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10.34133_2022_9870149.pdf	Code Availability. The source codes for our CS framework are available at https://github.com/bianzhiyu/ContinuityScaling.	Additional Points Code Availability. The source codes for our CS framework are available at https://github.com/bianzhiyu/ContinuityScaling .	AAAS Research Volume 2022, Article ID 9870149, 10 pages https://doi.org/10.34133/2022/9870149 Research Article Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately Xiong Ying,1,2,3 Si-Yang Leng ,2,4 Huan-Fei Ma ,5 Qing Nie and Wei Lin 1,2,3,8 ,6 Ying-Cheng Lai ,7 1School of Mathematical Sciences, SCMS, and SCAM, Fudan University, Shanghai 200433, China 2Research Institute for Intelligent Complex Systems, CCSB, and LCNBI, Fudan University, Shanghai 200433, China 3State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai 200032, China 4Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China 5School of Mathematical Sciences, Soochow University, Suzhou 215006, China 6Department of Mathematics, Department of Developmental and Cell Biology, And NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697-3875, USA 7School of Electrical, Computer, And Energy Engineering, Arizona State University, Tempe, Arizona 85287-5706, USA 8Shanghai Artiﬁcial Intelligence Laboratory, Shanghai 200232, China Correspondence should be addressed to Wei Lin; wlin@fudan.edu.cn Received 7 March 2022; Accepted 24 March 2022; Published 4 May 2022 Copyright © 2022 Xiong Ying et al. Exclusive Licensee Science and Technology Review Publishing House. Distributed under a Creative Commons Attribution License (CC BY 4.0). Data-based detection and quantiﬁcation of causation in complex, nonlinear dynamical systems is of paramount importance to science, engineering, and beyond. Inspired by the widely used methodology in recent years, the cross-map-based techniques, we develop a general framework to advance towards a comprehensive understanding of dynamical causal mechanisms, which is consistent with the natural interpretation of causality. In particular, instead of measuring the smoothness of the cross-map as conventionally implemented, we deﬁne causation through measuring the scaling law for the continuity of the investigated dynamical system directly. The uncovered scaling law enables accurate, reliable, and eﬃcient detection of causation and assessment of its strength in general complex dynamical systems, outperforming those existing representative methods. The continuity scaling-based framework is rigorously established and demonstrated using datasets from model complex systems and the real world. 1. Introduction Identifying and ascertaining causal relations are a problem of paramount importance to science and engineering with broad applications [1–3]. For example, accurate detection of causation is the key to identifying the origin of diseases in precision medicine [4] and is important to ﬁelds such as psychiatry [5]. Traditionally, associational concepts are often misinterpreted as causation [6, 7], while causal analysis in fact goes one step further beyond association in a sense that, instead of using static conditions, causation is induced under changing conditions [8]. The principle of Granger causality formalizes a paradigmatic framework [9–11], quantifying causality in terms of prediction improvements, but, because of its linear, multivariate, and statistical regression nature, the various derived methods require extensive data [12]. Entropy-based methods [13–20] face a similar diﬃculty. Another issue with the Granger causality is the fundamental requirement of separability of the underlying dynamical var- iables, which usually cannot be met in the real world sys- tems. To overcome these diﬃculties, the cross-map-based techniques, paradigms tailored to dynamical systems, have been developed and have gained widespread attention in the past decade [21–36]. 2 Research The cross-map is originated from nonlinear time series analysis [37–42]. A brief understanding of such a map is as follows. Consider two subsystems: X and Y. In the recon- structed phase space of X, if for any state vector at a time a set of neighboring vectors can be found, the set of the cross-mapped vectors, which are the partners with equal time of X, could be available in Y. The cross-map underlying the reconstructed spaces can be written as Y t = ΦðXtÞ (where Xt and Y t are delay coordinates with suﬃciently large dimensions) for the case of Y unidirectionally causing X while, mathematically, its inverse map does not exist [34]. In practice, using the prior knowledge on the true cau- sality in toy models or/and the assumption on the expanding property of Φ (representing by its Jacobian’s singular value larger than one in the topological causality framework [24]), scientists developed many practically useful tech- niques based on the cross-map for causality detection. For instance, the “activity” method, originally designed to mea- sure the continuity of the inverse of the cross-map, com- pares the divergence of the cross-mapped vectors to the state vector in X with the divergence of the independently- selected neighboring vectors to the same state vector [22, 23]. The topological causality measures the divergence rate of the cross-mapped vectors from the state vectors in Y [24], and the convergent cross-mapping (CCM), increasing the length of time series, compares the true state vector Y with the average of the cross-mapped vectors, as the estima- tion of Y [21, 25–36]. Then, the change of the divergence or the accuracy of the estimation is statistically evaluated for determining the causation from Y to X. Inversely, the causa- tion from X to Y can be evaluated in an analogous manner. The above evaluations [21, 24, 26–36] can be understood at a conceptional and qualitative level and perform well in many demonstrations. In this work, striving for a comprehensive understanding of causal mechanisms and inspired by the cross-map-based techniques, we develop a mathematically rigorous frame- work for detecting causality in nonlinear dynamical systems, turning eyes towards investigating the original systems from their cross-maps, which is also logically consistent with the natural interpretation of causality as functional dependences [2, 8]. The skills used in cross-map-based methods are assimilated in our framework, while we directly study the original dynamical systems or the reconstructed systems instead of the cross-maps. The foundation of our framework is the scaling law for the changing relation of ε with δ arising from the continuity for the investigated system, henceforth the term “continuity scaling”. In addition to providing a the- ory, we demonstrate, using synthetic and real-world data, that our continuity scaling framework is accurate, computa- tionally eﬃcient, widely applicable, showing advantages over the existing methods. 2. Continuity Scaling Framework To explain the mathematical idea behind the development of our framework, we use the following class of discrete time dynamical systems: xt+1 = fðxt, ytÞ and yt+1 = gðxt, ytÞ for t ∈ ℕ, where the state variables fxtgt∈ℕ, fytgt∈ℕ evolve in the compact manifolds M, N of dimension DM, DN under suﬃciently smooth map f, g, respectively. We adopt the common recognition of causality in dynamical systems. Deﬁnition 1. If the dependence of fðx, yÞ on y is nontrivial (i. e., a directional coupling exists), a variation in y results in a change in the value of fðx, yÞ for any given x, which, accord- ing to the natural interpretation of causality [2, 43], admits that y : fytgt∈ℕ has a direct causal eﬀect on x : fxtgt∈ℕ, denoted by y↪x, as shown in the upper panel of Figure 1(a). We now interpret the causal relationship stipulated by ð·Þ ≜ fðxg, ·Þ for a given the continuity of a function. Let fxg ∈ N , we denote its image under ∈ M. For any yP point xg ≜ fxg the given function by xI ðyPÞ. Applying the logic state- ment of a continuous function to fxg ð·Þ, we have that, for any neighborhood OðxI, εÞ centered at xI and of radius ε > 0, there exists a neighborhood OðyP, δÞ centered at yP of radius δ > 0, such that fxg ðOðyP, δÞÞ ⊂ OðxI, εÞ. The neigh- borhood and its radius are deﬁned by O p, hð Þ = s ∈ S distS f j s, pð Þ < h g, p ∈ S, h > 0, ð1Þ where distSð·, · Þ represents an appropriate metric describ- ing the distance between two given points in a speciﬁed manifold S with S = M or N . The meaning of this mathe- matical statement is that, if we have a neighborhood of the resulting variable xI ﬁrst, we can then ﬁnd a neighborhood for the causal variable yP to satisfy the above mapping and inclusion relation. This operation of “ﬁrst-ε-then-δ” pro- vides a rigorous base for the principle that the information about the resulting variable can be used to estimate the information of the causal variable and therefore to ascertain causation, as indicated by the long arrow in the middle panels of Figure 1(a). Note that, the existence of the δ > 0 neighborhood is always guaranteed for a continuous map . In fact, due to the compactness of the manifold N , a fxg largest value of δ exists. However, if yP does not have an explicit causal eﬀect on the variable xI, i.e., fxg is independent of yP, the existence of δ is still assured but it is independent of the value of ε, as shown in the upper panel of Figure 1(b). This means that merely determining the existence of a δ- neighborhood is not enough for inferring causation - it is necessary to vary ε systematically and to examine the scaling relation between δ and ε. In the following we discuss a num- ber of scenarios. Case I. Dynamical variables fðxt, ytÞgt∈ℕ are fully measur- x > 0, the set fxτ ∈ Mjτ ∈ It able. For any given constant ε ðε xÞg can be used to approximate the neighborhood Oðxt+1, ε xÞ, where the time index set is x It x ε xð Þ ≜ τ ∈ ℕ distMj f ð xt+1, xτ Þ < ε x g: ð2Þ The radius δt y = δt yðε xÞ of the neighborhood Oðyt, δt yÞ Research 3 (a) (b) Figure 1: Illustration of causal relation between two sets of dynamical variables. (a) Existence of causation from y in N to x in M, where each correspondence from xt+1 to yt is one-to-one, represented by the line or the arrow, respectively, in the upper and the middle panels. In y (the lower panel) with ε this case, a change in ln ε y denoting the neighborhood size of the resulting variable x and of the causal variable y, respectively. (b) Absence of causation from y to x, where every point on each trajectory, fytg, in N could be the correspondent point from xt+1 in M (the upper panel) and thus every point in N belongs to the largest δ-neighborhood of yt (the middle panel). In this case, δ x (the lower panel). Also refer to the supplemental animation for illustration. x results in a direct change in δ y does not depend on ε x and δ satisfying fxg=xt ðOðyt, δt yÞÞ ⊂ Oðxt+1, ε xÞ can be estimated as n h Þ ≜ # (cid:2)It x ε xð Þ δt y ε xð i o−1 〠 τ∈(cid:2)It x ε xð Þ distN yt, yτ−1 ð Þ, ð3Þ xg. xðε xÞ ≜ fτ ∈ It where #½·(cid:2) is the cardinality of the given set and the index set is (cid:2)It xÞjdistMðxt, xτ−1Þ < ε xðε The strict mathematical steps for estimating δt y are given in Section II of Supplementary Information (SI). We empha- size that here correspondence between xt+1 and yt is investi- gated, diﬀering from the cross-map-based methods, with one-step time diﬀerence naturally arising. This consider- ation yields a key condition [DD], which is only need when considering the original iteration/ﬂow and whose detailed description and universality are demonstrated in SI. We reveal a linear scaling law between hδt yit∈ℕ and ln ε x, as shown in the lower panels of Figure 1, whose slope s y↪x is an indicator of the correspondent relation between ε and δ and hence the causal relation y↪x. Here, h·it∈ℕ denotes the average over time. In particular, a larger slope value implies a stronger causation in the direction from y to x as represented by the map functions fðxt, ytÞ (Figure 1(a)), while a near zero slope indicates null causation in this direc- tion (Figure 1(b)). Likewise, possible causation in the reversed direction, x↪y, as represented by the function gð xt, ytÞ, can be assessed analogously. And the unidirectional case when fðx, yÞ = f0ðxÞ independent of y is uniformly con- sidered in Case II. We summarize the consideration below and an argument for the generic existence of the scaling law is provided in Section II of SI. Theorem 2. For dynamical variables fðxt, ytÞgt∈ℕ measured directly from the dynamical systems, if the slope s y↪x deﬁned above is zero, no causation exists from y to x. Otherwise, a directional coupling can be conﬁrmed from y to x and the slope s y↪x increases monotonically with the coupling strength. Case II. The dynamical variables fðxt, ytÞgt∈ℕ are not directly accessible but measurable time series futgt∈ℕ and fvtgt∈ℕ are available, where ut = uðxtÞ and vt = vðytÞ with u: M ⟶ ℝru and v: N ⟶ ℝrv being smooth observational functions. To assess causation from y to x, we assume one- dimensional observational time series (for simplicity): ru = rv = 1, and use the classical delay-coordinate embedding method [37–42, 44] to reconstruct the phase space: ut = T ðut, ut+τu,⋯,ut+ðdu−1ÞτuÞ and vt = ðvt, vt+τv ,⋯,vt+ðdv−1Þτv Þ , where τu,v is the delay time and du,v > 2ðDM + DN Þ is the embedding dimension that can be determined using some standard criteria [45]. As illustrated in Figure 2, the dynam- ical evolution of the reconstructed states fðut, vtÞgt∈ℕ is gov- erned by T ut+1 = ~ f ut, vt ð Þ, vt+1 = ~g ut, vt ð Þ: ð4Þ The map functions can be calculated as ∘ E−1 ~ fðu, vÞ ≜ Eu ∘ ∘ E−1 ∘ E−1 u ðuÞ, Π u 1 ∘ E−1 v ðvÞÞ, where the embedding (diﬀeomorphism) v ðvÞÞ, ~gðu, vÞ ≜ Ev ∘ ½f, g(cid:2)ðΠ 1 2 ½ f, g(cid:2)ðΠ ðuÞ, Π 2 4 Research (f(x, y), g(x, y)) M × N M × N y↪x or x↪y ) u ( 1 u – E ° 1 П ) v ( 1 – v E ° 2 П ) y , x ( u E ) y , x ( v E ˜ ˜ Lu × Lv ˜ ˜ Lu × Lv v↪u or u↪v ˜ ˜ (f(u,v),g(u,v)) {ut(x)}t𝜖N {vt(y)}t𝜖N Figure 2: Illustration of system dynamics before and after embedding for Case II. In the left panel, the arrows describe how ~ f, ~gÞ after the original systems ðf, gÞ is equivalent to the system ð embedding. In the right panel, causation between the internal variables x and y can be ascertained by detecting the causation between the variables u and v reconstructed from measured time series. Es: M × N ⟶ ~L s ⊂ ℝds with given by ~L s ≜ EsðM × N Þ, s = u or v, is Eu x, y ð f, g½ Ev x, y ð f, g½ ∘ 1 ∘ f, g½ Þ ≜ uð xð Þ, u ∘ Π (cid:2)2τu x, y ð Þ ≜ vð yð Þ, v ∘ Π 2 (cid:2)2τv x, y ð τu x, y (cid:2) ð du−1 Þ, ⋯, u ∘ Π ∘ f, g½ (cid:2) 1 τv x, y ∘ f, g½ (cid:2) ð dv−1 ∘ f, g½ ð Þ, ⋯, v ∘ Π Þ, u ∘ Π 1 Þτu x, y ð Þ, v ∘ Π 2 Þτv x, y ð (cid:2) ð ÞÞ, ∘ ÞÞ, 2 ð5Þ k ~L s, ½f, g(cid:2) s deﬁned on 1ðx, yÞ = x and Π with the inverse function E−1 represent- ing the kth iteration of the map and the projection mappings as Π 2ðx, yÞ = y. Case II has now been reduced to Case I, and our continuity scaling framework can be used to ascertain the causation from v to u based on the measured time series with the indices It uÞ and s v↪u (equations (2) and (3)). uÞ, δt uðε vðε ~ f0ðutÞ and vt+1 = ~gðut, vtÞ, where Does the causation from v to u imply causation from y to x? The answer is aﬃrmative, which can be argued, as follows. If the original map function f is independent of y: fðx, yÞ = f0ðxÞ, there is no causation from y to x. In this case, the embedding Euðx, yÞ becomes independent of y, degenerating into the form of Euðx, yÞ = Eu0ðxÞ, a diﬀeomorphism from M ~L u0 = Eu0ðMÞ only. As a result, equation (4) becomes to ~ ∘ f ∘ E−1 ut+1 = f0ðuÞ = Eu0 u0ð ~ f0 is independent of v. The uÞ and the resulting mapping independence can be validated by computing the slope v↪u associated with the scaling relation between hδt s vit∈ℕ and ln ε u, where a zero slope indicates null causation from v to u and hence null causation from y to x. Conversely, a ﬁnite slope signiﬁes causation between the variables. Thus, any type of causal relation (unidirectional or bi-directional) detected variables fðut, vtÞgt∈ℕ implies the same type of causal relation between the internal but inaccessible variables x and y of the original system. reconstructed between state the T Case III. The structure of the internal variables is completely unknown. Given the observational functions ~u, ~v: M × N ⟶ ℝ with ~ut = ~uðxt, ytÞ and ~vt = ~vðxt, ytÞ, we ﬁrst recon- struct the state space: ~ut = ð~ut, ~ut+τ,⋯,~ut+ðd−1ÞτÞ and ~vt = ð~vt, ~vt+τ,⋯,~vt+ðd−1ÞτÞ . To detect and quantify causation from ~v to ~u (or vice versa), we carry out a continuity scaling analysis with the modiﬁed indices It ~vðε~uÞ and s~v↪~u. Diﬀering from Case II, here, due to the lack of knowledge about the correspondence structure between the internal and observational variables, a causal relation for the latter does not deﬁnitely imply the same for the former. ~uðε~uÞ, δt T Case IV. Continuous-time dynamical systems possessing a suﬃciently smooth ﬂow fSt ; t ∈ ℝg on a compact manifold H : dStðu0Þ/dt = χðStðu0ÞÞ, where χ is the vector ﬁeld. Let f̂ut=ωn+νgn∈ℤ and f̂vt=ωn+νgn∈ℤ be two respective time series from the smooth observational functions ̂u, ̂v: H ⟶ ℝ with ̂ut = ̂uðStÞ and ̂vt = ̂vðStÞ, where 1/ω is the sampling rate and ν is the time shift. Deﬁning Ξ ≜ Sω: H ⟶ H and ̂Sn ≜ Sωn+νðu0Þ, we obtain a discrete-time system as ̂Sn+1 = Ξð̂SnÞ with the observational functions as ̂un = ̂uð̂SnÞ and ̂vn = ̂vð ̂SnÞ, reducing the case to Case III and rendering applicable our continuity scaling analysis to unveil and quantify the causal relation between f̂ut=ωn+νgn∈ℤ and f̂vt=ωn+νgn∈ℤ. If the domains of ̂u and ̂v have their own restrictions on some particular subspaces, e.g., ̂u: H u ⟶ ℝ and ̂v: H v ⟶ ℝ with H = H u ⊕ H v, the case is further reduced to Case II, so the detected causal relation between the observational variables imply causation between the internal variables belonging to their respective subspaces. 3. Demonstrations: From Complex Dynamical Models to Real-World Networks To demonstrate the eﬃcacy of our continuity scaling frame- work and its superior performance, we have carried out extensive numerical tests with a large number of synthetic and empirical datasets, including those from gene regulatory networks as well as those of air pollution and hospital admission. The practical steps of the continuity scaling framework together with the signiﬁcance test procedures are described in Methods. We present three representative examples here, while leaving others of signiﬁcance to SI. 2,t+1 = x The ﬁrst example is an ecological model of two unidirec- tionally interacting species: x 1,tð3:8 − 3:8x 1,t − μ 1,t+1 = x 12 x 2,t − μ 2,tÞ and x x 2,tð3:7 − 3:7x 1,tÞ. With time series 2,tÞgt∈ℕ obtained from diﬀerent values of the cou- 1,t, x fðx pling parameters, our continuity scaling framework yields correct results of diﬀerent degree of unidirectional causa- tion, as shown in Figures 3(a) and 3(b). In all cases, there exists a reasonable range of ln εx (neither too small nor too large) from which the slope sx of the linear scaling can be extracted. The statistical signiﬁcance of the estimated slope values and consequently the strength of causation can be assessed with the standard p-value test [46] (Methods and SI). An ecological model with bidirectional coupling has also been tested (see Section III of SI). Figures 3(c) and 3(d) ↪x 21 2 1 2 Research 5 0.6 0.4 t 〉 1 x 𝛿 〈 t 0.2 0.6 0.4 t 〉 2 x 𝛿 〈 t 0.2 0 –8 –6 –2 0 –4 ln 𝜀x2 0 –8 –6 –2 0 –4 ln 𝜀x1 𝜇21 = 0.00 𝜇21 = 0.05 𝜇21 = 0.10 𝜇21 = 0.15 (a) 𝜇12 = 0.00 𝜇12 = 0.00 𝜇12 = 0.00 𝜇12 = 0.00 (b) t c e ff E x5 x4 x3 x2 x1 j x ↪ x s i e p o l S t c e ff E x5 x4 x3 x2 x1 0.12 0.1 0.08 0.06 0.04 0.02 0 j x ↪ x s i e p o l S 0.15 0.12 0.09 0.06 0.03 0 x1 x2 x4 x5 x3 Cause (d) x1 x2 x4 x5 x3 Cause (c) Figure 3: Ascertaining and characterizing causation in various ecological systems of interacting species. (a, b) Unidirectional causation of two coupled species. In (a), the values of the slope sx 2 are approximately 0.0004, 0.1167, 0.1203, and 0.1238 for four diﬀerent values of the coupling parameter μ indicating its nonexistence. (c, d) Inferred causal network of ﬁve species whose interacting structure is, respectively, that of a ring: xi↪xi+1ðmod 5Þ (i = 1, ⋯, 5) and of a tree: x j↪xj+1,j+3 (j = 1, 2), where the estimated slope values are color-coded. Results of a statistical analysis of the accuracy and reliability of the determined causal interactions are presented in SI Section III. Time series of length 5000 are used in all these simulations. The embedding parameters are τs = 1 and ds = 3 with s = x 21. (b) Near zero slope values sx associated with the causal relation x for x ↪x ↪x 1, ↪x ↪x 1 2 2 1 1 2 1, ⋯, x 5. show the results from ecological networks of ﬁve mutually interacting species on a ring and on a tree structure, respec- tively, where the color-coded slope values reﬂect accurately the interaction patterns in both cases. The second example is the coupled Lorenz system: _xi = σiðyi − xiÞ + μijx j, _yi = xiðρi − ziÞ − yi, _zi = xiyi − βizi with i, j = 1, 2 and i=j. We use time series fy 2,tgt=nω for detecting diﬀerent conﬁgurations of causation (see Section III of SI). Figure 4 presents the overall result, where the color-coded estimated values of the slope from the continu- ity scaling are shown for diﬀerent combinations of the sam- pling rate 1/ω and coupling strength. Even with relatively low sampling rate, our continuity scaling framework can successfully detect and quantify the strength of causation. Note that the accuracy does not vary monotonously with the sampling rate, indicating the potential of our framework 1,t, y to ascertain and quantify causation even with rare data. Moreover, the proposed index can accurately reﬂect the true causal strength (denoting by the coupling parameter), which is also evidenced by numerical tests in Sections III and IV of SI. Robustness tests against diﬀerent noise perturbations are provided in Section III of SI demonstrating the practical usefulness of our framework. Additionally, analogous to the ﬁrst example, we present in SI several examples on cau- sation detection in the coupled Lorenz system with nonlin- ear couplings, and the Rössler-Lorenz system, etc., which further demonstrates the generic eﬃcacy of our framework. In addition, we present study on several real-world data- set, which brings new insights to the evolutionary mecha- nism of underlying systems. We study gene expression data from DREAM4 in silico Network Challenge [47, 48], whose intrinsic gene regulatory networks (GRNs) are known for veriﬁcation (Figure 5(a) and Figure S17 of SI). Applying 6 Research 1 2 𝜇 6 4 2 0 5 4 3 2 1 0 2 y ↪ 1 y s e p o l S 1 2 𝜇 6 4 2 0 1 y ↪ 2 y s e p o l S 5 4 3 2 1 0 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Sampling duration/0.001 (a) 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Sampling duration/0.001 (b) Figure 4: Detecting causation in the unidirectionally coupled Lorenz system. The results are for diﬀerent values of μ sampling rate 1/ω. (a, b) Color-coded values of the slopes sy embedding parameters are ds = 7, τs ≈ 0:05 with ωjτs (s = y including the time series lengths used in the simulations. 12 = 0) and , respectively. The integration time step is 10−3 and the and sy 2). See Section III and Table S9 of SI for all the other parameters 1 or y 21 (μ ↪y ↪y 1 2 1 2 100 45 44 43 1 0.8 0.6 0.4 0.2 e t a r e v i t i s o p e u r T 0 0 75 36 69 37 67 25 85 38 10 62 72 96 23 83 87 73 Enhancer Inhibitor (a) 0.2 0.4 0.6 0.8 1 False positive rate 1, AUROC = 0.765 2, AUROC = 0.667 3, AUROC = 0.693 4, AUROC = 0.693 5, AUROC = 0.868 (b) Figure 5: Detecting causal interactions in ﬁve GRNs. (a) One representative GRN containing 20 randomly selected genes. Other four structures can be found in Figure S17 of SI. (b) The ROC curves as well as their AUROC values demonstrate the eﬃcacy of our framework. our framework to these data, we ascertain the causations between each pair of genes by using the continuity scaling framework. The corresponding ROC curves for ﬁve diﬀerent networks as well as their AUROC values are shown in Figure 5(b), which indicates a high detection accuracy in dealing with real-world data. We then test the causal relationship in a marine ecosys- tem consisting of Paciﬁc sardine landings, northern anchovy landings and sea surface temperature (SST). We reveal new ﬁndings to support the competing relationship hypothesis stated in [49] which cannot be detected by CCM [25]. As pointed out in Figure 6, while common inﬂuence from SST to both species is veriﬁed with both methods, our continuity scaling additionally illuminates notable inﬂuence from anchovy to sardine with its reverse direction being less sig- niﬁcant. While competing relationship plays an important role in ecosystems, continuity scaling can reveal more essen- tial interaction mechanism. See Section III.E of SI for more details. Moreover, we study the transmission mechanism of the recent COVID-19 pandemic. Particularly, we analyze the daily new cases of COVID-19 of representative countries for two stages: day 1 (January 22 nd 2020) to day 100 (April 30 th 2020) and day 101 (May 1 st 2020) to day 391 (February 15 th 2021). Our continuity scaling is pairwisely applied to reconstruct the transmission causal network. As shown in Figure 7, China shows a signiﬁcant eﬀect on a few countries at the ﬁrst stage and this eﬀect disappears at the second stage. However, other countries show a diﬀerent situation with China, whose external eﬀect lasts as shown in Section III.E and Figure S18 of SI. Our results accord with that China holds stringent epidemic control strategies with Research 7 SST SST Sardine Anchovy Sardine Anchovy Continuity scaling CCM Figure 6: The comparison of causal network structure detected by continuity scaling and CCM among sea surface temperature, sardine, and anchovy. Figure 7: The causal eﬀect from China to other countries of the COVID-19 pandemic detected by continuity scaling between stages 1 and 2. Here, stage 1 is from January 22 nd 2020 to April 30 th 2020, and stage 2 is from May 1 st 2020 to February 15 th 2021. For those detected causal links between all countries, refer to Section III.E and Figure S18 of SI. These maps are for illustration only. sporadic domestic infections, as evidenced by oﬃcial daily brieﬁngs, demonstrating the potential of continuity scaling in detecting causal networks for ongoing complex systems. Additionally, We emphasize that day 100 is a suitable critical day to distinguish the early severe stage and the late well-under-control stage of the pandemic (see Figure S18(a) of SI), while slight change of the critical day will not nullify our result. As shown in Figure S18(b) of SI, when the critical day varies from day 94 to day 106, no signiﬁcant change (less than 5%) of the detected causal links occurs at both stages, and the number of countries under inﬂuence of China at Stage 2 remains zero. See more details in Section III.E of SI. Additional real world examples including air pollutants and hospital admission record from Hong Kong are also shown in Section III of SI. 4. Discussion To summarize, we have developed a novel framework for data-based detection and quantiﬁcation of causation in com- plex dynamical systems. On the basis of the widely used cross-map-based techniques, our framework enjoys a rigor- ous foundation, focusing on the continuity scaling law of the concerned system directly instead of only investigating the continuity of its cross-map. Therefore, our framework is consistent with the standard interpretation of causality, and works even in the typical cases where several existing typical methods do not perform that well or even they fail (see the comparison results in Section IV of SI). In addition, the mathematical reasoning leading to the core of our frame- work, the continuity scaling, helps resolve the long-standing issue associated with techniques directly using cross-map that information about the resulting variables is required to project the causal variables, whereas several works in the literature [50], which directly studied the continuity or the smoothness of the cross-map, likely yielded confused detected results on causal directions. Computational complexity. The computational com- plexity of the algorithm is OðT 2NεÞ, which is relatively smaller than the CCM method, whose computational com- plexity is OðT 2 log TÞ. the dynamical behavior of Limitations and future works. Nevertheless, there are still some spaces for improving the presently proposed framework. First, currently, only bivariate detection algo- rithm is designed, so generalization to multivariate network inference requires further considerations, as analogous to those works presented in Refs. [51–53]. Second, the causal time delay has not been taken into account in the current framework, so it also could be further investigated, similar to the work reported in Ref. [33]. Also, more advanced algo- rithms, such as the one developed in Ref. [54], could be 8 Research integrated into this framework for detecting those temporal causal structures. Deﬁnitely, we will settle these questions in our future work. Detecting causality in complex dynamical systems has broad applications not only in science and engineering, but also in many aspects of the modern society, demanding accurate, eﬃcient, and rigorously justiﬁed and hence trust- worthy methodologies. Our present work provides a vehicle along this feat and indeed resolves the puzzles arising in the use of those inﬂuential methods. 5. Methods Continuity scaling framework: a detailed description of algo- rithms. Let futgt=1,2,⋯,T and fvtgt=1,2,⋯,T be two experimen- tally measured time series of internal variables fðxt, ytÞgt∈ℕ. Typically, if the dynamical variables fðxt, ytÞgt∈ℕ are accessi- ble, fðut, vtÞg reduce to one-dimensional coordinate of the internal system. The key computational steps of our conti- nuity scaling framework are described, as follows. We reconstruct the phase space using the classical method of delay coordinate embedding [37] with the opti- mal embedding dimension dz and time lag τz determined by the methods in Refs. [55, 56] (i.e., the false nearest neigh- bors and the delayed mutual information, respectively): n (cid:2) L z ≜ z tð Þ = zt, zt+τz , ⋯, zt+ dz−1 ð Þτz (cid:3) j o t = 1, ⋯, T 0 , ð6Þ z = u, v, T where Euclidean distance is used for both L u,v. 0 = min fT − ðdz − 1Þτzjz = u, vg, and We present the steps for causation detection using the case of v↪u as an example. We calculate the respective diameters for L u,v as (cid:4) Dz ≜ max distLz z tð Þ, z τð Þ ð Þ 1 ≤ t, τ ≤ T j (cid:5) , 0 ð7Þ where z = u, v, and z = u, v. We set up a group of numbers, fε u,N ε = Du, with the other ele- u,jgj=1,⋯,N ε ments satisfying u,1 = e · Du, ε , as ε ln ε u,j − ln ε j − 1 u,1 = ln ε − ln ε u,N ε Nε − 1 u,1 , ð8Þ for j = 2, ⋯, N ε − 1. Then, in light of (2) with (3), we have δt v ε uð (cid:6) Þ = # It u ε uð Þ (cid:7)−1 〠 τ∈It u ε uð Þ distL v v tð Þ, v τ − 1 ð ð Þ Þ, ð9Þ with It u ε uð (cid:4) (cid:8) (cid:8) Þ = τ ∈ ℕ distLu u t + 1 ð Þ, u τð Þ ð Þ < ε u, t + 1 − τ j (cid:5) j > E ð10Þ where numerically, ε set fε u,jgj=1,⋯,N ε u alters its value successively from the , and the threshold E is a positive number 0 0 vðε chosen to avoid the situation where the nearest neighboring points are induced by the consecutive time order only. u,jÞg vðε u, where ℕT As deﬁned, hδt uÞit∈ℕ is the average of δt vðε uÞ over all t. We use a ﬁnite number of pairs possible time fðhδt , ln ε vðε u,jÞit∈ℕT to approximate the scaling j=1,⋯,N ε relation between hδt uÞit∈ℕ and ln ε = f1, 2,⋯,T 0g. Theoretically, a larger value of N ε and a smaller value of e will result in a more accurate approximation of the scaling relation. In practice, the accuracy is determined by the length of the observational time series, the sampling duration, and diﬀerent types of noise perturbations. In numerical simulations, we set e = 0:001 and N ε = 33. In addi- tion, a too large or a too small value of ε u can induce insuﬃ- cient data to restore the neighborhood and/or the entire manifold. We thus set δt u,jÞ = δt u,j+1Þ as a practical tech- nique as the number of points is limited practically in a small neighborhood. As a result, near zero slope values would appear on both sides of the scaling curve hδt uÞit∈ℕ-ln ε u, as demonstrated in Figure 3 and in SI. In such a case, to esti- mate the slope of the scaling relation, we take the following approach. vðε vðε vðε Deﬁne a group of numbers by (cid:11) (cid:9) − δt v − ln ε Sj ≜ ln ε u,j+1 δt v t∈ℕT (cid:11) (cid:12) (cid:10) ε 0 u,j+1 u,j (cid:9) (cid:10) (cid:12) ε u,j t∈ℕT 0 , ð11Þ where j = 1, ⋯, Nε − 1, sort them in a descending order, from which we determine the ½Nε + 1/2(cid:2) largest numbers, collect their subscripts - j’s together as an index set ̂J, and set H ≜ fj, j + 1jj ∈ ̂Jg. Applying the least squares method to the linear regression model: (cid:11) (cid:12) δt v ε uð Þ t∈ℕ = S · ln ε u + b ð12Þ with the dataset fðhδt optimal values ð̂S, ﬁnally obtain the slope of the scaling relation as s , we get the ̂bÞ for the parameters ðS, bÞ in (12) and u,jÞit∈ℕT , ln ε ≜ ̂S. u,jÞg vðε j∈H 0 v↪u For the other causal direction from u to v, these steps are equally applicable to estimating the slope s u↪v. 0 To assess the statistical signiﬁcance of the numerically determined causation, we devise the following surrogate test using the case of v causing u as an illustrative example. Divide the time series fuðtÞgt∈ℕT into NG consecutive segments of equal length (except for the last segment - the shortest segment). Randomly shuﬄe these segments and then regroup them into a surrogate sequence f̂uðtÞgt∈ℕT . Applying such a random permutation method to fvðtÞgt∈ℕT generates another surrogate sequence f̂vðtÞgt∈ℕT . Carrying out the slope computation yields ŝv↪̂u. The procedure can be repeated for a suﬃcient number of times, say Q, which consequently yields a group of estimated slopes, denoted as fsq ̂v↪̂u is set as s v↪u obtained from the original time series. For all the estimated slopes, we calculate ̂v↪̂ugq=0,1⋯,Q, where s0 0 0 0 Research 9 their mean bμ -value for s v↪u is calculated as v↪u and the standard deviation bσ v↪u. The p (cid:13) s ps v↪u ≜ 1 − normcdf (cid:14) , v↪u ð13Þ v↪u bσ − bμ v↪u where normcdf ½·(cid:2) is the cumulative Gaussian distribution function. The principle of statistical hypothesis testing guar- antees the existence of causation from v to u if ps < 0:05. In simulations, we set the number of segments to be N G = 25 and the number of times for random permutations to be Q ≥ 20. v↪u Additional Points Code Availability. The source codes for our CS framework are available at https://github.com/bianzhiyu/ContinuityScaling. Conflicts of Interest The authors declare no competing interests. Authors’ Contributions W.L. conceived idea. X.Y., S.-Y.L., and W.L. designed and performed the research. X.Y., S.-Y.L., H.-F.M., and W.L. analyzed the data. H.-F.M., Y.-C.L., and Q.N. contributed data and analysis tools, and all the authors wrote the paper. X.Y. and S.-Y.L. equally contributed to this work. Acknowledgments W.L. is supported by the National Key R&D Program of China (Grant No. 2018YFC0116600), by the National Natu- ral Science Foundation of China (Grant Nos. 11925103 and 61773125), by the STCSM (Grant No. 18DZ1201000), and by the Shanghai Municipal Science and Technology Major Project (No. 2021SHZDZX0103). Y.-C.L. is supported by AFOSR (Grant No. FA9550-21-1-0438). S.-Y.L. is supported by the National Natural Science Foundation of China (No. 12101133) and “Chenguang Program” supported by Shang- hai Education Development Foundation and Shanghai Municipal Education Commission (No. 20CG01). Q.N. is partially supported by NSF (Grant No. DMS1763272) and the Simons Foundation (Grant No. 594598). H.-F.M. is sup- ported by the National Natural Science Foundation of China (Grant No. 12171350) and by the National Key R&D Pro- gram of China (Grant No. 2018YFA0801100). Supplementary Materials Supplementary materials: SI.pdf (where we include analytic and computational details of the results in the main text. This SI is helpful but not essential for understanding the main results of the paper.) (Supplementary Materials) References [1] M. Bunge, Causality and Modern Science, Routledge, 2017. [2] J. Pearl, Causality, Cambridge university press, 2013. [3] J. Runge, S. Bathiany, E. Bollt et al., “Inferring causation from time series in earth system sciences,” Nature Communications, vol. 10, no. 1, p. 2553, 2019. [4] F. S. Collins and H. Varmus, “A new initiative on precision medicine,” New England Journal of Medicine, vol. 372, no. 9, pp. 793–795, 2015. [5] G. N. Saxe, A. 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10.1088_1361-6501_ad180c.pdf	Data availability statement The data cannot be made publicly available upon publication because they are owned by a third party and the terms of use prevent public distribution. The data that support the findings of this study are available upon reasonable request from the authors.	Data availability statement The data cannot be made publicly available upon publication because they are owned by a third party and the terms of use prevent public distribution. The data that support the findings of this study are available upon reasonable request from the authors.	Meas. Sci. Technol. 35 (2024) 035605 (12pp) Measurement Science and Technology https://doi.org/10.1088/1361-6501/ad180c Automated defect detection in precision forging ultrasonic images based on deep learning Jianjun Zhao1, Yuxin Zhang1, Xiaozhong Du1,3,∗ and Xiaoming Sun2 1 School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, People’s Republic of China 2 College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, People’s Republic of China 3 School of Energy and Materials Engineering, Taiyuan University of Science and Technology, Jincheng 048000, People’s Republic of China E-mail: xiaozhong_d@163.com Received 26 July 2023, revised 6 December 2023 Accepted for publication 21 December 2023 Published 29 December 2023 Abstract Ultrasonic testing is a widely used non-destructive testing technique for precision forgings. However, assessing defects in ultrasonic B-scan images can be prone to errors, misses, and inefficiencies due to human judgment. To address these challenges, we propose a method based on deep learning to automate the evaluation of such images. We started by creating a dataset comprising 8000 images, each measuring 224 × 224 pixels. These images were cropped from ultrasonic B-scan images of 7 specimens, each featuring different sizes and locations of holes and crack defects. We then used state-of-the-art deep learning models to benchmark the dataset and identified YOLOv5s as the best-performing baseline model for our study. To address the challenges of deploying deep learning models and the issue of small defects being easily confused with the background in ultrasonic B-scan images, we made lightweight improvements to the deep learning model. Additionally, we enhanced the quality of data labels through data cleaning. Our experiments show that our method achieved a precision of 97.8%, a recall of 98.1%, mAP@0.5 of 99.0%, and mAP@.5:.95 of 67.6%, with a frames per second (FPS) of 74.5. Furthermore, the number of model parameters was reduced by 43.2%, while maintaining high detection accuracy. Overall, our proposed method offers a significant improvement over the original model, making it a more reliable and efficient tool for automated defect assessment in ultrasonic B-scan images. Keywords: deep learning, ultrasonic testing, automated detection, lightweight improvement 1. Introduction The non-destructive testing of precision machined complex forgings is crucial, as they are irreplaceable core components in mechanical equipment. Ultrasonic testing is widely utilized in non-destructive testing of precision forgings due to its ease of use and ability to accurately locate defects [1]. While the acquisition of ultrasonic data is largely automated, the analysis ∗ Author to whom any correspondence should be addressed. of the acquired data is predominantly conducted manually by professionally trained experts. The quality of the analysis res- ults depends entirely on the knowledge and experience of the analysts, which can lead to issues such as missed detections, incorrect detections, and lengthy consumption times. As a res- ult, numerous researchers have made efforts to develop auto- mated methods for defect detection to streamline the analysis process. Figure 1 illustrates the basic scanning methods used in ultrasonic testing imaging, including A-scans, B-scans, and C-scans. In the past, most studies focused on using A-scan 1 © 2023 IOP Publishing Ltd Meas. Sci. Technol. 35 (2024) 035605 J Zhao et al Figure 1. Basic way of imaging with ultrasonic testing. data for automated analysis of ultrasonic data due to the poor imaging quality of B-scans images. For instance, in 2004, Bettayeb et al [2] proposed an automated ultrasonic NDE system that utilizes wavelet transform to suppress noise and enhance defect localization in ultrasonic signals, along with an artificial neural network classification algorithm, which achieved defect classification. In 2006, Matz et al [3] util- ized an ultrasonic signal filtering method with discrete wave- let transform and a pattern recognition method with support vector machines (SVMs) to classify A-scans signals into three categories: fault echoes, weld echoes, and back wall echoes. In 2007, Khelil et al [4] employed the principal component analysis method to optimize the wavelet parameters extrac- ted from ultrasonic echoes and establish a SVM algorithm to classify planar and volumetric defects. In 2011, Sambath et al [5] chose 12 coefficients from the wavelet representa- tion of the echo signal as features, such as mean, variance, energy, and amplitude, and inputted them to a neural net- work with two hidden layers for training to identify four types of defects with a 94% accuracy rate. In 2014, Chen et al [6] proposed a hierarchical multiclass SVM (LMSVM) with parameter optimization and feature selection using BA. The method was proven to be robust, accurate, and reliable for ultrasonic defect classification through experiments conducted on a welding defect dataset. In 2017, Cruz et al [7] used three different feature extraction methods, including the discrete Fourier transform, wavelet transform, and cosine transform, as well as two different artificial neural network architectures to automatically classify welding defects. They achieved effi- cient identification of defects using this approach. Meng et al [8] proposed a deep convolutional neural network (CNN) with a linear SVM top layer to classify cavity-layered ultrasonic signals in carbon fiber-reinforced polymer (CFRP) samples. In 2018, Munir et al [9] evaluated the performance of deep and shallow neural networks for automatic classification of weld defect ultrasonic signal data. They found that deep neural networks had better performance, achieving the highest accur- acy of 91.89% on a mixed-frequency dataset. In 2019, Munir et al [10] applied CNNs to noisy ultrasonic features to improve the classification performance and applicability of defects in welded parts. Their experimental results showed that CNNs can achieve fairly high defect classification accuracy even for noisy signals. Guo et al [11] combined principal component analysis (PCA) on adaptive enhancement (Adaboost), extreme gradient enhancement (XGBboost), and SVM—three machine learning models widely used in NDT—to compare their per- formance on 220 laser ultrasonic signal data collected from 22 samples with different subsurface defect sizes. PCA XGBoost achieved the highest recognition rate of 98.48%. While many researchers have achieved good results with automated analysis of A-scans, the evaluation datasets used have only contained a few hundred or a few thousand A-scans. Such datasets are hardly a complete representation of all pos- sible shape variations when compared to the millions of A- scans used in actual inspection tasks. Moreover, the lack of surrounding information in the A-scans makes it challenging to distinguish between noise and defect signals, which is not conducive to defect classification. Ultrasonic B-scan images provide valuable spatial inform- ation for automated analysis of ultrasonic testing, and recent advances in ultrasonic testing equipment have significantly improved their imaging quality. In 2019, Posilovic et al [12] 2 Meas. Sci. Technol. 35 (2024) 035605 J Zhao et al Figure 2. System framework. tested the performance of YOLO and SSD models in detecting defects from 98 real B-scan data by means of data augmenta- tion, and YOLO achieved better detection results with an aver- age accuracy (AP) of 89.7%. Virupakshappa et al [13] simu- lated a total of 400 ultrasonic B-scan images of various defect types and demonstrated that deep learning methods, such as CNN, can be used for defect identification in ultrasonic NDT with high classification accuracies. In 2021, Ye et al [14] cre- ated a new ‘USimgAIST’ dataset of over 7000 real ultrasonic testing images of 17 types of defects and benchmarked the dataset using state-of-the-art deep learning models. DenseNet achieved the best result with an f1 score of 95.33% in the work of Virkkunen et al [15], who used data augmentation to bring the deep learning network to human evaluation levels of per- formance in identifying cracks in pipe welds. Latete et al [16] used simulated and experimental data to train the Faster R- CNN, which allowed accurate identification, localization and sizing of flat bottom holes and side drilled holes in the speci- men. Medak et al [17] achieved 89.6% mAP for the detection of extreme aspect ratio defects commonly found in ultrasonic images by training the EfficientDet deep learning framework with adjusted hyperparameters. These studies demonstrate that deep learning has great potential for ultrasonic testing image recognition, and the com- bination of deep learning algorithms with ultrasonic testing B- scan images recognition has considerable significance in terms of improving detection efficiency and ensuring discriminatory accuracy. However, most of the research has been conducted based on simulation data, and there are no scholars who have systematically studied various aspects of data acquisition, data cleaning, deep learning model selection and model improve- ment in practical industrial applications. In this study, we have conducted comprehensive research on Dataset creation, benchmark selection for deep learn- ing models and model improvement methods. The overall framework of our approach is illustrated in figure 2. We have evaluated the performance of the latest deep learning models for automated defect evaluation of ultrasonic images in precision forgings using our self-built datasets. We have used YOLOv5s as the baseline and fine-tuned the automated ultrasonic image detection process through lightweight model improvements and data cleaning methods. Our study provides some insight into the deployment of deep learning models for automated defect assessment applications in ultrasonic images. 2. Construction of ultrasonic image dataset There is currently a lack of publicly available large-scale data- sets for ultrasonic testing due to the diversity and variability of defects and the difficulty in obtaining sample data. To address this gap and develop an automated evaluation algorithm for ultrasonic testing images, we collected data from 7 samples containing hole defects ranging from 0.5 mm to 1 mm in dia- meter and crack defects less than 1 mm in width. Data was acquired using an AOS phased-array ultrasonic real-time total focus imaging system, as shown in figure 3. The phased-array transducer utilized had a frequency of f = 5 MHz, 128 arrays, a width of e = 0.65 mm, a gap of g = 0.1 mm between arrays, a center distance of p = 0.75 mm, and a height of w = 10 mm. A total of 30 ultrasonic B-scan images were acquired using the Total Focus Imaging Algorithm (TFM), comprising 28 images with defects and 2 images without defects. To enhance the sample size for improved training of the deep learning model, the images were randomly cropped to generate 8000 B-scan images of 224 × 224 pixels. These images were ana- lyzed and labeled by multiple engineers to identify the types and locations of defects, as illustrated in table 1. The dataset was divided into a training set, validation set, and test set in a 3:1:1 ratio. 3 Meas. Sci. Technol. 35 (2024) 035605 J Zhao et al Figure 3. Phased array ultrasonic real-time total focus imaging system. Table 1. Dataset division. Number of hole defects Number of crack defects Number of defective images Number of defect-free images Total number of images Training set Validation set Test set Total 7543 2471 2465 12 470 1190 362 368 1920 4088 1379 1380 6847 692 231 230 1153 4780 1610 1610 8000 Figure 4 displays ultrasonic B-scan images of the defect- ive and healthy samples we acquired, which contain holes and cracks. While crack defects are clearly visible through inspection, some tiny hole defects are not eas- visual ily distinguishable from the background. This difficulty in detection can lead to missed or false detections during analysis. Due to the small size of hole defect targets and the pres- ence of some background noise in B-scan images, engineers may encounter problems during annotation, such as miss- ing defect annotations, mislabeled types, oversized bound- ing boxes, and bounding boxes with center points outside the image. To address these issues, we used the method shown in figure 5 to automatically retrieve data and obtained over 200 images with problematic annotations. We then re-annotated these images. The dataset after data cleaning was divided as shown in table 2. 3. Baseline testing trained all models on a machine equipped with a single NVIDIA GTX 1070 (8G) graphics card, using the default hyperparameter settings and the maximum batch size accept- able for that card. To evaluate the performance of each model, we used four statistics: TP (True Positive), FP (False Positive), TN (True Negative), and FN (False Negative). Based on these statist- ics, we introduced six evaluation metrics: Precision (P), Recall (R), mAP@0.5, mAP@.5:.95, number of model parameters (Params), and frames per second (FPS). Precision, which refers to the probability of detecting the correct target in all detected targets, can be calculated using equation (1). Recall, which refers to the probability of correct recognition among all positive samples, can be derived from equation (2) Precision = TP TP + FP Recall = TP TP + FN . (1) (2) In this study, we aimed to evaluate the performance of various deep learning models on our acquired ultrasonic defect dataset. Specifically, we compared YOLOv3 [18], YOLOv5, YOLOv7 [19], YOLOR [20], EffcientDet [21], and the two-stage detec- tion model Faster-RCNN [22], which are among the most commonly used models in the field of object detection. We A Precision-Recall curve can be plotted from an array con- taining Precision and Recall values, and the average precision AP is the area under the curve, calculated as follows: 1ˆ AP = p (r) dr. (3) 0 4 Meas. Sci. Technol. 35 (2024) 035605 J Zhao et al Figure 4. Ultrasonic B-scan images of (a) holes, (b) cracks, (c) no defects. mAP is the mean of all classes of AP: the YOLOv5s model was chosen as the baseline for further research. mAP = 1 n n∑ i =1 APi (4) 4. Methods where n represents the type of defect, mAP@0.5 represents the mAP value when IoU is set to 0.5, and mAP@.5:.95 represents the average mAP at different IoU thresholds (from 0.5 to 0.95, step size 0.05). Table 3 shows that among the deep learning models tested, the YOLOv5s model achieved the highest levels of preci- sion, mAP@.5:.95, and FPS. While the recall rate was only 3.0% lower than the best-performing YOLOR-P6 model. Compared to the best performing EfficientDet d0, the differ- ence in mAP@0.5 was only 0.7%. As the localization effect of defects and re-al-time detection are of more importance, Although the YOLOv5s model already exhibits good infer- ence speed and detection performance, this study still faces some challenges that need to be addressed. Firstly, during the detection of hole defects, the small size of the target leads to a low Precision and Recall of YOLOv5s, resulting in missed detections. Secondly, the large number of convolutional and deep neural network structures can lead to excessive model complexity, which is not suitable for deployment to mobile or embedded systems. To address these issues, this study optimized the model structure and used data cleaning methods to improve the 5 Meas. Sci. Technol. 35 (2024) 035605 J Zhao et al Figure 5. Data cleaning process. Table 2. Data cleaning results. Number of hole defects Number of crack defects Number of defective images Number of defect-free images Total number of images Training set Validation set Test set Total 7502 2481 2458 12 441 1190 362 368 1920 4082 1383 1371 6836 698 227 239 1164 4780 1610 1610 8000 Table 3. Benchmarking experiments. Model Faster-RCNN(resnet50) YOLOv3 spp YOLOv5s YOLOv7 YOLOR-P6 EffcientDet d0 Size 224 512 640 640 640 512 P 0.899 0.864 0.937 0.882 0.653 0.941 R 0.902 0.871 0.936 0.901 0.966 0.937 6 mAP@0.5 mAP@.5:.95 FPS Params 0.936 0.901 0.96 0.925 0.933 0.967 0.558 0.471 0.591 0.471 0.506 0.537 17 21 72 23 35 29 41.8M 62.5M 7.02M 36.9M 36.8M 3.9M Meas. Sci. Technol. 35 (2024) 035605 J Zhao et al Figure 6. Improved YOLOv5s model. Figure 7. CBS_CBAM module. efficiency of automated defect detection in ultrasonic images. The optimized YOLOv5s model structure is shown in figure 6. 4.1. YOLOv5s model improvement To improve detection efficiency and enhance the network’s focus on the target, we incorporated CBAM into the CBS module in layers 1, 3, and 5 of the backbone network. As shown in figure 7, the CBAM module [23] sequen- tially infers the attention graph through the channel atten- tion module and the spatial attention module. The chan- nel attention module leverages the information between fea- ture channels, while the spatial attention module leverages the information between feature spaces. The attention graph is then multiplied with the input feature graph for adapt- ive feature optimization, which effectively attends to small targets. To fulfill the requirements of industrial applications, we introduced the Ghost module [24] for a lightweight net- work design, by replacing the CBS module at layer 7 with GhostConv. In figure 8(a), GhostConv is performed in two steps: firstly, using normal convolution to obtain fewer fea- ture maps, then applying a second convolution on top of it to obtain more feature maps, and finally concatenating the different feature maps together to produce a new output. We replaced the C3 module in Backbone with C3Ghost, whose structure is shown in figure 8(c), mainly consisting of ordinary convolution and the GhostBottleneck as shown in figure 8(b). 7 Meas. Sci. Technol. 35 (2024) 035605 J Zhao et al Figure 8. (a) GhostConv, (b) GhostBottleneck and (c) C3Ghost modules. The GhostBottleneck module allows sufficient or redundant information to be provided in the feature layer, to always ensure the model’s understanding of the input data. To address the small and medium-sized target defects in this study, which are mainly concentrated in the shallow part of the neural network, we reduced the number of C3Ghost modules in layers 2, 4, and 6 of the backbone network from the ori- ginal [3, 6, 9] to [2, 4, 6]. This adjustment effectively improves the network’s detection capability for small and medium-sized targets. To reduce computational complexity and network structure while maintaining accuracy, the CBS module of the neck net- work was replaced with GhostConv, and the C3 module was replaced with C3Ghost, effectively compressing the network parameters of YOLOv5s. IOU = B1 ∩ B2 B1 ∪ B2 (6) where ρ2 (b, bgt) represents the Euclidean distance between the centroids of the prediction frame and the true frame. c repres- ents the diagonal distance of the smallest closed area that can contain both the prediction box and the true box, B1 for the true box and B2 for the prediction box. a = v 1 − IOU + v ( v = 4 π 2 arctan wgt hgt − arctan ) 2 w h LOSSCIOU = 1 − IOU + ρ2 (b, bgt) c2 + av (7) (8) (9) 4.2. Comparison of loss functions YOLOv5 defaults to using the CIOU loss function, and CIOU Loss takes into account the overlap area, centroid distance and aspect ratio of the bounding box regression though. As shown in equation (5): CIOU = IOU − ρ2 (b, bgt) c2 − av (5) where a is the weight function and v reflects the difference in aspect ratio rather than the true difference between the aspect ratio and its confidence level respectively, so this can some- times prevent the model from optimizing similarity effect- ively, for which we introduced the EIOU loss function and compared it with the CIOU loss function. EIOU loss was pro- posed by Zhang et al [25] in 2021, which minimizes the differ- ence between the width and height of the target frame and the 8 Meas. Sci. Technol. 35 (2024) 035605 J Zhao et al Anchor, producing faster convergence and better localization results. in Precision, a 0.8% increase in Recall, a 43.2% reduction in the amount of parameters, and an increase in FPS to 75.1. The comparison of solutions A, D, E, and G reveals that although optimizing the backbone network can reduce the model’s parameter count, it does not significantly improve the FPS. This is because the introduction of the CBAM attention mechanism in the improved backbone network increased the network layers, thus affecting the inference speed. Solution D involved lightweight design specifically for the backbone and neck networks, achieving a superior bal- ance between mAP and params. This approach resulted in the best detection performance. While ensuring detec- tion accuracy, it significantly reduced the model’s para- meter count, enhancing detection efficiency. It meets the requirements of accuracy and real-time performance in the lightweight industrial defect detection. Additionally, model is well-suited for practical deployment in production environments. In order to demonstrate the impact of data quality on the model’s detection accuracy, this study trained and tested the original YOLOv5s model and the improved model on the cleaned dataset. The results are presented in table 5, which clearly shows that data cleaning can effectively improve all aspects of model metrics. Precision improved by 4.5% and mAP@0.5 by 3.2%. Moreover, compared to the original YOLOv5s model, the improved model has only 0.1% lower mAP@.5:.95, but the number of model parameters is reduced by 43.2%, FPS is also slightly improved, and several other indicators have not changed significantly. Therefore, in prac- tical applications, it is more productive to identify the deep learning model first and then look for ways to improve the data. Figure 9 illustrates a comparison of the mAP between the improved algorithm and the original algorithm during the training phase. It can be observed that the convergence rate of the improved model is similar to that of the original model, indicating that the improvements made in this study do not affect the model’s convergence. Figure 10 illustrates the detection results of the YOLOv5s model before and after the improvement. The green circles represent the defects that were missed. Compared to the YOLOv5s model, the improved method in this study still has some cases of missing detection for small defects that are difficult to distinguish with the naked eye. However, it achieves a high accuracy rate of detection overall. This indic- ates that the lightweight model proposed in this study has excellent detection performance and can meet the accuracy and real-time performance requirements in industrial defect detection. The equation for EIOU loss is as follows: LOSSEIOU = LOSSIOU + LOSSdis + LOSSasp = 1 − IOU + ρ2 (b, bgt) c2 + ρ2 (w, wgt) C2 w + ρ2 (h, hgt) C2 h (10) where C2 rectangle of the predicted and real boxes. w and C2 h are the width and height of the smallest outer The EIOU equation consists of three parts. LOSSIOU is the loss of overlap between the predicted and true frames, LOSSdis is the loss of distance between the center of the predicted and true frames, which is the same as that of CIOU, and LOSSasp is the loss of width and height of the predicted and true frames. 5. Experimental results and discussion To validate the impact of the improvements described in this study on the detection performance of the model, an evaluation was carried out on the ultrasound B-scan dataset that we col- lected. We set up 7 solutions to analyze the different improve- ment components, each using the same training parameters. Table 4 shows the results of the evaluation. Solution A optimizes the backbone network by using the CBS_CBAM structure, adding a channel attention module and a spatial attention module to enhance the detection of small targets, replacing the Conv module at layer 7 with GhostConv, and replacing the C3 module with C3Ghost. The mAP@.5:.95 only reduced by 0.6%, while the amount of model paramet- ers was reduced by 24.8%, and the FPS increased to 73.6. Solution B improves the neck network by replacing the Conv module with GhostConv and the C3 module with C3Ghost, reducing the amount of model parameters by 18.5% and increasing the FPS considerably to 85.7. Solution C uses the EIOU loss function to replace the original CIOU loss to improve the localization accuracy of the model bounding box, with effective improvements in Precision, Recall, and FPS. Solutions D, E, and F were subjected to two-by-two cross- validation, and the comparison revealed that improving the backbone and neck networks could significantly reduce the number of model parameters. Improving the IOU loss could improve the Recall rate and reduce defect misses. Solution G has been improved for all three areas. Although mAP@0.5 is reduced by 0.2% and mAP@.5:.95 by 2.2% com- pared to the original YOLOv5s model, there is a 0.6% increase 9 Meas. Sci. Technol. 35 (2024) 035605 J Zhao et al Model Backbone Neck EIOU P R mAP@0.5 mAP@.5:.95 Params FPS Table 4. Ablation experiments. YOLOv5s Solution A Solution B Solution C Solution D Solution E Solution F Solution G 3 3 3 3 3 3 3 3 3 3 3 3 0.937 0.936 0.935 0.945 0.941 0.943 0.943 0.942 0.936 0.936 0.931 0.944 0.936 0.944 0.943 0.944 0.96 0.957 0.958 0.961 0.961 0.962 0.96 0.958 0.591 0.585 0.586 0.589 0.572 0.588 0.582 0.569 7.02M 5.28M 5.72M 7.02M 3.99M 5.28M 5.72M 3.99M Table 5. Data cleaning test results. Model YOLOv5s YOLOv5s + Data cleaning Solution D + Data cleaning P 0.937 0.982 0.978 R 0.936 0.981 0.981 mAP@0.5 mAP@.5:.95 0.96 0.992 0.990 0.591 0.677 0.676 Params 7.02M 7.02M 3.99M 72.6 73.6 85.7 86.1 74.5 74.4 85.5 75.1 FPS 72.6 72.6 74.5 Figure 9. Comparison of (a) mAP@0.5 and (b) mAP@.5:.95 for Solution D and YOLOv5s. Figure 10. Detection results of (a) YOLOv5s and (b) Solution D. 10 Meas. Sci. Technol. 35 (2024) 035605 J Zhao et al 6. Conclusion Consent for publication To achieve effective automation in the detection of ultrasound B-scan images, this study proposes an improved YOLOv5 model, making the following contributions: All authors have consented to have this work published and have approved of submission to the Measurement Science and Technology. (1) A baseline test was conducted on the constructed data- set, comparing the performance of YOLOv3, YOLOv5, YOLOv7, YOLOR, EfficientDet, and Faster-RCNN. The YOLOv5 model was identified as the most efficient method for analyzing ultrasound B-scan images currently available. (2) The YOLOv5 model was enhanced by incorporating the CBAM attention mechanism and GhostConv light- weight convolution, simplifying the model complexity and improving detection efficiency. (3) From a data nificantly accuracy. data-centric perspective, an cleaning method was employed enhance the algorithm’s automated to sig- detection The primary objective of this study is to validate the feasib- ility and effectiveness of the developed method. It is acknow- ledged that defects in real-world applications might be even more intricate. The breadth of collected ultrasound data and the extent of automated quantitative analysis will be explored in future work. Data availability statement The data cannot be made publicly available upon publication because they are owned by a third party and the terms of use prevent public distribution. The data that support the findings of this study are available upon reasonable request from the authors. Acknowledgments The author would like to thank the anonymous reviewers for constructive comments and suggestions which led to an improved presentation. Funding This work was supported by the General Project of the National Natural Science Foundation of China, Project No. in 51875501; Postgraduate Education Innovation Project Shanxi Province of China, Project No. 2022Y671, and the Natural Science Foundation of Shanxi Province, China, Project No. 202103021224273. Conflict of interest The authors declare that we have no competing interest. 11 Authors’ contributions Z J and D X wrote most of the manuscript text and gener- ated all the graphics. Z J and Z Y developed the algorithm and wrote the program. S X wrote the introduction and provided background information and references. ORCID iD Jianjun Zhao  https://orcid.org/0009-0003-2931-0379 References [1] Zhao J, Zhang Z, Zhang M and Du X 2022 Scanning path planning of ultrasonic testing robot based on deep image processing Russ. J. Nondestruct. 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10.1038_s41467-022-30406-4.pdf	Data availability The cryo-EM particle stacks, maps and models generated in this study have been deposited in EMPIAR image archive, EMDB database and the Protein Data Bank, respectively, under accession codes EMPIAR-10969, EMD-25757 and PDB-7T9G) for VcINDY-Na+ (300 mM) structure and under accession codes EMPIAR-10970, EMD- 25756 and PDB-7T9F) for VcINDY-Ch+ structure. Source Data for Fig. 4 are available with the paper.	Data availability The cryo-EM particle stacks, maps and models generated in this study have been deposited in EMPIAR image archive, EMDB database and the Protein Data Bank, respectively, under accession codes EMPIAR-10969, EMD-25757 and PDB-7T9G ) for VcINDY-Na + (300 mM) structure and under accession codes EMPIAR-10970, EMD- 25756 and PDB-7T9F ) for VcINDY-Ch + structure. Source Data for Fig. 4 are available with the paper.	ARTICLE https://doi.org/10.1038/s41467-022-30406-4 OPEN Structural basis of ion – substrate coupling in the Na+-dependent dicarboxylate transporter VcINDY David B. Sauer1,2,4, Jennifer J. Marden1,2, Joseph C. Sudar Da-Neng Wang 1,2✉ 2, Jinmei Song1,2, Christopher Mulligan 3✉ & ; , : ) ( 0 9 8 7 6 5 4 3 2 1 The Na+-dependent dicarboxylate transporter from Vibrio cholerae (VcINDY) is a prototype for the divalent anion sodium symporter (DASS) family. While the utilization of an electro- chemical Na+ gradient to power substrate transport is well established for VcINDY, the structural basis of this coupling between sodium and substrate binding is not currently understood. Here, using a combination of cryo-EM structure determination, succinate binding and site-directed cysteine alkylation assays, we demonstrate that the VcINDY protein couples sodium- and substrate-binding via a previously unseen cooperative mechanism by conformational selection. In the absence of sodium, substrate binding is abolished, with the succinate binding regions exhibiting increased ﬂexibility, including HPinb, TM10b and the substrate clamshell motifs. Upon sodium binding, these regions become structurally ordered and create a proper binding site for the substrate. Taken together, these results provide strong evidence that VcINDY’s conformational selection mechanism is a result of the sodium-dependent formation of the substrate binding site. 1 Department of Cell Biology, New York University School of Medicine, New York, NY 10016, USA. 2 Skirball Institute of Biomolecular Medicine, New York University School of Medicine, New York, NY 10016, USA. 3 School of Biosciences, University of Kent, Canterbury, Kent, UK. 4Present address: Centre for Medicines Discovery, Nufﬁeld Department of Medicine, University of Oxford, Oxford, UK. email: c.mulligan@kent.ac.uk; da-neng.wang@med.nyu.edu ✉ NATURE COMMUNICATIONS \| (2022) 13:2644 \| https://doi.org/10.1038/s41467-022-30406-4 \| www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS \| https://doi.org/10.1038/s41467-022-30406-4 VcINDY is a Na+-dependent dicarboxylate transporter that imports TCA cycle intermediates across the inner mem- brane of Vibrio cholerae1,2. The detailed structural and mechanistic understanding of VcINDY1–4 has made the protein a prototype of the divalent anion sodium symporter (DASS) family (Supplementary Fig. 1a, b)5. Within the human genome, the SLC13 genes encode for DASS members including the Na+-dependent, citrate transporter (NaCT) and dicarboxylate transporters 1 and 3 (NaDC1 and NaDC3)6. Besides functioning as TCA cycle intermediates, DASS-imported substrates are cen- tral to a number of cellular processes. In bacteria C4-carboxylates can serve as the sole carbon source for growth7, while imported citrate and tartrate are electron acceptors during fumarate respiration8. Citrate is also a precursor for both fatty acid bio- synthesis and histone acetylation in mammals9,10. Dicarboxylates such as succinate and α-ketoglutarate act as signaling molecules that regulate the fate of naive embryonic stem cells and certain types of cancer cells11,12. As a result of these roles in regulating cellular di- and tricarboxylate levels, mutations in DASS trans- porters have dramatic physiological consequences. Deletion of bacterial DASS transporters can abolish growth on particular dicarboxylates7,8. Mutations in the human NaCT transporter cause SLC13A5-Epilepsy in newborns13, whereas variants in the dicarboxylate transporter NaDC3 lead to acute reversible leukoencephalopathy14. In mice, knocking out NaCT results in protection from obesity and insulin resistance15. Such roles of SLC13 proteins in cell metabolism have made them attractive targets for the treatment against obesity, diabetes, cancer and epilepsy16–18. Therefore, mechanistic characterization of the prototype transporter VcINDY will help us to better understand the transport mechanism of the entire DASS family, including the human di- and tricarboxylate transporters. The VcINDY protein is a homodimer consisting of a scaffold domain and a transport domain (Supplementary Fig. 1b–f)1. The conservation of this architecture throughout the DASS/SLC13 family has been conﬁrmed by X-ray crystallography and cryo- electron microscopy (cryo-EM) structures of VcINDY, LaINDY, a dicarboxylate exchanger from Lactobacillus acidophilus, and the human citrate transporter NaCT1,4,19,20. Comparison of VcINDY in its inward-facing (Ci) conformation with the outward-facing (Co) structure of LaINDY, along with MD simulations, reveals that an elevator-type movement of the transport domain, through an ~12 Å translation along with an ~35° rotation, facilitates translocation of the substrate from one side of the membrane to the other19. In fact, the structural and mechanistic conserva- tion may extend beyond DASS to the broader Ion Transport Superfamily (ITS)5,21. Substrate transport of VcINDY is driven by the inwardly- directed Na+ gradient, with dicarboxylate import coupled to the co- transport of three sodium ions (Supplementary Fig. 1a, b)1,2,22. The binding sites for the substrate and two central Na+s have been identiﬁed in the structures of VcINDY in its Na+- and substrate- bound inward-facing (Ci-Na+-S) state (Supplementary Fig. 1e, f)1,4. The Na1 site on the N-terminal half of the transport domain is deﬁned by a clamshell formed by loop L5ab and the tip of hairpin HPin. A second clamshell encloses Na2, related to Na1 by inverted- repeat pseudo-symmetry in the sequence and structure, and formed by L10ab and the tip of hairpin HPout (Supplementary Fig. 1c). In addition to binding the Na+s, both hairpin tips also form parts of located between the Na+ sites. Each the substrate-binding site, hairpin tip consists of a conserved Ser-Asn-Thr (SNT) motif, and the two SNT motifs form part of the substrate-binding site, making direct contact with carboxylate groups of the substrate. Whereas these two SNT signature motifs are responsible for recognizing carboxylate, additional residues in neighboring loops have been proposed to distinguish between different kinds of substrates4. Furthermore, VcINDY’s structure with sodium in the absence of a substrate (the Ci-Na+ state), determined in 100 mM Na+, is very similar to that of the Na+- and substrate-bound state Ci-Na+-S19. While the Na+- and substrate-binding sites in VcINDY have been well-characterized1,4,23, the coupling mechanism between the electrochemical gradient and substrate transport24 is less well understood. There is strong evidence that charge compensation by sodium ions is essential in lowering the energy barrier for transporting the di- and trivalent anionic substrates across the membrane19. However, such charge compensation alone does not necessarily result in substrate binding as Li+ is able to bind to VcINDY similarly to Na+, but results in a lower afﬁnity substrate binding site and considerably reduced transport rates2,23. More importantly, charge neutralization cannot explain the sequential binding observed for VcINDY. As is the case for other DASS proteins25–28, all available experimental evidence from both whole cells and reconstituted systems supports the notion that in VcINDY sodium ions and substrate bind in a sequential manner, with Na+s binding ﬁrst, followed by dicarboxylate2,3,23,29. As a secondary-active transporter can transport substrate in either direction, it follows that the release of the substrate and Na+s is also ordered, with the substrates being released ﬁrst. Structures of VcINDY in the Na+- and substrate-bound state Ci-Na+-S, in which the Na+ sites share residues with the sub- strate site in their center, allowed us to propose that substrate binding in VcINDY follows a cooperative binding mechanism via conformational selection1,30. In this mechanism, the binding of sodium ions helps to induce a proper binding site for the substrate (Supplementary Fig. 1a, b). Conversely, in the absence of bound sodium ions the substrate-binding site will change signiﬁcantly, such that the substrate cannot bind. Not only can such a mechanism be part of Na+—substrate coupling, it may also explain the sequential binding observed for VcINDY. This conformational selection mechanism of substrate binding enables us to make two explicit, experimentally testable predic- tions. First, the afﬁnity of the transporter to a substrate must be much higher in the presence of Na+ than in its absence. Second, substantial structural changes will occur at the Na+ sites in the absence of sodium, affecting substrate binding. In this work, we aim to test these two predictions using a combination of structure determination by single-particle cryo-EM, substrate-binding afﬁnity measurements by intrinsic tryptophan ﬂuorescence quenching, and position accessibility quantiﬁcation via a newly-developed site-directed cysteine alkylation assay29. In particular, we characterize VcINDY in sodium-saturating and sodium-free conditions, including structures in Ci-Na+ and Ci-apo states. These experimental results allow us to directly test the conformational selection binding model of VcINDY. Results Succinate binding depends on the presence of Na+. To test the ﬁrst of the predictions generated from our conformational selection hypothesis, we measured VcINDY’s binding afﬁnity for the model substrate, succinate, in both the presence and absence of Na+ (Supplementary Fig. 2). We reasoned that VcINDY’s tryptophans, particularly Trp148 located at the tip of HPin of the Na1 site, may change its position or environment upon Na+-/substrate-binding. Thus, we used intrinsic tryptophan ﬂuorescence quenching, a technique that has been successfully applied to measure substrate binding for various membrane transporters20,31–36. In the presence of 100 mM Na+, detergent- puriﬁed VcINDY was found to bind succinate with an apparent Kd of 92.2 ± 47.4 μM (Fig. 1a, Supplementary Fig. 2b). For comparison, the human NaCT in the same protein family binds its substrate citrate at an apparent Kd of 148 ± 28 μM20. 2 NATURE COMMUNICATIONS \| (2022) 13:2644 \| https://doi.org/10.1038/s41467-022-30406-4 \| www.nature.com/naturecommunications NATURE COMMUNICATIONS \| https://doi.org/10.1038/s41467-022-30406-4 ARTICLE Fig. 1 Cryo-EM structure of VcINDY in the Ci-Na+ state determined in 300 mM Na+. a Measurements of succinate binding to detergent-puriﬁed VcINDY in the presence of 100 mM NaCl, using intrinsic tryptophan ﬂuorescence quenching (N = 4). Data are presented as mean values ± SEM. The apparent Kd was determined to be 92.2 ± 47.4 mM. When NaCl was replaced with Choline chloride, no binding of succinate to VcINDY could be measured (N = 4). b Cryo-EM map of VcINDY determined in the presence of 300 mM NaCl. The map is colored by local resolution (Å) and contoured at 5.1 σ. The overall map resolution is 2.83 Å. c Structure of VcINDY in the Ci-Na+ state. The structure is colored by the B-factor. d Na1 site structure and Coulomb map. e Na2 site structure and Coulomb map. f Overlay of VcINDY structures around the substrate and sodium binding sites in the Ci-Na+ state (green) and Ci- Na+-S state (PDB ID: 5UL7, blue). There is very little structural change observed between the two states. To measure the binding afﬁnity of succinate to VcINDY with empty Na+ binding sites, we searched for a cation to replace Na+ in the puriﬁcation buffer. This ion should not occupy the Na1 or Na2 sites while still allowing the transporter protein to remain stable in the solution. K+ is unable to power substrate transport in VcINDY, but was found to be unsuitable as the protein precipitated when puriﬁed in the presence of 100 mM KCl. We next tested the organic cation choline (C5H14NO+, Ch+ in abbreviation). We reasoned that Ch+ would be more stabilizing than K+ based on its position in the Hofmeister series37, and that its size would preclude it from occupying Na+ binding sites38. Indeed, VcINDY puriﬁed in 100 mM NaCl remained soluble at 0.5–1.0 mg/mL after diluting the sample 11,000-fold in 100 mM ChCl. VcINDY was therefore puriﬁed in the presence of 100 mM Ch+ as the only monovalent cation. The protein eluted as a sharp, symmetrical peak on a size- exclusion chromatography column (Supplementary Fig. 2a), con- ﬁrming its stability and structural homogeneity. intrinsic tryptophan ﬂuorescence quenching with VcINDY puriﬁed and assayed in the presence of 100 mM Ch+ revealed no succinate binding (Fig. 1a, Supplementary Fig. 2c). Thus, the binding measurements in the presence and absence of Na+ are consistent with a conformational selection model where bound sodium ions are necessary to VcINDY forming a proper binding site for succinate. Encouraged by these ﬁndings and our ability to produce stable, structurally homogeneous and Na+-free VcINDY, we next sought to uncover the structural basis of this Na+—substrate transporter’s structures using cryo-EM in different states. coupling by determining the Notably, Structure of VcINDY in 300 mM Na+. Generally speaking, the transport mechanism of a secondary-active transporter is rever- sible, in which the direction of substrate translocation depends on the direction and magnitude of the driving force (Supplementary Fig. 1a, b). Consequently, substrate binding is structurally equivalent to substrate release. Therefore, to provide structural insights into VcINDY’s binding process, we aimed to characterize the substrate release process in the inward-facing (Ci) con- formations by capturing the structures of VcINDY in the fol- lowing states: its Na+-and substrate-bound state (Ci-Na+-S), its Na+-bound state (Ci-Na+) and its Na+- and substrate-free state (Ci-apo). The Ci-Na+-S structure of VcINDY has previously been solved using X-ray crystallography1,4. Additionally, we had characterized the Ci-Na+ state using a cryo-EM structure of VcINDY puriﬁed in 100 mM Na+ without substrate19. However, as the apparent K0.5 for Na+ for VcINDY was measured to be 41.7 mM2, our earlier VcINDY sample in 100 mM Na+ likely represents a mixture of the Ci-Na+ and Ci-apo states. It is unclear whether the subsequent cryo-EM image processing of the particles was able to exclude all particles of the Na+-free Ci-apo state. To more clearly and deﬁnitively resolve the Ci-Na+ state structure, in the current work we puriﬁed and determined a structure of VcINDY in 300 mM Na+. This ion concentration was optimized to increase the Na+ occupancy, while, at the same time, ensuring a low enough noise level in the cryo-EM images to determine a Ci-Na+ state structure of this small membrane protein (total dimer mass: 126 kDa) at 2.83 Å resolution (Fig. 1b, c, Supplementary Figs. 3 and 4a–c, Table 1). Compared with the two previously determined cryo-EM structures of VcINDY in the presence of 100 mM NaCl19, the herein reported structure in 300 mM Na+ (Fig. 1c, Supplementary Fig. 4b) is identical to the one bound to a Fab and embedded in lipid nanodisc (PDB ID: 6WW5)19 (r.m.s.d. of 0.460 Å for all the non-hydrogen atoms), except for the position of the last three residues at the C-terminus, which interact with the Fab molecule used for structure determination (Supplementary Fig. 4d). Further- though the map obtained in 300 mM Na+ conditions more, clariﬁed the loop connecting HPoutb and TM10b, the model in 300 mM NaCl is effectively identical to the other previous Ci-Na+ structure in 100 mM NaCl, determined in amphipol and without Fab (PDB ID: 6WU3)19, with an r.m.s.d of 0.358 Å after excluding Val392 – Pro400 (Supplementary Fig. 4d). NATURE COMMUNICATIONS \| (2022) 13:2644 \| https://doi.org/10.1038/s41467-022-30406-4 \| www.nature.com/naturecommunications 3 ARTICLE NATURE COMMUNICATIONS \| https://doi.org/10.1038/s41467-022-30406-4 As expected from the higher Na+ occupancy in the 300 mM sample, better-deﬁned densities appeared within both the Na1 and Na2 clamshells (Fig. 1d, e), which were absent in the previous 100 mM Na+ maps19. In addition to coordination by side chains and backbone carbonyl oxygens, the sodium ion at the Na1 site is stabilized by the helix dipole moments from HPinb and TM5b (Fig. 1f; Supplementary Fig. 1e, f), as previously observed in other membrane proteins39,40. Similarly, the Na+ ion in the Na2 site is stabilized by HPoutb and TM10b. Finally, this higher resolution map conﬁrmed our earlier observations that succinate release caused only limited changes at the substrate-binding site without relaxing the two Na+ clamshells19. Both the overall structure and the sodium- and substrate-binding sites in the Ci-Na+ state are similar to those in the sodium- and substrate-bound Ci-Na+-S state (Fig. 1e, f, Supplementary Fig. 4e). The similarity of these structures agrees with our conformational selection model of Na+ – substrate coupling, which requires sodium-binding induce a Ci-Na+ state structure that can bind substrate directly as in the Ci-Na+-S state (Supplementary Fig. 1a, b). Apo structure of VcINDY in Choline+. With the structures of sodium- and succinate-bound1,4 and Na+-only bound (Fig. 1) states in hand, the missing piece of the puzzle to validate the Na+ con- formational selection mechanism was the Ci-apo state structure of the transporter protein. As a Ch+ ion is too large to ﬁt into a Na+ binding site36,38, and VcINDY was stable and monodisperse in the presence of 100 mM ChCl (Supplementary Fig. 2a), such a pre- paration allowed us to obtain cryo-EM maps of the Ci-apo state (Fig. 2, Supplementary Figs. 5 and 6, Table 1). Unlike the VcINDY map in 300 mM Na+ for which 3D classiﬁcation converged to a single map (Supplementary Fig. 3), the VcINDY-choline dataset yielded four distinct classes at a resolution range of 3.6—4.4 Å resolution (Supplementary Figs. 5 and 7). The 3D class with the highest resolution was further reﬁned to 3.23 Å resolution (Fig. 2a, b, Supplementary Fig. 6c). The least well-resolved regions of the map, and highest B-factors of the model, are found in L4-HPin and L9- HPout, two previously-identiﬁed hinge regions that facilitate move- ment of the transport domain19. Whereas the overall fold of the protein in Ch+ remains the same (Supplementary Fig. 6d, f), Na+ densities within the Na1 and Na2 clamshells are totally absent. Additional local changes are observed for the protein parts near the Na1 and Na2 sites (Fig. 2c), with a loss of density in each Ci-apo map at the HPinb and TM10b helices (Fig. 3b), indicating increased local structural ﬂexibility. Flexibility of Apo VcINDY near the Na1 and Na2 sites. While the VcINDY Ci-apo state overall structure is similar to those in the 300 mM Na+ (r.m.s.d of 0.672 Å) (Supplementary Fig. 6f), the model exhibited signiﬁcant changes near the Na1 and N2 sites (Figs. 2c and 3a, b). The tip of HPin and the L10a-b loop have moved away from the Na1 and Na2 sites, respectively, with the carbonyls of Ala376 and Ala420, and the side chain of Asn378 also rotated away from the Na2 site. Notably, HPinb near the Na1 site and TM10b near the Na2 site and their connecting loops showed marked decreased density in the cryo-EM map, corre- sponding to the increased ﬂexibility of these regions (Fig. 3a, b). Correspondingly, the model exhibited signiﬁcantly higher relative B-factors in the same regions compared to the rest of the model (Fig. 3c, d). However, we recognized that such a single, averaged model might not fully describe the true structural ensemble, and sought a method to describe the Ci-apo state’s mobility. To further analyze such local ﬂexibility, we used simulated annealing41,42 in a model reﬁnement protocol analogous to protein structure determination by NMR spectroscopy43. We reasoned that in multiple, separate reﬁnements with simulated annealing the rigid parts of the VcINDY would converge to the same coordinates, while mobile portions of the protein would arrive at distinct atomic positions in each run. We term this as NMR-style analysis in recognition of NMR’s power to characterize protein dynamics, though in cryo-EM the constraints are Coulomb potential maps rather than distances. Most parts of the VcINDY structure exhibit no variation in the Ci-Na+ state, including HPinb and TM10b in the 300 mM Na+ condition (Fig. 3e, Supplementary Fig. 8a). In contrast, in the Ci- apo state the NMR-style analysis clearly illustrated the structural heterogeneity near the Na1 and Na2 sites (Fig. 3f, Supplementary Fig. 8b). Instead of converging to one structure, the simulated annealing resulted in an ensemble of structures, with the greatest variations occurring in the HPinb and TM10b regions. The mean r.m.s.d. of the transport domain’s backbone atoms for the Ci-apo protomers is 0.589 Å, as opposed to 0.099 Å among Ci-Na+ protomers reﬁned using the same protocol to the same resolution. As the 3.23 Å apo map imposes C2 symmetry on one of four classes of particles in ChCl (Supplementary Fig. 5), and all four classes are different the degree of ﬂexibility of these helices in the Ci-apo state is likely to be even greater. Such helix ﬂexibility results from the absence of Na+ interactions with residues in the clamshells and with the dipoles of HPinb and TM10b44,45. (Supplementary Fig. 7), Site-directed alkylation supports structural changes to Na1 and Na2 sites. To conﬁrm the local conformational changes and helix ﬂexibility observed in our VcINDY structures, we implemented a site-directed cysteine alkylation strategy that can directly assess the solvent accessibility of speciﬁc positions in a protein. In this a L4-HPout b c 4.0 3.5 3.0 2.5 L9-HPin N378 A376 Na1 I149 N151 Na2 A420 P422 HPinb TM10b Fig. 2 Cryo-EM structure of VcINDY in the Ci-apo state determined in Choline+. a Cryo-EM map of VcINDY preserved in amphipol determined in the presence of 100 mM Choline Chloride. The map is colored by local resolution (Å) on the same scale as Fig. 1b and contoured at 4.8 σ. The overall map resolution is 3.23 Å. The two previously-identiﬁed hinge regions which facilitate movement of the transport domain19, L4-HPin and L9-HPout, are found to be most ﬂexible. b Structure of VcINDY in the Ci-apo state. The structure is colored by the B-factor on the same scale as Fig. 1b. c Overlay of VcINDY structures around substrate and sodium binding sites in the sodium-bound Ci-Na+ state (green) and the Ci-apo state (pink). The structures of the two Na1 and Na2 clamshells have changed in the absence of sodium ions, particularly around residues Ile149, Asn151, Ala376, Asn378, Ala420 and Pro422. 4 NATURE COMMUNICATIONS \| (2022) 13:2644 \| https://doi.org/10.1038/s41467-022-30406-4 \| www.nature.com/naturecommunications NATURE COMMUNICATIONS \| https://doi.org/10.1038/s41467-022-30406-4 ARTICLE a c e Na1 Na2 TM10b HPinb b Na1 Na2 TM10b HPinb d Na1 Na2 Na1 Na2 TM10b HPinb TM10b HPinb f Na1 Na2 Na1 Na2 TM10b HPinb TM10b HPinb Fig. 3 VcINDY ﬂexibility changes near the Na1 and Na2 sites between the Ci-Na+ state and Ci-apo states. a Cryo-EM density map in 300 mM NaCl. b. Cryo-EM density map in 100 mM Choline Chloride. In a and b, the respective protein models’ backbones are ﬁtted into the densities. Maps are contoured such that the scaffold domains have equal volume. c. Structure of VcINDY in its Ci-Na+ state. d. Structure of VcINDY in its Ci-apo state. In c and d, the structures are colored by normalized B-factors. e NMR-style analysis of the VcINDY structure in Na+. f NMR-style analysis of the VcINDY structure in Choline+. The resolution for reﬁnement of both structures in e and f was truncated to 3.23 Å. In the absence of sodium, the helices on the cytosolic side of Na1 and Na2, particularly HPinb and TM10b and their connecting loops, show markedly increase ﬂexibility. Instead of a single structure, the Ci-apo model consists of an ensemble of structures. approach, single cysteines are introduced into a Cys-less version of VcINDY, which is capable of robust Na+-driven transport2,3. Following puriﬁcation, the cysteine mutants of VcINDY are incubated with the thiol-reactive methoxypolyethylene glycol maleimide 5 K (mPEG5K). This tag reacts with solvent-accessible cysteines and increases the protein mass by ~5 kDa, which is separable from unmodiﬁed protein on an SDS-PAGE gel. As mPEG5K will react faster with cysteines that are more accessible, monitoring PEGylation over time provides us with the ability to follow changes in the accessibility of particular parts of the pro- tein under different conditions29. To test our conformational selection model using biochemical approaches, we designed a panel of single-cysteine mutants of VcINDY that would report on the Na+-dependent accessibility changes at the Na1 and Na2 sites predicted from structures (Fig. 4a, Supplementary Fig. 8d). We selected residues that, if our cooperative binding model is accurate, will exhibit a higher rate of PEGylation in the absence of Na+ compared to its presence due to the increased mobility of HPin and TM10b. To create our panel proximal to the Na1 site, we puriﬁed four cysteine mutants whose reactive thiol groups are buried in the Ci-Na+ state behind HPin (L138C on HPina, A155C and V162C on HPinb and A189C on TM5a). However, similar cysteine substitutions near Na2 (Val427, Ile433, Gly442 and Met438) resulted in diminished expression levels, likely indicating the importance of these residues to the stability of the protein. Fortunately, cysteine mutation of Val441 to cysteine, a residue located on TM11 and behind TM10b (Fig. 4a, Supplementary Fig. 8d), expressed well and allowed for puriﬁca- tion. Typically, well-expressing single cysteine VcINDY mutants that can be puriﬁed are capable of Na+-driven succinate transport29. We monitored the PEGylation of each mutant in the presence and absence of Na+. Under these reaction conditions there is no PEGylation of the Cys-less variant, demonstrating no background labelling that could hinder analysis (Fig. 4b, top row). In the presence of 300 mM Na+ we observed complete inhibition of PEGylation at every position (Leu138, Ala155, Val162, Ala189 and Val411) over the time course of 60 min (Fig. 4b, left panels), in agreement with our model that these residues are buried in the Na+-bound state. However, in the absence of Na+ (but with 300 mM Ch+), every mutant showed escalated levels of PEGyla- tion over time (Fig. 4b, right panels), indicating the increased ﬂexibility of HPinb and TM10b. To ensure that the change in PEGylation rate that we observed was due to changes in residue accessibility and not caused by an unforeseen effect the cations may have on the PEGylation reaction, we monitored the reaction rate of a position for which we observed no accessibility change in the structural analysis. A cysteine mutant at Ser436, positioned at the periphery of the transporter protein (Fig. 4a), exhibited minimal Na+-dependent accessibility changes (Fig. 4b, bottom row). These accessibility measurements, along with our previous PEGylation results on three other VcINDY residues near the Na1 site (T154C, M157C and T177C, Supplementary Fig. 8e)29, fully support the changes in protein dynamics predicted upon occupation of the Na1 and Na2 sites, and are consistent with a conformational selection coupling model. Structural comparison of Ci-Na+-S, Ci-Na+ and Ci-apo states. The VcINDY structures determined in 300 mM Na+ and apo as reported here, together with previously-determined X-ray struc- ture of the protein with both sodium and substrate bound1,4, allowed us to examine the structural changes of the transporter between the Ci-Na+-S, Ci-Na+ and Ci-apo states. In addition to the ﬂexibility observed in HPinb and TM10b, we observed amino acid sidechain movements both at the interface between the scaffold domain and the transport domain, as well as on the periplasmic surface of the protein. At the scaffold–transport domain interface, side chains of several bulky amino acids rotated or shifted between the three states, including Phe100, His111 and Phe326 (Fig. 5a). On the periplasmic surface, Trp461 at the C-terminus is buried in the apo and Na+-bound structures (Fig. 5b). However, in the Ci-Na+-S structure, the ring of the nearby Phe220 was rotated by ~90°, pushing out the side chain of Trp461, leaving the C-terminus pointing to the periplasmic space. Accordingly, the loop between HPoutb and TM10a moved into the periplasmic space of the apo VcINDY structure, displacing Glu394 and breaking its salt bridge with Lys337. Whereas no single switch was identiﬁed that can trigger conformational exchanges between the inward- and outward-facing states, local structural changes observed here suggest that small changes at multiple locations are required for inter-conformation transitions in VcINDY. In comparing maps of the three states, we noted the VcINDY Ci-Na+ map reported herein was sufﬁciently detailed to identify NATURE COMMUNICATIONS \| (2022) 13:2644 \| https://doi.org/10.1038/s41467-022-30406-4 \| www.nature.com/naturecommunications 5 ARTICLE NATURE COMMUNICATIONS \| https://doi.org/10.1038/s41467-022-30406-4 a Na1 HPina 138 HPinb Na2 441 TM10b TM5a 189 155 162 90 HPina 138 162 Na1 HPinb 441 TM5a 189 155 TM10b 436 Na2 b kDa 55 + Na+ Na+ Cys-less L138C 436 A155C 35 25 55 35 25 55 35 25 55 35 25 55 35 25 55 35 25 55 35 25 P U P U P U P U P U P U P U 0 5 10 30 60 0 5 10 30 60 min V162C A189C V441C S436C Fig. 4 Cysteine alkylation with mPEG5K of VcINDY near the Na1 and Na2 sites in the presence and absence of Na+. a Location of cysteine mutations. Our structures suggested that HPinb and TM10b become ﬂexible in the absence of sodium, increasing the solvent accessibility of Leu138, Ala155, Val162 and Ala189 near the Na1 site, and Val441 near the Na2 site. Position Ser436, for which no accessibility change was observed between our structures, is used as a control. On a Cys-less background, residues at these positions were individually mutated to a cysteine for mPEG5K labeling. For clarity, only amino acid numbers are labeled and the types are omitted. b Coomassie Brilliant Blue-stained non-reducing polyacrylamide gels showing the site-directed PEGylation of each cysteine mutant over time in the presence and absence of Na+. P: PEGylated protein; U: Un-PEGylated protein. Each reaction was performed on two separate occasions with the same result. Source data is provided as Source Data ﬁle. a b F326 F100 H111 ﬁve ordered water molecules buried at the dimer interface (Supplementary Fig. 8c). The water molecules are not visible in previous maps, or the VcINDY-apo map, indicating the high- the VcINDY Ci-Na+ map reported here was resolution of necessary for their identiﬁcation. These waters are arranged in a square pyramidal conﬁguration in the largely hydrophobic pocket, coordinated by only the symmetry-related carbonyls of Phe92 and inter-water hydrogen bonds. The role of these waters in VcINDY folding or transport are unclear, though protein folding defects underlie several pathogenic mutations on the equivalent dimerization interface of NaCT5. F220 W461 TM10a HPoutb E394 K337 Fig. 5 Movement of VcINDY’s amino acid side chains between its Ci-Na+-S, Ci-Na+ and Ci-apo states. VcINDY structures in three states are overlaid: Ci-Na+-S (blue), Ci-Na+ (green) and Ci-apo (pink) states. a At the scaffold-transport domain interface, the side chains of Phe100, His111 and Phe326 rotate between states. b On the periplasmic surface, some loops and side chains move between the states, including Phe220, Lys337, Glu394 and Trp462. Discussion Despite great advances in structural and mechanistic studies on the membrane transporters over the past ion–substrate coupling mechanism is well characterized for only very few co-transporters, this fundamental aspect of the secondary-active transport mechanism. Here, we have described the structural basis of ion–substrate coupling for VcINDY, which reveals a distinct conformational selection mechanism that ensures obligatory coupling. limiting our understanding of twenty years46–50, While Na+ sites in some other Na+-dependent transporters are buried in the middle of the protein38,48,49,51, the sodium sites in VcINDY are directly accessible from the extramembrane space. Previous experimental data support that Na+-driven DASS co- transporters operate via an ordered binding and release2,25–29. Speciﬁcally, Na+ binding occurs before substrate binding, while substrate release precedes Na+ release. For VcINDY, we have now observed that sodium release from the Na1 and Na2 sites in the cytoplasm allows increased conformational diversity going from the Ci-Na+ to the Ci-apo states, whereas the Ci-Na+ and Ci- 6 NATURE COMMUNICATIONS \| (2022) 13:2644 \| https://doi.org/10.1038/s41467-022-30406-4 \| www.nature.com/naturecommunications NATURE COMMUNICATIONS \| https://doi.org/10.1038/s41467-022-30406-4 ARTICLE Co Co-Na+ Co-Na+-S TM10b HPin Ci TM10b HPin TM10b HPin Ci-Na+ Ci-Na+-S Fig. 6 Schematic model of conformational selection mechanism for sodium—substrate coupling in VcINDY. In the absence of sodium ions, HPinb and TM10b, along with their connecting loops responsible for sodium and substrate binding, are ﬂexible. From the ensemble of ﬂexible structures, the binding of sodium ions (blue circles) selects a conformation with a proper binding site for the substrate, allowing its binding (red oval). The scaffold and transport domains in each protomer are colored as green and pink, respectively. Only the Na1 and Na2 sites are illustrated. Transport domain movements in the two protomers are shown as symmetric for simplicity but are functionally independent. Na+-S states are structurally similar (Fig. 6). Speciﬁcally, the movement of helices HPinb and TM10b is tightly coupled to Na+ binding. At the Na1 and Na2 sites, the sodium ions are stabilized via direct and ion—dipole interaction with the two helices. Therefore, upon Na+-release, the elimination of these interac- tions caused the relaxation of the HPinb and TM10b helices44,45, leading to increased mobility in the connected loops responsible for substrate binding. In the reverse reaction, ions binding at the Na1 and Na2 sites, concurrent with helix re-ordering, select from the ensemble a structure with the proper binding site for the substrate. While the effects of VcINDY’s cryptic third Na+ are still to be determined, we now have established a structural understanding of the Na+—substrate coupling mechanism for this co-transporter. By extension, other DASS transporters may utilise a similar structural mechanism for Na+—substrate cou- pling (Fig. 6). The structural basis of Na+-substrate coupling for VcINDY is distinct from that of GltPh/GltTk from the dicarboxylate/ amino acid:cation symporter family which otherwise share several commonalities with VcINDY including the presence of re-entrant hairpin elevator-like mechanism1,3,4,48,52–56. In addition, as we have shown here for VcINDY, a cooperative binding mechanism has been suggested for both GltPh and GltTk, which requires the initial binding of Na+ in order to prime the binding site for the substrate, aspartate38,57. However, the structural basis of Na+-substrate coupling in GltPh/ GltTk differs substantially from the coupling mechanism we observe for VcINDY. Rather than the general relaxation of a helix governing substrate-binding site formation, the binding of Na+ to GltPh/GltTk induces discrete conformational changes of a small number of amino acid residues centered on the highly conserved NMDGT motif53,57,58. As is the case here for VcINDY (Fig. 6), the fully loaded and Na+-only bound structures of GltPh/GltTk are largely identical52,53,57,58, demonstrating that Na+ binding drives the for- mation of the substrate-binding site, and not the substrate itself. utilization loops and the an of In addition to conformational selection, another mechanism for ion–substrate coupling of co-transporters has been proposed to be charge compensation5,19. Such a mechanism can greatly minimize the energy penalty for translocating charged substrates across the hydrophobic lipid bilayer59,60. Unlike for DASS exchangers19, where charge compensation is the major force for overcoming the ←→ Ci transition, both local structural energy barrier in the Co ordering and charge balance are needed for Na+-coupled co- transporters within the DASS family. Comparison of the VcINDY structures reported here with those determined earlier1,4,19, of three states in total, also sheds new light on the mechanism of the transporter’s conformational switching between the two sides of the membrane. As the Ci-apo structure is signiﬁcantly different from that of the Ci-Na+-S state, their corresponding transitions to the outward-facing state: Ci-apo to Co-apo and Ci-Na+-S to Co-Na+-S, are different at the transport-scaffold domain interface (Fig. 6). Whereas the transi- tion between Ci-Na+-S and Co-Na+-S state can be described as rigid-body movement, as was seen in the DASS exchangers5, the co-transporters’ Co-apo ←→ Ci-apo state transition likely involves large structural rearrangements of the transport domain. Considering the pseudo-symmetry of the DASS fold, the Ci-apo → Co-apo movement would require refolding of TM10b to pack against the scaffold domain, and possibly the concurrent unfolding of TM5b. This potential asymmetry between the apo- state transition (Co-apo ←→ Ci-apo) and transition of the fully- loaded transporter (Ci-Na+-S ←→ Co-Na+-S) needs further investigation. Finally, the pseudo-symmetry within the DASS fold and sequence1,3,19 and Na+ dependent solvent accessibility of the S381C mutant on HPoutb of VcINDY, which we investigated previously29, seem to indicate the Co state also undergoes Na+ dependent conformational selection to enable substrate binding. However, verifying this hypothesis will require structural char- acterization of a DASS symporter’s outward-facing state. Methods VcINDY expression and puriﬁcation. Expression and puriﬁcation of VcINDY were carried out according to our previous protocol1. Brieﬂy, E. coli BL21-AI cells (Invitrogen) were transformed with a modiﬁed pET vector61 encoding N-terminal 10x His tagged VcINDY. Cells were grown at 32 °C until OD595 reached 0.8, protein expression occurred at 19 °C following IPTG induction, and cells were harvested 16 h post-induction. Cell membranes were solubilized in 1.2 % DDM and the protein was puriﬁed on a Ni2+-NTA column. For cryo-EM and substrate binding experiments, the protein was puriﬁed using size-exclusion chromatography (SEC) in different buffers. The protein used for the cysteine alkylation assays was produced as described previously29. Tryptophan ﬂuorescence quenching assay. Tryptophan ﬂuorescence quenching was used to measure afﬁnity of succinate to puriﬁed VcINDY in detergent, using a protocol adapted from earlier work on other membrane transporters20,31–34. VcINDY puriﬁed by SEC in a buffer of 25 mM Tris pH 7.5, 100 mM NaCl and 0.05% DDM was used to measure succinate afﬁnity, while the 100 mM NaCl was replaced by 100 mM ChCl for afﬁnity measurements in the absence of sodium. Protein was diluted to a ﬁnal concentration of 4 μM in SEC buffer. Using a Horiba FluoroMax-4 ﬂuorometer (Kyoto, Japan) at 22 °C and a 280 nm excitation wave- length, the emission spectrum was recorded between 290 and 400 nm. The emis- sion maximum was determined to be 335 nm. Subsequently, the change in ﬂuorescent emission at 335 nm was monitored with increasing concentrations of succinic acid (pH 7.5), from 0.1 μM to 1 mM. Each experimental condition was repeated 4 times. The binding curve was ﬁt in Prism using a quadratic binding equation to account for bound substrate62. Amphipol exchange and cryo-EM sample preparation. From Ni2+-NTA pur- iﬁed VcINDY, DDM detergent was exchanged to PMAL-C8 (Anatrace, Maumee, OH) as previously described19,63. Following further puriﬁcation by SEC in buffer containing 25 mM Tris pH 7.5, 100 mM NaCl and 0.2 mM TCEP, the NaCl con- centration was increased to 300 mM and the protein sample was concentrated to 1.3 mg/mL. For the apo protein preparation, NaCl in the abovementioned SEC buffer was replaced with 100 mM ChCl, and the protein sample was concentrated to 1.3 mg/mL. Cryo-EM grids were prepared by applying 3 μL of protein to a glow-discharged QuantiAuFoil R1.2/1.3 300-mesh grid (Quantifoil, Germany) and blotted for 2.5 to 4 s under 100% humidity at 4 °C before plunging into liquid ethane using a Mark IV Vitrobot (FEI). Cryo-EM data collection. Cryo-EM data were acquired on a Titan Krios micro- scope with a K3 direct electron detector, using a GIF-Quantum energy ﬁlter with a NATURE COMMUNICATIONS \| (2022) 13:2644 \| https://doi.org/10.1038/s41467-022-30406-4 \| www.nature.com/naturecommunications 7 ARTICLE NATURE COMMUNICATIONS \| https://doi.org/10.1038/s41467-022-30406-4 20-eV slit width. SerialEM was used for automated data collection64. Each micro- graph was dose-fractioned over 60 frames, with an accumulated dose of 65 e-/Å2. Cryo-EM image processing and model building. Motion correction, CTF esti- mation, particle picking, 2D classiﬁcation, ab initio model generation, hetero- genous and non-uniform reﬁnement, and per-particle CTF reﬁnement were all performed with cryoSPARC65. Each dataset was processed using the same protocol, except as noted. Micrographs underwent patch motion correction and patch CTF estimation, and those with an overall resolution worse than 8 Å were excluded from subsequent steps. An ellipse-based particle picker identiﬁed particles used to generate initial 2D classes. These classes were used for template-based particle picking. Template identiﬁed particles were extracted and subjected to 2D classiﬁcation. A subset of well-resolved 2D classes were used for the initial ab initio model building, while all picked particles were subsequently used for heterogeneous 3D reﬁnement. After multiple rounds of 3D classiﬁcation (ab initio model generation and heterogeneous 3D reﬁnement with two or more classes), a single class was selected for nonuniform 3D reﬁnement with C2 symmetry imposed, resulting in the ﬁnal map. All Cryo-EM maps were sharpened using Auto-sharpen Map in Phenix66, models were built in Coot67, and reﬁned in Phenix real space reﬁne68. The model for VcINDY in NaCl was built using the structure of VcINDY embedded in a lipid nanodisc (PDB: 6WW5) as an initial model, with lipid and antibody fragments removed. The VcINDY model in choline used the structure of VcINDY in 300 mM NaCl, with ions and waters removed, as the starting model. The NMR-style analysis used 5 independent runs of phenix.real_space_reﬁne66 to reﬁne the models of VcINDY in apo and in 300 mM NaCl, with ions and waters removed, using unique computational seeds for each run. Each reﬁnement was performed with simulated annealing, without NCS constraints or secondary structure restraints, and a reﬁnement resolution limit of 3.23 Å for both maps. Analysis with or without map sharpening, or randomizing initial atomic positions using phenix.pdbtools, gave similar results. Transport domain maps were scaled to equivalent contours using the scaffold domain’s volume as an internal standard after extracting with phenix.map_box. Figures were made using UCSF Chimera69 and PyMOL70. Cysteine alkylation assay. For the cysteine alkylation experiments, each puriﬁed cysteine mutant was exchanged into reaction buffer containing 50 mM Tris, pH 7, 5% glycerol, 0.1% DDM and either 300 mM NaCl or 300 mM ChCl (Na+-free conditions). Protein samples were incubated with 6 mM mPEG5K and samples were taken at the indicated timepoints and immediately quenched by addition of SDS-PAGE samples buffer containing 100 mM methyl methanesulfonate (MMTS). Samples were analyzed with Coomassie Brilliant Blue-stained non-reducing polyacrylamide gels. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article. Data availability The cryo-EM particle stacks, maps and models generated in this study have been deposited in EMPIAR image archive, EMDB database and the Protein Data Bank, respectively, under accession codes EMPIAR-10969, EMD-25757 and PDB-7T9G) for VcINDY-Na+ (300 mM) structure and under accession codes EMPIAR-10970, EMD- 25756 and PDB-7T9F) for VcINDY-Ch+ structure. Source Data for Fig. 4 are available with the paper. Received: 11 January 2022; Accepted: 28 April 2022; References 1. Mancusso, R., Gregorio, G. G., Liu, Q. & Wang, D. N. Structure and mechanism of a bacterial sodium-dependent dicarboxylate transporter. Nature 491, 622–626 (2012). 2. Mulligan, C., Fitzgerald, G. A., Wang, D. N. & Mindell, J. A. Functional characterization of a Na+-dependent dicarboxylate transporter from Vibrio cholerae. J. Gen. Physiol. 143, 745–759 (2014). 3. Mulligan, C. et al. The bacterial dicarboxylate transporter VcINDY uses a two- domain elevator-type mechanism. Nat. Struct. Mol. 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D.B.S. was supported by the American Cancer Society Postdoctoral Fellowship (129844-PF-17-135-01-TBE) and Department of Defense Horizon Award (W81XWH-16-1- 0153). We thank the following colleagues for helpful discussions: N. Coudray, R. Gonzalez Jr., M. Lopez Redondo, J.A. Mindell and E. Tajkhorshid. We are also grateful to colleagues at the Biophysics Colab, C. Grewer, R.M. Ryan and X. Wang, for commenting on the manuscript. We thank the staff at the NYU Cryo-EM Facility and the NYU Microscopy Core for assistance in grid screening and the Paciﬁc Northwest Center for Cryo-EM in data collection. EM data processing used computing resources at the HPC Facility of NYULMC. Author contributions J.J.M., J.S. and C.M. puriﬁed the proteins. J.J.M., J.C.S. and D.B.S. collected and analyzed the substrate-binding data. C.M. did all the cysteine PEGylation experiments. D.B.S collected and processed the cryo-EM images and built the atomic models. D.B.S and D.N.W. analyzed the structures. D.B.S., C.M. and D.N.W. wrote the manuscript. All authors participated in the discussion and manuscript editing. C.M. and D.N.W. supervised the research. Competing interests The authors declare no competing interests. Additional information Supplementary information The online version contains supplementary material available at https://doi.org/10.1038/s41467-022-30406-4. Correspondence and requests for materials should be addressed to Christopher Mulligan or Da-Neng Wang. Peer review information Nature Communications thanks Jeff Abramson, Reinhart Reithmeier and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Reprints and permission information is available at http://www.nature.com/reprints Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afﬁliations. 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10.1085/jgp.202012858	null	null	ARTICLE EVAP: A two-photon imaging tool to study conformational changes in endogenous Kv2 channels in live tissues Parashar Thapa1, Robert Stewart1, Rebecka J. Sepela1, Oscar Vivas2, Laxmi K. Parajuli2, Mark Lillya1, Sebastian Fletcher-Taylor1,3, Bruce E. Cohen3,4, Karen Zito2, and Jon T. Sack1,5 A primary goal of molecular physiology is to understand how conformational changes of proteins affect the function of cells, tissues, and organisms. Here, we describe an imaging method for measuring the conformational changes of the voltage sensors of endogenous ion channel proteins within live tissue, without genetic modification. We synthesized GxTX-594, a variant of the peptidyl tarantula toxin guangxitoxin-1E, conjugated to a fluorophore optimal for two-photon excitation imaging through light-scattering tissue. We term this tool EVAP (Endogenous Voltage-sensor Activity Probe). GxTX-594 targets the voltage sensors of Kv2 proteins, which form potassium channels and plasma membrane–endoplasmic reticulum junctions. GxTX-594 dynamically labels Kv2 proteins on cell surfaces in response to voltage stimulation. To interpret dynamic changes in fluorescence intensity, we developed a statistical thermodynamic model that relates the conformational changes of Kv2 voltage sensors to degree of labeling. We used two-photon excitation imaging of rat brain slices to image Kv2 proteins in neurons. We found puncta of GxTX-594 on hippocampal CA1 neurons that responded to voltage stimulation and retain a voltage response roughly similar to heterologously expressed Kv2.1 protein. Our findings show that EVAP imaging methods enable the identification of conformational changes of endogenous Kv2 voltage sensors in tissue. image the conformational changes of endogenous Kv2 voltage- sensitive proteins. Introduction To move the field of voltage-sensitive physiology forward, we need new tools that indicate when and where voltage-sensitive Kv2 proteins form voltage-gated K+ channels (Frech et al., conformational changes in endogenous proteins occur. Many classes of transmembrane proteins have been found to be voltage 1989), bind endoplasmic reticulum proteins to form plasma membrane–endoplasmic reticulum junctions (Johnson et al., sensitive (Bezanilla, 2008). One important class of voltage-sensitive proteins is the voltage-gated ion channels. Electrophysiological 2018; Kirmiz et al., 2018a), regulate a wide variety of physio- techniques have enabled remarkably precise studies of the voltage logical responses in tissues throughout the body (Bocksteins, sensitivity of ionic conductances, primarily under reduc- 2016), and integrate their response to voltage with many other tionist experimental conditions where the channels have been cellular processes, including phosphorylation (Murakoshi et al., removed from their native tissue. In addition to their canon- 1997), SUMOylation (Plant et al., 2011), oxidation (MacDonald ical function as ion-conducting channels, voltage-gated ion et al., 2003), membrane lipid composition (Ramu et al., 2006), channel proteins have nonconducting functions that are in- and auxiliary subunits (Gordon et al., 2006; Peltola et al., 2011). dependent of their ion conducting functions (Tanabe et al., Kv2 proteins are members of the voltage-gated cation channel 1988; Kaczmarek, 2006). These nonconducting protein func- superfamily. The voltage sensors of proteins in this superfamily comprise a bundle of four transmembrane helices termed S1–S4 tions are largely inaccessible to study by electrophysiology and are more poorly understood. Novel approaches are needed (Long et al., 2005, 2007). The S4 helix contains positively to learn more about voltage sensing in intact tissues and to charged arginine and lysine residues, gating charges, that re- unlock the mysterious realm of nonconducting voltage- spond to voltage changes by moving through the transmem- sensitive physiology. Here, we present a new approach to brane electric field (Aggarwal and MacKinnon, 1996; Seoh et al., ............................................................................................................................................................................. 1Department of Physiology and Membrane Biology, University of California, Davis, Davis, CA; 3The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA; Laboratory, Berkeley, CA; 5Department of Anesthesiology and Pain Medicine, University of California, Davis, Davis, CA. 4Division of Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National 2Center for Neuroscience, University of California, Davis, Davis, CA; P. Thapa and R. Stewart contributed equally to this paper; Correspondence to Jon T. Sack: jsack@ucdavis.edu. © 2021 Thapa et al. This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms/). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 4.0 International license, as described at https://creativecommons.org/licenses/by-nc-sa/4.0/). Rockefeller University Press J. Gen. Physiol. 2021 Vol. 153 No. 11 e202012858 https://doi.org/10.1085/jgp.202012858 1 of 24 1996; Islas and Sigworth, 1999). When voltage sensor domains encounter a transmembrane voltage that is more negative on the inside of the cell membrane, voltage sensors are biased toward resting conformations, or down states, in which gating charges are localized intracellularly. When voltage becomes more posi- tive, gating charges translate toward the extracellular side of the membrane, and voltage sensors are progressively biased toward up states in a process of voltage activation (Armstrong and Bezanilla, 1973; Zagotta et al., 1994; Tao et al., 2010; Xu et al., 2019). Channel pore opening is distinct from, but coupled to, voltage sensor movement. In some voltage-gated ion channel proteins, voltage sensor movement is coupled to nonconducting protein functions (Tanabe et al., 1988; Kaczmarek, 2006). To study the functional outputs of voltage sensors, it is essential to measure voltage sensor activation itself. Conformational changes of voltage sensors have been detected with electro- physiological measurements of gating currents (Armstrong and Bezanilla, 1973; Schneider and Chandler, 1973; Bezanilla, 2018) or by optical measurements from fluorophores inserted near voltage sensors by genetic encoding (Lin and Schnitzer, 2016) or chemical modification (Zhang et al., 2015). However, the following experimental limitations prevent these existing techniques from measuring conformational changes of voltage sensors of most endogenous proteins: gating currents can only be measured when the proteins are expressed at high density in a voltage-clamped membrane; engineered proteins differ from endogenous channels; most chemical modification strategies result in off-target labeling; and conjugation of fluorophores into voltage sensors irreversibly alters structure and function. Here, we develop a different strat- egy to reveal conformational states of Kv2 proteins. To image where in tissue the voltage sensors of Kv2 pro- teins adopt a specific resting conformation, we exploited the conformation-selective binding of the tarantula peptide guang- xitoxin (GxTX)-1E, which can be conjugated to fluorophores to report Kv2 conformational changes (Tilley et al., 2014; Fletcher- Taylor et al., 2020; Stewart et al., 2021). Here, we synthesize GxTX-594, a Ser13Cys GxTX variant conjugated to Alexa Fluor 594, a fluorophore compatible with two-photon excitation imaging through light-scattering tissue. GxTX-594 dynamically binds Kv2 channels in living tissue. When GxTX-594 binds, it becomes immobilized and fluorescently labels Kv2 proteins at the cell surface. When Kv2 channels become voltage activated, GxTX-594 unbinds, resulting in unlabeling (see illustration). This labeling/unlabeling dynamic is similar to a recently re- ported point accumulation for imaging of nanoscale topology superresoluton imaging method (Legant et al., 2016), yet the method reported here is sensitive to changes in protein con- formation. GxTX-594 labeling of Kv2 proteins equilibrates on the time scale of seconds, revealing the probability (averaged over time) that unbound voltage sensors are resting or active. Here, we develop a method to calculate the average conforma- tional status of unlabeled Kv2 proteins from images of GxTX-594 fluorescence and deploy the GxTX-594 probe in brain slices to image voltage-sensitive fluorescence changes that reveal con- formational changes of endogenous neuronal Kv2 proteins. We refer to this type of imaging tool as an Endogenous Voltage- sensor Activity Probe (EVAP). This EVAP approach provides an imaging technique to study conformational changes of en- dogenous voltage-sensitive Kv2 proteins in samples that have not (or cannot) be genetically modified. Materials and methods GxTX-594 synthesis We used solid-phase peptide synthesis to generate a variant of GxTX, an amphiphilic 36-amino acid cystine knot peptide. We synthesized the same peptide used for GxTX-550, Ser13Cys GxTX, where a free thiolate side chain of cysteine 13 is predicted to extend into extracellular solution when the peptide is bound to a voltage sensor (Tilley et al., 2014). GxTX-1E folds by for- mation of three internal disulfides, and cysteine 13 was differen- tially protected during oxidative refolding to direct chemoselective conjugation. Following refolding and thiol deprotection, Alexa Fluor 594 C5 maleimide was condensed with the free thiol, and Ser13Cys (Alexa Fluor 594) GxTX-1E (called GxTX-594) was purified (Fig. S1). The Ser13Cys GxTX peptide was synthesized as previously described (Tilley et al., 2014). Methionine 35 of GxTX was re- placed by the oxidation-resistant noncanonical amino acid norleucine to avoid complications from methionine oxidation, and serine 13 was replaced with cysteine to create a spinster thiol. Ser13Cys GxTX was labeled with a Texas Red derivative (Alexa Fluor 594 C5 maleimide, cat. #10256; Thermo Fisher Scientific) to form GxTX-594. Ser13Cys GxTX lyophilisate was brought to 560 μM in 50% acetonitrile (ACN) + 1 mM Na2EDTA. 2.4 μl of 1M Tris (pH 6.8 with HCl), 4 μl of 10 mM Alexa Fluor 594 C5 maleimide in DMSO, and 17.9 μl of 560 μM Ser13Cys GxTX were added for a final solution of 100 mM Tris, 1.6 mM Alexa Fluor 594 C5 maleimide, and 0.4 mM GxTX in 24 μl of reaction solution. Reactants were combined in a 1.5-ml low- protein–binding polypropylene tube (LoBind, cat. #022431081; Eppendorf) and mixed at 1,000 rpm at 20°C for 4 h (Thermo- mixer 5355 R; Eppendorf). After incubation, the tube was centrifuged at 845 RCF for 10 min at room temperature. A purple pellet was observed after centrifugation. The supernatant was transferred to a fresh tube and centrifuged at 845 RCF for 10 min. After this second centrifugation, no visible pellet was seen. The supernatant was injected onto a reverse-phase HPLC C18 column (Biobasic 4.6-mm RP-C18 5 μm, cat. #2105-154630; Thermo Fisher Scientific) equilibrated in 20% ACN, 0.1% tri- fluoroacetic acid (TFA) at 1 ml/min, and eluted with a protocol holding in 20% ACN for 2 min, increasing to 30% ACN over 1 min, then increasing ACN at 0.31% per minute. HPLC effluent was monitored by fluorescence and an absorbance array detec- tor. 1-ml fractions were pooled based on fluorescence (280-nm Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 2 of 24 excitation, 350-nm emission) and absorbance (214 nm, 280 nm, and 594 nm). GxTX-594 peptide–fluorophore conjugate eluted at ∼35% ACN, and mass was confirmed by mass spectrometry using a Bruker ultrafleXtreme matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF; Fig. S1). Samples for identification from HPLC eluant were mixed 1:1 in an aqueous solution of 25% MeOH and 0.05% TFA saturated with α-cyano-4- hydrocinnamic acid, pipetted onto a ground-steel plate, dried under vacuum, and ionized with 60–80% laser power. Molecular species were detected using a reflector mode protocol and quantitated using Bruker Daltonics flexAnalysis 3.4 software. Lyophilizate containing GxTX-594 conjugation product was dissolved in cell external (CE) buffer (defined below) and stored at −80°C. GxTX-594 concentration was determined by 280-nm absorbance using a calculated molar attenuation coefficient of 18,900 M−1 cm−1. Chinese hamster ovary (CHO) cell methods CHO cell culture and transfection The CHO-K1 cell line (American Type Culture Collection) and a subclone transfected with a tetracycline-inducible rat Kv2.1 construct (Kv2.1-CHO; Trapani and Korn, 2003) were cultured as described previously (Tilley et al., 2014). To induce Kv2.1 expression in Kv2.1-TREx-CHO cells, 1 μg/ml minocycline (cat. #ALX-380-109-M050; Enzo Life Sciences), prepared in 70% ethanol at 2 mg/ml, was added to the maintenance media to induce Kv2.1 expression. Minocycline was added 40–48 h before imaging and voltage-clamp fluorometry experiments. Minocy- cline was added 1–2 h before whole-cell ionic current recordings to limit K+ conductance such that voltage clamp could be maintained. Transfections were achieved with Lipofectamine 2000 (cat. #1668027; Life Technologies). 1.1 μl Lipofectamine was diluted, mixed, and incubated in 110 μl of Opti-MEM (pro- duct no. 31985062, lot no. 1917064; Gibco-BRL) in a 1:100 ratio for 5 min at room temperature. Concurrently, 1 μg of plasmid DNA and 110 μl of Opti-MEM were mixed in the same fashion. DNA and Lipofectamine 2000 mixtures were mixed and left at room temperature for 20 min. Then, the transfection cocktail mixture was added to 2 ml of culture media in a 35-mm cell culture dish of CHO cells at ∼40% confluency and allowed to settle at 37°C in 5% CO2 for 4–6 h before the media were replaced. Cells were given 40–48 h recovery following transfection before being used for experiments. Rat Kv2.1-GFP (Antonucci et al., 2001), rat Kv2.2-GFP (Kirmiz et al., 2018b), rat Kv1.5-GFP (Li et al., 2001), rat Kv4.2-GFP (Shibata et al., 2003), mouse BK-GFP, rat KvBeta2 (Shibata et al., 2003), and rat KCHiP2 (An et al., 2000) plasmids were all gifts from James Trimmer (University of California, Davis, Davis, CA). Identities of constructs were confirmed by sequencing from their cytomegalovirus promoter. To minimize any day-to-day variances, the cells in experiments shown in Fig. 4 or Fig. S2 were each plated for all transfections from a single-cell suspension, transfected in parallel, and imaged 2 d later using the same thawed aliquot of GxTX-594. Confocal and Airy disk imaging Confocal images were obtained with an inverted confocal system (LSM 880 410900-247-075; Zeiss) run by ZEN Black 2.1 software. A 63×/1.40 NA oil DIC objective (420782-9900-799; Zeiss) was used for most imaging experiments; a 63×/1.2 NA water DIC objective (441777-9970-000; Zeiss) was used for voltage clamp fluorometry experiments. GFP and YFP were excited with the 488-nm line from an argon laser (3.2 mW at installation) powered at 0.5% unless otherwise noted. GxTX-594 was excited with a 594-nm helium–neon laser (0.6 mW at in- stallation) powered at 10% unless otherwise noted. Wheat germ agglutinin (WGA)-405 was excited with a 405-nm diode laser (3.5 mW at installation) powered at 1% unless otherwise noted. Temperature inside the microscope housing was 27–30°C. In confocal imaging mode, fluorescence was collected with the microscope’s 32-detector gallium arsenide phosphide de- tector array arranged with a diffraction grating to measure 400–700-nm emissions in 9.6-nm bins. Emission bands were 495–550 nm for GFP and YFP, 605–700 nm for GxTX-594, and 420–480 nm for WGA-405. Point spread functions were calcu- lated using ZEN Black software using emissions from 0.1-μm fluorescent microspheres prepared on a slide according to manufacturer’s instructions (cat. #T7279; Thermo Fisher Sci- entific). The point spread functions for confocal images with the 63×/1.40 NA oil DIC objective in the x–y direction were 228 nm (488-nm excitation) and 316 nm (594-nm excitation). In Airy disk imaging mode, fluorescence was collected with the microscope’s 32-detector gallium arsenide phosphide de- tector array arranged in a concentric hexagonal pattern (Airy- scan 410900-2058-580; Zeiss). After deconvolution, the point spread functions for the 63×/1.40 NA oil objective with 488-nm excitation was 124 nm in x–y and 216 nm in z and with 594-nm excitation, 168 nm in x–y and 212 nm in z. For the 63×/1.2 NA water objective, the point spread function with 488-nm excita- tion was 187 nm in x–y and 214 nm in z and with 594-nm ex- citation, 210 nm in x–y and 213 nm in z. Unless stated otherwise, cells were plated in uncoated 35-mm dishes with a 7-mm inset no. 1.5 coverslip (cat. #P35G-1.5-20-C; MatTek). The CHO CE solution used for imaging and electro- physiology contained (in mM): 3.5 KCl, 155 NaCl, 10 HEPES, 1.5 CaCl2, and 1 MgCl2, adjusted to pH 7.4 with NaOH. Measured osmolality was 315 mOsm/liter. When noted, solution was sup- plemented with either 10 mM glucose (CEG) or 10 mM glucose and 1% BSA (CEGB). For time lapse, GxTX-594 concentration–effect experiments, Kv2.1-CHO cells were plated onto 22 × 22-mm no. 1.5H cover- glass (Deckglaser), and Kv2.1 expression was induced with minocycline 48 h before experiments such that all Kv2.1-CHO cells expressed Kv2.1. Prior to imaging, cell maintenance media were removed and replaced with CEGB, then the coverslip was mounted on an imaging chamber (cat. #RC-24E; Warner In- struments) with vacuum grease. We performed three 10-fold serial dilutions of 1,000 nM GxTX-594 to generate the range of concentrations used for this concentration–effect experiment and applied each concentration of GxTX-594 to Kv2.1-CHO cells for 15 min followed by 15 min of washout before the next con- centration of GxTX-594 was applied. Solutions were added to the imaging chamber perfusion via a syringe at a flow rate of ∼1 ml per 10 s. Images were taken every 5 s. Laser power was set to 0.5% for the 488-nm laser and 1.5% for the 594-nm laser. For Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 3 of 24 colocalization experiments with GFP-tagged proteins, cells were incubated in 100 nM GxTX-594 for 5 min and then washed with 1 ml CEGB three times before imaging. Whole-cell voltage clamp for CHO cell imaging Kv2.1-CHO cells plated in glass-bottom 35-mm dishes were in- cubated in minocycline for 48 h to induce Kv2.1 channel expression. Cells were washed with CEGB, placed in the microscope, and then incubated in 100 μl of 100 nM GxTX-594 for 5 min to label cells. Before patch clamp, the solution was diluted with 1 ml of CEG for a working concentration of 9 nM GxTX-594 during experiments. We determined the time re- quired for GxTX-594 to reach a stable fluorescence after dilution from 100 nM to 9 nM by time-lapse imaging during dilution (Fig. 5 A). The rate of fluorescence change (kΔF) indicated that, on average, equilibration was 85% complete 9 min after dilution to 9 nM. After dilution to 9 nM, the mean fluorescence intensity decreased by 39.1 ± 8.4% and remained stable (Fig. 5 B). Cells with obvious GxTX-594 surface staining were voltage clamped in whole-cell mode with an EPC-10 patch-clamp amplifier (HEKA Elektronik) run by Patchmaster software (v2 × 90.2; HEKA Elektronik). The patch pipette contained the following potassium-deficient Cs+ internal pipette solution to reduce voltage error by limiting outward current: 70 mM CsCl, 50 mM CsF, 35 mM NaCl, 1 mM EGTA, and 10 mM HEPES, brought to pH 7.4 with CsOH. Osmolality was 310 mOsm/liter. The liquid junction potential was calculated to be 3.5 mV and was not corrected. Borosilicate glass pipettes (cat. #BF150-110-10HP; Sutter Instruments) were pulled with blunt tips to resistances <3.0 MΩ in these solutions. Cells were held at −80 mV (unless noted otherwise) and stepped to indicated voltages. The voltage step stimulus was maintained until any observed change in fluorescence was complete. Cells were stepped to −80 mV for at least 1 min and were visually inspected to determine sufficient fluorescence recovery before being stepped to another voltage. This recovery time did not always allow GxTX-594 labeling to equilibrate fully. For stimulus frequency dependence experi- ments, cells were given 2-ms steps to 40 mV at the stated fre- quencies (0.02, 5, 10, 20, 50, 100, 150, or 200 Hz). Images for voltage clamp fluorometry were taken in Airy disk imaging mode with the settings described above. During time-lapse imaging of voltage-clamped cells, we no- ticed that GxTX-594 fluorescence in the center of the glass- adhered surface responded more slowly to voltage changes than the periphery. At −80 mV, at the center of the glass- adhered cell surface, relabeling was incomplete after 500 s; however, at the cell periphery, relabeling neared completion within 200 s (Fig. S4 A). To quantify this observation, concentric circular regions of interest (ROIs) were drawn, with the smallest ROI in the center of the glass-adhered surface. The average fluorescence intensities from each of these ROIs were compared with each other and an ROI at the cell periphery (Fig. S4, B and C). We quantified kΔF by fitting the average fluorescence in- tensities from each ROI with a monoexponential function (Eq. 1 in Image analysis). In response to voltage change, the kΔF was consistently slower at the center of the glass-adhered surface and faster toward the outer periphery (Fig. S4, D and E). While kΔF was consistently slower at the center of the glass-adhered surface, we observed that kΔF was more pronounced during la- beling at −80 mV than unlabeling at 40 mV. When cells were held at a membrane potential of −80 mV, kΔF at the periphery was ∼10-fold faster than the kΔF at the center of the cell. In comparison, when the membrane potential was held at 40 mV, kΔF at the periphery was approximately threefold faster than the kΔF at the center of the cell. The gradual change in fluorescence intensity over many seconds after a voltage step is inconsistent with a fast electrochromic effect leading to fluorescence change as the change in fluorescence intensity does not occur instan- taneously when the membrane potential is stepped. Addition- ally, the slowing of kΔF in subcellular regions farther from the periphery of cells is inconsistent with a slower electrochromic effect as whole-cell voltage clamp should render the membrane surface isopotential. K+ channel–GFP ionic current recordings Whole-cell voltage clamp was used to measure currents from CHO cells expressing Kv2.1-GFP, Kv2.2-GFP, Kv1.5-GFP, Kv4.2- GFP, BK-GFP, or GFP that was transfected as described above. Cells were plated on glass-bottom dishes. Cells were held at −80 mV, then 100-ms voltage steps were delivered ranging from −80 mV to +80 mV in +5-mV increments. Pulses were repeated every 2 s. The external (bath) solution contained CE solution. The internal (pipette) solution contained (in mM) 35 KOH, 70 KCl, 50 KF, 50 HEPES, and 5 EGTA, adjusted to pH 7.2 with KOH. Liquid junction potential was calculated to be 7.8 mV and was not corrected. Borosilicate glass pipettes (cat. #BF150-110-10HP; Sutter Instruments) were pulled into pipettes with resistance <3 MΩ for patch clamp recording. Recordings were at room tem- perature (22–24°C). Voltage clamp was achieved with an Axon Axopatch 200B Amplifier (Molecular Devices) run by Patchmaster software (v2 × 90.2; HEKA Elektronik). Holding potential was −80 mV capacitance and ohmic leak were subtracted using a P/5 protocol. Recordings were low-pass filtered at 10 kHz and digitized at 100 kHz. Voltage clamp data were plotted with Igor Pro 7 (WaveMetrics). As the experiments plotted in Fig. S2 A were merely to confirm functional expression of ion channels at the cell surface, series resistance compensation was not used, and sub- stantial cell voltage errors are predicted during these experiments. Kv2.1 ionic current recordings Prior to patching, Kv2.1-CHO cells were washed in divalent-free PBS and then harvested in Versene (cat. #15040066; Gibco-BRL). Cells were scraped and transferred to a polypropylene tube, pelleted, and washed three times at 1,000 g for 2 min and then resuspended in the same external solution as used in the re- cording chamber bath. Cells were rotated in a polypropylene tube at room temperature (22–24°C) until use. Cells were then pipetted into a 50-μl recording chamber (RC-24N; Warner In- struments) prefilled with external solution and allowed to settle for ≥5 min. After adhering to the bottom of the glass recording chamber, cells were thoroughly rinsed with external solution using a gravity-driven perfusion system. Cells showing uniform intracellular GFP expression of intermediate intensity were se- lected for patching. Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 4 of 24 Voltage clamp was achieved with a patch clamp amplifier (Axon Axopatch 200B; Molecular Devices) run by Patchmaster software. Borosilicate glass pipettes (BF150-110-7.5HP; Sutter Instruments) were pulled with blunt tips, coated with silicone elastomer (Sylgard 184; Dow Corning), heat cured, and tip fire- polished to resistances <4 MΩ. Capacitance and ohmic leak were subtracted using a P/5 protocol. Recordings were low-pass fil- tered at 10 kHz using the amplifier’s built-in Bessel function and digitized at 100 kHz. For whole-cell ionic current measurements in Kv2.1-CHO cells, the external patching solution contained (in mM) 3.5 KCl, 155 NaCl, 10 HEPES, 1.5 CaCl2, and 1 MgCl2, adjusted to pH 7.4 with NaOH. The internal (pipette) solution contained (in mM) 70 KCl, 5 EGTA, 50 HEPES, 50 KF, and 35 KOH, adjusted to pH 7.4 with KOH. The osmolality was 315 mOsm/liter for the ex- ternal solution and 310 mOsm/liter for the internal solution measured by a vapor pressure osmometer. Following estab- lishment of the whole-cell seal, ionic K+ current recordings were taken in the presence of a vehicle, which consisted of 100 nM tetrodotoxin, 10 mM glucose, and 0.1% BSA prepared in external solution. Cells were held at −100 mV with channel activation steps ranging from −80 mV to +120 mV in increments of +5 mV (100 ms) before being returned to 0 mV (100 ms) to record tail currents. The intersweep interval was 2 s. To determine the bioactivity of GxTX-594, Kv2.1 ionic currents were recorded once more, 5 min following the wash-in of bath solution also containing 100 nM GxTX-594. Wash-ins were performed while holding at −100 mV; 100 μl was washed through the chamber and removed distally through vacuum tubing to maintain con- stant bath fluid level. Ionic current analysis The average current in the 100 ms before voltage step was used to zero subtract the recording. Outward current taken as the mean value between 90 and 100 ms of the channel activation step was used to calculate and correct for series resistance- induced voltage error. Tail current values were derived from the mean value between 0.2 and 1.2 ms of the 0-mV tail current step. Tail current was normalized by the mean activation step current from 50 to 80 mV and plotted against the estimated membrane potential, which had been corrected for voltage error and the calculated liquid junction potential of 8.5 mV. These tail GV plots were fit with a fourth-power Boltzmann function (Sack et al., 2004), and the fit parameters were used for statistical analysis. Brain slice methods Hippocampal slice culture preparation and transfection All experimental procedures were approved by the University of California, Davis, institutional animal care and use committee and were performed in strict accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. Animals were maintained under standard light–dark cycles and had ad libitum access to food and water. Organotypic hippocampal slice cultures were prepared from postnatal day 5–7 rats, as previously described (Stoppini et al., 1991) and detailed in a video protocol (Opitz-Araya and Barria, 2011). DIV15–30 neurons were transfected 2–6 d before imaging via biolistic gene transfer (160 psi, Helios gene gun; Bio-Rad) as described in a detailed video protocol (Woods and Zito, 2008). 10 μg of plasmid was coated to 6–8 mg of 1.6-μm gold beads. Two-photon excitation slice imaging Image stacks (512 × 512 pixels, 1-mm Z-steps, 0.035 μm/pixel) were acquired using a custom two-photon excitation microscope (LUMPLFLN 60XW/IR2 objective, 60×/1.0 NA; Olympus) with two pulsed Ti:sapphire lasers (Mai Tai; Spectra Physics) tuned to 810 nm (for GxTX-594 imaging) and 930 nm (for GFP imaging) and controlled with ScanImage software (Pologruto et al., 2003). After identifying a neuron expressing Kv2.1-GFP, perfusion was stopped, and GxTX-594 was added to the static bath solution to a final concentration of 100 nM. After 5-min incubation, perfu- sion was restarted, leading to washout of GxTX-594 from the slice bath. Red and green photons (565dcxr, BG-22 glass, HQ607/ 45; Chroma Technology) emitted from the sample were collected with two sets of photomultiplier tubes (R3896; Hamamatsu). Whole-cell voltage clamp for brain slice imaging Organotypic hippocampal slice cultures (6–7 DIV, not trans- fected) were transferred to an imaging chamber with re- circulating artificial cerebrospinal fluid (ACSF) maintained at 30°C. To hold the slice to the bottom of the chamber, a horseshoe-shaped piece of gold wire was used to weight the membrane holding the slice. ACSF contained (in mM) 127 NaCl, 25 NaHCO3, 25 D-glucose, 2.5 KCl, 1.25 NaH2PO4, 1 MgCl2, 2 CaCl2, and 200 nM tetrodotoxin, pH 7.3, and aerated with 95% O2/5% CO2 (∼310 mOsm). 4 ml of 100 nM GxTX-594 in ACSF was used in the circulating bath to allow the toxin to reach the slice and reach the desired concentration of 100 nM throughout the circulating bath. Images were acquired beginning 3 min after GxTX-594 was added. Apparent CA1 neurons with GxTX-594 labeling in a Kv2-like pattern were selected for whole-cell patch clamp. Voltage clamp was achieved using an Axopatch 200B Amplifier (Molecular Devices) controlled with custom software written in MATLAB (MathWorks, Inc.). Patch pipettes (5–7 MΩ) were filled with intracellular solution containing (in mM) 135 Cs-methanesulfonate, 10 Na2-phosphocreatine, 3 Na-L- ascorbate, 4 NaCl, 10 HEPES, 4 MgCl2, 4 Na2ATP, and 0.4 NaGTP, pH 7.2. Neurons were clamped at −70 mV. Input resistance and holding current were monitored throughout the experiment. Cells were excluded if the pipette series resistance was >25 MΩ or if the holding current exceeded −100 pA. To activate Kv2 channels, a 50- s depolarizing step from −70 mV to 0 mV was given. Image analysis Fluorescence images were analyzed using ImageJ 1.52n software (Schneider et al., 2012). ROIs encompassed the entire fluorescent region of an individual cell or neuron unless mentioned other- wise. ROIs were drawn manually. Analysis of images was con- ducted independently by multiple researchers who produced similar results, but analysis was not conducted in a blinded or randomized fashion. Fluorescence intensity (F) was background subtracted using the mean F of a region that did not contain cells. In experiments with CHO cells where the bath solution contained 9 nM GxTX-594, the apparent surface membrane of most cells (40 Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 5 of 24 of 47) lacking Kv2.1-GFP protein had lower F than the background region that did not contain cells (Fig. S3 B, horizontal black dashed line), indicating that the background F was overestimated. Based on the mean F from the apparent surface membrane of cells lacking Kv2.1 protein, the background was overestimated by 24%. The signal from the surface membrane of cells stably transfected with rat Kv2.1 (Kv2.1-CHO) was, on average, 18× higher than re- gions that did not contain cells, making the error generated by our overestimation of the background ∼1%. As the error produced by this background subtraction method was relatively small, it was not corrected. For F/Finit normalization, Finit was the mean fluo- rescence preceding the indicated voltage stimuli, or the max ob- served intensity in concentration–effect experiments. Further details of specific normalization and background subtraction procedures are provided in the figure legends. Time dependence of fluorescence intensity was fit with a monoexponential decay: F (cid:2) F∞ + (F0 − F∞) e −(t−t0 τ ) , (1) where τ = 1/kΔF, t = time, t0 = time at start of fit, F = fluorescence intensity, F0 = fluorescence at start of fit, and F∞ = fluorescence after infinite time. The Kv2.1–GxTX-594, fluorescence–voltage (FV) responses were fit with a Boltzmann function: f (V) (cid:2) offset + A 8 >< >: − 1 + e 9 >= >; , 1 h(cid:2) (cid:3) i V−V1 2 ∗ zF RT (2) where V = voltage, offset = the offset from zero of the Boltzmann distribution, A = the amplitude, z = number of elementary charges, F = Faraday’s constant, R = the universal gas constant, and T = temperature (held at 295°K). For colocalization analyses, the Pearson coefficient was calcu- lated using the JACoP plugin (Bolte and Cordelières, 2006). Co- localization analyses were conducted within ROIs defining individual cells. A Pearson correlation coefficient value of 0 sig- nifies uncorrelated pixels between two images, and a Pearson correlation coefficient value of 1 signifies complete correlation between pixels from two images. Correlation between pixels from two fluorescent recordings of an image suggests spatial colocali- zation of proteins. Plotting and curve fitting was performed with Igor Pro 7 or 8 (WaveMetrics), which performs nonlinear least squares fits using a Levenberg–Marquardt algorithm. Sample sizes of n ≥ 3 were selected to confirm reproducibility. Sample sizes of n ≥ 6 were selected to power nonparametric statistical compar- isons to discern P < 0.01. The α for statistical significance in nonparametric statistical comparisons was adjusted for multiple comparisons using the Bonferroni method. The Bonferroni- corrected P value = α , where α = 0.01 and n represents the number n of comparisons made in an experiment. Error values from indi- vidual curve fittings are SDs. All other errors, including error bars, indicate SEs. Arithmetic means are reported for intensity measurements and correlation coefficients. As the distributions underlying variability in results are unknown, nonparametric statistical comparisons were conducted with Mann–Whitney U tests, and two-tailed P values were reported individually if P > 0.0001. Parametric statistical tests, which include the Student’s t test, ANOVA, and Tukey’s post hoc test, were performed with paired data and on sample sizes of n ≤ 6 due to the weak statistical power of nonparametric tests when comparing small sample sizes. EVAP model In the EVAP model, at any given voltage, there is a probability that a voltage sensor is either in its resting conformation (Presting) or in its activated conformation (Pactivated) such that Pactivated = (1 – Presting). The equilibrium for voltage sensor activation is then a ratio of ac- tivated-to-resting voltage sensors (Pactivated/Presting) in which Pactivated Presting Pactivated Presting unlabeled (cid:2) e (V−V1/2,unlabeled ) zF RT labeled (cid:2) e (V−V1/2,labeled ) zF RT, (3a) (3b) where V1/2 is the voltage where Pactivated/Presting = 1. In a prior study, our analysis of the conductance–voltage relation of Kv2.1 yielded a V1/2 = −32 mV with z = 1.5 elementary charges (e0) for the early movement of four independent voltage sensors, and we found that with a saturating concentration of GxTX, the V1/2 = +42 mV (Fig. 1 C; Tilley et al., 2019). These values were used for V1/2,unlabeled, z, and V1/2,labeled, respectively (Table 1). To relate voltage sensor activation to transient labeling and unlabeling, we used microscopic binding (kon[EVAP]) and unbinding (koff) rates that are distinct for resting and activated voltage sensors. We estimated values for these rates assuming and kΔF (cid:2) kon[EVAP] + koff Kd (cid:2) koff kon . (4) (5) To calculate the kon,resting and koff,resting values reported in Table 1, – we used the saturating value at negative voltages of the kΔF voltage relation (see Fig. 6 E), and Kd from concentration–effect imaging (Fig. 2 D). In 9 nM GxTX-594, at greater than +40 mV, voltage-dependent unlabeling was nearly complete, indicating that koff,activated >> kon,activated[EVAP]. The model does not include EVAP signal that is insensitive to voltage (Fig. 6 C). We input the saturating amplitude of the Boltzmann fit to the kΔF at positive voltages as koff,activated (Fig. 6 E). The slow labeling of activated voltage sensors confounded attempts to measure kon,activated di- rectly, and we used the statistical thermodynamic principle of microscopic reversibility (Lewis, 1925) to constrain kon,activated: Pactivated Presting Pactivated Presting , labeled , unlabeled (cid:2) koff ,resting kon,resting koff ,activated kon,activated . (6) The EVAP model depicted in Scheme 2 has only a single microscopic binding rate, kon,total[EVAP], and unbinding rate, koff,total. kon,total is a weighted sum of both kon,resting and kon,activated from Scheme 1. The weights for kon,total are the relative probabilities that unlabeled voltage sensors are resting or activated, which is determined at any static voltage by an equilibrium constant, Pactivated Presting unlabeled: Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 6 of 24 kon,total (cid:2) kon,resting[EVAP] · 1 1 + Pactivated Presting + unlabeled ratio of fluorescence at a test voltage to fluorescence at a prior voltage (F/Finit) is equal to the probability that a Kv2 subunit is reversibly labeled by GxTX-594 (Plabeled): kon,active[EVAP] · 1 + 1 1 . Pactivated Presting unlabeled (7) F Finit (cid:2) Plabeled Plabeled,init . (9) The equilibrium Plabeled at any voltage can be determined from microscopic binding rates associated with Scheme 2 where Similarly, koff,total is determined by the unbinding rate from resting voltage sensors (koff,resting) and the unbinding rate from activated voltage sensors (koff,activated) and weighted such that Plabeled (cid:2) 1 1 + Kd,total [EVAP] (cid:2) 1 1 + koff ,total kon,total·[EVAP] . (10) koff ,total (cid:2) koff ,resting · 1 1 + Pactivated Presting labeled + koff ,active · 1 + . 1 1 Pactivated Presting labeled Predictions of F / Finit and kΔF during trains of 2-ms voltage steps from −80 mV to +40 mV were made from the EVAP model by summing the products of time-averaged probability of being at each voltage (PVn) and the fluorescence change predicted at that voltage (ΔFVn) : (8) (cid:4) F Finit (cid:2) (PV1 · ΔFV1) + (PV2 · ΔFV2) + ... + (PVn · ΔFVn). (11) Using kon,total and koff,total, we compute kΔF using Eq. 4, as im- plemented in Data S1. (Scheme 1) (Scheme 2) The EVAP model was also used to predict the magnitude of GxTX-594 fluorescence changes on cell surfaces. In theory, the For voltage steps from −80 to +40 mV, Eq. 11 is: (cid:4) F Finit (cid:2) (P40mV · ΔF40mV) + (P−80mV · ΔF−80mV). We predicted EVAP kinetic responses as kΔF (cid:2) (PV1 · kΔF,V1) + (PV2 · kΔF,V2) + ... + (PVn · kΔF,Vn), (12) where PVn is as in Eq. 11 and kΔF,n is kΔF at that particular voltage. For voltage steps from −80 to +40 mV, Eq. 12 is: kΔF (cid:2) (P40mV · kΔF40mV) + (P−80mV · kΔF−80mV). Online supplemental material Fig. S1 pertains to the synthesis of GxTX-594. It shows a model of GxTX-594, and provides HPLC chromatograms and MALDI-TOF mass spectrometry profiles of Ser13Cys GxTX and GxTX-594. Fig. S2 shows GxTX-594 selectively labeling Kv2 proteins at the cell surfaces. Fig. S3 shows that GxTX-594 labeling of surface membranes requires Kv2 proteins. Fig. S4 demonstrates that extracellular access can impact GxTX-594 labeling kinetics. Fig. S5 demonstrates that variation in bath temperature does not account for variability of GxTX-594 kinetics. Fig. S6 is an ex- tended image gallery of GxTX-594 labeling CA1 hippocampal pyramidal neurons transfected with Kv2.1 GFP. Video 1 is a time-lapse image sequence of GxTX-594 fluorescence on a voltage-clamped CA1 hippocampal pyramidal neuron while it is depolarized from −70 to 0 mV. Data S1 is a spreadsheet that sets up calculations to generate EVAP model predictions. Results GxTX-594 retains bioactivity for Kv2.1 after chemoselective modification To monitor activation of Kv2 proteins in tissue slices, we syn- thesized an EVAP compatible with two-photon imaging. We previously presented an EVAP that was a synthetic derivative of GxTX conjugated to a DyLight 550 fluorophore (GxTX-550; Tilley et al., 2014). DyLight 550 has poor two-photon excitation properties, and for this study, it was replaced with Alexa Fluor Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 7 of 24 Figure 1. GxTX-594 modulates Kv2.1 conductance. (A) Representative Kv2.1-CHO current response under whole-cell voltage clamp. Cells were given 100-ms, 5-mV increment voltage steps ranging from −80 mV (blue) to +120 mV (red) and then stepped to 0 mV to record tail currents. The holding potential was −100 mV. (B) Kv2.1 currents from the same cell 5 min after the addition of 100 nM GxTX-594. Scale bars are the same for A and B. (C) Normalized conductance–voltage relationships from Kv2.1 tail currents before application of GxTX-594 (n = 13). Different symbols correspond to individual cells, and the green corresponds to cell in A. (D) Normalized conductance—voltage relationships in 100 nM GxTX-594 (n = 11). (E) Mean midpoint of each of four independent voltage sensors in the fourth-power Boltzmann fit (V1/2) before (−31 ± 6 mV SD) and after (+27 ± 10 mV SD) 100 nM GxTX-594. *, P < 0.0001 by Mann–Whitney U test. (F) Mean e0 associated with Boltzmann fit (z) before (1.5 ± 0.3 e0 SD) and after (1.0 ± 0.4 e0 SD) 100 nM GxTX-594. , P = 0.0007 by Mann–Whitney U test. (G) Mean midpoint of conductance change in the fourth-power Boltzmann fit (Vmid) before (−2 ± 6 mV SD) and after (+73 ± 13 mV SD) 100 nM GxTX-594. , P < 0.0001 by Mann–Whitney U test. 594, a persulfonated Texas Red analogue with a large two- photon excitation cross section and ample spectral separation from GFP, making it well suited for multiplexed, two-photon excitation imaging experiments (Zito et al., 2004). We refer to this EVAP variant as GxTX-594. We performed electrophysiological analyses to determine whether GxTX-594 retains the ability to allosterically modulate Kv2.1 (Fig. 1). GxTX is a partial inverse agonist of Kv2.1, which lowers channel open probability by stabilizing voltage sensors in a resting conformation. Consequently, more positive intracellular voltage is required to activate voltage sensors and achieve the same open probability as without GxTX (Tilley et al., 2019). Previously, we estimated that a Kv2.1 voltage sensor with GxTX bound is 5,400-fold more stable in its resting conformation and requires more positive intracellular voltage to become activated (Tilley et al., 2019). To characterize the efficacy of GxTX-594 in allosterically modulating Kv2.1 gating, we voltage clamped Kv2.1- CHO cells and measured K+ currents in GxTX-594. We analyzed the Kv2.1 conductance–voltage (GV) relation by fitting with a fourth power Boltzmann function. The voltage at which the con- ductance of the fitted function is 50% of maximum, Vmid, was +73 ± 13 mV for 100 nM GxTX-594 (Fig. 1 G). For comparison, the Vmid of 100 nM GxTX was +67 ± 6 mV (Tilley et al., 2019). This shift of the GV indicates that GxTX-594 retains an efficacy similar to GxTX. Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 8 of 24 Table 1. Parameters used for calculations to generate Scheme 1: EVAP allosteric expansion Parameter kon,resting koff,resting kon,activated koff,activated V1/2,unlabeled V1/2,labeled z Value 0.30 μM−1 s−1 0.0081 s−1 0.21 μM−1s−1 0.39 s−1 −32 mV 41 mV 1.5 e0 GxTX-594 labels Kv2 proteins To determine the concentration range where GxTX-594 effec- tively labels Kv2.1 protein, we performed a concentration–effect experiment. As the Kd of our previously presented EVAP, GxTX- 550, was 30.0 ± 3.9 nM at a holding potential of −100 mV (Tilley et al., 2014), we presumed that 1,000 nM would be a sufficient upper bound for concentration–effect experiments with GxTX- 594 (Fig. 2, A and B). Cell surface fluorescence intensity was analyzed immediately after washout of 1, 10, 100, and 1,000 nM GxTX-594 to eliminate fluorescence from toxin in solution. This fluorescence labeling concentration–effect relation was fit with a Langmuir binding isotherm, resulting in a Kd of 26.9 ± 8.3 nM (Fig. 2 C). Due to the incomplete equilibration of labeling at 1 and 10 nM, our measure is expected to overestimate the Kd. The near saturation of the fluorescence concentration–effect relation with 100 nM GxTX-594, and the rate of fluorescence equilibration (Fig. 2 D) suggested that incubation with 100 nM GxTX-594 for 5 min results in near-maximal labeling on the glass-adhered surface of Kv2.1-CHO cells. We assessed whether GxTX-594 fluorescence on CHO-K1 cells is due to the presence of Kv2 proteins. CHO-K1 cells were transfected with either a rat Kv2.1-GFP or Kv2.2-GFP construct, each of which yielded delayed rectifier K+ currents (Fig. S2 A). 2 d after transfection, Kv2.1-GFP or Kv2.2-GFP fluorescence displayed distinct subcellular regions of high density at the glass-adhered surface (Fig. 3 A), which we refer to as Kv2 clusters, a term used to refer to similar high-density regions in neurons and other mammalian cell lines (Kirmiz et al., 2018b). Correlation coefficients indicated a high degree of subcellular colocalization of both Kv2 proteins with GxTX-594 (Fig. 3 B), quantitating the observation that GxTX-594 is not evenly dis- tributed throughout the membrane but is localized to Kv2 clusters. We did not detect a significant difference between the ratios of GxTX-594 to Kv2.1-GFP or Kv2.2-GFP fluorescence (Fig. 3 C; P = 0.74 by Mann–Whitney U test), consistent with the lack of discrimination of GxTX between Kv2.1 and Kv2.2 (Herrington et al., 2006). GxTX accesses the membrane- embedded voltage sensors of Kv2 proteins by partitioning into the outer leaflet of the plasma membrane bilayer (Milescu et al., 2009; Gupta et al., 2015), and a GxTX derivative labeled with a fluorophore that brightens in less polar environments is de- tectable in the membrane of CHO cells without Kv2 proteins (Fletcher-Taylor et al., 2020). However, we failed to find any GxTX-594 labeling of CHO cells in the absence of Kv2 proteins (Fig. S3). GxTX-594 selectively labels Kv2 proteins An important consideration for determination of whether GxTX-594 could reveal conformational changes of endogenous Kv2 proteins is whether the EVAP is selective for Kv2 proteins. Electrophysiological studies have concluded that the native GxTX peptide is selective for Kv2 channels, with some off-target Figure 2. Concentration–effect characteristics of GxTX-594 labeling. (A) Fluorescence from confluent Kv2.1-CHO cells incubated in indicated concen- trations of GxTX-594 for 15 min then washed out before imaging. Imaging plane was near the glass-adhered cell surface. Fluorescence shown corresponds to GxTX-594 (magenta). Scale bar, 20 μm. (B) Time-lapse fluorescence intensity from the concentration–effect experiment shown in A. Fluorescence was not background subtracted. Fmax is intensity while cells were incubated in 1,000 nM GxTX-594. (C) Relative fluorescence intensity of GxTX-594 that remains on cells immediately after washout of indicated concentrations of GxTX-594 from the extracellular solution. Symbols correspond to each of three experiments. Black line is the fit of a Langmuir isotherm for Kd = 26.9 nM ± 8.3. (D) Labeling or unlabeling kinetics for GxTX-594 at indicated concentrations. Symbols correspond to the same experiments as panel C. kΔF values obtained from monoexponential fits (Eq. 1). Error bars represent the SD of kΔF fitting. Black line is fit of the kΔF –concentration relation with Eq. 4, kon = 6.372 × 10−5 ± 0.049 × 10−5 nM−1 s−1; koff = 5.929 × 10−4 ± 0.043 × 10−4 s−1. Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 9 of 24 Figure 3. GxTX-594 colocalizes with Kv2-GFP. (A) Fluorescence from CHO cells transfected with Kv2.1-GFP (top) or Kv2.2-GFP (bottom) and labeled with GxTX-594. Optical sections were near the glass-adhered cell surface. Cells were incubated with 100 nM GxTX-594 for 5 min and rinsed before imaging. Fluorescence shown corresponds to emission of GFP (left), GxTX-594 (middle), or as an overlay of GFP and GxTX-594 (right). Scale bars, 20 μm. (B) Pearson correlation coefficients for GxTX-594 colocalization with Kv2.1-GFP or Kv2.2-GFP. (C) Ratio of fluorescence intensity of the same cells in B when excited at 594 nm versus 488 nm from cells expressing Kv2.1-GFP or Kv2.2-GFP. In B and C, pixel intensities were background subtracted before analyses by subtracting the average fluorescence of a background ROI that did not contain cells from the ROI containing the cell. Circles represent measurements from individual cells, and each transfection group contains data from two separate applications of GxTX-594. Bars represent the mean. No significant difference was detected between Kv2.1-GFP or Kv2.2-GFP by Mann–Whitney U test (P = 0.74). modulation of A-type Kv4 channels (Herrington et al., 2006; Liu and Bean, 2014; Speca et al., 2014). However, electrophysiological testing cannot determine whether ligands bind unless they also alter currents (Sack et al., 2013). Furthermore, structural differ- ences between wild-type GxTX and the GxTX-594 variant could potentially alter selectivity among channel protein subtypes. To test whether GxTX-594 binds other voltage-gated K+ channel subtypes, we quantified surface labeling and analyzed colocalization of GxTX-594 with a selection of GFP-tagged voltage-gated K+ channel subtypes (Fig. 4 A). The ratio of GxTX-594 fluorescence to each GFP-tagged K+ channel subtype was not distinguishable from zero for Kv4.2, Kv1.5, or BK channels (Fig. 4 B), indicating minimal, if any, binding. Fur- thermore, no colocalization was apparent between Kv4.2, Kv1.5, or BK channels and the residual GxTX-594 fluorescence (Fig. 4 C). An additional set of experiments conducted under different microscopy conditions and without the auxiliary subunits of Kv4.2 or Kv1.5 also gave no indication of GxTX-594 labeling (Fig. S2). While we cannot be certain that GxTX-594 does not bind any of the >80 known mammalian proteins containing voltage sensor domains, the lack of labeling of the related voltage-gated K+ channels indicates that GxTX-594 does not promiscuously label voltage sensors. The relationship between GxTX-594 cell-surface fluorescence and Kv2.1 voltage activation To understand the relationship between channel gating and GxTX-594 fluorescence, we determined how fluorescence intensity on cells expressing Kv2.1 responds to changes in membrane voltage. We found that GxTX-594 equilibrated more quickly on the sides of cells than on their glass-adhered surface, presumably due to restricted access to the extracellular space by the glass (Fig. S4). Due to this observation, we chose an imaging plane where fluorescence from GxTX-594 resembled an annulus (Fig. 6 A). This imaging plane varied between cells but was >1 μm above the glass. We developed a labeling protocol in which Kv2.1-CHO cells were incubated for 5 min in a bath solution (CEG) containing 100 nM GxTX-594, which was then diluted with extracellular solution to 9 nM (Fig. 5). Once GxTX-594 fluorescence intensity stabilized at the cell membrane (at least 9 min; Fig. 5), cells were voltage clamped in whole-cell mode. We measured the fluorescence response of GxTX-594 when the membrane voltage of Kv2.1-CHO cells was stepped from a holding potential of −80 mV to more positive voltages that ranged from −40 mV to +80 mV (Fig. 6 A). ROIs corresponding to the cell surface were manually identified and average fluo- rescence intensity quantified from time-lapse sequences. The voltage-dependent reduction in fluorescence equilibrates to a value above background. Even when cells were given a +80-mV depolarizing voltage stimulus, some GxTX-594 fluorescence re- mained (Fig. 6 B). Most of the residual fluorescence appeared to be localized to the cell surface membrane (Fig. 6 A) and varied between cells (Fig. 6 C). As such surface labeling was not pre- sent on CHO cells in the absence of Kv2 proteins (Fig. S3), we consider this residual fluorescence to originate from voltage- insensitive Kv2 proteins. Voltage-insensitive fluorescence Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 10 of 24 Figure 4. GxTX-594 selectively labels Kv2 proteins. (A) Fluorescence from live CHO cells transfected with Kv2.1-GFP, Kv2.2-GFP, Kv4.2- GFP + KChIP2, Kv1.5-GFP + Kvβ2, or BK-GFP (indicated by row) and labeled with GxTX-594. Airy disk imaging was from a plane above the glass-adhered surface. Cells were incubated with 100 nM GxTX-594 and 5 μg/ml WGA-405 then rinsed before imaging. Fluorescence corresponds to emission of GFP (column 1), GxTX-594 (col- umn 2), WGA-405 (column 3), or an overlay (column 4). Scale bars, 20 μm. (B) Intensity of GxTX-594 labeling for different K+ channel-GFP types. Fluorescence intensity resulting from 594-nm excitation of GxTX-594 is divided by fluorescence intensity resulting from 488-nm excitation of GFP. This value was normalized to the average 594:488 ratio from GxTX-594 and Kv2.1-GFP. Circles indicate measurements from individual cells. Only cells with obvious GFP expression were analyzed. For analysis, ROIs were drawn around the cell membrane indicated by WGA-405 fluorescence. Pixel in- tensities were background subtracted be- fore analyses by subtracting the average fluorescence of a background ROI that did not contain cells from the ROI containing the cell; this occasionally resulted in ROIs with negative intensity. Kv2.1, n = 16; Kv2.2, n = 10; Kv4.2, n = 13; Kv1.5, n = 13; and BK, n = 10; n indicates the number of individual cells analyzed in a single dish during a single application of GxTX-594 with the indicated K+ channel-GFP type. Bars represent the mean. Significant differences were observed be- tween 594:488 ratio for Kv2.1 or Kv2.2 and Kv1.5, Kv4.2, or BK by Mann–Whitney U test (P < 0.0001). The P value to determine signifi- cance is adjusted for multiple comparisons us- ing the Bonferroni method, where P < 0.0033 is considered significant, with the caveat that data points under each condition are technical repli- cates. (C) Pearson correlation coefficients between GxTX-594 and GFP. Same cells as B. Significant differences were observed between correlation coefficients for Kv2.1 or Kv2.2 and Kv1.5, Kv4.2, or BK by Mann-Whitney U test (P < 0.0001). could potentially be from Kv2.1–GxTX-594 complexes with im- mobilized voltage sensors or internalized Kv2.1–GxTX-594 com- plexes that remain just under the cell surface (Deutsch et al., 2012; Weigel et al., 2012; Fox et al., 2013a; Weigel et al., 2013). To compare voltage response properties between cells, we used a normalization procedure to analyze only the voltage- sensitive fraction of the fluorescence from each cell, which we defined as the fluorescence that changed between −80 mV and Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 11 of 24 Figure 5. GxTX-594 fluorescence equilibration after dilution from 100 nM to 9 nM. (A) CHO cells transfected with Kv2.1-GFP (left) in 100 nM GxTX-594 (middle) and after dilution to 9 nM GxTX-594 (right). Regions darker than the background bath solution are CHO cells not transfected with Kv2.1-GFP (ar- rowhead). Scale bar, 20 μm. (B) Fluorescence excited at 594 nm during dilution from 100 nM GxTX-594 to 9 nM GxTX-594. Timing of dilution is shown above the graph. Fluorescence was normalized to the mean 594 fluorescence before dilution to 9 nM. Data are from the cell labeled with an arrow in A. 100 nM and 9 nM images in A are from 0 and 12 min, respectively. (C) Rate of GxTX-594 fluorescence decay after dilution. Error bars are SDs of kΔF fit. n = 5 cells. +80 mV. The initial fluorescence at a holding potential of −80 mV was normalized to 100% F/Finit, and residual fluores- cence after a +80-mV step was normalized to 0% F/Finit (Fig. 6 D). To characterize the voltage dependence of the Kv2.1–GxTX-594 interaction, fluorescence–voltage (FV) responses were fit with a Boltzmann distribution (Eq. 2). This fit had a half maximal voltage midpoint (V1/2) of −27 mV and a steepness (z) of 1.4 e0 (Fig. 6 D, bottom panel, black line). This is similar to voltage sensor movement in Kv2.1-CHO cells without any GxTX present: V1/2 = −26 mV, z = 1.6 e0 (Tilley et al., 2019). These results suggest that at 9 nM GxTX-594, the FV appears to be a good surrogate for the gating current–voltage (QV) response of unlabeled Kv2 channels. To determine the voltage dependence of the kinetics of GxTX-594 labeling and unlabeling, we compared kΔF at varying step potentials. We quantified kΔF by fitting the average fluo- rescence from voltage-clamped cells with a monoexponential function (Eq. 1). In response to voltage steps from a holding potential of −80 mV to more positive potentials, kΔF increased progressively as step potential was increased above −40 mV and appeared to begin to saturate at higher voltages (Fig. 6 E). Upon return to −80 mV, kΔF was similar to −40 mV. While the kΔF did not clearly display saturation at positive voltages that would justify fitting with a Boltzmann function, a model of GxTX-594 dynamics, which we develop later in this study, indicated that Boltzmann fitting could yield physical insight (Fig. 6 E, bottom panel, black line). We noted that the degree of variability in kΔF measurements became greater at more positive potentials (Fig. 6 E, top and bottom panels). At −80 mV, there was a twofold range in kΔF values and a ninefold range at +80 mV. The relatively low variation in kΔF at −80 mV suggests that despite variance in fluo- rescence intensity after rebinding (Fig. S4 C), kΔF from fits of the upward relaxation at −80 mV are relatively consistent. The average kΔF equilibration at 10 nM GxTX-594 in concentration–effect ex- periments was comparable to Kv2.1-CHO cells incubated in 9 nM GxTX-594 and voltage clamped at −80 mV (0.0011 s−1 and 0.0014 s−1, respectively; Fig. S4 E and Fig. 2 D). This suggests that the Kv2 voltage sensors in the unclamped cells for concentration– effect experiments are in the same early resting conformation as voltage-clamped cells at −80 mV. Additionally, we determined that only a small fraction of the up to ninefold variability in kΔF at positive voltages could be attributed to temperature fluctuations (Fig. S5). Possible reasons for cell-to-cell variability at more positive voltages are discussed further in Limitations. The relation between voltage sensor activation and GxTX-594 dynamics can be recapitulated by rate theory modeling To enable translation of the intensity of fluorescence from GxTX-594 on a cell surface into a measure of Kv2 conformational change, we developed an EVAP model, a series of equations derived from rate theory that relate cell labeling to voltage sensor activation. The framework of the EVAP model is gener- alizable to fluorescent molecular probes that report conforma- tional changes by a change in binding affinity. In the EVAP model, the proportion of labeled versus unlabeled Kv2 in a membrane is determined by the proportion of voltage sensors in resting versus activated conformations. The model assumes that the innate voltage sensitivity of the Kv2 subunit is solely Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 12 of 24 responsible for controlling voltage dependence. EVAP labeling is voltage dependent because the binding and unbinding rates are different for resting and activated conformations of voltage sensors. Voltage activation of Kv2 channels involves many conformational changes (Scholle et al., 2004; Jara-Oseguera et al., 2011; Tilley et al., 2019). However, models that presume independent activation of a voltage sensor in each of the four Kv2.1 subunits accurately predict many aspects of voltage acti- vation and voltage sensor toxin binding (Lee et al., 2003; Tilley et al., 2019). For simplicity, we model Kv2 proteins as having only resting and activated conformations that are independent in each voltage sensor and developed a rate theory model con- sisting of four interconnected states (Scheme 1). When voltage sensors change from resting to activated con- formations, the binding rate of the GxTX-594 EVAP decreases, and the unbinding rate increases. When the membrane voltage is held constant for sufficient time, the proportions of labeled and unlabeled proteins reach an equilibrium. EVAP labeling requires seconds to equilibrate (Fig. 6), whereas Kv2 channel gating equilibrates in milliseconds (Tilley et al., 2019), three orders of magnitude more quickly. These distinct time scales of equilibration suggest an approximation to model the reversible EVAP labeling response: voltage sensor conformations achieve equilibrium quickly such that only their distribution at equi- librium is expected to greatly impact the kinetics of labeling and unlabeling, allowing Scheme 1 to collapse into Scheme 2, which depicts the structure of the EVAP model used for calculations. We constrained the EVAP model with measurements of GxTX-594 binding kinetics and GxTX impacts on Kv2.1 gating 9 nM GxTX-594. Color progression for pseudocoloring of fluorescence in- tensity is shown in vertical bar on right. Middle column in each row indicates voltage step taken from a holding potential of −80 mV. Times listed at top of each column correspond to time axis in panel B. Scale bar, 10 μm. (B) GxTX- 594 fluorescence during steps to indicated voltages. Smooth lines are mono- exponential fits (Eq. 1): −40 mV kΔF = 2.15 × 10−2 ± 0.22 × 10−2 s−1; 0 mV kΔF = 1.279 × 10−1 ± 0.023 × 10−1 s−1; 40 mV kΔF = 2.492 × 10−1 ± 0.062 × 10−1 s−1; and 80 mV kΔF = 4.20 × 10−1 ± 0.11 × 10−1 s−1. ROIs were hand-drawn around the apparent cell surface membrane based on GxTX-594 fluorescence. 0% was set by subtraction of background, which was the average intensity of a region that did not contain cells over the time course of the voltage proto- col. For each trace, 100% was set from the initial fluorescence intensity at −80 mV before the subsequent voltage step. Raw initial fluorescence values before normalization were within 10% of one another. (C) Fluorescence intensity remaining at the end of 50-s steps to +80 mV. Each circle repre- sents one cell. Background subtraction as in B. (D) Voltage dependence of fluorescence intensity at the end of 50-s steps. For each cell, 100% was set from the initial fluorescence intensity at −80 mV before the first step to another voltage. Cells did not always recover to initial fluorescence intensity during the −80-mV holding period between voltage steps. Top: Circle col- oring indicates data from the same cell, and lines connect points from the same cell. Gray circles represent data shown in B. Bottom: Black bars rep- resent the mean F/Finit at each voltage, and error bars represent the SEM. Black line is the fit of a first-order Boltzmann equation (Eq. 2): V1/2 = −27.4 ± 2.5 mV, z = 1.38 ± 0.13 e0. Green line is the prediction from the EVAP model at 9 nM GxTX. (E) Voltage dependence of fluorescence intensity kinetics (kΔF). Top: Circle coloring is the same as D. Bottom: Black bars represent the average kΔF at each voltage, and error bars represent the SEM. Black line is a first-order Boltzmann equation fit to the kΔF–voltage relation: V1/2 = +38 ± 15 mV, z = 1.43 ± 0.35 e0. Green line is the prediction from the EVAP model at 9 nM GxTX. Figure 6. GxTX-594 labeling responds to transmembrane voltage. (A) Fluorescence from an optical section of a voltage-clamped Kv2.1-CHO cell in Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 13 of 24 (see EVAP model and Data S1). We tested the viability of model predictions by comparison with GxTX-594 labeling measure- ments. The EVAP model predicts that the FV for GxTX-594 la- beling will conform to a Boltzmann distribution. The FV prediction in 9 nM GxTX-594 had a V1/2 and z that differ by only −4 mV and 0.06 e0, respectively, from the Boltzmann fit of ex- perimental data (Fig. 6 D, bottom panel, black and green lines). The EVAP model also predicts that the kΔF-V will conform to a Boltzmann distribution. The kΔF-V prediction differs by only 3 mV and 0.05 e0 from the Boltzmann fit of experimental data (Fig. 6 E, bottom panel, blue line). However, this fit was poorly constrained as we failed to obtain sufficient data at voltages above +80 mV where kΔF-V is predicted to saturate. In our at- tempts, the durations required at more positive voltages irre- versibly increased membrane leak. The V1/2 and z values from the GxTX-594 FV and kΔF-V were not used as constraints of the EVAP model, and the similarity between the predictions and empirical findings seemed remarkable enough to warrant fur- ther exploration of the EVAP model predictions. We used the EVAP model to investigate general principles of the relation between voltage sensor activation and reversible labeling. The EVAP model predicts that as GxTX-594 concen- tration decreases, the change in labeling (ΔF/ΔFmax) approaches the probability that unlabeled voltage sensors are resting (Fig. 7 A). This prediction explains the similarity between the FV in 9 nM GxTX-594 (V1/2 = −27 ± 3 mV, z = 1.4 ± 0.1 e0; Fig. 6 D) and the QV of Kv2.1 (V1/2 = −26 ± 1 mV, z = 1.6 ± 0.1 e0; Tilley et al., 2019). As the concentration of EVAP is increased, the FV shifts to more positive voltages such that the fractional change in fluorescence intensity is always less than the fraction of unlabeled voltage sensors that are active. As EVAP concentration increases and approaches the activated state Kd (1,790 nM), voltage sensor activation becomes less effective at dissociating the EVAP due to binding to activated voltage sensors (Fig. 7 B). The model pre- dicts that at any concentration, this simple interpretation will be valid: A decrease in EVAP surface fluorescence indicates acti- vation of unlabeled voltage sensors. The EVAP model also yields a simple interpretation of la- beling kinetics, it predicts that as GxTX-594 concentration de- creases, the rate of fluorescence change kΔF approaches the probability that labeled channels are active (Fig. 7 C). This pre- diction explains the similarity between the kΔF-V in 9 nM GxTX- 594 (V1/2 = 38 ± 15 mV, z = 1.4 ± 0.4 e0; Fig. 6 D) and the QV of Kv2.1 in saturating GxTX (V1/2 = 47 ± 1 mV, z = 1.6 ± 0.1 e0; Tilley et al., 2019). At low concentrations, the dependence of kΔF on the conformation of channels bound to GxTX-594 is due to the rate of unbinding dominating kΔF, with the rate of unbinding being solely determined by the conformation of channels bound to GxTX-594. Repetitive action potential–like stimuli amplify the GxTX-594 response Kv2 currents impact repetitive action potential firing (Du et al., 2000; Liu and Bean, 2014), making repetitive action potentials the voltage waveforms that are, arguably, most relevant to Kv2 function. However, action potentials occur on the millisecond time scale, orders of magnitude faster than the GxTX-594 response, which integrates Kv2 conformations occurring over many seconds. Kv2 channels have slow deactivation kinetics (Liu and Bean, 2014; Tilley et al., 2019), and high-frequency firing could prevent Kv2 proteins from fully deactivating be- fore a next action potential is triggered, creating a kinetic trap that progressively accumulates activated voltage sensors. This behavior of Kv2 proteins indicates that high-frequency firing could evoke a more robust fluorescence signal than the EVAP model predicts, as the model assumes continuous equilibrium of voltage sensors and cannot kinetically trap activated con- formations. To test this hypothesis, we crudely mimicked action potentials with trains of 2-ms voltage steps from −80 to +40 mV and observed the changes in fluorescence on GxTX-594–labeled Kv2.1-CHO cells (Fig. 8, A and B). To assess frequency response, step frequency was varied from 0.02 to 200 Hz. GxTX-594 un- labeling and kΔF increased with stimulus frequency (Fig. 8, C and D). We compared these fluorescence responses to those pre- dicted by the EVAP model. When the stimulus frequency was <50 Hz, the EVAP model did a reasonable job of predicting fluorescence change. At and >50 Hz, fluorescence decreased by more than the EVAP model predicted, even without accounting for a voltage-insensitive fraction of Kv2.1 proteins (Fig. 8 C, bottom panel, green line). As discussed above, this divergence from the equilibrium-based EVAP model is expected at frequencies where voltage steps are shorter than Kv2 equilibration times. The time constant of ac- tivating gating current decay from Kv2.1-CHO cells was 1.3 ms at +40 mV (Tilley et al., 2019), which means that the majority of voltage sensors are effectively activated during the 2-ms +40 mV steps. In contrast, the time constant of deactivating gating cur- rent decay from Kv2.1-CHO cells was 22 ms at −80 mV (Tilley et al., 2019), which means that Kv2.1 is expected to become ki- netically trapped in activated conformations when stimuli to +40 mV from −80 mV are ∼50 Hz or faster. Thus, the amplified EVAP response appears consistent with voltage sensors failing to deactivate before the next stimulus, leading to an accumulation of activated voltage sensors and a more dramatic fluorescence response than predicted by the EVAP model. Overall, these dy- namics indicate that the magnitude of the change in GxTX-594 fluorescence intensity will be amplified during repetitive action potentials, a regimen of electrophysiological signaling where Kv2 currents are critical. In contrast, the kinetics of the GxTX-594 response did not appear to deviate from EVAP model predictions at high fre- quencies (Fig. 8 D). As kΔF responds to the dynamics of Kv2 proteins bound by GxTX-594, it could be that the faster deacti- vation rate of voltage sensors bound by GxTX (Tilley et al., 2019) prevents the bound channels from being kinetically trapped. GxTX-594 labels brain slices transfected with Kv2.1-GFP To determine whether expression of Kv2 proteins embedded in tissue can be imaged with GxTX-594, we overexpressed Kv2.1- GFP in rat brain slices and examined CA1 pyramidal neurons of the hippocampus. We chose CA1 neurons for several reasons: They express Kv2 channels at a density typical of central neurons, the physiology of these neurons has been intensively studied, and their electrical properties are relatively homogeneous Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 14 of 24 Figure 7. Relationship of GxTX-594 labeling to probability of Kv2 voltage sensor activa- tion. (A) EVAP model predictions of concentra- tion and voltage dependence of cell surface fluorescence intensity at different concentrations of EVAP in solution. The bottom axis represents membrane voltage. The left axis represents the predicted fluorescence relative to when all voltage sensors are at rest (Finit) and does not include EVAP signal that is insensitive to voltage. The dashed line corresponds to the right axis and represents the probability that voltage sensors of unlabeled Kv2.1 are in their resting confor- mation. (B) EVAP model prediction of the cell surface fluorescence when cells are given a +40- mV depolarization relative to when all voltage sensors are at rest (Finit) at increasing concen- trations of EVAP. The orange dashed line repre- sents Kd of resting voltage sensors. The blue dashed line represents Kd of activated voltage sensors. (C) EVAP model predictions of con- centration and voltage dependence of kΔF. Col- ors correspond to A, except the dashed line is the probability that voltage sensors bound by GxTX-594 are an activated conformation. (Misonou et al., 2005). Organotypic hippocampal slice cultures prepared from postnatal day 5–7 rats were sparsely transfected with Kv2.1-GFP, resulting in a subset of neurons displaying green fluorescence. When imaged 2–4 d after transfection, GFP fluorescence was observed in the plasma membrane sur- rounding neuronal cell bodies and proximal dendrites (Fig. S6, A and B). Six days or more after transfection, Kv2.1-GFP fluo- rescence organized into clusters on the surface of the cell soma and proximal processes (Fig. 9 A and Fig. S6 C), a pattern consistent with a prior report of endogenous Kv2.1 in CA1 neurons (Misonou et al., 2005). After identifying a neuron expressing Kv2.1-GFP, solution flow into the imaging chamber was stopped, and GxTX-594 was added to the static bath solu- tion to a final concentration of 100 nM. After 5 min of incu- bation, solution flow was restarted, leading to washout of excess GxTX-594 from the imaging chamber. After washout, GxTX-594 fluorescence remained colocalized with Kv2.1-GFP (Fig. 9 and Fig. S6), indicating that GxTX-594 is able to per- meate through dense neural tissue and bind to Kv2 proteins on neuronal surfaces. Pearson correlation coefficients confirmed the colocalization of GxTX-594 with Kv2.1-GFP in multiple slices (Fig. 9 C). In most images of Kv2.1-GFP–expressing neu- rons, GxTX-594 also labeled puncta on neighboring neurons that did not express Kv2.1-GFP but at intensities that were roughly an order of magnitude dimmer (Fig. 9 B, white arrow). The clustered GxTX-594 fluorescence patterns on the cell body and proximal processes of CA1 neurons were strikingly similar to reported patterns of anti-Kv2 immunofluorescence patterns and are consistent with GxTX-594 labeling of endogenous Kv2 proteins in CA1 neurons. While we cannot exclude the possibility that CA1 neurons have a subset of Kv2 proteins on their surface that is not labeled by GxTX-594, we saw no indication of Kv2.1- GFP on neuronal surfaces that are not labeled by GxTX-594. While we observed GxTX-594 fluorescence that morpholog- ically resembles endogenous Kv2 protein localizations, we also found that GxTX-594 occasionally labels structures not consistent with Kv2 proteins (Fig. S6, bottom panel, arrows). This non-Kv2 labeling was most prevalent at the surface of the hippocampal slices and progressively decreased as the imaging plane was moved deeper into the tissue (data not shown). Our interpretation of this phenomenon is that GxTX-594 can accumulate in the dead tissue and debris that is present at the surface of a hippocampal section after it is cut. For this reason, we analyzed only GxTX-594 fluorescence with subcellular localizations consistent with Kv2 channels. GxTX-594 labeling in brain slices responds to neuronal depolarization To test whether reversible GxTX-594 labeling of neurons in brain slices is consistent with binding to endogenous Kv2 volt- age sensors, we determined whether GxTX-594 labeling re- sponds to voltage changes in tissue. First, we looked for Kv2-like patterns on CA1 pyramidal neurons in untransfected brain slices bathed in 100 nM GxTX-594. With two-photon excitation, Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 15 of 24 Figure 8. High-frequency repetitive stimuli amplify the GxTX- 594 response. (A) Fluorescence intensity from Kv2.1-CHO cells incubated in 9 nM GxTX-594. Arrow indicates voltage-clamped cell. Fluorescence at holding potential of −80 mV (left), after 50 s of 200-Hz stimulus (middle), and 100 s after the cell is returned to holding potential of −80 mV (right). Stimulus was a 2-ms step to +40 mV. Note that in each panel, the unclamped cell (left cell in each panel) does not show a change in fluorescence. Scale bar, 10 μm. (B) Representative trace with GxTX-594 unlabeling at 200 Hz. Red line, monoexponential fit (Eq. 1): kΔF = 0.2327 ± 0.0099 × 10−1 s−1. 