated HD domain was nearly inactive. Collectively, our results suggest that the CTD region functions as an allosteric activator of the hydrolase function of SpoTAb. Next, we set out to dissect the molecular mechanism of the CTD- mediated NTD control and assign the molecular functions to individual CTD domains. + +<|ref|>sub_title<|/ref|><|det|>[[115, 329, 588, 347]]<|/det|> +## The Core domain is a linchpin that controls the \(\tau\) -state + +<|ref|>text<|/ref|><|det|>[[112, 352, 884, 888]]<|/det|> +Both the overall structural arrangement of SpoTAb and our sequential domain truncation experiments (Fig. 4a- b) suggest that the Core- mediated allosteric crosstalk between the HD and rest of the domains of the enzyme is essential for enzyme's functionality. To specifically assess the role of the individual interdomain interactions we introduced single point substitutions at each of the interfaces of the Core with regulatory CTD domains (Fig. 4c) and measured the hydrolase activity of the SpoTAb variants (Fig. 4d). An intact HD:Core:TGS interface – the structure involved in scaffolding the HD active site – is crucial for HD activity, as the Y375G substitution at the HD:Core:TGS resulted in a 5- fold decrease in activity compared to the wild type. While substitutions at the ZFD (L373G / D374G) and RRM (A351K) domain interfaces also resulted in a pronounced defect (19 and 3- fold decrease, respectively), perturbations at the Core:pseudo- SYNTH domain interface (A348R) had only a minor effect on hydrolysis. Finally, decoupling the contacts of HD from the \(\tau\) - cap via the L356D substitution, located at the interface between Core domain and \(\alpha 6- \alpha 7\) motif of HD 23, has a dramatic 35- fold decrease in HD activity, suggestive of an allosteric signal transduction path between the cap and stem regions of the enzyme. When we monitored the thermodynamic stability of these Core variants of SpoTAb we observed they all have lower stability and loss of structure compared to the wild type (Supplementary Fig. 3a- e). This suggests that an increase in the configurational entropy of the Core has a global effect in the dynamics and compactness of the enzyme. The existence of an allosteric relay mediating a CTD- dependent activation of HD via the Core is further supported by the consistent decrease in hydrolysis associated with the aforementioned C- terminal truncations that affect the feedback of the Core to the HD (Fig. 4a- b), as well as by the observation that the deletion of domains HEL and TGS results in a 50- + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 881, 127]]<|/det|> +fold decrease in activity despite the presence of the other regulatory domains (pseudo- SYNTH, ZFD and RRM) (Fig. 4d). + +<|ref|>text<|/ref|><|det|>[[113, 130, 884, 495]]<|/det|> +We next used SEC- SAXS to directly probe the role of each contact at the interface of the Core with the different domains of SpoT \(_{Ab}\) on stabilisation of the \(\tau\) - sate. The L356D substitution (SpoT \(_{Ab}\) L356D, Fig. 4c and Supplementary Table 2) results in the segregation of the population into two conformational states with major differences in \(R_{G}\) (radius of gyration) and particle dimensions \((D_{MAX})\) . In SpoT \(_{Ab}\) L356D one state is the compact \(\tau\) - shape observed in the crystal structure (Fig. 4e), while the other state is more relaxed \((R_{G} = 41\mathrm{\AA}, D_{MAX} = 130\mathrm{\AA})\) with dimensions reminiscent of that of the less compacted Rel and RelA – but not quite as elongated as in the ribosome- bound state (Fig. 4f). In this relaxed state the Core and HEL domains appear to have transitioned to a more disordered state that is consistent with the conformational states of these regions in the fully elongated state observed in Rel/RelA (Fig. 4g- h); the other domains retain their structural integrity. Prompted by this analogy, we next probed A. baumannii monofunctional synthetase RelA and B. subtilis bifunctional RSH Rel \(_{Bs}\) with SAXS. The dimensions of RelA \(_{Ab}\) \((R_{G} = 42\mathrm{\AA}, D_{MAX} = 130\mathrm{\AA}, \mathrm{Mw} = 88\mathrm{kDa})\) are consistent with that of the relaxed state of SpoT \(_{Ab}\) L356D, whereas Rel \(_{Bs}\) is populated by both the relaxed and \(\tau\) - states (Fig. 4i- k and Supplementary Table 2). + +<|ref|>text<|/ref|><|det|>[[114, 500, 884, 790]]<|/det|> +Collectively, our results suggest that the Core domain functions as an allosteric relay that conveys signals from the CTD to the HD. At the structural level the composition of the Core is the key to the conformational state of the enzyme as defined by the three major conformations observed in SpoT, Rel and RelA (Fig. 4l). The correlation of the decrease in HD activity with entropy- increasing substitutions such as A351K, L356D, L371G / D374G, and Y375G supports the notion that the increase in structural disorder or flexibility of the Core domain (or the other IDRs) likely drives the conformational equilibrium of the enzyme away from the \(\tau\) - state. This decrease in activity observed by the disruption of the \(\tau\) - shape is also consistent with the lack of hydrolysis in Rel homologues that underwent an order- to- disordered transition while accommodating in ribosomal A site 28. In this context the aforementioned relaxed state is likely the idle resting state of long RSH enzymes in which the CTD precludes the function of SYNTH while not activating HD. + +<|ref|>sub_title<|/ref|><|det|>[[115, 821, 606, 839]]<|/det|> +## The TGS domain acts as a scaffold for the HD active site + +<|ref|>text<|/ref|><|det|>[[115, 845, 884, 912]]<|/det|> +The \(\alpha 6 - \alpha 7\) element plays a crucial role in the allosteric regulation of the opposing activities of bifunctional Rel \(_{T_{I}}\) 23. In Rel \(_{T_{I}}\) , \(\alpha 6 - \alpha 7\) of projects away from the HD catalytic centre to accommodate the \(3'\) and \(5'\) polyphosphate groups as well as allowing the catalytic \(^{82}\mathrm{ED}^{83}\) motif + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 883, 224]]<|/det|> +to get in position, close to the 3' phosphates, priming the enzyme for hydrolysis. In SpoT \(_{Ab}\) the outward- pointing conformation of \(\alpha 6 - \alpha 7\) is further stabilised by the N- terminal region of the TGS and the Core domains which function as a clamp to keep \(\alpha 6 - \alpha 7\) in the HD- compatible position, with the HEL domain providing an additional support via the Core (Fig. 5a). The dramatic drop in the activity of the SpoT \(_{Ab}\) variant lacking the TGS and HEL domains (Fig. 4d) substantiates the functional importance of this stabilising effect. + +<|ref|>text<|/ref|><|det|>[[113, 230, 884, 544]]<|/det|> +At the HD:TGS interface the \(\beta\) - hairpin of the TGS – the very element which is involved in tRNA recognition in Rel \(^{21,28,42}\) and RelA \(^{19,20,31}\) – is buried and stacking directly the \(\alpha 6 - \alpha 7\) element via a small hydrophobic interface formed by W382, Y384, L390 and the R124-E392 salt bridge (Fig. 5a). Disrupting this interface with the E379K / W382K substitutions (SpoT \(_{Ab}\) E379K/W382K) led to a 17- fold decrease in the hydrolase activity of the enzyme (Fig. 4d) suggesting that the HD:TGS interface constitutes as an important allosteric signal transduction pathway. This scaffolding role is complemented by the Core that wraps tightly around \(\alpha 7\) thus preventing the recoil of \(\alpha 6 - \alpha 7\) away of the HD active site, which, as we observed earlier in Rel \(_{Th}\) \(^{23}\) , induces the opening of the NTD. Indeed, substitutions at the Core: \(\alpha 6 - \alpha 7\) interface such as the aforementioned Y375G also affected hydrolysis (Fig. 4c- d). Interestingly, despite the strongly attenuated HD activity of SpoT \(_{Ab}\) E379K / W382K, SAXS showed SpoT \(_{Ab}\) E379K / W382K remains in the \(\tau\) - state \((R_{G} = 35\mathrm{\AA}, D_{MAX} = 104\mathrm{\AA})\) , suggesting an allosteric communication via the HD:Core:TGS axis (Fig. 5b and Supplementary Table 2). + +<|ref|>text<|/ref|><|det|>[[113, 549, 884, 788]]<|/det|> +Given that SpoT \(_{Ab}\) is SYNTH- inactive and is not specifically regulated by tRNA or ribosomes (Fig. 2d), it is not surprising that TGS residues involved in tRNA recognition – such as the crucial His residue involved in the recognition of the 3' CCA end by Rel \(^{21,28,42}\) and RelA \(^{19,31}\) (S407 in SpoT \(_{Ab}\) ) – are lost in the monofunctional SpoT \(_{Ab}\) (but are present in bifunctional SpoT \(_{Ec}\) \(^{1}\) ). Moreover, the \(\tau\) - state is sterically incompatible with the potential recognition of tRNA by TGS due to sequestration the \(\beta\) - hairpin and \(\alpha\) - helical elements. All these observations suggest that in SpoT \(_{Ab}\) the TGS has been repurposed as a scaffolding domain crucial to sustain hydrolysis, with both TGS and Core cooperating to lock the \(\alpha 6 - \alpha 7\) in place, stabilising the HD active site. This contrasts with its crucial function of recognition of uncharged tRNA in Rel/RelA \(^{19 - 21,28,31}\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 821, 727, 840]]<|/det|> +## The ZFD and RRM domains finetune the hydrolytic activity of SpoT \(_{Ab}\) + +<|ref|>text<|/ref|><|det|>[[115, 846, 883, 912]]<|/det|> +With ZFD and RRM positioned close to the disc- shaped cap and connecting with the pseudo- SYNTH domain, the resulting inter- domain interfaces are likely to play a role in the stability the \(\tau\) - state as well as to allosterically control of HD via the HD:pseudo- SYNTH relay. In + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 82, 883, 223]]<|/det|> +agreement with this hypothesis, disruptive substitutions at the Core:HD (L356D), Core:pseudo- SYNTH:RRM (A351K) and Core:ZFD (L373G / D374G) that decreased the stability of the \(\tau\) - state (Supplementary Fig. 3b- e) also decreased the HD activity of the enzyme by 35- , 3- and 22- fold, respectively (Fig. 4d). Therefore, we reasoned that substitutions stabilising the Core:pseudo- SYNTH:RRM and Core:ZFD interfaces would, conversely, trigger an allosteric activation of hydrolysis. + +<|ref|>text<|/ref|><|det|>[[113, 230, 884, 420]]<|/det|> +To probe this hypothesis, we introduced substitutions that would increase the contacts of RRM with pseudo- SYNTH via hydrogen bonds, I637D / R641D, and the Core with the ZDF, D374R (Fig. 5c). Denaturation experiments showed \(\mathrm{SpoT}_{Ab}\mathrm{D}^{374\mathrm{R}}\) and \(\mathrm{SpoT}_{Ab}^{1637\mathrm{D}} / \mathrm{R}641\mathrm{D}\) have higher stability and compactness than the WT (Supplementary Fig 4a- c) and SAXS measurements on \(\mathrm{SpoT}_{Ab}^{1637\mathrm{D}} / \mathrm{R}641\mathrm{D}\) confirmed this variant retained the \(\tau\) - state (Fig. 5d and Supplementary Table 2). As expected, the HD turnover of both enzyme variants increased (by 2.1- and 1.6- fold, respectively, Fig. 4d), and both behave like wild- type \(\mathrm{SpoT}_{Ab}\) in vivo (Fig. 5e). + +<|ref|>text<|/ref|><|det|>[[114, 428, 883, 520]]<|/det|> +Collectively, our results establish that HD activity is coupled to the stability of the \(\tau\) - state, with the Core domain working as an allosteric transducer that allows the catalytic HD to communicate with all the regulatory domains. Substitutions or interactions that stabilise the \(\tau\) - state increase hydrolysis, whereas \(\tau\) - state- destabilising substitutions lower the HD activity. + +<|ref|>sub_title<|/ref|><|det|>[[115, 550, 704, 567]]<|/det|> +## An intact \(\tau\) -shaped \(\mathrm{SpoT}_{Ab}\) is required for virulence of \(A\) . baumannii + +<|ref|>text<|/ref|><|det|>[[113, 572, 884, 912]]<|/det|> +Functional (p)ppGpp- mediated signalling plays a crucial role in antibiotic tolerance and virulence of \(A\) . baumannii \(^{15,43}\) . We used the wax moth \(G\) . mellonella larvae infection model to assess the functionality of mutant \(spo_{Ab}\) variants in supporting virulence of \(A\) . baumannii AB5075 (Fig. 5f). Only the strain with wild type- like virulence was the one expressing \(\mathrm{SpoT}_{Ab}\) D374R variant with a HD activity slightly higher than that of the WT \(\mathrm{SpoT}\) . The \(spo_{Ab}^{D374\mathrm{R}}\) strain has rapidly killed \(100\%\) of the larvae within the first two days whereas \(60\%\) of the larvae survived 6 days of infection with the (p)ppGpp \(^{0}\) \(\Delta relA\) strain. Infection with \(A\) . baumannii expressing the \(\Delta\) RRM- truncated enzyme \(\mathrm{SpoT}_{Ab}^{1 - 614}\) resulted in \(25\%\) survival rate of larvae after 6 days. Notably, the RRM- truncated \(\mathrm{SpoT}_{Ab}^{1 - 614}\) had 6- fold lower hydrolase activity as compared to wild type (Fig. 4d), and the strain displays no growth defects when grown on LB plates (Fig. 4a). The defect in virulence becomes more prominent with truncations beyond the TGS domain: \(\mathrm{SpoT}_{Ab}^{1 - 454}\) and \(\mathrm{SpoT}_{Ab}^{1 - 339}\) . The strong decrease in HD activity associated with the \(A\) . baumannii strains expressing these \(\mathrm{SpoT}\) variants results in \(100\%\) survival of the infected larvae (Fig. 5f). Collectively our results suggest that while a basal level of the HD hydrolase + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 82, 884, 151]]<|/det|> +activity is sufficient to sustain bacterial growth in non- stressed conditions (e.g. on a plate and in liquid culture), the pathogen requires fully functional CTD- and Core- mediated control of SpoTAb to tune the HD activity and efficiently establish a successful infection. + +<|ref|>sub_title<|/ref|><|det|>[[115, 182, 210, 198]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[113, 205, 884, 592]]<|/det|> +This study reveals the unexpected \(\tau\) - shaped architecture of full- length monofunctional SpoTAb, which enables auto- stimulation of the hydrolase activity of the enzyme by its CTD. With the loss of the synthetase function, the pseudo- SYNTH domain of SpoTAb becomes a regulatory and structure stabilising domain. Together with TGS, HEL, ZFD and RRM, pseudo- SYNTH defines the interaction network that transmits the allosteric signal from the CTD to the HD active site via the Core of the enzyme, to regulate (p)ppGpp hydrolysis. The Core element, together with the TGS and \(\mathrm{Mn}^{2 + }\) , aligns the active site residues of the HD in the correct position for catalysis. Compromising the functionality of either of these elements through substitutions of key residues results in major defects in hydrolysis activity. By contrast, pseudo- SYNTH, ZFD and RRM all subtly tune the HD activity of SpoTAb up or down by modulating its interactions with the Core. Interestingly, the ribosome- associated Rel/RelA (p)ppGpp synthetases, lacking the Core are non- functional in vivo and SYNTH- inactive, with the minimal enzyme version with SYNTH activity consisting of HD/pseudo- HD, SYNTH and Core domains22,28,36,41. Therefore, the presence of the Core and its crosstalk with the HD/pseudo- HD domain likely constitutes universal structural requirement for the efficient stabilization of the active states of long RSH enzymes. + +<|ref|>text<|/ref|><|det|>[[112, 597, 884, 912]]<|/det|> +We propose a unifying scheme that rationalises the evolution of the enzymatic output of long RSHs through fine- tuning of the conformational equilibrium of the \(\tau\) , relaxed and ribosome- bound states of these enzyme (Fig. 6). The very presence of catalytically- competent synthetase and hydrolase domains in bifunctional Rel[HS] and SpoT[HS] requires both the \(\tau\) and relaxed states as part of the conformational spectrum of these enzymes (Fig. 6a- b). While the \(\tau\) - state primes Rel/SpoT for efficient (p)ppGpp hydrolysis, the more elongated relaxed state sets the enzyme for low- efficiency (p)ppGpp synthesis. To fully activate its SYNTH activity, the enzyme needs to be further stimulated by starved ribosomes to attain the highly elongated ribosome- bound state; this transition is possible for amino acid starvation sensor Rel[HS], but not for SpoT, which is not under allosteric control by starved ribosomes and ppGpp36 (Fig. 6a). In the further subfunctionalised enzymes (dedicated hydrolase Moraxellaceae SpoT[HS] and dedicated synthetase RelA[hS]) the intrinsic structural equilibrium is limited to a subset of the conformations accessible to ancestral bifunctional Rel[HS] (Fig. 6c- d). Compared to + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 82, 883, 175]]<|/det|> +SpoT[HS], in SpoT[Hs] the equilibrium is further shifted towards the HD- active \(\tau\) - state required for hydrolysis (Fig. 6c). In contrast, in RelA[hS] the \(\tau\) - state becomes inaccessible, and the enzyme is primed for ribosomal recruitment upon which it is stabilised in the highly elongated ribosome- bound SYNTH- active state (Fig. 6d). + +<|ref|>text<|/ref|><|det|>[[114, 181, 884, 420]]<|/det|> +Expansion/contraction of the disordered regions is the likely molecular driver of the fine- tuning of the enzymatic output in long RSHs through the restriction of the conformational space (Supplementary Fig. 1c- e). Longer IDRs favour the relaxed state in RelA[hS] and increase the frustration of the enzyme, whereas the shorter IDRs favour the compact HD- active \(\tau\) - state in SpoT[Hs]. This genetic finetuning of a catalytic function, based on the optimization of the length and the forces generated by intrinsically disordered regions, is reminiscent of the evolution of human glucocorticoid receptor isoforms \(^{44}\) or the UDP- \(\alpha\) - d- glucose- 6- dehydrogenase \(^{45}\) . Such mechanisms seem to have evolved as a solution for conformationally heterogenous proteins with partially active resting states, that are under strong energetic and functional frustration. + +<|ref|>text<|/ref|><|det|>[[114, 426, 884, 592]]<|/det|> +The unifying scheme presented here provides a framework that can be used to rationalise the "hub" nature of SpoT and how binding partners such as the Acyl Carrier Protein (ACP) and the Regulator of RpoD - \(\sigma^{70}\) - (Rsd) could modulate its output \(^{46,47}\) or in the case of Rel/RelA how the ribosome prevents hydrolysis by exploiting this extensive allosteric network. Other protein partners of Rel such \(\mathrm{EIIA}^{\mathrm{NTR}}\) and \(\mathrm{Darb}^{41,48}\) could also modulate the intramolecular allosteric communication of the regulatory domains with HD by favouring of the \(\tau\) - or relaxed states, thus conditioning the catalytic output of the enzyme. + +<|ref|>sub_title<|/ref|><|det|>[[117, 625, 277, 641]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[114, 647, 884, 912]]<|/det|> +This work was supported by the Fonds National de Recherche Scientifique (FRFS- WELBIO CR- 2017S- 03, FNRS CDR J.0068.19, FNRS- EQP UN.025.19 and FNRS- PDR T.0066.18 to AGP); ERC (CoG DiStRes, \(\mathrm{n}^{\circ}\) 864311 to AGP) and Joint Programming Initiative on Antimicrobial Resistance, (JPIAMR) JPI- EC- AMR- R.8004.18 to AGP; the Program Actions de Recherche Concerté 2016- 2021, Fonds d'Encouragement à la Recherche of ULB (AGP); Fonds Jean Brachet and the Fondation Van Buuren (AGP); Chargé de Recherches fellowship from the FNRS \(\mathrm{n}^{\circ}\) CR/DM- 392 (HeT); the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska- Curie grant agreement \(\mathrm{N}^{\circ}\) 801505 (IF@ULB postdoctoral grant to AA) and (FRFS- WELBIO- CR- 2019S- 05 to RH). AP is an F.R.S. – FNRS Postdoctoral Researcher and RH is an F.R.S. – FNRS Research Associate. We are also grateful the Protein Expertise Platform at Umeå University for constructing plasmids. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 81, 884, 275]]<|/det|> +This work was supported by the European Regional Development Fund through the Centre of Excellence for Molecular Cell Technology (VH); project grant from the Knut and Alice Wallenberg Foundation (2020- 0037 to GCA); Ragnar Söderberg foundation (VH); Swedish Research council (2019- 01085 to GCA, 2017- 03783 and 2021- 01146 to VH, and 2018- 00956 to VH under the framework of Joint Programming Initiative on Antimicrobial Resistance, JPIAMR); the MIMS Excellence by Choice Postdoctoral Fellowship Programme grant 2018 (MR). The authors acknowledge the use of beamtimes PROXIMA 1 and 2A and SWING at the Soleil synchrotron (Gif- sur- Yvette, France). + +<|ref|>sub_title<|/ref|><|det|>[[116, 304, 303, 321]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[115, 328, 884, 420]]<|/det|> +VH and AGP drafted the manuscript with contributions from all authors. AGP and VH coordinated the study. VH, AGP, RH, HeT and MR designed experiments and analysed the data. MR, HiT, SZ, JP, AT, HeT, PK, AT, AP, HA, AA performed experiments. RD, GL and GCA performed bioinformatic analyses. + +<|ref|>sub_title<|/ref|><|det|>[[116, 454, 320, 470]]<|/det|> +## Declaration of interests + +<|ref|>text<|/ref|><|det|>[[116, 478, 466, 495]]<|/det|> +The authors declare no competing interests + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[55, 90, 884, 912]]<|/det|> +529 1. Atkinson, G.C., Tenson, T. & Hauryliuk, V. 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Nat Commun 12, 1210 (2021). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 83, 343, 100]]<|/det|> +## Main Figures and legends + +<|ref|>text<|/ref|><|det|>[[112, 106, 884, 425]]<|/det|> +Fig. 1. A. baumannii SpoT is a monofunctional alarTone hydrolase. (a) Evolution of long RSHs in Proteobacteria. Duplication of the ancestral bifunctional RSH Rel in Beta- and Gammaproteobacterial lineage gave rise to RelA and SpoT, leading to subfunctionalisation of RelA as monofunctional SYNTH- only alarTone synthetase and SpoT as a predominantly- HD RSH. In the Moraxellaceae family of Gammaproteobacteria, SpoT has undergone further subfunctionalisation, evolving into a monofunctional HD- only alarTone hydrolase. (b) Alignment of SYNTH- critical regions in long RSHs highlights the sequence divergence in Moraxellaceae SpoTs. (c) Co- expression of SpoT \(_{Ab}\) counteracts the growth defect in ppGpp \(^{0}\) \((\Delta relA \Delta spoT)\) E. coli caused by RelA expression. This demonstrates that SpoT \(_{Ab}\) is HD- active in the E. coli host. (d) While the SYNTH activity of ectopically expressed SpoT \(_{Ec}\) is essential and sufficient for promoting the growth of ppGpp \(^{0}\) E. coli on M9 minimal medium, SpoT \(_{Ab}\) fails to promote the growth of \(\Delta relA \Delta spoT\) E. coli on M9. This demonstrates that, unlike SpoT \(_{Ec}\) , which is SYNTH- active, SpoT \(_{Ab}\) is SYNTH- inactive. + +<|ref|>text<|/ref|><|det|>[[110, 453, 884, 916]]<|/det|> +Fig. 2. Full- length monomeric A. baumannii SpoT adopts a compact "mushroom"- shaped HD- active \(\tau\) - state. (a) Structure of "mushroom"- shaped SpoT \(_{Ab}\) - ppGpp complex in the \(\tau\) - state. The domain organization, from N to C terminus: NTD domains hydrolase (HD), pseudo- synthetase (pseudo- SYNTH) and Core, and CTD domains, TGS, Helical (HEL), Zn- finger (ZFD) and RNA recognition motif (RRM). The ppGpp alarTone is in red. (b) Cartoon representation the SpoT \(_{Ab}\) . The "stem" of the mushroom is formed by the enzymatic HD domain and the "cap" by the regulatory domains: NTD pseudo- SYNTH domain and the CTD domains. (c) Ribbon representation of the SpoT \(_{Ab}\) - ppGpp complex. The \(\alpha 6 / \alpha 7\) motif is held in the hydrolysis- compatible position by the folded Core domain and the TGS \(\beta\) - hairpin, with the Core domain communicating allosteric signals to HD from the regulatory domains. (d) The HD activity of SpoT \(_{Ab}\) is insensitive to the addition of E. coli 70S ribosomes, and non- specifically weakly inhibited by both aminoacylated and deacylated E. coli tRNA \(^{\mathrm{Val}}\) . (e) Analytical size exclusion chromatography (SEC) of SpoT \(_{Ab}\) supports its monomeric nature in solution. (f) Experimental X- ray scattering (SAXS) analysis of SpoT \(_{Ab}\) at 8 mg/mL further confirms the monomeric nature of SpoT \(_{Ab}\) . The analysis of the normalised Kratky plot (insert) of the SAXS curve reveals folded globular shape of SpoT \(_{Ab}\) . (g) Ab initio envelope of SpoT \(_{Ab}\) reconstructed from the experimental SAXS data superimposed on the crystal structure. Comparison of both models shows that in solution the enzyme adopts the same conformation as observed in the crystal. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 108, 885, 496]]<|/det|> +Fig. 3. A. baumannii SpoT is a \(\mathbf{Mn}^{2 + }\) - dependent (p)ppGpp hydrolase. (a) Surface representation of SpoT \(_{Ab}\) in the \(\tau\) - state. The active site cavity in the HD domain is boxed in dashed lines. (b) Zoom into the HD active site of the SpoT \(_{Ab}\) - ppGpp complex. The acidic half of the interface (residues K140, E82, D83, Y51 and R45) and the \(\mathrm{Mn}^{2 + }\) ion activate the water molecule for nucleophilic attack of the pyrophosphate bond pf ppGpp, while the basic half of the interface (K46, K158 and R161) stabilises the \(3^{\prime}\) and \(5^{\prime}\) phosphates of the alarMone substrate. (c) Ribbon representation of the active site of SpoT \(_{Ab}\) revealing the residues involved in coordination of ppGpp. (d) Effects of Ala-substitutions in the ppGpp binding site on the HD activity of SpoT \(_{Ab}\) . The residues for substitution were selected as per (c). (e) ITC titration of \(\mathrm{Mn}^{2 + }\) into unliganded apo- SpoT \(_{Ab}\) . (f) Hydrolase activity of unliganded apo- SpoT \(_{Ab}\) as a function of increasing concentrations of \(\mathrm{Mn}^{2 + }\) . (g) Structure of the \(\mathrm{Mn}^{2 + }\) - free N-terminal region of SpoT \(_{Ab}\) , \(\mathrm{SpoT}_{Ab}^{\mathrm{NTD}}\) . The HD domain is in purple and the pseudo- SYNTH is in yellow. The disordered active site is labeled. (h) Superposition of the HD domain of SpoT \(_{Ab}\) complexed with ppGpp (in light blue) onto \(\mathrm{Mn}^{2 + }\) - free SpoT \(_{Ab}\) (in purple). The key conformation differences in catalytically- crucial active site residues and the structural elements \(\alpha 3\) , \(\alpha 4\) and \(\alpha 8\) are highlighted as dashed arrows and shown in bold, respectively. + +<|ref|>text<|/ref|><|det|>[[111, 525, 885, 912]]<|/det|> +Fig. 4. The CTD controls the hydrolysis activity of SpoT by controlling the equilibrium between HD- active \(\tau\) - state and HD- inactive relaxed conformations. (a) The HD functionality test of truncated versions of SpoT \(_{Ab}\) . SpoT \(_{Ab}\) variants were co- expressed expressed with RelA \(_{Ab}\) in \(\Delta relA\Delta spoT\) Ptac::relA A. baumannii (AB5075). The ability of SpoT \(_{Ab}\) to promote the growth is reflective of its HD competence. (b) Hydrolase activity of SpoT \(_{Ab}\) and the C- terminally truncated SpoT \(_{Ab}\) variants. Tumovers corresponding to each protein variant are coloured as per the domain colour code on (a). (c) Cartoon representation of the allosteric network defined by the Core domain connecting the domains of the enzyme in the \(\tau\) - state. The key interface residues are shown as sticks and labeled. (d) Hydrolase activity of crucial Core residues involved in interactions with other domains of SpoT \(_{Ab}\) (A384R contacting SYNTH, A351K contacting RRM, L356D contacting HD, L373G/D374G contacting ZFD, Y375G contacting the TGS). The TGS:HD interface is also probed with the E379K/W382K point mutant and \(\Delta\) TGS- HEL versions. The \(\tau\) - state stabilising substitutions D374R and I637D/R641D increase the HD activity. (e, f) SAXS curves of L356D in the \(\tau\) - state (e) or relaxed state (f). (g) Pseudo- atomic model of the relaxed state of SpoT \(_{Ab}\) calculated with Dadimodo (Evrard et al., 2011) using the experimental SAXS data from (f). (h) Comparison of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 82, 884, 200]]<|/det|> +the experimental SAXS data from the relaxed state of L356D (in grey) with the theoretical scattering curve of the relaxed state (solid line) obtained from the Dadimodo model. (i) SAXS curve of RelAAb is consistent with the dimensions of the relaxed state. (j, k) SAXS curves of RelBs in the τ-state (j) or relaxed state (k). (l) Cartoon representation of experimentally observed conformational states as well as particle dimensions of long RSH enzymes. + +<|ref|>text<|/ref|><|det|>[[113, 229, 884, 740]]<|/det|> +Fig. 5. The Core domain of SpoT transduces the allosteric signal from the regulatory CTD and pseudo- SNTH to the enzymatic HD domain. (a, b) Cartoon representation of the interactions stabilising the α6- α7 motif of the HD active site (A). While the Core wraps around α7, the TGS β- hairpin forms a small hydrophobic patch that stabilises α6. These interactions preclude the movement of α6- α7 and maintain SpoTAb in a constitutive hydrolase- primed state. Key interface residues are shown as sticks and labelled. (b) The experimental SAXS curve of SpoTAbE379K/W382K is consistent with the dimensions of the τ- state. Cartoon representation of the HD:Core:RRM signal transduction axis. (c) The architecture of the τ- state suggests that the RRM is locked in place via the Core and supporting interaction provided by pseudo- SYNTH, suggesting that additional tethering of RRM to pseudo- SYNTH could further stabilise the τ- conformation. (d) SAXS curve of the SpoTAb1637D/R641D variant in which substitutions I637D/R641D and I637A/R641A promote H- bonding and stabilise the α- helical structure, respectively, is consistent with the dimensions of the τ- state. (e) In vivo HD functionality tests of SpoTAb variants D374G and I637D/R641D expressed from the inducible Ptac promoter in a ΔrelAΔspoT background of A. baumannii (AB5075) expressing relA from a replicative plasmid (pPrelA::relA). The stabilising substitutions D374R and I637D/R641D phenocopy the WT. (f) Virulence assays in the G. mellonella infection model demonstrate the essentiality of intact allosteric regulation of SpoTAb for virulence. G. mellonella larvae were injected with ≈2x10 CFU of A. baumannii (AB5075) strains (10 μl at ≈2x107 CFU/mL), eight larvae were inoculated per strain, and incubated at 37 °C in the dark. The viability of the larvae was scored every 24 h. + +<|ref|>text<|/ref|><|det|>[[114, 771, 884, 912]]<|/det|> +Fig. 6. The enzymatic output of subfunctionalised RelA and SpoT RSH enzymes is evolutionarily tuned through constrains of the conformational landscape. (a) Control of the enzymatic output of the ancestral bifunctional Rel[HS]. Upon amino acid starvation Rel is recruited to starved ribosomal complexes. The ribosome- bound Rel assumes an extended conformation in which the auto- inhibitory effect of the CTD region on the SYNTH activity is released. The full activation of SYNTH activity is achieved upon binding of (p)ppGpp to an + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 78, 885, 571]]<|/det|> +allosteric site within the NTD and release of the SYNTH inhibition by the HD domain. Conversely, off the ribosome the enzyme assumes the \(\tau\) - state. In this conformation locking of the \(\alpha 6 - \alpha 7\) motif by the CTD organises the HD active site residues to promote the HD activity. This, in turn, strongly inhibits the SYNTH activity via inter- NTD regulation. The full activation of either SYNTH or HD requires allosteric signalling from CTD to NTD enzymatic domains. (b, c) Evolution of SpoT as a predominantly dedicated hydrolase involved the loss of the allosteric control of the NTD by (p)ppGpp as well as by the ribosome. In bifunctional SpoT[HS] present in the majority of Gamma- and Betaproteobacteria, while the equilibrium is strongly shifted towards the HD- active \(\tau\) - state, the enzyme is capable of inefficient (p)ppGpp synthesis in the relaxed state (B). Subfunctionalisation of SpoT in Moraxellaceae has resulted in the monofunctional hydrolase SpoT[Hs], which naturally populates only the compact \(\tau\) - state and is SYNTH- inactive. (d) Subfunctionalisation of Gamma- and Betaproteobacterial RelA[hS] constitutes the other extreme case of evolutionary restriction of the conformational dynamics of the ancestral Rel[HS]. While losing its HD activity, RelA retains all the allosteric regulatory elements of Rel. Being a dedicated (p)ppGpp synthetase enzyme, off the ribosome RelA does not assume the \(\tau\) - state. Instead, it predominantly populates the functionally frustrated resting state equivalent to the relaxed state of Rel, primed to assume the elongated ribosome- associated state triggered by the 70S ribosome, uncharged tRNA and alarmones during stringency. Red circles represent inhibited catalytic centres, green circles represent fully activated catalytic centres, and dashed green circles represent idling catalytic centres. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 143, 68]]<|/det|> +## Figures + +<|ref|>sub_title<|/ref|><|det|>[[44, 131, 115, 150]]<|/det|> +## Figure 1 + +<|ref|>text<|/ref|><|det|>[[40, 171, 958, 444]]<|/det|> +A. baumannii SpoT is a monofunctional alarome hydrolase. (a) Evolution of long RSHs in Proteobacteria. Duplication of the ancestral bifunctional RSH Rel in Beta- and Gammaproteobacterial lineage gave rise to RelA and SpoT, leading to subfunctionalisation of RelA as monofunctional SYNTH-only alarome synthetase and SpoT as a predominantly-HD RSH. In the Moraxellaceae family of Gammaproteobacteria, SpoT has undergone further subfunctionalisation, evolving into a monofunctional HD-only alarome hydrolase. (b) Alignment of SYNTH-critical regions in long RSHs highlights the sequence divergence in Moraxellaceae SpoTs. (c) Co-expression of SpoTAb counteracts the growth defect in ppGpp0 (DrelA DspoT) E. coli caused by RelA expression. This demonstrates that SpoTAb is HD-active in the E. coli host. (d) While the SYNTH activity of ectopically expressed SpoTEc is essential and sufficient for promoting the growth of ppGpp0 E. coli on M9 minimal medium, SpoTAb fails to promote the growth of DrelA DspoT E. coli on M9. This demonstrates that, unlike SpoTEc, which is SYNTH-active, SpoTAb is SYNTH-inactive. + +<|ref|>sub_title<|/ref|><|det|>[[44, 499, 117, 518]]<|/det|> +## Figure 2 + +<|ref|>text<|/ref|><|det|>[[39, 538, 951, 927]]<|/det|> +Full- length monomeric A. baumannii SpoT adopts a compact "mushroom"- shaped HD- active \(\tau\) - state. (a) Structure of "mushroom"- shaped SpoTAb- ppGpp complex in the \(\tau\) - state. The domain organization, from N to C terminus: NTD domains hydrolase (HD), pseudo synthetase (pseudo- SYNTH) and Core, and CTD domains, TGS, Helical (HEL), Zn- finger (ZFD) and RNA recognition motif (RRM). The ppGpp alarome is in red. (b) Cartoon representation the SpoTAb. The "stem" of the mushroom is formed by the enzymatic HD domain and the "cap" by the regulatory domains: NTD pseudo- SYNTH domain and the CTD domains. (c) Ribbon representation of the SpoTAb- ppGpp complex. The a6/a7 motif is held in the hydrolysis- compatible position by the folded Core domain and the TGS \(\beta\) - hairpin, with the Core domain communicating allosteric signals to HD from the regulatory domains. (d) The HD activity of SpoTAb is insensitive to the addition of E. coli 70S ribosomes, and non- specifically weakly inhibited by both aminoacylated and deacetylated E. coli tRNAVal. (e) Analytical size exclusion chromatography (SEC) of SpoTAb supports its monomeric nature in solution. (f) Experimental X- ray scattering (SAXS) analysis of SpoTAb at 8 mg/mL further confirms the monomeric nature of SpoTAb. The analysis of the normalised Kratky plot (insert) of the SAXS curve reveals folded globular shape of SpoTAb. (g) Ab initio envelope of SpoTAb reconstructed from the experimental SAXS data superimposed on the crystal structure. Comparison of both models shows that in solution the enzyme adopts the same conformation as observed in the crystal. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 82, 116, 101]]<|/det|> +## Figure 3 + +<|ref|>text<|/ref|><|det|>[[40, 123, 952, 440]]<|/det|> +A. baumannii SpoT is a Mn2+-dependent (p)ppGpp hydrolase. (a) Surface representation of SpoTAb in the \(\tau\) -state. The active site cavity in the HD domain is boxed in dashed lines. (b) Zoom into the HD active site of the SpoTAb-ppGpp complex. The acidic half of the interface (residues K140, E82, D83, Y51 and R45) and the Mn2+ ion activate the water molecule for nucleophilic attack of the pyrophosphate bond pf ppGpp, while the basic half of the interface (K46, K158 and R161) stabilises the 3' and 5' phosphates of the alarome substrate. (c) Ribbon representation of the active site of SpoTAb revealing the residues involved in coordination of ppGpp. (d) Effects of Ala-substitutions in the ppGpp binding site on the HD activity of SpoTAb. The residues for substitution were selected as per (c). (e) ITC titration of Mn2+ into unliganded apo-SpoTAb. (f) Hydrolase activity of unliganded apo-SpoTAb as a function of increasing concentrations of Mn2+. (g) Structure of the Mn2+-free N-terminal region of SpoTAb, SpoTAbNTD. The HD domain is in purple and the pseudo-SYNTH is in yellow. The disordered active site is labeled. (h) Superposition of the HD domain of SpoTAb complexed with ppGpp (in light blue) onto Mn2+-free SpoTAb (in purple). The key conformation differences in catalytically-crucial active site residues and the structural elements a3, a4 and a8 are highlighted as dashed arrows and shown in bold, respectively. + +<|ref|>sub_title<|/ref|><|det|>[[43, 496, 116, 515]]<|/det|> +## Figure 4 + +<|ref|>text<|/ref|><|det|>[[39, 536, 950, 923]]<|/det|> +The CTD controls the hydrolysis activity of SpoT by controlling the equilibrium between HD- active \(\tau\) - state and HD- inactive relaxed conformations. (a) The HD functionality test of truncated versions of SpoTAb. SpoTAb variants were co- expressed expressed with RelAAb in \(\Delta\) relA \(\Delta\) spoT Ptac::relA A. baumannii (AB5075). The ability of SpoTAb to promote the growth is reflective of its HD competence. (b) Hydrolase activity of SpoTAb and the C- terminally truncated SpoTAb variants. Turnovers corresponding to each protein variant are coloured as per the domain colour code on (a). (c) Cartoon representation of the allosteric network defined by the Core domain connecting the domains of the enzyme in the \(\tau\) - state. The key interface residues are shown as sticks and labeled. (d) Hydrolase activity of crucial Core residues involved in interactions with other domains of SpoTAb (A384R contacting SYNTH, A351K contacting RRM, L356D contacting HD, L373G/D374G contacting ZFD, Y375G contacting the TGS). The TGS:HD interface is also probed with the E379K/W382K point mutant and \(\Delta\) TGS- HEL versions. The \(\tau\) - state stabilising substitutions D374R and I637D/R641D increase the HD activity. (e, f) SAXS curves of L356D in the \(\tau\) - state (e) or relaxed state (f). (g) Pseudo-atomic model of the relaxed state of SpoTAb calculated with Dadimodo (Evrard et al., 2011) using the experimental SAXS data from (f). (h) Comparison of 22 the experimental SAXS data from the relaxed state of L356D (in grey) with the theoretical scattering curve of the relaxed state (solid line) obtained from the Dadimodo model. (i) SAXS curve of RelAAb is consistent with the dimensions of the relaxed state. (j, k) SAXS curves of RelBs in the \(\tau\) - state (j) or relaxed state (k). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 861, 88]]<|/det|> +(I) Cartoon representation of experimentally observed conformational states as well as particle dimensions of long RSH enzymes. + +<|ref|>sub_title<|/ref|><|det|>[[43, 142, 117, 162]]<|/det|> +## Figure 5 + +<|ref|>text<|/ref|><|det|>[[40, 181, 950, 614]]<|/det|> +The Core domain of SpoT transduces the allosteric signal from the regulatory CTD and pseudo- SNTH to the enzymatic HD domain. (a, b) Cartoon representation of the interactions stabilising the a6- a7 motif of the HD active site (A). While the Core wraps around a7, the TGS \(\beta\) - hairpin forms a small hydrophobic patch that stabilises a6. These interactions preclude the movement of a6- a7 and maintain SpoTAb in a constitutive hydrolase- primed state. Key interface residues are shown as sticks and labelled. (b) The experimental SAXS curve of SpoTAbE379K/W382K is consistent with the dimensions of the \(\tau\) - state. Cartoon representation of the HD:Core:RRM signal transduction axis. (c) The architecture of the \(\tau\) - state suggests that the RRM is locked in place via the Core and supporting interaction provided by pseudo- SYNTH, suggesting that additional tethering of RRM to pseudo- SYNTH could further stabilise the \(\tau\) conformation. (d) SAXS curve of the SpoTAbI637D/R641D variant in which substitutions I637D/R641D and I637A/R641A promote H- bonding and stabilise the \(\alpha\) - helical structure, respectively, is consistent with the dimensions of the \(\tau\) - state. (e) In vivo HD functionality tests of SpoTAb variants D374G and I637D/R641D expressed from the inducible Ptac promoter in a \(\Delta\) relA\(\Delta\) spoT background of A. baumannii (AB5075) expressing relA from a replicative plasmid (pPrelA::relA). The stabilising substitutions D374R and I637D/R641D phenocopy the WT. (f) Virulence assays in the G. mellonella infection model demonstrate the essentiality of intact allosteric regulation of SpoTAb for virulence. G. mellonella larvae were injected with \(\approx 2 \times 10\) CFU of A. baumannii (AB5075) strains (10 \(\mu\) l at \(\approx 2 \times 10\)7 CFU/mL), eight larvae were inoculated per strain, and incubated at 37 °C in the dark. The viability of the larvae was scored every 24 h. + +<|ref|>sub_title<|/ref|><|det|>[[43, 670, 116, 690]]<|/det|> +## Figure 6 + +<|ref|>text<|/ref|><|det|>[[40, 710, 950, 962]]<|/det|> +The enzymatic output of subfunctionalised RelA and SpoT RSH enzymes is evolutionarily tuned through constrains of the conformational landscape. (a) Control of the enzymatic output of the ancestral bifunctional Rel[HS]. Upon amino acid starvation Rel is recruited to starved ribosomal complexes. The ribosome- bound Rel assumes an extended conformation in which the auto- inhibitory effect of the CTD region on the SYNTH activity is released. The full activation of SYNTH activity is achieved upon binding of (p)ppGpp to an 23 allosteric site within the NTD and release of the SYNTH inhibition by the HD domain. Conversely, off the ribosome the enzyme assumes the \(\tau\) - state. In this conformation locking of the a6- a7 motif by the CTD organises the HD active site residues to promote the HD activity. This, in turn, strongly inhibits the SYNTH activity via inter- NTD regulation. The full activation of either SYNTH or HD requires allosteric signalling from CTD to NTD enzymatic domains. (b, c) Evolution of SpoT as a predominantly dedicated hydrolase involved the loss of the allosteric control of the NTD by (p)ppGpp as + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[40, 44, 956, 338]]<|/det|> +well as by the ribosome. In bifunctional SpoT[HS] present in the majority of Gamma- and Betaproteobacteria, while the equilibrium is strongly shifted towards the HD- active t- state, the enzyme is capable of inefficient (p)ppGpp synthesis in the relaxed state (B). Subfunctionalisation of SpoT in Moraxellaceae has resulted in the monofunctional hydrolase SpoT[Hs], which naturally populates only the compact t- state and is SYNTH- inactive. (d) Subfunctionalisation of Gamma- and Betaproteobacterial RelA[hS] constitutes the other extreme case of evolutionary restriction of the conformational dynamics of the ancestral Rel[HS]. While losing its HD activity, RelA retains all the allosteric regulatory elements of Rel. Being a dedicated (p)ppGpp synthetase enzyme, off the ribosome RelA does not assume the t- state. Instead, it predominantly populates the functionally frustrated resting state equivalent to the relaxed state of Rel, primed to assume the elongated ribosome- associated state triggered by the 70S ribosome, uncharged tRNA and alarmones during stringency. Red circles represent inhibited catalytic centres, green circles represent fully activated catalytic centres, and dashed green circles represent idling catalytic centres. + +<|ref|>sub_title<|/ref|><|det|>[[44, 361, 311, 388]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 411, 765, 432]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 450, 527, 628]]<|/det|> +- SupplementalDataDec.xlsx- D1292120009valreportfullP1.pdf- Tammanv26Onlinemethods.pdf- Tammanv26SupplementalFigures.pdf- Tammanv26SupplementalTables.pdf- abuSpoTppGppfinalaltgood.pdf- nreditorialpolicychecklistNCHEMBA220114337.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__089483f12365d3a631f34ad6f3c9ff251e698c756a3d693ffcf84f0664f35fd7/images_list.json b/preprint/preprint__089483f12365d3a631f34ad6f3c9ff251e698c756a3d693ffcf84f0664f35fd7/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..348dad3d9b4f1c0556ccca92708dafcaddc1c178 --- /dev/null +++ b/preprint/preprint__089483f12365d3a631f34ad6f3c9ff251e698c756a3d693ffcf84f0664f35fd7/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1. The significance and synthetic challenges of heteroaromatic swapping. a, Importance of converting aromatic rings into heteroaromatic rings. b, One-step conversion of aromatic rings to heteroaromatic rings using state-of-the-art techniques (heteroaromatic swapping). c, Challenges and opportunities in the synthesis of aromatic and heteroaromatic ketones. d, Heteroaromatic swapping via Claisen/retro-Claisen reactions.", + "footnote": [], + "bbox": [ + [ + 122, + 85, + 880, + 555 + ] + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2. Substrate scope under optimized reaction conditions. a, Optimized reaction conditions. b, Scope for heteroaromatic esters: 1a (0.20 mmol), 2 (1.5 equiv), NaH (2.0 equiv), THF, 60 °C, 6 h. c, Scope for aromatic ketones: 1 (0.20 mmol), 2A (1.5 equiv), NaH (2.0 equiv), THF, 60 °C, 6 h. aYields were determined by \\(^1\\mathrm{H}\\) NMR analysis. b2A (3.0 equiv) and NaH (3.0 equiv) were added, and reactions were performed at 80 °C. cNaHMDS (1.0 equiv) was used instead of NaH, and reactions were performed at room temperature.", + "footnote": [], + "bbox": [ + [ + 120, + 85, + 866, + 655 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3. Mechanistic investigations. a, Investigation of reversibility of products. b, Retro-Claisen condensation of 5wA. c, Time course plot of heteroaromatic swapping. d, Proposed mechanism.", + "footnote": [], + "bbox": [ + [ + 120, + 100, + 844, + 622 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig 4. Applications of heteroaromatic swapping in complex substrates. a, Heteroaromatic swapping of esters derived from APIs and natural products. b, Heteroaromatic swapping of ketones derived from APIs. c, Further derivatization and expansion of the obtained products.", + "footnote": [], + "bbox": [ + [ + 120, + 80, + 875, + 789 + ] + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/preprint/preprint__089483f12365d3a631f34ad6f3c9ff251e698c756a3d693ffcf84f0664f35fd7/preprint__089483f12365d3a631f34ad6f3c9ff251e698c756a3d693ffcf84f0664f35fd7.mmd b/preprint/preprint__089483f12365d3a631f34ad6f3c9ff251e698c756a3d693ffcf84f0664f35fd7/preprint__089483f12365d3a631f34ad6f3c9ff251e698c756a3d693ffcf84f0664f35fd7.mmd new file mode 100644 index 0000000000000000000000000000000000000000..6a25ced01c02586f03bca992d5bd04ff1a593be1 --- /dev/null +++ b/preprint/preprint__089483f12365d3a631f34ad6f3c9ff251e698c756a3d693ffcf84f0664f35fd7/preprint__089483f12365d3a631f34ad6f3c9ff251e698c756a3d693ffcf84f0664f35fd7.mmd @@ -0,0 +1,221 @@ + +# Heteroaromatic Swapping in Aromatic Ketones + +Junichiro Yamaguchi junyamaguchi@waseda.jp + +Waseda University https://orcid.org/0000- 0002- 3896- 5882 + +Hikaru Nakahara Waseda University https://orcid.org/0000- 0001- 9170- 7703 + +Ryotaro Shirai Waseda University Yoshio Nishimoto Kyoto University Daisuke Yokogawa University of Tokyo + +Article + +Keywords: + +Posted Date: April 18th, 2025 + +DOI: https://doi.org/10.21203/rs.3. rs- 5583133/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on October 9th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 64041- 6. + +<--- Page Split ---> + +# Heteroaromatic Swapping in Aromatic Ketones + +Hikaru Nakaharaa, Ryotaro Shiraia, Yoshio Nishimotob, Daisuke Yokogawac, and Junichiro Yamaguchia\* a Department of Applied Chemistry, Waseda University, 513 Wasedatsurumakicho, Shinjuku, Tokyo 162- 0041, Japan. b Graduate School of Science, Kyoto University, Kyoto 606- 8502, Japan. c Graduate School of Arts and Sciences, The University of Tokyo, 3- 8- 1 Komaba, Meguro- ku, Tokyo 153- 8902, Japan. + +KEYWORDS Aromatic ketones, Claisen/retro Claisen Condensation, Heteroarenes, Metathesis, Aromatic Exchange Email: Junichiro Yamaguchi – junyamaguchi@waseda.jp \* Corresponding author + +## Abstract + +The modification of aromatic rings to heteroaromatic rings is a widely employed strategy in medicinal chemistry, often used to modulate lipophilicity and improve metabolic stability1,2. However, achieving a one- step, generalizable transformation of aromatic rings into diverse heteroaromatic rings—termed "heteroaromatic swapping"—remains a persistent challenge. Existing methods, such as skeletal editing3 and transition- metal- catalyzed aromatic ring exchange4,5, are limited in substrate scope and efficiency. Here, we present an efficient strategy for heteroaromatic swapping via a Claisen/retro- Claisen mechanism6, utilizing heteroaryl esters and aromatic ketones. This approach enables the selective exchange of aromatic rings with heteroaromatic rings across a broad substrate range, overcoming the limitations of existing techniques. Notably, it achieves high- yield conversions of bioactive aromatic ketones into their heteroaromatic counterparts. This method expands the molecular editing toolkit, offering a practical and versatile platform for synthesizing bioactive compounds with enhanced physicochemical properties. + +## Introduction + +Aromatic rings are fundamental building blocks in organic chemistry, playing a pivotal role in the structure and properties of a large variety of compounds, both of natural and synthetic origin. In bioactive molecules, they often serve as key structural elements, where their modification can profoundly influence both biological activity and selectivity. Substituting aromatic rings with heteroaromatic rings typically results in lower lipophilicity, a physicochemical parameter that positively impacts a compound's aqueous solubility, off- target toxicity, and some pharmacokinetic parameters (Fig. 1a)7,8. Prominent examples include the antiepileptic drug perampanel, adenosine A2A receptor antagonists, and hepatitis C therapies, where heteroaromatic substitutions (e.g., pyridine, pyrazole, and pyridazine) have significantly enhanced both efficacy and safety profiles9- 11. In organic synthesis, the ability to seamlessly convert aromatic rings into diverse heteroaromatic rings in a single step—referred to here as heteroaromatic + +<--- Page Split ---> + +swapping—would represent a transformative advance, greatly expanding the scope of synthetic methodologies. Among existing approaches, skeletal editing, which introduces nitrogen atom(s) into aromatic rings, is particularly notable (Fig. 1b). This strategy, primarily involving aryl azides to generate pyridine rings via rearrangement, holds significant potential. \(^{12 - 17}\) . However, while methods for nitrogen removal from aromatic rings are well- established \(^{18 - 21}\) , efficient and generalizable strategies for nitrogen incorporation remain limited. Similarly, transition- metal- catalyzed aromatic ring exchange reactions offer a promising route for transforming aromatic rings into heteroaromatic counterparts, yet successful examples involving heteroaromatic rings are exceedingly rare \(^{22 - 26}\) . Other ring transformation methodologies and molecular editing strategies for ketones offer alternative solutions, but these methods often introduce undesired functional groups or require multiple steps, limiting their utility \(^{27 - 30}\) . + +Aromatic ketones are particularly well- suited for heteroaromatic swapping due to their highly reactive carbonyl groups, which allow versatile transformations into a wide range of functional groups. These compounds feature highly reactive carbonyl groups, making them valuable synthetic intermediates and key structural motifs in a wide range of pharmaceuticals, including both aromatic and heteroaromatic derivatives (Fig. 1c). While aromatic ketones derived from benzene rings are often accessible through de novo synthesis, heteroaromatic ketones typically require multistep protocols to introduce heteroaromatic rings onto carbonyl groups, leading to inefficiency. \(^{31 - 34}\) A general, one- step method for the direct conversion of aromatic ketones into heteroaromatic ketones at a late stage would significantly accelerate the derivatization and development of novel compounds in medicinal chemistry. Such a strategy would enable access to structurally diverse molecules, thereby broadening the chemical space available for drug discovery and development. To address this challenge, we developed a strategy for heteroaromatic swapping of aromatic ketones by leveraging the classical Claisen/retro- Claisen reaction \(^{6,35,36}\) . We hypothesized that a reaction of aromatic ketones with heteroaromatic esters to form diketones, followed by a selective retro- Claisen reaction at the heteroaromatic site, would yield the desired heteroaromatic ketones and aromatic esters (Fig. 1d). While traditional textbooks describe Claisen reactions as driven by enolate stabilization through deprotonation of \(\beta\) - ketoesters, Claisen condensations between dissimilar partners—specifically ketones and esters—have rarely been explored. We hypothesized that diketones with an electronic bias favoring the ketone might undergo selective retro- Claisen reactions under carefully optimized conditions. Unexpectedly, the reaction proceeded with high selectivity under mild conditions, affording the desired heteroaromatic ketones in excellent yields. This discovery establishes a highly efficient and straightforward method for heteroaromatic swapping of aromatic ketones, providing a versatile tool for molecular editing that is compatible with late- stage functionalization of bioactive molecules. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Fig. 1. The significance and synthetic challenges of heteroaromatic swapping. a, Importance of converting aromatic rings into heteroaromatic rings. b, One-step conversion of aromatic rings to heteroaromatic rings using state-of-the-art techniques (heteroaromatic swapping). c, Challenges and opportunities in the synthesis of aromatic and heteroaromatic ketones. d, Heteroaromatic swapping via Claisen/retro-Claisen reactions.
+ +## Reaction Conditions and Scope + +We initiated our study by exploring the Claisen/retro- Claisen reaction between aromatic ketone 1a and methyl picolinate (2A) (Fig. 2a). Treatment 1a with 2A (1.5 equiv) and NaH (2.0 equiv) in THF at \(60^{\circ}\mathrm{C}\) for 6 h under the optimized conditions resulted in a highly efficient reaction, furnishing the desired pyridyl ketone 3aA in \(91\%\) yield, along with the corresponding aromatic ester 4a in \(65\%\) yield. Due to relatively high volatility of 4a under reduced pressure, accurate quantification of its yield remained challenging (Supplementary Table S1). The reaction also proceeded at room temperature, and the use of only 1.0 equiv of 2A was sufficient to achieve productive conversion (61% yield). Notably, the protocol is not limited to methyl esters; benzyl esters reacted smoothly, while bulkier esters diminished reactivity. The use of a base + +<--- Page Split ---> + +capable of irreversibly deprotonating the substrate was found to be essential for the reaction to proceed (Supplementary Table S2 and S3). + +We next investigated the substrate scope with the respect to heteroaromatic esters (Fig. 2b). A broad range of heteroaromatic esters proved compatible under the optimized conditions. Positional isomers of pyridine (2B and 2C), as well as other six- membered nitrogen containing heteroaromatic esters, including pyrimidine, pyrazine, pyridazine, and quinoline, provided the corresponding ketones 3aB- 3al in good yields. The reaction also demonstrated excellent functional group tolerance, accommodating halogens, esters, trifluoromethyl groups, and alkoxy groups, as evidenced by formation of ketones 3aJ- 3aR. Furthermore, five- membered heteroaromatic esters including furan, thiophene, and various azoles, also afforded the desired ketones 3aS- 3aX in good yields. However, no reaction occurred with methyl esters of pyrrole or heteroaromatic esters bearing a methyl ester at the C3 position. Comparative studies revealed that methyl picolinate (2A) exhibited superior reactivity relative to other heteroaromatic esters (Supplementary Fig. S1 and S2). In addition, reaction using aromatic esters bearing substituents at the C4 position were carried out, and a Hammett analysis based on these derivatives showed a positive \(\rho\) value \((p > 0)\) , indicating that electron- deficient aromatic rings facilitate the transformation (Supplementary Fig. S3). These results support the conclusion that electron- deficient heteroaromatic esters, such as 2A, are paticularly effective in promoting the reaction. + +Next, we examined the structural scope of the ketone component (Fig. 2c). The reaction proceeded regardless of substituents on the aryl group of the ketone (1b- 1g), and was compatible with bulky alkyl groups, benzyl groups, and ketones containing oxidation- or light- sensitive functionalities such as ferrocene and pyrene (3hA and 3kA). Acid- and base- sensitive functional groups, including oxetane, linear amines, and cyclic amines, were also well tolerated (3IA- 3qA). The transformation was applicable to \(\alpha\) - disubstituted ketones, although these required an excess amount of ester (3.0 equiv) and higher reaction temperatures (80 °C) (3rA and 3sA). For cyclic ketones, ring- opening heteroarylation was achieved; using NaHMDS as the base under milder conditions further improved the yields (3tA and 3uA). Interestingly, the reaction also proceeded with dialkyl ketones, suggesting potential for desymmetrization of such substrates (3vA). Notably, heteroaromatic swapping was more facile with \(\alpha\) - disubstituted ketones, which is surprising given that such substrates typically require harsher conditions (3rA: 0% vs 3wA: 59%). Moreover, benzyl ketones and alkyl ketones exhibited nearly equivalent reactivity (3bA: 22% vs 3iA: 20%). Collectively, these findings highlight the broad substrate scope of the method, with exceptions observed mainly for electron- rich esters and \(\alpha\) - unsubstituted ketones, such as methyl ketones (Supplementary Fig S4). + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2. Substrate scope under optimized reaction conditions. a, Optimized reaction conditions. b, Scope for heteroaromatic esters: 1a (0.20 mmol), 2 (1.5 equiv), NaH (2.0 equiv), THF, 60 °C, 6 h. c, Scope for aromatic ketones: 1 (0.20 mmol), 2A (1.5 equiv), NaH (2.0 equiv), THF, 60 °C, 6 h. aYields were determined by \(^1\mathrm{H}\) NMR analysis. b2A (3.0 equiv) and NaH (3.0 equiv) were added, and reactions were performed at 80 °C. cNaHMDS (1.0 equiv) was used instead of NaH, and reactions were performed at room temperature.
+ +## Mechanistic Investigations of the Reaction + +To elucidate the reaction mechanism, we systematically examined key factors governing its selectivity and reversibility. Our initial observations established the following critical requirements for the reaction to proceed: (1) The ketone substrate must possess an electronically neutral or electron- rich aromatic ring, while the ester substrate must have a relatively electron- deficient aromatic ring; (2) The ketone must + +<--- Page Split ---> + +bear a substituent at the \(\alpha\) - position; (3) The anticipated key intermediate, a 1,3- diketone, was never detected under the reaction conditions. These findings suggest that the inherent electronic properties of the substrates facilitate rapid fragmentation of the diketone intermediate, shifting the equilibrium toward product formation. To validate this hypothesis, we conducted a series of experiments. + +First, we subjected the reaction products, ketone 3wA and ester 4a, to the optimized reaction conditions (Fig. 3a). The reaction resulted in the near- complete recovery of the starting materials, with only a trace amount of 1w (1% yield), indicating that the reaction equilibrium is substrate- dependent. + +Next, we synthesized 1,3- diketone 5wA via an alternative synthetic route,37 and subjected it to reaction with NaOMe (1.0 equiv) under the standard conditions (Fig. 3a and Supplementary Figure S5 and S6). The reaction preferentially produced 3wA, though the product ratio \((3wA:1w = 65:29)\) indicated moderate regioselectivity. When the reaction was conducted with 1.0 equiv of methyl picolinate (2A), a similar selectivity was observed, confirming that the product distribution aligns with the expected reaction pathway (Supplementary Table S2). Moreover, we discovered that trace amounts of water significantly influenced the product distribution. Specifically, when NaOH was used as the base, 1w became the major product \((3wA:1w = 9:65)\) . This shift in selectivity upon using NaOH strongly suggests that the heteroaromatic swapping reaction is reversible, allowing the conversion of heteroaromatic ketones back into aromatic ketones. The practical application of this reverse swapping reaction is demonstrated in Fig. 4c. + +To gain further insight into the reaction mechanism, we monitored the reaction progress using time- course NMR analysis (Fig. 3c and Supplementary Fig. S7). The data confirmed that the starting ketone 1w was completely consumed, and that methyl picolinate (2A) was consumed in the stoichiometrically expected amount, leading to the formation of 3wA. Additionally, the enolate of 3wA and the corresponding methyl ester 4a were detected, with the reaction reaching completion within approximately one hour. Notably, no diketone intermediates were observed by NMR, suggesting that the diketone intermediate is highly reactive and rapidly undergoes the retro- Claisen reaction. + +Based on these experimental findings and additional computational analyses, we propose the following reaction mechanism (Fig. 3d). Under basic conditions, aromatic ketone 1 undergoes enolate formation, which subsequently reacts with heteroaromatic ester 2 to generate the diketone intermediate 5. Typically, the base (NaOMe) preferentially attacks the electron- deficient carbonyl of the heteroaromatic ketone, promoting the reverse reaction and regenerating the starting materials. The key factors governing product distribution are the relative electrophilicity and nucleophilicity of the involved species. Computational studies revealed that the electrophilicity index \((\omega)\) of ester 2 (2.00 eV) is higher than that of ester 4 (1.80 eV), making ester 4 a weaker electrophile.38,39 Similarly, the nucleophilicity index \((N)\) of enolate 1 (4.56 eV) is higher than that of enolate 3 (4.51 eV), rendering enolate 3 a weaker nucleophile (more details, see Supplementary Table S5).40,41 As a result, the reverse reaction is suppressed, leading to preferential accumulation of the final products, enolate 3 and ester 4. + +<--- Page Split ---> + +Furthermore, DFT and coupled- cluster calculations at the DLPNO- CCSD(T)/def2- TZVPP(D)/B3LYP- D3(BJ)/6- 311+G(d,p) were conducted to evaluate transition- state energies and product stabilities (Supplementary Fig. S8- S11 and Table S6- S7). The analysis revealed that the stability of the final products depends on the aggregation state of the sodium enolate. \(^{42,43}\) Specifically, enolate 3 and ester 4 are more thermodynamically stable than enolate 1 and ester 2. Additionally, the tetrameric aggregation of [enolate 3]4 is more stable than the [enolate 5]2 and [enolate 5]4, further favoring the formation of 3 and 4 over the intermediate diketone 5. Furthermore, the barrier heights for the interconversion between enolate 1/ester 2 and enolate 3/ester 4 are at most 22 kcal mol \(^{- 1}\) , indicating a reasonable height at the given reaction condition, and suggests that these products are likely in equilibrium. These findings strongly suggest that the reaction proceeds under thermodynamic control. + +In contrast, when NaOH is used as the base, nucleophilic attack occurs preferentially on the electron- deficient carbonyl of the heteroaromatic ketone in diketone 5, generating enolate 1 and carboxylic acid 2. Since the carboxylic acid cannot revert to the starting materials, it is ultimately converted into carboxylate 2, leading to selective product formation. As a result, the reaction under NaOH conditions exhibits kinetic control, and the observed product distribution aligns with Fig. 3b. Notably, transition- state calculations further support this kinetic model, confirming that under these conditions, the reaction pathway is governed by the rate of irreversible steps (Supplementary Fig. S12). + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 3. Mechanistic investigations. a, Investigation of reversibility of products. b, Retro-Claisen condensation of 5wA. c, Time course plot of heteroaromatic swapping. d, Proposed mechanism.
+ +## Applications of Heteroaromatic Swapping + +We further explored the versatility of our heteroaromatic swapping methodology by applying it to complex and pharmaceutically relevant substrates. By reacting simple benzene rings with complex pharmaceutical esters, we achieved a direct, one- step conversion into the corresponding ketones (Fig. 4a). For example, methyl esters derived from ticlopidine, caffeine, and febuxostat were efficiently transformed into their respective ketones 3aY, 3aZ, and 3aAA, demonstrating both the broad applicability and the operational simplicity of this reaction. + +The methodology was extended to a diverse range of ketones derived from active pharmaceutical ingredients (APIs) and natural products (Fig 4b). Notably, the aromatic ring of haloperidol was successfully replaced with a pyridine ring, affording 3xA in \(92\%\) yield on a gram scale, showcasing the robustness and scalability of the method. The + +<--- Page Split ---> + +structure of the obtained 3xA was determined byX- ray analysis after recrystallization (Supplementary Fig. S13 and Table S8). Additionally, haloperidol reacted nearly quantitatively with the caffeine- derived ester, producing 3xz in \(99\%\) yield. Fluanisone, similar to simpler ketone substrates, underwent efficient swapping with various heteroaromatic esters, including pyridine positional isomers (3yA and 3yB), furan (3yS), and benzothiophene (3yU), providing the corresponding products in good yields. Furthermore, substrates featuring pyridyl groups (3zA), alcohol (3aaA), and aromatic rings with multiple functional groups (3abA, 3acA, and 3adA) also reacted smoothly. Even complex frameworks, such as a steroidal scaffold (3aeA) and a chiral compound (3afA) underwent successful heteroaromatic swapping, converting aromatic ketones into pyridyl ketones. + +Importantly, this methodology allows a direct benzene- to- heteroarene swapping directly on the API molecule (Fig. 4c). Starting with haloperidol (1x), the aromatic ring was swapped to produce 3xA, which was subsequently transformed into fully heteroaromatic scaffolds, such as quinoline (6) and triazole (7) derivatives44. Additionally, employing 15N- labeled pyridyl esters onto azaperone (1z) directly led to the efficient synthesis of 15N- labeled compound 845. This approach was also applied to fluanisone (1y), where reaction with pyridyl methyl ester followed by reduction provided a piperidine derivative 946. Similarly, reaction with furan carboxylate followed by reduction yielded a tetrahydrofuran derivative 10. These examples highlight the method's utility in scaffold diversification, which is critical for drug discovery efforts47. + +Finally, this methodology enables a two- step transformation, converting benzene rings first into heteroaromatic rings and subsequently into electronically distinct, electron- rich aromatic rings. Specifically, fluanisone (1y) was efficiently converted into pyridyl ketone 3yA via heteroaromatic swapping. Subsequent treatment of 3yA with benzoyl cyanide and NaOH triggered a retro- Claisen reaction, affording phenyl ketone 11a in \(73\%\) yield. While the reaction also proceeded with acyl chlorides, these conditions resulted in lower yields due to competing O- acylation of the enolate. Among the reagents tested, benzoyl cyanide48 and benzoyl benzotriazole49 provided the best results, enabling the efficient synthesis of diketones and ensuring high product yields (Supplementary Table S4). Notably, this methodology also facilitated the direct conversion of fluanisone (1y) into electron- rich aromatic rings, such as p- methoxyphenyl, which is challenging to achieve with high efficiency in a single step (11b: \(82\%\) yield). Furthermore, substrates previously deemed unreactive under standard conditions, such as furans and thiophenes bearing ketones at the C3 position (noted as limitations in Fig. 2), were successfully synthesized with good yields (11c and 11d). + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig 4. Applications of heteroaromatic swapping in complex substrates. a, Heteroaromatic swapping of esters derived from APIs and natural products. b, Heteroaromatic swapping of ketones derived from APIs. c, Further derivatization and expansion of the obtained products.
+ +This method offers broad possibilities for the transformation of molecular frameworks, providing efficient access to structurally diverse and functionally rich compounds. The + +<--- Page Split ---> + +wide scope and operational simplicity of heteroaromatic swapping make it a valuable tool for the development of advanced synthetic strategies, particularly in the context of medicinal chemistry. + +## AUTHOR INFORMATION + +## Corresponding Author + +Junichiro Yamaguchi - Department of Applied Chemistry, Waseda University, 513 Wasedatsurumakicho, Shinjuku, Tokyo 162- 0041, Japan; orcid.org/0000- 0002- 3896- 5882; Email: junyamaguchi@waseda.jp + +## Author Information + +Hikaru Nakahara- Department of Applied Chemistry, Waseda University, 513 Wasedatsurumakicho, Shinjuku, Tokyo 162- 0041, Japan; orcid.org/0000- 0001- 9170- 7703. + +Ryotaro Shirai- Department of Applied Chemistry, Waseda University, 513 Wasedatsurumakicho, Shinjuku, Tokyo 162- 0041, Japan + +Yoshio Nishimoto- Graduate School of Science, Kyoto University, Kyoto 606- 8502, Japan; orcid.org/0000- 0001- 5581- 4712. + +Daisuke Yokogawa- Graduate School of Arts and Sciences, The University of Tokyo, 3- 8- 1 Komaba, Meguro- ku, Tokyo 153- 8902, Japan; orcid.org/0000- 0002- 7574- 0965. + +## Author Contributions + +J.Y. conceived this project. H.N and R. S. performed the experiments and analyzed the data. Y. N. and D. Y. performed computational studies. J.Y. and H.N. cowrote the manuscript with feedback from the other authors. + +## Notes + +The authors declare no competing financial interest. + +## ACKNOWLEDGMENT + +This work was supported by JSPS KAKENHI Grant Number JP21H05213 (Digi- TOS) (to J.Y.). This work was partly supported by JST CREST Grant Number JPMJCR24T3 (to J.Y.). We thank Dr. Y. Ishihara (Genesis Therapeutics) for discussion and critical comments. We thank Dr. Kenta Kato for the assistance of X- ray crystallographic analysis. The Materials Characterization Central Laboratory in Waseda University is acknowledged for the support of HRMS measurement. + +## REFERENCES + +<--- Page Split ---> + +(1) Pennington, L. D., Collier, P. N. & Comer, E. Harnessing the necessary nitrogen atom in chemical biology and drug discovery. Med. Chem. Res. 32, 1278–1293 (2023). +(2) Pennington, L. D. & Moustakas, D. T. 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Chem. Soc. 146, 22829-22839 (2024). +(30) Tetlow, D. J., Vincent, M. A., Hillier, I. H. & Clayden, J. Reversible aryl migrations in metallated ureas: controlled inversion of configuration at a quaternary carbon atom. Chem. Commun. 49, 1548-1550 (2013). +(31) Janssen, F. J. et al. Comprehensive Analysis of Structure-Activity Relationships of α-Ketoheterocycles as sn-1-Diacylglycerol Lipase α Inhibitors. J. Med. Chem. 58, 9742-9753 (2015). + +<--- Page Split ---> + +(32) Boger, D. L. et al. Discovery of a Potent, Selective, and Efficacious Class of Reversible \(\alpha\) -Ketoheterocycle Inhibitors of Fatty Acid Amide Hydrolase Effective as Analgesics \(\nabla\) . J. Med. Chem. 48, 1849–1856 (2005). +(33) Fan, X., He, Y., Zhang, X., Guo, S. & Wang, Y. Synthesis of heteroaryl ketones via tandem reaction of 1,1-dibromoethenes. Tetrahedron 67, 6369–6374 (2011). +(34) Chatani, N., Fukuyama, T., Tatamidani, H., Kakiuchi, F. & Murai, S. Acylation of Five-Membered N-Heteroaromatic Compounds by Ruthenium Carbonyl-Catalyzed Direct Carbonylation at a C–H Bond. J. Org. Chem. 65, 4039–4047 (2000). +(35) Barkley, L. B. & Levine, R. The Synthesis of Certain Ketones and α-Substituted β-Diketones Containing Perfluoroalkyl Groups. J. Am. Chem. Soc. 75, 2059–2063 (1953). +(36) Yang, D., Zhou, Y., Xue, N. & Qu, J. Synthesis of Trifluoromethyl Ketones via Tandem Claisen Condensation and Retro-Claisen C–C Bond-Cleavage Reaction. J. Org. Chem. 78, 4171–4176 (2013). +(37) Shokova, E. A., Kim, J. K. & Kovalev, V. V. 1,3-Diketones. Synthesis and properties. Russ. J. Org. Chem. 51, 755–830 (2015). +(38) Parr, R. G., Szentpály, L. v. & Liu, S. Electrophilicity Index. J. Am. Chem. Soc. 121, 1922–1924 (1999). +(39) Bickelhaupt, F. M. & Fernández, I. What defines electrophilicity in carbonyl compounds. Chem. Sci. 15, 3980–3987 (2024). +(40) Domingo, L. R., Chamorro, E. & Pérez, P. Understanding the Reactivity of Captodative Ethylenes in Polar Cycloaddition Reactions. A Theoretical Study. J. Org. Chem. 73, 4615–4624 (2008). +(41) Domingo, L. R. & Pérez, P. The nucleophilicity \(N\) index in organic chemistry. Org. Biomol. Chem. 9, 7168–7175 (2011). +(42) Tomasevich, L. L. & Collum, D. B. Method of Continuous Variation: Characterization of Alkali Metal Enolates Using \(^1\mathrm{H}\) and \(^{19}\mathrm{F}\) NMR Spectroscopies. J. Am. Chem. Soc. 136, 9710–9718 (2014). +(43) Luo, F. et al. Direct insertion into the C–C bond of unactivated ketones with NaH-mediated aryne chemistry. Chem 9, 2620–2636 (2023). +(44) Martínez, R., Ramón, D. J. & Yus, M. Transition-Metal-Free Indirect Friedländer Synthesis of Quinolines from Alcohols. J. Org. Chem. 73, 9778–9780 (2008). +(45) Nguyen, H. M. H. et al. Synthesis of \(^{15}\mathrm{N}\) -Pyridines and Higher Mass Isotopologs via Zincke Imine Intermediates. J. Am. Chem. Soc. 146, 2944–2949 (2024). +(46) Tanaka, N. & Usuki, T. Can Heteroarenes/Arenes Be Hydrogenated Over Catalytic Pd/C Under Ambient Conditions? Eur. J. Org. Chem. 2020, 5514–5522 (2020). +(47) Liu, D.- H. et al. Late-Stage Saturation of Drug Molecules. J. Am. Chem. Soc. 146, 11866–11875 (2024). + +<--- Page Split ---> + +(48) Wiles, C., Watts, P., Haswell, S. J. & Pombo-Villar, E. The regioselective preparation of 1,3-diketones. Tetrahedron Lett. 43, 2945–2948 (2002). +(49) Katritzky, A. R. & Pastor, A. Synthesis of \(\beta\) -Dicarbonyl Compounds Using 1-Acylbenzotriazoles as Regioselective C-Acylating Reagents. J. Org. Chem. 65, 3679–3682 (2000). + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +HN2980. cif HN2980checkCIF.pdf SlnakaharaswappingNaturerevfinal.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__089483f12365d3a631f34ad6f3c9ff251e698c756a3d693ffcf84f0664f35fd7/preprint__089483f12365d3a631f34ad6f3c9ff251e698c756a3d693ffcf84f0664f35fd7_det.mmd b/preprint/preprint__089483f12365d3a631f34ad6f3c9ff251e698c756a3d693ffcf84f0664f35fd7/preprint__089483f12365d3a631f34ad6f3c9ff251e698c756a3d693ffcf84f0664f35fd7_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..ff1b34a91571199a8f098e78894f7838d1f685ef --- /dev/null +++ b/preprint/preprint__089483f12365d3a631f34ad6f3c9ff251e698c756a3d693ffcf84f0664f35fd7/preprint__089483f12365d3a631f34ad6f3c9ff251e698c756a3d693ffcf84f0664f35fd7_det.mmd @@ -0,0 +1,273 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 864, 144]]<|/det|> +# Heteroaromatic Swapping in Aromatic Ketones + +<|ref|>text<|/ref|><|det|>[[44, 163, 300, 208]]<|/det|> +Junichiro Yamaguchi junyamaguchi@waseda.jp + +<|ref|>text<|/ref|><|det|>[[44, 234, 580, 273]]<|/det|> +Waseda University https://orcid.org/0000- 0002- 3896- 5882 + +<|ref|>text<|/ref|><|det|>[[44, 278, 580, 297]]<|/det|> +Hikaru Nakahara Waseda University https://orcid.org/0000- 0001- 9170- 7703 + +<|ref|>text<|/ref|><|det|>[[44, 304, 220, 440]]<|/det|> +Ryotaro Shirai Waseda University Yoshio Nishimoto Kyoto University Daisuke Yokogawa University of Tokyo + +<|ref|>text<|/ref|><|det|>[[44, 480, 104, 497]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 517, 137, 536]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 555, 300, 575]]<|/det|> +Posted Date: April 18th, 2025 + +<|ref|>text<|/ref|><|det|>[[44, 594, 475, 613]]<|/det|> +DOI: https://doi.org/10.21203/rs.3. rs- 5583133/v1 + +<|ref|>text<|/ref|><|det|>[[44, 631, 914, 674]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 692, 535, 712]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 747, 933, 790]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on October 9th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 64041- 6. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[118, 80, 795, 105]]<|/det|> +# Heteroaromatic Swapping in Aromatic Ketones + +<|ref|>text<|/ref|><|det|>[[115, 123, 884, 255]]<|/det|> +Hikaru Nakaharaa, Ryotaro Shiraia, Yoshio Nishimotob, Daisuke Yokogawac, and Junichiro Yamaguchia\* a Department of Applied Chemistry, Waseda University, 513 Wasedatsurumakicho, Shinjuku, Tokyo 162- 0041, Japan. b Graduate School of Science, Kyoto University, Kyoto 606- 8502, Japan. c Graduate School of Arts and Sciences, The University of Tokyo, 3- 8- 1 Komaba, Meguro- ku, Tokyo 153- 8902, Japan. + +<|ref|>text<|/ref|><|det|>[[118, 275, 881, 350]]<|/det|> +KEYWORDS Aromatic ketones, Claisen/retro Claisen Condensation, Heteroarenes, Metathesis, Aromatic Exchange Email: Junichiro Yamaguchi – junyamaguchi@waseda.jp \* Corresponding author + +<|ref|>sub_title<|/ref|><|det|>[[118, 371, 202, 387]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[117, 390, 884, 655]]<|/det|> +The modification of aromatic rings to heteroaromatic rings is a widely employed strategy in medicinal chemistry, often used to modulate lipophilicity and improve metabolic stability1,2. However, achieving a one- step, generalizable transformation of aromatic rings into diverse heteroaromatic rings—termed "heteroaromatic swapping"—remains a persistent challenge. Existing methods, such as skeletal editing3 and transition- metal- catalyzed aromatic ring exchange4,5, are limited in substrate scope and efficiency. Here, we present an efficient strategy for heteroaromatic swapping via a Claisen/retro- Claisen mechanism6, utilizing heteroaryl esters and aromatic ketones. This approach enables the selective exchange of aromatic rings with heteroaromatic rings across a broad substrate range, overcoming the limitations of existing techniques. Notably, it achieves high- yield conversions of bioactive aromatic ketones into their heteroaromatic counterparts. This method expands the molecular editing toolkit, offering a practical and versatile platform for synthesizing bioactive compounds with enhanced physicochemical properties. + +<|ref|>sub_title<|/ref|><|det|>[[118, 676, 238, 692]]<|/det|> +## Introduction + +<|ref|>text<|/ref|><|det|>[[117, 694, 884, 920]]<|/det|> +Aromatic rings are fundamental building blocks in organic chemistry, playing a pivotal role in the structure and properties of a large variety of compounds, both of natural and synthetic origin. In bioactive molecules, they often serve as key structural elements, where their modification can profoundly influence both biological activity and selectivity. Substituting aromatic rings with heteroaromatic rings typically results in lower lipophilicity, a physicochemical parameter that positively impacts a compound's aqueous solubility, off- target toxicity, and some pharmacokinetic parameters (Fig. 1a)7,8. Prominent examples include the antiepileptic drug perampanel, adenosine A2A receptor antagonists, and hepatitis C therapies, where heteroaromatic substitutions (e.g., pyridine, pyrazole, and pyridazine) have significantly enhanced both efficacy and safety profiles9- 11. In organic synthesis, the ability to seamlessly convert aromatic rings into diverse heteroaromatic rings in a single step—referred to here as heteroaromatic + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 85, 884, 312]]<|/det|> +swapping—would represent a transformative advance, greatly expanding the scope of synthetic methodologies. Among existing approaches, skeletal editing, which introduces nitrogen atom(s) into aromatic rings, is particularly notable (Fig. 1b). This strategy, primarily involving aryl azides to generate pyridine rings via rearrangement, holds significant potential. \(^{12 - 17}\) . However, while methods for nitrogen removal from aromatic rings are well- established \(^{18 - 21}\) , efficient and generalizable strategies for nitrogen incorporation remain limited. Similarly, transition- metal- catalyzed aromatic ring exchange reactions offer a promising route for transforming aromatic rings into heteroaromatic counterparts, yet successful examples involving heteroaromatic rings are exceedingly rare \(^{22 - 26}\) . Other ring transformation methodologies and molecular editing strategies for ketones offer alternative solutions, but these methods often introduce undesired functional groups or require multiple steps, limiting their utility \(^{27 - 30}\) . + +<|ref|>text<|/ref|><|det|>[[116, 312, 884, 845]]<|/det|> +Aromatic ketones are particularly well- suited for heteroaromatic swapping due to their highly reactive carbonyl groups, which allow versatile transformations into a wide range of functional groups. These compounds feature highly reactive carbonyl groups, making them valuable synthetic intermediates and key structural motifs in a wide range of pharmaceuticals, including both aromatic and heteroaromatic derivatives (Fig. 1c). While aromatic ketones derived from benzene rings are often accessible through de novo synthesis, heteroaromatic ketones typically require multistep protocols to introduce heteroaromatic rings onto carbonyl groups, leading to inefficiency. \(^{31 - 34}\) A general, one- step method for the direct conversion of aromatic ketones into heteroaromatic ketones at a late stage would significantly accelerate the derivatization and development of novel compounds in medicinal chemistry. Such a strategy would enable access to structurally diverse molecules, thereby broadening the chemical space available for drug discovery and development. To address this challenge, we developed a strategy for heteroaromatic swapping of aromatic ketones by leveraging the classical Claisen/retro- Claisen reaction \(^{6,35,36}\) . We hypothesized that a reaction of aromatic ketones with heteroaromatic esters to form diketones, followed by a selective retro- Claisen reaction at the heteroaromatic site, would yield the desired heteroaromatic ketones and aromatic esters (Fig. 1d). While traditional textbooks describe Claisen reactions as driven by enolate stabilization through deprotonation of \(\beta\) - ketoesters, Claisen condensations between dissimilar partners—specifically ketones and esters—have rarely been explored. We hypothesized that diketones with an electronic bias favoring the ketone might undergo selective retro- Claisen reactions under carefully optimized conditions. Unexpectedly, the reaction proceeded with high selectivity under mild conditions, affording the desired heteroaromatic ketones in excellent yields. This discovery establishes a highly efficient and straightforward method for heteroaromatic swapping of aromatic ketones, providing a versatile tool for molecular editing that is compatible with late- stage functionalization of bioactive molecules. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[122, 85, 880, 555]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[117, 559, 884, 672]]<|/det|> +
Fig. 1. The significance and synthetic challenges of heteroaromatic swapping. a, Importance of converting aromatic rings into heteroaromatic rings. b, One-step conversion of aromatic rings to heteroaromatic rings using state-of-the-art techniques (heteroaromatic swapping). c, Challenges and opportunities in the synthesis of aromatic and heteroaromatic ketones. d, Heteroaromatic swapping via Claisen/retro-Claisen reactions.
+ +<|ref|>sub_title<|/ref|><|det|>[[119, 711, 424, 728]]<|/det|> +## Reaction Conditions and Scope + +<|ref|>text<|/ref|><|det|>[[117, 730, 884, 919]]<|/det|> +We initiated our study by exploring the Claisen/retro- Claisen reaction between aromatic ketone 1a and methyl picolinate (2A) (Fig. 2a). Treatment 1a with 2A (1.5 equiv) and NaH (2.0 equiv) in THF at \(60^{\circ}\mathrm{C}\) for 6 h under the optimized conditions resulted in a highly efficient reaction, furnishing the desired pyridyl ketone 3aA in \(91\%\) yield, along with the corresponding aromatic ester 4a in \(65\%\) yield. Due to relatively high volatility of 4a under reduced pressure, accurate quantification of its yield remained challenging (Supplementary Table S1). The reaction also proceeded at room temperature, and the use of only 1.0 equiv of 2A was sufficient to achieve productive conversion (61% yield). Notably, the protocol is not limited to methyl esters; benzyl esters reacted smoothly, while bulkier esters diminished reactivity. The use of a base + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 85, 881, 122]]<|/det|> +capable of irreversibly deprotonating the substrate was found to be essential for the reaction to proceed (Supplementary Table S2 and S3). + +<|ref|>text<|/ref|><|det|>[[117, 124, 883, 464]]<|/det|> +We next investigated the substrate scope with the respect to heteroaromatic esters (Fig. 2b). A broad range of heteroaromatic esters proved compatible under the optimized conditions. Positional isomers of pyridine (2B and 2C), as well as other six- membered nitrogen containing heteroaromatic esters, including pyrimidine, pyrazine, pyridazine, and quinoline, provided the corresponding ketones 3aB- 3al in good yields. The reaction also demonstrated excellent functional group tolerance, accommodating halogens, esters, trifluoromethyl groups, and alkoxy groups, as evidenced by formation of ketones 3aJ- 3aR. Furthermore, five- membered heteroaromatic esters including furan, thiophene, and various azoles, also afforded the desired ketones 3aS- 3aX in good yields. However, no reaction occurred with methyl esters of pyrrole or heteroaromatic esters bearing a methyl ester at the C3 position. Comparative studies revealed that methyl picolinate (2A) exhibited superior reactivity relative to other heteroaromatic esters (Supplementary Fig. S1 and S2). In addition, reaction using aromatic esters bearing substituents at the C4 position were carried out, and a Hammett analysis based on these derivatives showed a positive \(\rho\) value \((p > 0)\) , indicating that electron- deficient aromatic rings facilitate the transformation (Supplementary Fig. S3). These results support the conclusion that electron- deficient heteroaromatic esters, such as 2A, are paticularly effective in promoting the reaction. + +<|ref|>text<|/ref|><|det|>[[117, 465, 884, 806]]<|/det|> +Next, we examined the structural scope of the ketone component (Fig. 2c). The reaction proceeded regardless of substituents on the aryl group of the ketone (1b- 1g), and was compatible with bulky alkyl groups, benzyl groups, and ketones containing oxidation- or light- sensitive functionalities such as ferrocene and pyrene (3hA and 3kA). Acid- and base- sensitive functional groups, including oxetane, linear amines, and cyclic amines, were also well tolerated (3IA- 3qA). The transformation was applicable to \(\alpha\) - disubstituted ketones, although these required an excess amount of ester (3.0 equiv) and higher reaction temperatures (80 °C) (3rA and 3sA). For cyclic ketones, ring- opening heteroarylation was achieved; using NaHMDS as the base under milder conditions further improved the yields (3tA and 3uA). Interestingly, the reaction also proceeded with dialkyl ketones, suggesting potential for desymmetrization of such substrates (3vA). Notably, heteroaromatic swapping was more facile with \(\alpha\) - disubstituted ketones, which is surprising given that such substrates typically require harsher conditions (3rA: 0% vs 3wA: 59%). Moreover, benzyl ketones and alkyl ketones exhibited nearly equivalent reactivity (3bA: 22% vs 3iA: 20%). Collectively, these findings highlight the broad substrate scope of the method, with exceptions observed mainly for electron- rich esters and \(\alpha\) - unsubstituted ketones, such as methyl ketones (Supplementary Fig S4). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 85, 866, 655]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 658, 884, 790]]<|/det|> +
Fig. 2. Substrate scope under optimized reaction conditions. a, Optimized reaction conditions. b, Scope for heteroaromatic esters: 1a (0.20 mmol), 2 (1.5 equiv), NaH (2.0 equiv), THF, 60 °C, 6 h. c, Scope for aromatic ketones: 1 (0.20 mmol), 2A (1.5 equiv), NaH (2.0 equiv), THF, 60 °C, 6 h. aYields were determined by \(^1\mathrm{H}\) NMR analysis. b2A (3.0 equiv) and NaH (3.0 equiv) were added, and reactions were performed at 80 °C. cNaHMDS (1.0 equiv) was used instead of NaH, and reactions were performed at room temperature.
+ +<|ref|>sub_title<|/ref|><|det|>[[117, 810, 528, 828]]<|/det|> +## Mechanistic Investigations of the Reaction + +<|ref|>text<|/ref|><|det|>[[117, 829, 884, 923]]<|/det|> +To elucidate the reaction mechanism, we systematically examined key factors governing its selectivity and reversibility. Our initial observations established the following critical requirements for the reaction to proceed: (1) The ketone substrate must possess an electronically neutral or electron- rich aromatic ring, while the ester substrate must have a relatively electron- deficient aromatic ring; (2) The ketone must + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 84, 884, 179]]<|/det|> +bear a substituent at the \(\alpha\) - position; (3) The anticipated key intermediate, a 1,3- diketone, was never detected under the reaction conditions. These findings suggest that the inherent electronic properties of the substrates facilitate rapid fragmentation of the diketone intermediate, shifting the equilibrium toward product formation. To validate this hypothesis, we conducted a series of experiments. + +<|ref|>text<|/ref|><|det|>[[117, 180, 884, 255]]<|/det|> +First, we subjected the reaction products, ketone 3wA and ester 4a, to the optimized reaction conditions (Fig. 3a). The reaction resulted in the near- complete recovery of the starting materials, with only a trace amount of 1w (1% yield), indicating that the reaction equilibrium is substrate- dependent. + +<|ref|>text<|/ref|><|det|>[[117, 256, 884, 502]]<|/det|> +Next, we synthesized 1,3- diketone 5wA via an alternative synthetic route,37 and subjected it to reaction with NaOMe (1.0 equiv) under the standard conditions (Fig. 3a and Supplementary Figure S5 and S6). The reaction preferentially produced 3wA, though the product ratio \((3wA:1w = 65:29)\) indicated moderate regioselectivity. When the reaction was conducted with 1.0 equiv of methyl picolinate (2A), a similar selectivity was observed, confirming that the product distribution aligns with the expected reaction pathway (Supplementary Table S2). Moreover, we discovered that trace amounts of water significantly influenced the product distribution. Specifically, when NaOH was used as the base, 1w became the major product \((3wA:1w = 9:65)\) . This shift in selectivity upon using NaOH strongly suggests that the heteroaromatic swapping reaction is reversible, allowing the conversion of heteroaromatic ketones back into aromatic ketones. The practical application of this reverse swapping reaction is demonstrated in Fig. 4c. + +<|ref|>text<|/ref|><|det|>[[117, 503, 884, 673]]<|/det|> +To gain further insight into the reaction mechanism, we monitored the reaction progress using time- course NMR analysis (Fig. 3c and Supplementary Fig. S7). The data confirmed that the starting ketone 1w was completely consumed, and that methyl picolinate (2A) was consumed in the stoichiometrically expected amount, leading to the formation of 3wA. Additionally, the enolate of 3wA and the corresponding methyl ester 4a were detected, with the reaction reaching completion within approximately one hour. Notably, no diketone intermediates were observed by NMR, suggesting that the diketone intermediate is highly reactive and rapidly undergoes the retro- Claisen reaction. + +<|ref|>text<|/ref|><|det|>[[117, 675, 886, 919]]<|/det|> +Based on these experimental findings and additional computational analyses, we propose the following reaction mechanism (Fig. 3d). Under basic conditions, aromatic ketone 1 undergoes enolate formation, which subsequently reacts with heteroaromatic ester 2 to generate the diketone intermediate 5. Typically, the base (NaOMe) preferentially attacks the electron- deficient carbonyl of the heteroaromatic ketone, promoting the reverse reaction and regenerating the starting materials. The key factors governing product distribution are the relative electrophilicity and nucleophilicity of the involved species. Computational studies revealed that the electrophilicity index \((\omega)\) of ester 2 (2.00 eV) is higher than that of ester 4 (1.80 eV), making ester 4 a weaker electrophile.38,39 Similarly, the nucleophilicity index \((N)\) of enolate 1 (4.56 eV) is higher than that of enolate 3 (4.51 eV), rendering enolate 3 a weaker nucleophile (more details, see Supplementary Table S5).40,41 As a result, the reverse reaction is suppressed, leading to preferential accumulation of the final products, enolate 3 and ester 4. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 84, 885, 312]]<|/det|> +Furthermore, DFT and coupled- cluster calculations at the DLPNO- CCSD(T)/def2- TZVPP(D)/B3LYP- D3(BJ)/6- 311+G(d,p) were conducted to evaluate transition- state energies and product stabilities (Supplementary Fig. S8- S11 and Table S6- S7). The analysis revealed that the stability of the final products depends on the aggregation state of the sodium enolate. \(^{42,43}\) Specifically, enolate 3 and ester 4 are more thermodynamically stable than enolate 1 and ester 2. Additionally, the tetrameric aggregation of [enolate 3]4 is more stable than the [enolate 5]2 and [enolate 5]4, further favoring the formation of 3 and 4 over the intermediate diketone 5. Furthermore, the barrier heights for the interconversion between enolate 1/ester 2 and enolate 3/ester 4 are at most 22 kcal mol \(^{- 1}\) , indicating a reasonable height at the given reaction condition, and suggests that these products are likely in equilibrium. These findings strongly suggest that the reaction proceeds under thermodynamic control. + +<|ref|>text<|/ref|><|det|>[[117, 313, 884, 484]]<|/det|> +In contrast, when NaOH is used as the base, nucleophilic attack occurs preferentially on the electron- deficient carbonyl of the heteroaromatic ketone in diketone 5, generating enolate 1 and carboxylic acid 2. Since the carboxylic acid cannot revert to the starting materials, it is ultimately converted into carboxylate 2, leading to selective product formation. As a result, the reaction under NaOH conditions exhibits kinetic control, and the observed product distribution aligns with Fig. 3b. Notably, transition- state calculations further support this kinetic model, confirming that under these conditions, the reaction pathway is governed by the rate of irreversible steps (Supplementary Fig. S12). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 100, 844, 622]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[116, 627, 881, 682]]<|/det|> +
Fig. 3. Mechanistic investigations. a, Investigation of reversibility of products. b, Retro-Claisen condensation of 5wA. c, Time course plot of heteroaromatic swapping. d, Proposed mechanism.
+ +<|ref|>sub_title<|/ref|><|det|>[[117, 703, 520, 720]]<|/det|> +## Applications of Heteroaromatic Swapping + +<|ref|>text<|/ref|><|det|>[[116, 721, 884, 854]]<|/det|> +We further explored the versatility of our heteroaromatic swapping methodology by applying it to complex and pharmaceutically relevant substrates. By reacting simple benzene rings with complex pharmaceutical esters, we achieved a direct, one- step conversion into the corresponding ketones (Fig. 4a). For example, methyl esters derived from ticlopidine, caffeine, and febuxostat were efficiently transformed into their respective ketones 3aY, 3aZ, and 3aAA, demonstrating both the broad applicability and the operational simplicity of this reaction. + +<|ref|>text<|/ref|><|det|>[[116, 855, 883, 930]]<|/det|> +The methodology was extended to a diverse range of ketones derived from active pharmaceutical ingredients (APIs) and natural products (Fig 4b). Notably, the aromatic ring of haloperidol was successfully replaced with a pyridine ring, affording 3xA in \(92\%\) yield on a gram scale, showcasing the robustness and scalability of the method. The + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 84, 885, 293]]<|/det|> +structure of the obtained 3xA was determined byX- ray analysis after recrystallization (Supplementary Fig. S13 and Table S8). Additionally, haloperidol reacted nearly quantitatively with the caffeine- derived ester, producing 3xz in \(99\%\) yield. Fluanisone, similar to simpler ketone substrates, underwent efficient swapping with various heteroaromatic esters, including pyridine positional isomers (3yA and 3yB), furan (3yS), and benzothiophene (3yU), providing the corresponding products in good yields. Furthermore, substrates featuring pyridyl groups (3zA), alcohol (3aaA), and aromatic rings with multiple functional groups (3abA, 3acA, and 3adA) also reacted smoothly. Even complex frameworks, such as a steroidal scaffold (3aeA) and a chiral compound (3afA) underwent successful heteroaromatic swapping, converting aromatic ketones into pyridyl ketones. + +<|ref|>text<|/ref|><|det|>[[115, 295, 885, 450]]<|/det|> +Importantly, this methodology allows a direct benzene- to- heteroarene swapping directly on the API molecule (Fig. 4c). Starting with haloperidol (1x), the aromatic ring was swapped to produce 3xA, which was subsequently transformed into fully heteroaromatic scaffolds, such as quinoline (6) and triazole (7) derivatives44. Additionally, employing 15N- labeled pyridyl esters onto azaperone (1z) directly led to the efficient synthesis of 15N- labeled compound 845. This approach was also applied to fluanisone (1y), where reaction with pyridyl methyl ester followed by reduction provided a piperidine derivative 946. Similarly, reaction with furan carboxylate followed by reduction yielded a tetrahydrofuran derivative 10. These examples highlight the method's utility in scaffold diversification, which is critical for drug discovery efforts47. + +<|ref|>text<|/ref|><|det|>[[115, 451, 885, 789]]<|/det|> +Finally, this methodology enables a two- step transformation, converting benzene rings first into heteroaromatic rings and subsequently into electronically distinct, electron- rich aromatic rings. Specifically, fluanisone (1y) was efficiently converted into pyridyl ketone 3yA via heteroaromatic swapping. Subsequent treatment of 3yA with benzoyl cyanide and NaOH triggered a retro- Claisen reaction, affording phenyl ketone 11a in \(73\%\) yield. While the reaction also proceeded with acyl chlorides, these conditions resulted in lower yields due to competing O- acylation of the enolate. Among the reagents tested, benzoyl cyanide48 and benzoyl benzotriazole49 provided the best results, enabling the efficient synthesis of diketones and ensuring high product yields (Supplementary Table S4). Notably, this methodology also facilitated the direct conversion of fluanisone (1y) into electron- rich aromatic rings, such as p- methoxyphenyl, which is challenging to achieve with high efficiency in a single step (11b: \(82\%\) yield). Furthermore, substrates previously deemed unreactive under standard conditions, such as furans and thiophenes bearing ketones at the C3 position (noted as limitations in Fig. 2), were successfully synthesized with good yields (11c and 11d). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 80, 875, 789]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 787, 884, 862]]<|/det|> +
Fig 4. Applications of heteroaromatic swapping in complex substrates. a, Heteroaromatic swapping of esters derived from APIs and natural products. b, Heteroaromatic swapping of ketones derived from APIs. c, Further derivatization and expansion of the obtained products.