0% F/Finit was set by subtraction of the average intensity of a region that did not contain cells. 100% was set by the initial fluo- rescence intensity at −80 mV. (C) Fluorescence–stimulus frequency relation. Points indicate F/Finit from individual cells. Top: Point coloring indicates data from the same cell. Bottom: Black bars represent the average F/Finit at each voltage, and error bars rep- resent the SEM. Green line is the prediction of the EVAP model at a concentration of 9 nM. (D) kΔF–stimulus frequency relation. Plotted as in C. optical sections are thinner than the neuronal cell bodies, and GxTX-594 fluorescence appeared as puncta circumscribed by dark intracellular spaces (Fig. 10 A). This was similar to the patterns of fluorescence in Kv2.1-GFP–transfected slices (Fig. 9 B) and consistent with the punctate expression pattern of Kv2.1 in CA1 pyramidal neurons seen in fixed brain slices (Misonou et al., 2005). We tested whether the punctate fluorescence was voltage sensitive using voltage clamp. To ensure voltage clamp of the neuronal cell body, slices were bathed in tetrodotoxin to block Na+ channels, and Cs+ was included in the patch pipette solution to block K+ currents. In each experiment, whole-cell configu- ration was achieved with a GxTX-594–labeled neuron, and holding potential was set to −70 mV. At this point, time-lapse imaging of a two-photon excitation optical section was initiated. Depolarization to 0 mV resulted in loss of fluorescence from a subset of puncta at the perimeter of the voltage-clamped neuron (Fig. 10 B, red arrows; and Video 1). Other fluorescent puncta appeared unaltered by the 0-mV step (Fig. 10 B, white arrows). These puncta could represent Kv2 proteins on a neighboring cell, off-target labeling by GxTX-594, or voltage-insensitive Kv2 proteins, possibly due to near-surface internalization. To assess whether the fluorescence changes were due to the voltage change, we compared fluorescence of the apparent cell mem- brane region with regions more distal from the voltage-clamped cell body. An ROI 30-pixels (1.2-μm) wide containing the ap- parent membrane of the cell body was compared with other regions within each image (Fig. 10 C). The region containing the membrane of the cell body lost fluorescence during the 0-mV step (Fig. 10 D, ROI 1), while neither of the regions more distal Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 16 of 24 Figure 9. GxTX-594 labels CA1 hippocampal pyramidal neurons transfected with Kv2.1-GFP. (A) Two-photon excitation images of fluorescence from the soma and proximal dendrites of a rat CA1 hippocampal pyramidal neuron in a brain slice 6 d after transfection with Kv2.1-GFP (left), labeled with 100 nM GxTX- 594 (middle), and the overlay (right). Image represents a Z-projection of 20 optical sections. Scale bar, 10 μm. (B) A single optical section of the two-photon excitation image shown in A. GxTX-594 labels both Kv2.1-GFP puncta from a transfected cell and apparent endogenous Kv2 proteins from an untransfected cell in the same cultured slice (right, arrow). Scale bar, 10 μm. (C) Pearson correlation coefficients from CA1 hippocampal neurons 2, 4, and 6 d after transfection with Kv2.1-GFP. Each circle represents a different neuron. Bars are arithmetic means. (ROI 2, ROI 3) or the intracellular region (ROI 0) showed a similar change. In three hippocampal slices from separate rats, we found a significant decrease in fluorescence in ROI 1 com- pared with ROI 2 and ROI 3 when neurons were given a voltage step to 0 mV (Fig. 10 E; ANOVA P < 0.001; Tukey’s post hoc test P < 0.001). The kinetics of fluorescence response of the voltage- clamped membrane region were similar between slices (Fig. 10, F and K). To determine whether the fluorescence response to depo- larization was driven by the Kv2-like puncta on the cell mem- brane, the fluorescence along a path containing the apparent cell membrane was selected by drawing a path connecting fluores- cent puncta surrounding the dark cell body region and averaging fluorescence in a region 10-pixels (0.4-μm) wide centered on this path (Fig. 10 G, yellow line). The fluorescence intensity along the path of the ROI revealed distinct peaks corresponding to puncta (Fig. 10 H, red trace). After stepping the neuron to 0 mV, the intensity of fluorescence of a subset of puncta lessened (Fig. 10 H, black trace, peaks 1, 3, 4, 5, and 8). While the data are noisy, it is clear that even more reduction is observed in indi- vidual fluorescence puncta in brain slice (Fig. 10 I, blue line). This suggests that the voltage sensors in these functionally se- lected puncta are extensively activated. The EVAP model pre- dicts only a 55% reduction of fluorescence from CHO cells stepped to 0 mV in 100 nM GxTX. The greater response of the endogenous puncta could be due to voltage sensors activating at more negative voltages in neurons than CHO cells. However, the GxTX-594 concentration within tissue may be more dilute than in the bath solution, and consequently, the sensitive fraction calculated from the EVAP model is a lower bound. Other puncta maintained or increased their brightness after depolarization (Fig. 10 H, black trace, peaks 2, 6, and 7), but it was unclear whether these voltage-insensitive puncta correspond to Kv2 proteins on the surface of the same voltage-clamped neuron. When the kinetics of fluorescence intensity decay of individual voltage-sensitive puncta were fit with Eq. 1, kΔF values were similar between puncta (Fig. 10 I), consistent with these spa- tially separated puncta all being on the surface of the voltage- clamped neuron. The kΔF changes measured in these puncta were similar to those predicted by the Fig. 7 model (Fig. 10 I, blue line), although the variability of these measurements was sub- stantial. To address whether the fluorescence change at the cell membrane was driven by decreases in regions of punctate fluo- rescence, the punctate and nonpunctate fluorescence intensity changes were analyzed separately. The regions with fluores- cence intensities above average for the path (Fig. 10 H, dotted line) were binned as one group. The above-average group, by definition, contained all punctate fluorescence. When compar- ing the fluorescence before and during the 0-mV step, the re- gions that were initially of below-average fluorescence Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 17 of 24 Figure 10. GxTX-594 puncta on hippocampal CA1 neurons are sensitive to voltage stimulus. (A) Two-photon excitation optical section from CA1 py- ramidal neurons in a cultured hippocampal slice after incubation with 100 nM GxTX-594. Scale bar, 5 μm. (B) 594 fluorescence from a single two-photon excitation optical section before and during depolarization of a whole-cell patch-clamped neuron. Text labels of holding potential (−70 mV or 0 mV) indicate approximate position of the patch-clamp pipette. Red arrows indicate stimulus-sensitive puncta; white arrows indicate stimulus-insensitive puncta. Left panel is the average fluorescence of the three frames at −70 mV before depolarization, while the right panel is the average fluorescence of three frames after holding potential was stepped to 0 mV. Scale bar, 5 μm. Video 1 contains time-lapse images from this experiment. (C) ROIs used in analysis for D–F. Same slice as B. ROI 1 contains the apparent plasma membrane of the cell body of the patch-clamped neuron; it was generated by drawing a path tracing the apparent plasma membrane and then expanding to an ROI containing 15 pixels on either side of the path (1.2 μm total width). ROI 2 contains the area 15–45 pixels outside the membrane path (1.2 μm total width). RO1 3 contains the area >45 pixels outside the membrane path. ROI 0 contains the area >15 pixels inside the membrane path. Scale bar, 5 μm. (D) Fluorescence from each ROI shown in C. Squares represent ROI 0, circles represent ROI 1, up triangles represent ROI 2, and down triangles represent ROI 3. Background was defined as the mean fluorescence of ROI 0 during the experiment. Finit was defined as the mean fluorescence of ROI 1 during the first six frames, after subtraction of background. Dotted lines represent the average fluorescence of the first six frames of each ROI. The voltage protocol is shown above the graph. (E) Change in fluorescence during a 0-mV step for different ROIs in three hippocampal slices from three separate rats. ROIs Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 18 of 24 for each slice were defined by methods described in C. Circles represent mean fluorescence from six frames during 0-mV stimulus (F), normalized to mean fluorescence from the same ROI in six frames before stimulus (Finit). Circle color is consistent between ROIs for each hippocampal slice; red circles are from the slice shown in D. Black bars are arithmetic mean ± SE from three hippocampal slices. A statistical difference between ROIs was detected by ANOVA (P = 0.0003) followed by the Tukey’s post hoc test, where ROI 1 versus ROI 2 (, P < 0.001), ROI 1 versus ROI 3 (*, P < 0.001), and ROI 2 versus ROI 3 (n.s., P = 0.98). (F) Kinetics of fluorescence change from ROI 1 during a 0-mV step in three hippocampal slices. ROI 1 for each slice was defined by the methods described in C. Lines are monoexponential fits (Eq. 1): kΔF = 6.8 × 10−2 ± 2.6 × 10−2 s−1 (yellow), 6.8 × 10−2 ± 4.6 × 10−2 s−1 (red), and 4.9 × 10−2 ± 2.10−2 s−1 (green). The voltage protocol is shown above the graph. Colors of individual circles indicate the same slices as E. (G) ROI used in analysis for H–J. Same image as C. The shaded ROI was generated by drawing a path tracing the apparent plasma membrane and then expanding to an ROI containing 5 pixels on either side of the path (0.4 μm total width). Numbers indicate puncta that appear as peaks in H. Scale bar, 5 μm. (H) A plot of the fluorescence intensity along the ROI shown in G before (red trace) and during (black trace) 0-mV stimulus. Numbers above peaks correspond to puncta labeled in G. Red trace: Mean fluorescence intensity during the three frames immediately before the stimulus, normalized to mean intensity of entire ROI (black dotted line), plotted against distance along path. Black trace: Mean fluorescence intensity during three frames at 0 mV, normalized by the same Finit value as the red trace. (I) Kinetics of fluorescence change of individual puncta from G. Puncta intensity are average intensity of points extending to half maximum of each peak in H. Asterisks indicate mean fluorescence intensity of puncta 1 (pink), 4 (red), and 5 (dark red). Lines are monoexponential fits (Eq. 1): kΔF = 7.10−2 ± 2.9 × 10−2 s−1 (pink), 6.7 × 10−2 ± 2.5 × 10−2 s−1 (red), and 1.2 × 10−1 ± 5.6 × 10−2 s−1 (dark red). Fits to other puncta had SDs larger than kΔF values and were excluded. Finit was defined as the mean background- subtracted fluorescence of the puncta during the six frames before stimuli. The voltage protocol is displayed above the graph. Blue line is prediction from Scheme 1. (J) Comparison of fluorescence change of puncta (above average) and interpuncta (below average) regions in response to 0-mV stimulus. The regions before stimulus shown by the red line of H that had F/Finit ≥1 (above average) were binned separately from regions with F/Finit <1. The mean fluorescence of each region during 0-mV stimulus (H, black trace) was compared with the fluorescence before stimulus (H, red trace). Circles indicate values from three independent hippocampal slices; colors indicate same slices as E and F. A weak statistical difference in fluorescence was detected between interpuncta and puncta regions by Student’s t test (, P = 0.046). Black bars are arithmetic mean ± SE. (K) Rate of fluorescence change of GxTX-594 after a 0-mV stimulus from puncta (as in I), ROI 1 (as in F), or Kv2.1-CHO cells in 100 nM GxTX-594. Kv2.1-CHO cells were imaged at the same temperature as neurons (30°C) using neuronal intracellular solution. CHO CE solution was used for Kv2.1-CHO cell experiments. Kv2.1-CHO measurements were made by Airy disk confocal imaging. Black bars are mean ± SEM from data shown. Blue dotted line is kΔF = 6.31 × 10−2 s−1 prediction of Scheme 1. No statistical difference was detected between groups by ANOVA (P = 0.11). maintained the same intensity (103 ± 8%); regions that were initially of above-average fluorescence decreased in intensity (70 ± 8%; Fig. 10 J). This suggests that the detectable unlabeling along the cell membrane originated in the Kv2-like puncta, not the regions of lower fluorescence intensity between them. To determine whether the dynamics of the voltage-dependent responses of GxTX-594 fluorescence on neurons in brain slices were consistent with reversible labeling of Kv2.1, we performed experiments with Kv2.1-expressing CHO cells under similar conditions as brain slice: 100 nM GxTX-594, 30°C, Cs+-containing patch pipette solution. The rate of fluorescence changes in Kv2.1- CHO cells was similar to neurons in brain slices and consistent with the kΔF of 0.06 s−1 predicted by the Fig. 7 model (Fig. 10 K). However, the data underlying of kΔF measurements were noisy, limiting our ability to detect differences. Discussion The molecular targeting, conformation selectivity, and spatial precision of fluorescence from GxTX-594 enable identification of where in tissue the conformational status of Kv2 voltage sensors becomes altered. However, the utility of GxTX-594 as an EVAP is limited by several factors, including emission intensity, variability between experiments, and inhibition of Kv2 proteins. We discuss the potential utility and limitations of the EVAP mechanism underlying GxTX-594. Unique capabilities of GxTX-594 The Kv2 EVAP presented here is the only imaging method we are aware of for measuring voltage-sensitive conformational changes of a specific, endogenous protein. As GxTX binding selectively stabilizes the fully resting conformation of Kv2.1 voltage sensors (Tilley et al., 2019), reversible GxTX-594 labeling is expected to bind with highest affinity specifically to the fully resting conformation of the Kv2 voltage sensor in which the first gating charge of the Kv2 S4 segment is in the gating charge transfer center (Tao et al., 2010). Images of GxTX-594 fluores- cence reveal this conformation’s occurrence with subcellular spatial resolution. Importantly, the EVAP model we developed allows deconvolution of the behavior of unlabeled Kv2 proteins. This enables the subcellular locations where Kv2 voltage sensing occurs to be seen for the first time. Electrophysiological approaches can detect the voltage- sensitive K+ conductance of Kv2 channels. However, the ma- jority of Kv2 proteins on cell surface membranes do not function as channels and are nonconducting (Benndorf et al., 1994; Malin and Nerbonne, 2002; O’Connell et al., 2010), and Kv2 proteins dynamically regulate cellular physiology by nonconducting functions (Antonucci et al., 2001; Singer-Lahat et al., 2007; Feinshreiber et al., 2010; Dai et al., 2012; Fox et al., 2015; Johnson et al., 2018; Kirmiz et al., 2018a, 2018b, 2019; Vierra et al., 2019). The Kv2 EVAP reports on the conformation of Kv2 voltage sensors independently from ion conductance, enabling the study of voltage sensor involvement in Kv2’s nonconducting physio- logical functions. We present this EVAP as a prototype molecular probe for imaging voltage sensing by endogenous proteins in tissue with molecular specificity. Here, during our initial testing of GxTX-594, we observed that the majority of Kv2 protein detected at discrete individual clusters was voltage sensitive. While it may not be surprising to find that voltage-gated ion channel proteins are voltage sensi- tive, the voltage sensors of surface-expressed proteins can be immobilized. For example, gating charge of the L-type Ca2+ channel Cav1.2 is immobilized until it is bound by an intracel- lular protein (Turner et al., 2020). Kv2.1 channel function is extensively regulated by neurons. In rat CA1 neurons, the clustered Kv2 channels are proposed to be nonconducting (Fox et al., 2013b). Our results show that clustered Kv2 proteins in rat Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 19 of 24 CA1 neurons remain voltage sensitive. Interestingly, when Kv2.1 is expressed in CHO cells, a fraction of the GxTX-594 fluorescence is voltage insensitive (Fig. 6 C). This observation is consistent with voltage sensor immobilization of some surface-expressed Kv2.1 protein, although it could be due to intracellular Kv2.1–GxTX-594 proteins that appear to be at the cell surface. We used images of GxTX-594 fluorescence to measure the coupling between endogenous Kv2 proteins and membrane potential at specific, subcellular anatomical locations. Similarly, GxTX-594 imaging should detect changes of voltage sensor status when Kv2 proteins become engaged or disengaged from nonconducting functions, such as formation of plasma membrane–endoplasmic reticulum junctions (Antonucci et al., 2001; Fox et al., 2015; Kirmiz et al., 2018a), regulation of exo- cytosis (Singer-Lahat et al., 2007; Feinshreiber et al., 2010), regulation of insulin secretion (Dai et al., 2012), interaction with kinases, phosphatases, and SUMOylases (Misonou et al., 2004; Park et al., 2006; Dai et al., 2009; Cerda and Trimmer, 2011; McCord and Aizenman, 2013), formation of specialized subcellular calcium signaling domains (Vierra et al., 2019), and interactions with astrocytic end feet (Du et al., 1998). GxTX-594 could potentially reveal conformational changes in organs throughout the body where Kv2 proteins are expressed, which include muscle, thymus, spleen, kidney, adrenal gland, pan- creas, lung, and reproductive organs (Bocksteins, 2016). Limitations of GxTX-594 There are important limitations to the GxTX-594 approach and of the underlying EVAP mechanism generally. We discuss sev- eral limitations that are worth considering in the design of any studies with GxTX-594. GxTX-594 labeling is slower than channel gating The kinetics of reversible GxTX-594 labeling are limited to measuring changes in Kv2 activity on a time scale of tens of seconds. While the temporal resolution of GxTX-594 is com- patible with live imaging and electrophysiology experiments, labeling kinetics do not provide sufficient time resolution to distinguish fast electrical signaling events. The response time of GxTX-594 is far slower than the kinetics of Kv2 conformational change, limiting measurements to the probability, averaged over time, that voltage sensors are resting or active. It is worth noting that the probability of a conformation’s occurrence can be a valuable measure and is the ultimate quantitation of many biophysical studies of ion channels (e.g., open probability, steady-state conductance, and gating charge–voltage relation). GxTX-594 dynamics are altered in confined extracellular spaces The kinetics of GxTX-594 dynamics varied within different re- gions of the same CHO cell (Fig. S4). The location dependence of kΔF was more pronounced during GxTX-594 labeling at −80 mV than unlabeling at +40 mV, and such a difference can be ex- plained by the distinct voltage-dependent affinities of Kv2.1 for GxTX-594. We suspect that the more extreme location depen- dence at −80 mV is due to a high density of Kv2.1 binding sites in the restricted extracellular space between the cell mem- brane and glass coverslip, such that GxTX-594 is depleted from solution by binding Kv2.1 before reaching the center of the cell. After unbinding at +40 mV, Kv2.1 proteins are in ac- tivated, low-affinity conformations, which are unlikely to re- bind GxTX-594 and, thus, do not slow their diffusion across the cell surface. The space extracellular to Kv2.1 channel clusters of hippocampal and cortical interneurons is restricted by as- trocytic end feet, which create an extracellular cleft only a few nanometers wide (Du et al., 1998). Such restricted spaces may slow the kinetics of labeling in the hippocampal slices (Fig. 10). GxTX-594 dynamics are variable between CHO cells The variability of GxTX-594 response rates and amplitudes be- tween CHO cells limited the precision of results. Some of this variability is expected from technical imprecisions: Fits of kΔF where the final value of the relaxation was poorly determined by the data, small changes due to variations of room temperature, photobleaching, and other potential sources. However, we no- ticed that results were more consistent between stimuli of the same cell, and the variability was greatest between cells (Fig. 6, D and E; and Fig. 8, C and D). We suspect that cell-to-cell differ- ences in Kv2.1 conformational equilibria are responsible for much of the variability in GxTX-594 response. The Kv2.1 conductance–voltage relation is regulated by many cellular pathways, including kinases, phosphatases, and SUMOylases (Misonou et al., 2004; Park et al., 2006; Dai et al., 2009; Cerda and Trimmer, 2011; McCord and Aizenman, 2013). Large cell-to- cell variation in Kv2.1 conductance–voltage and gating charge– voltage relations have been reported in CHO cells by our group and others (McCrossan et al., 2009; Tilley et al., 2014; Kang et al., 2019; Tilley et al., 2019). In this study, when we predicted the voltage sensor V1/2 of Kv2.1 from electrophysiology, we observed a 6.4-mV SD with a range of 19 mV, and this variance appeared to be exacerbated by GxTX-594 having a 9.7-mV SD and range of 36 mV (Fig. 1 E). As EVAP dynamics and the GV are both determined by voltage sensor activation, variability in the GxTX- 594 response is expected. The hypothesis that cell-to-cell varia- tion in fluorescence dynamics is due to the inherent variability of Kv2.1 voltage sensor activation could be more definitively tested by identifying whether a correlation exists between the V1/2 of the QV and fluorescence–voltage relationship from individual cells labeled with GxTX-594. While we have not attempted this, the structure of the variance in GxTX-594 fluorescence–voltage relationships is informative. The fluorescence–voltage relation- ships compiled from many cells become more variable near the midpoint of relevant voltage sensor movements. The response amplitude F/Finit (Fig. 6 D) is determined by unlabeled voltage sensor activation and appears most variable near the V1/2 of the unlabeled QV relation (−32 mV; Tilley et al., 2019). The response kinetics kΔF are determined by activation of voltage sensors, which have GxTX-594 bound and appear to become increasingly variable at voltages higher than −20 mV (Fig. 6 E, bottom panel). Despite this variability, the V1/2 and z from the Boltzmann fit of –voltage relationship from many cells were remarkably the kΔF close to the QV of the GxTX–Kv2.1 complex, with a V1/2 and z that differ by 3.3 mV and 0.07 e0, respectively (Tilley et al., 2019). Another possibility is that variability in surface membrane composition undergirds the variability of the GxTX-594 response. Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 20 of 24 GxTX affinities are influenced by the dynamically changing com- plement of lipids in the plasma membrane lipid composition. Lipids bind to the voltage-sensing domain near the GxTX binding site (Milescu et al., 2009; Gupta et al., 2015). Sphingomyelinase D treatment, which alters the membrane composition by converting sphingomyelin to ceramide-1-phosphate, has been shown to en- hance GxTX affinity for Kv2.1 fourfold (Milescu et al., 2009). Voltage activation of Kv2.1 is also affected by sphingomyelinase treatments. While variation in lipid composition is expected to cause variation in GxTX-594 dynamics, we do not know the degree to which the lipid composition varies between the Kv2.1-CHO cells in our studies. Fluorescence intensity Optical noise limits interpretation of EVAP imaging. While the GxTX-594 signal from CA1 neurons was sufficient to identify voltage sensing of endogenous Kv2 protein, fluorescence signal- to-noise issues limit interpretation with the EVAP model. This signal-to-noise ratio is influenced by the density of EVAP binding sites, the fluorescence intensity from each binding site, characteristics of the imaging system, and background fluores- cence from unbound EVAP or other sources. As the concentra- tion of fluorophore is lowered, background fluorescence from unbound EVAP will decrease, and the percentage of labeled binding sites will decrease. However, ΔF after voltage sensor activation remains similar, as does kΔF (Fig. 7). Due to this dy- namic, the minimum concentration of EVAP that is detectable is expected to be determined by the background fluorescence and the density of EVAP binding sites. Here, we see labeling of Kv2.1- CHO cells with 1 nM GxTX-594 (Fig. 2), and previously, we found a robust fluorescence–voltage response in 1 nM GxTX-550 (Tilley et al., 2014). in each case, <10% of resting voltage sensors are bound by the EVAPs. In theory, fluorescence measurements from a single EVAP molecule immobilized by binding to a single- voltage sensor could be informative, as fluorescence from a single binding site is eliminated altogether after unbinding and diffusion away. If the EVAP imaging method does not have single-molecule resolution, the density of EVAP binding sites can limit the signal to noise. CA1 hippocampal neurons express Kv2 proteins at a density typical of central neurons (Misonou et al., 2004; Vacher et al., 2008; Speca et al., 2014), and we expect that GxTX-594 imaging will have similar signal-to-noise characteristics in most brain regions. Improved signal to noise would be expected from such cells that express higher densities of Kv2 proteins, such as neurons of the subiculum, or the inner segment of photo- receptors (Maletic-Savatic et al., 1995; Gayet-Primo et al., 2018). Kv2 proteins are also expressed by many other cell types throughout the body (Bocksteins, 2016), where GxTX-594–labeling techniques may reveal Kv2 activity, if protein densities are sufficient. GxTX-594 inhibits Kv2 proteins GxTX-based probes inhibit the Kv2 proteins they label by sta- bilizing the resting conformation of Kv2 voltage sensors. The Kv2.1–GxTX-594 complex does not open to conduct K+ ions in the physiological voltage range (Fig. 1). Thus, GxTX-594 depletes the population of Kv2 proteins responding normally to physio- logical stimuli, which could alter Kv2 signaling. The concentra- tion of an EVAP can be lowered such that only an inconsequential minority of proteins are bound, with the trade-off being dim- mer fluorescence. With a related probe, we explored the impact of decreasing concentration on fluorescence response of a GxTX-based EVAP and saw substantial fluorescence responses to voltage while inhibiting only ∼10% of Kv2.1 current (Tilley et al., 2014). Here, we demonstrate that lower concentration and physiological stimuli are not always required for scientif- ically meaningful implementation of an EVAP. Even when GxTX-594 inhibits most Kv2 proteins, the behavior of unla- beled Kv2 proteins can be calculated using the EVAP model we have developed. Of course, the electrical feedback within cells will be altered by such protocols. The EVAP model is oversimplified Another limitation of the analysis developed here is that the model of Kv2 voltage sensor conformational change is an over- simplification. The gating dynamics of Kv2 channels are more complex than our model (Islas and Sigworth, 1999; Scholle et al., 2004; Jara-Oseguera et al., 2011; Tilley et al., 2019). Under some conditions, the assumption of voltage sensor independence will limit the model’s predictive power. With a related tarantula toxin, Hanatoxin, the concentration dependence for inhibition of Kv2.1 charge movement was consistent with independent binding to each voltage sensor inhibiting ∼25% of the total gating charge movement (Lee et al., 2003). This result suggests that the simplifying assumption of voltage sensor inde- pendence is reasonable. Additionally, the model of GxTX- 594–reversible labeling developed here assumes that voltage sensors are in continuous equilibrium. These deviations from equilibrium likely explain the deviation of the model from the data in response to high-frequency voltage steps (Fig. 8, C and D). This model could be further tested with a Kv2 mutant, which shifts channel gating without interfering with GxTX-594 binding. Conformation-selective probes reveal conformational changes of endogenous proteins Measurements of dynamic reversible labeling by a conformation- selective probe such as GxTX-594 can enable deduction of how unlabeled proteins behave. This is perhaps counterintuitive be- cause GxTX inhibits voltage sensor movement of the Kv2 protein it binds, and thus, only bound proteins generate optical signals. This approach is analogous to calcium imaging experiments, which have been spectacularly informative about physiological calcium signaling (Yang and Yuste, 2017), despite the fact that no optical signals originate from the physiologically relevant free Ca2+ but only from Ca2+ that is chelated by a dye. In all such experi- ments, fluorescence from Ca2+-bound dyes is deconvolved using the statistical thermodynamics of Ca2+ binding to calculate free Ca2+ (Adams, 2010). Similarly, GxTX-based probes dynamically bind to unlabeled Kv2 proteins, and the binding rate depends on the probability that unlabeled voltage sensors are in a resting conformation (Fig. 7). Thus, the conformations of unlabeled Kv2 proteins influence the dynamics of labeling with GxTX-based Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 21 of 24 probes. Consequently, the dynamics of labeling reveal the conformations of unlabeled Kv2 proteins. Deployment of GxTX-594 to report conformational changes of endogenous proteins demonstrates that conformation-selective ligands can be used to image occurrence of the conformations they bind to. The same principles of action apply to any conformation-selective labeling reagent, suggesting that probes for conformational changes of many different proteins could be developed. Probes could conceivably be developed from the many other voltage sensor toxins or other gating modifiers that act by a similar mechanism as GxTX yet target the voltage sensors of different ion channel proteins (McDonough et al., 1997; Sack et al., 2004; Catterall et al., 2007; Swartz, 2007; Schmalhofer et al., 2008; Peretz et al., 2010; McCormack et al., 2013; Ahuja et al., 2015; Dockendorff et al., 2018; Zhang et al., 2018). Conformation-selective binders have been engineered for a variety of other proteins, and methods to quantify con- formational changes from their fluorescence are needed. For example, fluorescently labeled conformation-selective binders have revealed that endocytosed GPCRs continue to remain in a physiologically activated conformation (Irannejad et al., 2013; Tsvetanova et al., 2015; Eichel and von Zastrow, 2018). A means to determine the conformational equilibria of GPCRs from fluorescence images has not yet been developed. We suggest that the statistical thermodynamic framework developed here could provide a starting point for more quantitative interpre- tation of other conformation-selective molecular probes. Acknowledgments Christopher J. Lingle served as editor. We thank Jim Trimmer (University of California, Davis) for numerous discussions and constructive critical reading of an early version of the manuscript. We thank Georgeann Sack (Afferent LLC) for critical reading, editing, and feedback. This research was supported by National Institutes of Health grants R01NS096317 (to J.T. Sack and B.E. Cohen), U01NS090581 (to J.T. Sack), R21EY026449 (to J.T. Sack), R01NS062736 (to K. Zito), U01NS103571 (to K. Zito), T32GM007377 (to R.J. Sepela), University of California, Davis New Research Initiative award (to J.T. Sack) and American Heart Association grant 17POST33670698 (to P. Thapa). GxTX variants were synthesized at the Molecular Foundry, supported by the Director, Office of Science, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering, U.S. Department of Energy under contract DE-AC02-05CH11231. The authors declare no competing financial interests. Author contributions: P. Thapa: Conceptualization, formal analysis, investigation, methodology, visualization, writing- original draft, writing-reviewing and editing. R. Stewart: Conceptualization, formal analysis, investigation, methodology, visualization, writing-original draft, writing-reviewing and editing. R.J. Sepela: Conceptualization, formal analysis, inves- tigation, methodology, visualization, writing-original draft, writing-reviewing and editing. O. Vivas: Investigation, meth- odology, writing-reviewing and editing. L.K. Parajuli: Investi- gation, methodology, writing-reviewing and editing. M. 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Peak 1 is Ser13Cys GxTX (retention time, 12.8 min; 33% ACN); peak 2 is a minor product from conjugation; and peak 3 is GxTX-594, the major product from conjugation (retention time, 16.4 min; 35% ACN). The fractions corresponding to peak 3 were combined. (D) HPLC chromatogram of the combined peak 3 fractions from C, a GxTX-594 preparation used in this study. 2 μl of 13.1 μM GxTX-594 diluted in 200 μl of 0.1% TFA was injected. MALDI-TOF mass spectrometry profile of the combined peak 3 fractions (inset). a.m.u., atomic mass unit; Rel. Int., relative intensity. Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 S1 Figure S2. GxTX-594 selectively labels Kv2 proteins on cell surfaces. GxTX-594 labeling was assessed in CHO cells expressing Kv2.1-GFP, Kv2.2-GFP, Kv4.2-GFP, Kv1.5-GFP, and BK-GFP. Each of these channel subtypes were assessed for voltage-dependent outward currents to identify cell surface expression of the K+ channels. CHO cells were not cotransfected with the β subunits for Kv4.2, KChIP2, or Kv1.5, Kvβ2, to assess whether these β subunits interfere with GxTX-594 binding. (A) Exemplar whole-cell voltage clamp recordings of CHO cells expressing Kv2.1-GFP, Kv2.2-GFP, Kv4.2-GFP, Kv1.5-GFP, or BK-GFP. Re- cordings shown are representative responses to 100-ms steps from −100 mV to −40, 0, and +40 mV. (B) Confocal imaging of fluorescence from live CHO cells transfected with Kv2.1-GFP, Kv2.2-GFP, Kv4.2-GFP, Kv1.5-GFP, or BK-GFP (indicated by row) and labeled with GxTX-594. Confocal imaging plane was >1 μm above the glass-adhered surface. Cells were incubated with 100 nM GxTX-594 and 5 μg/ml WGA-405 and rinsed before imaging. Fluorescence shown cor- responds to emission of GFP (column 1); Alexa Fluor 594 (column 2); WGA-405 (column 3); or an overlay of GFP, Alexa Fluor 594, and WGA-405 (column 4). Scale bars, 20 μm. (C) Ratio of fluorescence intensity resulting from excitation of GxTX-594 at 594 nm and GFP at 488 nm. Analysis methods as in Fig. 4 B. Kv2.1, n = 11; Kv2.2, n = 9; Kv4.2, n = 13; Kv1.5, n = 13; and BK, n = 12; n indicates the number of individual cells analyzed in a single dish during a single application of GxTX-594 with the indicated K+ channel-GFP type. Bars represent the mean. Each circle corresponds to a cell. Significant differences were observed between GxTX:GFP ratio for Kv2.1 or Kv2.2 and Kv1.5, Kv4.2, or BK by Mann–Whitney U test (P < 0.0001). The P value to determine significance is adjusted for multiple comparisons using the Bonferroni method, where P < 0.0033 is considered significant, with the caveat that data points under each condition are technical replicates. (D) Pearson correlation coefficients between GxTX-594 and GFP. Same cells as C. Analysis methods as in Fig. 4 C. Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 S2 Figure S3. GxTX-594 labeling of surface membranes requires Kv2 protein. GxTX-594 partitioning into the membrane was assessed with fluorescence from nontransfected CHO cells and cells transfected with Kv2-GFP proteins. (A) Fluorescence from live CHO cells transfected with Kv2.1-GFP (top row) or Kv2.2-GFP (bottom row) and labeled with GxTX-594. Airy disk imaging was at a plane above the glass-adhered surface. Cells were incubated with 100 nM GxTX-594 and 5 μg/ml WGA-405 then diluted to 9 nM GxTX-594 and 0.45 μg/ml WGA-405 before imaging. Scale bars, 20 μm. Fluorescence shown was excited at 488 nm (column 1), 594 nm (column 2), or 405 nm (column 3). Scale bars, 20 μm. (B) Fluorescence intensity from Kv2.1-GFP transfected cells with excitation of GxTX-594 at 594 nm versus GFP at 488 nm. Fluorescence from WGA-405 was used as a mask to manually draw ROIs on cells. Each point represents one cell. Cells with obvious GFP fluorescence are green points, cells without are black points. Mean background fluorescence from a region that did not contain cells is indicated by dashed lines. Red line represents a linear fit of cells with obvious GFP fluorescence. Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 S3 Figure S4. Extracellular access can impact GxTX-594 labeling kinetics. (A) Time-lapse Airy disk images of the glass-adhered surface of a voltage-clamped Kv2.1-CHO cell in 9 nM GxTX-594. Time index is in the upper left corner of each panel, and membrane potential is indicated in the upper right corner. Color progression for pseudocoloring of fluorescence intensity is shown in vertical bar on right. Scale bar, 10 μm. (B) Airy disk image of the glass-adhered surface of a voltage-clamped Kv2.1-CHO cell in 9 nM GxTX-594. Gray lines indicate boundaries of ROIs. ROIs 1, 2, and 3 are concentric circles, each with a respective diameter of 1.8, 4.9, and 9.1 μm. ROI 4 was hand-drawn to contain the apparent cell surface. In all cells analyzed, ROIs 1–3 were concentric circles of the same sizes, while ROI 4 varied based on cell shape. Scale bar, 10 μm. (C) Representative traces of GxTX-594 intensity response to voltage changes. Red lines are monoexponential fits (Eq. 1): 40-mV step ROI 1, kΔF = 4.29 × 10−2 ± 0.26 × 10−2 s−1; ROI 2, kΔF = 4.39 × 10−2 ± 0.16 × 10−2 s−1; ROI 3, kΔF = 5.65 × 10−2 ± 0.15 × 10−2 s−1; and ROI 4, kΔF = 9.69 × 10−2 ± 0.33 × 10−2 s−1. −80-mV step ROI 1, kΔF = 4.27 × 10−4 ± 0.11 × 10−4 s−1; ROI 2, kΔF = 7.999 × 10−4 ± 0.053 × 10−4 s−1; ROI 3, kΔF = 2.7556 × 10−3 ± 0.0074 × 10−3 s−1; and ROI 4, kΔF = 5.46 × 10−3 ± 0.13 × 10−3 s−1. Background for subtraction was the average intensity of a region that did not contain cells over the time course of the voltage protocol. Each trace was normalized to initial fluorescence intensity before the application of the voltage stimulus. (D) kΔF at +40 mV from individual cells. (E) kΔF at −80 mV from individual cells. Circle coloring in D and E indicates data from the same cell. Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 S4 Figure S5. Variation in temperature does not account for cell-to-cell variability of GxTX-594 kinetics. Temperature dependence of GxTX-594 labeling was assessed by holding the cell bath solution at either 27°C or 37°C and stepping the membrane voltage from −80 mV to 0 mV for a measurement of kΔF at both temperatures. (A) Representative traces of GxTX-594 fluorescence intensity response to voltage changes at 27°C (black) and 37°C (gray). Smooth lines are fits of a monoexponential function (Eq. 1): 27°C, kΔF = 2.47 × 10−2 ± 0.39 × 10−2 s−1; 37°C, kΔF = 7.54 × 10−2 ± 0.54 × 10−2 s−1. Background subtraction was performed as in Fig. 6 B. (B) kΔF at 27°C and 37°C. The rate of fluorescence change was significantly faster at higher temperatures (Mann–Whitney P = 0.0005). From geometric means (bars), a Q10 of 3.8 was calculated between 27°C and 37°C. Each circle represents one cell, n = 7 both groups. ***, P < 0.0001. Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 S5 Figure S6. GxTX-594 labels CA1 hippocampal pyramidal neurons transfected with Kv2.1-GFP. Two-photon excitation images of rat CA1 hippocampal pyramidal neurons in brain slices as in Fig. 9 A. Kv2.1-GFP (left), GxTX-594 (middle), and overlay (right). Scale bars, 10 μm in all panels. (A) Pyramidal neurons 2 d after transfection with Kv2.1-GFP. (B) Pyramidal neurons 4 d after transfection with Kv2.1-GFP. (C) Pyramidal neurons 6 d after transfection with Kv2.1- GFP. Video 1. Time-lapse image sequence of GxTX-594 fluorescence on a voltage-clamped CA1 hippocampal pyramidal neuron while it is depolarized from −70 to 0 mV. Frame rate, 0.1 fps. Playback speed, 7 fps. Data S1 is provided online as a separate Excel file and shows a spreadsheet containing model calculations that can be used to generate model prediction. Thapa et al. Imaging conformational change of endogenous Kv2 Journal of General Physiology https://doi.org/10.1085/jgp.202012858 S6	null
10.1007_s13280-020-01405-w.pdf	null	null	Ambio 2021, 50:586–600 https://doi.org/10.1007/s13280-020-01405-w R E S E A R C H A R T I C L E Ecosystem service lens reveals diverse community values of small-scale ﬁsheries Kara E. Pellowe , Heather M. Leslie Received: 3 March 2020 / Revised: 9 August 2020 / Accepted: 29 September 2020 / Published online: 3 November 2020 to of ocean beneﬁts Abstract The coastal provides communities around the world, however, the depth and interactions with marine complexity people’s represented in many marine ecosystems are not well management initiatives. Many ﬁsheries are managed to maximize provisioning value, which is readily quantiﬁed, while ignoring cultural values. An ecosystem services includes both provisioning and cultural approach that services will enable managers to better account for the diverse values marine ﬁsheries provide to coastal communities. In this study, we assess community values related to a top ﬁshed species, the Mexican chocolate clam, Megapitaria squalida, in Loreto, Baja California Sur, Mexico. We conducted an exploratory analysis based on 42 household surveys, and found that community members perceive multiple provisioning and cultural beneﬁts from the clam, including community economic, historical, and identity values. Despite reporting infrequent harvest and consumption of clams, participants perceive the species as an important part of community identity, highlighting the role of Mexican chocolate clams as a cultural keystone species in the Loreto region. Fisheries management that recognizes the full range of ecosystem services a species contributes to coastal communities will be better equipped to sustain these diverse values into the future. Keywords Community value (cid:2) Cultural ecosystem services (cid:2) Cultural keystone species (cid:2) Ecosystem services (cid:2) Gulf of california (cid:2) Small-scale ﬁsheries Electronic supplementary material The online version of this article (https://doi.org/10.1007/s13280-020-01405-w) contains sup- plementary material, which is available to authorized users. INTRODUCTION The ocean provides many beneﬁts to coastal communities, income, recreational opportunities, and including food, aesthetic values (Halpern et al. 2012; Loomis and Paterson 2014), yet interactions the depth and complexity of between people and marine ecosystems are not well understood (Villasante et al. 2013). Management of ﬁsh- eries and decisions related to governance of marine ecosystems reﬂect society’s values, priorities, and desires for ecosystems to produce certain beneﬁts. These decisions are complicated by multiple and sometimes contradictory goals, with priority often given to values that can be readily quantiﬁed in economic terms (Loomis and Paterson 2014). A holistic understanding of the values produced by marine ecosystems is necessary, if management is to accurately reﬂect diverse values and balance trade-offs between alternate priorities. Full consideration of the values associated with ecosystem services will better enable resource managers to address the needs and perspectives of different stakeholders (Chan et al. 2012b). Ecosystem services are the beneﬁts that an ecosystem provides to people (Millennium Ecosystem Assessment 2005). The Millennium Ecosystem Assessment (2005) outlined four categories of ecosystem services: supporting—those services that make it possible for ecosystems to continue providing the other three types of services (e.g., primary production); provisioning— products obtained from ecosystems (e.g., food); regulat- ing—beneﬁts produced through ecological processes (e.g., water puriﬁcation); and cultural—nonmaterial beneﬁts of ecosystems (e.g., recreation and sense of place). The ecosystem services approach is a useful tool for under- standing the connections between humans and ecosystems that goes beyond quantiﬁable outcomes such as income and 123 (cid:2) The Author(s) 2020 www.kva.se/en Ambio 2021, 50:586–600 587 food provision to include cultural and social values (Chan et al. 2012b). Earlier work on ecosystem services involved the integration of biophysical and economic perspectives to assess the value of biophysical processes in economic terms (Daily et al. 2000; Turner and Daily 2008). Eco- nomic approaches have been useful in advancing under- standing of human–nature relationships and facilitating integration of ecosystem-related values into decision- making (Turner and Daily 2008). However, economic approaches fail to encompass dimensions of value that cannot be quantiﬁed in economic terms, including many cultural and non-use values (Chan et al. 2011, 2012b). Resource management that is focused on a limited set of ecosystem services may lead to unexpected regime shifts and sudden losses of other ecosystem services (Gordon et al. 2008; Bennett et al. 2009). A growing body of lit- erature highlights the importance of considering and assessing cultural ecosystem services, in addition to pro- visioning services (Martı´n-Lo´pez et al. 2012, 2013; Her- na´ndez-Morcillo et al. 2013; Oteros-Rozas et al. 2014; Dickinson and Hobbs 2017). Cataloguing the complete suite of values marine ecosystems produce is a crucial step in managing in a way that both protects crucial beneﬁts and better attends to trade-offs among the diverse values and priorities of coastal communities (Loomis and Paterson 2014). Provisioning services, such as clean water, food, and income, are essential for providing the basic necessities of life, maintaining security, and protecting human health (Millennium Ecosystem Assessment 2005). As a country follows a development trajectory, human dependence on provisioning services tends to decrease, while dependence on cultural ecosystem services increases (Guo et al. 2010). Unlike provisioning services, which may be replaced by technical innovation or trade as they are degraded, cultural services are not as readily replaced (Millennium Ecosystem Assessment 2005). Cultural ecosystem services are more likely to be co-produced through the interactions between people and their environment, resulting in a tight coupling between the cultural beneﬁts of ecosystems and people’s held values and preferences (Russell et al. 2013; Dickinson and Hobbs 2017). Cultural services are also reﬂective of people’s environmental decision-making (Martı´n-Lo´pez et al. 2013), and can improve human health and well-being through personal and community connections to natural systems (Russell et al. 2013). Thus, it is critically important to account for and assess both provisioning and cultural values if marine management is to preserve both the basic necessities of life provided by ﬁsheries, as well as socio- cultural value that connects people and the sea. In ﬁsheries, resource exploitation by humans can sig- niﬁcantly affect system structure and functioning, and the long-term sustainability of human–resource impact interactions (Basurto et al. 2013; Partelow and Boda 2015). Fisheries provide many valuable beneﬁts to coastal com- munities, yet their sustainability is threatened by overex- ploitation, pollution, and environmental variability, among other stressors (Be´ne´ 2006; Halpern et al. 2012). Tradi- tional ﬁsheries management focuses primarily on ﬁsheries yield as a product of ecological processes and driver of economic beneﬁts, and has come a long way in acknowl- edging and understanding the heterogeneity of ecological systems. However, a parallel understanding of variety within social systems is often missing (St. Martin et al. 2007). Given the deep and complex ways in which people interact with marine ecosystems (Villasante et al. 2013), particularly through ﬁsheries, the focus of traditional ﬁsh- eries management is too narrow and overlooks the many other ways in which people interact with and derive ben- eﬁts from marine species and ecosystems. Meeting the challenge of ﬁsheries management requires moving beyond assessments solely of environmental variables and species interactions to develop a better understanding of socio- cultural values and local knowledge of coastal communi- ties and ﬁshers (St. Martin et al. 2007; Johnson 2018; Smith and Basurto 2019). For small-scale ﬁsheries, our very deﬁnitions, typically centered on technology and harvest, ignore the sociocul- tural characteristics of these ﬁsheries that set them apart from other types of ﬁshing (Smith and Basurto 2019). An ecosystem services approach can illuminate important connections between people and nature and help untangle complex interactions shaping small-scale ﬁshery systems. On the Gulf of California coast of Baja California Sur, Mexico, the Town of Loreto relies on ﬁshing and tourism to support the local economy. These activities are primarily focused on the marine park that the town hosts, Loreto Bay National Park. The national park is home to many species, including the Mexican chocolate clam, Megapitaria squalida. The clam is one of the top species harvested by biomass in Loreto (Pellowe and Leslie 2017), and is a local culinary specialty with a rich history of use. As is the case for many ﬁshed species, ﬁsheries management of Mexican chocolate clams in Loreto Bay National Park focuses on the maximization of ﬁsheries and economic yield. How- ever, based on the importance of Mexican chocolate clams to local (Pellowe and Leslie 2019), we hypothesize that the relationship between people and Mexican chocolate clams in the Loreto region is more multi-dimensional than is currently captured by ﬁsheries management. livelihoods This exploratory study presents a novel approach for assessing community values of a single ﬁshed species. Using household surveys, this study elicits data on the suite of ecosystem services provided by Mexican chocolate clams to households in this region, using a set of values (cid:2) The Author(s) 2020 www.kva.se/en 123 588 Ambio 2021, 50:586–600 adapted from previous studies of ecosystem services (Rolston and Coufal 1991; Reed and Brown 2003; Mil- lennium Ecosystem Assessment 2005; Raymond and Brown 2006). In addition to assessing the range of provi- sioning and cultural values that Mexican chocolate clams provide to households in Loreto, we also assess community perceptions of change related to the clams, since percep- tions of change shape people’s environmental decision- making and can help to illuminate priorities for manage- ment (Gobster et al. 2007). Finally, we explore how ﬁshery management might better account for trade-offs among varied community values and priorities. MATERIALS AND METHODS Location of study The Town of Loreto, Baja California Sur, Mexico, lies along the sea between the Sierra de la Giganta Mountains and the Gulf of California (Fig. 1). Loreto is home to roughly 19 000 people, and the town’s economy depends on ﬁsheries and tourism centered around the marine park it hosts (Instituto Nacional de Estadı´stica y Geografı´a, INEGI 2017). Loreto Bay National Park (LBNP) is one of the largest marine protected areas in Mexico with an area of 2065 km2. The park contains varied marine and estuarine habitat types, including rocky reefs, seagrass beds, man- groves, and sandy habitats, and hosts a variety of permitted including SCUBA diving, snorkeling, whale activities, watching, wildlife viewing, kayaking, and commercial and sport ﬁshing of select species (Comisio´n Nacional de A´ reas Naturales Protegidas 2019). The waters of LBNP are home to 800 marine species, including the Mexican chocolate clam, M. squalida (Fig. 2). Mexican chocolate clams are soft-sediment burrowers that inhabit sandy-bottom habitat from the intertidal to depths of 160 m (Keen 1971). In Loreto Bay, Mexican chocolate clams are an important source of food and income for local ﬁshing communities; they are among the top 5 species harvested by total bio- mass, and among the top 10 by total value (Pellowe and Leslie 2017). Mexican chocolate clams are in demand year-round, sometimes despite seasonal harvest bans. The clams are a long-standing culinary tradition in the region, headline the menu of local restaurants, and are the focus of an annual gastronomic festival held on Loreto’s waterfront. The clam also serves as a symbol of community pride and connection to the sea; murals around Loreto Bay depict smiling clams reminding locals to ﬁsh responsibly. For many families in the region, Mexican chocolate clams provide supplemen- tary food and income in times of limited resources, and serve as a safeguard against scarcity. Surveys From February to May 2019, we carried out 48 surveys with residents of Loreto, Baja California Sur, Mexico to explore community perspectives on a range of ecosystem services. Prior to survey administration, questions were carefully reviewed, translated, and pretested with local volunteers to ensure the validity and clarity of questions in both English and Spanish (Groves et al. 2011). Surveys with less than 25% of questions completed (12 or fewer questions answered out of 48 total questions) were removed from the sample. Forty-two surveys were inclu- ded in subsequent analyses. The participant population included adult community members (at least 18 years of age) of any occupation, residing in Loreto, Baja California Sur, Mexico at least 6 months of the year. Since Loreto has a large community of non-Mexican expat residents and Mexican nationals who are not originally from Loreto, the participant population included Mexican nationals origi- nally from Loreto (Loretanos), other Mexican nationals who reside in Loreto, and nationals of other countries who reside in Loreto. Survey participants were recruited via purposive sampling of contacts established during previous ﬁeldwork in the region. Sampling excluded residents with known economic dependence on the ﬁshery (e.g., ﬁshers), but included residents of Loreto thought to value Mexican chocolate clams based on their interest in our previous research. Participants were asked to answer survey ques- tions from the perspective of their entire household, even if they themselves were not heads of household. Due to variable literacy rates in the region, participants had the option of taking the survey themselves or having survey questions read aloud to them and their responses recorded by the researcher. Of 48 total surveys adminis- tered, 38 participants elected to take the survey themselves, and 10 elected to have the survey administered to them. Participants who took the survey themselves were less likely to complete it (32 of 38 surveys completed), as compared to participants who elected to have the survey administered to them by the researcher (10 out of 10 sur- veys completed). Informed consent was obtained from all participants prior to survey administration. Surveys were conducted in both Spanish and English. The survey instrument was written in both languages, allowing par- ticipants to read and respond in their preferred language. For surveys administered by the researcher, participants had the option to choose their preferred language for questions and responses. Each survey took approximately 10–20 min to complete. All procedures performed in this study were in accordance with the Ethical Standards of the Institutional Review Board (University of Maine IRB Permit # 2018-07-01). 123 (cid:2) The Author(s) 2020 www.kva.se/en Ambio 2021, 50:586–600 589 Fig. 1 Map of Loreto Bay National Park, Baja California Sur, Mexico Surveys were anonymous and collected information on the socioeconomic characteristics of households, how fre- quently members of their household harvest, buy, sell, and consume Mexican chocolate clams, and changes they have observed in the availability, market demand, quantity, time (survey quality, price, and size of clams over (cid:2) The Author(s) 2020 www.kva.se/en 123 590 Ambio 2021, 50:586–600 Fig. 2 Mexican chocolate clams. Photo by K. E. Pellowe instrument available as Supplementary Material). Partici- pants were then asked, using a three-item Likert scale (Likert 1932), to indicate whether they agreed, disagreed, or neither agreed nor disagreed with a set of statements (Table 1), each relating to an ecosystem service they and their household may receive from Mexican chocolate clams. Participants could also elect not to answer any questions of their choosing. Surveys were designed to elicit both use and non-use values. Selection of the services assessed in this study resulted from the compilation and adaptation of lists of multiple provisioning and cultural ecosystem services identiﬁed in diverse ecosystems (Rol- ston and Coufal 1991; Reed and Brown 2003; Millennium Ecosystem Assessment 2005; Raymond and Brown 2006). The ﬁnal list of services consisted of values appropriate for assessment for individual species, and included general (household level), general (community level), life-sustain- ing (household level), life-sustaining (ecological), eco- nomic (household level), economic (community level), tourism, subsistence, scientiﬁc/learning, recreation, aes- thetic, future use, historic, cultural, identity, community identity, existence, and intrinsic values (see individual 123 (cid:2) The Author(s) 2020 www.kva.se/en Ambio 2021, 50:586–600 591 Table 1 Value statements used to identify participants’ identiﬁcation of ecosystem service values. Participants’ indication that they agreed with each statement (as opposed to having disagreed or said that they neither agreed nor disagreed) indicated their belief that Mexican chocolate clams provide the associated ecosystem service value. Intrinsic value was reverse-coded, where disagreement with the associated statement was taken as indication that the participant believed that Mexican chocolate clams have intrinsic value Ecosystem service value assessed Value statement General Chocolate clams are important to me and my family Chocolate clams are important to my community Life sustaining Chocolate clams help sustain me and my family Economic Tourism Subsistence Scientiﬁc/learning Recreation Aesthetic Future use Historic Cultural Individual identity Community identity Existence Intrinsic Chocolate clams help sustain other animals in Loreto Bay Chocolate clams provide income to my household Chocolate clams are important to the local economy Tourists spend money on chocolate clams when they visit Loreto Chocolate clams are a tourist attraction of Loreto Chocolate clams provide some of my family’s basic needs Chocolate clams are important for scientists to study Chocolate clams should be protected so that people can learn about them Chocolate clams are important for recreation, including exercise and fun It is fun or relaxing to look for or harvest chocolate clams Chocolate clams are beautiful Chocolate clams contribute to the unique beauty of Loreto Chocolate clams should be conserved for future generations Chocolate clams should be conserved because I or my family might want to harvest them in the future Chocolate clams are important because of their history in this area Chocolate clams are important to the culture of this area Chocolate clams are an important part of who I am as an individual Chocolate clams are an important part of what it means to be a Loretano or to live in this area Even when I don’t use chocolate clams, I like to know they are there Chocolate clams have value primarily because they provide beneﬁts to people (reverse-coded) Table 1 for full ecosystem service values). list of statements used to determine We deﬁned general value at the household level to be the overall importance of the clam to the participant’s household, while general value at the community level was the overall importance of the clam to the community. Life- sustaining value at the household level was considered to be the clams’ provision of life-sustaining beneﬁts to the participant’s household, including food, income, or secu- rity, and life-sustaining value at the ecological scale was the clams’ role in sustaining other species or contributing to the broader coastal ecosystem. We deﬁned economic value as the provision of income to the participant’s household, or to the broader community. Tourism value was deﬁned as income generated from tourist activities (e.g., patronizing local restaurants to consume clams), or increased tourism as a result of the presence of Mexican chocolate clams in the region. Subsistence value was considered to be the provision of the participant’s basic needs, including food and/or income. Scientiﬁc/learning value was considered the potential for learning generated by the existence of the species, and the possibility for the advancement of science through studies of the species. Recreational value was deﬁned as the potential for fun, relaxation, or enjoyment from harvesting or searching for clams. Aesthetic value was considered to be the beauty of the clam itself or its contribution to the overall beauty of the region. Future use value was deﬁned as the ability of the participant or their household to harvest clams in the future, or the knowledge that future generations within the broader community would be able to harvest clams. His- toric value was considered to be the importance of the clam to regional history, and cultural value as the contribution of the clam to regional culture and practice. We considered individual identity value to be the importance of the clam in constructing individual worldview and sense of self. Community identity value was considered to be the con- tribution of the clam to a shared sense of what it means to be a member of the Loreto community. Existence value was considered to be the satisfaction of knowing that the clam exists in Loreto Bay National Park, and intrinsic value was the belief that Mexican chocolate clams have inherent value, outside of human interaction. (cid:2) The Author(s) 2020 www.kva.se/en 123 592 Ambio 2021, 50:586–600 The ecosystem services of tourism, scientiﬁc/learning, recreation, and aesthetic values were assessed each with two survey questions, and an average was taken from the two responses to determine whether participants identiﬁed these values from Mexican chocolate clams. Additionally, we assessed the following values both at the individual and the community level through two separate questions: gen- eral, economic, future use, and identity. For open-ended survey questions, including questions on the nature of changes observed, and participants’ perspectives on why changes had occurred, responses were coded into cate- gories. These categories emerged from analysis of partic- ipant responses by the researcher who conducted the surveys. Responses that were cited by two or more par- ticipants were considered response categories. We analyzed separately the responses of three partici- pant groups: Mexican nationals originally from Loreto; Mexican nationals not originally from Loreto; and foreign nationals. Maps and ﬁgures were created using R statistical software (R Core Team 2020) and the R packages ggplot2, ggspatial, rnaturalearth, and wesanderson (Wickham 2016; South 2017; Karthik and Wickham 2018; Dunnington 2020). RESULTS English (48%). Overall, 19% of survey participants were Mexican nationals originally from Loreto or Loretanos. Participants varied in the average length of time they had lived in Loreto, their mean monthly household income, mean household size, and reported frequency of use of Mexican chocolate clams (Table 2). None of the participants of any group reported clam- ming as a source of household income, despite Loretano participants reporting selling chocolate clams 7.4 times per year on average. 67% of Loretano participants responded that they had harvested Mexican chocolate clams at some point in the past. 50% of other Mexican participants and 25% of foreign participants reported harvesting Mexican chocolate clams at some point in the past. The participants originally from Loreto who indicated that they regularly harvest or used to regularly harvest Mexican chocolate clams had 13.3 years of harvest experience, on average, with a range of 5 to 20 years of experience. Loretano participants also had, on average, 34.6 years of experience buying Mexican chocolate clams, with a range of experi- ence from 1 to 82 years. Mexican participants not origi- nally from Loreto reported an average of 4.1 years of harvest experience and 15.7 years of buying experience, while foreign participants reported 8.7 years of harvest experience and 8.4 years of buying experience, on average. Perceptions of change Participant demographics and use behavior Of 42 survey participants whose responses were included in the ﬁnal analyses, 52% were Mexican nationals (of which, 40% were originally from Loreto) and 48% were nationals of other countries, including the United States, Canada, Germany, Australia, Chile, Switzerland, and the United Kingdom. These numbers also correspond to the number of surveys conducted in Spanish (52%) and 83% of Loretanos surveyed, 93% of Mexican participants not originally from Loreto, and 50% of foreign participants said they had noticed at least one change over time in terms of market demand, quantity, quality, size, price, and/or availability of the species. Observations of change varied by participant group and type of change (Fig. 3). Differing levels of observations of change may have been due, in part, to varying lengths of time spent in the region among Table 2 Demographic characteristics and reported use behavior by participant group Demographic characteristics and use behavior Participant group Mexican nationals from Loreto Mexican nationals from elsewhere Foreign nationals n Mean time in Loreto (years) Mean monthly household income (US Dollars) Mean household size (number of people) Reported harvest of clams (times per year) Reported purchase of clams (times per year) Reported sales of chocolate clams (times per year) Reported consumption of chocolate clams (times per 8 42 654 3.9 4.0 10.9 7.4 7.6 14 17 917 2.4 14.5 19.7 0.0 20.8 20 8 3924 2.0 0.4 17.7 0.0 19.1 year) 123 (cid:2) The Author(s) 2020 www.kva.se/en Ambio 2021, 50:586–600 593 Fig. 3 Survey participants have observed changes in Mexican chocolate clams, including in market demand, availability, price, quantity and/or quality, and size of individual clams. Percentages of survey participants who have observed these changes vary by type of change and participant group. Participant groups include Mexican nationals originally from Loreto (n = 8), Mexican nationals not originally from Loreto (n = 14), and foreign nationals (n = 20). The most highly cited changes were in market demand and availability of clams, followed by price, quantity and/or quality, and size of clams Fig. 4 Survey participants who provided qualitative descriptions of changes observed in Mexican chocolate clams largely agreed on the directionality of change. Participants who provided information on the nature of changes observed included Mexican nationals originally from Loreto (n = 8), Mexican nationals not originally from Loreto (n = 14), and foreign nationals (n = 20) the three participant groups. Participants largely agreed on the directionality of changes (Fig. 4), and reported that demand for and price of clams had increased over time, while the availability, quantity and/or quality, and average size had decreased. Despite the fact that most participants had noticed qualitative changes related to the market demand, quantity, quality, size, price, and/or availability of Mexican choco- late clams, none of the participants in any group said that the changes they had observed had directly affected their household. When asked whether they had any thoughts on why these changes had occurred, participant responses fell into four main categories, in the order of most to least (cid:2) The Author(s) 2020 www.kva.se/en 123 594 Ambio 2021, 50:586–600 Table 3 Participant perspectives on why changes that have occurred fell into four primary categories, in order of most to least cited: ﬁsheries management, overﬁshing, increased demand, and environmental change Do you have any thoughts on why these changes have occurred? Response category Times cited Illustrative quote(s) Fisheries management Overﬁshing Increased demand Environmental change 9 9 4 3 ‘‘It’s because of poor management of the clam’’, ‘‘It’s because of the cooperatives that use a compressor to harvest’’ ‘‘The uncontrolled exploitation’’ ‘‘It’s a tourist town, and this is the dish that represents our town’’; ‘‘There is more consumption now’’; ‘‘Supply and demand- there are more people in Loreto now’’ ‘‘The temperature—sometimes it’s too warm’’ cited: ﬁsheries management, overﬁshing, demand, and environmental change (Table 3). increased Ecosystem service values All but one ecosystem service value assessed was reported to be provided by chocolate clams to survey participants: this was personal economic value (assessed with the statement, ‘‘Chocolate clams provide income to my household’’). This is consistent with the lack of reported income from clamming among those surveyed. Relatedly, none of the participants originally from Loreto, 7% of participants from elsewhere in Mexico, and none of the foreign participants reported that their household receives life-sustaining value from Mexican chocolate clams (assessed with the statement, ‘‘Chocolate clams help sus- tain me and my family’’), and 33% of Loretano partici- pants, 14% of Mexican participants not originally from Loreto, and none of the foreign participants reported receiving subsistence value (assessed with the statement, ‘‘Chocolate clams provide some of my family’s basic needs’’). However, several participants noted that while their household does not receive life-sustaining or subsis- tence value from Mexican chocolate clams, many other households in the community do. In fact, all participants originally from Loreto, all Mexican participants not origi- nally from Loreto, and 90% of foreign participants agreed that Mexican chocolate clams are important to the com- munity of Loreto (assessed with the statement, ‘‘Chocolate clams are important to my community’’). Participants also agreed that chocolate clams help to shape the community identity of Loreto; 100% of Loretano participants, 79% of Mexican participants not originally from Loreto, and 60% of foreign participants agreed with the statement, ‘‘Cho- colate clams are an important part of what it means to be a Loretano or to live in this area.’’ Perhaps unsurprisingly, more participants originally from Loreto than participants from elsewhere felt that the clam also played a role in shaping their individual identity; 50% of Loretano partic- ipants agreed with the statement, ‘‘Chocolate clams are an important part of who I am as an individual,’’ as compared to 7% of Mexican participants not originally from Loreto, and none of foreign participants. While participants surveyed reported that their house- receive economic value from Mexican holds do not (0% agreement with the statement, chocolate clams ‘‘Chocolate clams provide income to my household’’ across all three participant groups), nearly all agreed that the clams provide economic value to the community (100% of Loretano participants, 86% of Mexican participants not originally from Loreto, and 100% of foreign participants agreed with the statement, ‘‘Chocolate clams are important to the local economy’’). Additionally, nearly all partici- pants agreed that the clam contributes to local tourism (100% Loretano, 93% other Mexican participants, and 83% foreign participant agreement with the two statements, ‘‘Tourists spend money on chocolate clams when they visit Loreto’’ and ‘‘Chocolate clams are a tourist attraction of Loreto’’). Additional ecosystem services with high levels of agreement among survey participants include cultural value (100%, 100%, and 95% agreement among the three groups, respectively, with the statement, ‘‘Chocolate clams are important to the culture of this area’’), historic value in the region (100%, 93%, and 75% agreement among the three groups, respectively, with the statement, ‘‘Chocolate clams are important because of their history in this area’’), existence value (100%, 100%, and 80% agreement among the three groups, respectively, agreement with the state- ment ‘‘Even when I don’t use chocolate clams, I like to know they are there’’), and future community use value (100%, 86%, and 85% agreement among the three groups, respectively, agreement with the statement, ‘‘Chocolate clams should be conserved for future generations’’). A full report of values assessed and responses for each participant group can be found in Fig. 5. 123 (cid:2) The Author(s) 2020 www.kva.se/en Ambio 2021, 50:586–600 595 Fig. 5 Ecosystem services with the highest levels of agreement among participants across participant groups include general community value, economic community value, cultural value, and tourism value. Ecosystem services with the lowest levels of agreement among participants across groups include life-sustaining household, economic household, and subsistence values. For values with two corresponding statements in surveys (tourism, scientiﬁc and learning, recreation, and aesthetic values), response percentage represents the average response for the two statements. For intrinsic value, which was reverse-coded, responses have been reversed for ease of comparison with other values. Participant groups include Mexican nationals originally from Loreto (n = 8), Mexican nationals from elsewhere (n = 14), and foreign nationals (n = 20) (cid:2) The Author(s) 2020 www.kva.se/en 123 596 DISCUSSION Mexican chocolate clams provide a host of ecosystem services to households in the Loreto region that include both provisioning and cultural services. As bivalves, the clams also provide regulating services in the form of water ﬁltration (Millennium Ecosystem Assessment 2005). The multitude of ecosystem services provided by the clams are not explicitly recognized in ﬁsheries management; current management focuses on ecological and economic factors. We ﬁnd that in addition to the provisioning services pro- duced by the ﬁshery, households in the Loreto region derive many cultural ecosystem services from Mexican chocolate clams. Community members agree that in addi- tion to economic value generated by the Mexican chocolate clam ﬁshery, this species also contributes to tourism, sci- entiﬁc/learning, recreation, aesthetic, historic, cultural, community identity, and existence values. This ﬁnding is consistent with other ecosystem service valuation studies that have found that high percentages of local stakeholders recognize their local ecosystems’ capacity to produce diverse ecosystem services including social and cultural values (Martı´n-Lo´pez et al. 2012; Oteros-Rozas et al. 2014). Community members report receiving many types of ecosystem services from the species, which supports our hypothesis that Mexican chocolate clams provide a diver- sity of both provisioning and cultural values to the com- munity of Loreto. None of the participants in the survey reported relying on income from clam harvest, yet nearly half of all participants indicated that they have collected Mexican chocolate clams at some point in the past, and a third said that they collect clams at least once per year. This indicates that residents of Loreto who are not ﬁshers also participate in the harvest of Mexican chocolate clams, and that the ﬁshery itself is much more heterogeneous than accounted for by current ﬁsheries management. This ﬁnd- ing is supported by the previous work demonstrating that multiple ﬁsher types harvest Mexican chocolate clams in Loreto, and that marginalized ﬁsher groups are excluded from ﬁsheries management processes (Pellowe and Leslie 2019). Fisheries management decisions have consequences not only for ﬁshers directly engaged in resource extraction, but also for the broader coastal community. In communities like Loreto, where relatively few individuals engage in regular harvest of the Mexican chocolate clam as com- mercial ﬁshers (Pellowe and Leslie 2019), the values pro- vided by the species to the broader coastal community are diverse and signiﬁcant. Accounting for diverse ecosystem services and community perspectives in management requires ﬁrst, identifying the values and aims of the com- munity, and then, creating management that accounts for trade-offs and conﬂicts among multiple priorities (Loomis Ambio 2021, 50:586–600 and Paterson 2014). Fisheries management in Baja Cali- fornia Sur is improving in its ability to integrate the heterogeneity of ecological systems into policies, but the sociocultural richness of ﬁsheries systems and coastal communities remains largely unaccounted for (see for example, Finkbeiner and Basurto 2015; Leslie et al. 2015). An ecosystem service assessment like what we present here can help inform ecosystem-based management that better incorporates and McLeod 2005). sociocultural (Rosenberg richness Cultural ecosystem services underpin stakeholders’ values and preferences (Russell et al. 2013). However, translating ecosystem service assessments into policy has many challenges, including reconciling the legitimacy of diverse knowledge types, and ﬁnding pathways to turn such knowledge into action (Posner et al. 2016). The purposive inclusion of cultural ecosystem services in these broader assessments is one way to ensure that the sociocultural richness of human–nature interactions as well as the knowledge and values of diverse stakeholders are incor- porated into management (Chan et al. 2012a; Loomis and Paterson 2014; Scholte et al. 2015). Previous work assessing diverse ecosystem services for management has largely focused on terrestrial environments (e.g., Martı´n- Lo´pez et al. 2012; Oteros-Rozas et al. 2014; Dickinson and Hobbs 2017), but there is growing interest in the utility of such approaches for integrating diverse values into the management of marine systems (Rees et al. 2010; Klain and Chan 2012; Loomis and Paterson 2014; Gregr et al. 2020). Stakeholders’ perceptions of change also provide valu- able information about changes in the delivery of beneﬁts that can help to identify management priorities (Martı´n- Lo´pez et al. 2012, 2013; Oteros-Rozas et al. 2014). In this study, perceptions of change provide important insight into how community members may make decisions regarding the clams and resulting marine conservation outcomes, since stakeholder perceptions of ecological conditions underpin environmental behavior (Gobster et al. 2007). Stakeholders’ perceptions of change have been important to understand temporal shifts in other marine populations and ecosystems in the Gulf of California (Sala et al. 2004; Sa´enz-Arroyo et al. 2005a, b, 2006). A study of ﬁsher perceptions of trends in the abundance of the Gulf grouper (Mycteroperca jordani) revealed dramatic declines in abundance that occurred prior to the collection of ﬁsheries data in the Gulf of California, and were thus unaccounted for in ﬁsheries management (Sa´enz-Arroyo et al. 2005b). Alongside ﬁsheries statistics and surveys, ﬁshers’ obser- vations of change over time have also revealed shifts in the species composition of coastal ecosystems of the Gulf of California, from mostly large, long-lived species in higher trophic levels to mostly small, short-lived species in lower 123 (cid:2) The Author(s) 2020 www.kva.se/en Ambio 2021, 50:586–600 597 trophic levels (Sala et al. 2004). Perceptions of change in marine environments are particularly valuable where long- term monitoring data are not available, as they contribute critical information for setting appropriate management targets (Sala et al. 2004). reduced availability, Participants in this study reported changes in Mexican chocolate clams over time in the form of increased market demand, higher prices, reduced quantity and quality, and smaller size. Changes were reported at higher rates by Mexican nationals than foreign nationals surveyed, perhaps because the Mexican nationals surveyed had lived in Loreto longer and had more years of experience harvesting and buying clams. Observed changes in demand, price, availability, quantity, quality, and size of clams affect the delivery of ecosystem services and reveal potential priorities for management. Survey participants proposed several possible causes of observed changes increased including ﬁsheries management, overﬁshing, demand for chocolate clams, and environmental change. Although survey participants were predominantly non- ﬁshers, the nature of their observations of change and attributed causes of change echo those reported by har- vesters of the Mexican chocolate clam in previous studies (Pellowe and Leslie 2019). Harvesters reported declines in Mexican chocolate clam populations over time, which they attributed to increased ﬁshing effort resulting from changes in ﬁsheries management (Pellowe and Leslie 2019). Stakeholders’ observations of change provide information on potential shifts in clam populations and the ecosystem services they generate that is critical for effective design and implementation of management strategies. Such stud- ies are particularly important in data-limited ﬁsheries, like the Mexican chocolate clam ﬁshery (Pellowe and Leslie 2020), where long-term abundance data may not be available. While survey participants did not feel acutely impacted by the changes they had observed, they believed other households in their community were affected. Similarly, community members we surveyed acknowledged the importance of the services provided by Mexican chocolate clams to the broader community of Loreto, even if they themselves did not feel that they received every service. Survey participants were more likely to report the delivery of both provisioning and cultural ecosystem services at the community level, especially for the values of general importance, life-sustaining value, economic value, future use value, and identity value, than they were to report the delivery of the same services at the individual or household level. Community members in Loreto recognize the com- munity value of the Mexican chocolate clam and the impacts of change on the delivery of ecosystem services at the community level. Of the values assessed in this study, the most important ecosystem services that the Mexican chocolate clam pro- vides to the community of Loreto include economic, tourism, future use, cultural, and existence values. Many locals recall childhood memories of collecting Mexican chocolate clams during family trips to the beach, learning to dig for clams in the sand with their toes, or holding their breath to grab a clam from the ocean ﬂoor (Pellowe unpublished data). Survey participants originally from Loreto were more likely to agree that the clam contributes to their individual identity than participants from else- where. However, most participants surveyed, regardless of their place of origin, agreed that the clam is an important it mean to be a member of the Loreto part of what community. Considering the wide recognition of cultural ecosystem services provided to Loreto households, and the clam’s contribution to local identity, the Mexican chocolate clam may be considered a cultural keystone species. Cultural keystone species are ‘‘culturally salient species that shape in a major way the cultural identity of a people’’ (Garibaldi and Turner 2004, p. 4). Such species are deﬁned by the key role they play in deﬁning cultural identity and are char- acterized by their high cultural signiﬁcance. Cultural key- stone species are also marked by their provision of important ecosystem services, particularly cultural value (Butler et al. 2012). The concept of the cultural keystone species highlights the importance of communities’ rela- tionship to place, and the conservation status of these species may be a starting point for identifying management priorities (Garibaldi and Turner 2004). In the Torres Strait Islands in Australia, two cultural keystone species, turtles and dugongs, were catalysts for a shift towards adaptive co- management, which involves the formal sharing of power between local stakeholders and regional ﬁsheries man- agers, and the formal local ecological knowledge into resource governance (Butler et al. 2012). the value of cultural keystone Their ﬁndings highlight species as catalysts for the integration of local knowledge into marine resource governance to enhance ﬁsheries pol- icy and protect the future delivery of ecosystem services (Butler et al. 2012). integration of In Loreto, embracing Mexican chocolate clams as a cultural keystone species may facilitate greater community participation in marine resource management decisions that is reﬂective of the heterogeneity among those involved in the ﬁshery, both directly and indirectly. It may also result in the integration of local ecological knowledge into future policy decisions including accounting for community and ﬁsher observations of change and investigating possible causes of change in order to identify management priori- ties. Managing for Mexican chocolate clams’ diverse val- include protecting habitat, regulating water ues might (cid:2) The Author(s) 2020 www.kva.se/en 123 598 Ambio 2021, 50:586–600 quality, and privileging low-impact ﬁshing practices to safeguard the future delivery of both provisioning and cultural ecosystem services. These practices would serve not only to conserve Mexican chocolate clams and the beneﬁts they provide to Loreto households, but would also beneﬁt many other marine species in Loreto’s nearshore waters including ﬁsh, rays, octopus, and other molluscs. While this study provides important insights about community members’ perceptions of change in the clam ﬁshery and the provisioning and cultural ecosystem ser- vices that Mexican clams provide to households in the Loreto region, a larger sample size of survey participants would be needed to generalize our ﬁndings to the broader population of Loreto residents. A future study with a larger number of participants, systematically recruited to ensure representativeness of the socioeconomic makeup of Loreto households, could conﬁrm whether our ﬁndings apply more broadly to the general population. Our participant pool consisted of many households of middle and upper socioeconomic status owing to the fact that nearly half of the participants surveyed were expats and nationals of the United States, Canada, and the European Union. The skewed socioeconomic characteristic of the participant pool in this study is a result of the purposive sampling method used to recruit participants. Future work should include surveys conducted with a more representative and wider participant pool in order to verify whether our ﬁndings hold true for Loreto residents more broadly. To investigate further the clam’s role in shaping community the identity in Loreto as a cultural keystone species, inclusion of more participants originally from Loreto should be prioritized in future work. The participants in this study did not rely on clams as a source of income, sustenance, or other basic needs, and we anticipate that the inclusion of more low-income households would lead to higher reporting of these services. Additionally, future work should include an expansion of the range of responses to value statements, in order to facilitate comparisons in the strength of participant response to different values. The three-item Likert scale employed in this study to assess survey participants’ agreement or disagreement with ecosystem service value statements could be expanded to a Likert scale that includes a greater range of degrees of agreement and disagreement. This would produce a richer understanding of participants’ experience of diverse values, as well as the relative importance of provisioning and cultural ecosystem services. The social and cultural values of species and ecosystems shape human–nature interactions, yet are often overlooked in decision-making and design of marine management (Chan et al. 2011). If such values are not explicitly understood and accounted for, they are likely to be poorly represented in natural resource policy (Klain and Chan 2012). Assessing these values and incorporating them into management creates robust policies that protect the future provision of valuable ecosystem services. Managing for a narrow set of ecosystem services may not only ignore other important values that a species or ecosystem provides to human communities, but can also reduce the ﬁshery’s capacity to cope with future disturbance (Gordon et al. 2008; Bennett et al. 2009). Understanding the full suite of ecosystem services provided by ﬁshed species is a critical step in designing resource management that protects cru- cial beneﬁts, while considering trade-offs among the diverse values and priorities of coastal communities. Acknowledgements We thank Yong Chen, Carla Guenther, Joshua Stoll, and Bridie McGreavy for their feedback on earlier conceptu- alizations of this paper. We thank Linda Ramirez and Eduardo Murillo for their help reviewing, translating, and pretesting survey questionnaires. We also thank Alfredo Baeza for logistical assistance in the ﬁeld, and the community members of the Loreto region for their participation in this study. We thank Melissa Britsch for her helpful feedback, which helped to improve the clarity of the paper. We also thank two anonymous reviewers for their thoughtful and constructive feedback, which signiﬁcantly improved the paper. Funding was pro- vided by the US National Science Foundation (Grant Number DEB 1632648 to HL). Funding Open access funding provided by Stockholm University. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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New York: Springer. https://ggplot2.tidyverse.org. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional afﬁliations. AUTHOR BIOGRAPHIES Kara E. Pellowe (&) is a Postdoctoral Fellow at the Stockholm Resilience Centre. Her research interests include sustainability sci- ence, marine conservation, and the dynamics and resilience of marine social–ecological systems. Address: Stockholm Resilience Centre, Stockholm University, Kra¨f- triket 2B, 106 91 Stockholm, Sweden. Address: Darling Marine Center, University of Maine, 193 Clarks Cove Road, Walpole, ME 04573, USA. e-mail: kara.pellowe@su.se Heather M. Leslie is Director of the Darling Marine Center and Associate Professor at the University of Maine. Her research interests include marine conservation science, and the ecology, policy, and management of coastal marine ecosystems. Address: Darling Marine Center, University of Maine, 193 Clarks Cove Road, Walpole, ME 04573, USA. Address: School of Marine Sciences, University of Maine, Orono, MA 04469, USA. e-mail: heather.leslie@maine.edu 123 (cid:2) The Author(s) 2020 www.kva.se/en	null
10.1088_1361-6595_ad03bd.pdf	Data availability statement The data cannot be made publicly available upon publication because no suitable repository exists for hosting data in this field of study. The data that support the findings of this study are available upon reasonable request from the authors.	Data availability statement The data cannot be made publicly available upon publication because no suitable repository exists for hosting data in this field of study. The data that support the findings of this study are available upon reasonable request from the authors.	Plasma Sources Sci. Technol. 32 (2023) 105015 (12pp) Plasma Sources Science and Technology https://doi.org/10.1088/1361-6595/ad03bd The role of recombination in constriction of a positive column of a glow discharge in inert gases A V Siasko ∗, V Yu Karasev and Yu B Golubovskii Faculty of Physics, St Petersburg State University, Ulianovskaia ul. 3, St Petersburg 198504, Russia E-mail: aleksei.siasko@gmail.com Received 30 August 2023, revised 5 October 2023 Accepted for publication 16 October 2023 Published 26 October 2023 Abstract The work is aimed at the experimental determination of the role of volume recombination in a positive column of a DC discharge in helium during the transition from a diffuse homogeneous state to a constricted stratified regime under real discharge conditions of high electron and gas ≈ 2 eV, Tg > 1000 K). The investigation is based on the probe measurements temperatures (Te of the wall current determined by the flux of charged particles towards the boundary of a cylindrical discharge tube. The experiments were carried out in helium, neon, and argon in the range of reduced pressures 1.2–300 Torr·cm. In heavy inert gases, the transition to a constricted regime is determined by the active loss of charged particles in the volume. In contrast to neon and argon, experiments in helium demonstrated that the role of volume recombination is insignificant during the transition to a constricted regime. The rate of volume losses in helium in real conditions is very low compared to neon and argon. The obtained results allow one to calculate the volume recombination rate by comparing the experimentally measured wall currents with the corresponding numerical calculations within the collision-radiative model. Keywords: glow discharge, positive column, constriction, optical constriction, plasma instabilities, recombination, probe diagnostics 1. Introduction The phenomenon of constriction—compression of plasma glow into a narrow filament near the discharge axis, which is often accompanied by the development of striations, has been described in numerous works beginning with the book of Stark [1]. The modern state of the problem of discharge constriction is described in classical experimental and the- oretical studies [2–21]. At present, the problem of plasma filamentation at intermediate and high pressures has not lost its relevance and there are several modern both exper- imental and theoretical works dedicated to plasma instabil- ities in sources of various types. For example, the paper [22] describes the development of striation-type ionization ∗ Author to whom any correspondence should be addressed. instability based on fluid simulation of a dielectric barrier dis- charge (DBD) discharge in argon at atmospheric pressure. The plasma parameters mimic the natural discharge condi- tions under which discharge stratification was experiment- ally observed in [23]. The development of instability is inter- preted as a disturbance of the spatial distribution of elec- trons along the discharge channel due to repeated stepwise ionization processes through metastable levels and ionization of excimers. Simultaneous constriction and stratification of a short discharge with an electrode spacing of a few millimeters were experimentally studied in argon and in helium with an admixture of nitrogen at pressures of hundreds of torr in [24, 25]. In [26], the constriction observed in a microwave discharge in molecular nitrogen at sub-atmospheric pressure was interpreted as an ionization-thermal instability due to a mixing of molecular states of nitrogen during rapid heating of the gas. A combined experimental and numerical study 1361-6595/23/105015+12$33.00 Printed in the UK 1 © 2023 IOP Publishing Ltd Plasma Sources Sci. Technol. 32 (2023) 105015 A V Siasko et al Figure 1. (a) Schottky linear diffusion theory (r∗ = R radiation compression or optical constriction. (c) Non-linear diffusion—recombination theory (r∗ < R formation of a plasma filament. ∗ = R/2.4), diffuse discharge. (b) Non-linear diffusion theory (r∗ < R; R ∗ < R/2.4), current constriction, ∗ ≈ R/2.4), −5–10 of microwave CO2 discharge [27] demonstrates the transition from a homogeneous state to a constricted state with an increase in pressure from 60 to 300 mbar due to a thermal- ionization mechanism at gas temperatures reaching 6500 K, −4. Along and the ionization degrees of the order of 10 with high pressures, the work on studying the instabilities is being carried out in low-pressure discharges. The development of S, P, and R ionization waves at low currents is described using a hybrid kinetic-fluid model in paper [28] and a fully kin- etic model in paper [29]. The simulation presented in the work [30] predicts the development of striations in the dusty plasma of the PK-4 system operating in micro-gravity on-board of the International Space Station. The obtained numerical results are confirmed by the experiments on the ground-based PK-4 rep- lica systems. To date, a certain point of view has been formed on the mechanism of discharge constriction. The initial equation for describing the compression of the electron density is the ioniz- ation balance equation. In the simplest case, under conditions of intermediate pressures and currents (tens to hundreds of torr and milliamp), ionization in each elementary plasma volume is balanced by the diffusion of charged particles and the volume recombination. Assuming two-body recombination with the rate αn2, for cylindrical geometry this equation can be written as: I (r) + ∇Da ∇n (r) − αn2 (r) = 0. (1) I is the number of ionization acts per unit volume per unit time, Da is the ambipolar diffusion coefficient, α is the coefficient of two-body recombination, and r is the radial coordinate. If for some reason the ionization sources are compressed towards the discharge axis and located in a region with a characteristic size ∗ , where I = 0, one r can assume Da/R is the distance over which charged particles diffuse during the recombina- tion time 1/αn. If the recombination coefficient α has a large ∗ < R may take place. R is the dis- value, then the relation R , the so-called recombination charge tube radius. The size R length, determines the radius of the current flow or the radius , then outside the ionization zone r > r ∗2 ∼ αn or R αn . Here R ∗2 ∼ Da ∗ ∗ ∗ ∗ ∗ charged particles dis- of the plasma filament since beyond R appear due to recombination. In the opposite case, when the ∗ > R. This recombination rate is low, it may turn out that R means that charged particles diffuse up to the wall, ionization is balanced by the ambipolar diffusion, and the recombination . In this case, a length coincides with the diffusion radius R diffuse discharge mode is established. Within the framework of the linear diffusion Schottky theory [31], a Bessel distribu- tion of the ionization rate and electron density is formed in the radial direction, which is described by the zero-order Bessel function J0(2.4 · r/R). This corresponds to the diffusion radius ∗ = R/2.4 and to the same characteristic radius of the ioniz- R ∗ = R/2.4. In the non-linear diffusion theory, ation sources r when the ionization rate depends exponentially on the elec- tron density, the size of the excitation zone can be noticeably ∗ < R). In the absence of smaller than the radius of the tube (r recombination, the ambipolar diffusion broadens the electron density profile. Thus, the excitation and the ionization zones are compressed, but the electron density profile only slightly differs from the Bessel one. This phenomenon, the so-called optical constriction, is discussed in detail in sections 2 and 5.2. More detailed discussions of discharge formation modes are described in the review [32]. Figure 1 illustrates all three cases described above. Figure 1(a)—formation of a diffuse discharge in the absence of recombination when the ionization is balanced by the ambi- ∗ = R/2.4). Figure 1(b) describes the polar diffusion (r compression of the ionization zone and a discharge glow with a smooth distribution of the electron density along the radius in the absence of recombination. In this case, a line radiation fil- ament is formed with a smooth decay of the bremsstrahlung ∗ ≈ continuum radiation- the optical constriction (r R/2.4). The characteristic radii of compression zones are ∗ ≈ R/10 [32]. Figure 1(c) of the order of R shows the case of the formation of a constricted current chan- nel, when the ionization zone is compressed, and the loss of charged particles occurs in the volume due to active recom- ∗ < R/2.4). In this case, charged particles are bination (r carried out of the ionization zone by the ambipolar diffusion and then disappear in the volume due on a small distance r ∗ ≈ R/5 and r ∗ < R, R ∗ = R ∗ < R ∗ 2 Plasma Sources Sci. Technol. 32 (2023) 105015 A V Siasko et al Figure 2. (a) Cross-sections of elastic electron-atom collisions depending on the electron energy in helium, argon, and neon. (b) Excitation frequencies of the lower metastable states depending on the ionization degree for characteristic electric fields under conditions of transition from a diffuse state to the constricted regime: E/N = 0.56 Td in argon and neon and E/N = 4.5 Td in helium. to recombination. In this mode, the characteristic values of the diffusion zone and ionization zone correspond to values of the ∗ ≈ R/50 [32], which is a small part ∗ ≈ R/40 and r order of R of the radius of the discharge tube. The main mechanisms of constriction of ionization sources can be associated with kinetic effects [33–37], as well as with the inhomogeneous heating of the gas (thermal mechanism) [37–48]. In the first case, one considers the depletion of the electron distribution function by fast electrons in the direction from the axis toward the wall. At the center of the tube, the electron density is high, the electron–electron collisions are efficient, and the distribution function is close to the Maxwell function. With the distance from the axis, the electron dens- ity decreases, the intensity of electron–electron collisions also decreases, and the distribution function becomes depleted by fast electrons capable of ionizing atoms. As a result, the ion- ization rate depends exponentially on the plasma ionization degree, which, in turn, decreases in the radial direction and leads to the constriction of ionization sources towards the dis- charge axis. The degree of compression of the electron density depends on the shape of the cross-section of elastic electron- atom collisions. This effect is especially pronounced in argon and other heavy gases—krypton and xenon. In these gases, the cross-section of elastic collisions increases with the increas- ing electron energy. For this reason, the distribution function in these gases differs greatly from the Maxwell one. It is lead- ing to a formation of a sharp non-linear dependence on the ionization degree, which results in a formation of a very thin zone of ionization and excitation in these gases. In neon, the elastic collisions cross-section is approximately constant and the non-linearity is less pronounced. The excitation and ioniz- ation zone is wider than in heavy gases. For helium, the elastic collision cross-section in the region exceeding 4 eV decreases with increasing velocity. The decrease in the cross-section contributes to the Maxwellization of the distribution func- tion, the non-linearity is very weakly expressed. This effect in helium, in contrast to other inert gases, plays a weak role in the constriction mechanism. Figure 2 compares the cross sections of elastic electron-atom collisions (a) in argon, neon, and helium and the non-linearities of the excitation frequency (b) caused by the competition of electron–atom and electron– electron collisions for these gases at fixed electric field corres- ponding to real discharge conditions under the transition from a diffuse state to the constricted one. The second possible mechanism of constriction of ioniza- tion sources is associated with inhomogeneous heating of the gas, which leads to a decrease in the reduced electric field E/N in the radial direction. Due to inhomogeneous heating, the temperature of the neutral gas decreases from the center toward the wall, the density of neutrals N inversely increases, and the reduced field E/N decreases. Accordingly, the electron temperature and the ionization rate decrease, which depends exponentially on the electron temperature. In works [49, 50], basing on a detailed experimental and theoretical study, the mechanisms leading to constriction of a discharge in neon and argon were investigated. It is shown that for these gases the main cause of constriction is the kin- etic mechanism arising from the depletion of the distribution function by fast electrons. The inhomogeneous heating of the neutral gas is of secondary importance. These works show the effect of each of these mechanisms on the current–voltage characteristics, on the radial size of the current filament and the size of line and bremsstrahlung radiation zones, on a hysteresis during the transition of a discharge from diffuse to constricted state and vice versa, on the appearance of ionization waves during this transition, etc. Such a subtle effect as an influence of the transport of resonance radiation on a formation of mac- roscopic characteristics of diffuse and constricted discharges was considered [20]. For such inert gases as neon and argon, an adequate theoretical description of the phenomena observed in the experiment is associated with available precise data on the cross-sections of elementary processes, on the probabilit- ies of radiative transitions, and, mainly, with the reliable data on the values and temperature dependences of the recombin- ation coefficients. The latter fact is of the greatest import- ance since the volume recombination leads to the compression 3 Plasma Sources Sci. Technol. 32 (2023) 105015 A V Siasko et al of the current channel and the formation of a thin plasma filament (figure 1(c)). distributions of line and bremsstrahlung radiation in these gases. Latest experimental study [51] was carried out to reveal the main mechanism of constriction in helium. It was shown that in helium, in contrast to neon and argon, optical constriction is observed (figure 1(b)) - with increasing current, an abrupt compression of the ionization zone occurs while the electron density smoothly decreases along the radius of the discharge tube. Experiments have shown that the main mechanism of constriction in helium is associated with the inhomogeneous heating of the gas. In this gas, the role of volume recombin- ation losses of charged particles remains unclear. On the one hand, the absence of the formation of a current filament upon constriction in helium indicates an insignificant role of the volume loss of charged particles due to recombination pro- cesses compared to the transport of particles due to ambipolar diffusion. On the other hand, the rate of dissociative recom- bination in helium, which is the main channel for particle loss at high pressures, depends on the density of the excited vibrational states of the helium molecular ion. As the gas temperature grows, a strong non-linear increase in the density of molecules in highly excited states can be observed. The literature data on the rate of dissociative recombination for helium are contradictory. The spread of experimental data is two orders of magnitude [52–57]. The most detailed study of recombination processes in plasma afterglow is presented in [55]. According to the data of this work, the rate of dissociative recombination is very low, and the temperature dependence remains indefinite—α < 5 · 10 −10(Te/293)−(1±1) cm3 s −1. The present work aims to conduct an experimental study to reveal the role of recombination processes in the construc- tion of a discharge in helium at high electron and gas tem- ≈ 2 eV, Tg > 1000 K [51]) at the wide range of perature (Te the reduced gas pressures from 2 to 100 Torr·cm and reduced −1. For this purpose, discharge currents from 8 to 140 mA cm it is proposed to measure the ion flux towards the tube wall using the wall probes. The flux of ions reaching the wall due to ambipolar diffusion is the difference between the number of ions born through the ionization and disappeared as a result of recombination in the plasma volume. This makes it pos- sible to estimate the integral rate of recombination losses over the volume. To reveal additional differences between constric- tion in helium and heavier inert gases, similar experiments were carried out in argon, where the role of volume losses and the rate constant of dissociative recombination are well studied. To complete the picture of the observed phenom- ena, some results will be generalized to the investigations in neon. 2. Brief phenomenology of discharge constriction in inert gases Despite the constricted positive column in helium being visu- ally similar to the constricted discharges in argon and neon (figure 3), there is a fundamental difference in the radial 4 In neon and helium, a bright luminous filament of line radi- ation is surrounded by a weaker glow of the bremsstrahlung continuum. In argon, the filament is so thin that the glow of lines and continuum is visually indistinguishable. In addition, due to thermal effects, the plasma column in argon emerges due to large temperature gradients and convective heat fluxes. These phenomenon was studied in detail in [50]. The radial distribution of line radiation approximately describes the ionization zone. The radial distribution of the bremsstrahlung continuum describes the zone where the elec- tron density is located and, accordingly, the current flow zone. Figure 4 shows the radial distributions of line radiation and bremsstrahlung continuum under conditions of diffuse and constricted regimes in argon (a and c) and helium (b and d) [51]. As can be seen from figure 4, for argon in the constricted regime, there is a strong compression of both line (figure 4(a)) and bremsstrahlung radiation (figure 4(c)) and a pronounced plasma filament is formed. In contrast, in helium only line radi- ation (figure 4(b)) is compressed, while the bremsstrahlung continuum (figure 4(d)) smoothly decreases from the axis to the wall in both regimes. In constricted mode in helium cur- rent flows through the entire cross-section as in the diffuse mode. This phenomenon has been called the optical constric- tion (figure 1(b)). The radial profile of the continuum describes the distri- bution of electrons over the radius of the discharge tube up to temperature inhomogeneity. As will be shown below- equation (6), the slope of the continuum near the tube radius describes the flux of charged particles toward the wall. From figure 4(c) it can be seen that for argon the slope of the con- tinuum sharply decreases during the transition to the con- striction regime. This indicates that in the constricted regime in argon charged particles almost do not reach the wall. In helium, the slope of the continuum almost coincides in both diffuse and constricted regimes, which indicates that the volume losses are low. Charged particles reach the wall in the ambipolar diffusion regime. It was shown in [51] that the formation of the constricted discharge in helium is strongly influenced by heat exchange conditions. When the tube walls are thermostated at room temperature, constriction is not observed in the investigated range of pressures and currents. In the regime of free heat exchange with the environment, an abrupt constriction of line radiation is observed, while the bremsstrahlung continuum does not experience noticeable compression. The transition is accompanied by a hysteresis, which is especially clearly seen in the current–voltage characteristics. By heating the walls of the discharge tube, it is possible to induce constric- tion at currents less than the critical ones in the free heat exchange regime. When the walls of the discharge tube are cooled, the opposite effect is observed—the radiation constric- tion disappears. In other inert gases, similar effects are not observed. Plasma Sources Sci. Technol. 32 (2023) 105015 A V Siasko et al Figure 3. Photographs of constricted discharges in argon at pR = 200 Torr·cm and i/R = 30 mA cm i/R = 47 mA cm −1 (b), and helium at pR = 200 Torr·cm and i/R = 100 mA cm −1 (c). −1 (a), neon at pR = 90 Torr·cm and Figure 4. Normalized radial profiles of spectral lines (a), (b) and bremsstrahlung continuum (c), (d) intensity in the diffuse (black lines) and constricted (red lines) states. Argon at pR = 96 Torr·cm. (a) - λ = 696.54 nm (c)- λ = 508. Helium at pR = 200 Torr·cm. (b) - λ = 587.56 nm, (d)- λ = 540 nm. 3. Experiment on the measurement of the wall currents To measure the ion fluxes toward the wall, discharge tubes with a radius of 1.2, 2.4, and 2.6 cm were used in the exper- iment. The tubes with a radius of 1.2 and 2.6 cm were made of molybdenum glass for studies in helium at low pressures and in argon. To work in helium at high pressures, a quartz tube with a radius of 2.4 cm was used. The use of a quartz tube is due to extremely high heating of the tube walls up to tem- peratures reaching the softening temperature of molybdenum glass. Using a forevacuum and diffusion pumps, the tubes were −6 Torr, which was registered pumped out to a pressure of 10 by the VIT-2 vacuum gauge, and filled with an inert gas up to the required pressure measured by a vacuum gauge which was calibrated by an oil pressure gauge. The discharge tubes were connected to two high-voltage DC power supply (figure 5) which had an output with positive and negative potentials, respectively. A ballast resistance was connected in series with the tube to establish a required discharge current. The exper- iments were carried out after multiple tube training by high currents and gas changes. The degree of purification was con- trolled by the emission spectrum. For reliable measurement of the wall currents, 5 mm diameter molybdenum and nickel probes were soldered into the tubes. The location and design of the probes are shown in figure 5. Some probes were addi- tionally equipped with a shielding ring (position 3 in figure 5) first proposed in [58]. To register the propagation of ionization waves in the form of striations, a photomultiplier (PMT) was placed across the discharge tube. The signal from the PMT was registered by an oscilloscope and then transferred to a PC. The electrical circuit to measure the probe characteristics is shown in figure 6. The value of the ion current towards the wall was obtained by linear extrapolation of the ion part of the probe characteristic to the potential of the isolated probe. Voltage from a stabilized power source, regulated by a poten- tiometer, was applied to the probe. The current from the probe was measured with a micro ammeter. Probes with a shield- ing ring were used to control the error in the measured wall 5 Plasma Sources Sci. Technol. 32 (2023) 105015 A V Siasko et al Figure 5. Scheme of a discharge tube with probes for measurement of the wall currents. 1 - molybdenum probe, 2 - nickel probe, 3 - molybdenum probe with a shielding ring. Figure 7. Probe characteristics in argon at pR = 100 Torr·cm and i/R = 4 mA cm 3- molybdenum probe with a shielding ring. −1. 1- molybdenum probe, 2- nickel probe, Figure 6. Electrical circuit for measurement of the probe characteristics. currents associated with edge effects. An example of probe characteristics registered in argon on a probe of nickel, molyb- denum with and without a shielding ring is shown in figure 7. For clarity, the characteristic of the molybdenum probe with a shielding ring is shifted along the abscissa axis by −20 V. It follows from this figure that, under the experimental condi- tions, the characteristics for nickel and molybdenum probes coincide and give the same value of the wall currents. The characteristic of a probe with a shielding ring has a differ- ent slope due to edge effects. With an increase in the voltage applied to the probe which is negative relative to the potential of the isolated probe, the ion current should reach saturation, while in a real experiment it constantly increases. This is due to an increase in the surface of the layer from which ions are attracted due to edge effects. Edge effects do not appear if a voltage equal to the voltage applied to the probe is applied to the shielding ring and the current flowing only from the probe is measured. Despite that the slope of the ion parts of the 6 Plasma Sources Sci. Technol. 32 (2023) 105015 A V Siasko et al probe characteristics is different for probes with and without the shielding ring, the wall current at the floating probe poten- tial almost coincides within the measurement errors (figure 7). This allows one to neglect the edge effects and also indicates that the thickness of the space charge layer is small compared to the transverse dimensions of the flat probe. It should be noted that the measured wall currents are not only the currents of charged particles moving from the plasma volume to the walls. In addition to electrons and ions, meta- stable atoms with a potential energy of the order of 11.5 eV for argon and 20 eV for helium diffuse towards the wall, as well as resonance photons with energies of the same orders of magnitude propagating in the plasma volume. These particles hitting the probe surface make it emit electrons which will lead to a distortion of the ion part of the probe characteristic and an increase in the measured wall current. Under the investigated conditions, the flux of secondary electrons is determined by the quantum yield of probe materials [59], as well as by the density of metastable and resonance states. When construct- ing a theory that would allow comparing the theoretical wall currents with the results of the experimental study, it would be necessary to take into account the contribution of the second- ary electron flux in the total value of the particle flux density. For correct measurement of the wall currents in the con- stricted state regime, four probes were soldered in each molyb- denum glass tube in one cross-section (figure 5). In the case of a small displacement of a filament from the axis of the dis- charge tube, the wall currents were recalculated according to the empirically determined equation: (cid:19) (cid:18) it = im , (2) 2 ′ R R where it is the true value of the current towards the probe, im is the measured value of the wall current at a small displacement ′ of the plasma filament from the axis of the discharge tube, R is the distance from the probe to the center of the plasma fila- ment. This allowed measuring the wall currents in the constric- ted state at small displacements of the filament from the axis of the tube, which is most important for measurements in argon, where the filament can experience Archimedes’ buoyant force [50]. Measurements of the wall current along the length of the discharge tube showed that the characteristics are uniform along the longitudinal direction. The value of the measured wall current provides informa- tion on the difference between the ionization and the recom- bination rates in the volume. The ionization balance equation for cylindrical geometry has the form: I (r) − Γ (r) = 1 r d dr rDa dn dr , with the corresponding boundary conditions: (cid:12) (cid:12) (cid:12) (cid:12) = 0, n\| r=R = 0. dn dr r=0 (3) (4) 7 Multiplying both sides of the equation (3) by rdr and integrat- ing from 0 to R, one can obtain an expression for the particle flux towards the wall and the wall current jw, accordingly: ˆ R 0 I (r) rdr − ˆ R 0 Γ (r) rdr = −Da dn dr (cid:12) (cid:12) (cid:12) (cid:12) , r=R (cid:12) (cid:12) (cid:12) (cid:12) r=R . (5) (6) jw = −eDa dn dr It can be seen that the wall current can be experimentally determined either directly from the probe characteristic or using the bremsstrahlung continuum profile and measuring the slope of the restored electron density profile near the wall (figures 4(c) and (d)). 4. Results. Measurement of the wall currents at low pressures in the absence of constriction Measurements in argon, neon, and helium were first performed at low pressures from 1 to 36 Torr·cm, when the ionization balance of charged particles is determined by the creation of particles in the plasma volume and their loss on the tube walls due to ambipolar diffusion in the absence of volume recom- bination. Figure 8 shows the values of the wall current density jw depending on the reduced discharge current i/R for differ- ent reduced pressures pR. In helium, the measurements were carried out in tubes with a radius of 1.2 cm (figure 8(a)) and 2.4 cm (figure 8(b)). The results for argon (figure 8(c)) and neon (figure 8(d)) are given for the tube radius of 1.2 cm. In all gases, in a diffuse regime when charged particles drift towards the discharge tube wall, the wall current increases monotonously with an increase in the discharge current. A slight deviation from linear dependences can be associated with a decrease in the electric field with an increase in the dis- charge current. It can be seen from the figure that the highest values of the wall currents at equal reduced pressures and dis- charge currents are observed in helium. The smallest wall cur- rents are observed in argon. This picture corresponds to the difference in ion mobilities and electron temperatures for these gases, which determine the ambipolar diffusion coefficient. Characteristics presented in figures 8(a) and (b) allow checking the applicability of the similarity rules. Under con- ditions when the drift of charged particles to the wall is main- tained in the diffusion mode, it was found that for tubes of different radii the value of the wall current multiplied by the radius jwR remains constant for the same values of the reduced discharge current i/R and reduced pressure pR (figure 9). It can be seen that the similarity rules are fulfilled quite well. The similarity rule can also be derived from the equation (6) if taking into account the dependence of the ambipolar dif- fusion coefficient on pressure (Da = Da0/p) and introducing the dimensionless coordinates x = r/R and y = n/n0. In these variables, Da0 is the coefficient of ambipolar diffusion at a pressure of 1 Torr and n0 is the electron density at the axis of Plasma Sources Sci. Technol. 32 (2023) 105015 A V Siasko et al Figure 8. Dependence of the wall current density jw on the reduced discharge current i/R at low pressures in helium at R = 1.2 cm (a) and R = 2.4 cm (b), in argon at R = 1.2 cm (c), and in neon at R = 1.2 cm (d). The above similarity rule is valid when the volume processes in the ionization balance are two-particle. Three-particle pro- cesses, for example, the conversion of atomic ions into molecular ones followed by dissociative recombination, as well as chemoionization processes, resonance radiation trap- ping, etc can lead to a deviation from the similarity rules. At low pressures, as shown in figure 9, deviation from the simil- arity rules is not observed. 5. Results. Measurement of the wall currents at high pressures in the presence of constriction 5.1. Argon Since, as mentioned above, the process of dissociative recom- bination in argon has been studied sufficiently well, and the rate constant is known, it seems appropriate to begin the investigation of the effect of recombination processes on the discharge constriction from this gas. The switch of the diffuse regime to the constricted one occurs if the gas pressure and the discharge current exceed the critical values. The experiments in the constricted discharge in argon were carried out at reduced pressures of 100, 200, and 300 Torr·cm in tubes with a radius of 1.2 and 2.6 cm. In the constricted dis- charge in argon, the electron density profile is strongly com- pressed, leading to a noticeable decrease in the value of dn r=R dr in the equation (6) compared to the diffuse discharge regime. This should lead to a decrease in the wall currents during (cid:12) (cid:12) Figure 9. Dependence of the reduced wall current density jwR on the reduced discharge current i/R at low pressures in helium. Black dots- R = 1.2 cm, red dots- R = 2.4 cm. the discharge tube. Then the expression for the reduced wall current jwR will be written in terms of the reduced similarity parameters pR and n0R which is proportional to i/R: jwR = − eDa pR n0R (cid:12) (cid:12) (cid:12) (cid:12) dy dx . x=1 (7) 8 Plasma Sources Sci. Technol. 32 (2023) 105015 A V Siasko et al Figure 10. Dependence of the wall current density jw on the reduced discharge current i/R at high pressures in diffuse and constricted regimes in argon at R = 1.2 cm and R = 2.6 cm at pR = 100 Torr·cm (a), pR = 200 Torr·cm (b), and pR = 300 Torr·cm (c). Figure 11. Dependence of the reduced wall current density jwR on the reduced discharge current i/R at high pressures in diffuse and constricted regimes in argon. Black dots- R = 1.2 cm, red dots- R = 2.6 cm. the discharge constriction. Figure 10 shows the dependencies of the wall currents on the reduced discharge current for the mentioned pressures and discharge tube radii. It can be seen from the figures that at low discharge currents in the region of the diffuse regime, a smooth increase in the wall current is observed with an increase in the discharge current, which is similar to diffuse discharges at low pressures (figure 8). When the critical values of the discharge current are exceeded, an abrupt decrease in the flux of charged particles towards the wall is observed. This jump is due to a rapid increase in the rate of recombination processes and the loss of charged particles in the volume. Thus, constriction in argon develops by the scenario presented in figure 1(c). After the transition from the diffuse to the constricted state, the wall current changes only slightly. For reduced pressures of 100 and 200 Torr·cm, the similar- ity rules were verified. On figure 11 the results are presented in the similarity parameters. It can be seen that the similarity rules are satisfied not only for low pressures (figure 9), but also for high ones. 5.2. Helium Constriction of the ionization zone in helium can be observed starting from reduced pressures of about 50 Torr·cm. Due to very high gas temperatures at such pressures, all measure- ments in a constricted discharge in helium were carried out in a quartz tube of the radius R = 2.4 cm. Figure 12 demonstrates the dependence of the wall current density on the reduced discharge current in helium at pressures of 53 and 100 Torr·cm (a) and the current–voltage character- istic at a pressure of 100 Torr·cm (b). It can be seen that the probe current increases linearly over the entire range of meas- ured discharge currents and decreases inversely to the pres- sure, despite the presence of an abrupt transition to optical constriction, which is clearly observed in the current–voltage characteristic. This result is drastically different from the phe- nomena observed in argon, when constriction was accom- panied by an abrupt decrease in the wall current (figures 10 and 11). Hence it follows that, upon the transition to optical constriction, the regime of loss of charged particles is determ- ined by the ambipolar diffusion, and the radial profile should not experience noticeable compression. This correlates with the spectral distributions of the bremsstrahlung continuum shown in figure 4(d). Thus, the low volume recombination rates do not play a decisive role in the formation of a con- stricted discharge in helium. This corresponds to the values of volume recombination given in [55]. The constriction phe- nomenon in the case of helium, unlike argon, develops by the scenario presented in figure 1(b). 9 Plasma Sources Sci. Technol. 32 (2023) 105015 A V Siasko et al Figure 12. (a) Dependence of the reduced wall current density jwR on the reduced discharge current i/R at high pressures in diffuse and constricted regimes in helium at pR = 53 Torr·cm and pR = 100 Torr·cm. (b) Volt-ampere characteristic at pR = 100 Torr·cm. Figure 13. Time dependence of radiation intensity on the axis of a discharge tube in helium at pR = 100 Torr·cm. Homogeneous glow in a diffuse discharge at i/R = 8.3 mA cm i/R = 83.3 mA cm −1 (a). Development of ionization waves in a constricted discharge at −1 and i/R = 16.7 mA cm −1 (c). −1 (b) and i/R = 125 mA cm Figure 14. Time dependence of radiation intensity on the axis of a discharge tube in neon at pR = 96 Torr·cm. Homogeneous glow in a diffuse discharge at i/R = 48 mA cm −1 (a). Development of ionization waves in a constricted discharge at i/R = 51 mA cm −1 (b). The discharge constriction in helium, as in other inert gases, is accompanied by the simultaneous development of ionization instability in the form of striations. Figure 13 shows the res- ult of registration of radiation across the tube using a PMT for currents corresponding to diffuse (a) and constricted (b and c) discharges at a pressure of 100 Torr·cm. For comparison, figure 14 shows similar temporal characteristics of radiation in neon in diffuse and constricted states near the critical value 10 Plasma Sources Sci. Technol. 32 (2023) 105015 A V Siasko et al Figure 15. Illustration of the extrapolation of the diffusion theory to the region of a constricted discharge to find the volume loss rate in the case of argon (a) and helium (b). of the discharge current. Despite the differences in constric- tion mechanisms in these gases noted above, it is clear that in neon and helium the phenomenon of stratification proceeds in the identical way. In the diffuse state, the glow is homogen- eous. The constricted state is accompanied by the appearance of striations. Despite that the role of recombination in the ionization balance in helium is negligible, nevertheless, it can contrib- ute to the development of ionization instability. This question requires the construction of a collision-radiative model and the study of the stationary solution for stability with respect to small perturbations of plasma parameters. 6. Conclusion A complex of experimental measurements of the wall currents in argon, neon, and helium has been carried out in a wide range of pressures and discharge currents in diffuse and constricted regimes of a DC discharge. It is shown that the measurements of the wall currents can serve as a method for estimating the role of volume recombination in the ionization balance. The performed measurements have shown that the similar- ity rules take place for the values of the reduced wall current jwR depending on the reduced discharge current i/R and the reduced pressure p/R. The similarity rules are fulfilled both at low pressures and at high pressures during the transition to the constricted regime. In diffuse discharge, a monotonic increase of the wall cur- rent is observed in all gases with an increase in the discharge current. With increasing pressure, the wall current decreases inversely. In heavy gases, when the critical values of the dis- charge current and pressure are exceeded, the wall current undergoes an abrupt decrease and then remains approxim- ately constant with the after increase in the discharge current. In contrast, in helium, such jumps are not observed during the transition to the constricted regime. The wall current in helium increases proportionally to the discharge current des- pite the jump-like compression of the line radiation and the voltage drop in the current–voltage characteristic. This fact confirms the insignificant role of the volume recombination in the ionization balance in helium. Despite the difference in the constriction mechanisms, in all investigated gases, simultaneously with the compression of the discharge one can observe the development of ionization instability in the form of striations. The whole range of observed phenomena in neon and argon can be described quite well by the theory [20]. A quantitat- ive description of the phenomena occurring in helium requires the development of a detailed collision-radiative model. This model should predict an abrupt compression of line radiation with a smooth distribution of the electron density upon the transition to the constricted state. An analysis of the station- ary solution for stability with respect to small perturbations of the plasma parameters should describe the development of the ionization instability. The presented results of the measured wall currents can serve to determine the volume recombination rate. For this purpose, based on a detailed collision-radiative model, in the first approximation it is necessary to calculate the plasma para- meters without account of the recombination losses in the framework of diffusion theory. The obtained solution makes it possible to calculate the flux of charged particles towards the wall of the discharge tube. It can be expected that at low pressures and currents when recombination losses can be neg- lected, the wall current in the diffusion theory should coincide with the experimental results. Extrapolation of the diffusion theory to the region of high pressures and discharge currents should give a difference between the measured and calculated wall currents. By extrapolation, one means the calculation of plasma parameters on the basis of a detailed collision-radiative model in the absence of recombination. From this difference, one can estimate the volume-averaged value of recombination losses- equation (5). In the next approximation, one can intro- duce volume losses in the collision-radiative model and, by the iterative method, achieve the convergence of the result. This method will give the dependence of the volume-averaged recombination rate on the current and pressure, i.e. depend- ing on the electron temperature, gas temperature, and elec- tron density in real plasma conditions. The proposed method for calculating the recombination loss rate from the measured wall currents is qualitatively illustrated in figure 15 on the example of argon (a) at a pressure pR = 100 Torr·cm and radius 11 Plasma Sources Sci. Technol. 32 (2023) 105015 A V Siasko et al R = 2.6 cm and helium (b) at pR = 100 Torr·cm and radius R = 2.4 cm. In the subsequent work, is planned to construct a it collision-radiative model for helium and implement the pro- posed method for determining the rate of recombination losses and their temperature dependences for the quantitative inter- pretation of the phenomena observed in helium. Data availability statement The data cannot be made publicly available upon publication because no suitable repository exists for hosting data in this field of study. The data that support the findings of this study are available upon reasonable request from the authors. Acknowledgment Authors gratefully acknowledge RSF Grant No. 22-12-00002. ORCID iDs A V Siasko  https://orcid.org/0000-0002-3546-9541 V Yu Karasev  https://orcid.org/0000-0003-2584-0068 Yu B Golubovskii  https://orcid.org/0000-0001-7757-0616 References [21] Ridenti M A, de Amorim J, Dal Pino A, Guerra V and Petrov G M 2018 Phys. Rev. E 97 13201 [22] Jovanovi´c A P, Hoder T, Höft H, Loffhagen D and Becker M M 2023 Plasma Sources Sci. 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10.1265_ehpm.22-00245.pdf	Availability of data and material The datasets generated and/or analyzed during the current study are not publicly available because the study involves human participants with a nondisclosure provision of individual data stated in the written informed consent in order to prevent compromise of study participants’ privacy but are available from the corresponding author upon reasonable request.	Availability of data and material The datasets generated and/or analyzed during the current study are not publicly available because the study involves human participants with a nondisclosure provision of individual data stated in the written informed consent in order to prevent compromise of study participants' privacy but are available from the corresponding author upon reasonable request.	Environmental Health and Preventive Medicine RESEARCH ARTICLE Environmental Health and Preventive Medicine (2023) 28:22 https://doi.org/10.1265/ehpm.22-00245 Cross-sectional associations between early mobile device usage and problematic behaviors among school-aged children in the Hokkaido Study on Environment and Children’s Health Chihiro Miyashita1, Keiko Yamazaki1, Naomi Tamura1, Atsuko Ikeda-Araki1,2, Satoshi Suyama3, Takashi Hikage4, Manabu Omiya5, Masahiro Mizuta6 and Reiko Kishi1＊＊Correspondence: rkishi@med.hokudai.ac.jp 1Center for Environmental and Health Sciences, Hokkaido University, Sapporo, Japan. 2Faculty of Health Sciences, Hokkaido University, Sapporo, Japan. 3Funded Research Division of Child and Adolescent Psychiatry, Hokkaido University Hospital, Sapporo, Japan. 4Graduate School, Faculty of Information Science and Technology, Hokkaido University, Sapporo, Japan. 5Information Initiative Center, Hokkaido University, Sapporo, Japan. 6Center for Training Professors in Statistics, The Institute of Statistical Mathematics, Tokyo, Japan. Abstract Background: Concerns have been raised about the adverse health impacts of mobile device usage. The objective of this cross-sectional study was to examine the association between a child’s age at the ﬁrst use of a mobile device and the duration of use as well as asso- ciated behavioral problems among school-aged children. Methods: This study focused on children aged 7–17 years participating in the Hokkaido Study on Environment and Children’s Health. Between October 2020 and October 2021, the participants (n = 3,021) completed a mobile device use-related questionnaire and the strengths and diﬃculties questionnaire (SDQ). According to the SDQ score (normal or borderline/high), the outcome variable was behavioral problems. The independent variable was child’s age at ﬁrst use of a mobile device and the duration of use. Covariates included the child’s age at the time of survey, sex, sleep problems, internet addiction, health-related quality of life, and history of developmental concerns assessed at health checkups. Logistic regression analysis was performed for all children; the analysis was stratiﬁed based on the elementary, junior high, and senior high school levels. Results: According to the SDQ, children who were younger at their ﬁrst use of a mobile device and used a mobile device for a longer duration represented more problematic behaviors. This association was more pronounced among elementary school children. Moreover, subscale SDQ analysis showed that hyperactivity, and peer and emotional problems among elementary school children, emotional problems among junior high school children, and conduct problems among senior high school children were related to early and long usage of mobile devices. Conclusions: Elementary school children are more sensitive to mobile device usage than older children, and early use of mobile devices may exacerbate emotional instability and oppositional behaviors in teenagers. Longitudinal follow-up studies are needed to clarify whether these problems disappear with age. Keywords: Hokkaido Study on Environment and Children’s Health, Mobile devices, Children, Behavioral problems 1 Introduction The usage of mobile devices, including smartphones and tablets, has rapidly increased across social classes. In 2018, the smartphone penetration rate was approximately 75% and 45% in developed and developing countries, re- spectively [24]. Globally, the age at which a child ﬁrst uses a mobile device is constantly tipping toward earlier ages. Some parents allow their children to use mobile devices at early ages for entertainment purposes. Concerns have been raised about the negative early health impacts of regular contact with mobile devices, especially in relation to neu- robehavioral developmental delays and imbalances in healthy activities in children [1, 26, 28]. The World Health Organization and some developed countries, including the United States of America, Canada, and Australia, have recommended that parents should avoid giving screen- based devices to infants younger than 18 months and re- strict screen time to <1 hour daily for preschool children aged 2–5 years [2, 3, 23, 30, 31]. While these recommendations are based on previous studies that mainly targeted traditional devices such as © The Author(s) 2023. 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The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Environmental Health and Preventive Medicine (2023) 28:22 2 of 11 the television, there is limited scientiﬁc evidence regarding the associations between the early use of contemporary mobile devices and development in children. Unstructured play with the hands and body and practical social commu- nication are important for the development of the central nervous system in early infancy [2, 3]. For establishing healthy behaviors, daytime activities, nighttime sleep, and routine mealtimes are essential. Early mobile device usage can interfere with comprehensive neurodevelopment and engagement in healthy activities, both of which foster lan- guage and cognitive development and social skills in in- fancy. According to a Korean study, children aged 1–3 years who spent longer times with touch screens displayed increased emotional problems and depression and anxiety symptoms [15]. Another study reported that the regular use of screen-based media among infants at 4 months was associated with poor performance on self-regulation tests but not cognitive ﬂexibility or working memory tests at assessed at 14 months [17]. In Japan, the internet penetration rate among school- aged children was 93.2% in 2019. The penetration rate rapidly increased from 12.5% to 49.5%, for smartphones and from 15.3% to 41.0%, for tablets, among elementary school students, between 2014 and 2019. More than 50% of children have contact with the internet in their early life, with 4.7% of those aged 0 years and 50.2% of those aged 3 years using the internet [18]. A Japanese study suggested that regular and frequent use of mobile devices among ﬁrst grade elementary school children was associated with in- creased emotional and behavioral problems [9]. However, this study did not assess school children aged >7 years. The association between mobile device usage in early life and developmental eﬀects was not evaluated. Therefore, the current study aimed to assess the association between a child’s age at ﬁrst use of a mobile device and the duration of use and the behavioral problems among elementary, junior high, and senior high school students. 2 Methods 2.1 Subjects This study focused on children aged 7–17 years between 2003 and 2012; the children were followed up until Oc- tober 2020 in a prospective birth cohort study of the Hokkaido Study on Environment and Children’s Health [11–13]. We mailed the strengths and diﬃculties question- naire (SDQ) and a questionnaire regarding mobile device use and lifestyle to 5,221 parent–child pairs between Oc- tober 2020 and January 2021 based on a random selection. A total of 3,364 responses were received by October 2021 (response rate = 64.4%). From the questionnaire, we as- sessed the associations between exposure to mobile de- vices (the child’s age at ﬁrst use of a mobile device and duration of use), the outcomes (child behavioral problems as determined by the SDQ), and potential covariates, in- cluding the child’s sleep problems, internet addiction, health-related quality of life, and history of developmental concerns assessed at health checkups (Fig. 1). The child- ren’s age at survey were recorded based on the response date on the questionnaire. The child’s date of birth and sex were obtained from medical records. Additional informa- tion, including the parents’ age and educational and house- hold incomes, were obtained from the baseline question- naire during maternal pregnancy [11, 13]. Of the 3,364 questionnaires returned, we excluded two pre-school chil- dren and 341 participants with missing response data. A total of 3,021 children, including 1,433 elementary school, 1,121 junior high school, and 467 senior high school chil- dren, were ﬁnally included in the study (Table 1 and Fig. 1). 2.2 Ethics statement The institutional ethics board for epidemiological studies at Hokkaido University Graduate School of Medicine and Hokkaido University Center for Environmental and Health Sciences approved the study protocol (approval number 19 - 118). Informed consent was obtained from all study participants before enrollment. 2.3 Exposure assessment We used 4-type exposure factors to monitor child mobile device usage (Table 2); the ﬁrst exposure factor was the child’s age at the ﬁrst use of a mobile device, according to parents’ answer to the following question: “At what age did your child ﬁrst use a mobile device? For example, you showed movies to your child on mobile devices, or your child used a mobile device by themselves?” The second exposure factor was the duration of mobile device usage, meaning usable years, calculated based on the child’s age at the ﬁrst use of a mobile device and that at the time of Participants were born from 2003 to 2012, and have been followed up until October 2020 in the Hokkaido Study on Environment and Children’s Health (n = 13,899) Random selection of participants to receive mobile device usage and child health questionnaire (n = 5,221) Participants returned mobile device usage and child health questionnaire (n = 3,364) Excluded 2 participants who were pre-school children Excluded 341 participants who had missing data Study participants (n = 3,021) Fig. 1 Flow chart of study participants Environmental Health and Preventive Medicine (2023) 28:22 3 of 11 Table 1 Characteristics of participants (children and their parents). All children Categories Number (%) Mean « SD or Median (IQR) School type Elementary school Number (%) Mean « SD or Median (IQR) Junior high school Number (%) Mean « SD or Median (IQR) Senior high school Number (%) Mean « SD or Median (IQR) p 3021 12.4 « 2.4 1433 10.2 « 1.2 1121 13.7 « 0.9 467 15.8 « 0.5 <0.001 Child Age at survey Sex Siblings History of developmental concerns Boy Girl No Yes No Yes 1499 (49.6) 1522 (50.4) 401 (16.0) 2100 (84.0) 2726 (90.2) 295 (9.8) Personal mobile device use and restriction 564 (18.7) No Having personal mobile devices 2457 (81.3) Yes Restricted use of mobile devices on weekdays No Yes No Yes Restricted use of mobile devices on holidays Child health quality Health-related quality of life Sleep problems Internet addiction 1310 (43.4) 1709 (56.6) 1456 (48.2) 1565 (51.8) 3021 3021 3021 721 (50.3) 712 (49.7) 192 (17.2) 926 (82.8) 1271 (88.7) 162 (11.3) 434 (30.3) 999 (69.7) 389 (27.2) 1042 (72.8) 472 (32.9) 961 (67.1) 550 (49.1) 571 (50.9) 151 (15.4) 830 (84.6) 1028 (91.7) 93 (8.3) 129 (11.5) 992 (88.5) 540 (48.2) 581 (51.8) 589 (52.5) 532 (47.5) 228 (48.8) 239 (51.2) 58 (14.4) 344 (85.6) 427 (91.4) 40 (8.6) 1 (0.2) 466 (99.8) 381 (81.6) 86 (18.4) 395 (84.6) 72 (15.4) 0.766 0.342 0.025 <0.001 <0.001 <0.001 43.0 (38.0, 47.0) 1433 44.0 (40.0, 48.0) 1121 43.0 (37.0, 47.0) 467 41.0 (35.0, 46.0) <0.001 24.0 (22.0, 26.0) 21.0 (16.0, 26.0) 1433 1433 24.0 (22.0, 26.0) 19.0 (15.0, 24.0) 1121 1121 23.0 (22.0, 26.0) 21.0 (17.0, 26.0) 467 467 23.0 (21.0, 25.0) <0.001 23.0 (19.0, 27.0) <0.001 Mother Age at time of survey Educational level 3021 44.1 « 5.1 1433 42.1 « 4.9 1121 45.3 « 4.6 467 47.2 « 4.2 <0.001 ¯9 10–12 13–15 >16 80 (2.6) 1152 (38.1) 1376 (45.5) 413 (13.7) 37 (2.6) 553 (38.6) 630 (44.0) 213 (14.9) 32 (2.9) 421 (37.6) 522 (46.6) 146 (13.0) 11 (2.4) 178 (38.1) 224 (48.0) 54 (11.6) 0.478 Father Age at time of survey Educational level ¯9 10–12 13–15 >16 <3.0 3.0–4.9 5.0–7.9 ²8 Annual household income (million Japanese Yen) 2984 45.7 « 5.9 1419 43.7 « 5.7 1103 46.9 « 5.4 462 48.9 « 5.2 <0.001 167 (5.6) 1099 (36.8) 785 (26.3) 938 (31.4) 510 (19.0) 1230 (45.8) 722 (26.9) 226 (8.4) 82 (5.8) 508 (35.6) 385 (27.0) 451 (31.6) 252 (19.5) 594 (46.0) 347 (26.9) 98 (7.6) 56 (5.1) 409 (37.1) 292 (26.5) 346 (31.4) 194 (19.6) 446 (45.1) 263 (26.6) 87 (8.8) 29 (6.3) 182 (39.6) 108 (23.5) 141 (30.7) 64 (15.7) 190 (46.7) 112 (27.5) 41 (10.1) 0.637 0.481 SD, standard deviation. SDQ, Strength and Diﬃculties Questionnaire. IQR, interquartile range is the 75th and 25th percentile p value by one-way ANOVA, »2 test, and Kruskal-Wallis test, which were used to compare data among elementary, junior high, and senior high school children. survey. The third exposure factor was the child’s age at which they were given personal mobile devices, according to answers by parents to the following question: “At what age did your child ﬁrst receive a personal mobile device such as a cell phone or tablet?” The fourth exposure factor was the duration of personal mobile device usage, indicat- ing that holding years were calculated using the age at which a child had a personal mobile device and that at the time of survey. 2.4 Assessment outcome We used the SDQ of the common methods for assessing behavioral and mental health problems among children and adolescents aged 4–17 in questionnaires, which were completed by the children’s parents or teachers [7]. The SDQ consists of 25 items, each rated as being not true (0), somewhat true (1), or certainly true (2). The items are divided into ﬁve subscales covering conduct problems, hyperactivity, emotional symptoms, peer problems, and Environmental Health and Preventive Medicine (2023) 28:22 4 of 11 Table 2 Child’s age at ﬁrst use of a mobile device and the duration of use. Age at ﬁrst use of a mobile device Duration of mobile device usage Age at ﬁrst having a personal mobile device Duration for having a personal mobile device All children Median (IQR) 7.0 (5.0, 10.0) 5.0 (3.0, 7.0) 12.0 (8.0, 13.0) 2.0 (1.0, 3.0) School type Elementary school children Median (IQR) 6.0 (4.0, 8.0) 4.0 (2.0, 6.0) 8.0 (7.0, 10.0) 2.0 (1.0, 3.0) Junior high school children Median (IQR) 9.0 (6.0, 11.0) 5.0 (3.0, 8.0) 12.0 (11.0, 13.0) 2.0 (1.0, 3.0) Senior high school children Median (IQR) 10.0 (7.0, 12.0) 6.0 (3.0, 9.0) 14.0 (12.0, 15.0) 2.0 (1.0, 4.0) p <0.001 <0.001 <0.001 0.49 IQR, interquartile range. p value by Kruskal-Wallis test, which were used to compare median among elementary, junior high, and senior high school children. prosocial behavior [7]. Summing up the scores on the four subscales, i.e., excluding prosocial behavior, gives the SDQ total diﬃculties score (TDS), which can range from 0 to 40. The TDS from the Japanese version of the SDQ has a cut-oﬀ score of 12/13 for normal/borderline and 15/ 16 for borderline/high [16]. In this study, according to the parents’ answers, the children were divided into two groups—normal or borderline/high—based on the TDS and ﬁve subscales. The cut-oﬀ score for the ﬁve subscales of conduct problems, hyperactivity, emotional symptoms, peer problems, and prosocial behavior were 3/4, 5/6, 3/4, 3/4, and 6/5 for the normal and borderline/high groups, respectively [8]. 2.5 Covariates Based on previous studies, we used several covariates, in- cluding the children’s age at the time of survey, sex, and history of developmental concerns at health checkups, health-related quality of life, sleep problems, internet ad- diction, school type, and interaction between a child’s mo- bile device usage and school type, all of which had poten- tial confounding eﬀects on a child’s mobile device usage and child behavioral problems based on the associations noted in this study (Table 4 and Supplemental Table 2 and 3) [9, 16, 19]. In fact, we assessed the generic health- related quality of life for children using the KIDSCREEN- 10 questionnaire [21]; this questionnaire consists of 10 items, including the physical, psychological, and social di- mensions of wellbeing [25]. We also assessed the tendency toward internet dependence using a modiﬁed Internet Ad- diction Test, which comprised 11 items [29, 32]. These questionnaires were completed by the children. We as- sessed sleep problems in the children based on 19 items from the short version of the sleep questionnaire for chil- dren [22], which was answered by their parents. Moreover, we used covariates to assess the interactions between a child’s mobile device usage and school type. The children included in this cross-sectional study had a wide birth-year period of 10 years, and their mobile device penetration rate had rapidly changed in the meantime [18]. The children’s behavioral problems not only changed as they developed but were also related to schooling from elementary to high school. We conducted school-speciﬁc analyses because we believed that the school type aﬀected both the exposure and outcomes. Meanwhile, we did not use covariates of paren- tal household income, educational levels, and the presence of siblings as these factors were not associated with a child’s mobile device usage in this study (Table 4). This indicates that the association between mobile device usage and social class could be weakening. 2.6 Statistical analysis Simple associations between the parents’ and children’s characteristics and the child’s age at ﬁrst use of a mobile device and duration of use as well as the children’s behav- ioral problems were assessed using one-way ANOVA, the »2 test, and the Kruskal–Wallis test. A logistic regression analysis was performed for all children, stratiﬁed by ele- mentary, junior high, and senior high school; the outcome (TDS and sub-analyses of child behavioral problems by SDQ) was considered the dependent variable, whereas ex- posure (child’s age at ﬁrst use of a mobile device and the duration of use) was considered the independent variable. The covariates included child age at time of survey, sex, sleep problems, internet addiction, health-related quality of life, history of developmental concerns assessed at health checkups, and school type. The logistic regression analysis among children stratiﬁed by school type was adjusted for the same variables, except for school type. p < 0.05 was considered statistically signiﬁcant. All statistical analyses were performed using SPSS software for Windows (ver- sion 21.0J; IBM, Armonk, NY, USA). 3 Results A total of 3,021 children, including 1,433 elementary school, 1,121 junior high school, and 467 senior high school children, were involved in this study. The children’s and parents’ characteristics and the diﬀerences among school types are shown in Table 1. The children’s and parents’ ages at the time of survey, the child’s internet addi- ction score, and the rate of having personal mobile devices increased as the children progressed from elementary to senior high school. The rate of restricted mobile device usage on weekdays and holidays and the health-related quality of life scores decreased as the children progressed from elementary to senior high school. Moreover, the his- tory of developmental concerns assessed at health check ups and sleep problem scores diﬀered among elementary, junior high, and senior high school children (Table 1). Environmental Health and Preventive Medicine (2023) 28:22 5 of 11 Table 3 Number (%) of child behavioral problems based on the TDS and subscales according to SDQ. TDS Categories Normal Borderline/High Overall Number (%) 2586 (85.6) 435 (14.4) School type Elementary school Number (%) 1205 (84.1) 228 (15.9) Junior high school Number (%) 972 (86.7) 149 (13.3) Senior high school Number (%) 409 (87.6) 58 (12.4) p 0.072 Conduct problems Normal Borderline/High 2727 (90.3) 294 (9.7) 1260 (87.9) 173 (12.1) Hyperactivity/inattention Normal Borderline/High 2727 (90.3) 294 (9.7) 1257 (87.7) 176 (12.3) Emotional problems Normal Borderline/High 2598 (86.0) 423 (14.0) 1228 (85.7) 205 (14.3) Peer problems Normal Borderline/High 2594 (85.9) 427 (14.1) 1255 (87.6) 178 (12.4) 1032 (92.1) 89 (7.9) 1036 (92.4) 85 (7.6) 964 (86.0) 157 (14.0) 949 (84.7) 172 (15.3) 435 (93.1) 32 (6.9) 434 (92.9) 33 (7.1) 406 (86.9) 61 (13.1) 390 (83.5) 77 (16.5) <0.001 <0.001 0.798 0.031 Prosocial behavior 1011 (70.7) 418 (29.3) TDS, Total diﬃculties score. SDQ, Strength and Diﬃculties Questionnaire. P value by »2 test, which was used to compare the data among elementary, junior high, and senior high school children. Normal Borderline/High 2006 (66.6) 1005 (33.4) 715 (64.0) 403 (36.0) 280 (60.3) 184 (39.7) <0.001 The median age at ﬁrst use of a mobile device and the duration of use was 7.0 and 5.0 years overall, 6.0 and 4.0 among elementary school children, 9.0 and 5.0 among junior high school children, and 10.0 and 6.0 among senior high school children, respectively (Table 2). The distribu- tion of child age at ﬁrst use of a mobile device is shown in Supplemental Table 1. The number of borderline/high (cases) according to TDS was 435 (14.4%) overall, 228 (15.9%) among elementary school children, 149 (13.3%) among junior high school children, and 58 (12.4%) among senior high school children (Table 3). The ﬁve subscales of childhood behavioral problems according to SDQ are shown in Table 3. The associations between the age at which a child ﬁrst used a mobile device and basic partic- ipant information are shown in Table 4, both unstratiﬁed and stratiﬁed by school type. Among all children, we ob- served positive associations between the child’s age at ﬁrst use of a mobile device and the child’s and parent’s age at the time of survey. Negative associations were observed between the health-related quality of life score and sleep problems score. The age of the children at ﬁrst use of a mobile device diﬀered by their sex, history of develop- mental concerns assessed at health checkups, possession of personal mobile devices, and mobile device restriction on weekdays and holidays. When the children were strati- ﬁed by school type, their age at ﬁrst use of a mobile device was associated with the child’s and parent’s age at the time of survey, their sex, their history of developmental con- cerns assessed at health checkups, their health-related quality of life, their sleep problems, and their internet ad- diction, among at least one school type (Table 4). The associations between child behavioral problems (TDS) and the characteristics of participants among all children and among those stratiﬁed by school type are shown in Supplemental Tables 2 and 3. Among all chil- dren, the prevalence of child behavioral problems diﬀered by the child’s and mother’s age at the time of survey, their sex, presence of siblings, their history of developmental concerns assessed by medical checkup, mobile device re- strictions on weekdays and holidays, their health-related quality of life, their sleep problems, their internet addic- tion, and their maternal educational levels (Supplemental Tables 2 and 3). When stratiﬁed by school type, the prev- alence of the children’s behavioral problems diﬀered by the children’s age at the time of survey, their sex, presence of siblings, their history of developmental concerns as- sessed at health checkups, mobile device restrictions on weekdays and holidays, their health-related quality of life, their sleep problems, and their internet addiction, among at least one school type (Supplemental Tables 2 and 3). According to the logistic regression analysis among all children, compared to the normal group, the adjusted odds ratios of the borderline/high group signiﬁcantly decreased with increasing child age at ﬁrst use of a mobile device (95% CI = 0.85 [0.77, 0.93]); by contrast, it signiﬁcantly increased with increasing duration for use of a mobile device (95% CI = 1.20 [1.08, 1.33]) (Table 5 and Fig. 2). In the logistic regression analysis stratiﬁed by school type, compared to the normal group, the adjusted odds ratios of child behavioral problems for the borderline/high group signiﬁcantly decreased with increasing child’s age at ﬁrst use of a mobile device (95% CI = 0.87 [0.81, 0.93]) and signiﬁcantly increased with increasing duration for use of a mobile device (95% CI = 1.15 [1.08, 1.23]) among ele- mentary school children. However, the adjusted odds ra- tios for the child behavioral problems were not signiﬁ- cantly associated with the child’s age and duration of mobile device use among junior and senior high school Environmental Health and Preventive Medicine (2023) 28:22 6 of 11 Table 4 Associations between child’s age at ﬁrst use of a mobile device and characteristics of participants. Categories All children Median (IQR) r School type Elementary school children Median (IQR) r Junior high school children Median (IQR) r High school children r Median (IQR) Child Age at time of survey Sex Siblings History of developmental concerns Boy Girl No Yes No Yes 6.0 (5.0, 10.0) 7.0 (5.0, 10.0) 7.0 (5.0, 10.0) 7.0 (5.0, 10.0) 7.0 (5.0, 10.0) 6.0 (5.0, 9.0) Personal mobile device use and restriction Having personal mobile devices No Yes 6.0 (4.0, 8.0) 7.0 (5.0, 10.0) Restricted use of mobile devices on weekdays Restricted use of mobile devices on holidays Child health quality Health-related quality of life Sleep problems Internet addiction Mother Age at time of survey Educational level Father Age at time of survey Educational level Annual household income (million Japanese Yen) No Yes No Yes 8.0 (5.0, 10.0) 7.0 (5.0, 9.0) 7.5 (5.0, 10.0) 7.0 (5.0, 9.0) ¯9 10–12 13–15 >16 ¯9 10–12 13–15 >16 <3.0 3.0–4.9 5.0–7.9 ²8 7.0 (5.0, 10.0) 7.0 (5.0, 10.0) 7.0 (5.0, 10.0) 7.0 (5.0, 10.0) 7.0 (5.0, 10.0) 7.0 (5.0, 10.0) 7.0 (5.0, 10.0) 7.0 (5.0, 10.0) 7.0 (5.0, 10.0) 7.0 (5.0, 10.0) 7.0 (5.0, 10.0) 7.0 (5.0, 10.0) 0.466 0.272 0.131 ¹0.031 5.0 (3.0, 7.0) 6.0 (4.0, 8.0) 6.0 (4.0, 8.0) 6.0 (4.0, 8.0) 6.0 (4.0, 8.0) 5.5 (3.0, 7.0) 6.0 (4.0, 7.0) 6.0 (3.0, 8.0) 6.0 (4.0, 8.0) 6.0 (4.0, 8.0) 6.0 (4.0, 8.0) 6.0 (4.0, 8.0) 8.0 (5.0, 10.0) 10.0 (6.0, 12.0) 8.0 (6.0, 11.0) 9.0 (6.0, 11.0) 9.0 (6.0, 11.0)* 7.0 (6.0, 10.0) 8.0 (6.0, 10.0) 9.0 (6.0, 11.0) 9.0 (6.0, 11.0) 9.0 (6.0, 11.0) 9.0 (6.0, 11.0) 9.0 (6.0, 11.0) 10.0 (6.0, 12.0) 10.0 (7.0, 12.0) 10.5 (6.0, 12.3) 10.0 (7.0, 12.0) 10.0 (7.0, 12.0) 10.0 (6.0, 12.0) 15.0 10.0 (7.0, 12.0) 10.0 (6.0, 12.0) 10.0 (7.0, 13.0) 10.0 (6.0, 12.0) 10.0 (7.3, 13.0) ¹0.097 ¹0.118** 0.002 0.013 ¹0.054* ¹0.118** ¹0.072* ¹0.048 ¹0.072* ¹0.080 ¹0.113* ¹0.024 0.251 0.157 0.061* 0.030 5.0 (4.0, 8.0) 6.0 (4.0, 7.0) 6.0 (4.0, 8.0) 6.0 (4.0, 8.0) 8.0 (5.0, 10.0) 8.0 (6.0, 11.0) 9.0 (6.0, 11.0) 9.0 (6.0, 11.0) 10.0 (6.0, 12.0) 10.0 (6.0, 12.0) 10.0 (7.0, 12.0) 10.5 (7.8, 14.0) 0.251 0.144 0.112** 0.082 5.0 (3.8, 7.0) 6.0 (3.0, 7.0) 6.0 (4.0, 8.0) 6.0 (4.0, 8.0) 6.0 (4.0, 8.0) 6.0 (4.0, 7.0) 6.0 (4.0, 8.0) 6.0 (3.8, 8.0) 10.0 (6.3, 12.0) 9.0 (6.0, 11.0) 8.0 (6.0, 10.0) 9.0 (6.0, 11.0) 9.0 (6.0, 11.0) 9.0 (6.0, 11.0) 9.0 (6.0, 11.0) 9.0 (6.0, 11.0) 10.0 (7.0, 12.0) 10.0 (6.0, 12.0) 10.0 (6.0, 12.0) 10.0 (7.0, 12.0) 10.0 (6.0, 12.0) 10.0 (6.0, 12.0) 10.0 (7.0, 13.0) 10.0 (6.5, 12.0) IQR, interquartile range is the 75th and 25th percentile. r, Spearman’s rank correlation coeﬃcient. P < 0.05, P < 0.01 by one-way ANOVA, »2 test, and Kruskal-Wallis test, which were used to compare data among all children, and among those stratiﬁed by school type children (Table 5 and Fig. 2). The eﬀects of the interaction between exposure (age at ﬁrst use of a mobile device and the duration) and the school type were statistically signiﬁ- cant (Table 5, and Supplemental Table 4 and 8). The adjusted odds ratios of the ﬁve subscales—conduct problems, hyperactivity, emotional symptoms, peer prob- lems, and prosocial behavior—according to the logistic regression analysis results for all children and those strati- ﬁed by the school type are shown in Table 6 and Fig. 2 (for the borderline/high and normal groups). Among all the children and among elementary school children only, the adjusted odds ratios for hyperactivity and peer problems signiﬁcantly decreased with increasing child’s age at ﬁrst use of a mobile device and decreasing child’s duration for the use of a mobile device. Among all the children and among those in elementary and junior high school, the adjusted odds ratios for emotional symptoms signiﬁcantly decreased with increasing age at ﬁrst use of a mobile de- vice and decreasing duration for use of a mobile device. Among all the children in senior high school, the adjusted Environmental Health and Preventive Medicine (2023) 28:22 7 of 11 Table 5 OR for child behavioral problems (TDS) according to child’s mobile device usage. Exposure All children OR (95% CI)a P for interaction Elementary school Junior high school Senior high school OR (95% CI)b OR (95% CI)b OR (95% CI)b Age at ﬁrst use of a mobile device 0.85 (0.77, 0.93)* 0.009 1.20 (1.08, 1.33)** 0.005 Duration for use of a mobile device Age at ﬁrst having personal mobile devices 0.776 0.94 (0.79, 1.11) 0.988 Duration for having personal mobile devices 1.10 (0.90, 1.34) TDS; total diﬃculties score. a (ALL); The logistic regression analysis models were adjusted for child age at time of survey, sex, history of developmental concerns, health-related quality of life, sleep problems, internet addiction, school type, and interaction between exposure and school type. b (Stratiﬁed by school type); The logistic regression analysis models were adjusted for child age at time of survey, sex, history of developmental concerns, health-related quality of life, sleep problems, and internet addiction. P < 0.05, P < 0.01 P for interaction; each exposure (child’s age at their ﬁrst use of a mobile device and duration of use) and school type. 0.87 (0.81, 0.93)* 1.15 (1.08, 1.23)** 0.89 (0.77, 1.02) 1.13 (0.98, 1.30) 1.02 (0.96, 1.09) 0.98 (0.92, 1.04) 0.95 (0.85, 1.05) 1.06 (0.95, 1.17) 0.99 (0.91, 1.08) 1.01 (0.93, 1.10) 0.90 (0.80, 1.02) 1.11 (0.98, 1.25) odds ratios for conduct problems signiﬁcantly decreased with increasing age at ﬁrst use of personal mobile devices and decreasing duration for having personal mobile de- vices (Table 6). In the supplemental logistic regression analysis stratiﬁed by the children’s sex, boys who were younger at their ﬁrst use of a mobile device and used such devices for longer durations were found to be hyperactive, have emotional instabilities, and experience peer problems in elementary school but displayed opposite behaviors in senior high school. Girls who were younger at their ﬁrst use of mobile devices and used such devices for longer durations were found to have emotional instabilities in junior high school but displayed opposite behaviors in senior high school (Supplemental Tables 4–7). 4 Discussion In this cross-sectional study, children who were younger at their ﬁrst use of a mobile device and used such devices for longer durations represented more problematic behaviors according to the SDQ. Moreover, when stratiﬁed by school type, the above associations remained statistically signiﬁ- cant for elementary school children, but not for junior high school and older children (Table 5). Our results suggest that elementary school children are more sensitive to mo- bile device usage than junior high and senior high school children because they are in the early stages of socializa- tion and their behavioral and mental development is rap- idly growing. Four exposure factors were determined in this study: the child’s age at ﬁrst use of a mobile device and the duration of use, and the child’s age at their ﬁrst owning of a personal mobile device and the duration of owning. Given the rapid spread of mobile devices in recent years, elementary school children have started using such devices earlier than high school children did. By contrast, elementary school children used mobile devices for shorter periods than high school children. However, elementary school children represented more problematic behaviors, suggesting that starting to use mobile devices at an early age causes negative eﬀects on developmental immature neural behaviors. Through a cross-sectional and longitudi- nal survey, the Danish National Birth Cohort has reported negative prenatal and postnatal eﬀects of cell phone use on emotional and behavioral diﬃculties in children aged 7 and 11 [4, 5, 27]; our study corroborated the results of this study, showing that early exposure to mobile devices can cause developmental impacts on school-aged children. Using SDQ subscales, our study assessed problematic behaviors among school children aged 7–17 years. Ele- mentary school children who were younger at their ﬁrst use of a mobile device and used these devices for longer durations had increased hyperactivity–inattention and dis- played peer and emotional problematic behaviors. More- over, junior high school children who were younger at their ﬁrst use of a mobile device and used such devices for longer durations represented more emotional problem- atic behaviors. One possible reason for these diﬀerences in the relationships with the school type is that the contents of mobile device use may diﬀer by the school type. Diﬀer- ent eﬀects with several content types were evaluated as screen time exposure, which has been noted as a risk factor for sensory development impacts, emotional and behavior- al problems, sleep disturbances, and internet addiction among school-aged children [6, 9]. A Swedish study tar- geting teenagers described that using mobile devices for social networking services is associated with increased communication skills as a positive eﬀect; however, it is also associated with increased anxiety as a negative eﬀect [10]. The results of our study corroborated those of the Swedish study, which stated that early exposure to mobile devices could exacerbate emotional instabilities in teen- agers. While the duration of personal mobile device usage did not diﬀer by the children’s school type (Table 2), se- nior high school children who were younger and had a personal mobile device for a longer period represented more conducted problematic behaviors. This suggests a correlation between having a mobile device and exhibiting oppositional and deﬁant behaviors among high-school teenagers. When stratiﬁed by sex, only boys exhibited an association between mobile device usage and hyperactivity and peer problems. Moreover, associations between mo- bile device usage and emotional problems were observed Environmental Health and Preventive Medicine (2023) 28:22 8 of 11 Fig. 2 Behavioral problems in children and child age at ﬁrst use of a mobile device OR: odds ratio. CI; conﬁdence interval. TDS; total each diﬃculties score. The OR (95% CI) for children of borderline/high group, who was compared to children of normal group based on total each TDS and the ﬁve subscales—conduct problems, hyperactivity, emotional symptoms, peer problems, and prosocial behavior were calculated by the logistic regression analysis, which was adjusted for child age at time of survey, sex, history of developmental concerns, health-related quality of life, sleep problems, internet addiction, school type, and interaction between exposure and school type among all children. The logistic regression analysis among children stratiﬁed by school type was adjusted for the same variables excluding school type and interaction between exposure and school type. P < 0.05, P < 0.01 Environmental Health and Preventive Medicine (2023) 28:22 9 of 11 Table 6 OR of subscale of SDQ according to child’s mobile device usage. Exposure All children OR (95% CI)a Conduct problems Age at ﬁrst use of a mobile device 0.97 (0.87, 1.08) Duration of mobile device use 1.02 (0.91, 1.14) Age at ﬁrst having personal mobile devices 0.95 (0.78, 1.15) Duration of having personal mobile devices 1.07 (0.85, 1.33) Elementary school P for interaction OR (95% CI)b Junior high school Senior high school OR (95% CI)b OR (95% CI)b 0.543 0.741 0.696 0.831 0.99 (0.93, 1.06) 1.01 (0.94, 1.08) 0.91 (0.78, 1.06) 1.11 (0.95, 1.30) 1.01 (0.94, 1.08) 0.99 (0.93, 1.07) 0.95 (0.85, 1.08) 1.05 (0.93, 1.19) 1.01 (0.91, 1.12) 0.99 (0.89, 1.10) 0.87 (0.76, 0.99) 1.15 (1.01, 1.32)* Hyperactivity/inattention Age at ﬁrst use of a mobile device 0.89 (0.79, 0.99)* 0.173 0.264 Duration of mobile device use 1.12 (0.99, 1.25) Age at ﬁrst having personal mobile devices 0.91 (0.74, 1.12) 0.829 0.875 Duration of having personal mobile devices 1.09 (0.86, 1.39) Age at ﬁrst use of a mobile device Duration of mobile device use Age at ﬁrst having personal mobile devices 1.01 (0.85, 1.19) Duration of having personal mobile devices 1.03 (0.85, 1.26) Emotional problems 0.91 (0.83, 1.00)* 0.541 1.15 (1.04, 1.26)** 0.129 0.652 0.996 Peer problems Age at ﬁrst use of a mobile device 0.93 (0.85, 1.02) Duration of mobile device use 1.09 (0.99, 1.20) Age at ﬁrst having personal mobile devices 0.90 (0.77, 1.06) Duration of having personal mobile devices 1.16 (0.96, 1.40) 0.490 0.350 0.191 0.130 0.93 (0.87, 1.00)* 1.08 (1.00, 1.15)* 0.94 (0.80, 1.11) 1.06 (0.90, 1.25) 0.96 (0.89, 1.04) 1.04 (0.96, 1.12) 0.91 (0.80, 1.04) 1.09 (0.96, 1.24) 1.01 (0.90, 1.13) 0.99 (0.88, 1.11) 0.95 (0.81, 1.11) 1.05 (0.90, 1.23) 0.92 (0.87, 0.98)* 1.08 (1.02, 1.15)* 0.97 (0.84, 1.11) 1.03 (0.90, 1.18) 0.92 (0.87, 0.97) 1.09 (1.03, 1.15) 0.98 (0.88, 1.08) 1.03 (0.92, 1.14) 0.99 (0.91, 1.08) 1.01 (0.93, 1.10) 0.97 (0.86, 1.10) 1.03 (0.91, 1.16) 0.93 (0.87, 0.99)* 1.07 (1.01, 1.15)* 0.93 (0.81, 1.07) 1.07 (0.93, 1.23) 0.97 (0.92, 1.02) 1.03 (0.98, 1.09) 0.96 (0.88, 1.06) 1.04 (0.94, 1.14) 1.00 (0.92, 1.07) 1.00 (0.93, 1.08) 1.13 (0.99, 1.28) 0.89 (0.78, 1.01) Prosocial behavior Age at ﬁrst use of a mobile device 0.98 (0.91, 1.05) Duration of mobile device use 1.01 (0.95, 1.09) Age at ﬁrst having personal mobile devices 1.05 (0.93, 1.19) Duration of having personal mobile devices 0.90 (0.77, 1.04) SDQ; The strengths and diﬃculties questionnaire. a (ALL); The logistic regression analysis models were adjusted for child age at time of survey, sex, history of developmental concerns, health-related quality of life, sleep problems, internet addiction, school type, and interaction between exposure and school type. b (Stratiﬁed by school type); The logistic regression analysis models were adjusted for child age at time of survey, sex, history of developmental concerns, health-related quality of life, sleep problems, and internet addiction. P for interaction; each exposure (child’s age at their ﬁrst use of a mobile device and duration of use) and school type. P < 0.05, *P < 0.01 0.98 (0.94, 1.03) 1.02 (0.97, 1.07) 1.05 (0.95, 1.17) 0.95 (0.85, 1.06) 1.01 (0.97, 1.05) 0.99 (0.95, 1.03) 1.04 (0.96, 1.12) 0.96 (0.89, 1.04) 1.00 (0.95, 1.06) 1.00 (0.95, 1.06) 0.99 (0.91, 1.07) 1.01 (0.93, 1.10) 0.513 0.690 0.606 0.235 in elementary school boys and junior high school girls (Supplemental Tables 4–7). The above results may be related to diﬀerences in developmental properties and tim- ing based on the children’s sex [16, 19]. The adverse eﬀects of having personal mobile devices are inconclusive in this cross-sectional analysis, and a longitudinal evalua- tion throughout adolescence is needed in future studies. Parental supervision of their children’s use of mobile devices is recommended. However, the results of this study remained unchanged even after adjusting the paren- tal usage restrictions for weekdays and holidays (Supple- mental Tables 8 and 9); taken together, our study demon- strates the importance of delaying the use of mobile de- vices rather than imposing parental restrictions. In our study, 15.3% of the children aged ¯3 years were found to have used mobile devices according to their parents, which is lower than the rate of 50.2% for children who used the internet according to a 2019 Japanese survey [18]. This diﬀerence may be attributed to this study targeting a wide birth-year period of 10 years and excluding the use of internet with a wired connection (TV or personal com- puter). In our study, 69.7% of the elementary school-aged children had personal mobile devices, higher than the rates of 49.5% and 41.0% for smartphones and tablets, respec- tively, in 2019 of Japanese’s survey [18]. This diﬀerence may be attributed to the fact that this study included mo- bile game devices and other devices as well. In addition, during the survey period in 2020, the penetration of mobile devices among the school-aged children in Hokkaido pre- fecture increased because the administration started to pro- vide mobile devices to a part of school children for online classes. 4.1 Strengths This study was conducted from 2020 to 2021 and allowed us to examine recent associations between mobile device usage and behavioral problems in children aged 7–17 years. This study used four exposure factors, including the age at their ﬁrst use of a mobile device and the duration of usage before or after owing a personal mobile device. Environmental Health and Preventive Medicine (2023) 28:22 10 of 11 This facilitated the evaluation of both early exposure to and having mobile devices. Previous studies have reported that the early use of mobile devices disrupts healthy activ- ities and increases the risk of sleep problems and internet addiction and lowers the health-related quality of life [2, 3, 31]. Children with developmental disorders often exhibit symptoms such as insomnia and anxiety [1]. These factors may be mutually related with mobile device usage and behavioral problems in children. Our results were obtained after adjusting for mutually related potential confounders, including sleep problems, internet addiction, health-related quality of life, and developmental concerns. 4.2 Limitations This cross-sectional study could not establish a clear causal direction. In fact, a previous study reported that children with developmental disorders are inclined toward heavy use of mobile devices owing to their weak self-regulation and the presence of restricted interests and repetitive behav- iors [14]. Also, parents of inattentive and hyperactive chil- dren are more likely to use mobile devices when calming down their children [20]. Furthermore, in this study, the parents retrospectively provided the age at which their chil- dren ﬁrst used a mobile device and the duration of use; hence, recall bias may be possible. The diﬀering results by school type may be associated with diﬀerences in the sample size, the rate of having personal mobile devices, and the varying content used among school types. 5 Conclusions Our ﬁndings suggest that elementary school children are more sensitive to mobile device usage than older children. Children who are younger at their ﬁrst use of a mobile device and use such devices for longer durations may be prone to emotional instabilities as teenagers. Children who are younger and have had a personal mobile device for longer may show oppositional behaviors as teenagers. However, longitudinal follow-up studies are needed to clarify whether these problems disappear with age. Supplementary information The online version contains supplementary material available at https://doi.org/ 10.1265/ehpm.22-00245. Additional ﬁle 1: Supplemental Table 1. Distribution of child’s age at ﬁrst use of a mobile device. Supplemental Table 2. Associations between child behavioral problems (TDS) and characteristics of participants (continuous variable). Supplemental Table 3. Associations between child behavioral problems (TDS) and characteristics of participants (categorical variable). Supplemental Table 4. OR for child behavioral problems (TDS) according to the child’s mobile device usage (boys). Supplemental Table 5. OR for subscales of SDQ according to children’s mobile device usage (boys). Sup- plemental Table 6. OR for child behavioral problems (TDS) according to child’s mobile device usage (girls). Supplemental Table 7. OR of subscale of SDQ according to child’s mobile device usage (girls). Supplemental Ta- ble 8. OR for child behavioral problems (TDS) according to child’s mobile device usage (Additional adjustment). Supplemental Table 9. OR of sub- scale of SDQ according to child’s mobile device usage (Additional adjust- ment). Declaration Ethics approval and consent to participate The institutional ethical board for human gene and genome studies at Hokkaido University Center for Environmental and Health Sciences (reference no. 139, August 30, 2022) and Hokkaido University Graduate School of Medicine (May 31, 2003) approved the study protocol. Written informed consent was obtained from all participants at the time of enrollment. Consent for publication Not applicable. Availability of data and material The datasets generated and/or analyzed during the current study are not publicly available because the study involves human participants with a nondisclosure provision of individual data stated in the written informed consent in order to prevent compromise of study participants’ privacy but are available from the corresponding author upon reasonable request. Competing interests The authors declare no conﬂict of interest. Funding This work was supported by the Grant-in-Aid for Health Science Research from the Japanese Ministry of Internal Affairs and Communications (JPMI10001). Authors’ contributions Reiko Kishi designed the study and developed the methodology. Chihiro Miyashita, Keiko Yamazaki, Naomi Tamura, and Atsuko Ikeda-Araki, collected the data and performed the analyses. Chihiro Miyashita, Keiko Yamazaki, and Naomi Tamura drafted the manuscript. Reiko Kishi, Satoshi Suyama, Takashi Hikage, Manabu Omiya, and Masahiro Mizuta provided critical revision of the manuscript. All authors take full responsibility for the content of this paper. All authors read and approved the ﬁnal manuscript. Acknowledgements We would like to express our appreciation to all of the study participants of the Hokkaido Study on Environment and Children’ Health. We also express our profound gratitude to all personnel in the hospitals and clinics that collaborated including Sapporo Toho Hospital, Keiai Hospital, Endo Kikyo with the study, Maternity Clinic, Shiroishi Hospital, Memuro Municipal Hospital, Aoba Ladies Clinic, Obihiro-Kyokai Hospital, Akiyama Memorial Hospital, Sapporo Medical University Hospital, Hokkaido University Hospital, Kitami Red Cross Hospital, Hoyukai Sapporo Hospital, Gorinbashi Hospital, Hashimoto Clinic, Asahikawa Medical College Hospital, Hakodate Central General Hospital, Ohji General Hospital, Nakashibetsu Municipal Hospital, Sapporo Tokushukai Hospital, Asahi- kawa Red Cross Hospital, Wakkanai City Hospital, Kushiro Rosai Hospital, Sapporo-Kosei General Hospital, Shibetsu City General Hospital, Nikko Memorial Hospital, Sapporo City General Hospital, Kohnan Hospital, Hakodate City Hospital, Hokkaido Monbetsu Hospital, Tenshi Hospital, Hakodate Goryoukaku Hospital, Nakamura Hospital, Kin-ikyo Sapporo Hospital, Kitami Lady’s Clinic, Engaru-Kosei General Hospital, Kushiro Red Cross Hospital, Nayoro City General Hospital, and Obihiro-Kosei General Hospital. 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10.1088_1402-4896_ad0bb9.pdf	Data availability statement The data cannot be made publicly available upon publication because they are owned by a third party and the terms of use prevent public distribution. The data that support the ﬁndings of this study are available upon reasonable request from the authors.	Data availability statement The data cannot be made publicly available upon publication because they are owned by a third party and the terms of use prevent public distribution. The data that support the findings of this study are available upon reasonable request from the authors.	Phys. Scr. 98 (2023) 125956 https://doi.org/10.1088/1402-4896/ad0bb9 RECEIVED 26 July 2023 REVISED 27 October 2023 ACCEPTED FOR PUBLICATION 10 November 2023 PUBLISHED 22 November 2023 PAPER Samarium-modiﬁed NBT ceramics: a comprehensive exploration of cumulative effects Jyothi Neeli1, Nitchal Kiran Jaladi1 Srinivasa Rao Kurapati2 1 Department of Physics, School of Applied Sciences and Humanities, Vignan’s Foundation for Science Technology and Research, , Nagamani Sangula1, Vijaya Lakshmi Garlapati1 and Vadlamudi, Guntur-522 213, A.P. India 2 Department of Physics, P.B.N college, Nidubrolu, 522124, A.P, India E-mail: kiran.nischal@gmail.com Keywords: rietveld analysis, band gap, color coordinates, vickers hardness, coefﬁcient of friction and wear, VSM study Abstract In the present report, ceramic specimens of sodium bismuth titanate [Na0.5(Bi1-xSmx)0.5TiO3] were prepared through solid-state reaction method with variations in the dopant concentrations speciﬁcally, x = 0.0, 0.1, 0.3, 0.5. The structural, optical, mechanical, and magnetic properties of lead- free NBT ceramics were investigated. The rhombohedral phase with space group R3c was conﬁrmed in all prepared ceramic samples using X-ray diffraction patterns and Rietveld analysis. SEM micrographs and Energy Dispersive x-ray spectroscopy (EDAX) assess the morphology, grain size, overall structure, and stoichiometry of the developed compounds. FTIR spectroscopy was used for characterizing and identifying the functional groups. UV–vis spectroscopy revealed that band gap values decreased as dopant concentration increased, conﬁrming the use of NBT-based perovskite as a photoactive material. PL spectra at room temperature exhibited reddish-orange emission. Colour coordinates and CCT values are in the range of 3483 K to 5912 K. At a concentration of x = 0.3, the materials displayed a high Vickers hardness of 8.20 GPa and exhibited minimal wear with low frictional coefﬁcient values. Ferromagnetic behaviour at room temperature (RTFM) was detected in Sm-modiﬁed ceramic samples, as conﬁrmed by the VSM study. The cumulative effect impact of the rare earth dopant cation at the Bi-site of NBT was widespread and demonstrated signiﬁcant potential for use in optoelectronic devices. 1. Introduction The remarkable advancement in science and technology, which has greatly enhanced our quality of life and made it intricately connected with the evolution of essential materials, has led to a concerted effort to create materials that meet our needs. In pursuit of this goal, extensive research has been conducted to uncover the intricate connection between the structural attributes of materials and their inherent properties. Over the past ﬁfty years, lead-based ferroelectric (FE) and piezoelectric materials have been the subject of intense study due to their wide variety of potential uses [1, 2]. Several classes of materials are explored as potentially attractive alternates to lead-based materials, such as KNN, BST, BNT, alkaline niobates and non-perovskite BLSF structures in order to develop environmental friendly lead-free materials as regulated by the European Union in 2004 [3]. However, researchers have yet to fully comprehend the structure–property correlations of NBT since Smolenskii et al discovered it in 1960. Na0.5Bi0.5TiO3, a lead-free perovskite-type ceramic with the general formula ABO3 and rhombohedral symmetry consisting of Na at the B-site, where oxygen is octahedrally linked to the B site and the A site is 12-fold coordinated with oxygen has received much attention [4]. It has an ambient temperature rhombohedral structure with a space group of R 3c. At 250 °C, this phase changes into a tetragonal phase; at 520 °C, it becomes a cubic phase [5–8]. It exhibits residual polarization of 38 μC/cm2 and possesses a high curie temperature of 320 °C [9]. Enhanced characteristics of NBT with appropriate doping at A-site were illustrated by He et al, and Bi-site doping was preferred to obtain superior at the A-site, and Ti4+ , Bi3+ + © 2023 IOP Publishing Ltd Phys. Scr. 98 (2023) 125956 J Neeli et al performance of NBT [10–12]. Rare-earth (Gd, Yb, Eu, Dy, Nd, and Er) doped NBT systems have previously been developed by researchers to investigate their optical and magnetic properties. The addition of rare earth ions to the A site of the NBT sample changes the number of available cations. It improves the polarization of the electric charge by increasing the oxygen vacancies in the ceramic matrix [13]. From the addition of the Gd3+ ion at the Bi-site of NBT (x = 0.00–0.02), the paramagnetic behaviour in NBT was distinguished from the diamagnetic behaviour [14]. E L T França et al [15] proposed the large dielectric constant and the Yb-doped (x = 0.005–0.020) NBT ceramics exhibit reduced dielectric losses, which makes them suitable for use in technological applications. Santosh Beharaa et al introduced a Eu3+ doped NBT (x = 0.0–0.08) that offers possibilities for exploiting concentration-dependent amphotericity in luminescence device applications. Further, proposed Dy3+ NBT (x = 0.00–0.14) amphoteric and site-dependent luminescence variance, which exhibits a strong correlation between structure–property relationships in material chemistry and holds promise for use in pc-WLEDs and optoelectronic devices [16, 17]. Kumara Raja Kandula et al, examined the effects of Nd3+ multifunctional properties of Na0.5[Bi1-xNdx]0.5TiO3 (NBT) [18]. When an erbium ion is present in the A-site, Na0.5[Bi1-xErx]0.5TiO3 improves the optical and ferroelectric properties [19]. T.Wei et al, introduced Sm3+ doped NBT (x = 0.0–0.16); it may act as a potentially multifunctional optical-electro-material [20]. S. Lenka et al, have examined the effect of the Sm3+ ion at the Bi-site of NBT, which is suitable for high-temperature applications [21]. Based on the previous literature for lead-free piezoelectric systems other than doped BT [22, 23], there is still a lack of knowledge of defect chemistry and the studies were limited to structural, optical, dielectric, and piezoelectric properties with low concentration rare earth doping at Bi-site of NBT. The novelty of this work is to study a cumulative effect of high-concentration rare earth doping at the Bi-site of NBT to unravel the structural, optical, magnetic and mechanical properties and hence to examine their viability as optoelectronic devices, magnetic memory materials and wear-resistant tribo-materials. The multifunctional attributes of Sm-modiﬁed NBT materials are relevant for electronic, automotive, aerospace, and medical industries owing to the stability, reliability, and ﬂexibility in meeting speciﬁc performance criteria by ﬁne-tuning the concentration of Sm dopant to derive high performance. ion doping on the doped 2. Experimental procedure 2.1. Synthesis Polycrystalline ceramic samples of Na0.5Bi0.5TiO3 (NBT) and Sm-modiﬁed Na0.5(Bi1-xSmx)0.5TiO3 with x = 0.0, 0.1, 0.3, and 0.5 (abbreviated as NBT, NBS1T, NBS3T, and NBS5T) ceramic samples from solid-state reaction method. Stoichiometric amounts of powders of oxides Bi2O3 (99.0%), TiO2 (99.0%), and Sm2O3 (99.9%) and carbonates Na2CO3 (99.0%), manufactured by high-media were utilized to prepare. The powders were triturated for 8 h in an agate mortar using methanol as a mixing medium. The resulting powders were double calcined for 3 h at 850 °C to increase their homogeneity. The pellets with a 10 mm diameter and 1 mm thickness were prepared by using hydraulically pressing polyvinyl alcohol (PVA) mixed calcined powders. The pellets are then subjected to sintering for 3 h at 1150 °C in the the air with a heating and cooling rates of 5° per minute. 2.2. Characterization The translucent design of the samples were analyzed utilizing an XRD machine RigakuMiniﬂex 300/600 with CuKα radiation (λ = 1.541 Å) with a step size of 2°/min, considering 2θ in the range of 0°−90°. Rietveld reﬁnement of XRD data has been conducted using FullProf software [24]. The three-dimensional crystal structures were set up using VESTA programming. Scanning electron microscopy (SEM) (vega3tescan) and Energy dispersive spectroscopy (EDAX) are used to conﬁrm the phase purity and stoichiometry of the prepared compassions.YLS-QC-WQP-004 was used for the FTIR spectroscopic analysis of the powders in KBr medium, −1. The absorption spectrum was acquired through diffuse reﬂectance covering a spectral range of 4000–400 cm measurements on the prepared ceramic materials using a UV–vis spectrometer (Analytik Jena, SPECORD 210 PLUS). Horiba JobinYvon Fluorolog-3–21 was employed to capture photoluminescence spectra at room temperature. Ferromagnetism at room temperature (RTFM) was evaluated using a MicroSense-20130523-01 vibrating sample magnetometer (VSM). Mechanical characteristics, including Vicker’s hardness (HV), wear coefﬁcient (K), coefﬁcient of friction (μ), and speciﬁc wear Rate and Speciﬁc wear energy, were assessed with the Tribometer-201. 2 Phys. Scr. 98 (2023) 125956 J Neeli et al Figure 1. (a) XRD patterns of NBT, NBS1T, NBS3T, NBS5T,and (b) Enlarged XRD patterns in 2θ range from 32° to 34° for NBT, NBS1T, NBS3T, NBS5T ceramic samples. Table 1. The crystallite size, and Lattice strain, Tolerance factor values for NBT, NBS1T, NBS3T, and NBS5T ceramics. S.No Sample 1 2 3 4 NBT NBS1T NBS3T NBS5T Crystallite size (nm) 30.97 35.16 23.83 17.15 Lattice strain 0.0034 0.0031 0.0038 0.0053 Tolerance factor 0.9716 0.9695 0.9655 0.9616 3. Results and discussions 3.1. Structural analysis 3.1.1. X-ray diffraction analysis (XRD) XRD patterns of sodium bismuth titanate (NBT) and Sm-modiﬁed NBT systems are shown in ﬁgure 1. Employed Rietveld reﬁnement method, examined all the prepared ceramic samples and veriﬁed the presence of rhombohedral structure with R 3c space group, conﬁrming the successful diffusion of samarium into NBT. In samarium-modiﬁed NBT systems, an increase in the dopant concentration resulted in a noticeable shift in high- intensity diffraction peaks, which can be attributed to the ionic radii of the Sm3+ ion. Considering the ionic radii of the samarium (1.24 Å), bismuth (1.32 Å), and sodium (1.39 Å), it is evident that Sm3+ ions are readily able to substitute for Bi3+ formula allowed us to establish the average crystal size, which clearly signiﬁes the diminishing trend in crystallite size. The crystallization percentage in 2θ range of 32 to 34° reiterates that there is a decrease with increasing in the concentration of samarium doping [26]. The lattice strain in all the studied materials was computed and observed to be increasing. The higher doping content has been linked to the decrease in crystallite size as presented in table 1. The Goldschmidt tolerance factor (tg) used to examine the structural stability of the compounds is expressed as [27] ions within a pristine NBT system [25]. The application of the Debye–Scherrer and Na1+ - + ) [( g t x 1 = Ra 1 ( r 2 B Where x is the dopant concentration, Ra1 is basic elements(Na, Bi) in A-site average ionic radii, Ra2 is the dopant element(Sm) in A-site ionic radii, rB is ionic radii of the B-site atoms, and ro is the ionic radius of oxygen. Goldschmidt tolerance factor (tg) values are listed in table 1. Incorporating Sm3+ place of larger ionic radii of Bi3+ provides evidence of reduced lattice parameters and a diminished unit cell volume in the examined samples. The ions caused the unit cells of Sm-doped NBT systems to contract [28]. Table 2 ions with smaller ionic radii in Ra 2 ) r o x + r o ] + ( ) 1 3 Phys. Scr. 98 (2023) 125956 J Neeli et al Table 2. Details of lattice parameters, cell volume, atomic position, occupancy, and goodness parameter values for NBT and samarium modiﬁed NBT ceramic samples. Compound NBT NBS1T NBS3T NBS5T Lattice Parameter a (Å) c (Å) Cell Volume(Å) Atomic Positions Na X Y Z Occupancy Bi X Y Z Occupancy Sm X Y Z Occupancy Ti X Y Z Occupancy O X Y Z Occupancy χ2 5.49287; 13.42146 350.695 5.48570 ± 0.00103 13.4180 ± 0.00480 349.587 ± 0.156 5.462 ± 0.00061 13.4545 ± 0.00192 347.680 ± 0.074 5.44375 ± 0.00068 13.4148 ± 0.00226 344.280 ± 0.00226 0.00000 0.00000 0.26311 0.143 0.00000 0.00000 0.23311 0.499 — — — — 0.00000 0.00000 0.01403 0.985 0.10863 0.33800 0.09333 1.510 0.926 0.00000 0.00000 0.26069 0.099 0.00000 0.00000 0.26069 0.449 0.00000 0.00000 0.26069 0.050 0.00000 0.00000 0.01164 0.975 0.0803 0.33741 0.06656 1.569 0.728 0.00000 0.00000 0.26338 0.165 0.00000 0.00000 0.26338 0.350 0.00000 0.00000 0.26338 0.149 0.00000 0.00000 0.01188 0.979 0.10899 0.33763 0.09459 1.216 0.970 0.00000 0.00000 0.26473 0.215 0.00000 0.00000 0.26473 0.250 0.00000 0.00000 0.26473 0.249 0.00000 0.00000 0.00978 0.974 0.10867 0.33810 0.07089 1.279 0.829 Full-Prof method was used for conducting the Rietveld reﬁnement to analyze the crystal structure of each sample. The samples’ Rietveld reﬁnement patterns are shown in ﬁgure 2. A nominal difference was noticed between the observed and calculated data and Bragg’s reﬂections of all the prepared ceramic samples were the same. The goodness parameter was observed to be less than 1, indicating a good ﬁt of the observed and calculated values. Table 2 displays the reﬁned lattice parameters, atomic positions, and goodness of ﬁt χ2, while table 3 presents the instrumental parameters, residual factor Rp, and weighted residual factor Rw. VESTA software was used for the visualization of the crystal structure of pure NBT, Sm3+ ion-modiﬁed composition. Figure 3 represents the three-dimensional crystal structures of the NBT, NBS1T, NBS3T, and NBS5T. The Ti occupancy was nearly the same in all compositions. Average bond lengths were observed for all compositions, and the average bond length of A-site and B-site atoms to oxygen was observed to decrease with dopant concentration. Bond angles (O-Ti-O) variation was observed for all compositions.The Ti4+ occupy an octahedral position; Ti-O distances exhibit short and long lengths that result in deformed octahedra; the average value of Ti4+ -O is around 1.96 Å. These octahedra are stretched in three dimensions and alternately joined. The analysis of various inter-atomic distances in table 4 reveals that Na/Bi/Sm atoms form (Na/Bi/Sm)O12 polyhedra, and the calculated average length of the Na/Bi/Sm–O bond is approximately 2.65 Å. cations 3.1.2. Density and microstructural studies The density of the prepared ceramic samples was determined by using the Archimedes water displacement equipment (model: TTB15) [29] and identiﬁed to possess high density (95%) with induced porosity, shown in table 5. In order to gain insight into the materials’s mechanical properties performed shrinkage measurements [30] on the pellets after sintering and noticed that the percentage density was to the tune of induced porosity. The studied samples’ SEM micrographs and corresponding histogram distribution are shown in ﬁgure 4. The average grain size of each sample has been calculated using ImageJ software. The determined average grain size of the NBT, NBS1T, NBS3T, and NBS5T compositions was 2.65 μm, 4.86 μm, 5.27 μm, and 4.13μm, 4 Phys. Scr. 98 (2023) 125956 J Neeli et al Figure 2. Rietveld reﬁnement XRD patterns of NBT and samarium-modiﬁed NBT ceramic samples. Table 3. Details of Rietveld reﬁnement parameters NBT, NBS1T, NBS3T, and NBS5T ceramic compositions. Compound Wavelength (Å) Step scan increment 2θ range (°) Program Caglioti parameters Pseudo-Voigt function PV = ɳ L + (1 − ɳ) G Space group RF RB Rp Rw Rexp NBT NBS1T NBS3T NBS5T 1.541862 0.02 20–80 FULLPROF U = 0.091985 V = −0.000528 W = 0.008432 ɳ = 0.63315 R −3 c 14.1 13.6 12.0 16.6 17.26 1.541862 0.02 20–80 FULLPROF U = 0.013862 V = −0.009936 W = 0.006103 ɳ = 1.83336 R −3 c 12.5 11.5 11.4 15.1 1.68 1.541862 0.02 20–80 FULLPROF U = 0.016799 V = −0.009915 W = 0.005638 ɳ = 3.40818 R −3 c 13.5 12.1 13 16.4 16.66 1.541862 0.02 20–80 FULLPROF U = 0.004133 V = −0.007618 W = 0.006255 ɳ = 0.0001 R −3 c 12.6 12.7 11.9 15.1 16.61 respectively. The increase in grain size can be attributed to the sintering mechanism, in which the surface free energy of particles decreases due to the solid–vapour interface energy and solid-state interface energy contributions along with the diffusion of rare earth ions in the NBT host matrix and the dopant’s ionic radii and atomic mass [31, 32]. Among various rare earth elements, samarium is considered as one of the excellent dopants due to its optical capacity [33]. In general, there is a strong correlation between changes in the structure and morphology of the material and its optical properties [34]. This prompted the use of Sm3+ luminophore in the NBT matrix. A Photoluminescence on an Sm3+ ion-modiﬁed NBT system was deciphered by the intensity correlation between the studied optical properties and structural parameters (Rietveld analysis). Energy Dispersive X-Ray Spectroscopy (EDAX): ﬁgure 5(a)–(d) depicts the Energy Dispersive x-ray spectra ions as a (EDAX) and atomic, weight percentages of each constituent atom for all compositions, conﬁrming the purity and stoichiometry. The XRD results conﬁrmed the presence of only the rhombohedral perovskite phase, and this corroborates the fact that sufﬁcient dissolution of samarium into the host matrix has occurred. 5 Phys. Scr. 98 (2023) 125956 J Neeli et al Table 4. Selected inter-atomic distances (Å) and O-Ti-O angles for NBT, NBS1T, NBS3T and NBS5T. Bond length Na/Bi-O Na/Bi-O Na/Bi-O Na/Bi-O Na/Bi-O Na/Bi-O <Na/Bi-O> Na/Bi/Sm -O Na/Bi/Sm -O Na/Bi/Sm -O Na/Bi/Sm -O Na/Bi/Sm -O Na/Bi/Sm -O 2.53134 2.71532 2.41529 2.43476 2.80849 3.00696 2.662466 2.665 2.8442 2.3566 2.3839 2.6902 3.0824 <Na/Bi/Sm –O> 2.6373 Na/Bi/Sm -O Na/Bi/Sm -O Na/Bi/Sm -O Na/Bi/Sm -O Na/Bi/Sm -O Na/Bi/Sm -O 2.4048 2.4273 2.7260 2.5119 3.0257 2.7956 <Na/Bi/Sm –O> 2.64855 Na/Bi/Sm -O Na/Bi/Sm -O Na/Bi/Sm -O Na/Bi/Sm -O Na/Bi/Sm -O Na/Bi/Sm -O 2.45 2.74 3.07 2.77 2.3935 2.421 NBT Ti-O Ti-O Ti-O Ti-O <Ti-O> NBS1T Ti-O Ti-O Ti-O Ti-O <Ti-O> NBS3T Ti-O Ti-O Ti-O Ti-O <Ti-O> NBS5T Ti-O Ti-O Ti-O Ti-O 1.98225 2.818436 1.78548 1.95351 1.97715 1.8062 2.0013 1.9545 2.2009 1.94275 2.17027 1.95084 1.97392 1.78487 1.97166 1.96 2.12 1.823 1.96 <Na/Bi/Sm –O> 2.6405 <Ti-O> 1.96575 Bond angles O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O O-Ti-O 176.6785 93.2166 89.2448 87.3889 88.8680 88.430 104.505 78.709 93.895 88.579 86.950 86.575 88.960 89.242 88.154 87.813 89.584 89.833 81.169 101.019 91.332 88.9 101.3 80.5 86.45 89.21 88.3 87.9 89.59 −1, ∼1460 cm 3.1.3. Fourier infrared spectroscopy (FTIR) The FT-IR spectra of Na0.5(Bi1-xSmx)0.5TiO3 at room temperature with x values of 0.0, 0.1, 0.3, and 0.5 are −1. The vibrational bands are observed at wave depicted in ﬁgure 6 with spectral range from 400 to 4000 cm −1 and ∼3477 cm −1, ∼2923 cm −1, ∼1649 cm −1, ∼834 cm numbers ∼636 cm The metal-oxide band observed close to 636 cm −1. The stretching vibration vibrations of TiO6 octahedra may be responsible for the absorption band at 834 cm of BO6 octahedra in the perovskite structure for B–O bonds along the c-axis is represented by a weak absorption band at 1460 cm extending vibrational methods of the caught water molecule. The shift and broadening of vibrational bands with dopant concentration correspond to the change in lattice parameters attributed to crystal growth [36, 37]. −1 is typical for perovskite materials [35]. The Ti–O–Ti −1 [35]. The band at 2923 cm −1 are aligned with the O-H −1 and around 3477 cm −1 for NBT sample. −1, 1649 cm 3.2. Optical studies 3.2.1. Ultraviolet-visible (UV–vis) reﬂectance spectroscopy(DRS) The ultraviolet–visible absorption spectra of all the prepared samples within the 200–800 nm wavelength range as shown in ﬁgure 7 illustrate the interband photon energy absorption leads to the optical conduction of electrons. As the dopant concentration increase, it was observed that the wavelength at which maximum absorption occurred consistently shifted towards a longer wavelengths. Using the Tauc equation, the optical band gaps (Eg) of Sm3+ ion-doped NBT materials have been determined [38]. 6 Phys. Scr. 98 (2023) 125956 J Neeli et al Figure 3. VESTA software developed NBT and Sm-modiﬁed NBT structures from Rietveld reﬁnement. Figure 4. SEM micrographs and corresponding histograms for NBT, NBS1T, NBS3T, and NBS5T. a n = n E ( h h – n ) g ( ) 2 Where the absorption coefﬁcient(α), Planck’s constant(h), incident photon frequency(ν), and a material- dependent constant (A) are present, the nature of the electronic transitions is revealed by the power index (n) value. Direct and indirect interband transitions are expressed by the numbers n = 1/2 and 2, respectively. In the case of NBT perovskite materials, there is a direct charge shift from the valence band’s higher edge to the conduction band’s lower edge. In addition, the theoretical research by Zeng et al on basic NBT perovskite material indicated that the O-2p state is at the top of the valence band while the Ti-3d and Bi-6s states are at the bottom of the conduction band for direct interband transitions [39]. Figure 8 exhibits linear ﬁts for direct band gap (n = ½) for all the ceramic samples produced elucidating the correlation between (αhv)2 versus hv plots. The direct band gap values of the NBT and samarium-modiﬁed ceramics were determined to fall within the descending range of 3.01 to 3.12 eV from that of NBT suggesting that the samples exhibit semiconducting properties, shown in table 6. A similar trend was observed by the earlier researchers [14, 40, 41]. This decrease in 7 Phys. Scr. 98 (2023) 125956 J Neeli et al Figure 5. EDAX spectra and corresponding atomic and weight percentages for NBT and Samarium modiﬁed NBT ceramics. Figure 6. FTIR spectra of all the prepared samples. 8 Table 5. Grain size, Relative density, and porosity values for NBT and Sm-modiﬁed NBT ceramic samples. 9 S. No Composition Experimental Density (g/cm3) Theoretical Density (g/cm3) Percentage of Shrinkage diameter (%) Grain size (μm) Relative Density (%) Porosity (%) 1 2 3 4 NBT NBS1T NBS3T NBS5T 6.158 5.217 5.529 5.273 6.580 5.497 5.826 5.572 14.1 15.8 14.4 15.3 2.65 4.86 5.27 4.13 95 95 95 95 0.049 0.051 0.051 0.054 P h y s . S c r . 9 8 ( 2 0 2 3 ) 1 2 5 9 5 6 J N e e l i e t a l Phys. Scr. 98 (2023) 125956 J Neeli et al Figure 7. Diffuse reﬂectance spectra (DRS) for NBSmxT with x = 0.0, 0.1, 0.3, and 0.5. Figure 8. Linear ﬁtting for the direct band gap values of NBSmxT with x = 0.0, 0.1, 0.3, and 0.5. band gap value may have been caused by the formation of new subbands within the band gap of NBT material. Additionally, it may be linked to the microstructural changes brought on by variations in dopant concentration. Among the studied ceramic compositions, the NBS3T had the lowest optical bang gap (Eg = 3.01 eV), and it might be a promising perovskite photoactive material [42]. 3.2.2. Photoluminescence study In order to investigate the photoluminescence (PL) characteristics of Na0.5Bi0.5TiO3 and Na0.5(Bi1-xSmx)0.5TiO3 (where x = 0.0, 0.1, 0.3, and 0.5), the samples were subjected to excitation at a wavelength of 407 nm. The resulting photo emissions were then examined within the range of 550 nm to 675 nm, as depicted in ﬁgure 9(a). The results showed that the photoluminescence intensity was proportional to Sm concentration initially and then reached the highest value at x = 0.3 (critical limit). Once the critical concentration of Sm is surpassed the 10 Phys. Scr. 98 (2023) 125956 J Neeli et al Figure 9. (a) Photoluminescence emission spectra for Na0.5(Bi1-xSmx)0.5TiO3 at x = 0.0, 0.1, 0.3, 0.5 (b) CIE chromaticity diagram. Table 6. Direct energy band gap values for NBT and Sm-modiﬁed NBT ceramics. S. No Composition Direct band gap Eg (eV) 1 2 3 4 NBT NBS1T NBS3T NBS5T 3.12 3.09 3.01 3.07 ions reduces, leading to mutual ion interactions that induce non-radiative transitions. distance between Sm3+ This phenomenon subsequently diminished the ﬂuorescence intensity. The decrease in photoluminescence intensity after the addition of excessive amounts of Sm can be attributed to the concentration quenching effect [43, 44]. The Blasse equation (3) can be used to determine the critical separation between two Sm3+ ions and is written as [45] =R 3 V p X N 4 C ⎡ ⎣⎢ 1 3 ( ) ⎤ ⎦⎥ ( ) 3 Here, N is the number of dopant-ready sites in the unit cell, and V is the unit cell volume at the critical doping concentration (Xc). The computed critical distance values for NBS1T, NBS3T, and NBS5T are 4.53 Å, 3.13 Å, and 2.63 Å, respectively. 3.2.3. CIE diagram Chromatic diagram based on Commission Internationale de l’Eclairage (CIE) (1931) [46], along with correlated colour temperature (CCT) values, for Na0.5(Bi1-xSmx)0.5TiO3 with x= 0.1, 0.3 and 0.5 are presented in ﬁgure 9(b). This diagram depicts the colour purity of the luminophores when excited at 407 nm. These are signiﬁcant from the perspective of the material performance on colour luminescent emission in applications like LEDs that take place in the real world. The McCamy empirical relation (4) is used to calculate the CCT values, from the perspective of material performance on colour luminescent emission in practical applications like LEDs [47] these are signiﬁcant. ( CCT x, y ) = - 449n3 + 3525n2 - 6823.3n + 5520.33 ( ) 4 , x e y e Where = - x n is the tangent of the angle between the y-axis, and this line is the reciprocal of the slope. (xe, n - y ye) = (0.3320, 0.1858) are the epicenter of the convergence at the point on the chromaticity diagram, and (x, y) is the colour coordinates of the sample. The colour purity of the dopant systems’ emitted colour is determined by using equation (5) [48] 11 Phys. Scr. 98 (2023) 125956 J Neeli et al Figure 10. M-H loops for NBT and Sm-modiﬁed NBT ceramics at room temperature. Table 7. Magnetization values for NBT and Samarium-modiﬁed NBT ceramic compositions at Room Temperature. S.No Composition Ms (memu /g) Mr (memu /g) 1 2 3 4 NBT NBS1T NBS3T NBS5T 0.0140 9.596 34.18 6.413 0.00036 0.703 1.49 0.337 S Hc(Oe) 0.07 0.49 0.43 0.42 0.00 0.50 96.31 116.23 Magnetic moment (μb) Anisotropy constant A(erg/cm3) 0.0005 0.0004 0.0012 0.0002 2 0.000 0.004 3.429 0.776 Color purity = ( x - 2 ) + (( y - x i x 2 ) y i y i 2 ´ 100% - d Where (xi, yi) is the illuminant point and (xd, yd) is the colour coordinates of a dominant wavelength. y d - + x ) ( ) ( i From ﬁgure 9(b), the Commission Internationale de l’Eclairage (CIE) colour coordinates for the NBSmxT systems (x = 0.1, 0.3, and 0.5) are (0.324, 0.329), (0.383. 0.326), (0.333. 0.301) respectively. The CCT values of Sm-modiﬁed NBT systems (x = 0.1, 0.3, and 0.5) are reported as 5912.12 K, 3483.53 K, and 5458.43 K, respectively. These systems are well within the reddish-orange region. Photometric parameters like CIE colour coordinates, CCT, and colour purity were different in all the studied compositions. The phosphors exhibit strong reddish-orange emission with acceptable CCT, colour purity, and reddish-orange chromaticity. Ceramic composition at x = 0.3 concentration displayed high-intensity reddish-orange emission based on the colour coordinates, CCT, and colour purity values. ( ) 5 3.3. Magnetic study The magnetic measurements of the prepared [Na0.5(Bi1-xSmx)0.5TiO3] ceramic samples with compositions x=0.0, 0.1, 0.3, and 0.5 were recorded at room temperature under the magnetic ﬁeld of −20 kOe H 20 kOe and are presented in ﬁgure10 along with NBT, which is with a diamagnetic attribute, the prepared compositions exhibited typical ferromagnetic (RTFM) behavior [49]. The equation (6), relates the magnetic moment (μb) with Bohr magneton [50]. m b = ´M M 5585 S ⎤ ⎦ ⎡ ⎣ 12 ( ) 6 Phys. Scr. 98 (2023) 125956 J Neeli et al Figure 11. (a) Vickers hardness as a function of concentration (b) Wear coefﬁcient, coefﬁcient of friction, and Speciﬁc wear energy as a function of concentration. Table 8. Vickers Hardness, Coefﬁcient of Wear, Coefﬁcient of Friction values for NBSmxT (x = 0.0, 0.1, 0.3 & 0.5) ceramics. S. No Composition 1 2 3 4 NBT NBS1T NBS3T NBS5T Vickers Hardness (HV) (GPa) Coefﬁcient of Wear (K) (mm3/ N.m) Coefﬁcient of friction (μ) SWE (* 104 J g −1) 5.59 6.78 8.20 5.86 –6 –6 0.68 × 10 0.78 × 10 –6 1.21×10 0.53 × 10 –6 0.582 0. 558 0. 641 0.545 14 76 93 58 Here, M is the molecular weight of the sample, Ms is saturation magnetization, and 5585 is the magnetic factor. The equation (7) was used to calculate the anisotropy constant [51] Anisotropy constant A = H C ´M S 0.96 ( ) 7 Where Ms is the saturation magnetization, Hc is the coercivity, and 0.96 is a constant. Table 7 presents the values of the reduced magnetization (S=Mr/Ms), magnetic moment (μb), and the anisotropy constant (A), as well as the saturation magnetization (Ms), remnant magnetization (Mr), and coercivity (Hc). The lattice distortion, indirect spin exchange interactions, the bond angle and length alteration impact ferromagnetic properties [52]. The reduced magnetization which is also known as the squareness factor, with values in between 0 and 1 is used in memory devices. All the prepared ceramic samples were identiﬁed to exhibit a soft magnetic nature with a multi-domain structure as the squareness factor values are less than 0.5 [53]. 3.4. Mechanical studies 3.4.1. Vickers microhardness Vickers hardness test was developed in 1924 by Smith and Sandland. Hardness is one of the most important properties of a ceramic. Vickers hardness values were calculated using equation (8) for NBT and Sm-modiﬁed NBT ceramic samples, the obtained results are noted in table 8. The input parameters applied are load 10 N and time 10 s. Vickers hardness as a function of concentration was shown in ﬁgure11(a) H = F 0.189 d ´ 2 Gpa ( ) 8 Where H is the hardness, F is the load in the indentation, and d is the average length of the diagonal line in the indentation. The NBS3T ceramic (8.20 GPa) has a hardness much higher than NBT (5.59 GPa) ceramics, and this may be due to submicron grains, which provide additional barriers to the movement of lattice dislocations in adjacent grains and, hence the increased number of grain boundaries [54–57]. 13 Phys. Scr. 98 (2023) 125956 J Neeli et al 3.4.2. Speciﬁc Wear Rate (SWR) The pins and ball-on-circle machines have been used to concentrate on the wear component in research, going from full-scale lab tests and downscale testing. Of the above techniques, the trunnion-on-circle machine is the easiest, most economical, and most efﬁcient research centre test for concentrating on wear components [58]. Speciﬁc wear rate (SWR) has been described as the loss of material volume per unit of load and sliding distance during the wear of two bodies by G. Suresh et al [59]. Pin-on-disc equipment was used to measure the Coefﬁcient −1 and applied load 10 N of of Wear (K) and Coefﬁcient of friction (μ) with input values sliding velocity 1.224 m s the prepared ceramic samples. It is calculated by using equation (9). SWR = ∆ W ´ ´ L ( r Sd ) ( ) 9 Where ΔW = Sample weight before wear test - Sample weight after wear test, ρ = density of the sample, L= Applied Load, Sd= Sliding distance –6 All prepared ceramic samples with mild wear were found to have a speciﬁc wear rate range of less than 10 mm3/(N m), and this demonstrates that the present materials are favourable to be utilized as wear-safe tribo- materials when contrasted with Polytetraﬂuoroethylene (PTFE) [60, 61]. 3.4.3. Speciﬁc wear energy (SWE) It is the ratio between the frictional energy consumed at the interface and the mass lost due to wear. The Speciﬁc Wear Energy (SWE) is derived from the coefﬁcient of friction and wear rate to determine the tribological characteristics of a material. It is said that composites with a high speciﬁc wear energy have a high speciﬁc wear rate [59]. SWE can be calculated using the following equation (10) ) Specific wear energy SWE ( = v m ´ ´ ´ w D m t ( ) 10 Where ‘v’ is the velocity in m/s, ‘w’ is the applied load in N, ‘μ’ is the coefﬁcient of friction, ‘t’ is time in seconds, and ‘Δm’ is the weight loss in grams. The greater SWE value may be ascribed to the high speciﬁc wear rate and coefﬁcient of friction listed in table 8. Speciﬁc wear energy values rise with the increase in Sm dopant concentration up to x = 0.3 after which they start to decrease. The decrease can be attributed to a reduction in the coefﬁcient of friction. The variation of SWR, SWE, and Coefﬁcient of friction with dopant concentration in NBT are shown in ﬁgure 11(b). 3.4.4. Coefﬁcient of friction (μ) Discovering a low friction coefﬁcient for speciﬁc conditions, such as temperature, time, applied load, sliding cycles on the sample, etc, is critical. This would prevent wear, save money on maintenance, and use less energy, all of which would signiﬁcantly extend the life of the product. The pin-on-disk tests were performed with a 50 mm-diameter track and a normal load of 10 N at a sliding speed of 1.224 m/s for 300 s. At room temperature, the concentrated creative materials’ friction coefﬁcients have produced results in the range of 0.5–0.6; these outcomes are coordinated with past researchers [59, 62]. The investigated ceramic materials might be a better choice for properties like self-lubrication. 4. Conclusions Sodium bismuth titanate [Na0.5(Bi1-xSmx)0.5TiO3] ceramic samples of varying concentrations with x = 0.0, 0.1, 0.3, & 0.5 were prepared from the solid-state reaction method. The rhombohedral phase with space group R3c was conﬁrmed in all prepared ceramic samples using x-ray diffraction patterns and obtained lattice parameters utilizing Rietveld analysis. As the dopant concentration increased, the particle size of the samples decreased, and the lattice strain ion doping increased the granularity value, indicating that the Ostwald ripening concurrently increased. Sm3+ mechanism causes the particles to become coarse during sintering, as received from SEM micrographs. The EDAX spectra for various compositions are in line with stoichiometry, afforming the presence of all the constituent elements. The vibration bands shift and expand with an increase in dopant concentration, indicating lattice deformation. Based on the ﬁndings from UV–vis spectroscopy it is evident that as the doping concentration increased the band gap values decreased, thereby highlighting the potential utility of NBT-based perovskites as photoactive materials. At a concentration of x = 0.3, the luminophore demonstrates a pronounced reddish-orange emission characterized by excellent reddish-orange chromaticity, CCT, and colour purity. The squareness factors of the produced compositions vary from 0.07 to 0.49, rendering them compelling 14 Phys. Scr. 98 (2023) 125956 J Neeli et al materials for applications in memory device. The ceramic material at x=0.3 concentration, the Vickers hardness was very high (8.20 GPa) and exhibited mild wear with relatively low frictional coefﬁcient values. Acknowledgments The authors would like to thank Vignan’s Foundation for Science Technology and Research (VFSTR) deemed to be University, for extending the CoExAMMPC facility for basic characterization of our research samples. 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10.1016_j.isci.2020.100959.pdf	DATA AND CODE AVAILABILITY RNA-seq data have been deposited in NCBI’s Gene Expression Omnibus (GEO). The accession number for the RNA-seq data reported in this paper is GEO: GSE145495.	DATA AND CODE AVAILABILITY RNA-seq data have been deposited in NCBI's Gene Expression Omnibus (GEO). The accession number for the RNA-seq data reported in this paper is GEO: GSE145495.	Article Loss of Asb2 Impairs Cardiomyocyte Differentiation and Leads to Congenital Double Outlet Right Ventricle Subﬆrate adapter polyubiquitinated Subﬆrate E3 Cullin5 Asb2 E2 X Heart Failure Embryonic Lethality DORV Flna Smad2 E7.5 FHF AHF Cardiac Crescent E9.0-9.5 OFT LV RV Looped Heart Negative regulation Positive regulation Abir Yamak, Dongjian Hu, Nikhil Mittal, ..., Christel Moog- Lutz, Patrick T. Ellinor, Ibrahim J. Domian fyamak@mgh.harvard.edu (A.Y.) idomian@mgh.harvard.edu (I.J.D.) HIGHLIGHTS Flna removal partially rescues embryonic lethality of Asb2-heart- speciﬁc knockout AHF-Asb2 knockouts harboring one Flna allele have double outlet right ventricle Asb2-Flna regulate TGFb-Smad2 signaling in the heart Conserved role of Asb2 in heart morphogenesis between mice and humans DATA AND CODE AVAILABILITY GSE145495 Yamak et al., iScience 23, 100959 March 27, 2020 ª 2020 The Author(s). https://doi.org/10.1016/ j.isci.2020.100959 Article Loss of Asb2 Impairs Cardiomyocyte Differentiation and Leads to Congenital Double Outlet Right Ventricle Abir Yamak,1,2,3,9,* Dongjian Hu,2,4 Nikhil Mittal,1,2 Jan W. Buikema,2,5 Sheraz Ditta,2,6 Pierre G. Lutz,7 Christel Moog-Lutz,7 Patrick T. Ellinor,1,2,3 and Ibrahim J. Domian1,2,8,* SUMMARY Deﬁning the pathways that control cardiac development facilitates understanding the pathogenesis of congenital heart disease. Herein, we identify enrichment of a Cullin5 Ub ligase key subunit, Asb2, in myocardial progenitors and differentiated cardiomyocytes. Using two conditional murine knockouts, Nkx+/Cre.Asb2ﬂ/ﬂ and AHF-Cre.Asb2ﬂ/ﬂ, and tissue clarifying technique, we reveal Asb2 requirement for embryonic survival and complete heart looping. Deletion of Asb2 results in upregu- lation of its target Filamin A (Flna), and concurrent Flna deletion partially rescues embryonic lethality. Conditional AHF-Cre.Asb2 knockouts harboring one Flna allele have double outlet right ventricle (DORV), which is rescued by biallelic Flna excision. Transcriptomic and immunoﬂuorescence analyses identify Tgfb/Smad as downstream targets of Asb2/Flna. Finally, using CRISPR/Cas9 genome editing, we demonstrate Asb2 requirement for human cardiomyocyte differentiation suggesting a conserved mechanism between mice and humans. Collectively, our study provides deeper mechanistic under- standing of the role of the ubiquitin proteasome system in cardiac development and suggests a pre- viously unidentiﬁed murine model for DORV. INTRODUCTION Congenital heart diseases (CHDs) are prenatal defects that affect the heart’s structure and/or function and are the leading cause of infant mortality under 1 year of age. Approximately 1%–2% of human babies are born with cardiac malformations that pose as major risk factors for adult cardiovascular problems (Bruneau, 2008; Nemer, 2008). The heart, the ﬁrst functional organ in the developing embryo, starts to form early on during development, before the end of gastrulation. The ﬁrst and second heart ﬁelds (FHF and SHF, respectively) as well as the proepicardial organ and the cardiac neural crest are the major contributors to the forming heart (Martinsen and Lohr, 2015). The FHF gives rise primarily to the left ventricle and most of the atria; the SHF contributes to the right ventricle, outﬂow tract, and parts of the atria (Srivastava, 2006; Yamak and Nemer, 2015). Induction of the cardiac fate and the proper morphogenesis of the verte- brate heart are controlled by a well-characterized and highly conserved combinatorial network of transcrip- tion factors and signaling molecules that act together to orchestrate the embryonic development of the four-chambered mammalian heart and the subsequent post-natal maturation. Of important note, the adult heart has minimal intrinsic regenerative capacity (Mercola et al., 2011). As a result, signiﬁcant stressors on the heart can result in loss of viable or functional myocardial tissue and ultimately heart failure. This renders cardiovascular disease a leading cause of death worldwide and highlights an unmet clinical need for novel approaches for heart regeneration. One major approach is the use of stem cells that can be induced to give rise to the different cell types that constitute the heart. Understanding the cellular processes and signaling pathways that govern in vivo heart formation and maturation is necessary for the generation of functional mature cardiac tissue for clinical and preclinical applications (Hu et al., 2018). Targeted protein degradation by the ubiquitin proteasome system (UPS) is important for the regulation of cellular physiology and is required for normal organ formation (Glickman and Ciechanover, 2002). The UPS consists of three enzymes: Ubiquitin (Ub) activating enzyme, E1, which transfers activated Ub to the Ub conjugating enzyme, E2. This then interacts with the E3 Ub ligase that covalently links the Ub or Ub chain to a lysine residue in the substrate thus targeting it for degradation by the proteasome. The E3 Ub ligase is responsible for substrate speciﬁcity (Jung et al., 2009). Recent evidence points to a role of the UPS in heart disease, particularly in myocardial remodeling, familial cardiomyopathies, chronic heart failure, and 1Harvard Medical School, Boston, MA 02115, USA 2Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, CPZN3200, Boston, MA 02114, USA 3Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA 4Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA 5University Medical Center Utrecht, 3584 CX Utrecht, Netherlands 6Department of Pharmaceutical Sciences, Utrecht University, 3512 JE Utrecht, Netherlands 7Institut de Pharmacologie et de Biologie Structurale, IPBS, Universite´ de Toulouse, CNRS, UPS, Toulouse, France 8Harvard Stem Cell Institute, Cambridge, MA 02138, USA 9Lead Contact Correspondence: fyamak@mgh.harvard.edu (A.Y.), idomian@mgh.harvard.edu (I.J.D.) https://doi.org/10.1016/j.isci. 2020.100959 iScience 23, 100959, March 27, 2020 ª 2020 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). 1 B D A C E Figure 1. Asb2 Is Expressed in the Developing and Adult Heart and Undergoes Isoform Switching during Differentiation (A) qPCR analysis of embryonic cardiomyocytes reveals predominant Asb2a expression in the R-G+ and the R+G+ populations. R-G+: Mef2c-.Nkx2-5+; R+G+: Mef2c+.Nkx2-5+; R+G-: Mef2c+.Nkx2-5-; NEG: Mef2c-.Nkx2-5-. (B) qPCR analysis of Asb2a and Asb2b on RNA from murine hearts of different embryonic stages as well as neonates and postnatal day 8–9. Note that the a isoform is equally expressed at all stages, whereas the b isoform expression increases with development. (C) Western blot analysis on whole tissue extracts from embryonic and adult heart, spleen, and skeletal muscle using Asb2-speciﬁc antibody. Note that Asb2 corresponding band in the embryonic heart co-migrates with that in the spleen where only the a isoform is expressed, whereas that in the adult heart co-migrates with that in the skeletal muscle that is known to express on the b isoform. These data are consistent with the qPCR data in (B). 2 iScience 23, 100959, March 27, 2020 Figure 1. Continued (D) In situ hybridization on E9.5 mouse embryo showing robust Asb2 expression in the LV, RV, and OFT and to a lesser extent the IFT. LV, left ventricle; RV, right ventricle; OFT, outﬂow tract; IFT, inﬂow tract. (E) Immunohistochemistry on E10.5 and E12.5 mouse embryos using Asb2-speciﬁc antibody (green) and Troponin T (red). DAPI (blue) marks nuclei. Note Asb2 expression colocalizes with Troponin T in the myocardium (arrows) and no expression is seen in the endocardial cells (arrow heads). Scale bar is equivalent to 250 mm in the ﬁrst three heart images left to right at E10.5 (top) and E12.5 (bottom), 25 mm in the E10.5 heart image top far right, and 50 mm in the E12.5 heart image bottom far right as indicated in the ﬁgure. ischemia-reperfusion injury (Pagan et al., 2013). Pharmacological inhibition of the proteasome is a new and promising means for cardioprotection (Pagan et al., 2013). Paradoxically, enhancing UPS activity has in some cases also provided protection against heart disease (Bulteau et al., 2001; Li et al., 2011; Powell et al., 2007) highlighting the importance of deﬁning the role of the UPS as a therapeutic target in cardiac disease. In addition to its role as a protein quality control, the UPS has also been shown to regulate the turnover of sarcomere proteins, including the myoﬁbrillar proteins myosin, actin, and troponin. Examples of this include the E3 Ub ligases MuRF1 (muscle-speciﬁc RING ﬁnger 1, targeting troponin I) and F box pro- tein Fbx122 (targeting a-actinin and ﬁlamin C) (Kedar et al., 2004; Spaich et al., 2012). E3 ligases have also been shown to regulate important signaling pathways in the heart, such as the JNK (c-Jun N terminal kinase) (Laine and Ronai, 2005), calcineurin (Fan et al., 2008), and the VEGF (vascular endothelial growth factor) signaling pathways (Murdaca et al., 2004). This important role of the UPS in the heart and its poten- tial for a therapeutic target in cardiac disease brings about a need to understand its speciﬁc function in heart development and disease. Using our previously described transgenic reporter system (Domian et al., 2009), we identiﬁed Asb2 (ankyrin repeat-containing protein with a suppressor of cytokine signaling box [SOCS box] 2) as being enriched in FHF and SHF cardiac progenitors. Asb2, which encodes speciﬁcity subunit of Cullin 5 RING E3 Ub ligase, exists in two isoforms: Asb2a and Asb2b in mouse, corresponding to variants 2 and 1 in humans, respectively (Bello et al., 2009). It has previously been shown to regulate differ- entiation of myeloid leukemia cells and skeletal myogenesis through proteasomal degradation of ﬁlamin proteins (Bello et al., 2009; Guibal et al., 2002; Heuze´ et al., 2008). Filamins (Flna, Flnb, and Flnc in mice) are actin-binding proteins important for the stabilization of the actin-cytoskeleton (van der Flier and Son- nenberg, 2001). Flnc is the only isoform expressed in the heart muscle where it is required for normal contractility (Fujita et al., 2012). Flna expression in the heart is restricted to endocardial and mesenchymal cells of the cardiac cushions during development and to valve leaﬂets in the adult heart (Norris et al., 2010). FLNA and FLNC mutations have been linked to cardiac defects in humans (de Wit et al., 2011, 2009; Kyndt et al., 2007; Valde´ s-Mas et al., 2014). In zebraﬁsh, an additional Asb2 target TCF3 was very recently identi- ﬁed where TCF3 was negatively regulated by Asb2 during cardiogenesis (Fukuda et al., 2017). Asb2 down- regulation was also shown to be a mediator of follistatin-induced muscle hypertrophy and SMAD2/3 regu- lation of skeletal muscle mass in young adults in mice. The repression of Asb2 was, however, ameliorated in aging mice, some of which also displayed increasing Asb2 baseline levels (Davey et al., 2016). Asb2 over- expression was also shown to drive skeletal muscle atrophy in mice (Davey et al., 2016). A recent study also showed that Asb2 knockout is embryonic lethal and that Asb2a targets Flna for proteasomal degradation during early cardiomyocyte differentiation (Me´ tais et al., 2018). The embryonic lethality of Asb2 mutants was shown to be primarily due to heart defects (Me´ tais et al., 2018). Herein, we show that Asb2 knockout in the FHF and SHF are both embryonic lethal by E10.5 and E12.5, respectively. Using tissue clearing combined with immunoﬂuorescence technique, we show that Asb2 mutant hearts have incomplete looping. Moreover, Asb2 regulates cardiac morphogenesis partly through Flna turnover, and we hereby propose a model where Asb2-Flna controls TGFb-SMAD signaling to drive early cardiac formation. Additionally, Asb2 lethality in the anterior heart ﬁeld (AHF) is partially rescued by Flna removal from these hearts. We also show that Asb2 ablation in the AHF leads to double outlet right ventricle (DORV), which is corrected upon further deletion of Flna from these hearts. Finally, we reveal that Asb2 role in cardiomyocyte differentiation is conserved in human cardiomyocytes as well. Collectively, our results shed light on the UPS regulation of heart development and its role as a cardio-therapeutic target and provide evidence for the ﬁrst time for the role of the UPS in the rare congenital heart defect, DORV. RESULTS Asb2 Is Highly Enriched in the Embryonic Heart We have previously characterized a transgenic reporter system for the isolation of three distinct mouse cardiac progenitor cells from developing embryos: FHF population, marked by Nkx2.5+.Mef2c- expression, and two SHF population subsets: Nkx2.5-.Mef2c+ and Nkx2.5+.Mef2c+ (Domian et al., 2009). Genome-wide iScience 23, 100959, March 27, 2020 3 E A B C D Figure 2. Asb2 Is Essential for Early Cardiac Development (A) Nkx2-5+/Cre.Asb2 E9.5 and E11.5 knockout (KO) embryos (ﬂ/ﬂ) versus wild-type littermates (Wt). Note the resorbing KO embryo at E11.5. Scale bar is equivalent to 0.4 mm for E9.5 and 0.5 mm for E11.5 as indicated. (B) AHF-Cre.Asb2 E10.5 and E12.5 knockout (KO) embryos (ﬂ/ﬂ) versus wild-type littermates (Wt). Note the resorbing KO embryo at E12.5. Scale bar is equivalent to 0.5 mm for E10.5 and E12.5 as indicated in the ﬁgure. (C) 3D reconstruction of CUBIC-cleared, Troponin-T-stained E9.5 whole control and Asb2 mutant embryos showing both ventral and dorsal views. Note the bulging in the right ventricle of the control heart that is lacking in the mutant (indicated by the red arrow heads). Scale bar is equivalent to 200 mm as indicated. 4 iScience 23, 100959, March 27, 2020 Figure 2. Continued (D) Measurement of the heart tube of control and Asb2 mutant hearts. Note the statistically signiﬁcant shorter heart tubes of the mutants. N = 5 per group. Data are represented as mean G SEM. = p < 0.005. Unpaired t test was used using GraphPad Prism; p < 0.05 is considered statistically signiﬁcant. (E) Heatmap analysis of a subset of cardiac looping differentially expressed genes in RNA-seq data from control (Nkx2-5+/Cre.Asb2ﬂ/+) versus Nkx2-5+/Cre.Asb2 knockout E9.5 murine hearts. N = 3 in each group (each sample is in itself a combination of three to four hearts to account for heterogeneity among different litters). transcriptional proﬁling and real-time PCR (qPCR) reveal Asb2 transcripts enrichment in the three popula- tions (Figure 1A) (Domian et al., 2009). To investigate the temporal expression of Asb2 in the developing heart, we performed qPCR analysis on RNA from mouse hearts at different stages of embryonic develop- ment. Our data show that Asb2a is expressed similarly throughout heart development, whereas Asb2b expression increases with development (Figure 1B). This is further conﬁrmed by western blot analysis, which shows that the Asb2 band in the embryonic heart co-migrates with that in the spleen (which expresses Asb2a [Spinner et al., 2015]), whereas the Asb2 band in the adult heart co-migrates with that in the skeletal muscle (known to express Asb2b [Bello et al., 2009]) (Figure 1C). To further investigate in vivo spatial cardiac expres- sion of Abs2, we performed in situ hybridization on E9.5 embryos. Our data reveal robust expression of Asb2 transcripts predominantly in the left (LV) and right ventricles (RV) and to a lower extent in inﬂow (IFT) and outﬂow tracts (OFT) (Figure 1D). Furthermore, immunostaining of E10.5 and E11.5 (Figure 1E, upper and lower panels, respectively) embryonic sections using Asb2-speciﬁc antibody shows that, in the heart, Asb2 expression (green) is restricted to the myocardium overlapping with cardiac Troponin T (red). White arrows in the zoomed merged image at E10.5 (right panel) indicate overlap of Asb2 and Troponin T in the myocardial layer, but no expression is seen in the endocardial layer indicated by arrow heads. Asb2 Is Required for Early Cardiac Formation To investigate the role of Asb2 during cardiac development, we generated two conditional knockout lines (KO): Nkx2-5+/Cre (a mouse line with the Cre recombinase knocked into the Nkx2-5 locus) and AHF-Cre (a mouse line with a transgene placing Cre under the transcriptional control of the AHF enhancer of the Mef2c gene). These mouse lines allow for the targeted removal of (Lombardi et al., 2009) Asb2 from the whole heart and the SHF, respectively (Lombardi et al., 2009). The ﬂoxed alleles are in common region and inactivate both Asb2 isoforms. Both conditional KOs have pericardial edema and are embryonic lethal: Nkx2-5+/Cre.Asb2ﬂ/ﬂ mice die at E10.5–11 and AHF-Cre.Asb2ﬂ/ﬂ die at E11.5–12 (Figures 2A and 2B, respec- tively). AHF-Cre.Asb2ﬂ/ﬂ mice analyzed at E10.5 also have shorter OFT compared with their control litter- mates (Figure S1D). For Nkx2-5+/Cre.Asb2ﬂ/ﬂ, mice were analyzed at E8.5 (3 litters), E9.5 (23 litters), E10.5 (3 litters), and E11.5 (2 litters); for AHF-Cre.Asb2ﬂ/ﬂ, mice were analyzed at E9.5 (3 litters), E10.5 (4 litters), and E12.5 (2 litters). Each litter consists of 8–11 embryos in total. All embryos were genotyped. Figure S1A shows the reduced level of Asb2 in the heterozygotes (Nkx2-5+/Cre.Asb2ﬂ/+) and the complete loss of Asb2 in the knockouts (Nkx2-5+/Cre.Asb2ﬂ/ﬂ). In order to perform a phenotypic analysis of the Nkx2-5+/Cre-Asb2ﬂ/ﬂ mutant embryos, we used state-of- the-art tissue clearing technique CUBIC combined with immunostaining. CUBIC can effectively clear mice embryos and embryonic hearts while preserving immunolabels (Kolesova´ et al., 2016; Tainaka et al., 2014). Nkx2-5+/Cre-Asb2ﬂ/ﬂ and control littermates e9.5 mice embryos were cleared with CUBIC and stained for Troponin T to mark cardiomyocytes as well as DAPI for nuclei. Confocal microscopy with optical sectioning followed by 3D-reconstruction allowed the precise visualization of the developing hearts without disruption of underlying anatomy. During cardiac morphogenesis, the straight heart tube undergoes sequential looping steps to get to the fully looped heart. The fully looped heart acquires a he- lical shape in mice that is also referred to as the mature S-loop in chicks (Le Garrec et al., 2017; Ma¨ nner, 2009). In Le Garrec et al. paper, they used computer modeling to simulate the biological process of mouse cardiac looping, incorporating in their model the left-right asymmetry and mechanical constraints seen in the looping heart. Their ﬁndings suggest that the lack of any of these parameters would lead to a C-shaped heart loop rather than the helical structure. In the chick, the heart is ﬁrst transformed into a C-shaped heart, a process known as dextral looping. The C-loop is then converted into an immature S-loop that then trans- forms into a mature S-looped heart where the ventricular segments are curved outward to generate the left and right chambers (Ma¨ nner, 2009). As shown in Figure 2C, the mutant embryos do not form the full helical structure seen in the control littermates. Instead, they have partially looped hearts that resemble the C-shaped hearts in the chick (Ma¨ nner, 2009). Moreover, measurement of the heart tube length in mutant versus control hearts reveals statistically signiﬁcant shorter tubes in the mutant hearts (Figure 2D). Video S1 is a z stack of stained control and mutant e9.5 embryos showing the incomplete looping in the mutant embryo. Figure S1C represents four images from the z stack at different depth in the embryo. Four to ﬁve iScience 23, 100959, March 27, 2020 5 A B C 6 iScience 23, 100959, March 27, 2020 Figure 3. Asb2 Targets Flna for Proteasomal Degradation in the Developing Heart and Asb2-Mutant Hearts Have an Altered Gene Expression Proﬁle (A) Immunohistochemistry on E9.5 Abs2 heterozygote (Nkx2-5+/Cre.Asb2ﬂ/+, middle pane) and mutant hearts (Nkx2-5+/Cre.Asb2ﬂ/ﬂ, lower panel) as well as Wt controls (top panel) using Flna (red) and Troponin-T (green)-speciﬁc antibodies. Note that FlnA expression is restricted to the endocardial layer (white arrow heads) in the Wt heart, whereas it is abnormally expressed in the myocardial layer in the Asb2-mutant hearts co-localizing with Troponin-T expression there (white arrows). Moreover, some cardiomyocytes in the outﬂow tract of the Asb2-heterozygous hearts also express Flna (yellow arrows) suggesting a dose- dependent regulation. Scale bar is equivalent to 250 mm in the ﬁrst column (left), 100 mm in the second, third, and fourth columns, and 25 mm in the ﬁfth (far right) column as indicated in the ﬁgure. (B) Heatmap analysis of RNA-seq data from control (Group1: Nkx2-5+/Cre.Asb2ﬂ/+), Asb2 mutant (Group2: Nkx2-5+/Cre.Asb2ﬂ/ﬂ), Flna mutant (Group3: Nkx2-5+/Cre.Flnaﬂ/y), and Asb2-Flna double mutant (Group4: Nkx2-5+/Cre.Asb2ﬂ/ﬂ.Flnaﬂ/y) E9.5 murine hearts. Note the high level of differentially expressed genes in the Asb2-mutant and Asb2-Flna double mutant versus the control groups. A small subset of genes (indicated by arrows) that are perturbed in the Asb2-mutant hearts are restored to normal in the Asb2-Flna double mutants. N = 3 in each group (each sample is in itself a combination of three to four hearts to account for heterogeneity among different litters). (C) Heatmap analysis of a subset of genes from the RNA-seq data in (B) that are part of the Tgfb/Smad signaling pathway. Note that the Foxa genes expression levels (indicated with a yellow line) that are downstream of the Tgfb/Smad are restored to normal in the Asb2-Flna double mutants versus the Asb2-mutant hearts. embryos were analyzed for each condition. The efﬁciency of the CUBIC/immunostaining technique on e9.5 mouse embryo is evidenced by the clearly visible striations of the cardiac muscle ﬁbers (Figure S1B). In order to identify Asb2 downstream targets in the heart, RNA sequencing (RNA-seq) analysis was per- formed on Nkx2-5+/Cre.Asb2ﬂ/ﬂ and control littermates (Figure 3B) (Figure S2C shows reduced levels of Asb2 transcripts in the Nkx2-5+/Cre.Asb2ﬂ/ﬂ knockout compared with the Nkx2-5+/Cre.Asb2ﬂ/+ heterozygote control). The gene expression proﬁle was greatly altered in the Asb2 mutant hearts compared with their control littermates (Group 2 versus Group 1) (Figure 3B). Of note, a number of genes that are mis-expressed in the Asb2 cardiac mutant hearts have been previously linked to abnormal cardiac looping in mice (Figure 2E) (Azhar et al., 2003; Bardot et al., 2017; Chen et al., 1997; Le Garrec et al., 2017; Mine et al., 2008; Ribeiro et al., 2007; Vincentz et al., 2011). Ingenuity Pathway Analysis also shows that ‘‘cardiovascular system devel- opment and function’’ as well as ‘‘cardiovascular disease’’ are among the top pathways altered in the Asb2 mutant hearts (Table S1, yellow highlights). Table S2 is an upstream analysis with the ones with a positive activation Z score > 1.5 highlighted in yellow. This list shows the pathways whose downstream targets are altered (upregulated or downregulated) in our knockouts versus controls. Targets with a positive Z score suggest upregulation pathways in the Asb2 mutant hearts. Asb2 Controls Cardiac Morphogenesis Partly through Regulating Filamin A Since Asb2 targets ﬁlamin proteins for degradation (Me´ tais et al., 2018) and Flna perturbations lead to car- diac defects and embryonic lethality (Feng et al., 2006), we investigated cardiac Flna expression in the Nkx2-5+/Cre.Asb2ﬂ/ﬂ. Flna expression in the control heart (Figure 3A, top panel) is restricted to endocardial and pericardial layers (red staining, white arrow heads). In the knockout embryos (Figure 3A, third panel), Flna’s expression domain is abnormally expanded to include the myocardial layer (white arrows), co-local- izing with Troponin T expression (green for Troponin and yellow for the co-localization). Moreover, in Nkx2-5+/Cre.Asb2ﬂ/+ heterozygous hearts (Figure 3A, second panel), Flna is abnormally expressed in some cardiomyocytes of the OFT myocardium (yellow arrows) suggesting that Asb2 regulation of Flna turn- over is dose dependent. We then hypothesized that, if Asb2 cardiac mutant phenotype is due to overexpression of Flna, then concurrently deleting Flna along with Asb2 should suppress the Asb2 phenotype. (Please note that Flna is an x-linked gene so a knockout is denoted by ﬂ/ﬂ for female or ﬂ/y for male, whereas a heterozygous is denoted by ﬂ/x or ﬂ/+.) To examine this hypothesis, we developed Nkx2-5+/Cre.Asb2ﬂ/ﬂ.Flnaﬂ/y double mutants. Removal of Flna from the hearts of Nkx2-5+/Cre.Asb2ﬂ/ﬂ did not rescue lethality (Figure S2A). Approximately 16 litters were analyzed at E9.5 and 3 litters at E10.5. As expected, Nkx2-5+/Cre.Asb2ﬂ/ﬂ.Flnaﬂ/ﬂ double knockouts no longer harbor ectopic Flna expression in the myocardium (Figure S2B) as was previously seen with the Nkx2-5+/Cre.Asb2ﬂ/ﬂ single knockouts (Figure 3A). Instead, the double knockouts have normal endocardial expression of Flna similar to their control litter- mates (Figure S2B). RNA-seq analysis on e9.5 hearts from these mice show that their gene expression pro- ﬁle is closely related to the Nkx2-5+/Cre.Asb2ﬂ/ﬂ group (Group 2 versus Group 4) (Figure S2C shows reduced levels of Asb2 transcripts in the Nkx2-5+/Cre.Asb2ﬂ/ﬂ.Flnaﬂ/y double knockout compared with the Nkx2-5+/Cre.Asb2ﬂ/+ heterozygote control). However, some genes whose expression was altered in the Nkx2-5+/Cre.Asb2ﬂ/ﬂ group are restored to normal in the Nkx2-5+/Cre.Asb2ﬂ/ﬂ.Flnaﬂ/y hearts (indicated by arrows and shown in Table S3). These results suggest that Flna concurrent deletion can restore the normal expression level of a subset of genes in the Asb2 mutant hearts. Among these genes are the iScience 23, 100959, March 27, 2020 7 A B C 8 iScience 23, 100959, March 27, 2020 Figure 4. Tgfb/Smad Signaling Activity Is Downstream Asb2-Flna in the Developing Heart (A) Schematic representation of Asb2-Flna-Smad2 interaction network using MetaCore Clarivate Analytics software. Note that Asb2 ubiquitinates and negatively regulates Flna, whereas Flna binds directly to and positively regulates Smad2. (B) Immunohistochemistry on Nkx2-5+/Cre.Asb2 mutant (middle panel) and Nkx2-5+/Cre.Asb2-Flna double mutant (last panel) murine hearts as well as wild- type controls (top panel) using pSmad2-speciﬁc antibody (green) and Troponin T (red). Note the nuclear localization of pSmad2 as a sign of Tgfb/Smad2 cycle activation. Examples of positive (purple arrowhead) and negative (yellow arrowheads) nuclei are indicated in the Wt sample (red box). DAPI marks all nuclei (AV, atrioventricular canal; V, primitive ventricle; OFT, outﬂow tract; Myo, myocytes; Endo, endocardial cells). Myocardial cells are marked by white arrows; endocardial cells are marked by white arrowheads. Scale bar is equivalent to 75 mm in the ﬁrst two columns from the left and 25 mm in the third, fourth, and ﬁfth columns as indicated in the ﬁgure. (C) Quantiﬁcation of the immunostaining in (B) of percentage of pSmad2-positive nuclei in cardiomyocytes ((AV+V) Myo and OFT Myo) as well as endocardial cells (AV endo). Note the increased level of pSmad2-positive nuclei in Asb2-mutant myocytes that are restored to normal in the Asb2-FlnA double mutants. This regulation is not seen in the endocardial cells that do not express Asb2 (Figure 1E). n = 7 for Wt; n = 4 for Nkx2-5+/Cre.Asb2ﬂ/ﬂ; n = 3 for Nkx2-5+/Cre.Asb2ﬂ/ﬂ.Flnaﬂ/ﬂ. Data are represented as mean G SEM. * = p < 0.05 Control versus Nkx2-5+/Cre.Asb2ﬂ/ﬂ; # = p < 0.05 Nkx2-5+/Cre.Asb2ﬂ/ﬂ versus Nkx2-5+/Cre.Asb2ﬂ/ﬂ.Flnaﬂ/ﬂ; NS = not signiﬁcant. Two-way ANOVA was used for analysis using GraphPad Prism. p < 0.05 is considered statistically signiﬁcant. Foxa genes, which are downstream of the Tgfb/Smad signaling (Figure 3C, yellow line) (Tang et al., 2011). Other genes in the Tgfb/Smad pathway are also altered in both Asb2-mutant and Asb2.Flna double mutant hearts (Figure 3C). Figure S2D is a qPCR analysis conﬁrming some of these altered genes. Tgfbr1 and InhA (which encodes a member of the Tgfb superfamily) are also among the positively regulated targets in the upstream analysis of the RNA-seq data of Asb2-mutant hearts versus control (Table S2). Both genes are no longer positively regulated in the upstream analysis of the list of genes corrected in the Asb2-Flna double mutant hearts (Table S3, yellow highlights). These data prompted further analysis of the Asb2/Flna regula- tion of TGFb/Smad signaling in the heart of these mice. Asb2 Regulates TGFb/Smad Signaling through Regulating Filamin A Protein TGFb signaling is initiated upon ligand-stimulated activation of serine/threonine receptor kinases that in turn lead to phosphorylation and activation of Smad proteins. Activated Smads interact with common signaling transducer Smad4, translocate to the nucleus, and activate downstream targets (Shi and Massague´ , 2003). Flna directly associates with Smad2 and Smad2 phosphorylation, and TGFb/Smad2 signaling is impaired in Fln-null human melanoma cells (Sasaki et al., 2001; Zhou et al., 2011). Moreover, FLNA mutations were linked to x-linked myxomatous valvular dystrophy, a multivalve degeneration disor- der, and disrupted TGFb/Smad2/3 signaling was implicated in the disease pathogenesis (Geirsson et al., 2012; Norris et al., 2010). Using the ‘‘Build Network’’ module in MetaCore Clarivate Analytics software, we investigated the Asb2-Flna-Smad2 interaction. As shown in Figure 4A, Asb2 negatively regulates Flna through ubiquitination and Flna positively regulates Smad2 through direct binding. Asb2, Flna, and Smad2 are shown in red for visualization. In order to investigate further Asb2/Flna regulation of TGFb/ Smad2 signaling in cardiac development, we immunostained E9.5 Asb2-mutant hearts with antisera directed against pSmad2 (Figure 4B) and then quantiﬁed the pSmad2-positive nuclei. Figure 4C shows signiﬁcant increase in the percentage of pSmad2-positive nuclei in the Nkx2-5+/Cre.Asb2ﬂ/ﬂ myocytes (pre- viously shown to have overexpression of Flna [Figure 3A]) compared with their littermate controls. This increase was not seen in the endocardial cells where Flna expression is normal (Figure 3A). Interestingly, pSmad2 levels were restored to normal in the Nkx2-5+/Cre.Asb2ﬂ/ﬂ.Flnaﬂ/ﬂ (double mutant) myocytes further conﬁrming that Asb2 regulates pSmad2 in the heart through the regulated turnover of Flna. Flna Removal from AHF-Cre.Asb2 Mutant Hearts Partially Rescues Embryonic Lethality To examine Flna expression in the AHF-Cre.Asb2ﬂ/ﬂ hearts (where Asb2 is knocked out in the RV and OFT only), Flna immunostaining was performed. As shown in Figure 5A, Flna expression (red) is restricted to the endocardial and epicardial layers in the control hearts (top panel, white arrow heads), whereas it is aber- rantly expressed in the myocardial layer of the OFT and RV only (red staining lower panel, white arrows), co-localizing with TroponinT expression (yellow staining lower panel) there. Flna expression was normal in the myocardial layer of the primitive left ventricle (PV) that harbors normal Asb2 expression and acts as an internal control in these mice. We then sought to examine the effect of further knocking out Flna from the AHF-Cre.Asb2-mutant hearts. To do this, we crossed Asb2ﬂ/ﬂ.Flnaﬂ/ﬂ with AHF-Cre.Asb2ﬂ/+ mice. Our results show that AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/y are born with the expected Mendelian ratios (Figure 5B); however, newborn pups die between P0.5 and P1.5. These results show that Flna deletion partially rescues Asb2 lethality. The AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/+ also survive to birth albeit at a lower percent- age from what is expected by Mendelian ratios; these mice also die right after birth at P0.5. iScience 23, 100959, March 27, 2020 9 Troponin FlnA OFT DAPI Merge Ε9.5 250μm Ε9.5 100μm 100μm 100μm 25μm PV Troponin FlnA OFT PV DAPI Merge Ε9.5 250μm Ε9.5 100μm 100μm 100μm 25μm + / l f 2 b s A . e r C - F H A l f / l f 2 b s A . e r C - F H A A B AHF-Cre.Asb2fl/+ X Asb2fl/fl Expected Observed at P0.5 AHFCreAsb2fl/fl AHFCreAsb2fl/+ Control 25% 25% 50% 0/29 (0%) 13/29 (44.8%) 16/29 (55.1%) AHF-Cre.Asb2fl/+ X Asb2fl/fl.FlnAfl/fl e r C F H A Asb2fl/fl.FlnAfl/+ Asb2fl/fl.FlnAfl/y Asb2fl/+.FlnAfl/+ Asb2fl/+.FlnAfl/y Expected Observed at P0.5 1/31 (3.2%) 4/31 (12.9%) 6/31 (19.3%) 6/31 (19.3%) 12.5% 12.5% 12.5% 12.5% Control 50% 14/31 (45.2%) Dead at P0.5-1.5 Figure 5. Flna Removal from AHFCre.Asb2 Mutant Hearts Partially Rescues Their Lethality (A) Immunohistochemistry on E9.5 AHF-Cre.Abs2 mutant hearts (AHF-Cre.Asb2ﬂ/ﬂ, lower panel) and littermate controls (AHF-Cre.Asb2ﬂ/+, top panel) using Flna (red)- and Troponin-T (green)-speciﬁc antibodies. Note overexpression of Flna in the OFT (white arrows) of the AHF-Cre.Asb2 mutant hearts but not the primitive left ventricle (PV) that harbors normal Asb2 levels thus serving as an internal control. Flna expression in the control hearts is restricted to the endocardial layer (white arrow heads). Scale bar is equivalent to 250 mm in the ﬁrst column (left); 100 mm in the second, third, and fourth columns; 25 mm in the ﬁfth column (far right) as indicated. (B) Table showing the survival of AHF-Cre.Asb2 mutant (top) and AHF-Cre.Asb2-Flna double mutant (bottom) mice. Note that no AHF-Cre.Abs2 mutant mice are observed at P0.5. However, the AHF-Cre.Asb2-Flna double mutant (Asb2ﬂ/ﬂ.Flnaﬂ/y) mice are born at the expected Mendelian ratios. AHF-Cre.Asb2- mutant mice harboring one copy of Flna (Asb2ﬂ/ﬂ.Flnaﬂ/+) are also born yet at lower percentage than what is expected by Mendelian genetics. These mice die, however, right after birth. P0.5, postnatal day 0.5. Asb2 Removal from the Anterior Heart Field Leads to Double Outlet Right Ventricle) in Mice To determine the cardiac defects of the AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/+, we examined these mice at e16.5–e17.5 after the completion of cardiac morphogenesis but prior to the perinatal mortality associated with this genotype. Five litters were analyzed. Figure 6A shows the survival rate of these mice at E16.5. Gross examination of these hearts revealed that both the aorta and the pulmonary artery originate in the RV (Figure 6B, middle panel, yellow circle). In contrast, both the control hearts (Figure 6B, left panel) and those with AHF-Cre.Asb2-Flna double mutant (Fig- ure 6B, right panel) were grossly normal with the pulmonary artery originating in the RV and the aorta originating in 10 iScience 23, 100959, March 27, 2020 C * A B D Figure 6. AHF-Asb2 Mutant Hearts Have Double Outlet Right Ventricle and Ventricular Septal Defect (A) Table showing the survival of AHF-Cre.Asb2-Flna double mutant mice at E16.5. (B) E16.5 whole hearts of AHF-Cre.Asb2-mutant embryos with one copy of Flna (AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/x), AHF- Cre.Asb2.Flna double mutants (AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/ﬂ), and wild-type control. Note that both the pulmonary artery (PA) and the aorta (Ao) are open in the right ventricle (RV) of the AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/x hearts (yellow circle). Scale bar is equivalent to 0.02 mm as indicated. (C) Masson trichrome staining of E16.5 heart sections of control (Wt) (top), AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/x (middle), and AHF- Cre.Asb2ﬂ/ﬂ.Flnaﬂ/ﬂ (bottom) embryos. Note that both the pulmonary artery and the aorta open in the right ventricle of the AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/x hearts (yellow circle, middle panel) but not the Wt or the AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/ﬂ hearts. The AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/x also have a VSD indicated by asterisk (middle panel, right). Ao, aorta; PA, pulmonary artery; RV, right ventricle; LV, left ventricle; IVS, interventricular septum. Scale bar is equivalent to 250 mm. (D) Number of E16.5 hearts with DORV in Wt, AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/x, and AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/ﬂ embryos. Note that 5/5 Asb2ﬂ/ﬂ.Flnaﬂ/x have DORV accompanied by a VSD suggesting 100% disease penetrance in these mice. the LV. Serial sections of mutant and control hearts (Figure 6C) further conﬁrm that the AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/x mice have DORV (Figure 6C middle panel, yellow oval). This is also accompanied by a ventricular septal defect (Figure 6C middle panel right, indicated by asterisk), a feature commonly associated with DORV in patients with congenital heart disease (Obler et al., 2008). As shown in Figure 6D, the DORV phenotype appeared to be fully penetrant in the AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/x hearts. Notably, the DORV phenotype is corrected in the AHF-Cre.Asb2-Flna double mutant hearts (AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/y, Figure 6C lower panel). Asb2 Is Required for Human Embryonic Stem Cell-Derived Cardiomyocyte Differentiation To further investigate if the requirement for Asb2 for cardiac development is conserved during human cardiomyo- (hESC)-derived cardiomyocyte in vitro cyte differentiation, we turned to human embryonic stem cell differentiation. Both ASB2 variants 1 (Asb2b in mice) and 2 (Asb2a in mice) are expressed at different stages of cardiomyocyte differentiation (Figure 7A, top and bottom graphs, respectively). Using CRISPR/Cas9 genome ed- iting technology, we then generated ASB2-null hESCs. The guides were designed in exon 2 (targeting variant 1 speciﬁcally) or exon 4 (targeting variants 1 and 2) (Figure S3A). Four wild-type (Wt) (received the CRISPR/Cas9 con- structs but failed to generate an in/del) and four knockout (KO) lines were generated. The genotype of all lines was conﬁrmed by sequencing (refer to Transparent Methods), and the knockouts were conﬁrmed by qPCR (Figure 7B, right panel). Wt clones were able to differentiate into beating cardiomyocytes, whereas all four KO lines failed to do so (Video S2, top panels for Wt clones and bottom panels for KO clones). Calcium cycling was also impaired in the Asb2-null derived hESCs (Video S3, left panel for Wt and right panel for KO, and Figure S3B). Two Wt and two KO lines were used for further investigation. qPCR analysis on RNA from cardiomyocytes derived from these cells iScience 23, 100959, March 27, 2020 11 A C B D Figure 7. ASB2 Is Essential for Human Embryonic Stem Cell (hESC)-derived Cardiomyocytes Differentiation (A) qPCR analysis of RNA from hESC-derived cardiomyocytes at different stages of differentiation. Note that both ASB2 variants are expressed at the different stages. N = 4 for D0, n = 5 for all other stages. Data are represented as mean G SEM. (B) qPCR analysis of RNA extracted from two different wild-type (Wt) clones and two ASB2 mutant (KO) clones d7 (left) and d15 (right) differentiated hESCs. Note reduced Troponin T (differentiation marker) transcripts in the mutants at d15 and no difference in MESP1 and NKX2-5 (cardiac progenitor markers) levels at d7. N = 3 per sample for d7 and N = 4 per sample for d14. Data are represented as mean G SEM. * = p < 0.05. Two-way ANOVA was used for analysis using GraphPad Prism. p < 0.05 is considered statistically signiﬁcant. (C and D) Immunostaining of d8 (C) and d15 (D) differentiated Wt and ASB2 mutant cells (KO). Note reduced Troponin T (red)-positive mutant cells at d15 but no difference in NKX2-5 (green) levels between Wt and mutant cells at both stages. DAPI (blue) marks all nuclei. Scale bar is equivalent to 8 mm in (C) and 10 mm in (D) as indicated. shows that cardiac Troponin T transcript levels (TNNT2, marker of cardiomyocyte differentiation) are greatly reduced in the KO lines at d15 of differentiation (Figure 7B, right). On the other hand, both NKX2-5 and MESP1 (markers of cardiac progenitors) are normally expressed at d7 of differentiation (Figure 7B, left). This was further conﬁrmed at the protein level by immunostaining that shows great reduction in cardiac Troponin T (red) expression at d15 (Figure 7D) and normal NKX2-5 levels (green) at days 15 and 8 (Figures 7C and 7D, respec- tively). These data suggest that Asb2-null hES cells can commit to the cardiac lineage but arrest in differentiation prior to the generation of functional cardiomyocytes. We then examined if ASB2 regulation of the TGFb/SMAD signaling seen in mice hearts is conserved in the human cells. As discussed above, upon TGFb/Smad activation, the signaling transducer Smad4 is 12 iScience 23, 100959, March 27, 2020 A B C Figure 8. ASB2 Is an Upstream Regulator of TGFb/SMAD Pathway in hESC-derived Cardiomyocytes (A) Immunostaining of d15 Wt and ASB2 mutant (KO) hESC-derived cardiomyocytes using SMAD4-speciﬁc antibody (green). Note reduced level of nuclear but not total mean ﬂuorescence intensity of SMAD4-positive cells in the KOs (quantiﬁcation graphs on the right). DAPI (blue) marks all nuclei. Scale bar is equivalent to 75 mm in the ﬁrst column and 25 mm in the second and third columns as indicated. : p < 0.005 signiﬁcant versus Wt. Unpaired t test was used for analysis using GraphPad Prism. (B and C) (B) Western blot analysis of Wt and ASB2 mutant (KO) hESC-derived cardiomyocytes using SMAD4, SMAD2, and pSMAD2 antibodies. Note increase of SMAD4 and pSMAD2 in the mutant clones. Data are representative of three separate experiments (C) Quantiﬁcation of the western blot analysis in (B). Data are average of quantiﬁcation from three separate experiments. : signiﬁcant versus Wt1 and Wt2. p < 0.05 is considered statistically signiﬁcant. One-way ANOVA was used for analysis using GraphPad Prism. translocated to the nucleus to activate downstream targets. Figure 8A shows an increase in nuclear SMAD4 (green) in the ASB2-null hES-derived cardiomyocytes. The nuclear versus total SMAD4 was quantiﬁed (Fig- ure 8A, right graph) showing that nuclear SMAD4 signal is doubled in the ASB2-null cells, whereas total Smad4 levels remain the same. Western blot analysis on total protein extracts from these cells also conﬁrms signiﬁcant increase in both SMAD4 and pSMAD2 protein levels (Figures 8B and 8C). This further conﬁrms that the TGFb/SMAD signaling pathway is activated in Asb2-null cardiomyocytes. DISCUSSION In this study, we provide strong evidence for the role of Asb2 in controlling heart morphogenesis partly through its regulation of the actin-binding protein, Filamin A (Flna), and Tgfb/Smad signaling. We further show that this regulation is part of the DORV disease pathogenesis. Using CUBIC clearing technique combined with immunoﬂuorescence and confocal microscopy, we show that the Asb2-mutant hearts have shorter heart tubes and do not form the fully looped helical structure. RNA-seq analysis also reveals that a number of genes that have been linked to cardiac looping defects iScience 23, 100959, March 27, 2020 13 are altered in the Asb2-mutant hearts. Recent morphological analysis of Asb2 null embryos suggested that cardiac looping in the total body null is largely intact (Me´ tais et al., 2018). To examine this more carefully, we exploited recent advances in tissue clearing coupled to optical sectioning and 3D reconstruction. This anal- ysis of the intact embryos, however, allows us to reﬁne these ﬁndings and to examine the Asb2 mutant hearts more thoroughly and at a slightly later point in development. Although the hearts do start to loop, they arrest early on before making it to the helical fully looped heart. Measurement of the heart tube length reveals shorter heart tubes in the mutant hearts, which could explain the inability of the heart to fully form the helical structure. These data further reveal the important role of Asb2 regulation of cardi- omyocyte differentiation on the normal growth of the heart tube. We show that the CUBIC technique combined with immunoﬂuorescence/confocal microscopy has distinct advantages over traditional morphological analysis for the phenotypic analysis of mouse embryos and allows for the detection of subtle phenotypes and morphological abnormalities. Our data further reveal that Filamin A (Flna) is aberrantly overexpressed in the Asb2-mutant cardiomyo- cytes that normally do not express Flna protein. This is consistent with the data that Metais et al. reported. We also show that this regulation is dose dependent. We further show that Asb2-Flna regulate Tgfb-Smad signaling. Nuclear pSmad2 is overexpressed in the Asb2-mutant hearts consistent with the upregulation of this signaling pathways. Its levels are restored to normal in the Asb2.Flna double mutants further showing that Asb2 regulates SMAD signaling through the Flna pathway. RNA-seq analysis also reveals that regula- tion of the Tgfb-Smad pathway in the Asb2-mutant hearts and the Foxa genes, which are downstream effectors of the Tgfb/Smad signaling (Tang et al., 2011), is in fact restored to normal in the Asb2-Flna dou- ble mutants. Flna has been previously shown to associate with Smad2 signaling (Sasaki et al., 2001). Moreover, Tgfb/Smad2/3 signaling is impaired in the multivalve degeneration disorder, X-linked myxoma- tous valvular dystrophy, in which FLNA mutations were reported (Geirsson et al., 2012; Norris et al., 2010). Our data provide further evidence for regulation of the Tgfb/Smad cycle by Flna and show that Asb2 is upstream of this regulatory pathway in the developing heart. Using human embryonic stem cell (hESC)-derived cardiomyocytes, we further show that the Asb2 role in embryonic heart differentiation is conserved in humans. Although ASB2-null hESCs are able to form cardiac progenitor cells (marked by expression of MESP1 and NKX2-5), they have an impaired ability to differen- tiate into beating TNNT+ cardiomyocytes. It is important to note here that the difference between Troponin T levels in the Asb2-null hESCs and the Asb2 mutants in vivo could be due to the total knockout in the cells that is more severe than the conditional in vivo knockout. Additionally, the cell system lacks signaling coming from the endocardium, which could also explain this difference. These results demon- strate that, in human PSCs differentiating in vitro, ASB2-mediated targeted degradation is required for the differentiation from NKX2-5+ progenitors to beating TNNT+ cardiomyocytes and that deletion of ASB2 results in a differentiation arrest at the progenitor stage. Moreover, the ﬁnding that these cells have increased levels of SMAD4 and pSMAD2, markers of TGFb/SMAD pathway activation, provides further evidence that ASB2 is an upstream regulator of the TGFb/SMAD pathway during the differentiation of human cardiomyocytes. These considerations become increasingly important given the potential of pluripotent stem cell-derived CMs to serve as a renewable cell source for cardiac regeneration in the injured heart. Given that Flna is a direct target of Asb2 that is aberrantly upregulated in Asb2-mutant cardiomyocytes, we then investigated whether Asb2 cardiac mutant embryonic lethality can be rescued by the concurrent deletion of Flna. Accordingly, we generated AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/y double mutants. Our data show that, as opposed to the AHF-Cre.Asb2ﬂ/ﬂ single mutants that die by E11.5, the AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/y double mutants are born with the expected Mendelian ratios (Figure 5B) but die shortly after birth. This suggests partial rescue of lethality seen in the AHF-Cre.Asb2ﬂ/ﬂ mutant hearts. Of note, we also generated Nkx2-5+/Cre.Asb2ﬂ/ﬂ.Flnaﬂ/Y double mutants that did not rescue the Asb2 lethality (Figure S2A), suggesting a greater Asb2 dependency or a more complex phenotype in these mice. These ﬁndings are not surprising owing to earlier and broader expression domain of the Nxk2-5Cre compared with the AHF-Cre line. Moreover, AHF-Asb2-mutant mice with one Flna allele (AHF-Cre.Asb2ﬂ/ﬂ.Flnaﬂ/x) sometimes also survive to P0, albeit at signiﬁcantly lower than expected ratios (Fig- ure 5B). Not only do these results suggest a dose dependency of Asb2 and its target Flna but they also allow us to identify DORV associated with ventricular septal defect (VSD) as a penetrant cardiac phenotype. More inter- estingly, this phenotype was rescued when Flna was abrogated by the concurrent deletion of Flna showing that both Asb2 and Flna play a functional role in the pathogenesis of DORV. 14 iScience 23, 100959, March 27, 2020 During cardiac development, the heart ﬁrst forms as a primitive heart tube that then elongates and starts to loop by addition of cells from the anterior, posterior, and second heart ﬁeld at both the venous and arterial poles. At the onset of looping, left-right asymmetry in the heart becomes morphologically evident and any defects in this process can lead to complex congenital heart problems, including DORV and VSDs (Rams- dell, 2005). The heart is the ﬁrst organ to break the left-right symmetry in the developing embryo, and it has been shown that the actin-cytoskeleton is fundamental for laterality and modulation associated with heart looping. It was shown to provide the built-in mechanism required for cells to acquire left-right asymmetry (Linask and Vanauker, 2007; Tee et al., 2015). Abnormalities in the control of construction of the cytoskel- eton has been previously shown to result in looping defects and ultimately lead to congenital heart prob- lems (Langdon et al., 2012; Linask and Vanauker, 2007). Our data and the data from Metais et al. provide solid evidence for the Asb2-Flna regulation of the actin cytoskeleton during heart morphogenesis (Me´ tais et al., 2018). Our data extend this regulation to show that it is important for normal heart tube and OFT development and, if perturbed, leads to DORV and VSD in the developing mammalian heart. Additionally, we suggest a mechanism where Asb2 downregulation leads to abnormal overexpression of Flna that ulti- mately leads to increased activity of the Tgfb/Smad2 signaling in the myocardium thus causing growth/ elongation defects and DORV in the mammalian heart. Indeed, prior reports have implicated the Tgfb superfamily and Smad2/3 in left-right asymmetry, and Tgfb2 mutant mice have been shown to develop DORV and die right after birth (Azhar et al., 2003; Sanford et al., 1997; Whitman and Mercola, 2001). In humans, a missense mutation in Flna (c.5290G>A (p.A1764T) has been reported in a patient with DORV (de Wit et al., 2011). Since missense mutations can result in both loss and gain of function, future studies will be required to determine the effect of this mutation on Flna expression and function. Thus, our data demonstrate a link between targeted protein turnover and the development of DORV and highlights the potential of the ASB2/FLNA axis as a diagnostic, prognostic, and/or therapeutic target for patients with DORV. Limitations of the Study Although our data show that the role of Asb2 in heart morphogenesis is conserved between mice and human, a limitation is that the in vivo murine system is a conditional knockout compared with the total knockout in the human cells system. Additionally, as we know, a cross talk between the endocardium and the myocardium occurs during heart morphogenesis, and this again is lacking in our human cell system. METHODS All methods can be found in the accompanying Transparent Methods supplemental ﬁle. DATA AND CODE AVAILABILITY RNA-seq data have been deposited in NCBI’s Gene Expression Omnibus (GEO). The accession number for the RNA-seq data reported in this paper is GEO: GSE145495. SUPPLEMENTAL INFORMATION Supplemental Information can be found online at https://doi.org/10.1016/j.isci.2020.100959. ACKNOWLEDGMENTS We thank all members of the Domian Lab for valuable insight and suggestions. We also thank the his- tology core at Dana Farber Cancer Institute/Harvard Medical School and the NextGen Sequencing core at Massachusetts General Hospital for technical support. This work was supported by the American Heart Association (17GRNT33630170), the Centre National de la Recherche Scientiﬁque, and the Univer- sity of Toulouse. A.Y. is a recipient of the Fund for Medical Discovery (FMD) Award from the Massachu- setts General Hospital/Harvard Medical School. AUTHOR CONTRIBUTIONS Conceptualization, A.Y. and I.J.D.; Methodology, A.Y. and I.J.D.; Investigation A.Y., D.H., N.M., J.W.B., and S.D.; Writing – Original Draft, A.Y.; Writing – Review & Editing, A.Y., P.G.L., C.M.-L, P.T.L., and I.J.D.; Visu- alization, A.Y.; Supervision, A.Y. and I.J.D.; Funding Acquisition, A.Y. and I.J.D. iScience 23, 100959, March 27, 2020 15 DECLARATION OF INTERESTS The authors declare no competing interests. Received: August 16, 2019 Revised: December 17, 2019 Accepted: February 26, 2020 Published: March 27, 2020 REFERENCES Azhar, M., Schultz, J.E.J., Grupp, I., Dorn, G.W., Meneton, P., Molin, D.G.M., Gittenberger-de Groot, A.C., and Doetschman, T. (2003). Transforming growth factor beta in cardiovascular development and function. Cytokine Growth Factor Rev. 14, 391–407. Bardot, E., Calderon, D., Santoriello, F., Han, S., Cheung, K., Jadhav, B., Burtscher, I., Artap, S., Jain, R., Epstein, J., et al. (2017). 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All animal experimentations were carried out in accordance with institutional guidelines for animal care. Experiments were approved by the Massachusetts General Hospital’s Subcommittee on Research Animal Care (SRAC), which serves as the Institutional Animal Care and Use Committee (IACUC) as required by the Public Health Service (PHS) Policy on Humane Welfare Regulations. The program and facilities have been fully accredited by the American Association for the Accreditation of Laboratory Animal Care (AAALAC) since July 30, 1993. The institutional assurance number with the Office for Protection from Research Risks at the N.I.H. is DI6-00361. All mice lines were kept on a C57BL/6 background. Approximately, 20 AHF-Cre, 20 Nkx2-5+/Cre, 100 Asb2fl/fl and 100 Asb2fl/fl.Flnafl/fl mice were used. To isolate embryos from pregnant females, cervical dislocation was used for euthanasia which is required for embryo collection in mice. Sex of the embryos was not an influence in this study due to the very early developmental stage. Embryos were analyzed at E8.5, E9.5, E10.5, and E11.5 as indicated in the results section where applicable. For the double outlet right ventricle analysis, hearts of E16.5 embryos were used. Generation of Asb2 and Flna knockout embryos. Asb2fl/fl or Asb2fl/fl.Flnafl/fl females were mated with Nkx2-5+/Cre or AHF-Cre male mice and plugs were checked on a daily basis. The day a plug is seen is considered embryonic day e0.5. Asb2fl/fl, Flnafl/fl, Nkx2-5+/Cre and AHF-Cre mice are previously described (Lamsoul et al., 2013; Lombardi et al., 2009; Pinto et al., 2014). Mice genotypes (adult and embryos) were determine by PCR genotyping. Genotyping oligos used are: Flna flox: 5’ TCT TCC TCT TTC AGC TGG 3’and 5’ ACA ACT GCT GCT CCA GAG 3’; Asb2 flox: 5’ CAGTGTCTGCTCTGAGGTCTCTC 3’ and 5’ CAATCTCTCCCTGGTAGAAACAGTTTGG 3’; Nkx2-5 Cre: 5’ GATTAGCTTAAGCGGAGCTGGGTGTCC 3’ and 5’ GCCGCATAACCAGTGAAACAGCATTGC 3’; AHF-Cre: 5’ CCAGGCAAAGGCAAGAATAA 3’ and 5’ ATGTTTAGCTGGCCCAAATG 3’. Immunohistochemistry. Immunofluorescence was done as previously described (Domian et al., 2009). Tissues were permeabilized with 0.3% Triton and antigen retrieval was done using citrate buffer. Tissues were blocked with goat or donkey serum and primary antibodies were incubated overnight at 4oC. Secondary antibodies linked to appropriate alexa fluor were incubated for 1 hour at room temperature. Excess antibodies were washed with Phosphate buffer saline with 0.2% tween-20. Tissues were mounted with prolong gold anti-fade mounting media. Antibodies used were: Asb2 (Abcam, ab13710); Filamin a (Abcam, ab76289); Nkx2-5 (Invitrogen, PA5-49431); pSmad2(Millipore, AB3849); Troponin T (Thermo- Scientific, MA5-12960; SMAD4 (Proteintech, 10231-1-AP). Masson Trichrome Staining was done on paraffin heart sections using the American Mastertech Scientific kit (Item No. KTMTR) according to the manufacturer’s protocol. Paraffin sections were deparaffinized with 3 rounds of xylene followed by rehydration with serial dilutions of ethanol baths prior to staining. Outflow tract measurements were done on 2D images using ImageJ. The landmarks used for measurement are as shown in supplementary figure 1D. CUBIC clearing and Immunostaining. Embryos were immersed in CUBIC-1 solution (25% urea, 15% TritonX-100, 25% N,N,N,N-tetrakis(2-hydroxypropyl)ethyl-enediamine) at 37oC with gentle shaking till efficiently cleared (2-5 days depending on developmental stage). Following clearing, embryos were washed thoroughly with PBS and stained with Troponin T and/or Filamin A antibodies for 4-5 days (at 4oC), washed with PBS and then incubated with the corresponding secondary antibodies coupled to Alexa Fluor 488 or 546 for additional 3-4 days (at 4oC). DAPI was added to CUBIC-1 solution and the following PBS washes to mark nuclei. Following staining, embryos were then cleared with CUBIC-2 solution (50% sucrose, 25% urea and 10% 2,2’,2’-nitrilotriethanol) for 1-2 days at 37oC with gentle shaking and then immediately transferred to immersion oil and imaged with laser confocal microscopy (Leica TCS SP8). 90-120 z-stacks were taken for each embryo that were then used to generate the 3D reconstructions using either the Leica software or image J. The 3D images were then further analyzed for phenotypic defects. At least 5 embryos were analyzed for each condition. The clearing/staining technique was adapted from the established protocol by Kolesova et al (Kolesová et al., 2016). Heart tube measurements were done on 3D images using ImageJ. The landmarks used for measuring the tube’s length are as described in Le Garrec et al paper (Le Garrec et al., 2017). RNA Extraction and qPCR. RNA extraction was done using the Qiagen RNeasy Micro Kit (Cat No. 74004) according to the manufacturer’s protocol. qPCR analysis was done using the Applied Biosystems PowerUp SYBR Green Master mix (Cat No. A25742) according to manufacturer’s protocol. Oligos used were: msAsb2a: 5’ GCTCTGTTTCACTCTGGCTCT 3’ and 5’ CTTCAGCACGGGGTCCATAG 3’; msAsb2b: 5’ AACCACCAGCCAGGACATTT 3’ and 5’ ACTTCTGCATGACCCCTTGG 3’; huASB2V1: 5’ ATTGGGCAGGAGGAGTACAG 3’ and 5’ AACTCTCAGGAGGTGCAGT 3’; huASB2V2: 5’ ATGACCCGCTTCTCCTATGC 3’ and 5’ CGAACTCTCAGGAGGTGCAG 3’. huTNNT2: 5’ ACTTGGAGGCAGAGAAGTTCG 3’ and 5’ CCCGGTGACTTTAGCCTTCC 3’; huNKX2-5: 5’ CGCACAGCTCTTTCTTTTCGG 3’ and 5’ CGCCTTCTATCCACGTGCC 3’; huMESP1: 5’ CTTTTTGGCCTCAGCACCTTC 3’ and 5’ AGTGTCTAGCCCTATGGGTCC 3’. RNA Sequencing. RNA was extracted from e9.5 embryo hearts using the Qiagen RNeasy Micro Kit (Cat No. 74004) and sent to the MGH Next Generation sequencing core. The libraries were sequenced using illumina HiSeq platform. Splice-aware alignment program STAR was used to map the sample sequencing reads to the Mus musculus mm10 reference genome. Gene expression counts were calculated using HTSeq based on current Ensembl annotation for mm10. The R package “edgeR” was then employed to make differential gene expression calls. Pathway analyses were done using “MetaCore-Clarivate” and “Ingenuity Pathway Analysis-Qiagen” softwares. Human Pluripotent Stem Cell Culture and Differentiation. HUES9 hESC line (NIH Human Embryonic Stem Cell Registry Number 0022, generated by HSCI iPS Core at Harvard University) was used in generating CRISPR KO cell line. hESC culture, differentiation and dissociation protocols were based on previously published works (Hu et al., 2018). Briefly, hESCs were cultured in Essential 8 Medium (Thermo Fisher Scientific, MA) in Matrigel (BD Biosciences) coated cell culture plates. hESCs were differentiated in RPMI GlutaMAX (Thermo Fisher Scientific, MA) plus Gem21 NeuroPlex Serum-Free Supplement without insulin (Gemini Bio Products, CA) for the first 5 days. Small molecules CHIR99021 (STEMCELL Technologies, Vancouver, Canada) and IWP-4 (STEMCELL Technologies, Vancouver, Canada) were added on day 1 and 3, respectively. Differentiation media was then switched to RPMI GlutaMAX plus Gem21 NeuroPlex Serum-Free Supplement from day 7 to 10. Differentiating hESCs then underwent glucose starvation for 6 days, which resulted in highly pure populations of beating CMs. hESC-CMs were re-plated onto Matrigel coated PDMS plates for confocal imaging. Imaging procedure and analysis were done based on previously published methods (Kijlstra et al., 2015). Briefly, Fluo-4, AM (Thermo Fisher Scientific, MA) calcium indicator were incubated with hESC-CMs prior to imaging. Movies of CMs at randomly selected regions were acquired in both DIC and GFP channels at 50 frames per second for 10 seconds. Calcium transients were analyzed using ImageJ software. In vitro differentiation of the SHF-dsRed/Nkx2.5-eGFP cells was done as previously described (Domian et al., 2009). Generation of ASB2-null hESCs. We used CRISPR/Cas9 genome editing technology to generate the ASB2-null hESCs according to the described protocol (Ran et al., 2013). Guide RNAs (gRNAs) specific for hASB2 variant 1 (equivalent to Asb2β in mouse) and those common for both variants 1 & 2 (mouse Asb2β & α respectively) were designed using CRISPR design online tool, cloned into CRISPR/Cas9-GFP plasmid backbone (pSpCas9 from Addgene) and sequenced. Plasmids with the efficient gRNAs were delivered by electroporation to hESCs. Single cell CRISPR clone selection, expansion and sequencing protocols were adapted from Peters et al (Peters et al., 2008). Following FACS selection, GFP+ hESCs were plated sparsely onto Matrigel coated dishes for growing single cell clones. After 10 days, individual clones were picked, plated into 96-well plates, and sequenced. Four clones harboring ASB2 gene locus modification along with four wild type (Wt) clones were expanded and differentiated into CMs for further analysis. 6 guides were tested individually (sequences below). Guides 1 and 6 were successful in inducing the knockouts. 1: used were: Guide 5' CACCGGTTGGTACATGCAGACGCGG 5' Guides AAACCCGCGTCTGCATGTACCAACC 3’; Guide 2: 5’ CACCGGTCCGCTAGGCTCTGCTCGA and 5’ AAACTCGAGCAGAGCCTAGCGGACC 3’; Guide 3: 5' CACCGGGCCCCTTGTCTTGTCCGCT 3’ and 5’ AAACAGCGGACAAGACAAGGGGCCC 3’; Guide 4: 5' CACCGGCCCGGGCCGGCGAACTCTC 3’ and 5' AAACGAGAGTTCGCCGGCCCGGGCC 3’; Guide 5: 5' CACCGCTCCTGAGAGTTCGCCGGCC 3’ and 5' AAACGGCCGGCGAACTCTCAGGAGC 3’; Guide 6: 5' CACCGCTGCACGAGGCCGCATACTA 3’ and 5' AAACTAGTATGCGGCCTCGTGCAGC 3’ and 3’ Western blot analysis. Total protein extracts were prepared using RIPA buffer. Proteins were run on 10% TGX pre-cast gels from biorad and transferred to PVDF membranes using Trans-blot turbo transfer kit (Biorad). Membranes were blocked with 5% non-fat milk or BSA (in case of pSmad2) and primary antibodies were incubated overnight at 4oC. Secondary antibodies linked to HRP (horseradish peroxidase) were incubated for 1 hour at room temperature and signal was revealed using super signal west femto or pico ECL substrates (Thermo-scientific). Antibodies used were Smad2 (5339, Cell Signaling), pSmad2 (3108, cell signaling) and Smad4 (ab40759, ABCAM). Western blots were then quantified using the Image Lab software. Statistical Analysis: Standard t-test was used for the QPCR analysis and the heart tube measurements. One-way ANOVA was used for the western blot quantification as well as the percentages of Smad4 and pSmad2 positive cells where. p<0.05 is considered statistically significant. Data and Software availability: RNAseq data have been deposited in NCBI’s Gene Expression Omnibus (GEO) and can be accessed through GEO Series accession number GSE145495. B. DAPI Troponin T Control Nkx2-5+/Cre.Asb2fl/+ Nkx2-5+/Cre.Asb2fl/fl 25μm + / fl 2 b s A . e r C / + 5 - 2 x k N fl / fl 2 b s A . e r C / + 5 - 2 x k N l o r t n o C H D P A G / 2 b s A l o r t n o C o t e v i t a e r l 1 0.8 0.6 0.4 0.2 0 Asb2 GAPDH E9.5 100μm A. C. 75 50 37 25 l o r t n o C t n a t u M E9.5 100μm D. Control AHF-Cre.Asb2fl/fl DAPI Troponin OFT OFT Ε10.5 100μm 100μm ) m μ ( h t g n e l T F O 600 400 200 0 * Control AHF-Cre.Asb2ﬂ/ﬂ Supplementary figure 1, related to figure 2. A. Western Blot analysis on hearts of e9.5 Nkx2- 5+/Cre.Asb2fl/+, Nkx2-5+/Cre.Asb2fl/fl, and their control littermates using Asb2 antibody and GAPDH for loading control. Notice reduced Asb2 protein levels in the heterozygous mice (fl/+) and the complete loss of Asb2 in the knockout mice (fl/fl) (quantification analysis on the rights) (5-6 hearts were uses per condition). B & C. CUBIC/Immunofluorescence of E9.5 mice embryos. B. High magnification showing the cardiac myocardial region of an E9.5 mouse embryo cleared with CUBIC and immuno-stained for Troponin T (green). Blue marls DAPI. Note the visible striations (yellow arrows). Scale bar is equivalent to 25µm. C. Serial sections of Control (top) and Mutant (bottom) E9.5 cleared/stained mice embryos, showing the heart region. Troponin T (green) was used to mark the myocardium. Note the bulging in the Control heart (right arrow) which is missing in the Mutant. D. Immunohistochemistry on E10.5 AHF-Cre.Asb2 hearts (AHF- Cre.Asb2fl/fl, right panel) and littermate control (left panel) using Troponin-T (gree)-specific antibody. Note the shorter outflow tract (OFT) of the AHF-Cre.Asb2fl/fl heart. Scale bar is equivalent to 100µm. 4 Control and 3 knockout hearts were analyzed. : p<0.005 significant vs. control. Unpaired t-test was used for analysis using Graphpad Prism. Supplementary Figure 1, related for ﬁgure 2. A. Western Blot analysis on hearts of e9.5 Nkx+/Cre.Asb2ﬂ/+, Nkx+/Cre.Asb2ﬂ/ﬂ and their control littermates using Asb2 antibody and GAPDH for loading control. Notice reduced Asb2 protein levels in the heterozygous mice (ﬂ/+) and the complete loss of Asb2 in the knock out mice (ﬂ/ﬂ) (quantiﬁcation analysis on the right) (5-6 hearts were used per condition). B. & C. CUBIC/Immunoﬂuorescence in e9.5 mice embryos. B&C. CUBIC/Immunoﬂuorescence in e9.5 mice embryos. B. High magniﬁcation showing the cardiac myocardial region of an E9.5 mouse embryo cleared with CUBIC and immuno-stained for TroponinT (green). Blue marks DAPI. Note the visible striations (yellow arrows). Scale bar is equivalent to 25μm. C. Serial sections of Control (top) and Mutant (bottom) E9.5 cleared/stained mice embryos, showing the heart region. TroponinT (green) was used to mark the myocar- dium. Note the bulging in the Control heart (right arrow) which is missing in the Mutant. D. Immunohistochem- istry on E10.5 AHF-Cre.Abs2 hearts (AHF-Cre.Asb2ﬂ/ﬂ, right panel) and littermate control (left panel) using Troponin-T (green)-speciﬁc antibody. Note the shorter outﬂow tract (OFT) of the AHF-Cre.Asb2ﬂ/ﬂ heart. Scale bar is equivalent to 100μm. 4 Control and 3 knockout hearts were analyzed. : p<0.005 signiﬁcant vs control. Unpaired t-test was used for analysis using Graphpad prism. A.A.A.A. B. Control Nkx2-5+/CreAsb2fl/fl.Flnafl/+ Nkx2-5+/CreAsb2fl/fl.Flnafl/y Troponin FlnA DAPI E9.5 0.2mm 0.2mm 0.2mm E10.5 0.4mm 0.4mm 0.4mm C. D. l o r t n o C ﬂ / ﬂ a n F l . ﬂ / ﬂ 2 b s A / . e r C + 5 - 2 x k N Ε9.5 Troponin 75μm 75μm 75μm FlnA DAPI Ε9.5 75μm 75μm 75μm + / ﬂ 2 b s A / . e r C + 5 - 2 x k N o t e v i t a e R l Asb2 * * 1.5 1.0 0.5 0.0 Nkx2-5+/Cre.Asb2ﬂ/+ Nkx2-5+/Cre.Asb2ﬂ/fl Nkx2-5+/Cre.Asb2ﬂ/ﬂ.Flnaﬂ/fl s l e v e l A N R m e v i t a l e R 6.0 4.5 3.0 3.0 2.5 2.0 1.5 1.0 0.5 0.0 * # @ * # * * # * Shh Hand2 Foxa2 Foxa3 : signiﬁcant vs Nkx2-5+/Cre.Asb2ﬂ/+ Nkx2-5+/Cre.Asb2ﬂ/+ Nkx2-5+/Cre.Asb2ﬂ/ﬂ #: signiﬁcant vs Nkx2-5+/Cre.Asb2ﬂ/+.Flnaﬂ/y Nkx2-5+/Cre.Asb2ﬂ/+.Flnaﬂ/y @: signiﬁcant vs Nkx2-5+/Cre.Asb2ﬂ/ﬂ.Flnaﬂ/y Nkx2-5+/Cre.Asb2ﬂ/ﬂ.Flnaﬂ/y Supplementary Figure 2, related to figure 3. A. Nkx2-5+/Cre.Asb2.Flna E9.5 and E10.5 embryos. Note Supplementary Figure 2, related to ﬁgure 3. A. Nkx2-5+/Cre.Asb2.Flna E9.5 and E10.5 embryos. Note the the smaller Nkx2-5+/Cre.Asb2fl/fl.Flnafl/+ and Nkx2-5+/Cre.Asb2fl/fl.Flnafl/y at E9.5 and E10.5. Nkx2- smaller Nkx2-5+/CreAsb2ﬂ/ﬂ.Flnaﬂ/+ and Nkx2-5+/CreAsb2ﬂ/ﬂ.Flnaﬂ/y at E9.5 and E10.5. Nkx2-5+/CreAsb2ﬂ/ﬂ.Flnaﬂ/+ and Nkx2- 5+/Cre.Asb2fl/fl.Flnafl/+ and Nkx2-5+/Cre.Asb2fl/fl.Flnafl/y often presented with pericardial edema at both stages. 5+/CreAsb2ﬂ/+.Flnaﬂ/y often presented with pericardial edema at both stages. 16 litters were analyzed at E9.5 and 16 litters were analyzed at E9.5 and 3 litters at E10.5. Scale bar is equivalent to 0.2mm at E9.5 and 0.4mm 3 litters at E10.5. Scale bar is equivalent to 0.2mm in E9.5 embryos and 0.4mm in E10.5 embryos as at E10.5 embryos as indicated. B. Immunofluorescence on Nkx2-5+/Cre.Asb2fl/fl.Flnafl/y double knockouts and indicated. B. Immunoﬂuorescence on Nkx2-5+/Cre.Asb2ﬂ/ﬂ.Flnaﬂ/ﬂ double knockouts and controls using Flna controls using Flna (red) and Troponin T (green) antibodies. Note absence of Flna expression in the (red) and Troponin T (green) antibodies. Note absence of Flna expression in the myocardium of the double myocardium of the double knockouts as opposed to its expression in the myocardium of the single knockouts as opposed to its expression in the myocardium of the single knockouts in ﬁgure 3A. Scale bar is knockouts in figure 3A. Scale bar is equivalent to 75µm. C. Asb2 transcipt levels from RNAseq data showing equivalent to 75μm. C. Asb2 transcript levels from RNAseq data showing reduced Asb2 levels in the single reduced Asb2 levels in the single and double knockouts compared to the Asb2 heterozygote control. : and double knockouts compared to the Asb2 heterozygote control. * p<0.05. One-way ANOVA was used for p<0.05. One-way ANOVA was used for analysis using Graphpad Prism. D. QPCR analysis of hearts from analysis using Graphpad prism. D. QPCR analysis of hearts from Nkx2-5+/Cre.Asb2ﬂ/+, Nkx2-5+/Cre.Asb2ﬂ/ﬂ, Nkx2-5+/Cre.Asb2fl/+, Nkx2-5+/Cre.Asb2fl/fl, Nkx2-5+/Cre.Asb2fl/+.Flnafl/y, Nkx2-5+/Cre.Asb2fl/fl.Flnafl/y E9.5 mice. Nkx2-5+/Cre.Asb2ﬂ/+.Flnaﬂ/y, and Nkx2-5+/Cre.Asb2ﬂ/ﬂ.Flnaﬂ/y E9.5 mice. n=5-6 for Nkx2-5+/Cre.Asb2ﬂ/+; n=5 for N=5-6 for Nkx2-5+/Cre.Asb2fl/+; n=5 for Nkx2-5+/Cre.Asb2fl/fl; n=5-6 for Nkx2-5+/Cre.Asb2fl/+.Flnafl/y; n=3 for Nkx2- Nkx2-5+/Cre.Asb2ﬂ/ﬂ; n=5-6 for Nkx2-5+/Cre.Asb2ﬂ/+.Flnaﬂ/y; n=3 for Nkx2-5+/Cre.Asb2ﬂ/ﬂ.Flnaﬂ/y (each sample was a 5+/Cre.Asb2fl/fl.Flnafl/y (each sample is a combination of 2-3 hears to account for littermate variability). The combination of 2-3 hearts to account for littermate variability). The selected genes are among those identiﬁed selected genes are among those identified in RNAseq analysis in fugures 2 and 3. P<0.05 is considered in RNAseq analysis in ﬁgures 2 and 3. p<0.05 is considered statistically signiﬁcant. T-test was used for anly- statistically significant. T-test was used for analysis using Graphpad Prism. sis using Graphpad Prism. A. 1 e d u G i Variant 1 1 2 3a 5’UTR 6 e d u G i 4 5 6 7 8 9 10 3’UTR Variant 2 3b 4 5 6 7 8 9 10 B. 3.0 2.5 0 F F / 2.0 1.5 1.0 0 5’UTR WT KO 3’UTR 2 4 6 Time (s) 8 10 Supplementary Figure 3, related to figure 7. A. Schematic representation of the two Asb2 isoforms Supplementary Figure 3, related to ﬁgure 7. A. Schematic representation of the two ASB2 isoforms showing the location of the guides used for CRISPR/Cas9 genome editing. B. Representative calcium showing the location of the guides used for CRISPR/Cas9 genome editing. B. Representative calcium transients of hiPSC-CMs (WT: blue, KO: red). transients of hiPSC-CMs (WT: blue, KO: red).	null
10.7554_elife.85878.pdf	Data availability All data generated or analyzed in this study are included in the manuscript and supporting files. Source data files have been provided for Figure 1b, Figure 1c, Figure 1f, Figure 1g, Figure 1- figure supplement 2a- c, Figure 2b, Figure 2g, Figure 2- figure supplement 1a- f, Figure 3a, Figure 3c, Figure 3e, Figure 3h, Figure 3 supplement 1b- c, Figure 4a, Figure 4b, Figure 4e, Figure 4f, Figure 4h, Figure 4i, Figure 4- figure supplement 1a- b, Figure 4- figure supplement 2a, Figure 5b.	Data availability All data generated or analyzed in this study are included in the manuscript and supporting files. Source data files have been provided for Figure 1b , Figure 1c, Figure 1f, Figure 1g, Figure 1-figure supplement 2a-c, Figure 2b, Figure 2g, Figure 2-figure supplement 1a-f, Figure 3a, Figure 3c, Figure 3e, Figure 3h, Figure 3 supplement 1b-c, Figure 4a, Figure 4b, Figure 4e, Figure 4f, Figure 4h, Figure 4i, Figure 4-figure supplement 1a-b, Figure 4-figure supplement 2a, Figure 5b.	RESEARCH ARTICLE Two RNA- binding proteins mediate the sorting of miR223 from mitochondria into exosomes Liang Ma, Jasleen Singh, Randy Schekman* Department of Molecular and Cell Biology, Howard Hughes Medical Institute, University of California, Berkeley, United States Abstract Fusion of multivesicular bodies (MVBs) with the plasma membrane results in the secre- tion of intraluminal vesicles (ILVs), or exosomes. The sorting of one exosomal cargo RNA, miR223, is facilitated by the RNA- binding protein, YBX1 (Shurtleff et al., 2016). We found that miR223 specif- ically binds a ‘cold shock’ domain (CSD) of YBX1 through a 5’ proximal sequence motif UCAGU that may represent a binding site or structural feature required for sorting. Prior to sorting into exosomes, most of the cytoplasmic miR223 resides in mitochondria. An RNA- binding protein local- ized to the mitochondrial matrix, YBAP1, appears to serve as a negative regulator of miR223 enrich- ment into exosomes. miR223 levels decreased in the mitochondria and increased in exosomes after loss of YBAP1. We observed YBX1 shuttle between mitochondria and endosomes in live cells. YBX1 also partitions into P body granules in the cytoplasm (Liu et al., 2021). We propose a model in which miR223 and likely other miRNAs are stored in mitochondria and are then mobilized by YBX1 to cyto- plasmic phase condensate granules for capture into invaginations in the endosome that give rise to exosomes. Editor's evaluation This important study presents a novel mechanism of miRNA223 sorting into exosomes involving its storage within mitochondria, specifically by a mitochondrially localized protein YBAP1. The evidence supporting the findings is convincing and opens avenues for future studies on molecular mecha- nisms. This paper is a valuable addition to the cellular sorting of miRNA involving interplay with and between the organelles, interesting for miRNAs researchers, as well as cell biologists. Introduction Extracellular vesicles (EVs) bud from the plasma membrane or are secreted when multivesicular bodies (MVB) fuse with the plasma membrane to release a population of vesicles called exosomes. EVs and their cargos are highly dependent on their membrane source. Microvesicles released by budding from the plasma membrane are a heterogeneous population of EVs ranging in size from 30 nm to 1000 nm (Cocucci et al., 2009). Exosomes are 30 nm to 150 nm in size and originate as vesicles invaginated into the interior of an MVB to form intraluminal vesicles (ILVs; Harding et al., 1983). Many RNAs are selectively sorted into EVs, especially small RNAs. Several studies have indicated that RNA binding proteins (RNPs) may be involved in the enrichment of RNAs into EVs (Mukherjee et al., 2016; Santangelo et al., 2016; Teng et al., 2017; Villarroya- Beltri et al., 2013). However, many of these studies used sedimentation at ~100,000 g to collect EVs, which may also collect RNP particles not enclosed within membranes which complicates the interpretation of these data. To address this question, we previously developed buoyant density- based methods to separate EVs For correspondence: schekman@berkeley.edu Competing interest: The authors declare that no competing interests exist. Funding: See page 20 Received: 30 December 2022 Preprinted: 11 January 2023 Accepted: 24 July 2023 Published: 25 July 2023 Reviewing Editor: Agnieszka Chacinska, IMol Polish Academy of Sciences, Poland Copyright Ma et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 1 of 23 Research article from non- vesicular aggregates and found that EVs form two distinct populations of high and low buoyant density (Shurtleff et al., 2016; Temoche- Diaz et al., 2020). We found that some miRNAs are selectively enriched in a high buoyant density vesicle fraction characterized by an enrichment in the exosomal marker protein CD63, whereas the low buoyant density EVs are fairly non- selective in the capture of miRNAs (Temoche- Diaz et al., 2019). We developed a cell- free reaction to identify YBX1 as required for miR223 sorting into exosomes and demonstrated that it plays an important role in the enrichment of miR223 into exosomes in HEK293T cells (Shurtleff et al., 2016). We subse- quently found that phase separated YBX1 condensates selectively recruit miR223 in vitro and sort it into exosomes in cells (Liu et al., 2021). In this study, we report that YBX1 directly and specifically binds miR223 by its ‘cold shock’ domain (CSD). We have identified a sequence motif, UCAGU, that facilitates the sorting of miR223 into exosomes. We also found a significant fraction of cytoplasmic miR223 localized within mitochondria, tightly associated with the mitochondrial envelope and that a mitochondrial RNA- binding protein, YBAP1, may control the transfer of miR223 from mitochondria to exosomes. Results YBX1 directly and specifically binds miR223 We previously documented that YBX1 facilitates miR223 sorting into exosomes (Shurtleff et al., 2016) and that exosomal miR223 is decreased in YBX1 knockout cells (Liu et al., 2021). We reexam- ined the enrichment and confirmed that exosomal miR223 was decreased in exosomes purified from YBX1 KO cells (Figure 1a). We used a Nanosight particle tracking device to quantify buoyant density purified vesicles and found that knockout of YBX1 did not affect exosome secretion (Figure 1—figure supplement 1). Whereas the importance of YBX1 for miR223 sorting has been established, the mechanism of their interaction was not known. To examine the direct interaction of YBX1 and miR223, we used an electrophoretic mobility shift assay (EMSA) with purified recombinant YBX1, expressed in insect cells (Figure 1—figure supplement 2a), and chemically synthetic miR223 and miR190, a cytoplasmic miRNA that is not enriched in exosomes. Purified YBX1 was titrated and incubated with 5’ fluorescently labeled miR223 at 30 ℃ for 30 min. miR223- YBX1 complexes were separated by electrophoresis and detected by in- gel fluorescence. The EMSA data showed that YBX1 directly and specifically bound to miR223, but ~140 fold less well with miR190 (Figure 1b–c). The measured Kd for YBX1:miR223 was 4.2 nM (Figure 1d). YBX1 has three major domains including an N- terminal alanine/proline- rich (A/P) domain, a central cold shock domain (CSD) and a C- terminal domain (CTD) (Figure 1e). To explore which specific domain of YBX1 binds miR223, we constructed a series of fragments: the A/P domain, CSD and CTD. The YBX1 fragments were expressed in and purified from insect cells (Figure 1—figure supplement 2b). EMSA data showed that the A/P domain and CTD had little or no affinity for miR223, whereas the CSD domain bound miR223 but with an affinity much reduced compared to full length YBX1 (Figure 1f). We then constructed two combined fragments of the A/P and CSD and CSD and CTD domains (Figure 1—figure supplement 2c). The EMSA data showed that the A/P domain was dispensable, whereas binding of miR223 to CSD plus CTD was comparable to full- length YBX1 (Figure 1g). YBX1- F85A in the CSD domain was reported to block the YBX1- specific binding of mRNA (Lyons et al., 2016). Purified YBX1- F85A protein failed to bind miR223 (Figure 1g, Figure 1—figure supple- ment 2c). These data suggest that YBX1 directly and specifically binds miR223 via the CSD. The CTD of YBX1 did not appear to bind miR223 but may somehow facilitate a higher affinity interaction of the CSD with miR223. A binding or structural motif on miR223 that promotes interaction with YBX1 and enrichment into exosomes We next sought to determine the miR223 sequence motif responsible for interaction with YBX1 and enrichment into exosomes. We used an EMSA competition assay with a series of miR223 mutants. Purified YBX1 and 5’ fluorescently labeled miR223 were incubated with miR223 mutant constructs titrated in a range from 1 nM to 1 μM. miR223 variants in a binding domain should not compete for interaction of YBX1 with 5’ fluorescently tagged miR223 whereas variations in sequences irrelevant to Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 2 of 23 Cell Biology Research article a. e g n a h c d o F l / ) T W O K 1 X B Y ( 8 4 2 1 0.5 0.25 d. miR223-3p miR190a-5p Cell Exosome 100 80 60 40 20 ) % ( d n u o B n o i t c a r F 0 10 0 M μ 1 [miRNA] = 1nM Kd of miR223 = 4.2nM Kd of miR190 = 574.5nM 10 1 10 2 YBX1 [ nM ] 10 3 c. M μ 0 YBX1 M μ 1 Bound miR190 Free miR190 miR223 miR190 b. M μ 0 YBX1 Bound miR223 Free miR223 YBX1 N 1 51 129 A/P CSD CTD 324 C M μ μ 0 0 YBX1(1-51) M μ 1 M μ μ 0 0 YBX1(52-129) M μ 1 YBX1(130-324) M μ μ 0 0 M μ 1 1 51 A/P 52 129 CSD 130 324 CTD YBX1(1-129) M μ μ 0 0 M μ 1 M μ μ 0 0 YBX1(52-324) M μ 1 M μ μ 0 0 YBX1(F85A) M μ 1 e. f. Bound miR223 Free miR223 g. Bound miR223 Free miR223 1 129 A/P CSD 52 324 1 51 324 CSD CTD A/P CSD CTD F85A Figure 1. YBX1 directly and specifically binds miR223. (a) RT- qPCR analysis of fold change of miR- 223 and miR- 190 in cells and purified exosomes from 293 T WT cells and YBX1 knockout cells. Data are plotted from three independent experiments, each independent experiment with triplicate qPCR reactions; error bars represent standard deviations. (b–c) EMSA assays using 1 nM 5’ fluorescently labeled miR223 or miR190 and purified YBX1. Figure 1 continued on next page Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 3 of 23 Cell Biology Research article Figure 1 continued Purified YBX1 was titrated from 500pM to 1 μM. In gel fluorescence was detected. Quantification of (d) shows the calculated Kd. (e) Schematic diagrams of the different domains of YBX1. (f) EMSA assay using 1 nM 5’ fluorescently labeled miR223 and purified YBX1 truncations. [YBX1(1–51) or YBX1(52–129) or YBX1(130–324).] (g) EMSA assay using 1 nM 5’ fluorescently labeled miR223 and purified YBX1 truncations [YBX1(1–129) or YBX1(52–324)] or YBX1(F85A) mutant. The online version of this article includes the following source data and figure supplement(s) for figure 1: Source data 1. Uncropped gel images corresponding to Figure 1. Figure supplement 1. Knockout of YBX1 did not change exosome secretion. Figure supplement 2. Purified YBX1 full length protein and different truncations and mutation. Figure supplement 2—source data 1. Uncropped gel images corresponding to Figure 1—figure supplement 2. interaction would compete. Using this EMSA competition assay to screen the miR223 binding motif, we found that the competitive binding of miR223mut (3- 6) and miR223mut (4- 7) were decreased (Figure 2—figure supplement 1). This suggested that the sequence UCAGU was critical for inter- action with YBX1. To test this directly, we employed a variant sequence, termed miR223mut, where the UCAGU was substituted with AGACA. As a positive control, we employed a variant of miR190, miR190sort, where the sequence AUAUG was substituted with UCAGU (Figure 2a). EMSA data showed a~27- fold reduced YBX1 interaction of with miR223mut, whereas the affinity of miR190sort with YBX1 was increased ~eightfold compared to wt sequences. To test whether this motif is critical for miR223 enrichment into exosomes, we purified exosomes from 293T cells transiently transfected to overexpress one of the four miRNA constructs (Figure 2d). RT- qPCR data showed that the level of miR223 in exosomes was ~fourfold dependent on the puta- tive exosomal sorting motif (Figure 3e) and the enrichment of miR190sort into exosomes was increased ~fivefold compared to miR190 WT (Figure 2f). In previous work, we developed a cell- free reaction to test the biochemical requirement for YBX1 in the sorting of miR223 into vesicles formed with membranes and cytosol isolated from broken HEK293 cells (Shurtleff et al., 2016). In this work, we showed that the sorting of miR223 and of a CD63- luciferase fusion protein into an enclosed membrane were coincidentally inhibited by GW4869 an inhibitor of neutral sphingomyelinase (NS2) known to interfere with exosome biogenesis and secre- tion. On this basis, we concluded that the cell- free reaction recapitulated the sorting event leading to the packaging of miR223 into exosomes. We refined this assay to measure the incorporation of 32P- 5’ end- labeled wt and mutant miR223 into vesicles formed in vitro. Isolated membranes and cytosol were incubated with 32P- labled wt or mutant miR223 at 30 °C for 20 min, after which RNase I was added to digest any unpackaged miRNA. Controls including 1% Triton X- 100 were used to measure background RNase resistant radiolabel. Samples were resolved on a gel for visual and quantitative evaluation of membrane sequestered RNA (Figure 2g and h). The results suggested that the UCAGU motif is critical for miR223 packaging into vesicles in the cell- free reaction. Taken together the results in Figure 2 show that the miR223 sequence UCAGA promotes the binding of YBX1 in order to sort the miRNA into vesicles formed in cells and in a cell- free reaction. We suggest this sorting facilitates the export of miR223 in exosomes secreted from HEK293 cells. Mitochondria contribute to miR223 enrichment into exosomes In a recent study, we showed that YBX1 is sorted into P- bodies in cells and that these biomolecular condensates may initiate the sorting of miR223 into vesicles budding into the interior of endosomes (Liu et al., 2021; Shurtleff et al., 2016). Mitochondria represent another apparent intracellular loca- tion of miR223 (Wang et al., 2020). We used cell fractionation of homogenates of HEK293 cells to evaluate the subcellular distribution of endogenous miR223. Fractionation was evaluated by immu- noblot using marker proteins characteristic of various cell organelles (Figure 3a). Analysis of RNA extracted from isolated membranous organelles confirmed that miR223 but not miR190 was signifi- cantly enriched in mitochondria but not in ER or cytosol (Figure 3b, Figure 3—figure supplement 1a; Wang et al., 2020). Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 4 of 23 Cell Biology Research article a. miR223-3p UGUCAGUUUGUCAAAUACCCCA miR223mut UGAGACAUUGUCAAAUACCCCA miR190-5p UGAUAUGUUUGAUAUAUUAGGU miR190sort UGUCAGUUUUGAUAUAUUAGGU c. 100 ) % ( d n u o B n o i t c a r F 75 50 25 b. M μ 0 YBX1 M μ 1 M μ 0 YBX1 0 0.1 M μ 1 1 M μ 0 10 100 YBX1 [nM] YBX1 miR223 miR190sort miR190a miR223mut [miRNA] = 1nM Kd of miR223mut = 112.4nM Kd of miR190sort = 77.5nM 1000 10000 M μ 1 M μ 0 YBX1 M μ 1 d. miR223 Transfect plasmid which express miRNAs or mutants miR223mut miR190 miR190sort medium 1,500 g Supernatant100,000 g 10% 40% 150,000 g Exosomes 10,000 g 120,000 g 60% Sucrose cushion EV 60% e. f. p=0.0013 1.00 0.27 1.2 1.0 0.8 0.6 0.4 0.2 0.0 g. Temp (℃) RNase I Membranes Cytosol Triton(1%) miR223 3030 30 30 4 4 _ + + + + + _ + + + + + + _ + + + + _ _ _ _ + _ miR223mut 30 + + _ + + _ _ _ _ 3030 304 + + + + + + + + + _ + 4 _ + + _ l e g n a h C d o F s A N R m i l a m o s o x E ) l l e c o t e v i t a e r ( l miR223 miR223mut p=0.0194 5.33 h. 1.00 miR190 miR190sort 8 6 4 2 0 i 3 2 2 R m d e t c e t o r p % 25 20 15 10 5 0 C M ) l l e c o t e v i t a e r ( l l e g n a h C d o F s A N R m i l a m o s o x E miR223 miR223mut degree) C+M+Triton C+M C+M(4 Figure 2. miR223 sequence motif UCAGU binds YBX1. (a) RNA oligonucleotides corresponding to miR223, miR190 and versions with mutated sorting motif (miR223mut) or mutation to introduce the sorting motif (miR190sort). (b) EMSA assays using 1 nM 5’ fluorescently labeled miR223 WT or miR223mut or miR190 WT or miR190sort and purified YBX1. Purified YBX1 was titrated from 500pM to 1 µM. In gel fluorescence was detected. (c) Binding affinity curves as calculated by EMSA data from (b) (d) Schematic shows exosome purification with buoyant density flotation in a sucrose Figure 2 continued on next page Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 5 of 23 Cell Biology Research article Figure 2 continued step gradient from 293T cells overexpressing miR223 WT or mutant or miR190 WT or miR190sort. (e) RT- qPCR analysis of relative abundance of miR223 or miR223mut detected in exosomes compared to cellular level in 293T cells overexpressing miR223 WT or miR223mut. Data are plotted from three independent experiments and error bars represent standard deviations. (f) RT- qPCR analysis of relative abundance of miR190 or miR190sort detected in exosomes compared to cellular level in 293T cells overexpressing miR190 WT or miR190sort. Data are plotted from three independent experiments and error bars represent standard derivations. (g) In vitro packaging assay using 32P 5’end- labeled miR223 and miR223mut. Cell- free packaging of miR223 and miR223mut measured as protected radioactive signal from 32P labeled miR223 and miR223mut. Reactions with or without membrane, cytosol, and 1% Triton X- 100, and incubated at 4 or 30 °C are indicated. For the samples containing only cytosol plus membrane at 4 °C, only one- third of the samples were loaded. Each sample was supplemented with 300 mM urea to reduce the background signal. (h) Data quantification showed protected fraction of miR223 and miR223mut as calculated from in vitro packaging data shown in (g). The online version of this article includes the following source data and figure supplement(s) for figure 2: Source data 1. Uncropped gel images corresponding to Figure 2. Figure supplement 1. Screening of exosomal sorting motif of miR223. Figure supplement 1—source data 1. Uncropped gel images corresponding to Figure 2—figure supplement 1. To determine the localization of miR223 on or within mitochondria, we prepared mitoplasts using digitonin to strip away the mitochondrial outer membrane followed by fractionation on a Percoll density gradient. Immunoblots of the enriched mitochondria and isolated mitoplasts showed that the outer membrane, marked by Tom20, was largely removed with retention of the inner membrane marker Tim23 (Figure 3c). RNA was extracted from the purified mitoplast and RT- qPCR data indicated that miR223 was enriched along with mRNA for COX1, but not with nuclear U6 snRNA (Figure 3d). As an independent means to assess the localization of cytoplasmic miR223, we used immunopre- cipitation to purify mitochondria. Isolated mitochondria were then converted to mitoplasts by osmotic shock and treated with proteinase K and RNase. Immunoblots of the immunoprecipitated mitochon- dria and isolated mitoplasts showed that the outer membrane, marked by Tom20, and intermembrane space, marked by AIF, were largely removed with retention of the mitochondrial matrix marker citrate synthase (Figure 3e). RNA was extracted from the immunoprecipitated mitochondria and mitoplasts and RT- qPCR data indicated that miR223 was enriched along with mRNA for COX1, but not with miR190 or nuclear U6 snRNA (Figure 3f). We also used immunoprecipitated mitochondria (Figure 3—figure supplement 1b) and either Triton X- 100 to solubilize the membrane or freeze- thaw to allow the matrix and envelope fractions to be separated by centrifugation. Mitochondrial membrane proteins, such as Tom20 and COX IV, were solubilized and retained in the supernatant fraction (Figure 3—figure supplement 1c). The freeze- thaw regimen released citrate synthase to a supernatant fraction whereas Tom20 and COX IV sedimented in the pellet fraction. RNA was extracted from the detergent supernatant and pellet fractions where we found similar distributions of COX1 and miR223, neither of which were as readily solubilized as the inner and outer membrane proteins (Figure 3—figure supplement 1d). RT- qPCR quantification of fractions from the freeze- thaw regimen showed that both COX1 mRNA and miR223 remained largely associated with the sedimentable membrane fraction (Figure 3—figure supplement 1e). We conclude that miR223 is enclosed within mitochondria, possibly in association with the inner membrane. We sought a test of the role of mitochondria in the secretion of miR223 in exosomes. For this purpose, we generated cells depleted of mitochondria (Correia- Melo et al., 2017). U- 2 OS cells expressing GFP- parkin were treated with CCCP for 48 hr, conditions that cause mitochondria to be removed by mitophagy. We confirmed mitochondrial depletion after CCCP treatment by RT- qPCR of mitochondrial COX1 mRNA (Figure 3g) and immunoblot of the mitochondrial inner membrane marker Tim23 (Figure 3h). We then compared the levels of both miR223 and miR190 from GFP- parkin expressing U- 2 OS cells with and without CCCP treatment. RT- qPCR data showed that miR223, but not miR190, increased threefold in cells treated with CCCP (Figure 3j). To test the possibility that miR223 accumulated in cells as a result of a failure of mobilization into exosomes, we compared the miR223 levels in exosomes purified from untreated and CCCP treated cells (Figure 3j). Although exosome secretion, as measured with a CD63- luciferase marker, did not change after CCCP treatment (Figure 3—figure supplement 2a), we found that CCCP treatment lowered the amount of miR223 in EVs fourfold (Figure 3j). The increase in cellular at the expense of exosomal miR223 may reflect a critical role for mitochondria in the mobilization of this RNA to exosomes. Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 6 of 23 Cell Biology c. d. l l e C T M P M ⍺-Tim23 ⍺-Tom20 ⍺-Tubulin ⍺-PDI t n e m h c i r n E A N R 10 8 6 4 2 0 COX1 miR223 U6 snRNA cell mitoplast cell mitoplast cell mitoplast COX1 miR223 miR190 U6 snRNA IP-MT Cell IP-MP IP-MT Cell IP-MP IP-MT IP-MP Cell IP-MT Cell IP-MP + CCCP Exosome Free Medium + Uridine CCCP removed exosome Cell Exosome Research article a. l l e C o t y C o t i M R E b. t n e m h c i r n E 3 2 2 R m i ⍺-Tim23 ⍺-Calnexin ⍺-Tubulin ) l l e c o t e v i t a e r ( l 40 30 20 10 0 Cyto ER Mito f. t n e m h c i r n E A N R 20 15 10 5 0 ⍺-Citrate Synthase ⍺-LAMP1 ⍺-Tom20 ⍺-AIF ⍺-GRP78 ⍺-GAPDH h. P=0.0004 i. ⍺-Tim23 ⍺-GAPDH Parkin-GFP e. g. l l e c n i A N R m 1 X O C f o % 120 100 80 60 40 20 0 NC 24uM CCCP j. e g n a h c d o F l ) / C N P C C C ( 8 4 2 1 0.5 0.25 0.125 miR223 miR190 Figure 3. Mitochondria contribute to miR223 enrichment into exosomes. (a) Immunoblot analysis of protein markers for different subcellular fractions isolated from 293T cells. (b) RT- qPCR analysis of miR223 fold changes of different subcellular fractions isolated from 293T cells relative to cell lysate. (c) Immunoblot analysis of protein markers for mitoplasts purified from 293T cells by Percoll gradient fractionation (MT: mitochondria; MP: mitoplast). (d) RT- analysis of COX1 mRNA, miR223 and U6 snRNA fold changes for mitoplasts purified from 293T cells relative to cell lysate. (e) Immunoblot analysis Figure 3 continued on next page Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 7 of 23 Cell Biology Research article Figure 3 continued of protein markers for immunoprecipitated mitochondria and osmotic shock generated mitoplasts. Mitochondria were purified from a 293T 3xHA- EGFP- OMP25 overexpressing cell line using anti- HA magnetic beads. Mitoplasts were purified following mitochondrial immunoprecipitation by osmotic shock, proteinase K and RNase treatment (IP- MT: immunoprecipitated mitochondria; IP- MP: immunoprecipitated mitoplasts). (f) RT- analysis of COX1 mRNA, miR223, miR190 and U6 snRNA fold changes for immunoprecipitaed mitochondria and mitoplasts purified from the 293T 3xHA- EGFP- OMP25 overexpressing cell line. Data are plotted from three independent experiments and error bars represent standard deviations. (g) RT- qPCR analysis of mitochondrial mRNA COX1 in U2OS cells expressing GFP- Parkin treated with or without CCCP. Data are plotted from three independent experiments and error bars represent standard deviations. (h) Immunoblot analysis of mitochondrial marker Tim23 in U2OS cells expressing GFP- Parkin treated with or without CCCP. (i) Schematic of exosome purification from mitochondria depleted GFP- Parkin expressing U2OS cells. (j) RT- qPCR analysis of fold change of miR- 223 and miR- 190 in cells and purified exosomes from U2OS cells expressing GFP- Parkin which were treated with or without CCCP. Data are plotted from three independent experiments and error bars represent standard deviations. The online version of this article includes the following source data and figure supplement(s) for figure 3: Source data 1. Uncropped immunoblot images corresponding to Figure 3. Figure supplement 1. miR223, but not miR190, enriched in mitochondria. Figure supplement 1—source data 1. Uncropped immunoblot images corresponding to Figure 3—figure supplement 1. Figure supplement 2. Mitochondrial depletion did not change exosome secretion. YBAP1 binds miR223 in the mitochondria and in vitro In the course of purifying a tagged version of YBX1 from 293T cells, we observed another protein that copurified and found that it corresponded to YBAP1 (Figure 4a and b). Such a complex of YBX1 and YBAP1 has previously been reported (Matsumoto et al., 2005). We confirmed that purified YBX1 and YBAP1 bind each other by coexpression and affinity purification from insect cells (Figure 4—figure supplement 1b). YBAP1 is a mitochondrial matrix protein with a standard N- terminal transit peptide sequence (Muta et al., 1997). We confirmed this mitochondrial localization in U- 2 OS cells expressing Tom22- mCherry transiently transfected with a YBAP1- GFP construct (Figure 4c–d). We also showed that YBAP1 is localized within mitochondria by performing a proteinase K protection assay on purified mitochondria. Mitochondria were isolated from non- transfected cells and exposed to proteinase K in the presence or absence of Triton X- 100 and the degradation of YBAP1 was evaluated by immuno- blot. YBAP1 was resistant to proteinase K digestion as was the mitochondrial inner membrane marker Tim23. Both were degraded by proteinase treatment in the presence of Triton X- 100 (Figure 4e), consistent with the localization of YBAP1 within mitochondria. To test whether YBAP1 was bound to miR223 in mitochondria, we used YBAP1 immunoprecipita- tion with mitochondria purified by fractionation on a Percoll density gradient (Figure 4f). RT qPCR data showed that mitochondrial miR223 was immunoprecipitated by YBAP1 antibody but not by a control antibody (Figure 4g). To determine whether the YBAP1 and miR223 interaction was direct, we used the EMSA assay and found that purified YBAP1 bound miR223, but not miR190. The YBAP1 interaction with miR223 was not dependent on the RNA sequence motif responsible for YBX1 binding (Figure 4—figure supplement 2a–b). Taken together, these data suggest that YBAP1 binds miR223 in mitochondria and in vitro. YBAP1 may control the transit of miR223 from mitochondria to exosomes To investigate the function of YBAP1 in the transit of miR223 into exosomes, we generated a 293T YBAP1 KO cell line and compared the level of miR223 enrichment in exosomes and mitochondria isolated from WT and mutant cells (Figure 5a and b). Although knockout of YBAP1 did not change exosome secretion (Figure 5—figure supplement 1a), RT- qPCR analysis showed that miR223 decreased twofold in mitochondria but increased eightfold in exosomes purified from mutant and WT cells, respectively (Figure 5c). This apparent inverse relationship is consistent with a role for YBAP1 protein in the retention of miR223 in mitochondria. YBX1 puncta shuttled from mitochondria to endosomes In previous work we reported the localization of YBX1 to P- bodies and suggested this may repre- sent an intermediate stage in the concentrative sorting of miRNAs for secretion in exosomes (Liu et al., 2021). In other earlier work, P- bodies were seen in association with mitochondria (Huang et al., Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 8 of 23 Cell Biology Research article a. b. d. e u a v l YBX1 Copuriﬁed Band ⍺-YBAP1 c. y a r G d e z i l a m r o N 1.5 1.0 0.5 0.0 0 Tom22 YBAP1 100 50 Distance (pixels) 150 Tom22-mC e. e. Mitochondria Protease K Triton X-100 + - - + + - + + + 10um YBAP1-GFP f. Input IP ⍺-Tim23 ⍺-Tom20 ⍺-YBAP1 ⍺-YBAP1 ⍺-Tom20 h. Bound miR223 Free miR223 i. M μ μ 0 0 YBAP1 M μ 1 M μ μ 0 0 YBAP1 M μ 1 Bound miR190 Free miR190 merge g. 3 2 2 R m i f o t u p n I % 60 40 20 0 j. 100 ) % ( d n o B n o i t c a r F 75 50 25 P<0.0001 mIgGIP YBAP1IP miR223-3p miR190a-5p 0 0.1 1 10 100 YBAP1 [nM] 1000 10000 [miRNA] = 1nM Kd of miR223 = 173.2nM Figure 4. YBAP1 directly and specifically binds miR223. (a) Strep II- YBX1 was overexpressed in HEK293T cells. Coomassie blue detection of unknown band copurified with YBX1 from 293T cells. (b) Immunoblot identified unknown band was YBAP1. (c) Tom22- mCherry expressing U2OS was transfected with a YBAP1- GFP- expressing plasmid, cultured for 12 hr and observed by confocal microscopy. Scale bar, 10 μm. (d) Quantification of the fluorescence intensity of the different channels indicated by the solid white line of (c). (e) YBAP1 resides in mitochondria. Proteinase K protection assay for YBAP1 Figure 4 continued on next page Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 9 of 23 Cell Biology Research article Figure 4 continued using purified mitochondria from 293T cells. Samples were treated with or without proteinase K (10 μg/ml) and or Triton X- 100 (0.5%). Immunoblots for Tim23, Tom20, and YBAP1 are shown. (f) Mitochondria were purified for immunoprecipitation with YBAP1 antibody. Immunoblot detection of YBAP1 and Tom20. (g) RT- qPCR analysis of miR223 fold changes of YBAP1 IP samples. Data are plotted from three independent experiments and error bars represent standard deviations. (h–i) EMSA assays using 1 nM 5’ fluorescently labeled miR223 or miR190. Purified YBAP1 was titrated from 500pM to 1 μM. In gel fluorescence was detected. Quantification of (j) shown the calculated Kd. The online version of this article includes the following source data and figure supplement(s) for figure 4: Source data 1. Uncropped immunoblot and gel images corresponding to Figure 4. Figure supplement 1. YBX1 and YBAP1 copurify as a complex from transfected SF9 cells. Figure supplement 1—source data 1. Uncropped gel images corresponding to Figure 4—figure supplement 1. Figure supplement 2. YBAP1 does not share the same miR223- binding motif as YBX1. Figure supplement 2—source data 1. Uncropped gel images corresponding to Figure 4—figure supplement 2. 2011). To explore this possibility, we visualized endogenous YBX1 and YBAP1 by IF and observed YBX1 puncta colocalized with mitochondria (Figure 6a and b). In order to detect the proximity of endosomes to this point of contact between YBX1 puncta and mitochondria, we used U- 2 OS cells transfected with Rab5(Q79L)- mCherry, which we employed previously to enlarge and detect the inter- nalization of YBX1 into endosomes (Liu et al., 2021). We then used three color visualization of the U- 2 OS cells also transfected with YFP- YBX1 and mito- BFP. Time- lapse imaging showed YBX1 puncta in close proximity to mitochondria or endosomes, followed quickly by transfer between them (Figure 6c and d). Taken together, these data suggest a mechanism whereby miR223 stored in mitochondria, possibly sequestered by YBAP1, may be captured in a tighter interaction with YBX1 in P- bodies and delivered to endosomes for sorting and secretion in exosomes. Discussion Selected miRNAs are sorted, some with very high fidelity, into invaginations in the endosome that give rise to exosomes secreted from cultured human cells and likely from many if not all cells in metazoan organisms. The means by which these miRNAs are sorted and the possible extracellular functions they serve is a subject of interest in normal and disease physiology. Here we report the role of the RNA- binding protein YBX1 and a sorting or structural signal on one target RNA, miR223, and the indirect path miR223 may take from storage in mitochondria into exosomes. We have identified a sequence motif on miR223, UCAGU, responsible for high- affinity interaction with YBX and for sorting into vesicles formed in a cell- free reaction as well as for secretion in exosomes by HEK293 cells. Previously we performed this in vitro packaging assay in the presence of an inhibitor (GW4869) of neutral sphingomyelinase (NS2). This inhibitor has been shown to reduce the secretion of exosomes and exosome- associated miRNAs in other studies (Li et al., 2013; Trajkovic et al., 2008; Yuyama et al., 2012). In our cell- free assay, GW4869 inhibited the protection of CD63- luciferase and miR- 223 at concentrations known to inhibit the activity of NS2 in partially purified enzyme frac- tions (Shurtleff et al., 2016). We concluded that our cell- free reaction provides a model that mimics aspects of exosome biogenesis. The YBX1 protein has three distinct domains, one of which, the cold- shock domain (CSD) appears to be the principal site for RNA binding, including at least one critical residue, F85, required for binding miR223 as well as other RNAs (Lyons et al., 2016). The C- terminal domain (CTD) includes an intrinsically disordered domain (IDR) that promotes the formation of a liquid- liquid phase separation likely responsible for the organization of YBX1 in P- bodies (Liu et al., 2021). This domain does not itself interact with RNA, but it appears to facilitate the folding or stabilization of the CSD to promote high affinity binding to miR223. In other work using a similar approach, we identified two separate sorting signals, a 5’UGGA and a 3’UUU, on miR122 to which the RNA- binding protein La binds en route to secretion in exosomes by the breast cancer cell line MDA- MB- 231 (Temoche- Diaz et al., 2019). Other distinct sorting signals and their cognate RNA- binding proteins have been documented in different cell lines. miRNAs with a GGAG sorting motif recognized by a sumolyated form of hnRNPA2B1 was shown to be enriched in exosomes (Villarroya- Beltri et al., 2013). Another sequence, AAUGC, was found to be enriched in Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 10 of 23 Cell Biology Research article a. Mitochondria Cell medium RNA Extraction and RT-qPCR WT or YBAP1-KO Exosome b. c. e g n a h c d o F l / ) T W O K 1 P A B Y ( 16 8 4 2 1 0.5 0.25 ⍺-YBAP1 ⍺-Citrate Synthase ⍺-COX IV ⍺-Tim23 ⍺-AIF ⍺-Tom20 ⍺-YBX1 ⍺-Tubulin Cell Exosome mitochondria miR223 miR190 Figure 5. YBAP1 sequesters miR223 which is released and secreted in YBAP1 KO cells. (a) Schematic shows exosome and mitochondria purification from 293 T WT cells and YBAP1 knock out cells for RT- qPCR analysis. (b) Analysis of 293 T WT and CRISPR/Cas9 genome- edited cells by immunoblot for YBAP1, YBX1 and mitochondrial markers (c) RT- qPCR analysis of miR223 enrichment in mitochondria purified from 293 T WT cells and YBAP1 KO cells relative to cell lysate. Data are plotted from three independent experiments and error bars represent standard deviations. (d) RT- qPCR analysis of miR223 and miR190 fold change in cells, purified mitochondria and exosomes from 293 T WT cells and YBAP1 KO cells. Data are plotted from three independent experiments and error bars represent standard deviations. The online version of this article includes the following source data and figure supplement(s) for figure 5: Source data 1. Uncropped immunoblot images corresponding to Figure 5. Figure supplement 1. Knockout of YBAP1 did not change exosome secretion. exosomal miRNA and dependent on the RNA- binding protein FMR1 for miRNA secretion (Wozniak et al., 2020). Diverse cell lines and likely tissues appear to invoke distinct sorting signals decoded by different RNA- binding proteins. Many of the proteins may engage in biomolecular condensates such as P- bodies as a mechanism to sort RNAs for secretion (Liu et al., 2021). Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 11 of 23 Cell Biology Research article a. b. d e h c a t t a a t c n u p 1 X B Y % a i r d n o h c o t i m e h t n o 80 60 40 20 0 d. s t n e v e e l t t u h s a t c n u p 1 X B Y l l e c r e p 40 30 20 10 0 YBX1 YBAP1 5μm c. YFP-YBX1 Rab5(Q79L)-mC mito-BFP 0s 3m48s 6m39s 2μm 9m30s 10m27s 28m28s Figure 6. YBX1 puncta relocalize from mitochondria to endosomes. (a) YBX1 puncta on the mitochondria. U2OS cells were stained with anti- YBX1 and anti- YBAP1 antibodies and observed by confocal microscopy. The right panel shows enlarged regions of interest from the left panel. Scale bar, 5 μm. (b) The statistics are of the percentage of YBX1 puncta detected in proximity to mitochondria. N=30 cells. (c) YBX1 puncta relocalize from mitochondria to endosomes. U2OS cells overexpressed YFP- YBX1, Rab5(Q79L)- mCherry and mito- BFP. Time- lapse images were acquired with a Zeiss LSM900 confocal microscope. Scale bar, 2 μm. (d) The statistics are of YBX1 puncta shuttle events per cell. The data was represented as violin plots. N=34 cells. Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 12 of 23 Cell Biology Research article miR223 appears to be an example of a number of small nuclear- encoded RNAs localized to mito- chondria (Jeandard et al., 2019). In some cases these RNAs, such as tRNAs, serve an essential func- tion such as in mitochondrial protein synthesis, however, for miRNAs with no obvious mitochondrial genome target, the function remains unclear (Jeandard et al., 2019). Nonetheless, others have documented the localization of these miRNAs enclosed within the mitochondrion and in the case of miR223, it appears to be tightly associated with the inner membrane. The exact organization and function of miR223 in this location remains to be investigated but in the context of exosomal secre- tion, the mitochondrial localization appears to serve as a reservoir. In relation to the mitochondrial localization of miR223, we found a mitochondrial RNA- binding protein, YBAP1, that copurified with a tagged form of YBX1 expressed in HEK293 cells. YBAP1 has previously been reported to interact with YBX1 and independently found associated with mitochon- dria where its localization is dependent on an N- terminal transit peptide sequence (Muta et al., 1997). The association of mitochondrial YBAP1 and cytoplasmic YBX1 was reproduced by coexpres- sion of recombinant forms of the two proteins in baculovirus- infected SF9 cells (Figure 4—figure supplement 1b). YBAP1 binds miR223 selectively but with an affinity significantly below that of YBX1 (Figure 4h–j). Although YBX1 does not localize to the mitochondrion, the stable interaction of the complex may suggest a transient relationship, perhaps during the biogenesis of YBAP1 as it transits from the cytoplasm into the mitochondrion. A functional relationship between YBAP1 and YBX1 is suggested by the reduction in miR223 in mito- chondria and increase in secretion of miR223 in exosomes secreted from YBAP1 KO cells (Figure 5). In contrast, removal of mitochondria by treatment of cells with CCCP resulted in an increase in cyto- plasmic miR223 at the expense of secretion in exosomes (Figure 3). Although YBAP1 may facilitate the retention of miR223 within mitochondria, mitochondrial RNA import and export may serve an MVB Exosomes YBX1 YBAP1 miR223 Figure 7. Diagram representing a model of miR223 sorting from mitochondria into exosomes. Stages in the transfer of miR223 from mitochondria. Cytosolic miR223 is enriched in mitochondria where it may be sequestered by a weak interaction with YBAP1. Cytoplasmic YBX1 interacts more tightly with miR223 which may drive the removal of miR223 from mitochondria. YBX1 in RNA granules may accumulate miR223 removed from mitochondria. YBX1 puncta may give rise to small particles carrying miR223 for uptake into endosomes and secretion in exosomes. Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 13 of 23 Cell Biology Research article independent role in the selective capture of miRNAs by YBX1 in cytoplasmic P- body condensates. YBX1 puncta appear to shuttle between mitochondria and endosomes at which point miR223 bound to YBX1 may be further sorted into invaginations budding into the interior of endosomes. The highly selective nature of miRNA sorting and secretion in exosomes suggests an important role in the trafficking of miRNAs between cells. Numerous studies have suggested a role for secreted miRNAs in recipient cells (Cha et al., 2015; Mittelbrunn et al., 2011; Pegtel et al., 2010; Valadi et al., 2007). Nonetheless, as miRNAs ordinarily act stoichiometrically on target mRNAs, the extremely low abundance and copy number of miRNAs/vesicle is hard to reconcile with such a functional role of secreted miRNA (Chevillet et al., 2014; Shurtleff et al., 2017). Our observation that the bulk of cellular miR223 is held within mitochondria suggests an alternative role in some structural or regula- tory process, perhaps essential for mitochondrial homeostasis, controlled by the selective extraction of unwanted miRNA into RNA granules and further by secretion in exosomes (Figure 7). Key resources table Materials and methods Reagent type (species) or resource Designation Source or reference Identifiers Additional information Cell line (Spodoptera frugiperda) Sf9 Cell line (Homo sapiens) HEK 293T cells Cell line (Homo sapiens) HEK 293T- YBX1 KO cells Other Other Other Cell culture facility at UC Berkeley Cell culture facility at UC Berkeley Obtained by CRISPR- Cas9 in Schekman Lab Cell line (Homo sapiens) HEK 293T- YBAP1 KO This study Obtained by CRISPR- Cas9 in Schekman Lab Cell line (Homo sapiens) HEK 293T- 3xHA- EGFP- OMP25 This study Obtained by overexpression of pLJM1- 3XHA- EGFP- OMP25 in Schekman lab Cell line (Homo sapiens) U- 2OS cells Other Cell culture facility at UC Berkeley Cell line (Homo sapiens) U- 2OS Parkin- GFP cells This study Obtained by overexpression of Parkin- GFP in Schekman lab Recombinant DNA reagent pFastBac His6 MBP N10 TEV LIC cloning vector (4 C) Addgene RRID: Addgene_30116 N/A Recombinant DNA reagent Tom22- mCherry (plasmid) This study Gift of Dr Li Yu lab Recombinant DNA reagent His- MBP- YBX1 (plasmid) This study Recombinant DNA reagent His- MBP- YBX1(1–51) (plasmid) This study Recombinant DNA reagent His- MBP- YBX1(52–129) (plasmid) This study Recombinant DNA reagent His- MBP- YBX1(130–324) (plasmid) This study Recombinant DNA reagent His- MBP- YBX1(1–129) (plasmid) This study Recombinant DNA reagent His- MBP- YBX1(F85A) (plasmid) This study Recombinant DNA reagent His- MBP- YBAP1 (plasmid) This study To express YBX1 in insect cells. Plasmid maintained in Schekman lab To express YBX1(1–51) in insect cells. Plasmid maintained in Schekman lab To express YBX1(52–129) in insect cells. Plasmid maintained in Schekman lab To express YBX1(1–51) in insect cells. Plasmid maintained in Schekman lab To express YBX1(1–129) in insect cells. Plasmid maintained in Schekman lab To express YBX1(F85A) in insect cells. Plasmid maintained in Schekman lab To express YBAP1 in insect cells. Plasmid maintained in Schekman lab Recombinant DNA reagent Mito- BFP This study Gift of Dr. Samantha Lewis lab Recombinant DNA reagent mCherry- Rab5(Q79L) (plasmid) Addgene RRID: Addgene_35138 Recombinant DNA reagent pLJM1- 3XHA- EGFP- OMP25 This study Continued on next page To express 3xHA- EGFP- OMP25 in HEK293T cells. Plasmid maintained in Schekman lab Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 14 of 23 Cell Biology Research article Continued Reagent type (species) or resource Designation Source or reference Identifiers Additional information Anti- YBX1 (Rabbit polyclonal) Anti- YBAP1 (Mouse monoclonal) Anti- YBAP1(Rabbit polyclonal) Abcam RRID: AB_1950384 WB 1:1000 Santa Cruz Thermo Fisher Scientific RRID: AB_10611471 WB 1:1000 RRID: AB_2638956 WB 1:1000 Anti- Tim23 (Mouse monoclonal) BD Biosciences RRID: AB_398754 WB 1:1000 Anti- Tom20 (Mouse monoclonal) Abcam RRID: AB_945896 WB 1:1000 Anti- Calnexin (Rabbit polyclonal) Abcam RRID: AB_2069006 WB 1:2000 Anti- HA (Rabbit monoclonal) Cell Signaling RRID: AB_1549585 WB 1:1000 Anti- COX IV (Rabbit Monoclonal) Cell signaling RRID: AB_2085424 WB 1:1000 Anti- Citrate Synthase (Rabbit monoclonal) Cell signaling RRID: AB_2665545 WB 1:1000 Anti- Rab5 (Rabbit monoclonal) Cell signaling RRID: AB_2300649 WB 1:1000 Anti- LAMP1 (Rabbit monoclonal) Cell signaling RRID: AB_2687579 WB 1:1000 Anti- GRP78 (Rabbit polyclonal) Abcam RRID: AB_2119834 WB 1:3000 Antibody Antibody Antibody Antibody Antibody Antibody Antibody Antibody Antibody Antibody Antibody Antibody Antibody Antibody Antibody Anti- GADPH (Rabbit monoclonal) Anti- alpha Tubulin (Mouse monoclonal) Anti- beta Actin (Mouse monoclonal) Cell signaling RRID: AB_561053 WB 1:5000 Abcam RRID: AB_2241126 WB 1:5000 Abcam LICOR NIH RRID: AB_449644 WB 1:5000 https://www.licor.com/bio/image-studio-lite/ RRID: SCR_002285 https://fiji.sc/ Software, algorithm Image Studio Lite Software, algorithm FIJI Software, algorithm Prism 9 GraphPad RRID: SCR_002798 https://www.graphpad.com Cell lines and cell culture All immortalized cell lines were obtained from the UC- Berkeley Cell Culture Facility and were confirmed by short tandem repeat (STR) profiling and tested negative for mycoplasma contamina- tion. HEK 293T cells were cultured in DMEM with 10% FBS(VWR), NEAA (Gibco, Cat No: 11140050) and 1 mM Sodium Pyruvate (Gibco, Cat No: 11360070). For exosome production, we seeded cells at 10~20% confluency in 150 mm tissue culture dishes (Fisher Scientific, Cat No: 12- 565- 100) containing 30 ml of exosome- free medium. Exosomes were collected from 80% confluent cells (~48 hr). Exosome purification Conditioned medium was harvested from 80% to 90% confluent HEK 293T cultured cells. All procedures were performed at 4 ℃. Cells and large debris were removed by centrifugation in a Sorvall R6 +centrifuge at 1000xg for 15 min followed by 10,000xg for 15 min using a FIBERlite F14−6x500 y rotor. The supernatant fraction was then centrifuged onto a 60% sucrose cushion in a buffer with 10 mM HEPES (pH 7.4) and 0.85% w/v NaCl at ~100,000 x g (28,000 RPM) for 1.5 h in a SW32Ti rotor. The interface over the sucrose cushion was collected and pooled for an additional centrifugation onto a 2 ml 60% sucrose cushion at ~120,000 x g (31,500 RPM) for 15 h using an SW41Ti rotor. The first collected interface was measured by refractometry and adjusted a sucrose concentration not exceeding 21%. For bulk purification, the EVs collected from the interface over the sucrose cushion after the first SW41Ti centrifugation were mixed with 60% sucrose to a final volume of 10 ml (the concentration of sucrose ~50%). One ml of 40% and 1 ml of 10% sucrose Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 15 of 23 Cell Biology Research article were sequentially overlaid and the samples were centrifuged at ~150,000 x g (36,500 rpm) for 15 h in an SW41Ti rotor. The exosomes were located at the 10%/40% interface and collected for RNA extraction or immunoblot. Density gradient isolation of mitochondria and mitoplasts Mitochondria were isolated according to a well- established published protocol (Wang et al., 2020). HEK293T Cells were harvested at 80% confluency and were homogenized in 6 vol of HB buffer (225 mM mannitol, 25 mM sucrose, 0.5% BSA, 0.5 mM EGTA, 30 mM Tris–HCl, pH 7.4, and protease inhibitors) in a prechilled Dounce homogenizer (Kontes). The lysate was centrifuged and the postnu- clear supernatant was collected. Crude mitochondria were centrifuged at 6300 x g for 8 min, washed once with MRB buffer (250 mM mannitol, 0.5 mM EGTA, and 5 mM HEPES, pH 7.4), resuspended in 1 ml MRB buffer, laid over a 30% Percoll solution (9 ml) and centrifuged at 95,000 g for 45 min. The buoyant, purified fraction of mitochondria was collected for further analysis. For mitoplast purification, crude mitochondria were resuspended into 10 vol MRB buffer with 0.2 mg/ml digitonin and incubated on ice for 15 min. Digitonin- treated crude mitochondria were laid over a 30% Percoll solution (9 ml) and centrifuged at 95,000 g for 45 min. A buoyant, purified fraction of mitoplasts was collected for further analysis. YBAP1 immunoprecipitation from mitochondria After the Percoll gradient purification, the enriched mitochondria were diluted 2 x into MRB buffer and centrifuged at 12,000 g for 10 min. The mitochondrial pellet was lysed in 0.5 ml RIPA buffer (50 mM Tris- HCl, pH 7.5, 150 mM NaCl, 2 mM EDTA, 1% DDM, 1 mM PMSF) containing protease inhibitors (1 mM 4- aminobenzamidine dihydrochloride, 1 µg/ml antipain dihydrochloride, 1 µg/ml aprotinin, 1 µg/ml leupeptin, 1 µg/ml chymostatin, 1 mM phenylmethylsulphonyl fluoride, 50 µM N- tosyl- L- phenylalanine chloromethyl ketone and 1 µg/ml pepstatin) and RNase inhibitors, followed by centrifugation at 12,000 g for 10 min. Supernatant fractions were incubated with 10 µl washed protein A Dynabeads (ThermoFisher Scientific, Catalog number: 10001D) and 0.5 µg mouse mono- clonal IgG antibody and rotated at 4 ℃ for 1 h. A magnetic rack was used to remove protein A beads and the resulting supernatant fractions were incubated with 40 µl washed protein A Dynabeads and 4 µg YBAP1 antibody or mouse IgG antibody and rotated at 4 ℃ overnight. The beads were collected using a magnetic rack, washed 3 x with 1 ml of RIPA buffer, and collected for immunoblot and RNA extraction. Mitochondria immunoprecipitation Mito- IP was performed as previously described with slight modifications (Chen et al., 2016). The mito- IP cell- line was grown to ~90% confluency in 15 cm dishes. All the subsequent steps of mito- IP were performed using ice- cold buffers either in a cold- room or on ice. Cells (2x107) were washed twice with 10 ml of PBS and then harvested in 10 ml of mito- IP buffer (10 mM KH2PO4, 137 mM KCl) containing protease inhibitors (1 mM 4- aminobenzamidine dihydrochloride, 1 µg/ml antipain dihydrochloride, 1 µg/ml aprotinin, 1 µg/ml leupeptin, 1 µg/ml chymostatin, 1 mM phenylmethyl- sulphonyl fluoride, 50 µM N- tosyl- L- phenylalanine chloromethyl ketone and 1 µg/ml pepstatin) and TCEP 0.5 mM. The final mito IP buffer also contained 6 ml of OptiPrep (Sigma) per 100 ml. Cells were collected at 700xg for 5 min and resuspended in 1 ml of mito- IP buffer per 15 cm plate and then lysed using 5–10 passes through a 22 G needle. A post- nuclear supernatant (PNS) fraction was obtained after centrifuging the lysate at 1500xg for 10 min to remove unbroken cells and nuclei. Whenever necessary, a fraction of PNS was saved for immunoblot analysis. The resulting PNS was incubated with 100 µl of anti- HA magnetic beads (Sigma) pre- equilibrated in the mito- IP buffer in 1.5 ml microcentri- fuge tubes and then gently rotated on a mixer for 15 min. The beads were collected using a magnetic rack and washed 3 x for 5 min with 1 ml of mito- IP buffer. For mitoplast purification by osmotic shock, the supernatant was discarded after the final wash of the mito- IP sample, and the beads were gently resuspended in 200 µl of hypotonic osmotic shock buffer (OSB) containing 20 mM HEPES at pH 7.4. The resuspended sample was incubated on ice for 30 min and then the beads were centrifuged at 15,000 g for 15 min to sediment mitochondria/ mitoplasts. Beads were then resuspended in 100 µl of KPBS and proteinase K was added to achieve a final concentration of 10 µg/ml and samples were incubated on a rotating mixer at 4 °C for 15 min. Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 16 of 23 Cell Biology Research article Subsequently, PMSF was added to a final concentration of 1 mM, along with a protease inhibitor cocktail, and the sample was incubated on ice for 5 min. Next, 2.5 units of RNase ONE (Promega) was added to the sample, which was further incubated on a rotating mixer at room temperature for 15 min. For protein analysis, the sample was eluted directly in the SDS loading buffer. Alternatively, Trizol was added to the sample to stop the reaction for RNA purification. To assess their quality, we assayed the immunoprecipitated mitochondria for the following protein markers by immunoblotting using the rabbit primary antibodies- anti- HA (1:1000), anti- COX IV (1:1000), anti- TOM20 (1:1000), anti- Citrate Synthase (1:1000), anti- RAB5 (1:1000), anti- LAMP1 (1:2000), anti- GAPDH (1:5000), and anti- GRP78 (Abcam) (1:3000). All the above antibodies were sourced from Cell Signaling Technology, unless stated otherwise. As a negative control, non- transduced HEK293T cells were used in these experiments to assess the non- specific capture of the marker proteins. Mitochondrial fractionation One mito- IP was performed per sample as described above. To ensure an even distribution of mito- chondria across the samples, we pooled washed beads from all the IPs and equally distributed aliquots for subsequent treatments. Mitochondria were lysed in a 50 μl final volume using either 1% vol/vol Triton X- 100 (final concentration) or by three sequential rounds of freeze/thaw using liquid- nitrogen, as indicated. Urea was added to a final concentration of 3 M. After a 10 min incubation on ice, samples were centrifuged at 15000xg for 15 min and supernatant and pellet fractions were collected as indi- cated. The total fractionated mitochondria were analyzed by immunoblotting for various mitochon- drial markers. To analyze the specific RNA content of total or fractionated mitochondria, we extracted RNA using Trizol (Invitrogen) as per manufacturer’s recommendations followed by q- PCR. In vitro packaging of miR223 and miR223mut Preparation of membranes and cytosol The membrane and cytosol fractions were prepared from HEK293T cells as previously described with slight modifications (Shurtleff et al., 2016; Temoche- Diaz et al., 2020). All steps were carried out in either the cold- room or on ice using ice- cold buffers and pre- chilled equipment. Briefly, HEK293T cells (80% confluency) were washed twice with PBS and then harvested in the homoge- nization buffer (HB) (250 mM sorbitol, 20 mM HEPES- KOH pH 7.4) containing protease inhibitor cocktail (1 mM 4- aminobenzamidine dihydrochloride, 1 µg/ml antipain dihydrochloride, 1 µg/ml aprotinin, 1 µg/ml leupeptin, 1 µg/ml chymostatin, 1 mM phenylmethylsulphonyl fluoride, 50 µM N- tosyl- L- phenylalanine chloromethyl ketone and 1 µg/ml pepstatin). The cell pellet was obtained by centrifuging the cells at 500xg for 5 min. After discarding the supernatant, cells were weighed and resuspended in two volumes of HB followed by lysis with 5–10 passages through a 22 G needle. The lysate was centrifuged at 1500xg for 10 min to remove unbroken cells and nuclei to obtain a PNS which was then centrifuged at 20,000xg for 30 min to obtain a membrane frac- tion. The supernatant from above was centrifuged at 150,000xg for 30 min using a TLA- 55 rotor (Beckman Coulter ) and the resulting supernatant was used as the cytosol fraction (~6 mg protein/ ml). Membranes from the first 20,000xg sedimentation were resuspended in 1 ml of HB and centri- fuged again at 20,000xg for 30 min. The pellet fraction was resuspended in one volume of HB and rested on an ice block for a minimum of 10 min until the insoluble components and debris settled at the bottom of the tube. The finely resuspended material in the resulting supernatant fraction was then transferred to a new microcentrifuge tube (to avoid the settled debris) and was used as the membrane fraction. Preparation of radiolabeled miR223 and miR223mut substrates HPLC purified miR223 and miR223mut oligos were obtained from IDT. A stock solution of these oligos (1 μl of a 10 μM) was 5’-end- labeled using T4PNK (NEB) and 5 μl of ATP, [γ–32P]- 6000 Ci/mmol 10mCi/ml EasyTide (PerkinElmer BLU502Z250UC) as per manufacturer’s recommendations in a 50 μl reaction volume. T4PNK was heat- inactivated at 70 °C for 15 min. Unincorporated radionucleotides were removed by passing through PerformaTM spin columns (EdgeBio). The flow- through (radiola- beled substrate) was collected and stored at –20 °C until further use. Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 17 of 23 Cell Biology Research article In vitro miR223 packaging reaction Wherever indicated, 10 μl of cytosol (~5 mg/ml), 17 μl of membranes, 2 μl of radiolabeled substrate, 9 μl of 5 x incorporation buffer (400 mM KCl, 100 mM CaCl2, 60 mM HEPES- NaOH, pH 7.4, 6 mM, MgOAc), 4.5 μl of 10 x ATP regeneration system (400 mM creatine phosphate, 2 mg/ml creatine phos- phokinase, 10 mM ATP, 20 mM HEPES pH 7.2, 250 mM sorbitol, 150 mM KOAc, 5 mM MgOAc), 1 μl of ATP (100 mM, Promega), 0.5 μl of GTP (100 mM, Promega), 1 μl of Ribolock (40 U/μl, Invitrogen) were mixed to setup a 45 μl in vitro packaging reaction. In samples without the cytosol or membranes, the final reaction volumes were adjusted to 45 μl using HB. The reactions were incubated at either 30 °C or on ice, as indicated, for 15 min. Following the incubation, the indicated samples were subjected to RNAse ONE(Promega) using 10 U of the enzyme in the presence of urea (300 mM final concentration) in a total reaction volume of 60 μl. Wherever indicated, TritonX- 100 was added to a final concentra- tion of 1%. The RNAse treatments were carried out for 20 min at 30 °C followed by RNA extraction using DirectZol (Zymo Research) kits as per manufacturer’s protocol. RNA was precipitated overnight at –20 °C by the addition of 3 vol of ethanol, 1/10th volume of 3 M sodium acetate (pH5.2) and 30 μg Glycoblue reagent (Invitrogen). Precipitated RNA was sedimented at 16,000xg for 30 min followed by washing with ice- cold 70% ethanol. The RNA pellet was resuspended in 2 X RNA loading dye (NEB) and heated for 5 min at 70 °C. RNA was separated using a 15% denaturing polyacrylamide gel, followed by gel drying using a vacuum gel dryer (Model 583, Biorad). Radioactive bands were visual- ized by phosphorimaging using a Kodak storage phosphor screen and the Pharos FX Plus Molecular Imager (Biorad). Immunoblots Cell lysates and other samples were prepared by adding 2% SDS and heated at 95 ℃ for 10 min. Protein was quantified using a BCA Protein Assay Kit (Thermo Fisher Scientific) and appropriate amounts were mixed with 5 x SDS loading buffer. Samples were heated at 95℃ for 10 min and sepa- rated on 4–20% acrylamide Tris- glycine gradient gels (Life Technologies). Proteins were transferred to PVDF membranes (EMD Millipore, Darmstadt, Germany) and the membrane was blocked with 5% fat- free milk powder in TBST and incubated for 1 h at room temperature or overnight at 4 °C with primary antibodies. Blots were then washed in three washes of TBST for 10 min each. Membranes were incubated with anti- rabbit or anti- mouse secondary antibodies (GE Healthcare Life Sciences, Pittsburgh, PA) for 1 hr at room temperature and rinsed in three washes of TBST for 10 min each. Blots were developed with ECL- 2 reagent (Thermo Fisher Scientific). Primary antibodies used in this study were as follows: anti- Tim23 (BD, 611222), Calnexin (Abcam, ab22595), Actin (Abcam, ab8224), Tubulin (Abcam, ab7291), YBAP1 (Santa Cruz Biotechnology, sc- 271200). Immunofluorescence Cells were cultured on 12 mm round coverslips (corning) and were fixed with 4% EM- grade parafor- maldehyde (Electron Microscopy Science, Hatfield, PA) in PBS pH7.4 for 10 min at room temperature. Cells were then washed 3 x with PBS for 10 min each, treated with permeabilizing buffer (10% FBS in PBS) containing 0.1% saponin for 20 min and treated in blocking buffer for 30 min. Subsequently, cells were incubated with primary antibodies in permeabilizing buffer for 1 hr at room temperature, washed 3 x with PBS for 10 min each and incubated with secondary antibodies in permeabilizing buffer for 1 hr at room temperature and finally washed 3 x with PBS for 10 min each. Cells were mounted on slides with Prolong Gold with DAPI (Thermo Fisher Scientific, P36931). Primary antibodies used in the immu- nofluorescence studies were as follows: anti- YBX1 (Abcam, ab12148), YBAP1 (Santa Cruz Biotech- nology, sc- 271200). Images were acquired with Zeiss LSM900 confocal microscope and analyzed with the Fiji software (http://fiji.sc/Fiji). Quantitative real-time PCR Cellular and EV RNAs were extracted using a mirVana miRNA isolation kit (Thermo Fisher Scien- tific, AM1560) or Direct- zol RNA Miniprep kits (Zymo Research). Taqman miRNA assays for miRNA detection were purchased from Life Technologies. Assay numbers were: hsa- miR- 223–3 p, 002295; hsa- mir- 190–5 p, 000489; U6 snRNA, 001973. Total RNAs were quantified using RNA bioanalyzer (Agilent). Taqman qPCR master mix with no Amperase UNG was obtained from Life Technologies for reverse transcription. For mRNA, RevertAid First Strand cDNA Synthesis Kit (Thermo Scientific, K1621) Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 18 of 23 Cell Biology Research article was used for reverse transcription. COX1 qPCR primers: Forward- 5’- TCTC AGGC TACA CCCT AGAC CA-3’, Reverse- 5’- ATCG GGGT AGTC CGAG TAAC GT-3’. GAPDH qPCR primers: Forward- 5’- CTGA CTTC AACA GCGA CACC -3’, Reverse- 5’- TAGC CAAA TTCG TTGT CATA CC-3’. Quantitative real- time PCR was performed using an QuantStudio 5 Real- Time PCR System (Applied Biosystems). Protein purification Twin Strep tag hybrid YBX1 was expressed and the protein was isolated 48 hr after PEI- mediated transfection of 293T cells. Cells were resuspended in PBS and collected by centrifugation for 5 min at 600 g. Pellet fractions were resuspended in 35 ml lysis buffer (50 mM Tris- HCl (pH 8),150mM NaCl,1mM EDTA, 2 mM DTT, 1 mM PMSF and 1 x protease inhibitor cocktail). After sonication of the cell suspension the crude lysate was centrifuged for 60 min at 20,000 rpm at 4 °C. The resulting supernatant fraction was incubated with 2 ml Strep- Tactin Sepharose resin (IBA, 2- 1201- 010) for 1 h. Strep- Tactin Sepharose resin samples were transferred to columns (18 ml) and protein- bound beads were washed with 60 ml wash buffer (50 mM Tris- HCl (pH 8), 500 mM NaCl, 1 mM EDTA, 2 mM DTT) until no protein was eluted as monitored by the Bio- Rad protein assay (Bio- Rad, Catalog #5000006). Proteins were eluted with 10 ml elution buffer (50 mM Tris- HCl (PH = 8),150mM NaCl, 10 mM desthi- obiotin, 1 mM EDTA, 2 mM DTT) and concentrated using an Amicon Ultra Centrifugal Filter Unit (50 kDa, 4 ml) (Fisher Scientific, EMD Millipore). Proteins were further purified by gel filtration chroma- tography (Superdex- 200, GE Healthcare) with columns equilibrated in storage buffer (50 mM Tris- HCl 7.4, 500 mM KCl, 5% glycerol, 1 mM DTT). Peak fractions corresponding to the appropriate fusion protein were pooled, concentrated, and distributed in 10 µl aliquots in PCR tubes, flash- frozen in liquid nitrogen and stored at –80 °C. Protein concentration was determined by known concentrations of BSA assessed by Coomassie Blue staining. Tagged (6xHis) and maltose- binding protein hybrid genes were expressed in baculovirus- infected SF9 insect cells (Lemaitre et al., 2019). Insect cell cultures (1 l, 1x106 cells/ml) were harvested 48 h after viral infection and collected by centrifugation for 20 min at 2000 rpm. The pellet fractions were resuspended in 35 ml lysis buffer (50 mM Tris- HCl 7.4, 0.5 M KCl, 5% glycerol, 10 mM imidazole, 0.5 µl/ml Benzonase nuclease (Sigma, 70746–3), 1 mM DTT, 1 mM PMSF and 1 x protease inhibitor cocktail). Cells were lysed by sonication and the crude lysate was centrifuged for 60 min at 20,000 rpm at 4 °C. After centrifugation, the supernatant fraction was incubated with 2 ml Ni- NTA His- Pur resin (Thermo Fisher, PI88222) for 1 hr. Ni- NTA resin samples were transferred to columns (18 ml) and protein- bound beads were washed with 60 ml lysis buffer until no protein was eluted as monitored by the Bio- Rad protein assay (Bio- Rad, Catalog #5000006). Proteins were eluted with 10 ml elution buffer (50 mM Tris- HCl 7.4, 0.5 M KCl, 5% glycerol, 500 mM imidazole). The eluted sample was incubated with 2 ml amylose resin (New England Biolabs, E8021L) for 1 hr at 4 °C. Amylose resin samples were transferred to columns and protein- bound beads were washed with 60 ml lysis buffer until no protein was eluted as monitored by the Bio- Rad protein assay. Proteins were eluted with 10 ml elution buffer (50 mM Tris- HCl 7.4, 500 mM KCl, 5% glycerol, 50 mM maltose) and were concentrated using an Amicon Ultra Centrifugal Filter Unit (50 kDa, 4 ml) (Thermo Fisher Scientific, EMD Millipore). Proteins were further purified by gel filtration chromatography (Superdex- 200, GE Healthcare) with columns equilibrated in storage buffer (50 mM Tris- HCl 7.4, 500 mM KCl, 5% glycerol, 1 mM DTT). Peak frac- tions corresponding to the appropriate fusion protein were pooled, concentrated, and distributed in 10 µl aliquots in PCR tubes, flash- frozen in liquid nitrogen and stored at –80 °C. Protein concentration was determined by known concentrations of BSA based on Coomassie blue staining. CRISPR/Cas9 genome editing A pX330- based plasmid expressing Venus fluorescent protein (Shurtleff et al., 2016) was used to clone the gRNAs targeting YBAP1. A CRISPR guide RNA targeting the first exon of the YBAP1 open reading frame was designed following the CRISPR design website (http://crispor.tefor.net/crispor.py): CGCT GCGT GCCC CGTG TGCT . Oligonucleotides encoding gRNAs were annealed and cloned into pX330- Venus as described (Cong et al., 2013). HEK293T cells were transfected by Lipofectamine 2000 for 48 hr at low passage number, trypsinized and sorted for single, Venus positive cells in 96- well plates by a BD Influx cell sorter. YBAP1 knockout candidates were confirmed by immunoblot. HEK 293T YBX1 knockout cells were generously provided by Dr. Xiaoman Liu (Liu et al., 2021). Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 19 of 23 Cell Biology Research article Electrophoretic mobility shift assay Fluorescently labeled RNAs (5’-IRD800CWN) for detecting free and protein- bound RNA were ordered from Integrated DNA Technologies (IDT, Coralville, IA). EMSA was performed as described with some modification (Rio, 2014). Briefly, 1 nM of IRD800CWN- labeled RNA was incubated with increasing amounts of purified proteins, ranging from 500 pM - 1 μM. Buffer E was used in this incubation (25 mM Tris pH8.0, 100 mM KCl, 1.5 mM MgCl2, 0.2 mM EGTA, 0.05% Nonidet P- 40, 1 mM DTT, 5% glycerol, 50 µg/ml heparin). Reactions were incubated at 30 ℃ for 30 min then chilled on ice for 10 min. Samples were mixed with 6 x loading buffer (60 mM KCl, 10 mM Tris pH 7,6, 50% glycerol, 0.03% (w/v) xylene cyanol). Mixtures (5 µl) were loaded onto a 6% native polyacrylamide gel and electrophoresed at 200 V for 45 min in a cold room. The fluorescence signal was detected using an Odyssey CLx Imaging System (LI- COR Biosciences, Lincoln, NE). The software of the Odyssey CLx Imaging System was used to quantify fluorescence. To calculate Kds, we fitted used Hill equations with quantified data points. CD63-Nluc exosome secretion assay The CD63- Nluc exosome secretion assay was carried out as described (Williams et al., 2023). Briefly, cells stably expressing CD63- Nluc were cultured in 24- well plates until reaching approximately 80% confluence. All subsequent procedures were performed at 4 °C. Conditioned medium (200 µl) was collected from the appropriate wells and transferred to microcentrifuge tubes. The tubes were subjected to centrifugation at 1000×g for 15 min to remove intact cells, followed by an additional centrifugation at 10,000×g for 15 min to eliminate cellular debris. Supernatant fractions (50 µl) were used for measuring CD63- Nluc exosome luminescence. Cells were kept on ice and washed once with cold PBS, and then lysed in 200 µl of PBS containing 1% TX- 100 and protease inhibitor cocktail. For the measurement of CD63- Nluc exosome secretion, a master mix was prepared by diluting the Extracellular NanoLuc Inhibitor at a 1:1000 ratio and the NanoBRET Nano- Glo Substrate at a 1:333 ratio in PBS (Promega, Madison, WI, USA). Aliquots of the Nluc substrate/inhibitor master mix (100 µl) were added to 50 µl of the supernatant fraction obtained from the medium- speed centrifugation. The mixture was briefly vortexed, and luminescence was measured using a Promega GlowMax 20/20 Luminometer (Promega, Madison, WI, USA). Following luminescence measurements, 1.5 µl of 10% TX- 100 was added to each reaction tube to achieve a final concentration of 0.1% TX- 100. Samples were vortexed briefly, and luminescence was measured again. For intracellular normalization, the lumi- nescence of 50 µl of cell lysate was measured using the Nano- Glo Luciferase Assay kit (Promega, Madison, WI, USA) following the manufacturer’s instructions. The exosome production index (EPI) for each sample was calculated using the formula: EPI = ([medium] - [medium +0.1% TX- 100])/cell lysate. Acknowledgements We thank Dr. Samantha Lewis for advice about localization to mitochondria and for sharing a plasmid; thanks Matthew J Shurtleff, David Melville, Shenjie Wu, Jordan Ngo, Congyan Zhang, Justin Williams, Morayma M Temoche- Diaz for suggestions and reading and editing the manuscript. We also thank staff at the UC Berkeley shared facilities, the Cell Culture Facility, the Flow Cytometry Facility and QB3- Berkeley (The California Institute for Quantitative Biosciences at UC Berkeley). LM and JS are supported as Research Associates of the HHMI. RS is an Investigator of the HHMI, a Senior fellow of the UC Berkeley Miller Institute of Science and Scientific Director of Aligning Science Across Parkin- son’s Disease. Additional information Funding Funder Howard Hughes Medical Institute Grant reference number Author Randy Schekman Ma et al. eLife 2023;12:e85878. DOI: https://doi.org/10.7554/eLife.85878 20 of 23 Cell Biology Research article Funder Grant reference number Author The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Author contributions Liang Ma, Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing; Jasleen Singh, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review and editing; Randy Schekman, Conceptualization, Resources, Supervision, Funding acquisition, Validation, Investigation, Method- ology, Writing – original draft, Project administration, Writing – review and editing Author ORCIDs Liang Ma Randy Schekman http://orcid.org/0000-0003-3227-5917 http://orcid.org/0000-0001-8615-6409 Decision letter and Author response Decision letter https://doi.org/10.7554/eLife.85878.sa1 Author response https://doi.org/10.7554/eLife.85878.sa2 Additional files Supplementary files • MDAR checklist Data availability All data generated or analyzed in this study are included in the manuscript and supporting files. 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10.1371_journal.pntd.0009424.pdf	Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files.	