+ +<|ref|>text<|/ref|><|det|>[[117, 881, 883, 919]]<|/det|> +This method offers broad possibilities for the transformation of molecular frameworks, providing efficient access to structurally diverse and functionally rich compounds. The + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[118, 85, 884, 141]]<|/det|> +wide scope and operational simplicity of heteroaromatic swapping make it a valuable tool for the development of advanced synthetic strategies, particularly in the context of medicinal chemistry. + +<|ref|>sub_title<|/ref|><|det|>[[118, 161, 353, 178]]<|/det|> +## AUTHOR INFORMATION + +<|ref|>sub_title<|/ref|><|det|>[[118, 200, 336, 216]]<|/det|> +## Corresponding Author + +<|ref|>text<|/ref|><|det|>[[118, 219, 884, 275]]<|/det|> +Junichiro Yamaguchi - Department of Applied Chemistry, Waseda University, 513 Wasedatsurumakicho, Shinjuku, Tokyo 162- 0041, Japan; orcid.org/0000- 0002- 3896- 5882; Email: junyamaguchi@waseda.jp + +<|ref|>sub_title<|/ref|><|det|>[[118, 295, 302, 311]]<|/det|> +## Author Information + +<|ref|>text<|/ref|><|det|>[[118, 314, 884, 369]]<|/det|> +Hikaru Nakahara- Department of Applied Chemistry, Waseda University, 513 Wasedatsurumakicho, Shinjuku, Tokyo 162- 0041, Japan; orcid.org/0000- 0001- 9170- 7703. + +<|ref|>text<|/ref|><|det|>[[118, 370, 884, 407]]<|/det|> +Ryotaro Shirai- Department of Applied Chemistry, Waseda University, 513 Wasedatsurumakicho, Shinjuku, Tokyo 162- 0041, Japan + +<|ref|>text<|/ref|><|det|>[[118, 427, 884, 464]]<|/det|> +Yoshio Nishimoto- Graduate School of Science, Kyoto University, Kyoto 606- 8502, Japan; orcid.org/0000- 0001- 5581- 4712. + +<|ref|>text<|/ref|><|det|>[[118, 484, 884, 521]]<|/det|> +Daisuke Yokogawa- Graduate School of Arts and Sciences, The University of Tokyo, 3- 8- 1 Komaba, Meguro- ku, Tokyo 153- 8902, Japan; orcid.org/0000- 0002- 7574- 0965. + +<|ref|>sub_title<|/ref|><|det|>[[118, 542, 323, 558]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[118, 559, 874, 640]]<|/det|> +J.Y. conceived this project. H.N and R. S. performed the experiments and analyzed the data. Y. N. and D. Y. performed computational studies. J.Y. and H.N. cowrote the manuscript with feedback from the other authors. + +<|ref|>sub_title<|/ref|><|det|>[[118, 678, 176, 694]]<|/det|> +## Notes + +<|ref|>text<|/ref|><|det|>[[118, 697, 581, 713]]<|/det|> +The authors declare no competing financial interest. + +<|ref|>sub_title<|/ref|><|det|>[[118, 734, 325, 750]]<|/det|> +## ACKNOWLEDGMENT + +<|ref|>text<|/ref|><|det|>[[118, 753, 884, 865]]<|/det|> +This work was supported by JSPS KAKENHI Grant Number JP21H05213 (Digi- TOS) (to J.Y.). This work was partly supported by JST CREST Grant Number JPMJCR24T3 (to J.Y.). We thank Dr. Y. Ishihara (Genesis Therapeutics) for discussion and critical comments. We thank Dr. Kenta Kato for the assistance of X- ray crystallographic analysis. The Materials Characterization Central Laboratory in Waseda University is acknowledged for the support of HRMS measurement. + +<|ref|>sub_title<|/ref|><|det|>[[118, 885, 257, 902]]<|/det|> +## REFERENCES + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 85, 880, 923]]<|/det|> +(1) Pennington, L. D., Collier, P. N. & Comer, E. Harnessing the necessary nitrogen atom in chemical biology and drug discovery. Med. Chem. Res. 32, 1278–1293 (2023). +(2) Pennington, L. D. & Moustakas, D. T. The Necessary Nitrogen Atom: A Versatile High-Impact Design Element for Multiparameter Optimization. J. Med. Chem. 60, 3552–3579 (2017). +(3) Joyson, B. W. & Ball, L. T. Skeletal Editing: Interconversion of Arenes and Heteroarenes. Helvetica Chim. Acta 106, (2023). +(4) Macias, M. D. L. H. & Arndtsen, B. A. Functional Group Transposition: A Palladium-Catalyzed Metathesis of Ar–X σ-Bonds and Acid Chloride Synthesis. J. Am. Chem. Soc. 140, 10140–10144 (2018). +(5) Lee, Y. H. & Morandi, B. 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Method of Continuous Variation: Characterization of Alkali Metal Enolates Using \(^1\mathrm{H}\) and \(^{19}\mathrm{F}\) NMR Spectroscopies. J. Am. Chem. Soc. 136, 9710–9718 (2014). +(43) Luo, F. et al. Direct insertion into the C–C bond of unactivated ketones with NaH-mediated aryne chemistry. Chem 9, 2620–2636 (2023). +(44) Martínez, R., Ramón, D. J. & Yus, M. Transition-Metal-Free Indirect Friedländer Synthesis of Quinolines from Alcohols. J. Org. Chem. 73, 9778–9780 (2008). +(45) Nguyen, H. M. H. et al. Synthesis of \(^{15}\mathrm{N}\) -Pyridines and Higher Mass Isotopologs via Zincke Imine Intermediates. J. Am. Chem. Soc. 146, 2944–2949 (2024). +(46) Tanaka, N. & Usuki, T. Can Heteroarenes/Arenes Be Hydrogenated Over Catalytic Pd/C Under Ambient Conditions? Eur. J. Org. Chem. 2020, 5514–5522 (2020). +(47) Liu, D.- H. et al. Late-Stage Saturation of Drug Molecules. J. Am. Chem. Soc. 146, 11866–11875 (2024). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 84, 857, 180]]<|/det|> +(48) Wiles, C., Watts, P., Haswell, S. J. & Pombo-Villar, E. The regioselective preparation of 1,3-diketones. Tetrahedron Lett. 43, 2945–2948 (2002). +(49) Katritzky, A. R. & Pastor, A. Synthesis of \(\beta\) -Dicarbonyl Compounds Using 1-Acylbenzotriazoles as Regioselective C-Acylating Reagents. J. Org. Chem. 65, 3679–3682 (2000). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[42, 43, 312, 71]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[42, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[59, 130, 424, 202]]<|/det|> +HN2980. cif HN2980checkCIF.pdf SlnakaharaswappingNaturerevfinal.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__089c9ef8b7852bfc9e4086386f4d035a0a402a924515dd23558dc3aebf2493aa/images_list.json b/preprint/preprint__089c9ef8b7852bfc9e4086386f4d035a0a402a924515dd23558dc3aebf2493aa/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..2e4658e714456e9a6f4853997b29d061a49ea1f3 --- /dev/null +++ b/preprint/preprint__089c9ef8b7852bfc9e4086386f4d035a0a402a924515dd23558dc3aebf2493aa/images_list.json @@ -0,0 +1,70 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 | Chemical structures of standard and non-standard nucleobases. By rearranging hydrogen bonding donor and acceptor groups on base pairs in a Watson Crick geometry, the number of independently replicable informational units in DNA/RNA can be increased from 4 to 12, increase the information density, functionality, and density of binders and catalysts in libraries of oligonucleotides built from an artificially expanded genetic information system (AEGIS).", + "footnote": [], + "bbox": [], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 | Sequencing 5-letter AEGIS (ATCGZ) DNA by deamination and transliteration. (A) Cytidine (C) is transliterated by cytidine deaminase to form uridine (U), which pairs to A in PCR. (B) AEGIS base Z becomes Z at pH (8.9) by deprotonation; Z' pairs with G during PCR, inducing a Z to C transliteration. (C) Schematic workflow shows the C to T and Z to C conversions after deamination and PCR amplification. (D) Denaturing PAGE-urea analysis of restriction digestion of PCR products by TakaRa Taq DNA polymerase at pH8.9 from DNA templates (Nat and ZZ) without deamination or with deamination (Nat-E and ZZ-E). Forward primer was labeled by FAM at 5'. (E) Sanger sequencing demonstrates the precise transliteration of C to U (then T during the PCR) and Z to C.", + "footnote": [], + "bbox": [ + [ + 90, + 45, + 870, + 277 + ] + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 | Enzyme-Assisted Sequencing of Expanded Genetic Alphabet (ESEGA) of single strand 6-letter DNA (ATCGZP). Complete NGS read summaries are in supplemental material. (A) Schematic diagram showing the transliteration of AEGIS (ATCGZP) DNA using 4-triphosphate or 5-triphosphate PCR at pH8.9, without or with deamination. (B) Quantitative PCR (qPCR) to evaluate transliteration efficiency of various DNA templates in 4-triphosphate and 5-triphosphate PCR. Error bars represent the standard deviation of three independent experiments. Addition of dZTP enhances the efficiency of PCR for 6-letter templates. (C) NGS data reveals the transliteration (%) of original bases [A, T, C, (U), G, Z, and P] in Nat, ZZ, ZP-1 and ZP-2 sequences under different PCR conditions (4-triphosphate or 5-triphosphate PCR at pH8.9, with or without deamination). Standard deviations represent the multiple bases in the three sequences. (D and E) Sequence logos of 6-letter DNA (ZP-1 and ZP-2) under different transliteration and PCR conditions. (F) Robustness and specificity of the ESEGA for ZZ and Nat sequences at different ratios (10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90%). (G) Sensitivity of ESEGA for ZZ (45%) and Nat (55%) sequences in a series of 10-fold dilutions. The red dashed line represents the reference value obtained from Figure 3F. (H) Robustness of ESEGA with libraries NNNCNNN (C-Ran, top), NNNZNNN (Z-Ran, middle) and NNNPNNN (P-Ran, bottom) sequences, without deamination (left panel) or with deamination (right panel), then, followed by 5-triphosphate PCR and NGS. These data show ESEGA has no strong context dependency.", + "footnote": [], + "bbox": [ + [ + 115, + 45, + 880, + 560 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 | Sequencing double-stranded 6-letter DNA and assessing the fidelity of different DNA polymerases in 6-triphosphate PCR. (A) Schematic of ESEGA workflow for double-stranded 6-letter (ATCGZP) DNA. (B) and (C) Retention rates of Z/P at each position in the ZP-1 template plotted after 6-triphosphate PCR with varying concentrations of dPTP (0.05-0.5 mM) at pH 8.9 or pH 8.0 conditions. (D) ESEGA evaluation of the fidelity of various polymerases in 6-triphosphate PCR. Complete NGS read summaries are in supplemental material.", + "footnote": [], + "bbox": [ + [ + 130, + 73, + 860, + 508 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5 | Chemical structures and retentions rate of functionalized-dPTP derivatives in 6-triphosphate PCR assessed by ESEGA. Chemical structures of (A) classical dPTP; (B) dPethTP, 7-(4Ethynyl)-dP triphosphate; and (C) dPphTP. 7-(4-Phenyl-1-butynyl)-dP triphosphate. (D) Sanger sequencing PCR amplicons from ZZ template with 6-triphosphate (dNTPs, dZTP, and dPTP, or dPethTP, or dPphTP). (E) ESEGA and NGS evaluate the retentions of dP, dPeth, and dPph in PCR amplicons after 6-triphosphate PCR by statistics of Z retentions.", + "footnote": [], + "bbox": [ + [ + 303, + 159, + 686, + 409 + ] + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/preprint/preprint__089c9ef8b7852bfc9e4086386f4d035a0a402a924515dd23558dc3aebf2493aa/preprint__089c9ef8b7852bfc9e4086386f4d035a0a402a924515dd23558dc3aebf2493aa.mmd b/preprint/preprint__089c9ef8b7852bfc9e4086386f4d035a0a402a924515dd23558dc3aebf2493aa/preprint__089c9ef8b7852bfc9e4086386f4d035a0a402a924515dd23558dc3aebf2493aa.mmd new file mode 100644 index 0000000000000000000000000000000000000000..071cccabeda62652a1c89c0a62c8d86247a14d41 --- /dev/null +++ b/preprint/preprint__089c9ef8b7852bfc9e4086386f4d035a0a402a924515dd23558dc3aebf2493aa/preprint__089c9ef8b7852bfc9e4086386f4d035a0a402a924515dd23558dc3aebf2493aa.mmd @@ -0,0 +1,347 @@ + +# Enzyme-Assisted High Throughput Sequencing of an Expanded Genetic Alphabet at Single Base Resolution + +Steven Benner + +manuscript@fname.org + +Foundation for Applied Molecular Evolution https://orcid.org/0000- 0002- 3318- 9917 + +Bang Wang Foundation for Applied Molecular Evolution + +Kevin Bradley Foundation for Applied Molecular Evolution + +Myong- Jung Kim Firebird Biomolecular Science + +Roberto Laos Foundation for Applied Molecular Evolution + +Cen Chen Foundation for Applied Molecular Evolution https://orcid.org/0000- 0002- 6883- 9369 + +Dietlind Gerloff Foundation for Applied Molecular Evolution + +Luran Manfio Foundation for Applied Molecular Evolution + +Zunyi Yang Foundation for Applied Molecular Evolution https://orcid.org/0000- 0002- 6245- 2040 + +## Article + +Keywords: Sequencing, Expanded Genetic Alphabets + +Posted Date: December 21st, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 3678081/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> + +Additional Declarations: Yes there is potential Competing Interest. Patent application have been submitted for certain of the technologies reported here. Z.Y, B.W, and S.B. own intellectual property in this area. Several of the compounds described here are sold by Firebird Biomolecular Sciences, LLC (www.firebirdbio.com), which employs the indicated authors and is owned by S.B. Source data are included alongside this paper + +Version of Record: A version of this preprint was published at Nature Communications on May 14th, 2024. See the published version at https://doi.org/10.1038/s41467-024-48408-9. + +<--- Page Split ---> + +# Enzyme-Assisted High Throughput Sequencing of an Expanded Genetic Alphabet at Single Base Resolution + +Bang Wang \(^{1,3}\) , Kevin M. Bradley \(^{2}\) , Myong- Jung Kim \(^{2}\) , Roberto Laos \(^{1}\) , Cen Chen \(^{1}\) , Dietlind L. Gerloff \(^{1}\) , Luran Manfio \(^{1}\) , Zunyi Yang \(^{1,2*}\) , Steven A. Benner \(^{1,2*}\) + +\(^{1}\) Foundation for Applied Molecular Evolution, 13709 Progress Blvd, Alachua, FL, USA, 32615. \(^{2}\) Firebird Biomolecular Sciences, LLC, Alachua, FL, USA, 32615. \(^{3}\) Department of Chemistry, University of Florida, Gainesville, FL, USA, 32611. + +<--- Page Split ---> + +ABSTRACT: Many efforts have sought to apply laboratory in vitro evolution (LIVE) to natural nucleic acid (NA) scaffolds to directly evolve functional molecules. However, synthetic biology can move beyond natural NA scaffolds to create molecular systems whose libraries are far richer reservoirs of functionality than natural NAs. For example, "artificially expanded genetic information systems" (AEGIS) add up to eight nucleotides to the four found in standard NA. Even in its simplest 6- letter versions, AEGIS adds functional groups, information density, and folding motifs that natural NA libraries lack. To complete this vision, however, tools are needed to sequence molecules that are created by AEGIS LIVE. Previous sequencing approaches, including approaches from our laboratories, exhibited limited performance and lost many sequences in diverse library mixtures. Here, we present a new approach that enzymatically transforms the target AEGIS DNA. With higher transliteration efficiency and fidelity, this Enzyme- Assisted Sequencing of Expanded Genetic Alphabet (ESEGA) approach produces substantially better sequences of 6- letter (AGCTZP) DNA than previous transliteration approaches. Therefore, ESEGA facilitates precise analysis of libraries, allowing 'next- generation deep sequencing' to accurately quantify the sequences of 6- letter DNA molecules at single base resolution. We then applied ESEGA to three tasks: (a) defining optimal conditions to perform 6- nucleotide PCR (b) evaluating the fidelity of 6- nucleotide PCR with various DNA polymerases, and (c) extending that evaluation to AEGIS components functionalized with alkynyl and aromatic groups. No other approach at present has this scope, allowing this work to be the next step towards exploiting the potential of expanded DNA alphabets in biotechnology. + +KEYWORDS: Sequencing, Expanded Genetic Alphabets. + +![](images/Figure_1.jpg) + + +<--- Page Split ---> + +## Introduction: + +A standard challenge in biotechnology arises from our inability to design molecules from first principles to meet the performance needed for biotechnological applications. Proteins, in principle, could deliver "performance on demand"; natural protein evolution does this for a spectacularly broad range of functions. However, computationally intensive protein design1 as well as protein- targeted laboratory evolution2 require enormous amounts of trial and error, as well as knowledge of thousands of pre- solved structures. Further, outside of privileged scaffolds (antibodies are exemplary), the enormous sequence space of proteins is dominated by molecules that do not fold or, worse, precipitate from water. Folding and dissolution in water are nearly universal requirements for biotechnological value. + +Nucleic acids (DNA, RNA) have better- defined folding rules. Further, they remain soluble throughout their sequence spaces due to their repeating backbone charges3, and enjoy direct evolvability without the intermediacy of complex ribosome- based translation. RNA catalysts may have supported life during an episode of its early evolution, the "RNA World"4. Accordingly, pioneers like Larry Gold, Jack Szostak, Gerald Joyce, and others suggested that nucleic acids might be platforms for laboratory in vitro evolution (LIVE) to create functional biopolymers5. + +Unfortunately, three decades of effort with LIVE on natural scaffolds have been often disappointing6. This disappointment has been attributed to the low information density of standard DNA/RNA (which hinders defined folding), their lack of functional groups needed for efficiently binding and catalysis, and the intrinsic difficulty of getting compact core folds from their polyanionic backbone. + +These limitations might be mitigated in DNA analogs that exploit alternative hydrogen bonding patterns to give "artificially expanded genetic information systems" (AEGIS, Fig. 1)8. For example, adding non- standard nucleobases adds alternative base- base interactions that dramatically expand the number of compact folds available to evolving AEGIS oligonucleotides. These include isoG pentapexes9 (with the one letter code B), fat and skinny duplexes10, and the recently reported fZ- motif11. This last fold exploits the low \(\mathsf{pK}_{\mathsf{a}}\) of Z to give "skinny" deprotonated Z: Z pairs in a novel parallel double helix. + +Consistent with this, AEGIS- LIVE is proving to be a useful alternative to phage display and computationally intensive design for proteins, and as an alternative for systematic evolution of ligands by exponential enrichment (SELEX) for standard nucleic acids. Evolved AEGIS- bodies, antibody analogs, inactivate toxins12, bind cancer cell surface proteins13, 14, and deliver drugs selectively to targeted malignant cells15. AEGIS libraries from 6- nucleotide AEGIS DNA (G, A, C, T, Z, P, Fig. 1) are at least 100,000 times richer than standard GACT libraries as reservoirs for GACTZP AEGISzyme ribonucleases, analogs of protein ribonucleases16. This is due to the ability of Z to act as a general acid- base catalyst. No comparable activity is seen with any standard nucleobase. + +The challenge to support AEGIS- LIVE now is to develop methods that efficiently sequence 6- nucleotide (GACTZP) AEGIS DNA. Since manufacturers of "next generation" sequencing instruments have not been persuaded to directly sequence non- standard AEGIS components of DNA, controlled transliteration of AEGIS DNA to standard DNA has been at the core of these methods. + +Previously in these laboratories, a sequencing method was developed that integrates both an "easy" and a "difficult" transliteration17. The "easy transliteration" occurs when Z:P is converted to C:G through pairing between deprotonated Z and G. The Z:G pair has a Watson- Crick geometry, allowing it to evade many proof- reading mechanisms. This makes it "easy". + +In contrast, a "difficult" transliteration requires T or C to pair opposite P. Neither is easy at standard PCR pHs, and therefore is not clean. This second transliteration means Z:P pairs are transliterated to a mixture of T:A and C:G pairs. The ratio in this mixture is very sensitive to condition, makes the bioinformatic analysis challenging, and preventing the analysis of very complex mixtures. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 1 | Chemical structures of standard and non-standard nucleobases. By rearranging hydrogen bonding donor and acceptor groups on base pairs in a Watson Crick geometry, the number of independently replicable informational units in DNA/RNA can be increased from 4 to 12, increase the information density, functionality, and density of binders and catalysts in libraries of oligonucleotides built from an artificially expanded genetic information system (AEGIS).
+ +In collaboration with Andrew Laszlo teams, we have recently published preliminary data suggesting the possibility of using nanopores to sequence expanded genetic alphabets \(^{18}\) . Similar approaches have been studied for hydrophobic unnatural nucleotides \(^{19}\) . Nevertheless, these approaches remain in infancy. + +Sequencing approaches of other unnatural nucleotide sets have suffered from similar challenges. For example, dye terminator Sanger sequencing \(^{20}\) with low throughput, the similar transliteration strategies \(^{21 - 23}\) were applied to hydrophobic pairs with NGS. Li and coworkers recently reported a clever transliteration strategy for Romesberg's TPT3- NaM pair \(^{24}\) . However, pairing between hydrophobic and hydrogen- bonding nucleobases, required for transliteration, need not always support quantitative sequencing results. + +Thus, to fully realize the potential of AEGIS, we need reliable, efficient, quantitative, and user- friendly methods to sequence GACTZP DNA. We report here such a method: Enzyme- Assisted Sequencing of Expanded Genetic Alphabet (ESEGA). + +Here, rather than using transliteration during PCR, we enzymatically transform a starting mix to convert all cytidines to uridines using a member of the "Apolipoprotein B mRNA Editing Catalytic Polypeptide- like" (AID/ APOBEC \(^{25}\) ) deaminase family \(^{26}\) . APOBEC converts standard cytidine (C) in an oligonucleotide to uridine (U), a deterministic transliteration that occurs in high yield. + +Separately, we exploit the relatively low \(\mathsf{pK}_{\mathsf{a}}\) \((\approx 7.8)\) of AEGIS Z, which in its deprotonated form mismatches with G. This allows clean transliteration of Z:P pairs to C:G pairs during PCR. Finally, since no standard nucleobase effectively mismatches with P, we developed a workflow that incorporates dZTP into transliterative PCR to make the only necessary mismatch in the workflow to be between deprotonated Z and G. + +Together, these allow us to exploit the power of next- generation sequencing (NGS) \(^{27}\) instruments. These deliver millions of reads from single samples for four- letter DNA. The final part of the workflow is bioinformatics. After deamination and 5- nucleotide PCR conversion, comparison of the results of deep sequencing of AEGIS PCR products, in parallel, with antisense and sense DNA, allows bioinformatics to infer the sequences of AEGIS- containing molecules in the starting mixture, even complex mixtures that arise from AEGIS- LIVE. + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Fig. 2 | Sequencing 5-letter AEGIS (ATCGZ) DNA by deamination and transliteration. (A) Cytidine (C) is transliterated by cytidine deaminase to form uridine (U), which pairs to A in PCR. (B) AEGIS base Z becomes Z at pH (8.9) by deprotonation; Z' pairs with G during PCR, inducing a Z to C transliteration. (C) Schematic workflow shows the C to T and Z to C conversions after deamination and PCR amplification. (D) Denaturing PAGE-urea analysis of restriction digestion of PCR products by TakaRa Taq DNA polymerase at pH8.9 from DNA templates (Nat and ZZ) without deamination or with deamination (Nat-E and ZZ-E). Forward primer was labeled by FAM at 5'. (E) Sanger sequencing demonstrates the precise transliteration of C to U (then T during the PCR) and Z to C.
+ +To demonstrate its utility, ESEGA was used to (1) Define optimal conditions to perform 6- triphosphate PCR conditions (such as buffer pH and dPTP concentrations). (2) Evaluate the 6- triphosphate PCR fidelity with various commercial and house- engineered DNA polymerases. (3) Extend that evaluation to functionalized AEGIS components, in particular, those with alkynyl and aromatic hydrophobic functional groups, which are sparsely introduced into AEGIS libraries because of the higher information density of a 6- letter GACTZP DNA alphabet. + +## Results: + +To develop ESEGA sequencing, two single- stranded DNA sequences were synthesized to serve as test beds. These were accompanied by control sequences made from standard nucleotides ("Nat"), and a Z- modified sequence, where C was replaced by with Z in the natural sequence (Table 1). The "Nat" sequence contains two restriction sites that are recognized by two restriction endonucleases, AluI (AGCT) and PspOMI (GGGCCC). The ZZ trial sequence contains Zs placed strategically so that if they are transliterated to C, the AluI and PspOMI sites are re- generated (Supplementary Fig. 1). This transliteration can be detected by strategic restriction digestion (Fig. 2D). + +To develop and metric ESEGA sequencing, samples of both Nat and ZZ sequences were treated with cytidine deaminase; controls were not treated. Then, treated and untreated sequences were PCR amplified (pH 8.9) in mixtures containing only four standard dNTPs (no dZTP, no dPTP "4- triphosphate PCR"). These conditions force template dZ to mis- direct incorporation of dGTP (Fig. 2B). + +As the pH of PCR buffer may affect PCR efficiency, a series of pH values of PCR buffer (from 7.4- 9.5, measured at room temperature) were evaluated by qPCR; \(\mathsf{C}_{\mathsf{q}}\) values were used as metrics. Both Nat and ZZ template were well amplified between pH 8.0 and 9.3 (Supplementary Fig. 4). As the preferred PCR conditions to facilitate \(\mathbf{Z}\rightarrow \mathbf{C}\) transliteration, pH 8.9 was chosen. PCR products were then digested by AluI or PspOMI. The PCR amplicons from the natural template without deaminase treatment gave one well identified low length digestion band in denatured Urea- PAGE analysis in Lanes 2 and 3 (Fig. 2D), as expected from faithful amplification of the two sites in the synthetic standard DNA. + +<--- Page Split ---> + + +Table 1 | Standard and AEGIS DNA sequences used in this study. + +
NameSequence (5'-3')
NatTAAGATGAGAGTTGAGGAGAGTTATTCCAAGCTATAGGCCCTTCAGTATAGTAGTGTAAGTAGATAGTGG
ZZTAAGATGAGAGTTGAGGAGAGTTATTCCAAGZTATAGGCCZTTTCAGTATAGTAGTGTAAGTAGATAGTGG
ZP-1TAAGATGAGAGTTGAGGAGAGTTACGTGZACGCCTPTGTCACZACACAGTATAGTAGTGTAAGTAGATAGTGG
ZP-2TAAGATGAGAGTTGAGGAGAGTTATTCAPCGTCACZCPCTTTTATAGTATAGTAGTGTAAGTAGATAGTGG
C-RanTAAGATGAGAGTTGAGGAGAGTTATNNNCNNNGTATAGTAGTGTAAGTAGATAGTGG
Z-RanTAAGATGAGAGTTGAGGAGAGTTATNNNZZNNNTATAGTAGTGTAAGTAGATAGTGG
P-RanTAAGATGAGAGTTGAGGAGAGTTATNNNPNNNNTATAGTAGTGTAAGTAGATAGTGG
+ +In contrast, PCR amplicons from the standard template that had been previously treated by deaminase ("Nat- E") resisted restriction digestion (Fig. 2D, Lanes 5 and 6). This showed that the deaminase completely converted the Cs to Us in the restriction sites (Fig. 2A); these appear as T in the PCR amplicons (Fig. 2C). + +When the ZZ template was amplified in PCR with just four standard triphosphates, amplicons were also well digested by endonucleases (Fig. 2D, Lanes 8 and 9). This showed that Z is converted to C during the PCR amplification. With ZZ template treated with cytidine deaminase ("ZZ- E") and then amplified by PCR, amplicons were digested by AluI (Lane 11). This indicated that: (i) Z is not affected by cytidine deaminase; (ii) an isolated Z can be successfully transliterated to C. + +However, the ZZ- E amplicons resisted the digestion by PspOMI (Fig. 2D, lane 12). This suggested that the PspOMI restriction site was changed from GGCZZ to GGTCG by deamination of the C to U and transliterating ZZ to CC. This also showed that C → U deamination by APOBEC was not affected by a neighboring ZZ. + +To confirm this by Sanger sequencing, the length of the sequencing DNA was extended by tagged PCR (from 71 bp to 323 bp, Supplementary Fig. 2). The sequencing results (Fig. 2E) agree well with the restriction digestion. The sequence of the "Nat" amplicons matched with original design. The sequences of the Nat- E amplicons showed that all the Cs were deaminated. In the ZZ template, all of the Zs were transliterated to C by PCR (at pH 8.9). For the ZZ- E sample, the sequences showed that the original Cs were completely transliterated to Ts (Fig. 2E). However, they also show three Cs signals arising from the positions originally holding Z, either isolated Z or consecutive ZZ. + +We then investigated how DNA sequences built from six nucleotides "letters" (A, C, T, G, Z, P) were amplified under different PCR conditions. Two other test AEGIS DNA molecules were designed to contain both Z and P (ZP- 1 and ZP- 2, Table 1) and synthesized. For DNA sequences containing P to work when only standard (A, T, C, G) triphosphates are present, P is forced to mismatch with either C or T during the initial PCR cycles. However, this encounters problem with conversion of P, since all mismatches available in this amplification are incompatible with the Watson- Crick geometry (Fig. 3A, left arrow). + +Isolated Zs and Ps in ZP- 1 were paired and read in primer extension experiments (Supplementary Fig. 3C). qPCR analysis showed that the \(\mathsf{C}_{\mathsf{q}}\) values of ZP- 1 ( \(\mathsf{C}_{\mathsf{q}} = 22.0\) ) were higher than those of the Nat sequence (9.8) and ZZ sequences ( \(\mathsf{C}_{\mathsf{q}} = 10.6\) ), indicating the problematic nature of P:C and P:T mismatches (Fig. 3B). Further, when Z and P were adjacent (ZP- 2), primer extension was completely inhibited (Supplementary Fig. 3D), and the \(\mathsf{C}_{\mathsf{q}}\) values of ZP- 2 in the 4- triphosphate PCR were even higher ( \(\mathsf{C}_{\mathsf{q}} = 24.7\) ) (Fig. 3B). This suggest that P mismatching to T or C is more problematic when Z is adjacent. + +This poor mismatching was mitigated by adding dZTP to the four standard dNTPs (5- triphosphate PCR) (Fig. 3A right arrow). This allows P to match with Z in the first PCR round. The Z in its deprotonated form then directs the mismatched incorporation of G, leading to cleaner conversion. Thus, ZP- 2 sequence work very well in 5- triphosphate primer extension (Supplementary Fig. 3E, F). Both ZP- 1 and ZP- 2 sequence show high efficiency in 5- triphosphate PCR, + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 3 | Enzyme-Assisted Sequencing of Expanded Genetic Alphabet (ESEGA) of single strand 6-letter DNA (ATCGZP). Complete NGS read summaries are in supplemental material. (A) Schematic diagram showing the transliteration of AEGIS (ATCGZP) DNA using 4-triphosphate or 5-triphosphate PCR at pH8.9, without or with deamination. (B) Quantitative PCR (qPCR) to evaluate transliteration efficiency of various DNA templates in 4-triphosphate and 5-triphosphate PCR. Error bars represent the standard deviation of three independent experiments. Addition of dZTP enhances the efficiency of PCR for 6-letter templates. (C) NGS data reveals the transliteration (%) of original bases [A, T, C, (U), G, Z, and P] in Nat, ZZ, ZP-1 and ZP-2 sequences under different PCR conditions (4-triphosphate or 5-triphosphate PCR at pH8.9, with or without deamination). Standard deviations represent the multiple bases in the three sequences. (D and E) Sequence logos of 6-letter DNA (ZP-1 and ZP-2) under different transliteration and PCR conditions. (F) Robustness and specificity of the ESEGA for ZZ and Nat sequences at different ratios (10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90%). (G) Sensitivity of ESEGA for ZZ (45%) and Nat (55%) sequences in a series of 10-fold dilutions. The red dashed line represents the reference value obtained from Figure 3F. (H) Robustness of ESEGA with libraries NNNCNNN (C-Ran, top), NNNZNNN (Z-Ran, middle) and NNNPNNN (P-Ran, bottom) sequences, without deamination (left panel) or with deamination (right panel), then, followed by 5-triphosphate PCR and NGS. These data show ESEGA has no strong context dependency.
+ +<--- Page Split ---> + +the Cq value (11.9 and 11.7) are close to Nat and ZZ template in 4-triphosphate PCR (Fig. 3B). + +To obtain quantitative metrics for the fidelity of converting sequences built from 6- letter (A, C, T, G, Z, P) DNA to sequencable nucleotides under different PCR conditions, the performance of ZP- 1 and ZP- 2 conversion was compared with these pre- treatments: + +(1) Direct amplification with 4-triphosphate PCR. This was expected to proceed with low efficiency with some ambiguous transliteration (Fig. 3A left arrow). + +(2) Treatment with deaminase, followed by amplification with 5-triphosphate PCR. This was expected to deliver high efficiency PCR, with clean conversion of Z:P to C:G, with all of the C:G in the original templates replaced by U:A, and then T:A pairs (Fig. 3A central arrow) + +(3) Direct amplification with 5-triphosphate PCR. This was expected to deliver high efficiency PCR, with clean transliteration of Z:P to C:G, and with all of the C:G in the original templates remaining as C:G pairs (Fig. 3A right arrow). + +These amplicons were sent to Sanger sequencing (Supplementary Fig. 9 and 10) and NextGen sequencing. The analysis of NGS data revealed very faithful transliteration (>99%) of the original bases [ATC(U)GZ] in the Nat, ZZ, ZP- 1, and ZP- 2 sequences to their corresponding bases (A, T, C, and G) in 5-triphosphate or 4-triphosphate PCR. However, in the case of P transliteration, a mixture of A (~63%) and G (~37%) was observed in 4-triphosphate PCR, with relatively larger fluctuations. Fixing this problem, P was transliterated almost exclusively to G (~94.5%) in 5-triphosphate PCR (Fig. 3C). To visualize the transliteration of base at a given position, we converted the table information into a sequence logo (Fig. 3D and 3E), which illustrating the transliteration of ZP- 1 and ZP- 2 templates under for 4-triphosphate PCR (top), 5-triphosphate PCR following deamination (middle), and 5-triphosphate PCR without deamination (bottom). + +As an innovative approach to test the robustness of ESEGA, ZZ and Nat templates were mixed with total concentrations of 100 nM, but with AEGIS- containing and standard oligonucleotides in various ratios (10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90%). Subsequently, each sample underwent a single- stranded ESEGA with NGS. + +The called populations of ZZ amplicons displayed a modest reduction of approximately 4.5% relative to the prepared ZZ percentage across all the templates (Fig. 3F). + +This observation might be attributed to the slightly lower efficiency of Z mismatch with G in the first round of PCR compared with standard DNA base pair. This is supported by the higher \(C_{\mathrm{a}}\) value recorded in qPCR for the ZZ template (10.6) in contrast to the Nat template (9.8) (Fig. 3B) + +To evaluate the sensitivity of ESEGA, ZZ (45%) and Nat templates (55%) were blended and subsequently diluted serially to give six different concentrations and input to ranging from \(10^{9}\) to \(10^{4}\) copies in sequencing. Each sample was subjected to a single- stranded AEGIS- DNA sequencing. Sequencing was possible even at the highest dilution (Fig. 3G). + +So far, the cytidine deamination and Z/P conversion were demonstrated with defined 6- letter DNA sequences. We were concerned that local sequence context might influence the outcome delivered by ESEGA. To determine whether neighboring nucleotides may affect C to U, Z to C, and P to G transliteration, three sequences were synthesized where a single C, Z or P was placed in the middle of six random nucleotides (C- Ran, Z- Ran and P- Ran, Table 1). The ESEGA workflow was applied. Low sequence bias was seen in the deamination results (Fig. 3H, top), consistent with the literature28. Further, no overall sequence context bias was observed in Z and P transliteration (Fig. 3H, middle and bottom). + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Fig. 4 | Sequencing double-stranded 6-letter DNA and assessing the fidelity of different DNA polymerases in 6-triphosphate PCR. (A) Schematic of ESEGA workflow for double-stranded 6-letter (ATCGZP) DNA. (B) and (C) Retention rates of Z/P at each position in the ZP-1 template plotted after 6-triphosphate PCR with varying concentrations of dPTP (0.05-0.5 mM) at pH 8.9 or pH 8.0 conditions. (D) ESEGA evaluation of the fidelity of various polymerases in 6-triphosphate PCR. Complete NGS read summaries are in supplemental material.
+ +Double- stranded DNA is a common outcome of AEGIS 6- nucleotide PCR. Thus, we developed a ESEGA workflow for double stranded DNA as well. First, the double- stranded DNA was denatured and the strands were separated. The two single- strands were separately treated with deaminase followed by 5- triphosphate PCR. The two amplicon pools were separately sequenced with barcode. Bioinformatics then matched the sequences to the strands that were originally paired. Then, the matches were analyzed to infer the original sequences of the paired strands (Fig. 4A). A:T and T:A pairs delivered A:T and T:A pairs in the duplexes matched by bioinformatics analysis, unchanged by the processes in the workflow. Thus, sites that hold A:T and T:A pairs in the surviving bioinformatics pairs were inferred to have been A:T and T:A pairs in the original duplex. + +Likewise, Z:P and P:Z in the original duplex gave C:G and G:C in the bioinformatics- paired sites. In both cases, they arise by transliteration involving deprotonate Z:G mismatches. Thus, sites that hold G:C and C:G pairs in the surviving bioinformatics pairs were inferred to have been Z:P and P:Z pairs in the original duplex. + +<--- Page Split ---> + +If the original duplexes have C:G or G:C pairs, then bioinformatics analysis gives a third outcome due to deamination. From amplicons arising from the strand that contained C, deamination gives amplicon duplexes with T:A pairs. From the complementary strand that contained G, the amplicon duplexes hold G:C pairs at the homologous site. Thus, bioinformatics assigns C:G in the original duplex when A:T appears in the amplicons derived from the "sense" DNA chain, if C:G appears also appears in amplicons derived from the anti- sense DNA chain. + +## Applications of ESEGA + +## Determining 6-nucleotide PCR conditions that optimally retain Z:P pairs + +To illustrate how ESEGA might be used in a practical setting, we first showed how ESEGA applied to double- stranded amplicons might be used to evaluate of various concentration of dPTP and various values of pH values the impact on the fidelity of GACTZP PCR. Here, the metric for fidelity was the percent retention of Z:P pairs when the ZP- 1 sequence was used as a template. + +FAM- labeled forward and Cy5- labeled reverse primers were used to amplify ZP- 1 in PCR with a complete set of six triphosphates, with a primer: template ratio of 50000:1 ( \(\sim 16\) nominal doublings). TaKaRa Taq HS polymerase and 6- triphosphate dNTP (dATP (0.1 mM), dCTP (0.2 mM), dGTP (0.1 mM), dTTP (0.1 mM), dZTP (0.1 mM), and dPTP) were used in the amplification. Six different dPTP concentrations (0.05 mM, 0.1 mM, 0.2 mM, 0.3 mM, 0.4 mM, and 0.5 mM) and two pH levels (8.0 and 8.9) were used, under the hypothesis that Z:P pairs would be better retained at higher concentrations of dPTP and better retained at lower pH (Supplementary Fig. 11). + +Following separation of the PCR duplex amplicons by PAGE- urea, ESEGA was used to compare the data from the sense and anti- sense strands to quantitate the retention of the Z and P nucleotides after 25 rounds of PCR. Consistent with the hypothesis, Z:P pairs were better retained during PCR at pH 8.0 (Fig. 4C) than at pH 8.9 (Fig. 4B) at each concentration of dPTP. This was attributed to greater deprotonation of Z at the higher pH, leading to more deprotonated Z:G mismatches. Greater retention of the Z:P pairs was also observed with increasing dPTP concentration. This was consistent with the hypothesis that dPTP competes with dGTP as a partner for template dZ. + +Thus, ESEGA supported an application to screening PCR conditions to identify parameters that best retained Z:P pairs. Under these conditions, \(\sim 90\%\) of the Z:P pairs were retained at pH 8.0 with 0.5 mM dPTP after 16 nominal doublings, with a nominal per cycle fidelity of \(99.34\%\) . + +## Identifying polymerases that optimally retain Z:P pairs + +Using these optimal conditions (pH 8.0, 0.5 mM dPTP), we then amplify the ZP- 1 template with a set of polymerases, including TaKaRa Taq HS, KOD exo', KlenTaq, Phire hot start II, Phusion TM, Go Taq, One Taq, and an in- house- engineered 6M Taq variant. All of these gave amplification and interpretable sequencing data. The amount of each polymerase was adjusted to ensure similar amplification efficiency. Other polymerases examined (LongAmp Taq, Q5 High- Fidelity, Sulfolobus, Vent exo', and HiFi KAPA) produced inconsistent results or no amplification at all. + +ESEGA was used to analyze amplicons from 25 cycles of 6- triphosphate PCR using these eight polymerases and the ZP- 1 template. The retention rates of Z:P pairs were visualized using a sequence logo (Fig. 4D). + +Our findings revealed that Z:P pairs were retained best by the KlenTaq polymerase under these conditions, retaining \(90 - 95\%\) of the Z:P pairs after 16 nominal doublings; this approximates the uncertainty in the ESEGA analysis itself. However, the KlenTaq polymerase gave less efficient amplification. Thus, TaKaRa Taq HS was identified as a preferred enzyme under a metric that combined fidelity, efficiency, and robustness. + +<--- Page Split ---> + +Additionally, we observed that KOD exo polymerase exhibited relatively good fidelity in the retention of Z and P, but added Z and P to the amplicons at positions that originated as C and G. The 6M Taq polymerase, which was developed in- house to encourage processivity, performed less well. A comprehensive description of the 6M Taq evolution process can be found in the Supplementary Materials. Overall, ESEGA provides a robust and reliable framework for the selection and development of high- fidelity polymerases in the context of 6- triphosphate PCR applications. + +![PLACEHOLDER_12_0] + +
Fig. 5 | Chemical structures and retentions rate of functionalized-dPTP derivatives in 6-triphosphate PCR assessed by ESEGA. Chemical structures of (A) classical dPTP; (B) dPethTP, 7-(4Ethynyl)-dP triphosphate; and (C) dPphTP. 7-(4-Phenyl-1-butynyl)-dP triphosphate. (D) Sanger sequencing PCR amplicons from ZZ template with 6-triphosphate (dNTPs, dZTP, and dPTP, or dPethTP, or dPphTP). (E) ESEGA and NGS evaluate the retentions of dP, dPeth, and dPph in PCR amplicons after 6-triphosphate PCR by statistics of Z retentions.
+ +## Assessing the fidelity of functionalized dPTP in 6-triphosphate PCR + +As noted in the introduction, one of the advantages of AEGIS- LIVE over LIVE with standard nucleotides is the increased information density of expanded genetic alphabets, and the consequent ability to sparsely introduce functional groups in AEGIS- LIVE that standard DNA/RNA lacks. This allows AEGIS- LIVE to compete with protein evolution (e.g. phage display) and protein computational design (e.g. ROSETTA) by increasing the diversity of functional groups towards that of proteins, and increasing the number of compact folds, without the troublesome features of proteins, in particular, their propensity to precipitate. + +Fig. 5B and Fig. 5C shows two variants of AEGIS P that carry functional groups, specifically, alkyl and phenylalkylnyl groups. The first can support "click chemistry"; proteins have no analogous capability. The second is able to support hydrophobic interactions (compare with phenylalanine in proteins). + +We used ESEGA to evaluate the performance of polymerases challenged to amplify AEGIS DNA containing these functionalized P variants. The ZZ sequence was chosen as template, with functionalized dPTP used in the triphosphate mix instead of normal dPTP. The amplification was done as before with 6- nucleotide PCR. The sense DNA chain was separated from resulting PCR products by PAGE- urea, and the sequences of the amplicons was evaluated by ESEGA, using both Sanger sequencing (Fig. 5D) and NGS (Fig. 5E). The 6- nucleotide PCR also monitored by qPCR (Eva green) when the fluor- labeled primers were replaced by unlabeled primers. (Supplementary Fig. 12B) + +<--- Page Split ---> + +Here, the efficiency of amplification by TaKaRa of oligonucleotides containing alkynyl P was close to that of those with unfunctionalized P. Amplification efficiency was modestly lower with phenylalkynyl P (Supplementary Fig. 12B). The fidelity of replication was comparable with alkynyl P by ESEGA (Fig. 5E). However, substantial loss of phenylalkynyl P was observed by ESEGA, especially at position 40 of the template. Here, the two adjacent Zs drive the insertion of two tagged P's. It is well known that unmodified polymerases such as TaKaRa do not easily synthesize DNA with two consecutive tagged nucleotides29. Thus, this result was not unexpected. + +## Discussion + +Nearly all life forms on Earth share the same informational biopolymers using A- T and C- G base pairs. Even the known exceptions, cyanoviruses that use diaminopurine instead of adenine as a partner for T, do not expand the number of independently replicable building blocks30- 32. + +However, standard nucleic acids lack the functional group diversity, the informational density, and the folding capability needed to give effective receptors, ligands, and catalysts. These factors account for the inability of laboratory in vitro evolution with standard DNA and RNA to compete effectively with antibody and laboratory protein evolution, even though proteins lacking a privileged scaffold are plagued by precipitation issues. + +Artificially expanded genetic information systems (AEGIS, Fig. 1) are not designed for any specific purpose, but rather to be richer reservoirs of functionality in a directly evolvable system. AEGIS has more building blocks, a greater diversity of functional groups, higher information density, better control over folding, and the ability to form compact folds via base- base interactions. Even with the limited sequencing tools previously available, AEGIS- LIVE has evolved molecules that neutralize toxins, cleave specific RNA molecules, bind to specific cells, and deliver drugs to cancer tissues33, 34. Alternative systems where pairing does not exploit inter- base hydrogen- bonding have been explored by Kool35, Hirao22, and Romesberg36. All have been shown to perform, at various levels of efficiency, in replication, transcription, translation and semi- synthetic organisms8, 37, 38. More exotically, AEGIS is helping us to seek alien life in the cosmos39, which may not have had the same pre- history as life on Earth, and thus may have different genetic biopolymers. + +Therefore, AEGIS has the potential for broad biotechnological applications, should its evolution under selective pressures chosen by experimentalists become routine40. ESEGA offers a key element needed to make AEGIS- LIVE routine. + +ESEGA represents a transformative use of the capabilities afforded by next generation sequencing, which has transformed the analysis of standard DNA sequences. By manipulating the pH level to alter the topological structure of nucleic bases, Z is deprotonated to form Z, Z equates to C (Fig. 1). This allows Z to pair with G quite well, leading to high fidelity transliteration (99%). Additionally, ESEGA employs 5- triphosphate transliteration PCR to ensure high- fidelity transfer of P to G (~95%), This ensures a clean outcome. + +Standard bioinformatics allow hundreds of thousands of reads from NGS to be analyzed in a library context. The cleanliness of this workflow supports high sequence diversity in those libraries, where each individual sequences present in the mix as only a few dozen exemplars. + +In addition to showing this robust workflow, we show three applications where ESEGA supports the development of synthetic biology using expanded DNA alphabets. These are, of course, not the only three that can be conceived. Thus, ESEGA- like workflows hold the potential to develop other expanded genetic alphabets. + +<--- Page Split ---> + +## ASSOCIATED CONTENT + +Experimental procedures, material and methods data, all synthetic dPTP derivatives, analytical characterization, and details of all assays included in the Supporting Information. + +## AUTHOR INFORMATION + +Corresponding Author : + +Zunyi Yang: Email: zyang@ffame.org + +Steven A. Benner: Email: sbenner@ffame.org + +## Author Contributions + +B.W., Z.Y. and S.B. conceived the project. B.W. designed, carried out all experiments and wrote the manuscript. K.B. performed bioinformatics analyses. M.K. synthesized the functional modified dPTP. R.L. and D.G. yielded the 6M Taq polymerase variant. C.C. synthesized the DNA containing AEGIS. Z.Y. and S.B. supervised the project, analyzed the data, and contributed to writing the manuscript. All individuals participated in the discussion. + +## Notes + +Patent application have been submitted for certain of the technologies reported here. Z.Y, B.W, and S.B. own intellectual property in this area. Several of the compounds described here are sold by Firebird Biomolecular Sciences, LLC (www.firebirdbio.com), which employs the indicated authors and is owned by S.B. Source data are included alongside this paper. + +## ACKNOWLEDGMENT + +This work was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Numbers R01GM141391. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This work was also supported by the AEGIS LIVE Endowment Fund from the Foundation for Applied Molecular Evolution (FfAME). + +## ABBREVIATIONS + +AEGIS, artificially expanded genetic information systems; LIVE, laboratory in vitro evolution. + +## REFERENCES + +1. 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An efficient unnatural base pair for PCR amplification. J. Am. Chem. Soc. 129, 15549-15555 (2007). + +21. Kimoto, M., Yamashige, R., Matsunaga, K.-i., Yokoyama, S. & Hirao, I. Generation of high-affinity DNA aptamers using an expanded genetic alphabet. Nat. Biotechnol. 31, 453-457 (2013). + +22. Kimoto, M. & Hirao, I. Genetic alphabet expansion technology by creating unnatural base pairs. Chem. Soc. Rev. 49, 7602-7626 (2020). + +23. Hamashima, K., Soong, Y.T., Matsunaga, K.-i., Kimoto, M. & Hirao, I. DNA Sequencing Method Including Unnatural Bases for DNA Aptamer Generation by Genetic Alphabet Expansion. ACS Synth. Biol. 8, 1401-1410 (2019). + +24. Wang, H. et al. Locating, tracing and sequencing multiple expanded genetic letters in complex DNA context via a bridge-base approach. Nucleic Acids Res. 51, e52-e52 (2023). + +25. Siriwardena, S.U., Chen, K. & Bhagwat, A.S. Functions and Malfunctions of Mammalian DNA-Cytosine Deaminases. Chem. Rev. 116, 12688-12710 (2016). + +26. Schutsky, E.K. et al. Nondestructive, base-resolution sequencing of 5-hydroxymethylcytosine using a DNA deaminase. Nat. Biotechnol. 36, 1083-1090 (2018). + +27. Goodwin, S., McPherson, J.D. & McCombie, W.R. Coming of age: ten years of next-generation sequencing technologies. Nature Reviews Genetics 17, 333-351 (2016). + +28. Williams, L. et al. Enzymatic Methyl-seq: the next generation of methylome analysis. NEB expressions (2019). + +29. Wu, W. et al. Termination of DNA synthesis by N6 -alkylated, not 3'-O -alkylated, photocleavable 2'-deoxyadenosine triphosphates. Nucleic Acids Res. 35, 6339-6349 (2007). + +30. Pezo, V. et al. Noncanonical DNA polymerization by aminoadenine-based siphoviruses. Science 372, 520-524 (2021). + +31. Kirnos, M., Khudyakov, I.Y., Alexandrushkina, N. & Vanyushin, B. 2-Aminoadenine is an adenine substituting for a base in S-2L cyanophage DNA. Nature 270, 369-370 (1977). + +32. Zhou, Y. et al. A widespread pathway for substitution of adenine by diaminopurine in phage genomes. Science 372, 512-516 (2021). + +33. Piccirilli, J.A., Benner, S.A., Krauch, T. & Moroney, S.E. Enzymatic incorporation of a new base pair into DNA and RNA extends the genetic alphabet. Nature 343, 33-37 (1990). + +34. Benner, S.A. et al. Alternative Watson-Crick synthetic genetic systems. Cold Spring Harb. Perspect. Biol. 8, a023770 (2016). + +<--- Page Split ---> + +35. Moran, S., Ren, R.X.-F. & Kool, E.T. A thymidine triphosphate shape analog lacking Watson–Crick pairing ability is replicated with high sequence selectivity. Proc. Natl Acad. Sci. USA 94, 10506-10511 (1997). +36. Feldman, A.W. & Romesberg, F.E. Expansion of the Genetic Alphabet: A Chemist’s Approach to Synthetic Biology. Acc. Chem. Res. 51, 394-403 (2018). +37. Malyshev, D.A. et al. A semi-synthetic organism with an expanded genetic alphabet. Nature 509, 385-388 (2014). +38. Zhang, Y. et al. A semi-synthetic organism that stores and retrieves increased genetic information. Nature 551, 644-647 (2017). +39. Špaček, J. & Benner, S.A. Agnostic Life Finder (ALF) for Large-Scale Screening of Martian Life During In Situ Refueling. Astrobiology 22, 1255-1263 (2022). +40. Benner, S.A. Rethinking nucleic acids from their origins to their applications. Philos. Trans. R. Soc. B 378, 20220027 (2023). + +<--- Page Split ---> + +16 + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +Supportinginformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__089c9ef8b7852bfc9e4086386f4d035a0a402a924515dd23558dc3aebf2493aa/preprint__089c9ef8b7852bfc9e4086386f4d035a0a402a924515dd23558dc3aebf2493aa_det.mmd b/preprint/preprint__089c9ef8b7852bfc9e4086386f4d035a0a402a924515dd23558dc3aebf2493aa/preprint__089c9ef8b7852bfc9e4086386f4d035a0a402a924515dd23558dc3aebf2493aa_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..bd13a85c3fff8ff9e84c7541b5cad99317a6d433 --- /dev/null +++ b/preprint/preprint__089c9ef8b7852bfc9e4086386f4d035a0a402a924515dd23558dc3aebf2493aa/preprint__089c9ef8b7852bfc9e4086386f4d035a0a402a924515dd23558dc3aebf2493aa_det.mmd @@ -0,0 +1,468 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 909, 207]]<|/det|> +# Enzyme-Assisted High Throughput Sequencing of an Expanded Genetic Alphabet at Single Base Resolution + +<|ref|>text<|/ref|><|det|>[[44, 230, 180, 247]]<|/det|> +Steven Benner + +<|ref|>text<|/ref|><|det|>[[52, 258, 289, 274]]<|/det|> +manuscript@fname.org + +<|ref|>text<|/ref|><|det|>[[44, 301, 792, 322]]<|/det|> +Foundation for Applied Molecular Evolution https://orcid.org/0000- 0002- 3318- 9917 + +<|ref|>text<|/ref|><|det|>[[44, 327, 432, 368]]<|/det|> +Bang Wang Foundation for Applied Molecular Evolution + +<|ref|>text<|/ref|><|det|>[[44, 373, 432, 414]]<|/det|> +Kevin Bradley Foundation for Applied Molecular Evolution + +<|ref|>text<|/ref|><|det|>[[44, 419, 316, 459]]<|/det|> +Myong- Jung Kim Firebird Biomolecular Science + +<|ref|>text<|/ref|><|det|>[[44, 465, 432, 506]]<|/det|> +Roberto Laos Foundation for Applied Molecular Evolution + +<|ref|>text<|/ref|><|det|>[[44, 512, 792, 552]]<|/det|> +Cen Chen Foundation for Applied Molecular Evolution https://orcid.org/0000- 0002- 6883- 9369 + +<|ref|>text<|/ref|><|det|>[[44, 557, 432, 598]]<|/det|> +Dietlind Gerloff Foundation for Applied Molecular Evolution + +<|ref|>text<|/ref|><|det|>[[44, 603, 432, 644]]<|/det|> +Luran Manfio Foundation for Applied Molecular Evolution + +<|ref|>text<|/ref|><|det|>[[44, 650, 792, 691]]<|/det|> +Zunyi Yang Foundation for Applied Molecular Evolution https://orcid.org/0000- 0002- 6245- 2040 + +<|ref|>sub_title<|/ref|><|det|>[[44, 733, 103, 751]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 771, 501, 791]]<|/det|> +Keywords: Sequencing, Expanded Genetic Alphabets + +<|ref|>text<|/ref|><|det|>[[44, 809, 348, 828]]<|/det|> +Posted Date: December 21st, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 847, 475, 866]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3678081/v1 + +<|ref|>text<|/ref|><|det|>[[42, 884, 912, 927]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[41, 44, 923, 157]]<|/det|> +Additional Declarations: Yes there is potential Competing Interest. Patent application have been submitted for certain of the technologies reported here. Z.Y, B.W, and S.B. own intellectual property in this area. Several of the compounds described here are sold by Firebird Biomolecular Sciences, LLC (www.firebirdbio.com), which employs the indicated authors and is owned by S.B. Source data are included alongside this paper + +<|ref|>text<|/ref|><|det|>[[41, 191, 911, 234]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on May 14th, 2024. See the published version at https://doi.org/10.1038/s41467-024-48408-9. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[116, 87, 880, 127]]<|/det|> +# Enzyme-Assisted High Throughput Sequencing of an Expanded Genetic Alphabet at Single Base Resolution + +<|ref|>text<|/ref|><|det|>[[113, 161, 881, 199]]<|/det|> +Bang Wang \(^{1,3}\) , Kevin M. Bradley \(^{2}\) , Myong- Jung Kim \(^{2}\) , Roberto Laos \(^{1}\) , Cen Chen \(^{1}\) , Dietlind L. Gerloff \(^{1}\) , Luran Manfio \(^{1}\) , Zunyi Yang \(^{1,2*}\) , Steven A. Benner \(^{1,2*}\) + +<|ref|>text<|/ref|><|det|>[[113, 234, 861, 308]]<|/det|> +\(^{1}\) Foundation for Applied Molecular Evolution, 13709 Progress Blvd, Alachua, FL, USA, 32615. \(^{2}\) Firebird Biomolecular Sciences, LLC, Alachua, FL, USA, 32615. \(^{3}\) Department of Chemistry, University of Florida, Gainesville, FL, USA, 32611. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 43, 912, 355]]<|/det|> +ABSTRACT: Many efforts have sought to apply laboratory in vitro evolution (LIVE) to natural nucleic acid (NA) scaffolds to directly evolve functional molecules. However, synthetic biology can move beyond natural NA scaffolds to create molecular systems whose libraries are far richer reservoirs of functionality than natural NAs. For example, "artificially expanded genetic information systems" (AEGIS) add up to eight nucleotides to the four found in standard NA. Even in its simplest 6- letter versions, AEGIS adds functional groups, information density, and folding motifs that natural NA libraries lack. To complete this vision, however, tools are needed to sequence molecules that are created by AEGIS LIVE. Previous sequencing approaches, including approaches from our laboratories, exhibited limited performance and lost many sequences in diverse library mixtures. Here, we present a new approach that enzymatically transforms the target AEGIS DNA. With higher transliteration efficiency and fidelity, this Enzyme- Assisted Sequencing of Expanded Genetic Alphabet (ESEGA) approach produces substantially better sequences of 6- letter (AGCTZP) DNA than previous transliteration approaches. Therefore, ESEGA facilitates precise analysis of libraries, allowing 'next- generation deep sequencing' to accurately quantify the sequences of 6- letter DNA molecules at single base resolution. We then applied ESEGA to three tasks: (a) defining optimal conditions to perform 6- nucleotide PCR (b) evaluating the fidelity of 6- nucleotide PCR with various DNA polymerases, and (c) extending that evaluation to AEGIS components functionalized with alkynyl and aromatic groups. No other approach at present has this scope, allowing this work to be the next step towards exploiting the potential of expanded DNA alphabets in biotechnology. + +<|ref|>text<|/ref|><|det|>[[87, 390, 548, 409]]<|/det|> +KEYWORDS: Sequencing, Expanded Genetic Alphabets. + +<|ref|>image<|/ref|><|det|>[[285, 469, 701, 616]]<|/det|> + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[87, 44, 199, 60]]<|/det|> +## Introduction: + +<|ref|>text<|/ref|><|det|>[[86, 70, 913, 210]]<|/det|> +A standard challenge in biotechnology arises from our inability to design molecules from first principles to meet the performance needed for biotechnological applications. Proteins, in principle, could deliver "performance on demand"; natural protein evolution does this for a spectacularly broad range of functions. However, computationally intensive protein design1 as well as protein- targeted laboratory evolution2 require enormous amounts of trial and error, as well as knowledge of thousands of pre- solved structures. Further, outside of privileged scaffolds (antibodies are exemplary), the enormous sequence space of proteins is dominated by molecules that do not fold or, worse, precipitate from water. Folding and dissolution in water are nearly universal requirements for biotechnological value. + +<|ref|>text<|/ref|><|det|>[[86, 219, 912, 323]]<|/det|> +Nucleic acids (DNA, RNA) have better- defined folding rules. Further, they remain soluble throughout their sequence spaces due to their repeating backbone charges3, and enjoy direct evolvability without the intermediacy of complex ribosome- based translation. RNA catalysts may have supported life during an episode of its early evolution, the "RNA World"4. Accordingly, pioneers like Larry Gold, Jack Szostak, Gerald Joyce, and others suggested that nucleic acids might be platforms for laboratory in vitro evolution (LIVE) to create functional biopolymers5. + +<|ref|>text<|/ref|><|det|>[[86, 332, 912, 401]]<|/det|> +Unfortunately, three decades of effort with LIVE on natural scaffolds have been often disappointing6. This disappointment has been attributed to the low information density of standard DNA/RNA (which hinders defined folding), their lack of functional groups needed for efficiently binding and catalysis, and the intrinsic difficulty of getting compact core folds from their polyanionic backbone. + +<|ref|>text<|/ref|><|det|>[[86, 410, 912, 515]]<|/det|> +These limitations might be mitigated in DNA analogs that exploit alternative hydrogen bonding patterns to give "artificially expanded genetic information systems" (AEGIS, Fig. 1)8. For example, adding non- standard nucleobases adds alternative base- base interactions that dramatically expand the number of compact folds available to evolving AEGIS oligonucleotides. These include isoG pentapexes9 (with the one letter code B), fat and skinny duplexes10, and the recently reported fZ- motif11. This last fold exploits the low \(\mathsf{pK}_{\mathsf{a}}\) of Z to give "skinny" deprotonated Z: Z pairs in a novel parallel double helix. + +<|ref|>text<|/ref|><|det|>[[86, 524, 912, 662]]<|/det|> +Consistent with this, AEGIS- LIVE is proving to be a useful alternative to phage display and computationally intensive design for proteins, and as an alternative for systematic evolution of ligands by exponential enrichment (SELEX) for standard nucleic acids. Evolved AEGIS- bodies, antibody analogs, inactivate toxins12, bind cancer cell surface proteins13, 14, and deliver drugs selectively to targeted malignant cells15. AEGIS libraries from 6- nucleotide AEGIS DNA (G, A, C, T, Z, P, Fig. 1) are at least 100,000 times richer than standard GACT libraries as reservoirs for GACTZP AEGISzyme ribonucleases, analogs of protein ribonucleases16. This is due to the ability of Z to act as a general acid- base catalyst. No comparable activity is seen with any standard nucleobase. + +<|ref|>text<|/ref|><|det|>[[86, 672, 911, 741]]<|/det|> +The challenge to support AEGIS- LIVE now is to develop methods that efficiently sequence 6- nucleotide (GACTZP) AEGIS DNA. Since manufacturers of "next generation" sequencing instruments have not been persuaded to directly sequence non- standard AEGIS components of DNA, controlled transliteration of AEGIS DNA to standard DNA has been at the core of these methods. + +<|ref|>text<|/ref|><|det|>[[86, 751, 911, 821]]<|/det|> +Previously in these laboratories, a sequencing method was developed that integrates both an "easy" and a "difficult" transliteration17. The "easy transliteration" occurs when Z:P is converted to C:G through pairing between deprotonated Z and G. The Z:G pair has a Watson- Crick geometry, allowing it to evade many proof- reading mechanisms. This makes it "easy". + +<|ref|>text<|/ref|><|det|>[[86, 830, 911, 900]]<|/det|> +In contrast, a "difficult" transliteration requires T or C to pair opposite P. Neither is easy at standard PCR pHs, and therefore is not clean. This second transliteration means Z:P pairs are transliterated to a mixture of T:A and C:G pairs. The ratio in this mixture is very sensitive to condition, makes the bioinformatic analysis challenging, and preventing the analysis of very complex mixtures. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[85, 44, 861, 228]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[85, 237, 912, 325]]<|/det|> +
Fig. 1 | Chemical structures of standard and non-standard nucleobases. By rearranging hydrogen bonding donor and acceptor groups on base pairs in a Watson Crick geometry, the number of independently replicable informational units in DNA/RNA can be increased from 4 to 12, increase the information density, functionality, and density of binders and catalysts in libraries of oligonucleotides built from an artificially expanded genetic information system (AEGIS).
+ +<|ref|>text<|/ref|><|det|>[[86, 360, 911, 413]]<|/det|> +In collaboration with Andrew Laszlo teams, we have recently published preliminary data suggesting the possibility of using nanopores to sequence expanded genetic alphabets \(^{18}\) . Similar approaches have been studied for hydrophobic unnatural nucleotides \(^{19}\) . Nevertheless, these approaches remain in infancy. + +<|ref|>text<|/ref|><|det|>[[86, 420, 911, 527]]<|/det|> +Sequencing approaches of other unnatural nucleotide sets have suffered from similar challenges. For example, dye terminator Sanger sequencing \(^{20}\) with low throughput, the similar transliteration strategies \(^{21 - 23}\) were applied to hydrophobic pairs with NGS. Li and coworkers recently reported a clever transliteration strategy for Romesberg's TPT3- NaM pair \(^{24}\) . However, pairing between hydrophobic and hydrogen- bonding nucleobases, required for transliteration, need not always support quantitative sequencing results. + +<|ref|>text<|/ref|><|det|>[[86, 535, 911, 588]]<|/det|> +Thus, to fully realize the potential of AEGIS, we need reliable, efficient, quantitative, and user- friendly methods to sequence GACTZP DNA. We report here such a method: Enzyme- Assisted Sequencing of Expanded Genetic Alphabet (ESEGA). + +<|ref|>text<|/ref|><|det|>[[86, 596, 911, 667]]<|/det|> +Here, rather than using transliteration during PCR, we enzymatically transform a starting mix to convert all cytidines to uridines using a member of the "Apolipoprotein B mRNA Editing Catalytic Polypeptide- like" (AID/ APOBEC \(^{25}\) ) deaminase family \(^{26}\) . APOBEC converts standard cytidine (C) in an oligonucleotide to uridine (U), a deterministic transliteration that occurs in high yield. + +<|ref|>text<|/ref|><|det|>[[86, 675, 911, 764]]<|/det|> +Separately, we exploit the relatively low \(\mathsf{pK}_{\mathsf{a}}\) \((\approx 7.8)\) of AEGIS Z, which in its deprotonated form mismatches with G. This allows clean transliteration of Z:P pairs to C:G pairs during PCR. Finally, since no standard nucleobase effectively mismatches with P, we developed a workflow that incorporates dZTP into transliterative PCR to make the only necessary mismatch in the workflow to be between deprotonated Z and G. + +<|ref|>text<|/ref|><|det|>[[86, 773, 911, 877]]<|/det|> +Together, these allow us to exploit the power of next- generation sequencing (NGS) \(^{27}\) instruments. These deliver millions of reads from single samples for four- letter DNA. The final part of the workflow is bioinformatics. After deamination and 5- nucleotide PCR conversion, comparison of the results of deep sequencing of AEGIS PCR products, in parallel, with antisense and sense DNA, allows bioinformatics to infer the sequences of AEGIS- containing molecules in the starting mixture, even complex mixtures that arise from AEGIS- LIVE. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[90, 45, 870, 277]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[85, 290, 912, 429]]<|/det|> +
Fig. 2 | Sequencing 5-letter AEGIS (ATCGZ) DNA by deamination and transliteration. (A) Cytidine (C) is transliterated by cytidine deaminase to form uridine (U), which pairs to A in PCR. (B) AEGIS base Z becomes Z at pH (8.9) by deprotonation; Z' pairs with G during PCR, inducing a Z to C transliteration. (C) Schematic workflow shows the C to T and Z to C conversions after deamination and PCR amplification. (D) Denaturing PAGE-urea analysis of restriction digestion of PCR products by TakaRa Taq DNA polymerase at pH8.9 from DNA templates (Nat and ZZ) without deamination or with deamination (Nat-E and ZZ-E). Forward primer was labeled by FAM at 5'. (E) Sanger sequencing demonstrates the precise transliteration of C to U (then T during the PCR) and Z to C.
+ +<|ref|>text<|/ref|><|det|>[[86, 438, 912, 542]]<|/det|> +To demonstrate its utility, ESEGA was used to (1) Define optimal conditions to perform 6- triphosphate PCR conditions (such as buffer pH and dPTP concentrations). (2) Evaluate the 6- triphosphate PCR fidelity with various commercial and house- engineered DNA polymerases. (3) Extend that evaluation to functionalized AEGIS components, in particular, those with alkynyl and aromatic hydrophobic functional groups, which are sparsely introduced into AEGIS libraries because of the higher information density of a 6- letter GACTZP DNA alphabet. + +<|ref|>sub_title<|/ref|><|det|>[[87, 552, 160, 568]]<|/det|> +## Results: + +<|ref|>text<|/ref|><|det|>[[86, 579, 913, 700]]<|/det|> +To develop ESEGA sequencing, two single- stranded DNA sequences were synthesized to serve as test beds. These were accompanied by control sequences made from standard nucleotides ("Nat"), and a Z- modified sequence, where C was replaced by with Z in the natural sequence (Table 1). The "Nat" sequence contains two restriction sites that are recognized by two restriction endonucleases, AluI (AGCT) and PspOMI (GGGCCC). The ZZ trial sequence contains Zs placed strategically so that if they are transliterated to C, the AluI and PspOMI sites are re- generated (Supplementary Fig. 1). This transliteration can be detected by strategic restriction digestion (Fig. 2D). + +<|ref|>text<|/ref|><|det|>[[86, 710, 912, 779]]<|/det|> +To develop and metric ESEGA sequencing, samples of both Nat and ZZ sequences were treated with cytidine deaminase; controls were not treated. Then, treated and untreated sequences were PCR amplified (pH 8.9) in mixtures containing only four standard dNTPs (no dZTP, no dPTP "4- triphosphate PCR"). These conditions force template dZ to mis- direct incorporation of dGTP (Fig. 2B). + +<|ref|>text<|/ref|><|det|>[[86, 789, 912, 910]]<|/det|> +As the pH of PCR buffer may affect PCR efficiency, a series of pH values of PCR buffer (from 7.4- 9.5, measured at room temperature) were evaluated by qPCR; \(\mathsf{C}_{\mathsf{q}}\) values were used as metrics. Both Nat and ZZ template were well amplified between pH 8.0 and 9.3 (Supplementary Fig. 4). As the preferred PCR conditions to facilitate \(\mathbf{Z}\rightarrow \mathbf{C}\) transliteration, pH 8.9 was chosen. PCR products were then digested by AluI or PspOMI. The PCR amplicons from the natural template without deaminase treatment gave one well identified low length digestion band in denatured Urea- PAGE analysis in Lanes 2 and 3 (Fig. 2D), as expected from faithful amplification of the two sites in the synthetic standard DNA. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[117, 70, 878, 188]]<|/det|> +<|ref|>table_caption<|/ref|><|det|>[[87, 44, 645, 62]]<|/det|> +Table 1 | Standard and AEGIS DNA sequences used in this study. + +
NameSequence (5'-3')
NatTAAGATGAGAGTTGAGGAGAGTTATTCCAAGCTATAGGCCCTTCAGTATAGTAGTGTAAGTAGATAGTGG
ZZTAAGATGAGAGTTGAGGAGAGTTATTCCAAGZTATAGGCCZTTTCAGTATAGTAGTGTAAGTAGATAGTGG
ZP-1TAAGATGAGAGTTGAGGAGAGTTACGTGZACGCCTPTGTCACZACACAGTATAGTAGTGTAAGTAGATAGTGG
ZP-2TAAGATGAGAGTTGAGGAGAGTTATTCAPCGTCACZCPCTTTTATAGTATAGTAGTGTAAGTAGATAGTGG
C-RanTAAGATGAGAGTTGAGGAGAGTTATNNNCNNNGTATAGTAGTGTAAGTAGATAGTGG
Z-RanTAAGATGAGAGTTGAGGAGAGTTATNNNZZNNNTATAGTAGTGTAAGTAGATAGTGG
P-RanTAAGATGAGAGTTGAGGAGAGTTATNNNPNNNNTATAGTAGTGTAAGTAGATAGTGG
+ +<|ref|>text<|/ref|><|det|>[[87, 211, 912, 280]]<|/det|> +In contrast, PCR amplicons from the standard template that had been previously treated by deaminase ("Nat- E") resisted restriction digestion (Fig. 2D, Lanes 5 and 6). This showed that the deaminase completely converted the Cs to Us in the restriction sites (Fig. 2A); these appear as T in the PCR amplicons (Fig. 2C). + +<|ref|>text<|/ref|><|det|>[[87, 289, 912, 376]]<|/det|> +When the ZZ template was amplified in PCR with just four standard triphosphates, amplicons were also well digested by endonucleases (Fig. 2D, Lanes 8 and 9). This showed that Z is converted to C during the PCR amplification. With ZZ template treated with cytidine deaminase ("ZZ- E") and then amplified by PCR, amplicons were digested by AluI (Lane 11). This indicated that: (i) Z is not affected by cytidine deaminase; (ii) an isolated Z can be successfully transliterated to C. + +<|ref|>text<|/ref|><|det|>[[87, 385, 912, 455]]<|/det|> +However, the ZZ- E amplicons resisted the digestion by PspOMI (Fig. 2D, lane 12). This suggested that the PspOMI restriction site was changed from GGCZZ to GGTCG by deamination of the C to U and transliterating ZZ to CC. This also showed that C → U deamination by APOBEC was not affected by a neighboring ZZ. + +<|ref|>text<|/ref|><|det|>[[87, 464, 912, 586]]<|/det|> +To confirm this by Sanger sequencing, the length of the sequencing DNA was extended by tagged PCR (from 71 bp to 323 bp, Supplementary Fig. 2). The sequencing results (Fig. 2E) agree well with the restriction digestion. The sequence of the "Nat" amplicons matched with original design. The sequences of the Nat- E amplicons showed that all the Cs were deaminated. In the ZZ template, all of the Zs were transliterated to C by PCR (at pH 8.9). For the ZZ- E sample, the sequences showed that the original Cs were completely transliterated to Ts (Fig. 2E). However, they also show three Cs signals arising from the positions originally holding Z, either isolated Z or consecutive ZZ. + +<|ref|>text<|/ref|><|det|>[[87, 595, 912, 716]]<|/det|> +We then investigated how DNA sequences built from six nucleotides "letters" (A, C, T, G, Z, P) were amplified under different PCR conditions. Two other test AEGIS DNA molecules were designed to contain both Z and P (ZP- 1 and ZP- 2, Table 1) and synthesized. For DNA sequences containing P to work when only standard (A, T, C, G) triphosphates are present, P is forced to mismatch with either C or T during the initial PCR cycles. However, this encounters problem with conversion of P, since all mismatches available in this amplification are incompatible with the Watson- Crick geometry (Fig. 3A, left arrow). + +<|ref|>text<|/ref|><|det|>[[87, 726, 912, 829]]<|/det|> +Isolated Zs and Ps in ZP- 1 were paired and read in primer extension experiments (Supplementary Fig. 3C). qPCR analysis showed that the \(\mathsf{C}_{\mathsf{q}}\) values of ZP- 1 ( \(\mathsf{C}_{\mathsf{q}} = 22.0\) ) were higher than those of the Nat sequence (9.8) and ZZ sequences ( \(\mathsf{C}_{\mathsf{q}} = 10.6\) ), indicating the problematic nature of P:C and P:T mismatches (Fig. 3B). Further, when Z and P were adjacent (ZP- 2), primer extension was completely inhibited (Supplementary Fig. 3D), and the \(\mathsf{C}_{\mathsf{q}}\) values of ZP- 2 in the 4- triphosphate PCR were even higher ( \(\mathsf{C}_{\mathsf{q}} = 24.7\) ) (Fig. 3B). This suggest that P mismatching to T or C is more problematic when Z is adjacent. + +<|ref|>text<|/ref|><|det|>[[87, 839, 912, 924]]<|/det|> +This poor mismatching was mitigated by adding dZTP to the four standard dNTPs (5- triphosphate PCR) (Fig. 3A right arrow). This allows P to match with Z in the first PCR round. The Z in its deprotonated form then directs the mismatched incorporation of G, leading to cleaner conversion. Thus, ZP- 2 sequence work very well in 5- triphosphate primer extension (Supplementary Fig. 3E, F). Both ZP- 1 and ZP- 2 sequence show high efficiency in 5- triphosphate PCR, + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 45, 880, 560]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[85, 568, 912, 861]]<|/det|> +
Fig. 3 | Enzyme-Assisted Sequencing of Expanded Genetic Alphabet (ESEGA) of single strand 6-letter DNA (ATCGZP). Complete NGS read summaries are in supplemental material. (A) Schematic diagram showing the transliteration of AEGIS (ATCGZP) DNA using 4-triphosphate or 5-triphosphate PCR at pH8.9, without or with deamination. (B) Quantitative PCR (qPCR) to evaluate transliteration efficiency of various DNA templates in 4-triphosphate and 5-triphosphate PCR. Error bars represent the standard deviation of three independent experiments. Addition of dZTP enhances the efficiency of PCR for 6-letter templates. (C) NGS data reveals the transliteration (%) of original bases [A, T, C, (U), G, Z, and P] in Nat, ZZ, ZP-1 and ZP-2 sequences under different PCR conditions (4-triphosphate or 5-triphosphate PCR at pH8.9, with or without deamination). Standard deviations represent the multiple bases in the three sequences. (D and E) Sequence logos of 6-letter DNA (ZP-1 and ZP-2) under different transliteration and PCR conditions. (F) Robustness and specificity of the ESEGA for ZZ and Nat sequences at different ratios (10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90%). (G) Sensitivity of ESEGA for ZZ (45%) and Nat (55%) sequences in a series of 10-fold dilutions. The red dashed line represents the reference value obtained from Figure 3F. (H) Robustness of ESEGA with libraries NNNCNNN (C-Ran, top), NNNZNNN (Z-Ran, middle) and NNNPNNN (P-Ran, bottom) sequences, without deamination (left panel) or with deamination (right panel), then, followed by 5-triphosphate PCR and NGS. These data show ESEGA has no strong context dependency.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[88, 70, 844, 88]]<|/det|> +the Cq value (11.9 and 11.7) are close to Nat and ZZ template in 4-triphosphate PCR (Fig. 3B). + +<|ref|>text<|/ref|><|det|>[[86, 98, 911, 151]]<|/det|> +To obtain quantitative metrics for the fidelity of converting sequences built from 6- letter (A, C, T, G, Z, P) DNA to sequencable nucleotides under different PCR conditions, the performance of ZP- 1 and ZP- 2 conversion was compared with these pre- treatments: + +<|ref|>text<|/ref|><|det|>[[85, 160, 911, 195]]<|/det|> +(1) Direct amplification with 4-triphosphate PCR. This was expected to proceed with low efficiency with some ambiguous transliteration (Fig. 3A left arrow). + +<|ref|>text<|/ref|><|det|>[[86, 204, 911, 257]]<|/det|> +(2) Treatment with deaminase, followed by amplification with 5-triphosphate PCR. This was expected to deliver high efficiency PCR, with clean conversion of Z:P to C:G, with all of the C:G in the original templates replaced by U:A, and then T:A pairs (Fig. 3A central arrow) + +<|ref|>text<|/ref|><|det|>[[86, 265, 911, 318]]<|/det|> +(3) Direct amplification with 5-triphosphate PCR. This was expected to deliver high efficiency PCR, with clean transliteration of Z:P to C:G, and with all of the C:G in the original templates remaining as C:G pairs (Fig. 3A right arrow). + +<|ref|>text<|/ref|><|det|>[[85, 328, 914, 500]]<|/det|> +These amplicons were sent to Sanger sequencing (Supplementary Fig. 9 and 10) and NextGen sequencing. The analysis of NGS data revealed very faithful transliteration (>99%) of the original bases [ATC(U)GZ] in the Nat, ZZ, ZP- 1, and ZP- 2 sequences to their corresponding bases (A, T, C, and G) in 5-triphosphate or 4-triphosphate PCR. However, in the case of P transliteration, a mixture of A (~63%) and G (~37%) was observed in 4-triphosphate PCR, with relatively larger fluctuations. Fixing this problem, P was transliterated almost exclusively to G (~94.5%) in 5-triphosphate PCR (Fig. 3C). To visualize the transliteration of base at a given position, we converted the table information into a sequence logo (Fig. 3D and 3E), which illustrating the transliteration of ZP- 1 and ZP- 2 templates under for 4-triphosphate PCR (top), 5-triphosphate PCR following deamination (middle), and 5-triphosphate PCR without deamination (bottom). + +<|ref|>text<|/ref|><|det|>[[86, 510, 911, 579]]<|/det|> +As an innovative approach to test the robustness of ESEGA, ZZ and Nat templates were mixed with total concentrations of 100 nM, but with AEGIS- containing and standard oligonucleotides in various ratios (10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90%). Subsequently, each sample underwent a single- stranded ESEGA with NGS. + +<|ref|>text<|/ref|><|det|>[[86, 590, 911, 624]]<|/det|> +The called populations of ZZ amplicons displayed a modest reduction of approximately 4.5% relative to the prepared ZZ percentage across all the templates (Fig. 3F). + +<|ref|>text<|/ref|><|det|>[[86, 634, 911, 686]]<|/det|> +This observation might be attributed to the slightly lower efficiency of Z mismatch with G in the first round of PCR compared with standard DNA base pair. This is supported by the higher \(C_{\mathrm{a}}\) value recorded in qPCR for the ZZ template (10.6) in contrast to the Nat template (9.8) (Fig. 3B) + +<|ref|>text<|/ref|><|det|>[[86, 696, 911, 765]]<|/det|> +To evaluate the sensitivity of ESEGA, ZZ (45%) and Nat templates (55%) were blended and subsequently diluted serially to give six different concentrations and input to ranging from \(10^{9}\) to \(10^{4}\) copies in sequencing. Each sample was subjected to a single- stranded AEGIS- DNA sequencing. Sequencing was possible even at the highest dilution (Fig. 3G). + +<|ref|>text<|/ref|><|det|>[[86, 775, 911, 895]]<|/det|> +So far, the cytidine deamination and Z/P conversion were demonstrated with defined 6- letter DNA sequences. We were concerned that local sequence context might influence the outcome delivered by ESEGA. To determine whether neighboring nucleotides may affect C to U, Z to C, and P to G transliteration, three sequences were synthesized where a single C, Z or P was placed in the middle of six random nucleotides (C- Ran, Z- Ran and P- Ran, Table 1). The ESEGA workflow was applied. Low sequence bias was seen in the deamination results (Fig. 3H, top), consistent with the literature28. Further, no overall sequence context bias was observed in Z and P transliteration (Fig. 3H, middle and bottom). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[130, 73, 860, 508]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[85, 520, 912, 625]]<|/det|> +
Fig. 4 | Sequencing double-stranded 6-letter DNA and assessing the fidelity of different DNA polymerases in 6-triphosphate PCR. (A) Schematic of ESEGA workflow for double-stranded 6-letter (ATCGZP) DNA. (B) and (C) Retention rates of Z/P at each position in the ZP-1 template plotted after 6-triphosphate PCR with varying concentrations of dPTP (0.05-0.5 mM) at pH 8.9 or pH 8.0 conditions. (D) ESEGA evaluation of the fidelity of various polymerases in 6-triphosphate PCR. Complete NGS read summaries are in supplemental material.
+ +<|ref|>text<|/ref|><|det|>[[86, 661, 912, 818]]<|/det|> +Double- stranded DNA is a common outcome of AEGIS 6- nucleotide PCR. Thus, we developed a ESEGA workflow for double stranded DNA as well. First, the double- stranded DNA was denatured and the strands were separated. The two single- strands were separately treated with deaminase followed by 5- triphosphate PCR. The two amplicon pools were separately sequenced with barcode. Bioinformatics then matched the sequences to the strands that were originally paired. Then, the matches were analyzed to infer the original sequences of the paired strands (Fig. 4A). A:T and T:A pairs delivered A:T and T:A pairs in the duplexes matched by bioinformatics analysis, unchanged by the processes in the workflow. Thus, sites that hold A:T and T:A pairs in the surviving bioinformatics pairs were inferred to have been A:T and T:A pairs in the original duplex. + +<|ref|>text<|/ref|><|det|>[[86, 826, 912, 898]]<|/det|> +Likewise, Z:P and P:Z in the original duplex gave C:G and G:C in the bioinformatics- paired sites. In both cases, they arise by transliteration involving deprotonate Z:G mismatches. Thus, sites that hold G:C and C:G pairs in the surviving bioinformatics pairs were inferred to have been Z:P and P:Z pairs in the original duplex. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[86, 43, 912, 147]]<|/det|> +If the original duplexes have C:G or G:C pairs, then bioinformatics analysis gives a third outcome due to deamination. From amplicons arising from the strand that contained C, deamination gives amplicon duplexes with T:A pairs. From the complementary strand that contained G, the amplicon duplexes hold G:C pairs at the homologous site. Thus, bioinformatics assigns C:G in the original duplex when A:T appears in the amplicons derived from the "sense" DNA chain, if C:G appears also appears in amplicons derived from the anti- sense DNA chain. + +<|ref|>sub_title<|/ref|><|det|>[[88, 157, 287, 174]]<|/det|> +## Applications of ESEGA + +<|ref|>sub_title<|/ref|><|det|>[[88, 183, 701, 201]]<|/det|> +## Determining 6-nucleotide PCR conditions that optimally retain Z:P pairs + +<|ref|>text<|/ref|><|det|>[[86, 211, 912, 280]]<|/det|> +To illustrate how ESEGA might be used in a practical setting, we first showed how ESEGA applied to double- stranded amplicons might be used to evaluate of various concentration of dPTP and various values of pH values the impact on the fidelity of GACTZP PCR. Here, the metric for fidelity was the percent retention of Z:P pairs when the ZP- 1 sequence was used as a template. + +<|ref|>text<|/ref|><|det|>[[86, 290, 912, 412]]<|/det|> +FAM- labeled forward and Cy5- labeled reverse primers were used to amplify ZP- 1 in PCR with a complete set of six triphosphates, with a primer: template ratio of 50000:1 ( \(\sim 16\) nominal doublings). TaKaRa Taq HS polymerase and 6- triphosphate dNTP (dATP (0.1 mM), dCTP (0.2 mM), dGTP (0.1 mM), dTTP (0.1 mM), dZTP (0.1 mM), and dPTP) were used in the amplification. Six different dPTP concentrations (0.05 mM, 0.1 mM, 0.2 mM, 0.3 mM, 0.4 mM, and 0.5 mM) and two pH levels (8.0 and 8.9) were used, under the hypothesis that Z:P pairs would be better retained at higher concentrations of dPTP and better retained at lower pH (Supplementary Fig. 11). + +<|ref|>text<|/ref|><|det|>[[86, 421, 912, 542]]<|/det|> +Following separation of the PCR duplex amplicons by PAGE- urea, ESEGA was used to compare the data from the sense and anti- sense strands to quantitate the retention of the Z and P nucleotides after 25 rounds of PCR. Consistent with the hypothesis, Z:P pairs were better retained during PCR at pH 8.0 (Fig. 4C) than at pH 8.9 (Fig. 4B) at each concentration of dPTP. This was attributed to greater deprotonation of Z at the higher pH, leading to more deprotonated Z:G mismatches. Greater retention of the Z:P pairs was also observed with increasing dPTP concentration. This was consistent with the hypothesis that dPTP competes with dGTP as a partner for template dZ. + +<|ref|>text<|/ref|><|det|>[[86, 552, 912, 604]]<|/det|> +Thus, ESEGA supported an application to screening PCR conditions to identify parameters that best retained Z:P pairs. Under these conditions, \(\sim 90\%\) of the Z:P pairs were retained at pH 8.0 with 0.5 mM dPTP after 16 nominal doublings, with a nominal per cycle fidelity of \(99.34\%\) . + +<|ref|>sub_title<|/ref|><|det|>[[88, 614, 552, 632]]<|/det|> +## Identifying polymerases that optimally retain Z:P pairs + +<|ref|>text<|/ref|><|det|>[[86, 641, 912, 744]]<|/det|> +Using these optimal conditions (pH 8.0, 0.5 mM dPTP), we then amplify the ZP- 1 template with a set of polymerases, including TaKaRa Taq HS, KOD exo', KlenTaq, Phire hot start II, Phusion TM, Go Taq, One Taq, and an in- house- engineered 6M Taq variant. All of these gave amplification and interpretable sequencing data. The amount of each polymerase was adjusted to ensure similar amplification efficiency. Other polymerases examined (LongAmp Taq, Q5 High- Fidelity, Sulfolobus, Vent exo', and HiFi KAPA) produced inconsistent results or no amplification at all. + +<|ref|>text<|/ref|><|det|>[[86, 754, 912, 806]]<|/det|> +ESEGA was used to analyze amplicons from 25 cycles of 6- triphosphate PCR using these eight polymerases and the ZP- 1 template. The retention rates of Z:P pairs were visualized using a sequence logo (Fig. 4D). + +<|ref|>text<|/ref|><|det|>[[86, 816, 912, 902]]<|/det|> +Our findings revealed that Z:P pairs were retained best by the KlenTaq polymerase under these conditions, retaining \(90 - 95\%\) of the Z:P pairs after 16 nominal doublings; this approximates the uncertainty in the ESEGA analysis itself. However, the KlenTaq polymerase gave less efficient amplification. Thus, TaKaRa Taq HS was identified as a preferred enzyme under a metric that combined fidelity, efficiency, and robustness. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[85, 42, 912, 148]]<|/det|> +Additionally, we observed that KOD exo polymerase exhibited relatively good fidelity in the retention of Z and P, but added Z and P to the amplicons at positions that originated as C and G. The 6M Taq polymerase, which was developed in- house to encourage processivity, performed less well. A comprehensive description of the 6M Taq evolution process can be found in the Supplementary Materials. Overall, ESEGA provides a robust and reliable framework for the selection and development of high- fidelity polymerases in the context of 6- triphosphate PCR applications. + +<|ref|>image<|/ref|><|det|>[[303, 159, 686, 409]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[85, 424, 912, 530]]<|/det|> +
Fig. 5 | Chemical structures and retentions rate of functionalized-dPTP derivatives in 6-triphosphate PCR assessed by ESEGA. Chemical structures of (A) classical dPTP; (B) dPethTP, 7-(4Ethynyl)-dP triphosphate; and (C) dPphTP. 7-(4-Phenyl-1-butynyl)-dP triphosphate. (D) Sanger sequencing PCR amplicons from ZZ template with 6-triphosphate (dNTPs, dZTP, and dPTP, or dPethTP, or dPphTP). (E) ESEGA and NGS evaluate the retentions of dP, dPeth, and dPph in PCR amplicons after 6-triphosphate PCR by statistics of Z retentions.
+ +<|ref|>sub_title<|/ref|><|det|>[[88, 565, 667, 584]]<|/det|> +## Assessing the fidelity of functionalized dPTP in 6-triphosphate PCR + +<|ref|>text<|/ref|><|det|>[[86, 592, 912, 715]]<|/det|> +As noted in the introduction, one of the advantages of AEGIS- LIVE over LIVE with standard nucleotides is the increased information density of expanded genetic alphabets, and the consequent ability to sparsely introduce functional groups in AEGIS- LIVE that standard DNA/RNA lacks. This allows AEGIS- LIVE to compete with protein evolution (e.g. phage display) and protein computational design (e.g. ROSETTA) by increasing the diversity of functional groups towards that of proteins, and increasing the number of compact folds, without the troublesome features of proteins, in particular, their propensity to precipitate. + +<|ref|>text<|/ref|><|det|>[[86, 723, 911, 777]]<|/det|> +Fig. 5B and Fig. 5C shows two variants of AEGIS P that carry functional groups, specifically, alkyl and phenylalkylnyl groups. The first can support "click chemistry"; proteins have no analogous capability. The second is able to support hydrophobic interactions (compare with phenylalanine in proteins). + +<|ref|>text<|/ref|><|det|>[[86, 785, 912, 908]]<|/det|> +We used ESEGA to evaluate the performance of polymerases challenged to amplify AEGIS DNA containing these functionalized P variants. The ZZ sequence was chosen as template, with functionalized dPTP used in the triphosphate mix instead of normal dPTP. The amplification was done as before with 6- nucleotide PCR. The sense DNA chain was separated from resulting PCR products by PAGE- urea, and the sequences of the amplicons was evaluated by ESEGA, using both Sanger sequencing (Fig. 5D) and NGS (Fig. 5E). The 6- nucleotide PCR also monitored by qPCR (Eva green) when the fluor- labeled primers were replaced by unlabeled primers. (Supplementary Fig. 12B) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[86, 42, 912, 165]]<|/det|> +Here, the efficiency of amplification by TaKaRa of oligonucleotides containing alkynyl P was close to that of those with unfunctionalized P. Amplification efficiency was modestly lower with phenylalkynyl P (Supplementary Fig. 12B). The fidelity of replication was comparable with alkynyl P by ESEGA (Fig. 5E). However, substantial loss of phenylalkynyl P was observed by ESEGA, especially at position 40 of the template. Here, the two adjacent Zs drive the insertion of two tagged P's. It is well known that unmodified polymerases such as TaKaRa do not easily synthesize DNA with two consecutive tagged nucleotides29. Thus, this result was not unexpected. + +<|ref|>sub_title<|/ref|><|det|>[[87, 174, 185, 191]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[87, 201, 911, 254]]<|/det|> +Nearly all life forms on Earth share the same informational biopolymers using A- T and C- G base pairs. Even the known exceptions, cyanoviruses that use diaminopurine instead of adenine as a partner for T, do not expand the number of independently replicable building blocks30- 32. + +<|ref|>text<|/ref|><|det|>[[86, 263, 912, 350]]<|/det|> +However, standard nucleic acids lack the functional group diversity, the informational density, and the folding capability needed to give effective receptors, ligands, and catalysts. These factors account for the inability of laboratory in vitro evolution with standard DNA and RNA to compete effectively with antibody and laboratory protein evolution, even though proteins lacking a privileged scaffold are plagued by precipitation issues. + +<|ref|>text<|/ref|><|det|>[[86, 358, 912, 550]]<|/det|> +Artificially expanded genetic information systems (AEGIS, Fig. 1) are not designed for any specific purpose, but rather to be richer reservoirs of functionality in a directly evolvable system. AEGIS has more building blocks, a greater diversity of functional groups, higher information density, better control over folding, and the ability to form compact folds via base- base interactions. Even with the limited sequencing tools previously available, AEGIS- LIVE has evolved molecules that neutralize toxins, cleave specific RNA molecules, bind to specific cells, and deliver drugs to cancer tissues33, 34. Alternative systems where pairing does not exploit inter- base hydrogen- bonding have been explored by Kool35, Hirao22, and Romesberg36. All have been shown to perform, at various levels of efficiency, in replication, transcription, translation and semi- synthetic organisms8, 37, 38. More exotically, AEGIS is helping us to seek alien life in the cosmos39, which may not have had the same pre- history as life on Earth, and thus may have different genetic biopolymers. + +<|ref|>text<|/ref|><|det|>[[86, 559, 911, 611]]<|/det|> +Therefore, AEGIS has the potential for broad biotechnological applications, should its evolution under selective pressures chosen by experimentalists become routine40. ESEGA offers a key element needed to make AEGIS- LIVE routine. + +<|ref|>text<|/ref|><|det|>[[86, 620, 911, 725]]<|/det|> +ESEGA represents a transformative use of the capabilities afforded by next generation sequencing, which has transformed the analysis of standard DNA sequences. By manipulating the pH level to alter the topological structure of nucleic bases, Z is deprotonated to form Z, Z equates to C (Fig. 1). This allows Z to pair with G quite well, leading to high fidelity transliteration (99%). Additionally, ESEGA employs 5- triphosphate transliteration PCR to ensure high- fidelity transfer of P to G (~95%), This ensures a clean outcome. + +<|ref|>text<|/ref|><|det|>[[86, 734, 911, 787]]<|/det|> +Standard bioinformatics allow hundreds of thousands of reads from NGS to be analyzed in a library context. The cleanliness of this workflow supports high sequence diversity in those libraries, where each individual sequences present in the mix as only a few dozen exemplars. + +<|ref|>text<|/ref|><|det|>[[86, 795, 911, 865]]<|/det|> +In addition to showing this robust workflow, we show three applications where ESEGA supports the development of synthetic biology using expanded DNA alphabets. These are, of course, not the only three that can be conceived. Thus, ESEGA- like workflows hold the potential to develop other expanded genetic alphabets. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[88, 42, 299, 60]]<|/det|> +## ASSOCIATED CONTENT + +<|ref|>text<|/ref|><|det|>[[88, 71, 910, 106]]<|/det|> +Experimental procedures, material and methods data, all synthetic dPTP derivatives, analytical characterization, and details of all assays included in the Supporting Information. + +<|ref|>sub_title<|/ref|><|det|>[[88, 115, 299, 133]]<|/det|> +## AUTHOR INFORMATION + +<|ref|>text<|/ref|><|det|>[[88, 144, 280, 161]]<|/det|> +Corresponding Author : + +<|ref|>text<|/ref|><|det|>[[88, 170, 400, 188]]<|/det|> +Zunyi Yang: Email: zyang@ffame.org + +<|ref|>text<|/ref|><|det|>[[88, 197, 454, 214]]<|/det|> +Steven A. Benner: Email: sbenner@ffame.org + +<|ref|>sub_title<|/ref|><|det|>[[88, 224, 271, 241]]<|/det|> +## Author Contributions + +<|ref|>text<|/ref|><|det|>[[87, 251, 912, 339]]<|/det|> +B.W., Z.Y. and S.B. conceived the project. B.W. designed, carried out all experiments and wrote the manuscript. K.B. performed bioinformatics analyses. M.K. synthesized the functional modified dPTP. R.L. and D.G. yielded the 6M Taq polymerase variant. C.C. synthesized the DNA containing AEGIS. Z.Y. and S.B. supervised the project, analyzed the data, and contributed to writing the manuscript. All individuals participated in the discussion. + +<|ref|>sub_title<|/ref|><|det|>[[87, 349, 140, 364]]<|/det|> +## Notes + +<|ref|>text<|/ref|><|det|>[[87, 366, 911, 436]]<|/det|> +Patent application have been submitted for certain of the technologies reported here. Z.Y, B.W, and S.B. own intellectual property in this area. Several of the compounds described here are sold by Firebird Biomolecular Sciences, LLC (www.firebirdbio.com), which employs the indicated authors and is owned by S.B. Source data are included alongside this paper. + +<|ref|>sub_title<|/ref|><|det|>[[88, 444, 272, 461]]<|/det|> +## ACKNOWLEDGMENT + +<|ref|>text<|/ref|><|det|>[[87, 471, 911, 541]]<|/det|> +This work was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Numbers R01GM141391. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This work was also supported by the AEGIS LIVE Endowment Fund from the Foundation for Applied Molecular Evolution (FfAME). + +<|ref|>sub_title<|/ref|><|det|>[[88, 550, 238, 567]]<|/det|> +## ABBREVIATIONS + +<|ref|>text<|/ref|><|det|>[[88, 578, 817, 596]]<|/det|> +AEGIS, artificially expanded genetic information systems; LIVE, laboratory in vitro evolution. + +<|ref|>sub_title<|/ref|><|det|>[[87, 605, 212, 622]]<|/det|> +## REFERENCES + +<|ref|>text<|/ref|><|det|>[[84, 633, 904, 905]]<|/det|> +1. Abbass, J. & Nebel, J.-C. 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Rev. 116, 12688-12710 (2016). + +<|ref|>text<|/ref|><|det|>[[85, 607, 880, 640]]<|/det|> +26. Schutsky, E.K. et al. Nondestructive, base-resolution sequencing of 5-hydroxymethylcytosine using a DNA deaminase. Nat. Biotechnol. 36, 1083-1090 (2018). + +<|ref|>text<|/ref|><|det|>[[85, 641, 833, 675]]<|/det|> +27. Goodwin, S., McPherson, J.D. & McCombie, W.R. Coming of age: ten years of next-generation sequencing technologies. Nature Reviews Genetics 17, 333-351 (2016). + +<|ref|>text<|/ref|><|det|>[[85, 675, 888, 708]]<|/det|> +28. Williams, L. et al. Enzymatic Methyl-seq: the next generation of methylome analysis. NEB expressions (2019). + +<|ref|>text<|/ref|><|det|>[[85, 709, 904, 743]]<|/det|> +29. Wu, W. et al. Termination of DNA synthesis by N6 -alkylated, not 3'-O -alkylated, photocleavable 2'-deoxyadenosine triphosphates. Nucleic Acids Res. 35, 6339-6349 (2007). + +<|ref|>text<|/ref|><|det|>[[85, 743, 908, 777]]<|/det|> +30. Pezo, V. et al. Noncanonical DNA polymerization by aminoadenine-based siphoviruses. Science 372, 520-524 (2021). + +<|ref|>text<|/ref|><|det|>[[85, 778, 844, 811]]<|/det|> +31. Kirnos, M., Khudyakov, I.Y., Alexandrushkina, N. & Vanyushin, B. 2-Aminoadenine is an adenine substituting for a base in S-2L cyanophage DNA. Nature 270, 369-370 (1977). + +<|ref|>text<|/ref|><|det|>[[85, 812, 891, 845]]<|/det|> +32. Zhou, Y. et al. A widespread pathway for substitution of adenine by diaminopurine in phage genomes. Science 372, 512-516 (2021). + +<|ref|>text<|/ref|><|det|>[[85, 846, 895, 880]]<|/det|> +33. Piccirilli, J.A., Benner, S.A., Krauch, T. & Moroney, S.E. Enzymatic incorporation of a new base pair into DNA and RNA extends the genetic alphabet. Nature 343, 33-37 (1990). + +<|ref|>text<|/ref|><|det|>[[85, 880, 900, 914]]<|/det|> +34. Benner, S.A. et al. Alternative Watson-Crick synthetic genetic systems. Cold Spring Harb. Perspect. Biol. 8, a023770 (2016). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[84, 44, 911, 250]]<|/det|> +35. Moran, S., Ren, R.X.-F. & Kool, E.T. A thymidine triphosphate shape analog lacking Watson–Crick pairing ability is replicated with high sequence selectivity. Proc. Natl Acad. Sci. USA 94, 10506-10511 (1997). +36. Feldman, A.W. & Romesberg, F.E. Expansion of the Genetic Alphabet: A Chemist’s Approach to Synthetic Biology. Acc. Chem. Res. 51, 394-403 (2018). +37. Malyshev, D.A. et al. A semi-synthetic organism with an expanded genetic alphabet. Nature 509, 385-388 (2014). +38. Zhang, Y. et al. A semi-synthetic organism that stores and retrieves increased genetic information. Nature 551, 644-647 (2017). +39. Špaček, J. & Benner, S.A. Agnostic Life Finder (ALF) for Large-Scale Screening of Martian Life During In Situ Refueling. Astrobiology 22, 1255-1263 (2022). +40. Benner, S.A. Rethinking nucleic acids from their origins to their applications. Philos. Trans. R. Soc. B 378, 20220027 (2023). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[860, 937, 883, 950]]<|/det|> +16 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[44, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 317, 150]]<|/det|> +Supportinginformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__08a17e35a9f30287f46ac8334be1320dddc37a6065b4ba95076e9f30f36e016a/images_list.json b/preprint/preprint__08a17e35a9f30287f46ac8334be1320dddc37a6065b4ba95076e9f30f36e016a/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..da28af2ee82417724e9e1eeffe1426b47458fa2f --- /dev/null +++ b/preprint/preprint__08a17e35a9f30287f46ac8334be1320dddc37a6065b4ba95076e9f30f36e016a/images_list.json @@ -0,0 +1,167 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "FIGURE 1", + "footnote": [], + "bbox": [ + [ + 130, + 163, + 856, + 472 + ] + ], + "page_idx": 18 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "FIGURE 2", + "footnote": [], + "bbox": [ + [ + 120, + 137, + 848, + 470 + ] + ], + "page_idx": 19 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "FIGURE 3", + "footnote": [], + "bbox": [ + [ + 131, + 118, + 870, + 430 + ] + ], + "page_idx": 20 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4: CETSA responses in gemcitabine sensitive versus resistant cells", + "footnote": [], + "bbox": [ + [ + 142, + 120, + 761, + 750 + ] + ], + "page_idx": 21 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "FIGURE 5", + "footnote": [], + "bbox": [ + [ + 130, + 130, + 857, + 660 + ] + ], + "page_idx": 23 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6: ADDR response in gemcitabine resistant cells", + "footnote": [], + "bbox": [ + [ + 117, + 125, + 840, + 767 + ] + ], + "page_idx": 25 + }, + { + "type": "image", + "img_path": "images/Figure_7.jpg", + "caption": "FIGURE 7", + "footnote": [], + "bbox": [ + [ + 156, + 152, + 816, + 490 + ] + ], + "page_idx": 27 + }, + { + "type": "image", + "img_path": "images/Supplementary_Figure_1.jpg", + "caption": "SUPPLEMENTARY FIGURE 1", + "footnote": [], + "bbox": [ + [ + 145, + 125, + 580, + 480 + ] + ], + "page_idx": 28 + }, + { + "type": "image", + "img_path": "images/Supplementary_Figure_2.jpg", + "caption": "Supplementary Figure 2: Early responses to gemcitabine in resistant and sensitive cells", + "footnote": [], + "bbox": [ + [ + 135, + 137, + 730, + 360 + ] + ], + "page_idx": 29 + }, + { + "type": "image", + "img_path": "images/Supplementary_Figure_3.jpg", + "caption": "SUPPLEMENTARY FIGURE 3", + "footnote": [], + "bbox": [ + [ + 140, + 120, + 737, + 530 + ] + ], + "page_idx": 30 + }, + { + "type": "image", + "img_path": "images/Supplementary_Figure_4.jpg", + "caption": "SUPPLEMENTARY FIGURE 4", + "footnote": [], + "bbox": [ + [ + 140, + 120, + 840, + 604 + ] + ], + "page_idx": 31 + } +] \ No newline at end of file diff --git a/preprint/preprint__08a17e35a9f30287f46ac8334be1320dddc37a6065b4ba95076e9f30f36e016a/preprint__08a17e35a9f30287f46ac8334be1320dddc37a6065b4ba95076e9f30f36e016a.mmd b/preprint/preprint__08a17e35a9f30287f46ac8334be1320dddc37a6065b4ba95076e9f30f36e016a/preprint__08a17e35a9f30287f46ac8334be1320dddc37a6065b4ba95076e9f30f36e016a.mmd new file mode 100644 index 0000000000000000000000000000000000000000..13332a687f455b798bd8ea774892b65fabe6ce8e --- /dev/null +++ b/preprint/preprint__08a17e35a9f30287f46ac8334be1320dddc37a6065b4ba95076e9f30f36e016a/preprint__08a17e35a9f30287f46ac8334be1320dddc37a6065b4ba95076e9f30f36e016a.mmd @@ -0,0 +1,515 @@ + +# MS-CETSA functional proteomics uncovers new DNA-repair programs leading to Gemcitabine resistance + +Pär Nordlund par.nordlund@ki.se + +Karolinska Institutet https://orcid.org/0000- 0002- 7794- 702X + +Ying Yu Liang A\*STAR + +Khalidah Khalid Agency for Science Technology and Research + +Hai Van Le Agency for Science Technology and Research + +Hui Min Teo Agency for Science Technology and Research + +Mindaugas Raitelaitis Karolinska Institutet + +Marc-Antoine Gerault Karolinska Institutet + +Jane Jia Hui Lee Genome Institute of Singapore + +Jiawen Lyu Karolinska Institutet + +Allison Chan National University of Singapore + +Anand Jeyashekaran National University of Singapore + +Wai Leong Tam Genome Institute of Singapore, Agency for Science, Technology and Research (A\*STAR) https://orcid.org/0000- 0003- 2365- 5264 + +Nayana Prabhu Nanyang Technological University https://orcid.org/0000- 0002- 2350- 6552 + +<--- Page Split ---> + +## Article + +Keywords: MS- CETSA, IMPRINTS, gemcitabine, resistance, DLBCL, ATR, ADDR, TLS, + +Posted Date: August 20th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4820265/v1 + +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: Yes there is potential Competing Interest. Prof. Nordlund is the inventor of patents related to the CETSA method and is a cofounder and board member of Pelago Biosciences AB. + +Version of Record: A version of this preprint was published at Nature Communications on May 7th, 2025. See the published version at https://doi.org/10.1038/s41467-025-59505-8. + +<--- Page Split ---> + +# MS-CETSA functional proteomics uncovers new DNA-repair programs leading to Gemcitabine resistance + +Ying Yu Liang \(^{1}\) , Khalidah Khalid \(^{1}\) , Hai Van Le \(^{1}\) , Hui Min Vivian Teo \(^{3}\) , Mindaugas Raitelaitis \(^{2}\) , Marc- Antoine Gerault \(^{2}\) , Jane Jia Hui Lee \(^{3}\) , Jiawen Lyu \(^{2}\) , Allison Chan \(^{4}\) , Anand Jeyashekaran \(^{4,5,7}\) , Wai Leong Tam \(^{3,4,5,6}\) , Pär Nordlund \(^{*1,2}\) , Nayana Prabhu \(^{*1}\) \(^{1}\) Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A\*STAR), 61 Biopolis Street, Proteos, Singapore 138673 \(^{2}\) Department of Oncology and Pathology, Karolinska Institutet, 171 77 Stockholm, Sweden \(^{3}\) Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A\*STAR), 60 Biopolis Street, Genome, Singapore 138672, Republic of Singapore \(^{4}\) Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Drive, Singapore 117599 \(^{5}\) Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597 \(^{6}\) NUS Center for Cancer Research, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599 \(^{7}\) Department of Haematology-Oncology, National University Hospital \(^{8}\) Correspondence: nayana_prabhu@imcb.a-star.edu.sg, par.nordlund@ki.se, tamwl@gis.a-star.edu.sg + +<--- Page Split ---> + +Mechanisms for resistance to cytotoxic cancer drugs are dependent on dynamic changes in the biochemistry of cellular pathways, information which is hard to obtain at the systems level. Here we use a deep functional proteomics implementation of CETSA (Cellular Thermal Shift Assay) revealing a range of induced biochemical responses to gemcitabine in resistant and sensitive diffuse large B cell lymphoma (DLBCL) cell lines. Initial responses in both, gemcitabine resistant and sensitive cells, reflect known targeted effects by gemcitabine on ribonucleotide reductase and DNA damage responses. However, after 3- 5 hours the responses diverge dramatically where sensitive cells show induction of characteristic CETSA signals for early apoptosis, while resistant cells reveal biochemical modulations reflecting transition through a distinct DNA- damage signaling state, including opening of cell cycle checkpoints and induction of translesion DNA synthesis (TLS) programs allowing bypass of damaged DNA- adducts. The data also reveal the induction of a new program, labeled the Auxiliary DNA Damage Repair (ADDR) protein ensemble likely supporting DNA replication at damaged sites. We show that this response can be attenuated in resistant cells by an ATR inhibitor reestablishing gemcitabine sensitivity and demonstrate ATR as a key signaling node of this response. + +Keywords: MS- CETSA, IMPRINTS, gemcitabine, resistance, DLBCL, ATR, ADDR, TLS, + +<--- Page Split ---> + +Cancer cells evade cytotoxic drugs through activation of resistance mechanisms. While drug- sensitive cancer cells induce cellular programs leading to cell death, resistant cells activate pathways to counteract such responses. A wide range of cellular processes have been implied in resistance to cytotoxic cancer drugs, including modulations of drug transport (1) or drug activation (2), induction of apoptosis blockade (3), bypass of oncogene inhibition by drug binding site mutations (4), activation of parallel driver pathways (5), as well as modulation of tumor microenvironment (6) and cell- to- cell signaling (7, 8). It is likely that many cancers establish multiple resistance mechanisms in parallel to overcome drug action. + +Detailed insights into which resistance promoting programs are operating in cancers of individual patients at different stages of therapy could arguably be transformative for selection of optimal drug combinations and staging in personalized therapy, as well as for identifying novel drug targets to attenuate resistance responses. Conclusive elucidation of resistance mechanisms of cancer drugs is however often challenging when they can involve complex remodeling of cellular pathways. Typically, resistance mechanisms are addressed using genomic or transcriptomic approaches, most often assessing static differences between cancer patient samples or sensitive and resistant cancer cells in model systems (9–11). Although such studies can access key mutations and RNA level changes implicative of resistance, cellular pathways and processes are highly regulated at the biochemical level and this information is only indirectly accessed in such studies. Moreover, comparison of static cells does not address drug- induced resistance responses. These responses can play key roles in resistance to cytotoxic cancer drugs but are normally not activated during ambient cancer cell growth. Notably, some cancer drug- induced resistance responses can be efficiently studied with focused assays, such as the induction of reactive oxygen species, autophagy and chaperone activation. While useful, these studies require a priori knowledge on putative mode of resistance mechanisms, and they do not provide an unbiased view on sequences of regulatory events. + +Here, we examined the induced modulation of cellular biochemistry leading to resistance towards one of the more commonly used cytotoxic cancer drugs – gemcitabine, which is an anti- neoplastic pyrimidine analog that replaces cytidine during DNA replication and inhibits ribonucleotide reductase (RNR) (12). In various cancers that include pancreatic, + +<--- Page Split ---> + +breast, ovarian, non- small cell lung cancer and lymphoma, gemcitabine is employed either in the first- line or refractory setting. Diffuse large B- cell lymphoma (DLBCL) represents the most frequently occurring and aggressive form of non- Hodgkin's lymphoma. The anthracycline- based regimen R- CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone) is the standard of care for first- line treatment with \(\sim 60\%\) of the patients achieving complete response (13). However \(20 - 50\%\) of patients do not respond, or relapse within the first two years of treatment (14). Gemcitabine has recently been used in salvage regimens for DLBCL although resistance often still develops (15). Gemcitabine is a nucleotide prodrug that needs to be metabolized into its active phosphorylated form within cells to exert its effects (Fig 1A). RNR catalyzes the conversion of ribonucleoside diphosphates to deoxyribonucleoside diphosphates and is a major protein target for gemcitabine (16, 17). In its diphosphate form, gemcitabine inhibits RNR by forming a covalent adduct to the catalytic subunit (RRM1), or alternatively scavenging the free radical cofactor of RNR, thus depleting dNTP pools (18). While genomic and transcriptomic studies have helped identify driver pathways and prognostic gene signatures in DLBCL (19, 20), this information remains of limited utility in guiding treatment regimens, especially in stratifying patients who may respond to specific salvage therapy agents. + +To better understand how sensitive or resistance biochemical pathways become selectively activated in response to therapeutics in DLBCL cells, we apply a time- dependent implementation of the deep functional proteomics method, IMPRINTS- CETSA (Integrated Modulation of Protein Interaction States - Cellular Thermal Shift Assay) to study gemcitabine- induced programs. CETSA reports on modulations of pathway activation at the biochemical level in intact cells by monitoring changes in protein interaction states (PRINTS), i.e., interactions made by individual proteins to other molecules in live cells reflecting protein activity and functional states (21). MS- CETSA (Mass Spectrometry- based CETSA) is the first integrative technology that can directly assess PRINTS in intact cells and tissues but has not been used previously for deep characterization of induced drug resistance. In the present study, we reveal comprehensive and distinct novel information on the time- dependent biochemical responses of gemcitabine in sensitive and resistant DLBCL cells. Initial responses in both cell types reveal similar RNR inhibition and activation of DNA- damage signaling. However, the downstream response in sensitive cells reflects the characteristic CETSA response for apoptosis induction, while in resistant cells the response is dominated by cell cycle checkpoints, translesion DNA synthesis (TLS) program and a new protein ensemble that likely + +<--- Page Split ---> + +support DNA repair. This response provides a rationale for gemcitabine resistance in DLBCL cells, which can be reversed by attenuating the DNA- repair inducing pathway with an ATR (ataxia telangiectasia and Rad3- related protein) inhibitor, and thereby re- establishing gemcitabine sensitivity. This study validates IMPRINTS- CETSA (22) as an efficient approach to dissect induced cancer drug resistance pathways at the biochemical level and provide drug targets and biomarkers for combination therapies with potential applications in the clinic. + +## RESULTS + +To study gemcitabine- induced resistance mechanisms we first evaluated the cell viability of a panel of DLBCL cell lines when challenged with a range of gemcitabine doses over \(48\mathrm{h}\) . Of the profiled cell lines, we selected two sensitive (OCI- LY19, \(\mathrm{IC}_{50} = 2.4\mathrm{nM}\) and OCI- LY3, \(\mathrm{IC}_{50} = 14.4\mathrm{nM}\) ) and two resistant (SUDHL4, \(\mathrm{IC}_{50} =\) not defined and HT, \(\mathrm{IC}_{50} =\) not defined) cell lines (SFig 1A) to employ the highly sensitive IMPRINTS implementation of MS- CETSA, whereby 3 biological replicates of treated cells were labeled together with their vehicle controls. To capture the dynamic cellular response upon drug treatment, sensitive OCI- LY19 and resistant SUDHL4 cells were treated for 4 time points (1h, 3h, 5h and 8h), while for comparison purposes sensitive OCI- LY3 and resistant HT cells were treated only at 2 timepoints (1h and 8h). For informative CETSA responses to be measurable, the drug concentration needs to be sufficiently high to induce molecular perturbations with high stoichiometry. We therefore selected \(20\mathrm{X}\mathrm{IC}_{50}\) for the sensitive cells, i.e. \(48\mathrm{nM}\) for OCI- LY19 and \(288\mathrm{nM}\) for OCI- LY3. For both resistant cells we used \(500\mathrm{X}\) the concentration in relation to OCI- LY19, i.e. \(24\mu \mathrm{M}\) , but have additionally collected CETSA data at lower concentrations ( \(480\mathrm{nM}\) and \(48\mathrm{nM}\) ) to monitor dose- dependent responses. We confirmed that treated cells remained intact and viable, as judged from a trypan blue assay, at the maximum timepoint for the CETSA experiments (SFig 1B). IMPRINTS CETSA was performed similarly in all cell lines using a 6- temperature protocol (Fig 1B). The protein coverages and numbers of hits scored using our standard hit selection criteria (described in Materials & Methods) are shown in STable 1. Of the 6 temperatures, the \(47^{\circ}\mathrm{C} - 57^{\circ}\mathrm{C}\) comprise the CETSA thermal shift, while the "unheated" \(37^{\circ}\mathrm{C}\) trace represents the CETSA protein abundance change. Different combinations of CETSA abundance and thermal shifts result in 8 typical IMPRINTS profiles as depicted in Fig 1C. + +<--- Page Split ---> + +The association of gemcitabine with RRM1 was expected to increase protein stability and produce a thermal shift. Indeed, within 1h, RRM1 displayed similar IMPRINTS profiles in both resistant and sensitive DLBCL cells, supporting extensive target engagement and inhibition of de novo deoxyribonucleotide synthesis (Fig 1D). This shift was also seen at subsequent timepoints of 3h, 5h, and 8h. An isothermal dose response (ITDR) experiment also showed comparable dose-response behavior, supporting similar RRM1 target engagement in sensitive and resistant cells (Fig 1E). + +## Initiation of DNA damage response in sensitive and resistant cells + +Apart from RRM1 inhibition, gemcitabine acts by being incorporated into DNA, inducing single strand DNA (ssDNA) breaks and stalled replication forks (23). Indeed, only 5 proteins shifted across all time points and cell lines (albeit weaker in sensitive cells) - DNMT1, RPA1, RPA2, RPA3, and CHEK1 (Fig 2). Notably, all 5 proteins can be localized at replication sites with 4 belonging to the core molecular machinery for sensing and signaling ssDNA damage and stalled replication fork. + +DNMT1 is a major enzyme involved in DNA methylation inheritance and plays a critical role in maintaining genome stability (24). Notably, gemcitabine is a cytidine analog with a difluoromodification at the C5 position where DNMT1 is supposed to act and transfer a methyl group on to endogenous cytidine. We therefore investigated the possibility of a direct soluble gemcitabine triphosphate- DNMT1 interaction in a cell lysate western blot CETSA experiment but did not see significant thermal stability shifts (SFig 2A). Additionally, gemcitabine treatment did not result in DNMT1 degradation as is described for other DNMT1 inhibitors such as decitabine (SFig 2B). It instead appeared plausible that the observed early CETSA effects on DNMT1 report on the modulations of specific protein or DNA interactions with DNMT1 induced at the stalled replication fork. + +The 3 replication protein A subunits (RPA1, RPA2, RPA3) are known to coat ssDNA generated at e.g. stalled replication forks, which is reflected in a thermal stabilization upon gemcitabine treatment. RPA- ssDNA complex is crucial for localization and activation of ATR kinase which initiates downstream DNA- damage response (DDR) pathways, including phosphorylation of CHEK1. Thermal destabilization of CHEK1 was concomitant with its phosphorylation at Ser345 (SFig 2C). These results demonstrate that CETSA can detect the activation of + +<--- Page Split ---> + +ATR/CHEK1 signaling axis as one of the early responses towards gemcitabine treatment. The fact that the thermal shifts are present in both sensitive and resistant cell lines suggest that resistance mechanisms occur downstream of these initial responses. + +## Activation of apoptosis in sensitive cells versus checkpoint release for cell cycle progression in resistant cells + +While the early responses (at 1h and 3h) in resistant and sensitive cells bore similarities in their engagement of RRM1 and signature proteins for stalled replication fork or DDR, by 5h and 8h, there was a segregation of proteins with thermal shifts between the two cell states. We observed a time- dependent increase in the total number of hits (Fig 3A), consistent with a sequential induction of unique GO biological processes in the two types of cell lines, likely initiated by the early DDR. Strikingly, by 8h post drug exposure, there was a clear divergence in the ensembles of proteins in either sensitive or resistant cell lines. In fact, only 3 proteins were overlapping (grey) between both resistant (green) and sensitive (red) cells (Fig 3B). + +Through the use and analyses of several apoptosis- inducing drugs, we recently identified a prototypic CETSA apoptosis response that is dominated by nuclear proteins and reflects very early apoptosis activation (under revision). This response was characterized by 47 proteins we termed the core CETSA apoptosis ensemble (CCAE), and this provided the first means for direct assessment of caspase activation in intact cells. When our hitlists from gemcitabine sensitive and resistant cells were separately compared with the CCAE described above, 24 proteins overlapped for the sensitive cells, while only one of the resistant hits were common with this ensemble (Fig 4A). These results unequivocally conclude that apoptosis induction was indeed unique to sensitive cells, despite the far higher gemcitabine concentration used to treat resistant cells. Furthermore, our current study resolved the sequence of early apoptosis events and showed that the emergence of the CCAE response in sensitive cells was clearly time dependent with proteins such as PARP1, XRCC5, XRCC6, MATR3, LMNB1, LMNB2, RBMX and ZC3H11A showing distinct thermal stability shifts as early as 3h and 5h following gemcitabine exposure (Fig 4B). For some CCAE proteins that are cleaved by caspases, a regional stabilization due to proteolysis (RESP) effect with stability changes in regions either N- or C- terminal of caspase cleavage sites, was also observed. Here, a subset of proteins also shows RESP effects indicative of direct caspase cleavage including known caspase targets such as PARP1, LMNB1, MATR3 and DDX21 (Fig 4C). To further verify that + +<--- Page Split ---> + +apoptosis is only induced in sensitive cells, we looked at PARP1 cleavage, a recognized hallmark of apoptosis, by western blot. Indeed, we only observed cleaved PARP1 upon gemcitabine treatment in sensitive but not in resistant cells (SFig 3A). Although apoptosis can be initiated by ATR/CHEK1 signaling via p53- activation (25), the lack of CETSA shifts in proteins recently defined as p53 regulated proteins in cell death, indicates that the observed processes here are independent of p53 (26). + +In contrast to the prominent CETSA apoptosis signatures featured in the sensitive cells, we observed cell- cycle regulating processes as one of the dominant features in the response of resistant cells. Contributing to this were significant shifts for cyclins and cyclin- dependent kinases (CDKs), most prominently CCNA2, CCNB1, CCNB2 and CDK1 (Fig 4D). These proteins showed distinct time- dependent thermal stabilizations or abundance changes with similar IMPRINTS profiles as compared to our previously published cell cycle study (SFig 3B) (22). The shifts indicated increased activation of CDK complexes that promoted G2/M and G1/S phase checkpoint transitions. To rule out that these effects are due to the higher gemcitabine concentration used in treatment of resistant cells, we consulted our additional low dose datasets that also included the same concentration as used for the sensitive cells (48nM). The findings, indeed, supported similar modulations of cell cycle checkpoints in resistant cells at these lower doses, and therefore the induced response was active over a wide concentration range (SFig 3C). Additionally, in our previous cell cycle study, RB1 phosphorylation during G1/S checkpoint release resulted in a thermal stabilization. Here, we observed the opposite effect, i.e. thermal destabilization and thus dephosphorylation, in gemcitabine sensitive cells (SFig 3D). Measuring cell cycle distribution using propidium iodide staining confirmed G1 arrest in sensitive cells, while resistant cells underwent normal cycling upon gemcitabine treatment (Fig 4E). + +## DDR initiates translesion DNA synthesis as a resistance mechanism + +Next, we sought to explain how resistant cells were able to proceed with the cell cycle, despite the exposure to a DNA synthesis inhibitor. + +DDR is dependent on the availability of dNTPs at appropriate levels for accurate DNA synthesis. SAMHD1, which exhibited significant destabilization across all timepoints in resistant cells (SFig 4A), is a regulator of dNTP homeostasis via its dNTPase activity (27). + +<--- Page Split ---> + +Like CHEK1, we tested whether phosphorylation of SAMHD1 is concomitant with its thermal destabilization, but we did not detect significant changes (SFig 4B). When we knocked down SAMHD1 (SFig 4C) as an attempt to re- establish gemcitabine sensitivity, we instead observed a slight increase in resistance (SFig 4D). LC/MS measurements of deoxyribonucleotide (and ribonucleotide) pools showed differences between SUDHL4 SAMHD1 WT and KO cells primarily in dGNP and dANP abundance; this is consistent with SAMHD1 being a dNTP hydrolase of purine nucleotides (SFig 4E). In a more compact 3- temperature IMPRINTS- CETSA experiment we found similar gemcitabine responses between SUDHL4 WT and KO cells. However, a notable difference was a much weaker stabilization of RRM1 after gemcitabine treatment in the KO cells (SFig 4F). As gemcitabine- triphosphate is a substrate of SAMHD1 (28, 29), we reasoned that the reduced cycling between different phosphorylation states of gemcitabine in the SAMHD1 KO cells affected the cellular concentration of the inhibitory diphosphate form of gemcitabine, thereby causing the reduction of RRM1 engagement. This might subsequently lead to the observed attenuated deoxyribonucleotide pools in the KO cells, explaining the increase in resistance. + +Interestingly, "translesion synthesis" (TLS) appeared as a prominent pathway only in resistant cells at 8h (Fig 5A). TLS is a process that facilitates DNA synthesis over damaged lesions by reorganizing replication complexes through the recruitment of specialized DNA repair polymerases. In addition to subunits of ssDNA binding proteins RPA1, RPA2 and RPA3, we observed pronounced time dependent thermal stabilization and abundance increases of two key proteins associated with TLS: PCNA binding protein (PCLAF) and Denticleless Protein Homolog (DTL). Accompanying these changes, we observed strong thermal destabilization of the core catalytic subunits of DNA polymerase \(\delta\) (PolD), POLD1, POLD2 and POLD4, the latter also depicting a decrease in abundance levels. To investigate the possible induction of dedicated TLS polymerases as a putative mechanism to overcome DNA damage and hence gemcitabine resistance, we examined the protein levels of several of the repair/TLS polymerases after 1h, 3h, 5h and 8h of gemcitabine exposure in both sensitive and resistant cells. POL \(\eta\) , POL \(\iota\) , and Rev1 did not show any difference in protein levels (SFig 4G). Notably, however, POL \(\kappa\) protein abundance was reduced in sensitive cells as early as 3h after gemcitabine treatment (Fig 5B). Together with the decrease of protein levels, smaller fragments of Polk were observed with gemcitabine treatment (Fig 5C). Blocking the proteasomal degradation pathway with the proteasomal inhibitor, MG132, did not rescue POL \(\kappa\) from being + +<--- Page Split ---> + +degraded. Instead, the use of a pan-caspase inhibitor, zVAD- FMK, was able to rescue POLk levels, supporting the notion that the degradation of POLk was likely dependent on the activation of caspase and apoptosis programs. This suggests that when cells further irreversibly commit to the early apoptotic process key, DDR mechanisms are being degraded in a caspase dependent manner. + +To further validate that the IMPRINTS CETSA shifts of the TLS proteins reported on TLS activation, we employed an orthogonal standard TLS assay, whereby the ubiquitination status of the DNA clamp protein, PCNA, is assessed. Upon DNA damage, mono- ubiquitination of PCNA primes access to DNA by TLS polymerases (30). We measured the levels of total versus mono- ubiquitinated PCNA after gemcitabine treatment and indeed only observed TLS activation in resistant cells (Fig 5D). Next, we sought to explore the effect on gemcitabine resistance by disrupting TLS with a REV7/REV3 interaction inhibitor. We indeed found synergistic effects between gemcitabine and REV7/REV3- In- 1 (Fig 5E). Based on these results we propose a gemcitabine resistance mechanism that involves the release of replicative DNA polymerase (PolD), reflected as thermal destabilizations, followed by mono- ubiquitination of PCLAF, which facilitates access to TLS polymerases and restart of replication fork (Fig 5F). This allows cells to bypass DNA damage induced replication arrest and apoptosis. + +## Auxiliary DNA Damage Repair (ADDR) response: A new protein ensemble induced by DNA damage drugs to drive resistance + +Apart from the TLS CETSA protein shifts, an ensemble of 5 proteins exhibited a strong concomitant protein abundance increase following gemcitabine treatment. This ensemble includes: RRM2 (Ribonucleoside diphosphate Reductase subunit M2) and TK1 (Thymidylate Kinase), which are involved in deoxyribonucleotide provision; GMNN (Geminin), which inhibits the formation of a pre- replication complex; SLBP (Stem Loop Binding Protein), which promotes histone transcription, and FBXO5 (F- box only protein 5), a regulator of the anaphase promoting complex. Together with DTL and PCLAF, these proteins were distinctly upregulated in the two resistant, but not sensitive DLBCL cell lines. We termed this new protein ensemble, the Auxiliary DNA Damage Repair (ADDR) response proteins (Fig 6A). We confirm that the ADDR CETSA signature was also present with lower doses of gemcitabine exposures in resistant cells (SFig 5A). We next examined whether the ADDR ensemble is more commonly activated in these cells and indeed found increased abundances upon treatment with + +<--- Page Split ---> + +other DNA damaging drugs such as cladribine and cytarabine (Fig 6B). To further validate whether the ADDR response is a conserved mechanism, we utilized a completely different cell system, MDA- MB- 231 breast cancer cells, treated with another class of DNA damaging agent, cisplatin. Strikingly, in the CETSA IMPRINTS dataset of this model, all 5 proteins of the ADDR response, as well as DTL and PCLAF, were among the strongest shifting proteins which displayed abundance changes (SFig 5B). These observations indicate that the ADDR program may have a broader role in conferring resistance towards a spectrum of DNA damaging agents. + +Interestingly, prominent thermal destabilization of PolD subunits (SFig 5C), noted above, were also observed in the cisplatin data. However, in contrast to gemcitabine- treated DLBCL cells, there was no shift for proteins in the ssDNA binding RPA complex or CHEK1 (SFig 5D) in cisplatin- treated breast cancer cells. Hence, across both experimental models, our data pointed to a CHEK1- independent mechanism for inducing TLS and ADDR responses. Finally, we confirmed that the gemcitabine- resistant SUDHL4 cells were, in fact, also resistant towards cytarabine, cladribine as well as cisplatin (Fig 6C). + +## ATR inhibition abrogates gemcitabine resistance through attenuation of TLS and ADDR induction + +Given the role of TLS and ADDR responses in overcoming DNA damage, we reasoned that perturbing DNA sensing mechanisms might be exploited to prevent otherwise- resistant cells from mounting the DDR in the first place. ATR is a serine/threonine kinase involved in sensing DNA damage together with RPA and phosphorylates proteins such as CHEK1 and inhibitors of ATR kinase have shown pre- clinical and clinical synergy with gemcitabine (31). Accordingly, we investigated the effects of the ATRI, AZD6738, on the gemcitabine response in resistant SUDHL4 cells. Interestingly, combination treatment resulted in attenuation of gemcitabine resistance as observed by a 100 fold lower \(\mathrm{IC}_{50}\) value at 72h (Fig 6D). + +Next, we investigated whether the resistance signatures in TLS and ADDR responses are affected and thus performed a 3- temperature IMPRINTS experiment in resistant SUDHL4 cells treated with either gemcitabine alone, AZD6738 alone, or in combination. Consistent with our hypothesis, the most prominent effects were seen for the proteins of the ADDR ensemble, as well as DTL and PCLAF from the TLS ensemble, whereby there was a dramatic decrease in abundance (Fig 6E). The destabilisation of POLD1 and POLD2, and level changes of + +<--- Page Split ---> + +POLD4, were also attenuated by ATRi (Fig 6F). This strongly supported the notion that the induction of these proteins was indeed ATR- dependent. The re- established sensitivity by ATRi reinforced the conclusion that the induction of the ADDR and TLS (DTL/PCLAF) ensembles was a key prerequisite for establishing resistance to DNA- damaging drugs. + +The quite dramatically decreased abundance of the ADDR ensemble upon combination treatment could be attributed to the relatively fast turnover rates of these proteins in exponentially growing cells, typically accomplished by a high rate of production and degradation. Indeed, in a Molm16 AML cell line protein turnover dataset used in our lab as reference, the 4 measured proteins all have rapid turnover rates (TK1- 15 h; RRM2- 11h; SLBP- 28h; PCLAF- 10h) (STable 2). Arguably, this design makes these proteins particularly useful for regulating urgent events in cellular processes. However, our current data is not conclusive on whether this is only due to decreased transcriptional activity for the corresponding genes in ATRi treated resistant cells, or whether there are posttranscriptional mechanisms or activation of proteosome degradation components induced. In the future, a more detailed elucidation of the signaling mechanisms post- ATR will be helpful to define contributions from different mechanisms to increased protein levels. + +## CETSA signature responses in DLBCL clinical samples + +To test the feasibility of applying MS- CETSA in clinical samples, we performed an ITDR- CETSA experiment on biopsies from two DLBCL patients who have relapsed after first line therapy and have not been previously treated with gemcitabine. Cells were extracted using Ficoll- paque and treated ex vivo for 5h with increasing doses of gemcitabine (Fig 7A). By comparing the CETSA signatures of resistant and responding cells, we can conclude that both clinical samples were dominated by shifts in proteins of the CCAE discussed above (Fig 7B), i.e. still sensitive to gemcitabine. + +## DISCUSSION + +Non- hypothesis driven system- wide methods have the potential to identify the most prominent molecular processes regulating cellular phenotypes. However, despite cellular biochemistry controlling most molecular processes of the cell, methods for efficient studies of cellular biochemistry at the systems level have been elusive. This has, arguably, also contributed to our + +<--- Page Split ---> + +relatively fragmented current understanding of the biochemical basis for pathway activation in cancer drug resistance. + +CETSA constitutes the first systems- wide method which can report on a range of different types of cellular biochemistry, from protein- protein and protein- DNA/RNA interactions to phosphorylation events and flux through metabolic pathways (21). However, so far CETSA studies have predominantly been focused on identifying drug interactions. Although it has been clear that CETSA can report on cellular pathway modulations downstream of drug binding, in our view this has not been systematically explored. Limitations of previous approaches have been the use of suboptimal CETSA implementations which don't allow for robust measurements of small stability shifts typically induced by functional biochemical changes. Furthermore, time- dependent studies have not been systematically explored to allow the dissection of sequences of activation of cellular processes/pathways. In one study, we have previously applied a 2 time- point MS- CETSA approach to study MoA and resistance to 5- FU, which revealed attenuation of anticipated toxic biochemistry in resistant cells, but no drug- induced resistance response (32). + +In the present work we use the highly sensitive IMPRINTS- CETSA implementation in a time- dependent approach to demonstrate applicability of this technology to study the biochemical pathways involved in gemcitabine MoA and resistance mechanisms. By focusing on the overlapping responses in cell pairs of resistant and sensitive cells, a distinct view of the biochemistry of the MoA of gemcitabine in the two cell types is revealed. The initial biochemical responses are very similar, reflecting the direct target engagement of RNR, as well as the establishment of a DNA- damage signaling hub activated by RPA binding to exposed ssDNA. The ITDR dose similarities for the RNR CETSA shifts exclude modifications in drug internalization and metabolism as dominant resistant mechanisms in our system. At the 3- 8h time- points, sensitive cells rapidly enter apoptosis as judged from the CCAE shifts and RESP effect, while resistant cells show CETSA shifts of CDK complexes supporting open cell cycle checkpoints, consistent with the continued proliferation. + +Most notably in the resistant cell CETSA data, distinct responses are seen for proteins related to activation of DNA repair, i.e., the abundance and thermal stability changes of two TLS biomarkers (PCLAF and DTL) as well as prominent destabilization in Pol8, likely reporting on the induction of TLS. This is further supported by the increased mono- ubiquitination of + +<--- Page Split ---> + +PCNA only in the resistant cells and the synergistic effects of gemcitabine with REV7/REV3 interaction inhibitor. The induced TLS program explains how resistant cells overcome stalled replication forks by allowing DNA- synthesis over damage lesions. TLS has not been previously implied in resistance to gemcitabine but has been suggested as a mechanism of resistance to cisplatin as derived from over- expression of TLS polymerases in resistant cells (33). However, in these cases TLS proteins are assumed to be constitutively expressed and not part of an induced TLS response, as uncovered in the present study. + +In addition to the induction of CDK activation and TLS programs, the induction of the ADDR ensemble of proteins is the most dominating feature of the response in resistant cells. These proteins appear to have functions that can support DNA- repair/replication and could therefore be supportive for TLS, although not previously identified as an ensemble in a DNA repair context. The distinct attenuation of both the induction of TLS proteins DTL and PCLAF, and the ADDR response by an ATR inhibitor strongly support that ATR is a signaling node in this response. However, we conclude that the response is likely not CHEK1 dependent, when ADDR response for cisplatin in MDA- MB- 231 breast cancer cells does not coincide with CHEK1 activation. The disparity likely reflected differences in DNA damage mechanisms between the two drugs: cisplatin is a DNA crosslinking agent while gemcitabine induces single strand breaks. + +Intriguingly, CHEK1 activation is expected to mediate cell cycle arrest, but in contrast, in gemcitabine resistant cells, the CETSA shifts of CDK complex and cell cycle assessments support the opposite effect, i.e., opening of cell cycle checkpoints. This gives further support for the activation of an alternative signaling pathway for the induction of a pathway downstream of ATR, controlling DNA- repair and cell cycle checkpoints to support cell proliferation during genotoxic challenges. However, despite significant efforts (data not shown) we have not been able to identify additional components of the signaling pathway downstream of ATR, which also might provide additional target proteins for specifically attenuating gemcitabine resistance. + +In addition to constituting a new pathway for induction of DNA- repair, the fact that ATR inhibition re- sensitized cells to gemcitabine, supports that this response is a key component of the gemcitabine resistance in this system. There have been previous reports of positive results for using ATRI in combination with gemcitabine in pancreatic cancer (31) and ovarian cancer + +<--- Page Split ---> + +(34) therapies. In a recent phase 2 trial in platinum-resistant high-grade serous ovarian cancer, a combination of the selective ATR inhibitor berzosertib, and gemcitabine showed significantly prolonged progression-free survival compared to treatment with gemcitabine alone (35). The current studies provide a mechanistic rationale for the combination of ATRi and gemcitabine for DLBCL. + +As a future strategy for patient stratification, CETSA could potentially be used to monitor whether this (or other) resistance mechanism(s) is in effect in clinical samples, or if instead the early apoptosis profile are detectable with CETSA, indicating sensitivity. The data from two clinical DLBCL patient samples also supports that high quality CETSA information can be obtained from clinical DLBCL samples. + +Together the present study supports that time- dependent IMPRINTS- CETSA constitutes a highly efficient strategy to discover sequences of prominent pathway activation explaining cancer drug MoA and resistance. Therefore, as an alternative to focused studies of cancer drug MoA, which are often limited in their scope by requirement of pathway/protein specific biochemical assays, this work establishes IMPRINTS- CETSA as an efficient strategy for global studies of the biochemistry of cancer drug resistance, where comprehensive insights into effects on many different cellular pathways can be directly accessed using a single method. These studies also provide a repertoire of MoA- based drug resistance biomarkers showing robust responses with potential applicability in the clinic. + +Acknowledgements: We gratefully acknowledge funding from the Swedish Research Council, the Swedish Cancer Society, Radiumhemmet's funds, the Knut and Alice Wallenberg Foundation, Singapore's National Research Foundation (NRF- NRFI08- 2022, NRF- CRP22- 2019- 0003), Agency for Science, Technology and Research, Singapore, and the Singapore Ministry of Education under its Research Centres of Excellence initiative. We also acknowledge all past members of the PN lab. + +Author contributions: Conceptualization: WLT, PN and NP; Methodology: YYL, LHV, KK, MR, HMT, WLT, PN and NP; Formal Analysis: YYL, LHV, KK, MR, MAG, WLT, PN and NP; Investigation: YYL, LHV, KK, HMT, MR, JJHL, JL, AC, ADJ, WLT, PN and NP; Writing- Original draft: YYL, WLT, PN and NP; Writing- Review & Editing: All; Funding Acquisition: PN, WLT and NP; Supervision: ADJ, PN, WLT, NP + +<--- Page Split ---> + +485 Declaration of Interests: PN is the inventor of patents related to the CETSA method and is a 486 cofounder and board member of Pelago Biosciences AB. + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
FIGURE 1
+ +488 + +489 + +(A) Structure and thus far known MoA of gemcitabine. (B) IMPRINTS CETSA experimental workflow. (C) Interpretation of IMPRINTS CETSA profiles. (D) IMPRINTS CETSA profiles of RRM1 in two sensitive, OCI-LY3 (orange) and OCI-LY19 (red), and two resistant, HT (dark green) and SUDHL4 (light green), cell lines after 1h, 3h, 5h or 8h of gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference ±SEM from biological replicates (n=3). (E) 3h Isothermal Dose Response (ITDR) of RRM1 in OCI-LY19 (red) and SUDHL4 (green) cells with different doses of gemcitabine and at 52°C CETSA heating. Data are presented as mean log2 fold change compared to the reference ±SEM from technical replicates (n=2). + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
FIGURE 2
+ +499 + +499Figure 2: Gemcitabine induced stalled replication forkHypothetical model indicating proteins at the stalled replication fork after gemcitabine induced DNA damage, and respective IMRPINTS profiles of DNMT1, RPA1, RPA2, RPA3 and CHEK1 in sensitive OCI-LY3 (orange) and OCI-LY19 (red), as well as resistant HT (dark green) and SUDHL4 cells (light green) after 1h, 3h, 5h or 8h of gemcitabine treatment. Data are presented as mean \(\log 2\) fold change compared to the reference \(\pm \mathrm{SEM}\) from biological replicates (n=3). + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
FIGURE 3
+ +(A) Heatmap showing the evolution of hit lists at the different time points (1h, 3h, 5h and 8h) after gemcitabine treatment in the sensitive OCI-LY19 cells and resistant SUDHL4 cells. (B) Venn diagram shows overlap of sensitive (red) and resistant (green) hits used for subsequent ClueGO analysis. Only common hits from both sensitive OCI-LY3 and OCI-LY19 (54 hits), and both resistant HT and SUDHL4 (45 hits) cells, after 8h gemcitabine treatment, are used. ClueGO analysis of common hits from sensitive (red) and resistant (green) cells after 8h gemcitabine treatment. Each node represents an enriched GO term whereby the colouring indicates an at least 60% hit contribution from a condition. Hits related to each GO term are depicted in small nodes with black label and coloured accordingly (sensitive = red, resistant = green, both = grey). + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4: CETSA responses in gemcitabine sensitive versus resistant cells
+ +(A) Venn diagram showing overlap of the hits from sensitive OCI-LY19 (top) and resistant SUDHL4 cells (bottom) with the previously identified CCAE (Core CETSA Apoptosis Ensemble) proteins. (B) STRING plot showing the overlapping proteins from A with IMPRINTS CETSA profiles for OCI-LY19 (red hues) and SUDHL4 (green hues) after 1h, 3h, 5h and 8h gemcitabine treatment. Data are presented as mean log2 fold change compared to + +<--- Page Split ---> + +the reference from biological replicates \((n = 3)\) . (C) IMPRINTS profiles of PARP1, MATR3, LMNB1 and DDX21 showing peptides before and after known caspase cleavage sites in sensitive OCI- LY19 cells (red hues) and resistant SUDHL4 cells (green hues) after 1h, 3h, 5h and 8h gemcitabine treatment. Data are presented as mean \(\log 2\) fold change compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . (D) IMPRINTS profiles CCNA2, CCNB1, CCNB2 and CDK1 for OCI- LY19 (red hues) and SUDHL4 (green hues) after 1h, 3h, 5h and 8h gemcitabine treatment. Data are presented as mean \(\log 2\) fold change compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . (E) Progression of cell cycle and distribution of cells in different cell cycle phases in the sensitive OCI- LY19 and resistant SUDHL4 cells after 8h and 24h with and without gemcitabine treatment. Data are presented as relative percentage of cells in each cycle phase \(\pm \mathrm{SEM}\) from biological replicates \((n = 4)\) . + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
FIGURE 5
+ +## Figure 5: Translusion synthesis in gemcitabine resistant cells + +(A) Nodes indicating translesion synthesis pathway as a GO term and IMPRINTS profiles of the involved proteins in OCI-LY19 (red hues) and SUDHL4 (green hues) after 1h, 3h, 5h and 8h gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference ±SEM from biological replicates (n=3). (B) Representative western blot (top) and quantification (bottom) of Polk expression in OCI-LY19 (red) and SUDHL4 (green) cells after 1h, 3h, 5h and 8h of gemcitabine treatment. Data are presented as mean relative fold change compared to the reference ±SEM from biological replicates (n=3). (C) Western blot of full-length and cleaved fragments of Polk in OCI-LY19 cells after 6h of gemcitabine treatment in + +<--- Page Split ---> + +the presence or absence of proteasomal inhibitor MG132 or pan-caspase inhibitor zVAD- FMK. (D) Representative western blot (top) and quantification (bottom) of PCNA and mono- ubiquitinated PCNA in the sensitive OCI- LY19 (red) and resistant SUDHL4 (green) cells after 6h of gemcitabine treatment. Data are presented as mean relative fold change compared to the reference ±SEM from biological replicates (n=3). (E) ZIP Synergy score of gemcitabine and rev7/3- in- 1 concentrations in SUDHL4 cells at 48h. (F) Hypothetical model of gemcitabine induced translesion synthesis polymerase switch. + +<--- Page Split ---> +![](images/Figure_6.jpg) + +
Figure 6: ADDR response in gemcitabine resistant cells
+ +(A) IMPRINTS profiles of ADDR protein ensemble in OCI-LY19 (red hues) and SUDHL4 (green hues) after 1h, 3h, 5h and 8h gemcitabine treatment. Data are presented as mean log2 + +<--- Page Split ---> + +fold change compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . (B) Quantification of ADDR proteins in SUDHL4 cells after 6h treatment with cladribine (orange) and cytarabine (red). Data are presented as mean \(\log 2\) fold change compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . (C) Relative viability and \(\mathrm{IC}_{50}\) values of OCI- LY19 (dotted lines) and SUDHL4 (continuous lines) cells after 48h treatment with increasing concentrations of gemcitabine (green), cisplatin (light blue), cytarabine (red) or cladribine (orange). Data are presented as mean relative viability compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . (D) Relative viability of SUDHL4 cells treated for 72h with increasing concentrations of gemcitabine, either alone (green) or in combination with \(1\mu \mathrm{M}\) AZD6738 (red). Data are presented as mean relative viability compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . (E) IMPRINTS profiles of ADDR protein ensemble in gemcitabine resistant SUDHL4 cells after 5h of treatment of gemcitabine alone, AZD6738 alone or in combination. Data are presented as mean \(\log 2\) fold change compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . (F) IMPRINTS profiles of POLD1, POLD2, POLD4 in gemcitabine resistant SUDHL4 cells after 5h of treatment of gemcitabine alone, AZD6738 alone or in combination. Data are presented as mean \(\log 2\) fold change compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . + +<--- Page Split ---> +![](images/Figure_7.jpg) + +
FIGURE 7
+ +575 + +576 + +577 + +(A) Experimental design for MS-CETSA treatment of patient samples from DLBCL patients. Patient samples were treated with different doses of gemcitabine ex vivo for 5h. The treated samples were CETSA heated and subjected to mass spectrometry. (B) MS-CETSA ITDR profiles of selected CCAE proteins in patient samples (top panel) and sensitive OCI-LY19 (bottom panel) cells. Data are presented as mean log2 fold change compared to the reference ±SEM from technical replicates (n=2). + +<--- Page Split ---> +![](images/Supplementary_Figure_1.jpg) + +
SUPPLEMENTARY FIGURE 1
+ +## Supplementary Figure 1: Cell viability after gemcitabine treatment + +(A) MTT viability assay and \(\mathrm{IC}_{50}\) values of OCI-LY3 (orange), OCI-LY19 (red), HT (dark green) and SUDHL4 (light green) cells after 48h treatment with increasing concentrations of gemcitabine. Data are presented as mean relative viability compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . (B) Trypan blue assay of OCI-LY3 (orange), OCI-LY19 (red), HT (dark green) and SUDHL4 (light green) cells after 8h treatment with indicated gemcitabine concentrations. + +<--- Page Split ---> +![](images/Supplementary_Figure_2.jpg) + +
Supplementary Figure 2: Early responses to gemcitabine in resistant and sensitive cells
+ +(A) Western blot detection of DNMT1 levels in soluble fraction in SUDHL4 lysates treated with increasing gemcitabine concentrations (0-1mM) for 1min and upon CETSA heat challenge at \(37^{\circ}\mathrm{C}\) (control) and \(54^{\circ}\mathrm{C}\) . (B) Western blot detection of DNMT1 levels in OCI-LY19 and SUDHL4 cells after 24h treatment with vehicle, gemcitabine or decitabine. Actin was used as loading control. (C) Western blot detection of phospho-Chk1 (S345) in OCI-LY19 and SUDHL4 after treatment with indicated gemcitabine concentrations for 1h, 3h, 5h or 8h. Actin was used as loading control. + +<--- Page Split ---> +![](images/Supplementary_Figure_3.jpg) + +
SUPPLEMENTARY FIGURE 3
+ +Supplementary Figure 3: Apoptosis induction in sensitive cells versus activation of checkpoint release for cell cycle progression in resistant cells + +(A) Western blot detection of PARP1 and cleaved PARP1 (c-PARP1) levels in OCI-LY19 and SUDHL4 cells after treatment with indicated gemcitabine concentrations for 1h, 3h, 5h, or 8h. Actin was used as loading control. (B) Schematic illustration of cyclin and CDKs interaction during different phases of cell cycle (left) and the IMPRINTS profiles of CDK1, CCNB1, CCNB2 and CCNA2 (right) as previously published by Dai et al (22). (C) IMPRINTS profiles CDK1, CCNB1, CCNB2 and CCNA2 in SUDHL4 cells treated for 1h (green) or 8h (blue) with 48nM, 480nM and 24μM gemcitabine. Data are presented as mean log2 fold change compared to the reference ±SEM from biological replicates (n=3). (D) IMPRINTS profile of RB1 in gemcitabine sensitive OCI-LY19 (red hues) and resistant SUDHL4 cells (green hues) after 1h, 3h, 5h and 8h of gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference ±SEM from biological replicates (n=3). + +<--- Page Split ---> +![](images/Supplementary_Figure_4.jpg) + +
SUPPLEMENTARY FIGURE 4
+ +## Supplementary Figure 4: Translesion synthesis polymerases + +(A) IMPRINTS profile of SAMHD1 in gemcitabine sensitive OCI-LY19 (red hues) and resistant SUDHL4 cells (green hues) after 1h, 3h, 5h and 8h of gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference ±SEM from biological replicates (n=3). (B) Western blot detection of phospho-SAMHD1 in SUDHL4 cells after 1h, 3h, 5h, or 8h of 24μM gemcitabine treatment. Actin was used as loading control. (C) Western blot detection of SAMHD1 levels in SUDHL4 SAMHD1 WT and KO cells. Actin was used as loading control. (D) Relative viability of SUDHL4 SAMHD1 WT (green) and KO (purple) cells after 72h treatment with increasing concentrations of gemcitabine. Data are presented as mean relative viability compared to the reference ±SEM from biological replicates (n=3). (E) LC/MS measurements of deoxyribonucleotide (and ribonucleotide) in SUDHL4 SAMHD1-KO cells treated with vehicle or 24μM gemcitabine for 6h. Graphs show relative fold changes + +<--- Page Split ---> + +as compared to SUDHL4 WT cells. (F) IMPRINTS profile of RRM1 in SUDHL4 SAMHD1 WT (green) and KO (purple) cells after 1h or 8h gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference \(\pm\) SEM from biological replicates (n=3). (G) Western blot detection of Poln, Polt and Rev1 levels in OCI-LY19 and SUDHL4 cells after treatment with indicated gemcitabine concentrations for 1h, 3h, 5h, or 8h. Actin was used as loading control. + +<--- Page Split ---> +![PLACEHOLDER_33_0] + + +<--- Page Split ---> + +638 presented as mean log2 fold change compared to the reference \(\pm\) SEM from biological replicates (n=3). IMPRINTS profiles of the (B) ADDR protein ensemble, (C) Polδ subunits and (D) proteins involved in stalled replication fork in MDA- MB- 231 breast cancer cells treated with \(25\mu \mathrm{M}\) cisplatin for 12h. Data are presented as mean log2 fold change compared to the reference \(\pm\) SEM from biological replicates (n=3). + +<--- Page Split ---> + +## MATERIALS AND METHODS + +## RESOURCE AVAILABILITY + +## Lead Contact + +Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Pär Nordlund (par.nordlund@ki.se). + +## Data and Code Availability + +The extracted protein abundance data from all MS- CETSA experiments are included in supplemental "Datafile S1 - Protein abundance all datasets". + +All mass spectrometry raw data files have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org/) via the jPOST repository with the dataset identifier PXD (to be filled in). Any additional information required to reanalyse the data reported in this paper is available from the lead contact upon request. + +## EXPERIMENTAL MODEL AND SUBJECT DETAILS + +## Cell Lines + +Human breast adenocarcinoma cell line MDA- MB- 231 (CRM- HTB- 26) was purchased from ATCC. Human lymphoma cell line SUDHL4 (CRL- 2957) was purchased from ATCC, HT cells (CRL- 2260) were a gift from the lab of Ernesto Guccione, Icahn School of medicine at Mt Sinai (formerly at IMCB, Singapore), OCI- LY19 and OCI- LY3 was obtained from the lab of Manikandan Lakshmanan at IMCB, Singapore. + +All the DLBCL cell lines and MDA- MB- 231 were maintained in RPMI- 1640 medium (R8758, Sigma) and L- glutamine, supplemented with \(20\%\) fetal bovine serum (FBS), 100units/ml penicillin and streptomycin in a \(37^{\circ}C\) CO2 incubator. + +## Generation of SUDHL4 SAMHD1 knockout cells + +Knockout was performed using LentiCRISPRv2GFP vector (82416, Addgene). Single- guide RNA encoding SAMHD1 was cloned into LentiCRISPRv2GFP vector. Briefly, lentiviruses were packaged using HEK293T cells via co- transfection of gene of interest, VSVG and delta 8.2 vector, using Lipofectamine 2000 transfection reagent (11668019, Thermo Fisher). Viruses collected were concentrated using Amicon Ultra Centrifugal filters (C2566709, Merck) and spinoculated onto the SUDHL4 cells in the presence of \(8\mu \mathrm{g / ml}\) polybrene (sc- 134220, Santa Cruz) at \(800\mathrm{g}\) for 30 minutes at room temperature. The target sequences of the sgRNAs are as follow: SAMHD1 sgRNA- 1 forward \(5^{\prime}\) - CACCGGAGTGTCTAGTTCCAGCCAC - 3'; + +<--- Page Split ---> + +SAMHD1 sgRNA- 1 reverse 5'- AAACGTGCGTGAACTAGACATCCTC - 3'. Single cells containing the CRISPR- GFP positive vector were then sorted through FACS and harvested as monoclines. + +## Primary DLBCL clinical patient samples + +Tumors were collected in MACS Tissue Storage Solution (130- 100- 08, Miltenyi) and kept on ice for transport. Tissue was cut into equally small pieces using a scalpel. To obtain single cell solutions, the cells were passed through a sterile \(70\mu \mathrm{M}\) cell filter mesh (352350, Corning) in RPMI- 1640 medium (R8758, Sigma) and L- glutamine, supplemented with \(10\%\) fetal bovine serum (FBS), 100units/ml penicillin and streptomycin. Cells number was determined and MSCETSA experiment was performed immediately. + +## METHOD DETAILS + +## Selected drugs + +Gemcitabine, AZD6738 (kindly provided by Prof. Anand Jeyasekharan, CSI, Singapore) and Decitabine were solubilized in water. Cisplatin, Cytarabine, Cladribine, Z- VAD- FMK, MG132 and REV7/REV3L- IN- 1 were solubilized in DMSO. All compound stocks were aliquoted and stored at \(- 20^{\circ}\mathrm{C}\) . + +## The Cellular Thermal Shift Assay (CETSA) + +CETSA in intact cells + +For the in vitro IMPRINTS- CETSA experiments, cell lines were seeded at \(0.5\mathrm{x}10^{6}\) cells/ml of media and preconditioned in complete RPMI with \(2\%\) FBS for 24h. The cells were then treated with either vehicle or drug at their respective final concentrations and incubated at \(37^{\circ}\mathrm{C}\) and \(5\% \mathrm{CO}_2\) for indicated time points. Cells were pelleted for 4min at \(400\mathrm{kg}\) , washed with PBS and resuspended in \(50\mu \mathrm{l}\) PBS. For the in vitro ITDR- CETSA experiments, cell lines were distributed into 6 tubes at \(0.3\mathrm{x}10^{6} / 100\mu \mathrm{l}\) in media, while the total cells of primary DLBCL clinical samples were distributed into 6 tubes in media. Cells were then treated with either vehicle or drug at their respective final concentrations and incubated at \(37^{\circ}\mathrm{C}\) and \(5\% \mathrm{CO}_2\) for indicated time points. Cells were pelleted for 4min at \(400\mathrm{kg}\) , washed with PBS and resuspended in \(50\mu \mathrm{l}\) PBS. Harvested cells and lysates were aliquoted into PCR tubes corresponding to each treatment condition and subjected to a 3min CETSA heating step in a Veriti thermal cycler (Applied Biosystems) with temperatures ranging from \(37^{\circ}\mathrm{C} - 57^{\circ}\mathrm{C}\) , followed by 3min cooling at \(4^{\circ}\mathrm{C}\) . + +<--- Page Split ---> + +For lysate CETSA experiments \(20 \times 10^{6}\) cells/ml were lysed by adding 2X kinase buffer to the final concentration of 50mM HEPES pH 7.5, 5mM beta- glycerophosphate, 0.1mM sodium orthovanadate (Na3VO4), 10mM MgCl2, 1mM TCEP (Sinopharm Chemical Reagent Co.), 1x protease inhibitor cocktail (Nacalai Tesque Inc.) and 25U/ml Benzonase. Cells were subjected to five freeze- thaw cycles with liquid nitrogen to release soluble proteins. The suspension was then centrifuged for 20min at 20,000xg and \(4^{\circ}\mathrm{C}\) to remove cell debris and 30μl of supernatants were treated with either vehicle or drug at their respective final concentrations for 1min. Lysates were aliquoted into PCR tubes corresponding to each treatment condition and subjected to a 3min CETSA heating step in a Veriti thermal cycler (Applied Biosystems) with temperatures ranging from \(37^{\circ}\mathrm{C} - 57^{\circ}\mathrm{C}\) , followed by 3min cooling at \(4^{\circ}\mathrm{C}\) . + +## Cell lysis and soluble protein extraction + +Following heat treatment, the cells were lysed by adding 2X kinase buffer to the final concentration of 50mM HEPES pH 7.5, 5mM beta- glycerophosphate, 0.1mM sodium orthovanadate (Na3VO4), 10mM MgCl2, 1mM TCEP (Sinopharm Chemical Reagent Co.), 1x protease inhibitor cocktail (Nacalai Tesque Inc.) and 25U/ml Benzonase. All the samples were subjected to five freeze- thaw cycles with liquid nitrogen to release soluble proteins. For lysate CETSA experiments this step was skipped and immediately proceeded to the next step. The suspension was then centrifuged for 20min at 20,000xg and \(4^{\circ}\mathrm{C}\) to remove cell debris. The supernatants were then analyzed using either LC- MS or western blotting. + +## Cell cycle analysis and Flow cytometry + +Cell lines were seeded at \(0.5 \times 10^{6}\) cells/ml of media and preconditioned in complete RPMI with \(2\%\) FBS for 24h. The cells were then treated with either vehicle or drug at their respective final concentrations and incubated at \(37^{\circ}\mathrm{C}\) and \(5\% \mathrm{CO}_{2}\) for indicated time points. Cells were pelleted for 4min at \(400\mathrm{xg}\) , washed with PBS. Cells were fixed in \(70\%\) ethanol overnight, washed twice with cold PBS, then resuspended in PI staining solution (100μg/ml ribonuclease A, \(50\mu \mathrm{g / ml}\) PI in PBS) and incubated in the dark for at least 30min at room temperature, followed by flow cytometric analysis on a LSR II (BD Biosciences, UK) flow cytometer. FlowJo (FlowJo, LLC, USA) was used to analyze the data. + +## Nucleotide quantification by LC-MRM/MS + +Cell lines were seeded at \(0.5 \times 10^{6}\) cells/ml of media and preconditioned in complete RPMI with \(2\%\) FBS for 24h. The cells were then treated with either vehicle or drug at their respective final + +<--- Page Split ---> + +concentrations and incubated at \(37^{\circ}\mathrm{C}\) and \(5\% \mathrm{CO}_2\) for indicated time points. Cells were pelleted for 4min at \(400\mathrm{xx}\) , washed with PBS. Cell pellets were harvested and snap frozen in liquid nitrogen. The samples were stored in \(- 80^{\circ}\mathrm{C}\) until transportation to Creative Proteomics for nucleotide quantification through LC- MS analysis. Each cell sample was resuspended in \(500\mu \mathrm{l}\) of \(80\%\) methanol and then lysed on a MM 400 mill mixer at a shaking frequency of \(30\mathrm{Hz}\) and with the aid of two metal balls for 2 min. The samples were subsequently sonicated for 1 min in an ice- water bath before centrifugal clarification at \(21,000\mathrm{g}\) and \(5^{\circ}\mathrm{C}\) for \(10\mathrm{min}\) . The clear supernatants were collected for the following assay. The precipitated pellets were used for protein assay using a standardized BCA procedure. Serially diluted standard solutions of the targeted nucleotides were prepared in \(80\%\) methanol. \(100\mu \mathrm{l}\) of each standard solution of the clear supernatant of each sample were dried under a nitrogen gas flow. The residues were dissolved in \(100\mu \mathrm{l}\) of a 13C- labeled internal standard solution. \(10\mu \mathrm{l}\) aliquots of the resulting solutions were injected into a C18 column \((2.1\times 110\mathrm{mm}\) , \(1.9\mu \mathrm{m}\) ) to run UPLC- MRM/MS with (- ) ion detection on a Waters Acquity UPLC system coupled to a Sciex QTRAP 6500 Plus MS instrument, with the use of tributylamine buffer (A) and acetonitrile (B) as the mobile phase for gradient elution. + +## Western blot + +Western blotting was performed on protein extracts obtained either by freeze- thawing or lysis by RIPA buffer (Thermo Scientific). Protein concentrations for each sample were quantified using bicinchoninic acid (BCA) assay according to manufacturer's instructions. + +Western blotting was performed on protein extracts obtained either by freeze- thawing or lysis by RIPA buffer (Thermo Scientific). Protein concentrations for each sample were quantified using bicinchoninic acid (BCA) assay according to manufacturer's instructions.Protein extract samples were mixed with NuPAGE loading buffer consisting of NuPAGE LDS sample buffer (NP0008, Life technologies) and reducing agent (NP0009, Life Technologies) and boiled at \(95^{\circ}\mathrm{C}\) . Proteins were separated on NuPAGE 4–12% Bis- Tris midi gels (WG1403BX10, Invitrogen) for 45- 55 min at 200 V. Separated proteins were transferred to nitrocellulose membranes using the iBlot system (Invitrogen) onto nitrocellulose membranes. Membranes were blocked in \(5\%\) (w/v) non- fat milk (Semper AB) in TBS with \(0.05\%\) Tween 20 (Medicago 09- 7510- 100) (TBS- T) for 1h with gentle shaking. Incubation with primary antibody was performed overnight at \(4^{\circ}\mathrm{C}\) and with gentle shaking. After washing in TBS- T for \(3 \times 10 \mathrm{~min}\) , the membranes were incubated with secondary antibodies for 1h, washed again \(3 \times 10 \mathrm{~min}\) in TBS- T and developed using Clarity™ Western ECL Substrate (170- 5061, BioRad). The chemiluminescent signal was detected using the ChemiDoc™ XRS+ imaging system from BioRad and the band intensities were quantified using ImageLab™ software (BioRad). + +<--- Page Split ---> + +## Sample preparation for LC-MS + +Protein concentrations were quantified after lysis using the BCA according to manufacturer's instructions and the same amount of protein was used for sample preparation. Samples were reduced with \(25\%\) TFE and \(20\mathrm{mM}\) TCEP at \(55^{\circ}\mathrm{C}\) for \(20\mathrm{min}\) , followed by alkylation with \(55\mathrm{mM}\) of 2- chloroacetamide (CAA) (C0267, Sigma) in the dark at room temperature for \(30\mathrm{min}\) . Samples were digested with LysC (1:25 enzyme to protein ratio, Wako Chemicals Ltd), for 4- 6h before adding trypsin (1:25, Promega) for overnight digestion at \(37^{\circ}\mathrm{C}\) . The samples were dried by a centrifugal vacuum evaporator and desalted with Oasis HLB 96- well plate following the manufacturer's instructions. The desalted peptides were re- solubilized in \(100\mathrm{mM}\) TEAB to \(1\mu \mathrm{g / \mu l}\) . All the peptides were labeled with Isobaric Tandem Mass Tags - 10plex TMT according to the manufacturer's protocol (90110, Thermo Scientific). The labeling was done at room temperature for at least 1hr and labeled samples were quenched using \(10\mu \mathrm{l}\) of 1M Tris (pH 7.4) solution. A high pH reverse phase Zorbax 300 Extend C- 18 4.6mm x 250mm (Agilent) column and liquid chromatography AKTA Micro (GE) system was used for offline sample pre- fractionation. The fractions were concatenated into 20 fractions and dried with a centrifugal vacuum evaporator. + +## LC-MS + +The digested, labeled, and dried peptide sample fractions were resuspended in \(0.1\%\) acetonitrile, \(0.5\%\) (v/v) acetic acid and \(0.06\%\) TFA in water immediately before analysis on LC- MS. Online chromatography was performed using Dionex UltiMate 3000 UPLC system coupled to a Q Exactive mass spectrometer (Thermo Scientific). Each fraction was separated on a \(50\mathrm{cm}\times 75\mu \mathrm{m}\) (ID) EASY- Spray analytical column (ES903, Thermo Scientific) in a \(80\mathrm{min}\) gradient of programmed mixture of solvent A ( \(0.1\%\) formic acid in \(\mathrm{H}_2\mathrm{O}\) ) and solvent B ( \(99.9\%\) acetonitrile, \(0.1\%\) formic acid). MS data were acquired using a top 12 data- dependent acquisition method. Full scan MS spectra were acquired in the range of \(350 - 1550\mathrm{m / z}\) at a resolution of 60,000 and AGC target of 3e6; Top 12 dd- MS \(^2\) 60,000 and 1e5 with isolation window at \(1.0\mathrm{m / z}\) . + +## QUANTIFICATION AND STATISTICAL ANALYSIS + +## Protein identification and quantification + +Protein identification was performed by Proteome Discoverer 2.5 software (Thermo Scientific), using both Mascot 2.6.0 (Matrix Science) and Sequest HT (Thermo Scientific) search engines to search against reviewed human Uniprot databases (downloaded on 13 Jan + +<--- Page Split ---> + +2017, including 42105 sequence entries and another downloaded on 23 Jul 2018, including 9606 sequence entries). MS precursor mass tolerance was set at 20ppm, fragment mass tolerance 0.05 Da, and maximum missed cleavage sites of 3. Dynamic modifications searched for Oxidation (M), Deamidation (NQ), and Acetylation (N-terminal protein). Static modifications: Carbamidomethyl (C) and TMT10plex (K and peptide N terminus). Only the spectrum peaks with signal- to- noise ratio (S/N) \(>4\) were chosen for searches. The false discovery rate (FDR) was set to \(1\%\) at both PSM and peptide levels. Only the unique and razor peptides were used for protein assignment and abundance quantification. Isotopic correction of the reporter ions in each TMT channel was performed according to the product sheet. Only the master proteins in the protein group were used for downstream analysis. For some datasets, the peptide abundances were obtained from Proteome Discoverer software (version 2.5). Every peptide with another modification than a TMT one was removed. To ensure the accuracy of TMT quantification, reporter S/N threshold was set at 10 and co- isolation threshold at \(30\%\) . Then, every peptide dataset has been treated the same way as the protein dataset according to the same method described in Dai et al., Cell 2018 (22). To illustrate the RESP effect, we summed the non Log2 transformed fold changes from the peptides located before the cleaved site and the ones after the cleaved site. Then those fold changes were Log2 transformed and plotted as bar plots. + +## Quantitative MS data analysis and visualization + +Quantified protein/peptide abundances were imported into the R environment (http://www.R- project.org/) to facilitate the data analysis and visualization. Only the proteins with at least two quantifying abundance counts were used for downstream analysis. Data cleaning, normalization, and calculations of protein abundance and thermal stability differences in each condition were performed using the IMPRINTS.CETSA and the IMPRINTS.CETSA.app R packages (36). Strict criteria for hit selection were applied for all datasets., For IMPRINTS- CETSA we require that proteins should be quantified by at least 2 abundance counts. As cut- off criteria we used an absolute mean fold change and a standard error of the mean (SEM)- scale factor as cut- off. For IMPRINTS- CETSA experiments we used a mean log2 fold change cut- off \(>0.25\) or \(0.2\) and \(\pm 4\times\) SEM as compared to reference condition was applied. + +## Protein-protein interaction network and gene ontology (GO) enrichment analysis + +Protein- protein interaction network for hits was obtained by importing the hitlist Uniprot IDs into Cytoscape v.3.9.1 (http://cytoscape.org). Using the embedded STRING interaction database (http://apps.cytoscape.org/apps/stringApp), a default confidence cut- off score of 0.4 + +<--- Page Split ---> + +was applied to retrieve the network. Each node represents one hit protein, and edges symbolize protein- protein interactions. Nodes explanation can be found on figure legends. Comparative GO analysis was performed using the ClueGO v2.5.1 plug- in in Cytoscape (http://apps.cytoscape.org/apps/cluego). Hitlist Uniprot IDs were imported to query the GO- Biological Processes database (EBI- QuickGO- GOA- 15783 terms/pathways with 17268 available unique genes- 20.11.2017). The parameters for analysis were set as follows: Evidence code – All; Use Go Term Fusion; GO tree interval – Level 3- 8; GO Term/Pathway Selection – Minimum 3 genes and threshold of 4% of genes per term; GO term connectivity threshold (Kappa score) – 0.4; Two- sided hypergeometric test with Bonferroni step down p- value correction. Only GO terms with p- value <0.05 are shown. GO terms are presented as nodes and clustered together based on term similarity. Node size is proportional to the p- value for GO term enrichment. Node colours are set according to the treatment condition showing the % of visible proteins of a term/pathway. + +## Data Analysis and visualization + +All graphs were generated using GraphPad Prism, R environment, cytoscape or Biorender. All data are presented as mean with error bars representing the standard error of the mean (SEM). Error bars that are smaller than the displayed data points are not displayed by the software. Details regarding replicates for each experiment can be found in the figure legends. Sigmoidal curves were fit (where appropriate) using R environment. Unpaired t- tests were performed using GraphPad Prism and the results are displayed in figures and figure legends. The flow cytometry data was analyzed on FlowJo v10.8 and GraphPad Prism was used to represent the data. + +<--- Page Split ---> + +
REAGENT or RESOURCESOURCEIDENTIFIER
Antibodies
Anti-SOD1 rabbit pAbSigmaHPA001401
Anti-RRM2Santa Cruz BiotechnologySc-81850
Anti-TK1GeneTexGTX113281
anti-GMNNSanta Cruz BiotechnologySc-74456
anti-SLBPInvitrogenPA5-53966
anti-FBX05Invitrogen37-6600
anti-DTLInvitrogenPA5-88380
anti-PCNASanta Cruz BiotechnologySc-56
anti-ubPCNACell Signaling TechnologyD5C7P
anti-PCLAFSanta Cruz BiotechnologySc-390515
Anti-DNMT1Abcamab19905
Anti-SAMHD1GeneTexGTX103751
Anti-phospho-SAMHD1Cell Signaling TechnologyD7O2M
Anti-pChk1 (S345)Cell Signaling Technology2348T
Anti-p53Santa Cruz BiotechnologySc-126
Anti-RRM1Santa Cruz BiotechnologySc-11733
Anti-PARP1Santa Cruz Biotechnologysc-8007
Anti-beta-actinSanta Cruz BiotechnologySc-69879
Anti-PolKSanta Cruz Biotechnologysc-166667
Anti-PolHSanta Cruz Biotechnologysc-17770
Anti-PollSanta Cruz Biotechnologysc-101026
Anti-Rev1Santa Cruz Biotechnologysc-393022
Anti-rabbit IgG HRP-conjugated secondary antibodyInvitrogen31460
Anti-mouse IgG HRP-conjugated secondary antibodyInvitrogen31430
Anti-goat IgG HRP-conjugated secondary antibodySanta Cruz BiotechnologySc-2354
Chemicals, Peptides, and Recombinant Proteins
RPMI-1640 mediumCytivaSH30096.01
+ +<--- Page Split ---> + +
Heat-inactivated fetal bovine serum (FBS)CytivaSV30160.03
Penicillin-StreptomycinGibco15140-122
MEM Non-Essential Amino AcidsPan BiotechP08-32100
L-GlutamineGibco25030-081
Sodium pyruvateLonza13-115E
PBSGibco20012-027
TrypLE Select (1X)Gibco12563-029
GemcitabineMedChemExpressHY-B0003
Z-VAD-FMKAbcamab-120487
MG-132Abcamab141003
CisplatinMedChemExpressHY-17394
CytarabineMedChemExpressHY-13605
CladribineMedChemExpressHY-13599
DecitabineMedChemExpressHY-A0004
REV7/REV3L-IN-1MedChemExpressHY-100468
Halt™ Protease Inhibitor Cocktail, EDTA-Free (100 X)Thermo Scientific1861279
Ribonuclease ASigmaR6513
NuPAGETM LDS Sample Buffer (4X)Thermo ScientificNP0008
NuPAGETM Sample Reducing Agent (10X)Thermo ScientificNP0009
NuPAGETM MES SDS Running Buffer (20X)Thermo ScientificNP0002
Tris Buffered Saline with 0,05% Tween 20 (TBS-T)Medicago09-7510-100
RIPA buffer (Radioimmunoprecipitation assay buffer)Thermo Scientific89900
+ +<--- Page Split ---> + +
HEPES (hydroxyethyl-piperazineethane-sulphonic acid buffer)GOLDBIOH-400-1
Beta-glycerophosphateSigmaG9422
Sodium orthovanadateSigma72060
Benzonase EndonucleaseEMD Millipore1.01695.0001
Triethylammonium bicarbonate buffer (TEAB)SigmaT7408
TCEP (tris(2-carboxyethyl)phosphine hydrochloride)Sinopharm Chemical Reagent Co, LtdXw518054592
2-chloroacetamide (CAA)SigmaC0267
Lys-CWako Chemicals Ltd129-02541
TrypsinPromegaV5117
TFA (Trifluoroacetic acid)SigmaT6508
Tris1st BaseBIO-1400
AcetonitrileSigmaT7408
TMT10PLEX Isobaric Label Reagent SetThermo Scientific90110
Ammonia solution 25%Merck5.33003.0050
Propidium IodideSigmaP4170
0.1% Formic Acid in ACETONITRILEFISHER USZZLFS/LS120-212
0.1% Formic Acid in WaterFISHER USZZLFS/HB523-4
LC-MS hypergrade acetonitrile (ACN)Merck100029
LC-MS grade acetic acidMerck533001
Critical Commercial Assays
BCA assayThermo Scientific23227
Clarity Western ECL substrateBio-Rad170-5060
Deposited Data
+ +<--- Page Split ---> + + +
Protein abundance (log2) data for MS-CETSAThis manuscriptDatafile S1 - Protein abundance all datasets
MS-CETSA raw dataProteomeXchange via the jPOST repositoryhttp://proteomecentral.proteomexchange.org/
Dataset identifiers PXDxxxxxx
Experimental Models: Cell Lines
SUDH-L4ATCCCRL-2957
HTATCCCRL-2260
OCI-LY3ATCC
OCI-LY19ATCC
MDA-MB-231ATCCCRM-HTB-26
Software and Algorithms
ImageLabTM softwareBioRadhttps://www.bio-rad.com/
Xcalibur v.4.0Thermo Scientifichttps://www.thermofisher.com/us/en/home.html
Proteome Discoverer v.2.5Thermo Scientifichttps://www.thermofisher.com/us/en/home.html
MASCOT 2.6.0MATRIX SCIENCEhttp://matrixscience.com
Sequest HTThermo Scientifichttps://www.thermofisher.com/us/en/home.html
RStudio v.1.2.5033RStudiohttps://www.rstudio.com
R v.3.6.3The R Foundationhttps://www.r-project.org/
+ +<--- Page Split ---> + +
mineCETSA 0.3.8.7Lim et al., 2018 (37)https://github.com/nkdailingyun/mine CETSA
IMPRINTS.CETSA 1.0.4Gerault et al., 2024 (36)https://github.com/nkdailingyun/IMPRINTS.CETSA
IMPRINTS.CETSA.app 3.4.2Gerault et al., 2024 (36)https://github.com/mgerault/IMPRINTS.CETSA.app
Cytoscape 3.9.2Cytoscapehttp://cytoscape.org
ClueGO plugin v2.5.1 for CytoscapeBindea et al., 2009http://apps.cytoscape.org/apps/cluego
GraphPad Prism v.8.3.0GraphPad Softwarehttps://www.graphpad.com/
Biorenderwww.biorender.com
FlowJo v.9.3.2FLOWJO, LLChttps://www.flowjo.com
Other
MicroAmp™ Fast 96-well Reaction plateApplied Biosystems4346907
Falcon® 150cm² cell culture flaskCorning355001
Falcon® 75cm² cell culture flaskCorning353136
Falcon® 25cm² cell culture flaskCorning353109
96-well plates (black)Greiner655090
NuPAGE 4–12% Bis-Tris midi gelInvitrogenWG1403BX10
iBlot 2 NC Regular StacksInvitrogenIB23001
Non-fat milk powderSemper ABN/A
Oasis HLB 1cc (10mg) extraction cartridgesWaters186000383
+ +<--- Page Split ---> + +
Xbridge Peptide BEH C18, 300 Å, 3.5 μm, 2.1 mm × 250 mm columnWaters186003610
Zorbax 300 Extend C-18 4.6 mm × 250 mm columnAgilent770995-902
50 cm x 75 μm(ID) EASY-Spray analytical columnThermo ScientificES903
Veriti™ 96-Well Thermal CyclerApplied BiosystemsP/N 4375786
XCell4 SureLock™ Midi-CellInvitrogenCat#WR0100
iBlot 2 systemInvitrogenCat#IB21001
ChemiDoc™ XRS+ imaging systemBioRadUniversal Hood III
SpeedVac vacuum concentratorThermo ScientificP/N SPD111V-230, 61010-1, and RV5 A65313906
Dionex UltiMate 3000 UPLC systemThermo ScientificP/N 5041.0010, 5826.0020, and 5035.9245
Q Exactive mass spectrometerThermo ScientificIQLAAEGAAPFA LGMBFZ
Veriti 96 well Thermal cyclerInvitrogen4375786
ChemiDoc MP Imaging SystemBio-Rad1708280
50cmx75μM (ID) EASY-Spray analytical columnThermoFisher ScientificP/N ES803
High pH reverse phase Zorbax 300 Extend C-18 4.6mm x 250mm columnAgilentP/N 770995-902
Liquid chromatography AKTA microsystemGE Healthcare28948303
Oasis HLB 96-well plateWatersWAT058951
Eppendorf concentrator plusEppendorf AG Hamburg5305000304
Centrifuge 5424 REppendorf5404000014
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Sellers, K. Polyak, M. Hu, G. 956 Peluffo, H. Chen, R. Gelman, S. Schmitt, K. Polyak, C. Kuperwasser, T. Chavarria, M. Wu, 957 G. Magrane, J. Gray, L. Carey, A. Richardson, R. Weinberg, H. Cheng, C. Kuo, J. Yan, H. 958 Chen, W. Lin, K. Wang, K. Tsai, H. Guvén, E. Flaberg, L. Szekely, G. Klein, K. Wu, D. 959 Wishart, C. Knox, A. Guo, R. Eisner, N. Young, B. Gautam, D. Hau, N. Psychogios, E. 960 Dong, S. Bouatra, R. Mandal, I. Sinelnikov, J. Xia, L. Jia, J. Cruz, E. Lim, C. Sobsey, S. + +<--- Page Split ---> + +961 Shrivastava, P. Huang, P. Liu, L. Fang, J. Peng, R. Fradette, D. Cheng, D. Tzur, M. +962 Clements, A. Lewis, A. De Souza, A. Zuniga, M. Dawe, C. Chan, C. Li, W. Yang, Y. Gao, S. +963 Lee, Z. Feng, H. Huang, K. Tsai, L. Flores, Y. Shao, J. Hazle, D. Yu, W. Wei, D. Sarbassov, +964 M. Hung, K. Nakayama, H. Lin, H. Horai, M. Arita, S. Kanaya, Y. Nihei, T. Ikeda, K. Suwa, +965 Y. Ojima, K. Tanaka, S. Tanaka, K. Aoshima, Y. Oda, Y. Kakazu, M. Kusano, T. Tohge, F. +966 Matsuda, Y. Sawada, M. Hirai, H. Nakanishi, K. Ikeda, N. Akimoto, T. Maoka, H. +967 Takahashi, T. Ara, N. Sakurai, H. Suzuki, D. Shibata, S. Neumann, T. Iida, K. Tanaka, K. +968 Funatsu, M. Soichot, B. Hennart, A. Al Saabi, A. Leloire, P. Froguel, C. Levy-Marchal, O. +969 Poulain-Godefroy, D. Allorge, T. Decker, P. Kovarik, A. Meinke, M. Du, W. Sotero-Esteva, +970 M. Taylor, T. Sumpter, A. Dangi, B. Matta, C. Huang, D. Stolz, Y. Vodovotz, A. Thomson, +971 C. Gandhi, C. Opitz, U. Litzenburger, F. Sahm, M. Ott, I. Tritschler, S. Trump, T. +972 Schumacher, L. Jestaedt, D. Schrenk, M. Weller, M. Jugold, G. Guillemin, C. Miller, C. Lutz, +973 B. Radlwimmer, I. Lehmann, A. von Deimling, W. Wick, M. Platten, F. Ohtake, A. Baba, I. +974 Takada, M. Okada, K. Iwasaki, H. Miki, S. Takahashi, A. Kouzmenko, K. Nohara, T. Chiba, +975 Y. Fujii-Kuriyama, S. Kato, A. Mansfield, P. Heikkila, A. Vaara, K. von Smitten, J. Vakkila, +976 M. Leidenius, G. Frumento, R. Rotondo, M. Tonetti, G. Damonte, U. Benatti, G. Ferrara, A. +977 Muller, J. DuHadaway, P. Donover, E. Sutanto-Ward, G. Prendergast, H. Yasui, K. Takai, R. +978 Yoshida, O. Hayaishi, M. Pan, M. Hou, H. Chang, W. Hung, S. Gross, R. Cairns, M. Minden, +979 E. Driggers, M. Bittinger, H. Jang, M. Sasaki, S. Jin, D. Schenkein, S. Su, L. Dang, V. +980 Fantin, T. Mak, P. Ward, J. Patel, D. Wise, O. Abdel-Wahab, B. Bennett, H. Coller, J. Cross, +981 V. Fantin, C. Hedvat, A. Perl, J. Rabinowitz, M. Carroll, S. Su, K. Sharp, R. Levine, C. +982 Thompson, M. Figueroa, O. Abdel-Wahab, C. Lu, P. Ward, J. Patel, A. Shih, Y. Li, N. +983 Bhagwat, A. Vasanthakumar, H. Fernandez, M. Tallman, Z. Sun, K. Wolniak, J. Peeters, W. +984 Liu, S. Choe, V. Fantin, E. Paietta, B. Lowenberg, J. Licht, L. Godley, R. Delwel, P. Valk, C. +985 Thompson, R. Levine, A. Melnick, W. Xu, H. Yang, Y. Liu, Y. Yang, P. Wang, S. Kim, S. +986 Ito, C. Yang, P. Wang, M. Xiao, L. Liu, W. Jiang, J. Liu, J. Zhang, B. Wang, S. Frye, Y. +987 Zhang, Y. Xu, Q. Lei, K. Guan, S. 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Sobota, P. 1052 Nordlund, An efficient proteome- wide strategy for discovery and characterization of cellular 1053 nucleotide- protein interactions, PLoS One 13, 1- 30 (2018). 1054 1055 1056 1057 + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementaryTable1. xlsx SupplementaryTable2. xlsx + +<--- Page Split ---> diff --git a/preprint/preprint__08a17e35a9f30287f46ac8334be1320dddc37a6065b4ba95076e9f30f36e016a/preprint__08a17e35a9f30287f46ac8334be1320dddc37a6065b4ba95076e9f30f36e016a_det.mmd b/preprint/preprint__08a17e35a9f30287f46ac8334be1320dddc37a6065b4ba95076e9f30f36e016a/preprint__08a17e35a9f30287f46ac8334be1320dddc37a6065b4ba95076e9f30f36e016a_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..9b43537ba120747e3098efb94ab670a4077367ba --- /dev/null +++ b/preprint/preprint__08a17e35a9f30287f46ac8334be1320dddc37a6065b4ba95076e9f30f36e016a/preprint__08a17e35a9f30287f46ac8334be1320dddc37a6065b4ba95076e9f30f36e016a_det.mmd @@ -0,0 +1,639 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 880, 207]]<|/det|> +# MS-CETSA functional proteomics uncovers new DNA-repair programs leading to Gemcitabine resistance + +<|ref|>text<|/ref|><|det|>[[44, 230, 255, 275]]<|/det|> +Pär Nordlund par.nordlund@ki.se + +<|ref|>text<|/ref|><|det|>[[44, 301, 592, 320]]<|/det|> +Karolinska Institutet https://orcid.org/0000- 0002- 7794- 702X + +<|ref|>text<|/ref|><|det|>[[44, 326, 165, 364]]<|/det|> +Ying Yu Liang A\*STAR + +<|ref|>text<|/ref|><|det|>[[44, 371, 456, 414]]<|/det|> +Khalidah Khalid Agency for Science Technology and Research + +<|ref|>text<|/ref|><|det|>[[44, 420, 456, 462]]<|/det|> +Hai Van Le Agency for Science Technology and Research + +<|ref|>text<|/ref|><|det|>[[44, 468, 456, 510]]<|/det|> +Hui Min Teo Agency for Science Technology and Research + +<|ref|>text<|/ref|><|det|>[[44, 515, 234, 553]]<|/det|> +Mindaugas Raitelaitis Karolinska Institutet + +<|ref|>text<|/ref|><|det|>[[44, 559, 234, 598]]<|/det|> +Marc-Antoine Gerault Karolinska Institutet + +<|ref|>text<|/ref|><|det|>[[44, 604, 325, 644]]<|/det|> +Jane Jia Hui Lee Genome Institute of Singapore + +<|ref|>text<|/ref|><|det|>[[44, 650, 234, 689]]<|/det|> +Jiawen Lyu Karolinska Institutet + +<|ref|>text<|/ref|><|det|>[[44, 695, 338, 737]]<|/det|> +Allison Chan National University of Singapore + +<|ref|>text<|/ref|><|det|>[[44, 743, 338, 784]]<|/det|> +Anand Jeyashekaran National University of Singapore + +<|ref|>text<|/ref|><|det|>[[44, 790, 820, 855]]<|/det|> +Wai Leong Tam Genome Institute of Singapore, Agency for Science, Technology and Research (A\*STAR) https://orcid.org/0000- 0003- 2365- 5264 + +<|ref|>text<|/ref|><|det|>[[44, 860, 712, 901]]<|/det|> +Nayana Prabhu Nanyang Technological University https://orcid.org/0000- 0002- 2350- 6552 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 45, 103, 63]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 82, 765, 103]]<|/det|> +Keywords: MS- CETSA, IMPRINTS, gemcitabine, resistance, DLBCL, ATR, ADDR, TLS, + +<|ref|>text<|/ref|><|det|>[[44, 121, 322, 141]]<|/det|> +Posted Date: August 20th, 2024 + +<|ref|>text<|/ref|><|det|>[[44, 159, 475, 179]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4820265/v1 + +<|ref|>text<|/ref|><|det|>[[42, 196, 916, 240]]<|/det|> +License: © © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 257, 937, 301]]<|/det|> +Additional Declarations: Yes there is potential Competing Interest. Prof. Nordlund is the inventor of patents related to the CETSA method and is a cofounder and board member of Pelago Biosciences AB. + +<|ref|>text<|/ref|><|det|>[[42, 334, 951, 378]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on May 7th, 2025. See the published version at https://doi.org/10.1038/s41467-025-59505-8. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[166, 95, 831, 140]]<|/det|> +# MS-CETSA functional proteomics uncovers new DNA-repair programs leading to Gemcitabine resistance + +<|ref|>text<|/ref|><|det|>[[70, 170, 884, 720]]<|/det|> +Ying Yu Liang \(^{1}\) , Khalidah Khalid \(^{1}\) , Hai Van Le \(^{1}\) , Hui Min Vivian Teo \(^{3}\) , Mindaugas Raitelaitis \(^{2}\) , Marc- Antoine Gerault \(^{2}\) , Jane Jia Hui Lee \(^{3}\) , Jiawen Lyu \(^{2}\) , Allison Chan \(^{4}\) , Anand Jeyashekaran \(^{4,5,7}\) , Wai Leong Tam \(^{3,4,5,6}\) , Pär Nordlund \(^{*1,2}\) , Nayana Prabhu \(^{*1}\) \(^{1}\) Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A\*STAR), 61 Biopolis Street, Proteos, Singapore 138673 \(^{2}\) Department of Oncology and Pathology, Karolinska Institutet, 171 77 Stockholm, Sweden \(^{3}\) Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A\*STAR), 60 Biopolis Street, Genome, Singapore 138672, Republic of Singapore \(^{4}\) Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Drive, Singapore 117599 \(^{5}\) Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 8 Medical Drive, Singapore 117597 \(^{6}\) NUS Center for Cancer Research, Yong Loo Lin School of Medicine, National University of Singapore, 14 Medical Drive, Singapore 117599 \(^{7}\) Department of Haematology-Oncology, National University Hospital \(^{8}\) Correspondence: nayana_prabhu@imcb.a-star.edu.sg, par.nordlund@ki.se, tamwl@gis.a-star.edu.sg + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 123, 883, 512]]<|/det|> +Mechanisms for resistance to cytotoxic cancer drugs are dependent on dynamic changes in the biochemistry of cellular pathways, information which is hard to obtain at the systems level. Here we use a deep functional proteomics implementation of CETSA (Cellular Thermal Shift Assay) revealing a range of induced biochemical responses to gemcitabine in resistant and sensitive diffuse large B cell lymphoma (DLBCL) cell lines. Initial responses in both, gemcitabine resistant and sensitive cells, reflect known targeted effects by gemcitabine on ribonucleotide reductase and DNA damage responses. However, after 3- 5 hours the responses diverge dramatically where sensitive cells show induction of characteristic CETSA signals for early apoptosis, while resistant cells reveal biochemical modulations reflecting transition through a distinct DNA- damage signaling state, including opening of cell cycle checkpoints and induction of translesion DNA synthesis (TLS) programs allowing bypass of damaged DNA- adducts. The data also reveal the induction of a new program, labeled the Auxiliary DNA Damage Repair (ADDR) protein ensemble likely supporting DNA replication at damaged sites. We show that this response can be attenuated in resistant cells by an ATR inhibitor reestablishing gemcitabine sensitivity and demonstrate ATR as a key signaling node of this response. + +<|ref|>text<|/ref|><|det|>[[115, 541, 864, 560]]<|/det|> +Keywords: MS- CETSA, IMPRINTS, gemcitabine, resistance, DLBCL, ATR, ADDR, TLS, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 125, 882, 316]]<|/det|> +Cancer cells evade cytotoxic drugs through activation of resistance mechanisms. While drug- sensitive cancer cells induce cellular programs leading to cell death, resistant cells activate pathways to counteract such responses. A wide range of cellular processes have been implied in resistance to cytotoxic cancer drugs, including modulations of drug transport (1) or drug activation (2), induction of apoptosis blockade (3), bypass of oncogene inhibition by drug binding site mutations (4), activation of parallel driver pathways (5), as well as modulation of tumor microenvironment (6) and cell- to- cell signaling (7, 8). It is likely that many cancers establish multiple resistance mechanisms in parallel to overcome drug action. + +<|ref|>text<|/ref|><|det|>[[115, 345, 882, 783]]<|/det|> +Detailed insights into which resistance promoting programs are operating in cancers of individual patients at different stages of therapy could arguably be transformative for selection of optimal drug combinations and staging in personalized therapy, as well as for identifying novel drug targets to attenuate resistance responses. Conclusive elucidation of resistance mechanisms of cancer drugs is however often challenging when they can involve complex remodeling of cellular pathways. Typically, resistance mechanisms are addressed using genomic or transcriptomic approaches, most often assessing static differences between cancer patient samples or sensitive and resistant cancer cells in model systems (9–11). Although such studies can access key mutations and RNA level changes implicative of resistance, cellular pathways and processes are highly regulated at the biochemical level and this information is only indirectly accessed in such studies. Moreover, comparison of static cells does not address drug- induced resistance responses. These responses can play key roles in resistance to cytotoxic cancer drugs but are normally not activated during ambient cancer cell growth. Notably, some cancer drug- induced resistance responses can be efficiently studied with focused assays, such as the induction of reactive oxygen species, autophagy and chaperone activation. While useful, these studies require a priori knowledge on putative mode of resistance mechanisms, and they do not provide an unbiased view on sequences of regulatory events. + +<|ref|>text<|/ref|><|det|>[[115, 813, 881, 906]]<|/det|> +Here, we examined the induced modulation of cellular biochemistry leading to resistance towards one of the more commonly used cytotoxic cancer drugs – gemcitabine, which is an anti- neoplastic pyrimidine analog that replaces cytidine during DNA replication and inhibits ribonucleotide reductase (RNR) (12). In various cancers that include pancreatic, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 75, 883, 489]]<|/det|> +breast, ovarian, non- small cell lung cancer and lymphoma, gemcitabine is employed either in the first- line or refractory setting. Diffuse large B- cell lymphoma (DLBCL) represents the most frequently occurring and aggressive form of non- Hodgkin's lymphoma. The anthracycline- based regimen R- CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone) is the standard of care for first- line treatment with \(\sim 60\%\) of the patients achieving complete response (13). However \(20 - 50\%\) of patients do not respond, or relapse within the first two years of treatment (14). Gemcitabine has recently been used in salvage regimens for DLBCL although resistance often still develops (15). Gemcitabine is a nucleotide prodrug that needs to be metabolized into its active phosphorylated form within cells to exert its effects (Fig 1A). RNR catalyzes the conversion of ribonucleoside diphosphates to deoxyribonucleoside diphosphates and is a major protein target for gemcitabine (16, 17). In its diphosphate form, gemcitabine inhibits RNR by forming a covalent adduct to the catalytic subunit (RRM1), or alternatively scavenging the free radical cofactor of RNR, thus depleting dNTP pools (18). While genomic and transcriptomic studies have helped identify driver pathways and prognostic gene signatures in DLBCL (19, 20), this information remains of limited utility in guiding treatment regimens, especially in stratifying patients who may respond to specific salvage therapy agents. + +<|ref|>text<|/ref|><|det|>[[113, 517, 883, 907]]<|/det|> +To better understand how sensitive or resistance biochemical pathways become selectively activated in response to therapeutics in DLBCL cells, we apply a time- dependent implementation of the deep functional proteomics method, IMPRINTS- CETSA (Integrated Modulation of Protein Interaction States - Cellular Thermal Shift Assay) to study gemcitabine- induced programs. CETSA reports on modulations of pathway activation at the biochemical level in intact cells by monitoring changes in protein interaction states (PRINTS), i.e., interactions made by individual proteins to other molecules in live cells reflecting protein activity and functional states (21). MS- CETSA (Mass Spectrometry- based CETSA) is the first integrative technology that can directly assess PRINTS in intact cells and tissues but has not been used previously for deep characterization of induced drug resistance. In the present study, we reveal comprehensive and distinct novel information on the time- dependent biochemical responses of gemcitabine in sensitive and resistant DLBCL cells. Initial responses in both cell types reveal similar RNR inhibition and activation of DNA- damage signaling. However, the downstream response in sensitive cells reflects the characteristic CETSA response for apoptosis induction, while in resistant cells the response is dominated by cell cycle checkpoints, translesion DNA synthesis (TLS) program and a new protein ensemble that likely + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 74, 882, 219]]<|/det|> +support DNA repair. This response provides a rationale for gemcitabine resistance in DLBCL cells, which can be reversed by attenuating the DNA- repair inducing pathway with an ATR (ataxia telangiectasia and Rad3- related protein) inhibitor, and thereby re- establishing gemcitabine sensitivity. This study validates IMPRINTS- CETSA (22) as an efficient approach to dissect induced cancer drug resistance pathways at the biochemical level and provide drug targets and biomarkers for combination therapies with potential applications in the clinic. + +<|ref|>sub_title<|/ref|><|det|>[[117, 248, 213, 265]]<|/det|> +## RESULTS + +<|ref|>text<|/ref|><|det|>[[115, 293, 882, 861]]<|/det|> +To study gemcitabine- induced resistance mechanisms we first evaluated the cell viability of a panel of DLBCL cell lines when challenged with a range of gemcitabine doses over \(48\mathrm{h}\) . Of the profiled cell lines, we selected two sensitive (OCI- LY19, \(\mathrm{IC}_{50} = 2.4\mathrm{nM}\) and OCI- LY3, \(\mathrm{IC}_{50} = 14.4\mathrm{nM}\) ) and two resistant (SUDHL4, \(\mathrm{IC}_{50} =\) not defined and HT, \(\mathrm{IC}_{50} =\) not defined) cell lines (SFig 1A) to employ the highly sensitive IMPRINTS implementation of MS- CETSA, whereby 3 biological replicates of treated cells were labeled together with their vehicle controls. To capture the dynamic cellular response upon drug treatment, sensitive OCI- LY19 and resistant SUDHL4 cells were treated for 4 time points (1h, 3h, 5h and 8h), while for comparison purposes sensitive OCI- LY3 and resistant HT cells were treated only at 2 timepoints (1h and 8h). For informative CETSA responses to be measurable, the drug concentration needs to be sufficiently high to induce molecular perturbations with high stoichiometry. We therefore selected \(20\mathrm{X}\mathrm{IC}_{50}\) for the sensitive cells, i.e. \(48\mathrm{nM}\) for OCI- LY19 and \(288\mathrm{nM}\) for OCI- LY3. For both resistant cells we used \(500\mathrm{X}\) the concentration in relation to OCI- LY19, i.e. \(24\mu \mathrm{M}\) , but have additionally collected CETSA data at lower concentrations ( \(480\mathrm{nM}\) and \(48\mathrm{nM}\) ) to monitor dose- dependent responses. We confirmed that treated cells remained intact and viable, as judged from a trypan blue assay, at the maximum timepoint for the CETSA experiments (SFig 1B). IMPRINTS CETSA was performed similarly in all cell lines using a 6- temperature protocol (Fig 1B). The protein coverages and numbers of hits scored using our standard hit selection criteria (described in Materials & Methods) are shown in STable 1. Of the 6 temperatures, the \(47^{\circ}\mathrm{C} - 57^{\circ}\mathrm{C}\) comprise the CETSA thermal shift, while the "unheated" \(37^{\circ}\mathrm{C}\) trace represents the CETSA protein abundance change. Different combinations of CETSA abundance and thermal shifts result in 8 typical IMPRINTS profiles as depicted in Fig 1C. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 75, 882, 243]]<|/det|> +The association of gemcitabine with RRM1 was expected to increase protein stability and produce a thermal shift. Indeed, within 1h, RRM1 displayed similar IMPRINTS profiles in both resistant and sensitive DLBCL cells, supporting extensive target engagement and inhibition of de novo deoxyribonucleotide synthesis (Fig 1D). This shift was also seen at subsequent timepoints of 3h, 5h, and 8h. An isothermal dose response (ITDR) experiment also showed comparable dose-response behavior, supporting similar RRM1 target engagement in sensitive and resistant cells (Fig 1E). + +<|ref|>sub_title<|/ref|><|det|>[[117, 273, 683, 292]]<|/det|> +## Initiation of DNA damage response in sensitive and resistant cells + +<|ref|>text<|/ref|><|det|>[[116, 319, 882, 465]]<|/det|> +Apart from RRM1 inhibition, gemcitabine acts by being incorporated into DNA, inducing single strand DNA (ssDNA) breaks and stalled replication forks (23). Indeed, only 5 proteins shifted across all time points and cell lines (albeit weaker in sensitive cells) - DNMT1, RPA1, RPA2, RPA3, and CHEK1 (Fig 2). Notably, all 5 proteins can be localized at replication sites with 4 belonging to the core molecular machinery for sensing and signaling ssDNA damage and stalled replication fork. + +<|ref|>text<|/ref|><|det|>[[115, 494, 882, 735]]<|/det|> +DNMT1 is a major enzyme involved in DNA methylation inheritance and plays a critical role in maintaining genome stability (24). Notably, gemcitabine is a cytidine analog with a difluoromodification at the C5 position where DNMT1 is supposed to act and transfer a methyl group on to endogenous cytidine. We therefore investigated the possibility of a direct soluble gemcitabine triphosphate- DNMT1 interaction in a cell lysate western blot CETSA experiment but did not see significant thermal stability shifts (SFig 2A). Additionally, gemcitabine treatment did not result in DNMT1 degradation as is described for other DNMT1 inhibitors such as decitabine (SFig 2B). It instead appeared plausible that the observed early CETSA effects on DNMT1 report on the modulations of specific protein or DNA interactions with DNMT1 induced at the stalled replication fork. + +<|ref|>text<|/ref|><|det|>[[115, 765, 882, 907]]<|/det|> +The 3 replication protein A subunits (RPA1, RPA2, RPA3) are known to coat ssDNA generated at e.g. stalled replication forks, which is reflected in a thermal stabilization upon gemcitabine treatment. RPA- ssDNA complex is crucial for localization and activation of ATR kinase which initiates downstream DNA- damage response (DDR) pathways, including phosphorylation of CHEK1. Thermal destabilization of CHEK1 was concomitant with its phosphorylation at Ser345 (SFig 2C). These results demonstrate that CETSA can detect the activation of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 75, 881, 144]]<|/det|> +ATR/CHEK1 signaling axis as one of the early responses towards gemcitabine treatment. The fact that the thermal shifts are present in both sensitive and resistant cell lines suggest that resistance mechanisms occur downstream of these initial responses. + +<|ref|>sub_title<|/ref|><|det|>[[117, 173, 880, 217]]<|/det|> +## Activation of apoptosis in sensitive cells versus checkpoint release for cell cycle progression in resistant cells + +<|ref|>text<|/ref|><|det|>[[117, 247, 883, 440]]<|/det|> +While the early responses (at 1h and 3h) in resistant and sensitive cells bore similarities in their engagement of RRM1 and signature proteins for stalled replication fork or DDR, by 5h and 8h, there was a segregation of proteins with thermal shifts between the two cell states. We observed a time- dependent increase in the total number of hits (Fig 3A), consistent with a sequential induction of unique GO biological processes in the two types of cell lines, likely initiated by the early DDR. Strikingly, by 8h post drug exposure, there was a clear divergence in the ensembles of proteins in either sensitive or resistant cell lines. In fact, only 3 proteins were overlapping (grey) between both resistant (green) and sensitive (red) cells (Fig 3B). + +<|ref|>text<|/ref|><|det|>[[115, 460, 883, 899]]<|/det|> +Through the use and analyses of several apoptosis- inducing drugs, we recently identified a prototypic CETSA apoptosis response that is dominated by nuclear proteins and reflects very early apoptosis activation (under revision). This response was characterized by 47 proteins we termed the core CETSA apoptosis ensemble (CCAE), and this provided the first means for direct assessment of caspase activation in intact cells. When our hitlists from gemcitabine sensitive and resistant cells were separately compared with the CCAE described above, 24 proteins overlapped for the sensitive cells, while only one of the resistant hits were common with this ensemble (Fig 4A). These results unequivocally conclude that apoptosis induction was indeed unique to sensitive cells, despite the far higher gemcitabine concentration used to treat resistant cells. Furthermore, our current study resolved the sequence of early apoptosis events and showed that the emergence of the CCAE response in sensitive cells was clearly time dependent with proteins such as PARP1, XRCC5, XRCC6, MATR3, LMNB1, LMNB2, RBMX and ZC3H11A showing distinct thermal stability shifts as early as 3h and 5h following gemcitabine exposure (Fig 4B). For some CCAE proteins that are cleaved by caspases, a regional stabilization due to proteolysis (RESP) effect with stability changes in regions either N- or C- terminal of caspase cleavage sites, was also observed. Here, a subset of proteins also shows RESP effects indicative of direct caspase cleavage including known caspase targets such as PARP1, LMNB1, MATR3 and DDX21 (Fig 4C). To further verify that + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 75, 881, 217]]<|/det|> +apoptosis is only induced in sensitive cells, we looked at PARP1 cleavage, a recognized hallmark of apoptosis, by western blot. Indeed, we only observed cleaved PARP1 upon gemcitabine treatment in sensitive but not in resistant cells (SFig 3A). Although apoptosis can be initiated by ATR/CHEK1 signaling via p53- activation (25), the lack of CETSA shifts in proteins recently defined as p53 regulated proteins in cell death, indicates that the observed processes here are independent of p53 (26). + +<|ref|>text<|/ref|><|det|>[[115, 238, 882, 677]]<|/det|> +In contrast to the prominent CETSA apoptosis signatures featured in the sensitive cells, we observed cell- cycle regulating processes as one of the dominant features in the response of resistant cells. Contributing to this were significant shifts for cyclins and cyclin- dependent kinases (CDKs), most prominently CCNA2, CCNB1, CCNB2 and CDK1 (Fig 4D). These proteins showed distinct time- dependent thermal stabilizations or abundance changes with similar IMPRINTS profiles as compared to our previously published cell cycle study (SFig 3B) (22). The shifts indicated increased activation of CDK complexes that promoted G2/M and G1/S phase checkpoint transitions. To rule out that these effects are due to the higher gemcitabine concentration used in treatment of resistant cells, we consulted our additional low dose datasets that also included the same concentration as used for the sensitive cells (48nM). The findings, indeed, supported similar modulations of cell cycle checkpoints in resistant cells at these lower doses, and therefore the induced response was active over a wide concentration range (SFig 3C). Additionally, in our previous cell cycle study, RB1 phosphorylation during G1/S checkpoint release resulted in a thermal stabilization. Here, we observed the opposite effect, i.e. thermal destabilization and thus dephosphorylation, in gemcitabine sensitive cells (SFig 3D). Measuring cell cycle distribution using propidium iodide staining confirmed G1 arrest in sensitive cells, while resistant cells underwent normal cycling upon gemcitabine treatment (Fig 4E). + +<|ref|>sub_title<|/ref|><|det|>[[117, 708, 699, 726]]<|/det|> +## DDR initiates translesion DNA synthesis as a resistance mechanism + +<|ref|>text<|/ref|><|det|>[[117, 758, 880, 800]]<|/det|> +Next, we sought to explain how resistant cells were able to proceed with the cell cycle, despite the exposure to a DNA synthesis inhibitor. + +<|ref|>text<|/ref|><|det|>[[117, 831, 880, 899]]<|/det|> +DDR is dependent on the availability of dNTPs at appropriate levels for accurate DNA synthesis. SAMHD1, which exhibited significant destabilization across all timepoints in resistant cells (SFig 4A), is a regulator of dNTP homeostasis via its dNTPase activity (27). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 75, 882, 440]]<|/det|> +Like CHEK1, we tested whether phosphorylation of SAMHD1 is concomitant with its thermal destabilization, but we did not detect significant changes (SFig 4B). When we knocked down SAMHD1 (SFig 4C) as an attempt to re- establish gemcitabine sensitivity, we instead observed a slight increase in resistance (SFig 4D). LC/MS measurements of deoxyribonucleotide (and ribonucleotide) pools showed differences between SUDHL4 SAMHD1 WT and KO cells primarily in dGNP and dANP abundance; this is consistent with SAMHD1 being a dNTP hydrolase of purine nucleotides (SFig 4E). In a more compact 3- temperature IMPRINTS- CETSA experiment we found similar gemcitabine responses between SUDHL4 WT and KO cells. However, a notable difference was a much weaker stabilization of RRM1 after gemcitabine treatment in the KO cells (SFig 4F). As gemcitabine- triphosphate is a substrate of SAMHD1 (28, 29), we reasoned that the reduced cycling between different phosphorylation states of gemcitabine in the SAMHD1 KO cells affected the cellular concentration of the inhibitory diphosphate form of gemcitabine, thereby causing the reduction of RRM1 engagement. This might subsequently lead to the observed attenuated deoxyribonucleotide pools in the KO cells, explaining the increase in resistance. + +<|ref|>text<|/ref|><|det|>[[115, 470, 883, 890]]<|/det|> +Interestingly, "translesion synthesis" (TLS) appeared as a prominent pathway only in resistant cells at 8h (Fig 5A). TLS is a process that facilitates DNA synthesis over damaged lesions by reorganizing replication complexes through the recruitment of specialized DNA repair polymerases. In addition to subunits of ssDNA binding proteins RPA1, RPA2 and RPA3, we observed pronounced time dependent thermal stabilization and abundance increases of two key proteins associated with TLS: PCNA binding protein (PCLAF) and Denticleless Protein Homolog (DTL). Accompanying these changes, we observed strong thermal destabilization of the core catalytic subunits of DNA polymerase \(\delta\) (PolD), POLD1, POLD2 and POLD4, the latter also depicting a decrease in abundance levels. To investigate the possible induction of dedicated TLS polymerases as a putative mechanism to overcome DNA damage and hence gemcitabine resistance, we examined the protein levels of several of the repair/TLS polymerases after 1h, 3h, 5h and 8h of gemcitabine exposure in both sensitive and resistant cells. POL \(\eta\) , POL \(\iota\) , and Rev1 did not show any difference in protein levels (SFig 4G). Notably, however, POL \(\kappa\) protein abundance was reduced in sensitive cells as early as 3h after gemcitabine treatment (Fig 5B). Together with the decrease of protein levels, smaller fragments of Polk were observed with gemcitabine treatment (Fig 5C). Blocking the proteasomal degradation pathway with the proteasomal inhibitor, MG132, did not rescue POL \(\kappa\) from being + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 75, 881, 196]]<|/det|> +degraded. Instead, the use of a pan-caspase inhibitor, zVAD- FMK, was able to rescue POLk levels, supporting the notion that the degradation of POLk was likely dependent on the activation of caspase and apoptosis programs. This suggests that when cells further irreversibly commit to the early apoptotic process key, DDR mechanisms are being degraded in a caspase dependent manner. + +<|ref|>text<|/ref|><|det|>[[115, 226, 882, 516]]<|/det|> +To further validate that the IMPRINTS CETSA shifts of the TLS proteins reported on TLS activation, we employed an orthogonal standard TLS assay, whereby the ubiquitination status of the DNA clamp protein, PCNA, is assessed. Upon DNA damage, mono- ubiquitination of PCNA primes access to DNA by TLS polymerases (30). We measured the levels of total versus mono- ubiquitinated PCNA after gemcitabine treatment and indeed only observed TLS activation in resistant cells (Fig 5D). Next, we sought to explore the effect on gemcitabine resistance by disrupting TLS with a REV7/REV3 interaction inhibitor. We indeed found synergistic effects between gemcitabine and REV7/REV3- In- 1 (Fig 5E). Based on these results we propose a gemcitabine resistance mechanism that involves the release of replicative DNA polymerase (PolD), reflected as thermal destabilizations, followed by mono- ubiquitination of PCLAF, which facilitates access to TLS polymerases and restart of replication fork (Fig 5F). This allows cells to bypass DNA damage induced replication arrest and apoptosis. + +<|ref|>sub_title<|/ref|><|det|>[[116, 546, 868, 589]]<|/det|> +## Auxiliary DNA Damage Repair (ADDR) response: A new protein ensemble induced by DNA damage drugs to drive resistance + +<|ref|>text<|/ref|><|det|>[[115, 618, 882, 909]]<|/det|> +Apart from the TLS CETSA protein shifts, an ensemble of 5 proteins exhibited a strong concomitant protein abundance increase following gemcitabine treatment. This ensemble includes: RRM2 (Ribonucleoside diphosphate Reductase subunit M2) and TK1 (Thymidylate Kinase), which are involved in deoxyribonucleotide provision; GMNN (Geminin), which inhibits the formation of a pre- replication complex; SLBP (Stem Loop Binding Protein), which promotes histone transcription, and FBXO5 (F- box only protein 5), a regulator of the anaphase promoting complex. Together with DTL and PCLAF, these proteins were distinctly upregulated in the two resistant, but not sensitive DLBCL cell lines. We termed this new protein ensemble, the Auxiliary DNA Damage Repair (ADDR) response proteins (Fig 6A). We confirm that the ADDR CETSA signature was also present with lower doses of gemcitabine exposures in resistant cells (SFig 5A). We next examined whether the ADDR ensemble is more commonly activated in these cells and indeed found increased abundances upon treatment with + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 74, 881, 243]]<|/det|> +other DNA damaging drugs such as cladribine and cytarabine (Fig 6B). To further validate whether the ADDR response is a conserved mechanism, we utilized a completely different cell system, MDA- MB- 231 breast cancer cells, treated with another class of DNA damaging agent, cisplatin. Strikingly, in the CETSA IMPRINTS dataset of this model, all 5 proteins of the ADDR response, as well as DTL and PCLAF, were among the strongest shifting proteins which displayed abundance changes (SFig 5B). These observations indicate that the ADDR program may have a broader role in conferring resistance towards a spectrum of DNA damaging agents. + +<|ref|>text<|/ref|><|det|>[[115, 272, 882, 440]]<|/det|> +Interestingly, prominent thermal destabilization of PolD subunits (SFig 5C), noted above, were also observed in the cisplatin data. However, in contrast to gemcitabine- treated DLBCL cells, there was no shift for proteins in the ssDNA binding RPA complex or CHEK1 (SFig 5D) in cisplatin- treated breast cancer cells. Hence, across both experimental models, our data pointed to a CHEK1- independent mechanism for inducing TLS and ADDR responses. Finally, we confirmed that the gemcitabine- resistant SUDHL4 cells were, in fact, also resistant towards cytarabine, cladribine as well as cisplatin (Fig 6C). + +<|ref|>sub_title<|/ref|><|det|>[[117, 470, 822, 513]]<|/det|> +## ATR inhibition abrogates gemcitabine resistance through attenuation of TLS and ADDR induction + +<|ref|>text<|/ref|><|det|>[[115, 543, 883, 735]]<|/det|> +Given the role of TLS and ADDR responses in overcoming DNA damage, we reasoned that perturbing DNA sensing mechanisms might be exploited to prevent otherwise- resistant cells from mounting the DDR in the first place. ATR is a serine/threonine kinase involved in sensing DNA damage together with RPA and phosphorylates proteins such as CHEK1 and inhibitors of ATR kinase have shown pre- clinical and clinical synergy with gemcitabine (31). Accordingly, we investigated the effects of the ATRI, AZD6738, on the gemcitabine response in resistant SUDHL4 cells. Interestingly, combination treatment resulted in attenuation of gemcitabine resistance as observed by a 100 fold lower \(\mathrm{IC}_{50}\) value at 72h (Fig 6D). + +<|ref|>text<|/ref|><|det|>[[115, 764, 883, 907]]<|/det|> +Next, we investigated whether the resistance signatures in TLS and ADDR responses are affected and thus performed a 3- temperature IMPRINTS experiment in resistant SUDHL4 cells treated with either gemcitabine alone, AZD6738 alone, or in combination. Consistent with our hypothesis, the most prominent effects were seen for the proteins of the ADDR ensemble, as well as DTL and PCLAF from the TLS ensemble, whereby there was a dramatic decrease in abundance (Fig 6E). The destabilisation of POLD1 and POLD2, and level changes of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[117, 75, 881, 169]]<|/det|> +POLD4, were also attenuated by ATRi (Fig 6F). This strongly supported the notion that the induction of these proteins was indeed ATR- dependent. The re- established sensitivity by ATRi reinforced the conclusion that the induction of the ADDR and TLS (DTL/PCLAF) ensembles was a key prerequisite for establishing resistance to DNA- damaging drugs. + +<|ref|>text<|/ref|><|det|>[[115, 198, 883, 488]]<|/det|> +The quite dramatically decreased abundance of the ADDR ensemble upon combination treatment could be attributed to the relatively fast turnover rates of these proteins in exponentially growing cells, typically accomplished by a high rate of production and degradation. Indeed, in a Molm16 AML cell line protein turnover dataset used in our lab as reference, the 4 measured proteins all have rapid turnover rates (TK1- 15 h; RRM2- 11h; SLBP- 28h; PCLAF- 10h) (STable 2). Arguably, this design makes these proteins particularly useful for regulating urgent events in cellular processes. However, our current data is not conclusive on whether this is only due to decreased transcriptional activity for the corresponding genes in ATRi treated resistant cells, or whether there are posttranscriptional mechanisms or activation of proteosome degradation components induced. In the future, a more detailed elucidation of the signaling mechanisms post- ATR will be helpful to define contributions from different mechanisms to increased protein levels. + +<|ref|>sub_title<|/ref|><|det|>[[117, 518, 598, 537]]<|/det|> +## CETSA signature responses in DLBCL clinical samples + +<|ref|>text<|/ref|><|det|>[[115, 567, 882, 733]]<|/det|> +To test the feasibility of applying MS- CETSA in clinical samples, we performed an ITDR- CETSA experiment on biopsies from two DLBCL patients who have relapsed after first line therapy and have not been previously treated with gemcitabine. Cells were extracted using Ficoll- paque and treated ex vivo for 5h with increasing doses of gemcitabine (Fig 7A). By comparing the CETSA signatures of resistant and responding cells, we can conclude that both clinical samples were dominated by shifts in proteins of the CCAE discussed above (Fig 7B), i.e. still sensitive to gemcitabine. + +<|ref|>sub_title<|/ref|><|det|>[[117, 765, 245, 781]]<|/det|> +## DISCUSSION + +<|ref|>text<|/ref|><|det|>[[115, 813, 881, 906]]<|/det|> +Non- hypothesis driven system- wide methods have the potential to identify the most prominent molecular processes regulating cellular phenotypes. However, despite cellular biochemistry controlling most molecular processes of the cell, methods for efficient studies of cellular biochemistry at the systems level have been elusive. This has, arguably, also contributed to our + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 75, 880, 120]]<|/det|> +relatively fragmented current understanding of the biochemical basis for pathway activation in cancer drug resistance. + +<|ref|>text<|/ref|><|det|>[[115, 149, 882, 465]]<|/det|> +CETSA constitutes the first systems- wide method which can report on a range of different types of cellular biochemistry, from protein- protein and protein- DNA/RNA interactions to phosphorylation events and flux through metabolic pathways (21). However, so far CETSA studies have predominantly been focused on identifying drug interactions. Although it has been clear that CETSA can report on cellular pathway modulations downstream of drug binding, in our view this has not been systematically explored. Limitations of previous approaches have been the use of suboptimal CETSA implementations which don't allow for robust measurements of small stability shifts typically induced by functional biochemical changes. Furthermore, time- dependent studies have not been systematically explored to allow the dissection of sequences of activation of cellular processes/pathways. In one study, we have previously applied a 2 time- point MS- CETSA approach to study MoA and resistance to 5- FU, which revealed attenuation of anticipated toxic biochemistry in resistant cells, but no drug- induced resistance response (32). + +<|ref|>text<|/ref|><|det|>[[115, 491, 882, 783]]<|/det|> +In the present work we use the highly sensitive IMPRINTS- CETSA implementation in a time- dependent approach to demonstrate applicability of this technology to study the biochemical pathways involved in gemcitabine MoA and resistance mechanisms. By focusing on the overlapping responses in cell pairs of resistant and sensitive cells, a distinct view of the biochemistry of the MoA of gemcitabine in the two cell types is revealed. The initial biochemical responses are very similar, reflecting the direct target engagement of RNR, as well as the establishment of a DNA- damage signaling hub activated by RPA binding to exposed ssDNA. The ITDR dose similarities for the RNR CETSA shifts exclude modifications in drug internalization and metabolism as dominant resistant mechanisms in our system. At the 3- 8h time- points, sensitive cells rapidly enter apoptosis as judged from the CCAE shifts and RESP effect, while resistant cells show CETSA shifts of CDK complexes supporting open cell cycle checkpoints, consistent with the continued proliferation. + +<|ref|>text<|/ref|><|det|>[[115, 812, 882, 907]]<|/det|> +Most notably in the resistant cell CETSA data, distinct responses are seen for proteins related to activation of DNA repair, i.e., the abundance and thermal stability changes of two TLS biomarkers (PCLAF and DTL) as well as prominent destabilization in Pol8, likely reporting on the induction of TLS. This is further supported by the increased mono- ubiquitination of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 75, 883, 242]]<|/det|> +PCNA only in the resistant cells and the synergistic effects of gemcitabine with REV7/REV3 interaction inhibitor. The induced TLS program explains how resistant cells overcome stalled replication forks by allowing DNA- synthesis over damage lesions. TLS has not been previously implied in resistance to gemcitabine but has been suggested as a mechanism of resistance to cisplatin as derived from over- expression of TLS polymerases in resistant cells (33). However, in these cases TLS proteins are assumed to be constitutively expressed and not part of an induced TLS response, as uncovered in the present study. + +<|ref|>text<|/ref|><|det|>[[115, 271, 883, 536]]<|/det|> +In addition to the induction of CDK activation and TLS programs, the induction of the ADDR ensemble of proteins is the most dominating feature of the response in resistant cells. These proteins appear to have functions that can support DNA- repair/replication and could therefore be supportive for TLS, although not previously identified as an ensemble in a DNA repair context. The distinct attenuation of both the induction of TLS proteins DTL and PCLAF, and the ADDR response by an ATR inhibitor strongly support that ATR is a signaling node in this response. However, we conclude that the response is likely not CHEK1 dependent, when ADDR response for cisplatin in MDA- MB- 231 breast cancer cells does not coincide with CHEK1 activation. The disparity likely reflected differences in DNA damage mechanisms between the two drugs: cisplatin is a DNA crosslinking agent while gemcitabine induces single strand breaks. + +<|ref|>text<|/ref|><|det|>[[115, 567, 883, 783]]<|/det|> +Intriguingly, CHEK1 activation is expected to mediate cell cycle arrest, but in contrast, in gemcitabine resistant cells, the CETSA shifts of CDK complex and cell cycle assessments support the opposite effect, i.e., opening of cell cycle checkpoints. This gives further support for the activation of an alternative signaling pathway for the induction of a pathway downstream of ATR, controlling DNA- repair and cell cycle checkpoints to support cell proliferation during genotoxic challenges. However, despite significant efforts (data not shown) we have not been able to identify additional components of the signaling pathway downstream of ATR, which also might provide additional target proteins for specifically attenuating gemcitabine resistance. + +<|ref|>text<|/ref|><|det|>[[115, 813, 883, 905]]<|/det|> +In addition to constituting a new pathway for induction of DNA- repair, the fact that ATR inhibition re- sensitized cells to gemcitabine, supports that this response is a key component of the gemcitabine resistance in this system. There have been previous reports of positive results for using ATRI in combination with gemcitabine in pancreatic cancer (31) and ovarian cancer + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 74, 881, 192]]<|/det|> +(34) therapies. In a recent phase 2 trial in platinum-resistant high-grade serous ovarian cancer, a combination of the selective ATR inhibitor berzosertib, and gemcitabine showed significantly prolonged progression-free survival compared to treatment with gemcitabine alone (35). The current studies provide a mechanistic rationale for the combination of ATRi and gemcitabine for DLBCL. + +<|ref|>text<|/ref|><|det|>[[115, 222, 881, 340]]<|/det|> +As a future strategy for patient stratification, CETSA could potentially be used to monitor whether this (or other) resistance mechanism(s) is in effect in clinical samples, or if instead the early apoptosis profile are detectable with CETSA, indicating sensitivity. The data from two clinical DLBCL patient samples also supports that high quality CETSA information can be obtained from clinical DLBCL samples. + +<|ref|>text<|/ref|><|det|>[[115, 370, 882, 586]]<|/det|> +Together the present study supports that time- dependent IMPRINTS- CETSA constitutes a highly efficient strategy to discover sequences of prominent pathway activation explaining cancer drug MoA and resistance. Therefore, as an alternative to focused studies of cancer drug MoA, which are often limited in their scope by requirement of pathway/protein specific biochemical assays, this work establishes IMPRINTS- CETSA as an efficient strategy for global studies of the biochemistry of cancer drug resistance, where comprehensive insights into effects on many different cellular pathways can be directly accessed using a single method. These studies also provide a repertoire of MoA- based drug resistance biomarkers showing robust responses with potential applicability in the clinic. + +<|ref|>text<|/ref|><|det|>[[115, 615, 882, 758]]<|/det|> +Acknowledgements: We gratefully acknowledge funding from the Swedish Research Council, the Swedish Cancer Society, Radiumhemmet's funds, the Knut and Alice Wallenberg Foundation, Singapore's National Research Foundation (NRF- NRFI08- 2022, NRF- CRP22- 2019- 0003), Agency for Science, Technology and Research, Singapore, and the Singapore Ministry of Education under its Research Centres of Excellence initiative. We also acknowledge all past members of the PN lab. + +<|ref|>text<|/ref|><|det|>[[115, 789, 882, 907]]<|/det|> +Author contributions: Conceptualization: WLT, PN and NP; Methodology: YYL, LHV, KK, MR, HMT, WLT, PN and NP; Formal Analysis: YYL, LHV, KK, MR, MAG, WLT, PN and NP; Investigation: YYL, LHV, KK, HMT, MR, JJHL, JL, AC, ADJ, WLT, PN and NP; Writing- Original draft: YYL, WLT, PN and NP; Writing- Review & Editing: All; Funding Acquisition: PN, WLT and NP; Supervision: ADJ, PN, WLT, NP + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 99, 880, 144]]<|/det|> +485 Declaration of Interests: PN is the inventor of patents related to the CETSA method and is a 486 cofounder and board member of Pelago Biosciences AB. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[130, 163, 856, 472]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[144, 123, 230, 139]]<|/det|> +