null	RESEARCH ARTICLE The utilization of advance telemetry to investigate critical physiological parameters including electroencephalography in cynomolgus macaques following aerosol challenge with eastern equine encephalitis virus 1, Franco D. Rossi1, Michael V. Accardi2, Brandi L. Dorsey1, Thomas John C. TrefryID R. SpragueID E. KimmelID P. CardileID L. PittID 1, Suzanne E. Wollen-RobertsID 1, Pamela J. GlassID 4, Darci R. SmithID 1* 1, Joshua D. Shamblin1, Adrienne 1, Lynn J. Miller3, Crystal W. Burke1, Anthony 1, Sina Bavari1, Simon Authier2, William D. PrattID 1, Farooq NasarID 1, Margaret 1 Virology Division, United States Army Medical Research Institute of Infectious Diseases, Frederick, Maryland, United States of America, 2 Charles River (formerly Citoxlab North America), Laval, Canada, 3 Veterinary Medicine Division, United States Army Medical Research Institute of Infectious Diseases, Frederick, Maryland, United States of America, 4 Division of Medicine, United States Army Medical Research Institute of Infectious Diseases, Frederick, Maryland, United States of America margaret.l.pitt.civ@mail.mil (MLP); fanasar@icloud.com (FN) Abstract Most alphaviruses are mosquito-borne and can cause severe disease in humans and domesticated animals. In North America, eastern equine encephalitis virus (EEEV) is an important human pathogen with case fatality rates of 30–90%. Currently, there are no therapeutics or vaccines to treat and/or prevent human infection. One critical impediment in countermeasure development is the lack of insight into clinically relevant parameters in a susceptible animal model. This study examined the disease course of EEEV in a cynomolgus macaque model utilizing advanced telemetry technology to continuously and simultaneously measure temperature, respiration, activity, heart rate, blood pressure, electrocardiogram (ECG), and electroencephalography (EEG) following an aerosol chal- lenge at 7.0 log10 PFU. Following challenge, all parameters were rapidly and substantially altered with peak alterations from baseline ranged as follows: temperature (+3.0–4.2˚C), respiration rate (+56–128%), activity (-15-76% daytime and +5–22% nighttime), heart rate (+67–190%), systolic (+44–67%) and diastolic blood pressure (+45–80%). Cardiac abnor- malities comprised of alterations in QRS and PR duration, QTc Bazett, T wave morphology, amplitude of the QRS complex, and sinoatrial arrest. An unexpected finding of the study was the first documented evidence of a critical cardiac event as an immediate cause of euthanasia in one NHP. All brain waves were rapidly (12–24 hpi) and profoundly altered with increases of up to 6,800% and severe diffuse slowing of all waves with decreases of ~99%. Lastly, all NHPs exhibited disruption of the circadian rhythm, sleep, and food/fluid a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Trefry JC, Rossi FD, Accardi MV, Dorsey BL, Sprague TR, Wollen-Roberts SE, et al. (2021) The utilization of advance telemetry to investigate critical physiological parameters including electroencephalography in cynomolgus macaques following aerosol challenge with eastern equine encephalitis virus. PLoS Negl Trop Dis 15(6): e0009424. https://doi.org/10.1371/journal. pntd.0009424 Editor: Alain Kohl, University of Glasgow, UNITED KINGDOM Received: December 4, 2020 Accepted: April 29, 2021 Published: June 17, 2021 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pntd.0009424 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 1 / 32 Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Funding: This study was supported by a grant from Medical Countermeasure Systems-Joint Vaccine Acquisition Program [Grant #A5XA0A7444182001 (FN and MLP)]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Utilization of advance telemetry in cynomolgus macaques infected with EEEV intake. Accordingly, all NHPs met the euthanasia criteria by ~106–140 hpi. This is the first of its kind study utilizing state of the art telemetry to investigate multiple clinical parameters rel- evant to human EEEV infection in a susceptible cynomolgus macaque model. The study provides critical insights into EEEV pathogenesis and the parameters identified will improve animal model development to facilitate rapid evaluation of vaccines and therapeutics. Author summary In North America, EEEV causes the most severe mosquito-borne disease in humans highlighted by fatal encephalitis and permeant debilitating neurological sequelae in survi- vors. The first confirmed human cases were reported more than 80 years ago and since then multiple sporadic outbreaks have occurred including one of the largest in 2019. Unfortunately, most human infections are diagnosed at the on-set of severe neurological symptoms and consequently a detailed disease course in humans is lacking. This gap in knowledge is a significant obstacle in the development of appropriate animal models to evaluate countermeasures. Here, we performed a cutting-edge study by utilizing a new telemetry technology to understand the course of EEEV infection in a susceptible macaque model by measuring multiple physiological parameters relevant to human dis- ease. Our study demonstrates that the infection rapidly produces considerable alterations in many critical parameters including the electrical activity of the heart and the brain lead- ing to severe disease. The study also highlights the extraordinary potential of new teleme- try technology to develop the next generation of animal models to comprehensively investigate pathogenesis as well as evaluate countermeasures to treat and/or prevent EEEV disease. Introduction The genus Alphavirus in the family Togaviridae is comprised of small, spherical, enveloped viruses with genomes consisting of a single stranded, positive-sense RNA ~11–12 kb in length. Alphaviruses comprise 31 recognized species classified into eleven complexes based on anti- genic and/or genetic similarities [1–5]. The two aquatic alphavirus complexes [Salmon pancre- atic disease virus (SPDV) and Southern elephant seal virus (SESV)] are not known to utilize arthropods in their transmission cycles, whereas all of the remaining complexes [Barmah For- est, Ndumu, Middelburg, Semliki Forest, Venezuelan (VEE), eastern (EEE), western equine encephalitis (WEE), Trocara, and Eilat], consist of arboviruses that almost exclusively utilize mosquitoes as vectors [6]. Mosquito-borne alphaviruses infect diverse vertebrate hosts includ- ing equids, birds, amphibians, reptiles, rodents, pigs, nonhuman primates (NHPs), and humans [6]. The ability to infect both mosquitoes and vertebrates enables the maintenance of alpha- viruses in natural endemic transmission cycles that occasionally spillover into the human pop- ulation and cause disease. Infections with Old World alphaviruses such as chikungunya, o’nyong-nyong, Sindbis, and Ross River are rarely fatal but disease is characterized by rash and debilitating arthralgia that can persist for months or years [6]. In contrast, New World alphaviruses such as eastern (EEEV), western (WEEV), and Venezuelan equine encephalitis virus (VEEV) can cause fatal encephalitis [6]. Of the New World alphaviruses, EEEV is of foremost importance in North America. EEEV is comprised of four lineages; one North American (NA) and three South American [7,8]. The PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 2 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV NA lineage is associated with severe human disease, and is endemic in the eastern United States and Canada, and the Gulf coast of the United States [6]. The main transmission cycle is between passerine birds and Culiseta melanura mosquitoes, with enzootic foci concentrated in the Mid-Atlantic, New England, Michigan, Wisconsin, and Florida [6,9]. This cycle can spill- over into humans and domesticated animals to cause severe disease with human and equid case-fatality rates of 30–90% and >90%, respectively [6,10]. Human survivors can suffer from debilitating and permanent long-term neurological sequelae at rates of 35–80% [6,10]. In addi- tion to natural infections, many properties of EEEV including ease of isolation from nature and amplification in tissue culture, high virus titers, virus stability, high infectivity and uni- form lethality via aerosol route in non-human primates are conducive to weaponization. These properties facilitated the development of EEEV as a potential biological weapon during the Cold War by the United States and the former Union of Soviet Socialist Republics (USSR) [11,12]. These traits have also led to the assignment of EEEV to the NIAID category B list and as a select agent. Currently, there are no licensed therapeutics and/or vaccines to treat or pre- vent EEEV infection and the U.S. population remains vulnerable to bioterror event/s or natu- ral disease outbreaks. In order to develop effective therapeutic and/or vaccine countermeasures, various rodent and NHP models have been utilized to recapitulate various aspects of human disease. Among these models, aerosol infection of the cynomolgus macaques can produce alterations in blood chemistry and hematology, febrile illness, viremia, neurological disease, and lethality [13–15]. However, the data in the model are limited and requires further examination to gain insights into EEEV clinical disease course and pathogenesis. In this study, we investigated disease course in the cynomolgus macaque model following an aerosol challenge with EEEV utilizing state of the art telemetry to measure clinical signs including temperature, activity, respiration, heart rate, blood pressure, electrocardiogram (ECG), and electroencephalography (EEG). Materials and methods Ethics statement This work was supported by an approved USAMRIID Institute Animal Care and Use Com- mittee (IACUC) animal research protocol. Research was conducted under an IACUC approved protocol in compliance with the Animal Welfare Act, PHS Policy, and other Federal statutes and regulations relating to animals and experiments involving animals. The facility where this research was conducted is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC International) and adheres to principles stated in the Guide for the Care and Use of Laboratory Animals, National Research Council, 2011 [16]. Virus and cells Eastern equine encephalitis virus isolate V105-00210 was obtained from internal USAMRIID collection. The virus (Vero-1) was received from the Centers for Disease Control and Preven- tion (CDC) Fort Collins, CO [17]. The virus stock was passed in Vero-76 cells (American Type Culture Collection, ATCC; Bethesda, MD) twice for the production of Master (Vero-2) and Working (Vero-3) virus stocks. The virus stock was deep sequenced to verify genomic sequence and to ensure purity. In addition, the stock was tested and determined to be negative for both endotoxin and mycoplasma. Vero-76 cells were propagated at 37˚C with 5% CO2 in Dulbecco’s Minimal Essential Medium (DMEM) (CellGro) containing 2% (v/v) fetal bovine serum (FBS) (Hyclone), sodium PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 3 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV pyruvate (1 mM) (CellGro), 1% (v/v) non-essential amino acids (CellGro), and 50 μg/mL gen- tamicin (Invitrogen). Nonhuman primate study design Four (2 males, 2 females) cynomolgus macaques (Macaca fascicularis) of Chinese origin ages 5–8 years and weighing 3–9 kg were obtained (Covance). All NHPs were prescreened and determined to be negative for Herpes B virus, simian T-lymphotropic virus 1, simian immuno- deficiency virus, simian retrovirus D 1/2/3, tuberculosis, Salmonella spp., Campylobacter spp., hypermucoviscous Klebsiella spp., and Shigella spp. NHPs were also screened for the presence of neutralizing antibodies to EEEV, VEEV IAB, and WEEV by plaque reduction neutralization test (PRNT80). Telemetry devices and data collection All NHPs were implanted with devices by Covance/DSI and 4-weeks post-implantation the NHPs were transported to USAMRIID. NHPs were implanted with a Data Sciences Interna- tional (DSI) PhysioTel Digital M11 and two DSI PhysioTel Digital M01 implants. Each M01 device was dedicated to each hemisphere of the brain to measure EEG activity. The devices were implanted at left and right scapula and the leads were placed intracranially. The M11 implant was utilized to measure temperature, activity, respiration and heart rates, blood pres- sure, and ECG. Following implantation, the NHPs completely recovered after 2–4 weeks. Implanted animals were placed in individual cages with a single DSI TRX-1 receiver per cage with additional TRX-1 receivers placed in the study room for redundancy. These receivers were connected via Cat 5e cable to communication link controllers. The digital data was routed to data acquisition computers, which captured and archived the digital data using the Notocord-hem Evolution software platform (Version 4.3, Notocord Inc., Newark, NJ). The telemetry devices were activated in the NHPs and pre-challenge baseline values for each parameter (temperature, activity, respiration, heart rate, blood pressure, ECG, and EEG) were obtained for five days. All physiological parameters were sampled at various rates; activity and temperature (1 Hz), blood pressure and ECG (500 Hz), and EEG (1,000 Hz). Respiration and heart rates were derived utilizing NOTOCORD software (NOTOCORD Systems, Instem Company, Le Pecq, France). EEG analysis was performed using NeuroScore software version 3.1 (Data Sciences International, St. Paul, Minnesota, USA). Establishing baseline for each physiological parameter To establish a baseline of each parameter for individual animals, telemetry data was collected continuously for five days prior to challenge. Data for each animal and parameter was utilized to generate a 0.5-hr interval by averaging up to 1,800 data points. Subsequently, a 48-point ref- erence baseline for a 24-hr period was generated by averaging time-matched five previously calculated baseline values. Baseline 0.5-hr averages and standard deviations (SD) were gener- ated. Analysis was also performed in 12-hr day/nighttime intervals. Daytime and nighttime are defined as 6 am to 6 pm and 6 pm to 6 am, respectively. To generate a 12-hr average, all raw data points with respective times were averaged to generate each day/nighttime values. All comparisons between pre- and post-challenge were time matched. Following determination of baseline values, NHPs were challenged with a target dose of 7.0 log10 PFU of EEEV via the aerosol route. The NHPs were observed for signs of clinical disease and data for each parameter (temperature, activity, respiration and heart rates, blood pressure, ECG, and EEG) was obtained. Time-matched comparisons were made between pre-challenge baseline and post-challenge values. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 4 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Aerosol challenge NHPs were exposed to the target inhalation dose of 7.0 log10 PFU of EEEV in the head-only Automated Bioaerosol Exposure System (ABES-II). The virus stock at 9.3 log10 PFU/mL was diluted to 9.0 log10 PFU/mL and utilized in the nebulizer. The inhalation challenge was gener- ated using a Collison Nebulizer to produce a highly respirable aerosol (flow rate 7.5±0.1 L/ minute). The system generated a target inhalation of 1 to 3 μm mass median aerodynamic diameters determined by TSI Aerodynamic Particle Sizer. Samples of the pre-spray suspension and inhalation collected from the exposure chamber using an all-glass impinger (AGI) during the challenge were analyzed by plaque assay to determine the inhaled PFU. The inhalation challenge dose for each NHP was calculated from the minute volume determined with a whole-body plethysmograph box using Buxco XA software. The total volume of inhaled dose was determined by the exposure time required to deliver the estimated inhaled dose. Individ- ual NHPs were challenged successively in the ABES-II. Post-exposure monitoring and score criteria NHP observations began five days prior to aerosol exposure to obtain baseline data. Following aerosol challenge all NHPs were monitored daily via continuous 24-hr remote monitoring to limit room entries. Clinical signs of disease were observed and a score for each NHP was deter- mined by evaluating three parameters; neurological score, temperature, and responsiveness score. The neurological score scale was as follows: 0 = normal; 1 = mild and infrequent trem- ors, 2 = hyperactivity, infrequent tremors, 3 = constant and repetitive tremors, and 10 = unre- sponsive. Temperature score scale was as follows; 0 = normal baseline, 1 = 1˚C above or below baseline, 2 = 2˚C above or below baseline, 3 = 3˚C above or below baseline, 10 = 4˚C above or below baseline. Responsive score scale was as follows; 0 = normal, 1 = mild unre- sponsiveness, 2 = moderate unresponsiveness, 3 = severe unresponsiveness, and 10 = unre- sponsive. NHPs with a total score �10 met the euthanasia criteria. Tissue preparation All tissues and fluids were collected at the time of euthanasia and frozen. For quantification of virus, frozen samples of brain (frontal cortex), olfactory bulb, cervical spinal cord, and heart were thawed, weighed, and suspended in 1X PBS to generate 10% (w:v) tissue suspensions using a Mixer Mill 300 (Retsch, Haan, Germany). Tissue homogenates were centrifuged at 5,000 x g for 5 mins and clarified supernatants were used immediately for plaque assay as described below. Cerebral spinal fluid (CSF), plasma, and serum samples were thawed and used immediately in plaque assays. Plaque assay ATCC Vero 76 cells were seeded overnight on 6-well tissue culture plates to achieve 90–95% confluence. Triplicate wells were infected with 0.1-ml aliquots from serial 10-fold dilutions in Hanks’ Balanced Saline Solution (HBSS) and virus was adsorbed for 1 hr at 37˚C, 5% CO2. After incubation, cells were overlaid with Eagle’s Basal Medium (BME) (Gibco A15950DK) containing 0.6% agarose supplemented with 10% heat-inactivated FBS, 2% Penicillin/Strepto- mycin (10,000 IU/mL and 10,000 μg/mL, respectively), and incubated for 24 hr at 37˚C, 5% CO2. A second agarose overlay, prepared as described above, containing 5% neutral red vital stain (Gibco 02-0066DG) was added to the wells and incubated for 18–24 hr for visualization of plaques. Plaques were counted and expressed in either plaque forming units (PFU) per mL (PFU/mL) or PFU/g of tissue. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 5 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Plaque reduction neutralization test (PRNT80) Serum samples were heat-inactivated at 56˚C for 30 mins. Samples were serially diluted 2-fold starting at 1:10 and were mixed with equal volumes of medium containing ~2,000 PFU/mL of virus and incubated at 37˚C, 5% CO2. Following incubation, six-well plates containing mono- layers of Vero-76 cells were infected with 100μL of virus-serum mixtures in triplicates and incu- bated at 37˚C, 5% CO2 for ~1 hr. Following incubation, a secondary agarose overlay containing 5% neutral red vital stain was added to the wells and incubated for ~24 hr for visualization of plaques. Plaques were counted, PRNT titers were calculated and expressed as the reciprocal of serum dilution yielding a >80% reduction (PRNT80) in the number of plaques. The limit of detection in PRNT80 assay is <1:20. All samples were analyzed three times in the assay. Statistics All comparisons between pre- and post-challenge were time matched. GraphPad Prism ver- sion 7.00 for Windows (GraphPad Software, La Jolla, California, USA) software was utilized for statistical analysis. Significant differences in each parameter were determined using one- way ANOVA followed by a Tukey Test. Results EEEV challenge study design, survival, and detection of infectious virus in tissues at terminal time point Four macaques (2 males and 2 females) were implanted with telemetry devices to continuously and simultaneously monitor physiological parameters for the duration of the study. Baseline for each parameter in each NHP was determined by obtaining data for five daytime and night- time cycles. Following establishment of baseline, the NHPs were challenged via the aerosol route with EEEV V105 strain at a target dose of 7.0 log10 PFU (Fig 1). The NHPs received a virus dose ranging between 6.4–6.8 log10 PFU (Fig 2A). Following challenge, the NHPs were observed for signs of clinical disease and each NHP was assigned a score comprising of alter- ations in temperature, responsiveness, and neurological signs. NHPs with a score of ten or higher met the euthanasia criteria. All NHPs exhibited signs of clinical disease by ~48–72 hours post-infection (hpi) (S1 Fig). NHP #1 and #2 exhibited rapid increase in scores and met the euthanasia criteria by ~106–120 hpi (Figs 2B and S1). The two remaining NHPs displayed a similar initial rise in scores, followed by a transient decline and a rapid progression to severe disease at ~140 hpi (Figs 2B and S1). Various NHP tissues were collected at the time of euthanasia and EEEV was quantitated via plaque assay. Virus was titrated from samples including serum, plasma, brain (frontal cortex), olfactory bulb, cervical spinal cord, and heart of each NHP (Fig 3). Cerebrospinal fluid (CSF) was collected and titrated for NHPs #1 and #2 (Fig 3). Infectious virus was detected in serum and plasma of only NHP #1 with titers of ~3.5 and ~3.2 log10 PFU, respectively (Fig 3). In con- trast, EEEV was detected in brain and olfactory bulb in all four NHPs with titers ranging from ~4.1 to ~7.9 log10 PFU (Fig 3). The cervical spinal cord of NHP #2 and #4 had virus titers of ~7.6 and ~4.1 log10 PFU, respectively (Fig 3). Virus was detected in the heart tissue of only one NHP (NHP #3) with a titer of ~4.0 log10 PFU (Fig 3). Lastly, the virus was present in CSF of NHP #1 and #2 with titers of ~5.9 and ~5.5 log10 PFU, respectively (Fig 3). Alteration of animal behavior The NHPs were observed were continuously monitoring with limited disruptions due to human activity. This provided a rare opportunity to study the impact of EEEV infection on PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 6 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Fig 1. Experimental design of the NHP study. All parameters were continuously and simultaneously measured throughout the study. Baseline was established for each parameter for an individual NHP by measuring 5 daytime (6 am-6 pm) and nighttime (6 pm– 6 am). All comparisons of post- challenge data was time-matched to pre-challenge baseline. https://doi.org/10.1371/journal.pntd.0009424.g001 animal behavior and determine the onset of neurological signs. The baseline behavior was determined for five days prior to challenge for each individual NHP for day and night times. Daytime and nighttime were defined as 6 am to 6pm and 6pm to 6 am, respectively. Alteration of animal behavior were evaluated by observing three parameters; sleep, activity, and food/ fluid consumption. Alteration of the circadian rhythm could be detected as early as 24 hpi in NHP #4 and ~42–54 hpi in the remaining three NHPs (S1 Table). The disruption of circadian rhythm was characterized by a decrease in sleep at night with a concomitant increase in night- time activity (S1 Table). The following daytime period was characterized by a decrease in day- time activity with short periods of sleep (S1 Table). However, following the onset of clinical signs, all NHPs exhibited a rapid progression to no or minimal sleep for the remainder of the study (S1 Table). Similar to sleep, food/fluid consumption also decreased between ~43–90 hpi in three of the four NHPs with minimal food/fluid consumption ~15–36 hrs prior to euthana- sia. Lastly, the onset of overt seizures was observed within last ~7 hrs of the study in three of the four NHPs (S1 Table). Neutralizing antibody response The presence of neutralizing antibodies was measured via PRNT80 at days -7, 0, and terminal time points (S2 Table). None of the NHPs had detectable neutralizing antibody titers prior to or at the time of challenge and were assigned the limit of detection of the assay (<1:20) (S2 Table). At the time of euthanasia, NHPs #1 and #2 did not have any detectable neutralizing PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 7 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Fig 2. Administered aerosol EEEV dose (A) and survival of the NHPs (B). https://doi.org/10.1371/journal.pntd.0009424.g002 antibody response (S2 Table). In contrast, both NHP #3 and #4 had PRNT80 titers of 1:40 and 1:80, respectively (S2 Table). Temperature The baseline temperature was determined for each NHP for five days and data was analyzed in 0.5- (Fig 4A) and 12-hr (Fig 4B) intervals. All post-challenge comparisons were time-matched to pre-challenge baseline values. The average temperature prior to challenge ranged from ~36.3–38˚C (Fig 4A). Following challenge, an increase in temperature was observed within ~26–56 hpi in all four NHPs (Fig 4A, S3 Table). The onset of fever (>1.5˚C above baseline) was at ~53–61 hpi and it remained considerably elevated for duration of ~47–53 hrs (Fig 4A, S3 Table). The peak temperature ranged from 40.1–41˚C, and peak magnitude of fever ranged from ~3.0–4.2˚C and ~2.2–3.8˚C for 0.5- and 12-hr interval analyses, respectively (Fig 4A and 4B, S3 Table). In both analyses, NHPs displayed the highest elevation in temperature at night time (Fig 4A and 4B). Three of the NHPs exhibited hyperpyrexia (>3.0˚C above baseline) for a duration of ~11–22 hrs (Fig 4A and 4B). Following the sustained fever, a decline in tempera- ture was observed in the last ~10–24 hrs prior to euthanasia in all four NHPs (Fig 4A). NHPs #3 and 4 exhibited a rapid decline in temperature ~4–6 hrs prior to euthanasia with NHP #3 displaying a decline of 3.3˚C (Fig 4A). Respiration rate Similar to temperature, respiration rate was analyzed in 0.5- (Fig 5A) and 12-hr (Fig 5B) inter- vals. The baseline respiration rate for each NHP ranged from ~15–32 breaths per minute (bpm) (Fig 5A). Following challenge, an increase in respiration rate was observed starting at PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 8 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Fig 3. Quantitation of infectious EEEV in NHP tissues collected at the time of euthanasia. The time of tissue collection for each NHP is provided in the x-axis. Limit of detection of plaque assay is 1.0 log10 PFU/mL or 1.0 log10 PFU/g and are indicated by a dashed line. https://doi.org/10.1371/journal.pntd.0009424.g003 24 hpi in all NHPs (Fig 5A and 5B). However, in contrast to temperature, the respiration rate displayed intermittent increase/decrease throughout first 96 hpi (Fig 5A). Sustained increase was observed at 100–112 hpi in all four NHPs, however the duration and magnitude of the increase differed considerably (Fig 5A). In NHPs #1 and #2, the sustained increase was observed ~3–8 hrs prior to euthanasia with peak respiration rate of 31–38 bpm, an increase of ~56–71% or ~17–22% in 0.5- and 12-hr interval analysis, respectively (Fig 5A and 5B). In con- trast, NHPs #3 and #4 experienced considerably higher peak respiration rate of ~49–50 bpm for a duration of ~32–40 hrs (Fig 5A and 5B). At peak, the percent increase in respiration rate in 0.5- and 12-hr interval analysis ranged from ~105–128% and ~85–95%, respectively (Fig 5A and 5B). Activity The activity of each NHP was analyzed in 6- (Fig 6A) and 12-hr (Fig 6B) intervals for both day- time and nighttime periods. Daytime activity differed markedly in the four NHPs. The average daytime activity for NHP #1 and #3 ranged from ~803–1704 units/6hrs, whereas the range for remaining two NHPs was ~410–505 units/6hrs (Fig 6A). Alteration in daytime activity could be observed within ~36–102 hpi with considerable decline in all NHPs at ~12–36 hrs prior to euthanasia (Fig 6A). The highest magnitude of decline was in NHP #1 and #3 with values of ~754–1303 units/6hrs, a decline of ~69–76% (Fig 6A). A 12-hr daytime interval analysis showed a sustained decline in activity ranging from ~64–73% in both NHPs (Fig 6B). NHP #2 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 9 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Fig 4. Body temperature of NHPs pre- and post-EEEV challenge. Data analysis is shown in 0.5- (A) and 12-hr (B) daytime/nighttime intervals. All NHPs were continuously monitored pre- and post-challenge. Pre-challenge baseline temperature was measured for five day/night cycles and a 0.5-hr interval baseline average was calculated by averaging raw data of five time-matched day or night time intervals. Forty-eight 0.5-hr interval averages were used to construct a baseline temperature for a day/night cycle and is shown as a grey line (A). Post-challenge values within �3 standard deviations (SD) are indicated with ( ). Hyperpyrexia is indicated by ( Temperature change in a 12-hr interval is shown in grey (daytime) and in black (nighttime) (B). All raw data from either 12-hr daytime or nighttime intervals were averaged and normalized. The last data point for NHP #3 and #4 is an average of 8 hrs (B). ), and >3 SD below baseline are indicated with ( ), >3 SD above baseline are indicated with ( ) (A). https://doi.org/10.1371/journal.pntd.0009424.g004 and #4 exhibited a decline in daytime activity ranging ~62–118 units/6hrs, a decline of ~15– 23% and a sustained decline of ~11–20% in the 12-hr interval analysis, respectively (Fig 6B). The baseline nighttime activity of all four NHPs was comparable, ranging from 333–383 units/6hrs (Fig 6A). Similar to daytime activity, a substantial increase in nighttime activity was observed as early as 24 hpi in NHP #4 and at 6–48 hrs prior to euthanasia in the other three NHPs (Fig 6A). Both 6- and 12-hr analysis showed an increase of ~5–22% and ~3–14%, respectively (Fig 6A and 6B). The considerable increase in nighttime activity was further cor- roborated via continuous monitoring (S1 Table). PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 10 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Heart rate The baseline heart rate in NHPs ranged from ~60–150 bpm (Fig 7A). Following challenge, intermittent alterations were observed within ~24 hpi in all four NHPs (Fig 7A). Elevation in nighttime heart rate was observed within ~56 hpi followed by a return to normal daytime base- line values (Fig 7A). Sustained elevated heart rate was observed ~79–105 hpi that peaked at ~24–42 hrs prior to euthanasia with values of ~185–243 bpm (Fig 7A). The peak elevations from baseline ranged from ~67–190% and ~60–134% in 0.5- and 12-hr interval analysis, respectively (Fig 7A and 7B). Fig 5. Respiration rate of NHPs pre- and post-EEEV challenge. Data analysis is shown in 0.5- (A) and 12-hr (B) daytime/nighttime intervals. All NHPs were continuously monitored pre- and post-challenge. Pre-challenge baseline respiration rate was measured for five day/night cycles and a 0.5-hr interval baseline average was calculated by averaging raw data of five time-matched day or night time intervals. Forty-eight 0.5-hr interval averages were used to construct a baseline respiration rate for a day/night cycle and is shown as a grey line (A).Post-challenge values within �3 standard deviations (SD) are indicated with ( interval is shown in grey (daytime) and in black (nighttime) (B). All raw data from either 12-hr daytime or nighttime intervals were averaged and normalized. The last data point for NHP #3 and 4 is an average of 8 hrs (B). p-values �0.03 are indicated with �. bpm = breaths per minute. ), and >3 SD below baseline are indicated with ( ), >3 SD above baseline are indicated with ( ). Percent change from baseline in 12-hr https://doi.org/10.1371/journal.pntd.0009424.g005 PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 11 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Fig 6. Measurement of activity in NHPs pre- and post-EEEV challenge. Data analysis is shown in 6- (A) and 12-hr (B) daytime/nighttime intervals. All NHPs were continuously monitored pre- and post-challenge. Pre-challenge baseline activity was measured for five day/night cycles and a 6-hr interval baseline average was calculated by averaging raw data of five time-matched day or night time intervals. Four 6-hr interval averages were used to construct a baseline activity for a day/night cycle and is shown as a grey line (A). Post-challenge values within �3 standard deviations (SD) are indicated with ( ), >3 SD above baseline are indicated with ( ). Percent change from baseline in 12-hr interval is shown in grey (daytime) and in black (nighttime) (B). All raw data from either 12-hr daytime or nighttime intervals were averaged and normalized. The last data point for NHP #3 and 4 is an average of 8 hrs (B). p-values �0.041 are indicated with �. ), and >3 SD below baseline are indicated with ( https://doi.org/10.1371/journal.pntd.0009424.g006 Blood pressure The baseline systolic and diastolic blood pressure values ranged from ~90–116 and ~58–87 mm Hg, respectively (Figs 8A and 9A). Following challenge, both systolic and diastolic pres- sure intermittently increased within 24 hpi followed by a sustained elevation at ~54–76 hpi for PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 12 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Fig 7. Heart rate of NHPs pre- and post-EEEV challenge. Data analysis is shown in 0.5- (A) and 12-hr (B) daytime/nighttime intervals. All NHPs were continuously monitored pre- and post-challenge. Pre-challenge baseline heart rate was measured for five day/night cycles and a 0.5-hr interval baseline average was calculated by averaging raw data of five time-matched day or night time intervals. Forty-eight 0.5-hr interval averages were used to construct a baseline heart rate for a day/night cycle and is shown as a grey line (A).Post-challenge values within �3 standard deviations (SD) are indicated with ( baseline are indicated with ( and in black (nighttime) (B). All raw data from either 12-hr daytime or nighttime intervals were averaged and normalized. The last data point for NHP #3 and 4 is an average of 8 hrs (B). p-values �0.035 are indicated with �. bpm = beats per minute. ), >3 SD above ). Percent change from baseline in 12-hr interval is shown in grey (daytime) ), and >3 SD below baseline are indicated with ( https://doi.org/10.1371/journal.pntd.0009424.g007 a duration of ~48–80 hrs (Figs 8A and 9A). At peak, both systolic and diastolic pressures ran- ged from ~153–180 and ~102–128 mm Hg, respectively (Figs 8A and 9A). The peak systolic pressure values in 0.5-hr and 12-hr intervals represent percent increases of ~44–67% and ~35– 57%, respectively (Fig 8A and 8B). Similarly, peak diastolic pressure values in 0.5-hr and 12-hr analysis ranged from ~45–80% and ~38–65%, respectively (Fig 9A and 9B). PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 13 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Fig 8. Systolic blood pressure of NHPs pre- and post-EEEV challenge. Data analysis is shown in 0.5- (A) and 12-hr (B) daytime/nighttime intervals. All NHPs were continuously monitored pre- and post-challenge. Pre-challenge baseline systolic blood pressure was measured for five day/night cycles and a 0.5-hr interval baseline average was calculated by averaging raw data of five time-matched day or night time intervals. Forty-eight 0.5-hr interval averages were used to construct a baseline systolic blood pressure for a day/night cycle and is shown as a grey line (A). Post-challenge values within �3 standard deviations (SD) are indicated with ( ). Percent change from baseline in 12-hr interval is shown in grey (daytime) and in black (nighttime) (B). All raw data from either 12-hr daytime or nighttime intervals were averaged and normalized. The last data point for NHP #3 and 4 is an average of 8 hrs (B). p-values �0.028 are indicated with �. ), and >3 SD below baseline are indicated with ( ), >3 SD above baseline are indicated with ( https://doi.org/10.1371/journal.pntd.0009424.g008 ECG Intermittent changes in ECG were observed in all four NHPs following challenge as early as 6–12 hpi (S2 and S3 Figs). As expected, the sustained elevation in heart rate at ~48–72 hpi led to reduction in QTc Bazett, RR, PR, and QRS duration (S2 and S3 Figs). However, 24 hrs prior to euthanasia multiple abnormalities were observed. An increase in QRS duration was observed for ~19 hrs with peak increase of ~11 msec in NHP #1 (S2 Fig). An increase in QTc Bazett was observed in three of the four NHPs (#1, 2, and 4) for duration of ~4–12 hrs with PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 14 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Fig 9. Diastolic blood pressure of NHPs pre- and post-EEEV challenge. Data analysis is shown in 0.5- (A) and 12-hr (B) daytime/nighttime intervals. All NHPs were continuously monitored pre- and post-challenge. Pre-challenge baseline diastolic blood pressure was measured for five day/night cycles and a 0.5-hr interval baseline average was calculated by averaging raw data of five time-matched day or night time intervals. Forty-eight 0.5-hr interval averages were used to construct a baseline diastolic blood pressure for a day/night cycle and is shown as a grey line (A). Post-challenge values within �3 standard deviations (SD) are indicated with ( ). Percent change from baseline in 12-hr interval is shown in grey (daytime) and in black (nighttime) (B). All raw data from either 12-hr daytime or nighttime intervals were averaged and normalized. The last data point for NHP #3 and 4 is an average of 8 hrs (B). p-values �0.023 are indicated with �. ), and >3 SD below baseline are indicated with ( ), >3 SD above baseline are indicated with ( https://doi.org/10.1371/journal.pntd.0009424.g009 peak increase ranging from ~22–44 msec (S2 Fig). The ECG of NHPs #1, 3, and 4 displayed either intermittent or sustained elongation of PR duration (S3 Fig). NHP #4 displayed an elon- gation in PR duration for ~6 hrs with a peak increase of ~24 msec prior to euthanasia (S3 Fig). Alteration of T wave morphology and a decline (~1.5 mV) in the magnitude of QRS complex were both observed in NHP #2 (Fig 10). Lastly, both NHPs #1 and 4 displayed sinoatrial arrest in the last 24-hrs prior to euthanasia (Fig 10). PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 15 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Fig 10. ECG abnormalities in EEEV infected NHPs 24 hrs prior to euthanasia. Representative ~5 second intervals of pre- and post-EEEV challenge are shown. https://doi.org/10.1371/journal.pntd.0009424.g010 Quantitative EEG The EEG data for each NHP pre- and post-challenge are shown as heat maps (Figs 11 and 12). Six night and five day 12-hr intervals were used to generate a pre-challenge heat map for each NHP. In all four NHPs, the baseline heat map displayed similar patterns with the majority of values between -50 to +50%. An increase in gamma waves were observed during the daytime (generally around anticipated feeding periods and increased human activity due to arrival of NHPs in biocontainment and one day prior to challenge) in all four NHPs during the pre-chal- lenge period attributed to electromyographic artifacts. However, post- challenge distinct pat- terns could be observed rapidly in all NHPs. NHP #1 displayed an increase in the individual frequencies comprising of the beta and gamma power bands at nighttime within ~15 hr post-challenge with maximal increases of ~1,300% and ~5,200%, respectively (Fig 11). The next ~6–10 hrs were characterized by PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 16 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Fig 11. Pre- and post-EEEV challenge quantitative electroencephalography (qEEG) heat maps of NHPs #1 and #2. The top and bottom x-axes display brain waves [(delta (δ), theta (θ), alpha (α), sigma (σ), and gamma (γ)] and frequency in hertz (Hz), respectively. The left and right y-axes display time (hr) and 12-hr day/night time intervals, respectively. 12-hr nighttime is indicated by (▐ ) and daytime by a gap. Pre-EEEV challenge baseline (left) and post-EEEV challenge (right) heat maps are shown. https://doi.org/10.1371/journal.pntd.0009424.g011 increases of up to ~800, 400, and 600% in the theta, alpha, and sigma bands, respectively, dur- ing the daytime while the NHP was awake. These increases were followed by a decline in all frequencies to near or below basal levels for ~6 hrs during the daytime (Fig 11). This pattern continued into the second night with a general increase of up to ~2,700% across all individual PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 17 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV frequencies within the gamma band and ~1,200% across all other frequencies (Fig 11). At 48 hpi, a prolonged decline of up to 93% was observed across all frequencies during the daytime (Fig 11). The third night was characterized by intermittent increases in high frequencies within the gamma band, however, a sustained increase of up to ~250% in all other brain waves was observed including low gamma frequencies (Fig 11). An increase of up to 6,800% in gamma frequencies was observed in the first ~3 hrs of the next daytime, followed by a sustained decline to below time-matched basal values (Fig 11). The decrease in gamma frequencies was accompanied by a simultaneous increase in theta, alpha, sigma, and low beta frequencies (in an awake NHP) with the alpha frequencies displaying the most pronounced increase with val- ues exceeding 1,000% (Fig 11). The fourth night displayed similar patterns as the previous night with sustained increase in alpha frequencies of ~600% (Fig 11). The last daytime for this NHP was characterized by a considerable and sustained decline in delta and gamma bands to at or below basal values, with a simultaneous increase in theta, alpha, sigma, and beta frequen- cies (Fig 11). The magnitude of the latter waves ranged from ~80–1,400%, with alpha band dis- playing the largest increases (Fig 11). NHP #2 displayed a similar pattern as NHP #1 with an early increase in the individual beta and gamma frequencies within ~12 hpi ranging from ~300 to 3,000% (Fig 11). This was fol- lowed by an elevation in delta, theta, alpha, and sigma frequencies during the daytime with increases of up to ~100 to 700% relative to time-matched baseline values (Fig 11). Concomi- tant to the rise in low frequencies, a decline to at or near baseline values was observed in both beta and gamma bands (Fig 11). This pattern continued into 24-hr period, however, the daytime displayed a sustained decline of both beta and gamma bands followed by an increase of up to ~750% in beta waves (Fig 11). The third night was characterized by increases in alpha, sigma, beta, and high gamma frequencies with increases of up to 2,100% in an awake NHP (Fig 11). The next daytime exhibited a prolonged decline in delta, theta, and gamma bands with a maximum reduction of nearly 99% from individual basal values (Fig 11). In contrast, sigma and alpha frequencies displayed increases of up to 660% (Fig 11). The fourth nighttime period exhibited a similar pattern to the previous nighttime. The next day- time was characterized by prolonged and considerable decline of all individual spectral fre- quencies to below baseline values (Fig 11). The last nighttime exhibited similar pattern to the two previous night times. Five hours prior to euthanasia was characterized by decline in all brain waves except delta and theta waves which had slight increases from ~42 to 370% in an awake NHP. NHP #3 predominately displayed an increase in gamma frequencies in the first three nights and two daytimes with values exceeding 1,000% as compared to the time-matched baseline val- ues (Fig 12). However, the third night also exhibited a rise in alpha, sigma, and beta frequen- cies not exceeding a 250% increase (Fig 12). The following daytime was predominately characterized by a prolonged and considerable decline in all individual frequencies to below time-matched baseline values. The subsequent evening and night were largely characterized by increases in alpha and gamma frequencies followed by an elevation in all frequencies except delta waves (Fig 12). The next daytime displayed a substantial reduction in all frequencies followed by a fifth nighttime with considerable increases in beta and gamma frequencies (Fig 12). The last day for NHP #3 was characterized by rise in theta, alpha, sigma, and beta waves in an awake NHP followed by a decline in all waves (Fig 12). The last nighttime showed a prolonged rise in all waves particularly in delta, theta, alpha, and sigma, in an awake NHP (Fig 12). NHP #4 displayed an increase in both beta and gamma frequencies 12 hpi that continued for most of the nighttime and daytime following challenge (Fig 12). Surprisingly, elevation in delta, theta, alpha, and sigma were also observed within 12 hpi and were sustained until PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 18 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Fig 12. Pre- and post-EEEV challenge quantitative electroencephalography (qEEG) heat maps of NHPs #3 and #4. The top and bottom x-axes display brain waves [(delta (δ), theta (θ), alpha (α), sigma (σ), and gamma (γ)] and frequency in hertz (Hz), respectively. The left and right y-axes display time (hr) and 12-hr day/night time intervals, respectively. 12-hr nighttime is indicated by (▐ ) and daytime by a gap. Pre-EEEV challenge baseline (left) and post-EEEV challenge (right) heat maps are shown. https://doi.org/10.1371/journal.pntd.0009424.g012 108–120 hpi with peak increases up to 1,800% in an awake NHP (Fig 12). This sustained increase over baseline was followed by a precipitous decline to below time-matched basal levels in frequencies from all four power bands from 108–136 hpi (Fig 12). Several hours prior to euthanasia, an increase in both delta and theta frequencies was observed (Fig 12). PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 19 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Fig 13. Timeline of events for NHP #1 3 hrs prior to euthanasia and profile of seizure events. Percent change in EEG waves with +/- standard deviations (error bars) are shown. https://doi.org/10.1371/journal.pntd.0009424.g013 Clinical parameters of nhp #1 three hours prior to a cardiac event NHP#1 had experienced considerable alterations of many important physiological parameters (temperature, respiration, heart rate, blood pressure, ECG, and EEG) ~24–80 hrs prior to euthanasia. In addition, there was substantial decline in sleep and food/fluid consumption ~40–52 hrs prior to euthanasia (S1 Table). During the last 3-hrs prior to euthanasia, NHP#1 experienced two seizures ~2 hrs apart (Fig 13). Both EEG seizure profiles showed involvement of all brain waves with increases ranging from ~99% to ~368% (Fig 13). The time frame of events occurred at the shift from daytime to nighttime, when the baseline values for each parameter would naturally decline in healthy animals (Fig 14). Following the onset of the first seizure, many of the parameters remained elevated and/or increased relative to baseline. The comparison of peak respiration and heart rates to baseline ranged from 31 vs. 18 bpm and 187 vs. 65 bpm, respectively (Fig 14A and 14B). Similar peak increases were also observed for sys- tolic and diastolic blood pressure relative to baseline with 180 vs.103 mmHg and 121 vs. 67 mmHg, respectively (Fig 14C and 14D). These elevations in parameters represent percent PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 20 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 21 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Fig 14. Alteration in respiration, heart rate, and systolic and diastolic blood pressure of NHP #1 3 hrs prior to euthanasia. 0.5-hr interval analysis (left) and percent change from baseline (right). The average for each time-matched 0.5 hr interval in shown in grey. https://doi.org/10.1371/journal.pntd.0009424.g014 increase from baseline of 71% (respiration rate), 190% (heart rate), 55% (systolic blood pres- sure), and 80% (diastolic blood pressure) (Fig 14). Following the onset of two seizures, NHP #1 experienced a cardiac event characterized by non-sustained ventricular tachycardia, fol- lowed by sustained ventricular tachycardia, and finally ventricular fibrillation (Fig 15). Imme- diately following the cardiac event, the NHP was euthanized. Discussion The susceptibility of cynomolgus macaques to North American lineage of EEEV via the aerosol route has been explored previously with two isolates, FL91-4679 and FL-93939. Both isolates were obtained from mosquito pools; Aedes albopictus (FL91-4679) and Culiseta melanura (FL- 93939) [14,15]. Aerosol exposure to either isolate at 107 PFU produced uniform disease and NHPs met the euthanasia criteria within ~96–130 hpi [14,15]. In this study, we utilized an iso- late from the brain tissue of a fatal human case in Massachusetts in 2005 [16,18]. The data from our study are in agreement with two previous studies with all four NHPs meeting eutha- nasia criteria by ~106–140 hpi [14,15]. Taken together, all three studies demonstrate that low passage isolates of EEEV-NA lineage, irrespective of the isolation from mosquito and human hosts, are uniformly lethal at 107 PFU dose via the aerosol route. Two decades ago, Pratt and colleagues successfully utilized telemetry in biocontainment to demonstrate continuous monitoring of temperature in NHPs following VEEV challenge [19]. This success led to incorporation of temperature in NHP studies for category A and B patho- gens including Ebola, Marburg, VEEV, EEEV, and WEEV [14,15,19–29]. However, the tech- nology of telemetry implants has advanced considerably and is now able to monitor many important physiological parameters continuously and simultaneously in biocontainment. The current devices can measure physiological parameters at 1–1,000 Hz and produce enormous data sets ranging from 1,800 to 1,800,000 data points every 30 mins to describe a given param- eter. Accordingly, the technology has substantial potential to improve animal model develop- ment particularly for Risk Group 3 and 4 agents. This is the first of its kind study in biocontainment to investigate clinical disease course of EEEV by implanting multiple devices in a single NHP to continuously and simultaneously monitor temperature, activity, respira- tion, heart rate, blood pressure, ECG, and EEG. All physiological parameters were altered con- siderably post-challenge and all four NHPs met the euthanasia criteria rapidly. However, the onset and sustained duration of each parameter differed considerably. Surprisingly, EEG was the earliest parameter to change within ~12–36 hpi in all four NHPs, followed by temperature, blood pressure, and activity/clinical signs at ~48–72 hpi, and all others �72 hpi. In previous studies, only temperature and heart rate parameters have been explored [14,15]. However, the heart rate data was limited as minimal baseline day/night time data was provided and partial post-challenge data was reported [14,15]. In both previous studies, the onset of sustained fever occurred ~48 hpi with peak increase in temperature ranging from 1.8– 3.5˚C, followed by a rapid decline prior to euthanasia [14,15]. The onset of sustained elevated heart rate was between ~40–72 hpi with peak heart rate increase of ~40–130 bpm [14,15]. Our temperature and heart rate data are in agreement with both of the previous studies. Cynomolgus macaques are a prey species and to avoid the potential confounding effects of prey response elicited by cage-side human observations, we utilized 24-hr continuous remote monitoring to gain greater insight into alteration of macaque behavior and clinical PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 22 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 23 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Fig 15. ECG profile of the cardiac event in NHP #1. Representative baseline and cardiac event ECG graphs ~5 seconds in duration are shown. https://doi.org/10.1371/journal.pntd.0009424.g015 manifestations post-EEEV infection. Disturbances in the circadian rhythm post-challenge were observed as early as 24 hpi. The NHPs were observed staying awake and alert at night time with a concomitant decrease in daytime activity accompanied by short periods of sleep. The alteration in sleep/wake cycle was also accompanied by decrease in fluid and food intake, followed by a rapid progression to minimal sleep, activity, and food/fluid consumption. The substantial alteration of these parameters is likely to considerably exacerbate the observed clin- ical signs in the terminal phase of infection. The weakness and lack of coordination observed in the last 24 hrs prior to euthanasia may be in part due to the lack of nutrients, electrolyte imbalances, and lack of sleep. Lastly, the continuous remote observation of the NHPs enabled the study to accurately measure the onset of neurological disease. Seizures were observed in three of the four NHPs, however, the on-set of seizures was very late in the disease, ~1–3 hrs prior to euthanasia. Taken together, these data demonstrate that remote monitoring of animals can substantially enhance the understanding of natural disease progression and should be incorporated in the objective assessment of clinical disease in future NHP studies. This study investigated ECG by measuring QTc Bazett, PR, RR, and QRS duration. These intervals are dependent on the heart rate. As the heart rate increases or decreases the intervals get shorter or longer, respectively [30]. Alterations in heart rate were observed as early as ~24 hpi and sustained increases were maintained through ~79–140 hpi. Consequently, the decrease in the intervals are consistent with an increase in heart rate. A recent study measured QT and RR intervals following EEEV infection via aerosol route [31]. A decrease in both intervals was observed following onset of severe disease [31]. Our data are in agreement with this study. However, there were ECG abnormalities detected within the last 24 hrs prior to euthanasia. The NHPs displayed ECG abnormalities consisting of alterations in QRS and PR duration, QTc Bazett, T wave morphology, amplitude of the QRS complex, and sinoatrial arrest. These abnormalities are indicative of electrical conductivity issues in the heart and are associated with ventricular arrhythmias [32]. In addition to these abnormalities, NHP #1 experienced a critical cardiac event leading immediate euthanasia. This is the first evidence of life-threaten- ing critical cardiac events as a consequence of EEEV infection. There are several potential explanations for the cardiac abnormalities. First, the abnormalities may be due to EEEV infec- tion of the myocardium and/or the pericardium [33–39]. EEEV infection of the myocardium and subsequent degeneration of the spontaneously contracting cardiac muscle tissue has been reported in equines, swine, and humans [40–42]. Second, the abnormalities could be due to host inflammatory responses resulting in myocarditis and/or pericarditis [33–39]. QRS and QT prolongation, ventricular arrhythmias, and T wave morphology changes have been reported for viral infections such as Coxsackievirus, HIV, influenza A, HSV, and adenovirus [32–39]. Third, the EEEV may target important autonomic control center such as the hypo- thalamus, thalamus, and medulla oblongata and thus interfere with electrical conductivity of the heart. Fourth, the electrolyte imbalance due to lack of food and fluid intake prior to meet- ing the euthanasia criteria may exacerbate any of the explanations outlined above. Many human EEEV infection cases are often misdiagnosed and/or diagnosed at the onset of severe symptoms, and accordingly a detailed clinical disease course is not available for human infection [40,43–57]. One important goal of animal model development is to gain insights into progression of brain abnormalities leading to encephalitis. To achieve this goal, we investigated EEG as a potential tool enabling continuous monitoring of the brain electrical activity following challenge. EEG has been utilized previously to monitor human EEEV, PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 24 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV WEEV, and VEEV infections [43–45,47,49–51,53,54,56,58–61]. The limited human EEG data shows diffuse slowing particularly of delta, theta, and alpha and gamma waves [43–45,47,49– 51,53,54,56,58–61]. Similar to human data, severe diffuse slowing was observed in all four NHPs post-EEEV challenge. The data in the present study is in agreement with previous human EEG data. However, in addition to diffuse slowing, other gross abnormalities were also observed. First, brain waves of all four NHPs displayed rapid and extreme fluctuations. In NHP #3, a profound decrease in all brain frequencies of up to nearly 99% was observed at ~94–108 hpi, followed by an increase of over 600, 650, 2,500 and 6,300% in alpha, sigma, beta, and gamma frequencies, respectively, for a duration of ~4–12 hrs. Similarly, NHP #4 displayed an increase of ~230 to 3,500% in delta, theta, alpha, and/or sigma frequencies at ~84–108 hpi followed by a near complete decline in all four waves for a duration of ~30 hr. Second, there was a profound decrease in gamma frequencies of nearly 99% during the daytime that contin- ued for up to ~10–15 hrs. Third, the presence of brain waves associated with sleep (delta, theta, and alpha waves) in awake NHPs was observed. All three brain abnormalities are a sign of sig- nificant brain injury and have been observed in human cases of viral encephalitis such as Japa- nese encephalitis virus and Herpes simplex virus [62–67]. Further studies are underway to characterize these profound alterations in the brain waves as well as the underlying mecha- nism/s responsible for these abnormalities. Traumatic brain injury (TBI) is defined as a non-degenerative and non-congenital insult to the brain that results in temporary or permanent impairment of cognitive and physical func- tions [68]. All four NHPs exhibited many signs associated with TBI such as disturbances in cir- cadian rhythm, food/fluid consumption, inability to initiate or maintain a normal sleep pattern, decrease overall activity, increased slow wave (delta, theta, and alpha) activity while awake, and neurological signs [68–72]. These data strongly suggest that EEEV infection via aerosol route can rapidly (~12–70 hpi) induce many signs of severe TBI. The potential mecha- nism/s that underlie such rapid induction of TBI signs require further exploration. The potential route of virus dissemination following aerosol infection has not been explored previously [14,15]. The infectious virus in the present study was either minimal or not detected in the periphery. In contrast, high level of infectious virus (>6.0 log10 PFU/g) was detected in the olfactory bulb and the central nervous system (CNS) in all animals at the time of euthana- sia. In addition, the physiological parameters measured in this study such as heart and respira- tion rates, blood pressure, temperature, sleep/wake cycle, and hunger/satiety are controlled by the autonomic nervous system (ANS). The rapid (~24–50 hpi) and considerable alteration of these functions suggest that EEEV infection via the aerosol route likely enables direct access to the ANS via the neuronal projections between the olfactory bulb and the critical control cen- ters such as the thalamus, hypothalamus, and the brainstem. Taken together these data suggest that following aerosol infection the virus spreads and infects the CNS and ANS producing injury to substantially disrupt animal behavior and the control of critical physiological param- eters to produce severe disease. However, this hypothesis requires further investigation to elu- cidate the potential mechanism. The clinical scores in of NHPs displayed two distinct patterns, a rapid rise (NHPs #1–2) or biphasic pattern with an initial rise followed by a decline (NHPs #3 and 4). The clinical score is comprised of neurological disease, temperature, and responsiveness. The initial rise in clinical scores of NHPs #3 and 4 were mainly due to alteration in both temperature and responsive- ness. However, the temperature declined between 90–110 hpi and the responsiveness improved to yield lower clinical scores. Nonetheless, both NHPs rapidly progressed to meet the euthanasia criteria (clinical score = 10). Surprisingly, the temperature rapidly declined to below baseline and both NHPs exhibited hypothermia ~1–3 hrs prior to euthanasia. These data suggest both NHPs likely were unable to regulate body temperature and support the PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 25 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV hypothesis of ANS dysregulation above. The data also highlight the rapid progression to termi- nal disease of EEEV infected NHPs. Neutralizing antibodies are generally considered to be a correlate of protection against alphavirus infection [73–78]. However, two NHPs in this study had neutralizing antibody at the time of euthanasia. One potential explanation is that EEEV infection and subsequent injury to the brain tissue is rapid and profound via the aerosol route, and by the time the neutralizing antibody response is generated it has minimal or no efficacy. This is supported by previous vaccine studies in cynomolgus macaques with similar rapid aerosol infection kinetics of EEEV [13,15]. In both studies, NHPs generated neutralizing antibody responses post-vaccination and yet met the euthanasia criteria following challenge [13,15]. Previous studies in cynomolgus macaques with lethal aerosol EEEV have focused mainly on five parameters (temperature, virus quantitation, hematological parameters, clinical dis- ease, and lethality) for NHP model development [13–15]. This study provides six additional parameters for countermeasure evaluation including activity, respiration and heart rates, blood pressure, ECG, and EEG in a lethal challenge model. For this first study, we utilized the most lethal of the three encephalitic alphaviruses, EEEV. Whether a similar alteration of these parameters following aerosol challenge in NHPs can be produced with WEEV and VEEV or a sub-lethal EEEV dose requires further investigation. The FDA approval of medical countermeasures for Risk Group 3 and 4 agents will rely on the U. S. Food and Drug Administration’s Animal Rule (21 CFR 601.90), which allows the utilization of animals in pivotal efficacy studies to support licensure in lieu of human efficacy studies. The mea- surement of clinical parameters via telemetry offers several advantages for animal model develop- ment in this regard. First, the technology enables measurements of important clinical parameters that are relevant to humans. Second, it enables identification of multiple parameters for rapid animal model development and countermeasure evaluation in event of a natural outbreak and/or bioterror event. The additional parameters may be particularly appealing for investigating and/or refining ani- mal model development for partially lethal or non-lethal agents such as WEEV and MERS. Further- more, the high sampling rate of the devices enables accurate and real-time (hourly/daily) evaluation of countermeasures. Third, it can identify potential side effects of a countermeasure prior to its utili- zation in human clinical trials. These are substantial advantages of utilization of advanced telemetry in NHP animal models for Risk Group 3 and 4 agents and require further investigation. In summary, we utilized state of the art telemetry to investigate important physiological parameters in the cynomolgus macaque model following EEEV aerosol challenge. All parame- ters measured, including 6 never-before examined including brain waves, were substantially and rapidly (~12 hpi) altered post-challenge. These alterations were the earliest documented signs of disease that were not readily observable without the use of physiological radio-telemetry devices, and add possible additional endpoints to future efficacy experiments for EEEV in an NHP model. This is the first detailed disease course of EEEV in an NHP model and the parame- ters identified will improve animal model development and countermeasure evaluation. Disclosure statement The views expressed in this article are those of the authors and do not reflect the official policy or position of the U.S. Department of Defense, or the Department of the Army. Supporting information S1 Fig. Clinical scores of NHPs post-EEEV challenge. Following aerosol challenge all NHPs were monitored daily and NHPs with a total score �10 met the euthanasia criteria. (TIF) PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 26 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV S2 Fig. QRS duration and QTc Bazett in NHPs pre- and post-EEEV challenge. Pre-chal- lenge baseline QRS duration and QTc Bazett are shown in grey (A). All NHPs were continu- ously monitored pre- and post-challenge. Pre-challenge baseline QRS duration and QTc Bazett were measured for five day/night cycles and a 0.5-hr interval baseline average was calcu- lated by averaging raw data of five time-matched day or night time intervals. Forty-eight 0.5-hr interval averages were used to construct baselines for QRS duration and QTc Bazett for a day/night cycle and are shown as a grey line (A). Post-challenge values within �3 standard deviations (SD) are indicated with ( ), and >3 SD below baseline are indicated with ( (TIF) ), >3 SD above baseline are indicated with ( ). S3 Fig. RR and PR duration in NHPs pre- and post-EEEV challenge. Pre-challenge baseline RR and PR duration are shown in grey (A). All NHPs were continuously monitored pre- and post-challenge. Pre-challenge baseline RR and PR duration were measured for five day/night cycles and a 0.5-hr interval baseline average was calculated by averaging raw data of five time- matched day or night time intervals. Forty-eight 0.5-hr interval averages were used to con- struct baselines for RR and PR duration for a day/night cycle and are shown as a grey line (A). Post-challenge values within �3 standard deviations (SD) are indicated with ( above baseline are indicated with ( (TIF) ), and >3 SD below baseline are indicated with ( ), >3 SD ). S1 Table. Alteration in NHP behavior and onset of neurological signs post-EEEV chal- lenge. NHP behavior comprised of food/fluid intake, sleep, activity, and onset of seizures were monitored pre- and post-EEEV challenge. # = modest decline, ## = moderate decline, and ### = severe decline. " = modest increase. (TIF) S2 Table. Neutralizing antibody response in terminal samples. Neutralizing antibody was measured via PRNT80 assay. The limit of detection in PRNT80 assay is indicated by italic font (<1:20). All samples were analyzed three times in the PRNT80 assay. (TIF) S3 Table. Summary of fever data in NHPs. Fever hours is calculated as the sum of the signifi- cant temperature elevations. ΔTmax = maximum change in temperature. (TIF) Author Contributions Conceptualization: William D. Pratt, Margaret L. Pitt, Farooq Nasar. Formal analysis: Franco D. Rossi, Michael V. Accardi, Simon Authier, William D. Pratt. Funding acquisition: Margaret L. Pitt, Farooq Nasar. Investigation: John C. Trefry, Franco D. Rossi, Michael V. Accardi, Brandi L. Dorsey, Thomas R. Sprague, Suzanne E. Wollen-Roberts, Joshua D. Shamblin, Adrienne E. Kimmel, Lynn J. Miller, Anthony P. Cardile, Darci R. Smith, Sina Bavari, Simon Authier, William D. Pratt, Farooq Nasar. Methodology: John C. Trefry, Franco D. Rossi, Michael V. Accardi, Simon Authier, William D. Pratt, Farooq Nasar. Project administration: John C. Trefry, Margaret L. Pitt, Farooq Nasar. PLOS Neglected Tropical Diseases \| https://doi.org/10.1371/journal.pntd.0009424 June 17, 2021 27 / 32 PLOS NEGLECTED TROPICAL DISEASESUtilization of advance telemetry in cynomolgus macaques infected with EEEV Resources: Pamela J. Glass, Crystal W. Burke. Supervision: Margaret L. Pitt, Farooq Nasar. Writing – original draft: John C. Trefry, Farooq Nasar. Writing – review & editing: John C. Trefry, Franco D. Rossi, Michael V. Accardi, Brandi L. Dorsey, Thomas R. Sprague, Suzanne E. Wollen-Roberts, Joshua D. Shamblin, Adrienne E. Kimmel, Pamela J. Glass, Lynn J. Miller, Crystal W. Burke, Anthony P. Cardile, Darci R. 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10.7554_elife.85521.pdf	Data availability Figure 3—source data 1, Figure 5—source data 1, Figure 6—source data 1, and Figure 7—source data 1 contain the numerical and statistical data used to generate the figures. The confocal imaging dataset is available at Brain Image Library under DOI https://doi.org/10.35077/g.933.	Data availability Figure 3 -source data 1, Figure 5 -source data 1, Figure 6 -source data 1, and Figure 7 -source data 1 contain the numerical and statistical data used to generate the figures. The confocal imaging dataset is available at Brain Image Library under DOI https://doi.org/10.35077/g.933 . The following dataset was generated:	RESEARCH ARTICLE Origin of wiring specificity in an olfactory map revealed by neuron type–specific, time- lapse imaging of dendrite targeting Kenneth Kin Lam Wong1, Tongchao Li1†, Tian- Ming Fu2‡, Gaoxiang Liu3, Cheng Lyu1, Sayeh Kohani1, Qijing Xie1, David J Luginbuhl1, Srigokul Upadhyayula3,4,5, Eric Betzig2,3,6, Liqun Luo1 1Department of Biology, Howard Hughes Medical Institute, Stanford University, Stanford, United States; 2Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, United States; 3Advanced Bioimaging Center, Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, United States; 4Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, United States; 5Chan Zuckerberg Biohub, San Francisco, United States; 6Departments of Molecular and Cell Biology and Physics, Howard Hughes Medical Institute, Helen Wills Neuroscience Institute, University of California, Berkeley, United States Abstract How does wiring specificity of neural maps emerge during development? Formation of the adult Drosophila olfactory glomerular map begins with the patterning of projection neuron (PN) dendrites at the early pupal stage. To better understand the origin of wiring specificity of this map, we created genetic tools to systematically characterize dendrite patterning across develop- ment at PN type–specific resolution. We find that PNs use lineage and birth order combinatorially to build the initial dendritic map. Specifically, birth order directs dendrite targeting in rotating and binary manners for PNs of the anterodorsal and lateral lineages, respectively. Two- photon– and adaptive optical lattice light- sheet microscope–based time- lapse imaging reveals that PN dendrites initiate active targeting with direction- dependent branch stabilization on the timescale of seconds. Moreover, PNs that are used in both the larval and adult olfactory circuits prune their larval- specific dendrites and re- extend new dendrites simultaneously to facilitate timely olfactory map organization. Our work highlights the power and necessity of type- specific neuronal access and time- lapse imaging in identifying wiring mechanisms that underlie complex patterns of func- tional neural maps. Editor's evaluation When a neuron is born it correlates with where it targets in the neuropil and this has been best demonstrated in the olfactory lobe of Drosophila. This important study uses sophisticated genetics and advanced live imaging to provide a compelling description of how neuronal dendrites explore the target field, eliminate excessive branches, and assort into the correct region during develop- ment. In the process, it develops valuable tools. The study brings us closer to a comprehensive understanding of how the birth order of a neuron translates to dendrite patterning within the Drosophila antennal lobe circuit. For correspondence: ltongchao@outlook.com (TL); lluo@stanford.edu (LL) Present address: †Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain- machine Integration, State Key Laboratory of Brain- machine Intelligence, Zhejiang University, Hangzhou, China; ‡Department of Electrical and Computer Engineering, Princeton University, Princeton, United States Competing interest: The authors declare that no competing interests exist. Funding: See page 25 Received: 11 December 2022 Preprinted: 29 December 2022 Accepted: 27 March 2023 Published: 28 March 2023 Reviewing Editor: Sonia Sen, Tata Institute for Genetics and Society, India Copyright Wong et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited. Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 1 of 33 Research article eLife digest The brain’s ability to sense, act and remember relies on the intricate network of connections between neurons. Organization of these connections into neural maps is critical for processing sensory information. For instance, different odors are represented by specific neurons in a part of the brain known as the olfactory bulb, allowing animals to distinguish between smells. Projection neurons in the olfactory bulb have extensions known as dendrites that receive signals from sensory neurons. Scientists have extensively used the olfactory map in adult fruit flies to study brain wiring because of the specific connections between their sensory and projection neurons. This has led to the discovery of similar wiring strategies in mammals. But how the olfactory map is formed during development is not fully understood. To investigate, Wong et al. built genetic tools to label specific types of olfactory projection neurons during the pupal stage of fruit fly development. This showed that a group of projection neurons directed their dendrites in a clockwise rotation pattern depending on the order in which they were born: the first- born neuron sent dendrites towards the top right of the antennal lobe (the fruit fly equivalent of the olfactory bulb), while the last- born sent dendrites towards the top left. Wong et al. also carried out high- resolution time- lapse imaging of live brains grown in the labora- tory to determine how dendrites make wiring decisions. This revealed that projection neurons send dendrites in all directions, but preferentially stabilize those that extend in the direction which the neurons eventually target. Also, live imaging showed neurons could remove old dendrites (used in the larvae) and build new ones (to be used in the adult) simultaneously, allowing them to quickly create new circuits. These experiments demonstrate the value of imaging specific types of neurons to understand the mechanisms that assemble neural maps in the developing brain. Further work could use the genetic tools created by Wong et al. to study how wiring decisions are determined in this and other neural maps by specific genes, potentially yielding insights into neurological disorders associated with wiring defects. Introduction Organization of neuronal connectivity into spatial maps occurs widely in the nervous systems across species (Luo and Flanagan, 2007; Cang and Feldheim, 2013; Luo, 2021). For example, in the retino- topic map of the visual system, nearby neurons in the input field project axons to nearby neurons in the target field (Cang and Feldheim, 2013). Such a continuous organization preserves spatial relation- ships in the visual world. Contrary to retinotopy, the olfactory glomerular map consists of discrete units called glomeruli in which input neurons connect with the cognate output neurons based on neuronal type rather than soma position (Mombaerts et al., 1996; Gao et al., 2000; Vosshall et al., 2000). This discrete map represents a given odor by the combinatorial activation of specific glomeruli. Whereas continuous maps are readily built using gradients of guidance cues (Cang and Feldheim, 2013), how glomeruli are placed at specific locations in discrete maps is less clear (Murthy, 2011). Understanding the developmental origins of these neural maps is fundamental for deciphering the logic of their func- tional organization through which information is properly represented and processed. The adult Drosophila olfactory map in the antennal lobe (the equivalent of the vertebrate olfactory bulb) has proven to be a powerful model for studying mechanisms of wiring specificity, thanks to the type- specific connections between the presynaptic olfactory receptor neurons (ORNs) and the cognate postsynaptic projection neurons (PNs). Molecules and mechanisms first identified in this circuit have been found to play similar roles in the wiring of the mammalian brain (e.g. Hong et al., 2012; Berns et al., 2018; Pederick et al., 2021). Assembly of the fly olfactory map begins with dendritic growth and patterning of PNs derived primarily from the anterodorsal (adPNs) and lateral (lPNs) lineages and born with an invariant birth order within each lineage (Jefferis et al., 2001; Jefferis et al., 2004; Marin et al., 2005; Yu et al., 2010; Lin et al., 2012; Figure 1A and B). This patterning creates a prototypic olfactory map, prior to ORN axon innervation, indicative of the PN- autonomous ability to target dendrites into specific regions. However, earlier studies could only unambiguously follow the development of one single PN type – DL1 PNs (Jefferis et al., 2004). It remains unclear to date how the prototypic olfactory map is organized and what cellular mechanisms PN dendrites use to achieve Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 2 of 33 Developmental Biology \| Neuroscience Research article Figure 1. Organization and development of the adult olfactory circuit in Drosophila. (A, B) Timeline (A) and schematic illustration (B) of Drosophila olfactory circuit development. Green, red, and blue circles denote the birth of embryonic- born anterodorsal projection neuron (adPN), larval- born adPN, and larval- born lPN, respectively. At the onset of metamorphosis, the larval- specific olfactory circuit degenerates; larval olfactory receptor neurons (ORNs) die while embryonic- born adPNs prune their larval- specific processes and re- extend new processes into the adult- specific olfactory circuit. In the adult- specific olfactory circuit, projection neuron (PN) dendrites extend first and form a prototypic map. This is followed by an extension of ORN axons and synaptic partner matching between cognate PN dendrites and ORN axons to form a mature map. Solid and open arrowheads in A indicate onset of innervation for PN dendrites and ORN axons, respectively. (C) Overview of this study investigating the logic of dendritic patterning (C1; see Figures 3 and 4) as well as cellular mechanisms of dendrite targeting specificity (C2; see Figures 6 and 7) and re- wiring (C3; see Figure 8) that contribute to the developmental origin of the adult Drosophila olfactory map. (D) Staining of fixed brains at indicated stages showing dendrite development of adPNs (VT033006+ run+ ; labeled in yellow) and lPNs (VT033006+ run–; labeled in cyan). As run- FLP is expressed before 0 h APF in adPN but not lPN neuroblasts, we can use it to label adPNs and lPNs with two distinct colors using an intersectional reporter (see Materials and methods for the genotype). Yellow arrowheads in (D1) mark larval- and adult- specific dendrites of adPNs in larval- and adult- specific antennal lobes, respectively. Cyan arrowheads in (D3) denote specific targeting of lPN dendrites at the opposite ends of the dorsomedial- ventrolateral axis. (D1): N=12; (D2): N=7; (D3): N=17; (D4): N=10; (D5): N=12. Common notations in this study: Unless otherwise indicated, all images in this and subsequent figures are partial z projections of confocal stacks of representative images. N indicates the number of antennal lobes imaged. Antennal lobe neuropils are revealed by N- Cadherin (Ncad; in blue) staining. Adult- specific (developing) antennal lobe is outlined with a white solid line. Larval- specific antennal lobe is outlined with an orange line (dashed line used to denote the degeneration stage) and is distinguished from the developing antennal lobe by the more intense nc82 staining as shown in Figure 1—figure supplement 1 (nc82 channel not shown here). Asterisks () indicate PN cell bodies, which are outside the antennal lobe neuropil (and sometimes appear on top because of the z- projections). Arrowheads mark PN dendrites. Arrows mark PN axons projecting towards higher olfactory centers (see Figure 1—figure supplement 2 for PN axons at their targets in the mushroom body and lateral horn). h APF: hours after puparium formation; h ALH: hours after larval hatching. DL: dorsolateral; DM: dorsomedial; VM: ventromedial; VL: ventrolateral. Scale bar = 10 µm. The online version of this article includes the following figure supplement(s) for figure 1: Figure supplement 1. Visualization of larval- and adult- specific antennal lobes by co- staining of Ncad and nc82. Figure supplement 2. Projection neuron (PN) axon development across pupal stages. Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 3 of 33 Developmental Biology \| Neuroscience Research article targeting specificity (Figure 1C1- 2). The initial map formation is further complicated by circuit remod- eling during which embryonic- born PNs used in both the larval and adult circuits reorganize their neurites (Marin et al., 2005). How embryonic- born PNs coordinate remodeling with re- integration into the adult circuit is not known (Figure 1C3). Here, we set out to explore the origin of the olfactory map by performing a systematic and compar- ative study of PN dendrite development at type- specific resolution in vivo, and two- photon– and adaptive optical lattice light- sheet microscope–based time- lapse imaging of PN dendrites in early pupal brain explants. As our overarching goal is to understand how the wiring specificity between ORNs and PNs arises, we focus on PNs that project to single glomeruli. Neurons from the lateral lineage that innervate multiple glomeruli or project to other regions of the adult brain (Lin et al., 2012) are not studied here. Our study uncovers wiring logic that directs PN dendrites to create an organized olfactory map, dendritic branch dynamics that lead to directional selectivity, and a novel re- wiring mechanism that facilitates timely olfactory map formation. These wiring strategies used in the initial map organization lay the foundation of precise synaptic connectivity between PNs and ORNs in the final glomerular map. Results Overview of Drosophila olfactory circuit development at a lineage- specific resolution We first described the development of the Drosophila olfactory circuit using pupal brains double- labeled for adPNs and lPNs (Figure 1D; see the genetic design in Figure 2). At the onset of metamor- phosis (0 hr after puparium formation; 0 hr APF), the adult- specific antennal lobe (also referred to as ‘developing antennal lobe’) remained relatively small, located dorsolateral and posterior to the larval- specific antennal lobe (also referred to as ‘degenerating antennal lobe’) (Figure 1D1). As PN dendrites continued to grow and innervate the developing antennal lobe, its size increased considerably (Figure 1D1–3). By 12 hr APF, PNs already appeared to be sorting their dendrites into specific regions to form a prototypic map, as revealed by the heterogeneous patterning of lPN dendrites (arrowheads in Figure 1D3). From 21 hr to 50 hr APF, dendrites of adPNs and lPNs gradually segregated and eventually formed intercalated but non- overlapping glomeruli (Figure 1D4–5). The development of the adult- specific antennal lobe partially overlapped with the degeneration of the larval- specific antennal lobe, as indicated by fragmentation of the larval- specific dendrites of embryonic- born PNs at 3 hr APF (Figure 1D2). This gross characterization at the resolution of two PN lineages was consistent with earlier studies (Jefferis et al., 2004; Marin et al., 2005). However, the resolution was not sufficiently high to answer the questions we raised in the Introduction (Figure 1C). Expanded genetic toolkit for type-specific labeling of PNs during early pupal development To reveal how PN dendrites initiate olfactory map formation at the high spatiotemporal resolution, we needed genetic access to specific PN types during early pupal development. From our recently deciphered single- cell PN transcriptomes (Xie et al., 2021), we searched for genetic markers that are expressed strongly and persistently in single or a few PN types across pupal development. This transcriptome- instructed search led to the identification of CR45223 (in place of this non- coding gene, we used the adjacent CG14322 that exhibits nearly identical expression pattern), lov, and tsh (Figure 2A and B; Figure 2—figure supplement 1). Next, using CRISPR/Cas9, we generated knock- in transgenic QF2 expression driver lines in which T2A- QF2 (or T2A- FLP for intersection) was inserted immediately before the stop codon of the endog- enous gene (Figure 2—figure supplement 2). The self- cleaving peptide T2A allows QF2 to be expressed in the same pattern as the endogenous gene (Diao and White, 2012). With these new QF2 lines together with existing GAL4 lines that label additional PN types (Xie et al., 2019), we now have an expanded toolkit accessing PNs ranging from early- to late- born PNs, from adPN to lPN lineages, and from PNs with neighboring glomerular projections to those with distant projections in the adult antennal lobe (Figure 2C and D). As QF2/QUAS and GAL4/UAS expression systems operate orthogo- nally to each other (Potter et al., 2010; Riabinina et al., 2015), we crossed our QF2 lines with existing GAL4 lines for simultaneous labeling of distinct PN types in the same brain (see inset in Figure 2C). Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 4 of 33 Developmental Biology \| Neuroscience Research article Figure 2. Expanded genetic toolkit for dual- color, type- specific labeling of projection neurons (PNs). (A) tSNE plot of PN single- cell transcriptomes, color- coded according to CR45223 expression level in [log2(CPM +1)], where CPM stands for transcript counts per million reads. Zoom- in of boxes in the tSNE plot (left) is shown on the right, and color- coded according to PN types and developmental stages. (B) Dot plot showing the expression of acj6, vvl, CR45223, CG14322, lov, and tsh in 0 hr APF PNs arranged according to their birth order and lineage (green: embryonic- born anterodorsal projection neuron (adPNs); red: larval- born adPNs; blue: larval- born lPNs). Unit of expression is [log2(CPM +1)] as in A. Data from panels A are B are from Xie et al., 2021. (C) Birth orders of adPNs and lPNs summarized by Lin et al., 2012; Yu et al., 2010 and genetic tools used to access them. Left: Accessible PN types are colored. Circles beneath the PN types denote QF2/GAL4 drivers used to access them. Asterisks beneath the PN types denote access by MARCM. Gray arrowhead marks neuroblast (NB) rest. Right: Genetic tools. Inset shows the combinatorial use of QF2/FLP and GAL4 (linked by dashed lines) for comparative analyses of dendrite development of two groups of PNs in the same animal. (D) Schematic of glomerular projections of QF2/ GAL4- accessible PNs in the adult antennal lobe. Indicated glomeruli are color- coded based on the genetic tools used to access them. See the color code in C. (E, F) Schematic of intersectional logic gates for dual- color labeling of PNs. See Figure 2—figure supplement 2 for newly generated FLP- out reporters. The online version of this article includes the following figure supplement(s) for figure 2: Figure supplement 1. Expression of projection neuron (PN) marker genes across development. Figure supplement 2. Generation of T2A- QF2/FLP transgenic flies by CRISPR/Cas9. Figure supplement 3. Design of single- and dual- color FLP- out reporters. This combinatorial use of driver lines permitted comparative analyses of the development of distinct PN types with minimal biological and technical variations (Supplementary file 1). To limit driver expression only in PNs, we applied intersectional logic gates (AND and NOT gates) using our newly generated conditional reporters genetically encoding either mGreenLantern, Halo tags, and/or SNAP tags (Kohl et al., 2014; Sutcliffe et al., 2017; Campbell et al., 2020; Figure 2E and F; Figure 2—figure supplement 3). These reporters can be broadly used in other systems. Finally, Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 5 of 33 Developmental Biology \| Neuroscience Research article we used MARCM (Lee and Luo, 1999) to label PNs that remain inaccessible due to a lack of drivers (Figure 2C; discussed in Figure 3). Early larval-born adPN dendrites initially share similar targeting regions Using the new genetic tools, we first re- visited the dendrite development of DL1 PNs—the first larval- born adPN type—using pupal brains double- labeled for DL1 PNs (labeled by 71B05- GAL4) and adPNs (Figure 3A). Consistent with our previous study (Jefferis et al., 2004), DL1 PNs already showed robust dendritic growth at the wandering third instar larval stage (Figure 3—figure supplement 1A). At 0 hr APF, DL1 PN dendrites extended radially outwards from the main process, reaching nearly the entire developing antennal lobe and often overshooting it (white arrowheads in Figure 3A1), likely surveying the surroundings. By 6 hr APF, most of the dendrites already occupied the dorsolateral (DL) corner of the antennal lobe (Figure 3A2). As the antennal lobe continued to grow, this dorsolateral positioning of the DL1 PN dendrites remained largely unchanged (Figure 3A3–6). From 21 hr APF onwards, the dendrites underwent progressive refinement: they were restricted into a smaller area by 30 hr APF (Figure 3A4–5), and eventually formed a compact, posterior glomerulus by 50 hr APF (Figure 3A6 showing a single z section). To assess whether other PN types follow the same developmental trajectory, we next examined CG14322+ PNs, which include DL1 PNs and DA3 PNs—the first and second larval- born adPN types, respectively. In the same brain, we also labeled with a different fluorophore DC2 PNs—the third larval- born adPN type (Figure 3B). The dendritic pattern of DL1/DA3 PNs appeared indistinguish- able from that of DL1 PNs from 0 hr to 12 hr APF (compare the yellow channel of Figure 3B1–3 with Figure 3A1–3), suggesting that DL1 and DA3 PN sent dendrites to the same region in the antennal lobe. We began to see differences in 21 hr APF pupal brains in which DL1/DA3 PN dendrites not only occupied the dorsolateral region but also spread ventrally (white arrowhead in Figure 3B4; compare with Figure 3A4). The more ventrally targeted dendrites likely belong to DA3 PNs. This suggests that ~21 hr APF marks the beginning of dendritic segregation of DL1 and DA3 PNs. By 30 h APF, DL1 and DA3 dendrites were clearly separable (Figure 3B5), which respectively formed more posteriorly and anteriorly targeted glomeruli at 50 hr APF (Figure 3B6; see single z sections in Figure 3—figure supplement 1C). Next, we focused on the third- born—DC2 PNs labeled by 91G04- GAL4 (Figure 3B). This GAL4 labeled additional embryonic- born adPNs from 0 hr to 6 hr APF, but the expression in these PNs diminished afterward. As embryonic- born adPNs do not have any dendrites in the developing antennal lobe at 0 hr APF (discussed in Figure 8), dendrites found in the antennal lobe should belong to the larval- born DC2 PNs. Like DL1/DA3 PNs, DC2 PNs initiated radial dendritic extension across the antennal lobe at 0 hr APF (Figure 3B1; Figure 3—figure supplement 1B). Notably, DL1/DA3 and DC2 PN dendrites exhibited substantial overlap from 0 hr to 12 hr APF and shared a similar targeting region at the dorsolateral corner from 6 hr to 12 hr APF (Figure 3B1–3). It was not until 21 hr APF that DL1, DA3, and DC2 dendrites began to segregate from each other along both medial- lateral and anterior- posterior axes (Figure 3B4–5). By 50 hr APF, the DC2 glomerulus was separated from DL1/DA3 glomeruli by intermediate glomeruli (Figure 3B6). In summary, dendrites of consecutively larval- born DL1, DA3, and DC2 adPNs (here collectively named ‘early larval- born adPNs’; see its definition in next section) develop in a similar fashion and share a similar targeting region at early pupal stages (0–12 hr APF). This is then followed by their segregation into distinct regions close to their adult glomerular positions during mid- pupal stages (21–50 hr APF). Larval-born adPNs with distant birth order send dendrites to distinct regions The analysis of early larval- born adPNs (Figure 3A and B) led us to hypothesize that larval- born adPNs might use their birth order to coordinate dendrite targeting during early pupal stages. If this were true, we would expect dendrites of larval- born adPNs with distant birth order to occupy distinct regions. To test this hypothesis, we compared dendrite- targeting regions of early larval- born adPNs with those of later- born adPNs. We first examined DC3/VA1d adPNs (referred to as ‘mid- early larval- born adPNs’) using Mz19- GAL4 (Figure 3C). This GAL4 is expressed in three PN types from 24 hr APF to adulthood: DC3 adPNs, VA1d Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 6 of 33 Developmental Biology \| Neuroscience Research article Figure 3. Birth order–dependent spatial patterning of anterodorsal projection neuron (adPN) dendrites in the developing antennal lobe. (A) Confocal images of fixed brains at indicated stages showing dendrite development of adPNs (acj6+; labeled in green) and DL1 adPNs (71B05+; labeled in yellow). Right column of A1 shows a zoom- in of the dashed box. The labeling of acj6+ adPNs outlines the developing antennal lobe and is used in dual- color Figure 3 continued on next page Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 7 of 33 Developmental Biology \| Neuroscience Research article Figure 3 continued AO- LLSM imaging later (see Figure 7A–C). White arrowheads in (A1) mark dendrites overshooting the antennal lobe. (A1): N=14; (A2): N=12; (A3): N=14; (A4): N=6; (A5): N=4; (A6): N=4. (B) Confocal images of fixed brains at indicated stages showing dendrite development of DL1/DA3 adPNs (CG14322+; labeled in yellow) and DC2 adPNs (91G04+; labeled in magenta). As 91G04- GAL4 labels some embryonic- born projection neurons (PNs) from 0 to 6 hr APF, their neurites are found in the larval- specific antennal lobe (B1, 2). Right column of (B1) shows a zoom- in of the dashed box. White arrowhead in (B4) denotes the more ventrally targeted DL1/DA3 dendrites. (B1): N=6; (B2): N=5; (B3): N=12; (B4): N=4; (B5): N=7; (B6): N=2. (C) Confocal images of fixed brains at indicated stages showing dendrite development of DC3/VA1d adPNs (Mz19+ acj6+; labeled in red) and DA1 lPNs (Mz19+ acj6–; labeled in cyan). (C1): N=14; (C2): N=6; (C3): N=4; (C4): N=10; (C5): N=10; (C6): N=6; (C7): N=4. (D) Confocal images of single- cell MARCM clones (in yellow) of DL1 PNs (D1–3), mid- late larval- born adPNs (D4–6), and late larval- born adPNs (D7–9) in 12 hr APF pupal brains, generated by heat shocks (hs) at indicated times. Three biological samples are shown for each of the indicated adPN cohorts. D1–3: N=5; D4–6: N=4; D7–9: N=8. (E) Summary of wiring logic of larval- born adPN dendrites to form an olfactory map in the 12 hr APF developing antennal lobe. See Figure 1 legend for common notations. The online version of this article includes the following video, source data, and figure supplement(s) for figure 3: Figure supplement 1. Dendrite development of early larval- born projection neurons (PNs). Figure supplement 2. MARCM- labeled single- cell projection neurons (PNs) of indicated lineages in adult brains. Figure supplement 3. Dendrite development of DL1, middle larval- born, and late larval- born projection neurons (PNs) at early stages. Figure supplement 3—source data 1. Source data for Figure 3—figure supplement 3F and G. Figure 3—video 1. 3D rendering of z stacks of indicated projection neurons (PNs) in 12 hr APF antennal lobe. https://elifesciences.org/articles/85521/figures#fig3video1 adPNs, and DA1 lPNs (Jefferis et al., 2004). To distinguish adPNs from lPNs, we previously adopted an FLP- out strategy labeling Mz19+ PNs with either GFP or RFP based on their lineages and studied dendrite segregation and refinement during mid- pupal stages (Li et al., 2021; Figure 3C4–7). However, the weak GAL4 expression before 24 hr APF prevented us from visualizing any dendrites at earlier stages. To overcome this, we incorporated Halo and SNAP chemical labeling (Kohl et al., 2014) in place of the immunofluorescence approach. This modification substantially extended the detection to developmental stages as early as 12 hr APF (Figure 3C1). We found that, from 12 hr to 21 hr APF, DC3/VA1d PN dendrites targeted the ventrolateral (VL) corner of the antennal lobe (Figure 3C1–4). Thus, early (DL1/DA3/DC2) and mid- early (DC3/VA1d) larval- born adPN dendrites occupy distinct regions at 12 hr APF. As we did not have reliable drivers to access other later- born PNs at early pupal stages, we turned to MARCM (Lee and Luo, 1999) to generate heat shock- induced single- cell clones of PNs born at different times (Figure 3—figure supplement 2). We used GH146- GAL4(IV), a PN driver that labels the majority of PN types, including later- born adPNs (Figure 3—figure supplement 2D–E), with a tight temporal control of heat shock and analyzed heat shock- induced animals that were among the first to form puparium to minimize the effects of unsynchronized development among individual animals (see Materials and methods for details). These optimizations permitted a systematic clonal analysis at higher PN type- specific resolution that correlates with birth time. Based on birth timing that corresponds to the heat shock time we applied to induce single- cell MARCM clones, we assigned larval- born adPNs to approximate temporal cohorts: (1) heat shock at 0–24 hr ALH (after larval hatching): first- born (DL1), (2) heat shock at 42–48 hr ALH: early- born (DL1, DA3, DC2, and D), (3) heat shock at 66–72 hr ALH: mid- late born (VM7v, VM7d, VM2, DM6, and VA1v), and (4) heat shock at 96–100 hr ALH: late- born (DM6, VA1v, DL2v, DL2d) (Figure 3E1). We assigned DC3/VA1d PNs labeled by Mz19- GAL4 to the mid- early cohort because they are born between the early and mid- late adPNs. We note that DM6 and VA1v PNs were assigned to both cohorts of mid- late and late- born adPNs, reflecting the nature of short birth timing differences and overlaps between adjacent cohorts. Using this strategy, we could also label lPNs born at different times and assigned them into approximate temporal cohorts (Figure 3—figure supplement 2F). Clonal analysis revealed that, at 12 hr APF, the first- born DL1 adPNs sent dendrites to the dorso- lateral corner of the antennal lobe as expected (Figure 3D1–3). By contrast, dendrites of mid- late larval- born adPNs occupied a large region on the medial/dorsomedial (M/DM) side (Figure 3D4–6). Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 8 of 33 Developmental Biology \| Neuroscience Research article The dendritic arborization patterns of these PNs varied widely, most likely because they belonged to different PN types. Intriguingly, late larval- born adPN dendrites targeted the peripheral, dorsomedial (abbreviated as pDM) corner where the staining of the pan- neuropil marker N- Cadherin was relatively weak (Figure 3D7–9). The weak staining implies that this area is less populated by PN dendrites (the major constituent of the antennal lobe neuropil at this stage), possibly because (1) this area is not innervated by many PNs and/or (2) the dendrites of late- born PNs innervate later and remain less elaborate than earlier- born PNs (we will explore this later). Together, our data (Figure 3A–D) suggest that larval- born adPNs with adjacent birth order send dendrites to similar regions of the developing antennal lobe whereas those with distant birth order send dendrites to distinct regions (Figure 3E2,3). Notably, the birth order of the examined PNs does not specify dendrite targeting randomly (Figure 3E4). Rather, the stereotyped dendritic pattern in the prototypic map correlates with the birth order in an organized manner (rotating clockwise in the right hemisphere when viewed from the front; anti- clockwise in the left: early↔DL; mid- early↔VL; mid- late↔M/DM; late↔pDM). One can, therefore, infer at least the approximate birth order of a larval- born adPN based on its initial dendrite targeting, and vice versa. As the antennal lobe is a 3D structure, we also visualized PN dendrite targeting in the 12 hr APF map with 3D rendering generated from z stacks with rotation along the y- axis (Figure 3—video 1). We found that, along the short anterior- posterior axis (spanning about 20 µm), PN dendrites were located primarily on the periphery of the antennal lobe, whereas the center housed the axon bundle projecting out of the antennal lobe. Some dendrites could reach almost the entire depth, suggesting active exploration of the surroundings in many directions. While 3D projections provide rich details in depth and different viewing angles, we did not find an apparent relationship between birth order and dendrite targeting along the anterior- posterior axis, at least for the examined PN types at 12 hr APF. Thus, the approximate 2D projection (Figure 3E2–4) conveys the logic of dendrite patterning effectively. Dendrite targeting timing of larval-born adPN depends on birth order Having provided evidence for birth order–dependent spatial patterning of larval- born adPN dendrites, we next asked whether the timing of dendritic extension and targeting is also influenced by birth order. We noticed that the extent of dendritic innervation of 0 hr APF first- born DL1 adPNs resembled that of 6 hr APF mid- late born adPNs (compare Figure 3—figure supplement 3A1–4 with Figure 3— figure supplement 3B5–8). Such a resemblance was also seen between 0 hr APF mid- late and 6 hr APF late- born adPNs (compare Figure 3—figure supplement 3B1–4 with Figure 3—figure supplement 3C). Quantitative analyses of the exploring volume of dendrites and the number of terminal branches showed that, at 0 hr APF, DL1 PN dendrites were more elaborate than mid- late born PN dendrites (Figure 3—figure supplement 3F). By 6 hr APF, the mid- late born appeared to catch up, showing an extent of innervation comparable to DL1 PNs. We next examined when the dendrites reach their targeting regions. We found that whereas early larval- born adPNs (DL1, DA3, DC2) concentrated their dendrites to the dorsolateral corner by 6 hr APF (Figure 3B2; Figure 3—figure supplement 3A5–8), later- born PNs concentrated their dendrites to the medial/dorsomedial or peripheral dorsomedial side at 12 hr APF (Figure 3D4- 9; Figure 3—figure supplement 3B5- 8, C). Thus, our results suggest larval- born adPN dendrites innervate and pattern the antennal lobe using a ‘first born, first developed’ strategy. Contribution of lineage to early PN dendritic patterning Both lineage and birth order of PNs contributes to the eventual glomerular choice of their dendrites (Jefferis et al., 2001). What is the involvement of lineage in the prototypic map formation? Do lPN dendrites pattern the developing antennal lobe following similar rules as adPNs? To characterize lPN dendrite development at type–specific resolution, we used tsh- GAL4 to genetically access DA1/ DL3 lPNs, and MARCM clones of lPNs as a complementary approach (Figure 4). We focused on the dendritic patterns of tsh+ DA1/DL3 lPNs from 0 hr to 12 hr APF as tsh- GAL4 labeled additional PNs from 21 hr APF onwards (Figure 4A4–6; Figure 4—figure supplement 1B4–6; Figure 4—figure supple- ment 2; Figure 2—figure supplement 1). Examination of pupal brains double- labeled with DA1/DL3 lPNs (referred to as ‘middle larval- born lPNs’) and DL1/DA3 adPNs revealed that, like the early larval- born adPNs, dendritic growth of DA1/ Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 9 of 33 Developmental Biology \| Neuroscience Research article Figure 4. Birth order–dependent spatial patterning of lPN dendrites in the developing antennal lobe. (A) Confocal images of fixed brains at indicated stages showing dendrite development of DL1/DA3 adPNs (CG14322+; labeled in yellow) and DA1/DL3 lPNs (tsh+; labeled in cyan). Right column of A1 shows a zoom- in of the dashed box. (A1): N=8; (A2): N=4; (A3): N=6; (A4): N=10; (A5): N=4; (A6): N=5. (B) MARCM clones (in cyan) of early (B1–3) and late (B4–6) larval- born lPNs in 12 hr APF pupal brains, generated by heat shocks (hs) at indicated times. In (B3), (B5), and (B6), single- cell clones of anterodorsal projection neuron (adPN) (yellow arrowheads) and lPN (cyan arrowheads) lineages were simultaneously labeled. Three biological samples are shown for each of the indicated lPN cohorts. B1–3: N=4; B4–6: N=6. (C) Summary of wiring logic of larval- born lPN dendrites to form an olfactory map in the 12 hr APF developing antennal lobe. (D) Summary of determination of dendrite targeting of larval- born PNs by lineage and birth order. See Figure 1 legend for common notations. The online version of this article includes the following video and figure supplement(s) for figure 4: Figure supplement 1. Dendrite development of DL1/DA3 and DA1/DL3 projection neurons (PNs). Figure supplement 2. Expression patterns of tsh in the developing antennal lobe during mid- pupal stages. Figure 4—video 1. 3D rendering of z stacks of indicated projection neurons (PNs) in 12 hr APF antennal lobe. https://elifesciences.org/articles/85521/figures#fig4video1 DL3 lPNs was evident by the wandering third instar larval stage (Figure 4—figure supplement 1A). At this stage, most DA1/DL3 lPN dendrites innervated the antennal lobe and intermingled with those of DL1/DA3 adPNs. From 0 hr to 12 hr APF, despite a high degree of overlap among those dendrites that explored the surroundings, DA1/DL3 lPN dendrites primarily targeted an area ventrolateral to those of DL1/DA3 adPNs (Figure 4A1–3; see 3D rendering in Figure 4—video 1). Such a spatial distinction was also observed between middle larval- born adPNs and lPNs in 0 hr and 6 hr APF pupal brains where occasionally single- cell clones from both lineages were simultaneously generated by MARCM Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 10 of 33 Developmental Biology \| Neuroscience Research article (Figure 3—figure supplement 3D1–4, 7–10). Thus, at least some adPNs and lPNs sort their dendrites into distinct regions very early on regardless of birth timing. Next, we used MARCM to ask if lPNs born earlier and later than DA1/DL3 lPNs would send dendrites to regions different from that of DA1/DL3 lPNs. We found that dendrites of early- born lPNs primarily occupied the medial/dorsomedial side of the antennal lobe (Figure 4B1–3); we note that adPNs born at the same time sent dendrites to the dorsolateral side (see yellow arrowhead in Figure 4B3). Also, in contrast to the ventrolateral targeting of middle- born lPN dendrites, late- born lPNs sent dendrites to the dorsomedial corner (Figures 4B4–6). Like larval- born adPNs, late- born lPNs innervated the antennal lobe later than earlier- born lPNs (Figure 3—figure supplement 3D7–12–E, G). These data suggest that, at early pupal stages, lPN dendrites pattern the developing antennal lobe following similar rules as larval- born adPNs: adjacent birth order → similar dendrite targeting; distant birth order → distinct dendrite targeting; ‘first born, first developed.’ However, unlike the correlation of birth order and target positions in a rotational manner for adPNs (Figure 3E), the lPN dendritic map formation appears binary: early↔M/DM; middle↔VL; late↔DM (Figure 4C). Our type- specific characterization corroborated with the gross examination of the lPN dendrites as previously reported (Jefferis et al., 2004): at 12 hr APF, lPN dendrites mostly occupied the opposite corners along the dorsomedial- ventrolateral axis, leaving the middle of the axis largely devoid of lPN dendrites (arrow- heads in Figure 1D3). In summary, we propose that lineage and birth order of larval- born PNs contribute to their dendrite targeting in a combinatorial fashion (Figure 4D). The wiring logic of PN dendrites in the developing antennal lobe can, therefore, be represented by [lineage, birth order]=dendrite targeting; one can deduce the unknown if the other two are known. An explant system for time-lapse imaging of PN development at early pupal stages So far, we have identified wiring logic governing the initial dendritic map formation (Figures 3 and 4) by examining specifically labeled neuron types in the fixed brain at different developmental stages. To examine dendrite targeting at the higher spatiotemporal resolution, we established an early- pupal brain explant culture system based on previous protocols (Özel et al., 2015; Rabinovich et al., 2015; Li and Luo, 2021; Li et al., 2021), and performed single- or dual- color time- lapse imaging with two- photon microscopy as well as adaptive optical lattice light- sheet microscopy (AO- LLSM) (Figure 5A–C). The following lines of evidence support that our explant system recapitulates key features of in vivo olfactory circuit development. First, during normal development, the morphology of the brain lobes changes from spherical at 0 hr APF to more elongated rectangular shapes at 15 hr APF (Rabinovich et al., 2015). After 22 hr ex vivo culture, the spherical hemispheres of brains dissected at 3 hr APF became more elongated, mimicking ~15 hr APF in vivo brains characterized by the separation of the optic lobes from the central brain (Figure 5D). Second, dual- color, two- photon imaging of PNs every 20 min for 22 hr revealed that lPNs in 3 hr APF brains initially produced dynamic but transient dendritic protrusions in many directions, followed by extensive innervation into the antennal lobe (arrowheads in Figure 5E1–3; Figure 5—video 1). In higher brain centers, lPN axons clearly showed direction- specific outgrowth of collateral branches into the mushroom body calyx as well as forward extension into the lateral horn (arrows in Figure 5E3), thus resembling in vivo development (Figure 1—figure supplement 2). Third, larval- specific dendrites observed in 0 hr APF brains cultured for 12 hr ex vivo (orange arrow- head in Figure 5F4) were no longer seen in those cultured for 24 hr ex vivo (Figure 5F5), indicative of successful pruning and clearance of larval- specific dendrites. Also, the size of the developing antennal lobe in the brains cultured for 24 hr ex vivo increased considerably (Figure 5F5). These imply that olfactory circuit remodeling (degeneration of larval- specific processes and growth of adult- specific processes) proceeds normally, albeit at a slower rate (compare with Figure 5F1–3). Fourth, dendrites from genetically identified DL1 and DA1/DL3 PNs targeted to their stereotyped locations in the antennal lobe in 0 hr APF brains cultured for 24 hr ex vivo (Figure 5G), mimicking in vivo development (Figure 4A). Finally, the segregation of dendrites of PNs targeting to neighboring proto- glomeruli could be recapitulated in brains dissected at 24 hr APF and cultured for 8 hr (Figure 5—figure supplement 1; Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 11 of 33 Developmental Biology \| Neuroscience Research article Figure 5. Establishment of an explant system for time- lapse imaging of olfactory map formation. (A) Schematic of the anatomical organization of the olfactory circuit in early pupal brain (0–3 hr APF). Green, red, and blue denote embryonic- born adPN, larval- born anterodorsal projection neuron (adPN), and larval- born lPN, respectively. MB: mushroom body; LH: lateral horn. (B) Schematic of explant culture system for early pupal brains. Wells created in the Sylgard plate from which brains were imbedded are shown in blue. (C) Schematic of explant culture and imaging system for early pupal brains. (D) Top: Schematic of morphological changes of brain lobes from 0 hr to ~15 hr APF during normal development. Bottom: Morphologies of a brain explant dissected at 3 hr APF and cultured for 0 hr ex vivo and cultured for 22 hr ex vivo. (E) Two- photon time- lapse imaging of adPNs (VT033006+ run+ ; labeled in magenta) and lPNs (VT033006+ run–; labeled in green) in pupal brain dissected at 3 hr APF and cultured for 0–22 hr ex vivo. Arrowheads mark dynamic but transient dendritic protrusions of lPNs in E1, 2, and extensive dendritic innervation of lPNs in (E3). Arrows in (E3) mark axonal innervation of lPNs in the mushroom body calyx and lateral horn. N=3. (F) Confocal images of antennal lobes labeled by VT033006+ projection neurons (PNs) (in green) at 0 hr (F1), 6 hr (F2), and 12 hr (F3) APF in vivo. Confocal images of antennal lobes labeled by VT033006+ PNs in pupal brains were dissected at 0 hr APF and cultured for 12 hr (F4) and 24 hr (F5) ex vivo. (F1): N=6; (F2): N=5; (F3): N=6; (F4): N=8; (F5): N=8. (G) Dendrite targeting regions of DL1 PNs (71B05+; in yellow; G1) and DA1/DL3 PNs (tsh+; in cyan; G2) in the antennal lobes in pupal brains dissected at 0 hr APF and cultured for 24 hr ex vivo. Antennal lobes are revealed by N- Cadherin (Ncad; in blue) staining. (G1): N=5; (G2): N=6. See Figure 1 legend for common notations. The online version of this article includes the following video, source data, and figure supplement(s) for figure 5: Figure supplement 1. Dendritic segregation of DC3/VA1d adPNs and DA1 lPNs targeting neighboring proto- glomeruli. Figure supplement 1—source data 1. Source data for Figure 5—figure supplement 1C and D. Figure 5—video 1. Two- photon time- lapse imaging of projection neuron (PN) development. https://elifesciences.org/articles/85521/figures#fig5video1 Figure 5—video 2. Two- photon time- lapse imaging of projection neuron (PN) dendritic segregation. https://elifesciences.org/articles/85521/figures#fig5video2 Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 12 of 33 Developmental Biology \| Neuroscience Research article Figure 5—video 2). Specifically, despite constant dynamic interactions among dendrites that explore the surroundings (arrowheads in Figure 5—figure supplement 1A2–4), DC3/VA1d and DA1 PNs exhib- ited a 1–2 µm increase in the distance between centers of the two dendritic masses and a substantial decrease in the overlap of their core targeting regions (Figure 5—figure supplement 1B–D). Taken together, these data support that the explant culture and imaging system established here reliably captures key neurodevelopmental events starting from early pupal stages. Single-cell, two-photon imaging reveals active dendrite targeting Our observation in fixed brains revealed that dendrites of DL1 adPNs transition from a uniform exten- sion in the antennal lobe at 0 hr APF to concentration at the dorsolateral corner of the antennal lobe at 6 hr APF (Figure 3A). To identify mechanisms of dendrite targeting specificity that could be missed in static developmental snapshots, we performed two- photon time- lapse imaging of single- cell MARCM clones of DL1 PNs in 3 hr APF brains (Figure 6; Figure 6—figure supplement 1; Figure 6—video 1). Although we did not have a counterstain outlining the antennal lobe, we could use the background signals to discern the orientation of DL1 PNs in the brain (Figure 6—figure supplement 1A). The final targeting regions relative to the antennal lobe revealed by post hoc fixation and immunostaining confirmed proper dendrite targeting (yellow arrowhead in Figure 6A10; Figure 6—figure supplement 1B–C). Using DL1 PN in Figure 6A (pseudo- colored in yellow; Figure 6—video 1) as an example, we observed that the PN initially extended dendrites in every direction (Figure 6A1–3), like what we observed in fixed tissues (Figure 3A1). The first sign of active targeting emerged at 2 hr 20 min ex vivo when DL1 PN began to generate long, albeit transient, dendritic protrusions in the dorsolateral direc- tion; these selective protrusions were more prominent at 3 hr ex vivo (arrowheads in Figure 6A4–6). The dorsolateral targeting continued to intensify, leading to the formation of a highly focal dendritic mass seen at 13 hr ex vivo (arrowhead in Figure 6A8). As the dendrites reached the dorsolateral corner and explored locally, the change in shape appeared less pronounced (Figure 6A9). To quantitatively characterize the active targeting process, we categorized the bulk dendritic masses emanating from the main process according to their targeting directions: DL, DM, VM, and VL (Figure 6B). During the initial phase, the percentage of dendritic volume in each direction varied from 10% to 40% (Figure 6C and D), indicative of active exploration with little targeting specificity. Despite these variations, the total amount of dendritic mass seen in the VM direction over the entire imaging time (area under the graph of Figure 6C) was the smallest across all samples examined (Figure 6E). The initial phase of exploration in every direction was followed by a ~4 hr transitional phase during which DL1 PNs predominantly extended dendrites in 2 of the 4 directions (Figure 6C; Figure 6—figure supplement 1D–E). One of the 2 directions was always DL whereas the other was either DM or VL but never VM. In the final phase, DL1 PN dendrites always preferred DL out of the two available directions. Lastly, we analyzed the bulk dendritic movements. We defined bulk extension and retraction events when dendrites respectively extended and retracted more than 2 μm between two consecutive time frames. The analyses showed a striking shift from frequent extension and retraction towards stabilization, reflecting the pre- and post- targeting dynamics, respectively (Figure 6F and G). Hence, long- term two- photon imaging of single- cell DL1 PNs revealed that dendrite targeting specificity increases over time via active targeting in a specific direction and stepwise elimination of unfavorable trajectory choices (see summary in Figure 7F1–3). AO-LLSM imaging suggests a cellular mechanism underlying dendrite targeting specificity To capture fast dynamics of single dendritic branches, we performed dual- color adaptive optical lattice sheet microscopy (AO- LLSM) imaging (Chen et al., 2014; Wang et al., 2014; Liu et al., 2018) of PNs every 30 s for 15 min, following a protocol we recently established (Li et al., 2021; Li and Luo, 2021). We selected 3 hr, 6 hr, and 12 hr APF pupal brains double- labeled with DL1 PNs and bulk adPNs (Figure 7A–C; Figure 7—videos 1–3). The labeling of adPNs with GFP outlined PN cell bodies and the developing antennal lobe but not the degenerating one, presumably because the GFP in larval- specific dendrites was quickly quenched upon glial phagocytosis (Marin et al., 2005). In the 15 min imaging window, we observed four types of terminal branches regardless of neuronal types or developmental stages: (1) stable branch that existed throughout the entire imaging time, Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 13 of 33 Developmental Biology \| Neuroscience Research article Figure 6. Two- photon time- lapse imaging reveals active dendrite targeting. (A) Two- photon time- lapse imaging of MARCM- labeled DL1 projection neuron (PN) (pseudo- colored in yellow) in a brain dissected at 3 hr APF and cultured for 21 hr ex vivo (A1–9). Arrowheads in A4–6 denote protrusions of dendritic branches towards the dorsolateral direction. After 21 hr culture, the explant was fixed and immuno- stained for N- Cadherin (Ncad; in blue) to outline the developing antennal lobe (A10). Yellow and cyan arrowheads indicate DL1 PN dendrites and processes of other GH146+ cells, respectively. (B) Neurite tracing of DL1 PN at the beginning of live imaging (3 hr APF + 0 hr ex vivo). Dendrites are categorized based on the directions to which they extend and color- coded accordingly. (C) Left: Quantification of the percentage of dendritic volume in indicated direction during the time- lapse imaging period reveals a transitional phase during which dendrites were found in only two out of the four directions. Right: Schematic of the initial, transitional, and final phases during the course of targeting. ‘½’ denotes the reduction of available trajectory directions by half. Timestamp 00:00 refers to HH:mm; H, hour; m, minute. See Figure 6—source data 1. (D) Quantification of the percentage of DL1 PN dendritic volume in an indicated direction in 3 hr APF cultured brains at the beginning (0 hr ex vivo) and at/near the end of imaging (18 hr ex vivo). DL1 PN sample size = 3. t- test; p<0.05. Timestamp 00:00 refers to HH:mm; H, hour; m, minute. (E) Quantification of the percentage of the sum of DL1 PN dendritic volume in indicated directions throughout the entire imaging time. DL1 PN sample size = 3. (F) Bulk dendrite dynamics of DL1 PN in Figure 6A. Each row represents bulk dendritic dynamics in the indicated direction (color- coded as in Figure 6B) across the 21 hr imaging period. Each block represents a 20 min window. Bulk extension (in green) and retraction (in magenta) events are defined as dendrites extending and retracting more than 2 μm between two consecutive time windows. The first and last six consecutive windows refer to the initial and final phases of imaging. (G) Quantification of the number of bulk extension and retraction events in the dorsolateral direction during the initial and final phases of imaging. DL1 PN sample size = 3. t- test; p<0.05. The online version of this article includes the following video, source data, and figure supplement(s) for figure 6: Source data 1. Source data for Figure 6C–G and Figure 6—figure supplement 1D and E. Figure supplement 1. Two- photon time- lapse imaging of DL1 projection neuron (PNs). Figure 6—video 1. Two- photon time- lapse imaging of DL1 projection neuron (PN) dendrites. https://elifesciences.org/articles/85521/figures#fig6video1 Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 14 of 33 Developmental Biology \| Neuroscience Research article (2) transient branch that was produced and eliminated within the imaging window, (3) emerging branch that was produced after imaging began, and (4) retracting branch that was eliminated within the imaging period (Figure 7—figure supplement 1A). To examine if terminal branch dynamics exhibit any directional preference, we assigned the branches according to their targeting directions (Figure 7D). Extension and retraction events were defined when the speed exceeded 0.5 μm/min. Terminal branches were selected for analyses as branches closer to the main process were too dense to resolve. Figure 7D1- 3 showed the dynamics of ~15 randomly selected terminal branches in each direction from the representative 3 hr, 6 hr, and 12 hr APF DL1 PNs (Figure 7A–C). Quantitative analyses revealed that at 3 hr APF, DL1 PNs constantly produced, eliminated, extended, and retracted dendritic branches (Figure 7A, Figure 7D1, Figure 7—video 1). Even stable branches were not immobile. Rather, they spent comparable amounts of time extending and retracting at ~1.5 μm/min (Figure 7—figure supplement 1A1, 1B). Transient, emerging, and retracting branches had similar, but more variable speeds, ranging from 1 to 2.5 μm/min. Although there was no correla- tion between targeting direction and frequency/speed of extension/retraction, the number of stable branches in the VM direction was significantly lower than in other directions across all 3 hr DL1 PN samples examined (Figure 7E1). This suggests that even though dendritic branches were developed in every direction at the early stages, those branches in the VM direction were short- lived and might be eliminated by retraction. The direction- dependent stability/lifespan of dendritic branches on the timescale of seconds uncovered from AO- LLSM imaging explains why bulk dendrites in unfavorable trajectories failed to persist in long- term two- photon imaging. From 6 hr to 12 hr APF, DL1 PNs no longer manifested direction- specific branch de/stabiliza- tion (Figure 7B–C, Figure 7D2–3, Figure 7—videos 2–3). At the same developmental stage, stable branches in one direction appeared indistinguishable from those in other directions in terms of abun- dance, frequency, and speed (Figure 7D2–3, Figure 7—figure supplement 1C–D). This suggests that the entire dendritic mass tends to stay in equilibrium upon arrival at target regions. At 12 hr APF, the abundance of stable branches of DL1 PNs was the highest (Figure 7D–E1). Also, the stable branches of 12 hr APF DL1 PNs moved at a significantly lower speed (~1 μm/min) (Figure 7E2) and spent more time being stationary than those at 3 hr and 6 hr (Figure 7—figure supplement 1B–D). The reduced branch dynamics at 12 hr APF is consistent with observations from two- photon imaging showing fewer bulk extension/retraction events in the final phase of targeting (Figure 6F–G). Despite the slowdown, dendritic arborization was evident in terminal branches of 12 hr APF DL1 PNs (Figure 7—figure supplement 1E), suggesting that PN dendrites are transitioning from simple to complex branch architectures. Although it remains unclear if there is a causal relationship between reduced branch dynamics and increased structural complexity, we propose that both contribute to the sustentation of dendrite targeting specificity. In summary, AO- LLSM imaging reveals that PNs selectively stabilize branches in the direction towards the target and destabilize those in the opposite direction, providing a cellular basis of dendrite targeting specificity. Upon arrival at the target, the specificity is sustained through branch stabilization in a direction- independent manner (summarized in Figure 7F4–7). Embryonic-born PNs timely integrate into an adult olfactory circuit by simultaneous dendritic pruning and re-extension In earlier sections, we uncovered wiring logic of larval- born PN dendritic patterning and cellular mech- anisms of dendrite targeting specificity used to initiate olfactory map formation (Figures 3–7). In this final section, we focused on embryonic- born PNs, which participate in both larval and adult olfactory circuits by reorganizing their processes (Marin et al., 2005). Our previous study demonstrates that embryonic- born PNs prune their larval- specific dendrites during early metamorphosis (Marin et al., 2005; Figure 1D1–3). Here, we examined when and how embryonic- born PNs re- extend dendrites used in the adult olfactory circuit. It is known that γ neurons of Drosophila mushroom body (γ Kenyon cells) and sensory Class IV dendritic arborization (C4da) neurons prune their processes between 4 hr and 18 hr APF and show no signs of re- extension at 18 hr APF (Lee et al., 2000; Watts et al., 2003; Lee et al., 2009). Do embryonic- born adPNs follow a similar timeframe? We first examined developing brains double- labeled for embryonic- born DA4l/VA6/VA2 adPNs (collectively referred to as ‘lov+ PNs’) and early larval- born DC2 adPNs (Figure 8A; Figure 8—figure supplement 1). We found that, by 12 hr APF, Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 15 of 33 Developmental Biology \| Neuroscience Research article Figure 7. AO- LLSM time- lapse imaging reveals cellular mechanisms of dendrite targeting specificity. (A–C) AO- LLSM imaging of DL1 projection neurons (PNs) (71B05+; labeled in yellow) and anterodorsal projection neurons (adPNs) (acj6+; labeled in blue) in cultured brains dissected at 3 hr (A), 6 hr (B), and 12 hr (C) APF. Zoom- in, single z- section images of (A1), (B1), and (C1) (outlined in dashed boxes) are shown in A2, B2 and C2, respectively. (D) Single dendritic branch dynamics of 3 hr (D1), 6 hr (D2), and 12 hr (D3) DL1 PNs shown in A–C. Terminal branches are analyzed and categorized based on the directions in which they extend. Their speeds are color- coded using purple- gray- green gradients (negative speeds, retraction; positive speeds, extension). Individual branches are also assigned into four categories: stable, transient, emerging, and retracting (color- coded on the right; see Figure 7— figure supplement 1A). Each block represents a 30s window. Each row represents individual branch dynamics across the 15 min imaging period. (E) Quantification of the abundance (in percentage) of DL1 PN stable branches in indicated direction at 3 hr, 6 hr, and 12 hr (E1). Average speed of DL1 PN stable branches in indicated direction at 3 hr, 6 hr, and 12 hr (E2). DL1 PN sample size: 3 hr=4; 6 hr=3; 12 hr=3. Error bars, SEM; t-test; One- way ANOVA; *p<0.05; n.s., p≥0.05. SEM, standard error of the mean; n.s., not significant. See Figure 7—source data 1. (F) Summary of mechanisms underlying the emergence of dendrite targeting specificity revealed by two- photon and AO- LLSM imaging of DL1 PN dendrites. The online version of this article includes the following video, source data, and figure supplement(s) for figure 7: Source data 1. Source data for Figure 7E. Figure supplement 1. Analyses of DL1 projection neuron (PN) dendritic branches captured by AO- LLSM imaging. Figure 7—video 1. AO- LLSM time- lapse imaging of 3 hr DL1 projection neuron (PN) dendrites. https://elifesciences.org/articles/85521/figures#fig7video1 Figure 7—video 2. AO- LLSM time- lapse imaging of 6 hr DL1 projection neuron (PN) dendrites. https://elifesciences.org/articles/85521/figures#fig7video2 Figure 7—video 3. AO- LLSM time- lapse imaging of 12 hr DL1 projection neuron (PN) dendrites. https://elifesciences.org/articles/85521/figures#fig7video3 Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 16 of 33 Developmental Biology \| Neuroscience Research article lov+ PNs already sent adult- specific dendrites to a region ventromedial to DC2 PN dendrites (green arrowhead in Figure 8A3; see 3D rendering in Figure 8—video 1). This implies that lov+ PNs have already caught up with DC2 PNs on dendrite development at this stage, and the re- extension of lov+ PN dendrites must have happened even earlier. Indeed, we observed lov+ PN dendrites innervated the developing antennal lobe extensively at 6 hr APF (Figure 8A2). Such innervation was not observed at 0 hr APF (Figure 8A1). After 12 hr APF, the time course of lov+ PN dendrite development was comparable to that of DC2 PNs (Figure 8A4–6). To characterize dendritic re- extension at single- cell resolution, we developed a sparse, stochastic labeling strategy to label single lov+ PNs (Figure 8B). We found that lov+ PNs produced nascent branches from the main process dorsal to larval- specific dendrites as early as 3 hr APF (Figure 8C2–3; arrowheads in Figure 8C6–7). At 6 hr APF, when larval- specific dendrites were completely segregated from lov+ PNs, the robust extension of adult- specific dendrites was seen across the developing antennal lobe (Figure 8C4). These data indicate that lov+ PNs re- extend their adult- specific dendrites at a more dorsal location before the larval- specific dendrites are completely pruned. Do other embryonic- born PNs prune and re- extend their dendrites simultaneously? Like lov drivers, Mz612- GAL4 labels embryonic- born PNs, one of which is VA6 PN (Marin et al., 2005). In 3 hr APF brains co- labeled for Mz612+ and lov+ PNs, we could unambiguously access three single embryonic- born PN types: (1) lov+ Mz612– PN, (2) lov– Mz612+ PN, and (3) lov+ Mz612+PN (Figure 8—figure supplement 2A–B). Tracing of individual dendritic branches showed that all these PNs already re- ex- tended dendrites to varying extents prior to the separation of larval- specific dendrites from the rest of the processes (Figure 8—figure supplement 2C). Thus, concurrent pruning and re- extension apply to multiple embryonic- born PN types. To capture the remodeling at the higher temporal resolution, we performed two- photon time- lapse imaging of single embryonic- born PNs labeled by Split7- GAL4 (Figure 8D, Figure 8—video 2, Figure 8—figure supplement 3). This GAL4 labels one embryonic- born PN (either VA6 or VA2 PN) at early pupal stages but eight PN types at 24 hr APF (Xie et al., 2021). Initially (3 hr APF + 0 hr ex vivo), no adult- specific dendrites were detected in live Split7+ PNs (Figure 8D1). The following ~3 hr ex vivo saw thickening of the main process (arrowhead in Figure 8D3). From 4 hr ex vivo onwards, re- extension occurred in the presumed developing antennal lobe located dorsal to larval- specific dendrites (arrowheads in Figure 8D4–8; see traces in Figure 8D9). Live imaging of Split7+ PNs also revealed that fragmentation of larval- specific dendrites occurred at the distal ends (Figure 8—figure supplement 3B1–5), and the process leading to larval- specific dendrites gradually disappeared as pruning approached completion (Figure 8—figure supplement 3B6–10). These observations suggest that pruning of embryonic- born PN dendrites is not initiated by severing at the proximal end. Distal- to- proximal pruning, rather than in the reversed direction, further supports concurrent but spatially segregated pruning and re- extension processes. It has been shown that dendritic pruning of embryonic- born PNs requires ecdysone signaling in a cell- autonomous manner (Marin et al., 2005). We asked if the re- extension process also depends on ecdysone signaling. We expressed a dominant negative form of ecdysone receptor (EcR- DN) in most PNs (including lov+ PNs) and monitored the development of lov+ PN dendrites (Figure 8— figure supplement 4). We found that inhibition of ecdysone signaling by EcR- DN expression not only suppressed pruning, but also blocked re- extension. This is consistent with a previous study reporting the dual requirement of ecdysone signaling in the pruning and re- extension of Drosophila anterior paired lateral (APL) neurons, although, unlike embryonic- born PNs, APL neurons prune and re- extend processes sequentially (at 6 hr and 18 hr APF, respectively) (Mayseless et al., 2018). We currently could not distinguish if the lack of re- extension is due to defective pruning, or if ecdysone signaling controls pruning and re- extension independently. Taken together, our data demonstrate that embryonic- born PNs prune and re- extend dendrites simultaneously at spatially distinct regions, and that both processes require ecdysone signaling (Figure 8E). Such a ‘multi- tasking’ ability explains how embryonic- born PNs can re- integrate into the adult olfactory circuit and engage in its prototypic map formation in a timely manner. Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 17 of 33 Developmental Biology \| Neuroscience Research article Figure 8. Embryonic- born projection neurons (PNs) timely participate in olfactory map formation via simultaneous pruning and re- extension. (A) Confocal images of fixed brains at indicated stages showing dendrite development of lov+ PNs (embryonic- born; labeled in green) and 91G04+DC2 PNs (larval- born; labeled in magenta). As 91G04- GAL4 also labels some embryonic- born PNs from 0 to 6 hr APF, their processes are found in the larval- specific antennal lobe (A1, 2). Right columns of A1, 2 show a zoom- in of the dashed boxes. Green arrowhead in (A2) indicates robust dendrite re- extension of embryonic- born PNs across the developing antennal lobe at 6 hr APF. (A1): N=6; (A2): N=12; (A3): N=9; (A4): N=12; (A5): N=9; (A6): N=5. (B) Schematic of the sparse, stochastic, and dual- color labeling strategy. In this strategy, the same cell has one copy of UAS- responsive conditional reporter 1 and one copy of QUAS- responsive reporter 2, both of which are integrated into the same 86Fb genomic locus (i.e. UAS- FRT- stop- FRT- reporter1/QUAS- FRT- stop- FRT- reporter2). FLP expression yields cis and trans recombination of FRT sites in a stochastic manner. Upon GAL4 expression, reporter 1 is expressed in cells with cis recombination, whereas reporter 2 is expressed only when cis and trans recombination events co- occur. (C) Sparse labeling of lov+ PNs (labeled in green; single- cell lov+ PNs in gray) at indicated developmental stages. (C6) and (C7) are zoom- in images of the rectangular boxes in (C2) and (C3), respectively. Arrowheads indicate nascent, adult- specific dendrites. Larval- specific dendrites are outlined by dashed orange lines. Arrows indicate axons projecting towards high brain centers. (C1): N=6; (C2–3): N=6; (C4): N=4; (C5): N=4. (D) Two- photon time- lapse imaging of a single Figure 8 continued on next page Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 18 of 33 Developmental Biology \| Neuroscience Research article Figure 8 continued embryonic- born PN (Split7+; pseudo- colored in yellow) in a brain dissected at 3 hr APF and cultured for 23 hr ex vivo. Arrowhead in (D3) denote the thickening of the main process. Arrowheads in D4, 5 denote dendritic protrusions dorsal to larval- specific dendrites. (D9) shows neurite tracing of the embryonic- born PN. Triangles in (D9) indicate the degenerating larval- specific dendrites. N=3. (E) Schematic summary of remodeling of embryonic- born PN dendrites. Following simultaneous pruning and re- extension, embryonic- born PNs timely integrate into an adult olfactory circuit and, together with larval- born PNs, participate in the prototypic map formation. The online version of this article includes the following video and figure supplement(s) for figure 8: Figure supplement 1. Dendrite development of lov+ embryonic- born projection neurons (PNs). Figure supplement 2. Dendrite re- extension of lov+ and Mz612+ embryonic- born projection neurons (PNs). Figure supplement 3. Two- photon time- lapse imaging of Split7+ projection neuron (PN) dendrites. Figure supplement 4. Dual requirement of ecdysone signaling in pruning and re- extension of embryonic- born projection neuron (PN) dendrites. Figure 8—video 1. 3D rendering of z stacks of indicated projection neurons (PNs) in 12 hr APF antennal lobe. https://elifesciences.org/articles/85521/figures#fig8video1 Figure 8—video 2. Two- photon time- lapse imaging of Split7+ projection neuron (PN) dendrites. https://elifesciences.org/articles/85521/figures#fig8video2 Discussion Wiring logic for the prototypic olfactory map Prior to this study, no apparent logic linking PN lineage, birth order, and adult glomerular position has been found. Our systematic analyses of dendritic patterning at the resolution of specific PN types across development identified wiring logic underlying the spatial organization of the prototypic olfac- tory map (Figures 3 and 4). We found that PNs of a given lineage and temporal cohort share similar dendrite targeting spec- ificity and timing. Notably, dendrites of adPNs and lPNs respectively pattern the antennal lobe in rotating and binary manners following birth order. Based on our new observations and previous find- ings, we discuss possible mechanisms that execute the wiring logic to form the initial map: (1) speci- fication of the initial dendrite targeting through combinatorial inputs from lineage and birth order, (2) PN dendrite- dendrite interactions, and (3) contribution of the degenerating larval- specific antennal lobe. The spatial distinctions of cell bodies (e.g. Figure 1D1), axons (e.g. Figure 1—figure supplement 2A), and dendrites (e.g. Figure 4A1) of adPNs and lPNs observed in 0 hr APF pupal brain suggest that lineage endows projection specificity very early on. Lineage- specific transcription factors have been identified to instruct PN neurite targeting (Komiyama et al., 2003; Komiyama and Luo, 2007; Li et al., 2017; Xie et al., 2022), which might explain the differences between the adPN and lPN dendritic maps. Nonetheless, lineage alone does not account for the characteristic dendritic patterns. Rather, dendrite targeting can be predicted using combinatorial inputs from lineage and birth order. This combinatorial strategy is also seen in neuronal fate diversification and wiring of the Drosophila optic lobe and ventral nerve cord (Erclik et al., 2017; Mark et al., 2021), suggesting that it is a general principle in wiring the fly brain and likely also used in vertebrates (Holguera and Desplan, 2018; Sen, 2023). Substantial advances have been made in understanding how temporal patterning arises for intra- lineage specification (Doe, 2017; Miyares and Lee, 2019). For instance, the embry- onic ventral nerve cord neuroblasts sequentially express a cascade of temporal transcription factors (TTFs) to specify temporal identity (Isshiki et al., 2001). Larval optic lobe neuroblasts also deploy the same strategy but use a completely different TTF cascade (Li et al., 2013). Earlier studies show Chinmo, a TTF, and RNA- binding proteins that regulate Chinmo translation, control neuronal cell fate of the adPN lineage (Zhu et al., 2006; Liu et al., 2015). Specifically, DL1 PNs mutant for Chinmo project dendrites to D glomerulus that is targeted by the fourth larval- born adPNs (Zhu et al., 2006), demonstrating temporal order specifies final glomerular targeting. However, whether approximate temporal cohorts of a given PN lineage we described arise from sequential expression of temporal factors, and how such factors translate into initial dendrite patterning remains a fertile ground for future studies. Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 19 of 33 Developmental Biology \| Neuroscience Research article Our time- lapse imaging data reveals robust PN dendritic dynamics during the initial targeting process (Figures 5–8), suggesting that cellular interactions among PN dendrites contribute to the initial map formation. This appears to contrast with the PN- ORN map in the mature antennal lobe, which is highly stable; connection specificity remains largely unchanged upon genetic ablation of their synaptic partners (Berdnik et al., 2006). Future works using early- onset genetic drivers for specific PN types for ablation can be used to investigate interactions between different PN groups, such as adPNs and lPNs, in the construction of the initial PN dendrite map. Does the degenerating larval- specific antennal lobe contribute to the initial dendrite patterning of the developing adult- specific antennal lobe? Earlier studies found that the larval- specific ORN axons secrete semaphorins, Sema- 2a and Sema- 2b, which act as repulsive ligands for dendrites of Sema- 1a- expressing PNs (including DL1 PNs) (Komiyama et al., 2007; Sweeney et al., 2011). As the larval- specific lobe is located ventromedial to the adult- specific lobe, Sema- 2a/b and Sema- 1a form opposing gradients along the dorsolateral- ventromedial axis. When DL1 PNs (the first- born/ developed) begin to target their dendrites, this repulsive action could destabilize branches in the ventromedial direction and thus favor dorsolateral targeting. This provides a plausible explanation as to why the adPN rotation pattern begins at the dorsolateral position. It would be interesting to see if the pattern is perturbed upon ablation of larval- specific ORNs. Our new tools for labeling and genetic manipulation of distinct PN types (Figure 2) will now enable in- depth investigations into the potential cellular interactions and molecular mechanisms leading to the initial map organization. Wiring logic evolves as development proceeds After the initial map formation at 12 hr APF, dendrite positions in the antennal lobe could change substantially in the next 36 hr (for example, see DC2 PNs in Figure 3B4–6 and DA1 and VA1d/DC3 PNs in Figure 3C4–7). These changes occur when dendrites of PNs with neighboring birth order begin to segregate and when ORN axons begin to invade the antennal lobe. Accordingly, the ovoid- shaped antennal lobe turns into a globular shape (30–50 hr APF; Figure 3C6- 7). These PN- autonomous and non- autonomous changes likely mask the initial wiring logic, explaining why previous studies, which mostly focused on examining the final glomerular targets in adults (Jefferis et al., 2001), have missed the earlier organization. Interestingly, the process of PN dendritic segregation coincides with the peak of PN transcriptomic diversity at 24 hr APF (Li et al., 2017; Xie et al., 2021). Recent proteomics and genetic analyses have indicated that PN dendrite targeting is mediated by cell- surface proteins cooperating as a combinatorial code (Xie et al., 2022). The evolving wiring logic, which is consistent with the stepwise assembly of an olfactory circuit (Hong and Luo, 2014), suggests the combinatorial codes are not static. We propose that PNs use a numerically simpler code for initial dendrite targeting. Following the expansion of transcriptomic diversity, PNs acquire a more complex code mediating dendritic segregation of neighboring PNs and matching of PN dendrites and ORN axons. Functional characterization of differentially expressed genes between 12 hr and 24 hr APF PNs may provide molecular insights into how the degree of discreteness in the olfactory map arises. Although the initial wiring logic is not apparent in the final map, several lines of evidence suggest the final map depends on the initial map. First, as mentioned above, the change of the temporal identity of DL1 PNs affects glomerular targeting (Zhu et al., 2006). Second, loss of Sema- 1a in DL1 PNs occasionally causes mistargeting in areas outside of the antennal lobe, and dendrite mistargeting phenotype along the dorsolateral- ventromedial axis is persistent across development as well as in adulthood (Komiyama et al., 2007). Our work thus demonstrates that identification of the wiring logic in the early stages should help us better resolve the architectures in complex neural circuits. Selective branch stabilization as a cellular mechanism for dendrite targeting Utilizing an early pupal brain explant culture system coupled with two- photon and AO- LLSM imaging (Figure 5), we presented the first time- lapse videos following dendrite development of a specific PN type – DL1 PNs (Figures 6 and 7). We found that DL1 PN dendrites initiate active targeting towards their dorsolateral target with direction- dependent branch stabilization. This directional selectivity provides a cellular basis for the emerging targeting specificity of PN dendrites at the beginning of olfactory map formation. Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 20 of 33 Developmental Biology \| Neuroscience Research article Although selective branch stabilization as a mechanism to achieve axon targeting specificity has been described in neurons in the vertebrate and invertebrate systems (e.g. Yates et al., 2001; Li et al., 2021), our time- lapse imaging showed, for the first time to our knowledge, that selective branch stabilization is also used to achieve dendrite targeting specificity. Furthermore, AO- LLSM imaging revealed that selective stabilization and destabilization of dendritic branches occur on the timescale of seconds. As the rate of olfactory circuit development in the brain explants was slower than normal development (Figure 5F), we might have captured PN dendritic dynamics in slow motion. Using AO- LLSM for high spatiotemporal resolution imaging, we just begin to appreciate how fast PN dendrites are coordinating trajectory choices with branch stabilization to make the appropriate deci- sion. Having characterized the dendritic branch dynamics of the wild- type DL1 PNs, we have set the stage for future studies addressing how positional cues and the downstream signaling instruct wiring, and whether other PN types follow similar rules as DL1 PNs. Simultaneous pruning and re-extension as novel remodeling mechanism for neuronal remodeling Our data on embryonic- born adPN dendrite development reveals a novel mode of neuronal remod- eling during metamorphosis (Figure 8). In mushroom body γ neurons and body wall somatosen- sory neurons, two well- characterized systems, larval- specific neurites are first pruned, followed by re- extension of adult- specific processes (Watts et al., 2003; Williams and Truman, 2005; Yaniv and Schuldiner, 2016). However, embryonic- born adPNs prune larval- specific dendrites and re- extend adult- specific dendrites simultaneously but at spatially separated subcellular compartments. Such spatial segregation suggests that regional external cues could elicit compartmentalized downstream signals leading to opposite effects on the dendrites. Subcellular compartmentalization of signaling and cytoskeletal organization has been observed in diverse neuron types across species (Rolls et al., 2007; Kanamori et al., 2013; O’Hare et al., 2022). Why do embryonic- born adPNs ‘rush’ to re- extend dendrites? During normal development, it takes at least 18 hr for embryonic- born adPNs to produce and properly target dendrites (growth at 3–6 hr APF, initial targeting at 6–12 hr APF, and segregation at 21–30 hr APF). Given that the dendritic re- extension of embryonic- born PNs is ecdysone dependent (Figure 8—figure supplement 4), if the PNs did not re- extend dendrites at 3 hr APF, they would have to wait for the next ecdysone surge at ~20 hr APF (Thummel, 2001), which might be too late for their dendrites to engage in the proto- typic map formation. Thus, embryonic- born PNs develop a remodeling strategy that coordinates with the timing of systemic ecdysone release. By simultaneous pruning and re- extension, embryonic- born adPNs timely re- integrate into the adult prototypic map that readily serves as a target for subsequent ORN axon innervation. In conclusion, our study highlights the power and necessity of type- specific neuronal access and time- lapse imaging to identify wiring logic and mechanisms underlying the origin of an olfactory map. Applying similar approaches to other developing neural maps across species should broaden our understanding of the generic and specialized designs that give rise to functional maps with diverse architectures. Materials and methods Drosophila stocks and husbandry Flies were maintained on a standard cornmeal medium at 25 °C. Fly lines used in this study included GH146- FLP (Hong et al., 2009), QUAS- FRT- stop- FRT- mCD8- GFP (Potter et al., 2010), UAS- mCD8- GFP (Lee and Luo, 1999), UAS- mCD8- FRT- GFP- FRT- RFP (Stork et al., 2014), VT033006- GAL4 (Tirian and Dickson, 2017), Mz19- GAL4 (Jefferis et al., 2004), 91G04- GAL4 (Jenett et al., 2012), Mz612- GAL4 (Marin et al., 2005), 71B05- GAL4 (Jenett et al., 2012), Split7- GAL4 (Xie et al., 2021), QUAS- FLP (Potter et al., 2010), and UAS- EcR.B1-ΔC655.F645A (Cherbas et al., 2003). The following GAL4 lines were obtained from Bloomington Drosophila Stock Center (BDSC): tsh- GAL4 (BDSC #3040) and lov- GAL4 (BDSC #3737). The following two stocks were used for MARCM analyses: (1) UAS- mCD8- GFP, hs- FLP; FRTG13, tub- GAL80;; GH146- GAL4, and (2) FRTG13, UAS- mCD8- GFP (Lee and Luo, 1999). Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 21 of 33 Developmental Biology \| Neuroscience Research article The following lines were generated in this study: UAS- FRT10- stop- FRT10- 3xHalo7- CAAX (on either II or III chromosome), UAS- FRT- myr- 4xSNAPf- FRT- 3xHalo7- CAAX (III), UAS- FRT- myr- mGreenLantern- FRT- 3xHalo7- CAAX (II), QUAS- FRT- stop- FRT- myr- 4xSNAPf (III), run- T2A- FLP (X), acj6- T2A- FLP (X), acj6- T2A- QF2 (X), CG14322- T2A- QF2 (III), and lov- T2A- QF2 (II). Drosophila genotypes tub- GAL80/FRTG13, UAS- mCD8- GFP;; supplement 1B: GH146- FLP/UAS- FRT10- stop- FRT10- 3xHalo7- CAAX; supplement 1A: GH146- FLP/UAS- FRT10- stop- FRT10- 3xHalo7- CAAX; Figure 1D, Figure 1—figure supplement 1, Figure 1—figure supplement 2: run- T2A- FLP/+; UAS- mCD8- FRT- GFP- FRT- RFP/+; VT033006- GAL4/+ Figure 3A: acj6- T2A- QF2/+; GH146- FLP, QUAS- FRT- stop- FRT- mCD8- GFP/UAS- FRT10- stop- FRT10- 3xHalo7- CAAX; 71B05- GAL4/+ Figure 3B, Figure 3—figure supplement 1C: GH146- FLP/UAS- FRT10- stop- FRT10- 3xHalo7- CAAX; 91G04- GAL4/CG14322- T2A- QF2, QUAS- FRT- stop- FRT- myr- 4xSNAPf Figure 3C: acj6- T2A- FLP/+; Mz19- GAL4; UAS- FRT- myr- 4xSNAPf- FRT- 3xHalo7- CAAX/+ Figure 3D, Figure 3—figure supplement 2, Figure 3—figure supplement 3: UAS- mCD8- GFP, hs- FLP/+; FRTG13, tub- GAL80/FRTG13, UAS- mCD8- GFP;; GH146- GAL4 (IV)/+ Figure 3—figure 71B05- GAL4/+ Figure 3—figure 91G04- GAL4/+ Figure 3—video 1: Please refer to Figure 3 for genotypes. Figure 4A, Figure 4—figure supplement 1: GH146- FLP, UAS- FRT10- stop- FRT10- 3xHalo7- CAAX/tsh- GAL4; CG14322- T2A- QF2, QUAS- FRT- stop- FRT- myr- 4xSNAPf/+ Figure 4B: UAS- mCD8- GFP, hs- FLP/+; FRTG13, GH146- GAL4 (IV)/+ Figure 8—figure supplement 2: acj6- T2A- FLP/+; tsh- GAL4, UAS- mCD8- FRT- GFP- FRT- RFP Figure 4—video 1: Please refer to Figure 4 for genotypes. Figure 5E, Figure 5—video 1: run- T2A- FLP/+; UAS- FRT- myr- mGreenLantern- FRT- 3xHalo7- CAAX/+; VT033006- GAL4/+ Figure 5F: UAS- mCD8- GFP/+; VT033006- GAL4/+ Figure 5G1: GH146- FLP/UAS- FRT10- stop- FRT10- 3xHalo7- CAAX; 71B05- GAL4/+ Figure 5G2: GH146- FLP/tsh- GAL4; UAS- FRT10- stop- FRT10- 3xHalo7- CAAX/+ Figure 5—figure supplement 1, Figure 5—video 2: acj6- T2A- FLP/+; Mz19- GAL4/ UAS- FRT- myr- mGreenLantern- FRT- 3xHalo7- CAAX Figure 6A, Figure 6—figure supplement 1, Figure 6—video 1: UAS- mCD8- GFP, hs- FLP/+; FRTG13, tub- GAL80/FRTG13, UAS- mCD8- GFP;; GH146- GAL4 (IV)/+ Figure 7A–C, Figure 7—figure supplement 1, Figure 7—videos 1–3: acj6- T2A- QF2/+; QUAS- FRT- stop- FRT- mCD8- GFP/UAS- FRT10- stop- FRT10- 3xHalo7- CAAX; GH146- FLP, 71B05- GAL4/+ Figure 8A, Figure 8—figure supplement 1: GH146- FLP, QUAS- FRT- stop- FRT- mCD8- GFP/lov- T2A- QF2; UAS- FRT10- stop- FRT10- 3xHalo7- CAAX/91G04- GAL4 8C: Figure QUAS- FRT- stop- FRT- myr- 4xSNAPf Figure 8D, Figure 8—figure supplement 3, Figure 8—video 2: UAS- mCD8- GFP/+; Split7- GAL4 (i.e. FlyLight SS01867: 72C11- p65ADZp; VT033006- ZpGDBD)/+ Figure 8—figure supplement 2: GH146- FLP, QUAS- FRT- stop- FRT- mCD8- GFP/lov- T2A- QF2, Mz612- GAL4; UAS- FRT10- stop- FRT10- 3xHalo7- CAAX/+ Figure 8—figure UAS- mCD8- FRT- GFP- FRT- RFP Figure 8—figure supplement 4B: lov- T2A- QF2, QUAS- FLP/UAS- EcR- DN; VT033006- GAL4/ UAS- mCD8- FRT- GFP- FRT- RFP Figure 8—video 1: Please refer to Figure 8 for genotypes. lov- T2A- QF2, QUAS- FLP/+; VT033006- GAL4/ UAS- FRT10- stop- FRT10- 3xHalo7- CAAX/ GH146- FLP/lov- GAL4; supplement 4A: Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 22 of 33 Developmental Biology \| Neuroscience Research article MARCM clonal analyses MARCM clonal analyses have been previously described (Lee and Luo, 1999). Larvae of the genotype UAS- mCD8- GFP, hs- FLP/+; FRTG13, tub- GAL80/FRTG13, UAS- mCD8- GFP;; GH146- GAL4/+ were heat shocked at 37 °C for 1 hr. To label the first- born DL1 PNs, heat shock was applied at 0–24 hr after larval hatching (ALH). MARCM clones of early, middle (mid- late for adPNs), and late larval- born PNs were generated by applying heat shocks at 42–48 hr, 66–72 hr, and 96–100 hr ALH, respectively. As larvae developed at different rates (Tennessen and Thummel, 2011), we reasoned that even if we could collect 0 hr–2 hr ALH larvae, their development might have varied by the time of heat shock. To minimize the effects of unsynchronized development, we selected those heat- shocked larvae that were among the first to form puparia and collected these white pupae in a ~3 hr window for the clonal analyses. Transcriptomic analyses Transcriptomic analyses have been described previously (Xie et al., 2021). tSNE plots and dot plots were generated in Python using PN single- cell RNA sequencing data and code available at https:// github.com/Qijing-Xie/FlyPN_development (Xie, 2021). Generation of T2A-QF2/FLP lines To generate a T2A- QF2/FLP donor vector for acj6 (we used the same strategy for run, CG14322 and lov), a ~2000 bp genomic sequence flanking the stop codon of acj6 was PCR amplified and introduced into pCR- Blunt II- TOPO (ThermoFisher Scientific #450245), forming pTOPO- acj6. To build pTopo- acj6- T2A- QF2, T2A- QF2 including loxP- flanked 3xP3- RFP was PCR amplified from pBPGUw- HACK- QF2 (Addgene #80276), followed by insertion into pTOPO- acj6 right before the stop codon of acj6 by DNA assembly (New England BioLabs #E2621S). To generate T2A- FLP, we PCR- amplified FLP from the genomic DNA of GH146- FLP strain. QF2 in pTopo- acj6- T2A- QF2 was then replaced by FLP through DNA assembly. Using CRISPR Optimal Target Finder (Gratz et al., 2014), we selected a 20 bp gRNA target sequence that flanked the stop codon and cloned it into pU6- BbsI- chiRNA (Addgene #45946). If the gRNA sequence did not flank the stop codon, silent mutations were introduced at the PAM site of the donor vector by site- directed mutagenesis. Donor and gRNA vectors were co- injected into Cas9 embryos in- house or through BestGene. Generation of FLP-out reporters To generate pUAS- FRT10- stop- FRT10- 3xHalo7- CAAX, FRT10- stop- FRT10 was PCR amplified from pUAS- FRT10- stop- FRT10- mCD8- GFP (Li et al., 2021) and inserted into pUAS- 3xHalo7- CAAX (Addgene #87646) through NotI and DNA assembly. To generate pUAS- FRT- myr- 4xSNAPf- FRT- 3xHalo7- CAAX, we first PCR amplified myr- 4xSNAPf from pUAS- myr- 4xSNAPf (Addgene #87637) using FRT- containing primers. FRT- myr- 4xSNAPf- FRT was then introduced into pCR- Blunt II- TOPO, forming pTOPO- FRT- myr- 4xSNAPf- FRT. Using NotI- containing primers, FRT- myr- 4xSNAPf- FRT was PCR amplified and subcloned into pUAS- 3xHalo7- CAAX through NotI. To generate pUAS- FRT- myr- mGreenLantern- FRT- 3xHalo7- CAAX, we first PCR amplified mGreen- Lantern from pcDNA3.1- mGreenLantern (Addgene #161912). Using MluI and XbaI, we replaced 4xSNAPf in pUAS- myr- 4xSNAPf with mGreenLantern to build pUAS- myr- mGreenLantern. myr- mGreenLantern was PCR amplified with the introduction of FRT sequence, followed by insertion into pCR- Blunt II- TOPO. Using the NotI- containing primers, FRT- myr- mGreenLantern- FRT was PCR ampli- fied and subcloned into pUAS- 3xHalo7- CAAX through NotI. To generate pQUAS- FRT- stop- FRT- myr- 4xSNAPf, we first PCR amplified FRT- stop from pJFRC7- 20XUAS- FRT- stop- FRT- mCD8- GFP (Li et al., 2021) and inserted it into pTOPO- FRT- myr- 4xSNAPf- FRT through DNA assembly to form pTOPO- FRT- stop- FRT- myr- 4xSNAPf- FRT. Using NotI- containing forward and KpnI- containing reverse primers, FRT- stop- FRT- myr- 4xSNAPf was PCR amplified and subcloned into p10XQUAST. p10XQUAST was generated using p5XQUAS (Addgene #24349) and p10xQUAS- CsChrimson (Addgene #163629). attP24 and 86Fb landing sites were used for site- directed integration. Immunofluorescence staining and confocal imaging Fly brain dissection for immunostaining and live imaging has been described (Wu and Luo, 2006). Briefly, brains were dissected in phosphate- buffered saline (PBS) and fixed with 4% Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 23 of 33 Developmental Biology \| Neuroscience Research article paraformaldehyde in PBS for 20 min on a nutator at room temperature. Fixed brains were washed with 0.1% Triton X- 100 in PBS (PBST) for 10 min twice. After blocking with 5% normal donkey serum in PBST for 1 hr at room temperature, the brains were incubated with primary antibodies overnight at 4 °C. After PBST wash, brains were incubated with secondary antibodies (1:1000; Jackson ImmunoResearch) in dark for 2 hr at room temperature. Washed and mounted brains were imaged with confocal laser scanning microscopy (ZEISS LSM 780; LSM 900 with Airyscan 2). Images were processed with ImageJ. Neurite tracing images were generated using Simple Neurite Tracer (SNT) (Arshadi et al., 2021). Primary antibodies used included chicken anti- GFP (1:1000; Aves Lab #GFP- 1020), rabbit anti- DsRed (1:500; TaKaRa #632496), rat anti- Cadherin DN (1:30; Developmental Studies Hybridoma Bank DSHB DN- Ex#8 supernatant), and mouse anti- Bruchpilot (1:30; DSHB nc82 supernatant). Chemical labeling Chemical labeling of Drosophila brains has been described (Kohl et al., 2014). Janelia Fluor (JF) Halo and SNAP ligands (stocks at 1 mM) were gifts from Dr. Luke Lavis (Grimm et al., 2017; Grimm et al., 2021). Fixed brains were washed with PBST for 5 min, followed by incubation with Halo and/or SNAP ligands (diluted in PBS) for 45 min at room temperature. Brains were then washed with PBST for 5 min, followed by blocking and immunostaining if necessary. For the co- incubation of Halo and SNAP ligands, JF503- cpSNAP (1:1000) and JF646- Halo (1:1000) were used. Alternatively, JFX650- SNAP (1:1000) and JFX554- Halo (1:10,000) were used. When only Halo ligands were needed, either JF646- Halo or JF635- Halo (1:1000) was used. For live brain imaging, dissected brains were incubated with Halo ligands diluted in culture media (described below) for 30 min at room temperature. For two- photon imaging, JF570- Halo was used at 1:5000. For AO- LLSM imaging, following JF646- Halo incubation at 1:1000, the brains were incubated with 1 µM Sulforhodamine 101 (Sigma) for 5 min at room temperature. The brains were then briefly washed with culture media before imaging. Brain explant culture setup and medium preparation Brain explant culture setup was modified based on Li et al., 2021; Li and Luo, 2021. A Sylgard plate with a thickness of ~2 millimeters was prepared by mixing base and curing agent at 10:1 ratio (DOW SYLGARD 184 Silicone Elastomer Kit). The mixture was poured into a 60 mm × 15 mm dish in which it was cured for two days at room temperature. Once cured, the plate was cut into small squares (~15 mm × ~15 mm). Indentations were created based on the size of an early pupal brain using a No.11 scalpel. Additional slits were made around the indentations for attaching imaginal discs which served as anchors to hold the brain position. A square Sylgard piece was then placed in a 60 mm × 15 mm dish or on a 25 mm round coverslip in preparation for two- photon/AO- LLSM imaging. Culture medium was prepared based on published methods (Rabinovich et al., 2015; Li and Luo, 2021; Li et al., 2021). The medium contained Schneider’s Drosophila Medium (ThermoFisher Scientific #21720001), 10% heat- inactivated Fetal Bovine Serum (ThermoFisher Scientific #16140071), 10 µg/mL human recombinant insulin (ThermoFisher Scientific #12585014; stock = 4 mg/mL), 1:100 Penicillin- Streptomycin (ThermoFisher Scientific #15140122). For 0 hr–6 hr APF brain culture, 0.5 mM ascorbic acid (Sigma #A4544; stock concentration = 50 mg/mL in water) was included. 20- hydroxyecdysone (Sigma #H5142; stock concentration = 1 mg/mL in ethanol) was used for 0 hr–6 hr and 12 hr brain explants at 20 µM and 2 µM, respectively. Culture medium was oxygenated for 20 min before use. Single- and dual-color imaging with two-photon microscopy Single- and dual- color imaging of PNs were performed at room temperature using a custom- built two- photon microscope (Prairie Technologies) with a Chameleon Ti:Sapphire laser (Coherent) and a 16 X water- immersion objective (0.8 NA; Nikon). Excitation wavelength was set at 920 nm for GFP imaging, and at 935 nm for co- imaging of mGreenLantern and JF570- Halo. z- stacks were obtained at 4 µm increments (10 µm increments for Figure 5—video 1). Images were acquired at a resolution of 1024 × 1024 pixel2 (512 × 512 for Figure 5—video 1), with a pixel dwell time of 6.8 µs and an optical zoom of 2.1, and at a frequency every 20 min for 8–23 hr. Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 24 of 33 Developmental Biology \| Neuroscience Research article Dual-color imaging with AO-LLSM For AO- LLSM- based imaging, the excitation and detection objectives along with the 25 mm coverslip were immersed in ~40 mL of culture medium at room temperature. Explant brains held on Sylgard plate were excited simultaneously using 488 nm (for GFP) and 642 nm (for JF- 646) lasers operating with ~2–10 mW input power to the microscope (corresponding to ~10–50 µW at the back aperture of the excitation objective). An exposure time of 20–50 msec was used to balance imaging speed and signal- to- noise ratio (SNR). Dithered lattice light- sheet patterns with an inner/outer numerical aperture of 0.35/0.4 or 0.38/0.4 were used. The optical sections were collected by an axial step size of 250 nm in the detection objective coordinate, with a total of 81–201 steps (corresponding to a total axial scan range of 20–50 µm). Emission light from GFP and JF- 646 was separated by a dichromatic mirror (Di03- R561, Semrock, IDEX Health & Science, LLC, Rochester, NY) and captured by two Hamamatsu ORCA- Fusion sCMOS cameras simultaneously (Hamamatsu Photonics, Hamamatsu City, Japan). Prior to the acquisition of the time series data, the imaged volume was corrected for optical aberrations using a two- photon guide star- based adaptive optics method (Chen et al., 2014; Wang et al., 2014; Liu et al., 2018). Each imaged volume was deconvolved using Richardson- Lucy algorithm on HHMI Janelia Research Campus’ or Advanced Bioimaging Center’s computing cluster (https://github.com/ scopetools/cudadecon, Lambert et al., 2023; https://github.com/abcucberkeley/LLSM3DTools, Ruan and Upadhyayula, 2020) with experimentally measured point spread functions obtained from 100 or 200 nm fluorescent beads (Invitrogen FluoSpheres Carboxylate- Modified Microspheres, 505/515 nm, F8803, FF8811). The AO- LLSM was operated using a custom LabVIEW software (National Instruments, Woburn, MA). Statistics For data analyses, t- test and one- way ANOVA were used to determine p values as indicated in the figure legend for each graph, and graphs were generated using Excel. Exact p values were provided in source data files. Material and data availability All reagents generated in this study are available from the lead corresponding author upon request. Figure 3—figure supplement 3—source data 1, Figure 6—source data 1, and Figure 7—source data 1 contain the numerical and statistical data used to generate the figures. The confocal imaging dataset is available at Brain Image Library under DOI https://doi.org/10.35077/g.933. Acknowledgements We thank the Luo lab members for constructive feedback on the manuscript; Tzumin Lee for sharing equipment at Janelia Research Campus; Luke Lavis for sharing JF dyes. This work was supported by a grant from NIH (R01 DC005982 to LL). TL was supported by NIH 1K99DC01883001. GL and SU are funded by Philomathia Foundation. SU is funded by the Chan Zuckerberg Initiative Imaging Scientist program. SU is a Chan Zuckerberg Biohub Investigator. EB and LL are HHMI investigators. Additional information Funding Funder National Institutes of Health Philomathia Foundation Chan Zuckerberg Initiative National Institutes of Health Grant reference number Author R01 DC005982 Liqun Luo Gaoxiang Liu Srigokul Upadhyayula Srigokul Upadhyayula 1K99DC01883001 Tongchao Li Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 25 of 33 Developmental Biology \| Neuroscience Research article Funder Grant reference number Author Howard Hughes Medical Institute Eric Betzig Liqun Luo The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. Author contributions Kenneth Kin Lam Wong, Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft; Tongchao Li, Resources, Investigation, Methodology, Writing – review and editing; Tian- Ming Fu, Gaoxiang Liu, Resources, Data curation, Investigation, Meth- odology, Writing – review and editing; Cheng Lyu, Resources, Methodology, Writing – review and editing; Sayeh Kohani, Data curation, Investigation; Qijing Xie, Data curation, Investigation, Writing – review and editing; David J Luginbuhl, Resources, Data curation, Writing – review and editing; Srigokul Upadhyayula, Eric Betzig, Resources, Supervision, Writing – review and editing; Liqun Luo, Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Project administra- tion, Writing – review and editing Author ORCIDs Kenneth Kin Lam Wong Tian- Ming Fu Liqun Luo http://orcid.org/0000-0001-6265-0859 http://orcid.org/0000-0001-5467-9264 http://orcid.org/0000-0002-5597-4051 Decision letter and Author response Decision letter https://doi.org/10.7554/eLife.85521.sa1 Author response https://doi.org/10.7554/eLife.85521.sa2 Additional files Supplementary files • Supplementary file 1. Sample variability among individual brains. A supplemental table describing the biological and technical variations we observed among individual brain samples, and measures we took to minimize them, if possible. • MDAR checklist Data availability Figure 3—source data 1, Figure 5—source data 1, Figure 6—source data 1, and Figure 7—source data 1 contain the numerical and statistical data used to generate the figures. The confocal imaging dataset is available at Brain Image Library under DOI https://doi.org/10.35077/g.933. The following dataset was generated: Author(s) Wong KLK Year 2023 Dataset title Dataset URL Database and Identifier https:// doi. org/ 10. 35077/ g. 933 Brain Image Library, 10.35077/g.933 Origin of wiring specificity in an olfactory map revealed by neuron type- specific, time- lapse imaging of dendrite targeting: Confocal imaging of developing fly brain Wong et al. eLife 2023;12:e85521. 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DOI: https://doi.org/10.7554/eLife.85521 30 of 33 Developmental Biology \| Neuroscience Research article Appendix 1 Appendix 1—key resources table Reagent type (species) or resource Designation Source or reference Identifiers Additional information Genetic reagent (D. melanogaster) GH146- FLP DOI: 10.1038/nn.2442 Genetic reagent (D. melanogaster) QUAS- FRT- stop- FRT- mCD8- GFP DOI: 10.1016 /j. cell.2010.02.025 Genetic reagent (D. melanogaster) UAS- mCD8- GFP DOI: 10.1016 / s0896- 6273(00)80701–1 Genetic reagent (D. melanogaster) UAS- mCD8- FRT- GFP- FRT- RFP DOI: 10.1016 /j. neuron.2014.06.026 Genetic reagent (D. melanogaster) VT033006- GAL4 Genetic reagent (D. melanogaster) Mz19- GAL4 Genetic reagent (D. melanogaster) 91 G04- GAL4 Genetic reagent (D. melanogaster) Mz612- GAL4 Genetic reagent (D. melanogaster) 71B05- GAL4 Genetic reagent (D. melanogaster) Split7- GAL4 Genetic reagent (D. melanogaster) QUAS- FLP DOI: 10.1101/198648 DOI: 10.1242/dev.00896 DOI: 10.1016 /j. celrep.2012.09.011 DOI: 10.1242/dev.01614 DOI: 10.1016 /j. celrep.2012.09.011 DOI: 10.7554/eLife.63450 FlyLight:SS01867 DOI: 10.1016 /j. cell.2010.02.025 Genetic reagent (D. melanogaster) UAS- EcR.B1-ΔC655.F645A DOI: 10.1242/dev.00205 Genetic reagent (D. melanogaster) tsh- GAL4 Genetic reagent (D. melanogaster) lov- GAL4 Bloomington Drosophila Stock Center BDSC:3040 Bloomington Drosophila Stock Center BDSC:3737 Genetic reagent (D. melanogaster) UAS- mCD8- GFP, hs- FLP; FRTG13, tub- GAL80;; GH146- GAL4 DOI: 10.1016 / s0896- 6273(00)80701–1 Genetic reagent (D. melanogaster) FRTG13, UAS- mCD8- GFP DOI: 10.1016 / s0896- 6273(00)80701–1 Genetic reagent (D. melanogaster) UAS- FRT10- stop- FRT10- 3xHalo7- CAAX this paper Genetic reagent (D. melanogaster) UAS- FRT- myr- 4xSNAPf- FRT- 3xHalo7- CAAX this paper Genetic reagent (D. melanogaster) UAS- FRT- myr- mGreenLantern- FRT- 3xHalo7- CAAX this paper Genetic reagent (D. melanogaster) QUAS- FRT- stop- FRT- myr- 4xSNAPf this paper Genetic reagent (D. melanogaster) run- T2A- FLP Genetic reagent (D. melanogaster) acj6- T2A- FLP Genetic reagent (D. melanogaster) acj6- T2A- QF2 this paper this paper this paper Genetic reagent (D. melanogaster) CG14322- T2A- QF2 this paper Genetic reagent (D. melanogaster) lov- T2A- QF2 this paper Antibody chicken polyclonal anti- GFP Aves Lab Appendix 1 Continued on next page on either II or III chromosome; see Materials and methods on III chromosome; see Materials and methods on II chromosome; see Materials and methods on III chromosome; see Materials and methods on X chromosome; see Materials and methods on X chromosome; see Materials and methods on X chromosome; see Materials and methods on III chromosome; see Materials and methods on II chromosome; see Materials and methods RRID:AB_10000240; Aves Lab:GFP- 1020 (1:1000) Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 31 of 33 Developmental Biology \| Neuroscience Research article Appendix 1 Continued Reagent type (species) or resource Designation Source or reference Identifiers Additional information Antibody rabbit polyclonal anti- DsRed TaKaRa RRID:AB_10013483; TaKaRa:632496 (1:500) Antibody rat monoclonal anti- Cadherin DN Developmental Studies Hybridoma Bank RRID:AB_528121; DSHB:DN- Ex#8 (1:30) Antibody mouse monoclonal anti- Bruchpilot Developmental Studies Hybridoma Bank RRID:AB_2314866; DSHB:nc82 supernatant (1:30) Recombinant DNA reagent Recombinant DNA reagent Recombinant DNA reagent Recombinant DNA reagent Recombinant DNA reagent pBPGUw- HACK- QF2 Addgene RRID:Addgene_80276 pU6- BbsI- chiRNA Addgene RRID:Addgene_45946 pUAS- 3xHalo7- CAAX Addgene RRID:Addgene_87646 pUAS- myr- 4xSNAPf Addgene RRID:Addgene_87637 pcDNA3.1- mGreenLantern Addgene RRID:Addgene_161912 Recombinant DNA reagent p5XQUAS Addgene RRID:Addgene_24349 Recombinant DNA reagent p10xQUAS- CsChrimson Addgene RRID:Addgene_163629 Recombinant DNA reagent pUAS- FRT10- stop- FRT10- 3xHalo7- CAAX this paper Recombinant DNA reagent pUAS- FRT- myr- 4xSNAPf- FRT- 3xHalo7- CAAX this paper Recombinant DNA reagent pUAS- FRT- myr- mGreenLantern- FRT- 3xHalo7- CAAX this paper Recombinant DNA reagent Recombinant DNA reagent pUAS- myr- mGreenLantern this paper pQUAS- FRT- stop- FRT- myr- 4xSNAPf this paper Chemical compound, drug SYLGARD 184 Silicone Elastomer Kit DOW Schneider’s Drosophila Medium ThermoFisher Scientific Fetal Bovine Serum ThermoFisher Scientific Human recombinant insulin ThermoFisher Scientific Penicillin- Streptomycin ThermoFisher Scientific backbone from pUAS- 3xHalo7- CAAX; see Materials and methods backbone from pUAS- 3xHalo7- CAAX; see Materials and methods backbone from pUAS- 3xHalo7- CAAX; see Materials and methods backbone from pUAS- myr- 4xSNAPf; see Materials and methods backbone from p5XQUAS; see Materials and methods DOW:2646340 ThermoFisher Scientific:21720001 ThermoFisher Scientific:16140071 ThermoFisher Scientific:12585014 ThermoFisher Scientific:15140122 used at 10% used at 10 µg/mL (1:100) Ascorbic acid Sigma Sigma:A4544 used at 50 mg/mL in water 20- hydroxyecdysone Sigma Sigma:H5142 used at 20 µM and 2 µM JF503- cpSNAP Chemical compound, drug JF646- Halo Chemical compound, drug Chemical compound, drug JFX650- SNAP JFX554- Halo Appendix 1 Continued on next page DOI: 10.1038/nmeth.4403; DOI: 10.1021/jacsau.1c00006 DOI: 10.1038/nmeth.4403; DOI: 10.1021/jacsau.1c00006 DOI: 10.1038/nmeth.4403; DOI: 10.1021/jacsau.1c00006 DOI: 10.1038/nmeth.4403; DOI: 10.1021/jacsau.1c00006 (1:1000); gift from Dr. Luke Lavis (1:1000); gift from Dr. Luke Lavis (1:1000); gift from Dr. Luke Lavis (1:10000); gift from Dr. Luke Lavis Chemical compound, drug Chemical compound, drug Chemical compound, drug Chemical compound, drug Chemical compound, drug Chemical compound, drug Chemical compound, drug Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 32 of 33 Developmental Biology \| Neuroscience Research article Appendix 1 Continued Reagent type (species) or resource Designation Source or reference Identifiers Additional information Chemical compound, drug JF635- Halo Chemical compound, drug JF570- Halo DOI: 10.1038/nmeth.4403; DOI: 10.1021/jacsau.1c00006 DOI: 10.1038/nmeth.4403; DOI: 10.1021/jacsau.1c00006 (1:1000); gift from Dr. Luke Lavis (1:5000); gift from Dr. Luke Lavis Chemical compound, drug Sulforhodamine 101 Sigma Sigma:S7635 used at 1 µM Software, algorithm ZEN Carl Zeiss RRID:SCR_013672 Software, algorithm ImageJ National Institutes of Health RRID:SCR_003070 Software, algorithm Python Programming Language Python RRID:SCR_008394 http://www.python.org/ Wong et al. eLife 2023;12:e85521. DOI: https://doi.org/10.7554/eLife.85521 33 of 33 Developmental Biology \| Neuroscience	null
10.1371_journal.pone.0258085.pdf	Data Availability Statement: Due to legal and participants confidentiality, data will only be available upon request. The data underlying the results presented in the study are available from Shenzhen Luohu Disease Prevention and Control Center via contacting Weihong Chen, director of Shenzhen Luohu Disease Prevention and Control Center, at 1433529760@qq.com.	Due to legal and participants confidentiality, data will only be available upon request. The data underlying the results presented in the study are available from Shenzhen Luohu	RESEARCH ARTICLE Level of engagement of recreational physical activity of urban villagers in Luohu, Shenzhen, China Lu ShiID 1, Willie Leung2, Qingming Zheng3, Jie Wu3 1 Public Health, School of Social and Behavioral Health Science, College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, United States of America, 2 Department of Health Sciences and Human Performance, College of Natural and Health Sciences, The University of Tampa, Tampa, FL, United States of America, 3 Shenzhen Luohu Disease Prevention and Control Center, Shenzhen, Guangdong, China shil@oregonstate.edu Abstract Physical activity is important for health. However, there is a lack of literature related to the physical activity levels of adults living in urban villagers, which is a vulnerable population in China. The aim of this study is to compare the physical activity and sedentary behavior engagements between urban villagers and non-urban villagers using the 2019 Luohu Shen- zhen, China Community Diagnosis Questionnaire. A total of 1205 adults living in urban vil- lages and non-urban villages were included in the analysis. Unadjusted and multiple multivariate logistic regression were conducted for the dependent variable of engagement in recreational physical activity, frequency of recreational physical activity per week, and hours spent in sedentary behaviors per day. Descriptive analysis was conducted to identify the reasons for not engaging in physical activity among urban villagers and non-urban villagers. Across the included sample, 29.05% were urban villagers and 70.95% were non-urban vil- lagers. The results suggested that urban villagers are more likely to engage in physical activ- ity than non-urban villager (OR = 1.90, 95% CI [1.40, 2.59], p < 0.001). However, it was also found that urban village status had no significant association for frequency in engaging in physical activity and average hours spent in sedentary behaviors. Both urban villagers and non-urban villages indicated that lack of time, lack of safe and appropriate environment, and working in labor intensive occupations as some of the reasons for not engaging in physical activity. There is a need for tailed interventions and policies for promoting physical activity among urban villagers and non-urban villagers. Additional studies are needed to further our understanding of the physical activity behaviors among urban villagers in China. Introduction Benefits of physical activity a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Shi L, Leung W, Zheng Q, Wu J (2021) Level of engagement of recreational physical activity of urban villagers in Luohu, Shenzhen, China. PLoS ONE 16(10): e0258085. https://doi. org/10.1371/journal.pone.0258085 Editor: Francisco Javier Huertas-Delgado, La Inmaculada Teacher Training Centre (University of Granada), SPAIN Received: November 21, 2020 Accepted: September 20, 2021 Published: October 28, 2021 Copyright: © 2021 Shi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Due to legal and participants confidentiality, data will only be available upon request. The data underlying the results presented in the study are available from Shenzhen Luohu Disease Prevention and Control Center via contacting Weihong Chen, director of Shenzhen Luohu Disease Prevention and Control Center, at 1433529760@qq.com. Funding: The authors received no specific funding for this work. The benefit of engagement of physical activity is well documented [1, 2]. The numerous bene- fits included weight management, lower blood cholesterol levels and blood pressure, PLOS ONE \| https://doi.org/10.1371/journal.pone.0258085 October 28, 2021 1 / 17 PLOS ONECompeting interests: The authors have declared that no competing interests exist. Urban villagers’ physical activity levels strengthening bones, muscles, and joint, and reducing risk of cardiovascular disease and cer- tain types of cancers [3]. In addition to physical health-related benefits, engagement in physical activity could lead to benefits of social and mental benefits. Regular engagement in physical activity is associated with reduced stress, improved mental health, emotional regulation, low- ered depression, increased social functioning, and increased sense of community [4]. Further, engagement of regular physical activity is related to reduce the risk of developing disabilities and maintenance of functional independences [5, 6]. Currently, physical inactivity is the fourth leading cause of mortality, according to the World Health Organization (WHO) [7]. WHO’s physical activity guidelines are 150 minutes of moderate physical activity or 75 minutes of vigorous physical activity per week or an equiva- lent combination of moderate- and vigorous-intensity activity for adults [7]. Individuals can perform various activities, such as leisure time physical activity, active transportation, and occupational activities to accumulate the minutes required to meet the guidelines. These guidelines apply to all individuals regardless of gender, race, ethnicity, or income levels. Physical activity levels of Chinese people Past literature had examined the physical activity levels individuals living in China [8, 9]. Using the data from the 2012 to 2015 China Hypertension Survey (CHS), it was found that 28.1% of Chinese adults were overweight and 5.2% were obese [10]. The results also found that regionals different of the prevalence of overweight and obesity different between Northern and Southern China with adults from Northern China more likely to be obese and overweight. According to a report published in the official Report on Cardiovascular Diseases in China 2017, 290 millions of Chinese adults are suffering from cardiovascular disease [11]. It was also found that China is facing a fast growing cardiovascular disease epidemic with a widening rural-urban disparities [12]. Similar physical activity trends found in Western countries were observed among Chinese adults as well. Trends such as male are more likely to engage in physical activity than female and older adults are less physical active than younger adults were found among individuals liv- ing in China [8, 9]. It was found that 66.3% of adults between the ages of 35 to 74 years were physically active according to the data from the International Collaborative Study of Cardio- vascular Disease in Asia from 2000–2001 [9]. Using accelerometers to measure physical activ- ity, it was found that Chinese adults in Shanghai spent 317 minutes per day in physical activity, while spent 509 minutes per day in sedentary behaviors [13]. It was reported that Chinese adults are more likely to report engaging in work-related or occupational physical activity (63.3%) than leisure time physical or recreational physical activity (24.5%) [9]. There were dis- parities between urban and rural residents with more rural residents (78.1%) spending time in physical activity than urban residents (21.8%) [9]. In addition to regional different, it was found that socioeconomic status (SES) impact physical activity levels among Chinese adults [14]. Using a community-based survey with 3567 adults living in Jiaxing, China, Chen et al. found that adults with lower SES are more likely to engage in household physical activity, adults with middle SES engages in higher intensity of occupational physical activity, and adults with higher SES levels were more likely to exercise but spent longer time in sedentary behav- iors [14]. The physical activity of subpopulation of Chinese adults had been well examined, especially for adults with different living area (rural vs. urban) and SES [14, 15]. However, there is a lack of literature examining the physical activity levels of urban villagers. Urban villagers refer to the individuals living in urban village. Urban village or chengzhongcun are typically low quality and high density with many closely packed apartment blocks of between 2 and 8 floors [16]. PLOS ONE \| https://doi.org/10.1371/journal.pone.0258085 October 28, 2021 2 / 17 PLOS ONEUrban villagers’ physical activity levels Urban villages are transitional neighborhoods typically found in urban areas or cities with rapid economic growth [16, 17]. Urban villages can be described as narrow roads, face-to-face buildings, a thin strip of sky, and inner streets packed with shops, grocery stores and service outlets [16]. Many of these urban villages are associated with unsuitable land use, poor housing construction, severe infrastructure deficiencies, intensified social disorder, and deteriorated urban environment [18]. In addition, urban villages often have poor sanitary condition, where pipelines and drainage systems are poorly constructed and water flows over the ground along with garage [17]. Many urban villagers are individuals with low SES status due to financial situ- ation. These urban villagers could include rural-to-urban migrants workers with limited skill- sets and educations or individuals who recently graduated from colleges and universities. They are attracted to urban villages due to the cheap housing accommodation. Overall, these urban villagers aggregate in urban village in large cities, such as Guangzhou and Beijing with limited infrastructure and poor living environments due to affordable living accommodations. Due to the unique living situations of urban villages and limited healthcare resources [19], urban villagers’ physical activity need to be better examined [20]. Knowing physical activity- related information of urban villagers could better design and develop interventions targeting the needs of urban villagers in the community. Regular engagement in physical activity is asso- ciated with better health-related outcomes [21], considering urban villagers is more at risk for poor health outcomes due to poor living situation [22, 23]. Previous studies had examined the physical activity levels of youths and adolescents living in urban village [24, 25]. Therefore, to better understand the physical activity levels of adult urban villagers, the purpose of this study is to compare the physical activity and sedentary behaviors engagements between urban villag- ers and non-urban villagers using the 2019 Luohu Shenzhen, China Community Diagnosis Questionnaire. Materials and methods Design and sample This study is secondary data analysis using data from the 2019 Luohu Shenzhen, China Com- munity Diagnosis Questionnaire. The questionnaire is part of a community health diagnosis program funded by the Center for Disease Control and Prevention of Shenzhen. Due to the unique status of Shenzhen as the Special Economic Zones (SEZ), it attracted various Chinese citizens with different background to settle in the areas. This allows assessments of health- related behaviors on various groups of Chinese citizens (e.g., household registration status, migrants status, employments status, income levels, etc.) within the same survey and living within the same area. The goal of the survey is to grasp the main health problems existing in the residents of Luohu District, determine the causes of community health problems, and determine the priority needs of the public health services and factors affecting residents’ health. The survey also served as an evaluation of Shenzhen residents satisfaction on the vari- ous healthcare institutes available to them, such as community health centers. The survey con- sisted of seven parts: 1) family demographics, 2) family medical history, 3) adults healthcare needs and access to healthcare, 4) health and quality of life of adults over the ages of 60 years old, 5) health, healthcare and reproductive healthcare needs of married women under the ages of 50 years old, 6) healthcare needs and health of children, and 7) examination of blood pres- sure, height, weight, hip length, and waist length. Data collection of the survey was approved by the IRB at Shenzhen Luohu Disease Prevention and Control Center. Analysis of the survey data was approved by the IRB at Oregon State University. Participants of the survey were selected by multiple stages of random selection. First, seven communities were randomly selected in Dongmen community, Luohu district, Shenzhen, PLOS ONE \| https://doi.org/10.1371/journal.pone.0258085 October 28, 2021 3 / 17 PLOS ONEUrban villagers’ physical activity levels Luohu as seen in Fig 1. Then 116 community grids were randomly selected from the seven selected communities in Dongmen community, Luohu district. Lastly, family household, serv- ing as survey unit, were randomly selected for interview based on the size of the community. All members of the household participated in the survey. Further, only individuals living in Shen- zhen for at least six months prior to the interview were included in the survey. The number of household participants in the survey is based on the size of the community. 200 households were randomly selected if the community sample size have more than two million individuals, 150 households for community sample size between one to two million, 100 households for community sample size between half of a million to one million, and 50 households for commu- nity less than half of a million. The random selection of communities was to identify individuals living in the various type of communities within the Shenzhen area. All data were collected between January and September of 2019. All data were collected through face-to-face interview. A total of 2122 participants were interviewed for the survey. However, only 2089 participants completed the survey with valid data. 1205 adults were included in the analysis. Across the sample, 54.52% of the participants were female and 45.48% of the participants were male. The average age of the participants were 38.8 years old. The average BMI were 22.88 Fig 1. Participants recruitment process. https://doi.org/10.1371/journal.pone.0258085.g001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0258085 October 28, 2021 4 / 17 PLOS ONEUrban villagers’ physical activity levels kg/cm2 with the hip-to-waist ratio of .89. A majority of the participants were employed (83.24%). Interestingly, 32.20% of the participants have no formal education and completed pri- mary education, which made up almost of one third of the sample. 28.54% of the participants completed middle school, 26.97% completed high school, and 12.28% completed professional school, college, and university. 76.02% of the participants were married or partnered and 23.98% were singled or not partnered. The sample consisted of more participants with non- Shenzhen hukou (62.41%). Across the sample, there were more participants without diagnosis of hypertension and diabetes. Only 6.56% and 1.91% of the participants reported having hyper- tension and diabetes, respectively. 76.43% of the participants reported they did not smoke. Measures The independent variable of the analysis is the living status of the participants. The variable is based on the location of the community grids participants resides in. Shenzhen used the com- munity grid system to identify local community [26]. Urban villages are typically located within one grid. Therefore, community grids serve as an indicator for urban villages. Partici- pants were classified as urban villagers if they live in an urban village and all other participants were classified as non-urban villagers. Physical activity and sedentary behavior-related variables from the survey component about health and quality of life of adults from 18 to 59 years old were selected for this analysis. A total of four variables were determined to be related to physical activity from the survey. The variables were engagement in recreational physical activity, frequency of recreational physical activity per week, hours spend in sedentary behaviors per day, and reasons for not engaging in physical activity. All variables were categorical variables. Engagement in recreational physical activity was based on the question of “Within the past six months, what types of recreational physical activity did you participated in?”. The respond options included: 1) did not participate in any activities, 2) machine equipment physical activity, 3) aerobic activity or aerobic dances, 4) swimming, 5) ambulatory activity (e.g., brisk walking, jogging, running, hiking), 6) ball- related sports (e.g. basketball, baseball, soccer, etc.), 7) sports or fitness competition, 8) martial arts, or 9) other. Participants were considered not to be engaged in physical activity when they responded with did not participate in any activities, else participants were classified as engaged in physical activity. Recreational physical activity is defined as physical activity that is done at leisure time. The variable of frequency of recreational physical activity were based on the ques- tion of “Within the past six months, how often do you exercise per week?” with the respond options of 1) 6 or more times per week, 2) 3 to 5 times per week, 3) 1 to 2 times per week, and 4) lesser than 1 time. The variable of hours spend in sedentary behaviors was based on the respond to the question of “In the past month, what is the average accumulated hours spend in sedentary activities (e.g., studying, working, watching TV, using computer, etc.)?”. The respond options included 1) lesser than 2 hours per day, 2) 2 to 4 hours per day, 3) 4 to 8 hours per day, 4) 8 to 12 hours per day, and 5) more than 12 hours per day. Reasons for not engaging in physical activity were only for participants who responded that they engaged in physical activity within the past six months. The survey item aims to identify how prevent them from engaging in physical activity throughout their routine. Participants were asked the reasons when they were unable to engage in physical activity weekly. Participants were able to select multiple options of 1) no recreational physical activity is needed due to labor intensive occupations, 2) no time to engage in physical activity, 3) there were no appropriate places and/ or environments for physical activity, 4) I feel healthy, I do not need physical activity, 5) do not want to engage in physical activity, 6) feeling ill, unable to participate in physical activity, and 7) other reasons. PLOS ONE \| https://doi.org/10.1371/journal.pone.0258085 October 28, 2021 5 / 17 PLOS ONEUrban villagers’ physical activity levels The covariates included in the analysis were gender, age, employment status, education, marital status, household registration, body mass index (BMI), diagnosis of hypertension, diagnosis of diabetes, and smoking status. Gender was a binary variable consisted of male and female. Age was a continuous variable between 18 to 59 years old. Employment status was a binary variable of being employed or unemployed. Education was a categorical variable including professional college and university, high school, middle school, and primary school or no formal education. Marital status was a binary variable of either being married or single. Household registration or Hukou were based on participants self-reporting their registration of either Shenzhen Hukou or non-Shenzhen Hukou. BMI is a continuous variable between 15.02 to 36.11 kg/cm2, which was calculated based on the participants’ height and weight by the survey. Hip-to-waist was calculated based on the hip and waist of the participants by the survey. Diagnosis of hypertension, diagnosis of diabetes, and smoking status were all binary variables with yes and no. These covariates were selected due to their relationship with physical activity engagement. Data analyses Descriptive analysis was conducted for the independent variables, dependent variables, and the covariates. To determine the physical activity engagement between urban villagers and non-urban villagers, unadjusted and multiple multivariate logistic regression were conducted for the dependent variable of engagement in recreational physical activity, frequency of recrea- tional physical activity per week, hours spend in sedentary behaviors per day, and reasons for not engaging in physical activity. All analyses were conducted using STATA version 16 (Stata- Corp LLC., College Station, TX, USA). The alpha levels were set at .05. The study protocol was approved by the Oregon State University (IRB: IRB-2020-0509). Results Across the sample, 29.05% (n = 350) of the participants were urban villagers and 70.95% (n = 855) were non-urban villagers. Pearson’s chi square test found significant different between education levels, marital status, and household registration status between the urban villagers and non-urban villagers. There were more non-urban villagers with completed mid- dle school, high school, and professional school, college, and university (χ2 = 99.46, p < 0.001). There were more non-urban villagers who were either married or partnered than urban villag- ers (χ2 = 3.77, p = 0.05). Regrading to household registration or hukou, there were higher pro- portion of non-urban villagers with Shenzhen hukou and higher proportion of urban villagers with non-Shenzhen hukou (χ2 = 180.60, p < 0.001). Also, there were significant different in age found between the two groups with non-urban villagers had a higher average age. Non-sig- nificant differences were found between urban villagers and non-urban villagers among other covariates (e.g., gender, employment, diagnosis of hypertension, diagnosis of diabetes, smok- ing status, BMI, and hip-to-waist ratio). Engagement in recreational physical activity From the total sample size (n = 1205), 63.73% (n = 768) of participants reported not engage in any recreational physical activity while 36.27% (n = 474) reported engaged in recreational physical activity. A significant difference in proportion of engaging in recreational physical activity were found between urban and non-urban villagers (χ2 = 60.79, p < 0.001) with higher proportion of urban villagers (53.14%) reported engaging in recreational physical activity than non-urban villagers (29.36%) as shown in Table 1. The unadjusted logistic regression found that urban villagers were 2.73 (95% CI [2.11, 3.53], p < 0.001) times the odds of non-urban PLOS ONE \| https://doi.org/10.1371/journal.pone.0258085 October 28, 2021 6 / 17 PLOS ONETable 1. Characteristics of urban villagers and non-urban villagers engaging in recreation physical activity. Urban Villagers Non-Urban Villagers Total n Mean/Proportion n Mean/Proportion n Mean/Proportion χ2/ t P Urban villagers’ physical activity levels Engagement in recreational physical activity, % Yes No Frequency of recreational physical activity per week, % > 6 times 3–5 times 1–2 times < 1 time Average hours spend in sedentary behaviors per day, % > 12 hours 9–12 hours 5–8 hours 2–4 hours < 2 hours Gender, % Female Male Age, years Employment status, % Yes No Education levels, % College & university High school Middle school Primary school & none Marital Status Married/partnered Single Household registration (hukou), % Shenzhen hukou Non-Shenzhen hukou Body Mass Index, kg/m2 Hip-to-waist ratio, % Hypertension, % Yes No Diabetes, % Yes No Smoking status, % Yes No 186 164 35 57 62 10 15 46 99 86 104 183 167 350 302 48 46 108 122 74 253 97 29 321 350 350 19 331 8 342 94 256 53.14 46.86 21.34 34.76 37.80 6.10 4.29 13.14 28.29 24.57 29.71 52.29 47.71 37.75 86.29 13.71 13.14 30.86 34.86 21.14 72.29 27.71 8.29 91.71 22.99 89 5.43 94.57 2.29 97.71 26.86 73.14 251 604 154 201 216 34 43 113 221 261 217 474 381 855 701 154 342 236 203 74 663 192 424 431 855 855 60 795 15 840 190 665 29.36 70.64 25.45 33.22 35.70 5.62 5.03 13.22 25.85 30.53 25.38 55.44 44.56 39.24 81.99 18.01 40.00 27.60 23.74 8.65 77.54 22.46 49.59 50.41 22.83 89 7.02 92.98 1.75 98.25 22.22 77.78 437 768 189 358 278 44 58 159 320 347 321 657 548 1205 1003 202 148 325 344 388 916 286 453 752 1205 1205 79 1126 23 1182 284 921 36.27 63.73 21.75 41.20 31.99 5.06 4.81 13.20 26.56 28.80 26.64 54.52 45.48 38.8 83.24 16.76 12.28 26.97 28.55 32.20 76.02 23.98 37.59 62.41 22.87 89 6.56 93.44 1.91 98.09 81.66 18.34 60.79 <0.001� 1.19 0.76 5.65 0.23 1.00 0.32 2.21 0.03� 3.29 0.07 99.46 <0.001� 3.77 0.05 180.60 0 < .001� -0.70 0.41 0.48 0.68 1.02 0.31 0.37 0.54 2.96 0.09 Note. n, sample size; χ2, chi-square statistic comparing between Urban Villagers and non-Urban Villagers for categorical variables; t, t-statistic comparing between Urban Villagers and non-Urban Villagers for continuous variables, p, p-value associated with the statistic comparison test; �, p < 0.05. https://doi.org/10.1371/journal.pone.0258085.t001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0258085 October 28, 2021 7 / 17 PLOS ONEUrban villagers’ physical activity levels villagers in engaging in recreational physical activity as shown in Table 2. The results of the multivariate logistic regression found that Urban Villagers were 1.90 (95% CI [1.40, 2.57], p < 0.001) times the odds of non-urban villagers in engaging in recreational physical activity after controlling for covariates. The analysis also found the education levels, household regis- tration, and BMI are significant factors contributing to the results of the odds ratios between urban villagers and non-urban villagers in engaging in recreational physical activity. Frequency of recreational physical activity per week 21.34% of urban villagers reported engaging in recreational physical activity more than six times per week, in compared to 25.45% of non-urban villagers reported the same frequency. 34.76% of urban villagers and 33.72% of non- urban villagers reported engaging in recreational physical activity 3 to 5 time per week, 37.80% of urban villagers and 35.70% of non- urban villagers reported engaging recreational physical activity 1 to 2 times per week. And 6.10% of urban vil- lagers and 5.62% of non-urban villagers reported engaged in lesser than recreational physical activity per week. No significant different was found between the two groups regarding the fre- quency of engaging recreational in physical activity per week (χ2 = 1.19, p = 0.76). The odds ratio of the unadjusted logistic regression for each level of the frequency of engaging in recrea- tional physical activity per week with references of less than 1 time per week were 0.98 (95% CI [0.46, 2.09], p = 0.95) for 1 to 2 time per week, 0.96 (95% CI [0.45, 2.07], p = 0.93) for 3 to 5 times per week, and 0.77 (95% CI [0.35, 1.71], p = 0.95) for more than six times per week for urban villagers in engaging in recreational physical activity compared to non-urban villagers. The results of the multivariate logistic regress found that urban villagers status is not a significant factor in estimating the odds ratio of frequency in engaging recreational physical activity per week with the reference groups of lesser than 1 time per week as shown in Tables 3 and 4. Average hours spend in sedentary behaviors per day 4.29% of urban villagers and 5.03% non-urban villagers reported spending more than 12 hours per day in sedentary, which made up the smallest proportion of the participants in their respective group. 13.14% of urban villagers and 13.22% of non-urban villagers reported Table 2. Odd ratios of urban villagers and non-urban villagers in engaging in recreational physical activity. Urban villagers Non-urban villagers Engagement in recreational physical activity Unadjusted Modelb Adjusted Modelc OR 2.73� 1 (ref.) 95% CI 2.11, 3.53 OR 1.90� 1 (ref.) 95% CI 1.40, 2.57 Abbreviations: OR, odds ratio; CI, confidence interval. aBoldfaced numerals indicate p-value <0.05. bOdd ratio from logistic regression model were computed for the outcome variable of engagement in recreational physical activity (yes/no) with the exposure variable of living situation (urban village/non-urban village). cOdd ratio from multivariable logistic regression model were computed for the outcome variable of engagement in recreational physical activity (yes/no) with the exposure variable of living situation (urban village/non-urban village) adjusted for gender (male/female), age (continuous), employment status (yes/no), education levels (college & university, high school, middle school, primary school & none), marital status (married & partnered/single), household registration (hukou) (Shenzhen/non-Shenzhen), BMI (continuous), hip-to-waist ratio (continuous), hypertension (yes/no), diabetes (yes/no), and smoking status (yes/no). d Detail adjusted model outcome were showed in S1 Table. https://doi.org/10.1371/journal.pone.0258085.t002 PLOS ONE \| https://doi.org/10.1371/journal.pone.0258085 October 28, 2021 8 / 17 PLOS ONEUrban villagers’ physical activity levels Table 3. Odd ratios of frequency of engaging in recreational physical activity per week between urban villagers and non-urban villagers. 1–2 times vs. < 1 time (ref.) OR 0.98 95% CI 0.46, 2.09 Urban villagers Non-urban villagers 1 (ref.) Unadjusted odd ratiosb 3–5 times vs. < 1 time (ref.) > 6 times vs. < 1 time (ref.) 1–2 times vs. < 1 time (ref.) Adjusted odd ratiosc 3–5 times vs. < 1 time (ref.) > 6 times vs. < 1 time (ref.) OR 0.96 1 (ref.) 95% CI 0.45, 2.07 95% CI 0.35, 1.71 OR 0.77 1 (ref.) 95% CI .44, 2.64 AOR 1.07 1 (ref.) AOR 0.98 1 (ref.) 95% CI .39, 2.43 AOR 0.83 1 (ref.) 95% CI .32, 2.15 Abbreviations: OR, odds ratio; CI, confidence interval. aBoldfaced numerals indicate p-value <0.05. bOdd ratio from logistic regression model were computed for the outcome variable of engagement in recreational physical activity (yes/no) with the exposure variable of living situation (urban village/non-urban village). cOdd ratio from multivariable logistic regression model were computed for the outcome variable of engagement in recreational physical activity (yes/no) with the exposure variable of living situation (urban village/non-urban village) adjusted for gender (male/female), age (continuous), employment status (yes/no), education levels (college & university, high school, middle school, primary school & none), marital status (married & partnered/single), household registration (hukou) (Shenzhen/non- Shenzhen), BMI (continuous), hip-to-waist ratio (continuous), hypertension (yes/no), diabetes (yes/no), and smoking status (yes/no). d Detail adjusted model outcome were showed in S2 Table. https://doi.org/10.1371/journal.pone.0258085.t003 spending 8 to 12 hours per day in sedentary behaviors. 28.29% of urban villagers and 25.85% of non-urban villagers reported spending 4 to 8 hours per day on sedentary behaviors, while 24.57% and 30.53% of urban villagers and non-urban villagers spend 2 to 4 hours per day on sedentary behaviors. For lowest amount of time spend in sedentary behaviors, 29.71% of urban villagers and 25.36% of non-urban villagers reported spending lesser than 2 hours on it. Non-significant different was found between the two groups regarded to the self-reported hours spend in sedentary hours (χ2 = 5.65, p = 0.23). From the unadjusted logistic regression with the reference group of spending less than 2 hours per day in sedentary behaviors and urban villagers, the odd ratios were 0.69 (95% CI [0.49, 0.96], p = 0.03) for 2 to 4 hours, 0.93 (95% CI [0.67, 1.30], p = .69) for 4 to 8 hours, and 0.85 (95% CI [0.56, 1.29], p = 0.44) for 8 to 12 hours. The results of the multivariate logistic regression found that urban villagers status is not a significant factor in estimating the hours spend in sedentary behaviors per day with the reference groups of lesser than 2 hours per day as shown in Table 4. However, across all levels Table 4. Odd ratios of average hours spend in sedentary behaviors per day between urban villagers and non-urban villagers. 2–4 hours vs. < 2 hours 5–8 hours vs. < 2 hours 9–12 hours vs. < 2 hours >12 hours vs. < 2 hours 2–4 hours vs. < 2 hours 5–8 hours vs. < 2 hours 9–12 hours vs. < 2 hours >12 hours vs. < 2 hours OR 0.69� 1 (ref.) Urban villagers Non-urban villagers 95% CI OR 95% CI .49, .96 0.93 .67, 1.30 OR 0.85 95% CI .56, 1.29 OR 0.73 95% CI .39, 1.37 OR 0.85 95% CI .58, 1.25 OR 1.18 95% CI .79, 1.75 OR 1.43 95% CI .86, 2.38 OR 0.06 95% CI 0, 2.07 1 (ref.) 1 (ref.) 1 (ref.) 1 (ref.) 1 (ref.) 1 (ref.) 1 (ref.) Abbreviations: OR, odds ratio; CI, confidence interval. aBoldfaced numerals indicate p-value <0.05. bOdd ratio from logistic regression model were computed for the outcome variable of engagement in recreational physical activity (yes/no) with the exposure variable of living situation (urban village/non-urban village). cOdd ratio from multivariable logistic regression model were computed for the outcome variable of engagement in recreational physical activity (yes/no) with the exposure variable of living situation (urban village/non-urban village) adjusted for gender (male/female), age (continuous), employment status (yes/no), education levels (college & university, high school, middle school, primary school & none), marital status (married & partnered/single), household registration (hukou) (Shenzhen/non- Shenzhen), BMI (continuous), hip-to-waist ratio (continuous), hypertension (yes/no), diabetes (yes/no), and smoking status (yes/no). d Detail adjusted model outcome were showed in S3 Table. https://doi.org/10.1371/journal.pone.0258085.t004 PLOS ONE \| https://doi.org/10.1371/journal.pone.0258085 October 28, 2021 9 / 17 PLOS ONEUrban villagers’ physical activity levels Fig 2. Reasons for not engaging in physical activity among urban villagers and non-urban villagers who engage in physical activity. https://doi.org/10.1371/journal.pone.0258085.g002 of hours spend in sedentary behaviors, completing professional school, college, and university had a higher odd of spending more time in sedentary behaviors. Reasons for not engagement in recreational physical activity Among participants who engage in recreational physical activity, many indicated that no time to exercise as the main reason why they did not engage in physical activity (n = 273) as shown in Fig 2. The second top reasons participants selected as the reasons for not engaging in recrea- tion physical activity was no need to exercise due to labor intensive occupation (n = 91), follow by unwilling to exercise and no place to exercise (n = 69). Some participants also respond that they did not engage in recreation physical activity due to feeling healthy (n = 14) and no need to exercise and unable to engage in recreational physical activity due to illness (n = 5). When stratified by urban village status, lack of time is the most cited reason for not engag- ing in physical activity for both urban villagers (n = 107) and non-urban villagers (n = 166). There were more urban villagers (n = 58) compared to non-urban villagers expressed that they do not need to engage in physical activity due to occupations being labor intensive. There were more non-urban villagers (n = 51) expressed that they were unwilling to engage in physical activity than urban villagers (n = 18). Also, higher number of non-urban villagers (n = 28) reported not having appropriate places and/or environments for physical activity compared to urban villagers (n = 19). Discussion The purpose of this secondary data analysis is to determine and compare the prevalence of physical activity engagement among the special population of Chinese urban villagers and non-urban villagers. Both the unadjusted and adjusted logistic regression identified that urban PLOS ONE \| https://doi.org/10.1371/journal.pone.0258085 October 28, 2021 10 / 17 PLOS ONEUrban villagers’ physical activity levels villagers are more likely to engage in recreational physical activity than their counterpart of non-urban villagers. No significant relationship was found between the frequency of engage- ment in recreational physical activity and urban village status. The multinomial logistic regres- sion also found no significant relationship between hours spend in sedentary behaviors and urban village status. Descriptive analysis shown that both urban villagers and non-urban vil- lagers shared reasons for not engaging in recreational physical activity, such as lack of time to exercise. However, more urban villagers indicated that their labor-intensive occupations are sufficient enough for physical activity. While more non- urban villagers indicated that they are more unwilling to exercise and there are no appropriate places and/or environments for recre- ational physical activity. While both urban villagers and non-urban villagers live in urban and well-developed area, the levels of engagement in recreational physical activity were different between the two groups. The results demonstrated that even within the same city, engagement in recreational physical activity could be different by social characteristics. Urban villagers, like non-urban vil- lagers, have access to different public physical activity facilities within the urban area. Physical activity facilities such as parks, sidewalks, and outside of the urban villages are facilities that urban villagers have access to. This is supported by the results that less urban villagers indi- cated that there are a lack of appropriate places and/or environments for recreational physical activity in compared to non-urban villagers. The ability of utilizing free public physical activity facilities increase the opportunities for urban villagers to engage in recreational physical activ- ity. Having these opportunities allow for urban villagers to obtain a healthier lifestyle of regu- larly engagement in recreational of physical activity. While it has been found that lower- income neighborhoods, such as urban villages, have less commercial physical activity-related facilities [27]. The results of this study was different from the study conducted by Ortiz-Her- na´ndez and Ramos-Iba´ñez [28], where they found that Mexican adults living in urban locali- ties and cities with low socio-economic status had a lower probability of engaging in physical activity. However, it is difficult to compare results across different countries as culture and environments are widely different between the countries. Therefore, it is not appropriate to compare the results between the studies. Studies conducted in the US [29] and in the Europe [30] found similar results of adults living in rural areas less likely to engage in physical activity and other psychosocial factors could influence physical activity behaviors. These highlight that there is a need of global effort to promote physical activity in various countries. Further, due to the unique situation of urban village in China, where the housing is surrounded by well-devel- oped buildings and infrastructures, urban villagers have easy access to these different infrastructures. Income status could potentially be one of the factors explaining the different proportion of urban villagers and non-urban villagers in engagement of recreational physical activity. Indi- viduals living in urban village are more likely to be individuals with lower economic status. Many of these individuals chose to reside in urban village due to the cheap accommodation [31, 32]. Further, many of these individuals might held lower wages and labor-intensive occu- pations. As evidence by the results of reasons for not engaging recreational physical activity, more urban villagers reported that their occupations are labor intensive enough that either they are too tired to engage in additional physical activity or they felt that they do not need to engage in additional physical activity. This aligned with previous study finding that more rural adults in China engage in work-related physical activity than urban adults [9]. In comparison to urban villagers, fewer participants in the non-urban village group reporting their occupa- tions are too physically demanding that they felt that engagement in recreational physical activity is not necessary. Non-urban villagers are more likely to held office-related occupations, therefore, it limits their ability to engage in physical activity. Past studies had demonstrated PLOS ONE \| https://doi.org/10.1371/journal.pone.0258085 October 28, 2021 11 / 17 PLOS ONEUrban villagers’ physical activity levels that officer workers are more likely to engage in less physical activity and more sedentary behaviors [33]. Further, non-urban villagers might be more likely to have better technology access than urban villagers. Technology such as television and media are found to be associated with lower physical activity levels and high sedentary behavior [34, 35]. This might relate to the higher number of non-Urban Villagers reporting unwilling to engage in recreational physi- cal activity. It is also surprising to find that there are higher numbers of non-urban villagers indicating that the reason for not engaging in recreational physical activity was lack of appro- priate places and/or environments. Being consistent with previous research by Munter et al. [9] where Chinese urban adults are less likely to engage in physical activity than Chinese adults with lower economic status living in rural area. Based on the results of this study, more tailed intervention is needed for Chinese adults not living in urban villages. Even though urban villagers are more likely to be in poor health due to poor housing situation [36, 37], they are more likely to engage in recreational physical activity than non-urban villagers. While the two groups have large number of participants reporting lack of time to engage in recreational physical activity, different interventions should be devel- oped for the two groups. Due to differences in living situations, economic status, and occupa- tions, different reactions and responses to interventions might be different between urban villagers and non-urban villagers. When designing physical activity interventions, there is a need to consider demographic characteristics and socioeconomic factors. For urban villagers, tailed interventions are needed to target group of individuals that believe that physical activity performed during their job are sufficient enough for health. Multiple studies had demon- strated that leisure time physical activity and recreational physical activity are associated with better health quality of life [38–40]. Occupational-related physical activity is not considered to be recreation or leisure physical activity. Therefore, specific interventions are needed targeting urban villagers. Developing interventions in targeting these reasons and solving these barriers for non-urban villagers will be important step for increase the proportion of non-urban villag- ers in engaging in recreational physical activity. For example, Gu et al. [41] found change in physical activity among office workers after the implementation of a worksite intervention programs at 17 worksites in the urban city of Shanghai with pedometers for 100 days. The goal of using and developing physical activity interventions are to promote recreational physical activity levels among both urban villagers and non-urban villagers. Further research and studies are warrants in determine the physical activity levels among urban villagers and non-urban villagers. Study had done in the past to examine the physical activity levels of Chines adults [9, 13, 42], but there is a lack of empirical evidence on the physical activity levels of urban villagers. Using additional techniques, such as accelerometers, to collected more detailed data could increase our understanding of physi- cal activity levels of urban villagers. More detailed data such as minutes spend in each intensity of physical activity or number of steps taken each day can better represent the physical activity levels of urban villagers. It has been proposed that an intersectionality approach should be taken when measuring and discussing physical activity levels [43–45]. The interacting factors could provide more detail information on the physical activity of special population such as urban villagers. Often, urban villagers might be considered as individuals living in urban area. However, due to the unique situation of urban village, they are considered a special population living in the urban area. This study demonstrated that there is a need to examine the physical activity levels of special populations living in China. As shown in this study, the proportion of urban villagers and non-urban villagers engaging in recreational physical activity is different, so more research is needed. This data could further facilitate the development of physical activity intervention targeting urban villagers and non-urban villagers. Future researches should also focus on urban PLOS ONE \| https://doi.org/10.1371/journal.pone.0258085 October 28, 2021 12 / 17 PLOS ONEUrban villagers’ physical activity levels villagers and non-urban villagers in meeting physical activity guidelines by the World Health Organization [21]. The current physical activity guidelines for adults over the ages of 18 years old is at least 150 minutes of moderate-to-vigorous physical activity or 75 min- utes of vigorous physical activity per week. Examining the prevalence of urban villagers and non-urban villagers in meeting these physical activity guidelines could increase our understanding of the physical activity behaviors and dose-response relationship between physical activity and health among these populations. It is important to note that there is a lack of national and regional physical activity guidelines in China [46]. Developing these physical activity guidelines could be beneficial for Chinese citizen as there is a guideline for them to follow. One interesting find of the analysis was that education might have an influence on physical activity-related outcomes among urban villagers and non-urban villagers. Based on the adjusted logistic regression model, in compare to no formal education and only completing primary education, other education levels (i.e., middle school, high school, professional college and university) are less likely to engage in physical activity. The analysis also found that higher education is associated with longer time spent in sedentary behaviors. The results align with previous study examining the decline of physical activity levels among Chinese adults [14]. The study found that the greater availability of higher educational institutions is strongly asso- ciated with the declines of physical activities based on data from the 1991–2006 China Health and Nutrition Surveys [47]. Individuals with higher education are more likely to have office- related positions. Officer workers are more likely to spend more time in sedentary behaviors [48]. In addition, it was found that Chinese adults who completed high school education are less likely to engage in occupational-related physical activity [9]. These results suggested that physical activity interventions are needed for individuals with higher education. To ensure that physical activity become a lifelong habit among Chinese adults, there is need to develop physical activity intervention targeting adults at various educational levels. For example, requiring physical education or physical activity classes for students in middle schools, high schools, and colleges and universities. Requirement of physical education in early childhood is positively associated with physical activity levels in adulthood [49]. Individuals who had taken a physical activity course while in colleges and universities report higher physical activity levels in adulthood compared to those that did not take a physical activity course [50]. Continuation promotion of physical activity through various different educational institutions could poten- tially increase physical activity levels of adults. Limitation To the authors’ knowledge, this is the first of the few studies that examined the physical activity levels of urban villagers in China. The strength of this study is including the special population of urban villagers. However, this study is not without its limitation. The data used in the analy- sis are based on self-reported data. There could be potential recall and social bias. These biases could lead to misclassification of data and results [51]. In addition to biases, there could be low generalizability of the results. Due to the data only included participants living in the Luohu, Shenzhen, China, the results might be only generalized to this particular populations living in Shenzhen. However, it is assumed that urban villagers across China shared the similar charac- teristics of lower economic status, migrant workers, labor intensive worker, poor living situa- tion, and lack of infrastructures. It is important to note that the survey did not utilized the International Physical Activity Questionary (IPAQ) in the surveillance system. This could lead to misunderstanding of questions by the participants. To limit misunderstanding, all data col- lected were in Chinese via face-to-face interview by trained personals. PLOS ONE \| https://doi.org/10.1371/journal.pone.0258085 October 28, 2021 13 / 17 PLOS ONEUrban villagers’ physical activity levels Conclusion Overall, the proportion of urban villagers and non-urban villagers in engaging in recreational physical activity are different with urban villagers more likely to engage in recreational physical activity. While participants from both groups expressed that lack of time as a barrier in engag- ing in recreational physical activity, non-urban villagers are more likely to reported that they are unwilling to participate in recreational physical activity and lack appropriate place and/ environment for recreational physical activity. Urban villagers are more likely to reported that they do not engage in recreational physical activity due to work-related physical activity. Physi- cal activity interventions are needed to target these various barriers in preventing urban villag- ers and non-urban villagers in participating from recreational physical activity. Further research is warranted in order to better understanding the physical activity levels of the special population of urban villagers living in China. Supporting information S1 Table. Odd ratios of urban villagers and non-urban villagers in engaging in recreational physical activity: Adjusted model outcomes. (DOCX) S2 Table. Odd ratios of frequency of engaging in recreational physical activity per week between urban villagers and non-urban villagers: Adjusted model outcomes. (DOCX) S3 Table. Odd ratios of average hours spend in sedentary behaviors per day between urban villagers and non-urban villagers. (DOCX) S1 File. Questionnaire Chinese. (DOCX) S2 File. Questionnaire English. (DOCX) Author Contributions Conceptualization: Lu Shi, Willie Leung, Qingming Zheng, Jie Wu. 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10.1371_journal.ppat.1011871.pdf	Data Availability Statement: Raw and processed RNA-sequencing data can be accessed from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database under accession number GSE212205.	Raw and processed RNA-sequencing data can be accessed from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database under accession number GSE212205.	RESEARCH ARTICLE Exposure to Mycobacterium remodels alveolar macrophages and the early innate response to Mycobacterium tuberculosis infection Dat Mai1, Ana Jahn1, Tara Murray1, Michael Morikubo1, Pamelia N. Lim2,3, Maritza M. Cervantes2, Linh K. Pham2,4, Johannes Nemeth1¤, Kevin Urdahl1, Alan H. Diercks1, Alan Aderem1, Alissa C. RothchildID 2* 1 Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, United States of America, 2 Department of Veterinary and Animal Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America, 3 Molecular and Cellular Biology Graduate Program, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America, 4 Animal Biotechnology and Biomedical Sciences Graduate Program, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America ¤ Current address: University Hospital Zurich, University of Zurich, Division of Infectious Diseases and Hospital Epidemiology, Zu¨rich, Switzerland * arothchild@umass.edu Abstract Alveolar macrophages (AMs) play a critical role during Mycobacterium tuberculosis (Mtb) infec- tion as the first cells in the lung to encounter bacteria. We previously showed that AMs initially respond to Mtb in vivo by mounting a cell-protective, rather than pro-inflammatory response. However, the plasticity of the initial AM response was unknown. Here, we characterize how previous exposure to Mycobacterium, either through subcutaneous vaccination with Mycobac- terium bovis (scBCG) or through a contained Mtb infection (coMtb) that mimics aspects of con- comitant immunity, impacts the initial response by AMs. We find that both scBCG and coMtb accelerate early innate cell activation and recruitment and generate a stronger pro-inflamma- tory response to Mtb in vivo by AMs. Within the lung environment, AMs from scBCG vaccinated mice mount a robust interferon-associated response, while AMs from coMtb mice produce a broader inflammatory response that is not dominated by Interferon Stimulated Genes. Using scRNAseq, we identify changes to the frequency and phenotype of airway-resident macro- phages following Mycobacterium exposure, with enrichment for both interferon-associated and pro-inflammatory populations of AMs. In contrast, minimal changes were found for airway-resi- dent T cells and dendritic cells after exposures. Ex vivo stimulation of AMs with Pam3Cys, LPS and Mtb reveal that scBCG and coMtb exposures generate stronger interferon-associated responses to LPS and Mtb that are cell-intrinsic changes. However, AM profiles that were unique to each exposure modality following Mtb infection in vivo are dependent on the lung environment and do not emerge following ex vivo stimulation. Overall, our studies reveal signifi- cant and durable remodeling of AMs following exposure to Mycobacterium, with evidence for both AM-intrinsic changes and contributions from the altered lung microenvironments. Com- parisons between the scBCG and coMtb models highlight the plasticity of AMs in the airway and opportunities to target their function through vaccination or host-directed therapies. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Mai D, Jahn A, Murray T, Morikubo M, Lim PN, Cervantes MM, et al. (2024) Exposure to Mycobacterium remodels alveolar macrophages and the early innate response to Mycobacterium tuberculosis infection. PLoS Pathog 20(1): e1011871. https://doi.org/10.1371/journal. ppat.1011871 Editor: Padmini Salgame, New Jersey Medical School, UNITED STATES Received: August 3, 2023 Accepted: November 27, 2023 Published: January 18, 2024 Copyright: © 2024 Mai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Raw and processed RNA-sequencing data can be accessed from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database under accession number GSE212205. Funding: This work was supported by National Institute of Allergy and Infectious Disease of the National Institute of Health under Awards U19AI135976 (A.A.), R01AI032972 (A.A.), 75N93019C00070 (K.U., A.C.R., A.A.), and PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 1 / 28 PLOS PATHOGENSR21AI163809 (A.C.R.). J.N. was supported by the Swiss National Foundation under grant 310030_200407. P.L. was supported by National Research Service Award T32 GM135096 from the National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: J.N. received honoraria for presentations from Oxford Immunotec, Gilead and ViiV. Alveolar macrophage remodeling by Mycobacterium Author summary Tuberculosis, a disease caused by the bacteria Mycobacterium tuberculosis (Mtb), claims around 1.6 million lives each year, making it one of the leading causes of death worldwide by an infectious agent. Based on principles of conventional immunological memory, prior exposure to either Mtb or M. bovis BCG leads to antigen-specific long-lasting changes to the adaptive immune response that can be effective at protecting against subsequent chal- lenge. However, how these exposures may also impact the innate immune response is less understood. Alveolar macrophages are tissue-resident myeloid cells that play an impor- tant role during Mtb infection as innate immune sentinels in the lung and the first host cells to respond to infection. Here, we examined how prior Mycobacterium exposure, either through BCG vaccination or a model of contained Mtb infection, impacts the early innate response by alveolar macrophages. We find that prior exposure remodels the alveo- lar macrophage response to Mtb through both cell-intrinsic changes and signals that depend on the altered lung environment. These findings suggest that the early innate immune response could be targeted through vaccination or host-directed therapy and could complement existing strategies to enhance the host response to Mtb. Introduction Mycobacterium tuberculosis (Mtb), the causative agent of Tuberculosis (TB), claimed more than 1.6 million lives in 2021. For the first time since 2005, the number of TB deaths worldwide is increasing [1,2]. These trends highlight the urgent need for new vaccine and therapeutic strategies. Traditionally, vaccine design has focused on generating a rapid, robust, and effective adaptive immune response. However, recent studies suggest that the innate immune system can undergo long-term changes in the form of trained immunity [3], which affect the outcome of infection and could function as important components of an effective TB vaccine [4,5]. Ini- tial trained immunity studies focused on central trained immunity, long-term changes to hematopoietic stem cells that lead to functional changes in short-lived innate cell compart- ments (i.e., monocytes, NK cells, dendritic cells) [3]. More recent studies have examined innate training in tissue-resident macrophages and demonstrated that these cells are also affected by prior exposures. Tissue-resident macrophages can respond to remote injury and inflammation [6], undergo long-term changes [3], and display altered responses to bacteria after pulmonary viral infection [7–9]. Lung resident alveolar macrophages (AMs) are the first cells to become infected with inhaled Mtb and engage a cell-protective response, mediated by the transcription factor Nrf2, that impedes their ability to effectively control bacterial growth [10,11]. In this study, we exam- ined how prior mycobacterial exposure reprograms AMs and alters the overall innate response in the lung to aerosol challenge with Mtb. To evaluate the range of AM plasticity, we chose to compare the effects of subcutaneous BCG vaccination (scBCG) with those arising from a con- tained Mtb-infection (coMtb) model. BCG, a live-attenuated TB vaccine derived from M. bovis and typically given during infancy, provides protection against disseminated pediatric disease but has lower efficiency against adult pulmonary disease [12–14]. In addition to enhancement of Mtb-specific adaptive responses, based on shared antigens, BCG vaccination also leads to changes in hematopoiesis and epigenetic reprogramming of myeloid cells in the bone marrow [15], early monocyte recruitment and Mtb dissemination [16], and innate acti- vation of dendritic cells critical for T cell priming [17]. Intranasal BCG vaccination protects PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 2 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium against Streptococcus pneumoniae and induces long term activation of AMs [18]. A recent study has shown that one mechanism by which BCG vaccination can elicit innate training effects on AMs, separate from alterations to the monocyte population, is through changes to the gut microbiome and microbial metabolites [19]. BCG vaccination is also associated with trained immunity effects in humans [20–22], including well-described reductions in all-cause neonatal mortality and protection against bladder cancer [3,23]. The coMtb model is generated by intradermal inoculation with virulent Mtb into the ears of mice and leads to a contained but persistent lymph node Mtb infection [24,25]. The model replicates observations in both humans and non-human primates (NHPs) that prior exposure to Mtb infection provides protection against subsequent exposure, through a form of concomi- tant immunity [26,27]. In a previous study, we found that coMtb leads to protection against challenge with aerosol Mtb infection and protects mice against heterologous challenges, including infection with Listeria monocytogenes and expansion of B16 melanoma cells, results which suggest there is substantial remodeling of innate immune responses [25]. We found that AMs from coMtb mice mount a more inflammatory response to Mtb infection compared to AMs from control mice, and the enhancement in AM activation after infection, as measured by MHC II expression, was dependent on IFNγR signaling [25]. Here, we show that while both coMtb and scBCG protect against low dose Mtb aerosol challenge, they remodel the in vivo innate response in different ways. In AMs, scBCG elicits a very strong interferon response in AMs, while coMtb promotes a broader pro-inflammatory response that is less dominated by Interferon Stimulated Genes. Prior exposure to Mycobacte- rium also remodels the frequency and phenotype of AM subsets in the lung prior to aerosol challenge and leads to significant changes in the early dynamics of the overall innate response. While changes in the AM responses that are unique to each exposure (scBCG, coMtb) depend on the lung environment, stronger interferon-associated responses following both LPS and Mtb stimulation ex vivo reveal cell-intrinsic changes. Results Prior exposure to Mycobacterium accelerates activation and innate cell recruitment associated with Mtb control We first determined the earliest stage of infection when the immune response was altered by prior exposure to Mycobacterium. Mice were vaccinated with scBCG or treated with coMtb, rested for 8 weeks, and then challenged with low-dose H37Rv aerosol infection. We measured both the cellularity and activation of innate immune cells in the lung at 10, 12 and 14 days fol- lowing infection, the earliest timepoints when innate cells are known to be recruited [10,11,28]. We observed a significant increase in MHC II Median Fluorescence Intensity (MFI) as early as day 10 for AMs from coMtb mice and day 12 for AMs from scBCG mice compared to controls (Figs 1A and S1). There were no significant differences in MHC II expression prior to challenge on day 0 (Fig 1A). There were also significant increases in the numbers of monocyte-derived macrophages (MDM), neutrophils (PMN), dendritic cells, and Ly6C+ CD11b+ monocytes by day 10 in coMtb mice compared to controls, with further increases by days 12 and 14 (Figs 1B and S1). scBCG elicited similar increases in these popula- tions starting at day 10, but the increases were not as robust or rapid as those observed in coMtb. Significant differences between scBCG and coMtb groups were found at days 10, 12, and 14 in MDM, day 14 in PMN, days 12 and 14 in dendritic cells, and day 14 in Ly6C+ CD11b+ monocytes (Fig 1B). While there were not significant differences in AM cell number between the three conditions, there was a modest drop in viability for both AMs from scBCG and coMtb mice by day 14 (Fig 1C). PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 3 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Fig 1. Prior exposure to Mycobacterium leads to faster activation and innate cell recruitment following aerosol Mtb challenge. Control, scBCG, and coMtb mice, 8 weeks following exposure, challenged with standard low-dose H37Rv. Lungs collected on day 10, 12, and 14 post-infection. A) AM MHC II MFI. B) Total numbers of MDMs, PMN, DC, and Ly6C+CD11b+ monocytes. C) AM viability (% Zombie Violet-). D) Total numbers of CD44+ CD4+ T PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 4 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium cells, ESAT6-tetramer+ CD4+ T cells, CD44+ CD8+ T cells, and TB10.4-tetramer+ CD8+ T cells. Mean +/- SEM, 5 mice per group, representative of 3 independent experiments. One-way ANOVA with Tukey post-test. * p< 0.05, p< 0.01, p < 0.001. B, C) , , and * scBCG or coMtb vs control; +, + + scBCG vs coMtb. https://doi.org/10.1371/journal.ppat.1011871.g001 In addition to early changes in innate cell activation and recruitment, we observed early recruitment of activated CD44+ CD4+ and CD8+ T cells in the lungs of both coMtb and scBCG mice starting at day 10 as well as TB antigen-specific T cells, ESAT6-tetramer+ CD4+ T cells and TB10.4-tetramer+ CD8+ T cells in coMtb mice starting at day 10 compared to con- trols and scBCG mice (Figs 1D and S1). The differences in the recruitment of ESAT6-tetra- mer+ CD4+ T cells between scBCG and coMtb were expected, as the ESAT6 antigen is expressed by H37Rv but not by BCG. We also evaluated whether these cell recruitment differences correlated with changes in bacterial burden. To compile CFU results from three independent experiments, each with slightly different bacterial growth (S2A Fig), we calculated a ΔCFU value that compared the bacterial burden of each sample to the average for the respective control based on timepoint, organ, and experiment. We found that both modalities generated a significant reduction in bacterial burden compared to controls in the lung, spleen, and lung-draining lymph node (LN) at day 14 and at day 28, as previously reported [16,25,29] (S2A–S2D Fig). At day 10, we observed no difference in lung bacterial burden in scBCG or coMtb mice compared to controls and a small increase in coMtb mice over scBCG. The majority of control mice had undetect- able bacteria in spleen and LN at this time. There was a significant reduction in bacterial bur- den in the lung by day 12 in coMtb but not scBCG mice and a significant reduction in CFU in the LN in both models compared to controls (S2B Fig). Our results demonstrate that prior Mycobacterium exposure leads to accelerated innate cell activation and recruitment, alongside an increase in activated T cells, within the first two weeks of infection, with coMtb generating a faster and more robust response compared to scBCG. These early immune changes are asso- ciated with reductions in bacterial load in the lung. Differences in bacterial burden in the LN and spleen suggest delays in bacterial dissemination, which first appear in the LN at day 12 and then in the spleen at day 14 (S2A Fig). Mycobacterium exposure alters the in vivo alveolar macrophage response to Mtb infection To examine the earliest response to Mtb, we measured the gene expression profiles of Mtb- infected AMs isolated by bronchoalveolar lavage and cell sorting, as previously described [10], 24 hours following aerosol challenge with high dose mEmerald-H37Rv (depositions: 4667, 4800) in scBCG-vaccinated mice and compared these measurements to previously generated profiles of AMs from control (unexposed) mice [10] and coMtb mice [25] (S1 Table). As pre- viously observed for the high dose infection, an average of 1.79% (range: 0.91–3.18%) of total isolated AMs were Mtb infected 24 hours after infection. Changes induced by Mtb infection were measured by comparing gene expression between Mtb-infected AMs and respective naïve AMs for each of the three groups (control, scBCG, coMtb). Principal Component Analy- sis on Mtb infection-induced changes showed that each of the three conditions led to distinct expression changes (Fig 2A) and the majority of up-regulated Differentially Expressed Genes (DEG) (fold change > 2, FDR < 0.05) were unique to each condition (control: 151 unique/257 total DEG, scBCG: 222/289, coMtb: 156/229) (Fig 2B). The divergence in the responses of Mtb-infected AMs from each of the 3 conditions was also reflected in the diversity in the Top 20 Canonical Pathways identified by Ingenuity Pathway Analysis (S3 Fig). PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 5 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Fig 2. Mycobacterium exposure alters the alveolar macrophage transcriptional response to Mtb infection in vivo. Bulk RNA-seq profiles of Mtb-infected AMs 24 hours following high-dose mEmerald-H37Rv infection. Gene expression changes are compared to respective naïve samples: Mtb-inf control vs naïve control; Mtb-inf scBCG vs naïve scBCG; Mtb-inf coMtb vs naïve coMtb (controls- reported in Rothchild et al, 2019 [10]; coMtb- reported in Nemeth et al, 2020 [25]). A) Principal Component Analysis using DEG (\|fold change\| > 2, FDR< 0.05) in Mtb-infected AMs compared to respective naïve AMs (control, scBCG, or coMtb). B) Venn Diagram and Intersection plot of overlap in up-regulated DEG between the 3 conditions. C) Gene Set Enrichment Analysis of 50 Hallmark Pathways. Pathways shown have \|NES\| > 1.5 and FDR< 0.05 for at least one of the conditions. * FDR< 0.05, FDR< 0.01, FDR< 0.001. D) Heatmap of 131 original in vivo DEG at 24 hours in Mtb-infected AM (left), Interferon Stimulated Genes, derived from macrophage response to IFNα (fold change >2, p- value < 0.01) Mostafavi et al, 2016 [30] (center-left), IL6 JAK STAT3 hallmark pathway (center-right) and selected coMtb signature genes (right, FDR< 0.05, PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 6 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium FC> 2). E) Scatterplots depicting fold change (log2) for Mtb-infected AMs over naïve AMs for scBCG versus coMtb. Highlighted pathways: Nrf2-associated genes out of 131 original in vivo DEG (56 genes, purple), shared leading edge genes for scBCG Interferon Alpha Response and Interferon Gamma Response pathways (61 genes, orange), and leading edge genes for coMtb IL6 JAK STAT3 pathway (23 genes, green). Compiled from 4 independent experiments per condition for control, 2 independent experiments per condition for scBCG and coMtb. https://doi.org/10.1371/journal.ppat.1011871.g002 To identify trends between groups, we performed Gene Set Enrichment Analysis using a set of 50 Hallmark Pathways. As we’ve shown previously, Mtb-infected AMs from control mice at 24 hours had strong enrichment for “Xenobiotic Metabolism” and “Reactive Oxygen Species” pathways, indicative of the Nrf2-associated cell-protective response (Fig 2C). While these two pathways were not among the most enriched pathways in the exposed groups, Mtb-infected AMs from all groups upregulated genes associated with the 131 in vivo DEG that make up the cell-protective Nrf2-driven response at 24 hours [10] (Fig 2D). Expression profiles for Mtb- infected AMs from scBCG mice showed the strongest enrichment for “Interferon Alpha Response” and “Interferon Gamma Response” pathways, which contain many shared genes (Fig 2C). The strength of the interferon response was further highlighted by examining gene expression changes in a set of Interferon Stimulated Genes (ISGs) identified from macro- phages responding to IFNα (fold change > 2, p-value < 0.01) [30] (Fig 2D). Expression pro- files for Mtb-infected AMs from coMtb mice showed a weaker enrichment for interferon response pathways with fewer up-regulated ISGs compared to scBCG, and instead showed enrichment across a number of inflammatory pathways including “IL6 JAK STAT3 signaling” in comparison to the other groups (Fig 2C and 2D). A direct comparison between the gene expression patterns for AMs from scBCG versus coMtb mice could be visualized more readily by scatterplots highlighting either Nrf2-associated, Interferon Alpha and Gamma Response, or IL-6 JAK STAT3 pathway genes (Fig 2E). In summary, Mycobacterium exposures alter the initial in vivo response of AMs to Mtb infection 24 hours after challenge and remodel the AM response in distinct ways. AMs from scBCG vaccinated animals mount a strong interferon-associated response, while AMs from coMtb mice express a more diverse inflammatory profile consisting of both interferon-associ- ated genes as well as other pro-inflammatory genes, including those within the IL-6 JAK STAT3 pathway. Mycobacterium exposure modifies the baseline phenotype of alveolar macrophages in the airway Although scBCG and coMtb exposures alter the AM responses to Mtb infection in vivo, tran- scriptional effects are not widely evident prior to infection as measured by bulk RNA-sequenc- ing of naïve AMs from control, scBCG, or coMtb mice, including expression of innate receptors and adaptors (S4 Fig). However, we posited that remodeling effects were likely not homogenous across the entire AM population and that small heterogenous changes to baseline profiles might be detectable using a single cell approach. We therefore analyzed pooled BAL samples taken from 10 age- and sex-matched mice from each of the three conditions (control, scBCG, coMtb) eight weeks following Mycobacterium exposure by single cell RNA-sequencing (scRNAseq). Gross cellularity was unaffected by mycobacterial exposure as measured by flow cytometry analysis of common lineage markers with AMs being the dominant hematopoietic cell type (57.4–85.8% of CD45+ live cells), followed by lymphocytes (5.26–22.7% of CD45+ live cells) with smaller contributions from other innate cell populations (S5 Fig). Six samples, with an average of 2,709 cells per sample (range: 2,117–4,232), were analyzed together for a total of 17,788 genes detected. The most prominent expression cluster mapped to an AM profile, with smaller clusters mapping to T and B lymphocytes, dendritic cells, and PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 7 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium neutrophils (Fig 3A). All cells that mapped to a macrophage profile were extracted and reclus- tered into 11 macrophage subclusters (Fig 3B and 3C). All but two of the macrophage subclus- ters (clusters 6 and 8) expressed AM lineage markers (Siglecf, Mertk, Fcgr1 (CD64), Lyz2 (LysM), and Itgax (CD11c) and had low expression of Itgam (CD11b) (Fig 3D). Cluster 6 showed high Itgam and Lyz2 expression and lower Siglecf expression, likely representing a small monocyte-derived macrophage population in the airway, while cluster 8 displayed high Lyz2 expression, low expression for other AM markers, and expression of Sftpa1 and Wfdc2 (S2 Table), genes most commonly expressed by pulmonary epithelial cells, suggesting that this cluster represents a small population of epithelial cells, To interpret the various expression subclusters, we identified the genes that most distin- guished each cluster from the others (S6 Fig and S2 Table). As has been reported by other groups [31,32], a small proportion of the AMs in two clusters (Clusters 4, 9) had high expres- sion of cell cycle genes (i.e., Top2a, Mki67), indicative of cell proliferation (Fig 3E and S2 Table). Cluster 0 was the most abundant macrophage cluster with high expression of lipid metabolism genes (i.e., Abcg1, Fabp1) (Fig 3F and S2 Table). Cluster 2 was significantly increased in relative frequency for scBCG samples compared to coMtb (p = 0.032, One-way ANOVA with Tukey post-test) and associated with oxidative stress response genes (Hmox1, Gclm). Several Cluster 2 associated genes, Slc7a11, Hmox1, and Sqstm1 also had higher overall expression level in scBCG samples compared to either control or coMtb (Fig 3G and S2 Table). Cluster 7 was the only cluster with an increase in relative frequency trending for both scBCG and coMtb (p = 0.076, One-way ANOVA). Cells in this cluster had high expression of Interferon Stimulated Genes (Ifit1, Isg15) and within this cluster, cells from scBCG samples had higher expression of Axl and Ifi204 than cells from coMtb samples. (Fig 3H and S2 Table). Cluster 3 had significantly higher relative frequency for coMtb samples compared to control and scBCG samples (p = 0.021, 0.039, respectively, One-way ANOVA with Tukey post- test) and was distinguished by expression of macrophage-associated transcription factors (Cebpb, Zeb2, Bhlhe40) [33,34], mitochondrial oxidative phosphorylation (mt-Co1, mt-Cytb, mt-Nd2), chromatin remodeling (Ankrd11, Baz1a), and immune signaling including the CARD9 complex (Malt1, Bcl10, Prkcd) (Figs 3I and S7 and S2 Table). This expression profile closely matches a subcluster of AMs previously described by Pisu et al, as an “interstitial mac- rophage-like” AM population (labeled “AM_2”) that expanded in relative frequency in lung samples 3 weeks following low-dose H37Rv infection [31]. Relative expression level for Cebpb, Mt-Cyb, and Lars2 within Cluster 3 was higher for cells from coMtb samples compared to either control or scBCG samples. Interestingly, Cluster 2 (higher relative frequency in scBCG) and Cluster 3 (higher relative frequency in coMtb) represent divergent endpoints of a pseudotime plot generated by a trajec- tory inference analysis, regardless of whether the starting point is the most abundant cluster in the control group (Cluster 0) (Fig 3J, top) or the cluster of proliferating cells (Cluster 4) (Fig 3J, bottom). This result suggests that scBCG and coMtb may drive AM phenotypes in diver- gent directions and indicates that AM responses can be remodeled into more than one flavor, rather than only a binary “on/off” state. To further investigate whether a sub-cluster of AMs might be responsible for the increased enrichment for Interferon Alpha/Gamma Response pathways in the in vivo Mtb response in scBCG and coMtb mice, we scored each cluster based on the ISG gene module, previously used in Fig 2D. As expected, we observed that only Cluster 7 showed strong enrichment for ISGs, which trended up in frequency for both scBCG and coMtb samples (Fig 3K). To investigate potential reprogramming of non-AM macrophages, we examined Cluster 6, the macrophage cluster with low Siglecf and high Itgam expression that is consistent with a monocyte-derived macrophage population. We observed no statistically significant differences PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 8 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Fig 3. Mycobacterium exposure modifies the alveolar macrophage phenotype in the airway pre-challenge. Single-cell RNA-sequencing of BAL samples from control, scBCG, and coMtb mice pre-aerosol challenge. A) Compiled scRNAseq data for all BAL samples, highlighted by major clusters, annotated based on closest Immgen sample match. B) Highlighting of the two clusters used for macrophage subcluster analysis. C) The 11 clusters generated by the macrophage subcluster analysis, separated by condition. D) Expression of major macrophage-specific markers: Siglecf, Mertk, Fcgr1, Lyz2, Itgam (CD11b), and Itgax (CD11c). E-I) Relative frequency of each macrophage subcluster by condition. (violin plots by cluster) Expression level of representative genes distinguished by that cluster compared to other clusters. One-way PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 9 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium ANOVA with Tukey post-test, * p< 0.05. (3-way violin plots by condition) Differentially expressed genes within Clusters 2, 7, and 3 between control vs scBCG vs coMtb samples. Wilcoxon Rank Sum Test, Bonferroni adjusted p-value. adj-p< 0.05, adj-p< 0.01, *adj-p< 0.001. J) Pseudotime analysis (Monocle3) with starting node at the largest cluster in control, Cluster 0 (top) and at the cluster of proliferating cells, Cluster 4,9 (bottom). K) ISG Module Score by cluster. Module derived from macrophage response to IFNα (fold change> 2, p-value< 0.01) (Mostafavi et al, 2016) [30]. Data is compiled from two independent experiments (circle, triangle) with 3 conditions each for a total of 6 samples. https://doi.org/10.1371/journal.ppat.1011871.g003 in the relative size of this cluster between each of the three conditions (S8A Fig). However, there were a number of Differentially Expressed Genes (DEGs) between the groups, including decreases in expression of CD11b (Itgam) and Macrophage scavenger receptor (Msr1) for scBCG and coMtb macrophages compared to controls, increases in MHC-related genes (H2-Aa, Cd74) and iron-metabolism associated genes (Cd63, Fth1, Ftl1) for coMtb macro- phages compared to controls (S8B Fig). A previous study found IV BCG induced chromatin accessibility changes in AMs and IMs for some of these genes [31]. Additionally, we compared baseline changes to AMs following scBCG and coMtb expo- sures to AM changes following ivBCG vaccination (S9 Fig). Overall, we found that ivBCG exposure led to similar changes in AM populations to that of scBCG vaccination, with increased frequency of AMs clustering to “oxidative stress response” and “interferon stimu- lated genes (ISGs)” (S9C Fig). These baseline changes by scRNAseq mirror what is observed for the response of Mtb-infected AMs from ivBCG mice 24 hours after infection by bulk RNA- seq (S9A and S9B Fig). Profiles of Mtb-infected AMs from ivBCG vaccinated mice most closely match those of Mtb-infected AMs from scBCG vaccinated mice, with robust up-regula- tion of Interferon Stimulated Genes. These results demonstrate that both SC and IV BCG vac- cination lead to similar remodeling of AMs, with profiles distinct from that of coMtb exposure. In summary, scRNAseq analysis of macrophages isolated by BAL demonstrate that Myco- bacterium exposure leads to subtle changes in a small minority of AM subsets in the airway, including ones associated with interferon responses and an interstitial macrophage phenotype, while leaving the most abundant subsets of AMs unchanged in frequency or gene expression. We hypothesize that these small changes in baseline profiles may be sufficient to drive the more substantial changes observed in the AM Mtb response in vivo, as described in Fig 2. Mycobacterium exposure has minimal impact on T cell populations in the airway While AMs are the dominant immune cell type in the airway, other cell populations make up an average of 18.4% of the cells within the BAL in controls (range: 10.4–26.3%) and 31.3% in exposed groups (range: 14.0–48.8%). To examine how Mycobacterium exposure influenced other cells in the airway, we focused on T cells and dendritic cells (DCs) which have two of the highest relative frequencies after AMs (Fig 4A and 4B). T cells and DCs were each combined from two original clusters each. Neither population showed a statistically significant difference in relative frequency (Fig 4B). To examine qualitative changes in the T cell population in greater detail, we next reclustered the T cells, resulting in 7 T cell clusters. We manually anno- tated each of the clusters based on the most closely matched Immgen profiles and the expres- sion of key lineage specific markers (Figs 4A–4C and S10). We focused on the 5 most abundant T cell subclusters (Clusters 0–4). While we observed subtle shifts in the relative fre- quency of each group, none reached statistical significance. Cluster 0, the most abundant clus- ter, had an expression profile most consistent with γδ T cells, including expression of Cd3e with low to nil Cd4 and Cd8a and some expression of Zbtb16 (PLZF) and Tmem176a, an ion channel regulated by RORγt and reported to be expressed by lung γδ T cells [35,36] (Figs 4D– PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 10 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Fig 4. Airway T cell and dendritic cell profiles following Mycobacterium exposure. Single-cell RNA-sequencing of BAL samples from control, scBCG, and coMtb mice pre-aerosol challenge. A) Compiled scRNAseq data for all BAL samples, with T cell and dendritic cell clusters highlighted. B) Relative frequency of T cells and DCs. C-F) T cell subcluster analysis. C) T cell subclusters compiled and split by condition. Annotations made following Immgen profile matches and manual marker inspection. D) Relative frequency of Clusters 0–4 for each condition. E) UMAP gene expression plot for general T cell markers. F) UMAP gene expression plot cluster-specific markers split by condition. G-J) Dendritic cell subcluster analysis. G) Dendritic cell subcluster, colored by each of 3 different clusters. H) Relative frequency of Clusters 0–2 for each condition. I) Violin plots for cluster-specific markers and genes of interest. J) Differentially expressed genes in Cluster 0 split by condition. adj-p< 0.05, adj-p< 0 .01, adj-p< 0.001, Wilcoxon Rank Sum Test, Bonferroni adjusted p-values. Data is compiled from two independent experiments with 3 conditions each for a total of 6 samples. https://doi.org/10.1371/journal.ppat.1011871.g004 4F and S10). Cluster 1 matched a profile for effector CD4+ T cells (Figs 4D–4F and S10), and Cluster 2 matched a profile for naïve CD8+ T cells (Figs 4D–4F and S10). Cluster 3 had a pro- file consistent with effector memory/resident memory CD8+ T cells (TEM/RM) (Figs 4D–4F and S10) and Cluster 4 had a profile consistent with NK cells. Overall, there were no PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 11 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium significant changes in the relative frequency of T cell or NK subclusters, despite detection of a number of different lymphocyte subsets in the airway. Mycobacterium exposure modifies the dendritic cell airway landscape Re-clustering of DCs yielded 2 major clusters (Cluster 0, 1) and 1 minor cluster (Cluster 2), which had a mixed phenotype with expression of genes from both major clusters (Fig 4G). Cells in Cluster 0 had high expression of Clec9a, Itgae (CD103), and MHC II genes (H2-Ab1, H2-DMa) consistent with an expression profile of lung CD103+ cDCs [37] (Fig 4I), while cells in Cluster 1 had higher expression of Batf3, Ccr7, and Fscn1. All three of the clusters had high Irf8 expression and lower expression of Xcr1, Irf4, and Itgam (CD11b) (Fig 4I). While the coMtb samples trended higher in relative frequency for Cluster 0 and low for Cluster 1, com- pared to the control or scBCG samples, these differences did not meet statistical significance (One-way ANOVA with Tukey post-test, p = 0.16, p = 0.11) (Fig 4H). This was likely due to the limit in statistical power with only 2 replicates. However, it was notable that for cells within Cluster 0, there was a significantly higher expression level for MHC II genes (H2-Aa, H2-DMb1, and Cd74) for coMtb cells compared to control or scBCG cells (Fig 4J). This sug- gests that coMtb might be able to elicit more mature or activated DCs in the airway. Overall, scRNAseq analysis shows that Mycobacterium exposure leads to minimal changes in T cell and dendritic cell populations in the airway, although we hypothesize that small changes in DC maturation/activation could have important impacts on adaptive immune priming dynamics after aerosol infection. Cell-intrinsic remodeling of alveolar macrophages following Mycobacterium exposure licenses an interferon response in vitro Analysis of the AM response to Mtb in vivo demonstrates that the very earliest immune response to Mtb is altered by previous Mycobacterium exposure. However, one limitation to this approach is the inability to discern whether changes to AMs are cell-intrinsic or dependent on the altered tissue environment, especially the presence of Mtb-specific T cells. Therefore, to determine whether Mycobacterium exposure induces cell-intrinsic changes to AMs, we iso- lated AMs from control, scBCG, and coMtb mice, stimulated them ex vivo with LPS, Pam3Cys, or H37Rv, and measured their transcriptional profiles 6 hours later (Fig 5A). First, PAMP-spe- cific trends were notable. AMs from coMtb and scBCG mice showed distinct responses com- pared to AMs from control mice following LPS and H37Rv stimulation, but only minimal changes following Pam3Cys stimulation(Fig 5B and S3 Table). No obvious changes in innate receptor or adaptor expression explain the PAMP-specific differences (S11 Fig). Second, as we have previously reported, Mtb-infected AMs did not strongly up-regulate Nrf2-associated genes ex vivo (Fig 5C). Third, when we examined the gene sets that distinguished the in vivo AM response between scBCG and coMtb mice, “Interferon Alpha/Gamma Response” and “IL6 JAK STAT3” (Fig 2E), we found that the differences between exposure modalities were diminished ex vivo, suggesting contribution of the lung environment to the quality of the response (Fig 5C). Using Gene Set Enrichment Analysis, we identified “Interferon Gamma Response”, “Interferon Alpha Response”, “TNFa signaling via NF-kB”, and”Inflammatory Response” pathways as the most strongly enriched for LPS and H37Rv responses from scBCG and coMtb AMs (Fig 5D). To assess whether the cell-intrinsic changes observed were long- lasting, we compared the responses of AMs at 8 or 23 weeks following scBCG vaccination by RT-qPCR. Increases in gene expression were as robust or even enhanced 23 weeks following exposure compared to 8 weeks, suggesting that exposure-induced changes to AMs are rela- tively long-lived (S12 Fig). To validate whether changes in gene expression were reflected at PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 12 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Fig 5. Cell-intrinsic remodeling of alveolar macrophages following Mycobacterium exposure. A) AM isolation 8 weeks following scBCG or coMtb exposure. AMs were stimulated with Pam3Cys (10 ng/ml), LPS (10 ng/ml), and H37Rv (effective MOI ~2:1) for 6 hours (RNA-seq) or 20 hours (flow cytometry). B-D) Gene expression changes measured by bulk RNA-seq for stimulated AMs compared to respective unstimulated AMs (i.e., LPS-stim control AM vs unstim control AM; LPS-stim scBCG AM vs unstim scBCG AM; LPS-stim coMtb AM vs unstim coMtb AM). B) Scatterplots, log2 fold change gene expression for stimulated to unstimulated AMs for each condition (control, scBCG, coMtb). Differentially expressed genes (DEG) are highlighted for one or both conditions (\|Fold change\| > 2, FDR< 0.05 for Pam3Cys and LPS; \|Fold change\| > 2, FDR< 0.2 for H37Rv). C) Scatterplots, log2 fold change gene expression for H37Rv-stimulated to unstimulated scBCG versus coMtb AMs. Genes highlighted derived from gene sets in Fig 2E. Nrf2-associated genes (56 genes, purple), Interferon Alpha/ Gamma Response (61 genes, orange), and IL6 JAK STAT3 (23 genes, green). D) Gene Set Enrichment Analysis results for 50 HALLMARK pathways. Pathways shown have NES> 1.5 and FDR< 0.05 for at least one of the conditions. FDR< 0.05, FDR< 0.01, FDR< 0.001. E) Gating strategy and MHC II and TNF histograms for coMtb AMs, no stimulation versus LPS. F-G) MHC II and TNF MFI in control, scBCG, and coMtb AMs after 20 hours of LPS stimulation. p< 0.05, p< 0.01, p< 0.001, One-way ANOVA with Tukey post-test. https://doi.org/10.1371/journal.ppat.1011871.g005 the protein level, we sought to develop a flow cytometry-based assay to assess AM-specific responses. Primary AMs were stimulated with LPS for 20 hours and both MHC II and TNF expression were measured by flow cytometry (Fig 5E). We found that AM from coMtb mice PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 13 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium had significantly higher MHC II expression than controls and a similar pattern was seen for scBCG AM in 1 of 2 experiments (Fig 5F). AMs from coMtb mice also showed a significant increase in TNF expression in 1 of 2 experiments (Fig 5G). Because the “Interferon Alpha Response” and “Interferon Gamma Response” pathways were most highly enriched for the H37Rv stimulation following Mycobacterium exposure, we decided to further investigate the Interferon-associated response [30]. We specifically sought out a dataset that would identify ISGs specific to Mtb-infected macrophages. To generate an IFNγ-derived signature, we would need a macrophage-T cell co-culture system and to sort out the Mtb-infected macrophages, because murine macrophages do not produce IFNγ during Mtb infection in vitro. Therefore, we decided to examine an IFNα/β-derived signature from a data set of Mtb-infected IFNAR-/- bone marrow derived macrophages (BMDMs). We catego- rized the macrophage response to H37Rv stimulation as “IFN-dependent” or “IFN-indepen- dent” based on gene expression of WT versus IFNAR-/- BMDMs following H37Rv infection (see methods section) (S4 Table) [38]. Expression of IFN-dependent genes was minimally induced in control AMs but strongly up-regulated in AMs from Mycobacterium exposed mice, as measured by the GSEA normalized enrichment score (NES) (Fig 6A, left). In contrast, expression of IFN-independent genes was modestly upregulated in control AMs and only slightly altered by Mycobacterium exposure (Fig 6A, right). When we applied these two gene sets to the in vivo response profiles described in Fig 2 generated for Mtb-infected AMs follow- ing high dose infection with mEmerald-H37Rv, we observe that Mtb-infected AMs from scBCG mice up-regulate the IFN-dependent response in vivo, suggesting that the licensing of the IFN-dependent response plays a role in vivo following BCG vaccination (Fig 6B). The dif- ference between the in vitro and in vivo response for AMs from coMtb mice points to an addi- tional contribution of the lung environment. These results demonstrate that prior Mycobacterium exposure leads to cell-intrinsic changes in AMs that license an enhancement of IFN-dependent responses to Mtb that are retained in vitro, while qualitative differences in the response between scBCG and coMtb in vivo are dependent on signals from the lung environment. Discussion Here we describe remodeling of AMs, long-lived airway-resident innate cells, following two modalities of Mycobacterium exposure, scBCG vaccination and coMtb, a model of contained Mtb infection. AMs are the first cells to be productively infected in the lung following aerosol Mtb infection [10,11]. We previously showed that AMs initially respond to Mtb infection with a cell-protective, Nrf2-driven program that is detrimental to early host control [10], suggesting that the lack of a robust response by AMs prevents effective host control early on. In line with this model, others have shown that depletion of AMs or strategies that “bypass” AMs including directly injecting antigen-primed DCs or activating DCs accelerate the immune response and reduce bacterial burden [17,39,40]. However, how vaccination or prior exposures impact the initial response of AMs and whether there are therapeutic strategies that would enhance their initial response to infection have not been well studied [41]. Most studies examining the impacts of prior exposure to either Mtb or other species of mycobacteria, including BCG vaccination, have focused on the durable antigen-specific changes to the adaptive immune response. In contrast, we focused on changes to tissue-resi- dent innate cells and their responses at the earliest stages of infection (� 14 days). Along with early changes to the T cell response, both scBCG and coMtb accelerate innate cell activation and immune cell recruitment in the first 10–14 days following Mtb aerosol infection, and even the very initial AM response to Mtb, within the first 24 hours of infection, is remodeled PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 14 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Fig 6. Mycobacterium exposure licenses an interferon-dependent response to H37Rv by alveolar macrophages. Gene expression changes measured by bulk RNA-seq for Mtb-infected AMs compared to respective unstimulated AMs (i.e., Mtb-inf control AM vs unstim control AM; Mtb-inf scBCG AM vs unstim scBCG AM; Mtb-inf coMtb AM vs unstim coMtb AM). A) Gene expression for control, scBCG, and coMtb AMs, 6 hour H37Rv infection ex vivo, log2 fold change (Mtb-infected/uninfected). IFN-dependent genes (339 total) and IFN-independent genes (352 total) based on WT vs IFNAR-/- BMDM bulk RNA-seq dataset (Olson et al, 2021) (see Methods section). B) Gene expression for control, scBCG, and coMtb AMs, 24 hour in vivo H37Rv infection, Mtb-infected sorted, log2 fold change (Mtb-infected/uninfected) for the same IFN-dependent and IFN-independent gene sets in (A). Grey bars indicate N.D. Normalized Enrichment Score (NES) calculated by GSEA for two data sets alongside Hallmark Pathways. +FDR< 0.05, ++FDR< 0.01, +++FDR< 0.001. https://doi.org/10.1371/journal.ppat.1011871.g006 following exposure to Mycobacterium. The durable changes observed fit with a number of recent studies which have uncovered either enhanced AM antimicrobial phenotypes [7–9] or impaired responses [42,43] following viral infection. Other studies have identified long-lasting changes to AMs following intranasal immunization of either adenoviral-based or inactivated whole cell vaccines [18,44,45]. We observe that the most robust cell-intrinsic changes to AM responses following scBCG or coMtb are found in IFN-dependent genes (Fig 6) and ISGs (Fig 2D), suggesting a critical role for interferon signaling in the changes to the early innate response in the lung during infection. Notably, this finding is not limited to the murine model. BAL from NHPs following IV, ID, or aerosol BCG vaccination similarly show AMs enriched for Interferon Gamma Response pathway genes [46]. AMs can respond to both Type I (IFNα/β) and Type II Interfer- ons (IFNγ) and it is not possible to distinguish between responses to IFNα/β and IFNγ based on transcriptional analysis alone. The presence of live bacteria within both scBCG and coMtb models limits system-wide perturbations, such as T cell depletion or anti-IFNγ blockade, which would reverse containment [24]. For this reason, we have not been able to directly test how interferon signals derived from scBCG or coMtb remodel AMs in a cell-autonomous manner, but we envision future studies to examine the specific effects of individual cytokines on AM remodeling. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 15 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Even though IFNAR-/- macrophages were used to generate the ISG signature identified in Fig 6, IFNγ is the more likely candidate to contribute to AM remodeling following Mycobacte- rium exposure. IFNγ is required for the generation of trained immunity in bone marrow- derived myeloid cells following IV BCG vaccination [15,47]. While IV and aerosol H37Rv infection was found to induce Type I IFNs and reduce myelopoiesis [47], we previously found that coMtb, in which Mtb is contained within the ear-draining lymph node, leads to low-level systemic cytokinemia, including IFNγ production. Using WT:Ifngr1-/- mixed bone marrow chimeras, we showed that IFNγ signaling was responsible for monocyte and AM activation fol- lowing establishment of coMtb [25]. Additionally, several reports have identified T cell-derived IFNγ as critical for altering AM function, although the immunological outcome varies sub- stantially based on the context. In one study, T cell-derived IFNγ following adenoviral infec- tion leads to AM activation, innate training and protection from S. pneumoniae [8], while in another study influenza-induced T cell-derived IFNγ leads to AM dysfunction and impaired clearance of S. pneumoniae [43]. A study of 88 SARS-CoV-2 patients identified AMs and T cell-derived IFNγ as part of a positive feedback loop in the airway [48]. In contrast, type I IFN signatures are associated with active TB or TB disease progression in both humans and non- human primates [49–51]. Host perturbations such as treatment with poly I:C or viral co-infec- tion that induce type I IFN lead to worsened disease [52,53], type I IFN has been shown to block production of IL-1β in myeloid cells during Mtb infection [54], and type I IFN drives mitochondrial stress and metabolic dysfunction in Mtb infected macrophages [38]. We note that the two modalities tested here consist of different mycobacterial species, dif- ferent doses, and different routes. We expect that all three of these factors likely contribute to the quality of AM remodeling. For example, they could be important for the location, timing, and amount of IFNγ that AMs are exposed to. While an in-depth examination of each of these factors is beyond the scope of this study, the side-by-side comparison of the two different exposure models, scBCG and coMtb, allows us to examine the plasticity of AM phenotypes and the impact of the local and/or systemic environments leading to different responses. It is notable that scBCG is quickly cleared from WT mice, while coMtb replication is sustained in the superficial cervical lymph node for up to a year or longer [25]. Protection from H37Rv challenge mediated by coMtb is abrogated following antibiotic treatment but not completely lost [25]. This suggests that there may be different contributions to AM remodeling from active bacterial replication and from long-term microenvironment changes following bacterial clearance, which will be addressed in follow-up studies. In particular, it is notable that the modality-specific signatures identified through in vivo transcriptional analysis disappeared following ex vivo isolation, along with the Nrf2 signature. The difference between in vivo and ex vivo signatures suggests a critical contribution of the altered lung microenvironments in AM remodeling, which deserves additional follow-up studies. One additional limitation of our approach is that the ex vivo samples were collected in bulk, in the absence of cell sorting, and so, unlike the in vivo studies, a very small number of bystander AMs were likely collected alongside the Mtb-infected AMs, which could have had minor impacts on the transcriptional signatures. The fact that AMs can be remodeled into more than one state suggests additional complexity in innate immune features that has not yet been fully explored. Heterogeneity in myeloid reprogramming is not limited to the murine model and has also been observed in human monocytes [55]. Several studies have recently described innate-adaptive interactions within the airway that are thought to impact infection dynamics [46,48]. We note that in these models we observe innate cell activation and recruitment occurring at the same time as T cell activation and recruitment, and that these events are likely promoting one another. We are particularly PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 16 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium intrigued by the changes in AM MHC class II expression that we observed in vivo during the first two weeks of infection (Fig 1A) and following ex vivo stimulation (Fig 5F). AMs are considered to be poor antigen presenters, relative to other myeloid subsets, yet the faster Mtb-specific T cells are recruited to the lung, the more likely it is that AMs will serve as pri- mary T cell targets [37,56–60]. Our results suggest that enhancement of AM antigen presenta- tion could be one innate mechanism that could be targeted to complement and synergize with the adaptive immune response during infection. Other potential mechanisms by which AM remodeling may contribute to enhanced bacterial control after Mtb aerosol challenge include enhanced phagocytic activity or increased direct antimycobacterial activity, as previously dem- onstrated by Jeyanathan et al [19]. Future studies are needed to further interrogate the contri- bution of these innate mechanisms. There are many other remaining questions. While we identify both cell-intrinsic changes and changes dependent on the lung environment, we do not yet know whether the cell-intrin- sic changes are retained long-term in the absence of environmental cues. We do not know the durability of the changes, both cell-intrinsic and environment-dependent, and whether they are mediated by epigenetic effects. Our longest experiment showed retention of cell-intrinsic changes to AMs after 23 weeks. In Nemeth et al, we showed that antibiotic treatment lessened the protection mediated by coMtb, suggesting that ongoing replication is a key part of host protection [25]. AM remodeling is retained 8 weeks or longer after the initial exposures, a timepoint when there is little to no detectable mycobacteria in the lung, ruling out a require- ment for local ongoing bacterial replication in AM remodeling, although systemic signals derived from remote bacterial replication may still play a role. We also performed several of these studies with intravenous BCG vaccination (ivBCG), which in the mouse model leads to more sustained bacterial replication than scBCG [61]. While we observed similar remodeling to AMs in the ivBCG model, these were not different in quality to those of scBCG vaccination, despite the major differences in bacterial replication and far greater T cell recruitment to the airway, suggesting that these changes are not required for AM remodeling (S9 Fig). There is still much unknown about the signals that drive reprogramming of tissue-resident innate cells. Ideally, vaccines would be designed to leverage these signals in order to promote the most effective interactions between innate and adaptive responses. Identifying the ways that AMs are reprogrammed by inflammatory signals and the effects of their changed pheno- types on the early stages of infection will help to improve future vaccines or host-directed therapies. Materials and methods Ethics statement Animal studies performed at Seattle Children’s Research Institute were performed in compli- ance with and approval by the Seattle Children’s Research Institute’s Institutional Animal Care and Use Committee. Animal studies performed at University of Massachusetts Amherst were performed in compliance with and approval by the University of Massachusetts Amherst’s Institutional Animal Care and Use Committee. All mice were housed and maintained in spe- cific pathogen-free conditions. Mice C57BL/6 mice were purchased from Jackson Laboratories (Bar Harbor, ME). 6–12 week old male and female mice were used for all experiments, except for RNA-sequencing, which used only female mice for uniformity. Mice infected with Mtb were housed in Biosafety Level 3 facilities in Animal Biohazard Containment Suites. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 17 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Mycobacteria exposure models: BCG immunization and establishment of coMtb BCG-Pasteur was cultured in Middlebrook 7H9 broth at 37˚C to an OD of 0.1–0.3. Bacteria was diluted in PBS and 1 x 106 CFU in 200 ml was injected subcutaneous (SC) or intravenous (IV). Intradermal infections to establish coMtb were performed as formerly described [24], with some modifications as detailed previously [25]. Briefly, 10,000 CFU of Mtb (H37Rv) in logarithmic phase growth were injected intradermally into the ear in 10 μL PBS using a 10 μL Hamilton Syringe, following anesthesia with ketamine/xylazine. M. tuberculosis aerosol infections and lung mononuclear cell isolation Aerosol infections were performed with wildtype H37Rv, including some transformed with an mEmerald reporter pMV261 plasmid, generously provided by Dr. Chris Sassetti and Christina Baer (University of Massachusetts Medical School, Worcester, MA). For both standard (~100 CFU) and high dose (1,473–4,800 CFU) infections, mice were enclosed in an aerosol infection chamber (Glas-Col) and frozen stocks of bacteria were thawed and placed inside the associated nebulizer. To determine the infectious dose, three mice in each infection were sacrificed one day later and lung homogenates were plated onto 7H10 plates for CFU enumeration. High dose challenge and sorting of Mtb-infected AM was performed 4 weeks following scBCG vac- cination and 2 weeks following coMtb vaccination as previously described [62]. All other anal- ysis was performed 8 weeks following Mycobacterium exposures. Lung single cell suspensions At each time point, lungs were removed, and single-cell suspensions of lung mononuclear cells were prepared by Liberase Blendzyme 3 (70 ug/ml, Roche) digestion containing DNaseI (30 μg/ml; Sigma-Aldrich) for 30 mins at 37˚C and mechanical disruption using a gentle- MACS dissociator (Miltenyi Biotec), followed by filtering through a 70 μM cell strainer. Cells were resuspended in FACS buffer (PBS, 1% FBS, and 0.1% NaN3) prior to staining for flow cytometry. For bacterial enumeration, lungs were processed in 0.05% Tween-80 in PBS using a gentleMACS dissociator (Miltenyi Biotec) and were plated onto 7H10 plates for CFU enumer- ation. ΔCFU (log) was calculated as follows: ΔCFU = log((sample CFU)/(average control CFU). For respective experiment, timepoint, and organ. A ΔCFU value of -1 corresponds to a 10-fold reduction in CFU for the sample, compared to the control. Similarly, a ΔCFU value of 1 corresponds to a 10-fold increase in CFU. Alveolar macrophage isolation AMs for cell sorting, bulk RNA-sequencing, single cell RNA-sequencing, and ex vivo stimula- tion were collected by bronchoalveolar lavage (BAL). BAL was performed by exposing the tra- chea of euthanized mice, puncturing the trachea with Vannas Micro Scissors (VWR) and injecting 1 mL PBS using a 20G-1” IV catheter (McKesson) connected to a 1 mL syringe. The PBS was flushed into the lung and then aspirated three times and the recovered fluid was placed in a 15mL tube on ice. The wash was repeated 3 additional times. Cells were filtered and spun down. For antibody staining, cells were suspended in FACS buffer. For cell culture, cells were plated at a density of 5 x 104 cells/well (96-well plate) in complete RPMI (RPMI plus FBS (10%, VWR), L-glutamine (2mM, Invitrogen), and Penicillin-Streptomycin (100 U/ml; Invitrogen) and allowed to adhere overnight in a 37˚C humidified incubator (5% CO2). Media with antibiotics were washed out prior to infection with Mtb. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 18 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium Cell sorting and flow cytometry Fc receptors were blocked with anti-CD16/32 (2.4G2, BD Pharmingen). Cell viability was assessed using Zombie Violet dye (Biolegend). Cells were suspended in 1X PBS (pH 7.4) con- taining 0.01% NaN3 and 1% fetal bovine serum (i.e., FACS buffer). Surface staining, performed at 4 degrees for 20 minutes, included antibodies specific for murine: Siglec F (E50-2440, BD Pharmingen), CD11b (M1/70), CD64 (X54-5/7.1), CD45 (104), CD3 (17A2, eBiosciences), CD19 (1D3, eBiosciences), CD11c (N418), I-A/I-E (M5/114.15.2), Ly6G (1A8), Ly6C (HK1.4), TNF (MP6-XT22). For ICS, Brefeldin A was added for duration of LPS stimulation. Cyto-Fast Fix/Perm and Cyto-Fast Perm Wash reagents were used for intracellular staining. Reagents are from Biolegend unless otherwise noted. MHC class II tetramers ESAT-6 (I-A(b) 4–17, sequence: QQWNFAGIEAAASA) and MHC class I tetramers TB10.4 (H-2K(b) 4–11, sequence: IMYNYPAM) were obtained from the National Institutes of Health Tetramer Core Facility. Cell sorting was performed on a FACS Aria (BD Biosciences). Sorted cells were col- lected in complete media, spun down, resuspended in TRIzol, and frozen at -80˚C overnight prior to RNA isolation. Samples for flow cytometry were fixed in 2% paraformaldehyde solu- tion in PBS and analyzed using a LSRII flow cytometer (BD Biosciences) and FlowJo software (Tree Star, Inc.). Bulk RNA-sequencing and analysis All high dose infections and sorting for bulk RNA-sequencing of Mtb-infected AMs (control, scBCG, and coMtb) were performed in the ABSL-3 facility at Seattle Children’s Research Insti- tute. All infections used the same Mtb strain, mEmerald-H37Rv, and the TRIzol-based RNA isolation protocol was performed by the same individual (D.M.). RNA isolation was performed using TRIzol (Invitrogen), two sequential chloroform extractions, Glycoblue carrier (Thermo Fisher), isopropanol precipitation, and washes with 75% ethanol. RNA was quantified with the Bioanalyzer RNA 6000 Pico Kit (Agilent). cDNA libraries were constructed using the SMAR- Ter Stranded Total RNA-Seq Kit (v2)–Pico Input Mammalian (Clontech) following the manu- facturer’s instructions. Libraries were amplified and then sequenced on an Illumina NextSeq (2 x 76, paired-end (sorted BAL cells) or 2 x 151, paired-end (ex vivo stimulation samples)). Stranded paired-end reads were preprocessed: The first three nucleotides of R2 were removed as described in the SMARTer Stranded Total RNA-Seq Kit–Pico Input Mammalian User Man- ual (v2: 063017) and read ends consisting of more than 66% of the same nucleotide were removed). The remaining read pairs were aligned to the mouse genome (mm10) + Mtb H37Rv genome using the gsnap aligner [63] (v. 2018-07-04) allowing for novel splicing. Con- cordantly mapping read pairs (~20 million / sample) that aligned uniquely were assigned to exons using the iocond program and gene definitions from Ensembl Mus_Musculus GRCm38.78 coding and non-coding genes. Genes with low expression were filtered using the “filterByExpr” function in the edgeR package [64]. Differential expression was calculated using the “edgeR” package [64] from ioconductor.org. False discovery rate was computed with the Benjamini-Hochberg algorithm. Hierarchical clusterings were performed in R using ‘Tsclust’ and ‘hclust’ libraries. Heat map and scatterplot visualizations were generated in R using the ‘heatmap.2’ and ‘ggplot2’ libraries, respectively. Gene Set Enrichment Analysis (GSEA) Input data for GSEA consisted of lists, ranked by -log(p-value), comparing RNAseq expression measures of target samples and naïve controls including directionality of fold-change. Mouse orthologs of human Hallmark genes were defined using a list provided by Molecular Signa- tures Database (MsigDB) [65]. GSEA software was used to calculate enrichment of ranked lists PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 19 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium in each of the respective hallmark gene lists, as described previously [66]. A nominal p-value for each ES is calculated based on the null distribution of 1,000 random permutations. To cor- rect for multiple hypothesis testing, a normalized enrichment score (NES) is calculated that corrects the ES based on the null distribution. A false-discovery rate (FDR) is calculated for each NES. Leading edge subsets are defined as the genes in a particular gene set that are part of the ranked list at or before the running sum reaches its maximum value. Ingenuity Pathway Analysis (IPA) IPA (QIAGEN) was used to identify enriched pathways for differentially expressed genes between naïve and Mtb-infected AMs (cut-off values: FDR < 0.01, \|FC\| > 2). The top 20 canonical pathways with enrichment score p-value < 0.05 with greater than 10 gene members are reported. Single cell RNA-sequencing BAL from 10 mice per condition was pooled for each sample, with two independent replicates per condition. Samples were prepared for methanol fixation following protocol “CG000136 Rev. D” from 10X Genomics [67]. Briefly, samples were filtered with 70 μm filters and red blood cells were lysed with ACK lysis buffer. Samples were resuspended in 1 mL ice-cold DPBS using a wide-bore tip and transferred to a 1.5 mL low-bind Eppendorf tube. Samples were centrifuged at 700 × g for 5 minutes at 4˚C. Supernatant was carefully removed with a p1000 pipette, and the cell pellet was washed two more times with DPBS, counted, and resus- pended in 200 μL ice-cold DPBS/1 × 106 cells. 800 μL of ice-cold methanol was added drop- wise for a final concentration of 80% methanol. Samples were incubated at -20˚C for 30 min- utes and then stored at -80˚C for up to 6 weeks prior to rehydration. For rehydration, frozen samples were equilibrated to 4˚C, centrifuged at 1,000 × g for 10 minutes at 4˚C, and resus- pended in 50 μL of Wash-Resuspension Buffer (0.04% BSA + 1mM DTT + 0.2U/μL Protector RNAase Inhibitor in 3× SSC buffer) to achieve ~1,000 cells/μL (assuming 75% sample loss). Single cell RNA-sequencing analysis Libraries were prepared using the Next GEM Single Cell 30 Reagent Kits v3.1 (Dual Index) (10X Genomics) following the manufacturer’s instructions. Raw sequencing data were aligned to the mouse genome (mm10) and UMI counts determined using the Cell Ranger pipeline (10X Genomics). Data processing, integration, and analysis was performed with Seurat v.3 [68]. Droplets containing less than 200 detected genes, more than 4000 detected genes (doublet discrimination), or more than 5% mitochondrial were discarded. Genes expressed by less than 3 cells across all samples were removed. Unbiased annotation of clusters using the Immgen database [69] as a reference was performed with “SingleR” package [70]. Pseudotime analysis was performed using the “SeuratWrappers” and “Monocle3” R packages [71]. Data visualiza- tion was performed with the “Seurat”, “tidyverse”, “cowplot”, and “viridis” R packages. Alveolar macrophage Ex Vivo stimulation AMs were isolated by bronchoalveolar lavage and pooled from 5 mice per group. Cells were plated at a density of 5 x 104 cells/well (96-well plate) in complete RPMI (RPMI plus FBS (10%, VWR), L-glutamine (2mM, Invitrogen), and Penicillin-Streptomycin (100 U/ml; Invitrogen) and allowed to adhere overnight in a 37˚C humidified incubator (5% CO2). Media with antibi- otics and non-adherent cells were washed out prior to stimulation. AM were stimulated with LPS (LPS from Salmonella Minnesota, List Biologicals, #R595, 10 ng/ml), Pam3Cys PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 20 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium (Pam3CSK4, EMC Microcollections, GmbH, 10 ng/ml), or H37Rv (effective MOI ~2:1). H37Rv was prepared by culturing from frozen stock in 7H9 media at 37˚C for 48 hours to O. D. of 0.1–0.3. The final concentration was calculated based on strain titer and bacteria was added to macrophages for two hours. Cultures were then washed three times to remove extra- cellular bacteria. Cell cultures were washed once in PBS after 6 hours to remove dead cells and collected in TRIzol for RNA isolation via chloroform/isopropanol extraction or collected after 20 hours for flow cytometry and ICS. Filtering for IFN dependent and independent gene sets “IFN dependent” and “IFN independent” gene sets were generated from data from Olson et al [38], using the following filters starting from a total of 1,233 genes up-regulated in H37Rv- stimulated WT BMDM with average CPM >1, log2 fold change > 1 and FDR < 0.01: “IFN dependent” = H37Rv-stimulated IFNAR-/- BMDM: log2 fold change < 1 AND H37Rv-stimulated WT vs IFNAR-/-: log2 fold change > 2 = 339 genes “IFN independent” = H37Rv-stimulated IFNAR-/- BMDM: log2 fold change > 1, FDR < 0.01 AND H37Rv-stimulated WT vs IFNAR-/-: log2 fold change < 2 = 352 genes qRT-PCR Quantitative PCR reactions were carried out using TaqMan primer probes (ABI) and TaqMan Fast Universal PCR Master Mix (ThermoFisher) in a CFX384 Touch Real-Time PCR Detec- tion System (BioRad). Data were normalized by the level of Ef1a expression in individual samples. Statistical analyses RNA-sequencing was analyzed using the edgeR package from Bioconductor.org and the false discovery rate was computed using the Benjamini-Hochberg algorithm. All other data are pre- sented as mean ± SEM and analyzed by one-way ANOVA (95% confidence interval) with Tukey post-test (for comparison of multiple conditions). Statistical analysis and graphical representation of data was performed using either GraphPad Prism v6.0 software or R. PCA plots generated using “Prcomp” and “Biplot” packages. Venn diagrams and gene set intersec- tion analysis was performed using Intervene [72]. p-values, p < 0.05, p < 0.01, * p < 0.001. Supporting information S1 Fig. (related to Fig 1). Flow cytometry gating schemes. Gating strategies for myeloid (A) and T cell (B) analysis. (TIF) S2 Fig. (related to Fig 1). Mycobacterium exposure provides protection against standard low-dose H37Rv aerosol challenge. A) Lung, spleen, and lung-draining lymph node (LN) CFU in control mice at deposition, days 10, 12, 14, and 28. B-E) Summary plots of ΔCFU (log) in lung, spleen, and LN following low-dose infection with H37Rv at day 10 (B), day 12 (C), day 14 (D), and day 28 (E). p < 0.05, p < 0.01, *p < 0.001. One-way ANOVA with Tukey post-test. Data compiled from 2–3 independent experiments per condition, with 5 mice per group for each experiment. (TIF) PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 21 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium S3 Fig. (related to Fig 2). Top 20 Canonical Pathways by Ingenuity Pathway Analysis for up-regulated genes by Mtb-infected alveolar macrophages. IPA analysis for Mtb-infected AMs from control, scBCG, and coMtb mice 24 hours following high dose mEmerald-H37Rv infection. Data representative of 3 independent experiments per condition. (TIF) S4 Fig. (related to Fig 2). Transcriptional changes to naive alveolar macrophages following Mycobacterium exposure by bulk RNA-sequencing. Bulk RNA-seq profiles of naive AMs (isolated alongside Mtb-infected AMs). Gene expression changes within naïve AMs are com- pared to AMs from control mice: naïve scBCG AM vs naïve control AM; naïve coMtb AM vs naïve control AM. A-B) Volcano plots depicting changes in baseline gene expression of naive AMs from scBCG (A) and coMtb(B) mice compared to naive AMs from control mice. Signifi- cantly changed genes (FDR < 0.05, \|FC\| > 2) highlighted and labeled. C) Gene expression for innate receptors and adaptors of interest, log2 fold change, unstimulated AMs from scBCG and coMtb mice compared to unstimulated AMs from control mice. FDR < 0.01. Compiled from 2 independent experiments for each condition. (TIF) S5 Fig. (related to Fig 3). Flow analysis of BAL samples prepared for 10X single-cell RNA- sequencing. Percentage of each population (AM, lymphocytes, eosinophils, MDM, other CD45+) out of CD45+ ZV-. AM = Siglec F+ CD64+, Eosinophils = Siglec F+ CD64-, lymphocytes = CD3/CD19+, MDM = Siglec F- CD64+, other CD45+ = CD3- CD19- Siglec F- CD64-. Note: One of the two coMtb samples analyzed by flow cytometry did not have an accompanying 10X sample. The second coMtb 10X sample was processed separately without flow analysis. (TIF) S6 Fig. (related to Fig 3). Top 10 genes differentially expressed for each of 11 macrophage subclusters. Heatmap of genes that are most differentially expressed for each of 11 clusters with all other clusters. Genes filtered with log fold change threshold of > 0.25 and minimum percentage expression of 25% of cells. All genes but one (Gsto1) had an adjusted p-value of < 1.0x10-5. Five genes (Fabp4, Fabp5, Stmn1, Mki67, Cbr2) met this criterion for more than one cluster, grouped with the more abundant cluster. Data is compiled from two inde- pendent experiments, 3 conditions each, for a total of 6 samples. (TIF) S7 Fig. (related to Fig 3). UMAP gene expression plots for genes associated with macro- phage subcluster 3 and found in AM_2 (Pisu et al) (31). Genes associated with mitochon- drial oxidative phosphorylation (mt-Co1, mt-Cytb, mt-Nd2), chromatin remodeling (Ankrd11, Baz1a), macrophage-associated transcription factors (Cebpb, Zeb2, Bhlhe40, Hif1a), and CARD9 signaling (Malt1, Bcl10). Data is compiled from two independent experiments with 3 conditions each, for a total of 6 samples. (TIF) S8 Fig. (related to Fig 3). Frequency and gene expression of Cluster 6 macrophages across exposure conditions. A) Single-cell RNA-sequencing from BAL samples from control, scBCG, and coMtb mice. Subcluster of macrophages with each cluster annotated. Relative fre- quency of Cluster 6 for each replicate. B) Differentially expressed genes within Cluster 6 between control vs scBCG vs coMtb samples. Wilcoxon Rank Sum Test, Bonferroni adjusted p-value, **adj-p < 0.001. (TIF) PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 22 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium S9 Fig. (related to Fig 3). IV BCG vaccination leads to similar remodeling of alveolar mac- rophages as SC BCG vaccination. A-B) Bulk RNA-seq gene expression analysis between naive and Mtb-infected AMs 24 hours following high-dose mEmerald-H37Rv infection in mice previously exposed to scBCG, ivBCG, and coMtb, compared to controls. (controls- reported in Rothchild et al, 2019(10); CMTB- reported in Nemeth et al, 2020(25)). A) Principal Component Analysis using DEG (fold change > \|2\|, FDR < 0.05) in Mtb-infected AMs at 24 hours. B) Heatmap of 131 DEG at 24 hours in Mtb-infected AM (left), Interferon Stimulated Genes, derived from macrophage response to IFNα (fold change >2, p-value < 0.01) Mosta- favi et al, 2016 (30)(middle), IL6 JAK STAT3 hallmark pathway (right). C) Relative frequency of 3 key clusters for macrophage subset from control, scBCG, ivBCG, and coMtb scRNAseq BAL samples. (A-B) Compiled from 3+ independent experiments per condition for control, 2 independent experiments per condition for scBCG, ivBCG, and coMtb. (C) Data is compiled from two independent experiments (circle, triangle) with 3 conditions each for a total of 6 samples. (TIF) S10 Fig. (related to Fig 4). UMAP gene expression plots of cluster and lineage marker genes of interest for T cell subclusters. Data is compiled from two independent experiments with 3 conditions each for a total of 6 samples. (TIF) S11 Fig. (related to Fig 5). Gene expression of alveolar macrophages from ex vivo stimula- tions. A) Gene expression changes measured by bulk RNA-seq for stimulated AMs compared to respective unstimulated AMs (i.e., LPS-stim control AM vs unstim control AM; LPS-stim scBCG AM vs unstim scBCG AM; LPS-stim coMtb AM vs unstim coMtb AM). AMs were stimulated for 6 hours with Pam3Cys (10 ng/ml), LPS (10 ng/ml), or H37Rv (effective MOI ~2:1). Volcano plots depict fold change (log2) and P-value (-log10) for each stimulation condi- tion for each of the three groups (control scBCG, coMtb) compared to the respective unstimu- lated controls. DEG (p-value < 0.001; \|fold change\| > 2) highlighted and labeled, space permitting. B) Baseline gene expression for innate receptors and adaptors of interest from scBCG and coMtb AM compared to control AM, log2 fold change, unstim scBCG AM vs unstim control AM; unstim coMtb AM vs unstim control AM. Compiled from 3 independent experiments. (TIF) S12 Fig. (related to Fig 5). Cell-intrinsic changes in alveolar macrophage response is retained 23 weeks following vaccination. Gene expression of Mx1, Cxcl10, Il1b, Cxcl2, Irf7, and Il6 as measured by qPCR in AMs isolated by BAL from mice 8 and 23 weeks following scBCG vaccination and from age-matched controls, with and without LPS (10 ng/ml) stimula- tion. Data is representative of technical AM duplicates from a single experiment. (TIF) S1 Table. RNA-Sequencing data for alveolar macrophages 24 hours following high dose H37Rv-mEmerald challenge from scBCG mice. (XLSX) S2 Table. Top differentially expressed genes for individual clusters for macrophage, T cell, and dendritic cell sub-cluster analysis. (XLSX) S3 Table. RNA-Sequencing data for ex vivo stimulated alveolar macrophages. (XLSX) PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1011871 January 18, 2024 23 / 28 PLOS PATHOGENSAlveolar macrophage remodeling by Mycobacterium S4 Table. IFN-independent and IFN-dependent genes based on WT and IFNAR-/- BMDM RNA-seq data. (XLSX) Acknowledgments We thank the Animal Care staff at Seattle Children’s Research Institute and University of Mas- sachusetts Amherst, Pamela Troisch and the Next Gen Sequencing core at the Institute for Sys- tems Biology. The authors acknowledge Research Scientific Computing at Seattle Children’s Research Institute for providing HPC resources that have contributed to the research results reported within this paper. We thank members of the Aderem, Urdahl, and Rothchild labs for helpful discussions. Author Contributions Conceptualization: Dat Mai, Johannes Nemeth, Kevin Urdahl, Alan H. Diercks, Alan Aderem, Alissa C. Rothchild. Data curation: Michael Morikubo, Alan H. Diercks, Alissa C. Rothchild. Formal analysis: Dat Mai, Michael Morikubo, Alan H. Diercks, Alissa C. Rothchild. Funding acquisition: Kevin Urdahl, Alan H. Diercks, Alan Aderem, Alissa C. Rothchild. 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10.1371_journal.pcbi.1011196.pdf	Data Availability Statement: Data can be found in the following doi: https://doi.org/10.6084/m9. figshare.21938651.v1.	Data can be found in the following 10.6084/m9. figshare.21938651.v1 .	RESEARCH ARTICLE Dynamic recycling of extracellular ATP in human epithelial intestinal cells Nicolas Andres SaffiotiID Virginia Gentilini5,6, Gabriel Eduardo Gondolesi5,6, Pablo Julio Schwarzbaum1,2, Julieta SchachterID 1,2,3, Cora Lilia Alvarez1,4, Zaher Bazzi1,2, Marı´a 1,2 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Saffioti NA, Alvarez CL, Bazzi Z, Gentilini MV, Gondolesi GE, Schwarzbaum PJ, et al. (2023) Dynamic recycling of extracellular ATP in human epithelial intestinal cells. PLoS Comput Biol 19(6): e1011196. https://doi.org/10.1371/journal. pcbi.1011196 Editor: Melissa L. Kemp, Georgia Institute of Technology and Emory University, UNITED STATES Received: January 25, 2023 Accepted: May 17, 2023 Published: June 29, 2023 Copyright: © 2023 Saffioti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data can be found in the following doi: https://doi.org/10.6084/m9. figshare.21938651.v1. Funding: Grants from Secretarı´a de Ciencia y Te´cnica, Universidad de Buenos Aires (PJS, UBACYT 20020170100152BA), Consejo Nacional de Investigaciones Cientı´ficas y Te´cnicas (PJS, CONICET PIP 1013) and Agencia Nacional de Promocio´n Cientı´fica y Tecnolo´gica (NAS, PICT- 2019 03218; JS, PICT 2019-0204; PJS, PICT 2021- 1 Instituto de Quı´mica y Fı´sico-Quı´mica Biolo´ gicas “Prof. Alejandro C. Paladini”, Universidad de Buenos Aires (UBA), Consejo Nacional de Investigaciones Cientı´ficas y Te´cnicas (CONICET), Facultad de Farmacia y Bioquı´mica, Buenos Aires, Argentina, 2 Universidad de Buenos Aires (UBA), Facultad de Farmacia y Bioquı´mica, Departamento de Quı´mica Biolo´gica, Ca´tedra de Quı´mica Biolo´ gica, Buenos Aires, Argentina, 3 Instituto de Nanosistemas, Universidad Nacional de General San Martin, Buenos Aires, Argentina, 4 Universidad de Buenos Aires (UBA), Facultad de Ciencias Exactas y Naturales, Departamento de Biodiversidad y Biologı´a Experimental, Buenos Aires, Argentina, 5 Fundacio´n Favaloro Hospital Universitario, Unidad de Insuficiencia, Rehabilitacio´n y Trasplante Intestinal, Buenos Aires, Argentina, 6 Instituto de Medicina Traslacional, Trasplante y Bioingenierı´a (IMETTyB, CONICET, Universidad Favaloro), Laboratorio de Inmunologı´a asociada al Trasplante, Buenos Aires, Argentina * pjs@qb.ffyb.uba.ar (PJS); jschachter@qb.ffyb.uba.ar (JS) Abstract Intestinal epithelial cells play important roles in the absorption of nutrients, secretion of elec- trolytes and food digestion. The function of these cells is strongly influenced by purinergic signalling activated by extracellular ATP (eATP) and other nucleotides. The activity of sev- eral ecto-enzymes determines the dynamic regulation of eATP. In pathological contexts, eATP may act as a danger signal controlling a variety of purinergic responses aimed at defending the organism from pathogens present in the intestinal lumen. In this study, we characterized the dynamics of eATP on polarized and non-polarized Caco-2 cells. eATP was quantified by luminometry using the luciferin-luciferase reaction. Results show that non-polarized Caco-2 cells triggered a strong but transient release of intracellular ATP after hypotonic stimuli, leading to low micromolar eATP accumulation. Subsequent eATP hydrolysis mainly determined eATP decay, though this effect could be counterbalanced by eATP synthesis by ecto-kinases kinetically characterized in this study. In polarized Caco-2 cells, eATP showed a faster turnover at the apical vs the basolateral side. To quantify the extent to which different processes contribute to eATP regulation, we cre- ated a data-driven mathematical model of the metabolism of extracellular nucleotides. Model simulations showed that eATP recycling by ecto-AK is more efficient a low micromo- lar eADP concentrations and is favored by the low eADPase activity of Caco-2 cells. Simula- tions also indicated that a transient eATP increase could be observed upon the addition of non-adenine nucleotides due the high ecto-NDPK activity in these cells. Model parameters showed that ecto-kinases are asymmetrically distributed upon polarization, with the apical side having activity levels generally greater in comparison with the basolateral side or the non-polarized cells. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 1 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells 00125). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Finally, experiments using human intestinal epithelial cells confirmed the presence of functional ecto-kinases promoting eATP synthesis. The adaptive value of eATP regulation and purinergic signalling in the intestine is discussed. Author summary Intestinal epithelial cells play important roles in the absorption of nutrients, secretion of electrolytes and food digestion. When intracellular ATP is released into the intestinal milieu, either at the lumen or the internal side, the resulting extracellular ATP can act as an alert signal to engage cell surface purinergic receptors that activate the immune defence of the organism against pathogens. We worked with Caco-2 and primary human intestinal cell, and our results showed that extracellular ATP regulation is a complex network of reactions that simultaneously consume or generate ATP in whole viable intestinal epithelial cells. In particular, we cre- ated a mathematical model and fitted it to experimental data allowing to quantify the degree to which intracellular ATP release and the activity of a variety of ectoenzymes con- trol the concentration of extracellular ATP. 1. Introduction The surface of the intestine is covered by a layer of cells that form the intestinal epithelium. Intestinal epithelial cells play important roles in the absorption of nutrients, secretion of elec- trolytes, digestion of food and host defence mechanisms [1,2]. The function of intestinal epi- thelial cells is strongly influenced by extracellular nucleotides, supporting a complex signalling network that mediates short-term functions such as secretion and motility, and long-term functions like proliferation and apoptosis [3,4]. Among these nucleotides, extracellular ATP (eATP) was found to be an early danger signal response to infection with enteric pathogens that eventually promote inflammation of the gut [4,5]. An important source of eATP is the intracellular ATP (iATP) found in the cytosol and vesi- cles of many cell types [6]. Activation of iATP release was found in subepithelial intestinal fibroblasts, human epithelial cell lines and enteroendocrine cells in response to several stimuli, including agents that elevate cAMP, such as forskolin and cholera toxin [7], low medium phos- phate, hypoosmotic swelling and bacterial infection [7,8]. Currently, several mechanisms have been postulated to mediate regulated iATP release, and these mechanisms can vary according to the cell type and the stimuli [6]. For example, after a hypotonic shock, Schwann cells release ATP via the anionic channel pannexin-1 [9], while the treatment with lipopolysaccharide (LPS) induces ATP release via connexin-43 in macrophages [10]. Additionally, the vesicular release pathway for ATP was also described, as in the case of endothelial cells under hypoxia [11]. Extracellular ATP and other di- and tri-phosphonucleosides can activate purinergic recep- tors 2 (P2 receptors) unevenly distributed in the small and large intestine [12]. Purinergic sig- nalling is controlled by membrane bound ecto-nucleotidases and ecto-kinases capable of promoting the synthesis and/or hydrolysis of eATP, and/or its conversion into other extracel- lular nucleotides and nucleosides. For any cell type and metabolic context, a specific set of ecto-enzymes may control the rate, amount and timing of nucleotide turnover [13]. Ecto-nucleoside triphosphate diphosphohydrolases (Ecto-NTPDases) are a family of enzymes promoting the extracellular hydrolysis of eATP, eADP, eUTP and eUDP. One or PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 2 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells more members of this family are present in almost every cell. Ecto-NTPDase-1, -2, and -3, which differ regarding the specific preferences for nucleotides, are responsible for the hydroly- sis of nucleoside diphosphates (NDPs) and nucleoside triphosphates (NTPs) in various tissues of the gastrointestinal tract [1]. Regarding eATP and eADP hydrolysis, ecto-NTPDase-1 hydrolyses both nucleotides at similar rates, while ecto-NTPDase-2 has a high preference for eATP over eADP and ecto-NTPDase3 is a functional intermediate which preferably hydrolyses eATP [14]. The intestinal cell line HT29 cells expressed functional ecto-NTPDase-2 displaying high ecto-ATPase activity [15], while Caco-2 cells and their exosomes were reported to exhibit ecto- NTPDases-1 and -2 at the cell membrane [16,17]. Extracellular ATP can be also metabolized by ecto-kinases, with ecto-adenylate kinase (Ecto-AK) facilitating the reversible conversion of eADP to eATP and eAMP, and ecto-nucleo- side diphosphate kinase (Ecto-NDPK) promoting the exchange of terminal phosphate between extracellular NDPs and NTPs [13]. All these ecto-enzymes, if present and active, should be able to control the concentration of eATP. Up to now, although some ecto-enzymes have been identified in intestinal cells, no attempts have been made to characterize the dynamic interaction of these membrane proteins on eATP regulation of intestinal cells. In this study, we aimed to characterize iATP release and eATP recycling by ecto-enzymes, contributing to the regulation of eATP concentration ([eATP]) in Caco-2 cell line. The Caco-2 cells derive from colorectal adenocarcinoma and easily differenti- ate into cells exhibiting the morphology and function of enterocytes, the absorptive cells of the small intestine [18]. The experimental studies on eATP dynamics in polarized and non-polar- ized Caco-2 were complemented with a mathematical model quantifying the complex relation- ship among the different processes contributing to [eATP] regulation. Our results provide a quantitative description of the eATP dynamics of human intestinal epithelial cells. 2. Results In this section we show experimental results on eATP kinetics of non-polarized and polarized Caco-2 cells. To understand the dynamics of the different processes contributing to [eATP] regulation, a mathematical model was fitted to experimental data, and predictions were made. Finally, for a comparative purpose, we show results of a few experiments made on epithelial cells obtained from intestinal surgical pieces. 2.1. Non-polarized Caco-2 cells 2.1.1. eATP kinetics after hypotonic shock. The kinetics of eATP accumulation, i.e., eATP kinetics, results from the dynamic balance between iATP release mechanisms and the activities of ecto-enzymes capable of degrading and/or synthetizing eATP. As a first step towards the characterization of eATP kinetics, iATP release was triggered by exposing Caco-2 cells to hypotonic media (Fig 1A–1D). Hypotonic swelling is a stimulus that influences the uptake of nutrients by epithelial cells [19] and represents an inducer of iATP release in most cell types and tissues [8]. Under unstimulated conditions, [eATP] remained stable. Whereas addition of isotonic medium triggered a slight increase of [eATP], hypotonic media (100–180 mOsm) activated a stronger iATP release with different kinetics according to the osmotic gradient imposed (Fig 1A–1C). As shown in Fig 1D, [eATP] increased non-linearly with the magnitude of the hypo- tonic stimulus. The experimental [iATP] amounted to 1.81 mM. By comparing [iATP] with [eATP] along eATP kinetics, it was possible to estimate the energy cost of iATP release. Calculations were PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 3 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells Fig 1. eATP kinetics of hypotonically-stimulated Caco-2 cells. The time course of [eATP] from Caco-2 cells after a hypotonic shock was quantified by luminometry performed at room temperature. (A-C) Cells were maintained in isotonic medium and, at the times indicated by the arrow, were exposed to isotonic medium (grey) or to hypotonic media (blue) of 180 mOsm (A), 150 mOsm (B) and 100 mOsm (C). Results are expressed as means of [eATP] from 4, 3 and 2 independent experiments run in triplicate for the 180, 150 a 100 mOsm experiments, respectively. (D) Increases in [eATP] from data in A-C were evaluated as ΔATP, i.e., the difference between [eATP] at 30 minutes post-stimulus and basal [eATP]. Cells were exposed to 300 mOsm (light blue bars), 180 mOsm (blue bars), 150 mOsm (dark blue bars) and 100 mOsm (grey bars). Bars show mean values + standard error of the mean (s.e.m) from 2 to 5 independent experiments. Points represent the independents values for each condition. https://doi.org/10.1371/journal.pcbi.1011196.g001 made for cells exposed to isotonic or 180 mOsm media, two conditions where no lysis was detected [17]. During the isotonic shock, representing a mechanical stimulus in the absence of osmotic gradient, [eATP] amounted to 0.33% of [iATP], while under 180 mOsm this figure amounted to 3.6%. Thus, the energy cost of eATP production by iATP efflux was very small (see section 4.9 for further details). No iADP release was detected in the 180 mOsm stimulus (S1 Fig). In our previous work, we showed that ecto-nucleotidases present in Caco-2 cells catalyse significant rates of eATP hydrolysis, leading to eADP accumulation [17]. In principle, the resulting accumulated eADP could be used by the potential presence of ecto-kinases like ecto- AK and ecto-NDPK, present in several cell types, to synthetize eATP. Thus, in the following experiments the activities of ecto-AK and ecto-NDPK were assessed by quantifying eATP kinetics under different conditions. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 4 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells Fig 2. Synthesis of eATP from eADP in Caco-2 cells. (A) The time course of [eATP] synthetized from exogenous eADP (6–48 μM) in the extracellular medium of intact Caco-2 cells was quantified by luminometry. The cells were incubated with the luciferin-luciferase reaction mix and the [eADP] indicated in the figure were added at the time indicated by the arrow. Data are means of at least 3 independent experiments run in duplicate for each [eADP]. (B) Effect of treatment with Ap5A (adenylate kinase inhibitor) on eATP synthesis from eADP in Caco-2 cells. The cells were treated or not (w/o treatment) with 10 μM Ap5A and the [eATP] at 30 minutes was measured by luminometry under similar conditions as experiments in (A). The bars are means ± s.e.m. from at 3–5 independent experiments run in duplicate in the absence of Ap5A and 2 independent experiments in the presence of the inhibitor. ND means that there was non-detected [eATP]. https://doi.org/10.1371/journal.pcbi.1011196.g002 2.1.2. Ecto-AK activity in Caco-2 cells. AK catalyses the following reversible reaction: 2 eADP $ eATP + eAMP and is inhibited by Ap5A [20]. Ecto-AK activity was then assessed by following eATP synthesis when Caco-2 cells were incubated with exogenous eADP (6–48 μM). Non-linear [eATP] increases were proportional to [eADP] (Fig 2A). At 30 minutes post-stimu- lus, treatment with 10 μM Ap5A, which does not permeate cells, inhibited eATP synthesis by 100% (6–24 μM eADP) or by 90% (48 μM eADP) (Fig 2B), thus showing the presence of a functional ecto-AK in Caco-2 cells membrane. 2.1.3. Ecto-NDPK activity in Caco-2 cells. NDPK catalyses the transfer of a γ–phosphate from NTP to NDP. Thus, in the presence of eADP and a given eNTP, the following reaction: eADP + eNTP $ eATP + eNDP leads to eATP synthesis when eADP is phosphorylated by NDPK. Accordingly, incubation of cells with 100 μM eCTP at different [eADP] (3–12 μM) resulted in the rapid synthesis of eATP (Fig 3A). Maximal [eATP] values were obtained 30 minutes after the addition of substrates (Fig 3A). The experiments were conducted in the presence of 10 μM Ap5A to rule out any contribution of ecto-AK to the observed eATP kinetics. Addition of 5 mM eUDP, together with 100 μM eCTP and 12 μM eADP, decreased the eATP synthesis by 91% (Fig 3B), a result compatible with high [eUDP] favouring eUDP to eUTP conversion by ecto-NDPK, rather than eATP synthesis from eADP. In separate experiments, addition of increasing [eUTP] (1–100 μM) without the addition of exogenous eADP (only endogenous eADP present), resulted in a concentration-dependent increase of [eATP] (Fig 3C). Because this increase was abolished by 5 mM eUDP (S2 Fig), we hypothesized that eATP synthesis was due to ecto-NDPK activity using exogenous eUTP and endogenous eADP. This is because there is a basal eADP concentration in the extracellular media of 0.77 ± 0.47 μM eADP/mg protein (S3 Fig). A similar experiment using 100 μM eGTP, instead of eUTP, provided qualitatively similar results (Fig 3D). Overall results showed a functional ecto-NDPK activity capable of synthetizing eATP from different γ-phosphate donors (eCTP, eUTP and eGTP) in the presence of endogenous and exogenous eADP. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 5 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells Fig 3. Extracellular synthesis of eATP from eADP and eCTP, eUTP and eGTP in Caco-2 cells. (A) The time course of eATP synthesis from eCTP (100 μM) and eADP (light blue for 3 μM, blue for 6 μM and grey for 12 μM) in the extracellular medium of Caco-2 cells was quantified by luminometry. Cells were incubated with the reaction mix and eCTP and eADP were added at the time indicated by the arrow. Data are means of 3 to 5 independent experiments run in duplicate for each [ADP]. (B) Production of eATP after 30 minutes exposure of Caco-2 cells to 100 μM eCTP and 12 μM eADP. Experiments were run in the presence of 5 mM eUDP (grey bar and squares) or in its absence (blue bar and points). The [eATP] was measured under conditions similar to the experiments in (A). Results are expressed as [eATP] in μM/mg of protein, bars are means ± s.e.m from 4 to 7 independent experiments run in duplicate. * means p- value <0.01 in comparison with the condition without treatment. (C) and (D) The time course of eATP accumulation in the presence of eUTP (C; grey for 100 μM, dark blue for 10 μM, blue for 1 μM) or 100 μM eGTP (D). Data are the means from 4 independent experiments in the case of 100 μM eUTP, 3 in the case of 100 μM eGTP, and 2 independent experiments in the case of 10 μM or 1 μM eUTP. Nucleotides were added at the time indicated by the arrow. https://doi.org/10.1371/journal.pcbi.1011196.g003 2.1.4. Modelling eATP kinetics of non-polarized Caco-2 cells. Caco-2 cells regulate eATP kinetics by iATP release, eATP synthesis by the activities of ecto-AK and ecto-NDPK (as shown in this study), and hydrolysis by ecto-nucleotidases [17]. These processes are active simultaneously when measuring the eATP dynamics in Caco-2 cells, except when a specific inhibitor was added (like Ap5A in the experiments of Fig 3A and 3B). Thus, to quantify the individual contribution of these processes to eATP kinetics, we built a mathematical model that was then fitted to experimental data. Model parameters contain the kinetic information of each enzyme, allowing to assess the individual contribution of ecto-enzymes to eATP dynamics. A scheme of the model is depicted in Fig 4A. In the model, [eATP] can increase by iATP release, by lytic and by non-lytic mechanisms. In addition, [eATP] can be modulated by the activities of ecto-ATPases, ecto-AK and ecto-NDPK. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 6 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells Fig 4. A model of extracellular purinergic regulation in non-polarized Caco- 2 cells. (A) The scheme shows a representation of the model created to explain the experimental results in non-polarized cells. The yellow bolt indicates that the JATP depended on the application of a hypotonic shock. ADO means extracellular adenosine. The green star behind “eATP” indicates that this is the metabolite measured directly during experiments. (B) The plot shows, in red, the model fitting to eATP kinetics exposed to media of different osmolarities (experimental data correspond to those shown in Fig 1A and 1C). (C) iATP efflux (JATP) predicted by the model upon the hypotonic or isotonic shocks indicated in the figure. https://doi.org/10.1371/journal.pcbi.1011196.g004 The model provides functions describing each of the fluxes involved in transport and metabolism of extracellular nucleotides (see S1 Table and section 4.13.1). Fitting the model to the experimental eATP kinetics under the different conditions allowed to obtain the best-fit values for the parameters of these functions (S1 Table). In that way the contributions of each flux to eATP kinetics were quantified, and several predictions were made. 2.1.4.1. iATP release. For experiments under iso- and hypotonic media, the model found a good fit to experimental data (continuous lines in Fig 4B), thus allowing to predict the rate of iATP efflux (JATP) over time (Fig 4C). JATP was rapid and transient in nature, leading to a 12-fold increase of [eATP] to a maximum in less than 2 seconds under the 180 mOsm shock, followed by rapid inactivation. The magnitude of the JATP peak depended on the osmotic gra- dient imposed. Inactivation of JATP was observed under conditions where no lysis was detected (isotonic and 180 mOsm media). On the other hand, a lytic flux (JL) explains the continuous increase of [eATP] at 100 mOsm (Figs 1C and 4B). 2.1.4.2. Ecto-enzymes. Another factor shaping eATP kinetics is eATP hydrolysis by ecto- ATPase activity. We have previously observed that, in intact non-polarized Caco-2 cells, ecto- ATPase activity follows a linear function of micromolar [eATP] [17]. Thus, following a stimu- lus promoting iATP release, any increase of [eATP] should be at least partially counterbal- anced by an increase of ecto-ATPase activity. Model predictions made at 180 mOsm show that the initial peak of [eATP] increase due to JATP is about 8-fold higher than the rate of eATP hydrolysis, i.e., JATP was 1.2 μM iATP/min/ mg of protein (Fig 4C) and eATP hydrolysis was 0.15 μM eATP/min/mg of protein at 1.5 μM eATP (S1 Table). Thus, during the first seconds of [eATP] increase, eATP kinetics was mainly governed by iATP release. At later times, however, the JATP inactivated, and the ecto-ATPase activity progressively gained importance in controlling [eATP]. This is illustrated by modelling a change in the amount of ecto-ATPase over a wide range, showing that a 5-fold increase of ecto-ATPase activity could lead to rapid decay of [eATP], while a 5-fold decrease would pro- long high levels of [eATP] over the entire incubation period (Fig 5A). However, a similar pro- cedure, i.e, increasing or decreasing 5 times the activity of ecto-AK, had no influence on the PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 7 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells Fig 5. Role of ecto-AK, ecto-ATPase and ecto-ADPase activity on eATP dynamics. (A) The simulation shows the [eATP] as a function of time upon a 180 mOsm hypotonic shock when the ecto-ATPase activity displayed its measured value (0.1, continuous line in blue), a 5-fold increase (0.5, dashed line in grey), and a 5-fold decrease (0.02, dashed line in light blue). The numbers in the plot indicate the kinetic constant of the activity in (μM eATP hydrolized)/(mg prot/ μM eATP/ min) units. (B) The simulation shows the [eATP] as a function of time upon a 180 mOsm shock at different initial [eADP] concentrations: calculated pre-stimulus eADP (0.22 μM continuous line in blue), a 3.5-fold increase (0.8 μM, dashed line in grey), and a 14-fold increase (3 μM, dashed line in light blue). (C) The simulation shows the [eATP] as a function of time upon addition of 6 μM eADP (the corresponding experimental results are shown in Fig 2A). The plot shows the eATP kinetics under various values of the kinetic constant for ecto-ADPase, i.e, the constant experimentally determined (0.008, continuous line in blue), a 5-fold increase (0.04, dashed line in grey), and a 12-fold increase (0.96, dashed line in light blue). The numbers in the plot indicate the kinetic constant of the activity in (μM eADP hydrolized)/(mg prot / μM eADP / min) units. (D) Ecto-ATPase, ecto-AK and ecto-ADPase activites as a function of their respective substrates, [eATP] for ecto-ATPase and [eADP] for ecto-AK and ecto-ADPase. The points show the initial velocities for eATP synthesis as a function of [eADP] by ecto-AK calculated from experimental data shown in Fig 2A. The points are means ± s.e.m. from 3 to 5 independent experiments run in duplicate. The continuous lines represent enzyme activities as a function of their respective substrates (see S1 Table for further details). Shadows behind the lines in panels A, B and C represent the uncertainty of the prediction calculated as indicated in section 4.13. Shadows behind the lines in panel D represent the interval ± the standard error of enzyme activity, calculated using the standard error of kinetic parameters of ecto-ATPase and ecto-ADPase activities. https://doi.org/10.1371/journal.pcbi.1011196.g005 [eATP] during the hypotonic shock (not shown). This can be attributed to the sigmoidal kinet- ics of ecto-AK, whose activity is very low below 3 μM eADP, but significantly higher above that concentration (Fig 5D). Thus, ecto-AK might influence eATP kinetics only when [eADP] is sufficiently high. Fig 5B shows a simulation where the initial [eADP] was raised up to 3 μM. At 3 μM eADP, eATP degradation was comparable to eATP synthesis by ecto-AK, indicating that ecto-AK can counterbalance ecto-ATPase activity. Note that a 180 mOsm hypoosmotic PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 8 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells medium does not change the activity of ecto-ATPase, ecto-ADPase, ecto-AMPase [17] or ecto- AK [21] in comparison with isosmotic medium. Another factor to consider is ecto-ADPase activity. We have previously shown that Caco-2 cells displays high ecto-ATPase but very low ecto-ADPase activity [17]. Nevertheless, a hypo- thetical increase of ecto-ADPase activity could negatively modulate ecto-AK activity. For example, an increase of 5- and 12-fold of ecto-ADPase activity would result in a 17% and 33% decrease in the [eATP] production respectively, at 6 μM eADP (Fig 5C). Model predictions showed above implied that the expression of ecto-AK in Caco-2 cells may have an important role in eATP kinetics. To assess this hypothesis, we compared ecto- ATPase, ecto-ADPase and ecto-AK activities as a function of their respective substrate’s con- centrations, that is, [eATP] for ecto-ATPase and [eADP] for ecto-ADPase and ecto-AK (Fig 5D). In Fig 5D, symbols of ecto-AK activities represent the initial velocities for eATP synthesis as a function of [eADP] calculated from experimental data shown in Fig 2A, and the continu- ous line represents the fit to data of the ecto-AK function included in the model (details in S1 Table and in the work of Sheng and collaborators [22]). The ecto-ATPase and ecto-ADPase activities are predictions made from data of our previous work [17]. Ecto-ATPase displayed the highest rate of the three reactions. On the other hand, although at low [eADP], ecto-AK and ecto-ADPase activities are similar and have relatively low values, the sigmoidal kinetics of ecto-AK allows a strong activity increase as [eADP] is raised, thus reaching activity levels well above those of ecto-ADPase activity (Fig 5D). Finally, in the presence of non-adenosine nucleotides, the influence of ecto-NDPK on eATP dynamics was assessed and analysed. Caco-2 cells synthetized eATP by ecto-NDPK activity in the presence of eCTP, eUTP and eGTP as NTP donors, and exogenous and endoge- nous eADP (Fig 3A–3D). The model found a good fit to the experimental eATP kinetics in the presence of 100 μM eCTP and different [eADP] (Fig 6A). Model predictions of ecto-NDPK activity at different [eADP] agreed well with initial velocities of experimental ecto-NDPK activities shown in Fig 3A (Fig 6B). We also studied the effect of eUTP addition without the addition of exogenous eADP (a condition where only endogenous eADP was present, S3 Fig) on the transient rise of [eATP] (Fig 3C, replicated in Fig 6C). To understand the role of eNTPs on ecto-NDPK activity, it is important to recall that ecto-NTPDases of Caco-2 cells can hydrolyse non-adenine nucleotides (S4 Fig). Model predictions show changes in ecto-NDPK and ecto-ATPase activities (Fig 6D), and the corresponding dynamics of [eATP] and [eADP] (Fig 6E), and of [eUTP] and [eUDP] (Fig 6F). Kinetics of eATP (Fig 6C and 6E) could be analysed in 3 stages. First, [eATP] increases due to a high an ecto-NDPK/ecto-ATPase activities ratio in the presence of high [eUTP] and basal [eADP] (stage 1 in Fig 6D, 6E and 6F). The resulting elevated [eATP] activates ecto- ATPase activity, while ecto-NDPK decreases deeply because its substrates (eUTP and eADP) are consumed by ecto-NTPase activity and by ecto-NDPK activity itself. A balance is then estab- lished between ecto-NDPK and ecto-ATPase activities in stage 2, where [eATP] is transiently stable. Finally in stage 3, [eUTP] continues decreasing, leading to a high ecto-ATPase/ecto- NDPK activities ratio, causing [eATP] to decrease and [eADP] to rise again (Fig 6E). 2.2. eATP regulation in polarized Caco-2 cells Because several reports showed differential activities of enzymes and transporters at each side of polarized epithelia [23,24], we speculated that [eATP] regulation might be different at the apical and basolateral sides of polarized Caco-2 monolayers. We then used polarized Caco-2 cells to test the effect of hypotonic shock on iATP release and resulting eATP kinetics at the apical and basolateral sides. Similarly to the procedure PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 9 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells Fig 6. Role of ecto-NDPK on eATP dynamics. (A) The plot shows the experimental results of eATP dynamics in the presence of 100 μM eCTP and various [eADP] (also shown in Fig 3A). Model fitting was applied to data and shown as continuous red lines. (B) The plot shows the ecto-NDPK activity expressed in μM of ATP synthetized per minute per mg of protein. The dots represent the initial velocity of ecto-NDPK (calculated from the experimental data shown in panel A) as a function of [eADP]. Points represent the means ± s.e.m. from 3 independent experiments run in duplicate. The continuous line represents the ecto-NDPK activity predicted by the model (details in S1 Table). (C) The plot shows the experimental eATP dynamics in the presence of various [eUTP] (also shown in Fig 3C) and the continuous red lines represent the model fitting. (D) The plot shows time changes of ecto-NDPK (blue line) and ecto- ATPase (grey line) activities predicted by the model. In the plot, the zones 1 (white background), 2 (pink background) and 3 (white background) represents the [eATP], increase, stabilization and decrease stages respectively. In (E) and (F) the plot shows the model predictions of [eATP] and [eADP], or [eUTP] and [eUDP] respectively as a function of time upon addition of 100 μM eUTP to non-polarized Caco-2 cells. Data is expressed in μM/mg protein, which was calculated by dividing the [eATP] at any time by the average protein mass in the experiments (0.25 mg in average). The shadows behind the lines in panels A, B and C represent the uncertainty of the prediction calculated as indicated in section 4.13. https://doi.org/10.1371/journal.pcbi.1011196.g006 employed for non-polarized cells, we fitted the model shown in Fig 4A to the experimental data to understand quantitatively the mechanisms involved in [eATP] regulation in differenti- ated monolayers of Caco-2 cells. Experimental results show that, following a 180 mOsm hypotonic shock, [eATP] increased at both sides of the monolayers, with qualitatively different kinetics. While at both sides the initial rate of [eATP] increase was fast, apical eATP kinetics achieved a maximum at 1.5 min- utes, followed by a rapid decay. This was not observed in the basolateral domain, where [eATP] continued increasing at a progressively lower rate, and a very slow [eATP] decay was observed only after 20 minutes (Fig 7A). The two different eATP kinetics suggested different activities of ecto-enzymes present at both sides of the monolayers. Therefore, we determined the activities of ecto-ATPase, ecto-AK and ecto-NDPK. For assessing ecto-ATPase activity, polarized Caco-2 cells were exposed to various [eATP] (0.2–7 μM) and eATP hydrolysis was estimated by quantifying [eATP] decay rates (S5 Fig). PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 10 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells Fig 7. Apical and basolateral eATP regulation in Caco-2 monolayers. (A) Effect of hypotonic shock on eATP kinetics. At the times indicated by the arrow, cells were exposed to 180 mOsm medium on the basolateral (grey) or the apical (blue) compartments. Data are the means from 5 independent experiments. (B) Ecto-ATPase activity measured from the eATP kinetics at different [eATP]. Data was obtained from eATP hydrolysis kinetics like the ones shown in S5A and S5B Fig. The points in the plot represent the mean ± s.e.m. of 3 independent experiments. The dashed lines represent a linear regression to the data allowing to obtain the ecto-ATPase kinetic constant which was 1.70 ± 0.08 and 0.36 ± 0.22 mM ATP hydrolized mM ATP mg prot min basolateral compartments respectively. (C) Ecto-AK activity. eATP kinetics in the presence of 12 μM eADP added to the basolateral (grey) or apical (blue) compartments. Data are the means from 2 independent experiments. (D) Ecto-NDPK activity. eATP kinetics in the presence of 100 μM eCTP + 12 μM eADP added to the basolateral (grey) or the apical (blue) compartments. Experiments were run in the presence of 10 μM Ap5A (adenylate kinase blocker). Data are the means from 3 independent experiments. (E) Ecto-AK initial velocities in polarized Caco-2 cells. Data are means + s.e.m. of 4 independent experiments. * indicates a p-value < 0.05 in comparison with the apical condition. (F) Ecto-NDPK initial velocities in polarized Caco-2 cells. Data are means ± s.e.m. of 3 independent experiments. (G) Scheme of the results interpretation showing that the increased activity of Ecto-AK, Ecto-NDPK and Ecto-ATPase leads to a faster eATP turnover. for the apical and https://doi.org/10.1371/journal.pcbi.1011196.g007 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 11 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells The initial rate values of [eATP] decay were used to calculate ecto-ATPase activity at each [eATP], so as to build a substrate curve (Fig 7B). Linear fitting to experimental data showed that ecto-ATPase activity was 4-fold higher in the apical than in the basolateral domain. To assess ecto-AK activity, Caco-2 cells were exposed to 12 μM eADP at the basolateral or apical domains. In the apical domain, [eATP] increased rapidly to a maximum, followed by a rapid decay towards pre-stimulated levels, while basolateral [eATP] increased steadily at a lower rate (Fig 7C). Initial velocity estimations showed that ecto-AK activity was significantly higher in the apical than in the basolateral compartment (Fig 7E). Ecto-NDPK activity was quantified using polarized cells exposed to 100 μM eCTP plus 12 μM eADP in the basal and apical domains. Experiments were run in the presence of 10 μM Ap5A to block ecto-AK activity. Production of eATP by ecto-NDPK was much higher than that observed under conditions used to measure ecto-AK activity, though the domain specific pattern of eATP kinetics was similar when assessing the two ecto-kinases, i.e., a biphasic pat- tern in the apical domain, and a steady [eATP] increase, at a lower rate, in the basal domain (Fig 7D). The initial velocity of ecto-NDPK was higher in the apical than in the basolateral domain although differences were not significant (Fig 7F, p value = 0.1). A good fitting of the model to all experimental data was achieved (red lines in Fig 7A, 7C and 7D). The model fitting allowed to obtain the ecto-NDPK and ecto-AK maximal velocity (Vmax) and compared them with the ones obtained from non-polarized cells (S6A and S6B Fig and S2 Table). Results indicated that the ecto-NDPK maximal activity in the apical compart- ment is a slightly higher than that of the non-polarized cells and significantly higher than that of the basolateral compartment. On the other hand, the ecto-AK maximal activity is signifi- cantly higher compared with the basolateral compartment or the non-polarized cells. Thus, the differences between the apical and basolateral eATP dynamics can be explained by an increase or decrease in ecto-enzymes activities. Altogether experimental results showed significantly higher activities of the ecto-enzymes (ecto-ATPase, ecto-AK and ecto-NPDK) in the apical, as compared to the basolateral domain (Fig 7G). Fig 8. Ecto-AK and ecto-NDPK activities in IECs. Time course of eATP synthetized from exogenous eADP (A) or eCTP and eADP (B) in the extracellular medium of IECs. (A) The cells were incubated with the luciferase-luciferin reaction mix and 12 μM eADP was added at the time indicated by the arrow in presence (grey) or absence (blue) of 10 μM Ap5A (B) 100 μM eCTP plus 12 μM eADP, in the presence of 10 μM Ap5A were added at the time indicated by the arrow. [eATP] was quantified by luminometry. Values are the means of 3 independent experiments run in duplicate. https://doi.org/10.1371/journal.pcbi.1011196.g008 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 12 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells 2.3. Ecto-AK and ecto-NDPK are active in human primary small intestinal epithelial cells Having characterized ecto-AK and ecto-NDPK activities of Caco-2 cells, we wondered whether these ecto-enzymes would be functional in IECs extracted from human small intes- tine. Accordingly, we used samples obtained from small intestine biopsies from healthy donors (Fig 8A and 8B). Results show that exposure of IECs to both 12 μM eADP (to assess ecto-AK) (Fig 8A) or to 12 μM eADP + 100 μM eCTP (to assess ecto-NDPK, Fig 8B) led to significant eATP produc- tion in the micromolar range. Furthermore, as expected, the presence of ApA5 totally inhibited the ecto-AK activity (Fig 8A). 3. Discussion Intestinal epithelial cells can release iATP and express several ecto-enzymes capable of regu- lating the amount and metabolism of eATP at the cell surface. The main goal of this study was to characterize quantitatively the dynamic interplay of iATP release, eATP hydrolysis and eATP synthesis contributing to the dynamic regulation of [eATP] in Caco-2 cells. Spe- cial emphasis was given to the role of ecto-kinases promoting eATP production under differ- ent conditions. Since Caco-2 cells undergo spontaneous enterocytic differentiation in culture, we decided to first approach the complexity of eATP regulation using the relatively simpler non-polarized cell model, and later extend the study to fully differentiated cells. These form apical and baso- lateral poles where morphological and biochemical features are segregated [23]. When exposed to hypotonicity, non-polarized Caco-2 cells triggered a strong iATP efflux that rapidly inactivated, leading to low μM [eATP] accumulation. A number of studies have confirmed that such micromolar [eATP] are capable of activating P2 receptors with high affin- ity for that nucleotide, such as P2Y2, P2Y11 and almost all P2X receptors [25]. In Caco-2 cells, eATP dose dependently activates P2Y receptors involved in the activation of MAPK cascades and transcription factors that promote cell proliferation [26,27], while higher [eATP] can induce apoptosis via P2X7 receptor [3]. In principle, purinergic activation by eATP should be transient, due to the presence of ecto- nucleotidases, the activities of which promotes strong eATP hydrolysis in Caco-2 cells [17]. Accordingly, our results show that hypotonicity induced iATP release and concomitant eATP accumulation, where [eATP] decay was accelerated by constitutive ecto-ATPase activity. This decay was even higher for a model predicted upregulation of eATP hydrolysis by one or more ecto-nucleotidases, as occurs in various cells and tissues during pathogen infection [28], cell differentiation [29] or tumorigenesis [30]. The above results imply that iATP release and eATP hydrolysis constitute two opposing fluxes shaping eATP kinetics of Caco-2 cells. However, the presence of ecto-kinases found in this study suggest that the dynamic regulation of [eATP] should also take the activities of these enzymes into account. In this respect, addition of exogenous eADP to Caco-2 cells dose dependently increased [eATP]. The fact that eATP synthesis was almost fully blunted by Ap5A, an AK blocker that does not permeate intact cells, suggested the presence of a functional ecto-AK. Results of the mathematical model allowed to envisage the contribution of ecto-AK to eATP kinetics. In the absence of exogenous eADP, the contribution of ecto-AK to eATP kinetics was negligible, so that [eATP] depended mainly on the balance between the rates of iATP release and eATP hydrolysis. This is due to the low endogenous [eADP] present under the experimental condi- tions. However, due to the sigmoidal nature of the AK reaction, model predictions show that PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 13 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells increasing [eADP] in the low micromolar range, suffices to promote significant eATP synthe- sis by ecto-AK, upregulating eATP kinetics. Thus, under certain conditions, e.g., when cell leak intracellular ADP (iADP) or eADP is supplied paracrinally by other cell types, eATP synthesis by ecto-AK of Caco-2 cells will transiently stabilize [eATP] levels, thereby favouring propaga- tion of eATP-dependent purinergic signalling. A similar stabilizing role of ecto-AK on [eATP] has been proposed for HT29 cells, lung epithelial cells and lymphocytes [14,15,31]. Modelling shows that ecto-ADPase activity, which facilitates eADP degradation, may com- pete with ecto-AK for the available eADP. However, Caco-2 cells -as HT29 cells [15]- displayed a relative low ecto-ADPase activity, in agreement with the presence of a functional ecto- NTPDase 2 in both cell types [15,17], and in addition the intrinsic sigmoidal nature of ecto- AK activity makes ecto-AK more sensitive to [eADP] than ecto-ADPase. Another consequence of ecto-AK activation relates to P1 signalling, since activity of this enzyme will provide eAMP from eADP for further hydrolysis to adenosine by ecto-5’NT present in Caco-2 cells [17], finally leading to extracellular adenosine accumulation. Our model predictions show how increasing [eADP] in the low μM range might lead to substantial adenosine accumulation, which may engage 4 different P1 receptors [32]. The con- sequences of P1 signalling on proliferation of Caco-2 cells and several other intestinal epithelial cell lines have been studied before [33]. In general, the balance between P1 and P2 receptors on epithelial cells regulate intestinal secretion [34–37] and absorption [38,39]; responses trig- gered by the P2 receptor stimulation by eATP and other nucleotides are sometimes counter- acted by P1 receptor stimulation by adenosine, though the potential role of ecto-AK was not considered in this context. Another factor affecting eATP kinetics is ecto-NDPK. Activity of this enzyme was detected in many cells and tissues such as astrocytoma cells [40], endothelial cells [41,42], lymphocytes [41], keratinocytes [43] and hepatocytes [44]. In general, ecto-NDPK will pri- marily serve to transfer phosphate groups between different extracellular nucleotides and thus potentially alter the pattern of P2 receptor activation. This is especially important since P2 receptor subtypes are differentially selective for adenine and uridine eNDPs and eNTPs [45,46]. Our results show that ecto-NDPK can use eCTP, eGTP and eUTP to phosphorylate eADP to eATP. As model predictions show, activities of ecto-NDPK (promoting eATP synthesis from eUTP and eADP) and ecto-nucleotidase (promoting eATP and eUTP hydrolysis) change in opposite directions to transiently stabilize [eATP]. Results analysed above show that, in non-polarized Caco-2 cells, [eATP] can increase by iATP release and ecto-kinase mediated eATP synthesis and decrease by ecto-nucleotidases mediated by eATP hydrolysis. Next, we studied eATP dynamics of polarized Caco-2 cells. These cells differentiate sponta- neously into polarized cells, with apical and basolateral domains exhibiting morphological and biochemical features of small intestine enterocytes [23,47]. In particular, the Caco-2 polarized phenotype is characterized by high levels of hydrolases typically associated with the brush bor- der membrane. The fact that in a variety of epithelia several ecto-nucleotidases and ecto-phos- phatases preferentially -but not exclusively- locate in the apical domain [48–50], anticipated a different eATP regulation at both poles of Caco-2 cells. Accordingly, hypotonically induced eATP kinetics had a faster resolution and was more effectively regulated at the apical, than at the basolateral side, a result in agreement with the observed higher apical (than basolateral) ecto-ATPase activity measured in this study. This is in agreement with several reports using intestinal epithelial cell from murine models and human intestinal cell lines, showing that various isoforms of ecto-NTPDases, ecto-phospha- tases and ecto-NPPases are preferentially located in the apical domain [50]. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 14 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells The mechanism mediating iATP release at both sides of the polarized Caco-2 cell mono- layer requires further investigation. We previously showed that iATP release in Caco-2 cells challenged by adrenergic stimulation or the presence of bacteria is reduced by treatment with carbenoxolone, a blocker of conductive iATP efflux [51]. Since the molecular mechanisms involved in iATP release may depend on the stimulus, further investigations will be conducted to clarify this topic. A qualitatively similar pattern was observed for ecto-AK and ecto-NDPK of Caco-2 cells, in that apical activities were much higher. Interestingly, the model describing eATP dynamics of non-polarized cells could be successfully fitted to eATP kinetics on each of the polarized domains, thus allowing to calculate the consequences of ectoenzymes sorting on eATP regulation. The results obtained with primary human IECs suggest that our model could be employed to explain the eATP kinetics in these cells, given the presence of both ecto-AK and ecto-NDPK. However, further studies would be required to adapt the Caco-2 model to IECs. These studies may need to account for bacterial periplasmic ATPases capable of hydrolyzing eATP, as we and others have shown that commensal bacteria can express periplasmic nucleotidases and release ATP into the intestinal lumen [51,52]. In this context, the model presented here may be taken as a starting point to progressively add other processes affecting [eATP] regulation in vivo. The fact that the apical domain exhibited a higher turnover of extracellular nucleotides, leading to higher eATP regulation may have adaptive value, considering that iATP release is a common response of epithelial intestinal cells to enteric pathogens [53]. Extracellular ATP may then act as a danger signal controlling a variety of purinergic responses aimed at defend- ing the organism from a variety of pathogens and their toxins present in the intestinal lumen. 4. Materials and methods 4.1. Ethics statement The protocol for handling human samples was approved by the Institutional Review Board of the Favaloro Foundation University Hospital (DDI [1587] 0621) and has been performed in accordance with the ethical standards laid down in the declarations of Helsinki and Istanbul. Informed written consent was obtained from donors. 4.2. Chemicals All reagents were of analytical grade. Bovine serum albumin (BSA), malachite green, adeno- sine 50 -triphosphate (ATP), adenosine 50 -diphosphate (ADP), cytidine 50-triphosphate diso- dium salt (CTP), adenosine 50 -monophosphate (AMP), uridine 5’-triphosphate (UTP), uridine 5’-diphosphate (UDP), guanosine-5’-triphosphate (GTP), phosphate-buffered saline (DPBS), 4-(2-hydroxyethyl)-1-piperazineetahnesulfonic acid (HEPES), ammonium molyb- date, Triton X-100, phenylmethylsulphonyl fluoride (PMSF), pyruvate kinase, phosphoenol- pyruvate (PEP), luciferase, coenzyme A and P1,P5-Di (adenosine-5´) pentaphosphate pentaso- dium salt (Ap5A) were purchased from Sigma-Aldrich (St Louis, MO, USA). D-luciferin was purchased from Molecular Probes Inc. (Eugene, OR, USA). 4.3. Solutions In the experiments to measure [eATP] by luminometry (section 4.5), cells were incubated with isotonic buffer called isosmotic DPBS (300 mOsm) containing: 137 mM NaCl, 2.7 mM KCl, 1 mM CaCl2, 2 mM MgCl2, 1.5 mM KH2PO4 and 8 mM Na2HPO4, pH 7.4 at 37˚C (assay medium). When applying a hypotonic shock to cells, the medium was changed for other con- taining the same components but with a lower NaCl concentration. Thus, DPBS with 100, 150 PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 15 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells and 180 mOsm were prepared. The osmolarity of all media was measured with a vapor pres- sure osmometer (5100B, Lugan, USA). When measuring phosphate (section 4.8.4) the following medium without phosphate was employed instead of isotonic buffer: 145 mM NaCl, 5 mM KCl, 1 mM CaCl2, 10 mM HEPES, and 1 mM MgCl2, pH 7.4 at 37˚C. 4.4. Caco-2 cell culture Caco-2 cells (ATCC, Molsheim, France) were grown in Dulbecco’s modified Eagle’s medium (DMEM-F12, Gibco, Grad Island, NY, USA) containing 4.5 g/L glucose (Sigma-Aldrich, St Louis, MO, USA) supplemented with 10% v/v fetal bovine serum (Natocor, Co´rdoba, Argen- tina), 2 mM L-glutamine (Sigma-Aldrich, St Louis, MO, USA), 100 U/mL penicillin, 100 μg/ mL streptomycin and 0.25 μg/mL fungizone (Invitrogen, Carlsbad, CA, USA) in a humidified atmosphere of 5% CO2 at 37˚C. For eATP kinetics measurements cells were directly seeded on glass coverslips. For ecto-nucleotidase activity experiments using the malachite green method, cells were seeded in cell culture 24-well plates (Corning Costar, NY, USA). 4.4.1. Polarisation of Caco-2 cells. For preparation of polarized Caco-2 monolayers, cells were seeded in permeable supports (inserts) made of polyester (Transwell; 0.1 μm pore size, 1.12 cm2 cell growth area; Jet Biofil, China) in 12-well plates at a density of 3 × 104 cells/0.5 mL per insert. The medium was changed after 3 days, and then after every 3 or 4 days. The polar- ized Caco-2 monolayers were used for experiments after the transepithelial electrical resistance reached a plateau (approximately 21 days after seeding). In polarized and non-polarized cul- tures contamination (including Mycoplasma) was routinely tested. 4.5. Human Intestinal Epithelial Cells (IECs) isolation IECs were isolated from ileum biopsies collected from healthy volunteers who were endoscopi- cally evaluated for colon cancer (N = 3) at the Favaloro Foundation University Hospital. Sam- ples of non-tumoral, non-injured intestinal biopsies were collected and transported in ice-cold Hanks’s balanced salt solution (HBSS) for immediate processing. The biopsies were incubated in 5 mM ethylenediaminetetra-acetic acid (EDTA) and 1.5 mM dithiothreitol HBSS with agita- tion for 25–30 minutes at room temperature to obtain IECs. Cells were pelleted, re-suspended in DPBS and used immediately. 4.6. ATP measurements The [eATP] of non-polarized Caco-2, polarized Caco-2 monolayers or IECs was measured using the firefly luciferase reaction (EC 1.13.12.7, Sigma-Aldrich, St Louis, MO, USA), which catalyses the oxidation of D-luciferin in the presence of ATP to produce light [54]. As described below, using this method it was possible to determine eATP kinetics, the iATP con- tent and the activities of ecto-enzymes. Before the experiments, the cells were washed two times with the assay medium (isosmotic DPBS with or without Pi). In this work, the cells’ medium was substituted by the assay medium before any measure- ment, therefore exoenzymes (enzymes released to extracellular medium not bound to the membrane) were removed and only ecto-enzymes (membrane bound extracellular enzymes) were investigated. 4.7. eATP kinetics of non-polarized Caco-2 and IECs Non-polarized Caco-2 cells and IECs were seeded on glass coverslips. Under all conditions cells were mounted in the assay chamber of a custom-built luminometer, as previously PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 16 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells described [55]. Because luciferase activity at 37˚C is only 10% of that observed at 20˚C [56], to maintain full luciferase activity, [eATP] measurements were performed at room temperature. The setup allowed continuous measurements of [eATP] by the luciferin-luciferase reaction. A calibration curve was used to transform the time course of light emission into [eATP] versus time. Increasing [eATP] from 13 to 1000 nM were sequentially added to the assay medium from a stock solution of pure ATP dissolved in isosmotic or hypotonic medium, according to the experiment. Calibration curves displayed a linear relationship within the range tested. After each experiment, cells were lysed with a solution containing 1 mM PMSF and 0.1% of Triton X-100 and the protein contents of each sample were quantified [57]. Results were expressed as [eATP] at every time point of a kinetics curve denoted as “eATP kinetics”, with [eATP] expressed as μM of eATP/mg protein in a final assay volume of 100 μL. 4.8. eATP kinetics of polarized monolayers Polarized Caco-2 cells monolayers were placed in the insert physically separating an apical and a basolateral compartment. Detection of eATP was performed separately on either side, by adding the luciferin-luciferase mixture in one compartment (apical or basolateral) and adding isosmotic DPBS to the other side. In preliminary experiments, we observed that the luciferin- luciferase mix added in one compartment did not cross the monolayer into the other compart- ment. Thus, luminescence registered when measuring the [eATP] in one compartment was not contaminated by light from the other compartment due to luciferin-luciferase leakage. When an hypoosmotic shock was applied, a luciferin-luciferase mix in DPBS with an osmo- larity of 180 mOsm was added to the compartment of interest while, isosmotic DPBS was added to the other side. 4.9. Activities of ecto-enzymes Ecto-ATPase, ecto-AK and ecto-NDPK activities of intact cells were measured by luminome- try (section 4.5). Ecto-nucleotidase activities were measured by measuring the inorganic phos- phate (Pi) release. 4.9.1. Ecto-ATPase activity Cells were exposed to different [eATP] (0.2, 1.2, 4.2 or 7 μM). Following an acute increase of [eATP], ecto-ATPase activity was estimated from the initial velocity of eATP decay at each [eATP]. 4.9.2. Ecto-AK activity. Cells were exposed to different [eADP] (6, 12, 24 or 48 μM) and the eATP kinetics was quantified in the absence and presence of 10 μM Ap5A (an AK blocker). Ecto-AK initial velocity was estimated as indicated in section 4.12. 4.9.3. Ecto-NDPK activity. Cells were exposed to different [eADP] (3, 6 or 12 μM) in the presence of eCTP (100 μM), eGTP (100 μM) or eUTP (1, 10 or 100 μM). Then, the eATP kinet- ics was quantified in the presence of Ap5A to block the eADP to eATP conversion by ecto-AK activity. In some experiments 5 mM eUDP was added to inhibit ecto-NDPK activity. Ecto- NDPK initial velocity was estimated as indicated in section 4.12. 4.9.4. Ecto-NTPDase activities. Cells were incubated with 500 μM of eCTP, eUTP or eGTP at 37˚C. Samples were taken at 30, 60, 90 and 120 minutes after nucleotides addition and, the inorganic phosphate concentration was measured by the malachite green method [17,58]. Activities measured in section 4.8.1 were expressed as μM of eATP hydrolysed per minute, normalized by the cell protein mass in the experimental sample (μM of eATP /mg protein/ min). Results from experiments explained in sections 4.8.2 and 4.8.3 were expressed as μM of PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 17 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells eATP synthetized per minute, normalized by the cell protein mass in the experimental sample (μM of eATP /mg protein/min). Activities measured in section 4.8.4 were expressed as μM of inorganic phosphate released per minute, normalized by the cell protein mass in the experi- mental sample (μM of Pi /mg protein/min). 4.10. Intracellular ATP measurements Caco-2 (0–30,000 cells) were laid on coverslips, incubated with 45 μL of luciferin-luciferase reaction mix for 5 minutes and subsequently permeabilized with digitonin (1.6 mg/mL final concentration). Light emission was transformed into [eATP] as a function of time as indicated in section 4.6. After considering the total volume occupied by Caco-2 present in the chamber, and the relative solvent cell volume (3.66 μl per mg of protein) [59], [iATP] was calculated in mM. To calculate the % of iATP release, the following equation was employed: %iATP ¼ 100 x ATPcell ð1Þ where ATPcell represents the [ATP] obtained when iATP from all cells is released into the assay medium. The "x" denotes the [eATP] measured at any time. The value of ATPcell was 66 μM/mg protein and was calculated by multiplying the [iATP] (1.8 mM, section 2.1.1) by the Caco-2 cell volume (3.66 μl per mg of protein [59]) and diving by the assay volume (0.1 mL). 4.11. Extracellular ADP measurements For detection of eADP of intact Caco-2 cells, 3 U/100 μL of pyruvate kinase and 100 μM PEP were added to the luciferin-luciferase mix. Using PEP as a substrate, pyruvate kinase promotes the stoichiometric conversion of eADP into eATP [60]. The resulting eATP was then mea- sured by light emission using the luciferin-luciferase procedure described above. 4.12. Data analysis Statistical significance was determined using the non-parametric Mann-Whitney test. Data were analyzed and graphically represented using GraphPad Prism software v5.0 (Graph Pad Software, San Diego, CA, USA). Each independent experiment was carried out in an indepen- dent cell culture or tissue sample in a different day. 4.13. Initial velocity estimation To measure the initial velocity of Ecto-AK or Ecto-NDPK, the eATP dynamics were measured as indicated in section 4.8.2 and 4.8.3. Only the values of [eATP] obtained during the first 5 minutes after substrates addition were considered for further analysis. The following equation was fitted to experimental data: ½ eATP � ¼ Að1 (cid:0) e(cid:0) k timeÞ ð2Þ where A and k are parameters, whose value are optimized to achieve a good fitting of Eq 2 to experimental data. The initial velocity is the derivative of [eATP] as a function of time at time 0 (the time when substrates were added). Thus, the initial velocity was calculated by multiply- ing the value of A by the value of k. 4.14. Mathematical modelling Chemical models of extracellular nucleotides were built using COPASI (Complex Pathway Simulator) software in version 4.29 (source: https://copasi.org/) [61]. Parameter optimization PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 18 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells was performed using COPASI “parameter estimation function” with Hooke & Jeeves (50 itera- tion steps, 10−5 tolerance and 0.2 rho factor), Levenberg-Marquardt (2000 iteration steps, 10−6 tolerance), or Evolutionary programming (200 generations with a 20 population size) as opti- mization methods. An initial guess of the parameter value was proposed based on literature data for each kinetic step. A detailed description of the models employed in this work can be found in S1 and S2 Tables. Parameters obtained from the model fitting are expressed as the best value ± standard deviation. The COPASI and SBML files of the models described in sec- tion 4.13.1 and 4.13.2 can be found in the data repository (see data availability statement). When performing time course simulations, we used the deterministic LSODA method with default parameters in COPASI version 4.29. Specifically, we set the relative tolerance to 10−6, the absolute tolerance to 10−12, and allowed for a maximum of 105 internal steps without a limit on maximal step size. Model prediction uncertainties were calculated using the parameter scan task in COPASI, which explores the parameter space within the interval given by the parameter best value ± the standard error (S1 Table). Four parameter values were considered within the range of the parameter best value ± the standard error, one at the lowest value of the interval, one at the highest and two in the middle. Simulations were performed by testing all possible combina- tions of the selected parameter values. The shaded regions depicted in Figs 5 and 6 correspond to the area that includes all simulations results obtained by varying the parameters values. In Fig 5, the prediction uncertainty was calculated by simulating the eATP kinetics while varying the parameters KATP, KADP, Vmax Ecto-AMPase and FtrAK. These are the kinetic parameters that control eATP kinetics under the hypotonic stimulus. In Fig 6D, 6E and 6F, the prediction uncertainty was calculated by simulating the nucleotides kinetics by varying the parameters KATP, KmAD, KmUTP Vmax NDPK and KNTPase. 4.1.4.1. A model of purinergic homeostasis in non-polarized Caco-2 cells. To explain the experimental observations, a data driven mathematical model was created (depicted in Fig 4A). The model has 7 reactions to explain the chemical fluxes of transformations or transport of extracellular nucleotides in Caco-2 cells: JATP, JEcto-ATPase, JEcto-ADPase, JEcto-AMPase, JEcto-AK, JEcto-NDPK and JEcto-NTPDase. A detailed description of each flux, its mathematical description and parameters can be found in S1 Table. In the model, the concentration of each species as a function of time was calculated from the following differential equations: � ½ @ eATP @t ¼ JATP (cid:0) ð JEcto(cid:0) ATPase þ JEcto(cid:0) AK þ JEcto(cid:0) NDPK Þ � ½ @ eADP @t ¼ JEcto(cid:0) ATPase (cid:0) JEcto(cid:0) ADPase þ 2∗JEcto(cid:0) AK þ JEcto(cid:0) NDPK � ½ @ eAMP @t ¼ JEcto(cid:0) ADPase (cid:0) ð JEcto(cid:0) AK þ JEcto(cid:0) AMPase Þ � ½ @ eADO @t ¼ JEcto(cid:0) AMPase � ½ @ eCTP @t ¼ JEcto(cid:0) NDPK � ½ @ eCDP @t ¼ (cid:0) JEcto(cid:0) NDPK ð3Þ ð4Þ ð5Þ ð6Þ ð7Þ ð8Þ PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 19 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells � ½ @ eUTP @t ¼ JEcto(cid:0) NDPK (cid:0) JEcto(cid:0) NTPase � ½ @ eUDP @t ¼ (cid:0) JEcto(cid:0) NDPK þ JEcto(cid:0) NTPase ð9Þ ð10Þ Note that in the equations JEcto-AK and JEcto-NDPK were considered in the direction of eATP consumption, i.e., eAMP + eATP $ 2 eADP for JEcto-AK and eNDP + eATP $ eNTP + eADP for JEcto-NDPK. The model was written in COPASI 4.29 and was fitted simultaneously to all experimental data shown in Figs 1A, 1C, 2A, 3A and 3B. The fitting of the model to experi- mental data can be seen in Figs 4B, 5C, 6A and 6C as red lines. Some kinetic parameters of the enzymes catalyzing the reactions were obtained from the lit- erature. Parameters from the Jecto-ATPase and Jecto-ADPase were obtained from our previous work [17]. The Vmax of the Jecto-AMPase reaction was obtained from our previous work [17], while the Km was obtained from the work of Navarro et al. [62]. Kinetic parameters of the Jecto- AK activity were obtained from the work of Sheng et al [22]. The equilibrium constant (Keq) and the affinity for ATP (KmAT) of the Jecto-NDPK were obtained from the work of Garces and Cleland [63]. The affinity constants for product inhibition in Jecto-NDPK (KiNDP and KiADP) were estimated from the work from Lascu et Gonin [64]. The rest of the model parameters were obtained from model fitting to experimental data (see S1 and S2 Tables for more details). The shape of the JATP flux as a function of time was modeled based on findings of a previous work from our group [65]. 4.1.4.2. A model of purinergic homeostasis in polarized Caco-2 cells. The model fitted to experimental data from the apical and basolateral compartments data is the same model indicated in section 4.13.1, although the parameters of some reactions were fitted again (S2 Table). The JATP expression for the 180 mOsm hypotonic shock in the polarized cells was dif- ferent from the one employed on non-polarized cells (S2 Table). The mathematical expressions of the other 6 reactions were not modified. Four parameters were refitted to the data to account for differences in the ecto-ADPase, ecto-AK and ecto-NDPK activities after polariza- tion (values can be found in S2 Table). Moreover, in the case of ecto-NTPDase, the eCTP hydrolysis could not be neglected in the apical compartment and was necessary to achieve a good fit to experimental data. In contrast the eCTP hydrolysis could be avoided in the basolat- eral compartment without affecting model fitting. This suggest that the ecto-CTPase activity is greater in the apical than in the basolateral compartment, in agreement with the increased activity of other enzymes on the apical side. The differential equations for [eCTP] and [eCDP] are modified in the apical side model to account for the eCTP hydrolysis: � ½ @ eCTP @t ¼ JEcto(cid:0) NDPK (cid:0) JEcto(cid:0) NTPase � ½ @ eCDP @t ¼ (cid:0) JEcto(cid:0) NDPK þ JEcto(cid:0) NTPase ð11Þ ð12Þ The models for the apical and basolateral compartments were written in COPASI 4.29 and fit- ted to experimental data shown in Fig 7A, 7C and 7D. The COPASI files can be found at https://doi.org/10.6084/m9.figshare.21938651.v1. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 20 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells Supporting information S1 Fig. iADP release estimation. Increase in [eATP] after a 180 mOsm hypotonic shock in absence (blue) or presence (grey) of PK (3 U) and PEP (100 μM) were evaluated as ΔATP, i.e., the difference between [eATP] at 1 min post-stimulus and basal [eATP]. (TIF) S2 Fig. Inhibition by exogenous eUDP of ecto-NDPK activity in the presence of eCTP and eADP. Time course of eATP accumulation in the presence of 100 eUTP μM in the absence (blue) or in the presence of 5 mM eUDP (grey). The data showed are the means of 3 indepen- dent experiments. (TIF) S3 Fig. Measurement of eADP by the conversion to eATP. Caco-2 cells were incubated with luciferin-luciferase and, at the time indicated with the arrow, PK (3 U) and PEP (100 μM) were added. The value of the [eADP] in resting conditions was 0.77 ± 0.44 μM eADP/mg. Given a usual protein cell mass of 0.2 mg, the [eADP] in resting conditions is 0.15 ± 0.09 μM. The data showed are the means of 5 independent experiments. (TIF) S4 Fig. Ecto-nucleotidase activity of Caco-2 cells. Experiments were performed in assay medium without Pi at room temperature, and Pi production was measured by the malachite green method (section 4.9.4). The time course of Pi accumulation in the extracellular media of Caco-2 cells was measured and values of enzyme activity were derived from initial rates of nucleotides hydrolysis for 500 μM of eUTP (grey), eGTP (blue) and eCTP (light blue). The data are the means of ± s.e.m. from 3 to 5 independent experiments. (TIF) S5 Fig. Basolateral and apical ecto-ATPase activity of Caco-2 cells. eATP kinetics of cells exposed to [eATP] (0.2–7 μM). Levels of [eATP] were measured by luminometry at the baso- lateral (A) and apical (B) sides of the polarized Caco-2 monolayers. Data is the mean of 3 inde- pendent experiments run in duplicate. The initial velocity of the ecto-ATPase activity was calculated by linear regression to experimental data obtaining the slope and y-intercept of the line. The slope represented the eATP hydrolysis as a function of time, i.e. the ecto-ATPase activity at each [eATP] and in each compartment. (TIF) S6 Fig. Enzyme Vmax calculated from model fitting. The plot shows the enzymes’ Vmax in the apical and basolateral compartments, and in non-polarized cells. The ecto-NDPK Vmax (A) were obtained from model fitting to experimental data and are the same shown in S2 Table (for the apical and basolateral compartments) and in S1 Table (for the non-polarized cells). The ecto-AK Vmax (B) was calculated from the model parameters using the following formula: FtrAK k(cid:0) 2k1 , where the FtrAK was obtained from the model fitting (S2 Table for the apical and baso- k(cid:0) 2þk1 lateral compartments and S1 Table for the non-polarized cells). The k-2 and k1 parameters value can be found in S1 Table. (TIF) S1 Table. Mathematical model of eATP regulation in non-polarized Caco-2 cells. Numeri- cal values of constants were normalized by the protein cell mass in the experiments (Mcell), measured by the Bradford method (section 4.7 in the manuscript). Parameter fitting and simu- lations were performed by selecting the average cell mass in the experiments (Mcell = 0.2 mg). JL and JNL represent the lytic and non-lytic iATP release respectively upon an osmotic shock. PLOS Computational Biology \| https://doi.org/10.1371/journal.pcbi.1011196 June 29, 2023 21 / 26 PLOS COMPUTATIONAL BIOLOGYeATP recycling in human epithelial intestinal cells The value of these terms was 0 before shock application. Jleakage represents a constant and small iATP release observed in the absence of any stimulus. The parameter values obtained from the model fitting are expressed as the best value ± standard deviation. (XLSX) S2 Table. A: JNL parameters obtained from fitting to experimental data at 180 mOsm shock in apical or basolateral compartments in polarized cells. The same value of kobs was considered for both compartments. Parameters obtained from the model fitting are expressed as the best value ± standard deviation. B: Parameters obtained from model fitting to experimental in apical or basolateral compartments in polarized cells. The model equations are the same shown in S1 Table, however, some parameters values were fitted again to experimental data from polarized cells. The parameters whose value has changed in comparison with the model of non-polarized cells are shown in this file. The rest of the parameters had the same value for non-polarized cells (shown in S1 Table). The KADPase and KNTPase (for eCTP) were considered 0 in the basolateral compartment. This does not mean that there is no ecto-ADPase or ecto- NTPase activity in the basolateral side but, they can be neglected in our experimental condi- tions. Parameters obtained from the model fitting are expressed as the best value ± standard deviation. (XLSX) Acknowledgments We are thankful to Dr. Cafferata for providing the Caco-2 cells. Author Contributions Conceptualization: Nicolas Andres Saffioti, Pablo Julio Schwarzbaum, Julieta Schachter. Data curation: Nicolas Andres Saffioti, Pablo Julio Schwarzbaum. Formal analysis: Nicolas Andres Saffioti, Julieta Schachter. Funding acquisition: Pablo Julio Schwarzbaum, Julieta Schachter. Investigation: Nicolas Andres Saffioti, Cora Lilia Alvarez, Zaher Bazzi, Marı´a Virginia Genti- lini, Julieta Schachter. Methodology: Nicolas Andres Saffioti, Cora Lilia Alvarez, Gabriel Eduardo Gondolesi, Pablo Julio Schwarzbaum, Julieta Schachter. Project administration: Pablo Julio Schwarzbaum, Julieta Schachter. Resources: Julieta Schachter. Supervision: Pablo Julio Schwarzbaum, Julieta Schachter. Writing – original draft: Nicolas Andres Saffioti, Pablo Julio Schwarzbaum, Julieta Schachter. Writing – review & editing: Nicolas Andres Saffioti, Cora Lilia Alvarez, Pablo Julio Schwarz- baum, Julieta Schachter. References 1. Lavoie EG, Gulbransen BD, Martı´n-Satue´ M, Aliagas E, Sharkey KA, Se´ vigny J. Ectonucleotidases in the digestive system: focus on NTPDase3 localization. 