FIGURE 1
+ +<|ref|>text<|/ref|><|det|>[[50, 472, 90, 487]]<|/det|> +488 + +<|ref|>text<|/ref|><|det|>[[50, 495, 88, 510]]<|/det|> +489 + +<|ref|>text<|/ref|><|det|>[[50, 518, 884, 737]]<|/det|> +(A) Structure and thus far known MoA of gemcitabine. (B) IMPRINTS CETSA experimental workflow. (C) Interpretation of IMPRINTS CETSA profiles. (D) IMPRINTS CETSA profiles of RRM1 in two sensitive, OCI-LY3 (orange) and OCI-LY19 (red), and two resistant, HT (dark green) and SUDHL4 (light green), cell lines after 1h, 3h, 5h or 8h of gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference ±SEM from biological replicates (n=3). (E) 3h Isothermal Dose Response (ITDR) of RRM1 in OCI-LY19 (red) and SUDHL4 (green) cells with different doses of gemcitabine and at 52°C CETSA heating. Data are presented as mean log2 fold change compared to the reference ±SEM from technical replicates (n=2). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 137, 848, 470]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[142, 98, 232, 114]]<|/det|> +
FIGURE 2
+ +<|ref|>text<|/ref|><|det|>[[63, 472, 90, 485]]<|/det|> +499 + +<|ref|>text<|/ref|><|det|>[[63, 493, 884, 660]]<|/det|> +499Figure 2: Gemcitabine induced stalled replication forkHypothetical model indicating proteins at the stalled replication fork after gemcitabine induced DNA damage, and respective IMRPINTS profiles of DNMT1, RPA1, RPA2, RPA3 and CHEK1 in sensitive OCI-LY3 (orange) and OCI-LY19 (red), as well as resistant HT (dark green) and SUDHL4 cells (light green) after 1h, 3h, 5h or 8h of gemcitabine treatment. Data are presented as mean \(\log 2\) fold change compared to the reference \(\pm \mathrm{SEM}\) from biological replicates (n=3). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[131, 118, 870, 430]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[139, 87, 228, 102]]<|/det|> +
FIGURE 3
+ +<|ref|>text<|/ref|><|det|>[[115, 457, 883, 722]]<|/det|> +(A) Heatmap showing the evolution of hit lists at the different time points (1h, 3h, 5h and 8h) after gemcitabine treatment in the sensitive OCI-LY19 cells and resistant SUDHL4 cells. (B) Venn diagram shows overlap of sensitive (red) and resistant (green) hits used for subsequent ClueGO analysis. Only common hits from both sensitive OCI-LY3 and OCI-LY19 (54 hits), and both resistant HT and SUDHL4 (45 hits) cells, after 8h gemcitabine treatment, are used. ClueGO analysis of common hits from sensitive (red) and resistant (green) cells after 8h gemcitabine treatment. Each node represents an enriched GO term whereby the colouring indicates an at least 60% hit contribution from a condition. Hits related to each GO term are depicted in small nodes with black label and coloured accordingly (sensitive = red, resistant = green, both = grey). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[142, 120, 761, 750]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 770, 752, 788]]<|/det|> +
Figure 4: CETSA responses in gemcitabine sensitive versus resistant cells
+ +<|ref|>text<|/ref|><|det|>[[115, 794, 881, 914]]<|/det|> +(A) Venn diagram showing overlap of the hits from sensitive OCI-LY19 (top) and resistant SUDHL4 cells (bottom) with the previously identified CCAE (Core CETSA Apoptosis Ensemble) proteins. (B) STRING plot showing the overlapping proteins from A with IMPRINTS CETSA profiles for OCI-LY19 (red hues) and SUDHL4 (green hues) after 1h, 3h, 5h and 8h gemcitabine treatment. Data are presented as mean log2 fold change compared to + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[110, 74, 883, 346]]<|/det|> +the reference from biological replicates \((n = 3)\) . (C) IMPRINTS profiles of PARP1, MATR3, LMNB1 and DDX21 showing peptides before and after known caspase cleavage sites in sensitive OCI- LY19 cells (red hues) and resistant SUDHL4 cells (green hues) after 1h, 3h, 5h and 8h gemcitabine treatment. Data are presented as mean \(\log 2\) fold change compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . (D) IMPRINTS profiles CCNA2, CCNB1, CCNB2 and CDK1 for OCI- LY19 (red hues) and SUDHL4 (green hues) after 1h, 3h, 5h and 8h gemcitabine treatment. Data are presented as mean \(\log 2\) fold change compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . (E) Progression of cell cycle and distribution of cells in different cell cycle phases in the sensitive OCI- LY19 and resistant SUDHL4 cells after 8h and 24h with and without gemcitabine treatment. Data are presented as relative percentage of cells in each cycle phase \(\pm \mathrm{SEM}\) from biological replicates \((n = 4)\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[130, 130, 857, 660]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[140, 90, 231, 105]]<|/det|> +
FIGURE 5
+ +<|ref|>sub_title<|/ref|><|det|>[[115, 685, 641, 703]]<|/det|> +## Figure 5: Translusion synthesis in gemcitabine resistant cells + +<|ref|>text<|/ref|><|det|>[[115, 709, 883, 905]]<|/det|> +(A) Nodes indicating translesion synthesis pathway as a GO term and IMPRINTS profiles of the involved proteins in OCI-LY19 (red hues) and SUDHL4 (green hues) after 1h, 3h, 5h and 8h gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference ±SEM from biological replicates (n=3). (B) Representative western blot (top) and quantification (bottom) of Polk expression in OCI-LY19 (red) and SUDHL4 (green) cells after 1h, 3h, 5h and 8h of gemcitabine treatment. Data are presented as mean relative fold change compared to the reference ±SEM from biological replicates (n=3). (C) Western blot of full-length and cleaved fragments of Polk in OCI-LY19 cells after 6h of gemcitabine treatment in + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 75, 882, 244]]<|/det|> +the presence or absence of proteasomal inhibitor MG132 or pan-caspase inhibitor zVAD- FMK. (D) Representative western blot (top) and quantification (bottom) of PCNA and mono- ubiquitinated PCNA in the sensitive OCI- LY19 (red) and resistant SUDHL4 (green) cells after 6h of gemcitabine treatment. Data are presented as mean relative fold change compared to the reference ±SEM from biological replicates (n=3). (E) ZIP Synergy score of gemcitabine and rev7/3- in- 1 concentrations in SUDHL4 cells at 48h. (F) Hypothetical model of gemcitabine induced translesion synthesis polymerase switch. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[117, 125, 840, 767]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[120, 840, 596, 858]]<|/det|> +
Figure 6: ADDR response in gemcitabine resistant cells
+ +<|ref|>text<|/ref|><|det|>[[115, 865, 880, 910]]<|/det|> +(A) IMPRINTS profiles of ADDR protein ensemble in OCI-LY19 (red hues) and SUDHL4 (green hues) after 1h, 3h, 5h and 8h gemcitabine treatment. Data are presented as mean log2 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 75, 883, 496]]<|/det|> +fold change compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . (B) Quantification of ADDR proteins in SUDHL4 cells after 6h treatment with cladribine (orange) and cytarabine (red). Data are presented as mean \(\log 2\) fold change compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . (C) Relative viability and \(\mathrm{IC}_{50}\) values of OCI- LY19 (dotted lines) and SUDHL4 (continuous lines) cells after 48h treatment with increasing concentrations of gemcitabine (green), cisplatin (light blue), cytarabine (red) or cladribine (orange). Data are presented as mean relative viability compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . (D) Relative viability of SUDHL4 cells treated for 72h with increasing concentrations of gemcitabine, either alone (green) or in combination with \(1\mu \mathrm{M}\) AZD6738 (red). Data are presented as mean relative viability compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . (E) IMPRINTS profiles of ADDR protein ensemble in gemcitabine resistant SUDHL4 cells after 5h of treatment of gemcitabine alone, AZD6738 alone or in combination. Data are presented as mean \(\log 2\) fold change compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . (F) IMPRINTS profiles of POLD1, POLD2, POLD4 in gemcitabine resistant SUDHL4 cells after 5h of treatment of gemcitabine alone, AZD6738 alone or in combination. Data are presented as mean \(\log 2\) fold change compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[156, 152, 816, 490]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[163, 92, 253, 108]]<|/det|> +
FIGURE 7
+ +<|ref|>text<|/ref|><|det|>[[57, 490, 88, 504]]<|/det|> +575 + +<|ref|>text<|/ref|><|det|>[[57, 512, 188, 528]]<|/det|> +576 + +<|ref|>text<|/ref|><|det|>[[57, 536, 188, 552]]<|/det|> +577 + +<|ref|>text<|/ref|><|det|>[[57, 560, 881, 700]]<|/det|> +(A) Experimental design for MS-CETSA treatment of patient samples from DLBCL patients. Patient samples were treated with different doses of gemcitabine ex vivo for 5h. The treated samples were CETSA heated and subjected to mass spectrometry. (B) MS-CETSA ITDR profiles of selected CCAE proteins in patient samples (top panel) and sensitive OCI-LY19 (bottom panel) cells. Data are presented as mean log2 fold change compared to the reference ±SEM from technical replicates (n=2). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[145, 125, 580, 480]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[144, 95, 395, 110]]<|/det|> +
SUPPLEMENTARY FIGURE 1
+ +<|ref|>sub_title<|/ref|><|det|>[[110, 515, 700, 534]]<|/det|> +## Supplementary Figure 1: Cell viability after gemcitabine treatment + +<|ref|>text<|/ref|><|det|>[[110, 539, 883, 682]]<|/det|> +(A) MTT viability assay and \(\mathrm{IC}_{50}\) values of OCI-LY3 (orange), OCI-LY19 (red), HT (dark green) and SUDHL4 (light green) cells after 48h treatment with increasing concentrations of gemcitabine. Data are presented as mean relative viability compared to the reference \(\pm \mathrm{SEM}\) from biological replicates \((n = 3)\) . (B) Trypan blue assay of OCI-LY3 (orange), OCI-LY19 (red), HT (dark green) and SUDHL4 (light green) cells after 8h treatment with indicated gemcitabine concentrations. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[135, 137, 730, 360]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[117, 390, 870, 409]]<|/det|> +
Supplementary Figure 2: Early responses to gemcitabine in resistant and sensitive cells
+ +<|ref|>text<|/ref|><|det|>[[115, 413, 883, 580]]<|/det|> +(A) Western blot detection of DNMT1 levels in soluble fraction in SUDHL4 lysates treated with increasing gemcitabine concentrations (0-1mM) for 1min and upon CETSA heat challenge at \(37^{\circ}\mathrm{C}\) (control) and \(54^{\circ}\mathrm{C}\) . (B) Western blot detection of DNMT1 levels in OCI-LY19 and SUDHL4 cells after 24h treatment with vehicle, gemcitabine or decitabine. Actin was used as loading control. (C) Western blot detection of phospho-Chk1 (S345) in OCI-LY19 and SUDHL4 after treatment with indicated gemcitabine concentrations for 1h, 3h, 5h or 8h. Actin was used as loading control. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[140, 120, 737, 530]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[144, 87, 397, 102]]<|/det|> +
SUPPLEMENTARY FIGURE 3
+ +<|ref|>text<|/ref|><|det|>[[115, 544, 881, 587]]<|/det|> +Supplementary Figure 3: Apoptosis induction in sensitive cells versus activation of checkpoint release for cell cycle progression in resistant cells + +<|ref|>text<|/ref|><|det|>[[115, 592, 883, 858]]<|/det|> +(A) Western blot detection of PARP1 and cleaved PARP1 (c-PARP1) levels in OCI-LY19 and SUDHL4 cells after treatment with indicated gemcitabine concentrations for 1h, 3h, 5h, or 8h. Actin was used as loading control. (B) Schematic illustration of cyclin and CDKs interaction during different phases of cell cycle (left) and the IMPRINTS profiles of CDK1, CCNB1, CCNB2 and CCNA2 (right) as previously published by Dai et al (22). (C) IMPRINTS profiles CDK1, CCNB1, CCNB2 and CCNA2 in SUDHL4 cells treated for 1h (green) or 8h (blue) with 48nM, 480nM and 24μM gemcitabine. Data are presented as mean log2 fold change compared to the reference ±SEM from biological replicates (n=3). (D) IMPRINTS profile of RB1 in gemcitabine sensitive OCI-LY19 (red hues) and resistant SUDHL4 cells (green hues) after 1h, 3h, 5h and 8h of gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference ±SEM from biological replicates (n=3). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[140, 120, 840, 604]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[139, 62, 396, 99]]<|/det|> +
SUPPLEMENTARY FIGURE 4
+ +<|ref|>sub_title<|/ref|><|det|>[[103, 623, 637, 642]]<|/det|> +## Supplementary Figure 4: Translesion synthesis polymerases + +<|ref|>text<|/ref|><|det|>[[113, 647, 883, 916]]<|/det|> +(A) IMPRINTS profile of SAMHD1 in gemcitabine sensitive OCI-LY19 (red hues) and resistant SUDHL4 cells (green hues) after 1h, 3h, 5h and 8h of gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference ±SEM from biological replicates (n=3). (B) Western blot detection of phospho-SAMHD1 in SUDHL4 cells after 1h, 3h, 5h, or 8h of 24μM gemcitabine treatment. Actin was used as loading control. (C) Western blot detection of SAMHD1 levels in SUDHL4 SAMHD1 WT and KO cells. Actin was used as loading control. (D) Relative viability of SUDHL4 SAMHD1 WT (green) and KO (purple) cells after 72h treatment with increasing concentrations of gemcitabine. Data are presented as mean relative viability compared to the reference ±SEM from biological replicates (n=3). (E) LC/MS measurements of deoxyribonucleotide (and ribonucleotide) in SUDHL4 SAMHD1-KO cells treated with vehicle or 24μM gemcitabine for 6h. Graphs show relative fold changes + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[58, 75, 881, 220]]<|/det|> +as compared to SUDHL4 WT cells. (F) IMPRINTS profile of RRM1 in SUDHL4 SAMHD1 WT (green) and KO (purple) cells after 1h or 8h gemcitabine treatment. Data are presented as mean log2 fold change compared to the reference \(\pm\) SEM from biological replicates (n=3). (G) Western blot detection of Poln, Polt and Rev1 levels in OCI-LY19 and SUDHL4 cells after treatment with indicated gemcitabine concentrations for 1h, 3h, 5h, or 8h. Actin was used as loading control. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[144, 130, 550, 760]]<|/det|> + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[57, 75, 883, 199]]<|/det|> +638 presented as mean log2 fold change compared to the reference \(\pm\) SEM from biological replicates (n=3). IMPRINTS profiles of the (B) ADDR protein ensemble, (C) Polδ subunits and (D) proteins involved in stalled replication fork in MDA- MB- 231 breast cancer cells treated with \(25\mu \mathrm{M}\) cisplatin for 12h. Data are presented as mean log2 fold change compared to the reference \(\pm\) SEM from biological replicates (n=3). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[117, 76, 400, 94]]<|/det|> +## MATERIALS AND METHODS + +<|ref|>sub_title<|/ref|><|det|>[[117, 108, 387, 125]]<|/det|> +## RESOURCE AVAILABILITY + +<|ref|>sub_title<|/ref|><|det|>[[117, 141, 240, 157]]<|/det|> +## Lead Contact + +<|ref|>text<|/ref|><|det|>[[117, 171, 880, 214]]<|/det|> +Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Pär Nordlund (par.nordlund@ki.se). + +<|ref|>sub_title<|/ref|><|det|>[[117, 228, 355, 245]]<|/det|> +## Data and Code Availability + +<|ref|>text<|/ref|><|det|>[[117, 258, 880, 301]]<|/det|> +The extracted protein abundance data from all MS- CETSA experiments are included in supplemental "Datafile S1 - Protein abundance all datasets". + +<|ref|>text<|/ref|><|det|>[[117, 314, 881, 406]]<|/det|> +All mass spectrometry raw data files have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org/) via the jPOST repository with the dataset identifier PXD (to be filled in). Any additional information required to reanalyse the data reported in this paper is available from the lead contact upon request. + +<|ref|>sub_title<|/ref|><|det|>[[117, 420, 605, 438]]<|/det|> +## EXPERIMENTAL MODEL AND SUBJECT DETAILS + +<|ref|>sub_title<|/ref|><|det|>[[117, 450, 208, 465]]<|/det|> +## Cell Lines + +<|ref|>text<|/ref|><|det|>[[117, 479, 881, 594]]<|/det|> +Human breast adenocarcinoma cell line MDA- MB- 231 (CRM- HTB- 26) was purchased from ATCC. Human lymphoma cell line SUDHL4 (CRL- 2957) was purchased from ATCC, HT cells (CRL- 2260) were a gift from the lab of Ernesto Guccione, Icahn School of medicine at Mt Sinai (formerly at IMCB, Singapore), OCI- LY19 and OCI- LY3 was obtained from the lab of Manikandan Lakshmanan at IMCB, Singapore. + +<|ref|>text<|/ref|><|det|>[[117, 608, 880, 676]]<|/det|> +All the DLBCL cell lines and MDA- MB- 231 were maintained in RPMI- 1640 medium (R8758, Sigma) and L- glutamine, supplemented with \(20\%\) fetal bovine serum (FBS), 100units/ml penicillin and streptomycin in a \(37^{\circ}C\) CO2 incubator. + +<|ref|>sub_title<|/ref|><|det|>[[117, 690, 544, 707]]<|/det|> +## Generation of SUDHL4 SAMHD1 knockout cells + +<|ref|>text<|/ref|><|det|>[[117, 713, 881, 905]]<|/det|> +Knockout was performed using LentiCRISPRv2GFP vector (82416, Addgene). Single- guide RNA encoding SAMHD1 was cloned into LentiCRISPRv2GFP vector. Briefly, lentiviruses were packaged using HEK293T cells via co- transfection of gene of interest, VSVG and delta 8.2 vector, using Lipofectamine 2000 transfection reagent (11668019, Thermo Fisher). Viruses collected were concentrated using Amicon Ultra Centrifugal filters (C2566709, Merck) and spinoculated onto the SUDHL4 cells in the presence of \(8\mu \mathrm{g / ml}\) polybrene (sc- 134220, Santa Cruz) at \(800\mathrm{g}\) for 30 minutes at room temperature. The target sequences of the sgRNAs are as follow: SAMHD1 sgRNA- 1 forward \(5^{\prime}\) - CACCGGAGTGTCTAGTTCCAGCCAC - 3'; + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 77, 880, 142]]<|/det|> +SAMHD1 sgRNA- 1 reverse 5'- AAACGTGCGTGAACTAGACATCCTC - 3'. Single cells containing the CRISPR- GFP positive vector were then sorted through FACS and harvested as monoclines. + +<|ref|>sub_title<|/ref|><|det|>[[118, 151, 474, 170]]<|/det|> +## Primary DLBCL clinical patient samples + +<|ref|>text<|/ref|><|det|>[[115, 183, 881, 325]]<|/det|> +Tumors were collected in MACS Tissue Storage Solution (130- 100- 08, Miltenyi) and kept on ice for transport. Tissue was cut into equally small pieces using a scalpel. To obtain single cell solutions, the cells were passed through a sterile \(70\mu \mathrm{M}\) cell filter mesh (352350, Corning) in RPMI- 1640 medium (R8758, Sigma) and L- glutamine, supplemented with \(10\%\) fetal bovine serum (FBS), 100units/ml penicillin and streptomycin. Cells number was determined and MSCETSA experiment was performed immediately. + +<|ref|>sub_title<|/ref|><|det|>[[118, 339, 306, 356]]<|/det|> +## METHOD DETAILS + +<|ref|>sub_title<|/ref|><|det|>[[118, 371, 247, 388]]<|/det|> +## Selected drugs + +<|ref|>text<|/ref|><|det|>[[117, 401, 881, 493]]<|/det|> +Gemcitabine, AZD6738 (kindly provided by Prof. Anand Jeyasekharan, CSI, Singapore) and Decitabine were solubilized in water. Cisplatin, Cytarabine, Cladribine, Z- VAD- FMK, MG132 and REV7/REV3L- IN- 1 were solubilized in DMSO. All compound stocks were aliquoted and stored at \(- 20^{\circ}\mathrm{C}\) . + +<|ref|>sub_title<|/ref|><|det|>[[118, 507, 499, 525]]<|/det|> +## The Cellular Thermal Shift Assay (CETSA) + +<|ref|>text<|/ref|><|det|>[[118, 540, 298, 556]]<|/det|> +CETSA in intact cells + +<|ref|>text<|/ref|><|det|>[[115, 570, 882, 883]]<|/det|> +For the in vitro IMPRINTS- CETSA experiments, cell lines were seeded at \(0.5\mathrm{x}10^{6}\) cells/ml of media and preconditioned in complete RPMI with \(2\%\) FBS for 24h. The cells were then treated with either vehicle or drug at their respective final concentrations and incubated at \(37^{\circ}\mathrm{C}\) and \(5\% \mathrm{CO}_2\) for indicated time points. Cells were pelleted for 4min at \(400\mathrm{kg}\) , washed with PBS and resuspended in \(50\mu \mathrm{l}\) PBS. For the in vitro ITDR- CETSA experiments, cell lines were distributed into 6 tubes at \(0.3\mathrm{x}10^{6} / 100\mu \mathrm{l}\) in media, while the total cells of primary DLBCL clinical samples were distributed into 6 tubes in media. Cells were then treated with either vehicle or drug at their respective final concentrations and incubated at \(37^{\circ}\mathrm{C}\) and \(5\% \mathrm{CO}_2\) for indicated time points. Cells were pelleted for 4min at \(400\mathrm{kg}\) , washed with PBS and resuspended in \(50\mu \mathrm{l}\) PBS. Harvested cells and lysates were aliquoted into PCR tubes corresponding to each treatment condition and subjected to a 3min CETSA heating step in a Veriti thermal cycler (Applied Biosystems) with temperatures ranging from \(37^{\circ}\mathrm{C} - 57^{\circ}\mathrm{C}\) , followed by 3min cooling at \(4^{\circ}\mathrm{C}\) . + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 106, 881, 348]]<|/det|> +For lysate CETSA experiments \(20 \times 10^{6}\) cells/ml were lysed by adding 2X kinase buffer to the final concentration of 50mM HEPES pH 7.5, 5mM beta- glycerophosphate, 0.1mM sodium orthovanadate (Na3VO4), 10mM MgCl2, 1mM TCEP (Sinopharm Chemical Reagent Co.), 1x protease inhibitor cocktail (Nacalai Tesque Inc.) and 25U/ml Benzonase. Cells were subjected to five freeze- thaw cycles with liquid nitrogen to release soluble proteins. The suspension was then centrifuged for 20min at 20,000xg and \(4^{\circ}\mathrm{C}\) to remove cell debris and 30μl of supernatants were treated with either vehicle or drug at their respective final concentrations for 1min. Lysates were aliquoted into PCR tubes corresponding to each treatment condition and subjected to a 3min CETSA heating step in a Veriti thermal cycler (Applied Biosystems) with temperatures ranging from \(37^{\circ}\mathrm{C} - 57^{\circ}\mathrm{C}\) , followed by 3min cooling at \(4^{\circ}\mathrm{C}\) . + +<|ref|>sub_title<|/ref|><|det|>[[117, 361, 461, 379]]<|/det|> +## Cell lysis and soluble protein extraction + +<|ref|>text<|/ref|><|det|>[[115, 391, 882, 583]]<|/det|> +Following heat treatment, the cells were lysed by adding 2X kinase buffer to the final concentration of 50mM HEPES pH 7.5, 5mM beta- glycerophosphate, 0.1mM sodium orthovanadate (Na3VO4), 10mM MgCl2, 1mM TCEP (Sinopharm Chemical Reagent Co.), 1x protease inhibitor cocktail (Nacalai Tesque Inc.) and 25U/ml Benzonase. All the samples were subjected to five freeze- thaw cycles with liquid nitrogen to release soluble proteins. For lysate CETSA experiments this step was skipped and immediately proceeded to the next step. The suspension was then centrifuged for 20min at 20,000xg and \(4^{\circ}\mathrm{C}\) to remove cell debris. The supernatants were then analyzed using either LC- MS or western blotting. + +<|ref|>sub_title<|/ref|><|det|>[[117, 596, 454, 614]]<|/det|> +## Cell cycle analysis and Flow cytometry + +<|ref|>text<|/ref|><|det|>[[115, 626, 882, 818]]<|/det|> +Cell lines were seeded at \(0.5 \times 10^{6}\) cells/ml of media and preconditioned in complete RPMI with \(2\%\) FBS for 24h. The cells were then treated with either vehicle or drug at their respective final concentrations and incubated at \(37^{\circ}\mathrm{C}\) and \(5\% \mathrm{CO}_{2}\) for indicated time points. Cells were pelleted for 4min at \(400\mathrm{xg}\) , washed with PBS. Cells were fixed in \(70\%\) ethanol overnight, washed twice with cold PBS, then resuspended in PI staining solution (100μg/ml ribonuclease A, \(50\mu \mathrm{g / ml}\) PI in PBS) and incubated in the dark for at least 30min at room temperature, followed by flow cytometric analysis on a LSR II (BD Biosciences, UK) flow cytometer. FlowJo (FlowJo, LLC, USA) was used to analyze the data. + +<|ref|>sub_title<|/ref|><|det|>[[117, 831, 494, 850]]<|/det|> +## Nucleotide quantification by LC-MRM/MS + +<|ref|>text<|/ref|><|det|>[[115, 862, 881, 905]]<|/det|> +Cell lines were seeded at \(0.5 \times 10^{6}\) cells/ml of media and preconditioned in complete RPMI with \(2\%\) FBS for 24h. The cells were then treated with either vehicle or drug at their respective final + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 75, 882, 465]]<|/det|> +concentrations and incubated at \(37^{\circ}\mathrm{C}\) and \(5\% \mathrm{CO}_2\) for indicated time points. Cells were pelleted for 4min at \(400\mathrm{xx}\) , washed with PBS. Cell pellets were harvested and snap frozen in liquid nitrogen. The samples were stored in \(- 80^{\circ}\mathrm{C}\) until transportation to Creative Proteomics for nucleotide quantification through LC- MS analysis. Each cell sample was resuspended in \(500\mu \mathrm{l}\) of \(80\%\) methanol and then lysed on a MM 400 mill mixer at a shaking frequency of \(30\mathrm{Hz}\) and with the aid of two metal balls for 2 min. The samples were subsequently sonicated for 1 min in an ice- water bath before centrifugal clarification at \(21,000\mathrm{g}\) and \(5^{\circ}\mathrm{C}\) for \(10\mathrm{min}\) . The clear supernatants were collected for the following assay. The precipitated pellets were used for protein assay using a standardized BCA procedure. Serially diluted standard solutions of the targeted nucleotides were prepared in \(80\%\) methanol. \(100\mu \mathrm{l}\) of each standard solution of the clear supernatant of each sample were dried under a nitrogen gas flow. The residues were dissolved in \(100\mu \mathrm{l}\) of a 13C- labeled internal standard solution. \(10\mu \mathrm{l}\) aliquots of the resulting solutions were injected into a C18 column \((2.1\times 110\mathrm{mm}\) , \(1.9\mu \mathrm{m}\) ) to run UPLC- MRM/MS with (- ) ion detection on a Waters Acquity UPLC system coupled to a Sciex QTRAP 6500 Plus MS instrument, with the use of tributylamine buffer (A) and acetonitrile (B) as the mobile phase for gradient elution. + +<|ref|>sub_title<|/ref|><|det|>[[117, 475, 233, 491]]<|/det|> +## Western blot + +<|ref|>text<|/ref|><|det|>[[117, 504, 881, 573]]<|/det|> +Western blotting was performed on protein extracts obtained either by freeze- thawing or lysis by RIPA buffer (Thermo Scientific). Protein concentrations for each sample were quantified using bicinchoninic acid (BCA) assay according to manufacturer's instructions. + +<|ref|>text<|/ref|><|det|>[[115, 584, 882, 875]]<|/det|> +Western blotting was performed on protein extracts obtained either by freeze- thawing or lysis by RIPA buffer (Thermo Scientific). Protein concentrations for each sample were quantified using bicinchoninic acid (BCA) assay according to manufacturer's instructions.Protein extract samples were mixed with NuPAGE loading buffer consisting of NuPAGE LDS sample buffer (NP0008, Life technologies) and reducing agent (NP0009, Life Technologies) and boiled at \(95^{\circ}\mathrm{C}\) . Proteins were separated on NuPAGE 4–12% Bis- Tris midi gels (WG1403BX10, Invitrogen) for 45- 55 min at 200 V. Separated proteins were transferred to nitrocellulose membranes using the iBlot system (Invitrogen) onto nitrocellulose membranes. Membranes were blocked in \(5\%\) (w/v) non- fat milk (Semper AB) in TBS with \(0.05\%\) Tween 20 (Medicago 09- 7510- 100) (TBS- T) for 1h with gentle shaking. Incubation with primary antibody was performed overnight at \(4^{\circ}\mathrm{C}\) and with gentle shaking. After washing in TBS- T for \(3 \times 10 \mathrm{~min}\) , the membranes were incubated with secondary antibodies for 1h, washed again \(3 \times 10 \mathrm{~min}\) in TBS- T and developed using Clarity™ Western ECL Substrate (170- 5061, BioRad). The chemiluminescent signal was detected using the ChemiDoc™ XRS+ imaging system from BioRad and the band intensities were quantified using ImageLab™ software (BioRad). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[118, 77, 395, 94]]<|/det|> +## Sample preparation for LC-MS + +<|ref|>text<|/ref|><|det|>[[115, 107, 881, 470]]<|/det|> +Protein concentrations were quantified after lysis using the BCA according to manufacturer's instructions and the same amount of protein was used for sample preparation. Samples were reduced with \(25\%\) TFE and \(20\mathrm{mM}\) TCEP at \(55^{\circ}\mathrm{C}\) for \(20\mathrm{min}\) , followed by alkylation with \(55\mathrm{mM}\) of 2- chloroacetamide (CAA) (C0267, Sigma) in the dark at room temperature for \(30\mathrm{min}\) . Samples were digested with LysC (1:25 enzyme to protein ratio, Wako Chemicals Ltd), for 4- 6h before adding trypsin (1:25, Promega) for overnight digestion at \(37^{\circ}\mathrm{C}\) . The samples were dried by a centrifugal vacuum evaporator and desalted with Oasis HLB 96- well plate following the manufacturer's instructions. The desalted peptides were re- solubilized in \(100\mathrm{mM}\) TEAB to \(1\mu \mathrm{g / \mu l}\) . All the peptides were labeled with Isobaric Tandem Mass Tags - 10plex TMT according to the manufacturer's protocol (90110, Thermo Scientific). The labeling was done at room temperature for at least 1hr and labeled samples were quenched using \(10\mu \mathrm{l}\) of 1M Tris (pH 7.4) solution. A high pH reverse phase Zorbax 300 Extend C- 18 4.6mm x 250mm (Agilent) column and liquid chromatography AKTA Micro (GE) system was used for offline sample pre- fractionation. The fractions were concatenated into 20 fractions and dried with a centrifugal vacuum evaporator. + +<|ref|>sub_title<|/ref|><|det|>[[118, 484, 187, 500]]<|/det|> +## LC-MS + +<|ref|>text<|/ref|><|det|>[[115, 515, 882, 754]]<|/det|> +The digested, labeled, and dried peptide sample fractions were resuspended in \(0.1\%\) acetonitrile, \(0.5\%\) (v/v) acetic acid and \(0.06\%\) TFA in water immediately before analysis on LC- MS. Online chromatography was performed using Dionex UltiMate 3000 UPLC system coupled to a Q Exactive mass spectrometer (Thermo Scientific). Each fraction was separated on a \(50\mathrm{cm}\times 75\mu \mathrm{m}\) (ID) EASY- Spray analytical column (ES903, Thermo Scientific) in a \(80\mathrm{min}\) gradient of programmed mixture of solvent A ( \(0.1\%\) formic acid in \(\mathrm{H}_2\mathrm{O}\) ) and solvent B ( \(99.9\%\) acetonitrile, \(0.1\%\) formic acid). MS data were acquired using a top 12 data- dependent acquisition method. Full scan MS spectra were acquired in the range of \(350 - 1550\mathrm{m / z}\) at a resolution of 60,000 and AGC target of 3e6; Top 12 dd- MS \(^2\) 60,000 and 1e5 with isolation window at \(1.0\mathrm{m / z}\) . + +<|ref|>sub_title<|/ref|><|det|>[[118, 768, 600, 787]]<|/det|> +## QUANTIFICATION AND STATISTICAL ANALYSIS + +<|ref|>sub_title<|/ref|><|det|>[[118, 800, 468, 818]]<|/det|> +## Protein identification and quantification + +<|ref|>text<|/ref|><|det|>[[115, 831, 881, 899]]<|/det|> +Protein identification was performed by Proteome Discoverer 2.5 software (Thermo Scientific), using both Mascot 2.6.0 (Matrix Science) and Sequest HT (Thermo Scientific) search engines to search against reviewed human Uniprot databases (downloaded on 13 Jan + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 75, 883, 514]]<|/det|> +2017, including 42105 sequence entries and another downloaded on 23 Jul 2018, including 9606 sequence entries). MS precursor mass tolerance was set at 20ppm, fragment mass tolerance 0.05 Da, and maximum missed cleavage sites of 3. Dynamic modifications searched for Oxidation (M), Deamidation (NQ), and Acetylation (N-terminal protein). Static modifications: Carbamidomethyl (C) and TMT10plex (K and peptide N terminus). Only the spectrum peaks with signal- to- noise ratio (S/N) \(>4\) were chosen for searches. The false discovery rate (FDR) was set to \(1\%\) at both PSM and peptide levels. Only the unique and razor peptides were used for protein assignment and abundance quantification. Isotopic correction of the reporter ions in each TMT channel was performed according to the product sheet. Only the master proteins in the protein group were used for downstream analysis. For some datasets, the peptide abundances were obtained from Proteome Discoverer software (version 2.5). Every peptide with another modification than a TMT one was removed. To ensure the accuracy of TMT quantification, reporter S/N threshold was set at 10 and co- isolation threshold at \(30\%\) . Then, every peptide dataset has been treated the same way as the protein dataset according to the same method described in Dai et al., Cell 2018 (22). To illustrate the RESP effect, we summed the non Log2 transformed fold changes from the peptides located before the cleaved site and the ones after the cleaved site. Then those fold changes were Log2 transformed and plotted as bar plots. + +<|ref|>sub_title<|/ref|><|det|>[[118, 526, 533, 545]]<|/det|> +## Quantitative MS data analysis and visualization + +<|ref|>text<|/ref|><|det|>[[115, 556, 883, 797]]<|/det|> +Quantified protein/peptide abundances were imported into the R environment (http://www.R- project.org/) to facilitate the data analysis and visualization. Only the proteins with at least two quantifying abundance counts were used for downstream analysis. Data cleaning, normalization, and calculations of protein abundance and thermal stability differences in each condition were performed using the IMPRINTS.CETSA and the IMPRINTS.CETSA.app R packages (36). Strict criteria for hit selection were applied for all datasets., For IMPRINTS- CETSA we require that proteins should be quantified by at least 2 abundance counts. As cut- off criteria we used an absolute mean fold change and a standard error of the mean (SEM)- scale factor as cut- off. For IMPRINTS- CETSA experiments we used a mean log2 fold change cut- off \(>0.25\) or \(0.2\) and \(\pm 4\times\) SEM as compared to reference condition was applied. + +<|ref|>sub_title<|/ref|><|det|>[[115, 810, 818, 829]]<|/det|> +## Protein-protein interaction network and gene ontology (GO) enrichment analysis + +<|ref|>text<|/ref|><|det|>[[115, 841, 881, 909]]<|/det|> +Protein- protein interaction network for hits was obtained by importing the hitlist Uniprot IDs into Cytoscape v.3.9.1 (http://cytoscape.org). Using the embedded STRING interaction database (http://apps.cytoscape.org/apps/stringApp), a default confidence cut- off score of 0.4 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 75, 882, 390]]<|/det|> +was applied to retrieve the network. Each node represents one hit protein, and edges symbolize protein- protein interactions. Nodes explanation can be found on figure legends. Comparative GO analysis was performed using the ClueGO v2.5.1 plug- in in Cytoscape (http://apps.cytoscape.org/apps/cluego). Hitlist Uniprot IDs were imported to query the GO- Biological Processes database (EBI- QuickGO- GOA- 15783 terms/pathways with 17268 available unique genes- 20.11.2017). The parameters for analysis were set as follows: Evidence code – All; Use Go Term Fusion; GO tree interval – Level 3- 8; GO Term/Pathway Selection – Minimum 3 genes and threshold of 4% of genes per term; GO term connectivity threshold (Kappa score) – 0.4; Two- sided hypergeometric test with Bonferroni step down p- value correction. Only GO terms with p- value <0.05 are shown. GO terms are presented as nodes and clustered together based on term similarity. Node size is proportional to the p- value for GO term enrichment. Node colours are set according to the treatment condition showing the % of visible proteins of a term/pathway. + +<|ref|>sub_title<|/ref|><|det|>[[118, 399, 392, 416]]<|/det|> +## Data Analysis and visualization + +<|ref|>text<|/ref|><|det|>[[115, 428, 882, 619]]<|/det|> +All graphs were generated using GraphPad Prism, R environment, cytoscape or Biorender. All data are presented as mean with error bars representing the standard error of the mean (SEM). Error bars that are smaller than the displayed data points are not displayed by the software. Details regarding replicates for each experiment can be found in the figure legends. Sigmoidal curves were fit (where appropriate) using R environment. Unpaired t- tests were performed using GraphPad Prism and the results are displayed in figures and figure legends. The flow cytometry data was analyzed on FlowJo v10.8 and GraphPad Prism was used to represent the data. + +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[115, 103, 892, 888]]<|/det|> +
REAGENT or RESOURCESOURCEIDENTIFIER
Antibodies
Anti-SOD1 rabbit pAbSigmaHPA001401
Anti-RRM2Santa Cruz BiotechnologySc-81850
Anti-TK1GeneTexGTX113281
anti-GMNNSanta Cruz BiotechnologySc-74456
anti-SLBPInvitrogenPA5-53966
anti-FBX05Invitrogen37-6600
anti-DTLInvitrogenPA5-88380
anti-PCNASanta Cruz BiotechnologySc-56
anti-ubPCNACell Signaling TechnologyD5C7P
anti-PCLAFSanta Cruz BiotechnologySc-390515
Anti-DNMT1Abcamab19905
Anti-SAMHD1GeneTexGTX103751
Anti-phospho-SAMHD1Cell Signaling TechnologyD7O2M
Anti-pChk1 (S345)Cell Signaling Technology2348T
Anti-p53Santa Cruz BiotechnologySc-126
Anti-RRM1Santa Cruz BiotechnologySc-11733
Anti-PARP1Santa Cruz Biotechnologysc-8007
Anti-beta-actinSanta Cruz BiotechnologySc-69879
Anti-PolKSanta Cruz Biotechnologysc-166667
Anti-PolHSanta Cruz Biotechnologysc-17770
Anti-PollSanta Cruz Biotechnologysc-101026
Anti-Rev1Santa Cruz Biotechnologysc-393022
Anti-rabbit IgG HRP-conjugated secondary antibodyInvitrogen31460
Anti-mouse IgG HRP-conjugated secondary antibodyInvitrogen31430
Anti-goat IgG HRP-conjugated secondary antibodySanta Cruz BiotechnologySc-2354
Chemicals, Peptides, and Recombinant Proteins
RPMI-1640 mediumCytivaSH30096.01
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[115, 73, 891, 844]]<|/det|> +
Heat-inactivated fetal bovine serum (FBS)CytivaSV30160.03
Penicillin-StreptomycinGibco15140-122
MEM Non-Essential Amino AcidsPan BiotechP08-32100
L-GlutamineGibco25030-081
Sodium pyruvateLonza13-115E
PBSGibco20012-027
TrypLE Select (1X)Gibco12563-029
GemcitabineMedChemExpressHY-B0003
Z-VAD-FMKAbcamab-120487
MG-132Abcamab141003
CisplatinMedChemExpressHY-17394
CytarabineMedChemExpressHY-13605
CladribineMedChemExpressHY-13599
DecitabineMedChemExpressHY-A0004
REV7/REV3L-IN-1MedChemExpressHY-100468
Halt™ Protease Inhibitor Cocktail, EDTA-Free (100 X)Thermo Scientific1861279
Ribonuclease ASigmaR6513
NuPAGETM LDS Sample Buffer (4X)Thermo ScientificNP0008
NuPAGETM Sample Reducing Agent (10X)Thermo ScientificNP0009
NuPAGETM MES SDS Running Buffer (20X)Thermo ScientificNP0002
Tris Buffered Saline with 0,05% Tween 20 (TBS-T)Medicago09-7510-100
RIPA buffer (Radioimmunoprecipitation assay buffer)Thermo Scientific89900
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[115, 73, 893, 853]]<|/det|> +
HEPES (hydroxyethyl-piperazineethane-sulphonic acid buffer)GOLDBIOH-400-1
Beta-glycerophosphateSigmaG9422
Sodium orthovanadateSigma72060
Benzonase EndonucleaseEMD Millipore1.01695.0001
Triethylammonium bicarbonate buffer (TEAB)SigmaT7408
TCEP (tris(2-carboxyethyl)phosphine hydrochloride)Sinopharm Chemical Reagent Co, LtdXw518054592
2-chloroacetamide (CAA)SigmaC0267
Lys-CWako Chemicals Ltd129-02541
TrypsinPromegaV5117
TFA (Trifluoroacetic acid)SigmaT6508
Tris1st BaseBIO-1400
AcetonitrileSigmaT7408
TMT10PLEX Isobaric Label Reagent SetThermo Scientific90110
Ammonia solution 25%Merck5.33003.0050
Propidium IodideSigmaP4170
0.1% Formic Acid in ACETONITRILEFISHER USZZLFS/LS120-212
0.1% Formic Acid in WaterFISHER USZZLFS/HB523-4
LC-MS hypergrade acetonitrile (ACN)Merck100029
LC-MS grade acetic acidMerck533001
Critical Commercial Assays
BCA assayThermo Scientific23227
Clarity Western ECL substrateBio-Rad170-5060
Deposited Data
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[117, 73, 894, 880]]<|/det|> + +
Protein abundance (log2) data for MS-CETSAThis manuscriptDatafile S1 - Protein abundance all datasets
MS-CETSA raw dataProteomeXchange via the jPOST repositoryhttp://proteomecentral.proteomexchange.org/
Dataset identifiers PXDxxxxxx
Experimental Models: Cell Lines
SUDH-L4ATCCCRL-2957
HTATCCCRL-2260
OCI-LY3ATCC
OCI-LY19ATCC
MDA-MB-231ATCCCRM-HTB-26
Software and Algorithms
ImageLabTM softwareBioRadhttps://www.bio-rad.com/
Xcalibur v.4.0Thermo Scientifichttps://www.thermofisher.com/us/en/home.html
Proteome Discoverer v.2.5Thermo Scientifichttps://www.thermofisher.com/us/en/home.html
MASCOT 2.6.0MATRIX SCIENCEhttp://matrixscience.com
Sequest HTThermo Scientifichttps://www.thermofisher.com/us/en/home.html
RStudio v.1.2.5033RStudiohttps://www.rstudio.com
R v.3.6.3The R Foundationhttps://www.r-project.org/
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[115, 73, 891, 868]]<|/det|> +
mineCETSA 0.3.8.7Lim et al., 2018 (37)https://github.com/nkdailingyun/mine CETSA
IMPRINTS.CETSA 1.0.4Gerault et al., 2024 (36)https://github.com/nkdailingyun/IMPRINTS.CETSA
IMPRINTS.CETSA.app 3.4.2Gerault et al., 2024 (36)https://github.com/mgerault/IMPRINTS.CETSA.app
Cytoscape 3.9.2Cytoscapehttp://cytoscape.org
ClueGO plugin v2.5.1 for CytoscapeBindea et al., 2009http://apps.cytoscape.org/apps/cluego
GraphPad Prism v.8.3.0GraphPad Softwarehttps://www.graphpad.com/
Biorenderwww.biorender.com
FlowJo v.9.3.2FLOWJO, LLChttps://www.flowjo.com
Other
MicroAmp™ Fast 96-well Reaction plateApplied Biosystems4346907
Falcon® 150cm² cell culture flaskCorning355001
Falcon® 75cm² cell culture flaskCorning353136
Falcon® 25cm² cell culture flaskCorning353109
96-well plates (black)Greiner655090
NuPAGE 4–12% Bis-Tris midi gelInvitrogenWG1403BX10
iBlot 2 NC Regular StacksInvitrogenIB23001
Non-fat milk powderSemper ABN/A
Oasis HLB 1cc (10mg) extraction cartridgesWaters186000383
+ +<--- Page Split ---> +<|ref|>table<|/ref|><|det|>[[115, 73, 891, 870]]<|/det|> +
Xbridge Peptide BEH C18, 300 Å, 3.5 μm, 2.1 mm × 250 mm columnWaters186003610
Zorbax 300 Extend C-18 4.6 mm × 250 mm columnAgilent770995-902
50 cm x 75 μm(ID) EASY-Spray analytical columnThermo ScientificES903
Veriti™ 96-Well Thermal CyclerApplied BiosystemsP/N 4375786
XCell4 SureLock™ Midi-CellInvitrogenCat#WR0100
iBlot 2 systemInvitrogenCat#IB21001
ChemiDoc™ XRS+ imaging systemBioRadUniversal Hood III
SpeedVac vacuum concentratorThermo ScientificP/N SPD111V-230, 61010-1, and RV5 A65313906
Dionex UltiMate 3000 UPLC systemThermo ScientificP/N 5041.0010, 5826.0020, and 5035.9245
Q Exactive mass spectrometerThermo ScientificIQLAAEGAAPFA LGMBFZ
Veriti 96 well Thermal cyclerInvitrogen4375786
ChemiDoc MP Imaging SystemBio-Rad1708280
50cmx75μM (ID) EASY-Spray analytical columnThermoFisher ScientificP/N ES803
High pH reverse phase Zorbax 300 Extend C-18 4.6mm x 250mm columnAgilentP/N 770995-902
Liquid chromatography AKTA microsystemGE Healthcare28948303
Oasis HLB 96-well plateWatersWAT058951
Eppendorf concentrator plusEppendorf AG Hamburg5305000304
Centrifuge 5424 REppendorf5404000014
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Sobota, P. 1052 Nordlund, An efficient proteome- wide strategy for discovery and characterization of cellular 1053 nucleotide- protein interactions, PLoS One 13, 1- 30 (2018). 1054 1055 1056 1057 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 43, 312, 71]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 131, 315, 177]]<|/det|> +SupplementaryTable1. xlsx SupplementaryTable2. xlsx + +<--- Page Split ---> diff --git a/preprint/preprint__08a32d4f18a4d90852d0a1f5f66103659aefcc1d8e292575b94fd513a2b10b92/images_list.json b/preprint/preprint__08a32d4f18a4d90852d0a1f5f66103659aefcc1d8e292575b94fd513a2b10b92/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..8ee42558f811fc42a2bdacaad0ab3df4dc4202a6 --- /dev/null +++ b/preprint/preprint__08a32d4f18a4d90852d0a1f5f66103659aefcc1d8e292575b94fd513a2b10b92/images_list.json @@ -0,0 +1,93 @@ +[ + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "Figures", + "footnote": [], + "bbox": [ + [ + 117, + 131, + 977, + 382 + ] + ], + "page_idx": 23 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2: Quality assessment of 43 phased diploid assemblies and gene duplication analysis. a, Assembly length comparison. Box plot detailing the assembly length for different haplotypes across both male and female samples. b, Assembly contiguity. Line graph showing contig length plotted against cumulative assembly coverage. Notably, reference contiguity for both CHM13 and GRCh38 genomes are included for comparison. c, Assembly accuracy and completeness. Scatter plot illustrating the mapping rate against consensus accuracy (QV), offering insights into the completeness and accuracy of the assembly. d, Assembly alignment coverage. Scatter plot comparing the alignment coverage of assemblies relative to benchmark references CHM13 and GRCh38. e, Duplication ratio analysis. Bar graph displaying the distribution of duplication ratios across assemblies. f, Gene and transcript annotation. Scatter plot showing the percentages of protein-coding and noncoding genes, as well as transcripts annotated from the reference set in each of the assemblies. g, Gene duplication per assembly. Histogram presenting the number of unique duplicated genes or gene families in each phased assembly in comparison to the number of duplicated genes annotated in GRCh38. h, Comparative duplicated gene analysis. Venn diagram visualizing the overlap and unique counts of duplicated genes across APR, HPRC, and CPC assemblies. i, Arab-HPRC duplicated gene overlap. Bar graph showcasing five overlapped duplicated genes with a notably higher frequency (≥5%) in Arab assemblies (blue) compared to HPRC (orange). j, Arab-CPC duplicated gene overlap. Bar chart illustrating five overlapped duplicated genes with a significantly higher frequency (≥5%) in Arab assemblies (blue) in contrast to CPC (yellow). k, Unique Arab duplicated genes. Bar graph representing the frequency of top 20 unique duplicated genes in Arab assemblies when compared against both HPRC and CPC. l, Bar graphs indicating the count of unique duplicated genes across three chromosome types: acrocentric, metacentric, and submetacentric. m, Bar graph showing the", + "footnote": [], + "bbox": [ + [ + 128, + 95, + 978, + 410 + ] + ], + "page_idx": 24 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3: Arab genome specific sequences. a, Bar graph demonstrating the total number of small variants for each sample, distinguishing between singleton and polymorphic variants. b, Bar graph showcasing the small variants specific to APR per sample, further differentiating between singleton and polymorphic variants. c, Venn diagram showcasing the small variants from the Arab pangenome in relation to HPRC and CPC datasets. d, Stacked bar graph detailing the total structural variants (SVs) per sample, categorizing between singleton and polymorphic variants for both insertions and deletions. e, Stacked bar graph illustrating the SVs that are APR-specific for each sample, for both insertions and deletions. f, Venn diagram visualizing the overlap and differences in SVs from the Arab pangenome with HPRC and CPC datasets. g, Visualization of Arab-specific SVs from the pangenome graph across autosomes. Sites of complex SVs are marked with blue circles. h, Bar graph displaying the length distribution of newly identified", + "footnote": [], + "bbox": [ + [ + 121, + 191, + 875, + 629 + ] + ], + "page_idx": 25 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4: Visualizing complex structural variation region. a, PRAMEF region subgraph. Diagram showcasing the specific location of the PRAMEF genes. b, Sample haplotypes in PRAMEF Region. Distinct paths taken by different samples through the PRAMEF region. c, PRAMEF region haplotype count. Linear structural diagrams representing the frequency and", + "footnote": [], + "bbox": [ + [ + 130, + 135, + 940, + 780 + ] + ], + "page_idx": 26 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Fig. 5: Mitochondrial pangenome analysis. a, Mitochondrial read length distribution. Frequency distribution graph depicting the mitochondrial read lengths of Hifi data. b, A ring structured representation of the mitochondrial pangenome, detailing the position and nomenclature of annotated mitochondrial genes and their relationships within the pangenome. Each bubble or loop represents a haplotype. c, mtAPR variant distribution. A bar chart showcasing the number of APR-specific small variants observed across different samples, differentiated between polymorphism (in dark blue) and singleton (in light blue). d, Mitochondrial pangenome indel length distribution. A histogram presenting the distribution of", + "footnote": [], + "bbox": [ + [ + 140, + 270, + 844, + 700 + ] + ], + "page_idx": 27 + }, + { + "type": "image", + "img_path": "images/Extended_Data_Figure_2.jpg", + "caption": "Extended Data Fig. 2: Analysis of gene duplication patterns. a, Histogram presenting the frequency of various gene copy numbers observed within APR. The x-axis displays the gene copy number, while the y-axis represents the frequency of each copy number. b, Bar graph", + "footnote": [], + "bbox": [], + "page_idx": 29 + }, + { + "type": "image", + "img_path": "images/Extended_Data_Figure_6.jpg", + "caption": "Extended Data Fig. 6: Length distribution of CSV sites. Histogram showcasing number of CSV sites (log scale y-axis) falling within each length interval (log scale x-axis).", + "footnote": [], + "bbox": [], + "page_idx": 30 + } +] \ No newline at end of file diff --git a/preprint/preprint__08a32d4f18a4d90852d0a1f5f66103659aefcc1d8e292575b94fd513a2b10b92/preprint__08a32d4f18a4d90852d0a1f5f66103659aefcc1d8e292575b94fd513a2b10b92.mmd b/preprint/preprint__08a32d4f18a4d90852d0a1f5f66103659aefcc1d8e292575b94fd513a2b10b92/preprint__08a32d4f18a4d90852d0a1f5f66103659aefcc1d8e292575b94fd513a2b10b92.mmd new file mode 100644 index 0000000000000000000000000000000000000000..3412a7c19fe6295056149800a10f35420f438d05 --- /dev/null +++ b/preprint/preprint__08a32d4f18a4d90852d0a1f5f66103659aefcc1d8e292575b94fd513a2b10b92/preprint__08a32d4f18a4d90852d0a1f5f66103659aefcc1d8e292575b94fd513a2b10b92.mmd @@ -0,0 +1,458 @@ + +# A draft Arab pangenome reference + +Mohammed Uddin mohammed.uddin@mbru.ac.ae + +Mohammed Bin Rashid University of Medicine and Health Sciences https://orcid.org/0000- 0001- 6867- 5803 + +Nasna Nassir Mohammed Bin Rashid University of Medicine and Health Sciences + +Mohamed Almarri Mohammed Bin Rashid University of Medicine and Health Sciences + +Muhammad Kumail Mohammed Bin Rashid University of Medicine and Health Sciences + +Nesrin Mohamed Mohammed Bin Rashid University of Medicine and Health Sciences + +Bipin Balan Mohammed Bin Rashid University of Medicine and Health Sciences + +Shehzad Hanif Mohammed Bin Rashid University of Medicine and Health Sciences + +Maryam AlObathani Mohammed Bin Rashid University of Medicine and Health Sciences + +Bassam Jamalalail Mohammed Bin Rashid University of Medicine and Health Sciences + +Hanan Elsokary Mohammed Bin Rashid University of Medicine and Health Sciences + +Dasuki Kondaramage Mohammed Bin Rashid University of Medicine and Health Sciences + +Suhana Shiyas Mohammed Bin Rashid University of Medicine and Health Sciences + +Noor Kosaji Mohammed Bin Rashid University of Medicine and Health Sciences + +Dharana Satsangi Mohammed Bin Rashid University of Medicine and Health Sciences + +Madiha Abdelmotagali Primary Health Care Services Sector + +Ahmad Tayoun + +<--- Page Split ---> + +Mohammed Bin Rashid University of Medicine and Health Sciences + +Olfat Ahmed Mohammed Bin Rashid University of Medicine and Health Sciences + +Douaa Youssef Primary Health Care Services Sector + +Hanan Suwaidi Mohammed Bin Rashid University of Medicine and Health Sciences + +Ammar Albanna Mohammed Bin Rashid University of Medicine and Health Sciences + +Stefan Plessis Mohammed Bin Rashid University of Medicine and Health Sciences + +Hamda Khansaheb Mohammed Bin Rashid University of Medicine and Health Sciences + +Alawi Alsheikh-Ali Mohammed Bin Rashid University of Medicine and Health Sciences + +Biological Sciences - Article + +Keywords: + +Posted Date: October 26th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 3490341/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on July 24th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 61645-w. + +<--- Page Split ---> + +## A draft arab pangenome reference + +Nasna Nassir \(^{1,2}\) Mohamed A. Almarri \(^{2,3}\) , Muhammad Kumail \(^{1}\) , Nesrin Mohamed \(^{1}\) , Bipin Balan \(^{1,2}\) , Shehzad Hanif \(^{4}\) , Maryam AlObahtani \(^{1}\) , Bassam Jamalalail \(^{1}\) , Hanan Elsokary \(^{1}\) , Dasuki Kondaramaqe \(^{1}\) , Suhana Shiyas \(^{1,4}\) , Noor Kosaji \(^{1,2}\) , Dharana Satsangi \(^{2}\) , Madiha Hamdi Saif Abdelmotagali \(^{5}\) , Ahmad Abou Tayoun \(^{6,7}\) , Olfat Zuhair Salem Ahmed \(^{5}\) , Douaa Fathi Youssef \(^{5}\) , Hanan Al Suwaidi \(^{2}\) , Ammar Albanna \(^{2,8}\) , Stefan Du Plessis \(^{1,2}\) , Hamda Hassan Khansaheb \(^{9}\) , Alwai Alsheikh- Ali \(^{1,2,10*}\) , Mohammed Uddin \(^{1,2,11,*}\) + +1. Center for Applied and Translational Genomics (CATG), Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE +2. College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE +3. Department of Forensic Science and Criminology, Dubai Police GHQ, Dubai, UAE +4. Manipal Centre for Biotherapeutics Research, Manipal Academy of Higher Education, Manipal, Karnataka, India +5. Primary Health Care Services Sector, Dubai Academic Health Corporation +6. Al Jalila Genomics Center of Excellence, Al Jalila Children’s Specialty Hospital, Dubai, UAE +7. Center for Genomic Discovery, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE +8. AlAmal Psychiatric Hospital, UAE +9. Medical Education and Research Department, Dubai Academic Health Corporation +10. Dubai Academic Healthcare Corporation (DAHC), Dubai, UAE +11. GenomeArc Inc., Mississauga, ON, Canada + +## \*Corresponding authors: + +Dr. Mohammed Uddin, Associate Professor, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE, GenomeArc Inc., Mississauga, ON, Canada. Email: mohammed.uddin@mbru.ac.ae + +Dr. Alawi Alsheikh-Ali, Professor, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE. Email: alawi.alsheikhali@mbru.ac.ae + +<--- Page Split ---> + +## Abstract + +Human pangenomes provide a comprehensive portrayal of genetic diversity of humans, yet it lacks representation of Arab populations. We constructed the Arab Pangenome Reference (APR) from 43 individuals with diverse Arab ethnicities. Nuclear and mitochondrial pangenomes were constructed utilizing 35.52X High fidelity long reads and 53.54X ultra- long reads. This yielded high- quality contiguous (average N50=106.81 Mb) de novo assemblies that used over 99% of the sequences constructing haplotype phased diploid genome assemblies with 88% exhibited larger genome length (average 3.01 gigabase) than the prevailing human reference GRCh38. We discovered 100.93 million base pairs of novel euchromatic sequences that were not present in recent human pangenomes and in the human genome references (T2T- CHM13 and GRCh38). We identified 10.68 million population- specific small variants, 108,709 structural variants, and 838 genes (13.24% recessive disease genes) duplication from the Arab pangenome. On exploring the mitochondria pangenome, we uncovered 718 bp of novel sequences. Our study provides a valuable resource for future genetic research and genomic medicine initiatives in the Arab populations and other populations with similar genetic backgrounds. + +<--- Page Split ---> + +## Main + +The human genome, with its vast complexity and diversity, has opened up new avenues of research in genomics, leading to significant advances in our understanding of human biology, and disease. However, the application of such genomic advances is limited by the quality, completeness, and representativeness of the reference genome used in various studies1. In the pursuit of understanding the intricate tapestry of human genome variation, large- scale sequencing projects have provided invaluable insights2,3. Recently, due to significant advancement of long- read genome technologies, the first complete telomere to telomere (T2T) sequencing of a haploid human genome CHM13 was possible. This is a remarkable achievement that fills the \(8\%\) of gaps that existed in the current reference genome GRCh38 (ref.4). Although it represents the first complete genome, it does not reflect human sequence diversity and a population- wide approach is necessary to detect population specific variants, sequences and regulatory elements. The Human Pangenome Reference Consortium (HPRC) has made significant strides in cataloging unprecedented amounts of human genomic variations from a diverse set of global populations5. The HPRC pangenome derived from 47 samples with diverse ethnicities, added an additional 119 million base pairs of euchromatic polymorphic sequences and identified 1,115 gene duplications compared to the current reference GRCh38. The application of the reference pangenome resulted in a \(104\%\) increase in the number of structural variants detected per haplotype compared to GRCh38 (ref.5). The recent construction of a pangenome from 58 samples representing 36 ethnic minorities in China reported 5.9 million small variants and 34,223 structural variants that were not reported in the HPRC multiehtnic global pangenome6. + +Arabs constitute culturally diverse communities with a combined population of nearly 500 million, comprising about \(5\%\) of the global population. They are unfortunately underrepresented in global sequencing projects (i.e. gnomAD database), with neither the HPRC pangenome nor the 1000 Genomes project sampling any Arab population7. The ancestral complexities between different Arab ethnic backgrounds are yet to be understood through large- scale genomic initiatives8,9. Moreover, the Arab population has a higher incidence of consanguinity, which leads to an increased rate of rare recessive disorders10- 12. The incidence of diabetes, heart disease and cancer are on the rise within Arab populations13,14. The lack of reference genomes for Arab populations has limited the investigation of genetic diversity and the genetic underpinning of numerous diseases; Population- specific reference pangenomes will enable the probing of disease associations with variants and sequences that are unique or prevalent in Arab populations. + +In this study, we sought to generate a comprehensive Arab Pangenome Reference (APR) using multiple long- read sequencing technologies and de novo assembly construction approaches. We present the first draft construction of the APR from 43 human genomes with high quality de novo diploid phased assemblies and their comparison with the HPRC GRCh38 reference genome and 1000 Genomes Project datasets. Our analysis also shows the application of the APR into + +<--- Page Split ---> + +functional annotation, short and long- read whole genome mapping for variant detection, and efficiency in mRNA transcript mapping. We anticipate our effort will produce pangenomes to allow population specific genome interpretation, unbiased and less gaps in the genome and will enable the high precision detection of structural and small variants. + +## Healthy arab sample cohort + +To construct a pangenome reference from healthy Arab individuals, we enrolled 40 unrelated adults (18 years or older) and a family trio with no known rare or common chronic diseases (i.e. no history of hypertension, diabetes mellitus, cancer, lung or heart disease). We collected extensive phenotype data over multiple clinic visits for all of our 43 study samples. Our cohort consists of 25 female and 18 male Emirati nationals from Dubai, UAE. Our comprehensive phenotypic assessment data confirmed (Supplementary Table 1) all individuals within our Arab study cohort have no identifiable common chronic diseases (i.e. diabetes, hypertension, cancer). All individuals were clinically assessed as healthy at the time of recruitment, and laboratory tests were within the normal range for all with the exception of some individuals with elevated serum cholesterol levels (Fig. 1a). + +## Assessment of sequencing quality + +The genomes were sequenced by employing both Pacific Biosciences (PacBio) high fidelity (HiFi) and Oxford Nanopore Technologies (ONT) ultra- long read sequencing kit (ULK) methodologies. Each sample yielded an average depth of coverage of 35.52X and 53.54X for PacBio HiFi and ONT ultra- long reads, respectively (Supplementary Fig.1). The sequencing quality yielded an average median Q score of 32.85 for Pacbio and 17.39 for ONT. Average N50 values of 16.78 kb and 58.55 kb for HiFi and ONT ultra- long read were obtained, respectively (Fig. 1b and Supplementary Table 2). Notably, the ultra- long reads surpassing 100 kb resulted in a substantial yield of 1,708 Gb, translating to 569.33X coverage (an average of 13.24X per sample) (Fig. 1c, Supplementary Table 3). When mapped against the CHM13 v2.0, both HiFi and ONT reads brought valuable insight on the capacity of sequencing coverage across accrocentric and metacentric chromosomes. Due to ultra- long protocols, ONT reads exhibited better mapping to all chromosomes compared to PacBio, particularly in the accrocentric chromosomes where they achieved 99.49% coverage, in comparison to 94.95% by PacBio (Fig. 1d, Supplementary Fig. 2, Supplementary Table 4a). We utilized both read types to scrutinize coverage across diverse regions of the human genome such as satellite DNA, centromeric transitions, and ribosomal DNA. Both platforms excelled in terms of coverage, with regions like AlphaSat_div, AlphaSat_mon, GammaSat, and Other CenSat exhibiting coverage above 95%. However, the ONT platform demonstrated a marked increase in rDNA coverage, with 99.92% and 62.40% for ONT and HiFi reads, respectively (Fig. 1e, Supplementary Table 4b). Our joint calling analysis incorporating Deepvariant (GRCh38) for HiFi data, identified an average of 4,421,702 single nucleotide variants (SNVs) and 847,117 indels (Supplementary Table 5a,b + +<--- Page Split ---> + +Supplementary Fig. 3, 4). We identified population specific variants comprising 5,663,728 SNVs and 1,717,397 indels, which were not previously reported in 1000 Genome project data \(^2\) . + +## Population structure of APR samples + +Through verbal interviews, \(30\%\) individuals confirmed migratory history of their recent ancestors from other Arab or middle eastern countries (Supplementary Table 6). Initial principal component analysis (PCA) showed distinct clustering from 1000 Genomes Project samples (Fig. 1f; Supplementary Fig. 5a,b). Haplotype sharing information confirmed the unrelated status of all 40 samples (Supplementary Fig. 5c). In addition, we have conducted PCA using genomic data from 11 Arab ethnic groups. Our analysis suggests the APR cohort samples ancestry are represent multiple different Arab subpopulations compare to the major continental populations (Extended Data Fig. 1). + +## Assembly construction of diverse Arab genomes + +Our analysis on the trio suggests comparable contig length between Hifiasm \(^{15}\) and Verkko \(^{16}\) (Supplementary Table 7). For individual samples without parental genome data, Hifiasm outperformed in terms of contig length and contiguity. Owing to Hifiasm's performance with non- trio samples, we performed de novo assembly using Hifiasm for our entire cohort and used those assemblies for downstream analysis. Assembly quality control was executed using Kraken, filtering out non- human eukaryotic pathogen genomes, ultimately retaining contigs classified as human. We removed 85 contigs that consisted of mostly mitochondrial sequences (Supplementary Table 8). One aberrantly long 242 Mb contig was manually corrected by removing small fragments of mitochondrial sequences. To resolve the mapping ambiguity of the pseudoautosomal region 1 (PAR1) homologous sequences in the assembly, we performed a contig splitting procedure based on the breakpoint boundaries identified by aligning the contigs to the X and Y reference chromosomes. Given PAR1's inherent repetitive nature and its recombination frequency, 35 contigs were split that contained PAR1 sequences and mapped to both X and Y chromosomes, resulting in two separate contigs for each original one (Supplementary Table 9). This enhanced the assembly precision by mitigating potential misassemblies caused by PAR1. Misjoins in the assemblies were catalogued based on their sequence identity (Supplementary Fig. 6, Supplementary Table 10). + +The resulting assemblies yielded an average genome size of 3.01 Gb per sample (Fig. 2a, Supplementary Table 11), with over \(88.37\%\) of assemblies surpassing the GRCh38 benchmark of 2.94 Gb. On average, assemblies comprised of 139 contigs with contig N50 length of 106.81 Mb (Fig. 2b). This exceeds the GRCh38 contig N50 of 57.88 Mb, with \(95.34\%\) of our assemblies having greater contiguity. All samples surpassed the 40 Mb N50 of the HPRC graph genome reference (Supplementary Fig. 7). The average QV scores of our assemblies, computed using yak v0.1-r66 was 57.05, indicating robust assembly quality (Fig. 2c). We compared our assemblies to the CHM13 and GRCh38 reference genomes and found that they covered on average \(93.61\%\) + +<--- Page Split ---> + +(92.62% for male, 94.32% for female) and 96.64% (95.39% for male, 97.53% for female) of each, respectively (Fig. 2d). Notably, fewer inversions and translocations indicated reduced structural discrepancies (Supplementary Fig. 8). There was an average of 30.84 Mb and 76.83 Mb of assembled contigs that did not align with CHM13 and GRCh38 respectively (Supplementary Fig. 9). This highlights the under- representation of the Arab genome diversity in these references. The duplication ratio, indicative of read mapping multiplicity against references, averaged 1.03 and 1.02 for GRCh38 and CHM13 respectively (Fig. 2e). These comprehensive quality control assessments verify the high- contiguity and accuracy of our de novo assembled genomes, providing a premier foundation for downstream genomic and clinical analyses. + +## Genomic features of the APR assemblies + +To investigate gene duplication events within Arab pangenome diploid assemblies, we employed liftoff v1.6.3 tool \(^{17}\) , and annotated using Gencode GRCh38.p14 (Gencode release 44). The resulting gene duplications revealed novel and diverse genomic features of the Arab population. Remarkably, a median of \(98.91\%\) protein- coding genes and \(99.12\%\) protein- coding transcripts were identified in each APR assembly (Fig. 2f). Each genome had an average of 31 genes with a gain in copy number relative to GRCh38 (Fig. 2g, Supplementary Table 12). A significant \(24.48\%\) of genes manifested a frequency surpassing \(5\%\) across all samples, with only \(11.51\%\) of these duplicated genes being exclusive to a single haplotype. + +We did a comparative assessment of gene duplications within the APR, HPRC, and CPC assemblies to identify genes that were specific to the Arab population. We identified 1039 duplicated genes unique to the APR assemblies, absent in the HPRC assemblies, and a further 838 duplicated genes absent in both HPRC and CPC assemblies (Fig. 2h, Extended Data Fig. 2 and Supplementary Table 13). There were 34 duplicated genes from the APR assemblies that were also observed in the HPRC and CPC assemblies. Among these overlapped genes, we found genes with a higher frequency \((\geq 5\%)\) in the APR assemblies than in the HPRC and CPC assemblies (Fig. 2i, 2j), of which USP17L genes were more frequent in APR. USP17L genes encode ubiquitin- specific proteases, which are involved in various cellular processes such as protein degradation, DNA repair, and cell cycle regulation. + +Of the APR unique duplicated genes, the TAF11L5 gene showed considerably high frequency in the APR assemblies (86 of 86) but were absent in the HPRC and CPC assemblies (Fig. 2k). TAF11L5 gene encodes TATA- box binding protein associated factor, which is predicted to be involved in the assembly of the RNA polymerase II preinitiation complex, a large complex of proteins that initiates transcription of protein- coding genes. These unique gene duplications were predominantly harbored within submetacentric chromosomes (Fig. 2l), majorly localized to chromosomes 16 and 1 (Fig. 2m). Conversely, the acrocentric chromosomes had a low number of duplication events. The unique gene duplications were notably enriched in microsatellite regions (151 out of 838), mostly in centromeric satellites (Fig. 2n, Supplementary Table 14). + +<--- Page Split ---> + +Furthermore, \(13.24\%\) of APR unique duplicated genes have been implicated in recessive conditions compared to \(11.79\%\) in HPRC unique and \(9.40\%\) in CPC unique duplicated genes. On the Horizon platform, \(2.14\%\) of unique duplicated genes were annotated as pan- ethnic disease genes associated with severe phenotypes18 (Supplementary Table 15). These genes were enriched significantly in ribonucleotide metabolism ( \(p< 6.22 \times 10^{- 4}\) ) and exocytosis regulation ( \(p< 6.44 \times 10^{- 3}\) ) pathways (Supplementary Table 16). + +## Pangenome graph construction and Arab genome-specific variants + +We constructed a pangenome graph of 43 Arab genomes using Minigraph Cactus (v2.6.7)19, integrating 86 long- read assemblies into a graph structure. The graph was seeded with the CHM13 and GRCh38 reference genomes and expanded using Minigraph to incorporate the new assemblies, resulting in a total length of 3,307,861,124 bp, and 72,256,745 nodes and 99,652,737 edges, whereas HPRC had a total length of 3,328,787,872 bp (Supplementary Table 17, Supplementary Fig. 10). We compared the Arab pangenome graph with the HPRC and CPC pangenome graph, which represents a diverse set of human genomes from different populations. We identified each individual genome containing an average of 5,044,179 total and 743,379 unique small variants (Fig. 3a, 3b), 10.68 million small variants were unique to APR (Fig. 3c). We observed each sample containing an average of 17,634 total and 8,302 unique SV (Fig. 3d, 3e), yielded 108,709 SVs that were unique to APR (Fig. 3f, Extended Data Fig. 3, Supplementary Table 18). + +The average SV length was 2.52 kb and median 246 bp (Extended Data Fig. 3), this validated the assembly integrity by showing Alu and LINE- 1 repeat content along the chromosomes. We further analyzed the Arab- specific SVs along the autosomes (Fig. 3g, Supplementary Table 19), harboring 8269 unique genes (Supplementary Table 20). These regions may harbor important genomic features that are specific to the Arab population. Genes affected by novel SVs in the APR were enriched in drug response ( \(p< 1.52 \times 10^{- 12}\) ), cytokine signaling and leukocyte activation ( \(p< 1.42 \times 10^{- 11}\) ) and nervous system development ( \(p< 1.54 \times 10^{- 10}\) ) pathways (Supplementary Table 21). + +## New euchromatic sequences in APR + +The pangenome graph added 100.93 Mb of non- reference sequences from the 86 diploid Arab genomes, including 92.16 Mb of singletons. The average non- reference sequence length per individual was 2.49 Mb (range 1.59- 3.86 Mb) (Fig. 3h, Supplementary Table 22). We also measured the amount of novel sequences that are not present in GRCh38, T2T- CHM13, HPRC or CPC, revealing that each Arab diploid genome harbored an average of 1,190 novel sequences (Supplementary Fig. 11). A small percentage of the total novel sequences were present in centromeric and telomeric regions, \(1.08\%\) and \(0.35\%\) , respectively (Supplementary Fig. 11). Nearly one- fifth (19.22%) of novel sequences were enriched in microsatellite regions (Fig 3i), especially in pericentromeric satellite, HSat3 and other centromeric satellites. To characterize the + +<--- Page Split ---> + +context of these novel sequences located within 20,267 loci, we performed repeat annotation using RepeatMasker, and we found LINE, SINE, LTR and satellite repeats constituted \(4.39\%\) , \(6.05\%\) , \(4.37\%\) and \(31\%\) of novel sequences, respectively (Extended Data Fig. 4a,b; Supplementary Table 23). We identified 20,267 loci with novel sequences. These novel sequences may represent novel functional elements or structural variations that are missed by conventional methods. These results demonstrate the power and utility of using long- read sequencing and pangenome graph construction to capture the genomic diversity and complexity of human populations. + +## Complex structural variation in the APR pangenome graph + +We used a pangenome graph to visualize and analyze complex structural variation (CSV) in APR samples. A complex structural variation site was defined as an SV site with at least one 10kb SV with minimum 5 haplotypes and we found that \(0.88\%\) (715 out of 80,763) of these SV sites were complex multiallelic bubbles (Supplementary Table 24). The CSVs were dispersed across all chromosomes, and they exhibited a notable concentration on acrocentric chromosomes (average 36.8 CSVs), particularly chromosome 22, which had the highest count. APR CSVs were predominantly located in pericentromeric satellite (in 16 autosomes), HSat3 and alpha satellite DNA (Fig. 3g and Extended Data Fig. 5). We found that the peak of CSV length distribution was at 10 kb (Extended Data Fig. 6). Utilizing rigorous criteria, we've delineated complex structural variation regions as areas within a 100- kilobase (kb) window that consist of two or more multiallelic SV sites, with each site containing at least one 10- kilobase (kb) SV within the haplotypes. This endeavor led to the discovery of 646 CSV regions. The genes (Supplementary Table 25) nestled within this 100kb window of complex regions were enriched in ubiquitin \(\mathrm{(p< 5.17x10^{- 11})}\) and cysteine peptidase activity \(\mathrm{(p< 8.57x10^{- 11})}\) pathways (Supplementary Table 26). We further investigated the potential functional impact of these complex variant regions by overlapping them with APR specific SVs (Supplementary Table 27). + +We compared the allelic variations of the APR against those of combined HPRC+CPC pangenomes in two regions of clinical relevance: the PRAMEF (Fig. 4a, Supplementary Table 28) and CT45A gene regions (Fig. 4b). In the PRAMEF region, we observed a common allele present in HPRC+CPC, also present in \(88.37\%\) of APR samples, while others were unique haplotypes to APR pangenome. We observed 2 novel haplotypes that were exclusively found in \(11.63\%\) of the Arab cohort. Notably, PRAME (Preferentially expressed antigen in melanoma) belongs to a group of cancer/testis antigens that are mainly expressed in the testis and an array of tumors, with pivotal roles in immunity and reproduction20. Delving into the CT45A region, we observed that the allele present in HPRC+CPC were absent in APR samples. We observed one APR specific novel allele absent in HPRC+CPC and one novel allele exclusively present in \(27.90\%\) of APR samples (absent in HPRC+CPC, CHM13 and GRCh38). CT45A genes encode cancer/testis antigen family 45, with known associations to pediatric acute leukemia21. Furthermore, allelic variations were observed in the HLA- DRB region which were absent in the + +<--- Page Split ---> + +references (Supplementary Fig. 12). Other CSV regions containing CYP2D6 showed variable haplotype presence within APR that are drastically different compared to CHM13 or GRCh38 references (Supplementary Fig. 13). The pangenome graph representation enabled us to discover new complex haplotypes that may contribute to numerous disorders, as well as to trace the origin and distribution of these haplotypes across the cohorts. + +## Mitochondrial pangenome construction + +The mitochondrial Arab pangenome (mtAPR) was constructed from high- quality long- reads from 43 individuals (Supplementary Fig. 14), with Hifireads mapping to ChrM greater than 15kb (Fig. 5a), with an average length of 16.28 kb. The graph consisted of 32,048 nodes and 56,607 edges when using CHM13 as reference (32,059 nodes and 56,735 edges when using GRCh38 as reference) (Fig. 5b). The graph showed a complex and diverse structure that reflected the genetic diversity of human mitochondrial DNA. We annotated the reads using the chrM annotation from GENCODE (v44) \(^{22}\) and identified the genes. We detected 60,812 single nucleotide variants (Fig. 5c) and 46,865 insertions/deletions (indels) (Fig. 5d) in the graph (Supplementary Table 29). We observed 11,801 unique small variants, of which 11,259 are singleton and 6,213 are polymorphic. Insertions \(> = 10\mathrm{bp}\) were extracted from the mitochondrial pangenome variant calling file and subsequently, the sequences were clustered using the cd- hit- est program with a \(90\%\) similarity threshold (- c 0.9) to obtain the novel insertions (Supplementary Table 30). Mitochondrial novel sequences not present in the references accounted for 718 bp. Additional analysis on gene duplication within the assemblies did not reveal any new gene duplication event. + +## Performance gains for pangenome-aided analysis + +We evaluated the impact of pangenome- based analysis on several aspects of genomic discovery. We aligned short reads from previously sequenced 21 Arab samples \(^{8}\) to the APR graph using Giraffe mapper \(^{23}\) and compared the results with the alignment to GRCh38 using BWA- MEM. We found that aligning to the pangenome graph, average mapping rate increased to \(85.01\%\) using APR compared to \(82.19\%\) using CHM13 (pval \(= 1.45\mathrm{x}10^{- 17}\) , paired t- test) (Supplementary Table 31). On average, \(2.82\%\) fewer reads mapped to CHM13 after graph alignment, indicating that these reads were better represented by the pangenome graph. We also assessed the long- read WGS mapping accuracy of 2 autism spectrum disorder (ASD) trios to the pangenome from the Arab cohort (Supplementary Table 32). These results suggest that pangenome- based analysis can improve the sensitivity and specificity of long- read variant detection. + +## Discussion + +We constructed a pangenome reference specific to the Arab population from 43 deeply phenotyped apparently healthy adults by processing 86 high- quality assemblies. These samples underwent careful phenotyping to monitor manifestations of the onset and progression of chronic complex diseases. Applying 35.52X HiFi reads and 53.54X ultra- long reads, we achieved N50 of + +<--- Page Split ---> + +106.81 Mb, which is 2.67- fold longer N50 than recently reported in HPRC. de novo assembly algorithms used \(99.96\%\) of all available long- read sequences to construct the contigs. Our average assembly quality score QV 57.05 suggests high quality assemblies underlying the pangenome. The average haplotype of APR samples shows \(93.61\%\) and \(96.64\%\) coverage of CHM13 and GRCh38, respectively, which is on par with HPRC and CPC assemblies. The coverage is lowest for the Y chromosome due to its intricate Yq12 heterochromatin repeat complex region. The construction of a high resolution reference pangenome for the Arab population brought novel insight into the unique genome organization within these populations. While HiFi provided the base with highly accurate long- reads, the ONT ultra long- reads contributed significantly to filling the gap on scaffolding and covering complex regions of APR genomes. The ULK data exhibited a coverage exceeding \(13x\) for each APR sample, contributing significantly to the extended N50 length. Our assemblies added 100.93 million bases of new euchromatic sequences that are outside of references (GRCh38, CHM13, HPRC and CPC) and reported 10.68 million population specific small variants, 108,709 structural variants and 838 protein coding gene duplications across the pangenome. + +The CHM13 reference is the most complete human genome to date and GRCh38 has the most complete annotations of functional elements. The Arab pangenome was constructed based on CHM13 by integrating GRCh38 and the APR assemblies. We observed a significant overlap between APR and HPRC+CPC common variants. In addition, we identified 6,467,909 novel SNVs and 4,212,811 indels that are outside of the reference genomes and the recently reported pangenomes (HPRC and CPC). A total of 10.68 million previously undiscovered small variants impacted 19,626 protein- coding genes. We anticipate that application of APR in clinical settings will markedly increase the diagnostic yield for numerous single gene disorders. The identified population- specific variants illustrate the old separation time of Middle Eastern populations from other continental groups and the subsequent affect of isolation and drift8. Unfortunately, the lack of representative data from the Middle Eastern populations towards large genomic initiatives (i.e. 1000 Genome project, gnomAD) did not generate impactful genomic resources for this region. To assess clinical significance of thousands of novel APR specific variants will require further disease cohort based analysis. + +APR specific structural variants reveal 100.93 Mb sequences (1.59- 3.86 Mb range of new sequences per haplotype) that are not present within the CHM13, GRCh38 references or the pangenomes (HPRC and CPC). These novel sequence show enrichment of highly repeated satellites which are inaccessible by short read technologies. A linear reference genome is limited by its static sequences that are unable to decipher the polymorphic nature of SVs. The clinical application of APR pangenome based SV detection will increase the clinical yield due to large number uncharacterized SVs. Moreover, will enable the detection of base resolution phased haplotype complexities to infer their association with diseases. The SVs have a profound effect on how APR differs from HPRC or CPC graphs. APR identified 108,709 unique SVs (average + +<--- Page Split ---> + +8,302 SVs per sample) that will create different bubbles or haplotype walks (involving genes) within these genomic regions compared to other pangenomes or human genome references. Our results suggest a larger sample size will produce a more accurate and comprehensive pangenome capturing additional rare gene duplication events and novel sequences. The rare genetic variants will have the potential to increase diagnostic yield for rare genetic diseases and pan cancer. From short read whole genome data of Arab individuals, we observed improved paired read mapping accuracy in APR compared to CHM13. Transcriptome annotation is highly complete, particularly for protein- coding transcripts that show approximately \(99\%\) mapping rate. Short or long read sequence alignment, variant calling and phasing accuracy workflows are still tedious for clinical labs to adopt. As we see mapping and variant calls improving using APR, with time the usability will significantly increase. + +Our APR specific gene duplication analysis identified 838 unique genes that are involved in ribonucleotide metabolism and exocytosis regulation pathways. Of the duplicated genes identified, \(13.24\%\) have been associated with recessive conditions. This is particularly notable due to the higher rate of consanguinity within Arab populations, predisposing them to higher homozygous stretches. Similar to previous reports24, gene duplications are highly active in segmental duplication regions. TAF11L5 is the most duplicated gene that encodes TATA- Box Binding Protein (TBP) predicted to be involved in RNA polymerase II preinitiation complex assembly. Our result suggests that TBP is a functionally active element within the Arab populations. The core conserved region of TBP that is also duplicated within APR is predominantly involved in double- stranded nucleic acid binding and that enables transcription initiation25. + +The mitochondrial APR consists of 718 bp of new sequence that is not found within the GRCh38 mitochondria. The diversity of haplotypes and identified variants shows a comprehensive reference for Arab mitochondria. Total mtAPR variant impacted \(37.49\%\) of the GRCh38 mtDNA bases, cataloging healthy heteroplasmy mitochondrial haplotypes within the Arab populations. Since the APR cohort was clinically assessed as healthy, the constructed Arab mitochondria pangenome reference will provide heteroplasmy frequencies that will be a valuable resource to determine mitochondrial diseases. + +The understanding of true human genomic diversity and complexity depends on the collective effort of producing numerous pangenomes across different ethnicities. Technological limitations are still an issue to decipher precisely the heterochromatic regions, specifically near the centromeres. Over time, sequencing technologies and algorithmic development will improve to parse out repeat complexity regions. Although we understand the current study requires more samples to capture sequence diversity among Arab populations, the APR will provide a foundational resource for human genetic studies. Large genomic databases have a notable + +<--- Page Split ---> + +underrepresentation of Arab populations, and the APR will address this gap by offering essential resources for clinical genomic laboratories to enhance the precision of variant interpretation. + +## Methods + +## Sample collection and phenotyping + +We recruited and collected 8- 10 ml blood samples from 43 healthy individuals (one trio and 40 unrelated individuals) of Arab descent from the United Arab Emirates (UAE). Informed consent was obtained from each participant. All participants underwent extensive phenotyping, including clinical, biochemical, and anthropometric assessments as well as review of their electronic health records (Supplementary methods section 1). In addition, we included a family trio (mother, father, and child) for in- depth analysis. The inclusion criteria was self- identified Arabs who are 18 years or older willing to participate voluntarily and are presumptively healthy (free from diseases such as diabetes, cardiovascular disease, hypertension, chronic kidney disease, lung disease and liver disease). To map long- read genome sequence data into APR, we recruited 5 trios, children with autism spectrum disorders (ASD). Furthermore, we processed short read illumina whole genome sequencing data from 21 Emirati individuals8. This study was approved by the Institutional Review Board at Dubai Scientific Research Ethics Committee (DSREC- 12/2022_01, DAHC/MBRU- IRB/2023- 15), and and Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU- IRB- 2017- 004). + +## DNA isolation and sequencing + +## Pacific Bioscience High Fidelity sequencing + +For Hifi long- read sequencing of APR cohort samples and 2 ASD trios, high molecular weight DNA was isolated from \(200 \mu L\) of flash frozen blood using both GenFind V3 and Nanobind DNA extraction kits per manufacturer's instructions. For shearing, DNA concentration was first measured using Qubit 3 Fluorometer with a dsDNA HS Assay kit (Thermo Fisher) and samples were adjusted to \(30 \mathrm{ng} / \mu \mathrm{L}\) in a total volume of \(130 \mu \mathrm{L}\) . DNA samples were then loaded on Diagenode Megaruptor 3 hydropore- Syringe at a speed of 31 targeting 15- 18 kb fragments length as recommended by the PacBio protocol. The sheared samples were run into 4200 Tapetation system to check the fragments length using Genomic DNA screen tape. SMRTbell libraries were prepared for sequencing using the SMRTbell Prep Kit 3.0. The libraries were attached to Sequel II primer 3.2 and Sequel II DNA polymerase 2.2 and sequenced on PacBio Sequel IIe equipment using SMRT cells 8M and Sequel II Sequencing kit 2.0. The runs were created to allow adaptive loading, 2 h of pre- extension and 30 h of movie times. For each sample on Sequel 11e, three SMRT cells were used to produce at least 30x coverage. 9 samples were run on Revio (Supplementary methods section 2). + +<--- Page Split ---> + +## ONT ultra-long DNA sequencing: + +ONT ultra- long DNA sequencing:For Oxford nanopore sequencing, blood samples from APR cohort samples were aliquoted into 1.8 ml cryovials and stored at \(- 80^{\circ}\mathrm{C}\) until further processing. Frozen aliquots were thawed and peripheral blood mononuclear cells (PBMCs) were isolated using red blood cell lysis buffer. The purified PBMCs were counted and \(\sim 60\) million cells per sample were carried forward for ultralong DNA extraction using the NEB Monarch Tissue DNA Extraction Kit. The extracted DNA was then prepared into sequencing libraries using the Oxford Nanopore Technologies Ultra- Long DNA Sequencing Kit (SQK- ULK114) following the manufacturer's protocol with minor modifications. Briefly, the DNA was tagnmented at room temperature for 10 min followed by heating at \(75^{\circ}\mathrm{C}\) for 10 min. Rapid sequencing adapters were then added and incubated for 30 min at room temperature. Cleanup of the adapted DNA was performed using the precipitation star and buffers provided in the kit. The final libraries were quantified and loaded onto a minimum 3 PromethION flow cells at approximately \(30\mathrm{ng}\) per flow cell (Supplementary Table 2). Sequencing was performed for 96 hours per flow cell using Oxford Nanopore latest R10.4.1 kits. Minimum read length was set to 1 kb. + +## Sequencing and variant QC + +Sequencing and variant QCFor ONT sequencing data, base- calling was performed using Oxford Nanopore's High Accuracy model utilizing Guppy basecaller v6.5.7 with modifications for detecting 5- methylcytosine (Supplementary methods section 3). We employed the High Accuracy (HAC) model to maximize the precision of the derived base sequences. The PacBio sequencing data were processed using the Circular Consensus Sequencing (CCS) algorithm. In the subsequent quality control phase, we employed the NanoStat v1.6.0, which provides an in- depth statistical overview of the sequence data. Additionally, metrics obtained from the smrtlink software were incorporated into the analysis to provide a comprehensive assessment. For refining the PacBio reads, HiFiAdapterFilt v2.0.1 was used to remove any residual adapter sequences that might hinder the assembly process. + +<--- Page Split ---> + +The PacBio human whole genome sequencing (WGS) pipeline was utilized for read alignment and variant calling. Reads were aligned to the GRCh38 and CHM13 reference genomes using pbmm2 v1.10.0. The CHM13 reference used was chm13v2.0_maskedY_rCRS.fa, while hg38 reference used was human_GRCh38_no_alt_analysis_set.fasta. Variant calling was then performed using Deepvariant v1.15.0. For SV calling, pbsv version 2.9 was utilized to jointly call SVs from the aligned long- reads. To identify small variants, DeepVariant and GLnexus (v1.4.1) were applied for joint SNP and indel calling. Variant annotation was performed using sliVAR (v0.2.2) for small variants and svPACK for SVs, which adds functional predictions and other annotations to the raw variant calls. We compared the number and types of variants detected in our samples using bcftools to those reported in the 1000 Genomes Project. We reported the number of SNPs, indels, and SVs, the ratio of base substitutions, and the indel length distribution for each sample. We used bedtools (Version 2.31.0) to compute the coverage of individual chromosomes and pericentromeric regions across all samples. + +## Population ancestry inference + +To discern the genetic diversity and ascertain the population structure of 43 APR genomes, Principal Component Analysis (PCA) was undertaken. We employed SNPRelate (v1.28.0) \(^{26}\) . For a merged dataset with APR (HiFi variants), 1000 Genomes (n=2,879 samples), and 11 Arabian subpopulation samples (n=330 samples), 1,700,155 SNPs per sample were utilized and we evaluated. Higher order PCA (1 and 2) was used for visualization. Next, from merged dataset with APR and Arabian subpopulation, total 8,012,255 SNPs were incorporated, and 32 principal components were evaluated for better representation of the variance among the populations. Since the samples within Arab ethnicities are very tightly clustered (Supplementary Fig 5), PCA was plotted using PC8 and PC21 for the Arab ethnicities where it shows the most visible membership. To confirm the unrelated status of all 40 APR samples, we have constructed a heatmap using haplotype sharing variance obtained from fineSTRUCTURE \(^{27}\) runs on the PCA SNPs. We applied ADMIXTURE tool \(^{28}\) to dissect the genetic diversity of all 43 APR samples and population inference form global ancestry. We have used three major continental populations (Han Chinese, Yoruba, and French) and 11 Arabian subpopulations. ADMIXTURE was run on the combined bed file using a SNPs set where we kept SNPs from every 10kb, that yielded 259,712 SNPs. For population k number of clustering analysis, we have used k ranging from 2 to 12. All 43 APR samples were labeled, and ADMIXTURE plot was produced with vertical lines color coded according to the \(k\) value. + +## de novo genome assembly + +High- quality de novo assemblies were generated for each individual using a hybrid assembly approach, combining PacBio HiFi and ONT ultra- long reads. A total of 43 samples were sequenced on 1- 6 SMRT Cells and 3- 7 ONT flow cells. We combined HiFi reads and ultra- long DNA sequencing kit (ULK) reads from multiple cells of each sample and applied Hifiasm + +<--- Page Split ---> + +(v0.19.5- r593) \(^{15}\) to carry out both the primary assembly and diploid assembly for 43 samples (Supplementary Methods section 4). + +## Genome assembly polishing + +Genome assembly polishingGenome assemblies were screened for contaminant sequences and polished to remove artifacts prior to analysis. Kraken \(^{29}\) was used to taxonomically classify assembled contigs, retaining only those annotated as human. Mitochondrial read pairs were identified by mapping to chrM using minimap2 v2.26 and samtools v1.6 and any corresponding mitochondrial contigs were removed. Due to large homologous sequences at the pseudoautosomal regions (PAR), contigs were misaligned between the sex chromosomes. PAR within individual samples were identified by performing blast search using the CHM13 reported PAR1 sequences (chrX:1- 2394410; chrY:1- 2458320) to the assembly. Only the contigs containing both X and Y PAR regions were taken into account, and the PAR region with the highest similarity (with a cutoff of blast \(\%\) identity \(> = 90\) ) was chosen. Contigs with flanking region lengths exceeding 1MB were the only ones subjected to splitting. The sequence headers of the contigs were modified by adding suffix "XP" or "YP" based on the selected PAR region and the headers of the splitted contig headers were modified by putting suffix "XP1", "XP2", "YP1", "YP2" based on the PAR region (Supplementary Table 9)(Supplementary Methods section 5). + +## Assembly quality assessment + +We assessed the quality and structural integrity of 43 primary assemblies and 86 haplotype assemblies using QUAST (v5.2.0) \(^{30}\) including completeness, N50, number of contigs, etc. This comprehensive tool was run with an extensive parameter set, as detailed: quast.py - o APR043. chm13v2.0 - r chm13v2.0. fa - t 16 APR043. bp. hap1. p_ctg. fa APR043. bp. hap2. p_ctg. fa - - large - e + +The yak suite v0.1- r66 of tools \(^{15}\) , including yak count and yak qv, were employed for genome quality validation. Mapping rate was assessed using minimap2 v2.26. Potential misjoins in the assembly were identified using the minigraph software, followed by pafools. The specific commands initiated were - cxasm chm13v2.0. fa APR043. bp. hap1. p_ctg. fa and pafools misjoin - e APR043.1. misjoins. paf, respectively (Supplementary Methods section 6). + +## Gene duplication + +We used Liftoff \(^{17}\) , a tool that accurately maps gene annotations between genome assemblies, to identify gene duplications in our data. Liftoff aligns gene sequences from a reference genome to a target genome and finds the mapping that maximizes sequence identity while preserving the gene structure. To remove partial matches from the analysis, we used the - exclude_partial option of Liftoff, which excludes partial mappings below the sequence identity (90%). We then counted the frequency of copy number variation (CNV) for each gene in the target genome by comparing + +<--- Page Split ---> + +the number of mapped copies to the number of copies in the reference genome (Gencode GRCh38.p14 (Gencode release 44)). + +## Pangenome graph construction + +We constructed Arab pangenome variation graphs using the Minigraph- Cactus pipeline (v2.6.7) \(^{19}\) , which combines structural variants and single nucleotide variants (SNVs) into a single graph representation. We used two reference genomes, GRCh38 and CHM13, as backbones for the graph construction following the steps described in the Minigraph- Cactus documentation. Initially, an SV graph (>50bp) was constructed using Minigraph (v0.19) \(^{31}\) by sequentially aligning the 86 assemblies to the reference genome. Centromeric and telomeric regions were masked using dna- brnn \(^{32}\) , and assemblies were remapped to exclude highly repetitive sequences. Contigs were then split and assigned to chromosomes based on their alignment coordinates. Base- level alignment was performed using Cactus v2.1.161, and the HAL output \(^{33}\) was converted to vg format (hal2vg). Paths >10kb unaligned to the graph were removed, and the graph was normalized with GFAffix. The chromosome graphs were combined into a whole genome graph, indexed with vg v1.50.1, and exported to VCF format (vg view). Subsequently, SNP variants were incorporated into the SV graph using the same pipeline. Assemblies were remapped to the SV graph, and quality control was applied, excluding softmasked sequences >100kb and alignments with MAPQ < 5. Cactus was executed on the chromosome graphs to introduce base- level variants, and the outputs were converted to vg format. The same filtering, normalization, combining, and indexing steps were applied to produce the final SNP+SV Arab pangenome graph. All analysis was conducted using CATG high performance computing cluster Flamingo. + +## Identification and visualization of population-specific variants + +The VCF file was separated into smaller variants and structural variants (SVs). To proceed with multilallic sites within the smaller variants, a two- step process was employed. First, the "bcftools norm - m-" command was applied to split multilallic sites into biallelic records. Then, the "vcfdccompose - - break- indels - - break- mmp" command from RTG tools (v.3.12.1) was used to further break down multi- nucleotide polymorphisms and complex indels into single nucleotide polymorphisms (SNPs) and indels. + +We used vg v1.50.1 to call structural variants (SVs) from the graph using the vg deconstruct command. Multiallelic SV sites were normalized to biallelic records with bcftools v1.9 using the - m - any option abd followed by annotation using Truvari (v4.0.0). Based on the Truvari annotations, the longest SVs at each SV site were selected for subsequent analysis. SVs found uniquely (<60% reciprocal overlap with HPRC & CPC) in the Arab assemblies were classified as population- specific. We visualized SV distributions and enrichment significance on chromosomes with RIdrogram (v0.2.2) \(^{34}\) . Subgraphs surrounding SVs were extracted with + +<--- Page Split ---> + +gfabase v0.6.0 and visualized in Bandage NG version (v2022.09) \(^{35}\) after aligning gene sequences with GraphAligner (v1.0.17) \(^{36}\) using the command: + +GraphAligner - g {viz_output} - f {pc_fasta_file} - t 32 - a {pc_alignment_file} - x vg --multimap- score- fraction 0.1 + +The gfatools package v0.5- r287 was subsequently used to derive in- depth statistical data from the pangenome graphs. Structural haplotypes were linearly visualized using the gggenes R package. xg file from CPC was used for comparative analysis in our study. + +## Novel euchromatic sequence identification + +To enhance the comparability of the call sets, clusters of closely related alleles in the HPRC- CPC SV sets were consolidated using the truvari command: truvari collapse - r 500 - p 0.95 - P 0.95 - s 50 - S 100000 followed by "truvari anno svinfo" command for annotation. The identical procedure was applied to APR as well. The unique SVs in APR were identified by comparing with HPRC- CPC SVs using truvari command: truvari bench - r 1000 - C 1000 - O 0.8 - p 0.8 - P 0.0 - s 50 - S 15 - - sizemax 100000. \(80\%\) reciprocal overlap was considered to call a SV unique. This collapses multiple SVs into one unique SVs within our cohort. To identify novel sequences within the APR unique SV insertions, we clustered them using cd- hit- est (4.8.1) with the default sequence identity threshold of 0.9. To identify the repeated elements within novel sequences, we have screened fasta file with rmlblastn version 2.14.1+. + +## Complex SV region and Bandage plotting + +A site was considered complex if at least one variation larger than 10 kb was present and had at least five different alleles. The 715 complex sites were identified by analyzing the snarls VCF. The unique SVs were then overlapped with the complex sites to find unique complex sites (see unique variants subsection) in the Arab Population. The precise location of the genes were found out by mapping the gene sequences to the graph using graph aligner with following parameters - multimap- score 0.1. If multiple genes mapped to the same location (in case of isoforms) only the gene mapping with highest accuracy was retained. Complex structural variation (CSV) regions were defined as areas within a 100- kilobase (kb) window that consist of two or more multilatellic complex SV sites, with each site containing at least one 10- kilobase (kb) SV within the haplotypes. The complex sites were plotted using Bandage. 50 Kbp flanking region for each complex site was extracted using Gfabase sub. To find out if a haplotype consists of a gene, the path was traversed to see if it traverses through the gene region. Variation among haplotype walks that do not involve genes are visualized through dark lines traversal and arrows to define the direction of the haplotype. + +<--- Page Split ---> + +## Mitochondria pangenome construction + +Mitochondria pangenome constructionTo construct a mitochondrial Arab pangenome (mtAPR) that captures the diversity of Arab mitochondrial DNA, we used high- quality long- reads from 43 individuals. We first mapped the reads to the chrM of the CHM13 reference genome using minimap2 (v2.26) \(^{37}\) and retained only reads that were longer than 15 kb, this threshold was set to substantially reduce the chances of inadvertent nuclear DNA contamination. It resulted in a total of 20,520 reads (19,251 ONT reads and 1,270 Hifi reads). We used PGBB (v0.5.4) \(^{38}\) to construct a mitochondrial pangenome graph from the mitochondrial contigs of HiFi reads of all individuals, each read of \(>15\mathrm{Kb}\) was QC passed as one haplotype to PGBB. We visualized the mtAPR graph using Bandage NG version (v2022.09) \(^{35}\) and displayed the nodes, edges and variants in different colors and shapes. + +## Pathway enrichment analysis + +Pathway enrichment analysisTo discern the biological pathways affected by gene sets, we leveraged the KEGG and GO pathway databases, a well curated repository featuring pathways that include molecular interactions, reactions, and relation networks. Our analytical framework included determining the degree of overlap between the gene sets and the pathways delineated in the KEGG- GO database. We excluded pathway gene sets that numbered fewer than 50 or exceeded 1000. A significant overlap measured using the Fisher's Exact Test (FET), implied that a pathway is enriched. To account for multiple hypothesis testing and control the false discovery rate (FDR), we applied the Benjamini- Hochberg procedure to adjust the p- values obtained from the Fisher's Exact Test. + +## Data availability + +Data availabilityThe first draft of the APR data from this work can be found at https://www.mbru.ac.ae/the- arab- pangenome- reference/. We hope the downloadable pangenome will impact research and clinical genetics communities. The raw sequencing data can be obtained from corresponding authors upon approval from Dubai Academic Healthcare Corporation (DAHC). + +## Code availability + +Code availabilityThe code to reproduce the pangenome from this work can be found at GitHub (https://github.com/muddinmbru/arab_pangenome_reference). Relevant commands used in other analyses can be found in the Methods or Supplementary Information. + +## References + +References1. Wang, T. et al. The Human Pangenome Project: a global resource to map genomic diversity. Nature 604, 437- 446 (2022).2. 1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68- 74 (2015). + +<--- Page Split ---> + +3. Bergström, A. et al. Insights into human genetic variation and population history from 929 diverse genomes. Science 367, (2020). +4. Nurk, S. et al. The complete sequence of a human genome. Science 376, 44–53 (2022). +5. Liao, W.-W. et al. A draft human pangenome reference. 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Building pangenome graphs. bioRxiv (2023) doi:10.1101/2023.04.05.535718. + +## Acknowledgements + +We would like to thank all the study participants for giving us this opportunity of building a reference pangenome. Yehia Zakaria Kotp provided constant support for the CATG Flamingo computation cluster. Avinash Krishnan, and Freeda Pinto provided constant help with logistics at CATG sequencing lab. HPRC team for commenting on our assembly data quality. Dubai Academic Healthcare Corporation (DAHC) funded this work and supported the procurement of reagents, chemicals, and computing hardwares. The funders had no role in the design of the study, data collection, analysis, publish or preparation of the manuscript. + +## Author information + +AAA, MU, SDP and MA conceived and designed the study. AAA, MU were responsible for ethical, legal and social implications. AAA, MU, NN coordinated and supervised the project. BJ, NN, BA, MAO, MHSA, AA, OZSA, DFY, HAS, HHK, were responsible for recruitment and + +<--- Page Split ---> + +collection of blood samples and clinical data from medical health records. AAA, MU, NN, SH, MA, HHK, BJ were responsible for sample selection and population genetic analysis. MAO, MU, NN, NM, HE, DK, DS, were responsible for DNA and RNA sequencing. MK, BB, SH, NN, MU were responsible for variant annotation. MK was responsible for assembly creation and MK,MU, NN were responsible for assembly quality control and assembly reliability analysis. MU, MK, BB, NN, SH were responsible for variant detection from assembly, pangenome empirical analysis and quality control. BB, SH, MU, NN were responsible for pangenome applications of SVs, short and long- read mapping. MK, NN, MU, NK, SS were responsible for pangenome visualization and complex loci analysis and pangenome graph creation. MU, MK, BB, NN, SH statistical analysis. AAA, MU, NN, SDP, MA, AAT were responsible for manuscript writing. MU, AAA, NN were responsible for manuscript editing. AAA, MU, and NN were responsible for data coordination and management. + +## Corresponding authors + +Correspondence and requests for materials should be addressed to Mohammed Uddin (mohammed.uddin@mbru.ac.ae) or Alawi Alsheikh Ali (alawi.alsheikhali@mbru.ac.ae). + +## Ethics declarations + +## Competing interests + +The authors declare no competing interests. + +## Supplementary information + +See submitted supplementary document and tables. + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + +
Figures
+ +Fig. 1: Cohort characterization and sequencing quality. a, Key serum measures kidney, thyroid, and liver function in addition to glucose and lipid values. Each plot showcases a specific phenotypic attribute, with dots representing individual measurements and the red line representing the normal threshold. b, Sequencing quality metrics. Bar chart comparing sequencing quality metrics, including the average Q scores and N50 read lengths for PacBio and Oxford Nanopore (ONT) platforms. The colors neon pink and teal blue denote PacBio and ONT respectively. c, Ultra- long read yield. Histogram depicting the yield of ultra- long reads (>100 kb) produced by ONT sequencing. The x- axis classifies the reads based on length categories, while the y- axis displays the yield for each category. d, Chromosome mapping distribution. Bar chart presenting the distribution of ONT and PacBio reads that align to acrocentric, metacentric, and submetacentric chromosomes. Neon pink and teal blue bars represent PacBio and ONT data respectively. e, Subtelomeric and pericentric read coverage. Bar chart illustrating the coverage of subtelomeric and pericentric regions by both ONT and PacBio reads. The depth of coverage is depicted for each region, emphasizing the difference between the two sequencing platforms. f, Principal Component Analysis (PCA) two- dimensional scatter plot visualizing the results of population ethnicity variance. Different ethnicities from 1000 genomes are color coded and each dot represents a sample from a designated ethnicity. APR cohort is color coded red and the two axes indicate the variance captured by first (PCA1) and second (PCA2) principal component, respectively. g, PCA clustering of APR cohort and numerous Arab ethnicities. Scatter plot emphasizing the PCA clustering specifically for APR cohort and Arab samples. The distribution is plotted using Principal Component 8 (PC8) on the y- axis against Principal Component 21 (PC21) on the x- axis, highlighting inherent clustering between these groups. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Fig. 2: Quality assessment of 43 phased diploid assemblies and gene duplication analysis. a, Assembly length comparison. Box plot detailing the assembly length for different haplotypes across both male and female samples. b, Assembly contiguity. Line graph showing contig length plotted against cumulative assembly coverage. Notably, reference contiguity for both CHM13 and GRCh38 genomes are included for comparison. c, Assembly accuracy and completeness. Scatter plot illustrating the mapping rate against consensus accuracy (QV), offering insights into the completeness and accuracy of the assembly. d, Assembly alignment coverage. Scatter plot comparing the alignment coverage of assemblies relative to benchmark references CHM13 and GRCh38. e, Duplication ratio analysis. Bar graph displaying the distribution of duplication ratios across assemblies. f, Gene and transcript annotation. Scatter plot showing the percentages of protein-coding and noncoding genes, as well as transcripts annotated from the reference set in each of the assemblies. g, Gene duplication per assembly. Histogram presenting the number of unique duplicated genes or gene families in each phased assembly in comparison to the number of duplicated genes annotated in GRCh38. h, Comparative duplicated gene analysis. Venn diagram visualizing the overlap and unique counts of duplicated genes across APR, HPRC, and CPC assemblies. i, Arab-HPRC duplicated gene overlap. Bar graph showcasing five overlapped duplicated genes with a notably higher frequency (≥5%) in Arab assemblies (blue) compared to HPRC (orange). j, Arab-CPC duplicated gene overlap. Bar chart illustrating five overlapped duplicated genes with a significantly higher frequency (≥5%) in Arab assemblies (blue) in contrast to CPC (yellow). k, Unique Arab duplicated genes. Bar graph representing the frequency of top 20 unique duplicated genes in Arab assemblies when compared against both HPRC and CPC. l, Bar graphs indicating the count of unique duplicated genes across three chromosome types: acrocentric, metacentric, and submetacentric. m, Bar graph showing the
+ +<--- Page Split ---> + +count of unique duplicated genes dispersed across all individual chromosomes, highlighting regions of enrichment. \(\mathbf{n}\) , Microsatellite region gene duplication. Bar graph depicting the count of unique duplicated genes located in microsatellite regions. + +![](images/Figure_3.jpg) + +
Fig. 3: Arab genome specific sequences. a, Bar graph demonstrating the total number of small variants for each sample, distinguishing between singleton and polymorphic variants. b, Bar graph showcasing the small variants specific to APR per sample, further differentiating between singleton and polymorphic variants. c, Venn diagram showcasing the small variants from the Arab pangenome in relation to HPRC and CPC datasets. d, Stacked bar graph detailing the total structural variants (SVs) per sample, categorizing between singleton and polymorphic variants for both insertions and deletions. e, Stacked bar graph illustrating the SVs that are APR-specific for each sample, for both insertions and deletions. f, Venn diagram visualizing the overlap and differences in SVs from the Arab pangenome with HPRC and CPC datasets. g, Visualization of Arab-specific SVs from the pangenome graph across autosomes. Sites of complex SVs are marked with blue circles. h, Bar graph displaying the length distribution of newly identified
+ +<--- Page Split ---> + +sequences for each sample, offering insights into the diversity of novel sequence lengths. i, Bar graph indicating counts of novel sequences across microsatellite regions. + +![](images/Figure_4.jpg) + +
Fig. 4: Visualizing complex structural variation region. a, PRAMEF region subgraph. Diagram showcasing the specific location of the PRAMEF genes. b, Sample haplotypes in PRAMEF Region. Distinct paths taken by different samples through the PRAMEF region. c, PRAMEF region haplotype count. Linear structural diagrams representing the frequency and
+ +<--- Page Split ---> + +structural visualization of haplotypes identified by the graph across 86 haploid assemblies, compared against the HPRC- CPC graph. d, CT45A region subgraph. Diagram highlighting the specific location of the CT45A region. e, Sample haplotypes in CT45A region. Unique paths traversed by different samples through the CT45A region. f, CT45A region haplotype count. Linear structural diagrams depicting the frequency and structural visualization of haplotypes as determined by the graph among 86 haploid assemblies, compared with the HPRC- CPC graph for a comprehensive comparison. + +![](images/Figure_5.jpg) + +
Fig. 5: Mitochondrial pangenome analysis. a, Mitochondrial read length distribution. Frequency distribution graph depicting the mitochondrial read lengths of Hifi data. b, A ring structured representation of the mitochondrial pangenome, detailing the position and nomenclature of annotated mitochondrial genes and their relationships within the pangenome. Each bubble or loop represents a haplotype. c, mtAPR variant distribution. A bar chart showcasing the number of APR-specific small variants observed across different samples, differentiated between polymorphism (in dark blue) and singleton (in light blue). d, Mitochondrial pangenome indel length distribution. A histogram presenting the distribution of
+ +<--- Page Split ---> + +insertion and deletion (indel) lengths within the mitochondrial pangenome, offering insights into the size and frequency of these sequence modifications. + +## Extended Data + +<--- Page Split ---> +![](images/Extended_Data_Figure_2.jpg) + + +Extended Data Fig. 1: Population genetic ancestry inference of the 43 APR samples using ADMIXTURE. Assuming ancestry components K ranging from 2 to 12, each plot shows independent ancestry fraction with its own color- coding representation labeled with short vertical lines. + +<--- Page Split ---> +![](images/Extended_Data_Figure_6.jpg) + +
Extended Data Fig. 2: Analysis of gene duplication patterns. a, Histogram presenting the frequency of various gene copy numbers observed within APR. The x-axis displays the gene copy number, while the y-axis represents the frequency of each copy number. b, Bar graph
+ +<--- Page Split ---> + +showcasing the frequency of the top 20 genes with gene duplications that are absent in the HPRC dataset. c, Bar graph showcasing the frequency of top 20 genes with gene duplications that are absent in the CPC dataset. + +<--- Page Split ---> +![PLACEHOLDER_32_0] + + +Extended Data Fig. 3: Structural variants from pangenome graph. a, Stacked bar graph illustrating the total structural variants (SVs) per haplotype, categorized by singleton or polymorphic variants for both insertions and deletions. b, Stacked bar graph illustrating the SVs that are APR- specific for each haplotype, for both insertions and deletions. c, SV length distribution for APR (blue) and HPRC+CPC (orange). The peak at 246 bp for Alu repeats are highlighted. + +<--- Page Split ---> +![PLACEHOLDER_33_0] + + +Extended Data Fig. 4: APR Novel euchromatic Sequences a, Repeatmasker to identify satellite repeats in novel sequences. b, Loci of APR specific novel sequences. Visualization of Arab- specific novel sequences across chromosomes. The line (red) width is proportional to the length of the novel sequence (kb). + +![PLACEHOLDER_33_1] + + +Extended Data Fig. 5: Chromosome wise distribution of complex structural variations (CSVs) sites. a, Bar graph indicating the count (y- axis) of CSV sites across three chromosome types: acrocentric, metacentric, and submetacentric. b, Bar graph showing the count of CSV sites dispersed across all individual chromosomes.c, Bar graph indicating counts of CSV sites across microsatellite regions. + +<--- Page Split ---> +![PLACEHOLDER_34_0] + +
Extended Data Fig. 6: Length distribution of CSV sites. Histogram showcasing number of CSV sites (log scale y-axis) falling within each length interval (log scale x-axis).