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10.1126_sciadv.adf9336.pdf	null	null	Supplementary Materials for Reconstitution of morphogen shuttling circuits Ronghui Zhu et al. Corresponding author: Michael B. Elowitz, melowitz@caltech.edu Sci. Adv. 9, eadf9336 (2023) DOI: 10.1126/sciadv.adf9336 The PDF file includes: Supplementary Text Figs. S1 to S11 Tables S1 to S4 Legends for movies S1 to S8 References Other Supplementary Material for this manuscript includes the following: Movies S1 to S8 Supplementary Text The mathematical model of BMP4-Chordin-Twsg1-BMP-1 circuit Here we introduce the mathematical model of BMP4-Chordin-Twsg1-BMP-1 circuit, which is based on previous models (10, 20, 30) but specifically incorporating the components analyzed here (Fig. 4A), including mobile extracellular components BMP4 ([𝐵𝑀𝑃4]), Chordin ([𝐶ℎ𝑜𝑟𝑑𝑖𝑛]), Twsg1 ([𝑇𝑤𝑠𝑔1]), BMP4-Chordin complex ([𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛]) and BMP4-Chordin-Twsg1 ([𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛-𝑇𝑤𝑠𝑔1]) complex, as well as immobile components receptors ([𝑅]), BMP4- receptor complex ([𝐵𝑅]), and fluorescent reporters ([𝐶𝑖𝑡𝑟𝑖𝑛𝑒]). We used a set of reaction-diffusion partial differential equations for mobile components. Thus, their equations have two parts. The first part is a diffusion term 𝐷∇!𝑐, where 𝑐 can be [𝐵𝑀𝑃4], [𝐶ℎ𝑜𝑟𝑑𝑖𝑛], [𝑇𝑤𝑠𝑔1], [𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛], [𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛-𝑇𝑤𝑠𝑔1], and 𝐷 can be 𝐷", 𝐷#, 𝐷$, 𝐷"#, 𝐷"#$, correspondingly. We used the effective diffusion coefficients for each mobile component, taking into account the effects of their interactions with extracellular matrix components such as heparan sulfate polyglycans, but not BMP4-receptor interactions. The second part contains reaction terms. First, BMP4 can bind to Chordin weakly, with estimated association rate 𝑘"# and dissociation rate 𝑟"#. Then BMP4-Chordin complex can further interact with Twsg1 to form BMP4-Chordin-Twsg1 complex, with association rate 𝑘"#$ and dissociation rate 𝑟"#$. Furthermore, BMP4 can bind to receptors with estimated association rate 𝑘% and dissociation rate 𝑟%. Finally, Chordin in its free form or in complex forms can be cleaved by BMP-1 secreted by Receiver-B1 cells with a rate 𝛽 dependent on BMP-1 expression level, and BMP4 and Twsg1 can be released from BMP4-Chordin and BMP4-Chordin-Twsg1 complex once Chordin is cleaved. Thus, we can write, 𝜕[𝐵𝑀𝑃4]/𝜕𝑡 = 𝐷"∇![𝐵𝑀𝑃4] − 𝑘"#[𝐵𝑀𝑃4][𝐶ℎ𝑜𝑟𝑑𝑖𝑛] + 𝑟"#[𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛] − 𝑘%[𝐵𝑀𝑃4][𝑅] + 𝑟%[𝐵𝑅] + 𝛽[𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛] + 𝛽[𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛-𝑇𝑤𝑠𝑔1] 𝜕[𝐶ℎ𝑜𝑟𝑑𝑖𝑛]/𝜕𝑡 = 𝐷#∇![𝐶ℎ𝑜𝑟𝑑𝑖𝑛] − 𝑘"#[𝐵𝑀𝑃4][𝐶ℎ𝑜𝑟𝑑𝑖𝑛] + 𝑟"#[𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛] − 𝛽[𝐶ℎ𝑜𝑟𝑑𝑖𝑛] 𝜕[𝑇𝑤𝑠𝑔1]/𝜕𝑡 = 𝐷$∇![𝑇𝑤𝑠𝑔1] − 𝑘"#$[𝑇𝑤𝑠𝑔1][𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛] + 𝑟"#$[𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛-𝑇𝑤𝑠𝑔1] + 𝛽[𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛-𝑇𝑤𝑠𝑔1] 𝜕[𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛]/𝜕𝑡 = 𝐷"#∇![𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛] + 𝑘"#[𝐵𝑀𝑃4][𝐶ℎ𝑜𝑟𝑑𝑖𝑛] − 𝑟"#[𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛] − 𝑘"#$[𝑇𝑤𝑠𝑔1][𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛] + 𝑟"#$[𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛-𝑇𝑤𝑠𝑔1] − 𝛽[𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛] 𝜕[𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛-𝑇𝑤𝑠𝑔1]/𝜕𝑡 = 𝐷"#$∇![𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛-𝑇𝑤𝑠𝑔1] + 𝑘"#$[𝑇𝑤𝑠𝑔1][𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛] − 𝑟"#$[𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛-𝑇𝑤𝑠𝑔1] − 𝛽[𝐵𝑀𝑃4-𝐶ℎ𝑜𝑟𝑑𝑖𝑛-𝑇𝑤𝑠𝑔1] Note that Twsg1 can also interact with BMP4 and Chordin individually (71), so there exist multiple interaction routes of forming the final BMP4-Chordin-Twsg1 complex. However, since interactions between Twsg1 and BMP4 or Chordin are much weaker than interactions between BMP4 and Chordin, we only consider one interaction route (BMP4 first interacts with Chordin, then BMP4-Chordin interacts with Twsg1) in our model. For the immobile BMP4 receptors ([𝑅]), other than reversible interactions with BMP4 ligands, we also considered receptor-mediated internalization and degradation of BMP4 ligands (72). Once the BMP4-receptor complex ([𝐵𝑅]) is internalized, we assumed that the BMP4 ligand is degraded and the receptor is recycled with a rate 𝛾. Thus, we can write, 𝜕[𝑅]/𝜕𝑡 = −𝑘%[𝐵𝑀𝑃4][𝑅] + 𝑟%[𝐵𝑅] + 𝛾[𝐵𝑅] 𝜕[𝐵𝑅]/𝜕𝑡 = 𝑘%[𝐵𝑀𝑃4][𝑅] − 𝑟%[𝐵𝑅] − 𝛾[𝐵𝑅] From these equations we can see that the total receptor concentration 𝑅𝑇𝑜𝑡𝑎𝑙 = [𝑅] + [𝐵𝑅] is held constant. Finally, we assume fluorescent reporter production rate follows a Hill function with BMP4-receptor complex concentration as a variable. The Citrine degrades with a ~24hr turnover time (70). Thus, we can write, 𝜕[𝐶𝑖𝑡𝑟𝑖𝑛𝑒]/𝜕𝑡 = 𝑏[𝐵𝑅]&/(𝐾& + [𝐵𝑅]&) − 𝛿’()[𝐶𝑖𝑡𝑟𝑖𝑛𝑒] To obtain more precise estimates of parameters that have relatively large estimated ranges from previous studies, such as 𝑅𝑇𝑜𝑡𝑎𝑙, 𝑘%, 𝑟% and 𝛾, as we as parameters that cannot be estimated from previous studies, such as 𝑏, 𝑛 and 𝐾, we fitted a simplified model containing only [𝐵𝑀𝑃4], [𝑅], [𝐵𝑅], [𝐶𝑖𝑡𝑟𝑖𝑛𝑒], and without diffusion terms, to a time-lapse movie (Movie S2-4) of Receiver-B1 cells turning on Citrine fluorescence in response to 5, 10, 20 ng/ml recombinant BMP4. All estimated parameters used in the model are listed in Table S4. There is a time delay for Citrine fluorescence to become detectable after BMP signaling, due to transcription, translation and maturation of Citrine fluorescence proteins (Fig. S1). To incorporate this delay into the model, we added a [𝐶𝑖𝑡𝑟𝑖𝑛𝑒]∗ term to lump together species like Citrine mRNA and immature Citrine proteins that are produced by Citrine fluorescence reporter but have not been converted into detectable mature Citrine proteins. The [𝐶𝑖𝑡𝑟𝑖𝑛𝑒]∗ can be converted into [𝐶𝑖𝑡𝑟𝑖𝑛𝑒] with a conversion rate 𝑟’(), i.e., 𝜕[𝐶𝑖𝑡𝑟𝑖𝑛𝑒]∗/𝜕𝑡 = 𝑏[𝐵𝑅]&/(𝐾& + [𝐵𝑅]&) − 𝛿’()[𝐶𝑖𝑡𝑟𝑖𝑛𝑒]∗ − 𝑟’()[𝐶𝑖𝑡𝑟𝑖𝑛𝑒]∗ 𝜕[𝐶𝑖𝑡𝑟𝑖𝑛𝑒]/𝜕𝑡 = 𝑟’()[𝐶𝑖𝑡𝑟𝑖𝑛𝑒]∗ − 𝛿’()[𝐶𝑖𝑡𝑟𝑖𝑛𝑒] When parameter values were not available, we made arbitrary but physiologically reasonable assumptions, and later tested whether key conclusions were sensitive to these values. As shown in Fig. S6, key conclusions in this paper were relatively insensitive to the precise values of unknown parameters, or to the incorporation of time delay of Citrine fluorescence reporter. Fig. S1. Dynamics of citrine fluorescence reporter after recombinant BMP4 addition. (A) Images at selected timepoints of time-lapse imaging data (one of two replicates, corresponding to Movie S1-4) of Receiver-B1 cells treated with various concentrations of recombinant BMP4 (rBMP4). (B) Citrine fluorescence reporter shows significant elevation at 4 hours after the addition of 5 ng/ml rBMP4 (left), and at 3 hours after the addition of 10 ng/ml rBMP4 (middle) and 20 ng/ml rBMP4 (right). At each timepoint, we calculated the average citrine level per image column and acquired a distribution of average citrine per column. Then we used Welch’s t-test to test whether citrine distribution of samples with rBMP4 is significantly larger than citrine distribution of the sample without rBMP4 for each timepoint (ns: p≥0.05, not significant; : p<0.05, significant). These results show that the time delay of Citrine fluorescence reporter is 3-4 hours, since BMP signaling can be detected by pSmad staining 20 minutes after rBMP4 addition (44). Fig. S2. BMP gradients can be reconstituted in vitro. (A) (Top) Images at selected timepoints of Movie S5 are from the same time-lapse imaging data as Fig. 2B (adding a 60hr image). (Bottom) mCherry expression at the sender region can be visualized more clearly by focusing on the RFP channel alone at the same selected timepoints. (B) Without 4-OHT induction (Movie S1), no gradients were formed (top) and no mCherry expression (bottom) was detected in the sender region. In both (A) and (B), the white line on each image labels the position of sender-receiver interface. The Citrine fluorescence is shown as yellow, and mCherry fluorescence is shown as red. Fig. S3. Raw flow cytometry traces and statistical tests of sender-receiver co-culture experiment in Fig. 3A. (A) These raw flow cytometry traces are from one of the three replicate experiments. To calculate the log2 fold change in Fig. 3A, we first obtained the mean Citrine from each trace, then calculated the log2 fold change by the equation log2[(Receiver Citrine - Cit0) / (Cit1 - Cit0)], where Cit0 is the receiver Citrine of the sample without 4-OHT (gray trace) and Cit1 is the receiver Citrine of the sample with only 4-OHT induction (red trace). The number in the parenthesis in the figure legend corresponds to the condition number in Fig. 3A. (B) For each matrix entry, the number is the p value of Welch’s t-test between the corresponding pair of conditions. The condition number corresponds to the condition number in Fig. 3A. Fig. S4. Noggin strongly inhibits BMP4 signaling. (A) We used the same doxycycline inducible system as Sender-C to construct an inducible Noggin sender cell line, Sender-N (Table S3). (B) Sender-receiver co-culture experiment verifies strong inhibition of BMP4 signaling by Noggin, which cannot be relieved by BMP-1 expression. This co-culture experiment was performed in a similar way to that in Fig. 3A, with Sender-N substituting Sender-C. Sender-N cells were engineered from Sender-N cells by adding a constitutively expressed mTurquoise2 cassette (Table S3), so that they can be distinguished from Receiver-B1 cells in flow cytometry. (C) Noggin can form inhibitory gradients, which cannot be modulated by BMP-1 expression. In samples with Dox induction, Dox was added 8 hours before other components (rBMP4, rTwsg1 and ABA) to pre-induce Chordin expression. In all samples, 15 ng/ml recombinant BMP4 was added to the culture, and we took images 24 hours after rBMP4 was added. (D) Noggin expression completely abolishes BMP4 gradients. Cells were plated using the Fig. 2A protocol, with Sender-N cells substituting filler cells. 4-OHT, Dox and ABA were added together and images were taken 48 hours after induction. In both (B) and (D), 4-OHT = 4 µM. In (B), (C) and (D), Dox = 100 ng/ml, rTwsg1 = 10 nM, ABA = 1000 µM. In (C), N is the number of replicates. The white line on each image labels the position of sender-receiver interface. The Citrine fluorescence is shown as yellow, and mTurquoise2 fluorescence is shown as blue. White corresponds to yellow+blue on the computer screen. We removed the mCherry channel from images to avoid interfering with the Citrine visualization. Fig. S5. Full images and individual traces of Fig. 3B. The white line on each image labels the position of sender-receiver interface. The Citrine fluorescence is shown as yellow, and mTurquoise2 fluorescence is shown as blue. White corresponds to yellow+blue on the computer screen. We removed the mCherry channel from images to avoid interfering with the Citrine visualization. The replicate #3 of rBMP4+Dox+ rTwsg1+ABA group used a small field of view due to an acquisition error, making the individual trace and average trace for that condition noisier than other traces in Fig. 3B. Fig. S6. Qualitative shuttling behaviors in Fig. 4 were insensitive to the precise values of unknown parameters or to incorporation of a time delay of Citrine reporter. The qualitative shuttling behaviors include (1) Chordin lengthens the gradient, (2) Twsg1 with Chordin suppresses gradients, (3) BMP-1 with other components generates a displaced gradient. (A) In the original model (left), we arbitrarily chose a 𝑘"#$ to be the same as 𝑘"# (Table S4), both within the diffusion limited rate range (0.006 nM-1min-1 – 0.06 nM-1min-1) (73). Holding other parameters constant, changing 𝑘"#$ to the upper (middle) or lower (right) limit of the diffusion limited rate range does not affect the qualitative shuttling behaviors. (B) Previous studies showed that Twsg1 enhances BMP-1 cleavage of Chordin in vitro (18, 74). In the original model (left), we set the BMP-1 cleavage rate 𝛽 to be the same for Chordin in both BMP4-Chordin and BMP4- Chordin-Twsg1. Holding other parameters constant, setting a higher BMP-1 cleavage rate for Chordin in BMP4-Chordin-Twsg1 (right) does not affect the qualitative shuttling behaviors. (C) Twsg1 is not necessary for a displaced gradient, but the contrast between proximal and distal BMP signals (compared with the plots of the original model in (A) or (B)). (D) Incorporating the time delay of Citrine reporter (Fig. S1) does not affect the qualitative shuttling behaviors. We incorporate the time delay by introducing a [𝐶𝑖𝑡𝑟𝑖𝑛𝑒]∗ term to lump together species that are produced by Citrine fluorescence reporter but not detectable yet, such as Citrine mRNA and immature Citrine fluorescence proteins, and a conversion rate 𝑟’() between [𝐶𝑖𝑡𝑟𝑖𝑛𝑒]∗ and detectable mature Citrine fluorescence protein [𝐶𝑖𝑡𝑟𝑖𝑛𝑒] (Supplementary Text). The 𝑟’() is set to 0.00385 min-1, corresponding to a 3-hour conversion time (Table S4, Fig. S1). Fig. S7. Pairwise comparison of Citrine traces in Fig. 4. For each pair of traces, we performed pixel-wise Welch’s t-test along the distance to the sender- receiver interface. The red line on the distance versus log10 p value plot is log10(0.05). Fig. S8. Twsg1 addition or BMP-1 expression minimally affects BMP4 gradients without Chordin. In the sender region, Sender-B cells were mixed with filler cells (Sender-C, see Materials and Methods). 4-OHT and (A) rTwsg1 or (B) ABA were added together, and images were taken 48 hours after induction. In both (A) and (B), the white line on each image labels the position of sender-receiver interface. The Citrine fluorescence is shown as yellow. We removed the mCherry channel from images to avoid interfering with the Citrine visualization. 4 µM 4-OHT was added to all samples. N denotes the number of replicates. Fig. S9. The mathematical model recapitulates the dynamic properties of shuttling. We generated the simulated dynamic gradient formation data using the same mathematical model in Fig. 4 (also see Supplementary Text), with all the parameters being the same except for the scaling factor of Citrine fluorescence (b). (A) Simulated data recapitulates the dynamic features of gradient formation for 4-OHT only condition (Fig. 2B and Fig. S2A), including gradient shapes approach a steady state at around 30hr (red line of normalized citrine) and instantaneous signal first increases near the source, then spread to more distal regions, and begin to diminish near the source after 30hr, possibly due to receptor saturation. (B) Simulated data recapitulates the dynamic features of shuttling. The dotted lines and bidirectional arrows between dotted lines in the top two instantaneous signal plots show that, both in model and in experiment, Chordin addition by Dox causes a delay of the Citrine signal to reach a detectable level. The arrows in the bottom instantaneous signal plot show that, both in model and in experiment, the Citrine signal in 4-OHT+Dox+rTwsg1+ABA condition initiates near the final displaced peak position and spreads outwards. Fig. S10. Raw flow cytometry traces and statistical tests of sender-receiver co-culture experiment in Fig. 6A. (A) These raw flow cytometry traces are from one of the four replicate experiments. To calculate the log2 fold change in Fig. 6A, we first obtained the mean Citrine from each trace, then calculated the log2 fold change by the equation log2[(Receiver Citrine - Cit0) / (Cit1 - Cit0)], where Cit0 is the receiver Citrine of the sample without 4-OHT (gray trace) and Cit1 is the receiver Citrine of the sample with only 4-OHT induction (red trace). The number in the parenthesis in the figure legend corresponds to the condition number on the right. (B) For each matrix entry, the number is the p value of Welch’s t-test between the corresponding pair of conditions. The condition number corresponds to the condition number in (A). Fig. S11. Pairwise comparison of Citrine traces in Fig. 6C. For each pair of traces, we performed pixel-wise Welch’s t-test along the distance to the sender- receiver interface. The red line on the distance versus log10 p value plot is log10(0.05). Table S1. List of BMP ligands, receptors and modulators expressed in NMuMG cells (data from Antebi et al., 2017). Gene (Ligand) FPKM Gene (Receptor) FPKM Gene (Modulator) FPKM Bmp2 Bmp3 Bmp3b Bmp4 Bmp5 Bmp6 Bmp7 Bmp8a Bmp8b Bmp9 Bmp10 Bmp15 0.966488 0 0 6.17382 0 0.128872 1.06013 0.240426 0.0148391 0 0 0 Bmpr1a Bmpr1b Bmpr2 Acvr1 Acvr2a Acvr2b 18.6297 0 5.64062 65.3538 54.2251 105.359 Nog Chrd Twsg1 Bmp1 Bmper Bambi 0.0946048 0.132197 57.4595 24.9904 0.486271 0.956501 Dragon/Rgmb 6.41953 Chrdl1 Chrdl2 Fst Fstl1 Fstl5 Sost Sostdc-1 Nbl1 Cer1 Grem1 Grem2 Crim1 Kcp 0 0.0308965 3.12281 1.01323 0 0 0 37.5115 0 0 0 37.1051 0.433142 Gapdh 39.5971 Table S2. List of plasmids used in this study. Index Construct name Cell line pJM009 PB-PGK-ERT2-Gal4-T2A-H2B-Citrine-SV40-HygroR Sender-B pJM018 PB-UAS-BMP4-IRES-H2B-mCherry-BGHpA-SV40- BlastR Sender-B pRZ007 PB-EF1α-TET3G-IRES-H2B-citrine-BGHpA-SV40- NeoR Sender-C, Sender-N, Sender- S pRZ011 PB-TRE3G-Chordin-IRES-H2B-mTurquoise2-SV40- Sender-C ZeoR pRZ012 PB-TRE3G-Noggin-IRES-H2B-mTurquoise2-SV40- Sender-N ZeoR pRZ018 PB-TRE3G-Sog-IRES-H2B-mTurquoise2-SV40-ZeoR Sender-S pRZ044 PB-UAS-BMP-1-IRES-H2B-mCherry-SV40-BlastR Receiver-B1 pRZ056 PB-EF1α-NLS-VP16-PYL-IRES-NLS-Gal4DBD-ABI- Receiver-B1 SV40-NeoR pRZ032 PB-EF1α-IRES-H2B-mTurquoise2-BGHpA-SV40- Sender-B* NeoR ES006 PB-CAG-H2B-mTurqoise2-BGHpA-SV40-HygroR Sender-C, Sender-N, Sender-S* Note: PB = PiggyBac backbone; ERT2-Gal4 = Gal4-VP16 transcriptional activator fused with human estrogen receptor (variant ERT2) (46); Gal4DBD = DNA-binding domain of Gal4; Tet3G = Tet-On 3G transactivator protein from Takara Bio; PYL, ABI = domains from PYL1 and ABI1 genes that confer ABA-induced proximity (47); PGK = constitutive promoter from mouse phosphoglycerate kinase 1 gene; UAS = inducible promoter with ERT2-Gal4 binding site; EF1α = constitutive EF1α promoter; TRE3G = inducible promoter with Tet3G binding sites; CAG = constitutive CAG promoter (75); SV40 = constitutive promoter from the early promoter of the simian virus 40 NLS = nuclear localization sequence; IRES = internal ribosome entry site; BGHpA = bovine growth hormone polyadenylation signal; HygroR, BlastR, NeoR, ZeoR = antibiotics resistance genes for hygromycin, blasticidin, geneticin/neomycin and zeocin, respectively; Construct maps in GenBank format are available at data.caltech.edu/records/0sdrn-73r13. Table S3. List of stable cell lines constructed for this study and their use in the figures. Cell lines Parental cells Figures Polyclonal or Monoclonal Integrated constructs Sender-B NMuMG (ATCC) Monoclonal Sender-C NMuMG (ATCC) Monoclonal Sender-N NMuMG (ATCC) Monoclonal Sender-S NMuMG (ATCC) Monoclonal Receiver- B1 NMuMG Sensor Line from (45) Monoclonal Sender-B* Sender-B Monoclonal Sender-C* Sender-C Monoclonal Sender-N* Sender-N Sender-S* Sender-S Polyclonal Polyclonal pJM009, pJM018 pRZ007, pRZ011 pRZ007, pRZ012 pRZ007, pRZ018 pRZ044, pRZ056 pRZ032 ES006 ES006 ES006 2-5, 6C, S2, S4C, S4D, S5, S8, Movie S1, Movie S5-8 3B, 4, 5, S5, S8, Movie S1, Movie S5-8 S4C, S4D 6C 2- 6, S1-5, S8, S10, Movie S1-8 3A, 6B, S3, S4B, S10 3A, S3 S4B 6B, S10 Table S4. List of parameters used in the model. Estimated range in the literature Parameters Value used in the model References 𝐷", 𝐷#, 𝐷$, 𝐷"#, 𝐷"#$ 0.1 – 20 µm2/s 𝑘% 𝑟% 𝑅𝑇𝑜𝑡𝑎𝑙 𝛾 𝑘"# 𝑟"# 𝑘"#$ 𝑟"#$ 𝛽 𝑏 𝑛 𝐾 𝛿’() 𝑟’() 0.00168 – 0.0438 nM-1min-1 0.018 – 0.09 min-1 0.0375 – 2.7 nM (corresponding to 18 – 1300 molecules/µm2 and a 800 µm Matrigel layer on the cell surface) 0.00693 – 0.173 min-1 (corresponding to t1/2 of 4 – 100 min) 0.0168 – 0.0234x10-2 nM-1min-1 15 µm2/s 0.00326 nM-1min-1 (fitted within literature range) 0.09 min-1 (fitted within literature range) 0.57 nM (fitted within literature range) 0.00693 min-1 (fitted within literature range) 0.018 nM-1min-1 (33, 34, 76, 77) (78–81) (78–81) (82, 83) (84–92) (58, 93) (58, 93) 0.003 – 0.204 min-1 0.06 min-1 N/A 0.0554 – 0.9 min-1 (corresponding to Chordin-Twsg1 dissociation constant ranging 3.08 – 50 nM, calculated by chosen kBCT) N/A N/A N/A N/A 0.000481 min-1 (corresponding to t1/2 of 24 hr) N/A 0.018 nM-1min-1 * N/A 0.0554 min-1 (40, 71) 0.002 (basal) – 0.01 (fully induced) min-1 # N/A 15.8 a.u./min (fitted) N/A 1.4 (fitted) 0.0126 nM (fitted) 0.000481 min-1 N/A N/A (70) 0.00385 min-1 (corresponding to t1/2 of 3 hr) Fig. S1. * Lacking direct measurements of 𝑘"#$, we set it arbitrarily to the same value as 𝑘"#, which lies within the diffusion limited range. We also verified that major conclusions are not sensitive to its precise value (Fig. S6). # Twsg has been shown to enhance Chordin cleavage by BMP-1 (18, 74), but we did not find a good estimate of the magnitude of this effect. We set the cleavage rate to be the same for Chordin with or without Twsg, but also verified that major conclusions are not sensitive to whether cleavage rates are set to be the same or not (Fig. S6). Movie S1. One replicate of the time-lapse movie of BMP4 gradients in the no induction condition. Movie S2. One replicate of the time-lapse movie of Receiver-B1 cells turning on Citrine fluorescence after the addition of 5 ng/ml rBMP4. Movie S3. One replicate of the time-lapse movie of Receiver-B1 cells turning on Citrine fluorescence after the addition of 10 ng/ml rBMP4. Movie S4. One replicate of the time-lapse movie of Receiver-B1 cells turning on Citrine fluorescence after the addition of 20 ng/ml rBMP4. Movie S5. 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10.1088_1361-6579_ad0f70.pdf	Data availability statement The data cannot be made publicly available upon publication because they contain commercially sensitive information. The data that support the ﬁndings of this study are available upon reasonable request from the authors.	Data availability statement The data cannot be made publicly available upon publication because they contain commercially sensitive information. The data that support the findings of this study are available upon reasonable request from the authors.	RECEIVED 10 October 2023 ACCEPTED FOR PUBLICATION 23 November 2023 PUBLISHED 11 December 2023 Physiol. Meas. 44 (2023) 125002 https://doi.org/10.1088/1361-6579/ad0f70 PAPER Magnetocardiography-based coronary artery disease severity assessment and localization using spatiotemporal features , Jiaojiao Pang3,4,5,∗, Dong Xu6, Ruizhe Wang1,2, Fei Xie3,4,5, Yanfei Yang1,2, Jiguang Sun7, Xiaole Han1,2 Yu Li3,4,5, Ruochuan Li3,4,5, Xiaofei Yin3,4,5, Yansong Xu3,4,5, Jiaxin Fan3,4,5, Yiming Dong3,4,5, Xiaohui Wu3,4,5, Xiaoyun Yang3,5,8, Dexin Yu3,5,9, Dawei Wang3,5,9, Yang Gao1,2,6,10 Jinji Sun1,2,6,11,10 1 Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and , Yuguo Chen3,4,5,∗ and Xiaolin Ning1,2,6,11,10 , Min Xiang1,2,6,11,10,∗ , Feng Xu3,4,5, Optoelectronic Engineering, Beihang University, People’s Republic of China 2 Zhejiang Provincial Key Laboratory of Ultra-Weak Magnetic-Field Space and Applied Technology, Hangzhou Innovation Institute, Beihang University, Hangzhou 310051, People’s Republic of China 3 Shandong Key Laboratory for Magnetic Field-free Medicine & Functional Imaging, Institute of Magnetic Field-free Medicine & Functional Imaging, Shandong University, People’s Republic of China 4 Department of Emergency Medicine, Qilu Hospital of Shandong University, Shandong Provincial Clinical Research Center for Emergency and Critical Care Medicine, People’s Republic of China 5 National Innovation Platform for Industry-Education Intearation in Medicine-Engineering Interdisciplinary, Shandong University, People’s Republic of China 6 National Institute of Extremely-Weak Magnetic Field Infrastructure, Hangzhou, People’s Republic of China 7 Hangzhou Nuochi Life Science Co., Ltd, People’s Republic of China 8 Department of Gastroenterology, Qilu Hospital of Shandong University, Shandong Provincial Clinical Research Center for Digestive Disease, People’s Republic of China 9 Department of Radiology, Qilu Hospital of Shandong University, People’s Republic of China 10 Institute of Large-Scale Scientiﬁc Facility and Centre for Zero Magnetic Field Science, Beihang University, People’s Republic of China 11 Hefei National Laboratory, People’s Republic of China ∗ Authors to whom any correspondence should be addressed. E-mail: jiaojiaopang@email.sdu.edu.cn, xiang_min@buaa.edu.cn and chen919085@sdu.edu.cn Keywords: coronary artery disease, disease severity and location, feature extraction, magnetocardiography, machine learning Supplementary material for this article is available online Abstract Objective. This study aimed to develop an automatic and accurate method for severity assessment and localization of coronary artery disease (CAD) based on an optically pumped magnetometer magnetocardiography (MCG) system. Approach. We proposed spatiotemporal features based on the MCG one-dimensional signals, including amplitude, correlation, local binary pattern, and shape features. To estimate the severity of CAD, we classiﬁed the stenosis as absence or mild, moderate, or severe cases and extracted a subset of features suitable for assessment. To localize CAD, we classiﬁed CAD groups according to the location of the stenosis, including the left anterior descending artery (LAD), left circumﬂex artery (LCX), and right coronary artery (RCA), and separately extracted a subset of features suitable for determining the three CAD locations. Main results. For CAD severity assessment, a support vector machine (SVM) achieved the best result, with an accuracy of 75.1%, precision of 73.9%, sensitivity of 67.0%, speciﬁcity of 88.8%, F1-score of 69.8%, and area under the curve of 0.876. The highest accuracy and corresponding model for determining locations LAD, LCX, and RCA were 94.3% for the SVM, 84.4% for a discriminant analysis model, and 84.9% for the discriminant analysis model. Signiﬁcance. The developed method enables the implementation of an automated system for severity assessment and localization of CAD. The amplitude and correlation features were key factors for severity assessment and localization. The proposed machine learning method can provide clinicians with an automatic and accurate diagnostic tool for interpreting MCG data related to CAD, possibly promoting clinical acceptance. © 2023 Institute of Physics and Engineering in Medicine Physiol. Meas. 44 (2023) 125002 1. Introduction X Han et al Coronary artery disease (CAD) is a leading cause of cardiovascular mortality and morbidity worldwide. Acute chest pain is the most common symptom of CAD (Xiao et al 2021). Approximately one-third of adults in the United States of America have some form of CAD, with more than 17 million suffering from CAD and nearly 10 million suffering from angina (Shin et al 2018, Tsao et al 2023). Some patients with CAD either experience atypical symptoms or present normal physical conditions, thus requiring further testing to detect CAD. The 12- lead electrocardiography (ECG) examination is the most common non-invasive technique for CAD detection. However, more than one-third of patients with acute coronary syndrome present normal results, which may represent an acute and severe manifestation of CAD (Rofﬁ et al 2016). When troponin is used for diagnosis, many patients with acute coronary syndrome exhibit normal troponin levels early in the disease. The gold standard for diagnosing CAD is coronary angiography, which allows for determining the severity and location of CAD; however, its high cost and invasiveness make it difﬁcult to use as a routine diagnostic tool. CAD is preventable, and early diagnosis and appropriate intervention are essential to reduce mortality. Magnetocardiography (MCG) is a non-invasive and passive technique that detects weak magnetic ﬁelds generated by the heart’s electrical activity (Stratbucker et al 1963). The main types of magnetometers are superconducting quantum interference devices (SQUIDs) and optically pumped magnetometers (OPMs). Compared with SQUIDs, OPM-based MCG features a small sensor array, light-weight, portable measurement ability, and room-temperature operation (Boto et al 2017, Hill et al 2019, Hill et al 2020, Yang et al 2021) as well as higher sensitivity and lower cost, rendering it more promising for clinical applications. Both ECG and MCG are functional examinations. Compared with the conventional 12-lead ECG, MCG enables non-contact measurements and has a higher sensitivity to tangential and eddy current signals caused by damaged myocardial tissue (Dutz et al 2006), and arrayed multichannel sensors achieve a higher spatial resolution (Tavarozzi et al 2002, Fenici et al 2003), thereby improving the acquisition of functional information from the heart (Smith et al 2007). Although MCG allows for the sensitive acquisition of functional signals from the heart, signal interpretation is time-consuming and requires experienced professionals, thus limiting its widespread clinical application. Therefore, automated MCG signal processing and disease detection may substantially improve clinical acceptance. With the maturing of machine learning algorithms, numerous researchers are introducing them into MCG and ECG research for tasks such as automated disease detection. The acute coronary syndrome was detected using 554 features extracted from 12-lead ECG examinations along with logistic regression, gradient boosting machine, and artiﬁcial neural network models, obtaining a sensitivity of 77%, speciﬁcity of 76%, positive predictive value of 43%, and negative predictive value of 94% (Al-Zaiti et al 2020). A logistic regression model was developed based on induction coil magnetometers using 10 features and diagnosed ischemic heart disease with a sensitivity of 95.4%, speciﬁcity of 35.0%, and negative predictive value of 97.8% (Mooney et al 2017, Ghasemi-Roudsari et al 2018). Six machine learning models based on SQUIDs were used to identify ischemic heart disease, and a back-propagation neural network achieved the highest performance with an accuracy of 78.43%, speciﬁcity of 68.18%, and sensitivity of 86.21% (Yosawin et al 2010). CAD detection in patients with chest pain was proposed using 10 two-dimensional (2D) MCG features and a multilayer perceptron based on SQUIDs, achieving an accuracy of 90.0%, sensitivity of 91.4%, and speciﬁcity of 87.7% (Xiao et al 2021). An automatic classiﬁcation system for CAD based on SQUIDs MCG information entropy was proposed using a multilayer perceptron based on linear discriminant analysis to classify 10 patients with coronary stenosis, achieving a sensitivity of 99%, speciﬁcity of 97%, accuracy of 98%, positive predictive value of 96%, and negative predictive value of 99% (Steinisch et al 2013). Using 164 MCG features based on SQUIDs and a support vector machine (SVM)—an extreme gradient boosting model for ischemic heart disease detection was proposed with an accuracy of 94.03%. Furthermore, 18 time-domain 2D features were extracted to localize ischemic heart disease with accuracies of 74%, 68%, and 65% for detection at the left anterior descending artery (LAD), left circumﬂex artery (LCX), and right coronary artery (RCA), respectively (Tao et al 2019). Previous studies have shown promising results for the automated detection of cardiovascular disease. However, some problems remain to be addressed. First, MCG features are mainly extracted from 2D maps, with features extracted from one-dimensional (1D) signals being mostly neglected, particularly regarding the sensitivity assessment of features from different channels. In addition, previous studies have focused on T-wave MCG features, consequently discarding the effects of other waves. Moreover, in clinical situations, physicians must determine the treatment modality based on the severity of CAD. However, existing methods can only detect CAD but cannot estimate its severity. Finally, although models to localize CAD are available, their performance is low and far from meeting clinical applicability. In this study, we developed a system for severity assessment and localization of CAD using OPM-based MCG. The proposed system utilizes spatiotemporal features of 1D MCG signals, including amplitude, correlation, local binary pattern (LBP), and shape features. We modeled CAD severity assessment and 2 Physiol. Meas. 44 (2023) 125002 X Han et al Figure 1. Photographs of the OPM-based MCG system. (a) position of the OPMs array relative to the thorax of the subject, (b) arrangement of 36 OPMs. localization using both conventional and the proposed MCG features, respectively, and compared their effectiveness. In addition, we analyzed the proposed features used for modeling. 2. Materials Figure 1(a) shows the OPM-based MCG system used in this study. This consisted of a semi-open magnetic shielded enclosure, an OPMs array, a magnetic-free bed, and data acquisition device (Han et al 2023). The system noise ﬂoor was 7–10 fT/Hz1/2. The operational dynamic range was ±5 nT. The relative position between the sensor array and the subject remained ﬁxed, including the sensor array lower edge center alignment with the xiphoid process of the subject and the vertical distance between the sensor array and the subject’s thorax (2 cm). Figure 1(b) depicts the arrangement of 36 OPMs, spanning a 275 × 275 mm area for acquisition at a 1000 Hz sampling frequency. The OPMs detected the magnetic ﬁeld signal along the Z axis, which was perpendicular to the sensor array. For each subject, MCG data were collected for a period of 3 min. A dataset was constructed from a prospective study of cardiovascular disease at the Qilu Hospital of Shandong University. All the enrolled subjects underwent coronary angiography or computed tomography angiography (CTA), and each subject had detailed medical records. Data on cardiovascular disease were collected and recorded using an OPM-based MCG system from 402 patients with symptoms of chest pain. A total of 93 cases were excluded based on the inclusion criteria, and 309 cases were enrolled. Among the excluded cases, 67 cases showed other medical conditions, 14 cases had non-removable metal implants in the body, and 12 cases did not have the correct MCG data. The 309 enrolled patients included 70 with stable angina, 208 with unstable angina, 13 with ST-segment elevation myocardial infarction, and 18 with non-ST-segment elevation myocardial infarction. The data from the 309 patients included 257 cases of coronary stenosis and 52 cases without coronary stenosis. In addition, 117 patients underwent CTA, and 192 patients underwent coronary angiography. Based on a coronary stenosis threshold of 50%, LAD (48 cases), LCX (6 cases), RCA (9 cases), LAD and LCX (26 cases), LAD and RCA (21 cases), LCX and RCA (9 cases), and LAD, LCX, and RCA (93 cases) stenoses were detected. Notably, patients commonly exhibited multiple stenoses in their coronary arteries. 3. Methods 3.1. System overview Figure 2 shows the data processing ﬂow of the CAD detection system. The raw MCG data were preprocessed to obtain a signal with a high signal-to-noise ratio. Preprocessing included median ﬁltering to remove baseline noise, notch ﬁltering to remove 50 Hz power frequency noise, lowpass ﬁltering to remove high-frequency noise, and superposition of the averaged heartbeats to remove common-mode noise. In addition, different heartbeat waves (e.g. P, QRS, and T waves) were segmented for feature extraction. Subsequently, four types of MCG features were extracted, and important feature subsets were selected for the system. Finally, the selected features were fed into a machine learning model for severity assessment and localization of CAD. Hierarchical tenfold 3 Physiol. Meas. 44 (2023) 125002 X Han et al Figure 2. Overview of data processing for CAD detection system. cross-validation was applied to improve the robustness of the system, with each fold containing approximately the same proportion of grouped labels. 3.2. Feature extraction 3.2.1. Conventional features The conventional MCG features are listed in table 1. Time, singular value decomposition (SVD), and amplitude class features were extracted from the butterﬂy diagram. The most common features extracted from ECG are time features, which mainly include the time intervals of P, QRS, and T waves and ST, PR, and QT segments. These features mainly describe changes in different segments of a heartbeat over time (Seki et al 2008, Sutter et al 2020). SVD features depict the number of independent sources in a signal (Tao et al 2019). Amplitude features are typically extracted from ECG to calculate ST segment amplitudes, and common statistical parameters include the maximum value, range, mean, standard deviation, and amplitude area. In the magnetic ﬁeld, the features typically extracted are the vector amplitude and direction from the negative magnetic pole pointing toward the positive magnetic pole (Beadle et al 2021) and the area ratio between positive and negative magnetic ﬁelds (Chen et al 2014). In the current ﬁeld, the maximum current vector (MCV) and the total current vector (TCV) are extracted as features (Ogata et al 2009). 3.2.2. Proposed features Based on the 36 channels of 1D MCG signals, four classes of MCG features were proposed, as listed in table 2. The 36 OPM sensors in the MCG system and relative positions between the sensor array and the subject were ﬁxed, resulting in a constant Pearson linear correlation coefﬁcient among the sensor signals for normal individuals. As CAD damages the myocardium, the altered electrical conduction pathways in the damaged part were reﬂected in the sensor array as alterations in the signal of each channel, thus changing the Pearson linear correlation coefﬁcient among the 36 channels. Figure 3(a) shows the MCG butterﬂy diagrams (Haberkorn et al 2006) of a healthy person and CAD patient, and ﬁgure 3(b) presents their 36-channel 1D MCG signals. Figure 3(c) depicts the Pearson linear correlation coefﬁcient of the 36-channel MCG signals in the T wave for the two individuals. The Pearson linear correlation coefﬁcient values and positions among the 36 channels drastically differed between the healthy person and the CAD patient. LBP is an operator used to describe the local texture of an image and is widely used in texture classiﬁcation, texture segmentation, and face image analysis (Prakasa 2016). We use LBP to describe the variation in the 1D MCG signal by constructing histograms to determine the frequency values of binary patterns, where each 4 Table 1. Conventional MCG features for CAD detection system. Map Butterﬂy diagram Class Time 5 SVD Amplitude Magnetic ﬁeld Vector Current ﬁeld Area MCV TCV Feature P wave time interval QRS wave time interval T wave time interval ST segment time interval PR segment time interval QT segment time interval SVD value SVD entropy Feature identiﬁer Time_P Time_QRS Time_T Time_ST Time_PR Time_QT SVD_Value# SVD_Entropy# Amp_Max Amp_Range Amp_Mean Amp_SD Amp_Area Mag_Dir Mag_Amp Feature description Number of features SVD_Value# contains the top 8 singular values SVD_Entropy# uses 8 singular values to calculate Shannon, Tsallis, and Renyi entropy values 6 11 × 8 5 × 36 × 8 2 1 2 2 1,541 Maximum value of wave Amplitude range of wave Mean value of wave Standard deviation of wave Area of wave Vector direction of negative magnetic pole pointing to the positive magnetic pole at T-wave peak Vector amplitude of negative magnetic pole pointing to the positive magnetic pole at T-wave peak Area ratio between positive and negative magnetic ﬁelds at T-wave peak Maximum current vector direction at T-wave peak Maximum current vector amplitude at T-wave peak Total current vector direction at T-wave peak Total current vector amplitude at T-wave peak Mag_Area MCV_Dir MCV_Amp TCV_Dir TCV_Amp Total number of features: The numbers 36 and 8 represent the spatial dimension with 36 channels and temporal dimension with 8 waves (P, QRS, and T waves, ST, ST-T, PR, and QT segments, and the whole heartbeat), respectively. P h y s i o l . M e a s . 4 4 ( 2 0 2 3 ) 1 2 5 0 0 2 X H a n e t a l Physiol. Meas. 44 (2023) 125002 X Han et al Figure 3. MCG signals and features of a healthy person (top) and CAD patient (bottom). (a) MCG butterﬂy diagram. (b) 36-channel 1D MCG signal. (c) Correlation features in T wave. Table 2. Proposed MCG features for CAD detection system. Map Class Feature Feature identiﬁer Feature description Butterﬂy diagram Correlation LBP Amplitude Shape Pearson linear correlation coefﬁcient of two-channel waveforms Bin values of LBP histogram Bin values of wave histogram Corr_channel# Corr_channel# contains 630 combinations LBP_Bin# Amp_Bin# LBP_Bin# contains 10 bins Amp_ Bin# contains 10 bins Kurtosis factor Skewness factor Waveform factor Peak factor Pulse factor Margin factor Volatility index Wave_Kurtosis Wave_Skewness Wave_Waveform Wave_Peak Wave_Pulse Wave_Margin Wave_Volatility Number of features 630 × 8 10 × 36 × 8 10 × 36 × 8 7 × 36 × 8 The numbers 36 and 8 represent the spatial dimension with 36 channels and temporal dimension with 8 waves (P, QRS, and T waves, ST, ST- T, PR, and QT segments, and the whole heartbeat), respectively. Total number of features: 12,816 pattern represents the probability of ﬁnding a binary pattern in the image. The number of histogram bins is set to 10. Figure 4(a) shows LBP features calculated from the T-wave MCG data of a healthy person and CAD patient at channel 1. The ﬁrst and tenth bin values considerably differ, likely establishing the difference between the healthy person and CAD patient. We propose using the amplitude histogram as a statistical parameter to compute the amplitude features. Figure 4(b) presents the amplitude features calculated from the T-wave MCG data of a healthy person and CAD patient at channel 1. Ten bin values were distinguished between the individuals. The shape features were extracted from the amplitude and shape changes between individuals as determined from the acquired MCG data. Figure 4(c) depicts the shape features calculated from the T-wave MCG data of a healthy person and CAD patient at channel 1. Seven shape features were distinguished between the individuals. 3.2.3. Fine-grained spatiotemporal features We extracted ﬁne-grained features in the spatiotemporal dimensions based on conventional and proposed MCG features (butterﬂy diagrams). Fine-grained spatial features are deﬁned by calculating the values for each 6 Physiol. Meas. 44 (2023) 125002 X Han et al Figure 4. MCG features of a healthy person and CAD patient. (a) LBP, (b) amplitude, and (c) shape features in the T wave. channel instead of averaging across the 36 channels. The ﬁne-grained spatial features are applied to amplitude, shape, correlation, and LBP features. The representative features and channels differ, possibly promoting CAD severity assessment and localization. Fine-grained temporal features are deﬁned for different waves, including the P, QRS, and T waves, ST, ST-T, PR, and QT segments, and the whole heartbeat. The ﬁne-grained temporal features are applied to the amplitude, SVD, shape, correlation, and LBP features. Furthermore, representative features selected by the models are distributed in different waves, and waves other than the T wave beneﬁt the models. To consider the spatiotemporal dimensions, the total number of conventional and proposed MCG features used were 1541 and 12 816, respectively. Compared to the population size, the number of conventional and proposed features was enormous and completely imbalanced. Therefore, an effective feature reduction was required (Patel et al 2022). Feature selection reduces the dimensionality of the data by selecting only a subset of the measured features to build a model. The main beneﬁts of feature selection are improved model prediction performance, faster and cost- effective feature subset, and improved data generation process understanding (Guyon and Elisseeff 2003, Richards 2022). The feature selection process was performed prior to inputting the features into the classiﬁcation model. First, feature importance was evaluated using a feature selection algorithm, and the features were sorted according to the importance score. Second, features with a large Pearson linear correlation coefﬁcient were removed (the threshold for the Pearson linear correlation coefﬁcient was set at 0.55). Finally, the number of selected features was 1/10 of the number of observations in the model. A hybrid feature selection algorithm (Tasci et al 2023) was utilized this study because the obtained results were considered superior to those obtained by separately utilizing ﬁlter and embedded feature selection algorithms. Initially, ﬁlter-type feature selection, in particular, the Chi-square test (Thaseen et al 2019) was used, followed by embedded-type feature selection which employs the linear discriminant analysis classiﬁer. 3.3. Classiﬁcation models We evaluated six machine learning classiﬁcation models: discriminant analysis, naïve Bayes, SVM, k-nearest neighbors (KNN), boosting tree, and bagging tree models. The model with the best classiﬁcation performance was selected for severity assessment and localization of CAD. Five metrics were calculated to evaluate the models based on the confusion matrix: accuracy, precision, sensitivity, speciﬁcity, and F1-score. In addition, the receiver operating characteristic (ROC) curves were obtained, and the AUC was calculated to assess the classiﬁcation performance. Because CAD severity assessment was a multi-label model, macro-average metrics were used for evaluation. 4. Results To validate the effectiveness of the MCG features proposed in this study, we developed models utilizing conventional MCG features (refer to table 1) and the proposed MCG features (refer to table 2) and then compared the modeling metrics obtained from the two models. 4.1. CAD severity assessment We used coronary angiography and CTA as references to determine the CAD severity. From CTA, we could only estimate mild, moderate, and severe stenoses. A machine learning model for estimating the CAD severity was developed to distinguish between individuals without stenosis and those with mild, moderate, or severe stenosis. Overall, data from 309 individuals were considered for modeling, with the cases distributed as listed in table 3. We selected 31 features based on the feature selection process, and the selected features were fed into the six classiﬁcation models for training and validation, obtaining the macro-average results listed in supplementary table 1. According to the accuracy metrics, the results of modeling the optimum based on conventional and proposed features are selected, respectively, as shown in table 4. Compared to the conventional feature-based 7 Physiol. Meas. 44 (2023) 125002 X Han et al Figure 5. ROC curves of two models for estimating CAD severity. (a) Bagging tree model (Conventional features), and (b) SVM model (Proposed features). Table 3. Cases for estimating CAD severity. CAD severity No CAD Mild stenosis Moderate stenosis Severe stenosis Coronary stenosis range Class index Number of cases 0% (0, 50%) [50%, 70%) [70%,100%] 1 2 3 4 52 45 39 173 Table 4. Performance of the best classiﬁcation models for CAD severity assessment. Best classiﬁer Accuracy Precision Sensitivity Speciﬁcity F1-score Mean AUC Bagging tree (conventional features, N = 31) SVM (proposed features, N = 31) 58.3 75.1 42.7 73.9 33.7 67.0 79.3 88.8 33.7 69.8 0.656 0.876 N denotes the number of features selected, and the highest metrics are in bold. modeling metrics, the proposed feature-based modeling metrics show a noteworthy improvement, particularly in the increase in accuracy by 16.8%. Figure 5(a) shows the ROC curves of the Bagging tree model (conventional features), and ﬁgure 5(b) shows the ROC curves of the SVM model (proposed features). Compared to the AUC values of the four classes using conventional features, all four AUC values using the proposed features exhibit an increase. To further understand the model based on the proposed features, we analyzed the 31 features (refer to supplementary table 2) utilized for the modeling. The classes, waves, and channels of these features were statistically distributed separately, as shown in ﬁgure 6. Figure 6(a) indicates the class of features, with a high percentage of features in the amplitude and correlation classes. Figure 6(b) presents the wave of features. All waves are features except for the ST and PR segments, with the highest percentage of features calculated using the whole wave. Figure 6(c) depicts the MCG channels and the number of features extracted per channel. The features were predominantly located on the diagonal, and there are channels with multiple features. In addition, one feature in the correlation class involved two channels, whereas the amplitude, LBP, and shape features involved only one channel. Overall, the performance of the CAD severity model is improved by the combined use of the proposed feature classes, feature extraction waves (temporal dimension), and channels (spatial dimension). 8 Physiol. Meas. 44 (2023) 125002 X Han et al Figure 6. The selected proposed features for predicting CAD severity. Feature (a) classes, (b) waves, and (c) channels. Figure 7. Seven-label model converted into three binary classiﬁcation models. Table 5. Data distribution for CAD localization formulated as binary classiﬁcation. Binary classiﬁcation Positive Negative LAD versus no LAD LCX versus no LCX RCA versus no RCA 188 134 132 24 78 80 4.2. CAD localization In ECG studies, changes in the ST segment of different leads are statistically counted to localize myocardial ischemia (Shu et al 2017). In MCG studies, features reﬂecting the 2D magnetic ﬁeld pattern are used to localize coronary stenosis (Tao et al 2019). Accordingly, we investigated whether CAD could be localized using features extracted from 1D MCG signals. CAD localization was applied to patients with moderate and severe coronary stenosis data. A seven-label model was constructed to account for the possible coronary stenosis locations, as illustrated in ﬁgure 7. However, building an accurate seven-label model was difﬁcult because of the high imbalance between labels in the collected data. To address this problem, we converted the seven-label model into three binary classiﬁcation models to independently predict each stenosis location (Tao et al 2019). The data distribution for the three binary classiﬁcation models is presented in table 5. Compared with the seven-label model, data were more balanced between the two classes of each binary classiﬁcation model. The three CAD localization models were constructed following a similar process to that used for obtaining the CAD severity model. Note that feature selection was performed for each of the three location models, and three feature sets were selected, each with a number of 21 (the number of observations for the location detection model is 212 cases). We inputted the three selected feature sets into each of the six classiﬁcation models for training and validation to obtain the metrics for the LAD, LCX, and RCA detection models, respectively (refer to 9 Physiol. Meas. 44 (2023) 125002 X Han et al Table 6. Performance of CAD localization using binary classiﬁcation. Binary classiﬁcation Best classiﬁer Accuracy Precision Sensitivity Speciﬁcity F1-score AUC LAD versus no LAD LCX versus no LCX RCA versus no RCA Discriminant analysis (Conventional features, N = 21) SVM (Proposed features, N = 21) Discriminant analysis (Conventional features, N = 21) Discriminant analysis (Proposed features, N = 21) Naïve Bayes (Conventional features, N = 21) Discriminant analysis (Proposed features, N = 21) 69.8 94.3 70.8 84.4 62.7 84.9 92.5 98.4 75.0 86.3 69.6 87.3 71.8 95.2 80.6 89.6 71.2 88.6 54.2 87.5 53.8 75.6 48.8 78.8 80.8 0.652 96.8 0.949 77.7 0.711 87.9 0.921 70.4 0.662 88.0 0.900 N denotes the number of features selected, and the highest metrics are in bold. supplementary tables 3–5). Under the accuracy metric, the optimal modeling results based on conventional and proposed features were selected, respectively, as illustrated in table 6. In table 6, all three proposed feature-based location detection model metrics are signiﬁcantly improved compared to the conventional feature-based modeling metrics. LAD detection has the highest accuracy, sensitivity, and speciﬁcity among the three location models. For further insight into the three localization models, we analyzed the three feature sets used for modeling. The three feature sets are speciﬁed in supplementary tables 6–8. The feature analysis was performed similarly to the feature analysis of the CAD severity model. The classes, waves, and channels of each of the three feature sets were statistically distributed, as shown in ﬁgure 8. Figure 8 shows three lines with the results of the feature sets analysis used by the LAD, LCX, and RCA models, respectively. Figure 8(a) illustrates the class distribution of the three feature sets; the amplitude class features are highest in the LCX and RCA models, and the correlation class features are highest in the LAD model. Figure 8(b) displays the wave distributions of the three feature sets; in the LAD model, the features are distributed in all waves, with the highest percentage in the P wave. In the LCX model, the features are selected to be computed in all waves except for the ST-T segment, with the highest percentage in the QT segment, and in the RCA model, the features are selected to be computed in all waves except the PR segment, with the highest percentage in the QT segment and the whole wave. Figure 8(c) presents the channel distributions of the three feature sets, which have different distributions of the channels where the features are located. In summary, the designed features contain three perspectives: feature class, feature wave (temporal dimension), and feature channel (spatial dimension). The features are extracted from these three perspectives separately for each CAD localization model, and the three CAD localization models obtained perform acceptably. 5. Discussion We investigated the severity assessment and localization of CAD. To determine the CAD severity, we selected a set of representative features evaluated in six classiﬁcation models. The SVM achieved the best performance, with an accuracy of 75.1%. Overall, the accuracy of the CAD severity assessment model was lower than that of the three CAD localization models for the following reasons. CAD severity required the classiﬁcation of four classes, whereas CAD localization was formulated as three binary classiﬁcation models. In addition, there was a higher imbalance across the four classes of CAD severity. These aspects undermined the performance of CAD severity assessment. For CAD localization, we used three binary classiﬁcation models to separately detect coronary stenosis at the LAD, LCX, and RCA. The average accuracy of CAD localization using proposed MCG features (average accuracy, 88%; LAD, 94%; LCX, 84%; RCA, 85%) improved by 19% compared with that obtained in a previous study (average accuracy, 69%; LAD, 74%; LCX, 68%; RCA, 65%) (Tao et al 2019). Three factors can explain the improved accuracy of CAD localization. (1) We used an MCG feature set combining spatial and temporal features, widely covering important information. (2) The important subsets of features for each of the three CAD localization models were selected separately. (3) We considered data from patients with moderate and severe stenoses and excluded those with mild stenosis. There are 94 features (31 + 21 × 3 = 94) used in the CAD severity and localization model, and we analyze these selected features as shown in ﬁgure 9. 10 Physiol. Meas. 44 (2023) 125002 X Han et al Figure 8. The selected proposed features for CAD localization. Feature (a) classes, (b) waves, and (c) channels. Figure 9(a) contains a higher percentage of amplitude and correlation class features, indicating that these two classes of features are more important in the CAD severity and localization model. In terms of physiology, various degrees of coronary artery stenosis or occlusion results in myocardial ischemia in the region supplied by a coronary artery (Nabel and Braunwald 2012). Local ischemia reduces the duration, resting potential, and propagation velocity of action potentials in the affected myocardium, resulting in wide variability in the conduction velocity between different myocardial regions. The variability in conduction velocity between the epicardial and endocardial walls of the affected region leads to signal variability in the speciﬁc MCG channels oriented to that region. To extract the signal variability information, we designed and selected channel-speciﬁc amplitude, LBP, and shape class features. The percentage of amplitude class features is much higher than that of LBP and shape class features, indicating that the amplitude class features better portray the variation of MCG signals affected by CAD. The variability in the ischemic region and normal myocardium leads to spatial variability between MCG channels. To extract the spatial variability information, we designed and selected the correlation features. The percentage of correlation class features is second only to the amplitude class features in CAD severity and location detection. Figure 9(b) presents the distribution of features in different waves, where the higher percentage is in the whole wave and QT segment rather than in the T wave. This reveals the potential beneﬁts of selecting multiple waves for detecting CAD severity and location. Regarding physiology, the repolarization sequence propagates from the endocardium to the epicardium in the normal ventricle (Zhu et al 2009). When cardiac ischemia occurs, there is a corresponding change in conductivity within the ischemic region, resulting in slower repolarization and impaired ventricular diastole, which is reﬂected in the alteration of the T wave signal. Myocardial injury may also lead to cardiac electrical conduction system abnormalities, which may cause arrhythmia signals, which are reﬂected in the P and QRS wave signals. Upon evaluating the screened feature results using the data-driven feature selection method, a contribution from other waves in addition to the T wave could be observed, which is consistent with the myocardial injury physiological process. To summarize, the higher percentage of relevant waves containing T wave (T wave, ST-T segment, QT segment, and whole wave) indicates that ventricular repolarization signals have a major contribution to the detection of disease, but that the other waves (P wave, QRS wave, ST segment, and PR segment) also have a certain role. 11 Physiol. Meas. 44 (2023) 125002 X Han et al Figure 9. The selected proposed features for CAD severity and localization. Feature (a) classes, (b) waves, and (c) channels. Figure 9(c) shows that the features use 35 channels, in which the channels with a higher number of features are mainly distributed in the middle region of the sensing array. It illustrates that the number of sensors can be reduced to lower the cost of the OPM-based MCG system while ensuring the effectiveness of the CAD severity and localization model. A promising ﬁnding was that when the top 5 important features of each model (20 features in total) were selected for analysis, there were only 14 channels where the features were located (refer to supplementary ﬁgure 1), which gives us an insight that if, in the development of portable MCG devices, the requirements for the detection model metrics are relatively low, it is possible to drastically reduce the number of 36-channel MCG sensors by retaining the sensors only in speciﬁc locations. In the ECG, stenosis at different CAD locations resulted in ST segment changes in different leads (Yuan et al 1991, Shu et al 2017). Likewise, we observed that among the features selected for the three CAD location detection models, the distribution of channels where important features were located was different (ﬁgure 8(c)). Finally, the primary contribution of this research is the extraction of new MCG features and their validation using clinical data acquired by an OPM-based MCG system. Speciﬁcally, owing to the shortcomings of the aforementioned studies, this study proposes spatiotemporal features, including amplitude, correlation, LBP, and shape, obtained from 1D MCG signals. Based on the database and feature selection method used in this study, selected features are considered signiﬁcant. However, because the database used in this study was relatively small, future research will focus on increasing the database to further validate and optimize the selected features and models. 6. Conclusion We propose a system to automatically estimate the severity and localize CAD. Our system can detect either the absence of stenosis or a mild, moderate, or severe case. In addition, it may promote the clinical diagnostic application of OPM-based MCG. Regarding the MCG features, we can draw three conclusions. (1) Amplitude and correlation features are essential for determining the severity and localizing CAD. (2) T wave signals are not the only waves useful for detecting CAD. Other waves also contribute to severity assessment and localization. (3) Not all channels in the 36-channel sensor array contribute to severity assessment and localization. There is a correlation between the channel distribution of important features and the location of coronary stenosis. Acknowledgments The authors would like to thank Dr Qinghua Sun for the scientiﬁc discussions pertaining to this study, the engineers from Hangzhou Nuochi Life Science Co., Ltd for their support during the experiments, and Huidong Wang, Sijia Zhang, Junliang Zhang, Chong Ma for their participation in the work data collection. The author’s have conﬁrmed that any identiﬁable participants in this study have given their consent for publication. This work was supported in part by the Innovation Program for Quantum Science and Technology under Grant No. 2021ZD0300500, the Development and Application of Ultra-Weak Magnetic Measurement Technology based on Atomic Magnetometer under Grant No. 2022-189-181, the National Natural Science Foundation of China under Grant No. 62101017, and the Key R&D Program of Shandong Province under Grant No. 2022ZLGX03. 12 Physiol. Meas. 44 (2023) 125002 X Han et al Data availability statement The data cannot be made publicly available upon publication because they contain commercially sensitive information. The data that support the ﬁndings of this study are available upon reasonable request from the authors. Ethical statement The study was reviewed and approved by the Ethics Committee of Scientiﬁc Research of Shandong University Qilu Hospital; the ethics board protocol approval number was KYLL-202204-017, and the approval date was 20 April, 2022. All the subjects provided written informed consent for the experimental procedure conducted in accordance with the Declaration of Helsinki. The trial name was Magnetocardiography in the Accurate Identiﬁcation of Severe Coronary Lesions and Myocardial Necrosis; the http://clinicalTrials.gov identiﬁer was NCT05392712. ORCID iDs Xiaole Han Yang Gao Min Xiang Jinji Sun Xiaolin Ning https://orcid.org/0000-0001-9152-8182 https://orcid.org/0000-0002-6841-9276 https://orcid.org/0000-0002-0239-3392 https://orcid.org/0000-0002-4804-637X https://orcid.org/0000-0003-3563-3601 References Al-Zaiti S et al 2020 Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram Nat. Commun. 11 1–10 Beadle R, Mcdonnell D, Roudsari S G and Unitt L 2021 Assessing heart disease using a novel magnetocardiography device Biomed. Phys. Eng. 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10.1093_molbev_msad069.pdf	Data availability No new data were generated in support of this research. TE models were deposited in the DFAM database.	Data availability No new data were generated in support of this research. TE models were deposited in the DFAM database.	Transposable Element Interactions Shape the Ecology of the Deer Mouse Genome Landen Gozashti ,1 Cedric Feschotte,2 and Hopi E. Hoekstra ,1 1Department of Organismic & Evolutionary Biology, Department of Molecular & Cellular Biology, Museum of Comparative Zoology and Howard Hughes Medical Institute, Harvard University, Cambridge, MA 2Department of Molecular Biology & Genetics, Cornell University, Ithaca, NY Corresponding author: E-mail: hoekstra@oeb.harvard.edu. Associate editor: Dr. Irina Arkhipova Abstract The genomic landscape of transposable elements (TEs) varies dramatically across species, with some TEs demonstrat- ing greater success in colonizing particular lineages than others. In mammals, long interspersed nuclear element (LINE) retrotransposons are typically more common than any other TE. Here, we report an unusual genomic land- scape of TEs in the deer mouse, Peromyscus maniculatus. In contrast to other previously examined mammals, long terminal repeat elements occupy more of the deer mouse genome than LINEs (11% and 10%, respectively). This pat- tern reflects a combination of relatively low LINE activity and a massive invasion of lineage-specific endogenous ret- roviruses (ERVs). Deer mouse ERVs exhibit diverse origins spanning the retroviral phylogeny suggesting they have been host to a wide range of exogenous retroviruses. Notably, we trace the origin of one ERV lineage, which arose ∼5–18 million years ago, to a close relative of feline leukemia virus, revealing inter-ordinal horizontal transmission. Several lineage-specific ERV subfamilies have very high copy numbers, with the top five most abundant accounting for ∼2% of the genome. We also observe a massive amplification of Kruppel-associated box domain-containing zinc finger genes, which likely control ERV activity and whose expansion may have been facilitated by ectopic recombin- ation between ERVs. Finally, we find evidence that ERVs directly impacted the evolutionary trajectory of LINEs by outcompeting them for genomic sites and frequently disrupting autonomous LINE copies. Together, our results il- luminate the genomic ecology that shaped the unique deer mouse TE landscape, shedding light on the evolutionary processes that give rise to variation in mammalian genome structure. Key words: mobile genetic elements, genome evolution, endogenous retrovirus, genomic conflict, Peromyscus maniculatus. A r t i c l e Introduction Transposable elements (TEs) are parasitic genetic elements capable of mobilizing in genomes and function as import- ant drivers of genome evolution (Orgel and Crick 1980; Kazazian 2004; Bourque et al. 2018). In mammals, for ex- ample, TEs account for at least 20% of the genome and, in some cases, have been exapted for significant functional innovations (van de Lagemaat et al. 2003; Platt et al. 2018; Senft and Macfarlan 2021). When TEs insert into new posi- tions in the genome, they generate mutations and thus re- present a significant burden on host fitness. This cost is compounded by the fact that TEs can contain gene regu- latory sequences and cause structural rearrangements even after they have lost the ability to transpose (Bourque et al. 2018; Klein and O’Neill 2018). Thus, the evolutionary success of a given TE lineage is dictated by its ability to replicate faster than the host genome but lim- ited by its cost to host fitness (Ford Doolittle and Sapienza 1980; Orgel and Crick 1980). TE lineages are in a constant coevolutionary conflict with each other as well as their host (Brookfield 2005; Venner et al. 2009). As a conse- quence, hosts have evolved various ways to suppress TE ac- tivity (Cosby et al. 2019). These genetic conflicts embody the “ecology of the genome” and play an important role in shaping the genomic landscape of TEs in a given species as well as its genome structure more broadly (Brookfield 2005; Venner et al. 2009). TEs are remarkably diverse, and TE landscapes can vary dramatically across species (Wells and Feschotte 2020). TEs are classified into two broad categories based on their transposition mechanism: class I elements (retrotranspo- sons), which mobilize through an RNA intermediate, and class II elements (DNA transposons), which do not. Most eukaryotic lineages harbor a diversity of TEs from multiple taxonomic subgroups within each of these broad classes (Bourque et al. 2018; Wells and Feschotte 2020). By con- trast, some phylogenetic groups have TE landscapes that are relatively similar across species (Abrusán and Krambeck 2006; Sotero-Caio et al. 2017). One such clade is mammals (Platt et al. 2018). In most mammalian © The Author(s) 2023. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/ licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Open Access Mol. Biol. Evol. 40(4):msad069 https://doi.org/10.1093/molbev/msad069 Advance Access publication March 22, 2023 1 Gozashti et al. · https://doi.org/10.1093/molbev/msad069 genomes, DNA transposons cannot actively mobilize and only exist as relics of anciently active elements (Platt et al. 2018). Actively mobilizing retrotransposons include long terminal repeat (LTR) retrotransposons, which are mostly endogenous retroviruses (ERVs), as well as non-LTR retrotransposons represented by long inter- spersed nuclear elements (LINEs) and their nonautono- mous counterparts, short interspersed nuclear elements (SINEs) (Eickbush 1992; Platt et al. 2018). LINEs are nearly always the most abundant TEs, and most are represented by a single family, L1, which typically occupies hundreds of megabases of the mammalian genome (Platt et al. 2018). However, the dearth of examples of alternative TE land- scapes has limited our ability to investigate the evolution- ary processes driving mammalian genome structure evolution and specifically, the maintenance of LINE dom- inance (Furano et al. 2004; Platt et al. 2018). The North American deer mouse, Peromyscus manicu- latus, has become an important model for studying the genetic basis of adaptation (Bedford and Hoekstra 2015). Early studies of deer mice and closely related spe- cies used polymerase chain reaction methods to explore TE abundance and reported evidence for an unprece- dented expansion of ERVs (Wichman et al. 1985; Cantrell et al. 2005). However, the landscape of TEs in the deer mouse remains unexplored on a genomic scale. Here, we report a highly distinct genomic landscape of TEs in the deer mouse genome. We find that, in contrast to nearly all examined mammalian genomes, LTR retro- transposons are more abundant in the deer mouse gen- ome than LINEs. We investigate the evolutionary origins and implications of the deer mouse’s distinct genomic landscape, revealing ecological processes that shaped its evolution. Results and Discussion Deer Mice Exhibit a Unique Landscape of TEs To evaluate the genomic landscape of TEs in the deer mouse genome, we first generated a lineage-specific TE li- brary de novo from the deer mouse (P. maniculatus bair- dii) genome using a combination of systematic and manual methods (see Methods). We identified 48 LINE, 28 SINE, and 118 LTR deer mouse-specific subfamilies (fig. 1A and supplementary table S1, Supplementary Material online). We then merged this lineage-specific TE library with all curated mammalian TEs from the Dfam database (Hubley et al. 2016) and annotated the genome using the combined library. We define lineage-specific sub- families with respect to those observed in house mice, Mus musculus (strain C57BL6) (∼25 Myr diverged from the deer mouse; Kumar et al. 2017). Our annotation revealed a distinct genomic landscape of TEs in the deer mouse, relative to other mammals, in which LTR elements occupy more of the genome than LINEs (fig. 1A). Specifically, LTR elements occupy ∼11% of the genome, followed by LINEs (∼10%), SINEs (7%), and other TEs (<2%) (fig. 1A and 2 MBE supplementary table S2, Supplementary Material online). Notably, the 10% LINE occupancy observed for the deer mouse is much lower compared to house mouse, rat, and human. It is worth noting that the dearth of LINE con- tent observed in the deer mouse genome is unlikely an artifact of our inability to detect lineage-specific LINEs since vertical propagation of LINEs has been accompanied by relatively little sequence changes. In total, TEs occupy ∼30% of the deer mouse genome, reflecting an increase in TE content relative to other species in the rodent Family Cricetidae, such as the grasshopper mouse (Onychomys torridus, 24%) and prairie vole (Microtus ochrogaster, 17%) (fig. 1A), but a reduction relative to house mouse (M. musculus, >40%), although differences in genome assembly and TE annotation quality may con- tribute to these patterns (Platt et al. 2016; Peona et al. 2021). Nonetheless, most of the difference in TE content between the deer mouse and house mouse can be attrib- uted to decreased LINE content in the deer mouse, where- as most of the difference in TE content among cricetid species can be attributed to LTR elements. Based on these observations, we hypothesized that the distinct TE landscape of deer mice is the result of a com- bination of reduced accumulation of lineage-specific LINE1s (L1s) and a proliferation of lineage-specific LTR ele- ments. To investigate this possibility, we first compared genomic representation as a function of within-subfamily divergence (as a proxy for subfamily age) across LINEs, SINEs, and LTR elements (fig. 1B). Consistent with our hy- pothesis, we observe reduced representation of LINEs with lower divergence from the consensus, suggesting de- creased LINE accumulation in the deer mouse lineage on more recent timescales (fig. 1B). However, despite this de- cline in the accumulation of LINEs, we still find multiple candidate L1s with intact protein machinery, suggesting that LINEs are still active, consistent with previous reports of LINE activity in deer mice (supplementary table S3, Supplementary Material online; Casavant et al. 1996). We also observed evidence for lineage-specific SINE activity (fig. 1B). Since SINEs parasitize LINE machinery for mobil- ization, evidence of recently active SINEs suggests that re- cently active LINEs still exist in the genome. In addition, we find a lineage-specific proliferation of LTR elements (fig. 1B): LTR elements are significantly overrepresented among the youngest TEs in the genome (<1% divergence from the consensus; two-sided Fisher’s exact test, P < 0.00001). Furthermore, the observed decline of LINE gains in the genome coincides with the peak of LTR gains in the gen- ome (fig. 1B). Together, these results suggest that both re- duced LINE gain and lineage-specific LTR proliferation have contributed to the deer mouse’s unique TE land- scape, and that the two may be associated. DNA Loss Fails to Explain Reduced LINE Content In addition to gain, TE loss can be an important driver of genomic TE content (Kapusta et al. 2017). Although we find evidence for a decline of LINE gain, the low LINE Transposable Element Interactions Shape the Ecology of the Deer Mouse Genome · https://doi.org/10.1093/molbev/msad069 MBE FIG. 1. TE landscape. (A) Phylogeny highlighting the relationship of deer mice (P. maniculatus) to other mammalian species considered in this study. Branch lengths in millions of years were obtained from Timetree (Kumar et al. 2017). Pie charts show the relative percent of the genome occupied by TE subclasses for each species. Color corresponds to the percent of the genome attributed to each type of TE (see legend); gray represents the percent of the genome not occupied by TEs. Stacked bar plots show the proportion of TE content represented by each TE type. Note, in deer mouse, LTRs occupy more of the genome than LINEs. (B) Percent of the genome as a function of CpG corrected Kimura divergence from the consensus for each TE subfamily of LINEs, SINEs, and LTR elements. The vertical dotted line represents the start of observed LINE decline in all plots. content in the deer mouse genome, relative to house mouse, could also have resulted from higher rates of ances- tral DNA loss in the deer mouse (fig. 2A and B). To inves- tigate this possibility, we calculated the DNA loss coefficient k (following Lindblad-Toh et al. 2005), using the formula E = Ae − kt, where E is the amount of extant ancestral DNA in the species considered, A is the ancestral assembly size, and t is time. Larger values of k suggest high- er rates of lineage-specific DNA loss. We calculated a k co- efficient of ∼0.0047 for the deer mouse, a value similar to, and in fact slightly lower than the k value estimated for the house mouse (∼0.006; Kapusta et al. 2017). These data sug- gest that the reduced LINE content observed in the deer mouse genome cannot be explained by generally higher rates of DNA loss in deer mice (fig. 2C). While the results above indicate that the genome-wide rate of DNA loss cannot explain the low LINE content of the deer mouse genome, it is still possible that LINEs are lost at a higher rate than other types of TEs. To investigate this possibility, we compared the proportions of DNA at- tributed to ancient mammalian LINEs present in the com- mon ancestor of the deer mouse and house mouse as well as lineage-specific elements. If the relative absence of LINEs in the deer mouse is due to higher rates of loss of these types of elements, we expect to find a decreased amount of DNA attributed to ancient LINEs in the deer mouse rela- tive to house mouse. To the contrary, we find that although LINEs contribute twice as much content to the house mouse as to the deer mouse (∼575Mb vs. ∼250Mb), ancient LINEs are significantly underrepre- sented in the house mouse genome (∼16% of total LINEs in deer mouse vs. ∼9% of total LINEs in house mouse; two-sided Fisher’s exact test, P = 0.006; fig. 2D). These re- sults suggest that LINE DNA is lost at a slower rate in the deer mouse lineage than in the house mouse lineage, consistent with our k calculations above. Together these data suggest that the low LINE content observed in the deer mouse cannot be attributed to high rates of LINE DNA loss, and instead, is most likely the result of relatively low rates of LINE activity in the deer mouse lineage. ERVK Elements Amplified Predominantly as Nonautonomous Subfamilies Most mammalian LTR retrotransposons are represented by ERVs, which are derived from germline infiltrations of exogenous retroviruses (Mager and Stoye 2015). ERVs are divided into three broad classes depending on their retroviral origins: ERV1 (Gammaretroviridae), ERVK (Betaretroviridae), and ERVL (Spumaretroviridae), with a subgroup of nonautonomous ERVLs called MaLRs (Hubley et al. 2016; Gifford et al. 2018). In the deer mouse, we find a pattern in which ERVK and ERVL/MaLR elements together account for over 80% of genomic ERV content, 3 Gozashti et al. · https://doi.org/10.1093/molbev/msad069 MBE FIG. 2. Non-mutually exclusive evolutionary scenarios that may have shaped the TE landscape in the deer mouse. (A) Higher rates of lineage- specific loss (larger red arrows) could have resulted in the reduced LINE content observed in deer mouse (P. maniculatus) relative to house mouse (M. musculus) and/or (B) higher rates of lineage-specific gain (larger blue arrows) in the house mouse relative to the deer mouse. (C ) k coefficients of DNA loss suggest lower rates of loss in deer mouse relative to house mouse. (D) Ancient elements account for a greater pro- portion of total LINE content in deer mouse relative to house mouse. consistent with previous reports in other rodents (Hubley et al. 2016; Platt et al. 2018; fig. 3A). Lineage-specific ERVs as a whole represent over half of genomic ERV content (∼57%), suggesting that the deer mouse has experienced a substantial ERV expansion. When we compare the pro- portion of lineage-specific ERV content to the proportion of shared ERV content represented by ERVKs, we find that ERVKs account for a disproportionately large part of lineage-specific ERV content (two-sided Fisher’s exact test, P < 0.00001), representing over 75% of observed lineage-specific ERV sequence in the genome (fig. 3B and supplementary table S1, Supplementary Material online). Thus, ERVK activity has been particularly pronounced in the deer mouse lineage. In well-annotated mammalian genomes, among full- length proviral elements (with two LTRs), ERVKs are typic- ally represented by autonomous elements that encode their own machinery for mobilization (Mager and Stoye 2015). After manually inspecting deer mouse-specific ERV subfamilies, and annotating gag, pro, pol, and env genes as well as predicting protein domains required for autonomous transposition, we find that the most abun- dant ERVK families do not possess any internal open read- ing frames (ORFs) predicted to encode proteins with conserved domains, suggesting that they are largely com- posed of nonautonomous elements. Many ERVs contain assembly gaps that interrupt or truncate their internal se- quences (507 of 1,119 candidate full-length ERVs), making it challenging to reconstruct full-length elements and 4 assess the presence or absence of coding machinery. In light of this caveat, we required that a putatively nonauto- nomous ERV subfamily display at least five full-length cop- ies with no gaps for it to be classified as nonautonomous, regardless of its consensus sequence length or content. Even with this conservative filter, we find that the most abundant deer mouse-specific ERV subfamilies are nonautonomous ERVK-like elements lacking any obvious coding capacity (fig. 3C and supplementary table S4, Supplementary Material online). Pman_ERV2_4.24, for ex- ample, is the most abundant ERV in the genome, account- ing for ∼5% of total ERV content. Furthermore, for the subset of ERVKs in which we could confidently reconstruct full-length copies and assess their coding capacity, nonau- tonomous elements occupy more of the genome than au- tonomous ones (supplementary table S4, Supplementary Material online). Overall, our results suggest that autono- mous ERVKs and their nonautonomous counterparts have had a significant impact on the deer mouse’s genome structure. Nonautonomous TEs parasitize autonomous elements for mobilization. Studies on the nonautonomous ERVK subfamily, ETn, in house mouse showed that ETn exhibits regions of sequence similarity to fully coding MusD ele- ments, suggesting that ETn likely arose from the ancestors of Mus-D (Mager and Freeman 2000) and now hijacks MusD machinery trans- complementation (Ribet et al. 2004). Given the high copy numbers of nonautonomous ERVKs in the deer for mobilization via Transposable Element Interactions Shape the Ecology of the Deer Mouse Genome · https://doi.org/10.1093/molbev/msad069 MBE FIG. 3. Relative contribution of different broad ERV classes to ERV content in the deer mouse genome across (A) all ERVs and (B) lineage-specific ERVs. (C ) Respective genomic occupancy across the 18 most common lineage-specific ERV subfamilies. Red font denotes nonautonomous sub- families. (D) VISTA plot showing regions of homology between and relative position of autonomous mysTR subfamilies (gray) and nonautono- mous subfamilies (red). (E) Comparison of LTR percent identity aggregated across all lineage-specific autonomous and nonautonomous elements. (F) Comparison of LTR percent identity across all ERV subfamilies that display at least one candidate full-length copy showing that the youngest subfamilies are autonomous (gray). mouse genome, we sought to identify related autonomous elements that may have been their progenitors and/or In addition to several facilitated their mobilization. prolific nonautonomous subfamilies, we identified three autonomous ERVK subfamilies, Pman_ERVK_4.503, Pman_ERVK_6.7639, and Pman_ERVK_5.247, that to- gether occupy ∼1% of the genome (fig. 3C). These subfam- ilies show sequence similarity to mysTR (∼89%, ∼96%, and ∼90% identity to mysTR pro-pol sequence, respectively), an ERV in Peromyscus (Cantrell et al. 2005), suggesting a shared origin from mysTR. Together, these mysTR-related subfamilies re- present the most abundant autonomous ERVs in the genome. family previously identified Previous studies failed to identify full-length mysTR copies with intact gag and pro-pol genes required for mo- bilization, raising questions about its overall origin and ability to mobilize, although these studies lacked the gen- omic resources to analyze mysTR sequences comprehen- sively (Cantrell et al. 2005; Erickson et al. 2011). Our reveals multiple copies of genome-wide analysis mysTR-related ERVs, which display apparently intact ORFs with homology to gag and pro-pol genes and contain all the protein domains expected to be encoded by au- tonomous elements, suggesting that these ERVs are indeed autonomous and may still be capable of mobilizing (supplementary table S4, Supplementary Material online). We also find several nonautonomous subfamilies related to mysTR (fig. 3D and supplementary table S4, Supplementary Material online; Wichman et al. 1985; Lee et al. 1996). The most conserved region of nucleotide se- quence homology between mysTR-related subfamilies is just downstream of the pro-pol gene and upstream of the 3′ LTR (fig. 3D). Interestingly, most candidate nonauto- nomous and autonomous mysTR-related subfamilies do not display strong homology outside of this region, sug- gesting that nonautonomous subfamilies may have evolved through a recombination event in an autonomous element, which replaced the original internal sequence of the autonomous element with a nonhomologous sequence (Mager and Freeman 2000). Maintenance of sequence similarity in this region is also consistent with functional constraint due to a possible role in ERVK mobil- ization, although its function remains unknown. Since ERV LTRs are identical upon insertion, LTR se- quence identity can provide an estimate for how recently an ERV inserted. To investigate the evolutionary dynamics of deer mouse ERVs, we compared the distributions of LTR identity across lineage-specific ERV subfamilies. We find that nonautonomous ERVs overall exhibit similar ages to 5 Gozashti et al. · https://doi.org/10.1093/molbev/msad069 autonomous ERVs (fig. 3E). However, when we compare nonautonomous and autonomous subfamilies independ- ently, we observe evidence for waves of autonomous elem- ent activity followed by waves of nonautonomous element activity, with the most recently active subfamilies being autonomous (fig. 3F). These observations are consistent with the hypothesis that nonautonomous subfamilies evolved from autonomous subfamilies. Diverse Origins of ERVs ERVs arise in a species when an exogenous retrovirus in- fects the germline. Thus, new families of ERVs evolve de novo through horizontal introduction more frequently than other autonomous mammalian TEs such as LINEs, which primarily evolve through the diversification of verti- cally inherited elements (Mager and Stoye 2015). To inves- tigate the origins of ERVs in the deer mouse, we focused on full-length ERVs across all identified subfamilies with flank- ing LTRs, pol genes, and reverse transcriptase (RT) domains (>450 bp), which we used for classification and phylogen- etic analysis. We initially identified 148 candidate full- length ERVs with pol genes and evidence of an RT domain. However, many ERVs contained ambiguous sites or gaps that interrupted or truncated the RT domain, leaving only 52 ERVs that met our conservative requirements tables S5 and S6, Supplementary (supplementary Material online). Thus, we note that our estimate of ERV diversity is likely an underestimate. We initially used a hidden Markov model approach (Finn et al. 2011) to classify ERVs based on their RT do- mains (supplementary table S6, Supplementary Material online). Using this approach, we find that of the 52 deer mouse ERVs with full-length RT domains, 11 are derived from gammaretroviruses (ERV1), 39 from betare- troviruses (ERVK), and 2 from spumaretroviruses (ERVL) (supplementary table S6, Supplementary Material online). Phylogenetic analysis of RT domains from these ERVs and other known retroviruses supports these initial classifica- tions and shows that deer mouse ERVs form 14 distinct clusters representing at least 14 independent endogeniza- tion events spanning retroviral diversity (fig. 4A). Most of these are derived from diverse betaretroviruses (9 of the 14), consistent with observations in other rodents (Baillie et al. 2004; Cui et al. 2015). Additionally, four ERV clusters show evidence of gammaretroviral origin, and one ERV cluster shows evidence of spumaretroviral origin (fig. 4A). To determine the age of ERVs, we conducted searches for deer mouse ERVs in grasshopper mouse, prairie vole, and house mouse. We find that most (9 of the 14) deer mouse ERVs arose before the divergence of the deer mouse and its close relative, the grasshopper mouse (∼5–13 mil- lion years ago [MYA]) (León-Paniagua et al. 2007; Leite et al. 2014), but after the divergence of their ancestor lineage of the prairie vole (∼18 MYA) and the (Abramson et al. 2009; Kumar et al. 2017), and are thus lineage-specific relative to house mouse (fig. 4B). Additionally, one ERV (Beta_Pman-ERV_cluster-5) was 6 MBE introduced even more recently, after the divergence be- tween the deer mouse and grasshopper mouse (fig. 4B). Intra-element LTR identity for ERVs in each respective cluster generally concurs with the timing estimate of their successive endogenization (supplementary table S6, Supplementary Material online). Given the relatively re- cent origins of several deer mouse ERVs (since the diver- gence between Peromyscus and Microtus ∼18 MYA), we reasoned that it may be possible to trace more precisely their origins by searching the databases for their closest ex- ogenous retrovirus relatives. This search revealed one po- tential case of a recent endogenization of an exogenous Feline Leukemia Virus (FeLV), or a closely related virus, in the ancestor of the deer mouse and grasshopper mouse within the last ∼5–18 million years (fig. 4C; Abramson et al. 2009; Kumar et al. 2017). Some Deer Mouse ERVs may Still be Infectious Although ERVs only require gag and pol genes to mobilize in the germline via retrotransposition, ERVs with intact env genes can also replicate via reinfection (Belshaw et al. 2004). Given the relatively recent evolution of several ERVs in the deer mouse, we inspected all intact ERVs as well as ERV subfamily consensus sequences for intact env genes. We find no evidence for env genes in mysTR- related subfamilies, consistent with previous studies on mysTR (Cantrell et al. 2005; fig. 4D and supplementary table S4, Supplementary Material online). However, we find putatively full-length env genes in multiple other ERV clusters, suggesting that some deer mouse ERVs may still be capable of infection (supplementary table S4, Supplementary Material online). One of these is the previ- ously mentioned FeLV-related Gamma_Pman-ERV_ cluster-10. The observation of a putatively intact env in these ERVs is consistent with previous studies showing that leukemia viruses remain infectious in other species (Hoover and Mullins 1991; Polat et al. 2017; fig. 4D). We also observe evidence of an intact env gene for ERVs within the Beta_Pman-ERV_cluster-5. Beta_Pman-ERV_cluster-5 ERVs are absent in grasshopper mice and thus represent some of the most recent ERVs to infiltrate the deer mouse germline (fig. 4B and D). Interestingly, env genes from this family show ∼60% sequence similarity to the env encoded by intracisternal A-type particle (IAP) elements in house mice, which are also capable of intercellular transmission (Ribet et al. 2008), suggesting a possible origin from a similar exogenous retrovirus (supplementary tables S4 and S6, Supplementary Material online). Together these data sug- gest that several ERVs derived from exogenous retroviruses recently and some may still be infectious. Negative Selection Shapes TE Distributions in the Deer Mouse Genome Most TE insertions are deleterious or neutral, and the genomic distribution of TEs is shaped in large part by se- lection against deleterious insertions. In the deer mouse genome, TEs account for nearly 25% of nucleotides in Transposable Element Interactions Shape the Ecology of the Deer Mouse Genome · https://doi.org/10.1093/molbev/msad069 MBE FIG. 4. Origins of ERVs in the deer mouse genome. (A) Cladogram displaying a maximum likelihood tree constructed with RT domains of deer mouse ERVs and publicly available endogenous and exogenous retroviruses for context. Internal nodes across monophyletic retroviral clades are all strongly supported (>95% bootstrap support). Deer mouse ERVs form 14 distinct clusters spanning broad retroviral diversity (highlighted in red). (B) Cladogram showing the approximate time of origin of deer mouse (P. maniculatus) ERVs based on the presence or absence in three other species at increasing phylogenetic distances. ERV cluster numbers are shown on the branch corresponding to their approximate origin. For each species, green boxes represent presence and gray boxes represent absence for each respective ERV cluster found in deer mouse. (C ) Neighbor-joining tree showing the phylogenetic relationship between Pman-ERV_cluster-10 copies in deer mouse, related ERVs in grasshopper mouse (O. torridus), and FeLV. (D) Structure of ERVs found in deer mouse with ORFs (colored boxes); important protein domains are annotated. Overlapping boxes represent overlapping ORFs. protein coding genes and 30% in long noncoding RNAs (lncRNAs) nucleotides but are relatively absent from coding exons (permutation test, P < 0.001), suggesting strong purifying selection on new insertions in coding exons (fig. 5A). These patterns are consistent with ob- servations in other mammals (Nellåker et al. 2012; Kapusta et al. 2013; Platt et al. 2018). Comparison of TE occupancies across chromosomes reveals that ERVs and LINEs are prevalent on the X chromosome (occupy- ing ∼15% and ∼17 of the X chromosome, respectively, compared to an average of ∼12% and ∼10% for other chromosomes; fig. 5B). This pattern is not observed for SINEs and likely reflects the more frequent removal of longer TEs such as LINEs and ERVs on autosomes by re- combination or purifying selection against new inser- tions (Kent et al. 2017; Dechaud et al. 2019). ERV insertions around protein coding genes are also usually deleterious since ERVs contain complex internal regula- tory elements that can disrupt gene expression. Consistent with this, ERVs are generally distant from genes and significantly more distant from genes in the same orientation (Mann Whitney U, P < 0.0001; fig. 5C). It is worth noting that this bias is most pronounced for mysTR-related ERV subfamilies, suggesting that these ERVs are highly deleterious, perhaps containing regulatory sequences particularly prone to disrupt gene expression when inserted in the same orientation as surrounding genes. Some ERV Subfamilies Have Possible Regulatory Function While most ERV subfamilies show patterns suggesting deleterious effects affecting the expression of neighboring genes, others display patterns consistent with possible regulatory function. Indeed, ERV LTRs may be co-opted for important regulatory functions over evolutionary time (Chuong et al. 2017). We find a small subset of ERV subfamilies to be enriched in the 5-kb region upstream of gene transcription start sites, suggesting that these ERVs minimally affect neighboring gene expression or that they may contribute to host gene regulation as either promoters or enhancers (fig. 5D). These ERVs also display significantly higher within-subfamily divergence relative to other lineage-specific deer mouse ERVs (Mann Whitney U, P = 0.0048), suggesting that they primarily represent older, inactive subfamilies (fig. 5E). Additionally, some subfam- ilies, including MT2B1 and ORR1B1, represent lineages of ancestrally shared elements that have been co-opted for regulatory functions in other mammalians (Franke et al. 7 Gozashti et al. · https://doi.org/10.1093/molbev/msad069 MBE FIG. 5. Genomic distribution of TEs in the deer mouse genome. (A) Respective coverage, defined as the proportion of nucleotides attributed to TEs for a given feature, for different TE subclasses across genomic features. CDS = protein coding sequence; lncRNA = long noncoding RNA. (B) Respective coverage for TEs across chromosomes. (C ) Box plots showing the distribution of ERV distances from the closest gene on the same strand (red) versus when strand is ignored (gray). (D) Enrichment ratio (number observed/expected) and Bonferroni-corrected Fisher’s exact test P-values (Q-values) for ERV subfamilies enriched within the 5 kb region upstream of genes in the same orientation. (E) Within-subfamily CpG corrected Kimura divergence for ERV subfamilies enriched within the 5 kb region upstream of genes in the same orientation (red) compared to all other ERV subfamilies (gray). (F ) Genomic distribution of ERV hotspots (red) across chromosomes. Lineage-specific KZNF genes are indicated (purple triangles) and are enriched in ERV hotspots. (G) KZNFs in ERV hotspots (red) show lower Kimura divergence than other KZNFs (gray), suggesting that they are younger. (H ) Kernel density estimates for the distribution of Kimura divergences for KZNFs outside ERV hotspots (gray), KZNFs in ERV hotspots (red), and ERVKs (blue). Vertical dotted lines show the peak value for each distribution. (I ) Cartoon displaying an ERV insertion interrupting a formerly intact LINE. N represents the range of observed candidate instances of ERV-mediated LINE interruption. 2017). Given the frequent and recurrent lineage-specific ERV co-option events observed across mammals (Feschotte 2008; Sakashita et al. 2020; Fueyo et al. 2022), these subfamilies represent promising candidates for co- option events in the deer mouse lineage. ERVs Accumulate in “Hotspots” Enriched for Kruppel-Associated Box-Zinc Finger Genes The distribution of ERVs in the genome is largely biased to- wards specific regions, or “hotspots”, which are enriched in Kruppel-associated box (KRAB) domain-containing zinc finger genes (KZNFs). We define “hotspots” as regions of the genome in the top 95th percentile of ERV density, where ERV density is the proportion of nucleotides attrib- uted to ERVs in a given 100-kb genomic window (fig. 5F). Lineage-specific ERVKs constitute over 70% of ERVs in hot- spots, suggesting that these genomic associations are likely lineage-specific. Furthermore, neighboring ERVs are signifi- cantly more divergent in ERV hotspots than in other 8 regions of the genome (Mann Whitney U, P = 3.317e −06), suggesting that hotspots arose primarily through in- dependent insertions rather than segmental duplication (SD) of existing insertions. ERV hotspots are largely devoid of genes, and we observe a strong negative correlation be- tween gene density and ERV density overall (generalized linear model, P < 0.0001). However, we do observe some genes in ERV hotspots. We performed a gene ontology (GO) enrichment analysis for genes in ERV hotspots and found significant enrichment for one biological pro- cess term: “regulation of transcription, DNA-templated” (two-sided Fisher’s exact test, Q < 0.00001). Scrutiny of genes overlapping ERV hotspots that match this GO term reveals that ∼85% (100/118) are deer mouse- specific KZNFs (fig. 5C). We define deer mouse-specific KZNFs based on refseq’s annotation of genes that do not have orthologs in other species (O’Leary et al. 2016). We find that deer mouse-specific KZNFs specific- ally are enriched in ERV hotspots, with ∼32% (100/312) of KZNFs occurring in ERV hotspots, despite the fact Transposable Element Interactions Shape the Ecology of the Deer Mouse Genome · https://doi.org/10.1093/molbev/msad069 MBE that ERV hotspots only represent <5% of the genome (two-sided Fisher’s exact test, P < 0.00001). Coevolution of ERVs and KZNFs It has become increasingly clear that the primary function of KZNFs is to suppress retroelement activity (Thomas and Schneider 2011; Yang et al. 2017; Cosby et al. 2019). KZNF gene clusters evolve rapidly through a birth-death model under positive selection and often expand in response to the lineage-specific activity of retroelements, including ERVs (Emerson and Thomas 2009; Najafabadi et al. 2015; Wolf et al. 2020). The colocalization of KZNF genes and ERVs in genomic space is intriguing and has been observed previously in the house mouse (Kauzlaric et al. 2017). Although this observation could simply be explained by re- laxed selection on nonessential KZNF genes, two alterna- tive, non-mutually exclusive hypotheses could explain the observed colocalization between KZNFs and ERVs: (1) KZNFs use neighboring ERVs as regulatory sequences to respond to the global derepression of ERVs (Pontis et al. 2019; Ito et al. 2020) or (2) ERVs contribute to KZNF gene family evolution by facilitating rapid gene du- plication and deletion (i.e., turnover) in these regions. Indeed, ERVs are known to facilitate structural rearrange- ments via ectopic recombination, and ERV-rich regions of the genome can be highly plastic (Hughes and Coffin 2001; Doxiadis et al. 2008; Jern and Coffin 2008; Hermetz et al. 2012). Interestingly, lineage-specific KZNF duplicates in ERV hotspots exhibit significantly lower divergence compared to other KZNFs, suggesting that genes in ERV hotspots duplicated relatively recently (Mann Whitney U, P = 0.0043; fig. 5G). This observation supports the idea that KZNFs overlapping with ERV hotspots duplicate more often, although the evolutionary processes driving this pattern remain unclear. Next, we examine whether lineage-specific KZNF gene family expansion located in ERV hotspots coincides with lineage-specific ERV activity. To do so, we compared the age (sequence divergence) distribution of KZNF gene du- plicates located within ERV hotspots to that of KZNFs res- iding outside ERV hotspots and that of ERVK subfamilies, as measured by intra-subfamily copy divergence (fig. 5H). The distribution of duplicate divergence for KZNFs in ERV hotspots suggests that the largest KZNF expansion oc- curred just before or around the same time as the peak of ERVK amplification (fig. 5H). Indeed, the median percent divergence for lineage-specific KZNF gene duplicates in ERV hotspots is ∼17.2%, while the within-subfamily diver- gence for the top three most abundant ERVs in the deer mouse genome is ∼17.4%. This pattern is consistent with a KZNF expansion driven by the amplification of highly ac- tive lineage-specific ERVKs. In contrast, the distribution of duplicate divergence for KZNFs not overlapping ERV hot- spots shows no obvious relationship to lineage-specific ERVK activity (fig. 5H), further suggesting that the ob- served colocalization between ERVKs and KZNFs may be causally associated. Furthermore, some KZNF gene clusters display much larger expansions than others: for example, a cluster on chromosome 1 contains >90 genes, represent- ing about one-third of lineage-specific KZNFs in the deer mouse genome (fig. 5F). This observation suggests that KZNFs in this chromosome 1 cluster may play an import- ant role in suppressing ERVKs. We observe another ex- ample on chromosome 22, which displays a cluster of 48 lineage-specific KZNF genes. Since members of the same KZNF clusters often bind to related ERV families, the mas- sive invasion of closely related ERVs predicts expansions of closely related KZNFs (Wolf et al. 2020). Together, these re- sults suggest that KZNFs in the deer mouse underwent a large expansion in response to lineage-specific ERV activity. Lineage-Specific ERV Insertions Interrupt LINE Sequences In addition to evaluating ERV distributions with respect to genes, we also assessed ERV distributions with respect to other TEs. We were specifically interested in how the ob- served ERV invasion in the deer mouse might directly im- pact pre-existing LINEs. Specifically, we sought to examine whether ERVs could have directly impacted L1 activity by inserting into and interrupting transposition-competent L1. L1 families typically only have from a hundred to a few thousand “master genes” that are transposition- competent in mammalian genomes (Deininger et al. 1992; Brouha et al. 2003; Zemojtel et al. 2007; Platt et al. 2018). Furthermore, the L1 retrotransposition mechanism is fairly inefficient, and the vast majority of new L1 inser- tions are defective and incapable of mobilizing thereafter (Fanning 1983; Grimaldi et al. 1984). Thus, disruption of many master genes could have a considerable impact on the evolutionary trajectory of L1s in a species. To explore the direct impacts of ERV insertions on L1s, we searched for ERV insertions directly flanked by L1 se- quences from the same L1 subfamily. We then filtered for cases in which flanking L1 sequences conjoined at the correct coordinates with respect to the subfamily con- sensus, forming a full-length L1. We also initially filtered for L1s that did not contain any additional TE insertions. These results revealed 322 prospective lineage-specific ERV inser- tions that interrupt full-length L1 (supplementary table S7, Supplementary Material online). However, this number is likely a large underestimate, since it does not include frag- mented L1s that, for example, have accumulated multiple indels. If we include fragmented L1s as well, we find 2,664 prospective ERV insertions interrupting LINEs, 900 of which are attributed to the two most abundant ERVK related subfamilies (Pman_ERV2_4.503 and Pman_ERV2_4.24; supplementary table S8, Supplementary Material online). Interestingly, within-subfamily percent divergences for these subfamilies (16.05% and 15.86%) suggest that they invaded just before the decline of L1 gain (see fig. 1B, ∼15%). We speculate that this association is no coincidence, and that ERV insertions within potentially active L1s were a signifi- cant driver in reducing L1 activity in the deer mouse lineage. Punctuated L1 interruptions on these scales (322 - > 2,500 9 Gozashti et al. · https://doi.org/10.1093/molbev/msad069 L1 interruptions) would eliminate most functional L1s in many mammalian species, and even on much smaller scales, could have a catastrophic effect on the evolutionary trajec- tory of L1s in a genome, especially given the poor success rates of the L1 retrotransposition mechanism in producing new fully functional L1 copies (Kazazian and Moran 1998; Szak et al. 2002). We also explored quantitative patterns of ERV content in LINEs more broadly. To do so, we tested for an enrichment of ERVs inserted within L1s across all ERV subfamilies. Interestingly, we find that several lineage-specific ERVK sub- families show significant enrichment within L1s relative to ran- dom expectation (Bonferroni-corrected permutation test, α = 0.01, N = 1000; supplementary table S9, Supplementary Material online). For example, Pman_ERVK_4.63 elements interrupt L1s more than 46 times more than expected by chance (Bonferroni-corrected permutation test, Q < 0.001). The observed enrichment for lineage-specific ERVK subfam- ilies, representing 29 of the 36 enriched subfamilies, and absence of enrichment for other (older) ERV subfamilies, is consistent with our hypothesis that lineage-specific ERVK expansion directly impacted L1 viability (supplementary table S9, Supplementary Material online). Together, these results suggest an intriguing model whereby ERVs and LINEs compete for genomic sites and that ERVKs may have directly impacted the evolutionary trajectory of LINEs in the deer mouse lineage. An “Ecology of the Genome” Model for the Evolution of the Deer Mouse Genome In the same way that species compete for space and re- sources, TEs compete with each other for sites in the gen- ome as well as metabolic resources (Brookfield 2005). TEs can occupy specific niches, which can allow them to coex- ist with limited competition, but TEs that occupy similar niches are more likely to compete and thereby drive one or another to extinction (Brookfield 2005; Venner et al. 2009). Furthermore, the relative success of a given TE also depends on host suppression mechanisms and their targets. For example, differential host targeting between two TE families in direct competition could limit the suc- cess of one family that would, in the absence of host de- fense mechanisms, be more fit than the other (Venner et al. 2009). Also, because TEs could threaten to kill their host in the absence of host-mediated suppression, it can be advantageous (for both the host and TEs) that host de- fenses evolve to suppress TE activity (Venner et al. 2009). Our model for the evolution of the distinctive TE land- scape of the deer mouse supports the notion of “genomic ecology” (Brookfield 2005). We postulate that the intro- duction of mysTR-related ERVs caused a shift in the deer mouse TE landscape through the following processes (fig. 6): first, mysTR ERVs evaded host defenses upon germ- line infiltration, which allowed them to expand to large numbers. This hypothesis is supported by the observation that mysTR ERVs are highly divergent from other known retroviruses as well as the remarkable expansion of deer 10 MBE mouse-specific KZNFs following peak ERV activity (Cantrell et al. 2005). In mammals, ERVs are the primary targets of KZNF suppression, whereas LINEs and SINEs are less frequently targeted, probably because ERV inser- tions are more regulatorily potent and therefore more deleterious (Wolf et al. 2015; Zhou et al. 2020). These host defenses keep ERVs in check, despite evidence that LINEs and ERVs compete for similar sites in the genome. First, many ERVs and LINEs both preferentially integrate into AT rich regions (Medstrand et al. 2002; Babushok et al. 2006; Nellåker et al. 2012; Campos-Sánchez et al. 2016). Thus, ERVs and LINEs often inhabit similar regions of the genome and frequently insert within each other (Campos-Sánchez et al. 2016). Second, ERV insertions in LINEs (or vice versa) are likely invisible to selection and exhibit a higher rate of fixation relative to deleterious insertions (Campos-Sánchez et al. 2016). Under these circumstances, in the absence of host defense mechanisms, we expect the pri- mary driver of ERV or LINE success in the genome to be rela- tive rates of gain of transposition-competent copies. Thus, we postulate that the massive expansion of mysTR ERVs nearly drove LINEs to extinction in the deer mouse genome. Since this initial ERV invasion, we postulate that expansions of host KZNF repertoires helped stabilize ERV activity in the gen- ome and were likely aided by the proliferation of nonautono- mous ERV derivatives. These suppression mechanisms likely enabled more sustainable ERV activity by limiting the rate of ERV expansion and reducing fitness cost. More generally, we propose that this model may explain the loss of LINE activity in other mammals. A subclade of sigmodontine rodents for example (∼13–18 MYA di- verged from the deer mouse; Abramson et al. 2009; Leite et al. 2014; Kumar et al. 2017; Gonçalves et al. 2020) repre- sents one of the few mammalian lineages to have experi- enced LINE extinction (Yang et al. 2019). Consistent with our model, previous studies suggest that LINE extinction in this group followed an invasion of mysTR-related ERVs on a similar or possibly larger scale to that observed for the deer mouse (Cantrell et al. 2005; Erickson et al. 2011). At present, the lack of genome assemblies for sigmodontine rodents makes it challenging to study TEs in these species. However, a recent study examining TE content in mammals notes that cricetid rodents exhibit the highest rates of recent LTR retrotransposon accumulation among mammals (Osmanski et al. 2022). Overall, we hypothesize that for an ERV invasion to have a similar effect on LINE ac- tivity in another mammal, the causal ERV must arise from a divergent retrovirus (unfamiliar to host suppression machin- ery), show similar integration preference to that of LINEs, and rapidly evolve nonautonomous derivatives. Future studies in other species, which show unique patterns of mammalian genome composition, will shed further light on evolutionary conflicts that drive mammalian genome evolution. Concluding Remarks Although TE landscapes differ drastically across species, most mammalian genomes are similarly dominated by Transposable Element Interactions Shape the Ecology of the Deer Mouse Genome · https://doi.org/10.1093/molbev/msad069 MBE FIG. 6. Model for deer mouse genome evolution. LINE (and SINE) non-LTR retrotransposons. The deer mouse, P. maniculatus, represents one of the few excep- tions to this pattern: LTR elements occupy more of the genome than LINEs. We find that the distinct genomic landscape of TEs in the deer mouse reflects a massive ex- pansion of ERVs as well as a dearth of LINE activity, and that the two phenomena are likely associated. Our results show that a broad diversity of ERVs invaded the deer mouse genome and that the infiltration of one ERV family in particular, mysTR, played a prominent role in establish- ing its unique TE landscape. Furthermore, we note that re- ported ERV copy numbers and diversity are likely a vast underestimate since the current deer mouse genome as- sembly was generated with short reads (Peona et al. 2021). Based on these findings, we postulate that the propen- sity for a mammalian genome to undergo a shift in TE con- tent and/or experience LINE extinction is directly related to its susceptibility to invasion by divergent TEs—in this case, ERVs. Furthermore, the accumulation of ERVs in specific genomic hotspots raises additional questions about how TE-dense regions can affect mammalian gen- ome evolution. Indeed, we would expect such regions to experience structural rearrangements more often than other regions of the genome. Previous studies in the deer mouse have identified many large inversions (>1 Mb in length), which are polymorphic, even within popu- lations (Hager et al. 2022; Harringmeyer and Hoekstra 2022). Could these ERV hotspots have played a role in fa- cilitating deer mouse inversions? We also observe enrich- ment of KZNF gene families which evolve rapidly via duplication within ERV hotspots. Is the colocalization of KZNFs and ERVs advantageous for the host due to the in- creased propensity for KZNF gene family expansion? We show that KZNFs in ERV hotspots are indeed younger than other KZNFs, providing some support for the coevolution of these genomic features. However, within- population studies are critical to further elucidate this coevolutionary relationship. Together, our results have 11 Gozashti et al. · https://doi.org/10.1093/molbev/msad069 MBE broad implications and open up a range of opportunities to investigate the evolutionary processes that give rise to the evolution of mammalian genome structure. deer mouse proteins with parameters (-max_target_seqs 25 -culling_limit 2 -evalue 10e-10) and filtered all TEs with unknown classifications that shared homology with proteins. Methods Obtaining Relevant Genomic Data We downloaded publicly available TE annotations for human, Homo sapiens (GCF_000001405.40; genome contig N50 = 57,879,411; contig L50 = 18); house mouse, M. musculus (GCF_000001635.27; genome contig N50 = 59,462,871; contig L50 = 15); Norway rat, Rattus norvegicus (GCF_015227675.2; genome contig N50 = 29,198,295; contig L50 = 27); and prairie vole, M. ochrogaster (GCF_000317375.1; genome contig N50 = 21,250; contig L50 = 29,205) from RepeatMasker (http://www.repeat masker.org/genomicDatasets/RMGenomicDatasets.html) and for grasshopper mouse, O. torridus from NCBI (GCF_903995425.1; genome contig N50 = 2,276,141; con- tig L50 = 308). We used the deer mouse, P. maniculatus, genome assembly available (refseq GCF_003704035.1; contig N50 = 30,111; contig L50 = 23,323) for all genomic analyses. Retroviral sequences for ERV phylogenetic analysis were downloaded from NCBI. Genbank accession numbers and for these sequences are shown in fig. 4A. through NCBI TE Discovery and Annotation We used a combination of systematic and manual techni- ques to identify and annotate TEs in the deer mouse gen- ome. We started by using an approach similar to the EarlGrey pipeline (github.com/TobyBaril/EarlGrey/; Baril and Hayward 2022). We first identified known rodent TEs in the deer mouse genome using RepeatMasker (ver- sion 4.1.2, https://www.repeatmasker.org/) with a curated set of rodent TEs from the DFAM database (Hubley et al. 2016) and the flags -nolow, -norna, and -s. Next, we constructed a de novo repeat library using RepeatModeler2 (version 2.0.1), with RECON (version 1.08) and RepeatScout (version 1.0.5) (Bao and Eddy 2002; Price et al. 2005; Flynn et al. 2020). Maximum-length consensus sequences were generated for putative de novo TEs identified by RepeatModeler using an automated version of the “Basic Local Alignment Search Tool (BLAST), Extract, Extend” process through EarlGray (Platt et al. 2016). Briefly, EarlGray first performs a BLASTn search to obtain the top hits for each TE subfamily (Camacho et al. 2009). Then, it aligns the 1,000 base pairs of flanking retrieved se- quences using multiple alignment using fast fourier trans- form (MAFFT; version 7.453; Katoh and Standley 2013). Following this, alignments are trimmed using trimAl (ver- sion 1.4) with -cons 60; Capella-Gutiérrez et al. 2009). Finally, consensus sequences are updated using European molecular biology open soft- ware suite cons (-plurality 3; Rice et al. 2000). This process is then repeated five times. Following this, we performed blastx (Camacho et al. 2009) searches against all known the options (-gt 0.6 12 families were Following the automated processes described above, alignments for TE families were individually inspected using AliView (Larsson 2014) and poorly represented po- sitions were manually trimmed as recommended by Storer et al. (2021). Families were also manually realigned using extract_align.py (Platt et al. 2016) and MAFFT (ver- sion 7.453; Katoh and Standley 2013) and then reexa- mined. Manually curated TE then re-clustered using cd-hit-est (Fu et al. 2012) and families were merged based on the 80-80-80 rule criterion (Wicker et al. 2007). We also used TE-Aid (https:// github.com/clemgoub/TE-Aid) to identify TE-associated ORFs and sequence features such as LTRs when classify- ing TEs. We combined our final de novo TE library with the Rodent DFAM TE library (Hubley et al. 2016) and an- notated TEs the deer mouse genome using RepeatMasker. To identify full-length LTR elements, we used LTR_FINDER (Xu and Wang 2007) and LTRharvest (Ellinghaus et al. 2008) through EDTA_raw with the flag -type ltr (version 2.0.0; Ou et al. 2019), which also re- port LTR divergence for each element. TE annotations were defragmented and refined using RepeatCraft with the flag -loose (Wong and Simakov 2019), and overlap- ping annotations were resolved in favor of the longer element using MGKit (version 0.4.1) filter-gff (Rubino and Creevey 2014). in Identifying Functional Machinery for Putatively Autonomous TEs To identify the protein machinery of potentially autono- mous LINE and LTR elements, we extracted all LINE ele- ments longer than 2700 bp and LTR elements longer than 5000 bp from the deer mouse genome. Then, we also used TE-Aid (https://github.com/clemgoub/TE-Aid) to identify ORFs in each retrieved LTR and LINE element with homology to known TE genes. We used hmmer (Finn et al. 2011) and relevant hmms available from GyDB (Llorens et al. 2011) and PFAM (Mistry et al. 2021) to identify retroviral protein domains as well as NCBI’s conserved domain search tool (Marchler-Bauer et al. 2015; Marchler-Bauer et al. 2017). Calculating k Coefficients We calculated the DNA loss coefficient k (Lindblad-Toh et al. 2005), using the formula E = Ae − kt, where E is the amount of extant ancestral DNA in the species considered, A is the ancestral assembly size, and t is the time. We cal- culated E for each species by subtracting the amount of genomic DNA attributed from the amount of DNA attributed to ancient shared mamma- lian TEs (retrieved from Kapusta et al. 2017). We used 2.8 Gb for A and 100 million years for t as in Kapusta et al. (2017). lineage-specific TEs Transposable Element Interactions Shape the Ecology of the Deer Mouse Genome · https://doi.org/10.1093/molbev/msad069 MBE Identifying Nonautonomous ERV-Like Elements Since the deer mouse genome was produced primarily with short reads, most ERVs have internal gaps or strings of low quality or ambiguous nucleotides. Thus, to decipher nonautonomous ERV-like elements from autonomous ERVs, we used a strict criterion. For a given ERV subfamily to be considered nonautonomous, we required at least five full-length copies which lack identifiable ORFs as well as ambiguous nucleotides. We performed global pairwise alignments between nonautonomous and autonomous ERVK consensus sequences using the global alignment software AVID with default parameters (Bray et al. 2003). We visualized alignments using VISTA (Frazer et al. 2004). ERV Classification and Phylogenetic Analysis We used two complementary approaches to classify deer mouse ERVs. First, we examined e-value statistics in the output from our GyDB hmm scans to discern which viral RT domain hmm best fit each ERV. In addition, we also used a phylogenetic approach. We annotated ERVs with their viral origin as predicted by our hmm scans. Next, we downloaded several endogenous and exogenous retro- viruses from NCBI (accessions shown in fig. 4A), extracted their RT domains, and annotated them with their respect- ive viral clade. Then, we filtered sequences with large strings of ambiguous characters, performed a multiple se- quence alignment of RT genes using MAFFT (Katoh and Standley 2013), and generated a maximum likelihood- based phylogeny using IQ-TREE (Minh et al. 2020) with a GTR + G model (general time reversible model with un- equal rates and unequal base frequencies and discrete gamma rate heterogeneity; Yang 1994). We constructed a consensus tree across 1,000 replicates using IQ-TREE’s -bb flag (Yang 1994). All internal nodes separating mono- phyletic ERV clades were strongly supported (>95% boot- strap support). We analyzed and edited the resulting phylogeny using ete3 (version 3.1.2) (Huerta-Cepas et al. 2016), collapsing clusters of deer mouse ERVs into repre- sentative nodes. We visualized the phylogenetic tree using the interactive tree of life (Letunic and Bork 2021) and FigTree (version 1.4.4; https://github.com/rambaut/ figtree). To search for homology between deer mouse ERVs and MysTR, we used blastn (Camacho et al. 2009) to align deer mouse ERV consensus sequences to a previ- ously (Genbank DQ139737.1; Cantrell et al. 2005). isolated MysTR pol-pro sequence Searching for Deer Mouse ERVs in Other Species To search for deer mouse ERVs in the house mouse, prairie vole, and grasshopper mouse genomes, we performed local BLASTn (Camacho et al. 2009) queries for each full-length deer mouse ERV to each respective genome. We ran BLASTn (Camacho et al. 2009) with the flag -outfmt 6 and required a minimum alignment length of 400 bp and minimum percent identity of 75 to limit possible erro- neous hits. As a proof of concept, we also made sure our results were consistent with expectations based on LTR divergences. For example, we would not expect an ERV with highly divergent LTRs (a signature of a more ancient insertion) to be specific to the deer mouse. We also per- formed broader BLASTn queries against NCBI’s nucleotide database. Queries for Gamma_Pman-ERV_cluster-10 se- quences yielded high-confidence hits in deer mouse spe- cies, the grasshopper mouse, and a FeLV reference genome (Genbank AB060732.3). BLAST queries of FeLV (AB060732.3) back to the non-redundant nucleotide data- base also showed best hits to the deer mouse and grass- hopper mouse genomes when other FeLV genomes were excluded. A neighbor-joining phylogeny constructed from deer mouse Gamma_Pman-ERV_cluster-10 se- quences, homologous ERVs in the grasshopper mouse gen- ome, and FeLV (AB060732.3) suggest a scenario in which Gamma_Pman-ERV_cluster-10 originated in the common ancestor the deer mouse and grasshopper mouse from FeLV or another closely related exogenous virus between 5 and 18 MYA. TE Distribution Analysis We used bedtools intersect (Quinlan and Hall 2010) to find overlaps between TE annotations and gene feature an- notations. We used bedtools closest (Quinlan and Hall 2010) with the parameter -s to identify TE distances from the nearest gene on the same strand and again with default parameters to ignore strand. All functional enrichment tests were performed using goatools (Klopfenstein et al. 2018). We also tested for enrichment or depletion of TEs 5 kb upstream of genes in the same orientation. Specifically, for each TE subfamily, we rando- mized all TE locations on each chromosome and com- pared the number of TEs within 5 kb of genes upstream in the same orientation with the observed value. We per- formed two-sided Fisher’s exact tests comparing the num- ber of observed and expected elements within these regions to obtain P-values. Fisher’s exact P-values and per- mutation test P-values were adjusted using the Bonferroni method to obtain Q-values. This revealed eight subfamilies that displayed enrichment for the 5 kb regions upstream of genes in the same orientation (supplementary table S10, Supplementary Material online). However, enrich- ment of SDs in these regions could also cause a similar pat- tern. To test this alternative, we used SEDEF (version 1.1) (Numanagic et al. 2018) with default parameters to iden- tify SDs in the deer mouse genome. Then, for each en- riched ERV subfamily, we intersected SD coordinates with the 5 kb regions upstream of genes harboring at least one element in the same orientation. We then compared SD coverage in these regions for each ERV subfamily with expectations from randomization for 1,000 permuta- tions. This revealed that genes containing RMER15 copies within 5 kb upstream in the same orientation also dis- played enrichment for SDs, suggesting that SDs could al- ternatively explain RMER15 (supplementary table S10, Supplementary Material online). Thus, we did not include RMER15 in figure 5D. We used bedtools coverage (Quinlan 13 Gozashti et al. · https://doi.org/10.1093/molbev/msad069 and Hall 2010) to calculate ERV and gene density along 100 kb windows in the genome. ERV hotspots were de- fined as windows which exhibit ERV densities within the top 95th percentile. ERV hotspots could arise through two possible non-mutually exclusive mechanisms: inde- pendent insertion of ERVs in specific genomic regions and SD of pre-existing ERV insertions. One expectation of the latter is that neighboring ERVs of the same subfamily would exhibit more similar divergences from the consen- sus in ERV hotspots (if they arose from a duplication of the one original insertion) than in other regions of the genome. To assess this possibility, we compared delta divergence from the consensus between neighboring ERVs (\|neighbor_1_div—neighbor_2_div\|) in ERV hot- spots to other regions of the genome and report a signifi- cant trend in the opposite direction (Mann Whitney U, P = 3.317e−06), suggesting that SD is not the primary con- tributing mechanism to ERV hotspot formation. Figure 5F was produced using RIdeogram (Hao et al. 2020). KZNF Gene Family Analysis We defined deer mouse-specific zinc finger (ZF) genes as genes which do not have recognizable orthologs as anno- tated by NCBI. We employed hmmscan (Finn et al. 2011) using KRAB hmms downloaded from PFAM (Mistry et al. 2021) to identify KRAB domain-containing ZFs (KZNFs). Then, we performed a multiple sequence align- ment of all KZNFs using Clustal omega (Sievers et al. 2011) with the parameters -use-kimura and -full in order to simultaneously produce a pairwise Kimura divergence matrix across all genes. We constructed a subsequent phyl- ogeny using IQ-TREE (Minh et al. 2020) with a general time reversible model. To test for phylogenetic clustering of KZNF that overlapped ERV hotspots, we used phyloclust through RRphylo R package (Castiglione et al. 2018) with 100 simulations. Since KZNF genes evolve via a birth-death process, we define duplicate genes as genes that exhibit the lowest divergence among all pairwise comparisons. ERV-Mediated LINE Interruption To identify candidate LINEs interrupted by ERVs, we searched for LINE fragments which would be full length (>5000 bp) if connected but exhibit an ERV sequence which splits them with respect to their subfamily consen- sus (supplementary table S7, Supplementary Material on- line). This yielded 322 candidate ERV-mediated LINE interruptions, 121 of which represented lineage-specific LINEs. In this first analysis, we excluded LINEs which showed more than two fragments. If we include those as well, we find 2,664 candidate ERV-mediated LINE interrup- tions. We employed a permutation test to quantitatively assess biased representation of ERVs in LINEs. We did this separately for each ERV subfamily. To do this, we com- pared the observed number of ERV insertions inside LINEs (ERV sequences flanked on both sides by LINE sequences from the same subfamily) to expectations by randomiza- tion 1,000 times. 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10.1088_2752-5295_acf4b5.pdf	Data availability statement The data that support the findings of this study are openly available in the Harvard Dataverse at https://doi. org/10.7910/DVN/2UT4GM (Hauer 2023).	Data availability statement The data that support the findings of this study are openly available in the Harvard Dataverse at https://doi. org/10.7910/DVN/2UT4GM (Hauer 2023) .	OPEN ACCESS RECEIVED 14 December 2022 REVISED 14 August 2023 ACCEPTED FOR PUBLICATION 29 August 2023 PUBLISHED 18 September 2023 Original Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Environ. Res.: Climate 2 (2023) 045004 https://doi.org/10.1088/2752-5295/acf4b5 PAPER Sea level rise already delays coastal commuters Mathew E Hauer1,4, Valerie Mueller2,3,∗ and Glenn Sheriff2 1 Department of Sociology, Florida State University, Tallahassee, FL, United States of America 2 School of Politics and Global Studies, Arizona State University, Tempe, AZ, United States of America 3 International Food Policy Research Institute, Washington, DC United States of America 4 Mathew Hauer is the primary author of the paper. Map data copyrighted OpenStreetMap contributors and available from www. openstreetmap.org. This material is based upon work supported by the National Science Foundation under Grant Number 1939841. ∗ Author to whom any correspondence should be addressed. E-mail: vmuelle1@asu.edu Keywords: sea level rise, adaptation, transportation, climate change impacts, king tides Supplementary material for this article is available online Abstract Although the most dire societal impacts of sea-level rise (SLR) typically manifest toward the end of the 21st century, many coastal communities face challenges in the present due to recurrent tidal flooding. Few studies have documented transportation disruptions due to tidal flooding in the recent past. Here, we address this issue by combining home and work locations for approximately 500 million commuters in coastal US counties from 2002 to 2017. We find tidal flooding delays coastal commuters by approximately 22 min per year in 2015–2017, increasing to between 200 and 650 min by 2060 under various SLR scenarios. Adjustments in residential and work locations reduce the growth in commuting delays for approximately 40% of US counties. For residents in coastal counties, SLR is not a distant threat—it is already lapping at their toes. 1. Main text Sea-level rise (SLR) is one of the most visible and costly impacts of climate change (Church et al 2013, Oppenheimer et al 2019). With sea levels expected to rise up to 2.5 m by 2100 (Sweet et al 2017, Oppenheimer et al 2019), scientists routinely quantify the potential multitude of impacts of SLR over the next eighty to 2000+ years (Strauss et al 2015, Clark et al 2016, Oppenheimer et al 2019), focusing on impacts deep into the future when they will be presumed greatest. Although the most worrying societal impacts of SLR (displacement and permanent submergence) typically manifest toward the end of the 21st century (Kulp and Strauss 2019, Oppenheimer et al 2019, Hauer et al 2020), many coastal communities face daunting impacts in the present due to recurrent tidal or high tide flooding (Dahl et al 2017, Moftakhari et al 2017, Sweet et al 2018). We use ‘tidal flooding’ to refer to only tide-based inundation, i.e. excluding precipitation (Hague and Taylor 2021). Our definition differs from that used by other studies focusing on tidal flooding above minimum thresholds (e.g. Sweet et al 2018)4. Studies focusing exclusively on directly affected areas find tidal flooding causes coastal erosion (Hinkel et al 2013), saltwater intrusion (Chang et al 2011), reduced property values (McAlpine and Porter 2018), damaged property (Bukvic and Harrald 2019), submerged transportation routes (Jacobs et al 2018), and exposure of thousands of people to general flood risks (Kulp and Strauss 2019). Given the interconnectedness of coastal communities, e.g. via commuting (Kasmalkar et al 2021), SLR impacts will likely indirectly affect inland, higher elevation coastal areas. The extent to which coastal commutes might be delayed due to inundation on roadways is presently uncertain for the coastal United States. Some research has linked tidal flooding to transportation networks. These studies use prescribed flood levels rather than historical tide data (Jacobs et al 2018, Kasmalkar et al 2020), limited transportation routes 4 Many factors can influence sea levels including storm surge, precipitation, currents, trapped waves, etc (Ezer and Atkinson 2014, Barnard et al 2017). Our definition of ‘tidal flooding’ includes all factors as measured at NOAA stations. © 2023 The Author(s). Published by IOP Publishing Ltd Environ. Res.: Climate 2 (2023) 045004 M E Hauer et al to major roads (Jacobs et al 2018, Kasmalkar et al 2020), annual daily traffic data rather than actual commuter data (Jacobs et al 2018), or limit their analysis to specific areas (Jacobs et al 2018, Kasmalkar et al 2020, Shen and Kim 2020, Hauer et al 2021, Praharaj et al 2021). Most notably, Jacobs et al (2018) and Fant et al (2021) link average annual traffic on major roadways to road inundation and calculate road-segment-specific travel delays in the absence of alternative routing to ‘dry’ routes. No such national-level estimate of commuting delays presently exist that account for alternative routing to avoid or minimize delays along flooded roadways. These shortcomings combine to create incomplete estimates of SLR and tidal flooding impacts on commuting in coastal communities. Using national-level routing, commuting, and elevation data, we overcome these issues to estimate the burden SLR driven tidal flooding imposes on commuting times with a dynamic routing algorithm to allow for commuters to route around flooding-related delays. By combining commuter data with detailed road networks in a flood hazard model we assess the commuting delays associated with present and future SLR driven tidal flooding. Our analytical framework addresses three primary questions concerning SLR impacts in coastal communities in the United States: (1) What is the current commuting time burden imposed by SLR driven tidal flooding and what areas does SLR driven tidal flooding burden the most? (2) How have changes in commuting behavior and residential location affected this burden? And (3) What is the future commuting time burden, absent further behavioral changes? We estimate commuting delays attributable to recurrent tidal flooding by combining multiple large-scale georeferenced data sources within a travel optimization routine. Namely, we combine (1) the home and work (HW) locations for 74 million census block group (CBG) located in 222 coastal counties and 158 non-coastal counties for 500 million commuter-years in the period 2002–2017 (U.S. Census Bureau 2020), (2) a complete road network from Open Street Map (OpenStreetMap contributors 2017), (3) data from eighty-four tide stations across the US from National Oceanographic and Atmospheric Administration’s (NOAA) Center for Operational Oceanographic Products and Services (CO-OPs) tide gauge database (Chamberlain 2020, NOAA CO-OPs 2020), (4) the National Levee Database (US Army Corps of Engineers 2020), and (5) digital elevation models from both NOAA (NOAA 2020) and the US Geological Survey (Gesch et al 2002). We converted OSM’s street network into dual-weighted directed graphs and calculated the minimum travel times between HW CBGs, conditional on flood depth on roadways, between 2002 and 2017 and projected in 2060 using NOAA’s global mean SLR scenarios (Sweet et al 2017). As roadways become inundated over time and travel velocity slows, we dynamically adjust travel routes to ensure commuters select the path with the least travel time for a given amount of roadway inundation. Both the change in the commuters along a HW pair and the change in tide heights due to SLR contribute to changes in commuting delays over time. Changes in commuters along a HW pair can theoretically reflect adaptive residential or work location changes to reduce flood-related commuting delays—all else equal—and we isolate this behavior using a two-factor decomposition (Gupta 1991). We refer to this adaptive behavior as ‘accommodation’, as opposed to ‘retreat’ or ‘adaptation’ broadly, since it is an adaptation response that reduces vulnerability to commuting delays without protection or necessarily retreat (Oppenheimer et al 2019). 2. Methods and materials First, we describe the data sets used in our analysis. Second, we describe the methods to estimate commuting delays. Data. We use five primary data sources to estimate commuting delays: commuting information, road network data, tide gauge data, levee data, and digital elevation models. Commuting information comes from the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) for the period 2002–2017 (U.S. Census Bureau 2020). LEHD-LODES is a partially synthetic dataset for paired CBG residential (home) and employment (work) locations in the United States. We downloaded these data for all coastal counties (n = 222), limiting our analysis to respondents with differing home and work CBGs. For computational tractability, we subset the complete ‘commuting area’ of any given coastal county to those counties that are (i) within 100 miles of the coastal county centroid, and that (ii) contain at least 1% of the commuters into the coastal county. As illustrated in figure 1, on average this procedure captures nearly 90% of a county’s commuters (median 89%, 80% of counties capture at least 81% of commuters). Overall, it yields 74 million CBG origin-destination pairs. Due to unavailability of historical data, data sharing limitations, or quality assurance problems, three states in our analysis have incomplete commuting data: New Hampshire in 2002, Mississippi in 2002 and 2003, and Massachusetts from 2002–2010. Massachusetts lack of data for 2002–2010 also impacts New 2 Environ. Res.: Climate 2 (2023) 045004 M E Hauer et al Figure 1. Distribution of county commuters within 100 miles of destination county and containing at least 1% of commuters. The median county captures approximately 90% of commuters. Hampshire, as nearly 30% of New Hampshire commuters are missing between 2003 and 2010. All other states contain complete commuting data for the entire period. We therefore exclude Mississippi commuters prior to 2004 and Massachusetts and New Hampshire commuters prior to 2011. We obtain road network data from Open Street Map (OSM). OSM is an open-source, free, editable map of world street networks and features built from volunteers. OSM data include speed limits, network connectivity, and road segment characteristics useful for our analysis such as ‘tunnel’ and ‘bridge.’ Previous analyses of the completeness and accuracy of OSM data suggest sufficient georeferenced accuracy (El-Ashmawy 2016, Brovelli et al 2017). We obtain all road network types (primary, motorway, residential, etc) except for service road types for any destination coastal county, any origin commuter county within 100 miles and containing at least 1% of commuters, and any county in between for our analysis. We obtain tide gauge data for 84 tide stations across the US from NOAA’s Center for Operational Oceanographic Products and Services (CO-OPs) tide gauge database. Figure 2 shows the geographic distribution of tide gauages. We download hourly verified water heights for the period 2002–2019 using the ‘rnoaa’ package (Chamberlain 2020), in the R programming language and subset the tide gauges for the max water height during readings between the hours of 05:00 and 20:00 for Monday through Friday—the primary commuting days and hours in most areas. We normalize the annual number of commuting days to a standardized 250-day year. To correct for extreme water levels which might correspond with extreme weather events such as tropical cyclones, we use an outlier detection algorithm (Chen and Liu 1993, de Lacalle 2019) to search and correct for these extreme water levels by building counterfactual tide gauge series. We use the counterfactual value in time periods where the observed gauge reading exceeds τ ⩾ 10 (t-score), correcting only the most extreme water levels. We assign a tide gauge to each county based on shortest distance as the crow flies. For computational tractability, we group tide gauge readings into five discrete bins corresponding to the <90th, <98th, <99.5th, <99.9th, and >99.9th percentiles for each gauge. Notably, the two periods in our historical analysis (2002–2004 and 2015–2017) take place at different points in both the perigean and El Ni˜no cycles (Goodman et al 2018). Consequently, this analysis is best interpreted as measuring the effects of changes in water levels between these periods resulting from the combined impact of all influences of water levels at tide gauges, rather than those attributable to global warming-induced sea level rise alone. Levee data come from the National Levee Database (NLD) maintained by the US Army Corps of Engineers. The NLD is a nationally comprehensive database on levee systems in the United States. We obtained georeferenced levee information for the 22 states in our analysis. Digital elevation models (DEMs) come from two sources. For areas threatened by SLR we use NOAA’s Digital Coast, which is based on 1/9 arcsecond (10 m) or less horizontal resolution. For counties outside of 3 Environ. Res.: Climate 2 (2023) 045004 M E Hauer et al Figure 2. Geographic distribution of the 84 tide gauges used. NOAA’s coverage, we download DEMs from the National Elevation Dataset (NED) at 1/3 arcsecond resolution (Gesch et al 2002). Methods. We calculate commuting delay in five steps (Hauer et al 2021). First, we model road inundation depth as a function of a given tide gauge reading. Second, we model travel velocity for each road segment as a function of inundation. Third, we select the fastest route between any two points, conditional on this velocity. Fourth, we use the observed distribution of annual tide gauge readings to calculate the average annual flooding delay for a given pair of points. Fifth, we obtain aggregate commuting delays for any given area as the weighted average annual delay over all home-work location pairs, with weights corresponding to the number of commuters traveling from a given home location to a work location in the period. Step 1: Road inundation as a function of tide guage. We combine road network, DEM, levee, and tide gauge data to calculate flood inundation for each road segment. We apply a ‘bathtub’ hydrological model, similar to NOAA’s tidal flooding mapping procedure 5, calculating road segment inundation levels as the difference between the water levels reported at the tide gauge and the road surface elevation. Any road segment in the OSM wholly located inside of the area of a levee is given an elevation of 100 m, setting its elevation far above any flood height, and any road segment partially located inside of the area of a levee (e.g. when a road segment begins outside of a levee and terminates inside of a levee) we assume the road segment elevation is the mean of the two points. Any road segment located outside of any DEM for any reason (due to being either very far inland via a circuitous route) is also assigned an elevation of 100 m. To aid in computational tractability, we divide tide gauge data into the five discrete bins outlined above. With each road segment assigned an elevation value, z, we subtract tide gauge bin gb from road segment z to produce the flood depth i for any given segment. Step 2: Travel velocity as a function of local inundation. We model travel velocity v in mph along any given road segment as a function of the speed limit L and the mm of road inundation i: 8 < v(i) = : (cid:8) L min 1 L, 0.6[0.0009i2 − 0.5529i + 86.9448] (cid:9) i <= 0 0 < i < 300 300 < i. (1) This is the depth-disruption function fitted by Pregnolato et al (2017) but modified in four key ways. First, if a road segment elevation z is above the tide gauge level gb and thus road inundation i is less than 0, we assume vehicle speed is the speed limit, i.e. the road segment is unaffected by tidal flooding. Second, if 5 https://coast.noaa.gov/data/digitalcoast/pdf/slr-inundation-methods.pdf. 4 Environ. Res.: Climate 2 (2023) 045004 M E Hauer et al flooding is less than 300 mm, we assume vehicle speed to be the minimum of the speed limit or the maximum safe speed estimated by Pregnolato et al (2017). This ensures when the maximum safe speed is above the speed limit, we assign travel velocity as the speed limit. Third, we assume a maximum safe speed of one mph, rather than zero, after inundation reaches 300 mm, which is the average depth at which passenger vehicles start to float. Fourth, any road segment in the OSM listed as either a ‘tunnel’ or a ‘bridge’ is presumed to be unaffected by road inundation and thus is assigned the speed limit. Step 3: Calculating optimal travel times. To find the optimal travel route between two points we first identify each individual origin (home) and destination (work) location conditional on tide gauge bin gb. We assume that the precise location of each origin and destination is the population weighted mean center for each CBG. We then locate the nearest road segment to each population weighted mean centroid as the origin and destination. Road networks can be conceptualized as dual-weighted directed graphs where the weight is given as the travel velocity. We use the dodgr (Padgham 2019) package in R to convert each county’s road network into a dual-weighted directed graph. The travel time along each road segment is calculated based on the segment’s length and its speed limit, producing a ‘weighted’ travel time along a road segment. The travel time between any two points is then calculated as the sum of the weight values. The ‘fastest’ route is simply calculated as the minimal total sum of weight values for all routes between two points. We calculate the fastest route between each origin and destination CBG under each tide gauge bin b and one ‘dry’ route, assuming the absence of any flooding. Step 4: Average annual flooding delays for each home-work pair. Let µh,w y denote daily mean annual flooding delay in min per commuter for any HW pair (h, w) in year y. It is the weighted average difference between the commute time conditional on the inundation level implied by tide bin b, mh,w , with weights pby, the number of days in year y with tide levels in bin b: , and the ‘dry’ commute time mh,w b b h P 5 b=1 µh,w y = i − mh,w by mh,w b P 5 b=1 pby · pby . Since the number of workdays varies year to year, we calculate the total annual round trip tide-induced delay per commuter for a normalized 250 workday year as Given a number of commuters, c h,w y , the total min of delay for (h, w) is then y = 2 · 250 · µh,w t h,w y . y = t h,w dh,w y c h,w y . Step 5: Projected SLR commuting delays. To compute anticipated water depths in 2060, we add the change in NOAA global mean low, intermediate, and extreme SLR scenarios in Sweet et al (2017) to the underlying tide distributions for each tide gauge. These scenarios correspond with 2100 SLR values of 0.3 m, 0.9 m, and 2.5 m, respectively, which translate to 2060 values of 0.19 m, 0.45 m, and 0.9 m. Our projections assume no changes in the shape of the distribution of annual tide values, just a shift in the distribution (Kirezci et al 2020, Taherkhani et al 2020). Accommodation effects from tide effects. For any given (h, w) pair between 2002 and 2017, changes in total commuting delays can come from two main changes: a change in the number of commuters (which we refer to as the ‘accommodation effect’) and a change in the tide values (which we refer to as the ‘tide effect’). We employ a Das Gupta decomposition (Gupta 1991) to isolate the change in commuting delays attributable to these two factors. For a given (h, w) pair and two periods (1,2) the change in the total delay can be expressed: dh,w 2 − dh,w 1 = 3. Results h 2 1 + th,w th,w 2 \| c h,w 2 {z Accomodation effect − c h,w 1 i } + h 1 + c h,w c h,w 2 \| 2 th,w 2 {z Tide effect − th,w 1 i } . (2) Our models show that tidal flooding delayed the average commuter by a total of 23.3 min in 2017 (figure 3). The total delay increased from 11.9 in 2002—more than doubling in fifteen years. Even with some of the lowest SLR scenarios (0.3 m by 2100), this commuting delay is projected to increase by a factor of seven by 5 Environ. Res.: Climate 2 (2023) 045004 M E Hauer et al Figure 3. Commuting delays in the United States, 2002–2017 and in 2060 under varying SLR scenarios. Estimates reflect total annual commuting delay for workers employed in coastal communities. Colored dots in 2060 correspond to predicted tide levels based on NOAA SLR curves (Sweet et al 2017) for low (0.3 m), intermediate (1.0 m), and extreme (2.5 m) by 2100. Uncertainty reflects the 90th percentile confidence or prediction interval. Sea-level rise is already delaying US coastal commuters. Figure 4. Commuting delays in US States, 2002–2017. Estimates reflect total annual commuting delay for workers employed in coastal communities. Results for Mississippi in 2002/2003, and New Hampshire and Massachusetts in 2002–2010 excluded due to data quality issues. Every coastal state experiences some amount of SLR commuting delay and all states experience increased delays since 2002. 2060 (183 min) and could increase by a factor of twenty-four under high end SLR scenarios by 2060 (643 min with 2.5 m of SLR by 2100). All coastal US states experience tidal-flood induced commuting delays and an increase in these delays since 2002 (figure 4). In 2015–2017, Georgia (251 min), North Carolina (92.4 min), and Massachusetts (64.5 min) were the only states with more than one hour of annual commuting delays (table 1). By 2060, 6 Environ. Res.: Climate 2 (2023) 045004 M E Hauer et al Table 1. Average Annual Commuting Delays in min for US States, 2002–2060. 2060 Intermediate (low-extreme) values refer to SLR under three 2100 scenarios — 1.0 m [0.3 m - 2.5 m]. These correspond to SLR in 2060 of 0.45 m [0.19 m - 0.9 m]. State 2002–2004 2015–2017 % Increase 2060 Intermediate [low-extreme] state 2002–2004 2015–2017 % Increase TOT 9.94 7.59 AL CA 19.6 CT 0.469 2.59 DE FL 1.64 GA 86.1 LA 6.11 ME 4.05 MD 2.3 MA — 22.4 16.5 32.2 0.797 3.94 7.54 251 7.98 7.54 5.46 64.5 126% 117% 66% 69% 51% 360% 192% 30% 87% 138% — MS — 359 [183 - 643] NH — 326 [136 - 495] NJ 9.45 457 [246 - 725] NY 11.4 27.6 [9.58 - 62.9] NC 55.8 223 [58.2 - 545] OR 13.1 109 [53.4 - 159] 0.108 2794 [1451 - 5296] RI SC 10.6 687 [155 - 1189] TX 0.0753 91.3 [51.1 - 187] 186 [68.8 - 284] VA 5.97 1051 [560 - 2148] WA 4.73 0.0711 13.4 20.9 18.4 92.4 19 0.178 34.2 0.148 14.9 8.3 — — 123% 60% 66% 45% 63% 225% 96% 150% 75% 2060 Intermediate [low-extreme] 16.5 [2.42 - 31.9] 227 [118 - 513] 411 [193 - 782] 325 [174 - 652] 2052 [864 - 3669] 209 [119 - 385] 5.3 [2.14 - 9.55] 362 [194 - 586] 3.7 [1.19 - 7.38] 269 [130 - 476] 92.6 [53.4 - 168] Figure 5. Annual Commuting delays in 2015–2017 and in 2060 with intermediate SLR (0.9 m by 2100) for US counties. Counties not included in the study area are colored in gray. even low-end SLR (0.3 m by 2100) will increase commuting delays in coastal areas by an order of magnitude within the next forty years without some form of adaptation. Commuting delays are unevenly distributed across space (figure 5). For many coastal counties, SLR commuting delays are presently minimal with the median annual commuting delay of just over 3 min in 2017 but by 2060 with intermediate SLR, we estimate the median annual commuting delay will increase to 99.5 min. Three counties—Washington NC (3057 min), McIntosh GA (1275 min), and Tyrrell NC (695 min)—experience over 500 min of annual delay. Twenty-three counties experienced more than 100 min of commuter delay in 2017—approximately the median delay in 2060 under intermediate SLR. For the residents in these counties, SLR is not a distant threat—it is already lapping at their toes. Unlike population exposure to inundation (Hauer et al 2016), which heavily threatens the US South, SLR impacts are not isolated to specific regions of the US. Places like San Francisco CA, Boston MA, and Savannah GA already see significant commuting delays due to recurrent tidal flooding. These are areas with high king tides, generally longer distance commutes, roadways along low-elevation coastlines, and generally, few, if any, alternative routes. Other major population centers, like Houston TX, Los Angeles CA, and New York NY, presently experience minimal, if any, commuting delays attributable to more alternative routes along higher-elevation inland areas. Areas with the greatest delays tend to contain more long-distance commutes, along roadways with higher maximum speed, containing fewer alternative, ‘drier’ routes. Additionally, we investigate how adjustments to commuter home or work locations (or both) have affected commuting delays. Some HW pairs might experience increasing delays due to tidal flooding but decreasing delays due to changes in the number of commuters along that HW pair. These changes are unlikely to be due to technological shifts to increased remote work as the number of non-commuters 7 Environ. Res.: Climate 2 (2023) 045004 M E Hauer et al Figure 6. Accommodation to commuting delays between 2002–2017 for Home-Work pairs with non-zero impacts in 2015-2017 for US counties . (a) shows the top and bottom eight counties with the greatest and least Accommodation effects. ‘Commuters’ refers to the number of commuters with non-zero commuting delays in 2015–2017. ‘Tot Increase’ refers to the total increase in commuting delays between 2002 and 2017. ‘Tide Effect’ is the raw increase in commuting delays if the home-work commuters remained unchanged in 2002–2004 but exposed to tides in 2015–2017. ‘Accommodation’ refers to the decomposed effect due to changes in either home or work locations between 2002–2004 and 2015–2017 with tides kept constant in 2002–2004. (b) maps these accommodation effects. Counties not included in the study area or excluded due to data limitations are colored in gray. (i.e. those commuting to the CBG in which they reside) accounted for 1.33% of commuters in 2002 and 1.39% in 2017, suggesting little increase in ‘home commuting.’ We decompose the change between 2002–2004 and 2015–2017 into the contributions from rising water levels and to changes in the allocation of commuters along HW pairs (what we call the ‘accommodation effect’(Hauer et al 2021)) using a two-factor Das Gupta style decomposition (Gupta 1991) for HW pairs with non-zero commuting delays in 2015–2017 (see Methods). Changes in commuter behavior regarding choices of residential and workplace location reduced the impact of flooding delays in 95 (43%) counties (figure 6). However, these accommodation effects are not uniform across the US coast. In parts of California, Georgia, and Florida, for example, the number of commuters increased in highly affected routes, further exacerbating flood-induced delays. 4. Discussion Understanding of SLR thresholds at which people adopt adaptive behavior to reduce their exposure and vulnerability to SLR impacts remains severely limited (Hauer et al 2020), though there is a general understanding that people theoretically must adapt to or accommodate higher water levels. Most examples of contemporary SLR adaptation tend to focus on large governmental infrastructure projects such as deployment of pumps or protective infrastructure (Fu et al 2017), whereas examples of individual adaptation strategies are mostly limited to either theoretical options for coastal residents (Kwadijk et al 2010) or localized case studies with minimal identification of thresholds or tipping points (Jamero et al 2017). Here, we describe how changes in commuter behavior across the coastal US have amplified or reduced the impact of rising tides on commute times. It remains to be seen whether some of the anticipated commuting delays (figure 5 and supplementary material) over the coming decades leave enough space for further accommodation to occur without large scale intervention. Much of the literature surrounding SLR impacts assesses flood impacts on housing (Hallegatte et al 2013, Hinkel et al 2014), critical infrastructure (Heberger et al 2011, Storlazzi et al 2018), or populations (Strauss et al 2015, Kulp and Strauss 2019). Temporal horizons associated with these impact assessments can be as short as 100 years (Hinkel et al 2014) and as long as 2000 years (Strauss et al 2015), pushing much of these impacts into the deep future. Additionally in many of these impact assessments, what is ‘impacted’ is defined by residence within a flooded area. Our results demonstrate two important considerations for SLR impact assessments. First, SLR impacts in the form of commuting delays are presently occurring in US coastal 8 Environ. Res.: Climate 2 (2023) 045004 M E Hauer et al communities. In principle, changes in commuter behavior could ameliorate these delays if people were to choose HW locations less vulnerable to tidal flooding. Although we are not able to determine a causal relationship between flood exposure and commuting choices, it does not appear that such accommodating behavior adaptations have been sufficiently widespread as to effectively counter the effect of rising tides. Changes in commuter behavior between 2002 and 2017 reduce flooding delays in less than half of the counties compared to a counterfactual in which residential and workplace locations did not change. Assuming a value of time equal to the median hourly wages of workers in 2019 in each county6, the cost of commuting delays increased from $154.7 M in 2002 to more than $439.7 M in 2017. Without significant additional accommodation, adaptation, or mitigation of carbon emissions, these costs could top $3.4 B in 2060 with low SLR or $11.8 B with extreme SLR. These estimates of lost wages are considerably lower than Fant et al (2021)’s estimates of economic damages of $1.3–$1.5 B in 2020 and $28–$37 B in 2050 likely due to our inclusion of alternative routing along ‘drier’ routes. Second, impacts are not limited to residents immediately adjacent to a coastline but extend to commuters elsewhere (see supplementary figure 1 for an interactive map). Repeated obstruction of transportation routes can impede the flow of goods, affecting business operating costs, employment opportunities, and the cost of living in the long term. Limiting research to flooding impacts of storm surges or inundation on property values underestimates potential SLR impacts in flooding-adjacent areas. Calls to uncover more specific SLR impacts beyond just flood risk (Hauer et al 2020) make clear the importance analyzing SLR impacts as integrated natural and social systems. For example, recent findings concerning reduced property values in repetitive flood loss areas (McAlpine and Porter 2018), soil salinization on agricultural livelihoods (Chen and Mueller 2018), and climate gentrification responses to SLR flooding (Keenan et al 2018) are some such mechanisms integrating natural and social systems together. In this article, we investigate another specific mechanism in the form of recurrent tidal flooding on commuting delays. In the future, scientists should consider more integrated natural-social systems when investigating SLR impacts, moving beyond just flood hazard and flood risk into the actual social systems in coastal communities. We make several simplifying assumptions for computational tractability. We assess road water depth with a bathtub model that assumes perfect hydrologic connectivity, potentially overestimating the inundation on roadways. Tidal flooding adversely impacts local drainage systems and since we do not model precipitation, we do not include the possibility of roadway inundation due to rainfall and tidal flooding, potentially underestimating the inundation on roadways (Gold et al 2023). Our model does not account for traffic congestion or public transportation, nor does it include the less than 1.5% of people who live and work within the same CBG, potentially underestimating the commuting delays. Finally, most tidal flooding occurs for several hours, possibly coinciding with morning, evening, or both commutes, depending on when and for how long the high tide lasts. By simplifying to round trip, it is possible we overstate our results. Data availability statement The data that support the findings of this study are openly available in the Harvard Dataverse at https://doi. org/10.7910/DVN/2UT4GM (Hauer 2023). ORCID iDs Mathew E Hauer  https://orcid.org/0000-0001-9390-5308 Valerie Mueller  https://orcid.org/0000-0003-1246-2141 Glenn Sheriff  https://orcid.org/0000-0001-9642-5529 References Barnard P L et al 2017 Extreme oceanographic forcing and coastal response due to the 2015–2016 El Ni˜no Nat. Commun. 8 14365 Brovelli M A, Minghini M, Molinari M and Mooney P 2017 Towards an automated comparison of openstreetmap with authoritative road datasets Trans. GIS 21 191–206 Bukvic A and Harrald J 2019 Rural versus urban perspective on coastal flooding: the insights from the U.S. mid-atlantic communities Clim. Risk Manag. 23 7–18 Chamberlain S 2020 rnoaa: NOAA weather data from R. R package version 1.0.0. 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10.1371_journal.pbio.3000080.pdf	null	The data for individual figures are available as Excel files (labeled, e.g., S1 Data) with links in the relevant figure captions. The full listing of these data files can be found following the captions for supplementary figures, as well as in the Excel file DataFileListings.xlsx. In addition, the full data are available. The corresponding author (Aniruddha Das) will maintain the data at Columbia University until all datasets will be shared openly with qualified scientists. Access will be granted by request to the corresponding	RESEARCH ARTICLE Task-related hemodynamic responses are modulated by reward and task engagement Mariana M. B. Cardoso1,2, Bruss LimaID 1,3, Yevgeniy B. Sirotin1,4, Aniruddha DasID 1,5* a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Cardoso MMB, Lima B, Sirotin YB, Das A (2019) Task-related hemodynamic responses are modulated by reward and task engagement. PLoS Biol 17(4): e3000080. https://doi.org/10.1371/ journal.pbio.3000080 Academic Editor: Frank Tong, Vanderbilt University, UNITED STATES Received: October 23, 2018 Accepted: March 29, 2019 Published: April 19, 2019 Copyright: © 2019 Cardoso et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data for individual figures are available as Excel files (labeled, e.g., S1 Data) with links in the relevant figure captions. The full listing of these data files can be found following the captions for supplementary figures, as well as in the Excel file DataFileListings.xlsx. In addition, the full data are available. The corresponding author (Aniruddha Das) will maintain the data at Columbia University until publication. Once published, all datasets will be shared openly with qualified scientists. Access will be granted by request to the corresponding author. It will be our intent to collaborate with 1 Department of Neuroscience, Columbia University, New York, New York, United States of America, 2 Center for Neural Science, New York University, New York, New York, United States of America, 3 Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil, 4 Identity and Data Science Laboratory of Science Applications International Corporation, Annapolis Junction, Maryland, United States of America, 5 Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, New York, United States of America * Aniruddha.Das@columbia.edu Abstract Hemodynamic recordings from visual cortex contain powerful endogenous task-related responses that may reflect task-related arousal, or “task engagement” distinct from atten- tion. We tested this hypothesis with hemodynamic measurements (intrinsic-signal optical imaging) from monkey primary visual cortex (V1) while the animals’ engagement in a peri- odic fixation task over several hours was varied through reward size and as animals took breaks. With higher rewards, animals appeared more task-engaged; task-related responses were more temporally precise at the task period (approximately 10–20 seconds) and mod- estly stronger. The 2–5 minute blocks of high-reward trials led to ramp-like decreases in mean local blood volume; these reversed with ramp-like increases during low reward. The blood volume increased even more sharply when the animal shut his eyes and disengaged completely from the task (5–10 minutes). We propose a mechanism that controls vascular tone, likely along with local neural responses in a manner that reflects task engagement over the full range of timescales tested. Introduction The use of functional magnetic resonance imaging (fMRI) in humans, complemented with electrode measurements from animal studies, has considerably advanced our understanding of cortical visual processing. This combination of tools has been particularly useful in under- standing exogenous, stimulus-evoked responses. Models of neural responses in humans based on electrophysiological recordings in animals, combined with linear models linking neural to hemodynamic responses, have been effective in accounting for stimulus-evoked fMRI mea- surements in human subjects and in quantitatively predicting the corresponding sensory per- cepts [1–9]. However, fMRI measurements from subjects performing visual tasks also contain large endogenous hemodynamic responses in the absence of or independent of visual stimuli, even at the earliest stages of visual processing [10–15]. There are at least two types of endogenous PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 1 / 34 individuals who contact us about sharing the data. But if that is not possible or does not make sense, then we will simply provide the data to them. Because of the large size of the optical imaging and electrode recording data, it might not be possible to keep it online. Hence, it will also be distributed (upon request) as a DVD box set for a nominal fee (sufficient to cover the costs of the DVDs, the time for a lab technician to burn the discs, and shipping expenses). Funding: National Institutes of Health (NIH) Grants R01EY025330, R01EY025673, R01 EY019500, and R01 NS063226 were given to AD, and a National Research Service Award was given to YBS, as well as grants from the Columbia Research Initiatives in Science and Engineering, the Gatsby Initiative in Brain Circuitry, and The Dana Foundation Program in Brain and Immuno Imaging and the Kavli Institute for Brain Science (to AD). BL received a fellowship from the The Italian Academy for Advanced Studies in America, Columbia University. MMBC was supported by Fundac¸ão para a Ciência e a Tecnologia (FCT), scholarship SFRH/BD/33276/2007. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Abbreviations: BF-ACh, basal forebrain- cholinergic; BOLD, blood oxygen level–dependent; CBV, cerebral blood volume; EEG, electroencephalographic; fMRI, functional magnetic resonance imaging; HR, heart rate; HRF, hemodynamic response function; IACUC, Institutional Animal Care and Use Committees; ISOI, intrinsic-signal optical imaging; LC-NA, locus coeruleus-adrenergic; LFP, local field potential; NIH, National Institutes of Health; NS, not significant. Modulating task-related hemodynamics response, “attention-like” and “task related” [16]. Unlike the case with exogenous responses, there has been mixed success in interpreting these endogenous hemodynamic responses. Selective visual attention has been characterized extensively through studies in human fMRI [10–15] with close parallels seen in animal electrophysiology [17–25]. Although likely driven by a unified mechanism [26], attention can take different forms. It could be selective for stimu- lus location [10,11,27–29], features (e.g., color versus motion [30]), or timing [28]. The related hemodynamic responses reflect corresponding attributes of the expected stimuli. Attentional responses also increase in strength along the visual cortical hierarchy [11,20]. Much less is known about the task-related endogenous hemodynamic response, including whether it comprises one or multiple types. It appears to be distinct from selective attention. It entrains to task structure and extends over large sections of cortical areas (e.g., primary visual cortex—i.e., V1) independent of the stimulus [16,31–33], where it can even be substantially stronger than stimulus-selective responses [34]. It is also strongest in V1 and progressively weaker in higher visual areas [16]. These differences may reflect distinct brain processes underlying these two endogenous responses. There is growing evidence of the importance of the task-related endogenous response. It may play a role in sensory processing, in temporally grouping otherwise unrelated sensory stimuli [33] or in switching between stimulus modalities [35]. As yet, relatively little is understood about the mechanism of the task-related response even though its presence has been known for over a decade [16,33,35–41]. This is largely due to the paucity of studies comparing hemodynamics with electrophysiology in behaving subjects. The current work derives from a task-related hemodynamic response measured using intrinsic-signal optical imaging (ISOI) [42,43] in V1 of behaving macaques performing cued visual tasks [31]. The observed task-related response entrained to task timing independent of visual stimulation, with amplitudes that could compare with or even exceed vigorous visually evoked responses [44]. It appeared to be spatially nonselective, being homogeneous over the optical imaging window and presumably extending beyond [32]. It is thus likely a good model for investigating the mechanism underlying the task-related response seen in humans. Con- current electrode recordings showed it to be poorly predicted by changes in local firing rates or local field potential (LFP) power at any frequency band [31], unlike stimulus-evoked hemo- dynamic responses that were well predicted by local electrophysiology [44,45]. Additionally, at a vascular level, this response corresponded to a coordinated contraction–dilation cycle engag- ing the arterial blood supply into the imaged cortical region [31]. These observations suggested an underlying mechanism distinct from exogenous, stimulus-evoked responses. Here, we explore the link between this task-related hemodynamic response and the level of engagement in a task. The link was suggested by earlier measurements showing correlations between the measured task-related response and task performance [32], as well as with sympa- thetic-like markers of mental effort in a task [46] such as phasic pupil dilation [31] and heart rate (HR) fluctuations [31]. To modulate the level of engagement, we changed reward size sys- tematically [47] while the monkeys performed a periodic visual fixation task over several hours. Using ISOI and electrophysiology, we looked for effects on the measured task-related hemodynamic response at multiple timescales: of individual trials (approximately 10–20 sec- onds), of blocks of trials (150–300 seconds), and finally, of extended segments of task engage- ment versus disengagement as the animal switched between working and resting with eyes closed (many minutes). Based on our results, we propose that the task-related hemodynamic response reflects mechanisms that entrain brain processing more sharply to a task during peri- ods of higher task engagement, possibly as a means of temporally filtering or binding compo- nents of a task. Although we use the term “task engagement” as a shorthand for the set of behavioral and hemodynamic responses described here, in the Discussion, we consider PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 2 / 34 Modulating task-related hemodynamics possible links with states of task-specific arousal that have variously been labeled “sustained attention,” “vigilance,” or “alertness” [48–50]. Additionally, we propose an overarching mech- anism that controls vascular tone over multiple timescales in coordination with ongoing changes in the level of engagement during a task. Understanding these links would be an important step forward in understanding the dynamic allocation of brain resources in the con- text of a task. Results Overview Two male rhesus macaques performed a cued, periodic visual fixation task, receiving a juice reward following every correct fixation with no time-out or other punishment for errors (see Methods). The task is known to evoke a robust task-related hemodynamic response in the monkeys’ V1 independently of visual stimulation [31,44]. Here, we systematically manipulated the size of the reward per correct trial as a means of modulating the animals’ level of engage- ment in the task. This was done either in alternating blocks of high and low reward or in sequences of progressively changing reward (see Methods). We recorded V1 hemodynamics using ISOI [42], a high-resolution optical analog of fMRI [51–53]. This technique deduces brain hemodynamic responses at the exposed cortical surface by measuring changes in reflected light intensity at wavelengths absorbed by hemoglobin. Here, we used a wavelength specific to total hemoglobin, which provides a measurement analogous to cerebral blood vol- ume (CBV) [54] (see Methods). Imaging was combined with concurrent extracellular elec- trode recording of multiunit spiking and LFP. All experimental procedures were performed in accordance with the National Institutes of Health (NIH) Guide for the Care and Use of Labo- ratory Animals and were approved by the Institutional Animal Care and Use Committees (IACUC) of Columbia University and the New York State Psychiatric Institute. We observed distinct effects on the task-related V1 hemodynamic responses at the three different timescales tested. At the shortest timescale (individual trials—a few seconds), higher reward led to crisper temporal alignment of the task-related response to each trial, accompa- nied by a significant, if modest, improvement in response amplitude. At a slower timescale of blocks of alternating high versus low reward (10 to 20 trials—i.e., 150 to 300 seconds per block), we observed consistent alternating ramp-like changes in the mean local cortical blood volume. The sign of the ramps was such as to decrease blood volume for blocks of high reward while increasing it for low. Finally, periods of disengagement from the task during which the animal shut his eyes and rested over many minutes led to further large, sustained increases in the mean local blood volume. None of these effects at any timescale could be accounted for by changes in local spike rate. The majority of the reported results came from tasks performed in essentially complete darkness (“dark-room fixation” N = 30 sites, 3 hemispheres, 2 animals). This allowed measure- ment of the effects on the endogenous task-related hemodynamic response while minimizing exogenous visual confounds [31]. A complementary section (N = 33 sites, 2 hemispheres, 2 animals) confirmed that the observed results generalized to the presence of visual stimuli. Timescale of single trials: Higher reward leads to greater temporal precision A section of recording made while the animal fixated periodically in the dark illustrates the pattern of task-entrained responses, as well as changes to these responses with reward size (Fig 1A). Despite the near-total absence of visual stimulation, the V1 hemodynamic recording PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 3 / 34 Modulating task-related hemodynamics Fig 1. High reward leads to greater engagement in the task. (A-C) Example data set, periodic fixation task in the dark. (A) Continuous records of hemodynamic response (“Hemo”), radial eye position (“Eye pos”), heart rate (“HR”), and pupil size (“Pupil”) while reward level alternated between high (“Hi”; 0.375 ml per correct trial) and low (“Lo”; 0.11 ml) in blocks of 10 correct trials (showing roughly 3 of 32 blocks total, with time indexed relative to the start of the experiment; red = high reward, cyan = low. Same color code is used all through the paper.). Trials with no color indicate incorrect fixation (compare “Eye pos”). Each continuous sequence of incorrect trials counts as one error (gray arrows). Monkeys made more frequent errors in low-reward blocks (0.29 for “Lo” versus 0.19 for “Hi” as fraction of correct trials [N = 330]). Hemodynamic response (dR R fractional change in light reflected off cortical surface; down indicates increasing light absorption (i.e., increasing local blood volume). (B) Comparing pupil dilation during the fix period, high- versus low-reward trials. “All Hi, Lo” compares all correct high-reward trials (N = 160) with low (N = 170). “Lo to Hi” and “Hi to Lo” compare the first trial after a change in reward size to the immediately preceding trial (N = 15 “Lo to Hi” transitions, 16 “Hi to Lo” [data in S3 Data]). Gray shaded rectangle indicates a period of steady fixation starting 1 second after fix onset, which is used for quantifying pupil dilation. Inset histograms show dilation difference (high minus low reward) for all experiments with reliable pupil recording (N = 9; x-axis labeling, shown only for the third histogram [“Pupil Hi to Lo”] to avoid clutter, is common to all [data in S2 Data]). Rewards were given at the end of each correct fixation (gray arrowheads below time line; the same reward timing was used in all experiments reported here [data in S1 Data]). (C) Comparing amplitude (defined as standard deviation) of mean trial-linked heart rate fluctuations, high versus low reward (0.038 s−1: high, 0.025 s−1: low [data in S4 Data]). Traces in (B, C) are shown as mean +/− SEM (lighter ribbon). (D) Scatterplot comparing errors as fraction of correct trials, high versus low reward, all experiments (N = 30 [data in S5 Data]). (E) Comparing amplitudes of mean HR fluctuation (standard deviation as in panel C), high versus low reward, all experiments (“expts”). Each data point in (D, E) corresponds to one recording site (data in S6 Data). In (D), error trials were counted as high or low reward based on the block in which they occurred (see Methods). In (E), values were averaged separately across all correct high-reward versus correct low-reward trials for the given recording site. p-Values in (D and E): Wilcoxon signed rank test. ) plots https://doi.org/10.1371/journal.pbio.3000080.g001 showed robust task-related fluctuations in local tissue blood volume [31]. These were accom- panied, as noted earlier [31], by sympathetic-like responses [46]—i.e., phasic pupil dilation and HR fluctuations—also entrained to the task period. These sympathetic-like responses increased with higher reward. The pupils dilated more per trial, switching dilation size across single trials at block transitions (Fig 1B). The mean HR fluctuations were stronger (Fig 1C and 1E). Furthermore, animals made fewer errors (fixations broken or never acquired) in high- PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 4 / 34 Modulating task-related hemodynamics reward blocks (Fig 1A and 1D). The mean hemodynamic response also appeared to ramp slowly upwards during the high-reward block—i.e., reducing mean local blood volume—as indicated by the slope of a linear regression line (red, Fig 1A); this observation is addressed in a later section on slow changes. We did note a weak fluctuation in recorded spiking that was periodic in the mean and appeared to relate to hemodynamics in some data sets. However, the correlation was unreliable and did not generalize (see S1 Fig), consistent with our earlier find- ings that the task-related response is not predicted by local spiking or LFP [31]. At the timescale of individual trials, the primary correlate of high reward on the task-related response appeared to be greater temporal precision—i.e., tighter alignment to trial timing. This was evident qualitatively in lower trial-to-trial temporal jitter for high reward (Fig 2A, left panel). The mean of these trial-by-trial responses, averaged across all correct trials, was also higher for high-reward trials. But it was unclear how much of that was due to a true difference in amplitude, as opposed to better temporal alignment of individual responses. To resolve this issue, it was necessary to separately estimate the timing and amplitude of the task-related response for each trial. We used a template-matching approach based on the observation that, other than temporal jitter, individual responses appeared similar to each other in shape independent of reward size (Fig 2A; also see [32]). The full hemodynamic recording was thus modeled implicitly as a sequence of task-related responses of stereotyped shape, one per trial, varying only in ampli- tude and timing from trial to trial. The template was defined to be the trial-triggered average response over all correct trials. This template was slid in a one-trial-long moving window over the recorded response, calculating the normalized local dot product at each time point (“Tem- plate Match” in Fig 2B, Methods, Eqs 1–3). The dot product is closely analogous to Pearson’s correlation (see Methods). We thus surmised that it would have maxima (peaks) at points of high correlation where the recorded hemodynamics locally matched the template in shape—in effect, defining locations of putative task-related responses. But in addition, unlike Pearson’s r, which is scale-invariant, dot products scale linearly with the amplitude of their arguments and thus provide a measure of response strength (Fig 2B and Methods). We therefore defined our estimates of task-related response time and amplitude per trial to be the location and height of the corresponding template match peak. After estimating response times and amplitudes as described above, we wanted to check our starting assumption that the measured hemodynamics are well modeled as a sequence of jittered but stereotyped shapes. If the assumption is valid, the segments of recorded hemody- namics centered on each peak of the template match should match each other closely in shape. To test, we centered each putative task-related response, as picked out through template matching, by its response time as estimated from the same template match (Fig 2C). Indeed, the realigned responses were strikingly well correlated with each other. This can be appreciated visually by normalizing realigned responses by their amplitudes to help compare shapes (Fig 2D) and quantitatively by correlating realigned responses to the template used for matching (Fig 2E and 2F). The strength of this correlation supports our approach. With the task-related response times and amplitudes thus quantified, we confirmed that the primary effect of higher reward was greater temporal precision. Response times were better aligned to the task period, with consistently tighter distributions (quantified by the 2 standard deviation width of the distribution). This was evident for the example data set (Fig 3A) as well as in essentially every other data set (Fig 3C). High reward also led to significantly higher response amplitude for the example data set (Fig 3B). However, that pattern was less consistent over the full set of experiments, with only a relatively modest improvement in median response amplitudes overall (Fig 3D). PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 5 / 34 Modulating task-related hemodynamics Fig 2. Estimating trial-by-trial timing and amplitude of task-related responses by template matching. (A) All correct trials in one data set, separated by reward size: high (N = 140 trials; left, red bar on top as in Fig 1A) and low (N = 148, right, cyan bar). Gray indicates individual trials, and black indicates the mean. The time axis is shared with (C), (D) (0 = trial onset; yellow indicates a fixation period [data in S7 Data]). (B) Elements of the template match. Black (“Hemo”) indicates a section of recorded hemodynamic response (z-scored, shifted down for visibility; time indexed from an arbitrary t = 0); vertical dashed lines indicate trial onsets. Green (“Template Match”) marks the sliding- window dot product of “Hemo” with “Tmplt” (inset: defined as mean hemodynamic response, all correct trials). The locations and heights of template match peaks (red dots) define estimated timing and amplitude of task-related response per trial. “Match Peak #1, #2” are examples illustrating the information carried by peaks. Both #1 and #2 mark locations where “Hemo” matches “Tmplt” in shape (see “Hemo” segments on green shading. Compare with “Match Trough,” gray shading, phase-reversed “Hemo”). Greater height of peak #1 versus #2 quantifies higher amplitude of “Hemo” fluctuation at #1. However, location of peak #2 is better centered in its trial. (C) Same traces as in (A), aligned by response times estimated from template match (data in S8 Data). (D) Same data as (C), normalized by amplitude (standard deviation; “SD-norm”). Orange indicates responses with standard deviation in the lowest 10th percentile over the full set. Gray marks the upper 90th percentile. Black indicates the mean of gray traces. The red dotted line marks the template. Gray traces match each other and the template well, particularly near the midpoint of the trial (data in S9 Data). (E) Histogram of correlations (“corr”) of aligned responses with the template (Pearson’s r; all correct trials, high and low reward, including responses in the lowest 10th percentile of standard deviation). (F) Histogram of correlation medians as in (E), all experiments (“expts”; data in S10 Data). https://doi.org/10.1371/journal.pbio.3000080.g002 We wondered if these results were due to our particular choice of template. We tested by repeating the analysis shown here across all data sets using a range of alternate templates. The alternate templates were also each one trial long and constructed from measured responses but using different criteria: for example, being phase shifted in time or using only “high signal-to- noise” responses with amplitudes exceeding a threshold. The task-related response times and amplitudes estimated by matching to these alternate templates showed a strikingly similar overall relationship to reward size as in Fig 3. This is illustrated in S2 Fig for a particular alter- nate template with timing and shape distinct from the one used in the main text. This result highlights the overall robustness of our findings. It also suggests that high reward leads to a state of greater temporal regularity and periodicity overall for the duration of the block, accounting for the higher temporal precision in estimated response times independent of the details of the template used. PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 6 / 34 Modulating task-related hemodynamics Fig 3. Higher reward leads to more temporally precise task-related responses. (A) Distributions of response (“Resp.”) times per trial, estimated as positions of the corresponding template match peaks (“Match Peak pos.”), same data set as in Fig 2A–2E. Distributions are separated by reward size, with color coding as indicated in the key (common to (A, B) and to all later figures). Data are shown as a vertical “violin plot” histogram with numbers of trials increasing from 0 (middle) upwards for high reward (“Hi”) and downwards for low (“Lo”). Similar displays are used for all such comparisons of distributions to avoid clutter (e.g., with interleaved histograms). Clustering of response times per reward size was quantified as the 2 standard deviation (“2x StdDev”) width of timing distributions. (B). Distributions of response amplitudes per trial, estimated from template matching (“Tmplt Match”), shown separated by reward size following the same conventions as in (A). Response amplitude per reward size was quantified as the median (indicated by arrowheads; medians are indicated similarly in all later amplitude distributions). Significance (p- values) in (A,B) were obtained from bootstrap with 10,000 resamples. (The 2x StdDev values per reward size [“hi”,”lo” in panel A] and median amplitude per reward size in panel B shown in these and other panels are not the sample medians from the measured distributions but rather are medians obtained from the same bootstrap procedure used to get p-values.) (C) Comparing the 2 standard deviation width of the response time distribution for all correct high- reward trials versus that for all correct low-reward trials, per experiment (“expt”; N = 30). (D) Comparing median response amplitudes for all correct high- versus all correct low-reward trials, per experiment. p-Values in (C), (D): Wilcoxon signed rank tests for pairwise comparisons (data in S11 Data). https://doi.org/10.1371/journal.pbio.3000080.g003 Timing precision is robust to noise in template match We were concerned that the apparently lower temporal precision with low reward could be an artifact of a noisier template match. When task-related responses had lower amplitudes, the template match could be poorer simply because of lower signal to noise. This could lead to noisier estimates of response time with wider distribution and thus apparently poorer preci- sion but, because of the poorer signal to noise alone, independent of reward size (Fig 4A; also consider, e.g., the responses with poor shape match in Fig 2D). Since lower rewards were asso- ciated with somewhat lower response amplitudes, this increased noise could make the low- reward responses appear artifactually less precise. PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 7 / 34 Modulating task-related hemodynamics Fig 4. Temporal precision is not an artifact of higher signal for high-reward responses. (A) An outline of the null hypothesis using a data set in which low-reward responses had substantially lower amplitudes. Left panel: Scatterplot of response amplitude versus time per trial, colored by reward size. Gray and white shading indicates quintiles along the amplitude axis, combining high (“hi”) and low (“lo”) rewards. Right panel: 2 standard deviation width of response time distribution in each quintile. The time axis is scaled to match that for the left panel. These 2 standard deviation widths (“2x StdDev”) increased progressively for lower response amplitudes, which were also more dominated by low-reward trials. The null hypothesis is that this covariance alone gives low-reward trials larger timing scatter. Arrows mark the y-axis locations indicating median amplitudes of quintiles (data in S12 Data). (B) Plot of response amplitude versus timing for a large data set (1,285 correct trials; 629 low reward; 626 high reward) separated into quartiles by response amplitude (gray/white shading). Each quartile also roughly matched for numbers of low- versus high-reward trials (data in S13 Data). (C1, C2, D1, D2, E1, E2, F1, F2) Pairs of distributions of response amplitude and timing per quartile separated by reward size. The numbers “N” in parentheses in (C1–F1) indicate numbers of high- and low-reward trials. The high-reward responses are significantly more precise than the corresponding low-reward ones in each quartile despite the similarity in response amplitudes (C2–F2). All distributions are shown as “violin plots” using the same conventions as Fig 3A and 3B. Panels (C1–F1) share a common abscissa scale, as do panels (C2–F2). The “NTrials” label for the ordinate is shown only for (F1) to avoid clutter. p-Values, bootstrap, 10,000 resamples (data in S14 Data). https://doi.org/10.1371/journal.pbio.3000080.g004 To test, we selected subsets from each experiment in which the high- and low-reward responses were matched in amplitude and in numbers of data points (Fig 4B, 4C1, 4D1, 4E1 and 4F1). If our concern about signal to noise in the template match were valid, high- and low-reward responses in each amplitude-matched subset should exhibit similar distributions of response times independent of reward size. Instead, even after matching for amplitudes, the high-reward responses remained consistently and significantly more temporally precise (see, particularly, Fig 4D1, 4D2, 4E1 and 4E2). Timing precision is independent of eye fixation timing and eye movements We wondered if there were some simple oculomotor explanation or correlate of our observa- tion. We considered two possible scenarios under which this could happen. PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 8 / 34 Modulating task-related hemodynamics We considered the null hypothesis that the timing of the task-related response per trial is determined by fix onset, with a stereotyped response time course and hence constant delay fol- lowing fixation (see S3 Fig). If that were the case, response times should be correlated to fix onset times, with unity slope and constant delay. The higher precision with high reward could reflect a behavioral pattern in which the animal is more precise in its fix onsets prior to those trials (S3 Fig, panel a). This null hypothesis turned out not to be the case, and task-related response times were uncorrelated with fixation onset. Parenthetically, both animals’ fixation behavior changed over the many months that we tested them intermittently on this task. Ini- tially, both animals tended to maintain fixation for long periods with very few breaks even dur- ing intertrial intervals. This led to extended periods of fixation prior to the start of each trial, or even across multiple trials, without any breaks but unrelated to the timing of the task-related response or reward size (S3 Fig, panel b). Later, both animals showed a different behavioral pattern, moving their eyes around during intertrial intervals and reacquiring fixation shortly before trial onset. This led to a pattern of brief fixation periods prior to each trial (S3 Fig, panel c). Task-related hemodynamic response times remained more precise with high reward, inde- pendent of the changing pattern of fixation. We next considered the possibility that animals may have steadier fixation or smaller eye movements during high-reward blocks, due to generally higher engagement in the task (S4 Fig). We failed to see any consistent patterns. There were no consistent differences in fixational jitter between high- and low-reward trials at the resolution of our measurements (60 Hz, 0.33 deg). There were also no consistent differences in eye movements during the intertrial periods during which the animals were free to look around. The animals also changed their patterns of intertrial eye movements over the many months of recording. In earlier sessions, they did move their eyes less during high-reward blocks (S4 Fig, panels a1-a3). Later, however, the ani- mals adopted a behavioral pattern of greater intertrial eye excursions for high-reward trials (S4 Fig, panels b1-b3). However, the task-related responses remained more precise for higher- reward trials (smaller 2 standard deviation width for task-related response time distributions), independent of this changing pattern of eye movements. Timing precision generalizes to the presence of visual stimulation The question that remained was whether reward size affected task-related responses only in the unnatural circumstance of visual tasks in the near absence of all visual stimulation or whether such effects generalized to the presence of visual stimuli. To test, we analyzed data from a separate set of experiments in which the animals were passively shown visual stimuli— gratings of different contrasts—while performing the same cued, periodic fixation task (Fig 5). Rewards were comparable to the dark room, if slightly higher (see Methods), ranging typically from 0.2 ml/trial (low) to 0.6 ml/trial (high). For this, we first needed to estimate the task- related response from recorded hemodynamics by estimating and removing stimulus-evoked responses. We did so by modeling the overall measured hemodynamics as a linear sum of the stimulus-evoked and task-related components, which we fitted to get the optimal kernels for the two components[55] (Methods, Eqs 4–6; also, S5 Fig). The optimal hemodynamic response function (HRF) kernel thus obtained was then convolved with the recorded spiking to estimate the stimulus-evoked component of hemodynamics and regress it away from the full hemody- namics. The residual—that was, by construction, the component of hemodynamics not pre- dicted by local spiking—was then defined to be the task-related component of the hemodynamic response, equivalent to the full hemodynamic response in the dark room. The task-related response thus estimated in the presence of visual stimuli was again tempo- rally more precise with high reward, just as with the task undertaken in the dark room. This PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 9 / 34 Modulating task-related hemodynamics Fig 5. Task-related responses in the presence of visual stimuli are temporally more precise and modestly higher in amplitude for high reward as in the dark room. (A-D) One example data set: (A) Residual task-related responses (“Task Rel. Resp.”) separated by trial and by reward size were obtained by regressing away stimulus-evoked responses (see S5 Fig) (data in S15 Data). (B) Comparing pupil dilation for high- (“Hi Reward”) versus low-reward (“Lo Reward”) trials. Gray shading indicates the period over which pupil dilations are compared, starting 1 second after fixation. (These pupil measurements were made in the presence of visual stimulation, unlike dark-room results [Fig 1B], likely accounting for different shape of trace including initial constriction on fixation) (data in S16 Data). (C) Distribution of response times from template match in this data set. (D) Distribution of response amplitudes in this data set. Conventions for “violin plot” histograms are used as in Fig 3A and 3B (data in S19 Data). (E) Comparing the 2 standard deviation widths of response times (“Resp time 2x StdDev”) for high versus low reward, per experiment (“expt”; N = 33) (data in S17 Data). (F) Comparing median response amplitudes (“Resp Amp Median”) for high versus low rewards, per experiment (N = 33). p-Values in (E), (F): Wilcoxon signed rank tests for the pairwise comparisons. The inset in panel F indicates overall behavioral performance as the total numbers of error trials as a fraction of the correct trials, per experiment (data in S18 Data). Eye pos, eye position; Fix on, fixation on; norm., normalized; NS, not significant; Stim on, stimulus on. https://doi.org/10.1371/journal.pbio.3000080.g005 can be seen qualitatively after separating the estimated task-related response into individual trials and segregating trials by reward size. These trial-wise responses were visibly less tempo- rally jittered for high reward (Fig 5A). The timing and amplitude of these task-related PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 10 / 34 Modulating task-related hemodynamics responses were quantified by matching to a template just as for recordings in the dark room; the template was taken to be the optimal mean kernel for the task-related component as esti- mated from the fit (Methods, Eqs 7 and 8. S5 Fig). The results of this template match closely paralleled those obtained in the dark room fixation task. The estimated response times were again more tightly clustered for high-reward trials, both for this specific data set (Fig 5A and 5C) and over the set of visually stimulated experiments (Fig 5E). Response amplitudes showed only a modest improvement (Fig 5D and 5F). High-reward trials were also associated with greater pupil dilation (Fig 5B). Timescale of blocks of trials: Mean blood volume decreases for high reward and increases for low Analyses up to this point were restricted to the scale of single trials—i.e., about 10 to 20 sec- onds. However, we also noted slow ramp-like drifts in the mean local blood volume over blocks of 10 to 20 trials of a given reward size—i.e., about 150 to 300 seconds (Fig 1A; Fig 6). The ramps decreased blood volume for high-reward blocks while increasing it for low. Regres- sion lines fitted through sequences of correct trials per block clustered into distinct sets of neg- ative slopes (increasing absorption of light during imaging—i.e., increasing blood volume) for low-reward blocks and positive slopes (decreasing blood volume) for high (Fig 6B and 6C). These slow hemodynamic drifts were not driven by slow changes in spiking (see S7 Fig). Blocks of trials with alternating ramps of mean blood volume failed to show similar alternating ramps of mean spiking (S7 Fig, Panels a-d). To test more quantitatively, we first simulated the spiking patterns required to generate the measured hemodynamic slopes on convolving with the corresponding optimal fitted HRF per experiment (S7 Fig, Panels e, f). The slopes of the simulated spiking ramps alternated in sign with reward size, as expected. Each measured spik- ing slope was then divided by the slope of its corresponding simulation to compare. If the mea- sured slopes had the same sign as their simulations, these ratios would consist of positive numbers, with some magnitude reflecting a scale factor. This was not the case; the ratios were equally likely to be positive or negative for both high and low reward. The measured spiking slopes were thus uncorrelated with those required to generate the measured hemodynamic slopes. Switching from task engagement to rest with eyes closed: Further profound increases in blood volume We wondered if the slow increase in mean local blood volume accompanying reduced reward could be part of a broader pattern of shifts in mean local blood volume accompanying shifts in the level of engagement. A potential clue was seen in the continuous measurements during long dark-room recording sessions lasting up to 3 hours (Fig 7). In these sessions, in between extended stretches of working well, the animals would take occasional breaks of many minutes during which they stopped working and rested with their eyes shut. The mean local blood vol- ume in V1 increased strikingly during these breaks, returning to baseline when the animal resumed working (Fig 7A). This pattern appeared to be an extreme manifestation of the ramp- like changes in blood volume with reward size in which lower reward, with its lower level of engagement (Fig 1), led to increasing mean blood volume (Fig 6). Before ascribing an association with reduced engagement in the task, we needed to test whether the increased blood volume could be accounted for simply by concurrent changes in neural or physiological drivers. As possible drivers, we considered the mean HR and the mean local multiunit spike rate (recorded separately at two electrodes spaced 4 mm apart in the recording chamber). We also measured the pairwise noise correlation of spike rates between PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 11 / 34 Modulating task-related hemodynamics Fig 6. Mean local cortical blood volume increases for low-reward blocks and decreases for high, in alternating ramp-like drifts. (A) Recordings from a sequence of correct trials, alternating between high (“Hi Reward”) and low reward (“Lo Reward”) in blocks of 10. Lines indicate regression fits to each block separately. Increasing slope implies decreasing local tissue blood volume. Correct trials were concatenated after excising incorrect ones while maintaining vertical position (see S6 Fig). This panel shows 20 blocks of 102 total: 1,160 trials total, 1,019 correct. (B) Histogram of regression slopes in high- versus low-reward blocks. Same data set as (A) (p-values, bootstrap, 10,000 resamples) (data in S21 Data). (C) Comparing median slopes of high-reward versus low-reward blocks over the set of all experiments (“expt.”). All the statistically significant data points lie in the upper left quadrant, “Lo(-)/Hi(+)”—i.e., with negative slopes for low- and positive slope for high-reward blocks (N = 19 experiments: using only those with at least 10 pairs of alternating blocks of 10 trials each). The results shown here were based on correct trials alone. Analyses that utilized all trials including incorrect ones gave results that were broadly similar but were sometimes harder to interpret because of the arbitrary duration of sequences of incorrect trials (S6 Fig) (data in S20 Data). Hemo, hemodynamic response; NS, not significant. https://doi.org/10.1371/journal.pbio.3000080.g006 the two electrodes over a 1-second sliding window, since that was expected to increase at rest [56]. To focus on slow changes, all recordings were downsampled to get, in effect, the smoothed average in a 60-second window (see Methods). We then assessed changes in these physiological and neural measurements as the animal switched state, marking the state based on the fraction of time within the 60-second window that the eyes were closed (Fig 7A, top row, “Eyes closed”). Spontaneous eye closures in the dark have been shown to provide a useful measure of drops in “vigilance” [49], correlating well with electroencephalographic (EEG) and fMRI indicators [57]. For this study, epochs during which the eyes were shut more than 60% of the time were defined as “rest,” whereas those with less than 5% of time with eye closure were considered “engaged.” To check, we compared with an LFP measure of arousal based on the ratio of power in the beta- and theta-range frequency bands (15–25 Hz and 3–7 Hz, respectively) as suggested by earlier studies ([57]; also reviewed in [48]). To get a measure that was low when the animal was engaged in his task and high when at rest [48], as with eye closure, we placed the theta power in the numerator. The square root of this ratio further compressed the dynamic range to roughly 0–1, as with eye closure. These two measures based on eye closure and on the LFP were closely comparable (Fig 7A, upper two rows and inset), supporting the use of eye closure to segregate physiological mea- surements by the state of task engagement. PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 12 / 34 Modulating task-related hemodynamics Fig 7. Mean local blood volume, spiking, and heart rate trace switches between states of task engagement and rest. (A) Traces show continuous 2.5-hour records of measured variables as indicated by adjoining labels, smoothed and downsampled to a 60-second sampling rate to track slow changes. (See text and Methods for more details.) Red and green bars on top mark epochs of rest (defined as “Eyes closed” > 0.6—i.e., >60% of the 60-second sample time) and task engagement (sections with eyes open, defined as “Eyes closed” < 0.05—i.e., <5% of the time). “Eyes closed” is highly correlated with the LFP measure (see text for definition. Pearson’s r = 0.94 for the example data set. Inset shows histogram of Pearson’s r for similar pairwise correlations over all data sets used for this analysis, N = 11). Performance in the task is quantified as the (smoothed) fraction of trials initiated in the 60-second (i.e., approximately 4-trial) window. “Hemo” marks the mean hemodynamic response (dR/R); “Spike1” and “Spike2” mark multiunit responses recorded from two electrodes spaced 4 mm apart in the imaged region. “Spk1-Spk2 Corr” marks the pairwise correlation between these two recordings over a 1-second moving window. “HR” marks mean heart rate. The red box indicates the section of hemodynamic and corresponding Spike1 spiking measurements (red asterisk) that are analyzed at a higher temporal resolution in Fig 8A. (B1-B6) Scatterplots of the measured values for the given experiment, as indicated, versus “Eyes closed.” Each data point represents a single smoothed, nonoverlapping 60-second sample. Data points are segregated into “task-engaged” (black) and “rest” (gray) using the value of “Eyes closed” as described. Red lines connect medians (data in S22 Data). (C1-C6) Lines connecting medians as in (B1-B6) for all experiments (“expts”) used (N = 11) (data in S23 Data). LFP, local field potential. https://doi.org/10.1371/journal.pbio.3000080.g007 The mean neural and HR measurements thus segregated showed systematic changes as the animal switched between states of rest and task engagement but in a direction opposite to that expected to increase blood volume at rest (Fig 7A). Thus, the mean HR, averaged over the PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 13 / 34 Modulating task-related hemodynamics moving 60-second window, reduced systematically relative to its local baseline value each time the animal disengaged from work and rested with eyes shut (Fig 7A: bottom trace [“HR”]: see shaded areas indicating rest). This is consistent with the abrupt falls in mean HR and blood pressure seen at sleep onset in human subjects [58]. But it suggests that the concurrent increase observed in V1 local blood volume is not a passive consequence of cardiovascular changes, as that would require an increase rather than a decrease in HR [59]. Similarly, the mean spike rate recorded at individual electrodes typically decreased as the animal rested. If the blood vol- ume at rest were driven linearly by local spiking, then the mean spike rate should have increased [1]. Although the mean spike rates at individual electrodes largely decreased, the pairwise correlation of spike rates over the pair of electrodes showed the expected [56] striking increases for the epochs of rest versus engagement. Comparing hemodynamics and spiking for the same data set at the higher imaging tempo- ral resolution (15 frames/second; Fig 8) supported our contention that the large blood volume increases at rest are not predicted from spiking. This conclusion was not immediately apparent on qualitative inspection (Fig 8A; same data segment as enclosed in the red box in Fig 7A). At the higher temporal resolution, the spiking response showed expected [60] bursts of high instantaneous spike rate (red arrow, Fig 8A) that stood out despite the overall reduction in mean spike rate as the animal rested with eyes shut (red asterisk in “Spike1,” Fig 7A). The cor- responding blood volume measurements showed large swings in amplitude that appeared, qualitatively, to follow the bursts of spiking. Our earlier work showed that the recorded hemo- dynamics is poorly predicted by spiking when the animal is engaged in his task, because of the presence of the task-related response (see S1 Fig; [31,44,55]). But there should be no task- related response, by definition, when the animal is disengaged from the task with his eyes shut and the hemodynamics could in principle be predictable from spiking. It was thus important to test the relationship between the two at this higher temporal resolution. We tested using deconvolution—i.e., multilinear regression (see Methods, Eqs 9–12), which has the advantage that it makes no assumptions about HRF shape [61]. The deconvolu- tion was done over partially overlapping 150-second windows (75-second steps; 150 seconds typically covered 10 trials) to get adequate temporal resolution for tracking rest states (e.g., the rest epoch in Fig 8A, indicated by the red bar, lasts about 400 seconds. Shorter deconvolution windows led to excessive noise). Each design matrix contained not only the spiking regressor for the given deconvolution window but also additional intercept and slope terms. The inter- cept is analogous to the “y intercept” in 1D linear regression, quantifying an inhomogeneous addition to a homogeneous linear equation. Here, we defined it as an estimated inhomoge- neous “mean shift” in the hemodynamic response, in addition to hemodynamic components that are linearly predictable from spiking. The full prediction using the deconvolved HRF ker- nel plus additional “mean shift” matched measured hemodynamics very well overall (Fig 8A, compare “Hemo, full pred,” green with “Hemo, meas,” black. Goodness of fit, R2 = 0.94, over this rest epoch). The HRFs from deconvolution windows falling within the rest epoch also matched each other well and resembled canonical HRFs (inset “HRFs,” Fig 8A; also see S5 Fig for an example canonical HRF). They predicted the high-frequency fluctuations in the mea- sured hemodynamics well, indicating that these high-frequency terms are accounted for by spiking (Fig 8A, “Hemo, spiking pred,” red). However, they failed to account for the increase in the mean blood volume, predicting a decrease instead (prediction rising above baseline), which is consistent with the decrease in the local mean spiking. The measured increase in the blood volume was well accounted for, on the other hand, by the fitted “mean shift” (Fig 8A, “Hemo, mean shift”; the slope terms made only small contributions). The same pattern was seen over the full 2.5-hour recording (Fig 8B). Linear predictions from spiking matched the high-frequency fluctuations of hemodynamics during rest epochs whereas the additional PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 14 / 34 Modulating task-related hemodynamics Fig 8. Hemodynamic response during rest is the sum of a high-frequency component predicted linearly by spiking plus an additional mean shift not predicted by spiking. The same data set as in Fig 7 is shown at a temporal resolution of 66.7 ms (15 Hz camera frame rate). (A) Expanded view of the sections of “Eyes closed,” “Hemo,” and “Spike1” data enclosed by a red box in Fig 7A, along with three alternate predictions of the measured “Hemo” response. “Eyes closed” is shown as in Fig 7A, top trace, with green and red bars indicating periods of task engagement and rest. Red arrow over the “Spike1” points to peaks of high instantaneous spike rate, indicating burst of spiking despite lower mean spike rate over this epoch (24.7 spikes/second average under “Eyes closed” red bar, Panel A, versus 29.4 spikes/second average in the two flanking green sections where eyes were open; same data as in Fig 7A, red asterisk). “Hemo” refers to 4 different hemodynamic traces color-coded as in the key. Black (“meas.”) = the measured response, same data as in Fig 7A. Green (“full pred.”) = full prediction following deconvolution. Red (“spiking pred.”) = linear prediction from spiking using deconvolved HRFs. The inset (“HRFs”) shows optimal HRF kernels from deconvolution windows in the “Eyes-closed” segment; colors are arbitrary. Magenta (“mean shift”) = fit to the intercept term in the design matrix, estimating components not predicted by spiking (see accompanying text). Black arrowheads pertain to an additional analysis in supplementary data; they point to two segments marked for comparison with an alternate deconvolution and prediction made without intercept terms in the design matrix (see S8 Fig). (B) Results of the deconvolution and prediction as in (A), shown over the full experiment. (The location of the expanded section in panel A is also indicated.) Only measured spiking, measured hemodynamic trace, and PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 15 / 34 Modulating task-related hemodynamics deconvolved “mean shift” are shown; full and “spiking” predictions are not shown to avoid clutter. Red arrow marks a burst of high instantaneous spiking despite lower overall mean; this burst is expanded in (A). HRF, hemodynamic response function. https://doi.org/10.1371/journal.pbio.3000080.g008 mean shift tracked the mean measured hemodynamic response (compare Fig 8B, “mean shift” with “Hemo” trace in Fig 7A). This result supports the suggestion that the large changes in mean blood volume during rest were likely driven by a mechanism acting in addition to spik- ing. Similar results were obtained for all extended recording sessions, including ones in which the mean spike rate increased during rest. It could be argued that the deconvolved “mean shift” is just the fit to the intercept term that we chose to include in the design matrix. It would thus necessarily fit the mean of the mea- sured response, by design, with no additional physiological significance. We tested by fitting the same data, using an identical deconvolution approach but without intercept terms in the design matrix (see S8 Fig). The resulting prediction was much worse at matching the measured hemodynamics. In addition, the deconvolved HRFs obtained from this new fit were markedly different from canonical spiking HRFs in two ways. First, the HRFs now incorporated the mean CBV for their respective deconvolution windows. They thus acquired large mean shifts that made them apparently acausal with large nonzero values prior to time zero. In addition, HRFs from successive deconvolution windows were noisy and matched each other poorly. This distinctly poorer fit without the intercept term suggests that the “mean shift” in the full fit (Fig 8) represents a physiological component of the hemodynamic response during the eyes- closed, disengaged behavioral state. Discussion Our goal is to understand the task-related endogenous component of hemodynamic responses recorded from visual cortex of subjects engaged in cued, predictable tasks. The existence of such responses has been known for more than a decade [16,33,35–41]. Their substantial strength relative to other brain hemodynamic components is well recognized [34]. Recent studies suggest their relevance to sensory processing [33,35]. Yet they have not been adequately studied, and little is known about their underlying mechanism or behavioral significance. Here, we consider the behavioral correlates of one particularly prominent task-related response recorded by us in V1 of behaving macaques [31], which is likely analogous to responses seen in human visual cortex [16,37]. Our work here suggests that this task-related response reflects brain mechanisms associated with the degree of task engagement. On increasing reward size to get the animal more engaged, the most notable effect, trial-by-trial, was improved temporal precision: the response became consistently more crisply aligned to task timing. It also became modestly stronger. At a slower timescale, different levels of task engagement led to consistent shifts in the mean local blood volume. High-reward blocks led to consistent decreases in the mean blood volume, whereas low-reward blocks led to corresponding increases. This effect was even more pronounced when the animal disengaged completely from his task and rested with eyes closed. The mean blood volume increased strikingly during these breaks, returning to baseline when the animal resumed working (also see [62]). Other than a high-frequency component while the animal slept, none of the hemodynamic measurements at any time scale—whether trial-by-trial or averaged across blocks while the animal worked, or the mean while the animal slept—could be accounted for by concurrent local spiking. On a methodological note, we recorded the hemo- dynamic response using ISOI at a wavelength tuned for measuring cortical blood volume. Such recordings have a steadier and more reliable baseline than blood oxygen level–dependent PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 16 / 34 Modulating task-related hemodynamics (BOLD) fMRI, suffering much less from instrumental noise and drift. This allowed us to moni- tor the response continuously over many hours, thus obtaining the reported results, including the prominent mean shifts. We propose that the task-related hemodynamic response and the effects reported in this paper comprise a marker of task-specific arousal tied to the level of engagement during an extended and possibly repetitive task. The term “arousal” is used in different senses in different areas of research, from arousal during tasks such as here to arousal in the face of fear or danger to nonspecific arousal along the sleep–wake axis [48]. The literature on task-specific arousal thus suggests avoiding the term “arousal” in favor of “vigilance,” “alertness,” or “sustained attention” [48–50]. This condition of sustained engagement in a task is known to fluctuate between states of higher stability, which are less prone to error (“in the zone”), and states that are more unstable and error-prone (out of “the zone”; see, e.g., [50]). The state of being “in the zone” is marked by higher regularity and temporal precision; responses “in the zone” show less variability in reaction time, trial-by-trial, even when the mean reaction time remains unchanged overall [50]. The state is further enhanced by reward, which leads to even less vari- ability in reaction times, in a manner that appears distinct from increased arousal [63]. This behavioral result may have a physiological analog in our finding of improved temporal preci- sion or regularity of the task-related hemodynamic response during high reward (Fig 3 and accompanying text). An attractive possibility is that the task-related response reflects the behavioral state variously labeled “vigilance,” “alertness,” or “sustained attention” in the cited literature. Our term “task engagement” is a shorthand reflecting this possibility as well as a nod to the initial report of this hemodynamic response component, which described it as “task structure” related [16]. Much remains to be done to flesh out these connections. An important question remains: How closely does the task-related hemodynamic response we record in macaque V1 correspond to the response identified in human visual cortex? And how distinct is it from selective visual attention [16]? Currently, the strongest evidence is that although varying substantially in time, the task-related response in the macaque is spatially homogeneous over the imaging window (a circular region 15 mm in diameter, typically extending approximately 1–6˚ eccentricity [32]). This makes it unlikely to be selective atten- tion at the fovea (e.g., for the task of discriminating the fixation cue color) that should lead to spatially graded activation over this cortical extent [16,64,65]. Additional evidence comes from the response timing. If it were selective attention cued to the fixation point, its time course at fix onset should be stereotyped independent of trial length. That was not the case in an earlier test; the starting time course even switched sign when switching between blocks of short versus long trials (e.g., 8 versus 20 seconds; see Fig 3 and Supplementary Fig S9 in [31]). This result also speaks to a corollary question arising from our describing the response as being entrained to task timing. It could be argued that the hemodynamics and the sympathetic-like changes in HR and pupil are, instead, responses to the reward acting as a stimulus. However, our earlier results noted above showed the hemodynamics to be entrained to the expected timing of upcoming trials rather than a stereotyped response to the reward. It should thus be interpreted as task-entrained albeit modulated by the current reward. However, although the evidence is strongly suggestive, the questions are interesting and open and remain topics of ongoing research in the lab. What could be the underlying mechanism or function? Although the poor prediction by recorded spiking does not rule out control by a small, hard-to-measure set of specialized neu- rons, it also suggests a different underlying mechanism such as neuromodulatory input (e.g., see [66]). The strong sympathetic-like responses (Fig 1) suggest the basal forebrain-cholinergic (BF-ACh) or the locus coeruleus-adrenergic (LC-NA) systems, both of which are linked to wakeful states, arousal, and attention. They powerfully facilitate hemodynamic responses via PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 17 / 34 Modulating task-related hemodynamics modulation of stimulus-evoked neural responses (reviewed in [67]) with additional control of cortical blood flow [68] through direct modulation of microvessels [69,70] or via astrocytes [71] or pericytes [72]. Other neuromodulators such as dopamine could also be involved [73]. All of this can lead to robust neuromodulator-mediated increases [68,74,75] or decreases [70– 72,76] in cortical blood flow. Our finding of a stereotyped response shape independent of tem- poral precision could be accounted for by a mechanism in which the response timing is deter- mined in a distal nucleus through temporal dynamics local to that nucleus. The result could then be transmitted in an all-or-nothing manner, like an action potential, to the target (here, V1), where it could release a fixed quantum of neurotransmitter. In addition, there could be a contribution from myogenic mechanisms independent of neural input, as suggested in a recent study of ongoing vascular fluctuations [77]. A combination of such mechanisms could modulate neural responsivity and vascular tone in a manner that reflects the level of task engagement. In single trials, this could help refresh the local blood supply ahead of task onsets [78]. Over more extended periods, it could also shift the mean vascular tone—e.g., by slow accumulation of the active substance—to higher values for high task engagement and the con- verse for low. Such a mean shift could account for the surprising finding of progressively lower mean blood volume for higher task engagement, since higher vascular tone does imply nar- rower blood vessels and thus lower tissue fraction occupied by blood. The increased vascular tone could have additional functional benefits of higher precision in stimulus-evoked hemody- namic responses. For example, adrenergic increase in vascular tone has been shown to lead to spatially and temporally sharper vascular responses to neural activity [71]. Exploring these issues through targeted experiments in behaving animals would be crucial to understanding brain mechanisms of task engagement. Methods Experimental model and subject details Animal use procedures were in accordance with the United States NIH Guide for the Care and Use of Laboratory Animals and were approved by the IACUC of Columbia University and the New York State Psychiatric Institute (Animal Care Protocol AC-AAAU1456). Two male rhe- sus macaques (Macaca mulatta) were used in the study. Access to water was scheduled to training or recording sessions that lasted 3–5 hours per day. Eye fixation and pupil diameter were recorded using an infrared eye tracker (ISCAN [79]). Before training, each animal was implanted with a stainless steel or titanium head post. After training, craniotomies were per- formed over the animals’ V1, and glass-windowed stainless steel or titanium recording cham- bers were implanted for subsequent ISOI in the behaving animal (see section “ISOI” below). The craniotomy exposed a 20-mm diameter area of V1 covering visual eccentricities from about 1 to 10˚. The exposed dura was resected and replaced with a soft, clear silicone artificial dura (GE Silicone RTV615 001). Recording chambers and artificial dura were fabricated in our laboratory following published designs [80,81]. Chambers were opened regularly for clean- ing, testing for infection, and treating if necessary, following published protocols [43]. Method details Summary. Extracellular electrode recording was carried out simultaneously with ISOI from V1 of behaving monkeys performing a periodic visual fixation task. Task and recording methods are essentially identical to those in earlier papers from our lab [31,44,45,55]. Task and reward schedules. All experiments were based on a simple fixation task carried out either under essentially complete darkness or in the presence of visual stimuli. In both con- ditions, animals held fixation periodically, cued by the color of a fixation spot (fixation PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 18 / 34 Modulating task-related hemodynamics window: 1.0–3.5 deg. diameter; monitor distance: 133 cm; fixation duration: 3–5 seconds; trial duration: 9–22 seconds; all parameters fixed for a given experiment but variable between experiments). A juice reward followed every correct (unbroken) fixation, with no time-out or other punishment for errors. The primary behavioral manipulation consisted of systematically changing reward size. For fixation trials in the dark room, the monitor was covered and the fixation point was presented behind a pinhole [31]. Reward sizes were alternated between high (typically 0.45 ml per correct trial, ranging from 0.35 to 0.6 ml) and low (typically 0.15 ml per correct trial, rang- ing from 0.1 to 0.2 ml. High- and low-reward sizes were fixed for an experiment; they were selected per day based on the animal’s willingness to work for the low reward). Rewards were alternated in blocks (typically 10 correct trials each; some experiments had longer blocks; some experiments had blocks of variable size). The animal had to correctly complete the full set of trials per block—i.e., not counting error trials—before the reward switched. Trials were grouped into “high-reward” and “low-reward” blocks for analysis. Errors were identified by the block in which they occurred—i.e., as “high” or “low” reward based on the preceding cor- rect trial. Continuous sequences of error trials were counted as a single error to avoid arbitrary overcounting during epochs in which the animal disengaged from work and took a nap. Thus, each counted error corresponded to a break following one or more correct trials. These experi- ments accounted for the majority of the reported results. For visually stimulated trials (Fig 5 and S5 Fig), the animals were passively shown gratings of different contrasts while holding fix- ation (sine-wave gratings; contrasts doubled in steps ranging typically from 6.25% to 100%; mean luminance = background luminance = 46 cd/m2; spatial frequency: 2 cycles/deg; drift speed 4 deg/second; diameter 2–4 deg; orientation optimized for the electrode recording site. These data are reanalyses of earlier experiments designed to relate hemodynamics to electro- physiology over a wide dynamic range of stimulated responses [44,45,55]). Reward sizes for these experiments increased progressively from a baseline (typically 0.2 ml per correct trial) to a maximum value (typically 0.6 ml per correct trial) for each successive correct fixation to keep the animals motivated. Again, the lowest reward size per day was chosen based on the animals’ willingness to work. For analysis, trials were grouped into “high-” and “low-reward” sets rela- tive to the median reward. Sequences of errors were also counted as single errors for these experiments, as with the dark room. However, errors were not identified as “high” or “low” reward. Since rewards were increased progressively for correct trials, errors (which often occurred at the end of a sequence of trials) typically followed a high reward; but that associa- tion was not informative. ISOI. ISOI is based on the finding that in vivo and in the visible spectrum, changes in light absorption in cortical tissue primarily measure changes in oxy- and deoxyhemoglobin in the blood flowing through cortical blood vessels [51,82,83]. ISOI deduces hemodynamic responses by imaging changes in light reflection at relevant wavelengths off the exposed corti- cal surface. CBV and oxygenation changes measured using ISOI can be used to predict concur- rently measured fMRI responses [53], making ISOI in effect an optical analog of fMRI albeit restricted to upper layers of exposed cortex. We imaged at 530 nm (green), an isosbestic wave- length that is equally absorbed by oxy- and deoxyhemoglobin. Increased absorption of light at this wavelength thus measures increased cortical tissue fraction of hemoglobin—in effect local cortical blood volume, independent of oxygenation state [54]. After the animals had recovered from surgery, we used this technique to image their V1 through the glass window of the recording chamber routinely while they engaged in the fixation task. Imaging hardware con- sisted of the following: camera (Dalsa 1M30P; binned to 256 × 256 pixels, 7.5 or 15 frames per second) and frame grabber (Optical PCI Bus Digital; Coreco Imaging). Imaging software was developed in our laboratory in C++ based on a previously described system [84]. Illumination PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 19 / 34 Modulating task-related hemodynamics was provided by high-intensity LEDs (Agilent Technologies, Purdy Technologies). The lens was a macroscope [85] of back-to-back camera lenses focused on the cortical surface. Imaging, trial data (trial onset, stimulus onset, identity and duration, etc.), and behavioral data (eye position, pupil size, timing of fixation breaks, fixation acquisitions, trial outcome) were acquired continuously. All data analyses were performed offline using custom software in MATLAB (MathWorks; RRID:nlx_153890). Electrophysiology. Electrode recordings were made simultaneously with optical imaging. Recording electrodes (FHC, AlphaOmega; typical impedances approximately 600–1,000 kO) were advanced into the recording chamber through a silicone-covered hole in the external glass window, using a custom-made low-profile microdrive. Recording sites were mostly but not exclusively confined to upper layers. Signals were recorded and amplified using a Plexon recording system (RRID:nif-0000-10382). The electrode signal was split into spiking (100 Hz to 8 kHz bandpass) and LFP (0.7–170 Hz). Subsequently, an additional analog 2-pole 250-Hz high-pass filter was applied to spiking, effectively eliminating any spectral power overlap between LFP and spiking. No attempt was made at isolating single units, and all measured spiking was multiunit activity (MUA) defined as each negative-going crossing of a threshold = roughly 4× the r.m.s. of the baseline obtained while the animal looked at a gray screen. The LFP recording was analyzed to obtain two bandpass-limited measurements in the beta- and theta-range frequency bands (15–25 Hz and 3–7 Hz, respectively; multitaper spectral analysis using the Chronux MATLAB toolbox). This gave an LFP measure of (low) vigilance defined by the square root of the ratio of power in theta versus beta. Analysis: Preprocessing. The imaging measurement was averaged over the imaged area, frame by frame (frame rate: 7.5 or 15 frames/second), and then divided by the mean value of this quantity for the given experiment (over all trials). This converted the measurement per image frame into dR R particular imaging wavelength of 530 nm, the negative of this quantity ((cid:0) the fractional increase in local tissue hemoglobin—i.e., the fractional increase in local cortical blood volume [54]. The dR was then detrended, and a prominent pulse artifact was filtered out R from the measured hemodynamics using Runline (Chronux) with a window of 2 seconds. This filtered dR R (i.e., the fractional change in light reflected off the cortical surface). At the ) is proportional to defined the measured hemodynamic response for all calculations. dR R The pulse artifact was used to estimate the instantaneous HR after upsampling 8× and iden- tifying peak times and thus the local pulse rate. Both the estimated HR and the spiking mea- surements were then resampled and aligned to the imaging frames. Neither the imaging nor spiking nor estimated HR were further temporally filtered. Unlike in our earlier papers, we did not high-pass filter to remove slow fluctuations [31,44,45,55], specifically so as to be able to estimate fluctuations over slow timescales of many minutes. Template matching (dark-room experiments: Fig 2). The amplitude and timing of the task-related response, per trial, were estimated as the height and location of the corresponding peak of a Template Match. This Template Match consisted of the continuous, normalized dot product of a template with the measured hemodynamic response. The calculation involved the following steps: 1. The default template “Tmplt” was defined to be the one-trial-long mean hemodynamic recording (z-scored to give “H(t)”) aligned to trial onsets, averaged across all correct trials, and mean-subtracted. 2. This Tmplt was then slid over H(t) in unit time steps (at the resolution of the imaging frame rate; e.g., 66.7 ms for 15 frames/s). At every time point t, the Template Match was defined to be the local dot product over the one-trial-long section of H centered on t, normalized by PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 20 / 34 Modulating task-related hemodynamics the (fixed) sum of squares of the Tmplt (Fig 2B): Template Match ¼ HðtÞ:Tmplt P jTmpltj2 ð1Þ This expression is identical in form to the Pearson’s correlation between the Tmplt and the same one-trial-long segment of H(t), other than the normalization. Thus, like Pearson’s r, this expression would have local maxima where the local one-trial-long segment of H(t) matched the Tmplt in shape (Fig 2B). The Template Match is also invariant to shifts in the mean of H(t), since the Tmplt is mean-subtracted and thus integrates to zero over any addi- tional constant. However, unlike Pearson’s r, this expression carries scale information. Pearson’s r has the standard deviation of both arguments in the denominator, making it scale-invariant. The Template Match on the other hand, with its fixed normalization inde- pendent of H(t), scales linearly with the amplitude of fluctuation in H(t). Thus, peaks of the Template Match carry information about both timing and amplitude of the task-related response per trial. 3. For computational efficiency in MATLAB, the above expression was rewritten as the nor- malized convolution of H(t) with the time-reversed version of the template Tmplt: Template Match ¼ HðtÞ � TmpltTR jTmpltj2 P ð2Þ where TmpltTR is the time-reversed Tmplt; i.e., TmpltTR(t) = Tmplt(−t), and the symbol � denotes convolution. The denominator for normalization remains unchanged. This expres- sion translated to the following script using MATLAB functions conv and sum: Template Match ¼ convðH; TmpltTR;0same0Þ sumðjTmpltj2Þ ð3Þ Peaks of Template Match were then identified as zero crossings of the first derivative at points where the second derivative was negative (marked by red dots in Fig 2B). 4. Alternate template matches used the same formalism but with different definitions of Tmplt. Thus, the Tmplt in S2 Fig was defined as the one-trial-long mean H(t) aligned to a point that was one-quarter trial ahead of trial onsets, averaged across all correct trials, and mean-subtracted. All other steps were the same. Template matching (with visual stimuli present: Fig 5, S5 Fig). This involved two sepa- rate sets of steps. 1. Estimating the task-related response from the net recorded hemodynamic response: i. We modeled the net recorded response as a linear sum of stimulus-evoked and task- related components. The stimulus-evoked response was modeled as the convolution of concurrent spiking with an “HRF” kernel. The task-related component was estimated iteratively. Our earlier approach [55] had modeled it as a stereotyped task-related func- tion (“TRF”) that was identical in timing and amplitude for each correct trial. Here, how- ever, we specifically need to estimate trial-by-trial variations in response timing and amplitude. As a first step, we assumed that the TRF had a fixed shape that could be esti- mated from the mean across trials. Optimal mean HRF and TRF kernels were obtained by PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 21 / 34 Modulating task-related hemodynamics fitting the mean recorded responses, separated by contrast, with the following equation, identical to Eq 1 in [55]: HðtÞ ¼ HRF � SðtÞ þ TRF � TrlðtÞ ð4Þ H(t) is the recorded hemodynamics and S(t) the concurrently measured spiking, and HRF�S(t) models the stimulus-evoked response. The second term on the RHS models the task-related response as a TRF kernel convolved with the set of delta functions at trial onsets, "Trl(t)”. The symbol � denotes convolution over time. The HRF kernel was parametrized, as before [31,44,45], as a gamma-variate function of time t: HRF t; t; W; A ð Þ ¼ A � �a t t � exp (cid:0) a � t (cid:0) t t ð5Þ The HRF parameters fitted during optimization are the amplitude A, time to peak τ, and full width at half maximum W [31,86,87]. The factor a ¼ 8:0 � log 2:0ð The TRF kernel was parametrized as the finite sum of a Fourier time series: � � � (cid:0) �2. Þ � t W � XN n¼1 � an cos n 2p PT t � þ bn sin n 2p PT t ð6Þ TRF t; a; b; P; N ð Þ ¼ Although the Fourier series was based on the trial period T, the fundamental Fourier period was allowed to vary as a fraction P of the trial period and optimized in the fit. The parameters an and bn, (with n ranging from 1 to the total number of terms in the Fourier series, N) are the pairs of cosine and sine coefficients, respectively, for the nth Fourier term. We showed earlier that only the fundamental and first harmonic—i.e., N = 2, carry significant information [55]. Thus, there are eight parameters in the model: three for the HRF, the two pairs of an and bn, and P. ii. All parameters were optimized simultaneously by matching the predicted to the mea- sured hemodynamics using a downhill simplex algorithm (fminsearch, MATLAB meth- ods as in [45]). To keep contrast information, we made concatenated sequences of the mean response per distinct contrast, randomized per contrast (same random sequence for hemodynamics, and spiking), and over multiple blocks (an arbitrarily large number 52, about 100× larger than a single HRF kernel convolution length, to minimize edge effects; we only matched traces two convolution lengths in from the edge). The error to � � be minimized was defined as the normalized sum squared error SSerror SStotal calculated sepa- rately per contrast and then averaged over all contrasts including the blank. This was intended to give equal weight to the fractional error at each stimulus contrast. The good- ness of fit R2 for the optimal prediction was defined as the coefficient of determination � 1 (cid:0) calculated separately per contrast and averaged across contrasts. This, again, � SSerror SStotal was intended to give equal weight to errors at each contrast. In order to reduce the chances of getting caught in a local minimum, we started with large sets of initial param- eter values, independently covering an order of magnitude for each fitted parameter. The fits were robust and converged to the same optimal parameters from multiple starting values, giving us confidence that we had reached global and not local minima. PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 22 / 34 Modulating task-related hemodynamics iii. Next, we used the optimal fitted HRF thus obtained from the mean hemodynamics and spiking, averaged per contrast, to get a continuous estimate of the exogenous, visually evoked component of measured hemodynamics. This was done by convolving the opti- mal HRF with the measured spiking, including both the spiking from the controlled visual stimuli and from uncontrolled visual stimulation as the animal looked around in between fixations: HSTIMULATEDðtÞ ¼ HRF � SðtÞ ð7Þ iv. This estimate of HSTIMULATED was subtracted from the full measured hemodynamics to get an estimate of the endogenous, task-related component of hemodynamics as the residual not accounted for by spiking: HTASK(cid:0) RELATEDðtÞ ¼ HðtÞ (cid:0) HSTIMULATEDðtÞ ð8Þ 2. Estimating task-related response peaks and amplitudes: i. The estimate of the task-related response HTASK−RELATED(t) as defined above was then used, exactly like the full hemodynamic response in the dark room, to estimate response times and amplitudes trial-by-trial. As a template, we used the optimal TRF obtained above by fitting to mean responses. The steps for template matching and identifying and analyzing peaks were identical to those outlined in Eqs 1–3, with the H(t) being replaced with HTASK−RELATED(t) and the Tmplt(t) being replaced with the optimal TRF. Peak times and amplitudes, per trial, were obtained exactly as in the dark-room template match. Tracking measured variables as animal switches from task-engaged to disengaged with eyes closed (Fig 7). To track slow changes in all measured variables, we downsampled the data. Data were averaged using a 15-second box car that corresponded roughly to a single trial and then decimated 4×, giving in effect a smoothed 60-second sample rate. Along with MUA spike rates at individual electrodes and the measured hemodynamics, the following measure- ments were thus tracked: 1. “Eyes closed”: Fraction of time over the 60-second averaging window that eyes are closed. Eye closure was monitored using the output from the IR eye tracker. All blinks or eye clo- sures appeared as sequences of missing points or “rails” (saturated output). Spontaneous eye blinks in macaques last roughly 200 ms (see [88,89]). Our own data showed a bimodal distribution with blink durations peaking either at 200 ms or multiple seconds to minutes. Thus, sequences of missing points lasting <500 ms were considered regular spontaneous blinks while awake and were marked as having duration = 0. Sequences lasting >500 ms were categorized as eye closures, and their durations were included in the moving average. 2. “LFP measure”: square root of the ratio of spectral band–limited power in the theta (3–7 Hz) and beta (15–25 Hz) frequency bands, each normalized by its standard deviation over the entire experiment. We chose this particular ratio to get a measure that was high during epochs of low engagement in the task to match “Eyes closed,” since theta power increases sharply on transitions from high to low engagement or to sleep [48]. We took the square root of the ratio to compress the measure to approximately 0–1 to make it comparable to “Eyes closed.” There was no attempt to separate the resting state more finely into sleep PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 23 / 34 Modulating task-related hemodynamics stages, since the goal was a broad separation into states of “task-engaged” versus “resting” with a time resolution of 60 seconds. 3. “Spike1,” “Spike2”: MUA responses recorded from two electrodes spaced 1 mm apart, in imaged V1. 4. “Spk1-Spk2 Corr”: Pairwise correlation of MUA spike rate from the two electrodes, calcu- lated over a 1-second moving window. 5. “HR”: Obtained from the pulse artifact in the measured hemodynamics, after upsampling 8× and identifying peak times and thus the local pulse rate. This instantaneous pulse rate, estimated at pulse time points, was then interpolated with spline smoothing to the imaging time base (7.5-Hz or 15-Hz sample rate depending on the experiment). Deconvolution, i.e., multilinear regression (Fig 8). We started with the assumption that the measured hemodynamics H(t) can be predicted from local spiking S(t) using a homoge- neous linear equation, along with two inhomogeneous terms: a scalar Intercept and a linear Slope in each 150-second window, at the resolution of the camera frame rate: HðtÞ ¼ HRF � SðtÞ þ Intercept þ Slope ð9Þ There are no assumptions about the shape of the HRF other than that it does not extend more than 10 seconds prior to time 0 and is back to baseline about 25 seconds after time 0. Using the formalism of deconvolution, this expression can be rewritten as a matrix equation H ¼ S � HRF þ Intercept þ Slope ð10Þ where H is a column vector of recorded hemodynamic responses (at the temporal resolution of the camera frame rate, 15 Hz); S is the spiking regressor expanded into a stimulus convolu- tion matrix (SCM) [37,61]; the symbol × indicates matrix multiplication; and HRF, Intercept, and Slope here refer to the same terms as in Eq 10 but expressed as column vectors. The SCM was constructed as a Toeplitz matrix comprising a horizontal concatenation of spiking column vectors, with circular time shifts ranging from −10 seconds to +25 seconds relative to t = 0. Formally, the SCM S can be extended (“Se”) to incorporate the Intercept and Slope by horizon- tally concatenating the two additional column vectors: a column of ones for the Intercept and a linear ramp from −1 to 1 for the Slope. The corresponding HRF can be formally extended (“HRFe”) by two coefficients, one for the Intercept and another for the Slope. H ¼ Se � HRFe ð11Þ Assuming that any noise is Gaussian and has zero mean, the optimal deconvolved HRFe can then be estimated using a least-squares solution to the linear regression [37,61]: HRFe ¼ ðSe T � SeÞ(cid:0) 1 � Se T � H ð12Þ where the superscript T indicates the matrix transpose, -1 indicates the matrix inverse, and × indicates matrix multiplication. The full prediction using the optimal deconvolved HRFe kernel is then computed using Eq 11. Similarly, just the (linear, homogeneous) prediction from spiking is obtained by taking the matrix multiplication over all column vectors of Se, save the last two, with all coefficients of the deconvolved HRFe, save the last two. Conversely, just the Intercept term or just the Slope term are obtained by appropriately multiplying the last two column vectors of Se with the corresponding last two coefficients of the deconvolved HRFe. The optimal deconvolved Intercept is defined as the additional “mean shift” not predicted by PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 24 / 34 Modulating task-related hemodynamics spiking. As with the model-based approach to fitting used earlier (see Eqs 4–6), the goodness � of fit R2 for the optimal prediction was defined as the coefficient of determination 1 (cid:0) SSerror SStotal This was used to compare fits made with versus without including an intercept term in the design matrix (Fig 8A versus S8 Fig). � . Getting bootstrap estimates for significance (p-values). All comparisons between distri- butions of amplitudes or peak times were tested for significance by bootstrapping, typically using 10,000 resamples with replacement. In cases with different numbers of trials for high and low reward, the smaller number of trials was chosen to make the bootstrap comparison. For comparing response amplitudes (e.g., Fig 3B), we tested for the median of high-reward amplitudes being less than that for low-reward amplitudes over the set of all resamples, against the null hypothesis that this difference has zero mean. We also tested for the complement— i.e., that median of low-reward amplitudes is less than that of high-reward amplitudes. For comparing widths of peak time distributions (e.g., Fig 3A), we first calculated the 2 standard deviation width (specifically, the +/− 34th percentile around the median, given the non-nor- mal distribution) of each bootstrapped set of peak times separately for high and low reward. We then tested for 2 standard deviation for high reward being less than that for low reward over the set of all resamples, against the null hypothesis that the difference has zero mean. We also tested for the complement—i.e., that 2 standard deviations for low reward was less than that for high reward. Fitting spiking to dark-room hemodynamic response using gamma-variate HRF (S1 Fig). To link to spiking, the dark-room response was modeled as a homogeneous prediction from spiking, fitted by optimizing a gamma-variate HRF kernel using fminsearch as described above: HðtÞ ¼ HRF � SðtÞ ð13Þ The fitting was done separately for the high-reward and low-reward trials at each recording site. Stimulus-evoked responses were fitted using a model with a task-related component: Eqs 4–6. In each case, the optimal fitted HRF kernel was then convolved with the continuous recorded spiking response to give a continuous prediction. Since the spiking response included both high-reward and low-reward segments, the prediction included sections of “same” pre- diction (e.g., low-reward spiking convolved with the low-reward kernel) and sections of cross predictions (e.g., high-reward spiking convolved with the low-reward kernel). Supporting information S1 Fig. Local spiking, although appearing to predict mean hemodynamic responses in indi- vidual recording conditions, is a poor and unreliable predictor of task-related responses overall. (a,b) Mean measured responses and optimal predictions for low-reward and high- reward trials, respectively, of a data set recorded in the dark-room task. In each case, the lower panel shows the mean measured spiking; the upper panel shows the mean measured hemody- namics as well as the prediction from spiking using the corresponding optimal fitted gamma- variate HRF kernel (see color code in each column). Low reward (N = 148 trials), R2 = 0.73 for the optimal prediction. High reward (N = 140 trials), R2 = 0.42 for the optimal prediction. (c) A separate set of visually stimulated trials at the same recording site, using visual stimuli con- sisting of optimally oriented drifting gratings at different contrasts, as indicated by the gray- scale coding (orange bar below depicts the visual stimulation period). Again, the top panel shows mean measured hemodynamics and optimal predictions grouped by stimulus contrast; predictions are shifted to the right for visibility (N = 141 trials total, i.e., 47 trials / contrast. R2 PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 25 / 34 Modulating task-related hemodynamics = 0.95). (d) The optimal fitted gamma-variate HRF kernels for the three recording conditions, color coded as shown. Note how poorly they match each other. (e) Comparing the measured low-reward hemodynamics to predictions using the low-reward dark-room set of spiking responses (as in panel a)—but with different optimal HRF kernels—from low-reward, high- reward, and stimulus-evoked sets. The cross predictions are poor (R2 of prediction using high- reward HRF = −0.014; stimulated HRF = −0.011). (f) Optimal HRFs from the full set of dark- room experiments, normalized in each case to the amplitude of the corresponding visually stimulated HRF (N = 56: pairs of high- and low-reward HRFs for each of 28 sets with electrode recordings). Scale truncates some HRFs of high absolute amplitude to help visualize those of smaller amplitude. Colors are arbitrary (MATLAB default). The different optimal HRFs match each other poorly, with some even reversed in sign. This makes cross prediction meaningless and suggests that apparently good predictions of mean responses in individual experiments are fortuitous. HRF, hemodynamic response function. (TIF) S2 Fig. Increase in temporal precision with reward size is not sensitive to the choice of tem- plate used to estimate response time and amplitude. (a-d) Same example data set as in Figs 2 and 3. (a) Orange indicates the alternate template defined as the mean hemodynamic response across correct trials, aligned to a time point one-quarter cycle ahead of trial onset (i.e., starting at the dashed vertical line 4.1 seconds ahead of time 0. Single trials are shown in gray). Green background (time points 0–16.4 second) marks the timing of the earlier template for compari- son (see Fig 2B, “Tmplt”). (b) New template match (orange, “Tmplt Match,” upper row) illus- trated using the same segment of recorded hemodynamics (“Hemo”) as in Fig 2B. The earlier template match from Fig 2B is shown alongside for comparison (green, dashed line). Black dots identify the peaks of the new Template Match, marking locations where the “Hemo” is locally best phase-matched to the new template (see “Match Peak,” compared to “Match Trough”). (c) Distributions of response times, defined as the positions of the new template match peaks. Compare with Fig 3A (same conventions). (d) Distributions of response ampli- tudes using the new template match. Compare with Fig 3B (same conventions). (e, f) New response timing distribution 2 standard deviation widths and amplitude medians for high- versus low-reward trials across all experiments, including p-values from Wilcoxon signed rank test for the pairwise comparisons. Compare with Fig 3C and 3D (data in S24 Data). (TIF) S3 Fig. Response timing does not correlate with fixation onset. (a) Simulation of the null hypothesis. The task-related response has a stereotyped time course following the onset of fixa- tion. Response times would then have a constant delay following fix onset, leading to a linear relation between the two with unity slope (the delay was taken to be 10 seconds for this simula- tion). The observed tighter clustering of response times for high reward could result from a corresponding clustering of fixation onsets (consider projection of red dots versus blue dots on the Response Time axis). (b) Relationship between measured response time (estimated as usual with a template match) and fixation onset in an early recording session. Animals tended to hold fixation for extended periods prior to trial onset, even across multiple trials. (c) Rela- tionship between response time and fix onset in a late recording session. Animals tended to move their eyes a lot during intertrial intervals, fixating shortly before trial onset. For both cases (b) and (c), response times were independent of fixation onset and very different from the pattern expected for the null hypothesis. In both data sets, response times for high-reward trials showed visibly lower scatter independent of fix onset (data in S25 Data). (TIF) PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 26 / 34 Modulating task-related hemodynamics S4 Fig. High reward does not correlate with tighter eye movements. (a1) Mean radial eye movement per intertrial interval in an early recording session. Each dot represents a single trial (mean eye movement during 7-second intertrial intervals per 9-second trial). Horizontal lines indicate median eye movement per block of high or low reward (blocks with varying numbers [13–31] of correct trials each). Intertrial eye movements were higher in low-reward blocks. (a2) Histogram of mean eye movement per trial. (a3) Relationship between response time and eye movement per trial, colored by reward size. (b1) Mean radial eye movement in a later recording session (12-second intertrial intervals in 16-second trials; alternating blocks of 10 correct trials each; all other conventions as in panel A1). Eye movements were higher in high-reward blocks. (b2) Corresponding histogram of mean eye movements per trial. (b3) Response time versus eye movement per trial colored by reward size. Low reward leads to wider scatter of response times in both panels (a3) and (b3) despite opposite effects on inter- trial eye movement (data in S26 Data). (TIF) S5 Fig. Estimating task-related response and its template match in the presence of visual stimulation (one example data set). (a-c) Estimating optimal fitted parameters (see Methods, Eqs 4–6). (a) The mean hemodynamic response per stimulus contrast (see key), averaged across trials. The response is modeled as the sum of the stimulus-evoked component (b) and the task-related component (c). The stimulus-evoked component is modeled as the convolu- tion (�) of the measured spiking with a gamma-variate HRF kernel (inset). The mean task- related component is modeled as the convolution of delta functions at trial onset with a “Mean TRF” kernel comprising a partial Fourier sum with its fundamental at the trial period (inset). Earlier work showed that the fundamental and the first harmonic terms of the Fourier series are adequate. Insets show the optimal fitted gamma-variate HRF (in b) and optimal mean TRF (in c), respectively. (d) Set of traces illustrating the process of estimating the residual task- related response and then estimating its timing and amplitude per trial by matching to a tem- plate (see Methods, Eqs 7 and 8). “Spiking,” “Hemo”: full measured responses, individually z- scored. “Hemo (predicted from spiking)” is the convolution of the spiking response with the optimal fitted HRF (b, inset). Subtracting this from the measured hemodynamic response gives the residual “Hemo (Unpredicted by spiking),” which we defined to be the task-related response. The moving-window dot product of this residual with the template (the optimal fit- ted mean TRF [c), inset]) gives the “Template Match” (shifted up for visibility). Timing and amplitude of task-related responses, per trial, are defined to be the location and height of each Template Match peak, as for the dark-room task. Showing a section of the full experiment of 483 trials (122 correct). (e) Set of all residual task-related responses, converted back from z- scored values, separated into trials grouped by reward size. The same data are shown in Fig 5A. HRF, hemodynamic response function; TRF, task-related function. (TIF) S6 Fig. Comparing regression lines through alternating blocks of high and low reward, before (top panel) and after (bottom panel) removing error trials. Color coding for high (red) and low reward (cyan) is the same as in the main text. Error trials are indicated in lighter colors and are grouped with the reward block corresponding to the immediately preceding correct trial. Straight lines show regression fits. Letters (“A,” “B,” “C”) and arrows identify correspond- ing blocks. Blocks A and B contain individual or short stretches of error trials. C includes a roughly 400-second stretch during which the animal napped. The time axis has the same scale for both top and bottom panels, with time 0 indicating the start of the experiment; the bottom concatenates time points for correct trials. Six consecutive blocks are shown from an PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 27 / 34 Modulating task-related hemodynamics experiment comprising 47 blocks (482 correct trials of 684 total). (TIF) S7 Fig. Ramp-like drifts in local blood volume are not accounted for by slow changes in local spiking. (a, c) Hemodynamics and spiking, respectively, showing correct trials from alternating blocks of high and low reward. Lines show regression fits per block (same data set as Figs 2 and 3). (b, d) Histograms with slopes of regression fits from (a), (c). (e) Simplified simulation of slow mean hemodynamic responses: triangle wave of matching period, with slopes equal to the median (absolute) slopes of the regression lines in (a) (= 4.1 × 10−5/second). (f) Simulated spiking response that generates the model hemodynamic response in (e) on convolving with the visually stimulated HRF for this recording site (see “HRF kernels,” S1 Fig; also, Methods). Measured spiking regression slopes (d) are only about 4× weaker than those in the simulation; but they do not alternate in sign with reward size. (g) Distributions of the ratios of measured spiking regression slope per block to the slope of the corresponding simulation, as in (e), (f), across all experiments (N = 752 blocks of 10 trials each, 376 blocks/reward size; from N = 11 experiments with electrode recordings and at least 10 blocks per reward size). p- Values test for the probability of the distributions being centered on zero (bootstrap, 10,000 resamples) (data in S27 Data). HRF, hemodynamic response function. (TIF) S8 Fig. Deconvolution fit of the same data segment as in Fig 8A but with no intercept term in the design matrix. The full prediction here matches the measured response reasonably well except for a few locations with large mismatches (black arrowheads; compare with the same locations in Fig 8A). The overall goodness of fit R2 = 0.76, averaged over this rest epoch, is worse than for the fit with an intercept (R2 = 0.94; see Fig 8A, text). The inset shows HRFs from the deconvolution windows covering this rest epoch, as in Fig 8A; colors identify corre- sponding HRFs for the two fits. HRF, hemodynamic response function. (TIF) S1 Data. Data for “Eye Pos” traces in Fig 1B. (XLSX) S2 Data. Data for “Hi–Lo” reward pupil dilation histograms in Fig 1B. (XLSX) S3 Data. Data for pupil traces in Fig 1B. (XLSX) S4 Data. Data for Fig 1C. (XLSX) S5 Data. Data for Fig 1D. (XLSX) S6 Data. Data for Fig 1E. (XLSX) S7 Data. Data for Fig 2A. (XLSX) S8 Data. Data for Fig 2C. (XLSX) PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 28 / 34 Modulating task-related hemodynamics S9 Data. Data for Fig 2D. (XLSX) S10 Data. Data for Fig 2E and 2F. (XLSX) S11 Data. Data for Fig 3. (XLSX) S12 Data. Data for Fig 4A. (XLSX) S13 Data. Data for Fig 4B. (XLSX) S14 Data. Data for Fig 4C–4F. (XLSX) S15 Data. Data for Fig 5A. (XLSX) S16 Data. Data for Fig 5B. (XLSX) S17 Data. Data for Fig 5E. (XLSX) S18 Data. Data for Fig 5F. (XLSX) S19 Data. Data for Fig 5C and 5D. (XLSX) S20 Data. Data for Fig 6C. (XLSX) S21 Data. Data for Fig 6A and 6B. (XLSX) S22 Data. Data for Fig 7B. (XLSX) S23 Data. Data for Fig 7C. (XLSX) S24 Data. Data for S2 Fig. (XLSX) S25 Data. Data for S3 Fig. (XLSX) S26 Data. Data for S4 Fig. (XLSX) S27 Data. Data for S7 Fig. (XLSX) PLOS Biology \| https://doi.org/10.1371/journal.pbio.3000080 April 19, 2019 29 / 34 Modulating task-related hemodynamics Acknowledgments Thanks to Maria Bezlepkina and Elena Glushenkova for technical support and lab manage- ment, to Liam Paninski for suggestions about analyzing slow changes in physiological responses, and to David Heeger, Mike Shadlen, Elisha Merriam, Charles Burlingham, and Saghar Mirbagherion for comments. Author Contributions Conceptualization: Yevgeniy B. Sirotin, Aniruddha Das. Data curation: Mariana M. B. Cardoso, Bruss Lima. Formal analysis: Mariana M. B. Cardoso, Bruss Lima, Aniruddha Das. Funding acquisition: Aniruddha Das. 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10.1021_acssynbio.1c00142.pdf	null	null	pubs.acs.org/synthbio Research Article Sequence Preference and Initiator Promiscuity for De Novo DNA Synthesis by Terminal Deoxynucleotidyl Transferase Erika Schaudy, Jory Lietard, and Mark M. Somoza* Cite This: ACS Synth. Biol. 2021, 10, 1750−1760 Read Online ACCESS Metrics & More Article Recommendations sı Supporting Information ABSTRACT: The untemplated activity of terminal deoxynucleotidyl transferase (TdT) represents its most appealing feature. Its use is well established in applications aiming for extension of a DNA initiator strand, but a more recent focus points to its potential in enzymatic de novo synthesis of DNA. Whereas its low substrate speciﬁcity for nucleoside triphosphates has been studied extensively, here we interrogate how the activity of TdT is modulated by the nature of length, chemistry, and the initiating strands, in particular nucleotide composition. Investigation of full permutational libraries of mono- to pentamers of D-DNA, L-DNA, and 2′O-methyl-RNA of diﬀering directionality immobilized to glass surfaces, and generated via the eﬃciency of photolithographic in situ synthesis, shows that extension strongly depends on the nucleobase sequence. We also show TdT being catalytically active on a non-nucleosidic substrate, hexaethylene glycol. These results oﬀer new perspectives on constraints and strategies for de novo synthesis of DNA using TdT regarding the requirements for initiation of enzymatic generation of DNA. KEYWORDS: TdT polymerase, microarray, synthetic biology, L-DNA, enzymatic DNA synthesis, photolithographic synthesis their T erminal deoxynucleotidyl transferase (TdT) is a member of the polX family of DNA polymerases ﬁrst puriﬁed from calf thymus glands.1,2 In contrast to template-dependent DNA polymerases, TdT extends DNA strands at their 3′ hydroxy terminus in the presence of divalent cation cofactors3 and deoxynucleoside triphosphates (dNTPs), but in the absence of a template strand. This activity is of major importance in the diversiﬁcation of immunoglobulins and T cell receptors in the process of V(D)J recombination of the adaptive immune system via random addition of nucleotides to nicked DNA strands.4,5 TdT’s unique ability to mediate template- independent polymerization has made it a valuable tool in a variety of molecular biology applications including ﬁnding strand breaks,6 modifying DNA oligomers with various NTPs,7 and identifying DNA damage and epigenetic modiﬁcations.8 Furthermore, the enzyme has proven useful for the generation of polynucleotides of high molecular weight9 and amphiphilic structures upon extension with BODIPY-dUTP,10 for detection of DNA and RNA on surfaces,11,12 and immobiliza- tion of DNA on solid supports.13 In the context of synthetic biology, template-independent DNA polymerization by TdT is, along with enzyme-based approaches,14 a promising alternative to chemical synthesis as many of the shortcomings of the phosphoramidite approach can be potentially avoided. In particular, coupling failures and depurination during the deblocking step limit chemical to about 200 nucleotides. The atom economy of phosphoramidite synthesis synthesis of DNA is also very poor, producing an approximately 1000- fold excess of chemical waste. Since polymerases work in aqueous solutions and are capable of fast and high-ﬁdelity synthesis of almost arbitrary length, they promise a greener and far more eﬃcient approach to DNA synthesis. Beyond genomics and biotechnological applications, DNA is an attractive medium for archiving digital information since it can achieve a storage density of hundreds of petabytes per gram,15 and data can be reliably recovered after being stored for thousands of years.16 Useful DNA data storage may depend on successful implementation of enzymatic synthesis since even high throughput chemical approaches are economically uncompetitive with, storage technologies.17 e.g., magnetic or optical Several recent publications have addressed sequence control in TdT-based enzymatic synthesis. In the context of digital information storage, a looser deﬁnition of sequence control can be tolerated, allowing dNTP degradation with apyrase to limit TdT-catalyzed extension to a controlled series of short Received: April 6, 2021 Published: June 22, 2021 © 2021 The Authors. Published by American Chemical Society 1750 https://doi.org/10.1021/acssynbio.1c00142 ACS Synth. Biol. 2021, 10, 1750−1760 ACS Synthetic Biology pubs.acs.org/synthbio Research Article homopolymers.18 Precise sequence control has been achieved using photocleavable TdT-dNTP conjugates,19,20 3′ photoc- aged dNTPs,21 and through the controlled release of divalent ion cofactors from photosensitive chelators.22,23 While it is too soon to tell which approach to sequence-controlled de novo DNA synthesis will be optimal, here we explore another factor critical to practical and eﬃcient enzymatic synthesis with TdT, its initiator preferences. Experiments demonstrating TdT- based synthesis have used relatively long primer DNA oligonucleotides20 to 60mersas starting substrates, an impractically large number since these are made chemically and remain attached.18,20,21,23 In the phosphoramidite chemistry approach it is standard practice to start with one of four solid-phase columns preloaded with the ﬁrst DNA nucleoside of the desired sequence. Such an approach might also be feasible in enzymatic synthesis if the initiator sequence length can be limited to one or two nucleotides, resulting respectively in 4 or 16 starting sequences or columns. This seems possible since very early research on TdT suggests a lower limit in length of the initiating DNA strand of at least 3 nt24 or as low as 2 nt.25 At the same time, we should ask whether some sequences are extended more eﬃciently than others, as this aﬀects not just the initiation, but potentially each subsequent cycle of the synthesis. for A related question is whether TdT is able to extend initiator molecules other than the 3′ terminus of DNA, enabling enzymatic synthesis of chimeric nucleic acid sequences, DNA/ RNA hybrids, or even conjugates where an unnatural initiator is extended with dNTPs or rNTPs. Regarding the diﬀerences in eﬃciency in the use of dNTPs and rNTPs, there appears to be limited ability for extension of DNA initiator strands with ribonucleotides.26,27 Furthermore, TdT was found to catalyze the extension of oligonucleotide strands with a variety of modiﬁed nucleoside triphosphates, instance biotiny- lated,11,28,29 ﬂuorescence-tagged,30 photo-cross-linkable31 or light-cleavable21 dNTPs and non-nucleosidic substrates,32 as well as ﬂuorescent nucleobase analogues33 and metal base- pairs,34 showing rather low substrate speciﬁcity in contrast to other DNA polymerases, which could be further loosened by protein engineering eﬀorts.35 An investigation of nucleoside triphosphate analogues, including arabinonucleosides and acyclic triphosphates of acyclovir and penciclovir, and their L- and D-stereoisomers showed that the stereochemistry of the triphosphates had a profound eﬀect on substrate recognition by TdT.36 Whereas nucleoside triphosphate substrate speciﬁc- ity is rather ﬂexible, DNA analogues in the initiating strand seem to hamper extension, for instance upon replacement of natural DNA nucleotides at the 3′ terminus with L-DNA,37 or when using RNA initiator strands.38,39 Herein, we report on the ability of TdT to extend ssDNA initiators between 1 and 5 nt in length and immobilized on a glass surface, as well as other nucleosidic and non-nucleosidic primers. Our the enzymatic results, which encompass extension of all 1364 possible sequence permutations of mono- up to pentamers for each of several nucleic acid chemistries, are based on the use of nucleic acid photo- lithography for the massively parallel synthesis of initiator strands on a common surface.40 We have recently expanded the toolbox of light-sensitive DNA phosphoramidites used in photolithographic synthesis beyond the standard 3′ → 5′ (“forward”) direction,41 and we are using this chemical diversity to investigate the activity of the TdT polymerase on from DNA oligonucleotides with a variety of initiators, accessible 3′ or 5′-OH groups (from “reverse” or “forward” DNA synthesis, respectively), to RNA-like nucleic acids with 2′O-methyl RNA (2′OMe-RNA), to mirror-image (L-)DNA primer strands with a terminal 5′-OH. We also examined the potential of non-nucleosidic molecules to act as initiators for TdT-mediated enzymatic synthesis by preparing polymers of hexaethylene glycol (HEG) linkers. Surprisingly, with the exception of 5′-OH D-DNA, all tested substrates were able to support some level of enzymatic extension, but with 3′ hydroxy terminated DNA clearly the optimal initiator. The extension eﬃciency of 3′ hydroxy terminated ssDNA by TdT is also strongly sequence dependent, with a factor of 3 eﬃciency diﬀerence between the best and worse pentamer initiator sequences. ■ MATERIALS AND METHODS Approach. In order to investigate the ability of TdT to extend terminal hydroxy groups of diﬀerent nucleic acid chemistries, multiple replicates of each oligonucleotide strand were synthesized on the same array, each present in two versions: one where the ﬁnal light-sensitive protecting group was removed at the end of the synthesis, exposing an accessible hydroxy group, whereas in the other version, the terminal hydroxy group was capped with a DMTr-dT phosphorami- dite.42 We have previously measured the coupling eﬃciency of most non-RNA phosphoramidites for light-directed synthesis to be ∼99.9%, including DMTr-dT in its role as capping agent; G being the exception at 97−98%.41,43−46 After synthesis and deprotection, the surface-bound oligonucleotides serve as initiator sequences for dT homopolymer extension with TdT polymerase. The eﬃciency of polymerization was evaluated by hybridization to the extension product. Absolute ﬂuorescent signal intensities of the capped and uncapped versions present on a single surface were compared in order to evaluate the ability of TdT to extend short oligonucleotide strands of diﬀering chemistry and nucleotide composition. In order to allow investigation of all diﬀerent monomers as initiators, and to distance the terminal hydroxy group from the glass surface, the synthesis was started with coupling of a hexaethylene glycol phosphoramidite as linker in an initial synthesis cycle. to the glass Photolithographic in Situ Synthesis. The detailed procedure for photolithographic in situ synthesis has already been described elsewhere.47,48 Brieﬂy, microscopy glass slides (Schott NEXTERION glass D) were functionalized with a 2% N-(3-triethoxysilylpropyl)-4-hydroxybutyramide (95%; abcr) solution in ethanol/water/acetic acid (95:5:0.1), washed, and cured at 120 °C under a vacuum for 2 h. An Expedite 8909 nucleic acid synthesizer was used to deliver reagents for substrate. Anhydrous acetonitrile synthesis (Biosolve) and DCI activator (Sigma-Aldrich, L032000) were maintained dry under molecular sieves (Sigma-Aldrich, Z509027). The exposure solvent consisted of 1% imidazole (Sigma-Aldrich, 56750) in anhydrous DMSO (Biosolve). The oxidizer was 20 mM I2 in H2O/pyridine/THF (Sigma-Aldrich L060060). Cyanoethyl phosphoramidites were used as 0.03 M solutions in dry acetonitrile and obtained from Orgentis (5′- BzNPPOC D-DNA, 3′-BzNPPOC D-DNA), ChemGenes (5′- NPPOC L-DNA; 3′-NPPOC 2′OMe-RNA; NPPOC-hexa- ethylene glycol), and LINK (DMTr-dT). Phosphoramidite purity and 3′ phosphitylation selectivity was veriﬁed by 31P and 2D 1H−31P NMR. Coupling times varied depending on the type of phosphoramidite, between 15 s (D-DNA), 60 s (L-DNA and 2′OMe-RNA), 120 s (DMTr-dT), and 300 s (hexa- 1751 https://doi.org/10.1021/acssynbio.1c00142 ACS Synth. Biol. 2021, 10, 1750−1760 ACS Synthetic Biology pubs.acs.org/synthbio Research Article ethylene glycol). After synthesis, cyanoethyl and base protecting groups were removed by treating the array with ethylenediamine/ethanol (1:1) for either 2 or 15 h (3′- BzNPPOC D-DNA, NPPOC-hexaethylene glycol). An optical system, focusing UV light from a 365 nm high- power UV-LED source (Nichia NVSU333A)49 onto a digital micromirror device (Texas Instruments 0.7 XGA DMD) with 1024 × 768 individually addressable micromirrors, and via an Oﬀner optical relay, further onto a functionalized glass slide, allows the photosensitive protecting groups according to a set of digital masks generated by a MATLAB program. spatially resolved removal of Synthesis Design. Oligonucleotide microarrays used in this study are based on the same layout and design. Using the full 1024 × 768 synthesis space, a 9:25 layout (blocks of 3 × 3 synthesis pixels surrounded by 2 pixel-wide unused margins) allowed for photolithographic synthesis of 31 008 individual sequences in parallel. The full permutation library of 1 to 5 nt length was synthesized with both free and capped terminal hydroxy groups. A 25mer (“QC25”: 5′-GTCATCATCATG- AACCACCCTGGTC-3′) was synthesized in parallel in order the synthesis quality via a to allow for evaluation of standardized hybridization. Furthermore, synthesis of T or U 18mers enabled the hybridization eﬃciency to be assessed during the detection of enzymatically generated dT homopol- ymers. All strands were grown on a single hexaethylene glycol (HEG) moiety as a non-nucleotide linker. Distribution of the sequencesand all replicates of individual sequenceson the array surface was randomized in order to compensate for any spatial eﬀects possibly occurring upon reaction and/or hybridization. The microarrays for the investigation of non- nucleotide initiator strands were synthesized using only HEG phosphoramidites in order to obtain strands of up to nine HEG units in length, both with accessible and blocked termini. Extension and Detection. After removal of cyanoethyl and nucleobase protecting groups, extension reactions were performed with a mix of 0.2 u/μL calf thymus TdT (NEB M0315; 20 u/μL stock) and 100 μM dTTP (Carl Roth; 100 mM stock) in 1× TdT buﬀer (NEB; 50 mM potassium acetate, 20 mM tris-acetate, 10 mM magnesium acetate, pH 7.9 at 25 °C) supplemented with 0.25 mM CoCl2 (NEB; 2.5 mM stock) at 37 °C in a hybridization oven with rotation for 120 min in an adhesive chamber (Grace Biolabs). After incubation, the reaction mix was removed from the hybridization chamber and the array rinsed brieﬂy by pipetting in and out nonstringent washing buﬀer (NSWB) (6× SSPE, 0.01% Tween-20), followed by a short wash (ca. 10 s) of the entire slide in ﬁnal washing buﬀer (FWB) (0.1× SSC) and drying in a microarray centrifuge. A hybridization solution containing probe rA18-Cy3 (IDT; 5′-Cy3-GDDDD(rA)18-3′; with D being either A,G,T; 90 nM) and acetylated BSA (Promega; 0.44 mg/mL) in 1× MES buﬀer (100 mM MES, 1 M Na+, 20 mM EDTA, 0.01% Tween-20) was applied to the array surface for incubation at 4 °C without rotation for 120 min. Stringency washes were performed by washing the slide for 2 min in NSWB, 1 min in stringent washing buﬀer (SWB) (100 mM MES, 0.1 M Na+, 0.01% Tween-20) and 10 s in FWB at 4 °C. After drying, the slides were scanned at 532 nm at a resolution of 5 μm using a GenePix Personal 4100A scanner. Data Analysis. The Cy3 ﬂuorescent signal intensities observed upon hybridization to the enzymatically generated homopolymer served as a measure of successful extension of initiator strands. Alignment of the scans with the underlying design using NimbleScan 2.1.68 (NimbleGen) allowed for data extraction for each individual feature. The data were analyzed using Microsoft Excel. Fluorescent signal intensities observed on features with blocked termini were treated as background noise and subtracted from the signal measured for the version with an accessible terminal hydroxy group. Sequence logos were created using WebLogo (weblogo.berkeley.edu).50 ■ RESULTS AND DISCUSSION The ability of TdT to extend all possible mono- to pentamers of nucleotide chains with diﬀering sugar chemistries was investigated via hybridization to the product of extension. This setup allowed not only for a comparison of the extension eﬃciency of diﬀerent chemistries, but also for the identiﬁcation of preferences in nucleotide composition as well as the minimal length still allowing for enzymatic polymerization. Besides nucleic acid pentamers, we also prepared polymers of hexaethylene glycol (HEG) containing up to nine units. Due to the uncontrolled mode of action of TdT randomly adding nucleoside triphosphates to the growing chain, we restricted our study to only dTTP as a substrate in order to generate poly dT strands detectable in a hybridization-based assay with a ﬂuorescently labeled complementary rA18 probe, as shown in Figure 1. Synthesis only using a single type of dNTP allows us to isolate the impact of the initiating sequence on extension eﬃciency from biases in the incorporation of dNTPs that have been observed in vivo51 and in vitro.19,24 The analysis of ﬂuorescent signal intensities measured upon hybridization to the enzymatic reaction products allowed for extension eﬃciencies to be compared. Figure 2 shows the intensities observed for the extension of range of signal initiators of diﬀering nucleic acid chemistries, with lowest and highest ﬂuorescent intensities detected and after background subtraction (sequences with blocked terminal the threshold to evaluate TdT’s hydroxy group). We set general ability to extend an initiator as the average of signal intensities for blocked sequences plus three times their standard deviation. D-DNA 3′-OH Extension. With the cognate substrate of TdT being single-stranded DNA with a 3′-OH terminus, we expected the highest extension eﬃciency for this substrate. Indeed, signal intensities plotted in Figure 2 clearly show 3′- terminated D-DNA as the favored substrate of all ﬁve diﬀerent chemistries. Focusing on the left panel of Figure 2, the extension reaction eﬃciency increases with the length of the initiating strands. However, there is also a clear dependence on the nucleotide composition, to the extent that some sequences of longer oligonucleotides can be less eﬃcient initiators than the shortest. signal Investigation of the full permutation library allowed us to identify which nucleotide sequences are preferentially ex- tended. Figure 3a provides an overview of the trends of initiating sequences yielding the 10% highest (left, framed in framed in red) extension green) and lowest (right panel, eﬃciency. Consensus sequence logos illustrate these trends. While the nucleobase sequence is less relevant for mono- and dimers, for tetra- and pentamers extension is least eﬃcient in the case of a deoxycytidine in the 3′ terminal position, with the lowest signal detected for the sequences TAGAC and GATC (all sequences 5′ → 3′). In the case of trimers, the two isolated data points at the top end of the range correspond to the sequences GGG and CGG, emphasizing the preference for G in the terminal positions of eﬃcient initiators of this length. 1752 https://doi.org/10.1021/acssynbio.1c00142 ACS Synth. Biol. 2021, 10, 1750−1760 ACS Synthetic Biology pubs.acs.org/synthbio Research Article Figure 2. Fluorescent signal intensities after background subtraction for ﬁve diﬀerent types of initiating strands. The structure of the corresponding dimer (monomer for HEG), immobilized to the surface is illustrated. For each type and length of oligonucleotide strands, the 0th, 25th, 75th, and 100th intensity percentiles are shown, based on all possible 4n data points for each initiator strand of length n. Hexaethylene glycol n-mers are plotted with dots. The greyed-out insert for 5′-OH D-DNA, 5′-OH L-DNA, and HEG shows this lower range of signal intensity in more detail. The two data points at the low end of the sigmoidal curve represent results for ACC and TGC, with the corresponding consensus sequence again clearly showing that a terminal C is not favored for extension. Investigation of the eﬀect of strand length is shown in Figure 3b, where the average signal intensity of all sequences of a speciﬁc length are normalized to the average signal Independent of intensity of monomers. nucleotide composition, the results show that the eﬃciency initiation increases with strand length, with pentamers of facilitatingon average2.2× higher initiation eﬃciency compared to monomers. Applying a second order polynomial ﬁt as guide suggests elongation eﬃciency asymptotically approaches a maximum, hinting that increasing initiating strand length further may not signiﬁcantly improve average eﬃciency. Still, the wide range of signal intensities detected for each length emphasizes even more the impact of nucleotide composition. 2′OMe-RNA 3′-OH Extension. Investigation of enzymatic extension of short strands of 2′OMe-RNA with a terminal 3′ hydroxy group synthesized on the surface clearly shows that TdT is able to use it as a substrate, albeit at lower eﬃciency than its D-DNA counterpart. In comparison to 3′-OH D-DNA, TdT exhibits distinct preferences for sequence composition in 2′OMe-RNA initiator strands, as shown in Figure 4a. Indeed, the nucleobase in the terminal position of the strand and the impact on the eﬃciency of adjacent one have a major extension, with adenine and cytidine nucleotides being favored in the terminal position when next to adenine or guanosine nucleotides. In contrast, both guanosine and uracil nucleotides at the 3′ terminus have a negative impact on the eﬃciency of strand extension. Of note, we found that extension of a 2′OMe uracil nucleotide is disfavored in almost all cases, including for mono- and dinucleotides. Comparison of the eﬃciency of initiation based on strand length shows a signiﬁcant leap from monomers to pentamers (Figure 4b). The second order 1753 https://doi.org/10.1021/acssynbio.1c00142 ACS Synth. Biol. 2021, 10, 1750−1760 Figure 1. Schematic representation of the experimental design and assays. (1) Two variants of all possible permutations of mono- to pentamers, either with accessible terminal hydroxy group (OH) or with DMTr-blocked terminus (×), were synthesized on a glass slide via photolithography. (2) The immobilized initiator strands were then extended enzymatically by TdT using dTTP as substrate, generating dT homopolymers. (3) Poly dT strands were detected via hybridization with a Cy3 labeled complementary probe. (4) Scanning of the microarray allows for ﬂuorescent signal intensities at diﬀerent positions to be assigned to speciﬁc sequences. The scan to the left corresponds to 2.4% of the total synthesis area (scale bar 300 μm). In more detail, the close-up of 16 features (scale bar 100 μm) and the corresponding layout beneath are shown with a grid next to it, indicating the sequences synthesized at speciﬁc positions. Features with blocked termini (×) exhibit much lower ﬂuorescence signal intensity than those with strands accessible for extension. TdT model adapted from PDB: 1JMS. ACS Synthetic Biology pubs.acs.org/synthbio Research Article Figure 3. Analysis of extension of 3′-OH D-DNA initiating strands. (a) Fluorescent signal intensities were normalized to the maximum and clustered according to length, with representative SEM error bars. Panels to the left illustrate sequence patterns (5′ → 3′ direction) from the data for the 10% highest signal intensities framed in green, whereas panels to the right show the data for the 10% lowest signal intensities for penta-, tetra-, and trimers (top to bottom) framed in red. Data for pentamers are repeated in gray in the subsequent plots for comparison. For monomers and dimers, data are plotted from highest to lowest signal intensity with the corresponding sequence speciﬁed by the labeling of the top and bottom x-axis for dimers and monomers, respectively. Next to this plot, the chemical structure of a dimer immobilized to the glass surface serves as a guide for straightforward identiﬁcation of diﬀerences between the chemical variants tested for initiation in this and subsequent ﬁgures. (b) Fluorescent signal intensities normalized to the average of all monomers and clustered according to strand length. Averages for each strand length are indicated by an “×”. The dotted second order polynomial ﬁt through the averages serves as a visual guide. polynomial ﬁt to the average signal of all sequences of a speciﬁc length levels oﬀ for tetramers and pentamers, suggesting a close-to-maximum eﬃciency already for initiating strands with ﬁve nucleotides in length. In comparison to the D-DNA (3′- OH) substrate, the position of the average signal intensity relative to the range of signal observed for longer initiators is striking. The distribution of data points clearly indicates that most of the sequences are being extended with low eﬃciency, keeping the average eﬃciency of initiation of pentamers at a level of approximately 4× compared to monomers, whereas some outstanding variants even show initiating eﬃciencies of more than 10× that of monomers. D-DNA 5′-OH Extension. In order to investigate if 5′-OH DNA extension is possible, the TdT reaction mix was applied to a microarray populated only with D-DNA tethered to the surface at the 3′ end and with a terminal 5′-OH. In this case, only very low ﬂuorescent signal intensities were detected (see Figure 4. Extension analysis of 2′OMe-RNA initiating strands with terminal 3′-OH. (a) Fluorescent signal intensities were normalized to the maximum signal detected and clustered according to their length, with representative error bars corresponding to 2× SEM for better visibility. Panels to the left illustrate sequence patterns (5′ → 3′) emerging from the data for the 10% highest signal intensities (framed in green), whereas panels to the right show the data for the 10% lowest signal intensities framed in red. Data for pentamers are also shown in the following plots for comparison, pointing to the similarity in shape between graphs for diﬀering strand lengths. For monomers and dimers, data are plotted from highest to lowest signal intensity with the corresponding sequence speciﬁed on the labeling of the top and bottom x-axis for dimers and monomers, respectively. Next to this plot, the chemical structure of a dimer immobilized to the glass surface serves as a guide for identiﬁcation of diﬀerences between the chemical variants initiation. (b) Fluorescent signal intensities normalized to the average of all monomers and clustered according to strand length. Averages for each strand length are indicated by “×”. A polynomial ﬁt through the averages serves as a visual guide. tested for Figure 2). The average values for all sequence permutations and for lengths between monomers and pentamers were below the limits of detection (determined using the data for DMTr- capped strands as unextendable controls), indicating that strands of D-DNA with terminal 5′ hydroxy group are not suitable substrates for extension with TdT. L-DNA 5′-OH Extension. Our recent report establishing photolithographic in situ synthesis for mirror-image DNA (L- DNA)46 motivated us to investigate the activity of TdT on this non-natural substrate. Surprisingly, we indeed were able to lower than for 3′-OH detect signiﬁcant extension, albeit terminated D-DNA (Figure 2). The sigmoidal curves generated to show the distribution of ﬂuorescent signal in order intensities among all sequence permutations of equal length in Figure 5a cover a considerable range, indicating that the sequence of the initiating strand has a critical impact on the 1754 https://doi.org/10.1021/acssynbio.1c00142 ACS Synth. Biol. 2021, 10, 1750−1760 ACS Synthetic Biology pubs.acs.org/synthbio Research Article intensity. Comparing the average signal intensities for each initiator length with one another in Figure 5b once again emphasizes the signiﬁcant increase in the eﬃciency of initiation with strand length. A second order polynomial ﬁt to the average serves as a visual guide and suggests a maximum eﬃciency of initiation for strands approximately ﬁve nucleo- tides in length. On average, signal intensities for pentamers are 4.3× higher than for monomers. However, a few isolated data points at the top end of the range show that the eﬃciency of initiation is strongly inﬂuenced by the L-DNA sequence, as initiating eﬃciencies for individual pentamers can be up to 20× higher than for the monomer average. Hexaethylene Glycol Extension. In order to assess the ability of TdT to act on primary hydroxy groups of non- nucleosidic substrates, microarrays with strands of hexa- ethylene glycol (molecular structure shown in Figure 6a), Figure 5. Analysis of 5′-OH L-DNA strand extension data. (a) Fluorescent signal intensities were normalized to the maximum and grouped by length, with representative error bars corresponding to 2× SEM for better visibility. Panels framed in green illustrate sequence patterns for the 10% highest signal intensities, whereas panels framed in red show the data for the 10% lowest. Pentamer data are repeated in subsequent plots for comparison, pointing to the similarity in shape between graphs for diﬀering strand lengths. For monomers and dimers, data are plotted from highest to lowest signal intensity with the corresponding sequence speciﬁed on the labeling of the top and bottom x-axis for dimers and monomers, respectively. Next to this plot, the chemical structure of a dimer on the glass surface serves as a guide for identiﬁcation of diﬀerences between the chemical variants tested for initiation. (b) Fluorescent signal intensities normalized to the average of all monomers and clustered according to strand length. Averages for each strand length are indicated by “×”. The dotted line is a second order polynomial ﬁt through the averages. eﬃciency of extension. Analysis of nucleotide composition of the L-DNA initiator strands unambiguously shows a strong preference for L-dT at both the 5′-OH terminus and at the adjacent position for TdT extension for all initiator lengths investigated. In contrast, the identities of nucleotides more from the site of extension are mostly irrelevant. distant Interestingly, poorly extended substrates fall into the same low ﬂuorescence regardless of primer length, as range of indicated by overlapping the greyed-out curve for pentamers with data points of tetramers and trimers. Whereas short strands do not allow for considerable extension, with signal intensities for monomers on average being hardly above the limit of detection, thymine is the favored nucleobase even in this context. Exceptionally high signal intensities compared to other sequences of the same length were measured for the pentamers TTAAA, TTAAG, and TTAAT, the tetramers TTAA and TTAT, and the trimers TTT, TTC, and TTA (all 5′ → 3′), as illustrated by their prominent positions as individually discernible data points at maximum signal Figure 6. Extension of hexaethylene glycol strands. (a) Molecular structure of a single HEG unit. (b) Fluorescent signal intensities, normalized to maximum signal detected for dimers, show a decreasing trend with increasing number of HEG units in the initiating strand (error bar representative for SEM). ranging from one to nine units in length, were synthesized. these initiator strands were extended by the Surprisingly, enzyme, with ﬂuorescence signals clearly above the LOD and in the same range as for 2′OMe-RNA and L-DNA (Figure 2). Investigating the dependence of ﬂuorescent signal intensities as a function of initiating strand length hints at shorter strands being extended more eﬃciently than longer ones (Figure 6b). DNA extension with TdT has been studied for over 60 years, but surprisingly few speciﬁc details have been established regarding the initiator preferences of this unique polymerase. Particularly in the context of de novo DNA synthesis, these preferences are crucial to developing a practical and eﬃcient approach competitive with phosphoramidite chemistry. Recent eﬀorts in this ﬁeld have used initiator strands 20 to 60 nt in length and of heterogeneous nucleobase composition. Although earlier research has shown that short TdT initiators are also functional, a lack of information on the initiator length dependence for TdT polymerization eﬃciency may have contributed to the choice of very long initiators. The crystal structure of murine TdT indicates that only three nucleotides at the 3′-hydroxy end are ordered within the polymerase, whereas additional ones are outside the polymerase and disordered.52 This along with the 3 nt minimum initiator length indicated by Kato et al.24 suggests that any beneﬁt to longer initiators would be due to more indirect mechanisms 1755 https://doi.org/10.1021/acssynbio.1c00142 ACS Synth. Biol. 2021, 10, 1750−1760 ACS Synthetic Biology pubs.acs.org/synthbio Research Article such as 1D diﬀusion along the strand facilitating the localization of TdT to the 3′-hydroxy end. While 1D diﬀusion along DNA has been identiﬁed for the T7 RNA polymerase,53 there is no evidence of a similar process for DNA polymerases in the absence of accessory sliding clamp factors.54 Although our data only extends to pentamers, it clearly shows that initiators longer than about 5 nt are unlikely to signiﬁcantly enhance TdT polymerization eﬃciency. This is true for TdT’s natural substrate, 3′-hydroxy terminated DNA, for which we observe polymerization eﬃciency ﬂattening beyond an initiator length of 4 nt (Figure 3b), as well as for the non-natural substrates 2′OMe-RNA and 5′-hydroxy terminated L-DNA (Figures 4b and 5b). On the short end of initiator length, we were able to observe signiﬁcant polymerization for both monomers and dimers. This observation stands in contrast to the 3 nt lower limit of Kato et al. However, in our experiments these short DNA strands are linked to relatively long hexaethylene glycol strands, which themselves can function as initiator strands. For 2′OMe-RNA, lower eﬃciencies of TdT extension were expected considering earlier reports of RNA primers not being extended, neither with dNTPs nor with rNTPs.38 Extension of a DNA primer with rNTPs showed an upper limit of 3−4 added nucleotides,26 leading to the hypothesis that the enzyme stops extension as soon as the initiator strand transitions from DNA to RNA. Comparing these reports with our own results, we observe that methylated RNA analogues can indeed be extended. Since a minimum extension length of seven dT nucleotides are necessary to provide a detectable hybridization signal with the rA18-Cy3 probe, our data show that the initiating strand must have been extended by at least seven dT nucleotides. Considering the additional steric hindrance from the 2′-methyl compared to unmodiﬁed RNA, the extension of 2′OMe-RNA with bulkier methyl groups suggests a more complex gating mechanism. Since 2′OMe-RNA, HEG, and L- initiating strands, DNA are functional, albeit whereas 5′-OH extension of D-DNA does not occur, the results suggest that TdT has evolved to exclude this last substrate rather than to be highly speciﬁc for 3′-OH DNA extension. ineﬃcient Regarding the activity of TdT on mirror-image DNA substrates, only a few reports exist. Already in 1995, Focher et al.55 demonstrated the ability of calf thymus terminal transferase to extend a dT20 primer of D-DNA (with a blocked 5′ terminus) upon addition of L-dTTP. However, extension stopped after 1−2 nt, indicating that this short stretch of L- DNA with a terminal 3′-OH is not a functional initiator. Another study on the extension of a D-DNA primer with a single L-dT incorporation at the 3′ end showed the extension using D-dNTPs is aborted after 1−2 nt. The authors speculated that a distortion of orientation initiated by presence of the L- nucleotide could result in termination of extension.37 However, all these investigations focus on extension of oligonucleotides with a terminal 3′ hydroxy group in solution. In contrast, the present study used L-DNA phosphoramidites in 3′ → 5′ synthesis direction using pure 5′-NPPOC 3′-L phosphorami- dites, resulting in strands immobilized to the surface and with an accessible terminal 5′ hydroxy group. In this context, comparing the results with those for the corresponding 5′-OH D-DNA initiator strands is especially surprising. As shown in the inset in Figure 2, signal intensities for hybridization after applying TdT to L-DNA initiator strands were signiﬁcantly higher relative to the corresponding 5′-OH D-DNA initiators, which were simply not extended at all, also indicating the absence of D-DNA contamination in the L-DNA building blocks. We surmise that the structural diﬀerences between D- and L-DNA play a role in the mirror-image form acting as a potential substrate. The left-handed conformation of L-DNA prevents not only hybridization to D-DNA, but also interaction with L-enzymes in the active center.56,57 Since the structure of D-DNA oligonucleotides with a 5′ terminal hydroxy group did not prove suitable as a substrate for extension, the conforma- tional change to its mirror-image pendant seems to represent the variation required to ﬁt the active center of the polymerase and allow for strand extension, albeit with much lower eﬃciency than at the 3′-OH of D-DNA substrates. Interaction of mirror-image DNA oligonucleotides with a natural DNA polymerase has been reported recently, however, with a substantial diﬀerence in location of the binding site compared to D-DNA.58 To the best of our knowledge, this is the ﬁrst report of a native DNA polymerase in L-conformation showing cross-chiral activity via catalysis of a reaction on a mirror-image DNA substrate, thereby generating chimeric L-/D-DNA strands. TdT was found to preferentially extend L-DNA strands featuring a thymidine residue at the 5′ terminus, and eﬃciency of initiation was enhanced considerably compared to extension of monomers by increasing the length of strands with one or more terminal T nucleotides. Given the enhanced intracellular stability of mirror-image oligonucleotides,59 their potential as drug delivery vehicles in the form of micelles generated via TdT-mediated extension of L-DNA aptamers is an alluring prospect.10 That TdT can elongate even short initiator sequences is of major importance for enzymatic de novo synthesis of DNA since any initiator must be either removed after synthesis or chosen to match the 5′ end of the desired sequence. Presumably, any initiator must be synthesized chemically, negating many beneﬁts of enzymatic synthesis, at least for longer initiators. Fortunately, short initiators work reasonably well, such that in the manner of current solid phase synthesis of DNA, synthesis columns preloaded with the ﬁrst 5′ nucleoside on a long and cleavable linker could be used. The elongation eﬃciency of monomers is about a third of that of pentamers, thus requiring longer initial cycles until a more optimal length is reached. The extension of non-natural initiators such as HEG and L-DNA by TdT could also be used as a workaround; even retained as a 5′ extension to the desired DNA sequence, these initiators are largely bio-orthogonal and would not interfere in many downstream applications, or could potentially be selectively removed chemically or enzymatically after synthesis. Nevertheless, the demonstrated success of nucleotide monomer initiators for de novo TdT synthesis seems more useful in most contexts. The use of alternative initiator chemistries still supports the possibility to use TdT to create mixed nucleic acid chimeras, particularly since several non-DNA nucleoside triphosphates have been found to be accepted by TdT.11,27,28,30−32 The strong sequence dependence of TdT initiator extension is a potential complication in TdT-based de novo synthesis. Crystallographic studies of murine TdT indicate that three consecutive nucleotides are at well-deﬁned positions within the polymerase,52 suggesting that TdT processivity is potentially sensitive to the identity of the last three bases, but unlikely to be signiﬁcantly aﬀected by further upstream bases. This hypothesis is largely conﬁrmed by our data. Consensus logos for the 3′-hydroxy DNA initiators extended most eﬃciently by 1756 https://doi.org/10.1021/acssynbio.1c00142 ACS Synth. Biol. 2021, 10, 1750−1760 ACS Synthetic Biology pubs.acs.org/synthbio Research Article Table 1. Results Summary Regarding Sequence and Length Dependence of Initiation Eﬃciency on Oligonucleotide Extension a with TdT Polymerase and dTTP for Various Types of Initiator Chemistries sequence motifs (5′→3′) for 10% highest/lowest signal most/least eﬃciently extended substrate dimers trimers tetramers pentamers highest lowest highest G _ T _ _ _ _ G G _ _ T/C _ G/A C/A _ _ _ _ _ _ _ C _ _ G/A C/A _ _ _ _ _c _ _ _ _ C _ _ _ G/A C/Ac sequence TTCAT TAGAC GGUGC corresponding normalized signald 1.000 0.315 0.264 normalized average signalb D-DNA 3′-OH 2′OMe-RNA 3′- OH L-DNA 5′-OH 0.652 0.086 0.021 lowest highest lowest n.s. n.e. U _ T _ _ _ n.s. n.e. U U U/G T T _ _ G _ n.s. n.e. _ _ U G/U T T _ _ A G _ _ n.s. n.e. _ _ U U G/U T T _ _ _c A G G _ _ n.s. n.e. UGUUG TTAAA AGT (HEG)2 n.e. 0.018 0.102 0.004 0.125 n.e. 0.083 0.002 HEG D-DNA 5′-OH a“_”, no distinct nucleotide occurring at higher frequency at this position; n.s., no sequence dependence; n.e., no extension. bFluorescent signal intensities averaged over all lengths and sequences, then normalized to highest signal intensity (1 = D-DNA 3′-OH “TTCAT”). cInitiator length showing highest ﬂuorescent signal for extension. dFluorescent signal intensity of best or worst sequence for initiation, respectively, normalized to highest signal intensity. TdT include only the last three 3′ bases for both the pentamers and the tetramers, and the last two 3′ bases in the case of the trimers (Figure 3a). In the case of the most poorly extended initiators, a consensus only appears for the terminal 3′ base for the pentamers and tetramers, whereas there is a small contribution from the second nucleobase in the case of the the 2′O-methyl-RNA and L-DNA trimers. In the case of initiating strands, again only two or three bases adjacent to the 3′-hydroxy end contribute signiﬁcantly, either positively or to the polymerase extension eﬃciency. For negatively, extension of TdT’s natural substrate, we found a 3-fold range in eﬃciency between the best and worse initiator sequences for pentamers, with the worst sequences resulting in polymer- ization yields similar to the average values obtained for monomer extension, about 2.5-fold lower than the average for pentamers. Poorly extended pentamers are characterized by a 3′ cytosine, whereas the more optimal initiators are less well- deﬁned but are generally missing cytosines in the two terminal positions. Very similar trends are apparent for tetramers, and for trimers the pattern is less well-deﬁned, but guanines in the ﬁrst two 3′ positions and cytosine or thymine at the 3′ are correlated with best and worse extension, respectively. For monomers and dimers, the reduced number of possible initiators and the smaller range between the best and worse initiators prevents a similar sequence assessment. In the case of 2′OMe-RNA initiating strands, we measured a ∼12-fold range in initiator extension eﬃciency between the best and worst pentamer sequences. For tetramers, trimers and dimers, the range decreases with length but is far larger than for 3′-hydroxy D-DNA initiators of the same length (Figure 4). Only in the case of the monomers is the eﬃciency largely independent of nucleobase identity. This strong sequence dependence results in well-deﬁned consensus sequence logos. The nucleobases immediately adjacent to the 3′ terminus are consistently cytosines and adenosines for the best initiators and guanines and uracils for the worst initiators. That the sequence dependence for 2′OMe-RNA is completely diﬀerent from that of D-DNA is not surprising given that the methoxy group must substantially alter the conformation of the initiator within TdT, such that, apparently, only sequences with the rather speciﬁc pattern revealed by the consensus logos are able to function as a substrate for polymerization. As for 2′OMe-RNA, TdT is also able to extend the 5′ hydroxy of L-DNA with low but clearly measurable yield. Similarly, the sequence-dependent range of extension eﬃciency is very large, about 20-fold, and associated with speciﬁc sequence patterns. Better initiators share a pair of terminal thymines, whereas the worse initiators omit this base in these positions and instead favor adenine and guanine. Since this substrate is the wrong end of the enantiomorph of the natural substrate of TdT, it appears that the polymerase is rather unspeciﬁc and will add nucleotides to many hydroxy-bearing molecules that ﬁt within its binding site. This hypothesis is supported by the extension of hexaethylene glycol, which has little resemblance to single-stranded DNA other than ﬂexibility and a terminal hydroxy group. Figure 6 clearly indicates ﬂuorescent intensity, corresponding to extension eﬃciency, reaching a maximum for two linked HEG molecules. For the cases of both one and two HEG units, the extension eﬃciency is greater than for any of the substrates except the natural DNA substrate and the ∼10% best 2′OMe-RNA initiators. We attribute the loss of eﬃciency with further extension to the primary alcohol becoming less accessible within a polyethylene glycol tangle. signal By comparing absolute signal intensities (Figure 2) for the diﬀerent chemistries and averaged values across all sequence variations (summarized in Table 1), D-DNA with available 3′- OH represents the most eﬃcient polymerization initiator. The data shown in Table 1 indicate that the extension of even the poorest initiator sequence made of 3′-OH D-DNA remains a better primer substrate. These diﬀerences should be taken into account when considering nonstandard initiators. In such cases, the reaction conditions should be adapted, with for instance longer reaction times or an increase of TdT concentration. than any other type of ■ CONCLUSIONS Our study brings important new information to the activity spectrum of TdT polymerase. In addition to its already well- described broad range of acceptance for diﬀerent types of modiﬁed (d)NTPs and their analogues, we show here its ability to extend other types of initiators as well. Although the natural substrate of TdT, the 3′ terminus of DNA, clearly outperforms 2′OMe-RNA (3′-OH), L-DNA (5′-OH), and in enzymatic extension eﬃciency, that hexaethylene glycol 1757 https://doi.org/10.1021/acssynbio.1c00142 ACS Synth. Biol. 2021, 10, 1750−1760 ACS Synthetic Biology these initiators are extended at all is remarkable. With the investigation of sequence dependence on the eﬃciency of extension, and the detection of initiation of extension even for single nucleotides, our results open up new opportunities for decoupling approaches for enzymatic de novo synthesis from chemical synthesis of DNA and illustrate substrate diversity coexisting with sequence speciﬁcity for the template- independent TdT polymerase. ■ ASSOCIATED CONTENT sı Supporting Information The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acssynbio.1c00142. Description of SI contents (PDF) Data S1: Fluorescent intensity data of all experimental data in spreadsheet format (XLSX) Data S2: Layout design ﬁle with the location and identity of all probes for the HEG arrays (TXT) Data S3: Layout design ﬁle with the location and identity of all probes for the oligonucleotide arrays (TXT) Data S4: High resolution ﬂuorescent scan data for the 2′OMe-RNA data (TIF) Data S5: High resolution ﬂuorescent scan data for the D- DNA 3′-OH extension data (TIF) Data S6: High resolution ﬂuorescent scan data for the D- DNA 5′-OH extension data (TIF) Data S7: High resolution ﬂuorescent scan data for the HEG extension data (TIF) Data S8: High resolution ﬂuorescent scan data for the L- DNA 5′-OH data (TIF) ■ AUTHOR INFORMATION Corresponding Author Mark M. Somoza − Institute of Inorganic Chemistry, Faculty of Chemistry, University of Vienna, 1090 Vienna, Austria; Chair of Food Chemistry and Molecular Sensory Science, Technical University of Munich, 85354 Freising, Germany; Leibniz-Institute for Food Systems Biology at the Technical University of Munich, 85354 Freising, Germany; orcid.org/0000-0002-8039-1341; Email: mark.somoza@ univie.ac.at Authors Erika Schaudy − Institute of Inorganic Chemistry, Faculty of Chemistry, University of Vienna, 1090 Vienna, Austria; orcid.org/0000-0002-2803-6684 Jory Lietard − Institute of Inorganic Chemistry, Faculty of Chemistry, University of Vienna, 1090 Vienna, Austria; orcid.org/0000-0003-4523-6001 Complete contact information is available at: https://pubs.acs.org/10.1021/acssynbio.1c00142 Author Contributions M.M.S. and E.S. conceived the study. E.S. performed the experiments and analyzed the data. E.S. and M.M.S. wrote the manuscript. E.S., M.M.S., and J.L. discussed the results and carefully revised the manuscript. Notes The authors declare no competing ﬁnancial interest. pubs.acs.org/synthbio ■ ACKNOWLEDGMENTS Research Article The authors thank Orgentis GmbH for the synthesis of D-DNA phosphoramidites and ChemGenes for L-DNA, 2′OMe-RNA, and HEG monomers. This work was supported by the Austrian Science Fund (FWF P30596) and the Faculty of Chemistry of the University of Vienna. ■ REFERENCES (1) Bollum, F. J. (1960) Calf Thymus Polymerase. J. Biol. Chem. 235, 2399−2403. (2) Fowler, J. D., and Suo, Z. (2006) Biochemical, Structural, and Physiological Characterization of Terminal Deoxynucleotidyl Trans- ferase. Chem. Rev. 106, 2092−2110. (3) Gouge, J., Rosario, S., Romain, F., Beguin, P., and Delarue, M. (2013) Structures of Intermediates along the Catalytic Cycle of Terminal Deoxynucleotidyltransferase: Dynamical Aspects of the Two-Metal Ion Mechanism. J. Mol. Biol. 425, 4334−4352. (4) Desiderio, S. 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10.1088_2632-2153_ad0e17.pdf	Data availability statement All data that support the findings of this study are included within the article (and any supplementary files).	Data availability statement All data that support the findings of this study are included within the article (and any supplementary files).	OPEN ACCESS RECEIVED 16 May 2023 REVISED 6 November 2023 ACCEPTED FOR PUBLICATION 20 November 2023 PUBLISHED 28 November 2023 Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author (s) and the title of the work, journal citation and DOI. Mach. Learn.: Sci. Technol. 4 (2023) 045039 https://doi.org/10.1088/2632-2153/ad0e17 PAPER Artificial intelligent identification of apatite fission tracks based on machine learning Zuoting Ren1, Shichao Li1,2,∗, Perry Xiao3, Xiaopeng Yang1 and Hongtao Wang1 1 College of Earth Sciences, Jilin University, Changchun 130061, People’s Republic of China 2 Key Laboratory of Mineral Resources Evaluation in Northeast Asia, Ministry of Natural Resources, Changchun 130061, People’s Republic of China 3 School of Engineering, London South Bank University, London SE1 0AA, United Kingdom ∗ Author to whom any correspondence should be addressed. E-mail: lsc@jlu.edu.cn Keywords: apatite fission track, OpenCV cascade classifier, TensorFlow object detection API, precision, recall, F1-Score Abstract Over the past half century, apatite fission track (AFT) thermochronometry has been widely used in the studies of thermal histories of Earth’s uppermost crust. The acquired thermal histories in turn can be used to quantify many geologic processes such as erosion, sedimentary burial, and tectonic deformation. However, the current practice of acquiring AFT data has major limitations due to the use of traditional microscopes by human operators, which is slow and error-prone. This study uses the local binary pattern feature based on the OpenCV cascade classifier and the faster region-based convolutional neural network model based on the TensorFlow Object Detection API, these two methods offer a means for the rapid identification and measurement of apatite fission tracks, leading to significant improvements in the efficiency and accuracy of track counting. We employed a training dataset consisting of 50 spontaneous fission track images and 65 Durango standard samples as training data for both techniques. Subsequently, the performance of these methods was evaluated using additional 10 spontaneous fission track images and 15 Durango standard samples, which resulted in higher Precision, Recall, and F1-Score values. Through these illustrative examples, we have effectively demonstrated the higher accuracy of these newly developed methods in identifying apatite fission tracks. This suggests their potential for widespread applications in future apatite fission track research. 1. Introduction The fission track method (Silk and Barnes 1959, Price and Walker 1962, Fleischer et al 1965) has been applied to resolve many geological problems such as determining the thermal history of sedimentary basins (Gleadow et al 1986, Emmel et al 2014), the timing of fault activities (Roden-Tice and Wintsch 2002, Abbey and Niemi 2018), source-sink coupling during mountain building processes (Ruiz et al 2004, Chen et al 2020), uplift histories of orogenic belts (He et al 2018, Bonilla et al 2020, Wang et al 2023), regional tectonic evolution (Grist and Zentilli 2003), and mineralization (Chakurian et al 2003). In the 1980s and 1990s, the apatite fission track dating method has been greatly improved after the introduction of ζ age parameter reference, the performance of annealing experiments, the development of annealing models, and the quantification of apatite multivariate kinetic annealing processes (Hurford and Green 1983, Laslett et al 1987, Green et al 1989, Vrolijk et al 1992). The fission track dating method is a valuable tool in geology as it provides information not only on the age of a geologic event but also on the thermal history of a sample. For instance, fission track lengths can be used to determine the age and cooling rate of an orogenic uplift event. The thermal history of orogenic development can also be modeled using software like HeFty and QtQt (Vermeesch and Tian 2014). There are several methods for fission track identification and counting including the external-detector method, subtraction method, re-etching method, and re-polishing method. Currently, the external detector method is © 2023 The Author(s). Published by IOP Publishing Ltd Mach. Learn.: Sci. Technol. 4 (2023) 045039 Z Ren et al the most widely used method for fission track thermochronology. This method determines the fission track age by counting the fossil fission tracks in minerals and induced-fission tracks in external detectors. In the present study, fission tracks were obtained using the external detector method. The procedure involved collecting rock samples, isolating apatite crystals, creating thin sections, etching the sections, irradiating the samples with thermal neutrons, processing post-irradiation, and counting the number and distribution of fission tracks. Determining the number and length of fission tracks may be the final step in the dating procedure, but it is also the most important and challenging step. This work forms the foundation for subsequent studies such as dating and thermal history simulation. Traditionally, fission track statistics were obtained through manual observations using a microscope to count the track lengths. This process can be time-consuming for the operator and prone to counting errors, leading to a low efficiency of fission track identification. To improve efficiency, technology with automatic recognition capabilities is necessary. The initial investigations into the automatic recognition of fission tracks utilized image morphology analysis (Petford et al 1993, Gleadow et al 2009). This approach entailed the conversion of the transmitted light-reflected light image of fission tracks into a binary image followed by using a threshold segmentation procedure to separate the two images. The overlapping features in the two binary images were then extracted and used for recognition. In recent years, machine learning has developed rapidly and has applications in many fields that work with large data sets. Many algorithms have been developed for data mining with particular applications to Earth science research (Fleming et al 2021, Recanati et al 2021, Zhang et al 2021), such as rock classification, stratigraphic analysis, earthquake prediction, landslide statistics, and geochemistry (Li et al 2018, Baraboshkin et al 2020, de Lima et al 2020, Xu et al 2021). Object detection technology is a widely researched area in machine learning. It can be divided into traditional algorithms and deep learning algorithms, with the latter being the current mainstream. Traditional object detection algorithms have been applied in image processing and face recognition, but they suffer from low efficiency and low recognition rates. The advent of regional convolution neural networks has propelled object detection technology into the deep learning era, resulting in a significant improvement in detection accuracy (Girshick 2015). In recent years, significant advancements have been made in the field of intelligent identification of apatite fission tracks, thanks to the progress in target detection technology. Researchers have achieved notable progress by employing cutting-edge techniques. For instance, Nachtergaele et al successfully developed a deep neural network that demonstrates exceptional capabilities in intelligently identifying apatite fission tracks, yielding highly accurate results (Nachtergaele and Grave 2021). Similarly, Li et al utilized a convolutional neural network (CNN) to extract semi-tracks through image semantic segmentation, thereby contributing to the study of intelligent identification methods for apatite fission tracks (Li et al 2022). These advancements highlight the promising trajectory of research in this area. This paper employs two novel object detection algorithms. The first is the TensorFlow Object Detection API based on faster region-based convolutional neural network (R-CNN) (Ren et al 2017), which integrates feature extraction, candidate area extraction, object location, and object classification into a single network, thereby enhancing recognition capabilities. We also employed the local binary pattern (LBP) feature-based OpenCV cascade classifier object detection method for comparison (Ojala et al 2000). After selecting the two approaches, we gathered and filtered the respective experimental data (photographs of apatite fission track samples). We carefully selected clear and representative photos of apatite fission track samples. These sample photos were preprocessed to establish the necessary experimental conditions for running the two methods. Subsequently, each method was individually trained while continuously adjusting the training duration, the amount of training samples, and the model parameters. The experimental results of both methods were calculated, and a comparison and discussion were conducted on their Precision, Recall, and F1-Score. 2. Data and methods Tracks that have not undergone chemical etching are referred to as latent tracks, while fission tracks that have been etched become linear grooves with defined lengths. Usually, these tracks lack a preferred orientation. However, in some cases, ‘noise’ on the etched surface of apatite, such as defects and scratches, can interfere with track identification. These challenges make the identification of fission tracks difficult. To overcome these issues, we use two newly developed methods for removing these effects. 2 Mach. Learn.: Sci. Technol. 4 (2023) 045039 Z Ren et al Figure 1. LBP schematic. 2.1. Experimental method 2.1.1. OpenCV processing image recognition OpenCV stands for open source computer vision library, which primarily uses image processing and machine learning algorithms to address related problems. Compared to other computer vision recognition libraries, OpenCV boasts several advantages, such as multiple language interfaces, cross-platform compatibility, robust development, and a plentiful API. In this paper, we use the OpenCV cascade classifier based on LBP features for our method. LBP is a tool for capturing the local texture features of an image, primarily utilized for texture feature extraction (Ojala et al 1996). It operates within a 3 × 3 window where the center pixel is used as a threshold and the grayscale values of the eight surrounding pixels are compared to it. If the peripheral pixel value is greater than the center pixel value, the pixel’s position is marked as 1; otherwise, it is marked as 0. By comparing the 8 points in the 3 × 3 neighborhood, 8-bit binary numbers are generated, which represent the LBP value of the center pixel and depict the texture information of the area (figure 1, Ojala et al 2000). The process is described by the equation: P−1∑ ( LBP(P,R) = s gp − ga ) 2p p=0 { 1x ⩾ 0 0x < 0 . s (x) = (1) (2) In the equation, gp is the gray value of the pixel on the circle with the center pixel and R as the radius, ga is the gray value of the center pixel in the corresponding local neighborhood, and P is the number of surrounding pixels points. The experiment employs the use of the cascade classifier, which is based on the LBP algorithm and provided by OpenCV. This classifier comprises many weak classifiers that are designed to classify different features of detection targets. The classifiers at each level are more complex than those at the previous level. Multiple weak classifiers work together, and different features are extracted from each window, which are then fed into different weak classifiers for judgment. If all the labels judged by the weak classifiers are positive samples, the target is detected in the smoothing window. The classifier at each layer of the cascade is trained and optimized based on the results of the previous layer. This enables negative samples to be quickly eliminated and reduces the number of misclassified samples, improving the overall classification performance without increasing the computational complexity. 2.1.2. TensorFlow object detection API processing image recognition The TensorFlow Object Detection API is a programming interface that utilizes TensorFlow to tackle object recognition issues such as real-time object identification. It boasts a reliable API, a streamlined workflow, excellent system compatibility, and highly efficient recognition capabilities. The API comprises object detection frameworks, including single shot multibox detector (SSD), region with CNN feature (R-CNN), and region-based fully convolutional network (R-FCN). For these models, integrated experiments can also be conducted by combining different feature extraction networks. Typical feature extraction networks include VGG, Inception v3, ResNet-101, Inception ResNet, etc (Al-Azzo et al 2018). The Faster R-CNN algorithm is the method employed in this study. As a typical two-stage object detection model, Faster R-CNN builds on the R-CNN and Fast R-CNN algorithms. The key difference is the use of a region proposal network (RPN) to generate candidate proposal windows. The Faster R-CNN detection process is composed of four major components: (1) a feature extraction network, which extracts features from the input image and outputs a feature map for use by the RPN and fully connected layer; (2) a 3 Mach. Learn.: Sci. Technol. 4 (2023) 045039 Z Ren et al Figure 2. Flow chart of Faster R-CNN detection. region candidate network (RPN), which generates anchor boxes and determines whether they contain an object, and also performs a bounding box regression to form a region proposal; (3) ROI Pooling Layer. Combine the feature map obtained from the first two modules with the region proposals to obtain a fixed-size proposal feature map which is then passed into the fully connected network for classification; (4) classification and regression. The proposal and feature map are used to calculate the specific class of the object, and a bounding box regression is done to obtain the exact position of the detection box (figure 2) (Ren et al 2017). When training RPN, the Anchor is divided into two categories. An anchor with a target in the box is labeled as a positive sample. An anchor without a target in the box is labeled as a negative sample. The loss function of the RPN network consists of two components, which are classification loss (Lcls) and boundary regression loss (Lreg). The equations are as follows (Ren et al 2017): L ({pi} , {ti}) = 1 Ncls ∑ i Lcls (pi, p ∗ i ) + λ 1 Nreg ∑ i ∗ i Lreg (ti, t ∗ i ) p (3) where pi represents the probability that the ith Anchor predicted by the network is the target, p∗ i represents the true value corresponding to pi. If the Anchor is positive, p∗ is 1, and if the Anchor is negative, it is 0, ti is i a vector representing the 4 parameterized coordinates of the prediction bounding box, indicating the offset between the prediction box and the Anchor box, t∗ i represents the true value corresponding to ti, indicating the offset between the true value and the Anchor box. Ncls is set to the size of the batch, and Nreg is set to the total number of Anchors, λ is the balance parameter used for the two loss functions. The faster_rcnn_inception_v2 model uses the Inception V2 network as its base network. This network is a pretrained CNN that has been trained on large-scale image data and has good feature extraction capabilities. It consists of multiple convolutional layers, pooling layers, and fully connected layers. In the Inception V2 network, advanced features of the image are gradually extracted through multiple convolution operations. Each convolutional layer slides a convolutional kernel over the input image to extract features and obtain an output feature map. As the number of layers increases, the receptive field gradually increases, allowing the model to model a larger range of image information (Ioffe and Szegedy 2015). 2.2. Data construction In this study, we utilized apatite fission tracks obtained through the external detector method. We employed two Durango samples, which are recognized as the standard in apatite fission track dating. The ages of the two samples are 31.4 ± 0.5 Ma (Wang et al 2018) and 31.02 ± 1.01 Ma (Mcdowell et al 2005), respectively. Our sample collection consisted of 20 images of Dur1 (Durango sample 1) and 60 images of Dur2 (Durango sample 2). A Zeiss microscope equipped with the Autoscan TrackWorks software, with a magnification setting of 1000, was used to generate sample images. The images of spontaneous fission tracks, with dimensions of 2048 px × 1536 px, were taken and numbered Qi (60 in total). These images were sourced from granite samples of the Qimen Tagh Range located on the northeastern margins of the Tibetan Plateau. 4 Mach. Learn.: Sci. Technol. 4 (2023) 045039 Z Ren et al Dataset Sample name Number of images Image format Image resolution Table 1. Fission track data table. Training samples Testing samples Qi Dur1 Dur2 Qi Dur1 Dur2 50 50 15 10 10 5 .jpg .jpg .jpg .jpg .jpg .jpg 2048 × 1536 2048 × 1536 2048 × 1536 2048 × 1536 2048 × 1536 2048 × 1536 The apatite fission tracks from these images dated from 58.7 ± 3.6 Ma to 239.5 ± 29.8 Ma. A total number of 50 images of Qi, 15 images of Dur1, and 50 images of Dur2 were selected as the training data images. Meanwhile, ten images of Qi, five images of Dur1, and ten images of Dur2 were used as test data images. These test samples were manually counted and compared against machine learning methods (table 1). 3. Experimental design Object detection is a subcategory of image recognition. In image recognition, the goal is to identify different objects present in an image, while in object detection, not only do the objects need to be recognized but also their specific location must be identified. Conducting experiments on object detection requires image labeling of the experimental data, where the objects to be recognized are assigned specific labels to distinguish them from other objects in the image. In this experiment, two methods were used, and the same training sample was used for both methods. However, the data processing methods were different. In the first method, which used OpenCV’s cascade classifier, the entire training image was cut into positive and negative samples, and this was used for data processing. In contrast, for the second method, which used TensorFlow Object Detection API, Labelimg software was used to label the entire image, and the fission track was divided into two labels: opaque and transparent. 3.1. OpenCV cascade classifier for image processing The experiment employed OpenCV’s cascade classifier to preprocess the images. The primary step in data preprocessing involved uniformly cutting the parts of the large image containing tracks into 40 × 40 pixel images and then converting them to grayscale to be used as positive samples (figure 3). The images without fission tracks were also cropped and utilized as negative samples. The sample size is specified below (table 2). The objective of creating positive and negative sample description files with corresponding samples is to produce vectorized data. The opencv_createsamples.exe program, which is part of OpenCV, can be used to create a vectorized positive sample set (.vec file) from the positive samples and their description files. The subsequent step involves utilizing OpenCV’s training classification tool (opencv_traincascade.exe) for the classification training. Upon completion of the training, a final model (cascade.xml) will be generated. Finally, the trained cascade classifier is employed for detecting the target of apatite fission track images. 3.2. TensorFlow object detection API for image processing The TensorFlow Object Detection API simplifies image preprocessing compared to the OpenCV cascade classifier, as it does not require the preparation of positive and negative samples. Instead, it is necessary to annotate the apatite fission track images. During experiments, the tracks exhibit diverse shapes, so to ease training, we categorize the training samples into two groups based on the transparency of the tracks: transparent and opaque (figure 4). We calculate the results of the two labels in a unified way in the final result testing stage. The training sample used is based on the data shown in the training set in table 1. The marking data quantity of opaque and opaque labels is shown in the following table (table 3). Labelimg is a graphical tool for image annotation, which converts label information into XML files for storage and exchange. The datasets were annotated manually using Labelimg by drawing bounding boxes around the targets and labeling them accordingly. Upon completion of the annotation, an XML file is generated automatically. However, to be compatible with TensorFlow’s operating environment, it is necessary to convert the XML file to a CSV file and then to a TensorFlow-compatible TFRECORD file. After the conversion is completed, the parameter setting and training of the model can be performed. 5 Mach. Learn.: Sci. Technol. 4 (2023) 045039 Z Ren et al Figure 3. Positive sample (A) negative sample (B). Table 2. OpenCV positive and negative sample data table. Sample name Positive samples Negative samples Number 1000 2000 Figure 4. Examples of labels. Table 3. TensorFlow object detection API label sample data table. Sample name Opaque samples Transparent samples Number 5200 800 Table 4. The important hyperparameters of TensorFlow object detection API. Name Num_classes Batch_size Initial_learning_rate Num_steps Eval_config:{num_examples} Eval_config:{max_evals} Number 2 1 0.0002 100 000 57 10 The training model used in this experiment is faster_rcnn_inception_v2. Using the TensorFlow-GPU version, compared to the TensorFlow-CPU version, the GPU operation will process graphics and images faster, shortening the training time. The important parameter settings in this training, are the batch size (the number of training samples at a time) is set to 10, and the training steps are set to 100 000. The important hyperparameters settings in this training are as follows (table 4): 6 Mach. Learn.: Sci. Technol. 4 (2023) 045039 Z Ren et al Table 5. OpenCV test result table. OpenCV Xa Xm TP FP FN Precision Recall F1-Score 23 29 16 23 29 35 27 39 7 28 27 24 28 26 23 30 44 34 37 41 31 39 29 34 41 17 25 16 18 19 24 19 29 6 22 23 22 22 22 18 20 30 21 27 32 20 27 20 24 31 6 4 0 5 10 11 8 10 1 6 4 2 6 4 5 10 14 13 10 9 11 12 9 10 10 5 9 5 5 6 8 9 10 3 5 5 7 1 9 5 5 15 2 10 11 8 11 6 7 8 Image1 Image2 Image3 Image4 Image5 Image6 Image7 Image8 Image9 Image10 Image11 Image12 Image13 Image14 Image15 Image16 Image17 Image18 Image19 Image20 Image21 Image22 Image23 Image24 Image25 Average 22 34 21 23 25 32 28 39 9 27 28 29 23 31 23 25 45 23 37 43 28 38 26 31 39 Qi Dur1 Dur2 All 73.9% 86.2% 100.0% 78.3% 65.5% 68.6% 70.4% 74.4% 85.7% 78.6% 85.2% 91.7% 78.6% 84.6% 78.3% 66.7% 68.2% 61.8% 73.0% 78.0% 64.5% 69.2% 69.0% 70.6% 75.6% 78.1% 83.7% 69.7% 75.9% 77.3% 73.5% 76.2% 78.3% 76.0% 75.0% 67.9% 74.4% 66.7% 81.5% 82.1% 75.9% 95.7% 71.0% 78.3% 80.0% 66.7% 91.3% 73.0% 74.4% 71.4% 71.1% 76.9% 77.4% 79.5% 74.7% 80.6% 76.2% 76.4% 75.6% 79.4% 86.5% 78.3% 70.4% 71.6% 69.1% 74.4% 75.0% 80.0% 83.6% 83.0% 86.3% 77.2% 78.3% 72.7% 67.4% 73.7% 73.0% 76.2% 67.8% 70.1% 72.7% 73.8% 77.5% 76.0% 81.7% 72.5% 75.7% 4. Experiment results The experimental results of different methods are evaluated by the same evaluation method. Evaluation indicators are an important basis for evaluating the goodness of target detection algorithms. There are many kinds of evaluation indicators, among which the more typical ones are Precision (P) and Recall (R), with the following formulas: P = R = TP TP + FP TP TP + FN . (4) (5) The true positive (TP) represents correctly identified fission tracks, the false positive (FP) represents the incorrectly identified fission tracks, and the false negative (FN) represents the fission tracks that were not identified. We used two different target detection models, thus it was necessary to choose between the two sets of Precision and Recall. Hence, we used F1-Score (F1) as a measure of the classification problem. It is the average of Precision and Recall, which integrates the results and ranges from 0 to 1, with 1 being the best and 0 the worst output of the model. The formula for the F1-Score is as follows: F1 = 2 × P × R P + R . (6) Tables 5 and 6 show the results of the OpenCV cascade classifier and TensorFlow Object Detection API, in which Image1–Image10 is sample Qi, Image11–Image15 is sample Dur1, and Image16–Image25 is sample Dur2. The number of fission tracks in artificially identified test pictures is Xa, and the number of fission tracks identified by the machine learning method is Xm. The experimental results using the OpenCV cascade classifier method are as follows (table 5). 7 Mach. Learn.: Sci. Technol. 4 (2023) 045039 Z Ren et al Table 6. TensorFlow object detection API test result table. TensorFlow object detection API Xa Xm TP FP FN Precision Recall F1-Score 17 26 10 17 18 28 22 32 9 22 28 27 23 26 20 28 43 25 37 41 30 34 24 32 40 16 24 10 15 17 26 21 32 9 21 28 27 22 26 20 24 39 23 33 38 25 32 24 28 38 1 2 0 2 1 2 1 0 0 1 0 0 1 0 0 4 4 2 4 3 5 2 0 4 2 5 8 11 8 8 6 7 7 0 6 0 2 1 5 3 1 6 0 4 5 3 6 2 3 1 Image1 Image2 Image3 Image4 Image5 Image6 Image7 Image8 Image9 Image10 Image11 Image12 Image13 Image14 Image15 Image16 Image17 Image18 Image19 Image20 Image21 Image22 Image23 Image24 Image25 Average 22 34 21 23 25 32 28 39 9 27 28 29 23 31 23 25 45 23 37 43 28 38 26 31 39 Qi Dur1 Dur2 All 94.1% 92.3% 100.0% 88.2% 94.4% 92.9% 95.5% 100.0% 100.0% 95.5% 100.0% 100.0% 95.7% 100.0% 100.0% 85.7% 90.7% 92.0% 89.2% 92.7% 83.3% 94.1% 100.0% 87.5% 95.0% 95.3% 99.1% 91.0% 94.4% 76.2% 75.0% 47.6% 65.2% 68.0% 81.3% 75.0% 82.1% 100.0% 77.8% 100.0% 93.1% 95.7% 83.9% 87.0% 96.0% 86.7% 100.0% 89.2% 88.4% 89.3% 84.2% 92.3% 90.3% 97.4% 74.8% 91.9% 91.4% 84.9% 84.2% 82.8% 64.5% 75.0% 79.1% 86.7% 84.0% 90.1% 100.0% 85.7% 100.0% 96.4% 95.7% 91.2% 93.0% 90.6% 88.6% 95.8% 89.2% 90.5% 86.2% 88.9% 96.0% 88.9% 96.2% 83.2% 95.3% 91.1% 88.8% The experimental results using the TensorFlow Object Detection API method are as follows (table 6). For the validation of the Qi sample (Wang et al 2018), a total of ten images (Image1–Image10) were used, with the manual identifications ranging from 9 to 39. The OpenCV cascade classifier had an average Precision of 78.1%, an average Recall of 74.7%, and an average F1-Score of 76.0%. The TensorFlow Object Detection API based on the Faster R-CNN algorithm had an average Precision of 95.3%, an average Recall of 74.8%, and an average F1-Score of 83.2%. For the verification of the Dur1 sample (Wang et al 2018), a total of five images (Image11–Image15) were used, with manual identifications ranging from 23 to 31. The OpenCV cascade classifier had an average Precision of 83.7%, an average Recall of 80.6%, and an average F1-Score of 81.7%. The TensorFlow Object Detection API based on the Faster R-CNN algorithm had an average Precision of 99.1%, an average Recall of 91.9%, and an average F1-Score of 95.3%. For the verification of the Dur2 sample (Mcdowell et al 2005), a total of ten images (Image16–Image25) were used, with manual identifications ranging from 23 to 45. The OpenCV cascade classifier had an average Precision of 69.7%, an average Recall of 76.2%, and an average F1-Score of 72.5%. The TensorFlow Object Detection API based on the Faster R-CNN algorithm had an average Precision of 91.0%, an average Recall of 91.4%, and an average F1-Score of 91.1%. For the apatite fission tracks tested using the OpenCV cascade classifier, the average Precision was 75.9%, the average Recall was 76.4%, and the average F1-Score was 75.7%. On the other hand, the TensorFlow Object Detection API, based on the Faster R-CNN algorithm, recorded an average Precision of 94.4%, Recall of 84.9%, and F1-Score of 88.8%. Figures 5 and 6 show the results of the sample OpenCV cascade classifier based on LBP and the TensorFlow Object Detection API based on the Faster R-CNN algorithm, respectively. 8 Mach. Learn.: Sci. Technol. 4 (2023) 045039 Z Ren et al Figure 5. Image 9, image 13, and image 23 are identified by the OpenCV cascade classifier. 5. Discussion 5.1. Experimental discussion From the experimental results table (table 5), it can be seen that the average accuracy of Precision, Recall, and F1-Score using the OpenCV cascade classifier based on LBP is above 75%. And it can be found that almost all of the test images show fission track recognition errors, and the number of recognition errors is high compared to that of using the TensorFlow Object Detection API. On the whole, the overall average accuracy rate of the Recall of the verified samples is higher than the overall average accuracy rate. This shows that there are more identified fission tracks than unidentified ones. In the experimental results (table 6), the TensorFlow Object Detection API based on the Faster R-CNN algorithm achieves an average accuracy of over 84% for Precision, Recall, and F1-Score. It outperforms the OpenCV cascade classifier approach in terms of average Precision, with a value close to 95%. However, the overall average Precision is higher than the overall average Recall, indicating that more fission tracks are not identified compared to the misidentified ones. From the experimental results table, it can be observed that the average Precision, Recall, and F1-Score accuracy of both methods is higher for the Dur1 sample than for the Qi and Dur2 samples. This is due to the lower background interference in the training images and fewer overlapping fission tracks in the Dur1 sample. The average Precision accuracy of both methods for the Dur1 and Qi samples is higher than the average Recall accuracy, indicating that there are more unidentified fission tracks than misidentified ones. In contrast, for the Dur2 sample, the average Precision accuracy is lower than the average Recall accuracy, meaning that there are more misidentified fission tracks than unidentified ones. 9 Mach. Learn.: Sci. Technol. 4 (2023) 045039 Z Ren et al Figure 6. Image 10, image 12, and image 18 are identified by tensorflow object detection API. The results of Precision, Recall, and F1-Score, depicted in the graphs (figure 7), demonstrate that the TensorFlow Object Detection API, based on the Faster R-CNN algorithm, outperforms the OpenCV cascade classifier based on LBP. The average accuracy of Precision, Recall, and F1-Score for the TensorFlow Object Detection API is higher, proving its superior performance in recognizing apatite fission tracks compared to the OpenCV cascade classifier. The results of both methods are based on trained data. By drawing Precision–Recall Curve (figure 8), the TensorFlow Object Detection API method has a more comprehensive algorithm and feature analysis, resulting in a higher accuracy rate in recognizing apatite fission tracks with many overlapping tracks, inconspicuous features, and short tracks. The OpenCV cascade classifier, on the other hand, has a certain accuracy in dealing with scattered, single, and well-defined fission tracks. Both methods can automatically identify apatite fission tracks. The OpenCV cascade classifier has the advantage of being easy to set up and faster in training speed, but its accuracy may be low in complex situations due to its algorithmic limitations. 5.2. Advantages and disadvantages From the data result table and experimental result chart of the two aforementioned experimental methods, it is evident that the average accuracy of Precision, Recall, and F1-Score achieved through the utilization of TensorFlow Object Detection API exceeds 84%. On the other hand, the average accuracy of Precision, Recall, and F1-Score obtained by employing the OpenCV cascade classifier surpasses 75%. While most fission tracks can be successfully identified, the identification efficiency requires enhancement when compared to other studies on intelligent identification of apatite fission tracks. There are still numerous instances of both non-identification and misidentification, indicating a lack of effective handling of overlapping fission tracks. 10 Mach. Learn.: Sci. Technol. 4 (2023) 045039 Z Ren et al Figure 7. TensorFlow object detection API and OpenCV cascade classifier Precision (A), Recall (B), F1-Score (C) result. P1–P25 are the test images 1–25. Figure 8. Precision–recall curve of tensorflow object detection API. In addition to the aforementioned issues, the two methods employed in this experiment offer certain advantages when compared to other existing studies on apatite fission track. Notably, both methods are characterized by their convenience of use. The TensorFlow Object Detection API boasts a comprehensive framework that allows for the utilization of various target detection models without necessitating extensive parameter adjustments. On the other hand, the OpenCV cascade classifier stands out due to its simple model configuration, short training duration, and minimal environmental requirements, setting it apart from alternative approaches. 11 Mach. Learn.: Sci. Technol. 4 (2023) 045039 Z Ren et al Figure 9. Fission track and scratch (1 is scratch and 2 is fission track). 5.3. Problems encountered in the experiment Compared to other intelligent research on apatite fission track, the two methods employed in this study exhibit lower recognition efficiency. The specific reasons for this are summarized as follows: (1). the number of training data samples is small compared to the amount of training data in most experiments. (2). Some of the training data samples have poor picture quality and many overlapping fission tracks. (3). The data samples have many background interference, which affects the recognition process. Firstly, the limited number of training data samples used in this experiment contributes to the insufficient diversity of the objectives. Additionally, the quality of the training data samples, which were captured through microscopy, is partially dependent on the accuracy of the experimental equipment. This can lead to inaccuracies in the pre-processing marking. To address these issues, we recommend increasing the sample size of the training data, improving the quality of the training data, and adjusting the model parameters. Secondly, during the verification process with fission track images, it is common to come across overlapping tracks, which greatly affects the object identification statistics. Although simple overlapping tracks can still be identified, a large number of overlapping tracks in some samples make some tracks unidentifiable. This presents the biggest challenge so far. Even manual recognition of overlapping track statistics is difficult. However, our study reveals that the overlapping track portion is not predominant. Hence, we suggest combining machine recognition with human recognition, enabling human recognition to correct machine recognition results to improve experiment accuracy. Finally, background interference in the test sample can also negatively impact the accuracy of fission track recognition. In some cases, ‘noise’ on the etched surface of apatite, such as defects and scratches, can interfere with track identification (figure 9). This makes it challenging to differentiate and identify fission tracks. Our solution to this issue is to minimize the interference factors in the sample through manual elimination during the pre-processing stage of the training data. 6. Conclusions The study presented in this paper employed two machine learning methods for fission track identification, with the following main conclusions: (1) Results from the experiments revealed that the TensorFlow Object Detection API outperforms the OpenCV cascade classifier in terms of fission track recognition. The API had an average Precision of 94.4%, Recall of 84.9%, and F1-Score of 88.8%, while the cascade classifier had an average Precision of 75.9%, Recall of 76.4%, and F1-Score of 75.7%. (2) The experiments encountered three challenges: a small number of training data samples with low quality, high levels of overlapping fission tracks, and high background interference in the samples. The solutions proposed were: increasing the number and quality of training data, combining machine 12 Mach. Learn.: Sci. Technol. 4 (2023) 045039 Z Ren et al recognition with manual recognition, and manually eliminating background interference factors during sample pre-processing. (3) In terms of ease of operation, the OpenCV cascade classifier environment is simpler and more convenient to build and operate compared to the TensorFlow Object Detection API. However, the API offers better integrity, allowing for monitoring of the entire training session. (4) The two target detection methods explored in this study offer the potential for wider use in the geology field. For instance, in rock research, target detection can aid in identifying rock types. Additionally, in the study of geological structures, utilizing target detection can assist in locating significant geological features such as fracture zones or volcanic craters through remote sensing images. To fully utilize the capabilities of deep learning in traditional geology, it is crucial to adopt a technically savvy approach and effectively integrate multiple methods to maximize their potential in furthering the development of geology. Data availability statement All data that support the findings of this study are included within the article (and any supplementary files). Acknowledgments This study was financially supported by the National Natural Science Foundation of China (Grant No. 41872234) and Science and Technology Research Project of Jilin Provincial Education Department (Grant No. JJKH20241255KJ). We are grateful to Professor An Yin for help, comments, and discussions on an earlier version of the manuscript. Thank you also to the all anonymous reviewers for their key comments on the revision of the manuscript. 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10.1186_s12875-019-0972-1.pdf	Availability of data and materials Table 1 provides a list of the 26 included papers and Additional file 1 shows the database search strategy.	Availability of data and materials Table 1 provides a list of the 26 included papers and Additional file 1 shows the database search strategy.	