+ +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- Arabpangenomesupplementary.pdf- SupplementaryTables.xlsx + +<--- Page Split ---> diff --git a/preprint/preprint__08a32d4f18a4d90852d0a1f5f66103659aefcc1d8e292575b94fd513a2b10b92/preprint__08a32d4f18a4d90852d0a1f5f66103659aefcc1d8e292575b94fd513a2b10b92_det.mmd b/preprint/preprint__08a32d4f18a4d90852d0a1f5f66103659aefcc1d8e292575b94fd513a2b10b92/preprint__08a32d4f18a4d90852d0a1f5f66103659aefcc1d8e292575b94fd513a2b10b92_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..869d9e059cbbb43c81ab2fcff4f05232d456c489 --- /dev/null +++ b/preprint/preprint__08a32d4f18a4d90852d0a1f5f66103659aefcc1d8e292575b94fd513a2b10b92/preprint__08a32d4f18a4d90852d0a1f5f66103659aefcc1d8e292575b94fd513a2b10b92_det.mmd @@ -0,0 +1,586 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 647, 142]]<|/det|> +# A draft Arab pangenome reference + +<|ref|>text<|/ref|><|det|>[[44, 161, 334, 207]]<|/det|> +Mohammed Uddin mohammed.uddin@mbru.ac.ae + +<|ref|>text<|/ref|><|det|>[[44, 234, 904, 277]]<|/det|> +Mohammed Bin Rashid University of Medicine and Health Sciences https://orcid.org/0000- 0001- 6867- 5803 + +<|ref|>text<|/ref|><|det|>[[44, 283, 642, 325]]<|/det|> +Nasna Nassir Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 330, 642, 372]]<|/det|> +Mohamed Almarri Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 376, 642, 418]]<|/det|> +Muhammad Kumail Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 422, 642, 464]]<|/det|> +Nesrin Mohamed Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 468, 642, 510]]<|/det|> +Bipin Balan Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 515, 642, 556]]<|/det|> +Shehzad Hanif Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 560, 642, 602]]<|/det|> +Maryam AlObathani Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 607, 642, 648]]<|/det|> +Bassam Jamalalail Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 653, 642, 695]]<|/det|> +Hanan Elsokary Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 700, 642, 741]]<|/det|> +Dasuki Kondaramage Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 746, 642, 787]]<|/det|> +Suhana Shiyas Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 792, 642, 833]]<|/det|> +Noor Kosaji Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 838, 642, 879]]<|/det|> +Dharana Satsangi Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 884, 372, 925]]<|/det|> +Madiha Abdelmotagali Primary Health Care Services Sector + +<|ref|>text<|/ref|><|det|>[[44, 931, 176, 949]]<|/det|> +Ahmad Tayoun + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[50, 45, 641, 66]]<|/det|> +Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 70, 641, 111]]<|/det|> +Olfat Ahmed Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 116, 375, 157]]<|/det|> +Douaa Youssef Primary Health Care Services Sector + +<|ref|>text<|/ref|><|det|>[[44, 163, 641, 204]]<|/det|> +Hanan Suwaidi Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 210, 641, 251]]<|/det|> +Ammar Albanna Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 255, 641, 297]]<|/det|> +Stefan Plessis Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 301, 641, 343]]<|/det|> +Hamda Khansaheb Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 347, 641, 389]]<|/det|> +Alawi Alsheikh-Ali Mohammed Bin Rashid University of Medicine and Health Sciences + +<|ref|>text<|/ref|><|det|>[[44, 430, 288, 450]]<|/det|> +Biological Sciences - Article + +<|ref|>text<|/ref|><|det|>[[44, 468, 137, 487]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 505, 329, 525]]<|/det|> +Posted Date: October 26th, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 544, 474, 563]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3490341/v1 + +<|ref|>text<|/ref|><|det|>[[44, 581, 914, 624]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 642, 535, 662]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 697, 910, 741]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on July 24th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 61645-w. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[328, 90, 670, 110]]<|/det|> +## A draft arab pangenome reference + +<|ref|>text<|/ref|><|det|>[[113, 134, 875, 255]]<|/det|> +Nasna Nassir \(^{1,2}\) Mohamed A. Almarri \(^{2,3}\) , Muhammad Kumail \(^{1}\) , Nesrin Mohamed \(^{1}\) , Bipin Balan \(^{1,2}\) , Shehzad Hanif \(^{4}\) , Maryam AlObahtani \(^{1}\) , Bassam Jamalalail \(^{1}\) , Hanan Elsokary \(^{1}\) , Dasuki Kondaramaqe \(^{1}\) , Suhana Shiyas \(^{1,4}\) , Noor Kosaji \(^{1,2}\) , Dharana Satsangi \(^{2}\) , Madiha Hamdi Saif Abdelmotagali \(^{5}\) , Ahmad Abou Tayoun \(^{6,7}\) , Olfat Zuhair Salem Ahmed \(^{5}\) , Douaa Fathi Youssef \(^{5}\) , Hanan Al Suwaidi \(^{2}\) , Ammar Albanna \(^{2,8}\) , Stefan Du Plessis \(^{1,2}\) , Hamda Hassan Khansaheb \(^{9}\) , Alwai Alsheikh- Ali \(^{1,2,10*}\) , Mohammed Uddin \(^{1,2,11,*}\) + +<|ref|>text<|/ref|><|det|>[[112, 277, 880, 576]]<|/det|> +1. Center for Applied and Translational Genomics (CATG), Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE +2. College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE +3. Department of Forensic Science and Criminology, Dubai Police GHQ, Dubai, UAE +4. Manipal Centre for Biotherapeutics Research, Manipal Academy of Higher Education, Manipal, Karnataka, India +5. Primary Health Care Services Sector, Dubai Academic Health Corporation +6. Al Jalila Genomics Center of Excellence, Al Jalila Children’s Specialty Hospital, Dubai, UAE +7. Center for Genomic Discovery, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE +8. AlAmal Psychiatric Hospital, UAE +9. Medical Education and Research Department, Dubai Academic Health Corporation +10. Dubai Academic Healthcare Corporation (DAHC), Dubai, UAE +11. GenomeArc Inc., Mississauga, ON, Canada + +<|ref|>sub_title<|/ref|><|det|>[[116, 679, 329, 696]]<|/det|> +## \*Corresponding authors: + +<|ref|>text<|/ref|><|det|>[[115, 698, 881, 755]]<|/det|> +Dr. Mohammed Uddin, Associate Professor, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE, GenomeArc Inc., Mississauga, ON, Canada. Email: mohammed.uddin@mbru.ac.ae + +<|ref|>text<|/ref|><|det|>[[115, 777, 865, 835]]<|/det|> +Dr. Alawi Alsheikh-Ali, Professor, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, UAE. Email: alawi.alsheikhali@mbru.ac.ae + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[455, 91, 542, 110]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[113, 132, 883, 412]]<|/det|> +Human pangenomes provide a comprehensive portrayal of genetic diversity of humans, yet it lacks representation of Arab populations. We constructed the Arab Pangenome Reference (APR) from 43 individuals with diverse Arab ethnicities. Nuclear and mitochondrial pangenomes were constructed utilizing 35.52X High fidelity long reads and 53.54X ultra- long reads. This yielded high- quality contiguous (average N50=106.81 Mb) de novo assemblies that used over 99% of the sequences constructing haplotype phased diploid genome assemblies with 88% exhibited larger genome length (average 3.01 gigabase) than the prevailing human reference GRCh38. We discovered 100.93 million base pairs of novel euchromatic sequences that were not present in recent human pangenomes and in the human genome references (T2T- CHM13 and GRCh38). We identified 10.68 million population- specific small variants, 108,709 structural variants, and 838 genes (13.24% recessive disease genes) duplication from the Arab pangenome. On exploring the mitochondria pangenome, we uncovered 718 bp of novel sequences. Our study provides a valuable resource for future genetic research and genomic medicine initiatives in the Arab populations and other populations with similar genetic backgrounds. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 92, 170, 110]]<|/det|> +## Main + +<|ref|>text<|/ref|><|det|>[[113, 113, 881, 533]]<|/det|> +The human genome, with its vast complexity and diversity, has opened up new avenues of research in genomics, leading to significant advances in our understanding of human biology, and disease. However, the application of such genomic advances is limited by the quality, completeness, and representativeness of the reference genome used in various studies1. In the pursuit of understanding the intricate tapestry of human genome variation, large- scale sequencing projects have provided invaluable insights2,3. Recently, due to significant advancement of long- read genome technologies, the first complete telomere to telomere (T2T) sequencing of a haploid human genome CHM13 was possible. This is a remarkable achievement that fills the \(8\%\) of gaps that existed in the current reference genome GRCh38 (ref.4). Although it represents the first complete genome, it does not reflect human sequence diversity and a population- wide approach is necessary to detect population specific variants, sequences and regulatory elements. The Human Pangenome Reference Consortium (HPRC) has made significant strides in cataloging unprecedented amounts of human genomic variations from a diverse set of global populations5. The HPRC pangenome derived from 47 samples with diverse ethnicities, added an additional 119 million base pairs of euchromatic polymorphic sequences and identified 1,115 gene duplications compared to the current reference GRCh38. The application of the reference pangenome resulted in a \(104\%\) increase in the number of structural variants detected per haplotype compared to GRCh38 (ref.5). The recent construction of a pangenome from 58 samples representing 36 ethnic minorities in China reported 5.9 million small variants and 34,223 structural variants that were not reported in the HPRC multiehtnic global pangenome6. + +<|ref|>text<|/ref|><|det|>[[114, 553, 880, 773]]<|/det|> +Arabs constitute culturally diverse communities with a combined population of nearly 500 million, comprising about \(5\%\) of the global population. They are unfortunately underrepresented in global sequencing projects (i.e. gnomAD database), with neither the HPRC pangenome nor the 1000 Genomes project sampling any Arab population7. The ancestral complexities between different Arab ethnic backgrounds are yet to be understood through large- scale genomic initiatives8,9. Moreover, the Arab population has a higher incidence of consanguinity, which leads to an increased rate of rare recessive disorders10- 12. The incidence of diabetes, heart disease and cancer are on the rise within Arab populations13,14. The lack of reference genomes for Arab populations has limited the investigation of genetic diversity and the genetic underpinning of numerous diseases; Population- specific reference pangenomes will enable the probing of disease associations with variants and sequences that are unique or prevalent in Arab populations. + +<|ref|>text<|/ref|><|det|>[[115, 794, 883, 893]]<|/det|> +In this study, we sought to generate a comprehensive Arab Pangenome Reference (APR) using multiple long- read sequencing technologies and de novo assembly construction approaches. We present the first draft construction of the APR from 43 human genomes with high quality de novo diploid phased assemblies and their comparison with the HPRC GRCh38 reference genome and 1000 Genomes Project datasets. Our analysis also shows the application of the APR into + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 866, 168]]<|/det|> +functional annotation, short and long- read whole genome mapping for variant detection, and efficiency in mRNA transcript mapping. We anticipate our effort will produce pangenomes to allow population specific genome interpretation, unbiased and less gaps in the genome and will enable the high precision detection of structural and small variants. + +<|ref|>sub_title<|/ref|><|det|>[[115, 191, 390, 211]]<|/det|> +## Healthy arab sample cohort + +<|ref|>text<|/ref|><|det|>[[114, 213, 877, 412]]<|/det|> +To construct a pangenome reference from healthy Arab individuals, we enrolled 40 unrelated adults (18 years or older) and a family trio with no known rare or common chronic diseases (i.e. no history of hypertension, diabetes mellitus, cancer, lung or heart disease). We collected extensive phenotype data over multiple clinic visits for all of our 43 study samples. Our cohort consists of 25 female and 18 male Emirati nationals from Dubai, UAE. Our comprehensive phenotypic assessment data confirmed (Supplementary Table 1) all individuals within our Arab study cohort have no identifiable common chronic diseases (i.e. diabetes, hypertension, cancer). All individuals were clinically assessed as healthy at the time of recruitment, and laboratory tests were within the normal range for all with the exception of some individuals with elevated serum cholesterol levels (Fig. 1a). + +<|ref|>sub_title<|/ref|><|det|>[[115, 437, 442, 458]]<|/det|> +## Assessment of sequencing quality + +<|ref|>text<|/ref|><|det|>[[113, 460, 879, 880]]<|/det|> +The genomes were sequenced by employing both Pacific Biosciences (PacBio) high fidelity (HiFi) and Oxford Nanopore Technologies (ONT) ultra- long read sequencing kit (ULK) methodologies. Each sample yielded an average depth of coverage of 35.52X and 53.54X for PacBio HiFi and ONT ultra- long reads, respectively (Supplementary Fig.1). The sequencing quality yielded an average median Q score of 32.85 for Pacbio and 17.39 for ONT. Average N50 values of 16.78 kb and 58.55 kb for HiFi and ONT ultra- long read were obtained, respectively (Fig. 1b and Supplementary Table 2). Notably, the ultra- long reads surpassing 100 kb resulted in a substantial yield of 1,708 Gb, translating to 569.33X coverage (an average of 13.24X per sample) (Fig. 1c, Supplementary Table 3). When mapped against the CHM13 v2.0, both HiFi and ONT reads brought valuable insight on the capacity of sequencing coverage across accrocentric and metacentric chromosomes. Due to ultra- long protocols, ONT reads exhibited better mapping to all chromosomes compared to PacBio, particularly in the accrocentric chromosomes where they achieved 99.49% coverage, in comparison to 94.95% by PacBio (Fig. 1d, Supplementary Fig. 2, Supplementary Table 4a). We utilized both read types to scrutinize coverage across diverse regions of the human genome such as satellite DNA, centromeric transitions, and ribosomal DNA. Both platforms excelled in terms of coverage, with regions like AlphaSat_div, AlphaSat_mon, GammaSat, and Other CenSat exhibiting coverage above 95%. However, the ONT platform demonstrated a marked increase in rDNA coverage, with 99.92% and 62.40% for ONT and HiFi reads, respectively (Fig. 1e, Supplementary Table 4b). Our joint calling analysis incorporating Deepvariant (GRCh38) for HiFi data, identified an average of 4,421,702 single nucleotide variants (SNVs) and 847,117 indels (Supplementary Table 5a,b + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 880, 129]]<|/det|> +Supplementary Fig. 3, 4). We identified population specific variants comprising 5,663,728 SNVs and 1,717,397 indels, which were not previously reported in 1000 Genome project data \(^2\) . + +<|ref|>sub_title<|/ref|><|det|>[[115, 151, 480, 171]]<|/det|> +## Population structure of APR samples + +<|ref|>text<|/ref|><|det|>[[114, 174, 882, 333]]<|/det|> +Through verbal interviews, \(30\%\) individuals confirmed migratory history of their recent ancestors from other Arab or middle eastern countries (Supplementary Table 6). Initial principal component analysis (PCA) showed distinct clustering from 1000 Genomes Project samples (Fig. 1f; Supplementary Fig. 5a,b). Haplotype sharing information confirmed the unrelated status of all 40 samples (Supplementary Fig. 5c). In addition, we have conducted PCA using genomic data from 11 Arab ethnic groups. Our analysis suggests the APR cohort samples ancestry are represent multiple different Arab subpopulations compare to the major continental populations (Extended Data Fig. 1). + +<|ref|>sub_title<|/ref|><|det|>[[115, 354, 586, 375]]<|/det|> +## Assembly construction of diverse Arab genomes + +<|ref|>text<|/ref|><|det|>[[113, 377, 877, 716]]<|/det|> +Our analysis on the trio suggests comparable contig length between Hifiasm \(^{15}\) and Verkko \(^{16}\) (Supplementary Table 7). For individual samples without parental genome data, Hifiasm outperformed in terms of contig length and contiguity. Owing to Hifiasm's performance with non- trio samples, we performed de novo assembly using Hifiasm for our entire cohort and used those assemblies for downstream analysis. Assembly quality control was executed using Kraken, filtering out non- human eukaryotic pathogen genomes, ultimately retaining contigs classified as human. We removed 85 contigs that consisted of mostly mitochondrial sequences (Supplementary Table 8). One aberrantly long 242 Mb contig was manually corrected by removing small fragments of mitochondrial sequences. To resolve the mapping ambiguity of the pseudoautosomal region 1 (PAR1) homologous sequences in the assembly, we performed a contig splitting procedure based on the breakpoint boundaries identified by aligning the contigs to the X and Y reference chromosomes. Given PAR1's inherent repetitive nature and its recombination frequency, 35 contigs were split that contained PAR1 sequences and mapped to both X and Y chromosomes, resulting in two separate contigs for each original one (Supplementary Table 9). This enhanced the assembly precision by mitigating potential misassemblies caused by PAR1. Misjoins in the assemblies were catalogued based on their sequence identity (Supplementary Fig. 6, Supplementary Table 10). + +<|ref|>text<|/ref|><|det|>[[114, 736, 884, 896]]<|/det|> +The resulting assemblies yielded an average genome size of 3.01 Gb per sample (Fig. 2a, Supplementary Table 11), with over \(88.37\%\) of assemblies surpassing the GRCh38 benchmark of 2.94 Gb. On average, assemblies comprised of 139 contigs with contig N50 length of 106.81 Mb (Fig. 2b). This exceeds the GRCh38 contig N50 of 57.88 Mb, with \(95.34\%\) of our assemblies having greater contiguity. All samples surpassed the 40 Mb N50 of the HPRC graph genome reference (Supplementary Fig. 7). The average QV scores of our assemblies, computed using yak v0.1-r66 was 57.05, indicating robust assembly quality (Fig. 2c). We compared our assemblies to the CHM13 and GRCh38 reference genomes and found that they covered on average \(93.61\%\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 882, 269]]<|/det|> +(92.62% for male, 94.32% for female) and 96.64% (95.39% for male, 97.53% for female) of each, respectively (Fig. 2d). Notably, fewer inversions and translocations indicated reduced structural discrepancies (Supplementary Fig. 8). There was an average of 30.84 Mb and 76.83 Mb of assembled contigs that did not align with CHM13 and GRCh38 respectively (Supplementary Fig. 9). This highlights the under- representation of the Arab genome diversity in these references. The duplication ratio, indicative of read mapping multiplicity against references, averaged 1.03 and 1.02 for GRCh38 and CHM13 respectively (Fig. 2e). These comprehensive quality control assessments verify the high- contiguity and accuracy of our de novo assembled genomes, providing a premier foundation for downstream genomic and clinical analyses. + +<|ref|>sub_title<|/ref|><|det|>[[115, 290, 512, 310]]<|/det|> +## Genomic features of the APR assemblies + +<|ref|>text<|/ref|><|det|>[[114, 313, 880, 473]]<|/det|> +To investigate gene duplication events within Arab pangenome diploid assemblies, we employed liftoff v1.6.3 tool \(^{17}\) , and annotated using Gencode GRCh38.p14 (Gencode release 44). The resulting gene duplications revealed novel and diverse genomic features of the Arab population. Remarkably, a median of \(98.91\%\) protein- coding genes and \(99.12\%\) protein- coding transcripts were identified in each APR assembly (Fig. 2f). Each genome had an average of 31 genes with a gain in copy number relative to GRCh38 (Fig. 2g, Supplementary Table 12). A significant \(24.48\%\) of genes manifested a frequency surpassing \(5\%\) across all samples, with only \(11.51\%\) of these duplicated genes being exclusive to a single haplotype. + +<|ref|>text<|/ref|><|det|>[[114, 492, 878, 693]]<|/det|> +We did a comparative assessment of gene duplications within the APR, HPRC, and CPC assemblies to identify genes that were specific to the Arab population. We identified 1039 duplicated genes unique to the APR assemblies, absent in the HPRC assemblies, and a further 838 duplicated genes absent in both HPRC and CPC assemblies (Fig. 2h, Extended Data Fig. 2 and Supplementary Table 13). There were 34 duplicated genes from the APR assemblies that were also observed in the HPRC and CPC assemblies. Among these overlapped genes, we found genes with a higher frequency \((\geq 5\%)\) in the APR assemblies than in the HPRC and CPC assemblies (Fig. 2i, 2j), of which USP17L genes were more frequent in APR. USP17L genes encode ubiquitin- specific proteases, which are involved in various cellular processes such as protein degradation, DNA repair, and cell cycle regulation. + +<|ref|>text<|/ref|><|det|>[[114, 713, 878, 893]]<|/det|> +Of the APR unique duplicated genes, the TAF11L5 gene showed considerably high frequency in the APR assemblies (86 of 86) but were absent in the HPRC and CPC assemblies (Fig. 2k). TAF11L5 gene encodes TATA- box binding protein associated factor, which is predicted to be involved in the assembly of the RNA polymerase II preinitiation complex, a large complex of proteins that initiates transcription of protein- coding genes. These unique gene duplications were predominantly harbored within submetacentric chromosomes (Fig. 2l), majorly localized to chromosomes 16 and 1 (Fig. 2m). Conversely, the acrocentric chromosomes had a low number of duplication events. The unique gene duplications were notably enriched in microsatellite regions (151 out of 838), mostly in centromeric satellites (Fig. 2n, Supplementary Table 14). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 89, 884, 209]]<|/det|> +Furthermore, \(13.24\%\) of APR unique duplicated genes have been implicated in recessive conditions compared to \(11.79\%\) in HPRC unique and \(9.40\%\) in CPC unique duplicated genes. On the Horizon platform, \(2.14\%\) of unique duplicated genes were annotated as pan- ethnic disease genes associated with severe phenotypes18 (Supplementary Table 15). These genes were enriched significantly in ribonucleotide metabolism ( \(p< 6.22 \times 10^{- 4}\) ) and exocytosis regulation ( \(p< 6.44 \times 10^{- 3}\) ) pathways (Supplementary Table 16). + +<|ref|>sub_title<|/ref|><|det|>[[116, 230, 768, 252]]<|/det|> +## Pangenome graph construction and Arab genome-specific variants + +<|ref|>text<|/ref|><|det|>[[113, 253, 875, 493]]<|/det|> +We constructed a pangenome graph of 43 Arab genomes using Minigraph Cactus (v2.6.7)19, integrating 86 long- read assemblies into a graph structure. The graph was seeded with the CHM13 and GRCh38 reference genomes and expanded using Minigraph to incorporate the new assemblies, resulting in a total length of 3,307,861,124 bp, and 72,256,745 nodes and 99,652,737 edges, whereas HPRC had a total length of 3,328,787,872 bp (Supplementary Table 17, Supplementary Fig. 10). We compared the Arab pangenome graph with the HPRC and CPC pangenome graph, which represents a diverse set of human genomes from different populations. We identified each individual genome containing an average of 5,044,179 total and 743,379 unique small variants (Fig. 3a, 3b), 10.68 million small variants were unique to APR (Fig. 3c). We observed each sample containing an average of 17,634 total and 8,302 unique SV (Fig. 3d, 3e), yielded 108,709 SVs that were unique to APR (Fig. 3f, Extended Data Fig. 3, Supplementary Table 18). + +<|ref|>text<|/ref|><|det|>[[114, 513, 880, 673]]<|/det|> +The average SV length was 2.52 kb and median 246 bp (Extended Data Fig. 3), this validated the assembly integrity by showing Alu and LINE- 1 repeat content along the chromosomes. We further analyzed the Arab- specific SVs along the autosomes (Fig. 3g, Supplementary Table 19), harboring 8269 unique genes (Supplementary Table 20). These regions may harbor important genomic features that are specific to the Arab population. Genes affected by novel SVs in the APR were enriched in drug response ( \(p< 1.52 \times 10^{- 12}\) ), cytokine signaling and leukocyte activation ( \(p< 1.42 \times 10^{- 11}\) ) and nervous system development ( \(p< 1.54 \times 10^{- 10}\) ) pathways (Supplementary Table 21). + +<|ref|>sub_title<|/ref|><|det|>[[114, 695, 468, 715]]<|/det|> +## New euchromatic sequences in APR + +<|ref|>text<|/ref|><|det|>[[114, 717, 880, 896]]<|/det|> +The pangenome graph added 100.93 Mb of non- reference sequences from the 86 diploid Arab genomes, including 92.16 Mb of singletons. The average non- reference sequence length per individual was 2.49 Mb (range 1.59- 3.86 Mb) (Fig. 3h, Supplementary Table 22). We also measured the amount of novel sequences that are not present in GRCh38, T2T- CHM13, HPRC or CPC, revealing that each Arab diploid genome harbored an average of 1,190 novel sequences (Supplementary Fig. 11). A small percentage of the total novel sequences were present in centromeric and telomeric regions, \(1.08\%\) and \(0.35\%\) , respectively (Supplementary Fig. 11). Nearly one- fifth (19.22%) of novel sequences were enriched in microsatellite regions (Fig 3i), especially in pericentromeric satellite, HSat3 and other centromeric satellites. To characterize the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 89, 870, 249]]<|/det|> +context of these novel sequences located within 20,267 loci, we performed repeat annotation using RepeatMasker, and we found LINE, SINE, LTR and satellite repeats constituted \(4.39\%\) , \(6.05\%\) , \(4.37\%\) and \(31\%\) of novel sequences, respectively (Extended Data Fig. 4a,b; Supplementary Table 23). We identified 20,267 loci with novel sequences. These novel sequences may represent novel functional elements or structural variations that are missed by conventional methods. These results demonstrate the power and utility of using long- read sequencing and pangenome graph construction to capture the genomic diversity and complexity of human populations. + +<|ref|>sub_title<|/ref|><|det|>[[115, 270, 699, 292]]<|/det|> +## Complex structural variation in the APR pangenome graph + +<|ref|>text<|/ref|><|det|>[[113, 293, 881, 612]]<|/det|> +We used a pangenome graph to visualize and analyze complex structural variation (CSV) in APR samples. A complex structural variation site was defined as an SV site with at least one 10kb SV with minimum 5 haplotypes and we found that \(0.88\%\) (715 out of 80,763) of these SV sites were complex multiallelic bubbles (Supplementary Table 24). The CSVs were dispersed across all chromosomes, and they exhibited a notable concentration on acrocentric chromosomes (average 36.8 CSVs), particularly chromosome 22, which had the highest count. APR CSVs were predominantly located in pericentromeric satellite (in 16 autosomes), HSat3 and alpha satellite DNA (Fig. 3g and Extended Data Fig. 5). We found that the peak of CSV length distribution was at 10 kb (Extended Data Fig. 6). Utilizing rigorous criteria, we've delineated complex structural variation regions as areas within a 100- kilobase (kb) window that consist of two or more multiallelic SV sites, with each site containing at least one 10- kilobase (kb) SV within the haplotypes. This endeavor led to the discovery of 646 CSV regions. The genes (Supplementary Table 25) nestled within this 100kb window of complex regions were enriched in ubiquitin \(\mathrm{(p< 5.17x10^{- 11})}\) and cysteine peptidase activity \(\mathrm{(p< 8.57x10^{- 11})}\) pathways (Supplementary Table 26). We further investigated the potential functional impact of these complex variant regions by overlapping them with APR specific SVs (Supplementary Table 27). + +<|ref|>text<|/ref|><|det|>[[113, 632, 880, 892]]<|/det|> +We compared the allelic variations of the APR against those of combined HPRC+CPC pangenomes in two regions of clinical relevance: the PRAMEF (Fig. 4a, Supplementary Table 28) and CT45A gene regions (Fig. 4b). In the PRAMEF region, we observed a common allele present in HPRC+CPC, also present in \(88.37\%\) of APR samples, while others were unique haplotypes to APR pangenome. We observed 2 novel haplotypes that were exclusively found in \(11.63\%\) of the Arab cohort. Notably, PRAME (Preferentially expressed antigen in melanoma) belongs to a group of cancer/testis antigens that are mainly expressed in the testis and an array of tumors, with pivotal roles in immunity and reproduction20. Delving into the CT45A region, we observed that the allele present in HPRC+CPC were absent in APR samples. We observed one APR specific novel allele absent in HPRC+CPC and one novel allele exclusively present in \(27.90\%\) of APR samples (absent in HPRC+CPC, CHM13 and GRCh38). CT45A genes encode cancer/testis antigen family 45, with known associations to pediatric acute leukemia21. Furthermore, allelic variations were observed in the HLA- DRB region which were absent in the + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 90, 880, 189]]<|/det|> +references (Supplementary Fig. 12). Other CSV regions containing CYP2D6 showed variable haplotype presence within APR that are drastically different compared to CHM13 or GRCh38 references (Supplementary Fig. 13). The pangenome graph representation enabled us to discover new complex haplotypes that may contribute to numerous disorders, as well as to trace the origin and distribution of these haplotypes across the cohorts. + +<|ref|>sub_title<|/ref|><|det|>[[114, 210, 504, 231]]<|/det|> +## Mitochondrial pangenome construction + +<|ref|>text<|/ref|><|det|>[[113, 233, 880, 533]]<|/det|> +The mitochondrial Arab pangenome (mtAPR) was constructed from high- quality long- reads from 43 individuals (Supplementary Fig. 14), with Hifireads mapping to ChrM greater than 15kb (Fig. 5a), with an average length of 16.28 kb. The graph consisted of 32,048 nodes and 56,607 edges when using CHM13 as reference (32,059 nodes and 56,735 edges when using GRCh38 as reference) (Fig. 5b). The graph showed a complex and diverse structure that reflected the genetic diversity of human mitochondrial DNA. We annotated the reads using the chrM annotation from GENCODE (v44) \(^{22}\) and identified the genes. We detected 60,812 single nucleotide variants (Fig. 5c) and 46,865 insertions/deletions (indels) (Fig. 5d) in the graph (Supplementary Table 29). We observed 11,801 unique small variants, of which 11,259 are singleton and 6,213 are polymorphic. Insertions \(> = 10\mathrm{bp}\) were extracted from the mitochondrial pangenome variant calling file and subsequently, the sequences were clustered using the cd- hit- est program with a \(90\%\) similarity threshold (- c 0.9) to obtain the novel insertions (Supplementary Table 30). Mitochondrial novel sequences not present in the references accounted for 718 bp. Additional analysis on gene duplication within the assemblies did not reveal any new gene duplication event. + +<|ref|>sub_title<|/ref|><|det|>[[114, 554, 598, 575]]<|/det|> +## Performance gains for pangenome-aided analysis + +<|ref|>text<|/ref|><|det|>[[114, 577, 880, 777]]<|/det|> +We evaluated the impact of pangenome- based analysis on several aspects of genomic discovery. We aligned short reads from previously sequenced 21 Arab samples \(^{8}\) to the APR graph using Giraffe mapper \(^{23}\) and compared the results with the alignment to GRCh38 using BWA- MEM. We found that aligning to the pangenome graph, average mapping rate increased to \(85.01\%\) using APR compared to \(82.19\%\) using CHM13 (pval \(= 1.45\mathrm{x}10^{- 17}\) , paired t- test) (Supplementary Table 31). On average, \(2.82\%\) fewer reads mapped to CHM13 after graph alignment, indicating that these reads were better represented by the pangenome graph. We also assessed the long- read WGS mapping accuracy of 2 autism spectrum disorder (ASD) trios to the pangenome from the Arab cohort (Supplementary Table 32). These results suggest that pangenome- based analysis can improve the sensitivity and specificity of long- read variant detection. + +<|ref|>sub_title<|/ref|><|det|>[[115, 799, 220, 817]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[115, 821, 883, 900]]<|/det|> +We constructed a pangenome reference specific to the Arab population from 43 deeply phenotyped apparently healthy adults by processing 86 high- quality assemblies. These samples underwent careful phenotyping to monitor manifestations of the onset and progression of chronic complex diseases. Applying 35.52X HiFi reads and 53.54X ultra- long reads, we achieved N50 of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 872, 390]]<|/det|> +106.81 Mb, which is 2.67- fold longer N50 than recently reported in HPRC. de novo assembly algorithms used \(99.96\%\) of all available long- read sequences to construct the contigs. Our average assembly quality score QV 57.05 suggests high quality assemblies underlying the pangenome. The average haplotype of APR samples shows \(93.61\%\) and \(96.64\%\) coverage of CHM13 and GRCh38, respectively, which is on par with HPRC and CPC assemblies. The coverage is lowest for the Y chromosome due to its intricate Yq12 heterochromatin repeat complex region. The construction of a high resolution reference pangenome for the Arab population brought novel insight into the unique genome organization within these populations. While HiFi provided the base with highly accurate long- reads, the ONT ultra long- reads contributed significantly to filling the gap on scaffolding and covering complex regions of APR genomes. The ULK data exhibited a coverage exceeding \(13x\) for each APR sample, contributing significantly to the extended N50 length. Our assemblies added 100.93 million bases of new euchromatic sequences that are outside of references (GRCh38, CHM13, HPRC and CPC) and reported 10.68 million population specific small variants, 108,709 structural variants and 838 protein coding gene duplications across the pangenome. + +<|ref|>text<|/ref|><|det|>[[113, 408, 881, 689]]<|/det|> +The CHM13 reference is the most complete human genome to date and GRCh38 has the most complete annotations of functional elements. The Arab pangenome was constructed based on CHM13 by integrating GRCh38 and the APR assemblies. We observed a significant overlap between APR and HPRC+CPC common variants. In addition, we identified 6,467,909 novel SNVs and 4,212,811 indels that are outside of the reference genomes and the recently reported pangenomes (HPRC and CPC). A total of 10.68 million previously undiscovered small variants impacted 19,626 protein- coding genes. We anticipate that application of APR in clinical settings will markedly increase the diagnostic yield for numerous single gene disorders. The identified population- specific variants illustrate the old separation time of Middle Eastern populations from other continental groups and the subsequent affect of isolation and drift8. Unfortunately, the lack of representative data from the Middle Eastern populations towards large genomic initiatives (i.e. 1000 Genome project, gnomAD) did not generate impactful genomic resources for this region. To assess clinical significance of thousands of novel APR specific variants will require further disease cohort based analysis. + +<|ref|>text<|/ref|><|det|>[[114, 709, 878, 888]]<|/det|> +APR specific structural variants reveal 100.93 Mb sequences (1.59- 3.86 Mb range of new sequences per haplotype) that are not present within the CHM13, GRCh38 references or the pangenomes (HPRC and CPC). These novel sequence show enrichment of highly repeated satellites which are inaccessible by short read technologies. A linear reference genome is limited by its static sequences that are unable to decipher the polymorphic nature of SVs. The clinical application of APR pangenome based SV detection will increase the clinical yield due to large number uncharacterized SVs. Moreover, will enable the detection of base resolution phased haplotype complexities to infer their association with diseases. The SVs have a profound effect on how APR differs from HPRC or CPC graphs. APR identified 108,709 unique SVs (average + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 880, 309]]<|/det|> +8,302 SVs per sample) that will create different bubbles or haplotype walks (involving genes) within these genomic regions compared to other pangenomes or human genome references. Our results suggest a larger sample size will produce a more accurate and comprehensive pangenome capturing additional rare gene duplication events and novel sequences. The rare genetic variants will have the potential to increase diagnostic yield for rare genetic diseases and pan cancer. From short read whole genome data of Arab individuals, we observed improved paired read mapping accuracy in APR compared to CHM13. Transcriptome annotation is highly complete, particularly for protein- coding transcripts that show approximately \(99\%\) mapping rate. Short or long read sequence alignment, variant calling and phasing accuracy workflows are still tedious for clinical labs to adopt. As we see mapping and variant calls improving using APR, with time the usability will significantly increase. + +<|ref|>text<|/ref|><|det|>[[113, 328, 860, 549]]<|/det|> +Our APR specific gene duplication analysis identified 838 unique genes that are involved in ribonucleotide metabolism and exocytosis regulation pathways. Of the duplicated genes identified, \(13.24\%\) have been associated with recessive conditions. This is particularly notable due to the higher rate of consanguinity within Arab populations, predisposing them to higher homozygous stretches. Similar to previous reports24, gene duplications are highly active in segmental duplication regions. TAF11L5 is the most duplicated gene that encodes TATA- Box Binding Protein (TBP) predicted to be involved in RNA polymerase II preinitiation complex assembly. Our result suggests that TBP is a functionally active element within the Arab populations. The core conserved region of TBP that is also duplicated within APR is predominantly involved in double- stranded nucleic acid binding and that enables transcription initiation25. + +<|ref|>text<|/ref|><|det|>[[114, 568, 880, 708]]<|/det|> +The mitochondrial APR consists of 718 bp of new sequence that is not found within the GRCh38 mitochondria. The diversity of haplotypes and identified variants shows a comprehensive reference for Arab mitochondria. Total mtAPR variant impacted \(37.49\%\) of the GRCh38 mtDNA bases, cataloging healthy heteroplasmy mitochondrial haplotypes within the Arab populations. Since the APR cohort was clinically assessed as healthy, the constructed Arab mitochondria pangenome reference will provide heteroplasmy frequencies that will be a valuable resource to determine mitochondrial diseases. + +<|ref|>text<|/ref|><|det|>[[114, 728, 875, 869]]<|/det|> +The understanding of true human genomic diversity and complexity depends on the collective effort of producing numerous pangenomes across different ethnicities. Technological limitations are still an issue to decipher precisely the heterochromatic regions, specifically near the centromeres. Over time, sequencing technologies and algorithmic development will improve to parse out repeat complexity regions. Although we understand the current study requires more samples to capture sequence diversity among Arab populations, the APR will provide a foundational resource for human genetic studies. Large genomic databases have a notable + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 876, 128]]<|/det|> +underrepresentation of Arab populations, and the APR will address this gap by offering essential resources for clinical genomic laboratories to enhance the precision of variant interpretation. + +<|ref|>sub_title<|/ref|><|det|>[[115, 151, 202, 170]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[115, 196, 412, 214]]<|/det|> +## Sample collection and phenotyping + +<|ref|>text<|/ref|><|det|>[[113, 215, 879, 495]]<|/det|> +We recruited and collected 8- 10 ml blood samples from 43 healthy individuals (one trio and 40 unrelated individuals) of Arab descent from the United Arab Emirates (UAE). Informed consent was obtained from each participant. All participants underwent extensive phenotyping, including clinical, biochemical, and anthropometric assessments as well as review of their electronic health records (Supplementary methods section 1). In addition, we included a family trio (mother, father, and child) for in- depth analysis. The inclusion criteria was self- identified Arabs who are 18 years or older willing to participate voluntarily and are presumptively healthy (free from diseases such as diabetes, cardiovascular disease, hypertension, chronic kidney disease, lung disease and liver disease). To map long- read genome sequence data into APR, we recruited 5 trios, children with autism spectrum disorders (ASD). Furthermore, we processed short read illumina whole genome sequencing data from 21 Emirati individuals8. This study was approved by the Institutional Review Board at Dubai Scientific Research Ethics Committee (DSREC- 12/2022_01, DAHC/MBRU- IRB/2023- 15), and and Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU- IRB- 2017- 004). + +<|ref|>sub_title<|/ref|><|det|>[[115, 517, 391, 536]]<|/det|> +## DNA isolation and sequencing + +<|ref|>sub_title<|/ref|><|det|>[[115, 558, 480, 576]]<|/det|> +## Pacific Bioscience High Fidelity sequencing + +<|ref|>text<|/ref|><|det|>[[113, 582, 877, 861]]<|/det|> +For Hifi long- read sequencing of APR cohort samples and 2 ASD trios, high molecular weight DNA was isolated from \(200 \mu L\) of flash frozen blood using both GenFind V3 and Nanobind DNA extraction kits per manufacturer's instructions. For shearing, DNA concentration was first measured using Qubit 3 Fluorometer with a dsDNA HS Assay kit (Thermo Fisher) and samples were adjusted to \(30 \mathrm{ng} / \mu \mathrm{L}\) in a total volume of \(130 \mu \mathrm{L}\) . DNA samples were then loaded on Diagenode Megaruptor 3 hydropore- Syringe at a speed of 31 targeting 15- 18 kb fragments length as recommended by the PacBio protocol. The sheared samples were run into 4200 Tapetation system to check the fragments length using Genomic DNA screen tape. SMRTbell libraries were prepared for sequencing using the SMRTbell Prep Kit 3.0. The libraries were attached to Sequel II primer 3.2 and Sequel II DNA polymerase 2.2 and sequenced on PacBio Sequel IIe equipment using SMRT cells 8M and Sequel II Sequencing kit 2.0. The runs were created to allow adaptive loading, 2 h of pre- extension and 30 h of movie times. For each sample on Sequel 11e, three SMRT cells were used to produce at least 30x coverage. 9 samples were run on Revio (Supplementary methods section 2). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 91, 399, 109]]<|/det|> +## ONT ultra-long DNA sequencing: + +<|ref|>text<|/ref|><|det|>[[113, 114, 881, 394]]<|/det|> +ONT ultra- long DNA sequencing:For Oxford nanopore sequencing, blood samples from APR cohort samples were aliquoted into 1.8 ml cryovials and stored at \(- 80^{\circ}\mathrm{C}\) until further processing. Frozen aliquots were thawed and peripheral blood mononuclear cells (PBMCs) were isolated using red blood cell lysis buffer. The purified PBMCs were counted and \(\sim 60\) million cells per sample were carried forward for ultralong DNA extraction using the NEB Monarch Tissue DNA Extraction Kit. The extracted DNA was then prepared into sequencing libraries using the Oxford Nanopore Technologies Ultra- Long DNA Sequencing Kit (SQK- ULK114) following the manufacturer's protocol with minor modifications. Briefly, the DNA was tagnmented at room temperature for 10 min followed by heating at \(75^{\circ}\mathrm{C}\) for 10 min. Rapid sequencing adapters were then added and incubated for 30 min at room temperature. Cleanup of the adapted DNA was performed using the precipitation star and buffers provided in the kit. The final libraries were quantified and loaded onto a minimum 3 PromethION flow cells at approximately \(30\mathrm{ng}\) per flow cell (Supplementary Table 2). Sequencing was performed for 96 hours per flow cell using Oxford Nanopore latest R10.4.1 kits. Minimum read length was set to 1 kb. + +<|ref|>sub_title<|/ref|><|det|>[[115, 415, 369, 435]]<|/det|> +## Sequencing and variant QC + +<|ref|>text<|/ref|><|det|>[[114, 437, 879, 635]]<|/det|> +Sequencing and variant QCFor ONT sequencing data, base- calling was performed using Oxford Nanopore's High Accuracy model utilizing Guppy basecaller v6.5.7 with modifications for detecting 5- methylcytosine (Supplementary methods section 3). We employed the High Accuracy (HAC) model to maximize the precision of the derived base sequences. The PacBio sequencing data were processed using the Circular Consensus Sequencing (CCS) algorithm. In the subsequent quality control phase, we employed the NanoStat v1.6.0, which provides an in- depth statistical overview of the sequence data. Additionally, metrics obtained from the smrtlink software were incorporated into the analysis to provide a comprehensive assessment. For refining the PacBio reads, HiFiAdapterFilt v2.0.1 was used to remove any residual adapter sequences that might hinder the assembly process. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 110, 872, 375]]<|/det|> +The PacBio human whole genome sequencing (WGS) pipeline was utilized for read alignment and variant calling. Reads were aligned to the GRCh38 and CHM13 reference genomes using pbmm2 v1.10.0. The CHM13 reference used was chm13v2.0_maskedY_rCRS.fa, while hg38 reference used was human_GRCh38_no_alt_analysis_set.fasta. Variant calling was then performed using Deepvariant v1.15.0. For SV calling, pbsv version 2.9 was utilized to jointly call SVs from the aligned long- reads. To identify small variants, DeepVariant and GLnexus (v1.4.1) were applied for joint SNP and indel calling. Variant annotation was performed using sliVAR (v0.2.2) for small variants and svPACK for SVs, which adds functional predictions and other annotations to the raw variant calls. We compared the number and types of variants detected in our samples using bcftools to those reported in the 1000 Genomes Project. We reported the number of SNPs, indels, and SVs, the ratio of base substitutions, and the indel length distribution for each sample. We used bedtools (Version 2.31.0) to compute the coverage of individual chromosomes and pericentromeric regions across all samples. + +<|ref|>sub_title<|/ref|><|det|>[[115, 396, 388, 415]]<|/det|> +## Population ancestry inference + +<|ref|>text<|/ref|><|det|>[[112, 416, 877, 777]]<|/det|> +To discern the genetic diversity and ascertain the population structure of 43 APR genomes, Principal Component Analysis (PCA) was undertaken. We employed SNPRelate (v1.28.0) \(^{26}\) . For a merged dataset with APR (HiFi variants), 1000 Genomes (n=2,879 samples), and 11 Arabian subpopulation samples (n=330 samples), 1,700,155 SNPs per sample were utilized and we evaluated. Higher order PCA (1 and 2) was used for visualization. Next, from merged dataset with APR and Arabian subpopulation, total 8,012,255 SNPs were incorporated, and 32 principal components were evaluated for better representation of the variance among the populations. Since the samples within Arab ethnicities are very tightly clustered (Supplementary Fig 5), PCA was plotted using PC8 and PC21 for the Arab ethnicities where it shows the most visible membership. To confirm the unrelated status of all 40 APR samples, we have constructed a heatmap using haplotype sharing variance obtained from fineSTRUCTURE \(^{27}\) runs on the PCA SNPs. We applied ADMIXTURE tool \(^{28}\) to dissect the genetic diversity of all 43 APR samples and population inference form global ancestry. We have used three major continental populations (Han Chinese, Yoruba, and French) and 11 Arabian subpopulations. ADMIXTURE was run on the combined bed file using a SNPs set where we kept SNPs from every 10kb, that yielded 259,712 SNPs. For population k number of clustering analysis, we have used k ranging from 2 to 12. All 43 APR samples were labeled, and ADMIXTURE plot was produced with vertical lines color coded according to the \(k\) value. + +<|ref|>sub_title<|/ref|><|det|>[[115, 799, 348, 817]]<|/det|> +## de novo genome assembly + +<|ref|>text<|/ref|><|det|>[[115, 819, 872, 898]]<|/det|> +High- quality de novo assemblies were generated for each individual using a hybrid assembly approach, combining PacBio HiFi and ONT ultra- long reads. A total of 43 samples were sequenced on 1- 6 SMRT Cells and 3- 7 ONT flow cells. We combined HiFi reads and ultra- long DNA sequencing kit (ULK) reads from multiple cells of each sample and applied Hifiasm + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 847, 128]]<|/det|> +(v0.19.5- r593) \(^{15}\) to carry out both the primary assembly and diploid assembly for 43 samples (Supplementary Methods section 4). + +<|ref|>sub_title<|/ref|><|det|>[[115, 150, 351, 168]]<|/det|> +## Genome assembly polishing + +<|ref|>text<|/ref|><|det|>[[113, 170, 875, 450]]<|/det|> +Genome assembly polishingGenome assemblies were screened for contaminant sequences and polished to remove artifacts prior to analysis. Kraken \(^{29}\) was used to taxonomically classify assembled contigs, retaining only those annotated as human. Mitochondrial read pairs were identified by mapping to chrM using minimap2 v2.26 and samtools v1.6 and any corresponding mitochondrial contigs were removed. Due to large homologous sequences at the pseudoautosomal regions (PAR), contigs were misaligned between the sex chromosomes. PAR within individual samples were identified by performing blast search using the CHM13 reported PAR1 sequences (chrX:1- 2394410; chrY:1- 2458320) to the assembly. Only the contigs containing both X and Y PAR regions were taken into account, and the PAR region with the highest similarity (with a cutoff of blast \(\%\) identity \(> = 90\) ) was chosen. Contigs with flanking region lengths exceeding 1MB were the only ones subjected to splitting. The sequence headers of the contigs were modified by adding suffix "XP" or "YP" based on the selected PAR region and the headers of the splitted contig headers were modified by putting suffix "XP1", "XP2", "YP1", "YP2" based on the PAR region (Supplementary Table 9)(Supplementary Methods section 5). + +<|ref|>sub_title<|/ref|><|det|>[[115, 471, 379, 490]]<|/det|> +## Assembly quality assessment + +<|ref|>text<|/ref|><|det|>[[115, 492, 877, 590]]<|/det|> +We assessed the quality and structural integrity of 43 primary assemblies and 86 haplotype assemblies using QUAST (v5.2.0) \(^{30}\) including completeness, N50, number of contigs, etc. This comprehensive tool was run with an extensive parameter set, as detailed: quast.py - o APR043. chm13v2.0 - r chm13v2.0. fa - t 16 APR043. bp. hap1. p_ctg. fa APR043. bp. hap2. p_ctg. fa - - large - e + +<|ref|>text<|/ref|><|det|>[[115, 611, 875, 710]]<|/det|> +The yak suite v0.1- r66 of tools \(^{15}\) , including yak count and yak qv, were employed for genome quality validation. Mapping rate was assessed using minimap2 v2.26. Potential misjoins in the assembly were identified using the minigraph software, followed by pafools. The specific commands initiated were - cxasm chm13v2.0. fa APR043. bp. hap1. p_ctg. fa and pafools misjoin - e APR043.1. misjoins. paf, respectively (Supplementary Methods section 6). + +<|ref|>sub_title<|/ref|><|det|>[[115, 731, 272, 749]]<|/det|> +## Gene duplication + +<|ref|>text<|/ref|><|det|>[[115, 751, 880, 869]]<|/det|> +We used Liftoff \(^{17}\) , a tool that accurately maps gene annotations between genome assemblies, to identify gene duplications in our data. Liftoff aligns gene sequences from a reference genome to a target genome and finds the mapping that maximizes sequence identity while preserving the gene structure. To remove partial matches from the analysis, we used the - exclude_partial option of Liftoff, which excludes partial mappings below the sequence identity (90%). We then counted the frequency of copy number variation (CNV) for each gene in the target genome by comparing + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 815, 128]]<|/det|> +the number of mapped copies to the number of copies in the reference genome (Gencode GRCh38.p14 (Gencode release 44)). + +<|ref|>sub_title<|/ref|><|det|>[[115, 151, 400, 170]]<|/det|> +## Pangenome graph construction + +<|ref|>text<|/ref|><|det|>[[113, 171, 879, 552]]<|/det|> +We constructed Arab pangenome variation graphs using the Minigraph- Cactus pipeline (v2.6.7) \(^{19}\) , which combines structural variants and single nucleotide variants (SNVs) into a single graph representation. We used two reference genomes, GRCh38 and CHM13, as backbones for the graph construction following the steps described in the Minigraph- Cactus documentation. Initially, an SV graph (>50bp) was constructed using Minigraph (v0.19) \(^{31}\) by sequentially aligning the 86 assemblies to the reference genome. Centromeric and telomeric regions were masked using dna- brnn \(^{32}\) , and assemblies were remapped to exclude highly repetitive sequences. Contigs were then split and assigned to chromosomes based on their alignment coordinates. Base- level alignment was performed using Cactus v2.1.161, and the HAL output \(^{33}\) was converted to vg format (hal2vg). Paths >10kb unaligned to the graph were removed, and the graph was normalized with GFAffix. The chromosome graphs were combined into a whole genome graph, indexed with vg v1.50.1, and exported to VCF format (vg view). Subsequently, SNP variants were incorporated into the SV graph using the same pipeline. Assemblies were remapped to the SV graph, and quality control was applied, excluding softmasked sequences >100kb and alignments with MAPQ < 5. Cactus was executed on the chromosome graphs to introduce base- level variants, and the outputs were converted to vg format. The same filtering, normalization, combining, and indexing steps were applied to produce the final SNP+SV Arab pangenome graph. All analysis was conducted using CATG high performance computing cluster Flamingo. + +<|ref|>sub_title<|/ref|><|det|>[[115, 573, 680, 592]]<|/det|> +## Identification and visualization of population-specific variants + +<|ref|>text<|/ref|><|det|>[[115, 594, 879, 712]]<|/det|> +The VCF file was separated into smaller variants and structural variants (SVs). To proceed with multilallic sites within the smaller variants, a two- step process was employed. First, the "bcftools norm - m-" command was applied to split multilallic sites into biallelic records. Then, the "vcfdccompose - - break- indels - - break- mmp" command from RTG tools (v.3.12.1) was used to further break down multi- nucleotide polymorphisms and complex indels into single nucleotide polymorphisms (SNPs) and indels. + +<|ref|>text<|/ref|><|det|>[[115, 733, 883, 871]]<|/det|> +We used vg v1.50.1 to call structural variants (SVs) from the graph using the vg deconstruct command. Multiallelic SV sites were normalized to biallelic records with bcftools v1.9 using the - m - any option abd followed by annotation using Truvari (v4.0.0). Based on the Truvari annotations, the longest SVs at each SV site were selected for subsequent analysis. SVs found uniquely (<60% reciprocal overlap with HPRC & CPC) in the Arab assemblies were classified as population- specific. We visualized SV distributions and enrichment significance on chromosomes with RIdrogram (v0.2.2) \(^{34}\) . Subgraphs surrounding SVs were extracted with + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[115, 90, 880, 129]]<|/det|> +gfabase v0.6.0 and visualized in Bandage NG version (v2022.09) \(^{35}\) after aligning gene sequences with GraphAligner (v1.0.17) \(^{36}\) using the command: + +<|ref|>text<|/ref|><|det|>[[115, 150, 877, 188]]<|/det|> +GraphAligner - g {viz_output} - f {pc_fasta_file} - t 32 - a {pc_alignment_file} - x vg --multimap- score- fraction 0.1 + +<|ref|>text<|/ref|><|det|>[[115, 210, 857, 268]]<|/det|> +The gfatools package v0.5- r287 was subsequently used to derive in- depth statistical data from the pangenome graphs. Structural haplotypes were linearly visualized using the gggenes R package. xg file from CPC was used for comparative analysis in our study. + +<|ref|>sub_title<|/ref|><|det|>[[115, 290, 500, 309]]<|/det|> +## Novel euchromatic sequence identification + +<|ref|>text<|/ref|><|det|>[[114, 311, 879, 510]]<|/det|> +To enhance the comparability of the call sets, clusters of closely related alleles in the HPRC- CPC SV sets were consolidated using the truvari command: truvari collapse - r 500 - p 0.95 - P 0.95 - s 50 - S 100000 followed by "truvari anno svinfo" command for annotation. The identical procedure was applied to APR as well. The unique SVs in APR were identified by comparing with HPRC- CPC SVs using truvari command: truvari bench - r 1000 - C 1000 - O 0.8 - p 0.8 - P 0.0 - s 50 - S 15 - - sizemax 100000. \(80\%\) reciprocal overlap was considered to call a SV unique. This collapses multiple SVs into one unique SVs within our cohort. To identify novel sequences within the APR unique SV insertions, we clustered them using cd- hit- est (4.8.1) with the default sequence identity threshold of 0.9. To identify the repeated elements within novel sequences, we have screened fasta file with rmlblastn version 2.14.1+. + +<|ref|>sub_title<|/ref|><|det|>[[115, 532, 495, 552]]<|/det|> +## Complex SV region and Bandage plotting + +<|ref|>text<|/ref|><|det|>[[114, 559, 880, 841]]<|/det|> +A site was considered complex if at least one variation larger than 10 kb was present and had at least five different alleles. The 715 complex sites were identified by analyzing the snarls VCF. The unique SVs were then overlapped with the complex sites to find unique complex sites (see unique variants subsection) in the Arab Population. The precise location of the genes were found out by mapping the gene sequences to the graph using graph aligner with following parameters - multimap- score 0.1. If multiple genes mapped to the same location (in case of isoforms) only the gene mapping with highest accuracy was retained. Complex structural variation (CSV) regions were defined as areas within a 100- kilobase (kb) window that consist of two or more multilatellic complex SV sites, with each site containing at least one 10- kilobase (kb) SV within the haplotypes. The complex sites were plotted using Bandage. 50 Kbp flanking region for each complex site was extracted using Gfabase sub. To find out if a haplotype consists of a gene, the path was traversed to see if it traverses through the gene region. Variation among haplotype walks that do not involve genes are visualized through dark lines traversal and arrows to define the direction of the haplotype. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 470, 110]]<|/det|> +## Mitochondria pangenome construction + +<|ref|>text<|/ref|><|det|>[[114, 111, 879, 291]]<|/det|> +Mitochondria pangenome constructionTo construct a mitochondrial Arab pangenome (mtAPR) that captures the diversity of Arab mitochondrial DNA, we used high- quality long- reads from 43 individuals. We first mapped the reads to the chrM of the CHM13 reference genome using minimap2 (v2.26) \(^{37}\) and retained only reads that were longer than 15 kb, this threshold was set to substantially reduce the chances of inadvertent nuclear DNA contamination. It resulted in a total of 20,520 reads (19,251 ONT reads and 1,270 Hifi reads). We used PGBB (v0.5.4) \(^{38}\) to construct a mitochondrial pangenome graph from the mitochondrial contigs of HiFi reads of all individuals, each read of \(>15\mathrm{Kb}\) was QC passed as one haplotype to PGBB. We visualized the mtAPR graph using Bandage NG version (v2022.09) \(^{35}\) and displayed the nodes, edges and variants in different colors and shapes. + +<|ref|>sub_title<|/ref|><|det|>[[115, 312, 383, 331]]<|/det|> +## Pathway enrichment analysis + +<|ref|>text<|/ref|><|det|>[[114, 333, 867, 512]]<|/det|> +Pathway enrichment analysisTo discern the biological pathways affected by gene sets, we leveraged the KEGG and GO pathway databases, a well curated repository featuring pathways that include molecular interactions, reactions, and relation networks. Our analytical framework included determining the degree of overlap between the gene sets and the pathways delineated in the KEGG- GO database. We excluded pathway gene sets that numbered fewer than 50 or exceeded 1000. A significant overlap measured using the Fisher's Exact Test (FET), implied that a pathway is enriched. To account for multiple hypothesis testing and control the false discovery rate (FDR), we applied the Benjamini- Hochberg procedure to adjust the p- values obtained from the Fisher's Exact Test. + +<|ref|>sub_title<|/ref|><|det|>[[115, 535, 279, 554]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[115, 556, 877, 636]]<|/det|> +Data availabilityThe first draft of the APR data from this work can be found at https://www.mbru.ac.ae/the- arab- pangenome- reference/. We hope the downloadable pangenome will impact research and clinical genetics communities. The raw sequencing data can be obtained from corresponding authors upon approval from Dubai Academic Healthcare Corporation (DAHC). + +<|ref|>sub_title<|/ref|><|det|>[[115, 658, 282, 678]]<|/det|> +## Code availability + +<|ref|>text<|/ref|><|det|>[[115, 680, 879, 739]]<|/det|> +Code availabilityThe code to reproduce the pangenome from this work can be found at GitHub (https://github.com/muddinmbru/arab_pangenome_reference). Relevant commands used in other analyses can be found in the Methods or Supplementary Information. + +<|ref|>sub_title<|/ref|><|det|>[[115, 761, 223, 781]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[114, 800, 872, 875]]<|/det|> +References1. Wang, T. et al. The Human Pangenome Project: a global resource to map genomic diversity. Nature 604, 437- 446 (2022).2. 1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68- 74 (2015). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[111, 90, 880, 877]]<|/det|> +3. Bergström, A. et al. Insights into human genetic variation and population history from 929 diverse genomes. Science 367, (2020). +4. Nurk, S. et al. The complete sequence of a human genome. Science 376, 44–53 (2022). +5. Liao, W.-W. et al. A draft human pangenome reference. Nature 617, 312–324 (2023). +6. Gao, Y. et al. A pangenome reference of 36 Chinese populations. Nature 619, 112–121 (2023). +7. Popejoy, A. B. & Fullerton, S. M. Genomics is failing on diversity. Nature 538, 161–164 (2016). +8. Almarri, M. A. et al. The genomic history of the Middle East. Cell 184, 4612-4625.e14 (2021). +9. Mbarek, H. et al. Qatar genome: Insights on genomics from the Middle East. Hum Mutat 43, 499–510 (2022). +10. Tadmouri, G. O. et al. Consanguinity and reproductive health among Arabs. Reprod Health 6, 17 (2009). +11. Teebi, A. S. Autosomal recessive disorders among Arabs: an overview from Kuwait. J Med Genet 31, 224–33 (1994). +12. Al-Gazali, L., Hamamy, H. & Al-Arrayad, S. Genetic disorders in the Arab world. BMJ 333, 831–4 (2006). +13. Rahim, H. F. A. et al. Non-communicable diseases in the Arab world. Lancet 383, 356–67 (2014). +14. El-Kebbi, I. M., Bidikian, N. H., Hneiny, L. & Nasrallah, M. P. Epidemiology of type 2 diabetes in the Middle East and North Africa: Challenges and call for action. World J Diabetes 12, 1401–1425 (2021). +15. Cheng, H., Concepcion, G. T., Feng, X., Zhang, H. & Li, H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat Methods 18, 170–175 (2021). +16. Rautiainen, M. et al. Telomere-to-telomere assembly of diploid chromosomes with Verkko. Nat Biotechnol 41, 1474–1482 (2023). +17. Shumate, A. & Salzberg, S. L. Liftoff: accurate mapping of gene annotations. Bioinformatics 37, 1639–1643 (2021). +18. Chen, C.-L. et al. Ethnically unique disease burden and limitations of current expanded carrier screening panels. Int J Gynaecol Obstet (2023) doi:10.1002/ijgo.15072. +19. Hickey, G. et al. Pangenome graph construction from genome alignments with Minigraph-Cactus. Nat Biotechnol (2023) doi:10.1038/s41587-023-01793-w. +20. Kern, C. H., Yang, M. & Liu, W.-S. The PRAME family of cancer testis antigens is essential for germline development and gametogenesis†. Biol Reprod 105, 290–304 (2021). +21. Cerveira, N. et al. A novel spliced fusion of MLL with CT45A2 in a pediatric biphenotypic acute leukemia. BMC Cancer 10, 518 (2010). +22. Frankish, A. et al. GENCODE 2021. Nucleic Acids Res 49, D916–D923 (2021). +23. Sirén, J. et al. Pangenomics enables genotyping of known structural variants in 5202 diverse genomes. Science 374, abg8871 (2021). +24. Vollger, M. R. et al. Increased mutation and gene conversion within human segmental duplications. Nature 617, 325–334 (2023). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 875, 181]]<|/det|> +25. Ravarani, C. N. J. et al. Molecular determinants underlying functional innovations of TBP and their impact on transcription initiation. Nat Commun 11, 2384 (2020).26. Zheng, X. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–8 (2012).27. Lawson, D. J., Hellenthal, G., Myers, S. & Falush, D. Inference of population structure using dense haplotype data. PLoS Genet 8, e1002453 (2012).28. Alexander, D. H. & Lange, K. Enhancements to the ADMIXTURE algorithm for individual ancestry estimation. BMC Bioinformatics 12, 246 (2011).29. Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 15, R46 (2014).30. Mikheenko, A., Prijibelski, A., Saveliev, V., Antipov, D. & Gurevich, A. Versatile genome assembly evaluation with QUAST-LG. Bioinformatics 34, i142–i150 (2018).31. Li, H., Feng, X. & Chu, C. The design and construction of reference pangenome graphs with minigraph. Genome Biol 21, 265 (2020).32. Li, H. Identifying centromeric satellites with dna-brnn. Bioinformatics 35, 4408–4410 (2019).33. Hickey, G., Paten, B., Earl, D., Zerbino, D. & Haussler, D. HAL: a hierarchical format for storing and analyzing multiple genome alignments. Bioinformatics 29, 1341–2 (2013).34. Hao, Z. et al. Rldeogram: drawing SVG graphics to visualize and map genome-wide data on the idiograms. PeerJ Comput Sci 6, e251 (2020).35. Wick, R. R., Schultz, M. B., Zobel, J. & Holt, K. E. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 31, 3350–2 (2015).36. Rautiainen, M. & Marschall, T. GraphAligner: rapid and versatile sequence-to-graph alignment. Genome Biol 21, 253 (2020).37. Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).38. Garrison, E. et al. Building pangenome graphs. bioRxiv (2023) doi:10.1101/2023.04.05.535718. + +<|ref|>text<|/ref|><|det|>[[112, 183, 875, 610]]<|/det|> +29. Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 15, R46 (2014).30. Mikheenko, A., Prijibelski, A., Saveliev, V., Antipov, D. & Gurevich, A. Versatile genome assembly evaluation with QUAST-LG. Bioinformatics 34, i142–i150 (2018).31. Li, H., Feng, X. & Chu, C. The design and construction of reference pangenome graphs with minigraph. Genome Biol 21, 265 (2020).32. Li, H. Identifying centromeric satellites with dna-brnn. Bioinformatics 35, 4408–4410 (2019).33. Hickey, G., Paten, B., Earl, D., Zerbino, D. & Haussler, D. HAL: a hierarchical format for storing and analyzing multiple genome alignments. Bioinformatics 29, 1341–2 (2013).34. Hao, Z. et al. Rldeogram: drawing SVG graphics to visualize and map genome-wide data on the idiograms. PeerJ Comput Sci 6, e251 (2020).35. Wick, R. R., Schultz, M. B., Zobel, J. & Holt, K. E. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 31, 3350–2 (2015).36. Rautiainen, M. & Marschall, T. GraphAligner: rapid and versatile sequence-to-graph alignment. Genome Biol 21, 253 (2020).37. Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).38. Garrison, E. et al. Building pangenome graphs. bioRxiv (2023) doi:10.1101/2023.04.05.535718. + +<|ref|>sub_title<|/ref|><|det|>[[117, 643, 306, 662]]<|/det|> +## Acknowledgements + +<|ref|>text<|/ref|><|det|>[[115, 664, 881, 803]]<|/det|> +We would like to thank all the study participants for giving us this opportunity of building a reference pangenome. Yehia Zakaria Kotp provided constant support for the CATG Flamingo computation cluster. Avinash Krishnan, and Freeda Pinto provided constant help with logistics at CATG sequencing lab. HPRC team for commenting on our assembly data quality. Dubai Academic Healthcare Corporation (DAHC) funded this work and supported the procurement of reagents, chemicals, and computing hardwares. The funders had no role in the design of the study, data collection, analysis, publish or preparation of the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[116, 824, 311, 844]]<|/det|> +## Author information + +<|ref|>text<|/ref|><|det|>[[115, 847, 879, 906]]<|/det|> +AAA, MU, SDP and MA conceived and designed the study. AAA, MU were responsible for ethical, legal and social implications. AAA, MU, NN coordinated and supervised the project. BJ, NN, BA, MAO, MHSA, AA, OZSA, DFY, HAS, HHK, were responsible for recruitment and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 880, 329]]<|/det|> +collection of blood samples and clinical data from medical health records. AAA, MU, NN, SH, MA, HHK, BJ were responsible for sample selection and population genetic analysis. MAO, MU, NN, NM, HE, DK, DS, were responsible for DNA and RNA sequencing. MK, BB, SH, NN, MU were responsible for variant annotation. MK was responsible for assembly creation and MK,MU, NN were responsible for assembly quality control and assembly reliability analysis. MU, MK, BB, NN, SH were responsible for variant detection from assembly, pangenome empirical analysis and quality control. BB, SH, MU, NN were responsible for pangenome applications of SVs, short and long- read mapping. MK, NN, MU, NK, SS were responsible for pangenome visualization and complex loci analysis and pangenome graph creation. MU, MK, BB, NN, SH statistical analysis. AAA, MU, NN, SDP, MA, AAT were responsible for manuscript writing. MU, AAA, NN were responsible for manuscript editing. AAA, MU, and NN were responsible for data coordination and management. + +<|ref|>sub_title<|/ref|><|det|>[[116, 350, 330, 370]]<|/det|> +## Corresponding authors + +<|ref|>text<|/ref|><|det|>[[116, 391, 820, 430]]<|/det|> +Correspondence and requests for materials should be addressed to Mohammed Uddin (mohammed.uddin@mbru.ac.ae) or Alawi Alsheikh Ali (alawi.alsheikhali@mbru.ac.ae). + +<|ref|>sub_title<|/ref|><|det|>[[116, 451, 303, 472]]<|/det|> +## Ethics declarations + +<|ref|>sub_title<|/ref|><|det|>[[116, 506, 300, 525]]<|/det|> +## Competing interests + +<|ref|>text<|/ref|><|det|>[[116, 537, 459, 555]]<|/det|> +The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[116, 577, 390, 597]]<|/det|> +## Supplementary information + +<|ref|>text<|/ref|><|det|>[[115, 600, 513, 618]]<|/det|> +See submitted supplementary document and tables. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[117, 131, 977, 382]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[115, 88, 186, 106]]<|/det|> +
Figures
+ +<|ref|>text<|/ref|><|det|>[[112, 386, 884, 830]]<|/det|> +Fig. 1: Cohort characterization and sequencing quality. a, Key serum measures kidney, thyroid, and liver function in addition to glucose and lipid values. Each plot showcases a specific phenotypic attribute, with dots representing individual measurements and the red line representing the normal threshold. b, Sequencing quality metrics. Bar chart comparing sequencing quality metrics, including the average Q scores and N50 read lengths for PacBio and Oxford Nanopore (ONT) platforms. The colors neon pink and teal blue denote PacBio and ONT respectively. c, Ultra- long read yield. Histogram depicting the yield of ultra- long reads (>100 kb) produced by ONT sequencing. The x- axis classifies the reads based on length categories, while the y- axis displays the yield for each category. d, Chromosome mapping distribution. Bar chart presenting the distribution of ONT and PacBio reads that align to acrocentric, metacentric, and submetacentric chromosomes. Neon pink and teal blue bars represent PacBio and ONT data respectively. e, Subtelomeric and pericentric read coverage. Bar chart illustrating the coverage of subtelomeric and pericentric regions by both ONT and PacBio reads. The depth of coverage is depicted for each region, emphasizing the difference between the two sequencing platforms. f, Principal Component Analysis (PCA) two- dimensional scatter plot visualizing the results of population ethnicity variance. Different ethnicities from 1000 genomes are color coded and each dot represents a sample from a designated ethnicity. APR cohort is color coded red and the two axes indicate the variance captured by first (PCA1) and second (PCA2) principal component, respectively. g, PCA clustering of APR cohort and numerous Arab ethnicities. Scatter plot emphasizing the PCA clustering specifically for APR cohort and Arab samples. The distribution is plotted using Principal Component 8 (PC8) on the y- axis against Principal Component 21 (PC21) on the x- axis, highlighting inherent clustering between these groups. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[128, 95, 978, 410]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 437, 880, 900]]<|/det|> +
Fig. 2: Quality assessment of 43 phased diploid assemblies and gene duplication analysis. a, Assembly length comparison. Box plot detailing the assembly length for different haplotypes across both male and female samples. b, Assembly contiguity. Line graph showing contig length plotted against cumulative assembly coverage. Notably, reference contiguity for both CHM13 and GRCh38 genomes are included for comparison. c, Assembly accuracy and completeness. Scatter plot illustrating the mapping rate against consensus accuracy (QV), offering insights into the completeness and accuracy of the assembly. d, Assembly alignment coverage. Scatter plot comparing the alignment coverage of assemblies relative to benchmark references CHM13 and GRCh38. e, Duplication ratio analysis. Bar graph displaying the distribution of duplication ratios across assemblies. f, Gene and transcript annotation. Scatter plot showing the percentages of protein-coding and noncoding genes, as well as transcripts annotated from the reference set in each of the assemblies. g, Gene duplication per assembly. Histogram presenting the number of unique duplicated genes or gene families in each phased assembly in comparison to the number of duplicated genes annotated in GRCh38. h, Comparative duplicated gene analysis. Venn diagram visualizing the overlap and unique counts of duplicated genes across APR, HPRC, and CPC assemblies. i, Arab-HPRC duplicated gene overlap. Bar graph showcasing five overlapped duplicated genes with a notably higher frequency (≥5%) in Arab assemblies (blue) compared to HPRC (orange). j, Arab-CPC duplicated gene overlap. Bar chart illustrating five overlapped duplicated genes with a significantly higher frequency (≥5%) in Arab assemblies (blue) in contrast to CPC (yellow). k, Unique Arab duplicated genes. Bar graph representing the frequency of top 20 unique duplicated genes in Arab assemblies when compared against both HPRC and CPC. l, Bar graphs indicating the count of unique duplicated genes across three chromosome types: acrocentric, metacentric, and submetacentric. m, Bar graph showing the
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 90, 863, 149]]<|/det|> +count of unique duplicated genes dispersed across all individual chromosomes, highlighting regions of enrichment. \(\mathbf{n}\) , Microsatellite region gene duplication. Bar graph depicting the count of unique duplicated genes located in microsatellite regions. + +<|ref|>image<|/ref|><|det|>[[121, 191, 875, 629]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 653, 874, 874]]<|/det|> +
Fig. 3: Arab genome specific sequences. a, Bar graph demonstrating the total number of small variants for each sample, distinguishing between singleton and polymorphic variants. b, Bar graph showcasing the small variants specific to APR per sample, further differentiating between singleton and polymorphic variants. c, Venn diagram showcasing the small variants from the Arab pangenome in relation to HPRC and CPC datasets. d, Stacked bar graph detailing the total structural variants (SVs) per sample, categorizing between singleton and polymorphic variants for both insertions and deletions. e, Stacked bar graph illustrating the SVs that are APR-specific for each sample, for both insertions and deletions. f, Venn diagram visualizing the overlap and differences in SVs from the Arab pangenome with HPRC and CPC datasets. g, Visualization of Arab-specific SVs from the pangenome graph across autosomes. Sites of complex SVs are marked with blue circles. h, Bar graph displaying the length distribution of newly identified
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 90, 866, 129]]<|/det|> +sequences for each sample, offering insights into the diversity of novel sequence lengths. i, Bar graph indicating counts of novel sequences across microsatellite regions. + +<|ref|>image<|/ref|><|det|>[[130, 135, 940, 780]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 823, 848, 903]]<|/det|> +
Fig. 4: Visualizing complex structural variation region. a, PRAMEF region subgraph. Diagram showcasing the specific location of the PRAMEF genes. b, Sample haplotypes in PRAMEF Region. Distinct paths taken by different samples through the PRAMEF region. c, PRAMEF region haplotype count. Linear structural diagrams representing the frequency and
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 89, 878, 228]]<|/det|> +structural visualization of haplotypes identified by the graph across 86 haploid assemblies, compared against the HPRC- CPC graph. d, CT45A region subgraph. Diagram highlighting the specific location of the CT45A region. e, Sample haplotypes in CT45A region. Unique paths traversed by different samples through the CT45A region. f, CT45A region haplotype count. Linear structural diagrams depicting the frequency and structural visualization of haplotypes as determined by the graph among 86 haploid assemblies, compared with the HPRC- CPC graph for a comprehensive comparison. + +<|ref|>image<|/ref|><|det|>[[140, 270, 844, 700]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 744, 860, 905]]<|/det|> +
Fig. 5: Mitochondrial pangenome analysis. a, Mitochondrial read length distribution. Frequency distribution graph depicting the mitochondrial read lengths of Hifi data. b, A ring structured representation of the mitochondrial pangenome, detailing the position and nomenclature of annotated mitochondrial genes and their relationships within the pangenome. Each bubble or loop represents a haplotype. c, mtAPR variant distribution. A bar chart showcasing the number of APR-specific small variants observed across different samples, differentiated between polymorphism (in dark blue) and singleton (in light blue). d, Mitochondrial pangenome indel length distribution. A histogram presenting the distribution of
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 89, 874, 128]]<|/det|> +insertion and deletion (indel) lengths within the mitochondrial pangenome, offering insights into the size and frequency of these sequence modifications. + +<|ref|>sub_title<|/ref|><|det|>[[115, 170, 262, 190]]<|/det|> +## Extended Data + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[198, 160, 833, 722]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[113, 771, 877, 878]]<|/det|> +Extended Data Fig. 1: Population genetic ancestry inference of the 43 APR samples using ADMIXTURE. Assuming ancestry components K ranging from 2 to 12, each plot shows independent ancestry fraction with its own color- coding representation labeled with short vertical lines. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[285, 111, 680, 810]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 819, 845, 890]]<|/det|> +
Extended Data Fig. 2: Analysis of gene duplication patterns. a, Histogram presenting the frequency of various gene copy numbers observed within APR. The x-axis displays the gene copy number, while the y-axis represents the frequency of each copy number. b, Bar graph
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 881, 160]]<|/det|> +showcasing the frequency of the top 20 genes with gene duplications that are absent in the HPRC dataset. c, Bar graph showcasing the frequency of top 20 genes with gene duplications that are absent in the CPC dataset. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[312, 95, 675, 725]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[113, 735, 872, 900]]<|/det|> +Extended Data Fig. 3: Structural variants from pangenome graph. a, Stacked bar graph illustrating the total structural variants (SVs) per haplotype, categorized by singleton or polymorphic variants for both insertions and deletions. b, Stacked bar graph illustrating the SVs that are APR- specific for each haplotype, for both insertions and deletions. c, SV length distribution for APR (blue) and HPRC+CPC (orange). The peak at 246 bp for Alu repeats are highlighted. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[118, 147, 883, 344]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[113, 362, 886, 467]]<|/det|> +Extended Data Fig. 4: APR Novel euchromatic Sequences a, Repeatmasker to identify satellite repeats in novel sequences. b, Loci of APR specific novel sequences. Visualization of Arab- specific novel sequences across chromosomes. The line (red) width is proportional to the length of the novel sequence (kb). + +<|ref|>image<|/ref|><|det|>[[118, 490, 958, 675]]<|/det|> + +<|ref|>text<|/ref|><|det|>[[113, 699, 884, 833]]<|/det|> +Extended Data Fig. 5: Chromosome wise distribution of complex structural variations (CSVs) sites. a, Bar graph indicating the count (y- axis) of CSV sites across three chromosome types: acrocentric, metacentric, and submetacentric. b, Bar graph showing the count of CSV sites dispersed across all individual chromosomes.c, Bar graph indicating counts of CSV sites across microsatellite regions. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[200, 100, 800, 450]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 456, 855, 503]]<|/det|> +
Extended Data Fig. 6: Length distribution of CSV sites. Histogram showcasing number of CSV sites (log scale y-axis) falling within each length interval (log scale x-axis).
+ +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[42, 93, 768, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 131, 393, 177]]<|/det|> +- Arabpangenomesupplementary.pdf- SupplementaryTables.xlsx + +<--- Page Split ---> diff --git a/preprint/preprint__08c896aeaed41f29a8b25b95b601c8dd1bba0170a826726f584aca97efa5d8d5/images_list.json b/preprint/preprint__08c896aeaed41f29a8b25b95b601c8dd1bba0170a826726f584aca97efa5d8d5/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..ba105a0fd614ba9140db2ebedc5efb90893fff97 --- /dev/null +++ b/preprint/preprint__08c896aeaed41f29a8b25b95b601c8dd1bba0170a826726f584aca97efa5d8d5/images_list.json @@ -0,0 +1,92 @@ +[ + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "Figures", + "footnote": [], + "bbox": [ + [ + 115, + 145, + 780, + 760 + ] + ], + "page_idx": 29 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2", + "footnote": [], + "bbox": [ + [ + 128, + 92, + 808, + 500 + ] + ], + "page_idx": 30 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3", + "footnote": [], + "bbox": [ + [ + 142, + 120, + 815, + 520 + ] + ], + "page_idx": 31 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4", + "footnote": [], + "bbox": [ + [ + 120, + 90, + 790, + 702 + ] + ], + "page_idx": 32 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5", + "footnote": [], + "bbox": [ + [ + 123, + 60, + 790, + 670 + ] + ], + "page_idx": 33 + }, + { + "type": "image", + "img_path": "images/Figure_6.jpg", + "caption": "Figure 6", + "footnote": [], + "bbox": [ + [ + 105, + 75, + 777, + 688 + ] + ], + "page_idx": 34 + } +] \ No newline at end of file diff --git a/preprint/preprint__08c896aeaed41f29a8b25b95b601c8dd1bba0170a826726f584aca97efa5d8d5/preprint__08c896aeaed41f29a8b25b95b601c8dd1bba0170a826726f584aca97efa5d8d5.mmd b/preprint/preprint__08c896aeaed41f29a8b25b95b601c8dd1bba0170a826726f584aca97efa5d8d5/preprint__08c896aeaed41f29a8b25b95b601c8dd1bba0170a826726f584aca97efa5d8d5.mmd new file mode 100644 index 0000000000000000000000000000000000000000..11088580909f74542f3c91a03f87357662e55b66 --- /dev/null +++ b/preprint/preprint__08c896aeaed41f29a8b25b95b601c8dd1bba0170a826726f584aca97efa5d8d5/preprint__08c896aeaed41f29a8b25b95b601c8dd1bba0170a826726f584aca97efa5d8d5.mmd @@ -0,0 +1,684 @@ + +# CellRank for directed single-cell fate mapping + +Marius Lange Institute of Computational Biology, Helmholtz Center Munich https://orcid.org/0000- 0002- 4846- 1266 + +Volker Bergen Institute of Computational Biology, Helmholtz Center Munich + +Michal Klein Institute of Computational Biology, Helmholtz Center Munich + +Manu Setty Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center + +Bernhard Reuter Department of Computer Science, University of Tubingen + +Mostafa Bakhti Institute of Diabetes and Regeneration Research, Helmholtz Center Munich, + +Heiko Lickert Helmholtz Zentrum München https://orcid.org/0000- 0002- 4597- 8825 + +Meshal Ansari Institute of Computational Biology, Helmholtz Center Munich + +Janine Schniering Institute of Lung Biology and Disease (ILBD), Helmholtz Zentrum München + +Herbert Schiller Helmholtz Zentrum München, Institute of Lung Biology and Disease, Group Systems Medicine of Chronic Lung Disease, Member of the German Center for Lung Research (DZL), CPC- M bioArchive, Munich https://orcid.org/0000- 0001- 9498- 7034 + +Dana Pe'er Memorial Sloan Kettering Cancer Center https://orcid.org/0000- 0002- 9259- 8817 + +Fabian Theis ( fabian.theis@helmholtz- muenchen.de) Helmholtz Zentrum München https://orcid.org/0000- 0002- 2419- 1943 + +## Article + +Keywords: CellRank, directed, single- cell fate mapping + +Posted Date: October 29th, 2020 + +DOI: https://doi.org/10.21203/rs.3.rs- 94819/v1 + +<--- Page Split ---> + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Version of Record: A version of this preprint was published at Nature Methods on January 13th, 2022. See the published version at https://doi.org/10.1038/s41592-021-01346-6. + +<--- Page Split ---> + +## CellRank - Online Methods + +Marius Lange \(^{1,2}\) , Volker Bergen \(^{1,2}\) , Michal Klein \(^{1}\) , Manu Setty \(^{3}\) , Bernhard Reuter \(^{4,5}\) , Mostafa Bakhti \(^{6,7}\) , Heiko Lickert \(^{6,7}\) , Meshal Ansari \(^{1,8}\) , Janine Schniering \(^{8}\) , Herbert B. Schiller \(^{8}\) , Dana Pe'er \(^{3*}\) , Fabian J. Theis \(^{1,2,9*}\) + +1 Institute of Computational Biology, Helmholtz Center Munich, Germany. 2 Department of Mathematics, TU Munich, Germany. 3 Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 4 Department of Computer Science, University of Tübingen, Germany. 5 Zuse Institute Berlin (ZIB), Takustr. 7, 14195 Berlin, Germany. 6 Institute of Diabetes and Regeneration Research, Helmholtz Center Munich, Germany. 7 German Center for Diabetes Research (DZD), Neuherberg, Germany. 8 Comprehensive Pneumology Center (CPC) / Institute of Lung Biology and Disease (ILBD), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany. 9 TUM School of Life Sciences Weihenstephan, Technical University of Munich, Germany. \*Corresponding authors: fabian.theis@helmholtz-muenchen.de and peerd@mskcc.org + +## Contents + +1 The CellRank algorithm 2 1.1 Modelling approach 3 1.2 Computing the transition matrix 5 1.3 Coarse-graining the Markov chain 7 1.4 Computing fate probabilities 10 1.5 Propagating velocity uncertainty 12 1.6 The CellRank software package 14 2 Computing a directed PAGA graph 16 3 Computing gene expression trends along lineages 16 4 Clustering gene expression trends 17 5 Uncovering putative driver genes 18 6 Robustness analysis 18 7 Pancreas data example 19 8 Lung data example 20 9 Methods comparison 21 10 Immunofluorescence stainings and microscopy on airway epithelial cells 23 + +<--- Page Split ---> + +## Online methods + +## 1 The CellRank algorithm + +The aim of the CellRank algorithm is to detect the initial, terminal and intermediate states of a cellular system and to define a global map of fate potentials that assigns each cell to these states in a probabilistic manner. Given our inferred fate potentials, we compute gene expression trends along trajectories in the fate map and provide several possibilities for visualizing these. The inputs to CellRank are a count matrix \(X\in \mathbb{R}^{N\times G}\) where \(N\) is the number of cells and \(G\) is the number of genes as well as a velocity matrix \(V = \mathbb{R}^{N\times G}\) , defining a vector field representing RNA velocity \(^{1,2}\) for each cell and gene. Note that CellRank can be generalized to any kind of vector field, i.e. \(V\) could equally represent directed information given by e.g. metabolic labeling \(^{3 - 6}\) . There are three main steps to the CellRank algorithm: + +1. Compute transition probabilities among observed cells. These reflect how likely a cell with a given cell state, defined by its gene expression profile, is to change its profile to that of a target cell. We compute these probabilities by integrating two sources of evidence: (1) transcriptomic similarity between the source and target cells and (2) an extrapolation of a cell's current gene expression profile into the near future using RNA velocity. We aggregate these transition probabilities in the transition matrix \(P\) and use it to model cell-state transitions as a Markov chain. + +2. Coarse-grain the Markov chain into a set of initial, terminal and intermediate macrostates of cellular dynamics. Each cell is assigned to each macrostate via a membership matrix \(\chi\) . The assignment is soft, i.e. each cell has a certain degree of confidence of belonging to each macrostate. We compute transition probabilities among macrostates in the matrix \(P_{c}\) . This matrix allows us to identify whether macrostates are initial, terminal or intermediate. + +3. Compute fate probabilities towards a subset of the macrostates. This will typically include the terminal states, but can also include intermediate states, depending on the biological question. We compute how likely each cell is to transition into each of the selected macrostates and return these probabilities in a fate matrix \(F\) . + +CellRank extracts the essence of cellular state transitions The principle of the CellRank algorithm is to decompose the dynamics of the biological system into a set of dynamical macrostates. We target macrostates that are associated with regions in the phenotypic manifold which cells are unlikely to leave once they have entered them. For each observed cell, we compute how likely it is to belong to each of these macrostates. We accumulate these soft assignments in a membership matrix \(\chi \in \mathbb{R}^{N\times n_{s}}\) . Further, we compute a coarse- grained transition matrix \(P_{c}\in \mathbb{R}^{n_{s}\times n_{s}}\) which specifies transition probabilities among macrostates. The coarse- grained transition matrix allows us to reduce the biological system to its essence: dynamical macrostates of observed cell- state transitions and their relationship to one another. Based on the coarse- grained transition matrix, we classify macrostates as either initial, intermediate or terminal. Initial states will be macrostates that have very small incoming but large outgoing transition probability. Intermediate states will be macrostates that have both incoming and outgoing transition probability. Terminal states will be macrostates that have large incoming but very little outgoing and large self- transition probability. + +CellRank computes probabilistic fate potentials Each macrostate is associated with a subset of the observed cells via the membership matrix \(\chi\) . Once we classified macrostates as either initial, intermediate or terminal using the coarse- grained transition matrix \(P_{c}\) , we may ask how likely each cell is to transition to each of the \(n_{t}\) terminal states. CellRank efficiently computes these probabilities and returns a fate matrix \(F\in \mathbb{R}^{N\times n_{t}}\) . The matrix \(F\) extends the short- range fate relationships given by RNA velocity to the global scale: from initial to terminal states along the phenotypic manifold. + +<--- Page Split ---> + +We account for high noise levels in the velocity vectors via a stochastic Markov chain formulation, by restricting predicted transitions to align with the phenotypic manifold and by propagating velocity uncertainty into the Markov chain. + +CellRank uncovers gene expression trends towards specific terminal populations The outputs of the CellRank algorithm are + +- a membership matrix \(\chi \in \mathbb{R}^{N\times n_s}\) where \(n_s\) is the number of macrostates. Row \(i\) in \(\chi\) softly assigns cell \(i\) to any of the macrostates. + +- a coarse-grained transition matrix \(P_{c} \in \mathbb{R}^{n_{s} \times n_{s}}\) that describes how likely these macrostates are to transition into one another. The matrix \(P_{c}\) allows macrostates to be classified as either initial, intermediate or terminal. + +- a fate matrix \(F \in \mathbb{R}^{N \times n_{t}}\) where \(n_{t}\) is the number of terminal states. Row \(i\) in \(F\) specifies how likely cell \(i\) is to transition towards any of the terminal states. + +We use the fate matrix \(F\) to model gradual lineage commitment. Fate biases can be aggregated to the cluster level and visualized as pie charts on a new directed version of PAGA graphs7 (Section 2). Further, we use the fate matrix \(F\) to uncover gene expression trends towards the identified terminal states (Section 3). Once the trends have been fit, they can be clustered to discover the main regulatory dynamics towards different terminal states (Section 4). For the identification of putative regulators towards specific terminal states, we correlate gene expression values with fate probabilities (Section 5). + +### 1.1 Modelling approach + +Similarly to other methods8- 10, CellRank models cell state transitions among observed cellular profiles. Unlike other velocity based methods, following the success of pseudotime methods, key to our model is that we restrict possible state changes to those consistent with the global structure of the phenotypic manifold via a KNN graph computed based on similarities in gene expression space. Our approach then biases the likely future state of an observed cell within its local graph neighborhood based on RNA velocity, by combining transcriptional similarity with RNA velocity to direct edges in the graph and to assign a probability to each cell state transition. When computing these probabilities, we take into account uncertainty in the velocity vectors. By aggregating individual, stochastic transitions within the global structure of the phenotypic manifold, we uncover the fate bias for individual cells. We make the following assumptions: + +- state transitions are gradual, daughter cells are in general transcriptomically similar to their mother cells. Cells traverse a low-dimensional phenotypic manifold from initial to terminal states via a set of intermediate states.- the set of sampled cellular profiles spans the entire state change trajectory, i.e. intermediate states have been covered, there are no 'gaps' in the trajectory.- while for an individual cell, its past history is stored in epigenetic modifications, we model average cellular dynamics where state transitions occur without memory.- RNA velocity approximates the first derivative of gene expression. This must not precisely hold for every gene in each individual cell as we treat state transitions as a stochastic process, enforce alignment with the manifold and propagate uncertainty, but it should hold in expectation for enough cells so that we are able to estimate the overall directional flow. + +Based on these assumptions, we model cellular state transitions using a Markov chain: a stochastic process \(X = (X_{t})_{t \in T}\) - a sequence of random variables \(X_{t}: \Omega \to E\) on a probability space \((\Omega , \mathcal{A}, \mathbb{P})\) over a countable set \(\Omega\) mapping to a measurable state space \((E, \Sigma)\) - that describes the evolution + +<--- Page Split ---> + +of a probability distribution over time where the future distribution only depends on the current distribution and not on the past, i.e. \(\operatorname *{Pr}(X_{t_{n + 1}} = x\mid X_{t_1} = x_1,X_{t_2} = x_2,\dots,X_{t_n} = x_n) = \operatorname *{Pr}(X_{t_{n + 1}} = x\mid X_{t_n} = x_n)\) . We use a Markov chain over a discrete and finite state space \(\Omega\) , where each state in the chain is given by an observed cellular transcriptional profile. To define the Markov chain, we need to compute a transition matrix \(P\in \mathbb{R}^{N\times N}\) which describes how likely one cell is to transition into another. We construct \(P\in \mathbb{R}^{N\times N}\) using a KNN graph based on transcriptional similarity between cells and a given vector field. While CellRank generalizes to any given vector field, we demonstrate it using RNA- velocities, based on unspliced to spliced read ratios, computed with scVelo9. + +Defining initial, intermediate and terminal states in biological terms We define an initial (terminal) state as an ensemble of measured gene expression profiles which, when taken together, characterize the starting (end) point of one particular cell- state change. We define an intermediate state as an ensemble of gene expression profiles which, when taken together, characterize a point on the cell- state transition trajectory which lies in between one or several initial and terminal states. + +Translating initial, intermediate and terminal states into mathematical terms To translate the above terms into mathematics, we make use of the coarse- graining given by the membership matrix \(\chi\) and the coarse- grained transition matrix \(P_{c}\) . We show below that our assignment of cells to macrostates maximizes a criterion we call the crispness: we obtain macrostates which have little overlap and large self- transition probability. In other words: we recover the kinetics of the Markov chain on slow- time scales, i.e. macrostates and their transitions reflect the limiting behavior of the Markov chain. Among the set of macrostates, we identify initial states as those which have little incoming large but large outgoing transition probability in \(P_{c}\) . Intermediate states will have both incoming and outgoing transition probability in \(P_{c}\) . Terminal states will have large incoming but little outgoing and large self- transition probability in \(P_{c}\) . An important term in the mathematical framework is metastability: a process starting in a metastable state will stay there with high probability for a long time. Accordingly, we define a metastable state of cellular dynamics as an area in phenotypic space that cells are unlikely to leave again once they have entered. A metastable state will typically correspond to a terminal state, while an intermediate state is typically only weakly metastable. Initial states can constitute weakly metastable states, if the probability of leaving them is small, potentially because of heavily cycling populations. + +Reversing the Markov chain to recover initial states Initial states may not be picked up as macrostates during coarse- graining of the Markov chain because they are not stable enough, i.e. cells in the initial state have very little probability of transitioning into one another and rapidly start traversing their state change trajectory. In these cases, we reverse the Markov chain, i.e. we flip the arrows in the velocity vector field \(V\) . The initial state now constitutes a terminal (i.e. metastable) state of the reversed dynamics and may be recovered by coarse- graining and interpreting the reversed Markov chain. + +Defining fate probabilities towards macrostates Biologically, we define the fate probability of cell \(i\) to reach macrostate \(j\in 1,\dots,M\) as the probability of cell \(i\) executing a series of gene expression programs which change its phenotype to match the phenotype of cells in macrostate \(j\) . Within the context of fate probabilities, we will typically be interested in macrostates which are either terminal or intermediate states. Mathematically, we translate this to the probability of a random walk on the Markov chain initialized in cell \(i\) to reach any cell belonging to macrostate \(j\) before reaching any cell belonging to another macrostate. CellRank efficiently computes these probabilities in closed form using absorption probabilities (Subsection 1.4). + +<--- Page Split ---> + +### 1.2 Computing the transition matrix + +We model each observed cell by one microstate in the Markov chain. To compute transition probabilities among cells, we make use of transcriptomic similarity to define the global topology of the phenotypic manifold and of RNA velocity to direct local movement on the manifold. To model the global topology of the phenotypic manifold, the first step of the CellRank algorithm is to compute a KNN graph. + +Computing a KNN graph to align local transitions with global topology We compute a KNN graph to constrain the set of possible transitions to those that are consistent with the global topology of the phenotypic manifold: Each cell is thus only allowed to transition into one of its \(K\) nearest neighbors. While CellRank can generalize to any reasonable similarity kernel, here, we compute the KNN graph as follows: + +1. project the data onto the first \(L\) principal components to obtain a matrix \(X_{PC}\in \mathbb{R}^{N\times L}\) , where rows correspond to cells and columns correspond to PC features. + +2. for each cell \(i\) , compute distances to its \(K\) -nearest neighbors based on euclidean distance in \(X_{PC}\) . Accumulate distances in a matrix \(D\in \mathbb{R}^{N\times N}\) . + +3. the KNN relationship will lead to a directed graph because it is not a symmetric relationship. Symmetrize the KNN relations encoded by \(D\) , such that cells \(i\) and \(j\) are nearest neighbors if either \(i\) is a nearest neighbors of \(j\) , or \(j\) is a nearest neighbors of \(i\) . This will yield an undirected symmetric version \(D_{sym}\) of \(D\) , where each cell has at least \(K\) nearest neighbors. + +4. compute a symmetric adjacency matrix \(A\) based on \(D_{sym}\) containing similarity estimates between neighboring cells according to the manifold structure. To approximate cell similarities, we use the method implemented in the UMAP algorithm, which adapts the singular set and geometric realization functors from algebraic topology to work in the context of metric spaces and fuzzy simplicial sets \(^{11,12}\) . + +We choose \(K = 30\) to be the number of nearest neighbors by default. We show in Supplementary Fig. 8a that CellRank is robust to the choice of \(K\) . To compute the similarity metric, the option presented is the default in SCANPY \(^{13}\) . Alternatively, similarity may be computed using a Gaussian kernel with density- scaled kernel width as introduced by ref. \(^{14}\) and adapted to the single cell context in ref. \(^{10}\) . We choose \(L = 30\) to be the number of principal components by default. This can be adapted based on knee- point heuristics or the percentage of variance explained, however, we show in Supplementary Fig. 8d that CellRank is robust to the exact choice of \(L\) . + +Directing the KNN graph based on RNA Velocity Next, we direct the edges of the KNN graph using RNA velocity information, giving higher probability to those neighbors whose direction best aligns with the direction of the velocity vector. Specifically, for cell \(i\) with gene expression profile \(x_{i}\in \mathbb{R}^{G}\) and velocity vector \(v_{i}\in \mathbb{R}^{G}\) , consider its neighbors \(j = 1,2,\dots,K_{i}\) with gene expression profiles \(\{x_{1},x_{2},\dots,x_{K}\}\) . Note that the graph construction outlined above leads to a symmetric KNN graph, where \(K_{i}\) is not constant across all cells, but \(K_{i}\geq K\forall i\in \{1,\dots,N\}\) . For each neighboring cell \(k\) , compute the corresponding state- change vector with cell \(i\) , \(s_{ik} = x_{k} - x_{i}\in R^{G}\) . Next, we compute Pearson correlations \(c_{i}\in R^{K}\) of \(v_{i}\) with all state change vectors via + +\[c_{ik} = \frac{(s_{ik} - e\bar{s}_{ik})^{\top}(v_{i} - e\bar{v}_{i})}{\|s_{ik} - e\bar{s}_{ik}\|\|v_{i} - e\bar{v}_{i}\|} \in [-1,1]^{K}, \quad (1)\] + +where \(e\) is a constant vector of ones and \(\bar{s}_{ik}\) and \(\bar{v}_{i}\) are averages over the state change vector and the velocity vector, respectively. Intuitively, \(c_{i}\) contains the cosines of the angles that the mean- centered \(v_{i}\) forms with the mean- centered state- change vectors \(s_{ik}\) . A value of 1 means perfect correlation between the gene expression changes predicted by the local velocity vector and the actual change + +<--- Page Split ---> + +observed when going from the reference cell to any of its nearest neighbors. Pearson correlations have been computed in similar ways by svelo \(^{9}\) and velocyto \(^{1}\) to project the velocity vectors into a given embedding. In Subsection 1.5 below, we show how their ideas can be formalized and extended to account for uncertainty in the velocity vector. + +Transforming correlations into transition probabilities To use the vector \(c_{i}\) as a set of transition probabilities to neighboring cells, we need to make sure it is positive and sums to one. For cell \(i\) , define a set of transition probabilities \(p_{i} \in \mathbb{R}^{K}\) via + +\[p_{ik} = \frac{\exp(\sigma c_{ik})}{\sum_{l = 1}^{K}\exp(\sigma c_{il})} \quad (2)\] + +where \(\sigma > 0\) is a scalar constant that controls how centered the categorical distribution will be around the most likely value, i.e. around the state- change transition with maximum correlation (see below). We repeat this for all \((i,k)\) which are nearest neighbors to compute the transition matrix \(P_{v} \in \mathbb{R}^{N \times N}\) . This scales linearly in the number of cells \(N\) , the number of nearest neighbors \(K\) and the number of genes \(G\) as the KNN graph is sparse. + +Automatically determine \(\sigma\) We reasoned that the value of \(\sigma\) should depend on typical Pearson correlation's between velocity vectors and state change vectors observed in the given data- set. For this reason, we use the following heuristic: + +\[\sigma = \frac{1}{\mathrm{median}(\{|c_{ik}|\forall i,k\})}. \quad (3)\] + +This means that if the median absolute Pearson correlation observed in the data is large (small), we use a small (large) value for \(\sigma\) . The intuition behind this is that for sparsely sampled data- sets where velocity vectors only roughly point into the direction of neighboring cells, we upscale all correlations a bit. Typical values for \(\sigma\) we compute this way range from 1.5 (lung example \(^{15}\) ) to 3.8 (pancreas example \(^{16}\) ). + +Coping with uncertainty in the velocity vectors scRNA- seq data is a noisy measurement of the underlying gene expression state of individual cells. RNA velocity is computed on the basis of these noisy measurements and is therefore itself a substantially noisy quantity. In particular the unspliced reads required by velocity and scVelo to estimate velocities are very sparse and their abundance varies depending on the amount of relevant intronic sequence of different genes. Besides this inherent noise, preprocessing decisions in the alignment pipeline of spliced and unspliced reads have been shown to impact the final velocity estimate \(^{17}\) . Further uncertainty in the velocity estimate arises because assumptions have to be made which may not always be satisfied in practice: + +- the original velocyto \(^{1}\) model assumes that for each gene, a steady state is captured in the data. The scVelo \(^{9}\) model circumvents this assumption by dynamic modeling, extending RNA velocity to transient cell populations, however there is often only a sparsity of transitional cells to estimate these dynamics.- both models assume that the key biological driver genes for a given cell-state transition are intron rich and may therefore be used to estimate spliced to unspliced ratios. This has been shown to be the case in many neurological settings, however, in other systems such as hematopoiesis, it remains unclear whether this assumption is met.- both models assume that per gene, a single set of kinetic parameters \(\alpha\) (transcription rate), \(\beta\) (splicing rate) and \(\gamma\) (degradation rate) may be used across all cells. However, we know that in many settings, this assumption is violated because of alternative splicing or cell-type specific regulation \(^{18-21}\) . + +<--- Page Split ---> + +- both models assume that there are no batch effects present in the data. To date, to the best of our knowledge, there are no computational tools to correct for batch effects in velocity estimates.- both models assume that the cell-state transition captured in the data is compatible with the time scale of splicing kinetics. However, this is often not known a priori and may explain the limited success of RNA velocity in studying hematopoiesis to date. + +The points outlined above highlight that RNA velocity is a noisy, uncertain estimate of the likely direction of the future cell state. To cope with the uncertainty present in RNA velocity, we adapt four strategies: + +- we restrict the set of possible transitions to those consistent with the global topology of the phenotypic manifold as described by the KNN graph. + +- we use a stochastic formulation based on Markov chains to describe cell-state transitions. For cell \(i\) with velocity vector \(v_{i}\) , we allow transitions to each nearest neighbor \(j\) with transition probability \(p_{ij}\) . This means that we even allow transitions backwards, against the flow prescribed by the velocity vector field, with small probability. This reflects our uncertainty in \(v_{i}\) . + +- we combine RNA velocity information with transcriptomic similarity, see below. + +- we propagate uncertainty in \(v_{i}\) into the downstream computations (Subsection 1.5). + +Emphasizing transcriptomic similarity Thus far, we have combined RNA velocity with transcriptomic similarity by computing a similarity- based KNN graph to restrict the set of possible transitions. To further take advantage of the information captured by the KNN graph and to increase robustness of the algorithm with respect to noisy velocity vectors, we combine the velocity based transition matrix \(P_{v}\) with a similarity based transition matrix \(P_{s}\) via + +\[P = \lambda P_{v} + (1 - \lambda)P_{s}\mathrm{~for~}\lambda \in [0,1]. \quad (4)\] + +The matrix \(P_{s}\) is computed by row- normalizing the adjacency matrix \(A\) . In practical applications, we have found that using values around \(\lambda = 0.2\) increase robustness with respect to noisy velocity estimates. The matrix \(P\) is the final transition matrix estimated by the CellRank algorithm. + +### 1.3 Coarse-graining the Markov chain + +The transition matrix \(P\) defines a Markov chain among the set of all observed cells, where each cell constitutes a microstate of the Markov chain. However, it is difficult to directly use \(P\) to interpret the cellular trajectory because \(P\) is a fine- grained, noisy representation of cell state transitions. Therefore, we seek to reduce \(P\) to its essence: macrostates representing key biological states and their transition probabilities among each other. We accomplish this using the Generalized Perron Cluster Cluster Analysis (GPCCA) \(^{22,23}\) , a method originally developed to study conformational dynamics in proteins. We adapt it to the single cell setting and utilize it to project the large transition matrix \(P\) onto a much smaller coarse- grained transition matrix \(P_{c}\) that describes transitions among a set of macrostates. A macrostate is associated with a subset \(M\) of the state space \(M \subset \Omega\) . The macrostates are defined through a so- called membership matrix \(\chi\) . Rows of \(\chi\) contain the soft assignment of cells to macrostates. + +Generalized Perron Cluster Cluster Analysis (GPCCA) The aim of the GPCCA algorithm is to project the large transition matrix \(P\) onto a much smaller coarse- grained transition matrix \(P_{c}\) , which describes transitions between macrostates of the biological system \(^{22,23}\) . For the projected or + +<--- Page Split ---> + +embedded dynamics to be Markovian, we require the projection to be based on an invariant subspace of \(P\) (invariant subspace projection), i.e. a subspace \(W\) for which + +\[P^{\top}x\in W\forall x\in W. \quad (5)\] + +In case of a reversible \(P\) , invariant subspaces are spanned by the eigenvectors of \(P^{24}\) . In our case however, \(P\) is non- reversible and the eigenvectors will in general be complex. Since the GPCCA algorithm can not cope with complex vectors, we rely on real invariant subspaces of the matrix \(P\) for the projection. Such subspaces are provided by the real Schur decomposition of \(P^{22,23}\) , + +\[P = QRQ^{\top}, \quad (6)\] + +where \(Q\in \mathbb{R}^{N\times N}\) is orthogonal and \(R\in \mathbb{R}^{N\times N}\) is quasi- upper triangular \(^{25}\) . \(R\) has 1- by- 1 or 2- by- 2 blocks on the diagonal, where the former are given by the real eigenvalues and the latter are associated with pairs of complex conjugate eigenvalues. + +Invariant subspaces of the transition matrix Columns of \(Q\) corresponding to real eigenvalues span real invariant subspaces. Columns of \(Q\) corresponding to pairs of complex conjugate eigenvalues span real invariant subspaces when kept together, but not if they are separated. Particularly, for columns \(q_{j}\) and \(q_{k}\) of \(Q\) belonging to a pair of complex conjugate eigenvalues, the space \(W_{0} = \operatorname {span}(q_{j},q_{k})\) is invariant under \(P\) , but the individual \(q_{j}\) and \(q_{k}\) are not \(^{26}\) . Depending on the constructed subspace, different dynamical properties of \(P\) will be projected onto \(P_{c}\) . Choosing Schur vectors belonging to real eigenvalues close to 1, metastabilities are recovered, while for Schur vectors with complex eigenvalues close to the unit circle, cyclic dynamics are recovered \(^{22,23}\) . Both options are available in CellRank, defaulting to the recovery of metastabilities. + +Projecting the transition matrix Let \(\bar{Q}\in \mathbb{R}^{N\times n_{s}}\) be the matrix formed by selecting \(n_{s}\) columns from \(Q\) according to some criterion (metastability or cyclicity). Let \(\chi \in \mathbb{R}^{N\times n_{s}}\) be a matrix obtained via linear combinations of the columns in \(\bar{Q}\) , i.e. + +\[\chi = \bar{Q} A, \quad (7)\] + +for an invertible matrix \(A\in \mathbb{R}^{n_{s}\times n_{s}}\) . We obtain the projected transition matrix via a Galerkin projection \(^{22,23}\) , + +\[P_{c} = (\chi^{\top}D\chi)^{-1}(\chi^{\top}DP\chi). \quad (8)\] + +Here, the matrix \(D\) is the diagonal matrix of a weighted scalar product. The Schur vectors must be orthogonal with respect to this weighted scalar product, i.e. \(Q^{\top}DQ = I\) with the \(n_{s}\) - dimensional unit matrix \(I\) , to yield the required invariant subspace projection. The diagonal elements of \(D\) are in principle arbitrary, but a convenient choice would be the uniform distribution or some distribution of the cellular states of interest. Choosing the uniform distribution, as is the default in CellRank, would result in a indiscriminate handling (without imposing any presumptions about their distribution) of the cellular states. Note that the matrix inversion in Equation (8) is performed on a very small matrix of size \(n_{s}\times n_{s}\) . + +Computing the membership vectors In principle, we could use any invertible \(A\) in Equation (7). However, we would like to interpret the rows of of \(\chi\) as membership vectors that assign cells to macrostates. For this reason, we seek a matrix \(A\) that minimizes the overlap between the membership vectors \(\chi\) , i.e. a matrix \(A\) that minimizes off- diagonal entries in \(\chi^{\top}D\chi\) . This is equivalent to maximizing + +\[\mathrm{trace}(\tilde{D}^{-1}\chi^{\top}D\chi), \quad (9)\] + +<--- Page Split ---> + +where \(\tilde{D}\) is chosen to row- normalize \(S = \tilde{D}^{- 1}\chi^{\top}D\chi\) , + +\[\tilde{D}^{-1} = \mathrm{diag}\left(\frac{1}{\sum_{j}(\chi^{\top}D\chi)_{1j}},\dots ,\frac{1}{\sum_{j}(\chi^{\top}D\chi)_{n_{s}j}}\right). \quad (10)\] + +Choosing Schur vectors with real eigenvalues close to one, thus recovering metastability, maximizing Equation (9) can be interpreted as maximizing the metastability of the macrostates in the system. In practice, we use + +\[f_{n_{s}}(A) = n_{s} - \mathrm{trace}(S), \quad (11)\] + +as our objective function, which is bounded below by zero and convex on the feasible set defined through the linear constraints \(^{24}\) . We must minimize \(f_{n_{s}}\) with respect to the constraints + +\[\begin{array}{r l} & {\sum_{j}\chi_{i j} = 1\forall i\in \{1,\dots,N\} \mathrm{~(partition~of~unity)~},}\\ & {\chi_{i j}\geq 0\forall i\in \{1,\dots,N\} ,j\in \{1,\dots,n_{s}\} \mathrm{~(positivity)~},} \end{array} \quad (13)\] + +which is not a trivial task. Among the several possibilities to solve the minimization problem, a convenient choice is to perform unconstrained optimization on \(A(2:n_{s},2:n_{s})\) using a trick: to impose the constraints after each iteration step, thus transforming the (unfeasible) solution into a feasible solution \(^{24}\) . The drawback of this approach is that this routine is non- differentiable. Thus a derivative- free method like the Nelder- Mead method, as implemented in the Scipy routine scipy.optimize.fmin, should be used for the optimization. + +Positivity of the projected transition matrix Note that the projected transition matrix \(P_{c}\) may have negative elements if macrostates share a large overlap. In practice, this is caused by a suboptimal number of macrostates \(n_{s}\) and can be resolved by changing that number. We may interpret \(P_{c}\) as the transition matrix of a Markov chain between the set of macrostates if it is non- negative within numerical precision. + +Tuning the number of macrostates The number of macrostates \(n_{s}\) can be chosen in a number of different ways: + +- using the eigengap heuristic for the real part of the eigenvalues close to one. + +- define the crispness \(\xi\) of the solution as the value of \(\mathrm{trace}(\tilde{D}^{-1}\chi^{\top}D\chi) / n_{s}\) . The larger this value, the smaller the overlap between the macrostates, and in turn, the sharper or "crisper" the recovered macrostates. The crispness can be computed for different numbers of macrostates \(n_{s}\) and the number \(n_{s}\) with the largest value of \(\xi\) should be selected. + +- to avoid having to solve the full problem for too many values of \(n_{s}\) , do a pre-selection using the minChi criterion: Based on an initial guess for \(A\) , compute a membership matrix \(\chi\) and calculate \(\min \mathrm{Chi} = \min_{i,j}(\chi_{ij})\) . In general, this value will be negative because the starting guess is infeasible. The closer to zero the value of minChi is, the more we can expect \(n_{s}\) to yield a crisp decomposition of the dynamics. + +- combining the minChi criterion and the crispness, to avoid solving the full problem for many \(n_{s}\) , but still select the \(n_{s}\) with the crispest decomposition. This is done by first selecting an interval of potentially good numbers of macrostates \(n_{s}\) via the minChi criterion and afterwards using the crispness to select the best \(n_{s}\) from the preselected macrostate numbers. + +All of the above are available through CellRank. + +<--- Page Split ---> + +Scalable Python implementation of GPCCA Following the original MATLAB implementation \(^{22,23}\) , we wrote up GPCCA as a general algorithm in Python and included it in the MSMTools \(^{27}\) package, which is widely used for studying protein folding kinetics. From CellRank, we interface to MSMTools for the GPCCA algorithm. A naive implementation of the Schur decomposition would scale cubically in cell number. We alleviate this problem by using SLEPSC to compute a partial real Schur decomposition using an iterative, Krylov- subspace based algorithm that optimally exploits the sparsity structure of the transition matrix \(^{28,29}\) . Overall, this reduces the computational complexity of our algorithm to be almost linear in cell number (Fig. 5d and Supplementary Table 1). This allows CellRank to scale well to very large cell numbers. + +Automatically determine terminal states We use the coarse- grained dynamics given by \(P_{c}\) to automatically identify terminal states. The idea is to look for the most stable macrostates according to the coarse- grained transition matrix \(P_{c}\) . Define the stability index (SI) of a macrostate \(m \in \{1, \ldots , n_{s}\}\) through its corresponding diagonal value in \(P_{c}\) , i.e. through its self- transition probability \(p_{c(m,m)}\) . The intuition behind this is that cells in terminal populations should have very little probability to transition to cells in other populations and should distribute most (if not all) of their probability mass to cells from the same terminal population. To identify the number of terminal states, we set a threshold on SI, i.e. we classify all states as terminal for which \(\mathrm{SI} \geq \epsilon_{\mathrm{SI}}\) with \(\epsilon_{\mathrm{SI}} = 0.96\) by default. + +Automatically determine initial states To identify the initial states automatically, we introduce the coarse- grained stationary distribution (CGSD) \(\pi_{p}\) , given by + +\[\pi_{p} = \chi^{\top}\pi \quad (14)\] + +where \(\pi\) is the stationary distribution of the original transition matrix \(P\) . The stationary distribution satisfies + +\[\pi^{\top}P = \pi^{\top},\pi_{i} > 0\forall i\mathrm{and}\sum_{i}\pi_{i} = 1. \quad (15)\] + +In other words, the stationary distribution \(\pi\) is an invariant measure of \(P\) and can be computed by normalizing the top left eigenvector of \(P\) (corresponding to the eigenvalue 1). Under certain conditions (ergodicity, see ref. \(^{30}\) ) imposed on the Markov chain, the stationary distribution is the distribution that the process converges to, if it evolves long enough, i.e. it describes the long- term evolution of the Markov chain. In the same vein, the CGSD \(\pi_{c}\) describes the long- term evolution of the Markov chain given by the coarse- grained transition matrix \(P_{c}\) . The CGSD \(\pi_{c}\) assigns large (small) values to macrostates that the process spends a large (little) amount of time in, if it is run infinitely long. As such, we may use it to identify initial states by looking for macrostates which are assigned the smallest values in \(\pi_{c}\) . The intuition behind this is that initial states should be states that the process is unlikely to visit again once it has left them. The number of initial states is a method parameter set to one by default which can be modified by the user to detect several initial states. + +Automatically determine intermediate states All remaining macrostates, i.e. macrostates which have neither been classified as terminal nor as initial, are classified as intermediate. Biologically, these correspond to intermediate, transient cell populations on the state change trajectory. + +### 1.4 Computing fate probabilities + +Given the soft assignment of cells to macrostates by \(\chi\) and the identification of terminal states through \(P_{c}\) , we compute how likely each cell is to transition towards these terminal states. Let \(n_{t}\) + +<--- Page Split ---> + +be the number of terminal states. For the sake of clarity, we assume that we are only interested in fate probabilities towards terminal states, however, the below computations apply just as well to intermediate states, depending on the biological question. For each terminal macrostate \(t\) for \(t \in \{1, \ldots , n_t\}\) , we choose \(f\) cells which are strongly assigned to \(t\) according to \(\chi\) . That is, for terminal macrostate \(t\) , we extract the corresponding column from \(\chi\) and we calculate the terminal index set \(\mathcal{R}_t\) of cells which have the largest values in this column of \(\chi\) . If cell \(i\) is part of terminal index set \(\mathcal{R}_t\) , we assume cell \(i\) is among the \(f\) most eligible cells to characterize the terminal macrostate \(t\) in terms of gene expression. We store the indices of the remaining cells in the transient index set \(\mathcal{T}\) . The index sets \(\{\mathcal{R}_t | t \in \{1, \ldots , n_t\} \}\) and \(\mathcal{T}\) form a disjoint partition of the state space, which means they do not overlap and they cover the entire state space. For each cell \(i\) in \(\mathcal{T}\) , we would like to compute a vector of probabilities \(f_i \in \mathbb{R}^{n_t}\) which specifies how likely this cell is to transition into any of the terminal sets \(\mathcal{R}_t\) . To interpret \(f_i\) as a categorical distribution over cell fate, we require \(f_{i,t} \geq 0 \forall i \in \mathcal{T} \forall t \in \{1, \ldots , n_t\}\) and \(\sum_t f_{i,t} = 1 \forall i \in \mathcal{T}\) . We accumulate the \(f_i\) column- wise in the fate matrix \(F \in R^{N \times n_t}\) . + +Absorption probabilities reveal cell fates We could approximate the \(f_{i}\) based on sampling: initialise a random walk on the Markov chain in cell \(i\) . Continue to simulate the random walk until any cell from a terminal set \(\mathcal{R}_t\) is reached. Record \(t\) and repeat this many times. Finally, count how often random walks initialized in cell \(i\) terminated in any of the terminal index sets \(\mathcal{R}_t\) . In the limit of repeating this infinitely many times, the normalized frequencies over reaching either terminal set will be equal to the desired fate probabilities for cell \(i\) , under reasonable assumptions on the Markov chain (irreducibility). Luckily, we do not have to do this in a sampling based approach, we can exploit the fact that a closed form solution exists for this problem: absorption probabilities. + +Computing absorption probabilities Key to the concept of absorption probabilities are recurrent and transient classes, which we will define here for the present case of a finite and discrete state space. Let \(i \in \Omega\) and \(j \in \Omega\) be two states of the Markov chain. In our case, \(i\) and \(j\) are cells. We say that \(i\) is accessible from \(j\) , if and only if there exists a path from \(j\) to \(i\) according to the transition matrix \(P\) . A path is a sequence of transitions which has non- zero transition probability. Further, \(i\) and \(j\) communicate, if and only if \(i\) is accessible from \(j\) and \(j\) is also accessible from \(i\) . Communication defines an equivalence relation on the state space \(\Omega\) , i.e. it is a reflexive, symmetric and transitive relation between two states \(^{30}\) . It follows that the state space \(\Omega\) can be partitioned into its communication classes \(\{\mathcal{C}_1, \ldots , \mathcal{C}_k\}\) . The communication classes are mutually disjoint non- empty and their union is \(\Omega\) . In other words: any two states from the same communication class communicate, states from different communication classes never communicate. We call a communication class \(\mathcal{C}_j\) closed if the submatrix of \(P\) restricted to \(\mathcal{C}_j\) has all rows sum to one. Intuitively, if \(\mathcal{C}_j\) is closed, then a random walk which enters \(\mathcal{C}_j\) will never leave it again. Closed communication classes are also called recurrent classes. If a communication class is not recurrent, we call it transient. In Theorem 1, we reproduce the statement of Thm. 28 in ref. \(^{30}\) to compute absorption probabilities towards states that belong to recurrent classes on the Markov chain. + +Theorem 1 (Absorption Probabilities) Consider a MC with transition matrix \(P \in \mathbb{R}^{N \times N}\) . We may rewrite \(P\) as follows: + +\[\begin{array}{r}\left[ \begin{array}{cc}\tilde{P} & 0\\ S & Q \end{array} \right], \end{array} \quad (16)\] + +where \(\tilde{P}\) and \(Q\) are restrictions of \(P\) to recurrent and transient states, respectively, and \(S\) is the restriction of \(P\) to transitions from transient to recurrent states. The upper right 0 is due to the fact that there are no transitions back from recurrent to transient states. Define the matrix \(M \in \mathbb{R}^{N \times N}\) via + +\[M = (I - Q)^{-1}. \quad (17)\] + +<--- Page Split ---> + +Then, the \(ij\) - th entry of \(M\) describes the expected number of visits of the process to state \(j\) before absorption, conditional on the process being initialised in state \(i\) . \(M\) is often referred to as the fundamental matrix of the MC. Further, the matrix + +\[A = (I - Q)^{-1}S, \quad (18)\] + +in the \(ij\) - th entry contains the probability of \(j\) being the first recurrent state reached by the MC, given that it was started in \(i\) . + +For a proof, see See Thm. 26 in ref. \(^{30}\) . To compute fate probabilities towards the terminal index sets \(\mathcal{R}_t\) defined above, we approximate these as recurrent classes, i.e. we remove any outgoing edges from these sets. We then apply Theorem 1, which, for each cell \(i \in \mathcal{T}\) yields absorption probabilities towards each of the \(f\) cells in each of the \(n_t\) recurrent index sets. We aggregate these to yield absorption probabilities towards the recurrent index sets themselves by summing up absorption probabilities towards individual cells in these sets. + +CellRank provides an efficient implementation to compute absorption probabilities A naive implementation of absorption probabilities scales cubically in the number of transient cells due to the matrix inversion in Equation (18). The number of transient cells is smaller than the total cell number only by a small constant, so the naive approach can be considered cubic in cell number. This will inevitably fail for large cell numbers. We alleviate this by re- writing Equation (18) as a linear problem, + +\[(I - Q)A = S. \quad (19)\] + +Note that \(Q\) is very sparse as it describes transitions between nearest neighbors. Per row, \(Q\) has approximately \(K\) entries. To exploit the sparsity, iterative solvers are very appealing as their per- iteration cost applied to this problem is linear in cell number and in the number of nearest neighbors. To apply an iterative solver, we must however re- write Equation (19) such that the right hand side is vector valued, + +\[(I - Q)a_{1} = s_{1},\ldots ,(I - Q)a_{f n_{t}} = s_{f n_{t}}, \quad (20)\] + +where \(f n_t\) is the total number of cells which belong to approximately recurrent classes. To solve these individual problems, we use the iterative GMRES \(^{31}\) algorithm which efficiently exploits sparsity. For optimal performance, we use the PETSc implementation, which makes use of efficient message passing and other practical performance enhancements. Lastly, we parallelize solving the \(f n_t\) linear problems. All of these tricks taken together allow us to compute absorption probabilities quickly even for large cell numbers (Fig. 5d and Supplementary Table 1). + +### 1.5 Propagating velocity uncertainty + +So far, we have assumed that individual velocity vectors are deterministic, i.e. they have no measurement error. However, this is not correct because RNA velocity is estimated on the basis of spliced and unspliced gene counts, which are noisy quantities. Hence, the velocity vectors \(v_i\) themselves should be treated as random variables which follows a certain distribution. Ultimately, our aim is to propagate the distribution in \(v_i\) into our final quantities of interest, i.e. state assignments and fate probabilities. However, this is difficult as these final quantities of interest depend on \(v_i\) in non- analytical ways, i.e. we cannot write down a closed- form equation which relates the final quantities to \(v_i\) . A possible solution to this is to use a Monte Carlo scheme where we draw velocity vectors, compute final quantities based on the draw and repeat this many times. In the limit of infinitely many draws, this will give us the distribution over final quantities, given the distribution in \(v_i\) . However, this has the disadvantage that we need to repeat our computation many times, which will get prohibitively expensive for large datasets. To get around this problem, and to allow CellRank to + +<--- Page Split ---> + +scale to large datasets, we construct an analytical approximation to the Monte Carlo based scheme. This analytical approximation will only have to be evaluated once and we can omit the sampling. We show in a practical example that the analytical approximation gives very similar results to the sampling based scheme and improves over a deterministic approach by a large margin. + +Modeling the distribution over velocity vectors Before we can propagate uncertainty, we need to describe the distribution over velocity vectors, i.e. we need to model the uncertainty present in the velocity vectors which are estimated by scVelo \(^9\) or velocity \(^1\) . Ideally, we would like these packages themselves to model uncertainty in the raw spliced and unspliced counts and to propagate this into a distribution over velocity vectors. However, as that is currently not being done, we will make an assumption about their distribution and use the KNN graph to fine- tune expectation and variance by considering neighboring velocity vectors. To ease notation and to illustrate the core ideas, we will drop the subscript \(i\) in this section and just focus on one fixed cell and it's velocity vector \(v\) . Let's assume that \(v\) follows a multivariate normal (MVN) distribution, + +\[v\sim \mathcal{N}(\mu ,\Sigma_{v}), \quad (21)\] + +with mean vector \(\mu \in \mathbb{R}^{G}\) and covariance matrix \(\Sigma_{v}\in \mathbb{R}^{G\times G}\) . The MVN is a reasonable choice here as velocities can be both positive and negative and for most genes, as we expect to see both up- and down- regulation, velocity values will be approximately symmetric around their expected value. Let's further assume the covariance matrix to be diagonal, i.e. gene- wise velocities are independent. This is a reasonable assumption to make as gene- wise velocities in both velocity \(^1\) and scvelo \(^9\) are computed independently. To compute values for \(\mu\) and \(\Sigma_{v}\) , consider velocity vector \(v\) and its \(K\) nearest neighbors. To estimate \(\mu\) and the diagonal elements of \(\Sigma_{v}\) , we compute first and second order moments over the velocity vectors of these neighboring cells. + +Propagating uncertainty into state assignments and fate probabilities We seek to approximate the expected value of the final quantities of interest (state assignments and fate probabilities), given the distribution in the velocity vectors. Let \(q\) be a final quantity of interest. There are two major steps involved in computing \(q\) , + +\[v\rightarrow T\rightarrow q, \quad (22)\] + +where \(v\) stands for our inputs, i.e. the velocity vectors, and \(T\) is the transition matrix defining the Markov chain. To get from \(v\) to \(T\) , we evaluate an analytical function which computes correlations and applies a softmax function. We can approximate this first part of the mapping with a Taylor series, which allows us to analytically propagate the distribution in \(v\) into \(T\) . For the second part of the mapping, we use the expected transition matrix to compute \(q\) . This yields an approximation to the expectation of the final quantity we can then compare with the approximation we obtain from a Monte Carlo scheme, which we treat as our ground truth. + +Approximating the expected transition matrix In the first step, we compute the expected value of the transition matrix, given the distribution of the velocity vectors. Given a particular draw \(v\) from the distribution in Equation (21) and a set of state- change vectors \(s_{k}\) , we compute a vector of probabilities \(p\) , which lives on a \(K\) - simplex in \(\mathbb{R}^{K}\) . Let's denote the mapping from \(v\) to \(p\) by \(h\) , + +\[\begin{array}{l}{h:\mathbb{R}^{G}\to R^{K}}\\ {v\mapsto h(v) = p.} \end{array} \quad (23)\] + +We can then formulate our problem as finding the expectation of \(h\) when applied to \(v\) , i.e. + +\[\operatorname {E}[h(v)]_{v\sim \mathcal{N}(\mu ,\Sigma_{v})}. \quad (24)\] + +<--- Page Split ---> + +To approximate this, expand the \(i\) - th component of \(h\) in a Taylor- series around \(\mu\) , + +\[h_{i}(v) = h_{i}(\mu) + \nabla_{v}^{\top}h_{i}(v)|_{\mu}(v - \mu) + \frac{1}{2} (v - \mu)^{\top}\nabla_{v}^{2}h_{i}(v)|_{\mu}(v - \mu) + \mathcal{O}(v^{3}). \quad (25)\] + +Define the Hessian matrix of \(h_{i}\) at \(v = \mu\) as + +\[H^{(i)} = \nabla_{v}^{2}h_{i}(v)|_{\mu}. \quad (26)\] + +Taking the expectation of \(h_{i}\) and using the Taylor- expansion, + +\[\operatorname {E}[h_{i}(v)]\approx h_{i}(\mu) + \frac{1}{2}\operatorname {E}[(v - \mu)^{\top}H^{(i)}(v - \mu)]. \quad (27)\] + +Note that the first order term cancels as \(\operatorname {E}[v - \mu ] = 0\) . The second order term can be further simplified by explicitly writing out the matrix multiplication, + +\[\operatorname {E}[(v - \mu)^{\top}H^{(i)}(v - \mu)] = \sum_{j,k = 1}^{G}H_{j,k}^{(i)}\operatorname {E}[(v - \mu)_{j}(v - \mu)_{k}], \quad (28)\] + +where we took the expectation inside the sum and the matrix elements outside the expectation as it does not involve \(v\) . For \(j \neq i\) , the two terms inside the expectation involving \(v\) are independent given our distributional assumptions on \(v\) and the expectation can be taken separately. Using again the fact that \(\operatorname {E}[v - \mu ] = 0\) , the sum equals zero for \(j \neq i\) . It follows + +\[\sum_{j,k = 1}^{G}H_{j,k}^{(i)}\operatorname {E}[(v - \mu)_{j}(v - \mu)_{k}] = \sum_{j}H_{j,j}^{(i)}\operatorname {E}[(v_{j} - \mu_{j})^{2}] = \sum_{j}H_{j,j}^{(i)}\operatorname {var}[v_{j}]. \quad (29)\] + +To summarize, our second order approximation to the transition probabilities given the distribution in \(v\) reads + +\[\operatorname {E}[h_{i}(v)]\approx h_{i}(\mu) + \frac{1}{2}\sum_{j}H_{j,j}^{(i)}\operatorname {var}[v_{j}]. \quad (30)\] + +We use automatic differentiation as implemented in JAX \(^{32}\) to compute the Hessian matrices \(H^{(i)}\) , which ensures they are highly accurate and can be computes in a scalable manner. Further, because we do not hard- code the derivatives, our approach is highly flexible to future changes in the way we compute transition probabilities. If for example it turns out at a later point that an alternative metric works better than Pearson correlation, this is automatically taken care of in the propagation of uncertainties and no changes need to be made, apart from changing the forwards function which computes the transition probabilities themselves. The above procedure can be repeated for all components \(i\) and for all cells to yield the second order approximation to the expected transition matrix \(T\) , given the distribution over each velocity vector. + +Approximating the expected final quantities To arrive at the final quantities of interest, i.e. state assignments and absorption probabilities, we use the expected transition matrix and proceed as in the deterministic case. We validate that this approximation gives very similar results to a fully stochastic approach based on Monte Carlo sampling (Supplementary Fig. 14a,b). + +### 1.6 The CellRank software package + +The CellRank software package implements two main modules: + +- kernels are classes that provide functionality to compute transition matrices based on (directed) single cell data. + +<--- Page Split ---> + +- estimators are classes that implement algorithms to do inference based on kernels. For example, estimators compute macrostates and fate probabilities. + +This modular and object oriented design allows CellRank to be extended easily into two directions. On the one hand, including more kernels to take into account further means of directional single cell data such as metabolic labeling or experimental time. On the other hand, including more estimators to learn new abstractions of cellular dynamics. The kernel module currently implements a + +- VelocityKernel which computes a transition matrix on the basis of a KNN graph and RNA velocity information. + +- PalantirKernel which mimicks the original routine outlined in the Palantir8 paper to compute a directed transition matrix on the basis of a KNN graph and a pseudotime. + +- ConnectivityKernel which takes the adjacency matrix underlying the KNN graph and row-normalizes it to obtain a valid transition matrix. This is essentially the transition matrix used in the DPT10 algorithm. + +- PrecomputedKernel which accepts any pre-computed transition matrix and allows for easy interfacing with the CellRank software. + +All kernel classes are derived from a base kernel class which implements density normalization as implemented in ref.10. Instances of kernel classes can be combined by simply adding them up using the + operator, potentially including weights. A typical code snippet to compute a transition matrix will look like this: + +from cellrank.tools.kernels import VelocityKernel, ConnectivityKernel + +vk = VelocityKernel(adata).compute_transition_matrix() ck = ConnectivityKernel(adata).compute_transition_matrix() + +combined_kernel = 0.9*vk + 0.1*ck + +The estimator module currently implements a + +- CFLARE estimator. CFLARE stands for Clustering and Filtering of Left and Right Eigenvectors. This estimator computes terminal states directly by filtering cells in the top left eigenvectors and clustering them in the top right eigenvectors, thereby combining ideas of spectral clustering and stationary distributions. + +- GPCCA estimator. The GPCCA estimator. + +All estimator classes are derived from a base estimator class which allows to compute fate probabilities, regardless of how terminal/intermediate states have been computed. A typical code snippet to compute macrostates and fate probabilities will look like this: + +from cellrank.tools.estimators import GPCCA + +# initialize the estimator gpcca = GPCCA(combined_kernel) + +# compute macrostates and identify the terminal states among them gpcca.compute_macrostates() gpcca.compute_terminal_states() + +# compute fate probabilities gpcca.compute_absorption_probabilities() + +<--- Page Split ---> + +Both kernels and estimators implement a number of plotting functions to conveniently inspect results. We designed CellRank to be highly scalable to ever increasing cell numbers, widely applicable and extendable to problems in single cell dynamical inference, user friendly with tutorials and comprehensive documentation and robust with \(88\%\) code coverage. CellRank is open source, fully integrated with SCANPY and scVelo and freely available at https://cellrank.org. + +## 2 Computing a directed PAGA graph + +Partition- based graph abstraction (PAGA) \(^{33}\) provides an interpretable graph- like connectivity map of the data manifold. It is obtained by associating a node with each manifold partition (e.g. cell type) and connecting each node by weighted edges that represent a statistical measure of connectivity between the partitions. The model considers groups/nodes as connected if their number of interedges exceeds what would have been expected under random assignment. The connection strength can be interpreted as confidence in the presence of an actual connection and allows discarding spurious, noise- related connections. + +Here, we extend PAGA by directing the edges as to reflect the RNA velocity vector field rather than transcriptome similarity. The connectivity strengths are defined based on the velocity graph. That is, for each cell correlations between the cell's velocity vector and its potential, cell- to- cell transitions are computed (Subsection 1.2). Inter- edges are considered whose correlation passes a certain threshold (default: 0.1). The number of inter- edges are then tested against random assignment for significance. + +To further constrain the single cell graph, compute a gene- shared latent time using scVelo \(^{34}\) . In short, this aggregates the per- gene time assignments computed in scVelo's dynamical model to a global scale which faithfully approximates a single- cells internal clock. Once we have computed the initial states using CellRank, we can use these as a prior for latent time to force it to start in this state. All of latent time, initial and terminal states can in turn be used as a prior to regularize the directed graph. At single- cell level, we use latent time as a constraint to prune the cell- to- cell transition edges to those that match the ordering of cells given by latent time. For the initial and terminal states, the edges are further constrained to only retain those cell- to- cell transitions that constitute outgoing flows for cells in initial cellular populations, and to incoming flows for cells in terminal populations. + +Finally, a minimum spanning is constructed for the directed abstracted graph. It is obtained by pruning node- to- node edges such that only the most confident path from one node to another is retained. If there are multiple paths to reach a particular node, only the path with the highest confidence is kept. + +## 3 Computing gene expression trends along lineages + +CellRank computes fate probabilities which specify how likely each individual cell is to transition towards each identified terminal state (Subsection 1.4). Combined with any pseudo- temporal measure like \(\mathrm{DPT}^{10}\) , scVelo's latent time \(^{2}\) or Palantir's pseudotime \(^{35}\) , this allows us to compute and to compare gene expression trends towards specific terminal populations. In contrast to other methods, we do not partition the set of all cells into clusters and define lineage each lineage via an ordered set of clusters. Instead, we use all cells to fit each lineage but we weigh each cell according to its fate probability, our measure of lineage membership. This means that for cells uncommitted between two or more fates, we allow them to contribute to each one of these, weighted by the fate probabilities. For cells committed towards any particular fate, their fate probabilities towards the remaining fates will be zero or almost zero which naturally excludes them when fitting these other lineages. + +<--- Page Split ---> + +Imputing gene expression recovers trends from noisy data To improve the robustness and resolution of gene expression trends, we adapt two strategies. First, we use imputed gene expression values and second, we fit Generalized Additive Models (GAMs). For gene expression imputation, we use MAGIC \(^{36}\) by default, however, any imputed gene expression matrix can be supplied. MAGIC is based on KNN imputation and makes use of the covariance structure among neighboring cells to estimate expression levels for each gene. The KNN graph is computed globally, based on the expression values of all genes and not just the one we are currently considering. + +Generalized Additive Models (GAMs) robustly fit gene expression values While sliding window approaches are known to be sensitive to local density differences and only take into account the current gene when determining gene expression trends, we fit GAMs to gene expression values which have been imputed borrowing information from neighboring cells via a KNN graph. Using GAMs allows us to flexibly model many different kinds of gene trends in a robust and scalable manner. We fit the gene expression trend for branch \(t\) in gene \(g\) via + +\[y_{gi} = \beta_0 + f(\tau_i)\forall i:F_{ib} > 0, \quad (31)\] + +where \(y_{gi}\) is gene expression of gene \(g\) in cell \(i\) , \(\tau_i\) is the pseudotemporal value of cell \(i\) and \(F\) is the fate matrix of Subsection 1.4. By default, we use cubic splices for the smoothing functions \(f\) as these have been shown to be effective in capturing non- linear relationships in trends \(^{37}\) . + +To visualize the smooth trend, we select 200 equally spaced testing points along pseudotime and we predict gene expression at each of them using the fitted model of Equation (31). To estimate uncertainty along the trend, we use the standard deviation of the residuals of the fit, given by + +\[\sigma_{\hat{y}_p} = \sqrt{\frac{\sum_{j = 1}^n(y_j - \hat{y}_j)^2}{n - 2}}\sqrt{1 + \frac{1}{n} + \frac{(\tau_p - \bar{\tau})^2}{\sum_{j = 1}^n(\tau_j - \bar{\tau})^2}}, \quad (32)\] + +where \(\hat{y}_p\) denotes predicted gene expression at test point \(p\) , \(\bar{\tau}\) denotes average pseudo- time across all cells and \(n\) is the number of test points \(^{38}\) . For the fitting of Equation (31), we provide interfaces to both the R package mgcv \(^{39}\) as well as the Python package pyGAM \(^{40}\) . We parallelize gene fitting to scale well in the number of genes, which is important when plotting heatmaps summarizing many gene expression trends. + +Visualizing gene expression trends for the pancreas example For CellRank's gene expression trends of lineage- associated genes along the alpha, beta, epsilon and delta fates, we used Palantir's pseudotime \(^{35}\) , MAGIC imputed data \(^{36}\) and the pyGAM \(^{40}\) package to fit GAMs. We used default values to fit the splines, i.e. we place 10 knots along pseudotime and we use cubic splines. For the delta lineage, fate probabilities among early cells were very low (0.01 average fate probability among Ngn3 high EP cells, see Fig. 2e). This reflects the small size of the delta population (70 cells or 3% of the data, see Supplementary Fig. 10a,b) as well as the fact that delta cells are produced mostly at later stages in pancreatic development \(^{41}\) . To still be able to reliably fit gene expression of early cells along the delta lineage, we thresholded weights at 0.05, i.e. weights smaller than this value were clipped to this value. This was done only for the fitting of gene expression trends. + +## 4 Clustering gene expression trends + +CellRank allows gene expression trends along a particular lineage to be clustered, thus recovering the major patterns of regulation towards a specific terminal state like (transient) up- or down- regulation. For the set of genes we are interested in, we recover their regulation along a specific lineage by fitting GAMs in pseudotime where we supply fate probabilites as cell- level lineage weights (Section 3). In the next step, we cluster the GAM- smoothed gene expression trends. For this, we z- transform + +<--- Page Split ---> + +expression values and we compute a PCA representation of the trends. By default, we use 50 PCs. We then compute a KNN graph in PC space as outlined in Subsection 1.2 and we cluster the KNN graph using the louvain \(^{42}\) or leiden \(^{43}\) algorithms. For each recovered cluster, we compute its mean and standard deviation (point- wise, for all testing points that were used for smoothing) and visualize them, together with the individual, smoothed trends per cluster. As gene- trend fitting is efficiently parallelized in CellRank, such an analysis can be performed in an unbiased fashion for large gene sets. For 10k genes, the run- time is about 6 min on a 2019 Mac book pro with 2,8 GHz Intel Core i7 processor and 16 GB RAM. + +Clustering gene expression trends towards the delta fate To cluster gene expression trends towards the delta fate in Fig. 3e, all genes which were expressed in at least 10 cells were included (12,987 genes). Smooth gene expression trends along the delta lineage were determined using Palantir's pseudotime \(^{35}\) . We used \(K = 30\) nearest neighbors for the gene- trend KNN graph and the louvain algorithm with resolution parameter set to 0.2 to avoid over- clustering the trends. + +## 5 Uncovering putative driver genes + +To find genes which are expressed at high levels in cells that are biased towards a particular fate, we compute Person's correlation between expression levels of a set of genes and fate probabilities. We sort genes according to their correlation values and consider high- scoring genes as candidate driver genes. By default, we consider all genes which have passed pre- processing gene filtering thresholds. The computation of correlation values can be restricted to a set of pre- defined clusters if one is interested in driver genes which act in a certain region of the phenotypic manifold. + +Uncovering putative driver genes for delta development To uncover putative driver genes towards the delta fate in Fig. 3d,e, we considered 12,987 genes which were expressed in at least 10 cells. We computed correlation of total- count normalized, log transformed gene expression values with the probability of becoming a delta cell. We restricted correlation computation to the \(\mathrm{Fev + }\) cluster, where we expected the fate decision towards delta to occur. + +## 6 Robustness analysis + +We were interested in evaluating how much CellRank's fate probabilities change in response to changes in the following key pre- processing parameters: + +- the number of neighbors \(K\) used for KNN graph construction (Subsection 1.2)- scVelo's gene-filtering parameter min_shared_counts which determines how many counts a gene must have in both spliced and unspliced layers- scVelo's gene filtering parameter n_top_genes which determines the number of most highly variable genes used for the velocity computation- the number of principal components n_pcs used for KNN graph construction (Subsection 1.2) + +In addition to the 4 key pre- processing parameters, we were interested to see how much CellRank's results change when we randomly sub- sample the number of cells to 90% of the original cell number and when we vary the number of macrostates. We used the pancreas example \(^{16}\) in all of the following comparisons. + +Robustness with respect to key pre- processing parameters To evaluate robustness with respect to changes in the pre- processing parameters, we varied one parameter at a time (keeping the others + +<--- Page Split ---> + +fixed), computed macrostates and fate probabilities towards them. We then compared the fate probabilities for different values of the parameter by computing pairwise Pearson correlation among all possible pairs of values we used for the parameter. We did this separately for each lineage, i.e. for the alpha, beta, epsilon and delta lineages. For each lineage, we recorded the median and minimum correlation achieved across all the different comparisons. We always computed enough macrostates so that the alpha, beta, epsilon and delta states were included. Naturally, the precise location of the terminal states changed slightly across parameter combinations. For this reason, the correlation values we recorded reflect robustness of the entire CellRank workflow, including both the computation of terminal states as well as fate probabilities. In a separate comparison, we were interested in evaluating the robustness of just the last step of the CellRank algorithm, i.e. the computation of fate probabilities. For this, we kept the terminal states fixed across parameter variations and proceeded as above otherwise, computing pairwise Pearson correlations among fate probabilities per lineage across all parameter value combinations. Furthermore, we were interested to see whether CellRank's robustness changes when we propagate uncertainty. For this, we repeated all the aforementioned computations using our analytical approximation to propagate uncertainty. + +Robustness with respect to random sub- sampling of cells We subsampled the data to \(90\%\) of cells, computed macrostates and fate probabilities towards the alpha, beta, epsilon and delta states. We repeated this 20 times, recorded all computed fate probabilities and compared them pairwise per lineage using Pearson's correlation for all possible pairs of random draws. As in the above evaluation for the key pre- processing parameters, we recorded minimum and median correlation per lineage across all pairs and we repeated this for fixed terminal states and for propagated uncertainty. + +Robustness with respect to the number of macrostates To evaluate sensitivity with respect to this parameter, we varied the number of macrostates between 10 and 16 and confirmed that within this range, the key terminal and initial states exist and remain in the same location. + +## 7 Pancreas data example + +We used a scRNA- seq time- series data set comprising embryonic days \(12.5 - 15.5\) of pancreatic development in mice assayed using 10x Genomics \(^{16}\) . We restricted the data to the last time point (E15.5) and to the Ngn3 low EP, Ngn3 high EP, Fev+ and endocrine clusters to focus on the late stages of endocrinogenesis where all of alpha, beta, epsilon and delta fates are present. We further filtered out cycling cells to amplify the differentiation signal. Our final subset contained 2531 cells. We kept the original cluster annotations which were available on a coarse level and on a fine level. On the fine level, the Fev+ cluster was sub- clustered into different populations which are biased towards different endocrine fates (Supplementary Fig. 10c). + +Data pre- processing and velocity computation For the following processing, we used scVelo \(^{9}\) and SCANPY \(^{13}\) with mostly default parameters. Loom files containing raw spliced and unspliced counts were obtained by running the velocyto \(^{1}\) command- line pipeline. We filtered genes to be expressed in at least 10 cells and to have at least 20 counts in both spliced and unspliced layers. We further normalized by total counts per cell, log transformed the data and kept the top 2000 highly variable genes. We then computed a PCA representation of the data and used the top 30 PCs to compute a KNN graph with \(K = 30\) nearest neighbors. For velocity computation, we used scVelo's dynamical model of splicing kinetics. We evaluate robustness of CellRank's results to changes in these pre- processing parameters (Section 6). + +Embedding computation We used the KNN graph to compute a PAGA \(^{7}\) representation of the data. The PAGA graph was used to initialize the computation of a UMAP \(^{11,44}\) representation of + +<--- Page Split ---> + +the data. Note that UMAP was only used to visualize the data and was not supplied to CellRank to compute the transition matrix or any downstream quantities. + +CellRank parameters We use CellRank's analytical stochastic approximation to compute transition probabilities and include a diffusion kernel with weight 0.2 (Subsection 1.2 and Subsection 1.5). We compute 12 macrostates and automatically detect the terminal alpha, beta and epsilon states. The delta population is picked up automatically as a macrostate. We manually assign it the terminal label. + +## 8 Lung data example + +We used a scRNA- seq time- series data- set of lung regeneration past bleomycin injury in mice assayed using Dropseq \(^{15,45}\) . The data- set contained 18 time points comprising days 0- 54 past injury. There was daily sampling from days 2- 13 and wider time- lags between the following time- points. Two replicate mice were used per time point. We restricted the data to days 2- 15 to make sure that the sampling is dense enough for velocities to be able to meaningfully extrapolate gene expression. If time points are too far apart, then RNA velocity cannot be used to predict the next likely cellular state because the linear extrapolation is only meaningful on the time scales of the splicing kinetics. Our final subset contained 24,882 cells. We kept the original cluster annotations. + +Data pre- processing and velocity computation For the following processing, we used scVelo \(^{9}\) and SCANPY \(^{13}\) with mostly default parameters. Loom files containing raw spliced and unspliced counts were obtained by running the velocity \(^{1}\) command- line pipeline. We filtered genes to be expressed in at least 10 cells and to have at least 20 counts in both spliced and unspliced layers. We further normalized by total counts per cell, log transformed the data and kept the top 2000 highly variable genes. We kept the PCA coordinates from the original study and computed a KNN graph with \(K = 30\) nearest neighbors using the top 50 PCs. For velocity computation, we used scVelo's dynamical model of splicing kinetics. + +Embedding computation The lung data was processed in three separate batches. We used BBKNN \(^{46}\) to compute a batch corrected KNN graph with 10 neighbors within each batch. The corrected KNN graph was used to compute a UMAP \(^{11,44}\) representation of the data. Note that UMAP was only used to visualize the data and was not supplied to CellRank to compute the transition matrix or any downstream quantities. We did not use BBKNN to correct the graph we used for velocity computation as it is an open question how to do batch correction for velocity computation. We used uncorrected data for velocity computation. + +CellRank parameters We use CellRank's analytical stochastic approximation to compute transition probabilities and include a diffusion kernel with weight 0.2 (Subsection 1.2 and Subsection 1.5). On the full data of Fig. 6a, we compute 9 macrostates. On the reduced data of Fig. 6d, we compute 3 macrostates. + +Defining stages of the differentiation trajectory We sub- setted cells to goblet and basal cells and re- run CellRank on the subset to investigate the trajectory at higher resolution. CellRank automatically detected initial and terminal states and computed fate probabilities towards the terminal states (Supplementary Fig. 24a- c). Further, we applied Palantir \(^{35}\) to the subset to compute a pseudotime (Supplementary Fig. 24d,e). We combined the pseudotime with CellRank's fate probabilities to define three stages of the dedifferentiation trajectory by requiring cells to have at least 0.66 basal probability. Cells passing this threshold were assigned to three bins of equal size along the pseudotemporal axis. We used this binning to define the three stages of the trajectory. + +<--- Page Split ---> + +## 9 Methods comparison + +9 Methods comparisonWe compared CellRank with the following similarity- based tools that compute probabilistic fate assignments on the single cell level: Palantir35, FateID47 and STEMNET48. We compared these methods in terms of the identification of initial and terminal states, fate probabilities, gene expression trends and run- time. + +The Palantir algorithm Palantir35 computes a KNN graph in the space of diffusion components and uses this graph to compute a pseudotime via iteratively updating shortest path distances from a set of waypoints. Palantir required us to provide a number of waypoint cells - essentially a smaller number of cells that the system is reduced to in order to make it computationally feasible. We set this number to 1200 cells for the pancreas data and to \(15\%\) of the total cell number of the runtime and memory benchmarks on the reprogramming dataset49 below. For pseudotime computation, an initial cells needs to be supplied by the user. The pseudotime is used to direct edges in the KNN graph by removing edges that point from later cells to earlier cells in pseudotime. The stationary distribution of the resulting directed transition matrix is combined with extrema in the diffusion components to identify terminal cells. Absorption probabilities towards the terminal cells serve as fate probabilities. Gene expression trends are computed similarly to CellRank, by fitting GAMs in pseudotime where each cell contributes to each lineage according to its fate probabilities. + +The FateID algorithm FateID47 either requires the user to provide terminal populations directly or through a set of marker genes. Terminal populations are used to train a random forest classifier. The classifier is applied to a set of cells in the neighborhood of each terminal cluster where it predicts the likely fate of these cells. The training set is iteratively expanded and the Random forest is re- trained on expanding populations, thus moving from the committed populations backward in time, classifying the fate of increasingly earlier cells. Two key parameters here are the size of the training and test sets used for the Random forest classifier, which we set to \(1\%\) of the data in all benchmarks. Gene expression trends are computed by selecting a (discrete) set of cells which pass a certain threshold for fate bias towards a specific terminal population. A principal curve is fit to these cells in a low dimensional embedding and pseudotime is assigned via projection onto this curve. Alternatively, the authors recommend to compute diffusion pseudotime10 (DPT) on the set of cells selected for a particular lineage. Gene expression values are then normalised and a local regression (LOESS) is performed to obtain mean trends. In contrast to CellRank and Palantir, this approach does not provide confidence intervals for the expression trends, it is dependent on low dimensional embeddings (principal curve fit) and it discreetly assigns cells to lineages, thereby ignoring the gradual nature of fate commitment when visualizing gene expression trends. Since different cells are selected for different lineages, the computed pseudo- temporal orderings are incompatible and gene trends along different lineages cannot be visualized jointly. + +The STEMNET algorithm STEMNET48 requires the user to provide the terminal populations directly as input to the algorithm. It then trains an elastic- net regularized generalized linear model on the terminal populations to predict state membership. This first step serves as feature selection - it selects a set of genes which are specific to their terminal populations. In the next step, the classifier uses expression of these genes to predict fate bias for the remaining, transient cells. STEMNET uses the computed fate probabilities to place cells on a simplex in 2 dimensions as a dimensionality reduction method. It does not offer a method to visualize gene expression trends. + +Fate probabilities In order to enable a fair comparison across methods, we supplied all methods with CellRank's identified terminal states and compared predicted fate probabilities. Methods differed in the format they require terminal state information to be passed: for Palantir, we passed individual cells, i.e. 4 cells, one for each of the three terminal states and one initial cell from the initial + +<--- Page Split ---> + +state. For STEMNET, we passed populations of cells defined through the underlying transcriptomic clusters. We passed the alpha, beta, epsilon and delta clusters defined through the sub- clustering of Supplementary Fig. 10c. For FateID, we passed marker genes to identify the terminal populations. For each terminal state, we passed its corresponding hormone- production associated gene, i.e. Ins1 for beta, Gcg for alpha, Ghrl for epsilon and Sst for delta50. We checked whether methods correctly predicted beta to be the dominant fate among early cells by computing the average fate prediction among Ngn3 high EP cells. + +Gene expression trends To visualize gene expression trends, we used the functionalities that each method provided. STEMNET did not have an option to compute gene expression trends. For CellRank, we visualized gene expression trends as described in Section 3. For Palantir, we used default parameters. For FateID, it was difficult to find a good threshold value to assign cells to lineages. If this value is too high, then early cells in the trajectory are not selected and the terminal states are isolated. If this value is too low, then for a subset of the lineages, very unlikely cells are assigned and the trends are very unspecific. The default value is 0.25, which was too high in our case. We decided to set the threshold at 0.15, which was a compromise between trying to have early cells in every lineage and making sure that irrelevant cells are not assigned. We computed DPT10 on the set of the selected cells, as recommended in the original publication. To identify a root cell for each lineage, we first computed DPT on the entire data- set, then subsetted to a lineage- specific set of cells and picked the cell with the earliest original DPT value as the root cell for the second DPT computation. We visualized expression trends for the key lineage drivers Pax451 and Pdx152- 54 (beta), Arx51 (alpha), Ghrl50 (epsilon) and Hhex55 and Cd24a56,57 (delta) as well as the lineage- associated genes Peg1058,59 (alpha) and Irs459 (epsilon). In Fig. 5c, we checked whether methods correctly predicted upregulation of Pdx1 along the beta fate. + +Runtime We compared run- time of the four methods applied to a scRNA- seq dataset comprising 100k cells undergoing reprogramming from mouse embryonic fibroblasts to induced endoderm progenitor cells49. We randomly subsampled the data- set to obtain 10 data- sets of increasing size, starting from 10k cells in steps of 10k until 100k cells. For each sub- sampled dataset, we applied each method 10 times and computed the mean runtime as well as the standard error on the mean. + +We used CellRank to compute 3 terminal states and we supplied these to all other methods to ensure that the number of terminal states is consistent across methods. Methods differed in the format they require terminal state information to be passed: for Palantir, we passed individual cells, i.e. three terminal cells and one initial cell (taken from the earliest time point of the reprogramming data). For STEMNET, we passed a set of cells for each terminal state by choosing the cells which have been most confidently assigned to each terminal state by CellRank. For each terminal state, we passed a number of cells that was equal to 1% of the total cell number. FateID requires marker genes to identify the terminal populations, so we computed the top 3 lineage drivers per CellRank- identified terminal state and passed these. + +For CellRank, we separately recorded the time it took to compute the terminal states and fate probabilities. For terminal states in CellRank, we included in this benchmark the entire workflow from computing the transition matrix via decomposing it into macrostates to identifying the terminal states among the macrostates. For fate probabilities, we benchmarked the compute_absorption_probabilities() method (CellRank), the run_palantir() function (Palantir), the fateBias() function (FateID) and the runSTEMNET() function (FateID). + +Comparisons were run on an Intel(R) Xeon(R) Gold 6126 CPU @ 2.60GHz and 32 cores. Each job was allocated at least 90 GiB RAM and we recorded the actual peak memory usage (see below). FateID did not finish on 100k cells because of a memory error due to densification of a large matrix. + +<--- Page Split ---> + +Peak memory usage The setup was identical to the setup for the runtime comparison above, only that we recorded peak memory usage of each method (Supplementary Table 2). For the Python- based methods CellRank and Palantir, we used the memory- profiler \(^{60}\) package whereas for the R- based packages STEMNET and FateID, we used the peakRAM \(^{61}\) profiler. CellRank and Palantir efficiently parallelize their computations across several cores which increases their peak memory consumption. We repeated our evaluation for these two methods on 100k cells using just a single core to estimate the size of this effect (Supplementary Table 3). + +## 10 Immunofluorescence stainings and microscopy on airway epithelial cells + +Formalin- fixed paraffin- embedded lung sections ( \(3.5\mu m\) thick) from bleomycin- treated mice at day 10 (n=2) and day 22 (n=2) after bleomycin instillation, and from phosphate- buffered saline (PBS)- treated controls (n=2) were stained as previously described \(^{15}\) . In brief, after deparaffinization, rehydration and heat- mediated antigen retrieval with citrate buffer (10 mM, pH = 6.0), sections were blocked with 5% bovine serum albumin for 1 h at room temperature and then incubated with the following primary antibodies overnight at 4°C: rabbit anti- Bpifb1 (kindly provided by C. Bingle \(^{62}\) , 1:500), mouse anti- Trp63 (abcam, ab735, clone A4A, 1:50) and chicken anti- Krt5 (BioLegend, Poly9059, 1:1,000). + +For visualization of stainings the following secondary antibodies were used: Goat anti- rabbit Alexa Fluor \(^{\textregistered}\) 488 (Invitrogen, A11008, 1:250), Goat anti- chicken Alexa Fluor \(^{\textregistered}\) 568 (Invitrogen, A11041,1:250) and goat anti- mouse Alexa Fluor \(^{\textregistered}\) 647 (Invitrogen, A21236, 1:250). Cell nuclei were visualized with 4',6- diamidino- 2- phenylindole (DAPI). + +Immunofluorescent images were acquired with an AxioImager.M2 microscope (Zeiss) using a PlanApochromat 20x/0.8 M27 objective. 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ISSN 1432- 0878. doi: 10.1007/s00441- 012- 1490- 9. + +<--- Page Split ---> +![](images/Figure_unknown_0.jpg) + +
Figures
+ +
Figure 1
+ +Combining RNA velocity with cell- cell similarity to determine initial and terminal states and compute a global map of cellular fate potential. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2
+ +Delineating fate choice in pancreatic development + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3
+ +Zooming into the delta state to elucidate differentiation paths + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Figure 4
+ +Uncertainty propagation adjusts for noise in RNA velocity vectors + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Figure 5
+ +CellRank outperforms methods that do not include RNA velocity + +<--- Page Split ---> +![](images/Figure_6.jpg) + +
Figure 6
+ +Cellrank predicts a novel differentiation trajectory in Murine lung regeneration + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +- 20201019cellranksupplementarytables.pdf- 20201019cellranksupplementaryfigures.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__08c896aeaed41f29a8b25b95b601c8dd1bba0170a826726f584aca97efa5d8d5/preprint__08c896aeaed41f29a8b25b95b601c8dd1bba0170a826726f584aca97efa5d8d5_det.mmd b/preprint/preprint__08c896aeaed41f29a8b25b95b601c8dd1bba0170a826726f584aca97efa5d8d5/preprint__08c896aeaed41f29a8b25b95b601c8dd1bba0170a826726f584aca97efa5d8d5_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..d6b3b036ca0f0c7061c7faa3de8bcde30bbfb627 --- /dev/null +++ b/preprint/preprint__08c896aeaed41f29a8b25b95b601c8dd1bba0170a826726f584aca97efa5d8d5/preprint__08c896aeaed41f29a8b25b95b601c8dd1bba0170a826726f584aca97efa5d8d5_det.mmd @@ -0,0 +1,952 @@ +<|ref|>title<|/ref|><|det|>[[44, 106, 840, 144]]<|/det|> +# CellRank for directed single-cell fate mapping + +<|ref|>text<|/ref|><|det|>[[44, 162, 940, 204]]<|/det|> +Marius Lange Institute of Computational Biology, Helmholtz Center Munich https://orcid.org/0000- 0002- 4846- 1266 + +<|ref|>text<|/ref|><|det|>[[44, 208, 583, 248]]<|/det|> +Volker Bergen Institute of Computational Biology, Helmholtz Center Munich + +<|ref|>text<|/ref|><|det|>[[44, 254, 583, 295]]<|/det|> +Michal Klein Institute of Computational Biology, Helmholtz Center Munich + +<|ref|>text<|/ref|><|det|>[[44, 300, 618, 342]]<|/det|> +Manu Setty Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center + +<|ref|>text<|/ref|><|det|>[[44, 348, 550, 389]]<|/det|> +Bernhard Reuter Department of Computer Science, University of Tubingen + +<|ref|>text<|/ref|><|det|>[[44, 394, 705, 435]]<|/det|> +Mostafa Bakhti Institute of Diabetes and Regeneration Research, Helmholtz Center Munich, + +<|ref|>text<|/ref|><|det|>[[44, 440, 670, 481]]<|/det|> +Heiko Lickert Helmholtz Zentrum München https://orcid.org/0000- 0002- 4597- 8825 + +<|ref|>text<|/ref|><|det|>[[44, 486, 583, 527]]<|/det|> +Meshal Ansari Institute of Computational Biology, Helmholtz Center Munich + +<|ref|>text<|/ref|><|det|>[[44, 532, 704, 574]]<|/det|> +Janine Schniering Institute of Lung Biology and Disease (ILBD), Helmholtz Zentrum München + +<|ref|>text<|/ref|><|det|>[[44, 579, 946, 665]]<|/det|> +Herbert Schiller Helmholtz Zentrum München, Institute of Lung Biology and Disease, Group Systems Medicine of Chronic Lung Disease, Member of the German Center for Lung Research (DZL), CPC- M bioArchive, Munich https://orcid.org/0000- 0001- 9498- 7034 + +<|ref|>text<|/ref|><|det|>[[44, 670, 761, 711]]<|/det|> +Dana Pe'er Memorial Sloan Kettering Cancer Center https://orcid.org/0000- 0002- 9259- 8817 + +<|ref|>text<|/ref|><|det|>[[44, 715, 670, 757]]<|/det|> +Fabian Theis ( fabian.theis@helmholtz- muenchen.de) Helmholtz Zentrum München https://orcid.org/0000- 0002- 2419- 1943 + +<|ref|>sub_title<|/ref|><|det|>[[44, 799, 102, 817]]<|/det|> +## Article + +<|ref|>text<|/ref|><|det|>[[44, 836, 510, 857]]<|/det|> +Keywords: CellRank, directed, single- cell fate mapping + +<|ref|>text<|/ref|><|det|>[[44, 875, 327, 894]]<|/det|> +Posted Date: October 29th, 2020 + +<|ref|>text<|/ref|><|det|>[[44, 912, 451, 932]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 94819/v1 + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 44, 911, 87]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[42, 123, 920, 167]]<|/det|> +Version of Record: A version of this preprint was published at Nature Methods on January 13th, 2022. See the published version at https://doi.org/10.1038/s41592-021-01346-6. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[102, 83, 483, 106]]<|/det|> +## CellRank - Online Methods + +<|ref|>text<|/ref|><|det|>[[101, 120, 851, 170]]<|/det|> +Marius Lange \(^{1,2}\) , Volker Bergen \(^{1,2}\) , Michal Klein \(^{1}\) , Manu Setty \(^{3}\) , Bernhard Reuter \(^{4,5}\) , Mostafa Bakhti \(^{6,7}\) , Heiko Lickert \(^{6,7}\) , Meshal Ansari \(^{1,8}\) , Janine Schniering \(^{8}\) , Herbert B. Schiller \(^{8}\) , Dana Pe'er \(^{3*}\) , Fabian J. Theis \(^{1,2,9*}\) + +<|ref|>text<|/ref|><|det|>[[101, 184, 864, 393]]<|/det|> +1 Institute of Computational Biology, Helmholtz Center Munich, Germany. 2 Department of Mathematics, TU Munich, Germany. 3 Program for Computational and Systems Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA. 4 Department of Computer Science, University of Tübingen, Germany. 5 Zuse Institute Berlin (ZIB), Takustr. 7, 14195 Berlin, Germany. 6 Institute of Diabetes and Regeneration Research, Helmholtz Center Munich, Germany. 7 German Center for Diabetes Research (DZD), Neuherberg, Germany. 8 Comprehensive Pneumology Center (CPC) / Institute of Lung Biology and Disease (ILBD), Helmholtz Zentrum München, Member of the German Center for Lung Research (DZL), Munich, Germany. 9 TUM School of Life Sciences Weihenstephan, Technical University of Munich, Germany. \*Corresponding authors: fabian.theis@helmholtz-muenchen.de and peerd@mskcc.org + +<|ref|>sub_title<|/ref|><|det|>[[102, 436, 208, 455]]<|/det|> +## Contents + +<|ref|>text<|/ref|><|det|>[[125, 468, 900, 727]]<|/det|> +1 The CellRank algorithm 2 1.1 Modelling approach 3 1.2 Computing the transition matrix 5 1.3 Coarse-graining the Markov chain 7 1.4 Computing fate probabilities 10 1.5 Propagating velocity uncertainty 12 1.6 The CellRank software package 14 2 Computing a directed PAGA graph 16 3 Computing gene expression trends along lineages 16 4 Clustering gene expression trends 17 5 Uncovering putative driver genes 18 6 Robustness analysis 18 7 Pancreas data example 19 8 Lung data example 20 9 Methods comparison 21 10 Immunofluorescence stainings and microscopy on airway epithelial cells 23 + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[102, 80, 288, 100]]<|/det|> +## Online methods + +<|ref|>sub_title<|/ref|><|det|>[[103, 111, 368, 129]]<|/det|> +## 1 The CellRank algorithm + +<|ref|>text<|/ref|><|det|>[[101, 136, 896, 283]]<|/det|> +The aim of the CellRank algorithm is to detect the initial, terminal and intermediate states of a cellular system and to define a global map of fate potentials that assigns each cell to these states in a probabilistic manner. Given our inferred fate potentials, we compute gene expression trends along trajectories in the fate map and provide several possibilities for visualizing these. The inputs to CellRank are a count matrix \(X\in \mathbb{R}^{N\times G}\) where \(N\) is the number of cells and \(G\) is the number of genes as well as a velocity matrix \(V = \mathbb{R}^{N\times G}\) , defining a vector field representing RNA velocity \(^{1,2}\) for each cell and gene. Note that CellRank can be generalized to any kind of vector field, i.e. \(V\) could equally represent directed information given by e.g. metabolic labeling \(^{3 - 6}\) . There are three main steps to the CellRank algorithm: + +<|ref|>text<|/ref|><|det|>[[124, 290, 896, 404]]<|/det|> +1. Compute transition probabilities among observed cells. These reflect how likely a cell with a given cell state, defined by its gene expression profile, is to change its profile to that of a target cell. We compute these probabilities by integrating two sources of evidence: (1) transcriptomic similarity between the source and target cells and (2) an extrapolation of a cell's current gene expression profile into the near future using RNA velocity. We aggregate these transition probabilities in the transition matrix \(P\) and use it to model cell-state transitions as a Markov chain. + +<|ref|>text<|/ref|><|det|>[[123, 410, 896, 492]]<|/det|> +2. Coarse-grain the Markov chain into a set of initial, terminal and intermediate macrostates of cellular dynamics. Each cell is assigned to each macrostate via a membership matrix \(\chi\) . The assignment is soft, i.e. each cell has a certain degree of confidence of belonging to each macrostate. We compute transition probabilities among macrostates in the matrix \(P_{c}\) . This matrix allows us to identify whether macrostates are initial, terminal or intermediate. + +<|ref|>text<|/ref|><|det|>[[123, 499, 895, 564]]<|/det|> +3. Compute fate probabilities towards a subset of the macrostates. This will typically include the terminal states, but can also include intermediate states, depending on the biological question. We compute how likely each cell is to transition into each of the selected macrostates and return these probabilities in a fate matrix \(F\) . + +<|ref|>text<|/ref|><|det|>[[101, 589, 896, 799]]<|/det|> +CellRank extracts the essence of cellular state transitions The principle of the CellRank algorithm is to decompose the dynamics of the biological system into a set of dynamical macrostates. We target macrostates that are associated with regions in the phenotypic manifold which cells are unlikely to leave once they have entered them. For each observed cell, we compute how likely it is to belong to each of these macrostates. We accumulate these soft assignments in a membership matrix \(\chi \in \mathbb{R}^{N\times n_{s}}\) . Further, we compute a coarse- grained transition matrix \(P_{c}\in \mathbb{R}^{n_{s}\times n_{s}}\) which specifies transition probabilities among macrostates. The coarse- grained transition matrix allows us to reduce the biological system to its essence: dynamical macrostates of observed cell- state transitions and their relationship to one another. Based on the coarse- grained transition matrix, we classify macrostates as either initial, intermediate or terminal. Initial states will be macrostates that have very small incoming but large outgoing transition probability. Intermediate states will be macrostates that have both incoming and outgoing transition probability. Terminal states will be macrostates that have large incoming but very little outgoing and large self- transition probability. + +<|ref|>text<|/ref|><|det|>[[101, 825, 896, 921]]<|/det|> +CellRank computes probabilistic fate potentials Each macrostate is associated with a subset of the observed cells via the membership matrix \(\chi\) . Once we classified macrostates as either initial, intermediate or terminal using the coarse- grained transition matrix \(P_{c}\) , we may ask how likely each cell is to transition to each of the \(n_{t}\) terminal states. CellRank efficiently computes these probabilities and returns a fate matrix \(F\in \mathbb{R}^{N\times n_{t}}\) . The matrix \(F\) extends the short- range fate relationships given by RNA velocity to the global scale: from initial to terminal states along the phenotypic manifold. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[101, 83, 895, 131]]<|/det|> +We account for high noise levels in the velocity vectors via a stochastic Markov chain formulation, by restricting predicted transitions to align with the phenotypic manifold and by propagating velocity uncertainty into the Markov chain. + +<|ref|>text<|/ref|><|det|>[[100, 158, 895, 190]]<|/det|> +CellRank uncovers gene expression trends towards specific terminal populations The outputs of the CellRank algorithm are + +<|ref|>text<|/ref|><|det|>[[128, 197, 895, 230]]<|/det|> +- a membership matrix \(\chi \in \mathbb{R}^{N\times n_s}\) where \(n_s\) is the number of macrostates. Row \(i\) in \(\chi\) softly assigns cell \(i\) to any of the macrostates. + +<|ref|>text<|/ref|><|det|>[[128, 238, 895, 286]]<|/det|> +- a coarse-grained transition matrix \(P_{c} \in \mathbb{R}^{n_{s} \times n_{s}}\) that describes how likely these macrostates are to transition into one another. The matrix \(P_{c}\) allows macrostates to be classified as either initial, intermediate or terminal. + +<|ref|>text<|/ref|><|det|>[[128, 293, 895, 326]]<|/det|> +- a fate matrix \(F \in \mathbb{R}^{N \times n_{t}}\) where \(n_{t}\) is the number of terminal states. Row \(i\) in \(F\) specifies how likely cell \(i\) is to transition towards any of the terminal states. + +<|ref|>text<|/ref|><|det|>[[101, 334, 895, 448]]<|/det|> +We use the fate matrix \(F\) to model gradual lineage commitment. Fate biases can be aggregated to the cluster level and visualized as pie charts on a new directed version of PAGA graphs7 (Section 2). Further, we use the fate matrix \(F\) to uncover gene expression trends towards the identified terminal states (Section 3). Once the trends have been fit, they can be clustered to discover the main regulatory dynamics towards different terminal states (Section 4). For the identification of putative regulators towards specific terminal states, we correlate gene expression values with fate probabilities (Section 5). + +<|ref|>sub_title<|/ref|><|det|>[[103, 474, 323, 490]]<|/det|> +### 1.1 Modelling approach + +<|ref|>text<|/ref|><|det|>[[101, 497, 896, 660]]<|/det|> +Similarly to other methods8- 10, CellRank models cell state transitions among observed cellular profiles. Unlike other velocity based methods, following the success of pseudotime methods, key to our model is that we restrict possible state changes to those consistent with the global structure of the phenotypic manifold via a KNN graph computed based on similarities in gene expression space. Our approach then biases the likely future state of an observed cell within its local graph neighborhood based on RNA velocity, by combining transcriptional similarity with RNA velocity to direct edges in the graph and to assign a probability to each cell state transition. When computing these probabilities, we take into account uncertainty in the velocity vectors. By aggregating individual, stochastic transitions within the global structure of the phenotypic manifold, we uncover the fate bias for individual cells. We make the following assumptions: + +<|ref|>text<|/ref|><|det|>[[128, 668, 896, 870]]<|/det|> +- state transitions are gradual, daughter cells are in general transcriptomically similar to their mother cells. Cells traverse a low-dimensional phenotypic manifold from initial to terminal states via a set of intermediate states.- the set of sampled cellular profiles spans the entire state change trajectory, i.e. intermediate states have been covered, there are no 'gaps' in the trajectory.- while for an individual cell, its past history is stored in epigenetic modifications, we model average cellular dynamics where state transitions occur without memory.- RNA velocity approximates the first derivative of gene expression. This must not precisely hold for every gene in each individual cell as we treat state transitions as a stochastic process, enforce alignment with the manifold and propagate uncertainty, but it should hold in expectation for enough cells so that we are able to estimate the overall directional flow. + +<|ref|>text<|/ref|><|det|>[[101, 877, 895, 925]]<|/det|> +Based on these assumptions, we model cellular state transitions using a Markov chain: a stochastic process \(X = (X_{t})_{t \in T}\) - a sequence of random variables \(X_{t}: \Omega \to E\) on a probability space \((\Omega , \mathcal{A}, \mathbb{P})\) over a countable set \(\Omega\) mapping to a measurable state space \((E, \Sigma)\) - that describes the evolution + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[101, 83, 895, 213]]<|/det|> +of a probability distribution over time where the future distribution only depends on the current distribution and not on the past, i.e. \(\operatorname *{Pr}(X_{t_{n + 1}} = x\mid X_{t_1} = x_1,X_{t_2} = x_2,\dots,X_{t_n} = x_n) = \operatorname *{Pr}(X_{t_{n + 1}} = x\mid X_{t_n} = x_n)\) . We use a Markov chain over a discrete and finite state space \(\Omega\) , where each state in the chain is given by an observed cellular transcriptional profile. To define the Markov chain, we need to compute a transition matrix \(P\in \mathbb{R}^{N\times N}\) which describes how likely one cell is to transition into another. We construct \(P\in \mathbb{R}^{N\times N}\) using a KNN graph based on transcriptional similarity between cells and a given vector field. While CellRank generalizes to any given vector field, we demonstrate it using RNA- velocities, based on unspliced to spliced read ratios, computed with scVelo9. + +<|ref|>text<|/ref|><|det|>[[101, 239, 895, 318]]<|/det|> +Defining initial, intermediate and terminal states in biological terms We define an initial (terminal) state as an ensemble of measured gene expression profiles which, when taken together, characterize the starting (end) point of one particular cell- state change. We define an intermediate state as an ensemble of gene expression profiles which, when taken together, characterize a point on the cell- state transition trajectory which lies in between one or several initial and terminal states. + +<|ref|>text<|/ref|><|det|>[[101, 345, 895, 602]]<|/det|> +Translating initial, intermediate and terminal states into mathematical terms To translate the above terms into mathematics, we make use of the coarse- graining given by the membership matrix \(\chi\) and the coarse- grained transition matrix \(P_{c}\) . We show below that our assignment of cells to macrostates maximizes a criterion we call the crispness: we obtain macrostates which have little overlap and large self- transition probability. In other words: we recover the kinetics of the Markov chain on slow- time scales, i.e. macrostates and their transitions reflect the limiting behavior of the Markov chain. Among the set of macrostates, we identify initial states as those which have little incoming large but large outgoing transition probability in \(P_{c}\) . Intermediate states will have both incoming and outgoing transition probability in \(P_{c}\) . Terminal states will have large incoming but little outgoing and large self- transition probability in \(P_{c}\) . An important term in the mathematical framework is metastability: a process starting in a metastable state will stay there with high probability for a long time. Accordingly, we define a metastable state of cellular dynamics as an area in phenotypic space that cells are unlikely to leave again once they have entered. A metastable state will typically correspond to a terminal state, while an intermediate state is typically only weakly metastable. Initial states can constitute weakly metastable states, if the probability of leaving them is small, potentially because of heavily cycling populations. + +<|ref|>text<|/ref|><|det|>[[101, 628, 895, 739]]<|/det|> +Reversing the Markov chain to recover initial states Initial states may not be picked up as macrostates during coarse- graining of the Markov chain because they are not stable enough, i.e. cells in the initial state have very little probability of transitioning into one another and rapidly start traversing their state change trajectory. In these cases, we reverse the Markov chain, i.e. we flip the arrows in the velocity vector field \(V\) . The initial state now constitutes a terminal (i.e. metastable) state of the reversed dynamics and may be recovered by coarse- graining and interpreting the reversed Markov chain. + +<|ref|>text<|/ref|><|det|>[[101, 766, 895, 896]]<|/det|> +Defining fate probabilities towards macrostates Biologically, we define the fate probability of cell \(i\) to reach macrostate \(j\in 1,\dots,M\) as the probability of cell \(i\) executing a series of gene expression programs which change its phenotype to match the phenotype of cells in macrostate \(j\) . Within the context of fate probabilities, we will typically be interested in macrostates which are either terminal or intermediate states. Mathematically, we translate this to the probability of a random walk on the Markov chain initialized in cell \(i\) to reach any cell belonging to macrostate \(j\) before reaching any cell belonging to another macrostate. CellRank efficiently computes these probabilities in closed form using absorption probabilities (Subsection 1.4). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[103, 83, 444, 99]]<|/det|> +### 1.2 Computing the transition matrix + +<|ref|>text<|/ref|><|det|>[[101, 107, 895, 189]]<|/det|> +We model each observed cell by one microstate in the Markov chain. To compute transition probabilities among cells, we make use of transcriptomic similarity to define the global topology of the phenotypic manifold and of RNA velocity to direct local movement on the manifold. To model the global topology of the phenotypic manifold, the first step of the CellRank algorithm is to compute a KNN graph. + +<|ref|>text<|/ref|><|det|>[[101, 214, 895, 295]]<|/det|> +Computing a KNN graph to align local transitions with global topology We compute a KNN graph to constrain the set of possible transitions to those that are consistent with the global topology of the phenotypic manifold: Each cell is thus only allowed to transition into one of its \(K\) nearest neighbors. While CellRank can generalize to any reasonable similarity kernel, here, we compute the KNN graph as follows: + +<|ref|>text<|/ref|><|det|>[[123, 301, 895, 336]]<|/det|> +1. project the data onto the first \(L\) principal components to obtain a matrix \(X_{PC}\in \mathbb{R}^{N\times L}\) , where rows correspond to cells and columns correspond to PC features. + +<|ref|>text<|/ref|><|det|>[[123, 342, 895, 376]]<|/det|> +2. for each cell \(i\) , compute distances to its \(K\) -nearest neighbors based on euclidean distance in \(X_{PC}\) . Accumulate distances in a matrix \(D\in \mathbb{R}^{N\times N}\) . + +<|ref|>text<|/ref|><|det|>[[123, 383, 895, 450]]<|/det|> +3. the KNN relationship will lead to a directed graph because it is not a symmetric relationship. Symmetrize the KNN relations encoded by \(D\) , such that cells \(i\) and \(j\) are nearest neighbors if either \(i\) is a nearest neighbors of \(j\) , or \(j\) is a nearest neighbors of \(i\) . This will yield an undirected symmetric version \(D_{sym}\) of \(D\) , where each cell has at least \(K\) nearest neighbors. + +<|ref|>text<|/ref|><|det|>[[123, 456, 895, 537]]<|/det|> +4. compute a symmetric adjacency matrix \(A\) based on \(D_{sym}\) containing similarity estimates between neighboring cells according to the manifold structure. To approximate cell similarities, we use the method implemented in the UMAP algorithm, which adapts the singular set and geometric realization functors from algebraic topology to work in the context of metric spaces and fuzzy simplicial sets \(^{11,12}\) . + +<|ref|>text<|/ref|><|det|>[[100, 544, 895, 658]]<|/det|> +We choose \(K = 30\) to be the number of nearest neighbors by default. We show in Supplementary Fig. 8a that CellRank is robust to the choice of \(K\) . To compute the similarity metric, the option presented is the default in SCANPY \(^{13}\) . Alternatively, similarity may be computed using a Gaussian kernel with density- scaled kernel width as introduced by ref. \(^{14}\) and adapted to the single cell context in ref. \(^{10}\) . We choose \(L = 30\) to be the number of principal components by default. This can be adapted based on knee- point heuristics or the percentage of variance explained, however, we show in Supplementary Fig. 8d that CellRank is robust to the exact choice of \(L\) . + +<|ref|>text<|/ref|><|det|>[[100, 682, 895, 812]]<|/det|> +Directing the KNN graph based on RNA Velocity Next, we direct the edges of the KNN graph using RNA velocity information, giving higher probability to those neighbors whose direction best aligns with the direction of the velocity vector. Specifically, for cell \(i\) with gene expression profile \(x_{i}\in \mathbb{R}^{G}\) and velocity vector \(v_{i}\in \mathbb{R}^{G}\) , consider its neighbors \(j = 1,2,\dots,K_{i}\) with gene expression profiles \(\{x_{1},x_{2},\dots,x_{K}\}\) . Note that the graph construction outlined above leads to a symmetric KNN graph, where \(K_{i}\) is not constant across all cells, but \(K_{i}\geq K\forall i\in \{1,\dots,N\}\) . For each neighboring cell \(k\) , compute the corresponding state- change vector with cell \(i\) , \(s_{ik} = x_{k} - x_{i}\in R^{G}\) . Next, we compute Pearson correlations \(c_{i}\in R^{K}\) of \(v_{i}\) with all state change vectors via + +<|ref|>equation<|/ref|><|det|>[[336, 823, 892, 857]]<|/det|> +\[c_{ik} = \frac{(s_{ik} - e\bar{s}_{ik})^{\top}(v_{i} - e\bar{v}_{i})}{\|s_{ik} - e\bar{s}_{ik}\|\|v_{i} - e\bar{v}_{i}\|} \in [-1,1]^{K}, \quad (1)\] + +<|ref|>text<|/ref|><|det|>[[100, 867, 895, 932]]<|/det|> +where \(e\) is a constant vector of ones and \(\bar{s}_{ik}\) and \(\bar{v}_{i}\) are averages over the state change vector and the velocity vector, respectively. Intuitively, \(c_{i}\) contains the cosines of the angles that the mean- centered \(v_{i}\) forms with the mean- centered state- change vectors \(s_{ik}\) . A value of 1 means perfect correlation between the gene expression changes predicted by the local velocity vector and the actual change + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[101, 83, 895, 148]]<|/det|> +observed when going from the reference cell to any of its nearest neighbors. Pearson correlations have been computed in similar ways by svelo \(^{9}\) and velocyto \(^{1}\) to project the velocity vectors into a given embedding. In Subsection 1.5 below, we show how their ideas can be formalized and extended to account for uncertainty in the velocity vector. + +<|ref|>text<|/ref|><|det|>[[101, 173, 895, 223]]<|/det|> +Transforming correlations into transition probabilities To use the vector \(c_{i}\) as a set of transition probabilities to neighboring cells, we need to make sure it is positive and sums to one. For cell \(i\) , define a set of transition probabilities \(p_{i} \in \mathbb{R}^{K}\) via + +<|ref|>equation<|/ref|><|det|>[[411, 232, 892, 270]]<|/det|> +\[p_{ik} = \frac{\exp(\sigma c_{ik})}{\sum_{l = 1}^{K}\exp(\sigma c_{il})} \quad (2)\] + +<|ref|>text<|/ref|><|det|>[[101, 276, 895, 358]]<|/det|> +where \(\sigma > 0\) is a scalar constant that controls how centered the categorical distribution will be around the most likely value, i.e. around the state- change transition with maximum correlation (see below). We repeat this for all \((i,k)\) which are nearest neighbors to compute the transition matrix \(P_{v} \in \mathbb{R}^{N \times N}\) . This scales linearly in the number of cells \(N\) , the number of nearest neighbors \(K\) and the number of genes \(G\) as the KNN graph is sparse. + +<|ref|>text<|/ref|><|det|>[[101, 382, 895, 431]]<|/det|> +Automatically determine \(\sigma\) We reasoned that the value of \(\sigma\) should depend on typical Pearson correlation's between velocity vectors and state change vectors observed in the given data- set. For this reason, we use the following heuristic: + +<|ref|>equation<|/ref|><|det|>[[391, 440, 892, 473]]<|/det|> +\[\sigma = \frac{1}{\mathrm{median}(\{|c_{ik}|\forall i,k\})}. \quad (3)\] + +<|ref|>text<|/ref|><|det|>[[101, 482, 895, 564]]<|/det|> +This means that if the median absolute Pearson correlation observed in the data is large (small), we use a small (large) value for \(\sigma\) . The intuition behind this is that for sparsely sampled data- sets where velocity vectors only roughly point into the direction of neighboring cells, we upscale all correlations a bit. Typical values for \(\sigma\) we compute this way range from 1.5 (lung example \(^{15}\) ) to 3.8 (pancreas example \(^{16}\) ). + +<|ref|>text<|/ref|><|det|>[[101, 588, 895, 718]]<|/det|> +Coping with uncertainty in the velocity vectors scRNA- seq data is a noisy measurement of the underlying gene expression state of individual cells. RNA velocity is computed on the basis of these noisy measurements and is therefore itself a substantially noisy quantity. In particular the unspliced reads required by velocity and scVelo to estimate velocities are very sparse and their abundance varies depending on the amount of relevant intronic sequence of different genes. Besides this inherent noise, preprocessing decisions in the alignment pipeline of spliced and unspliced reads have been shown to impact the final velocity estimate \(^{17}\) . Further uncertainty in the velocity estimate arises because assumptions have to be made which may not always be satisfied in practice: + +<|ref|>text<|/ref|><|det|>[[128, 725, 895, 933]]<|/det|> +- the original velocyto \(^{1}\) model assumes that for each gene, a steady state is captured in the data. The scVelo \(^{9}\) model circumvents this assumption by dynamic modeling, extending RNA velocity to transient cell populations, however there is often only a sparsity of transitional cells to estimate these dynamics.- both models assume that the key biological driver genes for a given cell-state transition are intron rich and may therefore be used to estimate spliced to unspliced ratios. This has been shown to be the case in many neurological settings, however, in other systems such as hematopoiesis, it remains unclear whether this assumption is met.- both models assume that per gene, a single set of kinetic parameters \(\alpha\) (transcription rate), \(\beta\) (splicing rate) and \(\gamma\) (degradation rate) may be used across all cells. However, we know that in many settings, this assumption is violated because of alternative splicing or cell-type specific regulation \(^{18-21}\) . + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[128, 83, 895, 190]]<|/det|> +- both models assume that there are no batch effects present in the data. To date, to the best of our knowledge, there are no computational tools to correct for batch effects in velocity estimates.- both models assume that the cell-state transition captured in the data is compatible with the time scale of splicing kinetics. However, this is often not known a priori and may explain the limited success of RNA velocity in studying hematopoiesis to date. + +<|ref|>text<|/ref|><|det|>[[102, 195, 895, 245]]<|/det|> +The points outlined above highlight that RNA velocity is a noisy, uncertain estimate of the likely direction of the future cell state. To cope with the uncertainty present in RNA velocity, we adapt four strategies: + +<|ref|>text<|/ref|><|det|>[[128, 252, 895, 288]]<|/det|> +- we restrict the set of possible transitions to those consistent with the global topology of the phenotypic manifold as described by the KNN graph. + +<|ref|>text<|/ref|><|det|>[[128, 293, 895, 375]]<|/det|> +- we use a stochastic formulation based on Markov chains to describe cell-state transitions. For cell \(i\) with velocity vector \(v_{i}\) , we allow transitions to each nearest neighbor \(j\) with transition probability \(p_{ij}\) . This means that we even allow transitions backwards, against the flow prescribed by the velocity vector field, with small probability. This reflects our uncertainty in \(v_{i}\) . + +<|ref|>text<|/ref|><|det|>[[128, 380, 780, 398]]<|/det|> +- we combine RNA velocity information with transcriptomic similarity, see below. + +<|ref|>text<|/ref|><|det|>[[128, 405, 816, 422]]<|/det|> +- we propagate uncertainty in \(v_{i}\) into the downstream computations (Subsection 1.5). + +<|ref|>text<|/ref|><|det|>[[101, 447, 895, 529]]<|/det|> +Emphasizing transcriptomic similarity Thus far, we have combined RNA velocity with transcriptomic similarity by computing a similarity- based KNN graph to restrict the set of possible transitions. To further take advantage of the information captured by the KNN graph and to increase robustness of the algorithm with respect to noisy velocity vectors, we combine the velocity based transition matrix \(P_{v}\) with a similarity based transition matrix \(P_{s}\) via + +<|ref|>equation<|/ref|><|det|>[[355, 540, 892, 558]]<|/det|> +\[P = \lambda P_{v} + (1 - \lambda)P_{s}\mathrm{~for~}\lambda \in [0,1]. \quad (4)\] + +<|ref|>text<|/ref|><|det|>[[101, 569, 895, 618]]<|/det|> +The matrix \(P_{s}\) is computed by row- normalizing the adjacency matrix \(A\) . In practical applications, we have found that using values around \(\lambda = 0.2\) increase robustness with respect to noisy velocity estimates. The matrix \(P\) is the final transition matrix estimated by the CellRank algorithm. + +<|ref|>sub_title<|/ref|><|det|>[[102, 644, 454, 661]]<|/det|> +### 1.3 Coarse-graining the Markov chain + +<|ref|>text<|/ref|><|det|>[[101, 667, 896, 846]]<|/det|> +The transition matrix \(P\) defines a Markov chain among the set of all observed cells, where each cell constitutes a microstate of the Markov chain. However, it is difficult to directly use \(P\) to interpret the cellular trajectory because \(P\) is a fine- grained, noisy representation of cell state transitions. Therefore, we seek to reduce \(P\) to its essence: macrostates representing key biological states and their transition probabilities among each other. We accomplish this using the Generalized Perron Cluster Cluster Analysis (GPCCA) \(^{22,23}\) , a method originally developed to study conformational dynamics in proteins. We adapt it to the single cell setting and utilize it to project the large transition matrix \(P\) onto a much smaller coarse- grained transition matrix \(P_{c}\) that describes transitions among a set of macrostates. A macrostate is associated with a subset \(M\) of the state space \(M \subset \Omega\) . The macrostates are defined through a so- called membership matrix \(\chi\) . Rows of \(\chi\) contain the soft assignment of cells to macrostates. + +<|ref|>text<|/ref|><|det|>[[101, 871, 895, 920]]<|/det|> +Generalized Perron Cluster Cluster Analysis (GPCCA) The aim of the GPCCA algorithm is to project the large transition matrix \(P\) onto a much smaller coarse- grained transition matrix \(P_{c}\) , which describes transitions between macrostates of the biological system \(^{22,23}\) . For the projected or + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[100, 83, 895, 116]]<|/det|> +embedded dynamics to be Markovian, we require the projection to be based on an invariant subspace of \(P\) (invariant subspace projection), i.e. a subspace \(W\) for which + +<|ref|>equation<|/ref|><|det|>[[419, 125, 892, 143]]<|/det|> +\[P^{\top}x\in W\forall x\in W. \quad (5)\] + +<|ref|>text<|/ref|><|det|>[[101, 155, 895, 220]]<|/det|> +In case of a reversible \(P\) , invariant subspaces are spanned by the eigenvectors of \(P^{24}\) . In our case however, \(P\) is non- reversible and the eigenvectors will in general be complex. Since the GPCCA algorithm can not cope with complex vectors, we rely on real invariant subspaces of the matrix \(P\) for the projection. Such subspaces are provided by the real Schur decomposition of \(P^{22,23}\) , + +<|ref|>equation<|/ref|><|det|>[[445, 230, 892, 248]]<|/det|> +\[P = QRQ^{\top}, \quad (6)\] + +<|ref|>text<|/ref|><|det|>[[101, 259, 895, 309]]<|/det|> +where \(Q\in \mathbb{R}^{N\times N}\) is orthogonal and \(R\in \mathbb{R}^{N\times N}\) is quasi- upper triangular \(^{25}\) . \(R\) has 1- by- 1 or 2- by- 2 blocks on the diagonal, where the former are given by the real eigenvalues and the latter are associated with pairs of complex conjugate eigenvalues. + +<|ref|>text<|/ref|><|det|>[[101, 334, 895, 479]]<|/det|> +Invariant subspaces of the transition matrix Columns of \(Q\) corresponding to real eigenvalues span real invariant subspaces. Columns of \(Q\) corresponding to pairs of complex conjugate eigenvalues span real invariant subspaces when kept together, but not if they are separated. Particularly, for columns \(q_{j}\) and \(q_{k}\) of \(Q\) belonging to a pair of complex conjugate eigenvalues, the space \(W_{0} = \operatorname {span}(q_{j},q_{k})\) is invariant under \(P\) , but the individual \(q_{j}\) and \(q_{k}\) are not \(^{26}\) . Depending on the constructed subspace, different dynamical properties of \(P\) will be projected onto \(P_{c}\) . Choosing Schur vectors belonging to real eigenvalues close to 1, metastabilities are recovered, while for Schur vectors with complex eigenvalues close to the unit circle, cyclic dynamics are recovered \(^{22,23}\) . Both options are available in CellRank, defaulting to the recovery of metastabilities. + +<|ref|>text<|/ref|><|det|>[[101, 504, 895, 554]]<|/det|> +Projecting the transition matrix Let \(\bar{Q}\in \mathbb{R}^{N\times n_{s}}\) be the matrix formed by selecting \(n_{s}\) columns from \(Q\) according to some criterion (metastability or cyclicity). Let \(\chi \in \mathbb{R}^{N\times n_{s}}\) be a matrix obtained via linear combinations of the columns in \(\bar{Q}\) , i.e. + +<|ref|>equation<|/ref|><|det|>[[460, 565, 892, 582]]<|/det|> +\[\chi = \bar{Q} A, \quad (7)\] + +<|ref|>text<|/ref|><|det|>[[100, 593, 895, 626]]<|/det|> +for an invertible matrix \(A\in \mathbb{R}^{n_{s}\times n_{s}}\) . We obtain the projected transition matrix via a Galerkin projection \(^{22,23}\) , + +<|ref|>equation<|/ref|><|det|>[[389, 635, 892, 655]]<|/det|> +\[P_{c} = (\chi^{\top}D\chi)^{-1}(\chi^{\top}DP\chi). \quad (8)\] + +<|ref|>text<|/ref|><|det|>[[101, 666, 895, 795]]<|/det|> +Here, the matrix \(D\) is the diagonal matrix of a weighted scalar product. The Schur vectors must be orthogonal with respect to this weighted scalar product, i.e. \(Q^{\top}DQ = I\) with the \(n_{s}\) - dimensional unit matrix \(I\) , to yield the required invariant subspace projection. The diagonal elements of \(D\) are in principle arbitrary, but a convenient choice would be the uniform distribution or some distribution of the cellular states of interest. Choosing the uniform distribution, as is the default in CellRank, would result in a indiscriminate handling (without imposing any presumptions about their distribution) of the cellular states. Note that the matrix inversion in Equation (8) is performed on a very small matrix of size \(n_{s}\times n_{s}\) . + +<|ref|>text<|/ref|><|det|>[[101, 821, 895, 903]]<|/det|> +Computing the membership vectors In principle, we could use any invertible \(A\) in Equation (7). However, we would like to interpret the rows of of \(\chi\) as membership vectors that assign cells to macrostates. For this reason, we seek a matrix \(A\) that minimizes the overlap between the membership vectors \(\chi\) , i.e. a matrix \(A\) that minimizes off- diagonal entries in \(\chi^{\top}D\chi\) . This is equivalent to maximizing + +<|ref|>equation<|/ref|><|det|>[[423, 911, 892, 930]]<|/det|> +\[\mathrm{trace}(\tilde{D}^{-1}\chi^{\top}D\chi), \quad (9)\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[101, 80, 523, 100]]<|/det|> +where \(\tilde{D}\) is chosen to row- normalize \(S = \tilde{D}^{- 1}\chi^{\top}D\chi\) , + +<|ref|>equation<|/ref|><|det|>[[299, 110, 892, 155]]<|/det|> +\[\tilde{D}^{-1} = \mathrm{diag}\left(\frac{1}{\sum_{j}(\chi^{\top}D\chi)_{1j}},\dots ,\frac{1}{\sum_{j}(\chi^{\top}D\chi)_{n_{s}j}}\right). \quad (10)\] + +<|ref|>text<|/ref|><|det|>[[101, 162, 895, 211]]<|/det|> +Choosing Schur vectors with real eigenvalues close to one, thus recovering metastability, maximizing Equation (9) can be interpreted as maximizing the metastability of the macrostates in the system. In practice, we use + +<|ref|>equation<|/ref|><|det|>[[400, 223, 892, 240]]<|/det|> +\[f_{n_{s}}(A) = n_{s} - \mathrm{trace}(S), \quad (11)\] + +<|ref|>text<|/ref|><|det|>[[101, 251, 895, 286]]<|/det|> +as our objective function, which is bounded below by zero and convex on the feasible set defined through the linear constraints \(^{24}\) . We must minimize \(f_{n_{s}}\) with respect to the constraints + +<|ref|>equation<|/ref|><|det|>[[288, 295, 892, 355]]<|/det|> +\[\begin{array}{r l} & {\sum_{j}\chi_{i j} = 1\forall i\in \{1,\dots,N\} \mathrm{~(partition~of~unity)~},}\\ & {\chi_{i j}\geq 0\forall i\in \{1,\dots,N\} ,j\in \{1,\dots,n_{s}\} \mathrm{~(positivity)~},} \end{array} \quad (13)\] + +<|ref|>text<|/ref|><|det|>[[101, 363, 895, 460]]<|/det|> +which is not a trivial task. Among the several possibilities to solve the minimization problem, a convenient choice is to perform unconstrained optimization on \(A(2:n_{s},2:n_{s})\) using a trick: to impose the constraints after each iteration step, thus transforming the (unfeasible) solution into a feasible solution \(^{24}\) . The drawback of this approach is that this routine is non- differentiable. Thus a derivative- free method like the Nelder- Mead method, as implemented in the Scipy routine scipy.optimize.fmin, should be used for the optimization. + +<|ref|>text<|/ref|><|det|>[[101, 485, 895, 566]]<|/det|> +Positivity of the projected transition matrix Note that the projected transition matrix \(P_{c}\) may have negative elements if macrostates share a large overlap. In practice, this is caused by a suboptimal number of macrostates \(n_{s}\) and can be resolved by changing that number. We may interpret \(P_{c}\) as the transition matrix of a Markov chain between the set of macrostates if it is non- negative within numerical precision. + +<|ref|>text<|/ref|><|det|>[[101, 592, 895, 624]]<|/det|> +Tuning the number of macrostates The number of macrostates \(n_{s}\) can be chosen in a number of different ways: + +<|ref|>text<|/ref|><|det|>[[128, 633, 755, 650]]<|/det|> +- using the eigengap heuristic for the real part of the eigenvalues close to one. + +<|ref|>text<|/ref|><|det|>[[128, 656, 895, 722]]<|/det|> +- define the crispness \(\xi\) of the solution as the value of \(\mathrm{trace}(\tilde{D}^{-1}\chi^{\top}D\chi) / n_{s}\) . The larger this value, the smaller the overlap between the macrostates, and in turn, the sharper or "crisper" the recovered macrostates. The crispness can be computed for different numbers of macrostates \(n_{s}\) and the number \(n_{s}\) with the largest value of \(\xi\) should be selected. + +<|ref|>text<|/ref|><|det|>[[128, 730, 895, 810]]<|/det|> +- to avoid having to solve the full problem for too many values of \(n_{s}\) , do a pre-selection using the minChi criterion: Based on an initial guess for \(A\) , compute a membership matrix \(\chi\) and calculate \(\min \mathrm{Chi} = \min_{i,j}(\chi_{ij})\) . In general, this value will be negative because the starting guess is infeasible. The closer to zero the value of minChi is, the more we can expect \(n_{s}\) to yield a crisp decomposition of the dynamics. + +<|ref|>text<|/ref|><|det|>[[128, 818, 895, 881]]<|/det|> +- combining the minChi criterion and the crispness, to avoid solving the full problem for many \(n_{s}\) , but still select the \(n_{s}\) with the crispest decomposition. This is done by first selecting an interval of potentially good numbers of macrostates \(n_{s}\) via the minChi criterion and afterwards using the crispness to select the best \(n_{s}\) from the preselected macrostate numbers. + +<|ref|>text<|/ref|><|det|>[[101, 890, 490, 906]]<|/det|> +All of the above are available through CellRank. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[101, 81, 900, 228]]<|/det|> +Scalable Python implementation of GPCCA Following the original MATLAB implementation \(^{22,23}\) , we wrote up GPCCA as a general algorithm in Python and included it in the MSMTools \(^{27}\) package, which is widely used for studying protein folding kinetics. From CellRank, we interface to MSMTools for the GPCCA algorithm. A naive implementation of the Schur decomposition would scale cubically in cell number. We alleviate this problem by using SLEPSC to compute a partial real Schur decomposition using an iterative, Krylov- subspace based algorithm that optimally exploits the sparsity structure of the transition matrix \(^{28,29}\) . Overall, this reduces the computational complexity of our algorithm to be almost linear in cell number (Fig. 5d and Supplementary Table 1). This allows CellRank to scale well to very large cell numbers. + +<|ref|>text<|/ref|><|det|>[[101, 254, 896, 400]]<|/det|> +Automatically determine terminal states We use the coarse- grained dynamics given by \(P_{c}\) to automatically identify terminal states. The idea is to look for the most stable macrostates according to the coarse- grained transition matrix \(P_{c}\) . Define the stability index (SI) of a macrostate \(m \in \{1, \ldots , n_{s}\}\) through its corresponding diagonal value in \(P_{c}\) , i.e. through its self- transition probability \(p_{c(m,m)}\) . The intuition behind this is that cells in terminal populations should have very little probability to transition to cells in other populations and should distribute most (if not all) of their probability mass to cells from the same terminal population. To identify the number of terminal states, we set a threshold on SI, i.e. we classify all states as terminal for which \(\mathrm{SI} \geq \epsilon_{\mathrm{SI}}\) with \(\epsilon_{\mathrm{SI}} = 0.96\) by default. + +<|ref|>text<|/ref|><|det|>[[101, 425, 895, 459]]<|/det|> +Automatically determine initial states To identify the initial states automatically, we introduce the coarse- grained stationary distribution (CGSD) \(\pi_{p}\) , given by + +<|ref|>equation<|/ref|><|det|>[[456, 468, 892, 488]]<|/det|> +\[\pi_{p} = \chi^{\top}\pi \quad (14)\] + +<|ref|>text<|/ref|><|det|>[[100, 498, 895, 531]]<|/det|> +where \(\pi\) is the stationary distribution of the original transition matrix \(P\) . The stationary distribution satisfies + +<|ref|>equation<|/ref|><|det|>[[346, 540, 892, 574]]<|/det|> +\[\pi^{\top}P = \pi^{\top},\pi_{i} > 0\forall i\mathrm{and}\sum_{i}\pi_{i} = 1. \quad (15)\] + +<|ref|>text<|/ref|><|det|>[[100, 583, 896, 778]]<|/det|> +In other words, the stationary distribution \(\pi\) is an invariant measure of \(P\) and can be computed by normalizing the top left eigenvector of \(P\) (corresponding to the eigenvalue 1). Under certain conditions (ergodicity, see ref. \(^{30}\) ) imposed on the Markov chain, the stationary distribution is the distribution that the process converges to, if it evolves long enough, i.e. it describes the long- term evolution of the Markov chain. In the same vein, the CGSD \(\pi_{c}\) describes the long- term evolution of the Markov chain given by the coarse- grained transition matrix \(P_{c}\) . The CGSD \(\pi_{c}\) assigns large (small) values to macrostates that the process spends a large (little) amount of time in, if it is run infinitely long. As such, we may use it to identify initial states by looking for macrostates which are assigned the smallest values in \(\pi_{c}\) . The intuition behind this is that initial states should be states that the process is unlikely to visit again once it has left them. The number of initial states is a method parameter set to one by default which can be modified by the user to detect several initial states. + +<|ref|>text<|/ref|><|det|>[[101, 804, 895, 852]]<|/det|> +Automatically determine intermediate states All remaining macrostates, i.e. macrostates which have neither been classified as terminal nor as initial, are classified as intermediate. Biologically, these correspond to intermediate, transient cell populations on the state change trajectory. + +<|ref|>sub_title<|/ref|><|det|>[[103, 878, 405, 894]]<|/det|> +### 1.4 Computing fate probabilities + +<|ref|>text<|/ref|><|det|>[[100, 902, 895, 934]]<|/det|> +Given the soft assignment of cells to macrostates by \(\chi\) and the identification of terminal states through \(P_{c}\) , we compute how likely each cell is to transition towards these terminal states. Let \(n_{t}\) + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[100, 83, 896, 308]]<|/det|> +be the number of terminal states. For the sake of clarity, we assume that we are only interested in fate probabilities towards terminal states, however, the below computations apply just as well to intermediate states, depending on the biological question. For each terminal macrostate \(t\) for \(t \in \{1, \ldots , n_t\}\) , we choose \(f\) cells which are strongly assigned to \(t\) according to \(\chi\) . That is, for terminal macrostate \(t\) , we extract the corresponding column from \(\chi\) and we calculate the terminal index set \(\mathcal{R}_t\) of cells which have the largest values in this column of \(\chi\) . If cell \(i\) is part of terminal index set \(\mathcal{R}_t\) , we assume cell \(i\) is among the \(f\) most eligible cells to characterize the terminal macrostate \(t\) in terms of gene expression. We store the indices of the remaining cells in the transient index set \(\mathcal{T}\) . The index sets \(\{\mathcal{R}_t | t \in \{1, \ldots , n_t\} \}\) and \(\mathcal{T}\) form a disjoint partition of the state space, which means they do not overlap and they cover the entire state space. For each cell \(i\) in \(\mathcal{T}\) , we would like to compute a vector of probabilities \(f_i \in \mathbb{R}^{n_t}\) which specifies how likely this cell is to transition into any of the terminal sets \(\mathcal{R}_t\) . To interpret \(f_i\) as a categorical distribution over cell fate, we require \(f_{i,t} \geq 0 \forall i \in \mathcal{T} \forall t \in \{1, \ldots , n_t\}\) and \(\sum_t f_{i,t} = 1 \forall i \in \mathcal{T}\) . We accumulate the \(f_i\) column- wise in the fate matrix \(F \in R^{N \times n_t}\) . + +<|ref|>text<|/ref|><|det|>[[101, 334, 896, 464]]<|/det|> +Absorption probabilities reveal cell fates We could approximate the \(f_{i}\) based on sampling: initialise a random walk on the Markov chain in cell \(i\) . Continue to simulate the random walk until any cell from a terminal set \(\mathcal{R}_t\) is reached. Record \(t\) and repeat this many times. Finally, count how often random walks initialized in cell \(i\) terminated in any of the terminal index sets \(\mathcal{R}_t\) . In the limit of repeating this infinitely many times, the normalized frequencies over reaching either terminal set will be equal to the desired fate probabilities for cell \(i\) , under reasonable assumptions on the Markov chain (irreducibility). Luckily, we do not have to do this in a sampling based approach, we can exploit the fact that a closed form solution exists for this problem: absorption probabilities. + +<|ref|>text<|/ref|><|det|>[[101, 488, 896, 746]]<|/det|> +Computing absorption probabilities Key to the concept of absorption probabilities are recurrent and transient classes, which we will define here for the present case of a finite and discrete state space. Let \(i \in \Omega\) and \(j \in \Omega\) be two states of the Markov chain. In our case, \(i\) and \(j\) are cells. We say that \(i\) is accessible from \(j\) , if and only if there exists a path from \(j\) to \(i\) according to the transition matrix \(P\) . A path is a sequence of transitions which has non- zero transition probability. Further, \(i\) and \(j\) communicate, if and only if \(i\) is accessible from \(j\) and \(j\) is also accessible from \(i\) . Communication defines an equivalence relation on the state space \(\Omega\) , i.e. it is a reflexive, symmetric and transitive relation between two states \(^{30}\) . It follows that the state space \(\Omega\) can be partitioned into its communication classes \(\{\mathcal{C}_1, \ldots , \mathcal{C}_k\}\) . The communication classes are mutually disjoint non- empty and their union is \(\Omega\) . In other words: any two states from the same communication class communicate, states from different communication classes never communicate. We call a communication class \(\mathcal{C}_j\) closed if the submatrix of \(P\) restricted to \(\mathcal{C}_j\) has all rows sum to one. Intuitively, if \(\mathcal{C}_j\) is closed, then a random walk which enters \(\mathcal{C}_j\) will never leave it again. Closed communication classes are also called recurrent classes. If a communication class is not recurrent, we call it transient. In Theorem 1, we reproduce the statement of Thm. 28 in ref. \(^{30}\) to compute absorption probabilities towards states that belong to recurrent classes on the Markov chain. + +<|ref|>text<|/ref|><|det|>[[101, 753, 896, 787]]<|/det|> +Theorem 1 (Absorption Probabilities) Consider a MC with transition matrix \(P \in \mathbb{R}^{N \times N}\) . We may rewrite \(P\) as follows: + +<|ref|>equation<|/ref|><|det|>[[459, 793, 893, 829]]<|/det|> +\[\begin{array}{r}\left[ \begin{array}{cc}\tilde{P} & 0\\ S & Q \end{array} \right], \end{array} \quad (16)\] + +<|ref|>text<|/ref|><|det|>[[101, 839, 896, 903]]<|/det|> +where \(\tilde{P}\) and \(Q\) are restrictions of \(P\) to recurrent and transient states, respectively, and \(S\) is the restriction of \(P\) to transitions from transient to recurrent states. The upper right 0 is due to the fact that there are no transitions back from recurrent to transient states. Define the matrix \(M \in \mathbb{R}^{N \times N}\) via + +<|ref|>equation<|/ref|><|det|>[[430, 911, 893, 930]]<|/det|> +\[M = (I - Q)^{-1}. \quad (17)\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[102, 83, 896, 131]]<|/det|> +Then, the \(ij\) - th entry of \(M\) describes the expected number of visits of the process to state \(j\) before absorption, conditional on the process being initialised in state \(i\) . \(M\) is often referred to as the fundamental matrix of the MC. Further, the matrix + +<|ref|>equation<|/ref|><|det|>[[427, 142, 892, 160]]<|/det|> +\[A = (I - Q)^{-1}S, \quad (18)\] + +<|ref|>text<|/ref|><|det|>[[101, 172, 895, 204]]<|/det|> +in the \(ij\) - th entry contains the probability of \(j\) being the first recurrent state reached by the MC, given that it was started in \(i\) . + +<|ref|>text<|/ref|><|det|>[[101, 212, 895, 310]]<|/det|> +For a proof, see See Thm. 26 in ref. \(^{30}\) . To compute fate probabilities towards the terminal index sets \(\mathcal{R}_t\) defined above, we approximate these as recurrent classes, i.e. we remove any outgoing edges from these sets. We then apply Theorem 1, which, for each cell \(i \in \mathcal{T}\) yields absorption probabilities towards each of the \(f\) cells in each of the \(n_t\) recurrent index sets. We aggregate these to yield absorption probabilities towards the recurrent index sets themselves by summing up absorption probabilities towards individual cells in these sets. + +<|ref|>text<|/ref|><|det|>[[101, 336, 895, 433]]<|/det|> +CellRank provides an efficient implementation to compute absorption probabilities A naive implementation of absorption probabilities scales cubically in the number of transient cells due to the matrix inversion in Equation (18). The number of transient cells is smaller than the total cell number only by a small constant, so the naive approach can be considered cubic in cell number. This will inevitably fail for large cell numbers. We alleviate this by re- writing Equation (18) as a linear problem, + +<|ref|>equation<|/ref|><|det|>[[437, 444, 892, 461]]<|/det|> +\[(I - Q)A = S. \quad (19)\] + +<|ref|>text<|/ref|><|det|>[[101, 472, 895, 555]]<|/det|> +Note that \(Q\) is very sparse as it describes transitions between nearest neighbors. Per row, \(Q\) has approximately \(K\) entries. To exploit the sparsity, iterative solvers are very appealing as their per- iteration cost applied to this problem is linear in cell number and in the number of nearest neighbors. To apply an iterative solver, we must however re- write Equation (19) such that the right hand side is vector valued, + +<|ref|>equation<|/ref|><|det|>[[336, 566, 892, 584]]<|/det|> +\[(I - Q)a_{1} = s_{1},\ldots ,(I - Q)a_{f n_{t}} = s_{f n_{t}}, \quad (20)\] + +<|ref|>text<|/ref|><|det|>[[101, 594, 895, 692]]<|/det|> +where \(f n_t\) is the total number of cells which belong to approximately recurrent classes. To solve these individual problems, we use the iterative GMRES \(^{31}\) algorithm which efficiently exploits sparsity. For optimal performance, we use the PETSc implementation, which makes use of efficient message passing and other practical performance enhancements. Lastly, we parallelize solving the \(f n_t\) linear problems. All of these tricks taken together allow us to compute absorption probabilities quickly even for large cell numbers (Fig. 5d and Supplementary Table 1). + +<|ref|>sub_title<|/ref|><|det|>[[102, 717, 442, 734]]<|/det|> +### 1.5 Propagating velocity uncertainty + +<|ref|>text<|/ref|><|det|>[[101, 741, 895, 935]]<|/det|> +So far, we have assumed that individual velocity vectors are deterministic, i.e. they have no measurement error. However, this is not correct because RNA velocity is estimated on the basis of spliced and unspliced gene counts, which are noisy quantities. Hence, the velocity vectors \(v_i\) themselves should be treated as random variables which follows a certain distribution. Ultimately, our aim is to propagate the distribution in \(v_i\) into our final quantities of interest, i.e. state assignments and fate probabilities. However, this is difficult as these final quantities of interest depend on \(v_i\) in non- analytical ways, i.e. we cannot write down a closed- form equation which relates the final quantities to \(v_i\) . A possible solution to this is to use a Monte Carlo scheme where we draw velocity vectors, compute final quantities based on the draw and repeat this many times. In the limit of infinitely many draws, this will give us the distribution over final quantities, given the distribution in \(v_i\) . However, this has the disadvantage that we need to repeat our computation many times, which will get prohibitively expensive for large datasets. To get around this problem, and to allow CellRank to + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[101, 83, 895, 148]]<|/det|> +scale to large datasets, we construct an analytical approximation to the Monte Carlo based scheme. This analytical approximation will only have to be evaluated once and we can omit the sampling. We show in a practical example that the analytical approximation gives very similar results to the sampling based scheme and improves over a deterministic approach by a large margin. + +<|ref|>text<|/ref|><|det|>[[101, 173, 896, 320]]<|/det|> +Modeling the distribution over velocity vectors Before we can propagate uncertainty, we need to describe the distribution over velocity vectors, i.e. we need to model the uncertainty present in the velocity vectors which are estimated by scVelo \(^9\) or velocity \(^1\) . Ideally, we would like these packages themselves to model uncertainty in the raw spliced and unspliced counts and to propagate this into a distribution over velocity vectors. However, as that is currently not being done, we will make an assumption about their distribution and use the KNN graph to fine- tune expectation and variance by considering neighboring velocity vectors. To ease notation and to illustrate the core ideas, we will drop the subscript \(i\) in this section and just focus on one fixed cell and it's velocity vector \(v\) . Let's assume that \(v\) follows a multivariate normal (MVN) distribution, + +<|ref|>equation<|/ref|><|det|>[[439, 330, 891, 348]]<|/det|> +\[v\sim \mathcal{N}(\mu ,\Sigma_{v}), \quad (21)\] + +<|ref|>text<|/ref|><|det|>[[101, 358, 896, 490]]<|/det|> +with mean vector \(\mu \in \mathbb{R}^{G}\) and covariance matrix \(\Sigma_{v}\in \mathbb{R}^{G\times G}\) . The MVN is a reasonable choice here as velocities can be both positive and negative and for most genes, as we expect to see both up- and down- regulation, velocity values will be approximately symmetric around their expected value. Let's further assume the covariance matrix to be diagonal, i.e. gene- wise velocities are independent. This is a reasonable assumption to make as gene- wise velocities in both velocity \(^1\) and scvelo \(^9\) are computed independently. To compute values for \(\mu\) and \(\Sigma_{v}\) , consider velocity vector \(v\) and its \(K\) nearest neighbors. To estimate \(\mu\) and the diagonal elements of \(\Sigma_{v}\) , we compute first and second order moments over the velocity vectors of these neighboring cells. + +<|ref|>text<|/ref|><|det|>[[101, 514, 895, 580]]<|/det|> +Propagating uncertainty into state assignments and fate probabilities We seek to approximate the expected value of the final quantities of interest (state assignments and fate probabilities), given the distribution in the velocity vectors. Let \(q\) be a final quantity of interest. There are two major steps involved in computing \(q\) , + +<|ref|>equation<|/ref|><|det|>[[448, 592, 891, 608]]<|/det|> +\[v\rightarrow T\rightarrow q, \quad (22)\] + +<|ref|>text<|/ref|><|det|>[[101, 620, 896, 734]]<|/det|> +where \(v\) stands for our inputs, i.e. the velocity vectors, and \(T\) is the transition matrix defining the Markov chain. To get from \(v\) to \(T\) , we evaluate an analytical function which computes correlations and applies a softmax function. We can approximate this first part of the mapping with a Taylor series, which allows us to analytically propagate the distribution in \(v\) into \(T\) . For the second part of the mapping, we use the expected transition matrix to compute \(q\) . This yields an approximation to the expectation of the final quantity we can then compare with the approximation we obtain from a Monte Carlo scheme, which we treat as our ground truth. + +<|ref|>text<|/ref|><|det|>[[101, 759, 896, 825]]<|/det|> +Approximating the expected transition matrix In the first step, we compute the expected value of the transition matrix, given the distribution of the velocity vectors. Given a particular draw \(v\) from the distribution in Equation (21) and a set of state- change vectors \(s_{k}\) , we compute a vector of probabilities \(p\) , which lives on a \(K\) - simplex in \(\mathbb{R}^{K}\) . Let's denote the mapping from \(v\) to \(p\) by \(h\) , + +<|ref|>equation<|/ref|><|det|>[[440, 833, 891, 875]]<|/det|> +\[\begin{array}{l}{h:\mathbb{R}^{G}\to R^{K}}\\ {v\mapsto h(v) = p.} \end{array} \quad (23)\] + +<|ref|>text<|/ref|><|det|>[[100, 885, 822, 902]]<|/det|> +We can then formulate our problem as finding the expectation of \(h\) when applied to \(v\) , i.e. + +<|ref|>equation<|/ref|><|det|>[[426, 914, 891, 931]]<|/det|> +\[\operatorname {E}[h(v)]_{v\sim \mathcal{N}(\mu ,\Sigma_{v})}. \quad (24)\] + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[100, 83, 758, 100]]<|/det|> +To approximate this, expand the \(i\) - th component of \(h\) in a Taylor- series around \(\mu\) , + +<|ref|>equation<|/ref|><|det|>[[204, 110, 893, 139]]<|/det|> +\[h_{i}(v) = h_{i}(\mu) + \nabla_{v}^{\top}h_{i}(v)|_{\mu}(v - \mu) + \frac{1}{2} (v - \mu)^{\top}\nabla_{v}^{2}h_{i}(v)|_{\mu}(v - \mu) + \mathcal{O}(v^{3}). \quad (25)\] + +<|ref|>text<|/ref|><|det|>[[100, 149, 452, 165]]<|/det|> +Define the Hessian matrix of \(h_{i}\) at \(v = \mu\) as + +<|ref|>equation<|/ref|><|det|>[[425, 175, 893, 195]]<|/det|> +\[H^{(i)} = \nabla_{v}^{2}h_{i}(v)|_{\mu}. \quad (26)\] + +<|ref|>text<|/ref|><|det|>[[100, 206, 593, 223]]<|/det|> +Taking the expectation of \(h_{i}\) and using the Taylor- expansion, + +<|ref|>equation<|/ref|><|det|>[[318, 232, 893, 263]]<|/det|> +\[\operatorname {E}[h_{i}(v)]\approx h_{i}(\mu) + \frac{1}{2}\operatorname {E}[(v - \mu)^{\top}H^{(i)}(v - \mu)]. \quad (27)\] + +<|ref|>text<|/ref|><|det|>[[100, 272, 895, 306]]<|/det|> +Note that the first order term cancels as \(\operatorname {E}[v - \mu ] = 0\) . The second order term can be further simplified by explicitly writing out the matrix multiplication, + +<|ref|>equation<|/ref|><|det|>[[274, 315, 893, 360]]<|/det|> +\[\operatorname {E}[(v - \mu)^{\top}H^{(i)}(v - \mu)] = \sum_{j,k = 1}^{G}H_{j,k}^{(i)}\operatorname {E}[(v - \mu)_{j}(v - \mu)_{k}], \quad (28)\] + +<|ref|>text<|/ref|><|det|>[[100, 370, 895, 436]]<|/det|> +where we took the expectation inside the sum and the matrix elements outside the expectation as it does not involve \(v\) . For \(j \neq i\) , the two terms inside the expectation involving \(v\) are independent given our distributional assumptions on \(v\) and the expectation can be taken separately. Using again the fact that \(\operatorname {E}[v - \mu ] = 0\) , the sum equals zero for \(j \neq i\) . It follows + +<|ref|>equation<|/ref|><|det|>[[211, 446, 893, 492]]<|/det|> +\[\sum_{j,k = 1}^{G}H_{j,k}^{(i)}\operatorname {E}[(v - \mu)_{j}(v - \mu)_{k}] = \sum_{j}H_{j,j}^{(i)}\operatorname {E}[(v_{j} - \mu_{j})^{2}] = \sum_{j}H_{j,j}^{(i)}\operatorname {var}[v_{j}]. \quad (29)\] + +<|ref|>text<|/ref|><|det|>[[100, 500, 895, 533]]<|/det|> +To summarize, our second order approximation to the transition probabilities given the distribution in \(v\) reads + +<|ref|>equation<|/ref|><|det|>[[348, 540, 893, 579]]<|/det|> +\[\operatorname {E}[h_{i}(v)]\approx h_{i}(\mu) + \frac{1}{2}\sum_{j}H_{j,j}^{(i)}\operatorname {var}[v_{j}]. \quad (30)\] + +<|ref|>text<|/ref|><|det|>[[100, 590, 895, 737]]<|/det|> +We use automatic differentiation as implemented in JAX \(^{32}\) to compute the Hessian matrices \(H^{(i)}\) , which ensures they are highly accurate and can be computes in a scalable manner. Further, because we do not hard- code the derivatives, our approach is highly flexible to future changes in the way we compute transition probabilities. If for example it turns out at a later point that an alternative metric works better than Pearson correlation, this is automatically taken care of in the propagation of uncertainties and no changes need to be made, apart from changing the forwards function which computes the transition probabilities themselves. The above procedure can be repeated for all components \(i\) and for all cells to yield the second order approximation to the expected transition matrix \(T\) , given the distribution over each velocity vector. + +<|ref|>text<|/ref|><|det|>[[100, 762, 895, 828]]<|/det|> +Approximating the expected final quantities To arrive at the final quantities of interest, i.e. state assignments and absorption probabilities, we use the expected transition matrix and proceed as in the deterministic case. We validate that this approximation gives very similar results to a fully stochastic approach based on Monte Carlo sampling (Supplementary Fig. 14a,b). + +<|ref|>sub_title<|/ref|><|det|>[[102, 853, 430, 870]]<|/det|> +### 1.6 The CellRank software package + +<|ref|>text<|/ref|><|det|>[[100, 878, 608, 895]]<|/det|> +The CellRank software package implements two main modules: + +<|ref|>text<|/ref|><|det|>[[127, 902, 895, 935]]<|/det|> +- kernels are classes that provide functionality to compute transition matrices based on (directed) single cell data. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[128, 84, 895, 116]]<|/det|> +- estimators are classes that implement algorithms to do inference based on kernels. For example, estimators compute macrostates and fate probabilities. + +<|ref|>text<|/ref|><|det|>[[101, 123, 895, 189]]<|/det|> +This modular and object oriented design allows CellRank to be extended easily into two directions. On the one hand, including more kernels to take into account further means of directional single cell data such as metabolic labeling or experimental time. On the other hand, including more estimators to learn new abstractions of cellular dynamics. The kernel module currently implements a + +<|ref|>text<|/ref|><|det|>[[128, 196, 895, 230]]<|/det|> +- VelocityKernel which computes a transition matrix on the basis of a KNN graph and RNA velocity information. + +<|ref|>text<|/ref|><|det|>[[128, 237, 895, 270]]<|/det|> +- PalantirKernel which mimicks the original routine outlined in the Palantir8 paper to compute a directed transition matrix on the basis of a KNN graph and a pseudotime. + +<|ref|>text<|/ref|><|det|>[[128, 277, 895, 326]]<|/det|> +- ConnectivityKernel which takes the adjacency matrix underlying the KNN graph and row-normalizes it to obtain a valid transition matrix. This is essentially the transition matrix used in the DPT10 algorithm. + +<|ref|>text<|/ref|><|det|>[[128, 333, 895, 366]]<|/det|> +- PrecomputedKernel which accepts any pre-computed transition matrix and allows for easy interfacing with the CellRank software. + +<|ref|>text<|/ref|><|det|>[[101, 372, 895, 439]]<|/det|> +All kernel classes are derived from a base kernel class which implements density normalization as implemented in ref.10. Instances of kernel classes can be combined by simply adding them up using the + operator, potentially including weights. A typical code snippet to compute a transition matrix will look like this: + +<|ref|>text<|/ref|><|det|>[[105, 445, 764, 462]]<|/det|> +from cellrank.tools.kernels import VelocityKernel, ConnectivityKernel + +<|ref|>text<|/ref|><|det|>[[105, 478, 660, 510]]<|/det|> +vk = VelocityKernel(adata).compute_transition_matrix() ck = ConnectivityKernel(adata).compute_transition_matrix() + +<|ref|>text<|/ref|><|det|>[[105, 526, 424, 543]]<|/det|> +combined_kernel = 0.9*vk + 0.1*ck + +<|ref|>text<|/ref|><|det|>[[101, 558, 472, 574]]<|/det|> +The estimator module currently implements a + +<|ref|>text<|/ref|><|det|>[[128, 582, 895, 647]]<|/det|> +- CFLARE estimator. CFLARE stands for Clustering and Filtering of Left and Right Eigenvectors. This estimator computes terminal states directly by filtering cells in the top left eigenvectors and clustering them in the top right eigenvectors, thereby combining ideas of spectral clustering and stationary distributions. + +<|ref|>text<|/ref|><|det|>[[128, 654, 480, 670]]<|/det|> +- GPCCA estimator. The GPCCA estimator. + +<|ref|>text<|/ref|><|det|>[[101, 678, 895, 727]]<|/det|> +All estimator classes are derived from a base estimator class which allows to compute fate probabilities, regardless of how terminal/intermediate states have been computed. A typical code snippet to compute macrostates and fate probabilities will look like this: + +<|ref|>text<|/ref|><|det|>[[105, 735, 518, 752]]<|/det|> +from cellrank.tools.estimators import GPCCA + +<|ref|>text<|/ref|><|det|>[[105, 770, 393, 802]]<|/det|> +# initialize the estimator gpcca = GPCCA(combined_kernel) + +<|ref|>text<|/ref|><|det|>[[105, 818, 727, 866]]<|/det|> +# compute macrostates and identify the terminal states among them gpcca.compute_macrostates() gpcca.compute_terminal_states() + +<|ref|>text<|/ref|><|det|>[[105, 883, 488, 915]]<|/det|> +# compute fate probabilities gpcca.compute_absorption_probabilities() + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[102, 83, 895, 164]]<|/det|> +Both kernels and estimators implement a number of plotting functions to conveniently inspect results. We designed CellRank to be highly scalable to ever increasing cell numbers, widely applicable and extendable to problems in single cell dynamical inference, user friendly with tutorials and comprehensive documentation and robust with \(88\%\) code coverage. CellRank is open source, fully integrated with SCANPY and scVelo and freely available at https://cellrank.org. + +<|ref|>sub_title<|/ref|><|det|>[[102, 190, 480, 207]]<|/det|> +## 2 Computing a directed PAGA graph + +<|ref|>text<|/ref|><|det|>[[102, 214, 895, 328]]<|/det|> +Partition- based graph abstraction (PAGA) \(^{33}\) provides an interpretable graph- like connectivity map of the data manifold. It is obtained by associating a node with each manifold partition (e.g. cell type) and connecting each node by weighted edges that represent a statistical measure of connectivity between the partitions. The model considers groups/nodes as connected if their number of interedges exceeds what would have been expected under random assignment. The connection strength can be interpreted as confidence in the presence of an actual connection and allows discarding spurious, noise- related connections. + +<|ref|>text<|/ref|><|det|>[[102, 335, 895, 431]]<|/det|> +Here, we extend PAGA by directing the edges as to reflect the RNA velocity vector field rather than transcriptome similarity. The connectivity strengths are defined based on the velocity graph. That is, for each cell correlations between the cell's velocity vector and its potential, cell- to- cell transitions are computed (Subsection 1.2). Inter- edges are considered whose correlation passes a certain threshold (default: 0.1). The number of inter- edges are then tested against random assignment for significance. + +<|ref|>text<|/ref|><|det|>[[102, 439, 895, 600]]<|/det|> +To further constrain the single cell graph, compute a gene- shared latent time using scVelo \(^{34}\) . In short, this aggregates the per- gene time assignments computed in scVelo's dynamical model to a global scale which faithfully approximates a single- cells internal clock. Once we have computed the initial states using CellRank, we can use these as a prior for latent time to force it to start in this state. All of latent time, initial and terminal states can in turn be used as a prior to regularize the directed graph. At single- cell level, we use latent time as a constraint to prune the cell- to- cell transition edges to those that match the ordering of cells given by latent time. For the initial and terminal states, the edges are further constrained to only retain those cell- to- cell transitions that constitute outgoing flows for cells in initial cellular populations, and to incoming flows for cells in terminal populations. + +<|ref|>text<|/ref|><|det|>[[102, 608, 895, 673]]<|/det|> +Finally, a minimum spanning is constructed for the directed abstracted graph. It is obtained by pruning node- to- node edges such that only the most confident path from one node to another is retained. If there are multiple paths to reach a particular node, only the path with the highest confidence is kept. + +<|ref|>sub_title<|/ref|><|det|>[[102, 699, 613, 716]]<|/det|> +## 3 Computing gene expression trends along lineages + +<|ref|>text<|/ref|><|det|>[[102, 725, 895, 885]]<|/det|> +CellRank computes fate probabilities which specify how likely each individual cell is to transition towards each identified terminal state (Subsection 1.4). Combined with any pseudo- temporal measure like \(\mathrm{DPT}^{10}\) , scVelo's latent time \(^{2}\) or Palantir's pseudotime \(^{35}\) , this allows us to compute and to compare gene expression trends towards specific terminal populations. In contrast to other methods, we do not partition the set of all cells into clusters and define lineage each lineage via an ordered set of clusters. Instead, we use all cells to fit each lineage but we weigh each cell according to its fate probability, our measure of lineage membership. This means that for cells uncommitted between two or more fates, we allow them to contribute to each one of these, weighted by the fate probabilities. For cells committed towards any particular fate, their fate probabilities towards the remaining fates will be zero or almost zero which naturally excludes them when fitting these other lineages. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[101, 83, 895, 196]]<|/det|> +Imputing gene expression recovers trends from noisy data To improve the robustness and resolution of gene expression trends, we adapt two strategies. First, we use imputed gene expression values and second, we fit Generalized Additive Models (GAMs). For gene expression imputation, we use MAGIC \(^{36}\) by default, however, any imputed gene expression matrix can be supplied. MAGIC is based on KNN imputation and makes use of the covariance structure among neighboring cells to estimate expression levels for each gene. The KNN graph is computed globally, based on the expression values of all genes and not just the one we are currently considering. + +<|ref|>text<|/ref|><|det|>[[101, 222, 895, 320]]<|/det|> +Generalized Additive Models (GAMs) robustly fit gene expression values While sliding window approaches are known to be sensitive to local density differences and only take into account the current gene when determining gene expression trends, we fit GAMs to gene expression values which have been imputed borrowing information from neighboring cells via a KNN graph. Using GAMs allows us to flexibly model many different kinds of gene trends in a robust and scalable manner. We fit the gene expression trend for branch \(t\) in gene \(g\) via + +<|ref|>equation<|/ref|><|det|>[[383, 331, 892, 349]]<|/det|> +\[y_{gi} = \beta_0 + f(\tau_i)\forall i:F_{ib} > 0, \quad (31)\] + +<|ref|>text<|/ref|><|det|>[[101, 360, 895, 409]]<|/det|> +where \(y_{gi}\) is gene expression of gene \(g\) in cell \(i\) , \(\tau_i\) is the pseudotemporal value of cell \(i\) and \(F\) is the fate matrix of Subsection 1.4. By default, we use cubic splices for the smoothing functions \(f\) as these have been shown to be effective in capturing non- linear relationships in trends \(^{37}\) . + +<|ref|>text<|/ref|><|det|>[[101, 416, 895, 466]]<|/det|> +To visualize the smooth trend, we select 200 equally spaced testing points along pseudotime and we predict gene expression at each of them using the fitted model of Equation (31). To estimate uncertainty along the trend, we use the standard deviation of the residuals of the fit, given by + +<|ref|>equation<|/ref|><|det|>[[289, 475, 892, 521]]<|/det|> +\[\sigma_{\hat{y}_p} = \sqrt{\frac{\sum_{j = 1}^n(y_j - \hat{y}_j)^2}{n - 2}}\sqrt{1 + \frac{1}{n} + \frac{(\tau_p - \bar{\tau})^2}{\sum_{j = 1}^n(\tau_j - \bar{\tau})^2}}, \quad (32)\] + +<|ref|>text<|/ref|><|det|>[[101, 531, 895, 612]]<|/det|> +where \(\hat{y}_p\) denotes predicted gene expression at test point \(p\) , \(\bar{\tau}\) denotes average pseudo- time across all cells and \(n\) is the number of test points \(^{38}\) . For the fitting of Equation (31), we provide interfaces to both the R package mgcv \(^{39}\) as well as the Python package pyGAM \(^{40}\) . We parallelize gene fitting to scale well in the number of genes, which is important when plotting heatmaps summarizing many gene expression trends. + +<|ref|>text<|/ref|><|det|>[[101, 638, 895, 799]]<|/det|> +Visualizing gene expression trends for the pancreas example For CellRank's gene expression trends of lineage- associated genes along the alpha, beta, epsilon and delta fates, we used Palantir's pseudotime \(^{35}\) , MAGIC imputed data \(^{36}\) and the pyGAM \(^{40}\) package to fit GAMs. We used default values to fit the splines, i.e. we place 10 knots along pseudotime and we use cubic splines. For the delta lineage, fate probabilities among early cells were very low (0.01 average fate probability among Ngn3 high EP cells, see Fig. 2e). This reflects the small size of the delta population (70 cells or 3% of the data, see Supplementary Fig. 10a,b) as well as the fact that delta cells are produced mostly at later stages in pancreatic development \(^{41}\) . To still be able to reliably fit gene expression of early cells along the delta lineage, we thresholded weights at 0.05, i.e. weights smaller than this value were clipped to this value. This was done only for the fitting of gene expression trends. + +<|ref|>sub_title<|/ref|><|det|>[[101, 825, 460, 842]]<|/det|> +## 4 Clustering gene expression trends + +<|ref|>text<|/ref|><|det|>[[101, 850, 895, 930]]<|/det|> +CellRank allows gene expression trends along a particular lineage to be clustered, thus recovering the major patterns of regulation towards a specific terminal state like (transient) up- or down- regulation. For the set of genes we are interested in, we recover their regulation along a specific lineage by fitting GAMs in pseudotime where we supply fate probabilites as cell- level lineage weights (Section 3). In the next step, we cluster the GAM- smoothed gene expression trends. For this, we z- transform + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[101, 83, 895, 213]]<|/det|> +expression values and we compute a PCA representation of the trends. By default, we use 50 PCs. We then compute a KNN graph in PC space as outlined in Subsection 1.2 and we cluster the KNN graph using the louvain \(^{42}\) or leiden \(^{43}\) algorithms. For each recovered cluster, we compute its mean and standard deviation (point- wise, for all testing points that were used for smoothing) and visualize them, together with the individual, smoothed trends per cluster. As gene- trend fitting is efficiently parallelized in CellRank, such an analysis can be performed in an unbiased fashion for large gene sets. For 10k genes, the run- time is about 6 min on a 2019 Mac book pro with 2,8 GHz Intel Core i7 processor and 16 GB RAM. + +<|ref|>text<|/ref|><|det|>[[101, 239, 895, 319]]<|/det|> +Clustering gene expression trends towards the delta fate To cluster gene expression trends towards the delta fate in Fig. 3e, all genes which were expressed in at least 10 cells were included (12,987 genes). Smooth gene expression trends along the delta lineage were determined using Palantir's pseudotime \(^{35}\) . We used \(K = 30\) nearest neighbors for the gene- trend KNN graph and the louvain algorithm with resolution parameter set to 0.2 to avoid over- clustering the trends. + +<|ref|>sub_title<|/ref|><|det|>[[102, 345, 455, 362]]<|/det|> +## 5 Uncovering putative driver genes + +<|ref|>text<|/ref|><|det|>[[101, 369, 895, 468]]<|/det|> +To find genes which are expressed at high levels in cells that are biased towards a particular fate, we compute Person's correlation between expression levels of a set of genes and fate probabilities. We sort genes according to their correlation values and consider high- scoring genes as candidate driver genes. By default, we consider all genes which have passed pre- processing gene filtering thresholds. The computation of correlation values can be restricted to a set of pre- defined clusters if one is interested in driver genes which act in a certain region of the phenotypic manifold. + +<|ref|>text<|/ref|><|det|>[[101, 494, 895, 574]]<|/det|> +Uncovering putative driver genes for delta development To uncover putative driver genes towards the delta fate in Fig. 3d,e, we considered 12,987 genes which were expressed in at least 10 cells. We computed correlation of total- count normalized, log transformed gene expression values with the probability of becoming a delta cell. We restricted correlation computation to the \(\mathrm{Fev + }\) cluster, where we expected the fate decision towards delta to occur. + +<|ref|>sub_title<|/ref|><|det|>[[102, 599, 323, 616]]<|/det|> +## 6 Robustness analysis + +<|ref|>text<|/ref|><|det|>[[101, 624, 895, 657]]<|/det|> +We were interested in evaluating how much CellRank's fate probabilities change in response to changes in the following key pre- processing parameters: + +<|ref|>text<|/ref|><|det|>[[128, 664, 895, 787]]<|/det|> +- the number of neighbors \(K\) used for KNN graph construction (Subsection 1.2)- scVelo's gene-filtering parameter min_shared_counts which determines how many counts a gene must have in both spliced and unspliced layers- scVelo's gene filtering parameter n_top_genes which determines the number of most highly variable genes used for the velocity computation- the number of principal components n_pcs used for KNN graph construction (Subsection 1.2) + +<|ref|>text<|/ref|><|det|>[[101, 794, 895, 858]]<|/det|> +In addition to the 4 key pre- processing parameters, we were interested to see how much CellRank's results change when we randomly sub- sample the number of cells to 90% of the original cell number and when we vary the number of macrostates. We used the pancreas example \(^{16}\) in all of the following comparisons. + +<|ref|>text<|/ref|><|det|>[[100, 884, 895, 916]]<|/det|> +Robustness with respect to key pre- processing parameters To evaluate robustness with respect to changes in the pre- processing parameters, we varied one parameter at a time (keeping the others + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[101, 83, 895, 325]]<|/det|> +fixed), computed macrostates and fate probabilities towards them. We then compared the fate probabilities for different values of the parameter by computing pairwise Pearson correlation among all possible pairs of values we used for the parameter. We did this separately for each lineage, i.e. for the alpha, beta, epsilon and delta lineages. For each lineage, we recorded the median and minimum correlation achieved across all the different comparisons. We always computed enough macrostates so that the alpha, beta, epsilon and delta states were included. Naturally, the precise location of the terminal states changed slightly across parameter combinations. For this reason, the correlation values we recorded reflect robustness of the entire CellRank workflow, including both the computation of terminal states as well as fate probabilities. In a separate comparison, we were interested in evaluating the robustness of just the last step of the CellRank algorithm, i.e. the computation of fate probabilities. For this, we kept the terminal states fixed across parameter variations and proceeded as above otherwise, computing pairwise Pearson correlations among fate probabilities per lineage across all parameter value combinations. Furthermore, we were interested to see whether CellRank's robustness changes when we propagate uncertainty. For this, we repeated all the aforementioned computations using our analytical approximation to propagate uncertainty. + +<|ref|>text<|/ref|><|det|>[[101, 351, 895, 448]]<|/det|> +Robustness with respect to random sub- sampling of cells We subsampled the data to \(90\%\) of cells, computed macrostates and fate probabilities towards the alpha, beta, epsilon and delta states. We repeated this 20 times, recorded all computed fate probabilities and compared them pairwise per lineage using Pearson's correlation for all possible pairs of random draws. As in the above evaluation for the key pre- processing parameters, we recorded minimum and median correlation per lineage across all pairs and we repeated this for fixed terminal states and for propagated uncertainty. + +<|ref|>text<|/ref|><|det|>[[101, 474, 895, 522]]<|/det|> +Robustness with respect to the number of macrostates To evaluate sensitivity with respect to this parameter, we varied the number of macrostates between 10 and 16 and confirmed that within this range, the key terminal and initial states exist and remain in the same location. + +<|ref|>sub_title<|/ref|><|det|>[[103, 548, 353, 564]]<|/det|> +## 7 Pancreas data example + +<|ref|>text<|/ref|><|det|>[[101, 573, 895, 702]]<|/det|> +We used a scRNA- seq time- series data set comprising embryonic days \(12.5 - 15.5\) of pancreatic development in mice assayed using 10x Genomics \(^{16}\) . We restricted the data to the last time point (E15.5) and to the Ngn3 low EP, Ngn3 high EP, Fev+ and endocrine clusters to focus on the late stages of endocrinogenesis where all of alpha, beta, epsilon and delta fates are present. We further filtered out cycling cells to amplify the differentiation signal. Our final subset contained 2531 cells. We kept the original cluster annotations which were available on a coarse level and on a fine level. On the fine level, the Fev+ cluster was sub- clustered into different populations which are biased towards different endocrine fates (Supplementary Fig. 10c). + +<|ref|>text<|/ref|><|det|>[[101, 728, 895, 872]]<|/det|> +Data pre- processing and velocity computation For the following processing, we used scVelo \(^{9}\) and SCANPY \(^{13}\) with mostly default parameters. Loom files containing raw spliced and unspliced counts were obtained by running the velocyto \(^{1}\) command- line pipeline. We filtered genes to be expressed in at least 10 cells and to have at least 20 counts in both spliced and unspliced layers. We further normalized by total counts per cell, log transformed the data and kept the top 2000 highly variable genes. We then computed a PCA representation of the data and used the top 30 PCs to compute a KNN graph with \(K = 30\) nearest neighbors. For velocity computation, we used scVelo's dynamical model of splicing kinetics. We evaluate robustness of CellRank's results to changes in these pre- processing parameters (Section 6). + +<|ref|>text<|/ref|><|det|>[[101, 898, 895, 930]]<|/det|> +Embedding computation We used the KNN graph to compute a PAGA \(^{7}\) representation of the data. The PAGA graph was used to initialize the computation of a UMAP \(^{11,44}\) representation of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[101, 83, 895, 115]]<|/det|> +the data. Note that UMAP was only used to visualize the data and was not supplied to CellRank to compute the transition matrix or any downstream quantities. + +<|ref|>text<|/ref|><|det|>[[101, 140, 895, 220]]<|/det|> +CellRank parameters We use CellRank's analytical stochastic approximation to compute transition probabilities and include a diffusion kernel with weight 0.2 (Subsection 1.2 and Subsection 1.5). We compute 12 macrostates and automatically detect the terminal alpha, beta and epsilon states. The delta population is picked up automatically as a macrostate. We manually assign it the terminal label. + +<|ref|>sub_title<|/ref|><|det|>[[102, 246, 316, 263]]<|/det|> +## 8 Lung data example + +<|ref|>text<|/ref|><|det|>[[101, 271, 896, 400]]<|/det|> +We used a scRNA- seq time- series data- set of lung regeneration past bleomycin injury in mice assayed using Dropseq \(^{15,45}\) . The data- set contained 18 time points comprising days 0- 54 past injury. There was daily sampling from days 2- 13 and wider time- lags between the following time- points. Two replicate mice were used per time point. We restricted the data to days 2- 15 to make sure that the sampling is dense enough for velocities to be able to meaningfully extrapolate gene expression. If time points are too far apart, then RNA velocity cannot be used to predict the next likely cellular state because the linear extrapolation is only meaningful on the time scales of the splicing kinetics. Our final subset contained 24,882 cells. We kept the original cluster annotations. + +<|ref|>text<|/ref|><|det|>[[101, 424, 896, 555]]<|/det|> +Data pre- processing and velocity computation For the following processing, we used scVelo \(^{9}\) and SCANPY \(^{13}\) with mostly default parameters. Loom files containing raw spliced and unspliced counts were obtained by running the velocity \(^{1}\) command- line pipeline. We filtered genes to be expressed in at least 10 cells and to have at least 20 counts in both spliced and unspliced layers. We further normalized by total counts per cell, log transformed the data and kept the top 2000 highly variable genes. We kept the PCA coordinates from the original study and computed a KNN graph with \(K = 30\) nearest neighbors using the top 50 PCs. For velocity computation, we used scVelo's dynamical model of splicing kinetics. + +<|ref|>text<|/ref|><|det|>[[101, 579, 895, 691]]<|/det|> +Embedding computation The lung data was processed in three separate batches. We used BBKNN \(^{46}\) to compute a batch corrected KNN graph with 10 neighbors within each batch. The corrected KNN graph was used to compute a UMAP \(^{11,44}\) representation of the data. Note that UMAP was only used to visualize the data and was not supplied to CellRank to compute the transition matrix or any downstream quantities. We did not use BBKNN to correct the graph we used for velocity computation as it is an open question how to do batch correction for velocity computation. We used uncorrected data for velocity computation. + +<|ref|>text<|/ref|><|det|>[[101, 716, 895, 780]]<|/det|> +CellRank parameters We use CellRank's analytical stochastic approximation to compute transition probabilities and include a diffusion kernel with weight 0.2 (Subsection 1.2 and Subsection 1.5). On the full data of Fig. 6a, we compute 9 macrostates. On the reduced data of Fig. 6d, we compute 3 macrostates. + +<|ref|>text<|/ref|><|det|>[[101, 806, 895, 934]]<|/det|> +Defining stages of the differentiation trajectory We sub- setted cells to goblet and basal cells and re- run CellRank on the subset to investigate the trajectory at higher resolution. CellRank automatically detected initial and terminal states and computed fate probabilities towards the terminal states (Supplementary Fig. 24a- c). Further, we applied Palantir \(^{35}\) to the subset to compute a pseudotime (Supplementary Fig. 24d,e). We combined the pseudotime with CellRank's fate probabilities to define three stages of the dedifferentiation trajectory by requiring cells to have at least 0.66 basal probability. Cells passing this threshold were assigned to three bins of equal size along the pseudotemporal axis. We used this binning to define the three stages of the trajectory. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[102, 83, 333, 100]]<|/det|> +## 9 Methods comparison + +<|ref|>text<|/ref|><|det|>[[102, 107, 895, 172]]<|/det|> +9 Methods comparisonWe compared CellRank with the following similarity- based tools that compute probabilistic fate assignments on the single cell level: Palantir35, FateID47 and STEMNET48. We compared these methods in terms of the identification of initial and terminal states, fate probabilities, gene expression trends and run- time. + +<|ref|>text<|/ref|><|det|>[[101, 198, 895, 392]]<|/det|> +The Palantir algorithm Palantir35 computes a KNN graph in the space of diffusion components and uses this graph to compute a pseudotime via iteratively updating shortest path distances from a set of waypoints. Palantir required us to provide a number of waypoint cells - essentially a smaller number of cells that the system is reduced to in order to make it computationally feasible. We set this number to 1200 cells for the pancreas data and to \(15\%\) of the total cell number of the runtime and memory benchmarks on the reprogramming dataset49 below. For pseudotime computation, an initial cells needs to be supplied by the user. The pseudotime is used to direct edges in the KNN graph by removing edges that point from later cells to earlier cells in pseudotime. The stationary distribution of the resulting directed transition matrix is combined with extrema in the diffusion components to identify terminal cells. Absorption probabilities towards the terminal cells serve as fate probabilities. Gene expression trends are computed similarly to CellRank, by fitting GAMs in pseudotime where each cell contributes to each lineage according to its fate probabilities. + +<|ref|>text<|/ref|><|det|>[[101, 416, 895, 708]]<|/det|> +The FateID algorithm FateID47 either requires the user to provide terminal populations directly or through a set of marker genes. Terminal populations are used to train a random forest classifier. The classifier is applied to a set of cells in the neighborhood of each terminal cluster where it predicts the likely fate of these cells. The training set is iteratively expanded and the Random forest is re- trained on expanding populations, thus moving from the committed populations backward in time, classifying the fate of increasingly earlier cells. Two key parameters here are the size of the training and test sets used for the Random forest classifier, which we set to \(1\%\) of the data in all benchmarks. Gene expression trends are computed by selecting a (discrete) set of cells which pass a certain threshold for fate bias towards a specific terminal population. A principal curve is fit to these cells in a low dimensional embedding and pseudotime is assigned via projection onto this curve. Alternatively, the authors recommend to compute diffusion pseudotime10 (DPT) on the set of cells selected for a particular lineage. Gene expression values are then normalised and a local regression (LOESS) is performed to obtain mean trends. In contrast to CellRank and Palantir, this approach does not provide confidence intervals for the expression trends, it is dependent on low dimensional embeddings (principal curve fit) and it discreetly assigns cells to lineages, thereby ignoring the gradual nature of fate commitment when visualizing gene expression trends. Since different cells are selected for different lineages, the computed pseudo- temporal orderings are incompatible and gene trends along different lineages cannot be visualized jointly. + +<|ref|>text<|/ref|><|det|>[[101, 732, 895, 844]]<|/det|> +The STEMNET algorithm STEMNET48 requires the user to provide the terminal populations directly as input to the algorithm. It then trains an elastic- net regularized generalized linear model on the terminal populations to predict state membership. This first step serves as feature selection - it selects a set of genes which are specific to their terminal populations. In the next step, the classifier uses expression of these genes to predict fate bias for the remaining, transient cells. STEMNET uses the computed fate probabilities to place cells on a simplex in 2 dimensions as a dimensionality reduction method. It does not offer a method to visualize gene expression trends. + +<|ref|>text<|/ref|><|det|>[[101, 871, 895, 935]]<|/det|> +Fate probabilities In order to enable a fair comparison across methods, we supplied all methods with CellRank's identified terminal states and compared predicted fate probabilities. Methods differed in the format they require terminal state information to be passed: for Palantir, we passed individual cells, i.e. 4 cells, one for each of the three terminal states and one initial cell from the initial + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[101, 83, 895, 196]]<|/det|> +state. For STEMNET, we passed populations of cells defined through the underlying transcriptomic clusters. We passed the alpha, beta, epsilon and delta clusters defined through the sub- clustering of Supplementary Fig. 10c. For FateID, we passed marker genes to identify the terminal populations. For each terminal state, we passed its corresponding hormone- production associated gene, i.e. Ins1 for beta, Gcg for alpha, Ghrl for epsilon and Sst for delta50. We checked whether methods correctly predicted beta to be the dominant fate among early cells by computing the average fate prediction among Ngn3 high EP cells. + +<|ref|>text<|/ref|><|det|>[[101, 222, 895, 480]]<|/det|> +Gene expression trends To visualize gene expression trends, we used the functionalities that each method provided. STEMNET did not have an option to compute gene expression trends. For CellRank, we visualized gene expression trends as described in Section 3. For Palantir, we used default parameters. For FateID, it was difficult to find a good threshold value to assign cells to lineages. If this value is too high, then early cells in the trajectory are not selected and the terminal states are isolated. If this value is too low, then for a subset of the lineages, very unlikely cells are assigned and the trends are very unspecific. The default value is 0.25, which was too high in our case. We decided to set the threshold at 0.15, which was a compromise between trying to have early cells in every lineage and making sure that irrelevant cells are not assigned. We computed DPT10 on the set of the selected cells, as recommended in the original publication. To identify a root cell for each lineage, we first computed DPT on the entire data- set, then subsetted to a lineage- specific set of cells and picked the cell with the earliest original DPT value as the root cell for the second DPT computation. We visualized expression trends for the key lineage drivers Pax451 and Pdx152- 54 (beta), Arx51 (alpha), Ghrl50 (epsilon) and Hhex55 and Cd24a56,57 (delta) as well as the lineage- associated genes Peg1058,59 (alpha) and Irs459 (epsilon). In Fig. 5c, we checked whether methods correctly predicted upregulation of Pdx1 along the beta fate. + +<|ref|>text<|/ref|><|det|>[[101, 505, 895, 586]]<|/det|> +Runtime We compared run- time of the four methods applied to a scRNA- seq dataset comprising 100k cells undergoing reprogramming from mouse embryonic fibroblasts to induced endoderm progenitor cells49. We randomly subsampled the data- set to obtain 10 data- sets of increasing size, starting from 10k cells in steps of 10k until 100k cells. For each sub- sampled dataset, we applied each method 10 times and computed the mean runtime as well as the standard error on the mean. + +<|ref|>text<|/ref|><|det|>[[101, 594, 895, 740]]<|/det|> +We used CellRank to compute 3 terminal states and we supplied these to all other methods to ensure that the number of terminal states is consistent across methods. Methods differed in the format they require terminal state information to be passed: for Palantir, we passed individual cells, i.e. three terminal cells and one initial cell (taken from the earliest time point of the reprogramming data). For STEMNET, we passed a set of cells for each terminal state by choosing the cells which have been most confidently assigned to each terminal state by CellRank. For each terminal state, we passed a number of cells that was equal to 1% of the total cell number. FateID requires marker genes to identify the terminal populations, so we computed the top 3 lineage drivers per CellRank- identified terminal state and passed these. + +<|ref|>text<|/ref|><|det|>[[101, 747, 895, 845]]<|/det|> +For CellRank, we separately recorded the time it took to compute the terminal states and fate probabilities. For terminal states in CellRank, we included in this benchmark the entire workflow from computing the transition matrix via decomposing it into macrostates to identifying the terminal states among the macrostates. For fate probabilities, we benchmarked the compute_absorption_probabilities() method (CellRank), the run_palantir() function (Palantir), the fateBias() function (FateID) and the runSTEMNET() function (FateID). + +<|ref|>text<|/ref|><|det|>[[101, 853, 895, 917]]<|/det|> +Comparisons were run on an Intel(R) Xeon(R) Gold 6126 CPU @ 2.60GHz and 32 cores. Each job was allocated at least 90 GiB RAM and we recorded the actual peak memory usage (see below). FateID did not finish on 100k cells because of a memory error due to densification of a large matrix. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[101, 83, 895, 196]]<|/det|> +Peak memory usage The setup was identical to the setup for the runtime comparison above, only that we recorded peak memory usage of each method (Supplementary Table 2). For the Python- based methods CellRank and Palantir, we used the memory- profiler \(^{60}\) package whereas for the R- based packages STEMNET and FateID, we used the peakRAM \(^{61}\) profiler. CellRank and Palantir efficiently parallelize their computations across several cores which increases their peak memory consumption. We repeated our evaluation for these two methods on 100k cells using just a single core to estimate the size of this effect (Supplementary Table 3). + +<|ref|>sub_title<|/ref|><|det|>[[101, 221, 844, 239]]<|/det|> +## 10 Immunofluorescence stainings and microscopy on airway epithelial cells + +<|ref|>text<|/ref|><|det|>[[101, 246, 895, 376]]<|/det|> +Formalin- fixed paraffin- embedded lung sections ( \(3.5\mu m\) thick) from bleomycin- treated mice at day 10 (n=2) and day 22 (n=2) after bleomycin instillation, and from phosphate- buffered saline (PBS)- treated controls (n=2) were stained as previously described \(^{15}\) . In brief, after deparaffinization, rehydration and heat- mediated antigen retrieval with citrate buffer (10 mM, pH = 6.0), sections were blocked with 5% bovine serum albumin for 1 h at room temperature and then incubated with the following primary antibodies overnight at 4°C: rabbit anti- Bpifb1 (kindly provided by C. Bingle \(^{62}\) , 1:500), mouse anti- Trp63 (abcam, ab735, clone A4A, 1:50) and chicken anti- Krt5 (BioLegend, Poly9059, 1:1,000). + +<|ref|>text<|/ref|><|det|>[[101, 384, 930, 449]]<|/det|> +For visualization of stainings the following secondary antibodies were used: Goat anti- rabbit Alexa Fluor \(^{\textregistered}\) 488 (Invitrogen, A11008, 1:250), Goat anti- chicken Alexa Fluor \(^{\textregistered}\) 568 (Invitrogen, A11041,1:250) and goat anti- mouse Alexa Fluor \(^{\textregistered}\) 647 (Invitrogen, A21236, 1:250). Cell nuclei were visualized with 4',6- diamidino- 2- phenylindole (DAPI). + +<|ref|>text<|/ref|><|det|>[[101, 457, 895, 537]]<|/det|> +Immunofluorescent images were acquired with an AxioImager.M2 microscope (Zeiss) using a PlanApochromat 20x/0.8 M27 objective. For quantification of immunofluorescence stainings, five different intrapulmonary regions were recorded per mouse and the percentage of positively stained cells normalized to the total number of airway cells was manually quantified using Fiji software (ImageJ, v. 2.0.0). + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[102, 81, 228, 100]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[100, 103, 900, 920]]<|/det|> +[1] Gioele La Manno, et al. RNA velocity of single cells. Nature, page 1, August 2018. ISSN 1476- 4687. doi: 10.1038/s41586- 018- 0414- 6. URL https://www.nature.com/articles/s41586- 018- 0414- 6. [2] Volker Bergen, et al. Generalizing RNA velocity to transient cell states through dynamical modeling. Nature Biotechnology, pages 1- 7, August 2020. ISSN 1546- 1696. doi: 10.1038/s41587- 020- 0591- 3. URL https://www.nature.com/articles/s41587- 020- 0591- 3. 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URL https://CRAN.R- project.org/package=peakRAM. + +<|ref|>text<|/ref|><|det|>[[100, 684, 896, 728]]<|/det|> +[62] Maslinda Musa, et al. Differential localisation of BPIFA1 (SPLUNC1) and BPIFB1 (LPLUNC1) in the nasal and oral cavities of mice. Cell and Tissue Research, 350(3):455- 464, December 2012. ISSN 1432- 0878. doi: 10.1007/s00441- 012- 1490- 9. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[115, 145, 780, 760]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 44, 142, 69]]<|/det|> +
Figures
+<|ref|>image_caption<|/ref|><|det|>[[42, 780, 115, 799]]<|/det|> +
Figure 1
+ +<|ref|>text<|/ref|><|det|>[[42, 822, 923, 865]]<|/det|> +Combining RNA velocity with cell- cell similarity to determine initial and terminal states and compute a global map of cellular fate potential. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[128, 92, 808, 500]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 519, 116, 538]]<|/det|> +
Figure 2
+ +<|ref|>text<|/ref|><|det|>[[42, 562, 478, 581]]<|/det|> +Delineating fate choice in pancreatic development + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[142, 120, 815, 520]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 541, 116, 561]]<|/det|> +
Figure 3
+ +<|ref|>text<|/ref|><|det|>[[42, 585, 577, 605]]<|/det|> +Zooming into the delta state to elucidate differentiation paths + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[120, 90, 790, 702]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 722, 116, 740]]<|/det|> +
Figure 4
+ +<|ref|>text<|/ref|><|det|>[[42, 764, 606, 784]]<|/det|> +Uncertainty propagation adjusts for noise in RNA velocity vectors + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[123, 60, 790, 670]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[44, 695, 117, 713]]<|/det|> +
Figure 5
+ +<|ref|>text<|/ref|><|det|>[[44, 737, 595, 757]]<|/det|> +CellRank outperforms methods that do not include RNA velocity + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[105, 75, 777, 688]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[42, 708, 117, 728]]<|/det|> +
Figure 6
+ +<|ref|>text<|/ref|><|det|>[[42, 750, 711, 771]]<|/det|> +Cellrank predicts a novel differentiation trajectory in Murine lung regeneration + +<|ref|>sub_title<|/ref|><|det|>[[44, 794, 311, 821]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[42, 844, 764, 865]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 881, 464, 928]]<|/det|> +- 20201019cellranksupplementarytables.pdf- 20201019cellranksupplementaryfigures.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__08c9c84db4a69f550730063a1db0239d35dc5acfc2ff936dd017039d40fa949f/images_list.json b/preprint/preprint__08c9c84db4a69f550730063a1db0239d35dc5acfc2ff936dd017039d40fa949f/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..9f67deecad81c67c246e1b300363243d960b431e --- /dev/null +++ b/preprint/preprint__08c9c84db4a69f550730063a1db0239d35dc5acfc2ff936dd017039d40fa949f/images_list.json @@ -0,0 +1,62 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Fig. 1 | Current-induced creation of skyrmions at 294 K. a-c, A sequence of Fresnel images of the skyrmion creation process after applying numbers of the current pulse, corresponding to conical, skyrmions, and skyrmion lattice, respectively. The red rectangular dashed lines are the thin area on both sides of the sample in a. Inset: in-plane magnetization mapping of skyrmion lattice within a hexagonal dashed line by Transport of Intensity Equation (TIE). d, Skyrmion number \\(N_{\\mathrm{s}}\\) as a function of pulse numbers, the pulse width is \\(20 \\mathrm{ns}\\) . e, Maximum skyrmion number \\(N_{\\mathrm{s - max}}\\) as a function of the current density, the pulse width is \\(20 \\mathrm{ns}\\) . f, Threshold current density \\(j_{\\mathrm{c}}\\) required to achieve the maximum skyrmion number. Defocused distance, \\(-1200 \\mu \\mathrm{m}\\) .", + "footnote": [], + "bbox": [ + [ + 156, + 280, + 840, + 545 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Fig. 2 | Diversity of room-temperature skyrmion bundles. a, Creation of skyrmion bundles at room temperature by applying reversed positive magnetic fields on skyrmion clusters with positive \\(Q\\) . b, Line profile of magnetic phase shift extracted from (a) (red dashed arrow). c,", + "footnote": [], + "bbox": [ + [ + 147, + 465, + 849, + 809 + ] + ], + "page_idx": 10 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Fig. 3 | Field-driven quantized topological annihilations. a, Field-driven magnetic evolutions from a skyrmion bundle with \\(Q = 8\\) at 295 K. b, Corresponding simulated averaged in-plane magnetization mapping during the field-driven magnetic evolution. c, Topological charge \\(Q\\) as a function of magnetic field \\(B\\) . d, Corresponding simulated topological charge \\(Q\\) as a function", + "footnote": [], + "bbox": [ + [ + 176, + 508, + 818, + 775 + ] + ], + "page_idx": 12 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Fig. 4 | Magnetic phase diagram of skyrmion bundles. a. The topological quantized annihilation of a \\(Q = 1\\) bundle in the field-increasing. b. Topological transformation from a \\(Q = 1\\) bundle to the magnetic helix in the field-decreasing process. c-e, Magnetic phase diagram of skyrmion bundles ( \\(Q = 0\\) , 1, and 2) as a function of temperature and magnetic field. The data points in the diagram are the critical points of the two-state transition. FM and PM represent ferromagnetic and paramagnetic states, respectively. The colorwheel represents the in-plane magnetization distributions in a and b.", + "footnote": [], + "bbox": [ + [ + 163, + 91, + 825, + 417 + ] + ], + "page_idx": 15 + } +] \ No newline at end of file diff --git a/preprint/preprint__08c9c84db4a69f550730063a1db0239d35dc5acfc2ff936dd017039d40fa949f/preprint__08c9c84db4a69f550730063a1db0239d35dc5acfc2ff936dd017039d40fa949f.mmd b/preprint/preprint__08c9c84db4a69f550730063a1db0239d35dc5acfc2ff936dd017039d40fa949f/preprint__08c9c84db4a69f550730063a1db0239d35dc5acfc2ff936dd017039d40fa949f.mmd new file mode 100644 index 0000000000000000000000000000000000000000..f782cf7c14725a06218e3f0a95abbd9a06352997 --- /dev/null +++ b/preprint/preprint__08c9c84db4a69f550730063a1db0239d35dc5acfc2ff936dd017039d40fa949f/preprint__08c9c84db4a69f550730063a1db0239d35dc5acfc2ff936dd017039d40fa949f.mmd @@ -0,0 +1,252 @@ + +# Stable skyrmion bundles at room temperature and zero magnetic field in a chiral magnet + +Jin Tang jintang@ahu.edu.cn + +School of Physics and Optoelectronic Engineering, Anhui University, Hefei, 230601, China + +Yongshen Zhang Chinese Academy of Sciences + +Yaodong Wu + +Key Laboratory for Photoelectric Detection Science and Technology of Education Department of Anhui Province, School of Physics and Materials Engineering, Hefei Normal University + +Meng Shi + +Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions, High Magnetic Field Laboratory, HFIPS, Anhui, Chinese Academy of Sciences + +Xitong Xu + +Chinese Academy of Sciences + +Shouguo Wang + +School of Materials Science and Engineering, Anhui University + +Mingliang Tian + +Anhui University https://orcid.org/0000- 0002- 0870- 995X + +Haifeng Du + +Hefei Institutes of Physical Science https://orcid.org/0000- 0003- 4263- 5023 + +Article + +Keywords: + +Posted Date: December 5th, 2023 + +DOI: https://doi.org/10.21203/rs.3.rs- 3639312/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> + +Version of Record: A version of this preprint was published at Nature Communications on April 22nd, 2024. See the published version at https://doi.org/10.1038/s41467-024-47730-6. + +<--- Page Split ---> + +# Stable skyrmion bundles at room temperature and zero magnetic field in a chiral magnet + +Yongsen Zhang \(^{1,2\#}\) , Jin Tang \(^{3\# *}\) , Yaodong Wu \(^{4}\) , Meng Shi \(^{1,2}\) , Xitong Xu \(^{2}\) , Shouguo Wang \(^{5}\) , Mingliang Tian \(^{2,3}\) and Haifeng Du \(^{2*}\) \(^{1}\) University of Science and Technology of China, Hefei 230026, China \(^{2}\) Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions, High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Anhui, Chinese Academy of Sciences, Hefei 230031, China \(^{3}\) School of Physics and Optoelectronic Engineering, Anhui University, Hefei, 230601, China \(^{4}\) School of Physics and Materials Engineering, Hefei Normal University, Hefei, 230601, China \(^{5}\) Anhui Key Laboratory of Magnetic Functional Materials and Devices, School of Materials Science and Engineering, Anhui University, Hefei 230601, China + +\*Corresponding author: + +jintang@ahu.edu.cn and duhf@hmfl.ac.cn + +<--- Page Split ---> + +## Abstract + +Topological spin textures are characterized by topological magnetic charges \(Q\) , which govern their fascinating electro- magneto properties. Recent studies have achieved skyrmion bundles with arbitrary integer values of \(Q\) , opening up possibilities for exploring topological spintronics based on the parameter freedom of \(Q\) . However, the realization of stable skyrmion bundles in chiral magnets at room temperature and zero magnetic field, which is the prerequisite for realistic device applications, has remained exclusive. Here, through the combination of pulsed currents and reversed magnetic fields, we experimentally achieved skyrmion bundles with different inter- \(Q\) values, reaching a maximum of 24 at above room temperature and zero magnetic field in a \(\beta\) - Mn- type \(\mathrm{Co8Zn_{10}Mn_{2}}\) chiral magnet. We demonstrate the field- driven topological quantitated annihilation of high- \(Q\) bundles and present a stable phase diagram as a function of temperature and field. Our experimental findings are consistently corroborated by micromagnetic simulations. The observation of skyrmion bundles and their multi- \(Q\) topological properties at room temperature and zero fields could promote realistic multi- \(Q\) - based topological spintronic devices. + +<--- Page Split ---> + +Magnetic skyrmions are vortex- like spin textures characterized by an integer topological magnetic charge, defined as \(Q = \frac{1}{4\pi}\int \mathbf{m}\cdot \left(\frac{\partial\mathbf{m}}{\partial x}\times \frac{\partial\mathbf{m}}{\partial y}\right)\mathrm{d}x\mathrm{d}y^{1 - 3}\) . Topological magnetic charge \(Q\) plays a crucial role in determining various topology- related properties of skyrmions, including skyrmion Hall effects \(^{4,5}\) , topological Hall effects \(^{6}\) , ultrasmall underpinning current \(^{7}\) , particle- like physics \(^{8}\) , and electric transport properties \(^{9,10}\) . Despite the significance of \(Q\) in determining magneto- electro properties of topological spin textures \(^{11 - 19}\) , traditional skyrmions are constrained to possess a fixed value of \(|Q| = 1\) . Recent studies propose a strategy to extend the values of \(Q\) from 1 to any integers in an assembly, called skyrmion bundles or skyrmion bags \(^{20 - 22}\) . Skyrmion bundles consist of various skyrmions encircled by a closed spin spiral. The \(N\) internal skyrmions contribute \(Q = N\) , while the outer spin spiral contributes \(Q = - 1\) . As a result, skyrmion bundles possess a total topological charge of \(Q = N - 1\) . Skyrmion bundles, holding the advantages of diversity \(Q\) and morphologies, have greatly enriched the family of topological magnetic solitons and shed a light on the topological spintronic devices based on multi- \(Q\) characteristics \(^{23}\) , such as multi- bit memories and information interconnect devices \(^{24 - 28}\) . + +The realization of skyrmion bundles is based on the first- order magnetic phase change from the helix to the skyrmion lattice \(^{29}\) , which allows for the coexistence of the two phases \(^{30,31}\) . Skyrmion bundles can be created by applying reversed magnetic fields to the coexisting skyrmion- helix phases \(^{20}\) . The formation of coexisting skyrmion- helix phases typically involves a complex operation, often through a cooling procedure from high temperatures \(^{20,32}\) . Furthermore, the observation of skyrmion bundles in chiral + +<--- Page Split ---> + +magnets has been limited to temperatures far below room temperature and under a certain magnetic field32, which poses practical challenges for their application in real-world devices. + +In this work, we successfully observed skyrmion bundles with varying inter- \(Q\) values, including a remarkable maximum of 24, and reported the unambiguous experimental realization of a type of three- dimensional (3D) multi- \(Q\) skyrmionic configurations at room temperature and zero magnetic field in \(\beta\) - Mn- type \(\mathrm{Co}_8\mathrm{Zn}_{10}\mathrm{Mn}_2\) chiral magnet by the combination of pulsed current and reversed magnetic fields. The creation of room- temperature skyrmion bundles avoids the cooling procedure. Furthermore, we demonstrate the field- driven topological phase transition and provide a stabilization diagram of skyrmion bundles as a function of magnetic field and temperatures. + +## Observation of skyrmion bundles at room temperature without a field cooling procedure + +Skyrmion bundles consist of an interior skyrmion bag and superficial multi- \(Q\) chiral vortices. The skyrmions inside the bundle exhibit conventional characteristics, with polarity \(p = 1\) and vorticity \(\nu = 1\) , resulting in a topological charge \((Q)\) of 1 for each skyrmion33- 35. The peripheral helical stripe possesses the same vorticity but opposite polarity. Consequently, the topological charge of skyrmion bundles is the summation of the central skyrmions and the boundary helical stripe, expressed as \(Q = N - 1\) , where \(N\) represents the number of skyrmions within the bundle. Given the fact for the first- order transition from spiral to skyrmions, the coexistence of skyrmions and spin spirals is + +<--- Page Split ---> + +allowed and serves as a precursor to the formation of skyrmion bundles. We first demonstrate the creation of skyrmions induced by currents29, 36. The micro- device utilized in the experiment comprises two Pt electrodes and a \(\sim 160 \mathrm{nm}\) thick lamella with two narrow regions of \(\sim 80 \mathrm{nm}\) thickness on both sides, fabricated from \(\mathrm{Co}_8\mathrm{Zn}_{10}\mathrm{Mn}_2\) bulk (Supplemental Fig. S1). + +![](images/Figure_1.jpg) + +
Fig. 1 | Current-induced creation of skyrmions at 294 K. a-c, A sequence of Fresnel images of the skyrmion creation process after applying numbers of the current pulse, corresponding to conical, skyrmions, and skyrmion lattice, respectively. The red rectangular dashed lines are the thin area on both sides of the sample in a. Inset: in-plane magnetization mapping of skyrmion lattice within a hexagonal dashed line by Transport of Intensity Equation (TIE). d, Skyrmion number \(N_{\mathrm{s}}\) as a function of pulse numbers, the pulse width is \(20 \mathrm{ns}\) . e, Maximum skyrmion number \(N_{\mathrm{s - max}}\) as a function of the current density, the pulse width is \(20 \mathrm{ns}\) . f, Threshold current density \(j_{\mathrm{c}}\) required to achieve the maximum skyrmion number. Defocused distance, \(-1200 \mu \mathrm{m}\) .
+ +Fig. 1 illustrates the process of skyrmion creation through the application of a series of current pulses with varying pulse widths and current densities in the \(x\) - direction. + +<--- Page Split ---> + +Initially, as shown Fig. 1a, we obtained a conical state shown by uniform Fresnel contrasts at \(B = - 70 \mathrm{mT}\) . Subsequently, upon applying current pulses with a duration of \(20 \mathrm{ns}\) and a density of \(8.19 \times 10^{10} \mathrm{A / m^2}\) , skyrmion clusters were generated after several pulses (Fig. 1b). As the number of pulses increased, the skyrmion clusters gradually merged to form the skyrmion lattice, as depicted in Fig. 1c. Fig. 1d shows the effect of the current pulse number on the skyrmion creation under a current density \(j \sim 8.42 \times 10^{10} \mathrm{A / m^2}\) . Initially, the number \(N_{\mathrm{s}}\) of skyrmions exhibits a positive linear relationship with the number of pulses \(N_{\mathrm{p}}\) . After approximately the application of 43 pulsed currents, skyrmions covered most regions of the sample, with a total count of \(N_{\mathrm{s}} \sim 1830\) . Subsequently, the number of skyrmions almost keeps a balance around \(\sim 1830\) , which is defined as the maximum skyrmion number \(N_{\mathrm{s - max}}\) , despite the further application of more pulsed currents (see Supplemental Fig. S2 for details). Fig. 1e shows the dependence of skyrmion maximum numbers \((N_{\mathrm{s - max}})\) on the current density when the pulse width was set to \(20 \mathrm{ns}\) . Firstly, as the current density increases, the number of skyrmions rises until it reaches a maximum value. However, once the current density exceeds a certain threshold, the current density no longer rises, as excessively high current densities could damage the sample \(^{30,37}\) . Fig. 1f displays the effect of pulse width on the critical current densities and directions. Despite the opposite current directions, the difference in critical current density is relatively small under the same pulse width. Skyrmions are first created at the edge of the sample and then pushed into the center \(^{38}\) . This creation process can be understood by the combined current effect of spin transfer torque and Joule thermal heating \(^{23,39,40}\) , as shown in Supplemental video S1. + +<--- Page Split ---> + +Because the current density is inversely proportional to the thickness, skyrmions are first created at the thin region because of larger temperature increases induced by the current. Then, the spin transfer torque could drive skyrmions nucleated in the thin region to the thick region. A previous study has shown the current- driven skyrmion- to- bobber transformations in stepped geometry41. However, magnetic bobbers are not observed in our experiments (Supplemental video S1). The step edge between thin and thick regions in realistic experiments cannot be very sharp. Our simulations show that a slight continuous deformation of the step edge can lead to the transformation of short skyrmion tubes in a thin region to long tubes in a thick region (Supplemental video S2), which explains the expansion of skyrmions from the thin region to the thick region. + +We have shown the creation of skyrmions with positive \(Q\) using pulsed current at room temperature without additional field cooling process (Fig. 2a). Additionally, after the pulse current was turned off, positive magnetic fields were applied to the skyrmions. Although \(Q = 1\) skyrmions are not thermodynamically stable under positive fields, they can usually survive in the non- equilibrium metastable state (Fig. 2a, 0 and 58 mT) at temperatures far below Curie temperature \(T_{\mathrm{c}}\) ( \(\sim 350 \mathrm{K}\) ). When the magnetic field increased to \(B = 80 \mathrm{mT}\) , most of the skyrmions were annihilated, leaving a skyrmion bundle consisting of a few skyrmions encircled by a dark ring. The Transport of Intensity Equation (TIE)42 analysis was employed to calculate the projected in- plane magnetization distribution of the skyrmion bundle. As shown in Fig. 2a (80 mT), it demonstrated that the surrounding circular spiral with weak contrast has the opposite rotation sense compared to the skyrmions inside. To quantitatively evaluate the fine + +<--- Page Split ---> + +variations in the phase shift inside and outside the skyrmion bundle, the line profiles of the experimental and simulated phases were plotted in Fig.2b and 2d, respectively. It is evident that the contrast intensity of the outer circular spiral is weaker than that of the inner skyrmions. + +These experimental observations are in excellent agreement with the average in- plane magnetization obtained through simulations, as shown in Supplemental Fig. S3. Fig. S3a depicts a representative simulated 3D magnetic configuration of skyrmion bundles containing 3 skyrmions. The iso- surfaces correspond to a value of - 0.1 for the normalized out- of- plane magnetization component, i.e., \(m_{z} = - 0.1\) (Fig. S3b). When approaching the sample surfaces (Fig. S3d and S3f), where the magnetic vortex is a bi- antiskyrmion with \(Q = 2\) , and the complete skyrmion bags with \(Q = 2\) are located only in the middle layers of the \(\mathrm{Co_8Zn_{10}Mn_2}\) lamella (Fig. S3e). Therefore, despite the strong spin twist along the depth dimension due to the conical background magnetizations, topological charges maintain \(Q = 2\) throughout all layers. The depth- modulated spin twists contribute weaker magnetic contrasts of the outer spiral compared to those of the interior skyrmions for the average in- plane magnetization mapping of skyrmion bundles (Fig. S3c). These characteristics of chiral skyrmion bundles in \(\mathrm{Co_8Zn_{10}Mn_2}\) are highly similar to those in chiral magnet \(\mathrm{FeGe^{20}}\) . + +By repeating the process of combining pulsed currents and reversed magnetic fields, a diversity of magnetic skyrmion bundles with a maximum \(Q\) of 24 at room temperature can be obtained, as shown in Fig. 2e. For the bubble bundles stabilized by magnetic dipole- dipole interaction, the internal skyrmion bubbles are much larger than + +<--- Page Split ---> + +the size of an isolate skyrmion, leading to the extremely expanded \(Q\) - related size of bubble bundles \(^{43}\) . In contrast, the skyrmion bundles stabilized by chiral interactions in \(\mathrm{Co_8Zn_{10}Mn_2}\) always reveal a compact configuration. The internal skyrmions of the bundles always tightly bind to each other and have a comparable size as that of an isolate skyrmion, leading to a linear increased area \(S\) as a function of \(Q\) . Fig. 2f shows the area \(S\) of skyrmion bundles as a function of topological charge \(Q\) , i.e., \(S = (Q + 1)(S_{N - skr} + S_{spiral})\) , which represents the sum of the area occupied by the inner skyrmions \(S_{N - skr}\) and the outer spin spiral \(S_{spiral}\) . It should be noted that in the repeating process, we could also obtain other special topological spin textures, such as the skyrmion- antiskyrmion pair (Supplemental Figure S4), at room temperature. + +![](images/Figure_2.jpg) + +
Fig. 2 | Diversity of room-temperature skyrmion bundles. a, Creation of skyrmion bundles at room temperature by applying reversed positive magnetic fields on skyrmion clusters with positive \(Q\) . b, Line profile of magnetic phase shift extracted from (a) (red dashed arrow). c,
+ +<--- Page Split ---> + +Corresponding simulated averaged in-plane magnetization mapping during the creation from skyrmion lattice to skyrmion bundles. d, Line profile of magnetic phase shift extracted from (c) (red dashed arrow). e, In-plane magnetic configurations of representative magnetic skyrmion bundles with varying \(Q\) at \(B \sim 80 \mathrm{mT}\) . f, Dependence between the area \(S\) of different skyrmion bundles and topological charge \(Q\) . The colorwheel represents the in-plane magnetizations. + +## Topological phase transition at high and zero magnetic fields + +Fig. 3a shows a representative topological phase transition of high- \(Q\) skyrmion bundles under high magnetic fields at room temperature. When increasing the external magnetic field from 70 to \(79 \mathrm{mT}\) , one interior skyrmion of the \(Q = 8\) bundle was annihilated, while the boundary spin spiral remained stable and shrank to tightly bind the remaining skyrmions, resulting in the formation of a \(Q = 7\) bundle. As the magnetic field continued to increase, the internal skyrmions gradually disappeared one by one until only one remained, forming a \(Q = 0\) bundle, also known as the \(2\pi\) - vortex or skyrmionium (Fig. 3a, \(107 \mathrm{mT}\) ) \(^{22, 44, 45}\) . The \(Q = 0\) bundle could survive in a wide magnetic field range from 107 to \(155 \mathrm{mT}\) . However, due to the consistent polarity between the center skyrmion in the \(Q = 0\) bundle and the direction of the positive magnetic field \(^{35, 46}\) , the internal skyrmion was inherently unstable and eventually disappeared at \(B = 155 \mathrm{mT}\) . Subsequently, the outer ring shrank to form a skyrmion with \(Q = - 1\) , with a polarity opposite to that of the skyrmion within the bundle. The field- driven topological quantized annihilation can be well reproduced in our zero- temperature simulations (Fig. 3b and Fig. 3d). + +<--- Page Split ---> + +We further observed skyrmion bundles in a broad temperature range from 150 to 320 K and explored their field- driven magnetic evolutions, as shown in Fig. 3c. The topological quantized one- by- one annihilations in the field- increasing process work for temperatures below 295 K. The threshold maximum magnetic field required for the stabilization of bundles decreased as the temperature increased. In contrast, at \(T \sim 310\) K, the topological quantized annihilation behavior of the \(Q = 8\) bundle was not gradual but instead resulted in a transition from 8 to 4, 2, 0, and finally - 1. Fig. 3c shows the simulated field- driven magnetic evolution from a \(Q = 8\) skyrmion bundle at zero temperature. The topological quantized annihilation in the zero- temperature simulation is hardly shown with a continuous one- by- one mode, but instead resulted in a transition from 8 to 5, 4, 2, 1, 0, and finally - 1. + +![](images/Figure_3.jpg) + +
Fig. 3 | Field-driven quantized topological annihilations. a, Field-driven magnetic evolutions from a skyrmion bundle with \(Q = 8\) at 295 K. b, Corresponding simulated averaged in-plane magnetization mapping during the field-driven magnetic evolution. c, Topological charge \(Q\) as a function of magnetic field \(B\) . d, Corresponding simulated topological charge \(Q\) as a function
+ +<--- Page Split ---> + +of magnetic field \(B\) . \(N\) represents the number of interior skyrmion tubes. The colorwheel represents the in-plane magnetizations. + +It should be noted that field- free or low- field conditions are also important for realistic device applications. Here, we further explore the stability of skyrmion bundles in the field- decreasing process, as shown in Fig. 4. When decreasing the magnetic field to zero from the high- field ferromagnetic state at room temperature, the Fresnel contrasts of the \(\mathrm{Co_8Zn_{10}Mn_2}\) lamella keep uniform, suggesting the stability of conical or stacked spiral with zero defocused Fresnel contrasts around zero magnetic fields (Supplemental Fig. S5). Our simulation confirm the formation of the stacked spiral at zero fields by decreasing field from ferromagnet at high field (Supplemental Fig. S5). Noted that the ferromagnet, conical, and stacked spiral are all shown no defocused Fresnel contrasts because of zero integral in- plane magnetization component along the depth. When the temperature further increases to \(320\mathrm{K}\) , the zero- field conical state is not stable and transforms to a helix (Supplemental Fig. S6) due to the strong thermal fluctuation energy near the Curie temperature. The zero- field stability of the stacked spiral state could support the stability of skyrmion bundles. + +Fig. 4a shows the topological quantized annihilation of a \(Q = 1\) bundle with a continuous topological quantized annihilation in the field- increasing process. In contrast, the magnetic evolution of the \(Q = 1\) bundle in the field- decreasing process is shown in Fig. 4b. The \(Q = 1\) skyrmion bundle remains stable even at zero field in the spiral magnetization background. The \(Q = 1\) skyrmion bundle cannot persist and + +<--- Page Split ---> + +transform to long helix domains until a negative magnetic field \(B = - 9 \mathrm{mT}\) is applied (Fig. 4b). + +Fig. 4c- e depicts the magnetic phase diagram of skyrmion bundles as a function of temperature and magnetic field in a field- increasing process from an initial \(Q = 0\) , 1, and 2 bundles. Skyrmion bundles in \(\mathrm{Co_8Zn_{10}Mn_2}\) magnets can survive in a broad field- temperature region. Our zero- temperature micromagnetic simulation also confirms the stability of skyrmion bundles at zero fields (Supplemental Fig. S7). However, for temperatures above \(320 \mathrm{K}\) , strong thermal fluctuations promote the transformation from high- energy metastable skyrmion bundles to helical phases with lower energy (Supplemental Fig. S6 and S8). The metastable skyrmion bundles and the helix at low fields are all disturbed in high temperatures near the curie temperature. It should be noted that the skyrmion bundle can be even stabilized in certain reversed negative fields. For example, the maximum negative field supporting the stability of \(Q = 2\) skyrmion bundles decreases from \(- 7 \mathrm{mT}\) at \(150 \mathrm{K}\) to \(0 \mathrm{mT}\) at \(295 \mathrm{K}\) . + +<--- Page Split ---> +![](images/Figure_4.jpg) + +
Fig. 4 | Magnetic phase diagram of skyrmion bundles. a. The topological quantized annihilation of a \(Q = 1\) bundle in the field-increasing. b. Topological transformation from a \(Q = 1\) bundle to the magnetic helix in the field-decreasing process. c-e, Magnetic phase diagram of skyrmion bundles ( \(Q = 0\) , 1, and 2) as a function of temperature and magnetic field. The data points in the diagram are the critical points of the two-state transition. FM and PM represent ferromagnetic and paramagnetic states, respectively. The colorwheel represents the in-plane magnetization distributions in a and b.
+ +## Conclusions + +In summary, we have demonstrated the thermal stability of skyrmion bundles at physical conditions of realistic devices, i.e., room temperature and zero magnetic fields. We propose the creation of skyrmion bundles using a combination of pulsed current and reversed magnetic field without an additional field- cooling procedure. We also demonstrate the room- temperature multi- \(Q\) characteristics of skyrmion bundles, including topological quantized annihilation, magnetic phase diagram, and \(Q\) - related + +<--- Page Split ---> + +bundle size. Metastable conical background magnetization can be stabilized at zero field and low temperatures, which contributes to the stability of skyrmion bundles at zero fields below \(320\mathrm{K}\) . However, the strong thermal fluctuation near curie temperature led to the only ground helix domains, resulting in the instability of skyrmion bundles above \(320\mathrm{K}\) . 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Finally, \(\mathrm{Co}_8\mathrm{Zn}_{10}\mathrm{Mn}_2\) alloy was obtained. + +## Fabrication of \(\mathrm{Co}_8\mathrm{Zn}_{10}\mathrm{Mn}_2\) micro-devices + +The \(\mathrm{Co}_8\mathrm{Zn}_{10}\mathrm{Mn}_2\) micro- devices with a thickness of \(\sim 160 \mathrm{nm}\) for TEM observation were fabricated from a polycrystal \(\mathrm{Co}_8\mathrm{Zn}_{10}\mathrm{Mn}_2\) alloy by the lift- out method using the focus ion beam (FIB) dual- beam system (Helios NanoLab 600i; FEI). + +## TEM measurements + +The Lorentz Fresnel imaging was recorded by Lorentz mode at room temperature, and the accelerating voltage of TEM (Talos F200X, FEI) is \(200 \mathrm{kV}\) . A perpendicular varying magnetic field is applied by changing the object's current. The current pulses were provided using a voltage source (AVR- E3- B- PN- AC22, Avtech Electrosystems), and the pulse widths were set to 10- 200 ns with a frequency of \(1 \mathrm{Hz}\) . + +## Micromagnetic Simulation + +The micromagnetic simulation package JuMag was used to investigate the evolution of skyrmion bundles with magnetic field. The sum of micromagnetic energy \(E\) is + +\[E = \int_{V_{s}}(\epsilon_{e} + \epsilon_{a} + \epsilon_{D} + \epsilon_{z} + \epsilon_{d})d\mathbf{r}\] + +Where \(\epsilon_{e} = A(\partial_{x}m^{2} + \partial_{y}m^{2} + \partial_{z}m^{2})\) , \(\epsilon_{a} = - K_{u}(\mathbf{u}\cdot \mathbf{m})^{2}\) , + +<--- Page Split ---> + +\[\epsilon_{D} = D\left(m_{z}\frac{\partial m_{x}}{\partial_{x}} -m_{x}\frac{\partial m_{z}}{\partial_{x}} -m_{z}\frac{\partial m_{y}}{\partial_{y}} +m_{y}\frac{\partial m_{z}}{\partial_{y}}\right),\quad \epsilon_{z} = -M_{s}\mathbf{H}\cdot \mathbf{m},\quad \mathrm{and}\quad \epsilon_{d} =\] + +where exchange interaction constant \(A_{\mathrm{ex}} = 3.25 \times 10^{- 12} \mathrm{J m}^{- 1}\) , \(M_{\mathrm{s}} = 2.78 \times 10^{5} \mathrm{A m}^{- 1}\) , DMI constant \(D = 4.8 \times 10^{- 4} \mathrm{J m}^{- 2}\) , \(K_{\mathrm{u}} = 8.1 \times 10^{3} \mathrm{J m}^{- 3}\) , and the applied external magnetic field is perpendicular to the sample. The thickness of the sample was \(\sim 160\) nm and the cell size was \(2 \mathrm{nm} \times 2 \mathrm{nm} \times 2 \mathrm{nm}\) . + +For simulating the current- driven dynamic motions of the skyrmion tube in the continuous of the step edge of nanostructures, a spin- transfer torque term based on the Zhang- Li model was applied with the expression: + +\[\epsilon_{ZL} = \frac{1}{1 + \alpha^{2}}\{(1 + \beta \alpha)\mathbf{m}\times [\mathbf{m}\times (\mathbf{\mu}\cdot \nabla)\mathbf{m}] + (\beta -\alpha)\mathbf{m}\times (\mathbf{\mu}\cdot \nabla)\mathbf{m}\} ,\] + +Where \(\mu = \frac{\mu_{B}\mu_{0}}{2e\gamma_{0}B_{sat}(1 + \beta^{2})} P\mathbf{J}\) , and \(\mathbf{J}\) , \(P\) , \(\beta\) , \(B_{\mathrm{sat}}\) , \(\mu_{\mathrm{B}}\) are the current density, the spin current polarization of the chiral magnet, the degree of non- adiabaticity, the Bohr magneton, and the saturation magnetization expressed in Tesla, respectively. We set \(\alpha = 0.3\) and \(\beta = 0.05\) . + +## Data availability + +The data that support the plots provided in this paper and other finding of this study are available from the corresponding author upon reasonable request. + +## Acknowledgments + +This work was supported by the National Key R&D Program of China, Grant No. 2022YFA1403603; the Natural Science Foundation of China, Grants No. 12174396, + +<--- Page Split ---> + +12104123, and 12241406; the National Natural Science Funds for Distinguished Young Scholar, Grant No. 52325105; the Anhui Provincial Natural Science Foundation, Grant No. 2308085Y32; the Natural Science Project of Colleges and Universities in Anhui Province, Grant No. 2022AH030011; the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDB33030100; CAS Project for Young Scientists in Basic Research, Grant No. YSBR- 084; and Systematic Fundamental Research Program Leveraging Major Scientific and Technological Infrastructure, Chinese Academy of Sciences, Grant No. JZHKYPT- 2021- 08. + +## Author contributions + +H. D. and J. T. supervised the project. J.T. conceived the idea and designed the experiments. X.X. synthesized \(\mathrm{Co_8Zn_{10}Mn_2}\) crystals. Y.Z. and Y.W fabricated the \(\mathrm{Co_8Zn_{10}Mn_2}\) microdevices and performed TEM measurements. M.S performed the simulations. Y.Z., J.T., and H.D. wrote the manuscript with input from all authors. All authors discussed the results and contributed to the manuscript. + +## Competing interests + +The authors declare no competing interests. + +## Additional information + +Supplementary information is available for this paper. + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementaryVideoS1S2. zip Supplementaryinformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__08c9c84db4a69f550730063a1db0239d35dc5acfc2ff936dd017039d40fa949f/preprint__08c9c84db4a69f550730063a1db0239d35dc5acfc2ff936dd017039d40fa949f_det.mmd b/preprint/preprint__08c9c84db4a69f550730063a1db0239d35dc5acfc2ff936dd017039d40fa949f/preprint__08c9c84db4a69f550730063a1db0239d35dc5acfc2ff936dd017039d40fa949f_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..ac1da952a84f919447700dffd0780b75ad799e5a --- /dev/null +++ b/preprint/preprint__08c9c84db4a69f550730063a1db0239d35dc5acfc2ff936dd017039d40fa949f/preprint__08c9c84db4a69f550730063a1db0239d35dc5acfc2ff936dd017039d40fa949f_det.mmd @@ -0,0 +1,321 @@ +<|ref|>title<|/ref|><|det|>[[44, 107, 919, 177]]<|/det|> +# Stable skyrmion bundles at room temperature and zero magnetic field in a chiral magnet + +<|ref|>text<|/ref|><|det|>[[44, 196, 259, 240]]<|/det|> +Jin Tang jintang@ahu.edu.cn + +<|ref|>text<|/ref|><|det|>[[44, 267, 825, 290]]<|/det|> +School of Physics and Optoelectronic Engineering, Anhui University, Hefei, 230601, China + +<|ref|>text<|/ref|><|det|>[[44, 294, 234, 334]]<|/det|> +Yongshen Zhang Chinese Academy of Sciences + +<|ref|>text<|/ref|><|det|>[[44, 340, 158, 358]]<|/det|> +Yaodong Wu + +<|ref|>text<|/ref|><|det|>[[44, 361, 940, 405]]<|/det|> +Key Laboratory for Photoelectric Detection Science and Technology of Education Department of Anhui Province, School of Physics and Materials Engineering, Hefei Normal University + +<|ref|>text<|/ref|><|det|>[[44, 409, 130, 428]]<|/det|> +Meng Shi + +<|ref|>text<|/ref|><|det|>[[44, 430, 916, 474]]<|/det|> +Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions, High Magnetic Field Laboratory, HFIPS, Anhui, Chinese Academy of Sciences + +<|ref|>text<|/ref|><|det|>[[44, 479, 130, 497]]<|/det|> +Xitong Xu + +<|ref|>text<|/ref|><|det|>[[55, 500, 320, 519]]<|/det|> +Chinese Academy of Sciences + +<|ref|>text<|/ref|><|det|>[[44, 525, 180, 544]]<|/det|> +Shouguo Wang + +<|ref|>text<|/ref|><|det|>[[53, 546, 592, 566]]<|/det|> +School of Materials Science and Engineering, Anhui University + +<|ref|>text<|/ref|><|det|>[[44, 571, 180, 590]]<|/det|> +Mingliang Tian + +<|ref|>text<|/ref|><|det|>[[53, 592, 556, 612]]<|/det|> +Anhui University https://orcid.org/0000- 0002- 0870- 995X + +<|ref|>text<|/ref|><|det|>[[44, 617, 145, 636]]<|/det|> +Haifeng Du + +<|ref|>text<|/ref|><|det|>[[53, 639, 719, 659]]<|/det|> +Hefei Institutes of Physical Science https://orcid.org/0000- 0003- 4263- 5023 + +<|ref|>text<|/ref|><|det|>[[44, 700, 101, 718]]<|/det|> +Article + +<|ref|>text<|/ref|><|det|>[[44, 737, 136, 756]]<|/det|> +Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 775, 335, 794]]<|/det|> +Posted Date: December 5th, 2023 + +<|ref|>text<|/ref|><|det|>[[44, 813, 474, 833]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 3639312/v1 + +<|ref|>text<|/ref|><|det|>[[44, 850, 910, 894]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[44, 911, 530, 931]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[42, 45, 914, 88]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on April 22nd, 2024. See the published version at https://doi.org/10.1038/s41467-024-47730-6. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[128, 92, 835, 155]]<|/det|> +# Stable skyrmion bundles at room temperature and zero magnetic field in a chiral magnet + +<|ref|>text<|/ref|><|det|>[[100, 175, 856, 647]]<|/det|> +Yongsen Zhang \(^{1,2\#}\) , Jin Tang \(^{3\# *}\) , Yaodong Wu \(^{4}\) , Meng Shi \(^{1,2}\) , Xitong Xu \(^{2}\) , Shouguo Wang \(^{5}\) , Mingliang Tian \(^{2,3}\) and Haifeng Du \(^{2*}\) \(^{1}\) University of Science and Technology of China, Hefei 230026, China \(^{2}\) Anhui Province Key Laboratory of Condensed Matter Physics at Extreme Conditions, High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Anhui, Chinese Academy of Sciences, Hefei 230031, China \(^{3}\) School of Physics and Optoelectronic Engineering, Anhui University, Hefei, 230601, China \(^{4}\) School of Physics and Materials Engineering, Hefei Normal University, Hefei, 230601, China \(^{5}\) Anhui Key Laboratory of Magnetic Functional Materials and Devices, School of Materials Science and Engineering, Anhui University, Hefei 230601, China + +<|ref|>text<|/ref|><|det|>[[147, 654, 342, 672]]<|/det|> +\*Corresponding author: + +<|ref|>text<|/ref|><|det|>[[147, 694, 494, 712]]<|/det|> +jintang@ahu.edu.cn and duhf@hmfl.ac.cn + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[147, 96, 227, 111]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[144, 128, 854, 682]]<|/det|> +Topological spin textures are characterized by topological magnetic charges \(Q\) , which govern their fascinating electro- magneto properties. Recent studies have achieved skyrmion bundles with arbitrary integer values of \(Q\) , opening up possibilities for exploring topological spintronics based on the parameter freedom of \(Q\) . However, the realization of stable skyrmion bundles in chiral magnets at room temperature and zero magnetic field, which is the prerequisite for realistic device applications, has remained exclusive. Here, through the combination of pulsed currents and reversed magnetic fields, we experimentally achieved skyrmion bundles with different inter- \(Q\) values, reaching a maximum of 24 at above room temperature and zero magnetic field in a \(\beta\) - Mn- type \(\mathrm{Co8Zn_{10}Mn_{2}}\) chiral magnet. We demonstrate the field- driven topological quantitated annihilation of high- \(Q\) bundles and present a stable phase diagram as a function of temperature and field. Our experimental findings are consistently corroborated by micromagnetic simulations. The observation of skyrmion bundles and their multi- \(Q\) topological properties at room temperature and zero fields could promote realistic multi- \(Q\) - based topological spintronic devices. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[140, 92, 853, 680]]<|/det|> +Magnetic skyrmions are vortex- like spin textures characterized by an integer topological magnetic charge, defined as \(Q = \frac{1}{4\pi}\int \mathbf{m}\cdot \left(\frac{\partial\mathbf{m}}{\partial x}\times \frac{\partial\mathbf{m}}{\partial y}\right)\mathrm{d}x\mathrm{d}y^{1 - 3}\) . Topological magnetic charge \(Q\) plays a crucial role in determining various topology- related properties of skyrmions, including skyrmion Hall effects \(^{4,5}\) , topological Hall effects \(^{6}\) , ultrasmall underpinning current \(^{7}\) , particle- like physics \(^{8}\) , and electric transport properties \(^{9,10}\) . Despite the significance of \(Q\) in determining magneto- electro properties of topological spin textures \(^{11 - 19}\) , traditional skyrmions are constrained to possess a fixed value of \(|Q| = 1\) . Recent studies propose a strategy to extend the values of \(Q\) from 1 to any integers in an assembly, called skyrmion bundles or skyrmion bags \(^{20 - 22}\) . Skyrmion bundles consist of various skyrmions encircled by a closed spin spiral. The \(N\) internal skyrmions contribute \(Q = N\) , while the outer spin spiral contributes \(Q = - 1\) . As a result, skyrmion bundles possess a total topological charge of \(Q = N - 1\) . Skyrmion bundles, holding the advantages of diversity \(Q\) and morphologies, have greatly enriched the family of topological magnetic solitons and shed a light on the topological spintronic devices based on multi- \(Q\) characteristics \(^{23}\) , such as multi- bit memories and information interconnect devices \(^{24 - 28}\) . + +<|ref|>text<|/ref|><|det|>[[144, 696, 853, 903]]<|/det|> +The realization of skyrmion bundles is based on the first- order magnetic phase change from the helix to the skyrmion lattice \(^{29}\) , which allows for the coexistence of the two phases \(^{30,31}\) . Skyrmion bundles can be created by applying reversed magnetic fields to the coexisting skyrmion- helix phases \(^{20}\) . The formation of coexisting skyrmion- helix phases typically involves a complex operation, often through a cooling procedure from high temperatures \(^{20,32}\) . Furthermore, the observation of skyrmion bundles in chiral + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 94, 852, 188]]<|/det|> +magnets has been limited to temperatures far below room temperature and under a certain magnetic field32, which poses practical challenges for their application in real-world devices. + +<|ref|>text<|/ref|><|det|>[[144, 207, 852, 527]]<|/det|> +In this work, we successfully observed skyrmion bundles with varying inter- \(Q\) values, including a remarkable maximum of 24, and reported the unambiguous experimental realization of a type of three- dimensional (3D) multi- \(Q\) skyrmionic configurations at room temperature and zero magnetic field in \(\beta\) - Mn- type \(\mathrm{Co}_8\mathrm{Zn}_{10}\mathrm{Mn}_2\) chiral magnet by the combination of pulsed current and reversed magnetic fields. The creation of room- temperature skyrmion bundles avoids the cooling procedure. Furthermore, we demonstrate the field- driven topological phase transition and provide a stabilization diagram of skyrmion bundles as a function of magnetic field and temperatures. + +<|ref|>sub_title<|/ref|><|det|>[[145, 545, 850, 602]]<|/det|> +## Observation of skyrmion bundles at room temperature without a field cooling procedure + +<|ref|>text<|/ref|><|det|>[[144, 620, 852, 905]]<|/det|> +Skyrmion bundles consist of an interior skyrmion bag and superficial multi- \(Q\) chiral vortices. The skyrmions inside the bundle exhibit conventional characteristics, with polarity \(p = 1\) and vorticity \(\nu = 1\) , resulting in a topological charge \((Q)\) of 1 for each skyrmion33- 35. The peripheral helical stripe possesses the same vorticity but opposite polarity. Consequently, the topological charge of skyrmion bundles is the summation of the central skyrmions and the boundary helical stripe, expressed as \(Q = N - 1\) , where \(N\) represents the number of skyrmions within the bundle. Given the fact for the first- order transition from spiral to skyrmions, the coexistence of skyrmions and spin spirals is + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 93, 852, 265]]<|/det|> +allowed and serves as a precursor to the formation of skyrmion bundles. We first demonstrate the creation of skyrmions induced by currents29, 36. The micro- device utilized in the experiment comprises two Pt electrodes and a \(\sim 160 \mathrm{nm}\) thick lamella with two narrow regions of \(\sim 80 \mathrm{nm}\) thickness on both sides, fabricated from \(\mathrm{Co}_8\mathrm{Zn}_{10}\mathrm{Mn}_2\) bulk (Supplemental Fig. S1). + +<|ref|>image<|/ref|><|det|>[[156, 280, 840, 545]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 560, 852, 777]]<|/det|> +
Fig. 1 | Current-induced creation of skyrmions at 294 K. a-c, A sequence of Fresnel images of the skyrmion creation process after applying numbers of the current pulse, corresponding to conical, skyrmions, and skyrmion lattice, respectively. The red rectangular dashed lines are the thin area on both sides of the sample in a. Inset: in-plane magnetization mapping of skyrmion lattice within a hexagonal dashed line by Transport of Intensity Equation (TIE). d, Skyrmion number \(N_{\mathrm{s}}\) as a function of pulse numbers, the pulse width is \(20 \mathrm{ns}\) . e, Maximum skyrmion number \(N_{\mathrm{s - max}}\) as a function of the current density, the pulse width is \(20 \mathrm{ns}\) . f, Threshold current density \(j_{\mathrm{c}}\) required to achieve the maximum skyrmion number. Defocused distance, \(-1200 \mu \mathrm{m}\) .
+ +<|ref|>text<|/ref|><|det|>[[145, 828, 852, 885]]<|/det|> +Fig. 1 illustrates the process of skyrmion creation through the application of a series of current pulses with varying pulse widths and current densities in the \(x\) - direction. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 92, 854, 920]]<|/det|> +Initially, as shown Fig. 1a, we obtained a conical state shown by uniform Fresnel contrasts at \(B = - 70 \mathrm{mT}\) . Subsequently, upon applying current pulses with a duration of \(20 \mathrm{ns}\) and a density of \(8.19 \times 10^{10} \mathrm{A / m^2}\) , skyrmion clusters were generated after several pulses (Fig. 1b). As the number of pulses increased, the skyrmion clusters gradually merged to form the skyrmion lattice, as depicted in Fig. 1c. Fig. 1d shows the effect of the current pulse number on the skyrmion creation under a current density \(j \sim 8.42 \times 10^{10} \mathrm{A / m^2}\) . Initially, the number \(N_{\mathrm{s}}\) of skyrmions exhibits a positive linear relationship with the number of pulses \(N_{\mathrm{p}}\) . After approximately the application of 43 pulsed currents, skyrmions covered most regions of the sample, with a total count of \(N_{\mathrm{s}} \sim 1830\) . Subsequently, the number of skyrmions almost keeps a balance around \(\sim 1830\) , which is defined as the maximum skyrmion number \(N_{\mathrm{s - max}}\) , despite the further application of more pulsed currents (see Supplemental Fig. S2 for details). Fig. 1e shows the dependence of skyrmion maximum numbers \((N_{\mathrm{s - max}})\) on the current density when the pulse width was set to \(20 \mathrm{ns}\) . Firstly, as the current density increases, the number of skyrmions rises until it reaches a maximum value. However, once the current density exceeds a certain threshold, the current density no longer rises, as excessively high current densities could damage the sample \(^{30,37}\) . Fig. 1f displays the effect of pulse width on the critical current densities and directions. Despite the opposite current directions, the difference in critical current density is relatively small under the same pulse width. Skyrmions are first created at the edge of the sample and then pushed into the center \(^{38}\) . This creation process can be understood by the combined current effect of spin transfer torque and Joule thermal heating \(^{23,39,40}\) , as shown in Supplemental video S1. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 94, 853, 455]]<|/det|> +Because the current density is inversely proportional to the thickness, skyrmions are first created at the thin region because of larger temperature increases induced by the current. Then, the spin transfer torque could drive skyrmions nucleated in the thin region to the thick region. A previous study has shown the current- driven skyrmion- to- bobber transformations in stepped geometry41. However, magnetic bobbers are not observed in our experiments (Supplemental video S1). The step edge between thin and thick regions in realistic experiments cannot be very sharp. Our simulations show that a slight continuous deformation of the step edge can lead to the transformation of short skyrmion tubes in a thin region to long tubes in a thick region (Supplemental video S2), which explains the expansion of skyrmions from the thin region to the thick region. + +<|ref|>text<|/ref|><|det|>[[144, 471, 853, 905]]<|/det|> +We have shown the creation of skyrmions with positive \(Q\) using pulsed current at room temperature without additional field cooling process (Fig. 2a). Additionally, after the pulse current was turned off, positive magnetic fields were applied to the skyrmions. Although \(Q = 1\) skyrmions are not thermodynamically stable under positive fields, they can usually survive in the non- equilibrium metastable state (Fig. 2a, 0 and 58 mT) at temperatures far below Curie temperature \(T_{\mathrm{c}}\) ( \(\sim 350 \mathrm{K}\) ). When the magnetic field increased to \(B = 80 \mathrm{mT}\) , most of the skyrmions were annihilated, leaving a skyrmion bundle consisting of a few skyrmions encircled by a dark ring. The Transport of Intensity Equation (TIE)42 analysis was employed to calculate the projected in- plane magnetization distribution of the skyrmion bundle. As shown in Fig. 2a (80 mT), it demonstrated that the surrounding circular spiral with weak contrast has the opposite rotation sense compared to the skyrmions inside. To quantitatively evaluate the fine + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 94, 853, 225]]<|/det|> +variations in the phase shift inside and outside the skyrmion bundle, the line profiles of the experimental and simulated phases were plotted in Fig.2b and 2d, respectively. It is evident that the contrast intensity of the outer circular spiral is weaker than that of the inner skyrmions. + +<|ref|>text<|/ref|><|det|>[[144, 243, 853, 754]]<|/det|> +These experimental observations are in excellent agreement with the average in- plane magnetization obtained through simulations, as shown in Supplemental Fig. S3. Fig. S3a depicts a representative simulated 3D magnetic configuration of skyrmion bundles containing 3 skyrmions. The iso- surfaces correspond to a value of - 0.1 for the normalized out- of- plane magnetization component, i.e., \(m_{z} = - 0.1\) (Fig. S3b). When approaching the sample surfaces (Fig. S3d and S3f), where the magnetic vortex is a bi- antiskyrmion with \(Q = 2\) , and the complete skyrmion bags with \(Q = 2\) are located only in the middle layers of the \(\mathrm{Co_8Zn_{10}Mn_2}\) lamella (Fig. S3e). Therefore, despite the strong spin twist along the depth dimension due to the conical background magnetizations, topological charges maintain \(Q = 2\) throughout all layers. The depth- modulated spin twists contribute weaker magnetic contrasts of the outer spiral compared to those of the interior skyrmions for the average in- plane magnetization mapping of skyrmion bundles (Fig. S3c). These characteristics of chiral skyrmion bundles in \(\mathrm{Co_8Zn_{10}Mn_2}\) are highly similar to those in chiral magnet \(\mathrm{FeGe^{20}}\) . + +<|ref|>text<|/ref|><|det|>[[145, 772, 851, 903]]<|/det|> +By repeating the process of combining pulsed currents and reversed magnetic fields, a diversity of magnetic skyrmion bundles with a maximum \(Q\) of 24 at room temperature can be obtained, as shown in Fig. 2e. For the bubble bundles stabilized by magnetic dipole- dipole interaction, the internal skyrmion bubbles are much larger than + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 92, 852, 455]]<|/det|> +the size of an isolate skyrmion, leading to the extremely expanded \(Q\) - related size of bubble bundles \(^{43}\) . In contrast, the skyrmion bundles stabilized by chiral interactions in \(\mathrm{Co_8Zn_{10}Mn_2}\) always reveal a compact configuration. The internal skyrmions of the bundles always tightly bind to each other and have a comparable size as that of an isolate skyrmion, leading to a linear increased area \(S\) as a function of \(Q\) . Fig. 2f shows the area \(S\) of skyrmion bundles as a function of topological charge \(Q\) , i.e., \(S = (Q + 1)(S_{N - skr} + S_{spiral})\) , which represents the sum of the area occupied by the inner skyrmions \(S_{N - skr}\) and the outer spin spiral \(S_{spiral}\) . It should be noted that in the repeating process, we could also obtain other special topological spin textures, such as the skyrmion- antiskyrmion pair (Supplemental Figure S4), at room temperature. + +<|ref|>image<|/ref|><|det|>[[147, 465, 849, 809]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 825, 852, 899]]<|/det|> +
Fig. 2 | Diversity of room-temperature skyrmion bundles. a, Creation of skyrmion bundles at room temperature by applying reversed positive magnetic fields on skyrmion clusters with positive \(Q\) . b, Line profile of magnetic phase shift extracted from (a) (red dashed arrow). c,
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 90, 852, 220]]<|/det|> +Corresponding simulated averaged in-plane magnetization mapping during the creation from skyrmion lattice to skyrmion bundles. d, Line profile of magnetic phase shift extracted from (c) (red dashed arrow). e, In-plane magnetic configurations of representative magnetic skyrmion bundles with varying \(Q\) at \(B \sim 80 \mathrm{mT}\) . f, Dependence between the area \(S\) of different skyrmion bundles and topological charge \(Q\) . The colorwheel represents the in-plane magnetizations. + +<|ref|>sub_title<|/ref|><|det|>[[144, 263, 759, 284]]<|/det|> +## Topological phase transition at high and zero magnetic fields + +<|ref|>text<|/ref|><|det|>[[144, 300, 852, 850]]<|/det|> +Fig. 3a shows a representative topological phase transition of high- \(Q\) skyrmion bundles under high magnetic fields at room temperature. When increasing the external magnetic field from 70 to \(79 \mathrm{mT}\) , one interior skyrmion of the \(Q = 8\) bundle was annihilated, while the boundary spin spiral remained stable and shrank to tightly bind the remaining skyrmions, resulting in the formation of a \(Q = 7\) bundle. As the magnetic field continued to increase, the internal skyrmions gradually disappeared one by one until only one remained, forming a \(Q = 0\) bundle, also known as the \(2\pi\) - vortex or skyrmionium (Fig. 3a, \(107 \mathrm{mT}\) ) \(^{22, 44, 45}\) . The \(Q = 0\) bundle could survive in a wide magnetic field range from 107 to \(155 \mathrm{mT}\) . However, due to the consistent polarity between the center skyrmion in the \(Q = 0\) bundle and the direction of the positive magnetic field \(^{35, 46}\) , the internal skyrmion was inherently unstable and eventually disappeared at \(B = 155 \mathrm{mT}\) . Subsequently, the outer ring shrank to form a skyrmion with \(Q = - 1\) , with a polarity opposite to that of the skyrmion within the bundle. The field- driven topological quantized annihilation can be well reproduced in our zero- temperature simulations (Fig. 3b and Fig. 3d). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 94, 852, 490]]<|/det|> +We further observed skyrmion bundles in a broad temperature range from 150 to 320 K and explored their field- driven magnetic evolutions, as shown in Fig. 3c. The topological quantized one- by- one annihilations in the field- increasing process work for temperatures below 295 K. The threshold maximum magnetic field required for the stabilization of bundles decreased as the temperature increased. In contrast, at \(T \sim 310\) K, the topological quantized annihilation behavior of the \(Q = 8\) bundle was not gradual but instead resulted in a transition from 8 to 4, 2, 0, and finally - 1. Fig. 3c shows the simulated field- driven magnetic evolution from a \(Q = 8\) skyrmion bundle at zero temperature. The topological quantized annihilation in the zero- temperature simulation is hardly shown with a continuous one- by- one mode, but instead resulted in a transition from 8 to 5, 4, 2, 1, 0, and finally - 1. + +<|ref|>image<|/ref|><|det|>[[176, 508, 818, 775]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 787, 850, 890]]<|/det|> +
Fig. 3 | Field-driven quantized topological annihilations. a, Field-driven magnetic evolutions from a skyrmion bundle with \(Q = 8\) at 295 K. b, Corresponding simulated averaged in-plane magnetization mapping during the field-driven magnetic evolution. c, Topological charge \(Q\) as a function of magnetic field \(B\) . d, Corresponding simulated topological charge \(Q\) as a function
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 90, 850, 137]]<|/det|> +of magnetic field \(B\) . \(N\) represents the number of interior skyrmion tubes. The colorwheel represents the in-plane magnetizations. + +<|ref|>text<|/ref|><|det|>[[144, 179, 852, 689]]<|/det|> +It should be noted that field- free or low- field conditions are also important for realistic device applications. Here, we further explore the stability of skyrmion bundles in the field- decreasing process, as shown in Fig. 4. When decreasing the magnetic field to zero from the high- field ferromagnetic state at room temperature, the Fresnel contrasts of the \(\mathrm{Co_8Zn_{10}Mn_2}\) lamella keep uniform, suggesting the stability of conical or stacked spiral with zero defocused Fresnel contrasts around zero magnetic fields (Supplemental Fig. S5). Our simulation confirm the formation of the stacked spiral at zero fields by decreasing field from ferromagnet at high field (Supplemental Fig. S5). Noted that the ferromagnet, conical, and stacked spiral are all shown no defocused Fresnel contrasts because of zero integral in- plane magnetization component along the depth. When the temperature further increases to \(320\mathrm{K}\) , the zero- field conical state is not stable and transforms to a helix (Supplemental Fig. S6) due to the strong thermal fluctuation energy near the Curie temperature. The zero- field stability of the stacked spiral state could support the stability of skyrmion bundles. + +<|ref|>text<|/ref|><|det|>[[144, 707, 852, 876]]<|/det|> +Fig. 4a shows the topological quantized annihilation of a \(Q = 1\) bundle with a continuous topological quantized annihilation in the field- increasing process. In contrast, the magnetic evolution of the \(Q = 1\) bundle in the field- decreasing process is shown in Fig. 4b. The \(Q = 1\) skyrmion bundle remains stable even at zero field in the spiral magnetization background. The \(Q = 1\) skyrmion bundle cannot persist and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 94, 850, 150]]<|/det|> +transform to long helix domains until a negative magnetic field \(B = - 9 \mathrm{mT}\) is applied (Fig. 4b). + +<|ref|>text<|/ref|><|det|>[[144, 169, 852, 603]]<|/det|> +Fig. 4c- e depicts the magnetic phase diagram of skyrmion bundles as a function of temperature and magnetic field in a field- increasing process from an initial \(Q = 0\) , 1, and 2 bundles. Skyrmion bundles in \(\mathrm{Co_8Zn_{10}Mn_2}\) magnets can survive in a broad field- temperature region. Our zero- temperature micromagnetic simulation also confirms the stability of skyrmion bundles at zero fields (Supplemental Fig. S7). However, for temperatures above \(320 \mathrm{K}\) , strong thermal fluctuations promote the transformation from high- energy metastable skyrmion bundles to helical phases with lower energy (Supplemental Fig. S6 and S8). The metastable skyrmion bundles and the helix at low fields are all disturbed in high temperatures near the curie temperature. It should be noted that the skyrmion bundle can be even stabilized in certain reversed negative fields. For example, the maximum negative field supporting the stability of \(Q = 2\) skyrmion bundles decreases from \(- 7 \mathrm{mT}\) at \(150 \mathrm{K}\) to \(0 \mathrm{mT}\) at \(295 \mathrm{K}\) . + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[163, 91, 825, 417]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[145, 429, 852, 616]]<|/det|> +
Fig. 4 | Magnetic phase diagram of skyrmion bundles. a. The topological quantized annihilation of a \(Q = 1\) bundle in the field-increasing. b. Topological transformation from a \(Q = 1\) bundle to the magnetic helix in the field-decreasing process. c-e, Magnetic phase diagram of skyrmion bundles ( \(Q = 0\) , 1, and 2) as a function of temperature and magnetic field. The data points in the diagram are the critical points of the two-state transition. FM and PM represent ferromagnetic and paramagnetic states, respectively. The colorwheel represents the in-plane magnetization distributions in a and b.
+ +<|ref|>sub_title<|/ref|><|det|>[[147, 668, 272, 687]]<|/det|> +## Conclusions + +<|ref|>text<|/ref|><|det|>[[144, 706, 852, 914]]<|/det|> +In summary, we have demonstrated the thermal stability of skyrmion bundles at physical conditions of realistic devices, i.e., room temperature and zero magnetic fields. We propose the creation of skyrmion bundles using a combination of pulsed current and reversed magnetic field without an additional field- cooling procedure. We also demonstrate the room- temperature multi- \(Q\) characteristics of skyrmion bundles, including topological quantized annihilation, magnetic phase diagram, and \(Q\) - related + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 93, 853, 415]]<|/det|> +bundle size. Metastable conical background magnetization can be stabilized at zero field and low temperatures, which contributes to the stability of skyrmion bundles at zero fields below \(320\mathrm{K}\) . However, the strong thermal fluctuation near curie temperature led to the only ground helix domains, resulting in the instability of skyrmion bundles above \(320\mathrm{K}\) . The further increase of thermal stability of skyrmion bundles at room temperature can be expected to be achieved in chiral magnets with higher curie temperatures. The stability of skyrmion bundles at both room temperature and zeo field could promote topological spintronic device applications based on the freedom parameter of \(Q\) . + +<|ref|>sub_title<|/ref|><|det|>[[148, 472, 260, 490]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[140, 508, 853, 884]]<|/det|> +1. Muhlbauer, S. et al. Skyrmion Lattice in a Chiral Magnet. Science 323, 915-919 (2009). +2. Kanazawa, N., Seki, S. & Tokura, Y. Noncentrosymmetric Magnets Hosting Magnetic Skyrmions. Adv. Mater. 29, 1603227 (2017). +3. Fert, A., Reyren, N. & Cros, V. Magnetic skyrmions: advances in physics and potential applications. Nat. Rev. Mater. 2, 15 (2017). +4. Jiang, W.J. et al. Direct observation of the skyrmion Hall effect. Nat. Phys. 13, 162-169 (2017). +5. Litzius, K. et al. Skyrmion Hall effect revealed by direct time-resolved X-ray microscopy. Nat. Phys. 13, 170-175 (2017). +6. Kolesnikov, A.G., Stebliy, M.E., Samardak, A.S. & Ognev, A.V. Skyrmionium - high velocity without the skyrmion Hall effect. Sci. Rep 8, 8 (2018). +7. Wang, W.W. et al. Electrical manipulation of skyrmions in a chiral magnet. Nat. Commun. 13, 7 (2022). +8. Wang, X.S., Qaiumzadeh, A. & Brataas, A. Current-Driven Dynamics of Magnetic Hopfions. Phys. Rev. Lett. 123, 6 (2019). +9. Tang, J. et al. Combined Magnetic Imaging and Anisotropic Magnetoresistance Detection of Dipolar Skyrmions. Adv. Funct. Mater. 33, 7 (2023). +10. Maccariello, D. et al. Electrical detection of single magnetic skyrmions in metallic multilayers at room temperature. Nat. 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Fert, A., Cros, V. & Sampaio, J. Skyrmions on the track. Nat. Nanotechnol. 8, 152-156 (2013). 317 26. Tomasello, R. et al. A strategy for the design of skyrmion racetrack memories. Sci. Rep 4, 7 (2014). 318 27. Grollier, J. et al. Neuromorphic spintronics. Nat. Electron. 3, 360-370 (2020). 319 28. Luo, S.J. & You, L. Skyrmion devices for memory and logic applications. APL Mater. 9, 11 (2021). 320 29. Wang, Z.D. et al. Thermal generation, manipulation and thermoelectric detection of skyrmions. Nat. Electron. 3, 672-+ (2020). 321 30. Yu, X.Z. et al. Current-Induced Nucleation and Annihilation of Magnetic Skyrmions at Room Temperature in a Chiral Magnet. Adv. Mater. 29, 6 (2017). 322 31. Koshibae, W. & Nagaosa, N. Creation of skyrmions and antiskyrmions by local heating. Nat. Commun. 5, 11 (2014). + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[89, 78, 855, 730]]<|/det|> +343 32. Powalla, L. et al. Seeding and Emergence of Composite Skyrmions in a van der Waals Magnet. Adv. Mater. 9, 2208930 (2023). 344 33. Wachowiak, A. et al. Direct observation of internal spin structure of magnetic vortex cores. Science 298, 577-580 (2002). 346 34. Van Waeyenberge, B. et al. Magnetic vortex core reversal by excitation with short bursts of an alternating field. Nature 444, 461-464 (2006). 348 35. Yu, X.Z. et al. Real- space observation of a two- dimensional skyrmion crystal. Nature 465, 901-904 (2010). 350 36. Zhao, X.B., Wang, S.S., Wang, C. & Che, R.C. Thermal effects on current- related skyrmion formation in a nanobelt. Appl. Phys. Lett. 112, 4 (2018). 352 37. Legrand, W. et al. Room- Temperature Current- Induced Generation and Motion of sub- 100 nm Skyrmions. Nano Lett. 17, 2703-2712 (2017). 354 38. Zhao, X.B. et al. Direct imaging of magnetic field- driven transitions of skyrmion cluster states in FeGe nanodisks. Proc. Natl. Acad. Sci. U. S. A. 113, 4918-4923 (2016). 356 39. Litzius, K. et al. The role of temperature and drive current in skyrmion dynamics. Nat. Electron. 3, 30-36 (2020). 360 40. Heinrich, B. Skyrmion birth at the notch. Nat. Nanotechnol. 16, 1051-1051 (2021). 361 41. Zhu, J. et al. Current- driven transformations of a skyrmion tube and a bobber in stepped nanostructures of chiral magnets. Sci. China- Phys. Mech. Astron. 64, 6 (2021). 363 42. Tang, J., Kong, L.Y., Wang, W.W., Du, H.F. & Tian, M.L. Lorentz transmission electron microscopy for magnetic skyrmions imaging. Chin. Phys. B 28, 9 (2019). 366 43. Tang, J. et al. Skyrmion- Bubble Bundles in an X- Type Sr2Co2Fe28O46 Hexaferrite above Room Temperature. Adv. Mater. 9, 2306117 (2023). 367 44. Tang, J. et al. Target Bubbles in Fe3Sn2 Nanodisks at Zero Magnetic Field. Acs Nano 14, 10986-10992 (2020). 368 45. Zhang, S.L., Kronast, F., van der Laan, G. & Hesjedal, T. Real- Space Observation of Skyrmionium in a Ferromagnet- Magnetic Topological Insulator Heterostructure. Nano Lett. 18, 1057-1063 (2018). 369 46. Yu, X.Z. et al. Near room- temperature formation of a skyrmion crystal in thin- films of the helimagnet FeGe. Nat. Mater. 10, 106-109 (2011). 370 377 + +<|ref|>sub_title<|/ref|><|det|>[[149, 772, 240, 789]]<|/det|> +## Methods + +<|ref|>sub_title<|/ref|><|det|>[[149, 810, 354, 828]]<|/det|> +## Sample Preparation + +<|ref|>text<|/ref|><|det|>[[89, 847, 850, 901]]<|/det|> +Polycrystalline samples of Co8Zn10Mn2 crystals were synthesized by a high- temperature reaction method. Stoichiometric cobalt (> 99.9%), zinc (> 99.99%), and + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[145, 94, 849, 150]]<|/det|> +manganese ( \(>99.95\%\) ) were mixed into a quartz tube, and initially heated, then slowly cooled down. Finally, \(\mathrm{Co}_8\mathrm{Zn}_{10}\mathrm{Mn}_2\) alloy was obtained. + +<|ref|>sub_title<|/ref|><|det|>[[147, 206, 568, 226]]<|/det|> +## Fabrication of \(\mathrm{Co}_8\mathrm{Zn}_{10}\mathrm{Mn}_2\) micro-devices + +<|ref|>text<|/ref|><|det|>[[147, 244, 850, 340]]<|/det|> +The \(\mathrm{Co}_8\mathrm{Zn}_{10}\mathrm{Mn}_2\) micro- devices with a thickness of \(\sim 160 \mathrm{nm}\) for TEM observation were fabricated from a polycrystal \(\mathrm{Co}_8\mathrm{Zn}_{10}\mathrm{Mn}_2\) alloy by the lift- out method using the focus ion beam (FIB) dual- beam system (Helios NanoLab 600i; FEI). + +<|ref|>sub_title<|/ref|><|det|>[[148, 395, 356, 414]]<|/det|> +## TEM measurements + +<|ref|>text<|/ref|><|det|>[[147, 432, 851, 602]]<|/det|> +The Lorentz Fresnel imaging was recorded by Lorentz mode at room temperature, and the accelerating voltage of TEM (Talos F200X, FEI) is \(200 \mathrm{kV}\) . A perpendicular varying magnetic field is applied by changing the object's current. The current pulses were provided using a voltage source (AVR- E3- B- PN- AC22, Avtech Electrosystems), and the pulse widths were set to 10- 200 ns with a frequency of \(1 \mathrm{Hz}\) . + +<|ref|>sub_title<|/ref|><|det|>[[148, 658, 420, 678]]<|/det|> +## Micromagnetic Simulation + +<|ref|>text<|/ref|><|det|>[[147, 696, 851, 751]]<|/det|> +The micromagnetic simulation package JuMag was used to investigate the evolution of skyrmion bundles with magnetic field. The sum of micromagnetic energy \(E\) is + +<|ref|>equation<|/ref|><|det|>[[350, 767, 645, 813]]<|/det|> +\[E = \int_{V_{s}}(\epsilon_{e} + \epsilon_{a} + \epsilon_{D} + \epsilon_{z} + \epsilon_{d})d\mathbf{r}\] + +<|ref|>text<|/ref|><|det|>[[147, 828, 653, 848]]<|/det|> +Where \(\epsilon_{e} = A(\partial_{x}m^{2} + \partial_{y}m^{2} + \partial_{z}m^{2})\) , \(\epsilon_{a} = - K_{u}(\mathbf{u}\cdot \mathbf{m})^{2}\) , + +<--- Page Split ---> +<|ref|>equation<|/ref|><|det|>[[144, 85, 850, 155]]<|/det|> +\[\epsilon_{D} = D\left(m_{z}\frac{\partial m_{x}}{\partial_{x}} -m_{x}\frac{\partial m_{z}}{\partial_{x}} -m_{z}\frac{\partial m_{y}}{\partial_{y}} +m_{y}\frac{\partial m_{z}}{\partial_{y}}\right),\quad \epsilon_{z} = -M_{s}\mathbf{H}\cdot \mathbf{m},\quad \mathrm{and}\quad \epsilon_{d} =\] + +<|ref|>text<|/ref|><|det|>[[144, 168, 850, 300]]<|/det|> +where exchange interaction constant \(A_{\mathrm{ex}} = 3.25 \times 10^{- 12} \mathrm{J m}^{- 1}\) , \(M_{\mathrm{s}} = 2.78 \times 10^{5} \mathrm{A m}^{- 1}\) , DMI constant \(D = 4.8 \times 10^{- 4} \mathrm{J m}^{- 2}\) , \(K_{\mathrm{u}} = 8.1 \times 10^{3} \mathrm{J m}^{- 3}\) , and the applied external magnetic field is perpendicular to the sample. The thickness of the sample was \(\sim 160\) nm and the cell size was \(2 \mathrm{nm} \times 2 \mathrm{nm} \times 2 \mathrm{nm}\) . + +<|ref|>text<|/ref|><|det|>[[144, 319, 850, 413]]<|/det|> +For simulating the current- driven dynamic motions of the skyrmion tube in the continuous of the step edge of nanostructures, a spin- transfer torque term based on the Zhang- Li model was applied with the expression: + +<|ref|>equation<|/ref|><|det|>[[216, 428, 778, 458]]<|/det|> +\[\epsilon_{ZL} = \frac{1}{1 + \alpha^{2}}\{(1 + \beta \alpha)\mathbf{m}\times [\mathbf{m}\times (\mathbf{\mu}\cdot \nabla)\mathbf{m}] + (\beta -\alpha)\mathbf{m}\times (\mathbf{\mu}\cdot \nabla)\mathbf{m}\} ,\] + +<|ref|>text<|/ref|><|det|>[[144, 465, 850, 602]]<|/det|> +Where \(\mu = \frac{\mu_{B}\mu_{0}}{2e\gamma_{0}B_{sat}(1 + \beta^{2})} P\mathbf{J}\) , and \(\mathbf{J}\) , \(P\) , \(\beta\) , \(B_{\mathrm{sat}}\) , \(\mu_{\mathrm{B}}\) are the current density, the spin current polarization of the chiral magnet, the degree of non- adiabaticity, the Bohr magneton, and the saturation magnetization expressed in Tesla, respectively. We set \(\alpha = 0.3\) and \(\beta = 0.05\) . + +<|ref|>sub_title<|/ref|><|det|>[[147, 658, 317, 678]]<|/det|> +## Data availability + +<|ref|>text<|/ref|><|det|>[[144, 695, 850, 752]]<|/det|> +The data that support the plots provided in this paper and other finding of this study are available from the corresponding author upon reasonable request. + +<|ref|>sub_title<|/ref|><|det|>[[147, 809, 336, 828]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[144, 845, 850, 904]]<|/det|> +This work was supported by the National Key R&D Program of China, Grant No. 2022YFA1403603; the Natural Science Foundation of China, Grants No. 12174396, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[144, 93, 852, 377]]<|/det|> +12104123, and 12241406; the National Natural Science Funds for Distinguished Young Scholar, Grant No. 52325105; the Anhui Provincial Natural Science Foundation, Grant No. 2308085Y32; the Natural Science Project of Colleges and Universities in Anhui Province, Grant No. 2022AH030011; the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDB33030100; CAS Project for Young Scientists in Basic Research, Grant No. YSBR- 084; and Systematic Fundamental Research Program Leveraging Major Scientific and Technological Infrastructure, Chinese Academy of Sciences, Grant No. JZHKYPT- 2021- 08. + +<|ref|>sub_title<|/ref|><|det|>[[149, 433, 365, 453]]<|/det|> +## Author contributions + +<|ref|>text<|/ref|><|det|>[[144, 469, 852, 640]]<|/det|> +H. D. and J. T. supervised the project. J.T. conceived the idea and designed the experiments. X.X. synthesized \(\mathrm{Co_8Zn_{10}Mn_2}\) crystals. Y.Z. and Y.W fabricated the \(\mathrm{Co_8Zn_{10}Mn_2}\) microdevices and performed TEM measurements. M.S performed the simulations. Y.Z., J.T., and H.D. wrote the manuscript with input from all authors. All authors discussed the results and contributed to the manuscript. + +<|ref|>sub_title<|/ref|><|det|>[[149, 697, 352, 716]]<|/det|> +## Competing interests + +<|ref|>text<|/ref|><|det|>[[149, 734, 504, 752]]<|/det|> +The authors declare no competing interests. + +<|ref|>sub_title<|/ref|><|det|>[[149, 800, 384, 819]]<|/det|> +## Additional information + +<|ref|>text<|/ref|><|det|>[[148, 838, 586, 857]]<|/det|> +Supplementary information is available for this paper. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[43, 42, 312, 70]]<|/det|> +## Supplementary Files + +<|ref|>text<|/ref|><|det|>[[43, 93, 765, 113]]<|/det|> +This is a list of supplementary files associated with this preprint. Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 353, 177]]<|/det|> +SupplementaryVideoS1S2. zip Supplementaryinformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__08d5be710fda340f8176868396f7164bafc541ee9e5230b9c4d443f3c9838375/images_list.json b/preprint/preprint__08d5be710fda340f8176868396f7164bafc541ee9e5230b9c4d443f3c9838375/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..a53fe24b42171ac567e3a63eb89941723fb5cfdf --- /dev/null +++ b/preprint/preprint__08d5be710fda340f8176868396f7164bafc541ee9e5230b9c4d443f3c9838375/images_list.json @@ -0,0 +1,56 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1", + "footnote": [], + "bbox": [ + [ + 133, + 144, + 860, + 333 + ] + ], + "page_idx": 46 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Extended Data Figure 5", + "footnote": [], + "bbox": [], + "page_idx": 46 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_0.jpg", + "caption": "b", + "footnote": [], + "bbox": [], + "page_idx": 47 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_1.jpg", + "caption": "C", + "footnote": [], + "bbox": [ + [ + 170, + 364, + 757, + 580 + ] + ], + "page_idx": 48 + }, + { + "type": "image", + "img_path": "images/Figure_unknown_2.jpg", + "caption": "b", + "footnote": [], + "bbox": [], + "page_idx": 49 + } +] \ No newline at end of file diff --git a/preprint/preprint__08e00c94056e03db3ebcdc2920d9ec2ef75181d657a1f6242f88ac52aaa88e5f/images_list.json b/preprint/preprint__08e00c94056e03db3ebcdc2920d9ec2ef75181d657a1f6242f88ac52aaa88e5f/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..bc9b664e960710085b3255bd5084468bdf7e6d70 --- /dev/null +++ b/preprint/preprint__08e00c94056e03db3ebcdc2920d9ec2ef75181d657a1f6242f88ac52aaa88e5f/images_list.json @@ -0,0 +1,77 @@ +[ + { + "type": "image", + "img_path": "images/Figure_1.jpg", + "caption": "Figure 1 | Sr2IrO4 thin film systems. Schematic diagram of Sr2IrO4 thin film on different ruthenates substrates and reference LSAT substrate. The Sr2RuO4 and LSAT single crystals have comparable in-plane lattice constants yet exhibit metallic, and insulating properties, respectively. The Ca3Ru1.98Ti0.02O7 single crystal exhibits metallic properties above 55 K and becomes insulating below 55 K.", + "footnote": [], + "bbox": [ + [ + 142, + 90, + 852, + 315 + ] + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2 | RIXS spectra of \\(\\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\\) heterostructure. a Energy loss spectra of \\(\\mathrm{Sr_2IrO_4 / Sr_2RuO_3}\\) heterostructure at \\(20\\mathrm{K}\\) along high symmetry lines. The inset shows high symmetry points in the Brillouin zone of the undistorted tetragonal unit cell and the magnetic unit cell. b The image plot of the data shown in (b). The detections of well-defined dispersive magnons and spin-orbit excitons indicate high-quality \\(\\mathrm{Sr_2IrO_4}\\) thin film. The orange dotted line in both (a) and (b) is the eye guide to the magnon dispersion. c A visual representation depicting the horizontal scattering geometry utilized in RIXS measurements.", + "footnote": [], + "bbox": [ + [ + 200, + 125, + 812, + 480 + ] + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/Figure_3.jpg", + "caption": "Figure 3 | Softening of magnon in \\(\\mathrm{Sr_2IrO_4}\\) interfaced to metallic substrates at \\((\\pi /2,\\pi /2)\\) zone boundary. a RIXS spectra of \\(\\mathrm{Sr_2IrO_4}\\) thin films on two types of substrates, with single crystal (light green) at \\((\\pi ,0)\\) and \\((\\pi /2,\\pi /2)\\) : metallic substrates \\((\\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\\) (orange) and \\(\\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\\) at \\(\\mathrm{T} > 55\\mathrm{K}\\) (red)) and insulating substrates \\((\\mathrm{Sr_2IrO_4 / LSAT}\\) (cyan) and \\(\\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\\) at \\(\\mathrm{T}< 55\\mathrm{K}\\) (blue)). The \\(\\mathrm{Sr_2IrO_4}\\) thin films interfaced on the metallic substrates show significant softening of the single magnon at the \\((\\pi /2,\\pi /2)\\) . b Magnon dispersion along the high symmetry lines extracted from the RIXS spectra in comparison with data obtained on \\(\\mathrm{Sr_2IrO_4}\\) single crystal. The solid orange and cyan lines represent the theoretical fitting using the model Hamiltonian for the \\(\\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\\) and \\(\\mathrm{Sr_2IrO_4 / LSAT}\\) , respectively.", + "footnote": [], + "bbox": [ + [ + 192, + 95, + 792, + 355 + ] + ], + "page_idx": 8 + }, + { + "type": "image", + "img_path": "images/Figure_4.jpg", + "caption": "Figure 4 | Softening of two-magnon in samples with metallic substrates. a Raman spectra of \\(B_{2\\mathrm{g}}\\) two-magnon modes in \\(\\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\\) and \\(\\mathrm{Sr_2IrO_4 / LSAT}\\) heterostructures measured at \\(10\\mathrm{K}\\) . b Temperature-dependent Raman spectra of \\(B_{2\\mathrm{g}}\\) two-magnon modes of \\(\\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\\) heterostructure. Two-magnon energy in \\(\\mathrm{Sr_2IrO_4}\\) interfaced to metallic substrates \\((\\mathrm{Sr_2RuO_4}\\) and \\(\\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\\) ( \\(T > 55\\mathrm{K}\\) )) has lower peak energy compared to that interfaced to insulating substrates (LSAT and \\(\\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\\) ( \\(T< 55\\mathrm{K}\\) )). Two-magnon peak positions for both figures are estimated by using two Lorentz oscillator curve fits which are represented by smooth solid lines and their sum is shown by black solid line.", + "footnote": [], + "bbox": [ + [ + 313, + 299, + 656, + 675 + ] + ], + "page_idx": 9 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5 | Hardening of phonons in \\(\\mathrm{Sr_2IrO_4}\\) interfaced with metallic substrates. a \\(A_{1\\mathrm{g}}\\) and \\(B_{2\\mathrm{g}}\\) phonon modes of \\(\\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\\) and \\(\\mathrm{Sr_2IrO_4 / LSAT}\\) heterostructures. The solid black lines represent the Lorentzian fit. b Temperature-dependent \\(B_{2\\mathrm{g}}\\) phonon modes of \\(\\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\\) heterostructure. Inset: Temperature-dependent peak position of the \\(B_{2\\mathrm{g}}\\) phonon modes of \\(\\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\\) heterostructure.", + "footnote": [], + "bbox": [ + [ + 306, + 95, + 678, + 440 + ] + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/preprint/preprint__08e00c94056e03db3ebcdc2920d9ec2ef75181d657a1f6242f88ac52aaa88e5f/preprint__08e00c94056e03db3ebcdc2920d9ec2ef75181d657a1f6242f88ac52aaa88e5f.mmd b/preprint/preprint__08e00c94056e03db3ebcdc2920d9ec2ef75181d657a1f6242f88ac52aaa88e5f/preprint__08e00c94056e03db3ebcdc2920d9ec2ef75181d657a1f6242f88ac52aaa88e5f.mmd new file mode 100644 index 0000000000000000000000000000000000000000..2fce9befe6372cc4aa0df7a072a9892d0fa89312 --- /dev/null +++ b/preprint/preprint__08e00c94056e03db3ebcdc2920d9ec2ef75181d657a1f6242f88ac52aaa88e5f/preprint__08e00c94056e03db3ebcdc2920d9ec2ef75181d657a1f6242f88ac52aaa88e5f.mmd @@ -0,0 +1,267 @@ + +# Tunable magnons of an antiferromagnetic Mott insulator via interfacial metal-insulator transitions + +Ambrose Seo + +a.seo@uky.edu + +University of Kentucky https://orcid.org/0000- 0002- 7055- 5314 + +Sujan Shrestha + +University of Kentucky + +Maryam Souri + +University of Kentucky + +Christopher Dietl + +Argonne National Laboratory + +Ekaterina M. Parschke + +University of Alabama at Birmingham + +Maximilian Krautloher + +Max Planck Institute for Solid State Research + +Gabriel Calderon Ortiz + +Ohio State University + +Matteo Minola + +Max Planck Institute https://orcid.org/0000- 0003- 4084- 0664 + +Xiaotong Shi + +Max Planck Institute for Solid State Research + +A. +V. Boris + +Max Planck Institute for Solid State Research https://orcid.org/0000- 0002- 2062- 5046 + +Jinwoo Hwang + +Ohio State University + +Giniyat Khaliullin + +Max Planck Institute for Solid State Research https://orcid.org/0000- 0001- 9395- 6447 + +Gang Cao + +University of Colorado Boulder https://orcid.org/0000- 0001- 9779- 430X + +Bernhard Keimer + +Max Planck Institute for Solid State Research https://orcid.org/0000- 0001- 5220- 9023 + +Jong-Woo Kim + +Argonne National Laboratory https://orcid.org/0000- 0001- 9641- 2947 + +Jung Ho Kim + +<--- Page Split ---> + +## Article + +## Keywords: + +Posted Date: July 26th, 2024 + +DOI: https://doi.org/10.21203/rs.3.rs- 4753008/v1 + +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +Additional Declarations: There is NO Competing Interest. + +Version of Record: A version of this preprint was published at Nature Communications on April 15th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 58922- z. + +<--- Page Split ---> + +# Tunable magnons of an antiferromagnetic Mott insulator via interfacial metal-insulator + +## transitions + +S. Shrestha, \(^{1}\) M. Souri, \(^{1}\) C. Dietl, \(^{2}\) E. M. Pärschke, \(^{3}\) M. Krautloher, \(^{4}\) G. A. Calderon Ortiz, \(^{5}\) M. Minola, \(^{4}\) X. Shi, \(^{4}\) A. V. Boris, \(^{4}\) J. Hwang, \(^{5}\) G. Khaliullin, \(^{4}\) G. Cao, \(^{6}\) B. Keimer, \(^{4}\) J.-W. Kim, \(^{2}\) J. Kim, \(^{2}\) and A. Seo \(^{1}\) + +\(^{1}\) Department of Physics and Astronomy, University of Kentucky, Lexington, KY 40506, USA \(^{2}\) Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA \(^{3}\) Department of Physics, University of Alabama at Birmingham, Birmingham, AL 35294, USA \(^{4}\) Max- Planck- Institut für Festkörperforschung, D- 70569 Stuttgart, GERMANY \(^{5}\) Department of Materials Science and Engineering, The Ohio State University, Columbus, OH 43210, USA \(^{6}\) Department of Physics, University of Colorado at Boulder, Boulder, CO 80309, USA + +## Abstract + +Antiferromagnetic insulators offer an alternative to ferromagnets due to their ultrafast spin dynamics essential for low- energy terahertz spintronic device applications. One way is to utilize magnons, i.e., quantized spin waves, which can carry information through excitations. However, finding external knobs for tuning the magnons has been a significant challenge. Here we report that interfacial metal- insulator transitions can be an effective means for controlling the magnons of a strongly spin- orbit- coupled antiferromagnetic Mott insulator, \(\mathrm{Sr_2IrO_4}\) . From resonant inelastic X- ray scattering and Raman spectroscopy, we have observed a pronounced softening of zone- boundary magnon energies in several \(\mathrm{Sr_2IrO_4}\) thin- film systems that are epitaxially contacted with metallic \(4d\) transition- metal oxides (TMOs). Therefore, the magnon dispersion of \(\mathrm{Sr_2IrO_4}\) is tunable by metal- insulator transitions of the \(4d\) TMO crystals. Remarkably, this non- trivial behavior of magnons is a long- range phenomenon coupled with intriguing magnon- phonon interactions. Our experimental finding proposes a new scheme for magnonics. + +<--- Page Split ---> + +Magnons, i.e., collective spin wave excitations originating from spin precession in magnetically ordered materials, have the potential to serve as a promising medium for quantum information devices. Since the propagation of magnons does not require the transport of a charge, preventing electrical losses such as Joule heating1, it gives rise to a burgeoning research field known as magnonics2,3. Antiferromagnetic insulators have garnered considerable attention in this emerging field, primarily due to their ultrafast spin dynamics compared to ferromagnetic counterparts, which are essential for device operation in the terahertz range4-6. Nevertheless, effectively guiding and coherently manipulating magnons using external stimuli is still a significant challenge. + +Hetero- interfaces between two different materials can provide model systems for investigating the relation between external stimuli and collective spin waves. Examples are the effects of lattice strain7, interfacial coupling, and charge transfer on magnons8,9 and their spin currents10- 12. In particular, interfaces between an antiferromagnetic insulator and a metal have been considered for novel spin- charge conversion13,14. Despite some astonishing predictions from magnetic insulator/metal interfaces15, the fundamental understanding of how a metallic interface affects the spin- wave dispersion of an antiferromagnetic insulator remains elusive. + +\(\mathrm{Sr_2IrO_4}\) , a \(5d\) transition- metal oxide, is a quasi- two- dimensional antiferromagnetic insulator with strong spin- orbit interaction resulting in the \(J_{\mathrm{eff}} = 1 / 2\) pseudospins. The distinctive canted antiferromagnetism and magnetic anisotropy in the \(J_{\mathrm{eff}} = 1 / 2\) state can be useful for spintronic applications16,17. Notably, \(\mathrm{Sr_2IrO_4}\) hosts spin waves at terahertz frequencies with a significant stress response mediated by strong spin- orbit interactions7. Its similarities to \(\mathrm{La_2CuO_4}\) , a parent compound of high \(T_{\mathrm{c}}\) superconductors, suggest a potential for superconducting antiferromagnetic magnonics18. Therefore, \(\mathrm{Sr_2IrO_4}\) presents a compelling avenue for studying the influence of + +<--- Page Split ---> + +metallic interfaces on spin- wave dispersion and its heterostructures offer opportunities to explore intriguing phenomena19,20. + +In this article, we report a systematic study of the spin- wave dispersion of \(\mathrm{Sr2IrO_4}\) thin films that are epitaxially interfaced with various metallic or insulating single crystals. The recent advancement of high- resolution resonant inelastic x- ray scattering (RIXS) has enabled us to access the low- energy magnetic dynamics of iridates throughout the entire Brillouin zone21. Our RIXS experiments show a significant softening of single magnon peaks (with no broadening) near the \((\pi /2, \pi /2)\) zone boundary for various \(\mathrm{Sr2IrO_4}\) thin films neighboring metallic crystals, while the single magnon spectrum of \(\mathrm{Sr2IrO_4}\) thin films remains unchanged when adjoining insulating crystals. Similar behavior of the two- magnon excitation, predominantly indicating zone boundary excitation, is observed in Raman spectra. Conversely, the phonon mode of \(\mathrm{Sr2IrO_4}\) thin films exhibits significant hardening when interfaced with metallic crystals. We propose that electron- phonon interaction, occurring either at the heterointerface or within the metallic substrate, could have modified the phonon of \(\mathrm{Sr2IrO_4}\) thin films, subsequently influencing the magnons via long- range magnon- phonon interactions throughout the entire thin film. Our systematic measurements using various bulk- sensitive experimental techniques such as Raman spectroscopy, optical spectroscopy, and resonant x- ray scattering, indicate that conventional interfacial interactions such as strain, doping, and proximity effects are unlikely to account for this experimental result. Our experimental results present a novel approach in magnonics, utilizing metal- insulator transitions in adjacent crystals as a mechanism for manipulating the propagation of terahertz magnons. + +<--- Page Split ---> + +## Experimental Layout + +We constructed epitaxial heterostructures by depositing \(\mathrm{Sr_2IrO_4}\) epitaxial thin films on ruthenates single crystals (Fig. 1a) by using pulsed laser deposition \(^{22,23}\) . To circumvent the potential influence of spin- spin interactions, we opt for ruthenates exhibiting paramagnetic characteristics. The \(\mathrm{Sr_2RuO_4}\) single crystals exhibit a tetragonal crystal structure and metallic transport behavior whereas the single crystals of \(\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) are orthorhombic and exhibit a metal- insulator transition at 55 K (i.e., metallic above 55 K and insulating below 55 K) \(^{24}\) with a slight change of the \(b\) and \(c\) - lattice constants (Supplementary Fig. 1) \(^{24}\) . For a systematic examination and to address the strain effect, the \(\mathrm{Sr_2IrO_4}\) thin film on an insulating \(\mathrm{(LaAlO_3)_{0.3}(Sr_2TaAlO_6)_{0.7}}\) (LSAT) perovskite single crystal is also investigated, as LSAT and \(\mathrm{Sr_2RuO_4}\) single crystals have a similar lattice mismatch with \(\mathrm{Sr_2IrO_4}\) . A high- resolution Z- contrast scanning transmission electron microscopy image of the \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructure (Supplementary Fig. 2) reveals an atomically sharp heterointerface, akin to the reported sharpness in the \(\mathrm{Sr_2IrO_4 / Ca_3Ru_2O_7}\) heterointerface \(^{25}\) , which is expected to be replicated in the \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) heterostructure. Supplementary Figure 3a exhibits distinct (0 0 \(l\) )- diffraction peaks for both the \(\mathrm{Sr_2IrO_4}\) thin film and the \(\mathrm{Sr_2RuO_4}\) , \(\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) , and LSAT substrates, including interference fringes near the (0 0 12) peak. X- ray reciprocal space mapping clearly shows that the in- plane lattice of \(\mathrm{Sr_2IrO_4}\) thin films is coherently strained with all three substrates (Supplementary Fig. 3b and 3c). It is noteworthy that the \(\mathrm{Sr_2IrO_4}\) thin film grown on both \(\mathrm{Sr_2RuO_4}\) and LSAT experiences the same amount of compressive strain of - 0.51% (Supplementary Table 1). However, the \(\mathrm{Sr_2IrO_4}\) thin film deposited on the \(\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) substrate experiences a - 2.22% compressive strain along the \(a\) - axis and a 0.33% tensile strain along the \(b\) - axis above 55 K, whereas the tensile strain along the \(b\) - axis increases to 2% below 55 K (Supplementary Table 2). + +<--- Page Split ---> +![](images/Figure_1.jpg) + +
Figure 1 | Sr2IrO4 thin film systems. Schematic diagram of Sr2IrO4 thin film on different ruthenates substrates and reference LSAT substrate. The Sr2RuO4 and LSAT single crystals have comparable in-plane lattice constants yet exhibit metallic, and insulating properties, respectively. The Ca3Ru1.98Ti0.02O7 single crystal exhibits metallic properties above 55 K and becomes insulating below 55 K.
+ +In recent years, RIXS has become the tool of choice for collecting momentum- resolved and element- specific information about collective magnetic excitations such as magnons, and spin- orbit excitons in transition metal oxide thin films26- 28. Figure 2a shows representative energy loss spectra of Sr2IrO4/Sr2RuO4 heterostructure collected along high- symmetry directions throughout the magnetic Brillouin zone. These spectra were acquired using RIXS measurements at the 27- ID beamline of the Advanced Photon Source, utilizing a horizontal scattering setup with incident photons polarized in the \(\pi\) - direction, as depicted in Fig. 2c. Additionally, Figure 2b depicts a color intensity map generated from energy loss spectra resembling those presented in Fig. 2a. The distinctive features of these spectra such as a dispersive magnetic excitation (magnon) in the low energy range of 0 - 0.25 eV and a dispersive orbital excitation (spin- orbit exciton) in the higher energy range of 0.40 - 0.90 eV exhibit inherent properties of the system18,28. Furthermore, the detections of well- defined dispersive magnons and spin- orbit excitons indicate high crystallinity of Sr2IrO4 thin film. + +<--- Page Split ---> +![](images/Figure_2.jpg) + +
Figure 2 | RIXS spectra of \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructure. a Energy loss spectra of \(\mathrm{Sr_2IrO_4 / Sr_2RuO_3}\) heterostructure at \(20\mathrm{K}\) along high symmetry lines. The inset shows high symmetry points in the Brillouin zone of the undistorted tetragonal unit cell and the magnetic unit cell. b The image plot of the data shown in (b). The detections of well-defined dispersive magnons and spin-orbit excitons indicate high-quality \(\mathrm{Sr_2IrO_4}\) thin film. The orange dotted line in both (a) and (b) is the eye guide to the magnon dispersion. c A visual representation depicting the horizontal scattering geometry utilized in RIXS measurements.
+ +## Results + +The RIXS measurements reveal that the low- energy magnon dispersion of \(\mathrm{Sr_2IrO_4}\) exhibits a sudden softening by about \(20\mathrm{meV}\) at the \((\pi /2,\pi /2)\) zone boundary when the neighboring single crystal goes across a transition from insulating to metallic. Figure 3a shows the low- energy magnon peaks of all \(\mathrm{Sr_2IrO_4}\) thin films at \((\pi ,0)\) and \((\pi /2,\pi /2)\) . As observed in \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) , + +<--- Page Split ---> +![](images/Figure_3.jpg) + +
Figure 3 | Softening of magnon in \(\mathrm{Sr_2IrO_4}\) interfaced to metallic substrates at \((\pi /2,\pi /2)\) zone boundary. a RIXS spectra of \(\mathrm{Sr_2IrO_4}\) thin films on two types of substrates, with single crystal (light green) at \((\pi ,0)\) and \((\pi /2,\pi /2)\) : metallic substrates \((\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) (orange) and \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) at \(\mathrm{T} > 55\mathrm{K}\) (red)) and insulating substrates \((\mathrm{Sr_2IrO_4 / LSAT}\) (cyan) and \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) at \(\mathrm{T}< 55\mathrm{K}\) (blue)). The \(\mathrm{Sr_2IrO_4}\) thin films interfaced on the metallic substrates show significant softening of the single magnon at the \((\pi /2,\pi /2)\) . b Magnon dispersion along the high symmetry lines extracted from the RIXS spectra in comparison with data obtained on \(\mathrm{Sr_2IrO_4}\) single crystal. The solid orange and cyan lines represent the theoretical fitting using the model Hamiltonian for the \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) and \(\mathrm{Sr_2IrO_4 / LSAT}\) , respectively.
+ +well- defined magnons are present in other thin films on both metallic \((\mathrm{Sr_2RuO_4}\) and \(\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) above \(55\mathrm{K}\) ) and insulating substrates \((\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) below \(55\mathrm{K}\) and LSAT). While the magnon peak energy remains almost similar (around \(200\mathrm{meV}\) ) at \((\pi ,0)\) for all five systems, i.e., \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) , \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) ( \(\mathrm{T}< 55\mathrm{K}\) ), \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) ( \(\mathrm{T} > 55\mathrm{K}\) ), \(\mathrm{Sr_2IrO_4 / LSAT}\) , and \(\mathrm{Sr_2IrO_4}\) single crystal, a notable difference is observed in the magnon peak energy at \((\pi /2,\pi /2)\) . The \(\mathrm{Sr_2IrO_4}\) thin films interfaced on the metallic substrates \((\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) (orange) and \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) above \(55\mathrm{K}\) (red)) show significant softening, with a reduction of about \(20\mathrm{meV}\) at \((\pi /2,\pi /2)\) . In contrast, for thin films on insulating + +<--- Page Split ---> + +substrates \(\mathrm{(Ca_3Ru_{1.98}Ti_{0.02}O_7}\) below \(55\mathrm{K}\) (blue) and LSAT (cyan)), the magnon peak energy at \((\pi /2,\pi /2)\) is indistinguishable from that of the single crystal. This peak shift is greater than the experimental error bar \(^{21}\) . Essentially, we can group them into two categories: \(\mathrm{Sr_2IrO_4}\) heterostructures with insulating substrates show higher magnon energy, while the other with metallic substrates have a softened magnon energy reduced by about \(20\mathrm{meV}\) . + +![](images/Figure_4.jpg) + +
Figure 4 | Softening of two-magnon in samples with metallic substrates. a Raman spectra of \(B_{2\mathrm{g}}\) two-magnon modes in \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) and \(\mathrm{Sr_2IrO_4 / LSAT}\) heterostructures measured at \(10\mathrm{K}\) . b Temperature-dependent Raman spectra of \(B_{2\mathrm{g}}\) two-magnon modes of \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) heterostructure. Two-magnon energy in \(\mathrm{Sr_2IrO_4}\) interfaced to metallic substrates \((\mathrm{Sr_2RuO_4}\) and \(\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) ( \(T > 55\mathrm{K}\) )) has lower peak energy compared to that interfaced to insulating substrates (LSAT and \(\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) ( \(T< 55\mathrm{K}\) )). Two-magnon peak positions for both figures are estimated by using two Lorentz oscillator curve fits which are represented by smooth solid lines and their sum is shown by black solid line.
+ +<--- Page Split ---> + +The two- magnon peak energies in the high- resolution Raman spectra corroborate the RIXS measurements. Figure 4a illustrates the \(B_{2\mathrm{g}}\) two- magnon modes in \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) and \(\mathrm{Sr_2IrO_4 / LSAT}\) at \(10\mathrm{K}\) , while Fig. 4b displays the temperature- dependent Raman spectra of \(B_{2\mathrm{g}}\) two- magnon modes in \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) . We determine the two- magnon peak energies \((\omega_{2\mathrm{M}})\) through fits to a model function comprising two Lorentz oscillators. Note that the two- magnon energies of thin films on metallic substrates \((\mathrm{Sr_2RuO_4}\) and \(\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) above \(55\mathrm{K}\) ) are lower than those on insulating substrates \((\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) below \(55\mathrm{K}\) and LSAT). Considering that the two- magnon mode primarily reflects zone boundary excitations \(^{29}\) , this observation aligns with the softening of the \((\pi /2, \pi /2)\) zone boundary magnon and establishes consistency between the two measurements. + +By fitting the magnon dispersion data (Fig. 3b) of \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) and \(\mathrm{Sr_2IrO_4 / LSAT}\) heterostructures using a Heisenberg spin model, we extracted the in- plane exchange interactions, i.e., the nearest \((J_1)\) , next nearest \((J_2)\) , next- next nearest \((J_3)\) , and fourth nearest \((J_4)\) neighbors, between the \(J_{\mathrm{eff}} = 1 / 2\) pseudospins. The best- fit results in the values of \(J_1 = 50 \mathrm{meV}\) , \(J_2 = - 21 \mathrm{meV}\) , \(J_3 = 20 \mathrm{meV}\) , and \(J_4 = 6.3 \mathrm{meV}\) for \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructure and \(J_1 = 50 \mathrm{meV}\) , \(J_2 = - 17 \mathrm{meV}\) , \(J_3 = 16 \mathrm{meV}\) , and \(J_4 = 9 \mathrm{meV}\) for \(\mathrm{Sr_2IrO_4 / LSAT}\) heterostructure. Note that the change of the experimentally measured magnon dispersion for metallic substrates suggests increasing in- plane interactions \(J_2\) and \(J_3\) compared to insulating substrates and \(\mathrm{Sr_2IrO_4}\) single crystals (Supplementary Table 3). + +Raman spectroscopy indicates that the phonon modes of \(\mathrm{Sr_2IrO_4}\) undergo a noticeable hardening by approximately \((1 - 1.4) \mathrm{meV}\) when the adjacent single crystal substrate is altered from insulating to metallic, likely attributed to electron- phonon interaction. Figure 5a illustrates + +<--- Page Split ---> +![](images/Figure_5.jpg) + +
Figure 5 | Hardening of phonons in \(\mathrm{Sr_2IrO_4}\) interfaced with metallic substrates. a \(A_{1\mathrm{g}}\) and \(B_{2\mathrm{g}}\) phonon modes of \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) and \(\mathrm{Sr_2IrO_4 / LSAT}\) heterostructures. The solid black lines represent the Lorentzian fit. b Temperature-dependent \(B_{2\mathrm{g}}\) phonon modes of \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) heterostructure. Inset: Temperature-dependent peak position of the \(B_{2\mathrm{g}}\) phonon modes of \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) heterostructure.
+ +the \(A_{1\mathrm{g}}\) and \(B_{2\mathrm{g}}\) phonon modes of the \(\mathrm{Sr_2IrO_4}\) thin film in the \(\mathrm{Sr_2IrO_4 / LSAT}\) and \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructures. While the phonon modes of \(\mathrm{Sr_2IrO_4 / LSAT}\) closely resemble \(\mathrm{Sr_2IrO_4}\) single crystal, a notable upward energy shift by about \(1.4\mathrm{meV}\) and \(1\mathrm{meV}\) for \(A_{1\mathrm{g}}\) and \(B_{2\mathrm{g}}\) modes, respectively, is observed in \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructure. A similar upward shift of up to 0.7 meV in the phonon energy is also observed in \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) heterostructures when transitioning from an insulating to metallic state of the \(\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) substrate at \(55\mathrm{K}\) (Fig. 5b). Nevertheless, we acknowledge that the sudden structural change at \(55\mathrm{K}\) might also have some contributions to the phonon mode shift in \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) heterostructures. Additionally, + +<--- Page Split ---> + +we examined several other heterostructures, such as \(\mathrm{Sr_2IrO_4 / Ca_2Ru_{0.91}Mn_{0.09}O_4}\) (insulator), \(\mathrm{Sr_2IrO_4 / Sr_2RhO_4}\) (metal), and \(\mathrm{Sr_2IrO_4 / Ca_3Ru_2O_7}\) (metal) at \(10\mathrm{K}\) . These heterostructures displayed similar patterns based on the substrate's metallicity, showing a stiffening of the \(B_{2\mathrm{g}}\) phonons in the \(\mathrm{Sr_2IrO_4}\) thin film by about \(8\mathrm{cm^{- 1}}\) when interfaced with metallic substrates compared to insulating ones (Supplementary Fig. 4). Clearly, a common factor in the results is the metallic state of the substrates. The presence of delocalized carriers could explain the anomalous phonon behavior observed. One potential explanation is that charge carrier-phonon interactions within the metallic substrate alter its phonons, which then resonate with the phonons of the \(\mathrm{Sr_2IrO_4}\) thin film. Alternatively, an interaction between electrons from the substrate and phonons from the \(\mathrm{Sr_2IrO_4}\) thin film at the interface could propagate the modified phonons throughout the entire \(\mathrm{Sr_2IrO_4}\) thin film. + +## Discussion + +Combining our observations, we noted a significant softening of magnons and hardening of phonon modes in \(\mathrm{Sr_2IrO_4}\) thin films when deposited on a metallic substrate compared to the insulating substrate. These phonons and magnon modes may be interconnected with the simple formula: \(c\Delta \omega_{\mathrm{phonon}} = \Delta \omega_{\mathrm{magnon}}\) , where \(c\) represents the coupling constant between magnon and phonon, \(\Delta \omega_{\mathrm{phonon}}\) and \(\Delta \omega_{\mathrm{magnon}}\) is the change in phonon mode and magnon energy between metallic and insulating substrate, respectively. The coupling constant for the single magnon with \(B_{2\mathrm{g}}\) phonon mode is about - 19 whereas with \(A_{1\mathrm{g}}\) mode is about - 15. This suggests that any alteration in magnon energy arises from the modifications in the phonon modes. Therefore, the long- range phenomenon accompanied by magnon- phonon couplings and electron- phonon couplings explains the intriguing change of the magnon dispersion of \(\mathrm{Sr_2IrO_4}\) thin films interfacing with metallic crystals. The delocalized electrons in the metallic crystals may interact with the phonon modes of + +<--- Page Split ---> + +\(\mathrm{Sr_2IrO_4}\) thin films through interfacial electron-phonon interactions, or they may interact with the phonon modes of metallic substrates through bulk electron- phonon interactions, thereby modifying the phonon modes of \(\mathrm{Sr_2IrO_4}\) thin film. Consequently, the modified phonon mode may interact with the magnon within the \(\mathrm{Sr_2IrO_4}\) thin film, resulting in the magnon's softening and the phonon mode's hardening. It's crucial to highlight that the magnon- phonon interaction has minimal impact on the first nearest- neighbor interaction, but it significantly affects interactions with the second and third nearest neighbors. + +Other possible tuning parameters, including lattice strain, interfacial proximity effects, and carrier doping, are inadequate to account for our observations. The strain effect, for instance, fails to justify the magnon softening observed in the \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructure as the \(\mathrm{Sr_2IrO_4 / LSAT}\) heterostructure, which exhibits similar strain states, does not show the softening. Additionally, proximity effects at the interface are ruled out as our observations are made through bulk- sensitive techniques27 i.e., the experimental data encompassed the entire volume of 20- 30 nm (8- 12 layers) films, not just near the interface. Furthermore, \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructures of varying thicknesses (12, 30, and 50 nm) showed consistent magnon softening without a noticeable thickness dependence (Supplementary Fig. 5), indicating a long- range effect throughout the thin film. In case the charge transfer between a metallic crystal to \(\mathrm{Sr_2IrO_4}\) thin film at the interface is hole transfer, it typically hardens magnon peak energy at the zone boundary30,31, contrary to our findings. Electron doping typically softens magnon peak energy but broadens it significantly and collapses the long- range magnetic order of \(\mathrm{Sr_2IrO_4}^{32,33}\) . In both \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) and \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructures, we observed neither broadened magnon peaks nor a collapse of long- range magnetic order (Supplementary Fig. 6). Resonant x- ray scattering near the Ru \(L_2\) edge also confirms the absence of Ru ions near the \(\mathrm{Sr_2IrO_4}\) interface (Supplementary Fig. 7), dismissing + +<--- Page Split ---> + +the potential intermixing between Ir and Ru ions at the interface. Also, optical spectroscopy reveals a clear insulating gap of \(\sim 0.3 \mathrm{eV}\) in the \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructure, similar to \(\mathrm{Sr_2IrO_4}\) single crystals (Supplementary Fig. 8), suggesting minimal charge transfer or doping in the \(\mathrm{Sr_2IrO_4}\) thin films. + +In summary, we have observed a pronounced softening of the zone boundary magnon energy and hardening of phonon modes when \(\mathrm{Sr_2IrO_4}\) thin films are epitaxially linked with the metallic \(4d\) TMO single crystals. We discuss that an electron- phonon coupling at the interface or within the substrate might have affected the magnon dispersion of \(\mathrm{Sr_2IrO_4}\) via a long- range magnon- phonon interaction. This phenomenon has increased second and third- nearest- neighbor interaction while its impact on the first- nearest neighbor is minimal. Our experimental results call for further theoretical studies and calculations with a new understanding of microscopic interactions between magnons and phonons. Nevertheless, besides the prevailing acoustic waves responsible for magnon- phonon coupling34, we posit that metal- insulator heterointerfaces serve as a novel mechanism for inducing magnon- phonon coupling essential for magnonics. Our work also suggests some interesting questions and perspectives. For instance, whether this phenomenon is specific to \(5d / 4d\) heterostructures or if it will also occur in other types of heterostructures. In other words, it sparks questions regarding how spin- orbit interaction influences the magnon dispersion of antiferromagnetic insulators and whether this phenomenon can also be observed in Van der Waals heterostructures. Future studies of various heterostructures with different materials would shed light on these questions. + +<--- Page Split ---> + +## Acknowledgments + +AcknowledgmentsWe acknowledge the support of National Science Foundation Grant No. DMR- 2104296 for sample synthesis and characterization. This research used resources of the Advanced Photon Source; a U.S. Department of Energy (DOE) Office of Science user facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE- AC02- 06CH11357. Electron microscopy was performed at the Center for Electron Microscopy and Analysis at the Ohio State University supported by National Science Foundation Grants No. DMR- 1847964. G.C. acknowledges NSF support via Grant No. DMR 2204811. B.K. acknowledges financial support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through Project No. 107745057- TRR 80. + +## References + +References1. Grundler, D. Reconfigurable magnonics heats up. Nature Physics 11, 438- 441 (2015).2. Kruglyak, V. V., Demokritov, S. O. & Grundler, D. Magnonics. Journal of Physics D: Applied Physics 43, 264001 (2010).3. Lenk, B., Ulrichs, H., Garbs, F. & Münzenberg, M. The building blocks of magnonics. Physics Reports 507, 107- 136 (2011).4. Baltz, V. et al. Antiferromagnetic spintronics. Reviews of Modern Physics 90, 015005 (2018).5. Jungwirth, T., Marti, X., Wadley, P. & Wunderlich, J. Antiferromagnetic spintronics. Nature Nanotechnology 11, 231- 241 (2016). + +<--- Page Split ---> + +6. Rezende, S. M., Azevedo, A. & Rodríguez-Suárez, R. L. Introduction to antiferromagnetic magnons. Journal of Applied Physics 126, 151101 (2019). + +7. Kim, H.-H. et al. Giant stress response of terahertz magnons in a spin-orbit Mott insulator. Nature Communications 13, 6674 (2022). + +8. Meng, Y. et al. Direct evidence of antiferromagnetic exchange interaction in Fe(001) films: Strong magnon softening at the high-symmetry \(\overline{\mathrm{M}}\) point. Physical Review B 90, 174437 (2014). + +9. Chuang, T. H. et al. Magnetic properties and magnon excitations in Fe(001) films grown on Ir(001). Physical Review B 89, 174404 (2014). + +10. Zhou, Y. et al. Piezoelectric Strain-Controlled Magnon Spin Current Transport in an Antiferromagnet. Nano Letters 22, 4646-4653 (2022). + +11. Liang, Y. et al. Observation of Magnon Spin Transport in BiFeO₃ Thin Films. Advanced Functional Materials 34, 2308944 (2024). + +12. Li, J. et al. Spin current from sub-terahertz-generated antiferromagnetic magnons. Nature 578, 70-74 (2020). + +13. Rongione, E. et al. Emission of coherent THz magnons in an antiferromagnetic insulator triggered by ultrafast spin-phonon interactions. Nature Communications 14, 1818 (2023). + +14. Kholid, F. N. et al. The importance of the interface for picosecond spin pumping in antiferromagnet-heavy metal heterostructures. Nature Communications 14, 538 (2023). + +15. Fjærbu, E. L., Rohling, N. & Brataas, A. Superconductivity at metal-antiferromagnetic insulator interfaces. Physical Review B 100, 125432 (2019). + +16. Lee, N. et al. Antiferromagnet-Based Spintronic Functionality by Controlling Isospin Domains in a Layered Perovskite Iridate. Advanced Materials 30, 1805564 (2018). + +<--- Page Split ---> + +17. Wang, H. et al. Giant anisotropic magnetoresistance and nonvolatile memory in canted antiferromagnet \(\mathrm{Sr_2IrO_4}\) . Nature Communications 10, 2280 (2019). + +18. Kim, J. et al. Magnetic excitation spectra of \(\mathrm{Sr_2IrO_4}\) probed by resonant inelastic x-ray scattering: establishing links to cuprate superconductors. Physical Review Letters 108, 177003 (2012). + +19. Meng, K.-Y. et al. Observation of nanoscale skyrmions in \(\mathrm{SrIrO_3 / SrRuO_3}\) bilayers. Nano letters 19, 3169-3175 (2019). + +20. Matsuno, J. et al. Interface-driven topological Hall effect in \(\mathrm{SrRuO_3 - SrIrO_3}\) bilayer. Science Advances 2, e1600304. + +21. Kim, J. et al. Quartz-based flat-crystal resonant inelastic x-ray scattering spectrometer with sub-10 meV energy resolution. Scientific Reports 8, 1958 (2018). + +22. Nichols, J. et al. Tuning electronic structure via epitaxial strain in \(\mathrm{Sr_2IrO_4}\) thin films. Applied Physics Letters 102, 141908 (2013). + +23. Seo, S. S. A. et al. Selective growth of epitaxial \(\mathrm{Sr_2IrO_4}\) by controlling plume dimensions in pulsed laser deposition. Applied Physics Letters 109, 201901 (2016). + +24. Krautloher, M. Neutron scattering studies on layered ruthenates. (2018). + +25. Shrestha, S. et al. Emergent interlayer magnetic order via strain-induced orthorhombic distortion in the 5d Mott insulator \(\mathrm{Sr_2IrO_4}\) . Physical Review B 105, L100404 (2022). + +26. Paris, E. et al. Strain engineering of the charge and spin-orbital interactions in \(\mathrm{Sr_2IrO_4}\) . Proceedings of the National Academy of Sciences 117, 24764-24770 (2020). + +27. Ament, L. J. P., van Veenendaal, M., Devereaux, T. P., Hill, J. P. & van den Brink, J. Resonant inelastic x-ray scattering studies of elementary excitations. Reviews of Modern Physics 83, 705-767 (2011). + +<--- Page Split ---> + +28. Kim, J. et al. Excitonic quasiparticles in a spin–orbit Mott insulator. Nature communications 5, 4453 (2014). + +29. Fleury, P. A. & Loudon, R. Scattering of Light by One- and Two-Magnon Excitations. Physical Review 166, 514-530 (1968). + +30. Zhong, Z. & Hansmann, P. Band Alignment and Charge Transfer in Complex Oxide Interfaces. Physical Review X 7, 011023 (2017). + +31. Bertinshaw, J. et al. Spin-wave gap collapse in Rh-doped Sr₂IrO₄. Physical Review B 101, 094428 (2020). + +32. Liu, X. et al. Anisotropic softening of magnetic excitations in lightly electron-doped Sr₂IrO₄. Physical Review B 93, 241102 (2016). + +33. Gretarsson, H. et al. Persistent paramagnons deep in the metallic phase of Sr₂-ₓLaₓIrO₄. Physical review letters 117, 107001 (2016). + +34. Lyons, T. P. et al. Acoustically Driven Magnon-Phonon Coupling in a Layered Antiferromagnet. Physical Review Letters 131, 196701 (2023). + +<--- Page Split ---> + +## Supplementary Files + +This is a list of supplementary files associated with this preprint. Click to download. + +SupplementaryInformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__08e00c94056e03db3ebcdc2920d9ec2ef75181d657a1f6242f88ac52aaa88e5f/preprint__08e00c94056e03db3ebcdc2920d9ec2ef75181d657a1f6242f88ac52aaa88e5f_det.mmd b/preprint/preprint__08e00c94056e03db3ebcdc2920d9ec2ef75181d657a1f6242f88ac52aaa88e5f/preprint__08e00c94056e03db3ebcdc2920d9ec2ef75181d657a1f6242f88ac52aaa88e5f_det.mmd new file mode 100644 index 0000000000000000000000000000000000000000..641536cdeb2e493842ea4e6433663b23257a1352 --- /dev/null +++ b/preprint/preprint__08e00c94056e03db3ebcdc2920d9ec2ef75181d657a1f6242f88ac52aaa88e5f/preprint__08e00c94056e03db3ebcdc2920d9ec2ef75181d657a1f6242f88ac52aaa88e5f_det.mmd @@ -0,0 +1,356 @@ +<|ref|>title<|/ref|><|det|>[[44, 108, 909, 175]]<|/det|> +# Tunable magnons of an antiferromagnetic Mott insulator via interfacial metal-insulator transitions + +<|ref|>text<|/ref|><|det|>[[44, 196, 155, 214]]<|/det|> +Ambrose Seo + +<|ref|>text<|/ref|><|det|>[[52, 223, 201, 240]]<|/det|> +a.seo@uky.edu + +<|ref|>text<|/ref|><|det|>[[50, 270, 610, 288]]<|/det|> +University of Kentucky https://orcid.org/0000- 0002- 7055- 5314 + +<|ref|>text<|/ref|><|det|>[[44, 294, 201, 312]]<|/det|> +Sujan Shrestha + +<|ref|>text<|/ref|><|det|>[[52, 317, 252, 334]]<|/det|> +University of Kentucky + +<|ref|>text<|/ref|><|det|>[[44, 340, 201, 358]]<|/det|> +Maryam Souri + +<|ref|>text<|/ref|><|det|>[[52, 363, 252, 380]]<|/det|> +University of Kentucky + +<|ref|>text<|/ref|><|det|>[[44, 386, 191, 404]]<|/det|> +Christopher Dietl + +<|ref|>text<|/ref|><|det|>[[52, 409, 308, 426]]<|/det|> +Argonne National Laboratory + +<|ref|>text<|/ref|><|det|>[[44, 432, 241, 450]]<|/det|> +Ekaterina M. Parschke + +<|ref|>text<|/ref|><|det|>[[52, 455, 381, 473]]<|/det|> +University of Alabama at Birmingham + +<|ref|>text<|/ref|><|det|>[[44, 479, 240, 496]]<|/det|> +Maximilian Krautloher + +<|ref|>text<|/ref|><|det|>[[52, 501, 450, 518]]<|/det|> +Max Planck Institute for Solid State Research + +<|ref|>text<|/ref|><|det|>[[44, 525, 238, 543]]<|/det|> +Gabriel Calderon Ortiz + +<|ref|>text<|/ref|><|det|>[[52, 548, 238, 565]]<|/det|> +Ohio State University + +<|ref|>text<|/ref|><|det|>[[44, 572, 171, 589]]<|/det|> +Matteo Minola + +<|ref|>text<|/ref|><|det|>[[52, 593, 595, 611]]<|/det|> +Max Planck Institute https://orcid.org/0000- 0003- 4084- 0664 + +<|ref|>text<|/ref|><|det|>[[44, 617, 156, 635]]<|/det|> +Xiaotong Shi + +<|ref|>text<|/ref|><|det|>[[52, 640, 450, 657]]<|/det|> +Max Planck Institute for Solid State Research + +<|ref|>text<|/ref|><|det|>[[44, 664, 135, 681]]<|/det|> +A. +V. Boris + +<|ref|>text<|/ref|><|det|>[[52, 685, 808, 703]]<|/det|> +Max Planck Institute for Solid State Research https://orcid.org/0000- 0002- 2062- 5046 + +<|ref|>text<|/ref|><|det|>[[44, 710, 175, 727]]<|/det|> +Jinwoo Hwang + +<|ref|>text<|/ref|><|det|>[[52, 732, 238, 749]]<|/det|> +Ohio State University + +<|ref|>text<|/ref|><|det|>[[44, 756, 191, 774]]<|/det|> +Giniyat Khaliullin + +<|ref|>text<|/ref|><|det|>[[52, 778, 808, 796]]<|/det|> +Max Planck Institute for Solid State Research https://orcid.org/0000- 0001- 9395- 6447 + +<|ref|>text<|/ref|><|det|>[[44, 803, 130, 820]]<|/det|> +Gang Cao + +<|ref|>text<|/ref|><|det|>[[52, 825, 682, 843]]<|/det|> +University of Colorado Boulder https://orcid.org/0000- 0001- 9779- 430X + +<|ref|>text<|/ref|><|det|>[[44, 849, 191, 867]]<|/det|> +Bernhard Keimer + +<|ref|>text<|/ref|><|det|>[[52, 871, 808, 889]]<|/det|> +Max Planck Institute for Solid State Research https://orcid.org/0000- 0001- 5220- 9023 + +<|ref|>text<|/ref|><|det|>[[44, 896, 171, 913]]<|/det|> +Jong-Woo Kim + +<|ref|>text<|/ref|><|det|>[[52, 917, 666, 935]]<|/det|> +Argonne National Laboratory https://orcid.org/0000- 0001- 9641- 2947 + +<|ref|>text<|/ref|><|det|>[[44, 942, 158, 959]]<|/det|> +Jung Ho Kim + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[44, 106, 104, 124]]<|/det|> +## Article + +<|ref|>sub_title<|/ref|><|det|>[[44, 144, 136, 162]]<|/det|> +## Keywords: + +<|ref|>text<|/ref|><|det|>[[44, 181, 295, 201]]<|/det|> +Posted Date: July 26th, 2024 + +<|ref|>text<|/ref|><|det|>[[43, 220, 475, 239]]<|/det|> +DOI: https://doi.org/10.21203/rs.3.rs- 4753008/v1 + +<|ref|>text<|/ref|><|det|>[[43, 257, 914, 300]]<|/det|> +License: © This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License + +<|ref|>text<|/ref|><|det|>[[43, 317, 535, 337]]<|/det|> +Additional Declarations: There is NO Competing Interest. + +<|ref|>text<|/ref|><|det|>[[42, 372, 914, 416]]<|/det|> +Version of Record: A version of this preprint was published at Nature Communications on April 15th, 2025. See the published version at https://doi.org/10.1038/s41467- 025- 58922- z. + +<--- Page Split ---> +<|ref|>title<|/ref|><|det|>[[130, 88, 866, 108]]<|/det|> +# Tunable magnons of an antiferromagnetic Mott insulator via interfacial metal-insulator + +<|ref|>sub_title<|/ref|><|det|>[[451, 124, 545, 142]]<|/det|> +## transitions + +<|ref|>text<|/ref|><|det|>[[128, 156, 870, 230]]<|/det|> +S. Shrestha, \(^{1}\) M. Souri, \(^{1}\) C. Dietl, \(^{2}\) E. M. Pärschke, \(^{3}\) M. Krautloher, \(^{4}\) G. A. Calderon Ortiz, \(^{5}\) M. Minola, \(^{4}\) X. Shi, \(^{4}\) A. V. Boris, \(^{4}\) J. Hwang, \(^{5}\) G. Khaliullin, \(^{4}\) G. Cao, \(^{6}\) B. Keimer, \(^{4}\) J.-W. Kim, \(^{2}\) J. Kim, \(^{2}\) and A. Seo \(^{1}\) + +<|ref|>text<|/ref|><|det|>[[128, 254, 872, 378]]<|/det|> +\(^{1}\) Department of Physics and Astronomy, University of Kentucky, Lexington, KY 40506, USA \(^{2}\) Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA \(^{3}\) Department of Physics, University of Alabama at Birmingham, Birmingham, AL 35294, USA \(^{4}\) Max- Planck- Institut für Festkörperforschung, D- 70569 Stuttgart, GERMANY \(^{5}\) Department of Materials Science and Engineering, The Ohio State University, Columbus, OH 43210, USA \(^{6}\) Department of Physics, University of Colorado at Boulder, Boulder, CO 80309, USA + +<|ref|>sub_title<|/ref|><|det|>[[461, 446, 536, 464]]<|/det|> +## Abstract + +<|ref|>text<|/ref|><|det|>[[113, 481, 885, 789]]<|/det|> +Antiferromagnetic insulators offer an alternative to ferromagnets due to their ultrafast spin dynamics essential for low- energy terahertz spintronic device applications. One way is to utilize magnons, i.e., quantized spin waves, which can carry information through excitations. However, finding external knobs for tuning the magnons has been a significant challenge. Here we report that interfacial metal- insulator transitions can be an effective means for controlling the magnons of a strongly spin- orbit- coupled antiferromagnetic Mott insulator, \(\mathrm{Sr_2IrO_4}\) . From resonant inelastic X- ray scattering and Raman spectroscopy, we have observed a pronounced softening of zone- boundary magnon energies in several \(\mathrm{Sr_2IrO_4}\) thin- film systems that are epitaxially contacted with metallic \(4d\) transition- metal oxides (TMOs). Therefore, the magnon dispersion of \(\mathrm{Sr_2IrO_4}\) is tunable by metal- insulator transitions of the \(4d\) TMO crystals. Remarkably, this non- trivial behavior of magnons is a long- range phenomenon coupled with intriguing magnon- phonon interactions. Our experimental finding proposes a new scheme for magnonics. + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 388]]<|/det|> +Magnons, i.e., collective spin wave excitations originating from spin precession in magnetically ordered materials, have the potential to serve as a promising medium for quantum information devices. Since the propagation of magnons does not require the transport of a charge, preventing electrical losses such as Joule heating1, it gives rise to a burgeoning research field known as magnonics2,3. Antiferromagnetic insulators have garnered considerable attention in this emerging field, primarily due to their ultrafast spin dynamics compared to ferromagnetic counterparts, which are essential for device operation in the terahertz range4-6. Nevertheless, effectively guiding and coherently manipulating magnons using external stimuli is still a significant challenge. + +<|ref|>text<|/ref|><|det|>[[113, 402, 884, 631]]<|/det|> +Hetero- interfaces between two different materials can provide model systems for investigating the relation between external stimuli and collective spin waves. Examples are the effects of lattice strain7, interfacial coupling, and charge transfer on magnons8,9 and their spin currents10- 12. In particular, interfaces between an antiferromagnetic insulator and a metal have been considered for novel spin- charge conversion13,14. Despite some astonishing predictions from magnetic insulator/metal interfaces15, the fundamental understanding of how a metallic interface affects the spin- wave dispersion of an antiferromagnetic insulator remains elusive. + +<|ref|>text<|/ref|><|det|>[[113, 656, 884, 886]]<|/det|> +\(\mathrm{Sr_2IrO_4}\) , a \(5d\) transition- metal oxide, is a quasi- two- dimensional antiferromagnetic insulator with strong spin- orbit interaction resulting in the \(J_{\mathrm{eff}} = 1 / 2\) pseudospins. The distinctive canted antiferromagnetism and magnetic anisotropy in the \(J_{\mathrm{eff}} = 1 / 2\) state can be useful for spintronic applications16,17. Notably, \(\mathrm{Sr_2IrO_4}\) hosts spin waves at terahertz frequencies with a significant stress response mediated by strong spin- orbit interactions7. Its similarities to \(\mathrm{La_2CuO_4}\) , a parent compound of high \(T_{\mathrm{c}}\) superconductors, suggest a potential for superconducting antiferromagnetic magnonics18. Therefore, \(\mathrm{Sr_2IrO_4}\) presents a compelling avenue for studying the influence of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[114, 88, 883, 144]]<|/det|> +metallic interfaces on spin- wave dispersion and its heterostructures offer opportunities to explore intriguing phenomena19,20. + +<|ref|>text<|/ref|><|det|>[[112, 168, 886, 789]]<|/det|> +In this article, we report a systematic study of the spin- wave dispersion of \(\mathrm{Sr2IrO_4}\) thin films that are epitaxially interfaced with various metallic or insulating single crystals. The recent advancement of high- resolution resonant inelastic x- ray scattering (RIXS) has enabled us to access the low- energy magnetic dynamics of iridates throughout the entire Brillouin zone21. Our RIXS experiments show a significant softening of single magnon peaks (with no broadening) near the \((\pi /2, \pi /2)\) zone boundary for various \(\mathrm{Sr2IrO_4}\) thin films neighboring metallic crystals, while the single magnon spectrum of \(\mathrm{Sr2IrO_4}\) thin films remains unchanged when adjoining insulating crystals. Similar behavior of the two- magnon excitation, predominantly indicating zone boundary excitation, is observed in Raman spectra. Conversely, the phonon mode of \(\mathrm{Sr2IrO_4}\) thin films exhibits significant hardening when interfaced with metallic crystals. We propose that electron- phonon interaction, occurring either at the heterointerface or within the metallic substrate, could have modified the phonon of \(\mathrm{Sr2IrO_4}\) thin films, subsequently influencing the magnons via long- range magnon- phonon interactions throughout the entire thin film. Our systematic measurements using various bulk- sensitive experimental techniques such as Raman spectroscopy, optical spectroscopy, and resonant x- ray scattering, indicate that conventional interfacial interactions such as strain, doping, and proximity effects are unlikely to account for this experimental result. Our experimental results present a novel approach in magnonics, utilizing metal- insulator transitions in adjacent crystals as a mechanism for manipulating the propagation of terahertz magnons. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 297, 109]]<|/det|> +## Experimental Layout + +<|ref|>text<|/ref|><|det|>[[112, 128, 886, 890]]<|/det|> +We constructed epitaxial heterostructures by depositing \(\mathrm{Sr_2IrO_4}\) epitaxial thin films on ruthenates single crystals (Fig. 1a) by using pulsed laser deposition \(^{22,23}\) . To circumvent the potential influence of spin- spin interactions, we opt for ruthenates exhibiting paramagnetic characteristics. The \(\mathrm{Sr_2RuO_4}\) single crystals exhibit a tetragonal crystal structure and metallic transport behavior whereas the single crystals of \(\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) are orthorhombic and exhibit a metal- insulator transition at 55 K (i.e., metallic above 55 K and insulating below 55 K) \(^{24}\) with a slight change of the \(b\) and \(c\) - lattice constants (Supplementary Fig. 1) \(^{24}\) . For a systematic examination and to address the strain effect, the \(\mathrm{Sr_2IrO_4}\) thin film on an insulating \(\mathrm{(LaAlO_3)_{0.3}(Sr_2TaAlO_6)_{0.7}}\) (LSAT) perovskite single crystal is also investigated, as LSAT and \(\mathrm{Sr_2RuO_4}\) single crystals have a similar lattice mismatch with \(\mathrm{Sr_2IrO_4}\) . A high- resolution Z- contrast scanning transmission electron microscopy image of the \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructure (Supplementary Fig. 2) reveals an atomically sharp heterointerface, akin to the reported sharpness in the \(\mathrm{Sr_2IrO_4 / Ca_3Ru_2O_7}\) heterointerface \(^{25}\) , which is expected to be replicated in the \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) heterostructure. Supplementary Figure 3a exhibits distinct (0 0 \(l\) )- diffraction peaks for both the \(\mathrm{Sr_2IrO_4}\) thin film and the \(\mathrm{Sr_2RuO_4}\) , \(\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) , and LSAT substrates, including interference fringes near the (0 0 12) peak. X- ray reciprocal space mapping clearly shows that the in- plane lattice of \(\mathrm{Sr_2IrO_4}\) thin films is coherently strained with all three substrates (Supplementary Fig. 3b and 3c). It is noteworthy that the \(\mathrm{Sr_2IrO_4}\) thin film grown on both \(\mathrm{Sr_2RuO_4}\) and LSAT experiences the same amount of compressive strain of - 0.51% (Supplementary Table 1). However, the \(\mathrm{Sr_2IrO_4}\) thin film deposited on the \(\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) substrate experiences a - 2.22% compressive strain along the \(a\) - axis and a 0.33% tensile strain along the \(b\) - axis above 55 K, whereas the tensile strain along the \(b\) - axis increases to 2% below 55 K (Supplementary Table 2). + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[142, 90, 852, 315]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[114, 320, 884, 420]]<|/det|> +
Figure 1 | Sr2IrO4 thin film systems. Schematic diagram of Sr2IrO4 thin film on different ruthenates substrates and reference LSAT substrate. The Sr2RuO4 and LSAT single crystals have comparable in-plane lattice constants yet exhibit metallic, and insulating properties, respectively. The Ca3Ru1.98Ti0.02O7 single crystal exhibits metallic properties above 55 K and becomes insulating below 55 K.
+ +<|ref|>text<|/ref|><|det|>[[112, 465, 885, 905]]<|/det|> +In recent years, RIXS has become the tool of choice for collecting momentum- resolved and element- specific information about collective magnetic excitations such as magnons, and spin- orbit excitons in transition metal oxide thin films26- 28. Figure 2a shows representative energy loss spectra of Sr2IrO4/Sr2RuO4 heterostructure collected along high- symmetry directions throughout the magnetic Brillouin zone. These spectra were acquired using RIXS measurements at the 27- ID beamline of the Advanced Photon Source, utilizing a horizontal scattering setup with incident photons polarized in the \(\pi\) - direction, as depicted in Fig. 2c. Additionally, Figure 2b depicts a color intensity map generated from energy loss spectra resembling those presented in Fig. 2a. The distinctive features of these spectra such as a dispersive magnetic excitation (magnon) in the low energy range of 0 - 0.25 eV and a dispersive orbital excitation (spin- orbit exciton) in the higher energy range of 0.40 - 0.90 eV exhibit inherent properties of the system18,28. Furthermore, the detections of well- defined dispersive magnons and spin- orbit excitons indicate high crystallinity of Sr2IrO4 thin film. + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[200, 125, 812, 480]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 509, 884, 650]]<|/det|> +
Figure 2 | RIXS spectra of \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructure. a Energy loss spectra of \(\mathrm{Sr_2IrO_4 / Sr_2RuO_3}\) heterostructure at \(20\mathrm{K}\) along high symmetry lines. The inset shows high symmetry points in the Brillouin zone of the undistorted tetragonal unit cell and the magnetic unit cell. b The image plot of the data shown in (b). The detections of well-defined dispersive magnons and spin-orbit excitons indicate high-quality \(\mathrm{Sr_2IrO_4}\) thin film. The orange dotted line in both (a) and (b) is the eye guide to the magnon dispersion. c A visual representation depicting the horizontal scattering geometry utilized in RIXS measurements.
+ +<|ref|>sub_title<|/ref|><|det|>[[115, 707, 179, 723]]<|/det|> +## Results + +<|ref|>text<|/ref|><|det|>[[113, 749, 884, 875]]<|/det|> +The RIXS measurements reveal that the low- energy magnon dispersion of \(\mathrm{Sr_2IrO_4}\) exhibits a sudden softening by about \(20\mathrm{meV}\) at the \((\pi /2,\pi /2)\) zone boundary when the neighboring single crystal goes across a transition from insulating to metallic. Figure 3a shows the low- energy magnon peaks of all \(\mathrm{Sr_2IrO_4}\) thin films at \((\pi ,0)\) and \((\pi /2,\pi /2)\) . As observed in \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) , + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[192, 95, 792, 355]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 395, 883, 576]]<|/det|> +
Figure 3 | Softening of magnon in \(\mathrm{Sr_2IrO_4}\) interfaced to metallic substrates at \((\pi /2,\pi /2)\) zone boundary. a RIXS spectra of \(\mathrm{Sr_2IrO_4}\) thin films on two types of substrates, with single crystal (light green) at \((\pi ,0)\) and \((\pi /2,\pi /2)\) : metallic substrates \((\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) (orange) and \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) at \(\mathrm{T} > 55\mathrm{K}\) (red)) and insulating substrates \((\mathrm{Sr_2IrO_4 / LSAT}\) (cyan) and \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) at \(\mathrm{T}< 55\mathrm{K}\) (blue)). The \(\mathrm{Sr_2IrO_4}\) thin films interfaced on the metallic substrates show significant softening of the single magnon at the \((\pi /2,\pi /2)\) . b Magnon dispersion along the high symmetry lines extracted from the RIXS spectra in comparison with data obtained on \(\mathrm{Sr_2IrO_4}\) single crystal. The solid orange and cyan lines represent the theoretical fitting using the model Hamiltonian for the \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) and \(\mathrm{Sr_2IrO_4 / LSAT}\) , respectively.
+ +<|ref|>text<|/ref|><|det|>[[113, 619, 884, 888]]<|/det|> +well- defined magnons are present in other thin films on both metallic \((\mathrm{Sr_2RuO_4}\) and \(\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) above \(55\mathrm{K}\) ) and insulating substrates \((\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) below \(55\mathrm{K}\) and LSAT). While the magnon peak energy remains almost similar (around \(200\mathrm{meV}\) ) at \((\pi ,0)\) for all five systems, i.e., \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) , \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) ( \(\mathrm{T}< 55\mathrm{K}\) ), \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) ( \(\mathrm{T} > 55\mathrm{K}\) ), \(\mathrm{Sr_2IrO_4 / LSAT}\) , and \(\mathrm{Sr_2IrO_4}\) single crystal, a notable difference is observed in the magnon peak energy at \((\pi /2,\pi /2)\) . The \(\mathrm{Sr_2IrO_4}\) thin films interfaced on the metallic substrates \((\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) (orange) and \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) above \(55\mathrm{K}\) (red)) show significant softening, with a reduction of about \(20\mathrm{meV}\) at \((\pi /2,\pi /2)\) . In contrast, for thin films on insulating + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 248]]<|/det|> +substrates \(\mathrm{(Ca_3Ru_{1.98}Ti_{0.02}O_7}\) below \(55\mathrm{K}\) (blue) and LSAT (cyan)), the magnon peak energy at \((\pi /2,\pi /2)\) is indistinguishable from that of the single crystal. This peak shift is greater than the experimental error bar \(^{21}\) . Essentially, we can group them into two categories: \(\mathrm{Sr_2IrO_4}\) heterostructures with insulating substrates show higher magnon energy, while the other with metallic substrates have a softened magnon energy reduced by about \(20\mathrm{meV}\) . + +<|ref|>image<|/ref|><|det|>[[313, 299, 656, 675]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 715, 884, 875]]<|/det|> +
Figure 4 | Softening of two-magnon in samples with metallic substrates. a Raman spectra of \(B_{2\mathrm{g}}\) two-magnon modes in \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) and \(\mathrm{Sr_2IrO_4 / LSAT}\) heterostructures measured at \(10\mathrm{K}\) . b Temperature-dependent Raman spectra of \(B_{2\mathrm{g}}\) two-magnon modes of \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) heterostructure. Two-magnon energy in \(\mathrm{Sr_2IrO_4}\) interfaced to metallic substrates \((\mathrm{Sr_2RuO_4}\) and \(\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) ( \(T > 55\mathrm{K}\) )) has lower peak energy compared to that interfaced to insulating substrates (LSAT and \(\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) ( \(T< 55\mathrm{K}\) )). Two-magnon peak positions for both figures are estimated by using two Lorentz oscillator curve fits which are represented by smooth solid lines and their sum is shown by black solid line.
+ +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 88, 885, 423]]<|/det|> +The two- magnon peak energies in the high- resolution Raman spectra corroborate the RIXS measurements. Figure 4a illustrates the \(B_{2\mathrm{g}}\) two- magnon modes in \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) and \(\mathrm{Sr_2IrO_4 / LSAT}\) at \(10\mathrm{K}\) , while Fig. 4b displays the temperature- dependent Raman spectra of \(B_{2\mathrm{g}}\) two- magnon modes in \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) . We determine the two- magnon peak energies \((\omega_{2\mathrm{M}})\) through fits to a model function comprising two Lorentz oscillators. Note that the two- magnon energies of thin films on metallic substrates \((\mathrm{Sr_2RuO_4}\) and \(\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) above \(55\mathrm{K}\) ) are lower than those on insulating substrates \((\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) below \(55\mathrm{K}\) and LSAT). Considering that the two- magnon mode primarily reflects zone boundary excitations \(^{29}\) , this observation aligns with the softening of the \((\pi /2, \pi /2)\) zone boundary magnon and establishes consistency between the two measurements. + +<|ref|>text<|/ref|><|det|>[[112, 437, 886, 736]]<|/det|> +By fitting the magnon dispersion data (Fig. 3b) of \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) and \(\mathrm{Sr_2IrO_4 / LSAT}\) heterostructures using a Heisenberg spin model, we extracted the in- plane exchange interactions, i.e., the nearest \((J_1)\) , next nearest \((J_2)\) , next- next nearest \((J_3)\) , and fourth nearest \((J_4)\) neighbors, between the \(J_{\mathrm{eff}} = 1 / 2\) pseudospins. The best- fit results in the values of \(J_1 = 50 \mathrm{meV}\) , \(J_2 = - 21 \mathrm{meV}\) , \(J_3 = 20 \mathrm{meV}\) , and \(J_4 = 6.3 \mathrm{meV}\) for \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructure and \(J_1 = 50 \mathrm{meV}\) , \(J_2 = - 17 \mathrm{meV}\) , \(J_3 = 16 \mathrm{meV}\) , and \(J_4 = 9 \mathrm{meV}\) for \(\mathrm{Sr_2IrO_4 / LSAT}\) heterostructure. Note that the change of the experimentally measured magnon dispersion for metallic substrates suggests increasing in- plane interactions \(J_2\) and \(J_3\) compared to insulating substrates and \(\mathrm{Sr_2IrO_4}\) single crystals (Supplementary Table 3). + +<|ref|>text<|/ref|><|det|>[[113, 760, 884, 850]]<|/det|> +Raman spectroscopy indicates that the phonon modes of \(\mathrm{Sr_2IrO_4}\) undergo a noticeable hardening by approximately \((1 - 1.4) \mathrm{meV}\) when the adjacent single crystal substrate is altered from insulating to metallic, likely attributed to electron- phonon interaction. Figure 5a illustrates + +<--- Page Split ---> +<|ref|>image<|/ref|><|det|>[[306, 95, 678, 440]]<|/det|> +<|ref|>image_caption<|/ref|><|det|>[[113, 479, 884, 580]]<|/det|> +
Figure 5 | Hardening of phonons in \(\mathrm{Sr_2IrO_4}\) interfaced with metallic substrates. a \(A_{1\mathrm{g}}\) and \(B_{2\mathrm{g}}\) phonon modes of \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) and \(\mathrm{Sr_2IrO_4 / LSAT}\) heterostructures. The solid black lines represent the Lorentzian fit. b Temperature-dependent \(B_{2\mathrm{g}}\) phonon modes of \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) heterostructure. Inset: Temperature-dependent peak position of the \(B_{2\mathrm{g}}\) phonon modes of \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) heterostructure.
+ +<|ref|>text<|/ref|><|det|>[[113, 633, 884, 899]]<|/det|> +the \(A_{1\mathrm{g}}\) and \(B_{2\mathrm{g}}\) phonon modes of the \(\mathrm{Sr_2IrO_4}\) thin film in the \(\mathrm{Sr_2IrO_4 / LSAT}\) and \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructures. While the phonon modes of \(\mathrm{Sr_2IrO_4 / LSAT}\) closely resemble \(\mathrm{Sr_2IrO_4}\) single crystal, a notable upward energy shift by about \(1.4\mathrm{meV}\) and \(1\mathrm{meV}\) for \(A_{1\mathrm{g}}\) and \(B_{2\mathrm{g}}\) modes, respectively, is observed in \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructure. A similar upward shift of up to 0.7 meV in the phonon energy is also observed in \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) heterostructures when transitioning from an insulating to metallic state of the \(\mathrm{Ca_3Ru_{1.98}Ti_{0.02}O_7}\) substrate at \(55\mathrm{K}\) (Fig. 5b). Nevertheless, we acknowledge that the sudden structural change at \(55\mathrm{K}\) might also have some contributions to the phonon mode shift in \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) heterostructures. Additionally, + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[112, 87, 886, 458]]<|/det|> +we examined several other heterostructures, such as \(\mathrm{Sr_2IrO_4 / Ca_2Ru_{0.91}Mn_{0.09}O_4}\) (insulator), \(\mathrm{Sr_2IrO_4 / Sr_2RhO_4}\) (metal), and \(\mathrm{Sr_2IrO_4 / Ca_3Ru_2O_7}\) (metal) at \(10\mathrm{K}\) . These heterostructures displayed similar patterns based on the substrate's metallicity, showing a stiffening of the \(B_{2\mathrm{g}}\) phonons in the \(\mathrm{Sr_2IrO_4}\) thin film by about \(8\mathrm{cm^{- 1}}\) when interfaced with metallic substrates compared to insulating ones (Supplementary Fig. 4). Clearly, a common factor in the results is the metallic state of the substrates. The presence of delocalized carriers could explain the anomalous phonon behavior observed. One potential explanation is that charge carrier-phonon interactions within the metallic substrate alter its phonons, which then resonate with the phonons of the \(\mathrm{Sr_2IrO_4}\) thin film. Alternatively, an interaction between electrons from the substrate and phonons from the \(\mathrm{Sr_2IrO_4}\) thin film at the interface could propagate the modified phonons throughout the entire \(\mathrm{Sr_2IrO_4}\) thin film. + +<|ref|>sub_title<|/ref|><|det|>[[114, 483, 206, 501]]<|/det|> +## Discussion + +<|ref|>text<|/ref|><|det|>[[112, 526, 886, 897]]<|/det|> +Combining our observations, we noted a significant softening of magnons and hardening of phonon modes in \(\mathrm{Sr_2IrO_4}\) thin films when deposited on a metallic substrate compared to the insulating substrate. These phonons and magnon modes may be interconnected with the simple formula: \(c\Delta \omega_{\mathrm{phonon}} = \Delta \omega_{\mathrm{magnon}}\) , where \(c\) represents the coupling constant between magnon and phonon, \(\Delta \omega_{\mathrm{phonon}}\) and \(\Delta \omega_{\mathrm{magnon}}\) is the change in phonon mode and magnon energy between metallic and insulating substrate, respectively. The coupling constant for the single magnon with \(B_{2\mathrm{g}}\) phonon mode is about - 19 whereas with \(A_{1\mathrm{g}}\) mode is about - 15. This suggests that any alteration in magnon energy arises from the modifications in the phonon modes. Therefore, the long- range phenomenon accompanied by magnon- phonon couplings and electron- phonon couplings explains the intriguing change of the magnon dispersion of \(\mathrm{Sr_2IrO_4}\) thin films interfacing with metallic crystals. The delocalized electrons in the metallic crystals may interact with the phonon modes of + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 87, 884, 319]]<|/det|> +\(\mathrm{Sr_2IrO_4}\) thin films through interfacial electron-phonon interactions, or they may interact with the phonon modes of metallic substrates through bulk electron- phonon interactions, thereby modifying the phonon modes of \(\mathrm{Sr_2IrO_4}\) thin film. Consequently, the modified phonon mode may interact with the magnon within the \(\mathrm{Sr_2IrO_4}\) thin film, resulting in the magnon's softening and the phonon mode's hardening. It's crucial to highlight that the magnon- phonon interaction has minimal impact on the first nearest- neighbor interaction, but it significantly affects interactions with the second and third nearest neighbors. + +<|ref|>text<|/ref|><|det|>[[112, 340, 886, 890]]<|/det|> +Other possible tuning parameters, including lattice strain, interfacial proximity effects, and carrier doping, are inadequate to account for our observations. The strain effect, for instance, fails to justify the magnon softening observed in the \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructure as the \(\mathrm{Sr_2IrO_4 / LSAT}\) heterostructure, which exhibits similar strain states, does not show the softening. Additionally, proximity effects at the interface are ruled out as our observations are made through bulk- sensitive techniques27 i.e., the experimental data encompassed the entire volume of 20- 30 nm (8- 12 layers) films, not just near the interface. Furthermore, \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructures of varying thicknesses (12, 30, and 50 nm) showed consistent magnon softening without a noticeable thickness dependence (Supplementary Fig. 5), indicating a long- range effect throughout the thin film. In case the charge transfer between a metallic crystal to \(\mathrm{Sr_2IrO_4}\) thin film at the interface is hole transfer, it typically hardens magnon peak energy at the zone boundary30,31, contrary to our findings. Electron doping typically softens magnon peak energy but broadens it significantly and collapses the long- range magnetic order of \(\mathrm{Sr_2IrO_4}^{32,33}\) . In both \(\mathrm{Sr_2IrO_4 / Ca_3Ru_{1.98}Ti_{0.02}O_7}\) and \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructures, we observed neither broadened magnon peaks nor a collapse of long- range magnetic order (Supplementary Fig. 6). Resonant x- ray scattering near the Ru \(L_2\) edge also confirms the absence of Ru ions near the \(\mathrm{Sr_2IrO_4}\) interface (Supplementary Fig. 7), dismissing + +<--- Page Split ---> +<|ref|>text<|/ref|><|det|>[[113, 88, 884, 214]]<|/det|> +the potential intermixing between Ir and Ru ions at the interface. Also, optical spectroscopy reveals a clear insulating gap of \(\sim 0.3 \mathrm{eV}\) in the \(\mathrm{Sr_2IrO_4 / Sr_2RuO_4}\) heterostructure, similar to \(\mathrm{Sr_2IrO_4}\) single crystals (Supplementary Fig. 8), suggesting minimal charge transfer or doping in the \(\mathrm{Sr_2IrO_4}\) thin films. + +<|ref|>text<|/ref|><|det|>[[112, 238, 885, 781]]<|/det|> +In summary, we have observed a pronounced softening of the zone boundary magnon energy and hardening of phonon modes when \(\mathrm{Sr_2IrO_4}\) thin films are epitaxially linked with the metallic \(4d\) TMO single crystals. We discuss that an electron- phonon coupling at the interface or within the substrate might have affected the magnon dispersion of \(\mathrm{Sr_2IrO_4}\) via a long- range magnon- phonon interaction. This phenomenon has increased second and third- nearest- neighbor interaction while its impact on the first- nearest neighbor is minimal. Our experimental results call for further theoretical studies and calculations with a new understanding of microscopic interactions between magnons and phonons. Nevertheless, besides the prevailing acoustic waves responsible for magnon- phonon coupling34, we posit that metal- insulator heterointerfaces serve as a novel mechanism for inducing magnon- phonon coupling essential for magnonics. Our work also suggests some interesting questions and perspectives. For instance, whether this phenomenon is specific to \(5d / 4d\) heterostructures or if it will also occur in other types of heterostructures. In other words, it sparks questions regarding how spin- orbit interaction influences the magnon dispersion of antiferromagnetic insulators and whether this phenomenon can also be observed in Van der Waals heterostructures. Future studies of various heterostructures with different materials would shed light on these questions. + +<--- Page Split ---> +<|ref|>sub_title<|/ref|><|det|>[[115, 90, 271, 108]]<|/det|> +## Acknowledgments + +<|ref|>text<|/ref|><|det|>[[112, 133, 886, 433]]<|/det|> +AcknowledgmentsWe acknowledge the support of National Science Foundation Grant No. DMR- 2104296 for sample synthesis and characterization. This research used resources of the Advanced Photon Source; a U.S. Department of Energy (DOE) Office of Science user facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE- AC02- 06CH11357. Electron microscopy was performed at the Center for Electron Microscopy and Analysis at the Ohio State University supported by National Science Foundation Grants No. DMR- 1847964. G.C. acknowledges NSF support via Grant No. DMR 2204811. B.K. acknowledges financial support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through Project No. 107745057- TRR 80. + +<|ref|>sub_title<|/ref|><|det|>[[115, 549, 209, 566]]<|/det|> +## References + +<|ref|>text<|/ref|><|det|>[[110, 590, 886, 892]]<|/det|> +References1. Grundler, D. Reconfigurable magnonics heats up. Nature Physics 11, 438- 441 (2015).2. Kruglyak, V. V., Demokritov, S. O. & Grundler, D. Magnonics. Journal of Physics D: Applied Physics 43, 264001 (2010).3. Lenk, B., Ulrichs, H., Garbs, F. & Münzenberg, M. The building blocks of magnonics. Physics Reports 507, 107- 136 (2011).4. Baltz, V. et al. Antiferromagnetic spintronics. Reviews of Modern Physics 90, 015005 (2018).5. 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Click to download. + +<|ref|>text<|/ref|><|det|>[[60, 130, 353, 150]]<|/det|> +SupplementaryInformation.pdf + +<--- Page Split ---> diff --git a/preprint/preprint__0913196261d535330330d1e998de6b5f2eff345031b264d7786eff4a9e3ac6a7/images_list.json b/preprint/preprint__0913196261d535330330d1e998de6b5f2eff345031b264d7786eff4a9e3ac6a7/images_list.json new file mode 100644 index 0000000000000000000000000000000000000000..4bb3d15b750af9e84fb9e7180167ebd796ad3af8 --- /dev/null +++ b/preprint/preprint__0913196261d535330330d1e998de6b5f2eff345031b264d7786eff4a9e3ac6a7/images_list.json @@ -0,0 +1,18 @@ +[ + { + "type": "image", + "img_path": "images/Figure_2.jpg", + "caption": "Figure 2. Altered stromal and epithelial cell differentiation states of DR in sPE condition. (a) UMAP of cell subpopulation identification of stromal and perivascular fraction of endometrium in late secretory phase. (b) UMAP of stromal merged cells of both sPE (blue) and control (red) samples. (c) Neighbourhood graph represents the differential abundance of stromal cell in late secretory endometrium. Dot size represents neighbourhoods, while edges thickness (weight) depicts the", + "footnote": [], + "bbox": [], + "page_idx": 23 + }, + { + "type": "image", + "img_path": "images/Figure_5.jpg", + "caption": "Figure 5. Decidualization resistance in sPE confirmed with spatial transcriptomics. (a) Immunofluorescence of enriched stromal ROIs selected of one representative sPE sample (PanCK in green, Vimentin in yellow, CD31 in red and nucli in blue) and unsupervised hierarchical clustering based on Pearson distances of the normalized data z-scores of the top genes of enriched stromal ROIs. (b) Volcano plots depicting DEGs between sPE and controls within stromal ROIs. (c) Immunofluorescence of enriched glandular epithelial ROIs selected of one representative sPE sample and unsupervised hierarchical clustering based on Pearson distances of the normalized data z-scores of the top genes of enriched glandular epithelium ROIs. (d) Volcano plots depicting DEGs between sPE and controls within Glandular epithelial ROIs. (e) Immunofluorescence of enriched luminal epithelial", + "footnote": [], + "bbox": [], + "page_idx": 25 + } +] \ No newline at end of file