Wadsworth et al. BMC Family Practice (2019) 20:97 https://doi.org/10.1186/s12875-019-0972-1 R E S E A R C H A R T I C L E Open Access Shared medical appointments and patient- centered experience: a mixed-methods systematic review Kim H. Wadsworth1* Adam S. Hoverman3 , Trevor G. Archibald1, Allison E. Payne1, Anita K. Cleary1, Byron L. Haney1,2 and Abstract Background: Shared medical appointments (SMAs), or group visits, are a healthcare delivery method with the potential to improve chronic disease management and preventive care. In this review, we sought to better understand opportunities, barriers, and limitations to SMAs based on patient experience in the primary care context. Methods: An experienced biomedical librarian conducted literature searches of PubMed, Cochrane Library, PsycINFO, CINAHL, Web of Science, ClinicalTrials.gov, and SSRN for peer-reviewed publications published 1997 or after. We searched grey literature, nonempirical reports, social science publications, and citations from published systematic reviews. The search yielded 1359 papers, including qualitative, quantitative, and mixed method studies. Categorization of the extracted data informed a thematic synthesis. We did not perform a formal meta-analysis. Results: Screening and quality assessment yielded 13 quantitative controlled trials, 11 qualitative papers, and two mixed methods studies that met inclusion criteria. We identified three consistent models of care: cooperative health care clinic (five articles), shared medical appointment / group visit (10 articles) and group prenatal care / CenteringPregnancy® (11 articles). Conclusions: SMAs in a variety of formats are increasingly employed in primary care settings, with no singular gold standard. Accepting and implementing this nontraditional approach by both patients and clinicians can yield measurable improvements in patient trust, patient perception of quality of care and quality of life, and relevant biophysical measurements of clinical parameters. Further refinement of this healthcare delivery model will be best driven by standardizing measures of patient satisfaction and clinical outcomes. Keywords: Shared medical appointment, Group visit, Cooperative health care clinic, Group prenatal care, Patient satisfaction, Patient experience, Health services, Primary care, Primary health care, Coproduction Background Shared medical appointments (SMAs), or group visits, are a healthcare delivery innovation arising from the changing demands of patient-centered medical home (PCMH) set- tings and the primary care context. The model emphasizes prompt access and improved service, increased doctor- patient contact time, greater patient education, enhanced prevention and disease self-management, closer attention to routine health maintenance and performance measures, * Correspondence: kim_ha@stanfordalumni.org 1Pacific Northwest University of Health Sciences, College of Osteopathic Medicine, Yakima, WA, USA Full list of author information is available at the end of the article and the central role of patient and clinician experience within the Triple Aim: enhancing patient experience, im- proving population health, and reducing costs [1–3]. More recently, Bodenheimer and Sinsky recommended that “the Triple Aim be expanded to a Quadruple Aim, adding the goal of improving the work life of health care providers, including clinicians and staff [4].” We chose SMA as the overarching term to encompass shared visit, group appointment, group medical appoint- ment, group visit (GV), group medical clinic, shared in- group medical appointment, group prenatal care (GPNC) and group-based antenatal care. SMAs prioritize the deliv- ery of care within interprofessional environments utilizing © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Wadsworth et al. BMC Family Practice (2019) 20:97 Page 2 of 13 peer-to-peer interactions [5]. Multiple standardized SMA delivery models have been established, from the drop-in group medical appointment, cooperative health care clinic (CHCC) and physicals shared medical appointment, to CenteringPregnancy® (CP) and parenting visits [3, 6]. These visits frequently emphasize the “coproduction” roles of patients as experts in their own circumstances and health professionals as facilitators rather than fixers, thus fostering a shared experience of illness and health to bet- ter inform, empower, and support [2]. SMAs have garnered a body of evidence in chronic disease management and preventive care. The various interpretations of the group clinical model have been ap- plied to a wide array of settings and a myriad of health promotion and disease-focused visits, including patients with diabetes, hypertension, congestive heart failure, chronic lung disease, asthma, arthritis, stroke, kidney disease, cancer, hearing impairment, and prenatal care, among other conditions [7–15]. Several systematic reviews summarize the effects of SMAs on healthcare delivery, economic factors, and bio- physical outcomes. Health systems have begun to em- brace the need for this transformative approach in achieving patient goals [2, 16–18]. In an era recognizing the role of patient-centeredness in improving healthcare quality, numerous authors have highlighted the need for a review that addresses the impacts of SMAs on patient experience of care [3, 7, 16, 17, 19]. This review aims to meet this need by examining the patient experience from the published literature alongside an assessment of SMAs to improve biophysical outcomes in the adult pri- mary care setting. Analyzing the existing body of evidence for shared medical appointments, we sought to understand the op- portunities, barriers, and limitations to SMAs based on self-reported patient experience, a notable component of the Triple Aim [2]. Specifically, our goal was to highlight effective approaches for patients participating in SMAs and determinants of effectiveness. Methods librarian conducted pre- An experienced biomedical planned literature searches of PubMed, Cochrane Li- brary, PsycINFO, Cumulative Index of Nursing and Allied Health Literature (CINAHL), Web of Science, ClinicalTrials.gov, and Social Science Research Network (SSRN) for peer-reviewed publications, using controlled vocabulary, keywords, and text words (see Additional file 1 for search strategy details). The search was limited to publications from 1997 or after. We also searched grey literature, non-empirical reports, social science publica- tions, and citations from published systematic reviews. The search yielded 1359 papers, including qualitative, quantitative, and mixed-methods studies. Case studies, pilot/feasibility studies, protocols, opinions, or advocacy articles were excluded. Eligibility criteria and methods of analysis were specified a priori. Two researchers independently reviewed citation titles, abstracts, and full-text articles to determine eligibility as well as extracted the data and performed quality and risk of bias assessment on included articles, as detailed below. Before general use, we pilot-tested the abstraction form templates on a sample of included articles and then re- vised accordingly to ensure that all relevant data elements were captured. Disagreements were resolved by consensus of the two reviewers or by obtaining a third investigator’s opinion when consensus could not be reached. Studies were required to meet five process (p) and out- come (o) criteria: clinical intervention (o), clinician-led visit (p), patient experience of care (o), primary care (p), and availability of individual clinical consultation (p), as detailed below. Studies were excluded if any participants were < 18 years of age. To limit potential bias, we ex- cluded studies involving addiction medicine, substance dependence / rehabilitation treatment, inpatient settings (both short and long term) or chronic care clinics that implemented multiple interventions, and SMAs requir- ing management by a specialist. We deemed SMAs to be clinician led if led by an inde- pendent licensed prescriber or clinician. This included medical doctors (MDs), doctors of osteopathy (DOs), ad- vanced registered nurse practitioners (ARNPs), certified nurse midwives (CNMs), and in some regions, nurse practitioners (NPs). We verified prescriptive authority and care responsibility by consulting organizational web- sites from the countries in which our identified studies were conducted [20–22]. Our review emphasized biophysical metrics of adult pa- tients in primary care environments. The study team in- cluded articles focused on SMAs that implemented a clinical intervention, such as vital sign measurements, lab checks (e.g., hemoglobin A1c, lipid panels), medication ad- justments, or physical exams. We excluded studies if the intervention was limited to patient education, facilitation, peer-facilitated support groups, or group talk therapy. We tracked confounders within targeted studies, such as participant inclusion/exclusion criteria, local barriers to implementation, reimbursement framework, types of SMA interventions, and patient characteristics including language, culture, and socioeconomic status. In our consideration of quantitative research, we in- cluded only those studies with a comparative control group. Studies with quantitative primary outcomes were evaluated using the modified Jadad score, which assesses the overall quality of the individual studies, including risk of bias, and has shown high inter-rater reliability [23–26]. To evaluate qualitative studies, our team used the “Trustworthiness of Qualitative Inquiry” framework to Wadsworth et al. BMC Family Practice (2019) 20:97 Page 3 of 13 assess credibility, transferability, dependability, and ob- jectivity [27]. Inter-rater reliability was assessed during the data ex- traction phase via two-way mixed measures intraclass cor- relation (ICC) value for average agreement presented [28]. In consideration of ENTREQ and PRISMA frameworks for this mixed-methods systematic review, categorization of the extracted data informed a thematic synthesis [29– 32]. We did not perform a formal meta-analysis. Results Thirteen quantitative controlled trials, 11 qualitative pa- pers, and two mixed methods studies met inclusion cri- teria. Three models were identified: CHCC (five articles), SMA / GV (10 articles) and GPNC / CP (11 articles). Figure 1 shows the Preferred Reporting Items for Sys- tematic Reviews and Meta-Analyses (PRISMA) flowchart for all included studies [32]. Summary of included studies SMA / GV is the most frequently mentioned model in quantitative studies whereas the GPNC / CP model is the most common in qualitative studies in this re- is the least represented in view. The CHCC model this review (Table 1). Table 2 breaks down the included articles into locale, healthcare system, reimbursement model, study design, single site or multiple sites, and study duration. Table 3 provides details of the typical configuration of the three models included in this review: CHCC, SMA / GV, and GPNC / CP. Generally, CHCC has a larger group size compared to SMA / GV and GPNC / CP. Physician- led intervention teams were cited in most SMA / GV studies, whereas certified nurse midwives were most often cited as leaders of the GPNC / CP visits. Per inclusion criteria, all 26 articles reported patient satisfaction and experience (Table 4). Only one article reported outcomes for all four aims [8]. Patient experience and satisfaction Methodologies for tracking patient experience and satis- faction were grouped by data collection method into the following five categories: One-on-One Interviews (via tele- phone or in person), Focus Group Style Interviews, Self- Efficacy / Participation / Satisfaction Questionnaires, Diabetes-Related Quality of Life (DQoL) Related Scales; and Primary Care Assessment Tool / Trust in Provider Outcomes (Table 5). When comparing the results of the patient experience / satisfaction data in these 26 articles, the following six Citations identified through database searches (n = 1537) Citations identified through grey literature searches (n = 22) Additional citations identified through bibliography sources (n = 73) Citations available for initial screening (n = 1632) Titles and abstracts screened, after duplicates removed (n = 1359) Full-text articles assessed for eligibility (n = 299) Citations included in mixed- methods systematic review (n = 26) Citations excluded at title / abstract level (n = 1060) Full-text articles excluded, with reasons (n = 273) Quantitative articles (n = 13) Qualitative articles (n = 11) Mixed methods articles (n = 2) Fig. 1 The PRISMA flowchart for all included studies Wadsworth et al. BMC Family Practice (2019) 20:97 Page 4 of 13 Table 1 List of 26 included articles in the primary care setting, categorized by model of group clinic and study type Model: CHCC SMA / GV GPNC / CP Quantitative (13 articles) X X X X X Beck, 1997 Clancy, 2007 Jafari F, 2010 Junling, 2015 Kennedy, 2011 Naik, 2011 Scott, 2004 Tandon, 2013 Trento, 2001 Trento, 2002 Trento, 2004 Trento, 2005 Trento, 2010 Qualitative (11 articles) Andersson, 2012 Andersson, 2013 Capello, 2008 Clancy, 2003 Herrman, 2012 Kennedy, 2009 McDonald, 2014 McNeil, 2012 Novick, 2011 Raballo, 2012 Wong, 2015 Mixed-methods (2 articles) Heberlein, 2016 Krzywkowski-Mohn,2008 Total no. of articles (26) 5 X X X X X X X X X X 10 X X X X X X X X X X X 11 Abbreviations: CHCC Cooperative health care clinic, CP CenteringPregnancy®, GPNC Group prenatal care, GV Group visit, SMA Shared medical appointment major themes emerged (also see Additional file 2 for more details). Patient-clinician dynamic Overall, data on the patient-clinician dynamic that emerged during SMAs were positive. SMAs saw quantita- tive advantages over individual visits in domains ranging from improved communication to overall satisfaction with the visit [7, 15, 33]. In SMA environments, more time was allotted to discuss healthcare issues with the clinician compared to traditional individual visits, and physicians were perceived as less hurried [7, 14]. One study indicated that SMA experiences resulted in markedly enhanced trust in one’s primary care physician [33]. Qualitative feedback similarly supported the patient- clinician dynamic as a notable aspect of SMAs. Inter- views with CP patients indicated that extra time with cli- nicians helped them to develop strong, supportive, and positive relationships with their healthcare clinicians, and reduced anxiety about potentially not being familiar with the practitioner who would oversee their obstetric deliveries [9–11, 34]. Feedback from patients indicated that room for further improvement of the patient-clinician dynamic in SMAs lies in the avoidance of a paternalistic, didactic style of communication from the clinician leader [12]. Patients appreciated being empowered by their clinicians and preferred a more encouraging and empowering commu- nication style within their groups. Overall quality of care Multiple studies demonstrated that patients participating in SMAs were significantly more satisfied with their care than those in individual models of care [7, 13–15]. When compared to patients receiving traditional individ- ual care, those participating in SMAs were more likely to describe their overall quality of care as excellent, to feel that their care was meeting all their needs, and to feel that their care was well coordinated [8, 35]. No studies showed significant decreases in patient percep- tions of quality of care in SMAs. Overall quality of care was not a direct theme ex- tracted from qualitative investigations of SMAs. How- interviews from Herrman’s research on the CP ever, program revealed that “multiparous women frequently commented that [SMAs were] far superior to their pre- vious experiences” [11]. Quality of life Trento’s research thoroughly addressed the theme of quality of life, using a modified version of the Diabetes Quality of Life Measure (DQoL) questionnaire consisting of 39 questions ranked along a 5-point Likert scale. This assessment scale was used across all five of Trento’s arti- cles, and demonstrated consistent results over 10 years of varied research on SMAs for patients with Diabetes Mellitus, Type 2 (T2DM). In all five of Trento’s studies discussed in this paper, DQoL scores significantly im- proved among group participants while worsening or remaining the same in control subjects [36–40]. Sense of community Patients in multiple studies reported that the feeling that they were not alone in their experience was central to the positive impact of SMAs and persisted whether the subject of the SMA was pregnancy, navigation of the VA Wadsworth et al. BMC Family Practice (2019) 20:97 Page 5 of 13 Table 2 Characteristics of included studies in the primary care setting Study characteristics No. of studies, by medical condition N studies (participants) Diabetes 10 (1881) Country United States Canada Europe (Italy, Sweden) Middle East (Iran) Asia (China) Healthcare system Govt (VA, FQHC, NHS, PHD) Private (HMO, MCO) University-affiliated clinic Healthcare payment model Public (Medicaid, Medicare, govt funded) Private (fee-for-service, managed care) Uninsured /underinsured Study design Randomized controlled trial Non-randomized controlled trial Observational / interviews / focus groups Mixed methods Sites Single Multisite Study duration < 6 months 6 months 7 to 11 months 12 to 18 months 24 months > 2 years 4 (426) 0 6 (1455) 0 0 3 (362) 1 (120) 6 (1399) 8 (1575) 0 2 (306) 9 (1848) 0 1 (33) 0 9 (1066) 1 (815) 1 (87) 1 (120) 0 2(219) 3 (1169) 3 (286) HTN 2 (1262) 1 (58) 0 0 0 1 (1204) 2 (1262) 0 0 2 (1262) 0 0 1 (1204) 0 1 (58) 0 1 (58) 1 (1204) 1 (1204) 1 (58) 0 0 0 0 MCC 3 (645) 2 (616) 1 (29) 0 0 0 1 (29) 2 (616) 0 3 (645) 0 0 2 (616) 0 1 (29) 0 1 (321) 2 (324) 0 0 0 2 (350) 1 (295) 0 Pregnancy 11 (2010) 6 (926) 2 (21) 2 (435) 1 (628) 0 8 (1908) 3 (102) 0 8 (1908) 3 (102) 0 4 (1591) 1 (268) 5 (122) 1 (29) 4 (84) 7 (1926) 0 0 11 (2010) 0 0 0 Abbreviations: FQHC Federally qualified health center, HMO Health maintenance organization, HTN Hypertension, MCC Multiple chronic conditions, MCO Managed care organization, NHS National health service, PHD Public health district, VA Veterans Administration system, or hypertension [6, 10, 12, 33, 41–44]. Creation of community via SMAs supported patients’ emotional health by providing validation and stemming the isola- tion often experienced when managing chronic condi- tions. This sense of community was viewed as a benefit, though one study referenced a member who reported that at times she avoided discussion of “disturbing topics for fear that it would negatively impact her cohort” [34]. Patient empowerment / role in healthcare This body of research suggests that a strength of SMAs over usual care is the ability to engage and empower pa- tients as active participants in their own healthcare. This empowerment bore out in both qualitative and quantita- tive research participants. Quantitatively, patients reported that they were more able to participate in their care and had significant improvements on scales of Coping Skills and Health Distress as compared to their counterparts [13, 14, 43]. In the realm of qualitative analyses, it was de- scribed that patients felt they were better able to interpret their medical data, thus making them more likely to dis- cuss their issues with their clinicians [42]. Within the CP model, patients reported feeling “reassured, prepared, less anxious, and confident,” and they felt that the group ses- sions made them more proactive with respect to their own health [9]. Raballo’s research also indicated that after Wadsworth et al. BMC Family Practice (2019) 20:97 Page 6 of 13 Table 3 Typical configuration of group models, as represented by included studies in the primary care setting Model (no. of articles) CHCC (5) Duration of each group session 90– 120 min Duration of individual consultation 5–10 min each at end of group session Group size 6–20 SMA / GV(10) 60– 90 min Optional 10 mins each or 24 mins total allotted at end of group session 5–15 GPNC / CPa(11) 90– 120 min 10 mins each at beginning of group session 8–12 Clinical intervention Nonclinical components Intervention team Disciplines (no. of articles) Size Vital signs Lab results review and medical records update Medication management Preventive measures Scheduling Medical-related paperwork requested by pts Brief 1:1 visits with physician, as necessary Vital signs Lab results review and medical records update Routine lab test orders 1:1 indiv consultation with physician, as necessary Health risk assessment Medication management Referrals, coordination of public health services Vital signs Physical exam Routine prenatal screening and labs Routine ultrasound Flu vaccine (seasonal) Postpartum visit Individual assessments prior to prenatal care within group setting Socialization Health education Group cohesion Orientation and socialization Interactive health education Group cohesion Self-monitoring Group discussion Medication compliance Group discussion, self-care, skills- building Active tracking of pregnancy changes (done by pts) Tour of birth unit, labor and delivery nurse Pediatric care resources Postpartum reunion 2–5 2–7 PCP (5) Nurse, RN or diabetes nurse educator (5) Clinical pharmacist (2) PT, OT (2) Dietitian (2) Community health worker (1) 1–2 physicians (9) Nurse, NP, RN (2) Diabetes educator/ RD (4) Clin psychologist, psychopedagogist (3) 1–2 postgraduate med students (1) Others (2) 2 + others invited 1–2 CNMs (8) NP (3) Medical asst (3) Physician (2) Health / perinatal educator (1) Others (1) Abbreviations: CHCC Cooperative health care clinic, CNM Certified nurse midwife, CP CenteringPregnancy®, GPNC Group prenatal care, GV Group visit, NP Nurse practitioner, OT Occupational therapist, PCP Primary care physician, PT Physical therapist, RD Registered dietitian, RN Registered nurse, SMA Shared medical appointment aWk 5–10: First visit w/ nurse. Wk 10–12: First visit with clinician. Wk 12–16: Start CP program experiencing SMAs, patients were significantly more likely to describe an internal locus of control for their health than those followed by usual care [45]. communication between clinicians, decreased waiting times, increased opportunities for learning throughout their visits, and improved administrative support [41, 42, 46]. Access / efficiency Several articles also establish benefits of SMAs with respect to access and efficiency. Quantitatively, participants re- ported that appointments were easily scheduled “as soon as [they liked]” and were more likely to report that visit waiting time was acceptable [8, 14]. Qualitatively, patients described experiencing “more comprehensive services,” smoother Biophysical outcomes Less than half of the included articles reported biophys- ical outcomes by health condition—either diabetes mellitus (DM) or hypertension (HTN)—as summarized in Table 6 [36–40, 42, 43, 45, 47, 48]. These studies claimed significant and non-significant improvements in biophysical metrics; however, heterogeneity of study Table 4 Quadruple aim reported in included studies Model (no. of articles) CHCC (5) SMA / GV (10) GPNC / CP (11) No. of articles Patient experience Population health 5 10 11 2 1 3 Cost 2 1 0 Clinician experience 3 3 1 Abbreviations: CHCC Cooperative health care clinic, CP CenteringPregnancy®, GPNC Group prenatal care, GV Group visit, SMA Shared medical appointment Wadsworth et al. BMC Family Practice (2019) 20:97 Page 7 of 13 Table 5 Methods used to collect patient experience data Method 1:1 phone or in-person interviewsa Focus group style interviewsa Self-efficacy / participation / satisfaction questionnaires Diabetes-related quality of life scales (DQoL) Primary care assessment tool & trust in clinician outcomes Total: aAndersson 2012 is double coded as it included both 1:1 and group interviews No. of articles 10 3 6 6 2 27 populations, methods and outcomes did not allow data across studies to be combined and analyzed. (one article) This data subset was categorized into quantitative (seven articles), qualitative (two articles), and mixed to include additional studies methods details (Table 7). Eight articles had a control comparator of usual care while two articles (one qualitative study and one mixed methods study) only compared pre- and post-group intervention. Only one article utilized the CHCC model while the remaining nine articles were SMAs / GVs. From the ten studies included in this sub- set, the reported biophysical profile data varied, keeping with previous systematic reviews on SMAs by Booth et al. and Edelman et al. [17, 18]. Barriers to implementation Few studies addressed barriers, as shown in Additional file 3. Prior reviews by Edelman et al., Booth et al., and Jones et al. cite several barriers to implementation of SMAs overall, including patient participation and at- tendance, group dynamic incompatibilities, cost-benefit concerns, and staff/facilities inadequacies [16, 17, 49]. Prior studies cited poor attendance at SMAs [7, 13, 33]. In tracking attendance and patient-centered out- comes through different group visit formats, durations and patient populations, a great variation of attendance rates was found, as shown in Additional file 4. Inter-rater reliability As shown in Additional file 5, the ICC(2,k) inter-rater reliability values are 0.956 for Jadad-modified score of quantitative studies, 0.923 for trustworthiness score of qualitative studies, and indeterminable for mixed method studies due to sample size of n = 2 studies. Values greater than 0.90 indicate excellent reliability [28]. Table 6 Overview of biophysical data from available studies, categorized by health condition (no. of articles = 10) HbA1c FBG Lipids BP BMI Body First author, year Diabetes X HDL, TG X HDL X HDL, TG X TC, HDL, TG X TC, LDL, HDL, TG X TC, HDL, TG X LDL X X X Trento, 2001 Trento, 2002 Trento, 2004 Trento, 2005 Trento, 2010 Naik, 2011 X X X X X X Raballo, 2012 X Krzywkowski- Mohn, 2008 X Hypertension Junling, 2015 Capello, 2008 wt X X X X X X X X X X X X X SBP X X X CV risk DM Rx dosage Kidney Eye Foot Physical activity X X X retinopathy X insulin X Cr X ACR X Cr X foot ulcers X retinal exam X foot exam X Abbreviations: ACR Albumin/Creatinine ratio, BMI Body mass index, BP Blood pressure, Cr Creatinine, CV Cardiovascular, DM Diabetes mellitus, FBG Fasting blood glucose, HbA1c Glycated hemoglobin, HDL High-density lipoprotein, LDL Low-density lipoprotein, Rx Prescription, SBP Systolic blood pressure, TC Total cholesterol, TG Triglycerides Wadsworth et al. BMC Family Practice (2019) 20:97 Page 8 of 13 Table 7 Biophysical data from available studies, categorized by research type (no. of articles = 10) First author, year Quantitative Model Health cond(s) Sample size (n) Biophysical measures Reported findings (with p-values) Junling, 2015 CHCC HTN 600 group, 604 control ● BP ● BMI SBP decreased significantly in both group (p < 0.001) and control (p = 0.001) from baseline to follow-up, although decreases in group > control. ● Physical activity DBP decreased significantly in group (p = 0.001) but did not decrease significantly in control. Trento, 2001 SMA / GV T2DM 56 group, 56 control ● HbA1c ● BMI ● HDL ● Fasting TG Trento, 2002 SMA / GV T2DM 56 group, 56 control ● Dosage of anti- hyperglycemic agents ● Body wt, BP and CV risk ● Metabolic control: - HbA1c - BMI - HDL - Retinopathy BMI did not change in both. Increases in physical activity in group (p < 0.001) more remarkable than in control. HbA1c stable in group, worsened in control (p < 0.002). Tendency toward lower BMI in group (p = 0.06). HDL cholesterol initially similar in both but later lower in group only (p < 0.05). Trend toward lower TG in group (p = 0.053). Dosage of hypoglycemic agents decreased (p < 0.001) among group compared to control. Body wt (p < 0.001) and BMI (p < 0.001) decreased in group but not in control. Similar reductions in BP and CV risk in group vs control, but diff significant only for DBP (p < 0.001). Significant decrease in HbA1c (p < 0.001) in group. HDL increased (p < 0.001) in group but not in control. Retinopathy progressed less in group (p = 0.009). Trento, 2004 SMA / GV T2DM (NIDDM) 56 group, 56 control ● HbA1c ● BMI ● HDL, TG ● Cr HbA1c remained stable in group but progressively increased among control (p < 0.001). BMI, HDL, TG and Cr improved over 5 yrs. in group, but not significantly different from control. Trento, 2005 SMA / GV T2DM 31 group, 31 control ● HbA1c HbA1c decreased in both, though not significantly. Trento, 2010 SMA / GV T2DM (NIDDM) 421 group, 394 control TC decreased in controls (p < 0.05), while HDL increased in group (p = 0.027). No significant modifications in other clinical variables monitored (body wt, BMI, FBG, insulin dosage, TG, ACR, foot ulcers). FBG, HbA1c, TC, TG, LDL cholesterol, SBP, DBP, and BMI decreased in group from baseline to year 4 compared to control (p < 0.001, for all measures). HDL increased in group (p < 0.001). Cr did not change significantly in group. BMI, HbA1c, TG, and Cr increased in control, whereas total, HDL, and LDL cholesterol and SBP did not change and DBP decreased. ● Lipids (TC, HDL, TG) ● Body wt, BMI ● FBG ● Insulin dosage ● ACR ● Foot ulcers ● FBG ● HbA1c ● TC, LDL, HDL, TG ● BP ● BMI ● Cr Naik, 2011 SMA / GV T2DM 45 group, 42 control ● HbA1c ● SBP ● BMI Significantly greater improvements in HbA1c immediately following active Intervention and persisted at 1-year follow-up (p = 0.05). SBP and BMI were only reported at baseline, but not significantly different between both. Wadsworth et al. BMC Family Practice (2019) 20:97 Page 9 of 13 Table 7 Biophysical data from available studies, categorized by research type (no. of articles = 10) (Continued) First author, year Qualitative Reported findings (with p-values) Biophysical measures Sample size (n) Health cond(s) Model Capello, 2008 SMA / GV HTN 58 group (no control) Raballo, 2012 SMA / GV T1DM, T2DM 121 group, 121 control Mixed Methods Krzywkowski- Mohn, 2008 SMA / GV T2DM 33 group (no control) ● BP Significant effects on SBP and DBP (p < 0.01). ● HbA1c ● Lipids (TC, HDL, TG) ● FBG ● BMI HbA1c lower in T1DM group than in control (p = 0.001) and not significantly so in T2DM (NS). Lower HDL in T1DM control (p = 0.002), but no other significant differences among both. Lower HbA1c after group intervention (p < 0.05). Diabetic clinical indicators: ● HbA1c ● LDL ● BP ● Retinal exam Increase in diabetic eye exams. ● Foot exam Lower LDL after 18 mos (p < 0.05). No significant diff. in SBP or DBP after 18 mos. No diff in diabetic foot exams (96.9% pre + post). Abbreviations: ACR Albumin/Creatinine ratio, BMI Body mass index, BP Blood pressure, Cr Creatinine, CV Cardiovascular, DBP Diastolic blood pressure, FBG Fasting blood glucose, HbA1c Glycated hemoglobin, HDL High density lipoprotein, HTN Hypertension, LDL Low density lipoprotein, NIDDM Non-insulin dependent diabetes mellitus, SBP Systolic blood pressure, T1DM Diabetes mellitus, type 1, T2DM Diabetes mellitus, type 2, TC Total cholesterol, TG Triglycerides Discussion This review limited SMA models to three general cat- egories: cooperative health care clinic, shared medical appointment / group visit, and group prenatal care / the focus on group CenteringPregnancy®. To meet in- intervention, we considered visits clinical cluded the following clinical components: review of labs, medication management, physical examination, or other medical interventions. From a strength of evidence perspective, 16 of the studies reflected a ran- domized controlled design and one non-randomized controlled design. The remaining nine studies were cohort and case study designs, with a median study duration of 12 months. that As SMAs are generalizable to primary care environ- ments, we prioritized reviews that included Internal Medicine, Obstetrics/Gynecology, Family Medicine, and Psychiatry. Though non-clinician-led SMAs have been applied in myriad ways in primary care settings, such as group-based acupuncture clinics, group psy- chotherapy for post-traumatic stress disorder and group interventions for disabled adults, we excluded them to evaluate SMAs as a variation of clinician-led primary care. To the best of our knowledge, our current review up- dates the evidence base to date and provides a necessary segue to patient-oriented outcomes. In the spirit of the Triple Aim, SMAs uniquely enhance patient-centered experience, thus we limited our review to settings that provide individual primary care consultation alongside the group visit. Individual consultation provides a re- served space for private concerns. This is an important distinction as privacy concerns have been a prominent drawback of the model identified by prior research [13, 15, 34]. We prioritized this element, recognizing the trust it fosters in the patient-clinician relationship. Summary of findings In sum, designing, promoting, and running SMAs from tested and proven formats proves to be vital for implemen- tation. Model and content fidelity demonstrate significant outcome improvement, most notably in the prenatal care and birth outcomes through the CenteringPregnancy® group process. Standardized training also improves facilita- tion of group care. Therefore, clinicians learning to facilitate group care are encouraged to receive training in facilitative leadership with emphasis on the role that a participatory at- mosphere has in improving outcomes [50]. Several models describe a physical design component to enhance the effect on patient experience or group process [3, 42, 51]. Some studies use displayed patient biophysical data for comparison and a visual aid for decision-making. Patient seating design has also been identified as a driver, both circular and U-shaped formats. Krzywokwski-Mohn stipulates that SMAs occur with participants seated around a circular conference table, with no one at the “head of the table,” balancing power and significantly influencing SMA participant outcomes [42]. Additionally, the emergence of the patient-centered medical home motivates improvement in patient educa- tion, experience of care, and measurable outcomes Wadsworth et al. BMC Family Practice (2019) 20:97 Page 10 of 13 without increasing clinical workload [3]. The interprofes- sional team plays a prominent role in SMAs across the lit- erature, including nurses, nutritionists, NPs, pharmacists, physical therapists, PAs, primary healthcare coordinators and nurse midwives [7, 8, 14, 34, 52]. Despite these reallo- cation of tasks, roles, and resources, SMAs demonstrate efficacy and feasibility across a wide range of healthcare systems [39, 53]. Despite SMAs objectively providing patients more time with their clinicians, the degree to which this affects satis- faction is unknown and patient characteristics and outside influences can affect satisfaction outcomes [7, 13, 49, 54]. Furthermore, evaluating and effectively responding to the social determinants of health requires improved identifica- tion of patient needs and outcomes assessment [55]. Nonetheless, our evaluation includes consideration of pa- tient experience fundamental for evaluating health-related quality of life, including disease-related health locus of control, health behaviors, self-efficacy, and other measures of patient perspective of care and quality of life. Lastly, studies emphasizing biophysical outcomes re- port statistically significant improvement in at least one biophysical metric, yet are too heterogeneous to com- pare across studies. Nonetheless, results are consistent with other systematic reviews by Booth et al., Edelman et al., and Jones et al. [17, 18, 49]. Limitations of review Our inclusion criteria and focus on the primary care context limited the number of articles that we evaluated in this review, which may impact the generalizability of our conclusions. Previous systematic reviews looked at a broader number of articles, though their approach also introduced more heterogeneity [17, 18, 49]. Single center studies, representing the majority for our included arti- cles on diabetes patients, may also limit generalizability. We also note that half of our included articles for the SMA / GV format were authored by the same researcher [36–40]. Other previous reviews have mentioned the im- possibility of blinding the participant and clinician / care team. Given that trials of SMA interventions cannot be designed in a traditional double-blinded manner, our quality assessment scores for quantitative studies could only receive a maximum of seven out of a total of eight points on the modified Jadad score. However, a few studies described minimizing performance bias by hav- ing the same clinician and care team manage the same intervention and control subjects and by measuring outcomes blindly for the treatment group. Furthermore, there may be sampling bias in nonrandomized con- trolled trials as well as focus groups and interviews due to the possibility that patients who are high frequency attenders may self-select to be included in the interven- subjects who have negative tion group; likewise, experiences with SMAs may decline to be interviewed or refuse to be randomized into the intervention group. Moreover, information bias may have appeared due to variation in attendance and/or completion of visits within our sample. Critiques exist concerning the evaluation of patient ex- perience through patient satisfaction measures. Aside from a lack of agreement on a converging definition of “satisfaction,” there are methodological challenges in re- liably and precisely measuring and interpreting percep- tions of the healthcare environment (survey content, mode and timing of survey administration, bias, con- founding, need for post-hoc adjustment, and subjective nature of interpersonal experiences, including patient- clinician communication as a unique dimension of qual- ity). Despite these challenges, patient experience has a meaningful role in quality improvement discussions and determination of perceived quality and sense of commu- nity [56]. Implications for practice, policy, and future research Improved resilience and coping skills, in concert with pa- tient agency and activation, are valuable outcomes of the spectrum of SMAs [34]. The primary care environment is an optimum setting to build the necessary trust, health lit- eracy, and awareness of health beliefs required for suc- cessful intersection with the broader healthcare system [35, 38]. Honoring adult learning strategies often requires nonclinical skill sets for interdisciplinary care clinicians [38]; yet, few studies focused on interprofessional practice despite widespread presence across differing SMA models. SMAs emphasize patient empowerment through peer ac- countability, socialization, and appreciation of local cul- tural context as well as patients’ familiarity and comfort with the setting [40, 43, 53]. Engaging group members in the design of these SMAs can maximize responsiveness to [43]. cultural context and acceptability of GPNC / CP have demonstrated efficacy in increasing health-related knowledge, social support, personal locus of control, emotional care, and self-care [52, 57]. the model In general, to improve quality and validity of report- ing patient experience as well as improved reporting of population health outcomes, we recommend longer duration of follow up in each study setting. We also recommend specific evaluation of team-based care, in- cluding perspectives of administrators and supporting clinical staff. As provision of healthcare is a service, measures of quality should include assessment of the extent to which patients and care teams reach a com- mon understanding of treatment course and health out- comes [2]. This intersection of shared well-being with health improvement warrants further evaluation to optimize healthcare delivery models, such as SMAs, to achieve the quadruple aim. Wadsworth et al. BMC Family Practice (2019) 20:97 Page 11 of 13 Conclusions Shared medical appointments are increasingly employed in primary care settings. This mixed-methods systematic review concludes that accepting and implementing this nontraditional approach by both patients and clinicians can yield measurable improvements in patient trust, pa- tient perception of quality of care and quality of life, and relevant biophysical measurements of clinical parameters. Compared to usual care, SMAs have a greater ability to engage and empower patients as active participants in their own healthcare while improving patient access and healthcare efficiency. The cumulative benefits of SMAs are most notable when implemented within a conducive environment such as a PCMH. No singular model of SMA best serves all settings. Similarly, there does not appear to be a priority set of their outcome measures nor consistent means for evaluation from our review. Our analysis indicates that both quantitative and qualitative methods are equally valid for evaluating patient experience. Further refine- ment of this healthcare delivery model will benefit from standardizing measures of patient satisfaction and clin- ical outcomes. Not surprisingly, critiques and cost-benefit concerns remain. Demonstration of global payment models result- ing in improved population health outcomes alongside economies of scale may be essential for wider acceptance of SMAs. We recommend further evaluation of the en- ablers and barriers to advance SMA integration in pri- mary care practice settings. We also recommend more thorough and longitudinal evaluations to better describe the consumer-minded approach for care delivery design and responsiveness to the voice of the customer to achieve the most efficient models possible. Additional files Additional file 1: Database search strategies (DOCX 27 kb) Additional file 2: Description of data: Reported significant findings related to patient experience and satisfaction, as reported in included articles (DOCX 31 kb) Additional file 3: Description of data: Barriers to implementation from available studies (no. of articles = 8) (DOCX 28 kb) Additional file 4: Description of data: SMA patient-centered variables vs. attendance and outcomes (no. of articles = 26) (DOCX 40 kb) Additional file 5: Inter-rater reliability of included articles using two-way mixed measures intraclass correlation (ICC) value for average agreement presented. (DOCX 29 kb) Abbreviations CHCC: Cooperative health care clinic; CP: CenteringPregnancy®; DQoL: Diabetes-Related Quality of Life; GPNC: Group prenatal care; GV: Group visit; PCMH: Patient centered medical home; SMA: Shared medical appointment Acknowledgments The authors thank Mary Giovanini for her help with full-text citations; Drs. Sean Cleary, MD, PhD, and Jennifer Best, MD, for their thorough edits of our manuscript; Dr. William Elliott, MD, PhD, for his critical suggestions on quality assessment of included articles; Dr. Bernadette Howlett, PhD, for her early in- put on our research methodology; Dr. Michele McCarroll, PhD, Carla S. Case and Anita Quintana, MA, for their kind assistance and support; and Tracy Dana, MLS, and Sarah Safranek, MLIS, for reviewing our literature search strategies. Authors’ contributions All listed authors significantly contributed to this project. KHW, AKC and ASH developed the study protocol. KHW, TGA and ASH conducted the title and abstract screening. KHW, TGA, AEP, and ASH conducted the full-text screen- ing, data extraction, quality assessments, and data synthesis. BLH provided the content expertise. AKC is the biomedical librarian who conducted the lit- erature search and managed the citations. All authors had access to the data, played a role in writing the manuscript, and read and approved the final manuscript. Funding This project was made possible with a Mapping the Landscape, Journeying Together grant from the Arnold P. Gold Foundation (APGF). The APGF did not have any role in the design of the study, collection, analysis and interpretation of data, nor writing the manuscript. Availability of data and materials Table 1 provides a list of the 26 included papers and Additional file 1 shows the database search strategy. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Author details 1Pacific Northwest University of Health Sciences, College of Osteopathic Medicine, Yakima, WA, USA. 2Family Health Care of Ellensburg, Ellensburg, WA, USA. 3Multnomah County Health Department, Oregon Health and Science University–Portland State University School of Public Health, Portland, OR, USA. Received: 23 February 2018 Accepted: 31 May 2019 References 1. Berwick DM, Nolan TW, Whittington J. The triple aim: care, health, and cost. Health Aff Proj Hope. 2008;27:759–69. Batalden M, Batalden P, Margolis P, Seid M, Armstrong G, Opipari-Arrigan L, et al. Coproduction of healthcare service. BMJ Qual Saf. 2016;25:509–17. Noffsinger EB. Group visits -- the “secret sauce” of the medical home. Med Home News. 2013;5:1,6-8. Bodenheimer T, Sinsky C. From triple to quadruple aim: care of the patient requires care of the provider. Ann Fam Med. 2014;12:573–6. Noffsinger EB. Running group visits in your practice. New York: Springer; 2009. Novick G. 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J Perinat Educ. 2011;20:210–7. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.	null
10.1186_s13059-023-02963-4.pdf	Availability of data and materials The results published here are in part based upon data generated by the TCGA Research Network (https:// www. cancer. gov/ tcga), METABRIC (https:// ega‑ archi ve. org/ studi es/ EGAS0 00000 00083), MSK‑IMPACT (https:// www. mskcc. org/ msk‑ impact) or deposited at cBioPortal (https:// www. cbiop ortal. org/). The following expression datasets from the Gene Expression Omnibus (GEO, https:// www. ncbi. nlm. nih. gov/ geo/) have also been employed: GSE114012 [48], GSE131594 [45], GSE137912 [12], GSE152699 [49], GSE75367 [47], GSE83142 [46], GSE93991 [44], GSE134836 [13], GSE134838 [13], GSE134839 [13], GSE124854 [93], GSE135215 [94], GSE99116 [93], GSE178839 [149], GSE149224 [100], GSE139944 [102], GSE191127 [150], GSE109211 [151], GSE50509 [152], GSE65185 [153], GSE66399 [154], GSE68871 [155] and GSE99898 [156]. The GEO datasets employed in the analyses are summarised in Additional file 1: Tables S2 and S3. All codes developed for the purpose of this study can be found at the following repository, released under a GNU Gen‑ eral Public License v3.0 at github: https:// github. com/ secri erlab/ Cance rG0Ar rest [157] and Zenodo (doi: 1	Availability of data and materials The results published here are in part based upon data generated by the TCGA Research Network ( https:// www. cancer. gov/ tcga ), METABRIC ( https:// ega-archi ve. org/ studi es/ EGAS0 00000 00083 ), MSK-IMPACT ( https:// www. mskcc. org/ msk- impact ) or deposited at cBioPortal ( https:// www. cbiop ortal. org/ ). The following expression datasets from the Gene Expression Omnibus (GEO, https:// www. ncbi. nlm. nih. gov/ geo/ ) have also been employed: GSE114012 [48] , GSE131594 [45] , GSE137912 [12] , GSE152699 [49] , GSE75367 [47] , GSE83142 [46] , GSE93991 [44] , GSE134836 [13] , GSE134838 [13] , GSE134839 [13] , GSE124854 [93] , GSE135215 [94] , GSE99116 [93] , GSE178839 [149], GSE149224 [100], GSE139944 [102], GSE191127 [150], GSE109211 [151], GSE50509 [152], GSE65185 [153], GSE66399 [154], GSE68871 [155] and GSE99898 [156]. The GEO datasets employed in the analyses are summarised in Additional file 1: Tables S2 and S3 . All codes developed for the purpose of this study can be found at the following repository, released under a GNU General Public License v3.0 at github: https:// github. com/ secri erlab/ Cance rG0Ar rest [157] and Zenodo (doi: 10. 5281/ zenodo. 78406 72 ) [158].	Wiecek et al. Genome Biology (2023) 24:128 https://doi.org/10.1186/s13059-023-02963-4 RESEARCH Genome Biology Open Access Genomic hallmarks and therapeutic implications of G0 cell cycle arrest in cancer Anna J. Wiecek1, Stephen J. Cutty2, Daniel Kornai1, Mario Parreno‑Centeno1, Lucie E. Gourmet1, Guidantonio Malagoli Tagliazucchi1, Daniel H. Jacobson1,3, Ping Zhang4, Lingyun Xiong4, Gareth L. Bond5, Alexis R. Barr2,6 and Maria Secrier1* Correspondence: m.secrier@ucl.ac.uk 1 UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, UK 2 Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK 3 UCL Cancer Institute, Paul O’Gorman Building, University College London, London, UK 4 Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK 5 Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, UK 6 Cell Cycle Control Team, MRC London Institute of Medical Sciences (LMS), London, UK Abstract Background: Therapy resistance in cancer is often driven by a subpopulation of cells that are temporarily arrested in a non‑proliferative G0 state, which is difficult to capture and whose mutational drivers remain largely unknown. Results: We develop methodology to robustly identify this state from transcriptomic signals and characterise its prevalence and genomic constraints in solid primary tumours. We show that G0 arrest preferentially emerges in the context of more stable, less mutated genomes which maintain TP53 integrity and lack the hallmarks of DNA damage repair deficiency, while presenting increased APOBEC mutagenesis. We employ machine learning to uncover novel genomic dependencies of this process and validate the role of the centrosomal gene CEP89 as a modulator of proliferation and G0 arrest capacity. Lastly, we demonstrate that G0 arrest underlies unfavourable responses to various therapies exploiting cell cycle, kinase signalling and epigenetic mechanisms in single‑cell data. Conclusions: We propose a G0 arrest transcriptional signature that is linked with therapeutic resistance and can be used to further study and clinically track this state. Keywords: Cell cycle arrest, G0, Cancer, Persister cells, Genomic dependencies, Machine learning, Data integration, Bulk/single‑cell sequencing Background Tumour proliferation is one of the main hallmarks of cancer development [1] and has been extensively studied. While most of the cells within the tumour have a high prolif- erative capacity, occasionally under stress conditions, some cells will become arrested temporarily in the G0 phase of the cell cycle, in a reversible state often referred to as ‘quiescence’, ‘dormancy’, ‘diapause-like’ or a potentially irreversible state called ‘senes- cence’, where they maintain minimal basal activity [2–5]. It has been proposed that G0 arrest enables cells to become resistant to anti-cancer compounds that target actively dividing cells, such as chemotherapy [5–7]. Moreover, a drug-tolerant ‘persister’ cell © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the mate‑ rial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Wiecek et al. Genome Biology (2023) 24:128 Page 2 of 35 state represented by slow cycling, entirely quiescent or even senescent cells [4, 8–11] has been observed in a variety of pre-existing or acquired resistance scenarios, also in the context of targeted therapies [12, 13]. As neoplastic cells evolve, G0 arrest can also be employed as a mechanism to facilitate immune evasion [14, 15] or adaptation to new environmental niches during metastatic seeding [16, 17]. In the context of disseminated tumour cells, these G0 cycle arrest states can facilitate minimal residual disease, a major cause of relapse in the clinic [18]. Although G0 arrest is a widely conserved cellular state, essential for the normal devel- opment and homeostasis of eukaryotes [2, 19], and has been extensively studied in a variety of organisms including bacteria and yeast [20, 21], its role and different facets in cancer are still poorly defined. Hampering our understanding is the fact that it rep- resents a number of heterogeneous states [19, 22]. Canonically, cells can be forced into G0 arrest through serum starvation, mitogen withdrawal or contact inhibition [19]. Cells can also undergo G0 arrest spontaneously in response to cell-intrinsic factors like rep- lication stress [23–25]. This process is controlled by p53 [26], which triggers the inhibi- tion of cyclin-CDK complexes by activating p21 [24]. This in turn allows the assembly of the DREAM complex—a key effector responsible for repression of cell-cycle-dependent gene expression [27]. Min and Spencer [28] recently demonstrated a much broader sys- temic coordination of 198 genes underlying distinct types of G0 arrest by profiling the transcriptomes of cells that entered this state either spontaneously or upon different stimuli. Additionally, proliferation-G0 decisions can be impacted by oncogenic changes such as MYC amplification [29] or altered p38/ERK signalling [30]. Despite these advances, the identification of G0-arrested cells within tumours pre- sents an ongoing challenge due to their scarcity and lack of universal, easily measura- ble markers for the activation and maintenance of this state. As they are often defined by a lack of proliferative markers [31, 32], different forms of G0 arrest such as quies- cence, senescence, dormancy and (to a lesser extent) stemness might sometimes be used interchangeably [4, 33]. Quiescent and dormant cells can readily resume their prolif- erative state, senescent cells are irreversibly arrested [28] while cancer stem cells have a high capacity for self-renewal and sit at the top of the differentiation hierarchy [34]. Even though the same cell cycle arrest programme underlies all of these states, they are linked with distinct environmental stimuli and drive cancer progression and therapeu- tic resistance in different ways [11, 12, 35, 36]. Increasing evidence from the literature points towards the rapid adaptation of tumour cells to drug treatments being enabled by a slow dividing or a quiescent state that persists for a short period of time before the cells start reproliferating [37]. Thus, quiescence could more frequently be encountered at the early stages of therapeutic resistance compared to other cell cycle arrest phenotypes, although senescence and stemness are also often discussed in this context. Biomarkers of cell cycle arrest and persistence that are sufficiently specific and robust to be clinically useful are clearly needed. Furthermore, our understanding of how cancer evolution is shaped by proliferation and G0 arrest decisions is limited. The proliferative heterogeneity of cancer cell popula- tions has been previously described and linked with FAK/AKT1 signalling [38], but the constraints and consequences of these cell state switches have not been systematically profiled across cancer tissues. The extent to which G0 arrest in cancer is enacted through Wiecek et al. Genome Biology (2023) 24:128 Page 3 of 35 transcriptional or genetic control is unknown [5, 39], and neither are the mutational processes and genomic events modulating this state. Understanding the evolutionary triggers and molecular mechanisms that enable cancer cells to enter and maintain G0 arrest would enable us to develop pharmacological strategies to selectively eradicate these arrested cancer cells or prevent them from re-entering proliferative cycles. To address these challenges, we have developed a new method to reliably quantify G0 arrest in cancer using transcriptomic data, and employed it to characterise this phenom- enon in bulk and single-cell datasets from a variety of solid tumours. We describe the spectrum of proliferation and G0 arrest decisions in primary tumours, which reflects a range of stress adaptation mechanisms during the course of cancer development from early to advanced disease. We identify and validate mutational constraints for the emer- gence of G0 arrest, hinting at potential new therapeutic targets that could exploit this mechanism. We also demonstrate the relevance of G0 arrest to responses to a range of compounds targeting cell cycle, kinase signalling and epigenetic mechanisms in single- cell datasets and propose an expression signature that could be employed to detect treat- ment resistance induced by G0 arrested tumour cells. Results Evaluating G0 arrest in cancer from transcriptomic data We hypothesised that primary tumours contain varying numbers of cells temporarily or permanently arrested in the cell cycle, which reflect evolutionary adaptations to cellular stress and may determine their ability to overcome antiproliferative therapies. To capture this elusive phenotype, we developed a computational framework that would allow us to quantify G0 arrest signals in bulk and single-cell sequenced cancer samples (Fig. 1a). To define a signature of G0 arrest, we focused on genes that have been shown by Min and Spencer [28] to be specifically activated or inactivated during quiescence that arises spontaneously or as a response to serum starvation, contact inhibition, MEK inhibition or CDK4/6 inhibition. The activity of 139 of these genes changed in a coordinated man- ner across all these five distinct forms of quiescence, likely representing generic tran- scriptional consequences of G0 arrest. The expression levels of these markers were used to derive a score reflecting the relative abundance of G0 arrested cells within individual tumours (see ‘Methods’, Additional file 1: Table S1). (See figure on next page.) Fig. 1 Methodology for quantifying G0 arrest in cancer. a Workflow for evaluating G0 arrest from RNA‑seq data; 139 genes differentially expressed in multiple forms of quiescence were employed to score G0 arrest across cancer tissues. b Receiver operating characteristic (ROC) curves illustrating the performance of the Z‑score methodology on separating actively proliferating and G0 arrested cells in seven single‑cell (continuous curves) and bulk RNA‑seq (dotted curves) datasets. AUC area under the curve. c Compared classification accuracies of the G0 arrest Z‑score approach and classic cell proliferation markers across the seven single‑cell/bulk RNA‑seq validation datasets. d G0 arrest levels of embryonic fibroblast cells under serum starvation for various amounts of time. Replicates are depicted in the same colour. e Representative images of lung cancer cell lines immunostained and analysed to detect the G0 arrest fraction. Hoechst (labels all nuclei) is in blue, phospho‑Rb in green and EdU in red in the merged image. White dashed circles highlight G0 arrested cells that are negative for both phospho‑Rb and EdU signals. Scale bar: 100 µm. f Graphs show single‑cell quantification of phospho‑Rb and EdU intensities taken from images and used to define the cut‑off to calculate the G0 arrest fraction (green boxes). Images in e and graphs in f are taken from the A549 cell line. g–h Correlation between theoretical estimates of a G0 or G1 state and the fraction of cells entering G0 arrest in nine lung adenocarcinoma cell lines, as assessed through g phospho‑Rb assays and h 3 is shown for the average percentage of G0 arrested cells EdU assays. Mean of n = Wiecek et al. Genome Biology (2023) 24:128 Page 4 of 35 Fig. 1 (See legend on previous page.) To validate this signature and select the optimal method to score G0 arrest in indi- vidual samples amongst different enrichment/rank-based scoring methodologies [40–43], we used seven single-cell and bulk datasets [12, 44–49] where actively prolif- erating and quiescent/dormant cells had been independently isolated and sequenced (Additional file 1: Table S2, Methods). We tested the performance of our signature and scoring methodology, as well as that of other commonly used gene signatures, in dis- tinguishing between the truly quiescent/dormant and truly proliferating cells in these seven datasets while varying the expression cut-offs for labelling cells as G0 arrested or proliferating based on the respective signature. A combined Z-score approach had Wiecek et al. Genome Biology (2023) 24:128 Page 5 of 35 the highest accuracy in detecting signals of G0 arrest, with a 91% mean performance in classifying cells as G0 arrested or cycling (Fig. 1b, Additional file 2: Fig. S1a-b). Indeed, the individual cells that had been identified as arrested in G0 in the experi- ments showed a significantly higher Z-score than the dividing cells across all data- sets (Additional file 2: Fig. S1c). Our signature reflected an expected increase in p27 protein levels, which are elevated during G0 arrest [50] (Additional file 2: Fig. S1d). It also outperformed classical cell cycle and arrest markers, such as the expression of targets of the DREAM complex, CDK2, Ki67 and of mini-chromosome replication maintenance (MCM) protein complex genes—which are involved in the initiation of eukaryotic genome replication, as well as recently defined G1/S and G2/M signatures [51] (Fig. 1c). Importantly, our approach provided a good separation between G0 and proliferating samples across a variety of cancer types and models including cancer cell lines, 3D organoid cultures, circulating tumour cells and patient-derived xeno- grafts (Additional file 1: Table S2), thereby demonstrating its broad applicability. Fur- thermore, the strength of the score appeared to reflect the duration of G0 arrest [52] (Fig. 1d). We further experimentally validated our methodology in nine lung adenocarci- noma cell lines. We estimated the fraction of G0 cells in each of these cell lines using quantitative, single-cell imaging of phospho-Ser807/811-Rb (phospho-Rb, which labels proliferative cells [53]) and 24-h EdU proliferation assays (Fig. 1e–h). In these assays, cells were pulse-labelled with the nucleotide analogue, EdU, for 24 h before fixation and immunostaining. Only cells which have proliferated in the last 24 h will be labelled by EdU. EdU-negative cells are classed as G0. Cells that were negative for phospho-Rb were also defined as G0, and not G1, since they have not yet passed the restriction point (phospho-Rb negative; see ‘Methods’, Fig. 1e–f ). This G0 frac- tion was further validated in A549 and NCI-H1944 cells where endogenous PCNA has been labelled with an mRuby fluorophore to enable tracking of cell cycle phase lengths by live-cell imaging (Zerjatke et al. [54], ‘Methods’). By quantifying the G0/ G1 length in individual cells (i.e. time taken to enter S-phase after mitotic exit) over a 48-h period, we could see that these cells were quiescent and not senescent (or in deep quiescence), as all G0/G1 cells did eventually enter S-phase, albeit with variable timing (Additional file 2: Fig. S1e). There was a remarkably good correlation between our predicted G0 arrest levels based on the expression of these cell lines from the Cancer Cell Line Encyclopedia (CCLE) and the fraction of G0 cells in the experiment as assessed by lack of EdU incorporation over a 24-h period (EdU incorporation only occurs during S phase) but particularly by lack of Rb phosphorylation. Phosphorylation and inactivation of the retinoblastoma protein is often used to define the boundary between G0 and G1 and was specifically shown to distinguish the G0 state recently by Stallaert et al. [53]. Furthermore, a G1 signature (see ‘Methods’) was not associated with these experimental measurements, suggesting our method recovers a state more similar to G0 arrest rather than a prolonged G1 state (Fig. 1g–h). The G0 arrest correlations appeared robust to random removal of individual genes from the signature, with no single gene having an inordinate impact on the score (Supplementary Fig. 1f-h). This provided further reassurance that our Z-score-based methodology is successful in capturing G0 arrest signals from bulk tumour data. Wiecek et al. Genome Biology (2023) 24:128 Page 6 of 35 The spectrum of G0 arrest capacity in solid primary tumours Having established a robust framework for quantifying G0 cell cycle arrest in cancer, we next profiled 8005 primary tumour samples across 31 solid cancer tissues from The Can- cer Genome Atlas (TCGA). After accounting for potential confounding signals of non- cycling non-tumour cells from the microenvironment by correcting for tumour purity (see ‘Methods’, Supplementary Fig. 1i-j), we observed an entire spectrum of fast prolif- erating to slowly cycling tumours, with the latter presenting stronger G0-linked signals (Fig. 2a). While we acknowledge that no tumour would be entirely quiescent/senescent and we cannot identify individual G0 arrested cells within the tumour, this analysis does capture a broad range of phenotypes reflecting varying proliferation and cell cycle arrest rates, which suggests that G0 arrest is employed to different extents by tumours as an adaptive mechanism to various extrinsic and intrinsic stress factors. Cancers known to be frequently dormant, such as glioblastoma [6, 44], were amongst the highest ranked in terms of G0 arrest levels, along with kidney and adrenocortical carcinomas (Fig. 2b). This is likely explained by the innate proliferative capacity of the respective tissues. Indeed, tissues with lower stem cell division rates presented a greater propensity for G0 arrest (Fig. 2c) [55]. Our score showed strong negative correlations with the expression of proliferation markers (Fig. 2d), suggesting that it captures a cellular state that could potentially act as a baseline for all major forms of cell cycle arrest, including quiescence, senescence, stemness and clinical dormancy. Indeed, we found that our signature could to a certain Fig. 2 Pan‑cancer evaluation of proliferative heterogeneity and linked tumour hallmarks. a PHATE plot illustrating the wide spectrum of proliferative to slow cycling/arrested states across 8005 primary tumour samples from TCGA. Each sample is coloured according to the relative G0 arrest level. b Variation in tumour G0 arrest levels across different cancer tissues. c Correlation between mean G0 arrest capacity and stem cell division estimates for various tissue types. d Correlating tumour G0 arrest scores with cancer cell stemness (Stemness Index), telomerase activity (EXTEND score), p21 activity (CDKN1A) and the expression of several commonly used proliferation markers. The Pearson correlation coefficient is displayed. RC replication complex. e Consistently higher levels of G0 arrest are detected in samples with functional p53. f Lower G0 arrest scores are observed in tumours with one or two whole‑genome duplication events. Wilcoxon rank‑sum test p‑values are displayed in boxplots, p < 0.05; p < 0.01; p < 0.001; **p < 0.0001 Wiecek et al. Genome Biology (2023) 24:128 Page 7 of 35 extent also separate senescent cells from proliferating ones in single-cell data from Her- nandez-Segura et al. [56], which is unsurprising since these cells are also in the G0 phase (Additional file 2: Fig. S2a). Indeed, the authors of this resource highlight that some of the pathways uncovered in these senescent cells may be shared with quiescence, which is also backed up by a study from Fujimaki and Yao [57] suggesting similarities between deep quiescence and senescence. Our score did not show strong correlations with other markers of senescence such as the senescence-associated secretory phenotype (SASP) and β-galactosidase activity [58–60] in single cells or TCGA samples (Fig. 2d, Supple- mentary Fig. 2b-d), although we cannot exclude the possibility of a senescent state being captured occasionally given that neither β-galactosidase nor the SASP are obligatory for maintaining senescence [61]. However, the underpinning programme appears to be dis- tinct from that of cancer stem cells, marked by signatures associated with high telomer- ase activity and an undifferentiated state [62, 63] (Additional file 2: Fig. S2e-f ). Lastly, we confirmed expected dependencies on the p53/p21/DREAM activation axis: tumours that were proficient in TP53 or the components of the DREAM complex, as well as those with higher p21 expression, had elevated G0 arrest levels across numerous tissues (Fig. 2e, Supplementary Fig. 2g-h), although only 8 out of 139 genes in our signa- ture are directly transcriptionally regulated by p53 [64]. Nevertheless, p53 proficiency appears to be a non-obligatory dependency of G0 arrest, which is also observed to arise in p53 mutant scenarios in 21% of cases. p53 has also been shown to play a role in pre- venting the occurrence of larger structural events and polyploidy [65–67], potentially explaining the lower G0 arrest levels we observed in tumours that had undergone whole- genome duplication (Fig. 2f ). The genomic background of G0 arrest in cancer Cancer evolution is often driven by a variety of genomic events, ranging from single base substitutions to larger scale copy number variation and rearrangements of genomic seg- ments. It is reasonable to expect that certain mutations accumulated by the cancer cells might enable a more proliferative phenotype, impairing the ability of cells to enter G0 arrest, or - on the contrary - might favour cell cycle exit as a temporary adaptive mecha- nism to extreme levels of stress. Having obtained G0 arrest estimates for primary tumour samples, we set out to identify potential genomic triggers or constraints that may shape proliferation versus G0 arrest decisions in cancer. We identified 285 cancer driver genes that were preferentially altered (via mutations or copy number alterations) either in slow cycling or fast proliferating tumours (Fig. 3a). Reassuringly, this list included genes pre- viously implicated in driving cell cycle exit decisions such as TP53 and MYC [26, 29]. We also investigated associations with mutagenic footprints of carcinogens (termed ‘muta- tional signatures’), which can be identified as trinucleotide substitution patterns in the genome [68, 69]. Fifteen mutational signatures were linked with G0 arrest levels either within individual cancer studies or pan-cancer (Additional file 2: Fig. S2i). Following the initial prioritisation of putative genomic constraints of G0 arrest, we employed machine learning to identify those events that could best distinguish slow cycling tumours with higher abundance of G0 arrested cells from fast proliferating ones, while accounting for tissue effects. An ensemble elastic net selection approach similar to the one described by Pich et al. [70] was applied for this purpose (Fig. 3b, see ‘Methods’). Wiecek et al. Genome Biology (2023) 24:128 Page 8 of 35 Our pan-cancer model identified tissue type to be a major determinant of G0 arrest lev- els (Additional file 2: Fig. S3a). It also uncovered a reduced set of 57 genomic events linked with proliferation/G0 arrest switches, including SNVs and copy number losses in 17 cancer genes, as well as amplifications of 10 cancer genes (Fig. 3c). These events could then be successfully employed to predict G0 arrest in a separate test dataset, thus inter- nally validating our model (Additional file 2: Fig. S3b). Thus, while these events are not necessarily causative, the link is strong enough to be identifying G0 arrest states from genomic data alone. Such events may also pinpoint cellular vulnerabilities that could be exploited therapeutically. Overall, the genomic dependencies of G0 arrest mainly comprised genes involved in cell cycle pathways, p53 regulation and ubiquitination (most likely of cell cycle targets), and RUNX3 regulation, which have previously been shown to play a role in controlling proliferation and cell cycle entry [71] (Additional file 2: Fig. S3c). Invariably, this analysis has captured several events that are well known to promote cellular proliferation in can- cer: this is expected and confirms the validity of our model. It was reassuring that a func- tional TP53, lack of MYC amplification and lower mutation rates (Fig. 3c) were amongst the top ranked characteristics of tumours with high levels of G0 arrest, which also dis- played less aneuploidy. However, our analysis has also uncovered novel dependencies of G0 arrest-proliferation decisions that have not been reported previously, such as CEP89 and LMNA amplifications observed in fast cycling tumours, or ZMYM2 deletions preva- lent in samples with high levels of G0 arrest. ZMYM2 has recently been described as a novel binding partner of B-MYB and has been shown to be important in facilitating the G1/S cell cycle transition [72]. p16 (CDKN2A) deletions, one of the frequent early events during cancer evolution [73, 74], were enriched in tumours with high proportions of cells in G0. RB1 deletions and amplifications were both associated with a reduction in G0 arrest, which might reflect the dual role of RB1 in regulating proliferation and apop- tosis [75]. Our model also calls to attention to the broader mutational processes associated with this cellular state. Such processes showed fairly weak and heterogeneous correlations with G0 arrest within individual cancer tissues (Additional file 2: Fig. S2g), but their contribution becomes substantially clearer pan-cancer once other genomic sources (See figure on next page.) Fig. 3 Genomic landscape of G0 arrest decisions in cancer. a Cancer drivers with mutations or copy number alterations depleted pan‑cancer in a G0 arrest context. Features further selected by the pan‑cancer model are highlighted. b Schematic of the ensemble elastic net modelling employed to prioritise genomic changes associated with G0 arrest. c Genomic events significantly associated with G0 arrest, ranked according to their importance in the model (highest to lowest). Each point depicts an individual tumour sample, coloured by the value of the respective feature. For discrete variables, purple indicates the presence of the feature and green its absence. The Shapley values indicate the impact of individual feature values on the G0 arrest score prediction. d G0 arrest levels are significantly reduced in microsatellite unstable (MSI) samples in stomach adenocarcinoma (STAD) and uterine corpus endometrial carcinoma (UCEC), with the same trend (albeit not significant) shown in colon adenocarcinoma (COAD). Wilcoxon rank‑sum test p < 0.05; *p < 0.01. e Genomic alterations are depleted across DNA repair pathways during G0 arrest. Odds ratios of mutational load on pathway in G0 arrest are depicted, along with confidence intervals. CS, chromosome segregation; p53, p53 pathway; UR, ubiquitylation response; CPF, checkpoint factors; TM, telomere maintenance; CR, chromatin remodelling; TLS, translesion synthesis; NHEJ, non‑homologous end joining; NER, nucleotide excision repair; MMR, mismatch repair; FA, Fanconi Anaemia; BER, base excision repair. f G0 arrest scores are increased in cell lines with slow doubling time across MCF7 strains, which also show lower prevalence of PTEN mutations. g Tissue‑specific changes in G0 arrest between samples with/without quiescence‑associated deletions (blue), amplifications (red) and SNVs (brown) within the TCGA cohort (top) and external validation datasets (bottom) Wiecek et al. Genome Biology (2023) 24:128 Page 9 of 35 Fig. 3 (See legend on previous page.) are accounted for. In particular, we identified an association between G0 arrest and mutagenesis induced by the AID/APOBEC family of cytosine deaminases as denoted by signature SBS2 [68] (Fig. 3c). As highlighted by Mas-Ponte and Supek [76], APOBEC/ AID driven mutations tend to be directed towards early-replicating, gene-rich regions of the genome, inducing deleterious events on several genes including ZMYM2, which our pan-cancer model has linked with G0 arrest. In turn, defective DNA mismatch repair, as evidenced by signatures SBS44, SBS20, SBS15, SBS14 and SBS6 [68], was prevalent in fast cycling tumours (Fig. 3c). Mismatch Wiecek et al. Genome Biology (2023) 24:128 Page 10 of 35 repair deficiencies lead to hypermutation in a phenomenon termed ‘microsatellite insta- bility’ (MSI), which has been linked with increased immune evasion [77]. Cancers par- ticularly prone to MSI include colon, stomach and endometrial carcinomas [78], where this state was indeed linked with reduced G0 arrest (Fig. 3d). Furthermore, tumours with high proportions of cells in G0 were depleted of alterations across all DNA damage repair pathways (Fig. 3e). Our measurements of G0 arrest also reflected expected cycling patterns across 27 MCF7 strains [79]: cell lines with longer doubling times exhibited increased G0 arrest (Fig. 3f ). This coincided with a depletion of PTEN mutations, a dependency highlighted by the pan-cancer model. When checking for dependencies in individual cancer tissues, 24 out of the 25 genes identified by the model were significantly associated with G0 arrest or proliferation deci- sions in at least one tissue, most prominently in breast, lung and liver cancers which also represent the largest studies within TCGA (Fig. 3g, top panel). Most of these genomic insults were linked with a decrease in G0 arrest. In external validation datasets, these associations, including deletions in PTEN and LRP1B or amplifications of MYC, CEP89 and ETV6, featured most prominently in the largest cohort of breast cancer samples (Fig. 3g, bottom panel). These results highlight the fact that although a pan-cancer approach is suited to capture genomic events that are universally associated with cell cycle exit, certain genetic alterations may facilitate a higher or lower propensity of G0 arrest in a single tissue only. Indeed, when building a tissue-specific breast cancer model of G0 arrest using a com- bined ANOVA and random forest classification approach (Additional file 2: Fig. S4a), we not only recovered the associations with the TP53, MYC, LMNA and ETV6 events already seen in the pan-cancer model (Additional file 2: Fig. S4b) but also identified additional events which validated in the METABRIC cohort and were also seen in sev- eral other cancers, e.g. bladder, lung and lower grade glioma (Additional file 2: Fig. S4c). Notably, the APOBEC mutational signature SBS2 was the strongest genomic signal linked with G0 arrest in breast cancer (Supplementary Fig. 4b,d) and was most prevalent in Her2+ tumours, although the Luminal A subtype showed the highest levels of G0 arrest overall, as expected given its well-known lower proliferative capacity [80] (Sup- plementary Fig. 4e-f ). Validation of CEP89 as a modulator of G0 arrest capacity To gain more insight into the underlying biology of G0 arrest in cancer, we sought to experimentally validate associations highlighted by the pan-cancer model. We focused on the impact of CEP89 activity on proliferation/arrest decisions due to the high ranking of this putative oncogene in the model, the relatively unexplored links between CEP89 and cell cycle control, as well as its negative association with G0 arrest across a variety of cancer cell lines (Supplementary Fig. 5a-c). The function of CEP89 is not well char- acterised; however, the encoded protein has been proposed to function as a centroso- mal-associated protein [81, 82]. Centrosomes function as major microtubule-organising centres in cells, playing a key role in mitotic spindle assembly [83] and the mitotic entry checkpoint [84]. Moreover, centrosomes act as sites of ubiquitin-mediated proteolysis of cell cycle targets [85], and members of several growth signalling pathways, such as Wiecek et al. Genome Biology (2023) 24:128 Page 11 of 35 Wnt and NF-kB, localise at these structures [86, 87]. Several genetic interactions have also been reported between CEP89 and key cell cycle proteins, including cyclin D2 [88] (Fig. 4a). Our model linked CEP89 amplification with fast cycling tumours (Fig. 3c). Centro- some amplification is a common feature of tumours with high proliferation rates and high genomic instability [89], and overexpression of centrosomal proteins can alter cen- triole structure [90, 91]. Indeed, CEP89 amplified tumours presented elevated expres- sion of a previously reported centrosome amplification signature (CA20) [89] (Fig. 4b), which was strongly anticorrelated with G0 arrest levels (Fig. 4c). Furthermore, CEP89 expression was prognostic across multiple cancer tissues (Fig. 4d) and linked with toxic- ity of several cancer compounds in cell line models (Additional file 2: Fig. S5d). Fig. 4 CEP89 amplification is associated with lower G0 arrest capacity. a Network illustrating CEP89 interactions with cell cycle genes (from GeneMania). The edge colour indicates the interaction type, with green representing genetic interactions, orange representing predicted interactions and purple indicating pathway interactions. The edge width illustrates the interaction weight. b CA20 scores are significantly increased in TCGA primary tumours containing a CEP89 amplification. c Pan‑cancer relationship between CA20 and G0 arrest scores across the TCGA cohort. d Cox proportional hazards analysis estimates of the log hazards ratio for the impact of CEP89 expression on patient prognosis within individual cancer studies, after adjusting for tumour stage. Patients with high expression of CEP89 show significantly worse prognosis within ACC, LUSC, LIHC, KIRC and STAD, but significantly better prognosis within HNSC, PAAD and KIRP studies. e Western blot showing depletion of Cep89 protein 48 h after siRNA transfection of NCI‑H1299 cells. Mock is lipofectamine only; NTC is non‑targeting control siRNA. B‑actin is used as a loading control. f Graphs show that Cep89 depletion in NCI‑H1299 cells leads to a reduction in nuclear number and an increase in the fraction of G0 arrested cells, measured by an increase in the percentage of EdU negative (24 h EdU pulse) and Phospho‑Ser 807/811 Rb negative cells. One‑way ANOVA, p < 0.05, **p < 0.01. Mean of n 3 = Wiecek et al. Genome Biology (2023) 24:128 Page 12 of 35 We validated this target in the lung adenocarcinoma cell line NCI-H1299 showing high levels of CEP89 amplification. Cep89 depletion via siRNA knockdown caused a consistent decrease in cell number, in the absence of any detectable cell death, and an increase in the fraction of G0 cells as measured by phospho-Rb and EdU assays (Fig. 4e– f ). Thus, we propose CEP89 as a novel cell proliferation regulator that may be exploited in certain scenarios to control tumour growth. Characterisation of individual stress response programmes of G0 arrest While we had previously examined a generic programme of G0 arrest, cancer cells can enter this state due to different stimuli [19] and this may inform its aetiology and mani- festation. To explore this, we re-scored tumours based on gene expression programmes specific to serum starvation, contact inhibition, MEK inhibition, CDK4/6 inhibition or spontaneously occurring quiescence as defined by Min and Spencer [28] (see ‘Methods’). We observed a good correlation between our estimates representing individual stress response programmes and the expression of genes associated with the corresponding form of G0 arrest in the literature (Fig. 5a–e, Additional file 2: Fig. S6, see ‘Methods’). Specifically, strong inverse correlations were seen between our CDK4/6 inhibition scores and the mean expression of CDK4 and CDK6, or between our MEK inhibition scores and the expression of genes involved in the MAPK pathway [92]. Spontaneous quies- cence and serum starvation scores were most correlated with the activity of p21, or of genes involved in the cellular response to starvation, respectively. The contact inhibition programme was also captured, but with lesser specificity. CDK4/6 inhibition-induced G0 arrest levels were further validated using exter- nal RNA-seq datasets from cancer cell lines and xenograft mice sequenced before and after treatment with the CDK4/6 inhibitor palbociclib [93, 94] (Fig. 5f, Additional file 1: Table S3). The fact that the estimates for a generic G0 arrest phenotype were equally or, in some cases, more discriminative of cells treated with palbociclib confirms the gener- alisability of this score, which may be more broadly applicable to different tissues and/ or model systems, as shown previously in the single-cell validation data. The CDK4/6 inhibition scores outperformed all the other stress response subtype scores, suggesting that a combination of individual programmes and the generic score might best identify a specific stimulus driving G0 arrest. Interestingly, we also observed significant differ- ences in spontaneous quiescence scores before and after treatment. Indeed, p21 activ- ity has been linked with the palbociclib mechanism of action [95, 96], and this analysis suggests potential similarities between CDK4/6 inhibition and p21-dependent G0 arrest phenotypes. Having validated our framework for quantifying stimulus-specific G0 arrest pro- grammes, we proceeded to estimate the dominant form of stress that may induce cell cycle arrest in different cancer types (Fig. 5g). We found a range of G0 arrest aetiolo- gies across most tissues, while a minority of cancers were dominated by a single form of stress response, e.g. serum starvation in all G0 arrested pheochromocytomas and paragangliomas, contact inhibition in 88% of head and neck carcinomas and CDK4/6 inhibition in 80% of adrenocortical carcinomas. While we do not wish to claim that the state of cell cycle arrest will have necessarily been induced by the actual pre- dicted stimulus (impossible in the case of CDK4/6 or MEK inhibition, as the analysed Wiecek et al. Genome Biology (2023) 24:128 Page 13 of 35 Fig. 5 Pan‑cancer characterisation of individual G0 stress response programmes. a‑e Comparison of correlation coefficients between stress response programme scores and a mean expression of CDK4 and CDK6, b mean expression of curated contact inhibition genes, c a transcriptional MAPK Pathway Activity Score (MPAS), d mean expression of curated serum starvation genes and e CDKN1A expression (encoding for p21), across TCGA cancers. The correlations expected to be strongest (either negative or positive) are denoted by an asterisk. The generic G0 arrest score refers to scores calculated using the original list of 139 genes differentially expressed across all 5 forms of G0 arrest. f Comparison of stress response programme scores measured in cancer cell lines before (grey) and after (red) palbociclib treatment across three validation studies. Datasets used for validation are denoted by their corresponding GEO series accession number. g Predicted stress response diversity in samples with high levels of G0 arrest across individual cancer types. The same colour legend as in a is applied. Grey bars represent the proportion of samples for which the G0 arrest inducer could not be estimated samples are all treatment-naïve), we suggest that the downstream signalling cascade may resemble that triggered by such stimuli, e.g. via CDK4/6 or MEK loss of function mutations. Some of the differences observed might be explained by the dependency between p53 activity and the form of stress response that is enacted. Amongst the five differ- ent forms, spontaneous G0 arrest appeared most strongly dependent on p53 func- tionality, with a nearly two-fold enrichment of p53 proficient tumours in this group (Additional file 2: Fig. S6f ). Indeed, significantly higher levels of spontaneous G0 arrest were observed in the majority of cancers (56%) when p53 was functional rather than mutated. The second most dependent state was that of CDK4/6 inhibition, with increased levels in 36% of cancer types displaying p53 proficiency (Additional file 2: Fig. S6g). Overall, these analyses of stress response states point to common transcriptional features of drug-tolerant G0 arrested cells in different cancer settings that could be employed in designing ways to eradicate these cells in the future. Wiecek et al. Genome Biology (2023) 24:128 Page 14 of 35 Role of G0 arrest in driving therapeutic resistance in cancer uncovered from single‑cell data Overall, G0 arrest appears to be beneficial for the long-term outcome of cancer patients, even when accounting for potential confounders such as stage, sex and tissue (Fig. 6a, Additional file 2: Fig. S7a). No clear relation was observed between G0 arrest levels within the primary tumour and risk of relapse, although higher G0 arrest was occasion- ally deemed favourable to avoiding disease recurrence or progression (Additional file 2: Fig. S7b-e). Indeed, such slow cycling, indolent tumours would have higher chances of being eradicated earlier in the disease, which is consistent with reported worse prog- nosis of patients with higher tumour cell proliferation rates [97]. As expected, G0 arrest levels were increased in stage 1 tumours, although later stages also exhibited this phe- notype occasionally (Additional file 2: Fig. S7f ). However, outcomes do vary depend- ing on the stress source, with worse survival observed upon contact inhibition (Fig. 6b). The outcomes also vary by tissue: when the cut-offs between increased G0 arrest and high proliferation were defined on an individual cancer basis rather than pan-cancer, we found that lung, colon or oesophageal carcinoma patients displayed significantly worse prognosis in the context of high proportions of G0 arrested cells in the tumour (Fig. 6c, see ‘Methods’). Indeed, p53 wild-type colorectal cancers expressing a quiescence-linked fetal phenotype have been recently associated with metastasis and poor prognosis [98]. In contrast, adrenocortical and kidney papillary cell carcinoma ranked in the top of can- cers with improved survival. It is noticeable that the cancers in the former, worse prog- nosis group are also amongst the ones displaying lower than average G0 arrest (Fig. 2b), so the observed inferior outcomes could in part be linked to these cancers being intrinsi- cally faster progressing. It is possible there is a lower limit below which G0 arrest stops being useful for delaying growth and becomes detrimental instead, perhaps in conjunc- tion with treatment. Indeed, other factors such as the type of therapy received could play a role too. While we are limited in the investigation of such factors in TCGA due to the incomplete records available, these discrepancies should be subject to future research. While G0 arrest may confer an overall survival advantage in most cancers, it can also provide a pool of cells that are capable of developing resistance to therapy [12, 99]. Using our methodology, we indeed observed an increase in G0 arrest levels in cell lines (See figure on next page.) Fig. 6 Impact of G0 arrest on patient prognosis and treatment response. a Disease‑specific survival based on proliferation/G0 arrest levels for patients from TCGA within 15 years of follow‑up. Patients with increased levels of G0 arrest in primary tumours showed significantly better prognosis than patients with fast proliferating tumours. b–c Hazard ratio ranges illustrating the impact of different forms of G0 induction (b) and different tissues (c) on patient prognosis, after taking into account potential confounding factors. Values above 0 indicate significantly better prognosis when tumours contain high proportions of cells arrested in G0. d Change in G0 arrest scores inferred from bulk RNA‑seq across breast, pancreatic, colorectal and skin cancer cells in response to treatment with the CDK4/6 inhibitor palbociclib, 5‑FU or the BRAF inhibitor vemurafenib. e–f UMAP plot illustrating the response of the TP53‑proficient RKO colorectal cancer cell line to various 5‑FU doses and the corresponding proportions of cells predicted to be arrested/proliferating. Each dot is an individual cell, coloured according to its G0 arrest level. g–h The same as previous, but for the TP53‑deficient SW480 cell line. i–j UMAP plot illustrating the response of individual PC9 NSCLC cells to the EGFR inhibitor erlotinib across several time points and the corresponding proportion of cells predicted to be arrested/proliferating. k Principal component analysis illustrating the superimposition of single‑cell RNA‑seq profiles (circles) of G0 arrested NSCLC cells before/after EGFR inhibition onto the bulk RNA‑seq reference data (triangles) for MCF10A cells occupying various stress response states. l The proportion of NSCLC cells in k predicted to occupy different stress response states across several time points Wiecek et al. Genome Biology (2023) 24:128 Page 15 of 35 Fig. 6 (See legend on previous page.) following treatment with EGFR, BRAF and CDK4/6 inhibitors, as well as conventionally used chemotherapies such as 5-fluorouracil (5-FU) in multiple bulk RNA-seq datasets (Fig. 6d). Furthermore, the recent widespread availability of single-cell transcriptomics offers the opportunity to investigate the impact of G0 arrest on such therapies with much greater granularity than is allowed by bulk data. Using our G0 arrest signature and single-cell data from RKO and SW480 colon cancer cell lines treated with 5-FU [100], we could observe G0 arrest and proliferation decisions following conventional chemotherapy Wiecek et al. Genome Biology (2023) 24:128 Page 16 of 35 treatment. Within the p53 proficient cell line RKO, the fraction of G0 arrested cells increased from 41 to 93% after treatment with a low dose (10 μM) of 5-FU and per- sisted at higher doses (Fig. 6e–f ). In contrast, a comparable increase in G0 arrest was not observed in TP53 mutant SW480 cells, further emphasizing the key role of p53 as a regulator of cell cycle exit (Fig. 6g–h). This implies that although TP53 mutations confer a more aggressive tumour phenotype and may drive resistance via other mechanisms, TP53 wild-type tumour cells are more likely to be capable of entering a G0 ‘persistent’ state associated with drug resistance. SW480 cells showed higher apoptotic activity following treatment compared to RKO cells, particularly within actively cycling cells, further corroborating that cells capable of entering G0 arrest may be intrinsically less vulnerable to this therapy (Supplementary Fig. 8a-b). Similarly, using single-cell data from an EGFR mutant non-small cell lung cancer (NSCLC) cell line treated with the EGFR inhibitor erlotinib [13], we predicted that 40% of cells were likely to exist in a G0 arrest state prior to treatment. EGFR inhibition led to a massive decrease in cell numbers immediately after treatment, mostly due to pro- liferating cells dying off (Supplementary Fig. 8c-d), while the proportion of arrested cells increased to 96% at day 1, indicating an immediate selective advantage for such cells (Fig. 6i–j). These cells appear to gradually start proliferating again in the following days during continuous treatment, with the percentage of proliferating cells approach- ing pre-treatment levels by day 11 (Fig. 6j). The same trend captured by our signature could be observed upon KRAS and BRAF inhibition in different cell line models (Addi- tional file 2: Fig. S8e-h, Additional file 1: Table S3) [12, 13]. Furthermore, during the first days of treatment, the NSCLC cells that survived EGFR inhibition appeared to reside in a state most resembling that induced by serum starvation (Fig. 6k–l). Both EGFR kinase inhibitors and serum starvation have been shown to trigger autophagy [101], which may explain the convergence between this inhibitory trigger and the type of stress response. At day 11, most of the remaining arrested cells appeared in a state similar to that preced- ing the treatment (Fig. 6l). Thus, G0 arrest appears to explain resistance to broad acting chemotherapy agents as well as targeted molecular inhibitors of the Ras/MAPK signalling pathway, being either selected for, or induced immediately upon treatment, and gradually waning over time as cells start re-entering the cell cycle. Using massively multiplexed chemical transcrip- tomic data, we also analysed responses to 188 small molecule inhibitors in cell lines at single-cell resolution [102] (Additional file 2: Fig. S9). We observed a large increase in G0 arrest following treatment with not only compounds targeting cell cycle regulation and tyrosine kinase signalling, consistent with our previous results, but also for com- pounds modulating epigenetic regulation, e.g. histone deacetylase inhibitors—thus high- lighting the broad relevance of G0 arrest. While links between G0 arrest and therapeutic resistance are prevalently observed in cell lines, one would question whether this translates to similar pathology in cancer patients. While we observed significantly higher G0 arrest levels in pre-treated tumours of non-responders to neoadjuvant chemotherapy in a breast cancer study by Hatzis et al. [103] (Additional file 2: Fig. S10a), surveying various targeted therapy datasets from the SELECT study [104] and the TCGA data for links with response to various thera- pies (single agent and combinations) showed little to no evidence for a bulk signature Wiecek et al. Genome Biology (2023) 24:128 Page 17 of 35 of G0 being useful for predicting resistance in these studies (Supplementary Fig. 10b- c). Although the studies available for inspection are rather sparse, evidence from all the analyses presented here suggests there is no universal a priori role that G0 arrest has within the pre-treated primary tumour in determining response to treatment: favour- able overall outcomes are observed occasionally due to slower progressing malignancy, but resistance is also observed in the case of chemotherapy in breast cancer. Instead, the role of G0 arrest in enabling therapeutic resistance as a short-lived acquired phenotype as demonstrated in single-cell datasets appears more consistent. Tumour cell G0 arrest signature for use in single‑cell transcriptomics data Our ability to probe the nature of G0 arrest phenotypes in single-cell RNA-seq data using a defined G0 signature could aid the development of methods to selectively tar- get G0 arrested drug-resistant persister cells. However, a major challenge of single-cell RNA-seq data analysis is the high percentage of gene dropout, which could impact our ability to evaluate G0 arrest using the full 139 gene signature. The single-cell RNA-seq datasets we analysed exhibited an average drop-out of 8.5 genes out of the full gene sig- nature. While our scoring method remains robust to such levels of dropout (Supple- mentary Fig. 1d-f ), we also employed machine learning to reduce our initial list of 139 markers of quiescence to a robust 35-gene signature, comprised mainly of RNA metabo- lism and splicing-regulating factors, and also of genes involved in cell cycle progression, ageing and senescence, which could be applied to sparser datasets with larger levels of gene dropout (see ‘Methods’, Fig. 7a-b, Additional file 1: Table S4). The optimised sig- nature of G0 arrest performed similarly to the initial broadly defined programme in distinguishing fast cycling tumours from those containing high proportions of G0 cells (Fig. 7c). It also showed an average dropout of only 0.5 genes across the single-cell RNA- seq datasets used in this study (Fig. 7d), was similarly prognostic (p = 0.004) and showed comparable profiles of resistance to treatment (Fig. 7e, Additional file 2: Fig. S11). This minimal expression signature could be employed to track and further study emerging G0 arrest-enabled resistance in a variety of therapeutic scenarios. Discussion Despite its crucial role in cancer progression and resistance to therapies, G0 arrest in all its forms remains poorly characterised due to the scarcity of suitable models and bio- markers for large-scale tracking in the tissue or blood. The lack of proliferative mark- ers such as Ki67 or CDK2 [31, 105] does not uniquely distinguish G0 arrest from other cell cycle phases, e.g. G1. Miller et al. [32] have shown that the Ki67 is expressed at the mRNA level but the protein is degraded continuously both in G0 and G1, and it rather acts as a graded marker of S/G2/M. Similarly, CDK2 activity is low in G0 and G1, builds up at the restriction point, is high in the S phase and is then replaced by CDK1 in mito- sis. Reduced CDK2 expression can manifest due to not only quiescence and mitosis but also to DNA damage [106], and thus cut-offs to uniquely distinguish its activity in G0 would be difficult to define. Furthermore, these and other reliable markers of G0 arrest such as p27 or p130 [50] are best captured at protein level, which is much more sparsely measured, and expression does not accurately reflect their activity. This study overcame this limitation by employing genes active in different forms of quiescence whose patterns Wiecek et al. Genome Biology (2023) 24:128 Page 18 of 35 Fig. 7 Optimisation of the G0 arrest signature for use in single‑cell RNA‑seq data. a Methodology for refining the gene signature of G0 arrest: random forest classifiers are trained to distinguish arrested from cycling tumours on three high confidence datasets; Gini index thresholding is optimised to prioritise a final list of 35 genes. b Gini index variation, correlation with experimentally measured quiescence via EdU and phospho‑Rb staining assays, and corresponding p‑values are plotted as the number of genes considered in the model is increased. The vertical black dashed line indicates the threshold chosen for the final solution of 35 genes. The horizontal grey dotted line indicates the threshold for p‑value significance. c Additional external validation of the 35 gene signature acting as a classifier of G0 arrested and proliferating cells in single‑cell and bulk datasets. d Dropout in single‑cell data by gene signature. The percentage of genes out of the 35 (red) and 139 (grey) gene lists with reported expression across the single‑cell RNA‑seq datasets analysed in this study. e Proportion of cycling and G0 arrested cells estimated in single‑cell datasets of p53 wild‑type and mutant lines treated with 5FU, as well as cells treated with EGFR inhibitors. Data as in Fig. 6 of expression are distinct from markers of a longer G1 phase and capture cell cycle arrest as also observed in senescence, stemness or dormancy. We have extensively validated our method and signature in single-cell datasets and cancer cell lines and have demon- strated that it can reliably and robustly capture signals of G0 arrest both in bulk tissue as well as in single cells. Within bulk tissue, we are limited in our capacity to distinguish between large fractions of cells residing in short-lived G0 arrest and a smaller fraction of cells that are in deeper Wiecek et al. Genome Biology (2023) 24:128 Page 19 of 35 G0 arrest, as our score seems to reflect both parameters to a certain extent. Bearing in mind this limitation, our score could potentially also be used in a single-cell setting to capture longer-lived cell cycle arrest states such as ones demonstrated in senescence or dormancy and could assist in identifying such states, but only with the help of additional cell-state specific, immune or secretory biomarkers. Indeed, gene activity linked to cell cycle arrest is not exclusive to quiescence, but can be shared with senescence or dor- mancy in certain scenarios, as also demonstrated in some of our analyses of senescent cells. This makes it difficult to clearly distinguish states like dormancy, senescence and quiescence (particularly deep quiescence), as even their definitions can be contentious at times both in the context of human cancers [4, 57, 107–109] as well as in physiological conditions in other organisms [110, 111]. Since our signature was derived and validated in experiments that were tailored specifically to induce and/or measure quiescence, we believe the signature proposed in this study best reflects a quiescent-like, reversible G0 cell cycle arrest state. While senescent and dormant cells could be distinguished from their quiescent counterparts simply based on additional senescence and dormancy markers, further research is nevertheless required in the future to delineate signatures that are both necessary and sufficient to unambiguously discriminate all three states. In the meantime, future studies utilising our G0 signature should also test for such addi- tional markers like β-galactosidase activity, the SASP and other senescence markers, or NRF2, NR2F1, SOX9, RARβ [112, 113] and other dormancy markers to ascertain the type of G0 arrest that is being captured. The versatility of our signature is evidenced by high classification accuracies across a variety of solid cancer datasets. More variable performance was observed when applied to haematopoietic stem cells as it was not designed to capture signals in this context (Additional file 2: Fig. S1b). While we cannot exclude that the patterns captured may also occasionally reflect cell cycle arrest in G1 or G2, this broad signature would still capture phenotypes resulting from intrinsic or extrinsic cellular stress that reflect tem- porary tumour adaptation during the course of cancer evolution or upon treatment with drugs. Thus, studying such states is relevant for identifying vulnerabilities that could be exploited at different time points during the course of cancer treatment. We show that G0 arrest is pervasive across different solid cancers and generally associ- ated with more stable, less mutated genomes with intact DNA damage repair pathways. We also find a link between APOBEC mutagenesis and higher levels of G0 arrest. Some neoplastic events enriched in tumours with increased G0 arrest, such as p16 or ZMYM2 deletions, could mark elevated genomic stress that renders cells more prone to cell cycle exit. We also identified mutational events affecting a variety of genes such as PTEN, CEP89, CYLD and LMNA that appear unfavourable to cell cycle arrest, thus potentially implicating them in influencing G0 arrest-proliferation decisions. Amongst these, we propose and validate CEP89 as a novel modulator of G0 arrest capacity in non-small cell lung cancer. A recent paper describes how increased CEP89 copy number and expres- sion correlates with a worse prognosis in ovarian cancer [114], which we hypothesise could be linked to Cep89’s role in modulating G0 arrest. Although we do not yet know how Cep89 regulates G0 arrest, two roles have been ascribed to Cep89 which could be significant. First, Cep89 is required for primary cilium assembly [115, 116]. The primary cilium acts as a signalling hub, transducing extracellular signals to intracellular signalling Wiecek et al. Genome Biology (2023) 24:128 Page 20 of 35 networks, many of which regulate growth and proliferation [117]. Cep89 deficiency also leads to defects in Complex IV assembly in the electron transport chain in mitochon- dria, leading to decreased mitochondrial function and ATP production [118]. Decreased ATP would impair the ability of cells to proliferate. Since Cep89 is a coiled-coil protein with no obviously targetable regulatory domains, it will be important to ascertain which Cep89 function is key to regulating the balance between proliferation and arrest in can- cer cells to be able to potentially target that process, rather than Cep89 itself, to induce or maintain G0 arrest. These large-scale genomic associations with G0 arrest phenotypes are only currently feasible in bulk datasets. However, bulk sequenced data has a major limitation in cap- turing an average signal across all cells within the tumour, which prevents individual cell state identification and counting. Our subsequent exploration of single-cell datasets across 193 therapeutic scenarios complements this analysis and illustrates the power of applying our signature in single cells. Our signature of G0 arrest is prognostic and marks primary tumours with a lower proliferative capacity before treatment, but we also clearly demonstrate that it can be employed to track resistance to multiple cell cycle, kinase signalling and epigenetic tar- geting regimens, where it often appears as a short-lived phenotype. While this discrep- ancy may appear incompatible at first glance, it is not unlike other cellular processes that have been shown to present dual roles in a cancer setting, such as reactive oxygen spe- cies [119], but also p38α [120] or NRF2 [121], both of which have been implicated in qui- escence or dormancy [113, 122]. It is possible that there is a tipping point between G0 arrest acting beneficially or detrimentally during tumour development and treatment. Furthermore, this is likely influenced by a myriad of other complex factors that we have not had the chance to analyse in depth here, and in some cases, it may just be the base- line for acquiring cancer cell stemness or senescent properties. While we acknowledge this conundrum requires further study, we believe this phenotype also offers a unique opportunity to further understand mechanisms of tumour resistance. A key open ques- tion remains: if G0 arrest drives resistance, does it do so in a Darwinian fashion, as a pre- existing population that is selected for upon drug treatment, or is it instead an acquired phenotype? Our single-cell analyses cannot exclude either scenario. Given the variable links to treatment response and lack of clear evidence for relapse when surveying G0 arrest in primary tumours before treatment, it is likely our G0 arrest signature in its cur- rent form cannot be used to predict resistance to chemotherapy or targeted therapy, and we would not recommend it for this purpose unless further validated in a specific cancer setting. We have also not inspected the role of G0 arrest in the context of immunother- apy, which remains an area of future study. However, we believe our signature has high value for the study of emerging resistance in an in vitro/in vivo setting, as a short-lived enabler of drug tolerance. The optimised signature we propose for single-cell data makes it tractable to a variety of future studies in this area. In a treatment setting, vulnerabilities of G0 arrested cells could be exploited for com- bination therapies. Cells which have exited the cell cycle utilise several mechanisms to achieve drug resistance, including upregulation of stress-induced pathways such as anti-apoptotic BCL-2 signalling [123], anti-ROS programmes [28] or immune evasion Wiecek et al. Genome Biology (2023) 24:128 Page 21 of 35 [15]. Further studies are needed to elucidate which of these mechanisms are specifically employed on a case-by-case basis. Our findings contribute to the understanding of the aetiology and genetic context of G0 arrest in cancer. This is particularly relevant to not only identifying new anti-prolif- erative targets but also for the detection and eradication of drug-tolerant persister cells, which have been frequently, although not always, observed to be slow cycling or entirely quiescent/senescent [8, 9]. Importantly, the state of G0 arrest that we have studied here is distinct from that of disseminated tumour cells causing clinical dormancy and can- cer relapse, often after many years from the treatment of the primary tumour [4, 124]. Here, we have focused on understanding how tumours make proliferation and G0 arrest decisions during the earlier stages of cancer development, within the treatment-naïve primary tumour and as an immediate response to anti-cancer therapies. However, since the dormancy of disseminated tumour cells is fundamentally enabled through a long but temporary cell cycle arrest, we believe our findings of the fundamental processes linked with G0 arrest could in the future help inform a better characterisation of dor- mant tumour cells when combined with specific microenvironmental signatures that are critical for enabling that process. Conclusions Overall, our study provides, for the first time, a pan-cancer view of G0 arrest and its evolutionary constraints, underlying novel mutational dependencies which could be exploited in the clinic. We propose a G0 arrest signature which can be robustly meas- ured in bulk tissue or single cells and could potentially inform therapeutic strategies in the longer term. This signature could be assessed in the clinic to track rapidly emerg- ing resistance, e.g. through liquid biopsies or targeted gene panels. We hope these insights can be used as building blocks for future studies into the different regulators of G0 arrest, including epigenetics and microenvironmental interactions, as well as the mechanisms by which it enables therapeutic resistance both in solid and haematological malignancies. Methods Selection of G0 arrest marker genes Generic G0 arrest markers Differential expression analysis results comparing cycling immortalised, non-trans- formed human epithelial cells and cells in five different forms of quiescence (sponta- neous quiescence, contact inhibition, serum starvation, CDK4/6 inhibition and MEK inhibition) were obtained from Min and Spencer [28]. A total of 195 genes were differen- tially expressed in all five forms of quiescence under an adjusted p-value cut-off of 0.05. This gene list, reflective of a generic G0 arrest phenotype, was subjected to the follow- ing refinement and filtering steps: (1) selection of genes with a unidirectional change of expression across all five forms of quiescence; (2) removal of genes involved in other cell cycle stages included in the ‘KEGG_CELL_CYCLE’ gene list deposited at MSigDB; (3) removal of genes showing low standard deviation and low levels of expression within the TCGA dataset, or which showed low correlation with the pan-cancer expression of Wiecek et al. Genome Biology (2023) 24:128 Page 22 of 35 the transcriptional targets of the DREAM complex, the main effector of quiescence, in TCGA. The resulting 139-gene signature is presented in Additional file 1: Table S1. G0 arrest stress response‑specific markers Gene lists representing spontaneous quiescence, contact inhibition, serum starvation, CDK4/6 inhibition and MEK inhibition programmes were obtained using genes differ- entially expressed in each individual quiescence form using an adjusted p-value cut-off of 0.05. The gene lists were subjected to filtering steps 2 and 3 described above. Follow- ing the refinement steps, 10 upregulated and 10 downregulated genes with highest log2 fold changes were selected for each stress response type. Quantification of G0 arrest in tumours The GSVA R package was used to implement the combined Z-score [40], ssGSEA [41] and GSVA [42] gene set enrichment methods. For the above three methods, a sepa- rate score was obtained for genes upregulated in quiescence and genes downregulated in quiescence, following which a final G0 arrest score was obtained by subtracting the two scores. The singscore single-sample gene signature scoring method [43] was imple- mented using the singscore R package. In addition to these, we also calculated a mean scaled G0 arrest score based on the refined list of genes upregulated and downregulated in quiescence, as well as a curated housekeeping genes from the ‘HSIAO_HOUSEKEEP- ING_GENES’ list deposited at MSigDB, as follows: 1 n G0m = GD GU − 1 n 1 GH n G0m = mean scale G0 arrest score GU = expression of genes upregulated in quiescence GD = expression of genes downregulated in quiescence GH = expression of housekeeping genes n = number of genes in each gene set G0 arrest scores for the TCGA cohort were derived from expression data scaled by tumour purity estimates. The pan-cancer TCGA samples were also classified into groups with ‘high’ or ‘low’ levels of G0 arrest based on k-means clustering (k = 2) on the expres- sion data of 139 G0 biomarker genes, following the removal of tissue-specific expression differences using the ComBat function from the sva R package [125]. Measuring the duration of G0 arrest We employed the GSE124109 dataset from Fujimaki et al. [52] where rat embryonic fibroblasts were transcriptomically profiled as they moved from short- to long-term qui- escence in the absence of growth signals. The derived G0 arrest scores using our com- bined Z-score methodology increased from short- to longer-term quiescence. Wiecek et al. Genome Biology (2023) 24:128 Page 23 of 35 Validation of G0 arrest scoring methodologies Single‑cell RNA‑sequencing validation datasets Datasets were obtained from the ArrayExpress and Gene Expression Omnibus (GEO) databases though the following GEO Series accession numbers: GSE83142, GSE75367, GSE137912, GSE139013, GSE90742 and E-MTAB-4547. Quality control analysis was standardised using the SingleCellExperiment [126] and scater [127] R packages. Normal- isation was performed using the scran [128] R package. Bulk RNA‑sequencing validation datasets Datasets were obtained from the GEO database through the following GEO Series accession numbers: GSE93391, GSE114012, GSE131594, GSE152699, GSE124854, GSE135215, GSE99116, GSE124109, GSE61130, GSE64553 and GSE63577. GSE114012 count data were normalised to TPM values using the GeoTcgaData R package. All nor- malised datasets were log-transformed before further analysis. The accuracy with which the G0 arrest scoring methods could separate proliferating and quiescent samples within the validation datasets was determined by calculating the area under the curve of the receiver operating characteristic (ROC) curves, using the plotROC R package. Experimental validation in lung adenocarcinoma cell lines The average fraction of cancer cells spontaneously entering quiescence was estimated for nine lung adenocarcinoma cell lines (NCIH460, A549, NCIH1666, NCIH1944, NCIH1563, NCIH1299, NCIH1650, H358, L23) using EdU and phospho-Rb staining proliferation assays. Cell lines were obtained from ATCC or Sigma and regularly checked for mycoplasma. A549 and NCIH460 were cultured in DMEM (Gibco). NCIH358, NCIH1299 and NCIH1563 were maintained in RPMI-1640 (Gibco) supplemented with 5 mM sodium pyruvate and 0.5% glucose. NCIH1944, NCIH1666, NCIH1650 and L23 were grown in RPMI-1640 ATCC formulation (Gibco). A427 were cultured in EMEM (ATCC). A549, NCIH460, H358, NCIH1299, NCIH1563, and A427 were supplemented with 10% heat inactivated FBS. NCIH1666 with 5% heat-inactivated FBS and all other cell lines with 10% non-heat inactivated FBS. All cell lines had penicillin–streptomycin (Gibco) added to 1%. Cells were maintained at 37 °C and 5% CO2. To calculate the quiescent fraction, A549 and NCIH460 cells were plated at a density of 500 cells/well, and all other cell lines at a density of 1000/well, in 384-well CellCarrier Ultra plates (PerkinElmer) in the rel- evant media. Twenty-four hours later, 5 μM EdU was added and cells were incubated for a further 24 h before fixing in a final concentration of 4% formaldehyde (15 min, RT), permeabilization with PBS/0.5% Triton X-100 (15 min, RT) and blocking with 2% BSA in PBS (60 min, RT). The EdU signal was detected using Click-iT chemistry, according to the manufacturer’s protocol (ThermoFisher). Cells were also labelled for phospho- Ser807/811 Rb (phospho-Rb) using Rabbit mAb 8516 (CST) at 1:2000 in blocking solu- tion, overnight at 4 °C. Unbound primary antibody was washed three times in PBS and secondary Alexa-conjugated antibodies were used to detect the signal (ThermoFisher, 1:1000, 1 h at RT). Finally, nuclei were labelled with Hoechst 33258 (1 μg/ml, 15 min RT) Wiecek et al. Genome Biology (2023) 24:128 Page 24 of 35 before imaging on a high-content widefield Operetta microscope, 20 × N.A. 0.8. Auto- mated image analysis (Harmony, PerkinElmer) was used to segment and quantify nuclear signals in imaged cells. Quiescent cells were defined by the absence of EdU or phospho- Rb staining, determined by quantification of their nuclear expression (Fig. 1e–f ). Endogenous PCNA was labelled at the N-terminus with a cDNA encoding mRuby in both A549 and NCI-H1944 cells, using AAV-mediated gene-targeting, according to methods described in Zerjatke et al. [54]. mRuby-expressing cells were sorted into 50:50 conditioned:fresh media at single-cell density into 96-well plates by FACS and single-cell clones expanded. For live-cell imaging, 500 cells in phenol-red free media were plated per well of a 384 CellCarrierUltra plate (PerkinElmer) the day before imaging. Prior to imaging, a breathable membrane was applied to the plate and cells were imaged on the Operetta HCS microscope (PerkinElmer) at 37 °C, 5% CO2 using the 20 × N.A. 0.8 objective and at 10–12 min intervals for 48 h. Images were then exported and G0/G1 length (time from mitotic exit to S-phase entry) was analysed manually in FIJI. The G0 arrest scores for cancer cell lines were calculated using corresponding log- transformed RPKM normalised bulk RNA-seq data from the Cancer Cell Line Encyclo- pedia (CCLE) database [129]. CEP89 was depleted by ON-Target siRNA Pool from Horizon. NCI-H1299 cells were reverse transfected in 384-well plates with 20 nM of non-targeting control (NTC) or CEP89-targeting siRNA using Lipofectamine RNAiMax (ThermoFisher), according to the manufacturer’s instructions. Cells were left for 24 h, before 5 μM EdU was added for the final 24 h and then cells were processed as above to determine the quiescent fraction. To determine the level of Cep89 depletion by western blot, cells were reverse transfected with siRNA in 24-well plates. Forty-eight hours after transfection, cells were lysed directly in 1 × SDS sample buffer with 1 mM DTT (ThermoFisher). Samples were separated on pre-cast 4–20% Tris-Glycine gels, transferred to PVDF using the iBlot2 system and membranes blocked in blocking buffer (5% milk in TBS) for 1 h at RT. The membrane was then cut and the upper half was incubated in 1:1000 Cep89 antibody (Sigma, HPA040056), the bottom half in B-actin antibody 1:2000 (CST; 3700S) diluted in blocking buffer overnight at 4 °C. Membranes were washed three times in TBS-0.05% TritonX-100 before being incubated in secondary anti-rabbit (Cep89) or anti-mouse (B-actin) HRP conjugated antibodies (CST 7074P2 and CST 7076P2, respectively) diluted 1:2000 in blocking buffer for 1 h at RT. Membranes were washed three times again and signal detected using Clarity ECL solution (BioRad) and scanned on an Amer- sham ImageQuant 800 analyser. Multi‑omics discovery cohort FPKM normalised RNA-sequencing expression data, copy number variation gene- level data, RPPA levels for p27 as well as mutation annotation files aligned against the GRCh38 human reference genome from the Mutect2 pipeline were downloaded using the TCGABiolinks R package [130] for 9712 TCGA primary tumour samples across 31 solid cancer types. Haematological malignancies were excluded as the G0 markers were derived in epithelial cells and might not be equally suited to capture this pheno- type in blood. For patients with multiple samples available, one RNA-seq barcode entry was selected for each individual patient resulting in 9631 total entries. All expression Wiecek et al. Genome Biology (2023) 24:128 Page 25 of 35 data were log-transformed for downstream analysis. During G0 arrest score calculation, expression data for the primary tumour samples was scaled according to tumour purity estimates reported by Hoadley et al. [131] to account for potential confounding cell cycle arrest signals coming from non-tumour cells in the microenvironment. Samples with purity estimates lower than 30% were removed, leaving 8005 samples for downstream analysis. The mutation rates of all TCGA primary tumour samples were determined by log- transforming the total number of mutations in each sample divided by the length of the exome capture (38 Mb). TP53 functional status was assessed based on somatic mutation and copy num- ber alterations as described in Zhang et al. [132]. TP53 mutation and copy number for the TCGA tumours were downloaded from cBioPortal (http:// www. cbiop ortal. org). Tumours with TP53 oncogenic mutations (annotated by OncoKB) and copy-number alterations (GISTIC score ≤ − 1) were assigned as TP53 mutant and CNV loss. Tumours without these TP53 alterations were assigned as TP53 wild type. The effects of the TP53 mutation status on G0 arrest were then determined with a linear model approach with the G0 arrest score as a dependent variable and mutational status as an independent variable. The P values were FDR-adjusted. APOBEC mutagenesis enriched samples were determined through pan-cancer clus- tering of mutational signature contributions as described in Wiecek et al. [133]. The APOBEC mutagenesis cluster was defined as the cluster with highest mean SBS2 and SBS13 contribution. This was repeated 100 times and only samples which appeared in the APOBEC cluster at least 50 times were counted as being APOBEC enriched. Aneuploidy scores and whole-genome duplication events across TCGA samples were obtained from Taylor et al. [134]. Microsatellite instability status for uterine corpus endometrial carcinoma, as well as stomach and colon adenocarcinoma samples were obtained from Cortes-Ciriano et al. [78]. Telomerase enzymatic activity ‘EXTEND’ scores were obtained from Noureen et al. [62]. Expression-based cancer cell stemness indices were obtained from Malta et al. [63]. Centrosome amplification transcriptomic signature (CA20) scores were obtained from Almeida et al. [89]. PHATE dimensionality reduction The phateR R package [135] was used to perform the dimensionality reduction with a constant seed for reproducibility. The ComBat function from the sva R package [136] was used to remove tissue-specific expression patterns from the TCGA RNA-seq data. Cancer stem cell division estimates The mean stem cell division estimates for different cancer types used in this study were obtained from Tomasetti and Vogelstein [55]. Mutational signature estimation Mutational signature contributions were inferred as described in Wiecek et al. [133]. Wiecek et al. Genome Biology (2023) 24:128 Page 26 of 35 Machine learning of G0 arrest‑linked features via ensemble elastic net regression models The COSMIC database was used to source a list of 723 known drivers of tumorigenesis (tiers 1 + 2); 285 oncogenes and tumour suppressors from a curated list showed a sig- nificant enrichment or depletion of mutations or copy number variants in samples with high levels of G0 arrest either pan-cancer or within individual TCGA studies. To classify G0 arrest-prone from fast proliferating tumours, the 285 genes were used as input features for an ensemble elastic net regression model along the tumour muta- tional rate, whole-genome doubling estimates, ploidy, aneuploidy scores and 15 muta- tional signatures, which showed a significant correlation with G0 arrest scores either pan-cancer or within individual TCGA studies. The caret R package was used to build an elastic net regression model 1000 times on the training dataset of 3753 TCGA pri- mary tumour samples (80% of the total dataset). Only samples with at least 50 mutations were used in the model, for which mutational signatures could be reliably estimated. For each of the 1000 iterations, we randomly selected 90% of the samples from the training dataset to build the model. Only features which were included in all 1000 model itera- tions were selected for further analysis. To test the performance of our approach, a linear regression model was built using the reduced list of genomic features and their corre- sponding coefficients averaged across the 1000 elastic net regression model iterations. When applying the resulting linear regression model on the internal validation dataset of 936 samples, we found a strong correlation between the observed and predicted G0 arrest scores (R = 0.73, p < 2.2e − 16). SHAP values for the linear regression model used to predict G0 arrest scores were obtained using the fastshap R package. Gene enrichment and network analysis Gene set enrichment analysis was carried out using the ReactomePA R package, as well as GeneMania [137] and ConsensusPathDB [138]. Interactions between CEP89 and other cell cycle components were inferred using the list of cell cycle genes provided by cBioPortal and GeneMania to reconstruct the expanded network with direct interactors (STAG1, CCND2, STAT3). Networks were visualised using Cytoscape [139]. Gene lists Genes associated with the G1 phase of the cell cycle were obtained from the curated ‘REACTOME_G1_PHASE’ list deposited at MSigDB. Genes associated with the G1/S and G2/M phases of the cell cycle were obtained from Tirosh et al. [51]. Genes associated with apoptosis were obtained from the curated ‘HALLMARK_ APOPTOSIS’ list deposited at MSigDB. Genes associated with the senescence-associated secretory phenotype were obtained from Basisty et al. [60]. Lists of genes making up the various DNA damage repair path- ways were derived from Pearl et al. [140]. Genes associated with contact inhibition were obtained from the curated ‘contact inhi- bition’ gene ontology term. Genes associated with serum starvation were obtained from the curated ‘REACTOME_CELLULAR_RESPONSE_TO_STARVATION’ list deposited at MSigDB. MEK inhibition was assessed based on the activity of the MAPK pathway as Wiecek et al. Genome Biology (2023) 24:128 Page 27 of 35 determined using an expression signature (MPAS) consisting of 10 downstream MAPK transcripts [92]. Validation of the genomic constraints of G0 arrest For elastic net model feature validation, RNA-seq data was downloaded for six cancer studies from cBioPortal [141], along with patient-matched whole-genome, whole-exome and targeted sequencing data. The 6 datasets used comprise breast cancer (SMC [142] and METABRIC [143]), paediatric Wilms’ tumour (TARGET [144]), bladder cancer, prostate adenocarcinoma and sarcoma (MSKCC [145–147]) studies. The data were pro- cessed and analysed in the same manner as the TCGA data. RNA-seq data for 27 MCF7 cell line strains, alongside cell line growth rates and targeted mutational sequencing data were obtained from Ben-David et al. [79]. Genomic dependency modelling in breast cancer An ANOVA-based feature importance classification was used and identified 30 genomic features most discriminative of samples with lower and higher than average G0 arrest scores. A random forest model was then built using the identified features and correctly classified samples according to their G0 arrest state with a mean accuracy of 74% across five randomly sampled test datasets from the cohort. Survival analysis Multivariate Cox proportional hazards analysis was carried out using the coxph func- tion from the survival R package. The optimal quiescence score cut-off value of 2.95 was determined pan-cancer using the surv_cutpoint function. We also used this function to determine optimal cut-offs for individual cancer types, as presented in Fig. 6c. Treatment response single‑cell and bulk RNA‑seq datasets Datasets have been obtained from the GEO database through the following GEO Series accession numbers for the cell line experiments: GSE134836, GSE134838, GSE134839, GSE137912, GSE149224, GSE124854, GSE135215, GSE99116, GSE152699, GSE178839, GSE139944, and the following accession numbers for the patient sample datasets: GSE191127, GSE109211, GSE50509, GSE65185, GSE66399, GSE68871, GSE99898 (Additional file 1: Table S3). Unified treatment response data for TCGA was obtained from Moiso, medRxiv 2021 [148]. The umap R package was used for dimensionality reduction with constant seed for reproducibility. Stress response subtype determination TCGA cohort studies Samples with evidence of cell cycle arrest characterised by a generic G0 score > 0 were further subclassified based on the most likely form of stress response, among CDK4/6 inhibition, contact inhibition, MEK inhibition, spontaneous quiescence or serum Wiecek et al. Genome Biology (2023) 24:128 Page 28 of 35 starvation, using stress-specific expression signatures. We opted for a conservative approach and classed each sample with a high level of G0 arrest into a specific stress response subtype if the arrest score for the corresponding programme was higher than one standard deviation of the distribution across the TCGA cohort and if the score was significantly higher than for the remaining programmes when assessed using a Student’s t test. Samples which could not be classified into any of the five stress response states characterised in this study were classified as ‘uncertain’. Single‑cell RNA seq treatment response datasets The stress response subtype of individual single cells was inferred by mapping such individual cells onto the reference dataset of MCF10A cells reflecting different forms of G0 arrest obtained from Min and Spencer [28]. The ComBat R package was used to remove the study batch effect between the expression data to be classified and the reference bulk RNA-seq data. PCA dimensionality reduction analysis was then used on the combined datasets using the prcomp R function. For each patient sample or single-cell expression data entry, a k-nearest neighbour algorithm classification was performed using the knn function from the class R package. During the classifica- tion, the three nearest reference bulk RNA-seq data points were considered, with two nearest neighbours with identical class needed for classification. Optimisation of the G0 arrest signature We investigated if a subset of the 139 G0 phase-related genes could act as a more reli- able marker of cell cycle arrest that would bypass dropout issues in single-cell data. This was performed in three steps: (1) Assessment of individual importance as G0 arrest marker for a given gene We collected three high confidence single-cell expression datasets separating arrested from proliferating cells. A random forest model was trained on each dataset sepa- rately to predict the state (G0 arrest/cycling) of a given cell based on the expression levels of the 139 genes in the signature. The Gini indices corresponding to each gene in the model were normalised to a range of values between 0 and 1, which would reflect how important an individual gene was for determining G0 arrest state relative to the other 138 genes. The procedure was repeated 1000 times for each of the three datasets, and the average Gini coefficients across iterations were stored. (2) Prioritisation of gene subsets based on cumulative importance in the model Genes were placed in the candidate subset if their importance metric was above a given threshold in at least one of the datasets. By gradually increasing the threshold from 0 to 1, different gene combinations were produced. (3) External validation of candidate subsets The gene combinations in (2) were tested for their ability to predict G0 arrest. For this, a separate validation dataset was utilized, which contained gene expression levels for the 139 genes in the 10 lung cancer cell lines previously employed for experimental Wiecek et al. Genome Biology (2023) 24:128 Page 29 of 35 validation, along with the quiescence state of the lines as inferred by phospho-Rb and EdU staining. For each gene subset, a combined Z-score of G0 arrest was cal- culated from the expression levels as described previously. The correlations between this Z-score and the two experimental measurements of quiescence were used to establish the ability of a gene combination to predict quiescence. Among the top performing subsets, a 35 gene signature with a mean correlation of 78% between predicted and measured G0 arrest levels in the test data (p = 0.016) showed the highest correlation with phospho-Rb measurements capturing short-lived G0 arrest, the more common state observed in single-cell treatment datasets. Therefore, this signature was deemed to achieve the best trade-off between gene numbers and sig- nal capture. The optimised gene signature is provided in Additional file 1: Table S4. Statistical analysis Groups were compared using a two-sided Student’s t test, Wilcoxon rank-sum test or ANOVA, as appropriate. p-values were adjusted for multiple testing where appropriate using the Benjamini–Hochberg method. Graphs were generated using the ggplot2 and ggpubr R packages. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s13059‑ 023‑ 02963‑4. Additional file 1: Table S1. The 139‑gene signature of G0 arrest. The up‑ or downregulation status in G0 arrest is indicated for each gene. Table S2. Summary of external G0 arrest score validation datasets. Table S3. Summary of external cancer cell line treatment response data. Table S4. Results of the signature optimisation analysis. Additional file 2: Fig. S1. G0 arrest score evaluation and validation. Fig. S2. Expression and genomic patterns of G0 arrest in tumours. Fig. S3. Modelling and validating a pan‑cancer classifier of G0 arrest. Fig. S4. Genomic landscape of G0 arrest in breast cancer. Fig. S5. CEP89 expression is associated with G0 arrest and has prognostic value. Fig. S6. Stress response programme validation and links with p53 status. Fig. S7. Relevance of G0 arrest to clinical outcome in cancer. Fig. S8. G0 arrest signatures of drug tolerance in single cell data. Fig. S9. G0 arrest dynamics upon various treatment modalities. Fig. S10. G0 arrest levels in patient samples compared between responders and non‑respond‑ ers to various cancer treatments. Fig. S11. Application of the reduced G0 arrest signature to scRNA‑seq data. Additional file 3. Review history. Acknowledgements We would like to thank Prof Chris Barnes for the very helpful discussions and input on the findings of the study. Review history The review history is available as Additional file 3. Peer review information Wenjing She was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Authors’ contributions MS designed the study and supervised the computational analyses. ARB designed and supervised the experimental validation in cell lines. GB supervised the analysis of p53 functional association. AJW developed the quiescence scoring methodology and performed all computational analyses to validate and apply it in bulk and single‑cell datasets, as well as link it to genomic features. SC performed the experimental validation of G0 arrest prevalence and CEP89 association with G0 arrest in cell lines. DK performed the inference of the minimal signature of G0 arrest applicable in single‑cell data. MPC performed the random forest modelling and feature selection in breast cancer. LG helped with the gene prior‑ itisation. GMT wrote the code for batch effect correction and for PCA mapping of single‑cell data on a reference dataset. DHJ performed the APOBEC enrichment classification. PZ and LX performed the G0 arrest comparison of p53 wild‑type and mutated cancers. MS, AJW, ARB and SC wrote the manuscript, with contributions from all other authors. All authors read and approved the manuscript. Authors’ Twitter handles Twitter handles: @Alexis_Barr (Alexis R. Barr), @mariasecrier (Maria Secrier). Wiecek et al. Genome Biology (2023) 24:128 Page 30 of 35 Funding AJW and DHJ were supported by MRC DTP grants (MR/N013867/1). MPC was supported by an Academy of Medical Science Springboard award (SBF004\1042). GMT was supported by a Wellcome Seed Award in Science (215296/Z/19/Z). MS was supported by a UKRI Future Leaders Fellowship (MR/T042184/1). Work in MS’s lab was supported by a BBSRC equipment grant (BB/R01356X/1) and a Wellcome Institutional Strategic Support Fund (204841/Z/16/Z). ARB and SC are supported by a CRUK CDF (C63833/A25729) and work in ARB’s lab is supported by MRC core‑funding to the London Institute of Medical Sciences (MC‑A658‑5TY60). Availability of data and materials The results published here are in part based upon data generated by the TCGA Research Network (https:// www. cancer. gov/ tcga), METABRIC (https:// ega‑ archi ve. org/ studi es/ EGAS0 00000 00083), MSK‑IMPACT (https:// www. mskcc. org/ msk‑ impact) or deposited at cBioPortal (https:// www. cbiop ortal. org/). The following expression datasets from the Gene Expression Omnibus (GEO, https:// www. ncbi. nlm. nih. gov/ geo/) have also been employed: GSE114012 [48], GSE131594 [45], GSE137912 [12], GSE152699 [49], GSE75367 [47], GSE83142 [46], GSE93991 [44], GSE134836 [13], GSE134838 [13], GSE134839 [13], GSE124854 [93], GSE135215 [94], GSE99116 [93], GSE178839 [149], GSE149224 [100], GSE139944 [102], GSE191127 [150], GSE109211 [151], GSE50509 [152], GSE65185 [153], GSE66399 [154], GSE68871 [155] and GSE99898 [156]. The GEO datasets employed in the analyses are summarised in Additional file 1: Tables S2 and S3. All codes developed for the purpose of this study can be found at the following repository, released under a GNU Gen‑ eral Public License v3.0 at github: https:// github. com/ secri erlab/ Cance rG0Ar rest [157] and Zenodo (doi: 10. 5281/ zenodo. 78406 72) [158]. Declarations Ethics approval and consent to participate All data employed in this study comply with ethical regulations, with approval and informed consent for collection and sharing already obtained by the relevant consortia where the data were obtained from (TCGA, METABRI, MSK‑IMPACT). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Received: 1 June 2022 Accepted: 7 May 2023 References 1. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000;100(1):57–70. 2. van Dijk D, Dhar R, Missarova AM, Espinar L, Blevins WR, Lehner B, et al. Slow‑growing cells within isogenic popula‑ tions have increased RNA polymerase error rates and DNA damage. 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10.1371_journal.pone.0261071.pdf	Data Availability Statement: Data are available from the Zenodo database (DOI: 10.5281/zenodo. 5774499).	Data are available from the Zenodo database (DOI: 10.5281/zenodo. 5774499 ).	RESEARCH ARTICLE Effects of weather and moon phases on emergency medical use after fall injury: A population-based nationwide study Min Ah Yuh1, Kisung KimID Jinwoo Kim5, Sungyoup HongID 1* 2, Seon Hee Woo3, Sikyoung Jeong1, Juseok OhID 4, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Yuh MA, Kim K, Woo SH, Jeong S, Oh J, Kim J, et al. (2021) Effects of weather and moon phases on emergency medical use after fall injury: A population-based nationwide study. PLoS ONE 16(12): e0261071. https://doi.org/10.1371/journal. pone.0261071 Editor: Quan Yuan, Tsinghua University, CHINA Received: May 8, 2021 Accepted: November 23, 2021 Published: December 31, 2021 Copyright: © 2021 Yuh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 1 Department of Emergency Medicine, Daejeon St Mary’s Hospital, The Catholic University of Korea College of Medicine, Seoul, Republic of Korea, 2 BioBrain Inc, Daejeon, Republic of Korea, 3 Department of Emergency Medicine, Incheon St Mary’s Hospital, The Catholic University of Korea College of Medicine, Seoul, Republic of Korea, 4 Department of Emergency Medicine, Uijeongbu St Mary’s Hospital, The Catholic University of Korea College of Medicine, Seoul, Republic of Korea, 5 Department of Emergency Medical Service, Daejeon Health Institute of Technology, Daejeon, Republic of Korea * emhong@catholic.ac.kr Abstract Background Previous studies reported that changes in weather and phases of moon are associated with medical emergencies and injuries. However, such studies were limited to hospital or com- munity level without explaining the combined effects of weather and moon phases. We investigated whether changes in weather and moon phases affected emergency depart- ment (ED) visits due to fall injuries (FIs) based on nationwide emergency patient registry data. Methods Nationwide daily data of ED visits after FI were collected from 11 provinces (7 metropolitan cities and 4 rural provinces) in Korea between January 2014 and December 2018. The daily number of FIs was standardized into FI per million population (FPP) in each province. A mul- tivariate regression analysis was conducted to elucidate the relationship between weather factors and moon phases with respect to daily FPP in each province. The correlation between weather factors and FI severity was also analyzed. Data Availability Statement: Data are available from the Zenodo database (DOI: 10.5281/zenodo. 5774499). Results Funding: We declare that this study was supported by Daejeon St. Mary’s Hospital, Clinical Research Institute Grant No. CMCDJ-P-2021013. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. The study analyzed 666,912 patients (418,135 in metropolitan and 248,777 in rural areas) who visited EDs on weekdays. No regional difference was found in age or gender distribu- tion between the two areas. Precipitation, minimum temperature and wind speed showed a significant association with FI in metropolitan areas. In addition, sunshine duration was also substantial risk factors for FI in rural areas. The incidence of FIs was increased on full moon days than on other days in rural areas. Injury severity was associated with weather factors such as minimum temperature, wind speed, and cloud cover. PLOS ONE \| https://doi.org/10.1371/journal.pone.0261071 December 31, 2021 1 / 14 PLOS ONEEffects of weather and moon phases on EM use Conclusion Weather changes such as precipitation, minimum temperature, and wind speed are associ- ated with FI in metropolitan and rural areas. In addition, sunshine duration and full moon are significantly associated with FI incidence only in rural areas. Weather factors are associated with FI severity. Introduction Accurate prediction of the need for emergency medical care is critical to provide appropriate services for patients with injuries. Therefore, many countries have an emergency medical sys- tem data registry collected during pre-hospital and in-hospital phases to design emergency medical service (EMS) and implement public health monitoring and planning. Fall injury (FI) is the second major cause of accidental or unintended injury-related deaths worldwide [1]. The mechanism of injury for falls is vertical deceleration due to the force of gravity high place or loss of balance on a slippery surface. FIs in older or disabled individuals increase in winter due to low temperatures and long nights [2]. The slippery ground caused by melting ice, snow-covered ice, and ice is a typical cause of FI in winter [3]. The incidence of FIs is known to have a seasonal variation depending on geographical location, such as coun- tries with a cold climate (Russia, Canada, Sweden, Finland, and Norway) [4, 5] and countries with a warm tropical climate, such as Hong Kong [6]. Weather conditions have been reported to influence the occurrence of trauma and disease. Poor weather conditions may lead to traumatic events [7, 8]. However, other studies reported that outdoor activities even in good weather are related to increased incidence of all kinds of injuries [9]. If we target FI only, snowfall and icy surfaces were associated with FIs in late autumn and winter [6, 7, 10]. But another study reported the increased frequency of FIs was found in better weather with medium mean air temperature and atmospheric pressure during warm season [4]. A full moon has been reportedly associated with potential emergency department (ED) visits after traffic accidents [11] and mortality after motorcycle crashes and accidents [12]. However, Stomp et al. reported that phases other than full moon increased ED visits after all kinds of trauma [9]. Such difference might be attributed to the use of nationwide statistics of road car accidents in two of the three studies, whereas Stomp et al. [9] used all types of trauma data from one ED located in a small suburban area of Netherlands. Therefore, findings from these studies were limited by small sample size in specific regions, restricted data sources or target injury. In summary, previous studies evaluated the role of weather and lunar phases; however, the findings were limited to specific region or involved a small sample size. To prevent unwanted medical errors due to ED crowding and provide prompt and appropriate EMS, it is vital to foresee the demand for emergency medical use due to FIs. The primary purpose of this study was to assess the FI prevalence according to regional characteristics, weather changes and moon phases using the nationwide longitude data. The secondary goal was to determine the effects of weather factors and moon phases on FI severity. Materials and methods Study design and data collection To analyze the association of FI incidence with weather factors and lunar phases, a nationwide epidemiological analysis was conducted based on emergency department usage data obtained PLOS ONE \| https://doi.org/10.1371/journal.pone.0261071 December 31, 2021 2 / 14 PLOS ONEEffects of weather and moon phases on EM use from all emergency centers in Korea. The population of mainland Korea and its affiliated islands of 99,000 km2 is approximately 50 million. Korea has a total of 420 registered ERs that are open to all beneficiaries without restriction. The National Emergency Department Infor- mation System (NEDIS) operated by the National Emergency Medical Center (NEMC) pro- spectively collected data of patients who visited all Korean EDs since 2005. This study used the NEDIS data of patients who visited emergency centers after FI, including age, gender, region of occurrence, onset time, injury mechanism, injury severity with Korean Triage and Acuity Scale (KTAS), and outcome of emergency care from January 2014 to December 2018. No per- sonal identifier was included in these data. Data were stored in a secured personal computer. KTAS score consists of five levels of acuity: level 1 (resuscitation), level 2 (urgent), level 3 (emergent), level 4 (non-urgent), and level 5 (delayed). The KTAS was developed as a single triage tool for emergency patients in Korea and has since become nationalized [13]. Daily weather data including precipitation, minimum temperature, mean wind speed (wind speed), sunshine and fog duration, and cloud cover were obtained from the Korea Mete- orological Agency (KMA). Daily precipitation was calculated as the sum of hourly measure- ments for 24 hours. Cloud cover was calculated in integers ranging from 0 to 10 tenths based on visual cloud cover observations from each observation site. A weather station located in the capital city of each province in Korea was selected to represent the weather data collection point. Moon phase data were obtained from a website (https://www.timeanddate.com/moon/ phases/south-korea/). First, pediatric patients under the age of 15 years were excluded from the collected data. FI patients on weekdays excluding Saturday, Sunday, and public holidays (New Year’s Day, Lunar New Year’s Day, Children’s Day, Korean Independence Day, Lunar Thanksgiving Day and Christmas) were also excluded from this study. We analyzed data from a total of 743 nights (182 new moon days, 186 first quarter and full moon days, and 189 3rd quarter nights). The full moon period was determined for three days starting from one night before to one night after the peak full moon night. The same rule was applied to other moon phases. Annual mid-population data of each province were obtained from the central organization for Statistics of Korea (http://kostat.go.kr/portal/eng/index.action). Outcome measurement We counted the daily number of patients who visited EDs after a FI for each province. The number of daily FIs was standardized into FIs per million population (FPP) by dividing with an annual mid-population of the province. This study compared FPP and severity of FI between two regions: 1) metropolitan areas consisting of provinces with a population of more than one million; and 2) rural areas without metropolitan cities within the perimeter of the provincial limit (S1 Fig). The primary outcome was the daily number of ED visits due to FI. It was calculated as the number of patients per million people. The secondary outcome was injury severity of patients and was determined by the mean KTAS score of all daily FI patients in the designated area. Statistical analysis The chi-square test is extremely sensitive to sample size. If the sample size is too large (> 500), any small differences appear statistically significant [14]. Hence, we used Cramer’s V statistics instead of Chi-square test to estimate the association of ordinary factors between two regions. The means of the continuous variable were compared using Student’s t-test between two regions. The number of FI events in a fixed time interval was modeled using Poisson distribu- tion, and thus a generalized linear model (GLM) with a Poisson distribution and log-linear PLOS ONE \| https://doi.org/10.1371/journal.pone.0261071 December 31, 2021 3 / 14 PLOS ONEEffects of weather and moon phases on EM use function was used to assess the significance of association between dependent variable (FPP for each day) and independent variables including weather factors and lunar phases. All vari- ables with a p value < 0.2 in the univariate analysis were entered into multivariate analysis. The incidence risk ratios (IRRs) and their 95% confidence intervals (CIs) were calculated for each independent variable. Theoretical FI incidence-factor curve was approximated via non- linear curve fitting with Boltzmann sigmoidal function and illustrated with scatter plots. The correlation between weather factors and daily mean KTAS was analyzed using Pearson corre- lation coefficients. All statistical analyses were analyzed using Origin Pro (OriginLab, North- ampton, MA) and Rstudio 1.4.1717 (RStudio Inc, Boston, MA). Statistically significant difference was indicated by a p value 0.050 or less. Ethical approval The study protocol was reviewed and approved by the Institutional Review Board of Daejeon St Mary’s Hospital, The Catholic University of Korea (DC21ZIS10034). Results Demographic characteristics of the study subjects Of 1,476,652 people included in the registry with FI, 1,065,637 were older than 15 years in Korea between Jan 2014 and Dec 2018 (Fig 1). FPPs were significant higher on weekends than on weekdays (P < 0.010, S2 Fig). Hence, we excluded FI cases on weekend and holidays to pre- vent bias. Finally, 666,912 patients (418,135 in metropolitan and 248,777 in rural areas) on weekdays were analyzed in this study. The mean age of patients finally enrolled was 54.4 ± 20.4 years. There was no significant association with age distribution between the two regions, but the proportion of male patients was significantly higher in the rural areas (Table 1, P < 0.01). The distribution of FI between the two regions was balanced with no monthly difference. Of the total patients, 69, 563, 66, 755, 69, 573, and 69,783 patients suffered FIs on the new moon, 1st quarter, full moon, and 3rd quarter days, respectively. A notably higher number of FIs occurred on full moon days (Table 1, P < 0.010) and a significantly higher proportion of patients were brought to ED in an ambulance in the rural areas (Table 1, p = 0.017). In the rural areas of this study, the proportion of severe patients with KTAS scores of 1 to 2 was significantly lower, and the proportion of patients with KTAS scores from 3 to 5 was higher (Table 1, Cramer’s V = 0.076). The proportion of mentally alert patients was higher (V = 0.022) than in the metropolitan areas. The systolic and diastolic blood pressure and pulse rate per minute of FI patients in the rural areas were significantly higher in rural areas than in metropolitan areas. Relationship of FI incidence with weather and moon phase Pooled associations of the daily FPP with weather and moon phase are presented in Table 2, Fig 2 (metropolitan area), and Table 3, Fig 3 (rural area). Among weather factors, precipita- tion, minimum temperature, and wind speed showed a significant association with FI in met- ropolitan areas (Table 2). FIs occurred frequently on days with lower precipitation, lower minimum temperature, and low-wind days in metropolitan areas (Fig 2). In rural areas, FIs have been shown to increase significantly on days with lower precipitation levels, higher mini- mum temperatures, higher wind speed and longer sunshine duration (Table 3, Fig 3). The distribution of FI patients was compared according to moon phase. The frequency of FIs was higher on full moon days than on new moon days in rural areas (Table 1, p < 0.010). Full moon was a significant predictor of FIs in univariate analysis in rural area (Table 3, PLOS ONE \| https://doi.org/10.1371/journal.pone.0261071 December 31, 2021 4 / 14 PLOS ONEEffects of weather and moon phases on EM use Fig 1. Schematic diagram showing the selection of study population for this study. https://doi.org/10.1371/journal.pone.0261071.g001 p = 0.048) but not significant in multivariate analysis. Based on the interaction analysis, the new moon phase showed a significant interaction with precipitation and wind speed in rural areas. However, there was no significant difference in the incidence of FI as similar FIs occurred on all days in metropolitan areas (Tables 1 and 2). Correlation of weather factors with FI severity The severity of FI was measured using KTAS assessed upon ED arrival. KTAS 1 refers to a state warranting emergency resuscitation, and KTAs 5 indicates absence of emergency. Injury severity (daily mean KTAS for each province) was significantly correlated with minimum tem- perature and wind speed and thus the injury severity was increased on cold windless days in both areas (Table 4). Additionally, in rural areas, the daily mean KTAS was significantly corre- lated with cloud cover. Precipitation, sunshine, and fog duration were not associated with the PLOS ONE \| https://doi.org/10.1371/journal.pone.0261071 December 31, 2021 5 / 14 PLOS ONETable 1. Demographic features of subjects who visited ED after a fall injury. Effects of weather and moon phases on EM use Variable Age Sex Month Moon phase Route 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80–84 85–89 90–94 95–99 100–104 105–109 110–120 Male Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec New moon 1st quarter Full moon Last quarter other Ambulance Private car Ambulation Metropolitan (n = 418,135) N (%) Rural Cramer’s V or p value (n = 248,777) N (%) 25,423 (5.0) 28,389 (5.6) 29,192 (5.6) 29,566 (5.8) 27,869 (5.5) 30,840 (6.1) 36,172 (7.1) 43,583 (8.6) 48,404 (9.5) 38,986 (7.7) 35,154 (6.9) 37,268 (7.3) 39,169 (7.7) 31,347 (6.2) 17,844 (3.5) 6,660 (1.3) 1,393 (0.3) 183 (0.0) 24 (0.0) 8 (0.0) 7,299 (5.7) 7,864 (5.5) 7,795 (4.7) 7,988 (5.0) 7,780 (5.6) 8,706 (6.5) 10,245 (7.7) 12,272 (9.1) 13,521 (9.1) 10,742 (7.3) 9,593 (6.3) 10,214 (6.8) 10,960 (7.9) 8,844 (6.6) 5,034 (3.7) 1,893 (1.4) 409 (0.3) 59 (0.1) 13 (0.0) 3 (0.0) 258,579 (51.0) 132,101(53.1) 44,515(8.8) 38,357 (7.7) 39,581 (7.8) 40,136 (7.9) 44,224 (8.7) 38,029 (7.5) 39,968 (7.9) 41,564 (8.2) 43,425 (8.6) 46,051 (9.1) 42,183 (8.3) 49,410 (9.7) 44,461 (10.6) 41,531 (9.9) 43,107 (10.3) 44,059 (10.5) 244,713 (58.6) 439,439 (86.6) 65,574 (12.9) 2,227 (0.4) 12,288 (8.7) 10,381 (7.4) 10,448 (7.4) 10,939 (7.8) 12,420 (8.8) 10,928 (7.7) 11,315 (8.0) 12,131(8.6) 12,371 (8.8) 13,404 (9.5) 11,675 (8.3) 12,931 (9.2) 25,102 (10.1) 25,224 (10.1) 26,466 (10.6) 25,679 (10.3) 146,306 (58.8) 124,180 (87.9) 16,504 (11.7) 520 (0.3) V = 0.001 P< 0.010 V = 0.020 p <0.010 p = 0.017 (Continued ) PLOS ONE \| https://doi.org/10.1371/journal.pone.0261071 December 31, 2021 6 / 14 PLOS ONETable 1. (Continued) Variable KTAS Mental SBP DBP PR 1 2 3 4 5 Alert Verbal response Pain response Unresponsive Effects of weather and moon phases on EM use Metropolitan (n = 418,135) N (%) Rural Cramer’s V or p value (n = 248,777) N (%) 920 (0.80) 5334 (4.61) 34519 (29.83) 63769 (55.11) 11155 (9.64) 487775 (96.1) 11928 (2.4) 5038 (1.0) 2544 (0.5) 134.0 ± 26.6 79.8 ± 19.3 88.8 ± 40.4 223 (0.67) 1195 (3.59) 12620 (37.95) 15897(47.81) 3317(9.97) 137035 (97.0) 2358 (1.7) 1089 (0.8) 738 (0.5) 135.7 ± 27.1 80.7 ± 19.5 89.5 ± 19.5 V = 0.076 V = 0.022 p = 0.001 p <0.010 p <0.010 KTAS, Korean triage and acuity scale; SBP, systolic blood pressure; DBP, diastolic blood pressure; PR, pulse rate. https://doi.org/10.1371/journal.pone.0261071.t001 severity of FI in the rural or metropolitan areas. There was no significant difference in mean KTAS depending on the lunar phase in the metropolitan or rural areas (p = 0.394, p = 0.457, respectively). Discussion During the study period of five years, we found that the prevalence and severity of FI were associated with multiple weather factors such as daily precipitation, minimum temperature, Table 2. Multivariate regression analysis of relationships between weather factors and moon phase with fall injuries in metropolitan areas. Univariate analysis Multivariate analysis IRR 0.99 0.98 0.78 1.07 1.03 1.00 1.10 1.12 1.25 p value 0.043 <0.001 0.003 0.269 0.020 0.677 0.567 0.532 0.724 Precipitation Minimum temperature Wind speed Cloud cover Sunshine duration Fog duration Moon phase (versus full moon) New moon 1st quarter 3rd quarter Interaction effects precipitation:minimum precipitation:wind precipitation:sunshine minimum:wind minimum:sunshine wind:sunshine IRR, incidence risk ratio; SE, standard error; CI, confidence interval. https://doi.org/10.1371/journal.pone.0261071.t002 IRR 0.93 1.27 0.80 1.01 1.00 1.01 1.01 0.99 1.02 0.99 p value 0.045 0.005 <0.001 0.516 0.001 0.755 0.414 0.001 0.023 0.131 CI 0.93–0.97 0.97–0.98 0.95–1.01 1.01–1.02 1.00–1.01 1.01–1.01 1.01–1.01 0.99–1.00 1.01–1.03 0.99–0.99 PLOS ONE \| https://doi.org/10.1371/journal.pone.0261071 December 31, 2021 7 / 14 PLOS ONEEffects of weather and moon phases on EM use PLOS ONE \| https://doi.org/10.1371/journal.pone.0261071 December 31, 2021 8 / 14 PLOS ONEFig 2. Scatter plots of the number of ED visits per million people after fall injuries versus weather components in metropolitan areas of Korea. A theoretical Gaussian regression line estimated by nonlinear curve fitting with the Boltzmann sigmoidal function is shown in red. https://doi.org/10.1371/journal.pone.0261071.g002 Effects of weather and moon phases on EM use and wind speed in both metropolitan and rural areas. However, we found that the longer the sunshine duration was linked with the higher FI in rural area. Moon phases were weakly asso- ciated with FI, especially in rural areas. The FI severity was closely related to weather factors. This study enrolled the largest dataset ever collected to determine the association between weather factors and ED visits due to FI in all 11 provinces of Korea over a 5-year period. A pre- vious study conducted in a small city of 23,000 people in northern Netherlands reported that better weather conditions were associated with the incidence of all types of trauma [9]. The study location was similar to rural South Korea, where weather components including maxi- mum temperature, sunshine duration, humidity, and precipitation were associated with all kinds of injury. The present study also found that precipitation, minimum temperature, and wind speed were typically related to FI. Additionally, sunshine duration was a significant pre- dictor of FI in rural areas with high agricultural activities. These findings suggest that it is essential to consider a variety of factors such as geographic location, main industry in the region, and weather changes during the investigation of injury prevalence. Ramgopal et al. [8] investigated the association of weather factors with all EMS dispatches using longitudinal data of ambulance transport in western Pennsylvania and reported increased EMS responses with rising temperature, snowfall, and rain based on a stratified anal- ysis of seasonal variables and a day-of-the-week effect week. We found that additional factors such as wind speed, cloud cover, and sunshine duration were associated with emergency Table 3. Multivariate regression analysis of relationships between weather factors and moon phase with fall injuries in rural areas. Univariate analysis Multivariate analysis IRR p value IRR p value 0.96 1.25 1.24 2.01 1.19 0.03 0.75 0.61 0.68 <0.001 <0.001 <0.001 0.383 <0.001 0.817 0.002 0.948 0.984 Precipitation Minimum temperature Wind speed Cloud cover Sunshine duration Fog duration Moon phase (versus full moon) new moon 1st quarter 3rd quarter Interaction effects precipitation:wind precipitation:minimum precipitation:sunshine minimum:wind minimum:sunshine wind:sunshine new moon:precipitation new moon:wind new moon:sunshine IRR, incidence risk ratio; SE, standard error; CI, confidence interval. https://doi.org/10.1371/journal.pone.0261071.t003 0.98 1.20 1.18 1.20 0.78 0.60 0.68 1.01 1.00 0.98 1.01 1.00 1.08 1.01 1.02 0.99 <0.001 <0.001 <0.001 0.040 0.043 0.556 0.984 <0.001 <0.001 <0.001 <0.001 0.590 <0.001 0.027 0.028 0.055 CI 0.90–0.97 0.99–1.51 1.23–1.25 0.67–2.20 0.61–0.85 0.95–1.02 0.99–1.03 1.01–1.01 1.00–1.00 0.96–0.99 1.01–1.02 1.00–1.00 1.06–1.11 0.00–14.6 0.79–1.16 0.02–3.13 PLOS ONE \| https://doi.org/10.1371/journal.pone.0261071 December 31, 2021 9 / 14 PLOS ONEEffects of weather and moon phases on EM use PLOS ONE \| https://doi.org/10.1371/journal.pone.0261071 December 31, 2021 10 / 14 PLOS ONEFig 3. Scatter plots of the number of ED visits per million people after fall injuries versus weather components in rural areas of Korea. A theoretical Gaussian regression line estimated by nonlinear curve fitting with the Boltzmann sigmoidal function is shown in red. https://doi.org/10.1371/journal.pone.0261071.g003 Effects of weather and moon phases on EM use resource use after FI. However, seasonal changes were not included as independent variables in this study because changes in minimum temperature and precipitation implicated seasonal variations in weather. We also excluded FI on weekends and holidays because of increased trauma due to enhanced outdoor leisure activity and distant travel on days that might act as a confounding variable. We expected no major challenges in the analysis of FIs on weekdays because of a sufficient number of cases using 5-year large-scale longitudinal data for at the nationwide level. Stomp et al. [9] reported that better weather conditions in rural areas were associated with the incidence of all traumas. Our analysis also found that the frequency of FIs in rural areas was increased under less precipitation, higher minimum temperature, and longer sunshine duration such as busy farming seasons. This is the first study to compare FI-related factors between developed metropolitan cities and rural areas. During the course of our study, another research paper reported the correla- tion between weather changes and FI in a small Russian city [4]. It was the only longitudinal study for FIs like this study but was limited by geographic location of the study area or by small number of subjects. The daily average of outdoor falls in the cold season was 20.2 per 100,000 people and the slippery surfaces covered with wet snow or ice and temperatures between -7.0˚C and -0.7˚C were risk factors. As mentioned above, our study results showed a distinct increase in FIs according to regional characteristics, with a lower temperature trigger- ing falls on slippery surface in metropolitan areas, and a higher temperature during increased agricultural activity in rural areas associated with increased FIs. They also reported that the FIs were increased when the 12-hour precipitation was greater than 0.4 mm; however, the present study showed that the FIs were increased under low precipitation. This difference is probably explained by the falling of snow leading to slippery surfaces in Russia with a high altitude, whereas in Korea located in mid-latitude weather, rain accompanied by summer storms with strong winds reduced the frequency of outdoor activities. Northern Russia is located at the highest latitude among countries in the world. As the highest temperature in summer was near zero, the study failed to reflect changing weather patterns in mid-latitude areas with four clear seasons. Good weather conditions accompanied by active agricultural activities and increased night visibility under moonlight on a full moon might be associated with FIs in rural areas. A moon phase occurs every 29.53 days and 12.37 times in a year. The four principal moon phases include: new moon, the 1st quarter, full moon, and the last quarter. Moon phases are known to drive periodic changes in nighttime illumination, geomagnetic fields, gravitational pull, and other factors associated with major meteorological and biological changes [15]. We found that Table 4. Results of Pearson’s correlation analysis between injury severity (mean KTAS) with weather factors. Precipitation Minimum temperature Mean wind speed Cloud cover Sunshine duration Fog duration Metro CC Rural p value CC p value 0.006 0.701 -0.008 0.718 CC, correlation coefficient. 0.049 0.003 0.065 0.004 https://doi.org/10.1371/journal.pone.0261071.t004 0.157 <0.001 0.108 <0.001 -0.045 0.700 0.298 0.007 -0.012 0.957 0.005 0.809 <0.001 0.995 0.030 0.175 PLOS ONE \| https://doi.org/10.1371/journal.pone.0261071 December 31, 2021 11 / 14 PLOS ONEEffects of weather and moon phases on EM use FI-related ED visits on full moon days were significantly increased than on new moon days only in rural areas. Two of the four rural provinces in this study border the sea and another province is an island with active fishing activities (S1 Fig). The full moon is a time of full tide and active fishing activities due to vertical migration of fishes [16]. Therefore, active nighttime activities and fishing activities might increase FIs in rural areas. However, FIs in metropolitan areas were less affected by lunar phase due to good visibility under night light that offset the effects of lunar phases. A previous study from Japan revealed a significant increase in the risk of emergency trans- port after traffic accidents on full moon days among those aged �40 years [11]. This finding is consistent with the results of our study showing a significant increase in FIs during full moon days especially in rural areas where the elderly individuals reside under weak artificial lighting at night. A population-based double control study conducted in the United States reported that deaths from motor traffic accidents are more frequent on full moon nights [12]. The authors postulated that a full moon might be associated with speeding, long distances, and unknown routes, resulting in more frequent deaths. In our study, FIs were increased on full moon days only in rural areas with weak artificial lighting, suggesting that increased visibility and outdoor activity under moonlight on full moon days are associated with increased FIs. The analysis of interaction between lunar phases and weather factors showed that the new moon phase interacted with precipitation, wind speed, and cloud cover, which is consistent with the findings of a previous study showing an increased number of storms during new moon phases [17]. The finding suggests that the decrease in FIs in rural areas during new phases may be a result of weather changes. Thus, the effect of the lunar phase is complex with increased near-field vision due to moonlight mainly in rural areas and secondary weather changes associated with lunar phases. We found that weather factors were correlated with FI severity measured by KTAS, a uni- fied triage scoring system. KTAS is a five-level triage scale developed in Korea based on Cana- dian Triage and Acuity Scale (CTAS) and the score is a strong predictor of severity of patients with higher 30-day mortality [18]. The strength of the study is that it is a population-based analysis of longitudinal data involv- ing FIs in a mid-latitude country with four clearly distinguished seasons. A few unknown envi- ronmental factors may confound the study results. Future studies should use more complex modeling methods and evaluate the effects of moon phases and weather changes. Patients sus- taining FIs may visit the ED the next day or later instead of on the day of injuries. Morency et al. [19] reported a significant increase in outdoor falls on days 1–3 after falling temperatures or snowfall. Therefore, it might be a challenge to compare changes in weather phenomena and patients visiting the hospital on the same day. We believed that the interval between the weather change and FIs is not a hindrance because of the gradual changes in weather and FI incidence over a period of several days. We enrolled subjects regardless of indoor or outdoor injuries because exposure to slippery terrain under snow or rain can still trigger injuries indoors. Additionally, snowy and rainy days lead to behavioral changes due to thick clothes and protective gears. Our study was conducted using large-scale nationwide databases without analyzing clinical data of patients with emotional stress, alcohol use, and violence. Further, the effects of other natural events such as earthquakes leading to mass casualties were not considered. In summary, we found that the incidence of FI is related to weather factors. Emergency medical personnel should understand that FIs occur frequently during days of low precipita- tion, high temperature and low winds linked with active outdoor activity in metropolitan areas. Additional weather factors have been shown to affect FI incidence in rural areas so that increased FI rates were noticed on days of low precipitation, high temperature, low winds and PLOS ONE \| https://doi.org/10.1371/journal.pone.0261071 December 31, 2021 12 / 14 PLOS ONEEffects of weather and moon phases on EM use longer sunshine duration in rural areas. Moon phases are weakly linked to FI incidence rates. FIs increased only in rural areas during the full moon days compared with new moon days. FI severity is also affected by weather factors. In both urban and rural areas, the severity of FI sig- nificantly increased on cold and windy days. Supporting information S1 Fig. Areas to be studied were selected by dividing them into A) metropolitan areas (red color) including seven metropolitan cities with a population exceeding one million and B) rural areas (blue color) consisting of four provinces without containing metropolitan cities within its perimeter. (PDF) S2 Fig. Distribution of daily fall injuries by weekday for metropolitan and rural areas. (PDF) Author Contributions Conceptualization: Kisung Kim, Sungyoup Hong. Data curation: Min Ah Yuh, Kisung Kim, Juseok Oh, Jinwoo Kim, Sungyoup Hong. Formal analysis: Kisung Kim, Seon Hee Woo, Jinwoo Kim, Sungyoup Hong. Funding acquisition: Juseok Oh, Sungyoup Hong. Methodology: Min Ah Yuh. Project administration: Seon Hee Woo. Software: Kisung Kim. Supervision: Sungyoup Hong. Validation: Kisung Kim, Juseok Oh. Visualization: Min Ah Yuh, Sungyoup Hong. Writing – original draft: Sikyoung Jeong, Sungyoup Hong. Writing – review & editing: Seon Hee Woo, Sikyoung Jeong. References 1. Organization WH, Ageing WHO, Unit LC. WHO global report on falls prevention in older age: World Health Organization; 2008. 2. Smulders E, Enkelaar L, Weerdesteyn V, Geurts A, van Schrojenstein Lantman-de Valk H. Falls in older persons with intellectual disabilities: fall rate, circumstances and consequences. J Intellect Disabil Res. 2013; 57(12):1173–82. https://doi.org/10.1111/j.1365-2788.2012.01643.x PMID: 23106830 3. Lepy E, Rantala S, Huusko A, Nieminen P, Hippi M, Rautio A. Role of Winter Weather Conditions and Slipperiness on Tourists’ Accidents in Finland. Int J Environ Res Public Health. 2016; 13(8):822. https:// doi.org/10.3390/ijerph13080822 PMID: 27537899 4. Unguryanu TN, Grjibovski AM, Trovik TA, Ytterstad B, Kudryavtsev AV. Weather conditions and out- door fall injuries in Northwestern Russia. Int J Environ Res Public Health. 2020; 17(17):6096. https://doi. org/10.3390/ijerph17176096 PMID: 32825697 5. Sundfør HB, Sagberg F, Høye AJAA, Prevention. Inattention and distraction in fatal road crashes– Results from in-depth crash investigations in Norway. Accid Anal Prev. 2019; 125:152–7. https://doi. org/10.1016/j.aap.2019.02.004 PMID: 30763812 6. Yeung P-Y, Chau P-H, Woo J, Yim VW-T, Rainer TH. Higher incidence of falls in winter among older people in Hong Kong. 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10.1186_s12866-023-02901-1.pdf	Data Availability Raw sequences from this study are available and were deposited in the European Nucleotide Archive (ENA) with bio project accession PRJEB56537 in the ENA bio project database: https://www.ebi.ac.uk/ena/browser/view/ PRJEB56537.	Data Availability Raw sequences from this study are available and were deposited in the European Nucleotide Archive (ENA) with bio project accession PRJEB56537 in the ENA bio project database: https://www.ebi.ac.uk/ena/browser/view/ PRJEB56537 .	Akinyemi et al. BMC Microbiology (2023) 23:164 https://doi.org/10.1186/s12866-023-02901-1 BMC Microbiology Whole genome sequencing of Salmonella enterica serovars isolated from humans, animals, and the environment in Lagos, Nigeria Kabiru Olusegun Akinyemi1, Christopher Oladimeji Fakorede1, Jörg Linde2, Ulrich Methner2, Gamal Wareth2,3,4, Herbert Tomaso2 and Heinrich Neubauer2 Abstract Background Salmonella infections remain an important public health issue worldwide. Some serovars of non- typhoidal Salmonella (NTS) have been associated with bloodstream infections and gastroenteritis, especially in children in Sub-Saharan Africa with circulating S. enterica serovars with drug resistance and virulence genes. This study identified and verified the clonal relationship of Nigerian NTS strains isolated from humans, animals, and the environment. Methods In total, 2,522 samples were collected from patients, animals (cattle and poultry), and environmental sources between December 2017 and May 2019. The samples were subjected to a standard microbiological investigation. All the isolates were identified using Microbact 24E, and MALDI-TOF MS. The isolates were serotyped using the Kauffmann-White scheme. Antibiotic susceptibility testing was conducted using the disc diffusion method and the Vitek 2 compact system. Virulence and antimicrobial resistance genes, sequence type, and cluster analysis were investigated using WGS data. Results Forty-eight (48) NTS isolates (1.9%) were obtained. The prevalence of NTS from clinical sources was 0.9%, while 4% was recorded for animal sources. The serovars identified were S. Cotham (n = 17), S. Give (n = 16), S. Mokola (n = 6), S. Abony (n = 4), S. Typhimurium (n = 4), and S. Senftenberg (n = 1). All 48 Salmonella isolates carried intrinsic and acquired resistant genes such as aac.6…Iaa, mdf(A), qnrB, qnrB19 genes and golT, golS, pcoA, and silP, mediated by plasmid Col440I_1, incFIB.B and incFII. Between 100 and 118 virulence gene markers distributed across several Salmonella pathogenicity islands (SPIs), clusters, prophages, and plasmid operons were found in each isolate. WGS revealed that strains of each Salmonella serovar could be assigned to a single 7-gene MLST cluster, and strains within the clusters were identical strains and closely related as defined by the 0 and 10 cgSNPs and likely shared a common ancestor. The dominant sequence types were S. Give ST516 and S. Cotham ST617. Correspondence: Kabiru Olusegun Akinyemi kabiru.akinyemi@lasu.edu.ng Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. RESEARCHOpen AccessPage 2 of 17 Conclusion We found identical Salmonella sequence types in human, animal, and environmental samples in the same locality, which demonstrates the great potential of the applied tools to trace back outbreak strains. Strategies to control and prevent the spread of NTS in the context of one’s health are essential to prevent possible outbreaks. Keywords Salmonella, WGS, MLST, Virulence gene, Resistant gene, Serotyping, Nigeria Background Salmonellosis in humans is caused by Gram-negative zoonotic bacteria of the species Salmonella enterica and Salmonella bongori and remains an important public health problem worldwide. Salmonellosis accounts for 93.8 million cases of gastroenteritis, with an estimated 155,000 deaths recorded globally every single year [1]. Most cases of salmonellosis are acquired from contami- nated food such as dairy and poultry products [2]. Of the more than 2,600 different serovars of S. enterica, only a few non-typhoidal serovars (NTS) are respon- sible for most human infections [2]. Salmonella Typhi and S. Paratyphi, which are human-restricted, cause sys- temic illness, i.e., typhoid or paratyphoid fever [3]. NTS serovars are diverse in their host range and vary in their pathogenicity [4]. For severe cases of salmonellosis like sepsis or SIRS (Systemic Inflammatory Response Syn- drome), antibiotic treatment is indicated. Unfortunately, multidrug resistance (MDR) with resistance to ampicil- lin, chloramphenicol, and trimethoprim-sulfamethox- azole has become a growing concern in NTS and some invasive non-typhoidal Salmonella (iNTS) infections [5]. Fluoroquinolone resistance in isolates from African countries has increased in recent decades, and resistance to β-lactam antibiotics, particularly third- and fourth generation cephalosporins, was found in Nigeria [6]. The development of NTS isolates resistant to extended spectrum cephalosporins such as ceftriaxone represents another substantial public health issue [7]. In Africa, NTS strains appear to be different from those that cause diarrheal disease in industrialized countries and cause invasive disease with bacteraemia more often in children, with 4100 deaths per year [8, 9]. The role of animal reser- voirs and human-to-human transmission of iNTS strains is unclear [10, 11]. The control of zoonotic diseases in Nigeria is difficult due to the diversity of reservoirs and lack of surveillance. Zoonotic pathogens can contami- nate the environment, spill over into the food chain, and appear at any time during processing and post-process- ing procedures. 29% of the national burden of human dis- ease has been linked to the environment in Nigeria, while the remaining 71% has been traced to diarrheal diseases, malaria, respiratory infections, etc. [11]. A high preva- lence of Salmonella in commercial poultry farms (43.6%) was reported [12]. Products from cattle and poultry have been identified as major sources of human Salmonella infections in Nigeria [13]. Furthermore, poultry and poul- try products are the major sources of protein for humans. The domestic industry (Poultry Industry) rose from an estimated 350,000 metric tons (MT) of eggs and 200,000 MT of poultry meat produced in 2003 to an estimated production of 650,000 MT of eggs and 290,000 MT of poultry meat in 2013 [14, 15]. The industry is estimated to be worth $600 million, i.e., approximately 165 million birds in 2013. By 2015, these numbers had risen to about 180 million birds, with the bulk of the poultry production coming from backyard poultry farming. This increase is attributed to the ban on the import of chicken (excluding day-old chicks) in 2003 [14, 15]. A recent survey (2008– 2015) on Salmonella bacteraemia in children in central and northwest Nigeria revealed that 20.7–23.6% of the Salmonella bacteraemia cases were due to non-typhoidal Salmonella, with up to 45% and 39% of the isolates being Salmonella Typhimurium and S. Enteritidis, respectively [16]. Bacterial resistance to antibiotics can be attributed to irrational use due to advertising strategies, application without previous susceptibility testing, or consumption of food produced from previously treated animals [17]. Antimicrobial resistance due to the acquisition of resis- tance gene clusters either horizontally or vertically is on the rise [18]. These gene clusters have been reported to have an epidemiological link with community-associated infections in many countries, including Nigeria [18–20]. The benefit of the recent explosion of genomic sequence information for Salmonella to study drug resistance in Salmonella serotypes cannot be overemphasized. In outbreak investigations and epidemiological surveil- lance, many laboratories, including reference labora- tories, have employed serotyping and phage typing for decades [21]. But due to the polyphyletic nature of Sal- monella serovars, evolutionary groupings may fail if phe- notypic methods such as serotyping are used alone [21]. Thus, in the last two decades, pathogen characteriza- tion has already shifted to genomic analysis techniques, e.g., multi-locus sequence typing (MLST) and the vari- able number of tandem repeats (VNTR) [22, 23]. Since whole-genome sequencing (WGS) has become widely available and affordable as a tool for genotyping bacteria, it is replacing older genomic techniques [24]. The Salmo- nella Typhi genome was first reported in 2001, and sev- eral thousand Salmonella strains of various serovars have been sequenced [7, 24]. The challenge of discriminating highly related lineages of bacteria was resolved by analy- sis of the genome sequence using WGS and bioinformat- ics pipelines [24, 25]. In Nigeria, there are currently few Akinyemi et al. BMC Microbiology (2023) 23:164 Page 3 of 17 studies on the molecular detection of virulence and anti- microbial resistance genes [26, 27]. The aim of this study is to investigate virulence and antimicrobial resistance genes among the Salmonella enterica serovars and to identify potential clonal relation- ships between strains from different sources using whole genome sequencing. Materials and methods Ethical approvals Ethical approval from the ethics committee of the follow- ing institutions was obtained before patients’ enrolment: The Human Research and Ethics Committee of the Lagos State University Teaching Hospital with reference num- ber LREC/06/10/1012 and the Lagos State Health Service Commission with reference number LSHSC/2222/VOL. VC/352. Preliminary investigation A total of 2,522 samples were collected between Decem- ber 2017 and January 2019, comprising 2,002 human samples (blood 1,042 and stool 960) from hospitalized and outpatients with clinical diagnoses of febrile illness and diarrheal disease, 150 samples of cattle dung (animal from the point of slaughter) and 270 samples of poul- try feces (birds ready for sale), and 100 hospital effluent (wastewater) samples. Samples were screened for Sal- monella growth using standard protocols [28, 29] at the Department of Microbiology, Lagos State University, Nigeria. Bacteria identification A total of 51 presumptive Salmonella isolates, compris- ing 13 strains from in-patients of the general hospitals (Alimosho General Hospital and Massy Street Children Hospital), 9 strains from out-patients of the Lagos State University Teaching Hospital, 11 and 6 strains from dung and faecal samples of cattle and poultry respectively, and 12 strains from wastewater of Gbagada General Hospital, were initially identified using a MICROBACT 24E iden- tification system (Oxoid Ltd, Basingstoke, UK). The iso- lates were stored in cryotubes with Nutrient-Agar (Oxoid Ltd., Basingstoke, UK) at room temperature and sent to the Institute of Bacterial Infections and Zoonoses (IBIZ) of the Friedrich-Loeffler-Institut (FLI) in Jena, Germany. These isolates were further analyzed following DIN EN ISO 6579-1:2017-07. Briefly, they were re-suspended in 3 mL of buffered peptone water for 6–18 ± 2 h (Oxoid Ltd., Basingstoke, UK) at 37 °C. Enrichment in a selective liquid medium was done in Rappaport-Vassiliadis (RVS) Soya Peptone Broth (RVS Broth) (OMNILAB-Laborzen- trum GmbH, Bremen, Germany) for 24 ± 3 h at 41.5 °C. For selective media cultivation, two methods according to DIN EN ISO 6579-1:2017-07 were applied. Animal isolates were cultured on Xylose-Lysin-Deoxycholate Agar (XLD) (Oxoid Ltd., Basingstoke, UK) and Rambach Agar (Merck KGaA, Darmstadt, Germany) at 37 °C for 24 ± 3 h. Human and sewage isolates were cultivated on Rambach Agar and Bismuth Sulfite Agar (Becton Dick- inson, Franklin Lakes, USA) at 37 °C for 24 ± 3 h for the early screening and detection of typhoidal and paraty- phoid Salmonella. For species confirmation of the animal isolates, an Ultraflex II MALDI-TOF MS instrument (Bruker Dal- tonik GmbH, Bremen, Germany) was used as described by [30]. After positive Salmonella confirmation, serologi- cal testing was done according to ISO/TR 6579-3:2014 for serovar identification. The early identification of typhoidal and paratyphoi- dal Salmonella serovars for the human and sewage iso- lates was done with serological tests according to ISO/ TR 6579-3:2014. The species confirmation for Salmonella isolates was done with anti-Salmonella A-67 + Vi omni- valent (Sifin Diagnostics GmbH, Berlin, Germany). Then, the O groups for S. Typhi (O: 9), S. Paratyphi A (O: 2), S. Paratyphi B (O: 4), and S. Paratyphi C (O: 7) were tested. A BSL-3 laboratory was available to further characterize isolates that would be found positive for anti-Salmonella O: 9 (Sifin Diagnostics GmbH, Berlin, Germany) and therefore suspicious to be S. Typhi. The non-suspicious isolates were also identified using an Ultraflex II MALDI- TOF MS instrument as Salmonella at the genus level (Bruker Daltonik GmbH, Bremen, Germany). Analysis was carried out with the Biotyper 3.1 software (Bruker Daltonik GmbH). MALDI-TOF MS Isolates were identified using MALDI-TOF MS [30]. Briefly, bacteria from overnight cultures were suspended in 300 µl of bi-distilled water and mixed with 900 µl of 96% ethanol (Carl Roth GmbH, Karlsruhe, Germany) for precipitation. After centrifugation for 5 min at 10,000 x g, the supernatant was removed, and the pellet was re- suspended in 50 µl of 70% (vol/vol) formic acid (Sigma- Aldrich Chemie GmbH, Steinheim, Germany). Fifty microliters of acetonitrile (Carl Roth GmbH) were added, mixed, and centrifuged for 5 min at 10,000 x g. One and a half microliters of the supernatant were transferred onto an MTP 384 Target Plate Polished Steel TF (Bruker Daltonik GmbH, Bremen, Germany). After air-drying, the material was overlaid with 2 µl of a saturated solu- tion of -cyano-4-hydroxycinnamic acid (Sigma-Aldrich Chemie GmbH) in a mix of 50% acetonitrile and 2.5% trifluoroacetic acid (Sigma-Aldrich Chemie GmbH). After air-drying, spectra were acquired with an Ultraflex II instrument (Bruker Daltonik GmbH). The instrument was calibrated using the IVD Bacterial Test Standard (Bruker Daltonik GmbH). Analysis was carried out with Akinyemi et al. BMC Microbiology (2023) 23:164 Page 4 of 17 the Biotyper 3.1 software (Bruker Daltonik GmbH). The following interpretation of results was performed accord- ing to the manufacturer’s recommendation: A score of ≥ 2.3 represented reliable species-level identification; a score of 2.0–2.29 represented probable species level iden- tification; a score of 1.7–1.9 represented probable genus- level identification; and a score ≤ 1.7 was considered an unreliable identification [31]. Pure cultures were stored appropriately in cryo-tubes at -80 °C (Mast Diagnostica GmbH, Reinfeld, Germany). Serotyping using the traditional White-Kaufman Le-Minor serotyping of 48Salmonellaisolates All Salmonella strains were serotyped using poly- and monovalent anti-O as well as anti-H sera (SIFIN, Ger- many) according to the Kauffmann-White scheme [32]. Antibiotic resistance testing The isolates were re-cultivated on Columbia agar plates with 5% sheep blood for 24 h at 37 °C for antibiotic sus- ceptibility testing with the Vitek 2 Compact system (Bio- Mérieux, Marcy-etoile, France) according to EUCAST guidelines [33] for 24 different antibiotics on two cards (AST-N195 and AST-N248): ampicillin, amoxicillin + cla- acid, piperacillin, piperacillin-tazobactam, vulanic cefalexin, cefuroxime, cefuroxime-acetyl, cefotaxime, ceftazidime, cefepime, azithronam, ertapenem, imipe- nem, meropenem, amikacin, gentamicin, Tobramycin, ciprofloxacin, tigecycline, fosfomycin, nitrofurantoin, colistin, trimethoprim, and trimethoprim-sulfamethox- azole. Appropriate dilutions of the colonies were made according to the manufacturer’s instructions for MIC evaluation. The strains with Vitek Extended Spectrum ß-Lactamase (ESBL) were further characterized phe- notypically: ESBL (CTX-M like), AmpC (High-Level Case (AmpC)), or/and carbapenemase (Carbapenemase (+ Oder - ESBL) phenotype were confirmed using a combination disk test (CDT) according to the manufac- turer’s instructions and EUCAST guidelines. For ESBL resistance testing, MASTDISCS® Combi Extended Spec- trum ß lactamase (ESBL) Set (CPD10) (Mast Diagnostica GmbH, Reinfeld, Germany) with Ceftazidime 30 µg and Ceftazidime 30 µg + Clavulanic Acid 10 µg, Cefotaxime 30 µg, and Cefotaxime 10 µg + Clavulanic Acid 10 and cefpodoxime 30 µg and Cefpodoxime 10 µg + Clavulanic Acid 1 µg were used. For the detection of carbapen- emases, KPC, MBL, and OXA-48 MASTDISCS® were used: carbapenemases (Rosco, Taastrup, Denmark) with Meropenem 10 µg, Meropenem 10 µg + Phenylboronic acid, Meropenem 10 µg + Dipicolinic acid, Merope- nem 10 µg + Cloxacillin, and Temocillin. For the AmpC detection, the 69 C AmpC Detection Disc Set (Mast Diagnostica GmbH, Reinfeld, Germany) was used with cefpodooxime 10 µg, AmpC stimulator, an ESBL inhibi- tor, and an AmpC inhibitor. Whole genomic sequence and bioinformatics analysis Next-generation-sequencing (NGS) The QIAGEN® Genomic-tip 20/G kit (QIAGEN, Ger- many) was used to prepare genomic DNA. NGS librar- ies were prepared using the NextEra XT DNA Library Preparation Kit (Illumina Inc., USA). An Illumina MiSeq instrument (Illumina Inc., USA) was used for paired-end sequencing. Raw sequences from this study are available and were deposited in the European Nucleotide Archive (ENA) with bio project accession PRJEB56537 in the ENA bio project database: https://www.ebi.ac.uk/ena/ browser/view/PRJEB56537. Bioinformatics analysis: data analysis 2.2.0 The Linux-based bioinformatics pipeline WGSBAC v. (https://gitlab.com/FLI_Bioinfo/WGSBAC) was used to analyze raw sequencing data as previously described [34, 35]. For quality control, WGSBAC used FastQC v. 0.11.7 [36] and calculated sequencing cover- age. As a next step, the pipeline assembled sequencing reads using Shovill v. 1.0.4 [37] and accessed assembly quality using QUAST v. 5.0 [38]. To check for poten- tial contamination, Kraken v2.1.1 [39] was used to clas- sify the raw sequencing. To predict serovars based on sequencing data, WGSBAC used SISTR v. 1.0.2 [40] and SeqSero2 [41]. For the detection of genes and point mutations potentially leading to antimicrobial resistance (AMR), AMRFinderPlus (v. 3.6.10) [42] was used within WGS- BAC. ABRicate (v. 0.8.10) [43] together with the data- bases Virulence Factor Database (VFDB) [44] and PlasmidFinder [45] were used to detect virulence fac- tors and plasmids, respectively. For genotyping, WGS- BAC first performed 7-gene multi-locus sequence typing (MLST) on assembled genomes using the software mlst v. 2.16.1 [46]. High-resolution genotyping was performed using both an SNP-based approach and an allele-based approach. Snippy v. 4.3.6 to identify core-genome single nucleotide polymorphisms (cgSNPs) was utilized within WGSBAC in standard settings [47]. As a reference genome, the complete genome sequence of Salmonella enterica subsp. enterica serovar Typhimurium strain LT2 (GenBank accession GCA_000006945.2) was used. To cal- culate pairwise SNP distances, SNPs-dists (v 0.63) were applied. Hierarchical clustering was performed using the hierClust function v.5.1 of the statistical language R. A cut-off of 10 cgSNPs was used to define closely related strains and 0 cgSNPs to define identical strains. For the allele-based approach, core-genome multi- locus sequence typing (cgMLST) was performed by applying Ridom Seqsphere + v. 5.1.0 [48] with default Akinyemi et al. BMC Microbiology (2023) 23:164 Page 5 of 17 Table 1 Sample summary and Salmonella enterica positive isolates distributed across human, animal and environmental sources Human samples Blood 1042 163 10 153 Stool 960 308 9 299 Animal samples Cattle Poultry Dung 150 54 11 43 Feaces 270 72 6 66 Environmental samples Effluent 100 70 12 58 Total 2,522 667 48 619 Number of samples Number of + ve cultures Salmonellaisolates (n) Other isolates +ve: positive bacterial culture Fig. 1 Distribution of Salmonella enterica serovars according to sample source from Lagos, Nigeria settings together with the specific core-genome scheme (cgMLST v2) for Salmonella enterica developed by EnteroBase [49]. Again, a cut-off of 10 alleles was used to define clusters. Results Identification, distribution, and serotyping of NigerianSalmonella enterica isolates Out of 2,522 samples analyzed in this study, 667 sam- ples showed bacterial growth on selective media. From these positive bacterial culture samples, 51 presumptive Salmonella isolates were identified by Microbact 24E (Oxoid, England). Of the 51 presumptive Salmonella iso- lates, 48 isolates were identified as Salmonella enterica using MALDI-TOF MS (Table 1), and the remain- ing three isolates were Citrobacter spp. The serotyping results of the 48 Salmonella isolates revealed six different serovars from human, animal, and environmental sources (Fig. 1). The serovars with their predicted antigenic profiles included S. Cotham (n = 17), S. Give (n = 16), S. Mokola (n = 6), S. Abony (n = 4), S. Typhimurium (n = 4) and S. Senftenberg (n = 1) and are presented in Table S1. The distribution of the serovars is as follows: S. Cotham (2 from human, 10 from animal, and 5 from sewage Akinyemi et al. BMC Microbiology (2023) 23:164 samples), S. Give (12 from human, 2 from animal, and 2 from sewage samples), S. Mokola (5 from animal samples and 1 from a sewage sample), S. Abony (1 from a clinical sample and 3 from sewage samples), S. Typhimurium (3 from human samples and 1 from a sewage sample), and S. Senftenberg ( 1 from a human sample), In this study, seven strains of S. Give, one strain of S. Typhimurium, and one strain of S. Senftenberg were iNTS. Whole genome sequencing data Genome sequencing of the 48 Salmonella enterica isolates analyzed in this study yielded an average of 1,513,603 reads per isolate (range 465,448-3,046,296; Table S1). The mean coverage of the 48 Salmonella iso- lates was 52-fold (ranging from 23-fold to 148-fold in Table S2). To check for putative contamination, the software Kraken2 was used, which classified each read (or contig). On the species level, the top hit for all 48 isolates was always “Salmonella”. On average, 96% of the reads were classified as “Salmonella”. Table S3. The N50 of the 48 assembled genomes ranges from 153,458 bp to 708,946 bp. (Table S4) Page 6 of 17 Resistance profiling and AMR genes in 48 Nigerian Salmonella enterica serovar isolates from different sources All forty-eight Salmonella enterica isolates were 100% susceptible to ampicillin, piperacillin-tazobactam, cefo- taxime, ceftazidime, cefepime, azithronam, ertapenem, imipenem, meropenem, tigecycline, fosfomycin, colis- tin, trimethoprim, and trimethoprim-sulfamethoxazole. Meanwhile, 16 (33.33%) isolates were resistant to moxi- floxacin and 14 (29.2%) isolates showed intermediate resistance to Ciprofloxacin. There was no phenotypic expression of extended-spectrum β-lactamase (ESβL), inducible AmpC, Metallo- β-Lactamase, or blaOXA-48 among the isolates (Fig. 2). All isolates contained intrinsic chromosomal encoded aminoglycoside acetyltransferase aac(6)-Iaa resistance genes as well as mdf(A) genes coding for a multidrug efflux pump. Acquired quinolone resistance gene qnrB was detected in four strains of Salmonella Give 8.3% (4/48), while 12 strains of Salmonella Give harbored qnrB19. All Salmonella serovars harbor efflux mecha- nism genes sinH, mdsB, and mdsA and genes golT and golS coding for resistance to copper/gold and gold, respectively. However, genes pcoE, pcoS, pocR, pcoD, pcoC, pcoB, pcoA, silP, silA, silB, silF, silC, silR, silS, and silE coding for resistance to copper, silver, and copper/ silver were found in only one Salmonella Senftenberg Fig. 2 Antibiogram of Salmonella enterica serovars isolated from different sources in Nigeria in accordance with EUCAST Expert Rules x 3.2 Akinyemi et al. BMC Microbiology (2023) 23:164 strain. Plasmid replicons were detected in 20 of the 48 isolates. All 16 Salmonella Give strains harbored plasmid Col440I_1, while plasmid incFIB.B/incFII.S was detected in four Salmonella Typhimurium strains (Table 2). An average of 100 to 118 virulence gene markers dis- tributed across several Salmonella pathogenicity islands (SPIs), clusters, and plasmid operons were detected in all 48 Salmonella isolates. A total of 95 virulence genes were common to all Salmonella isolates. The virulence genes ctdB, fae, faeD, and faeE were found only in Sal- monella Give and S. Cotham strains. IpfA, IpfB, IpfC, IpfD, IpfE, pipB2, and sopD2 genes were detected in Sal- monella Senftenberg, S. Abony and S. Typhimurium. The virulence genes spvB, spvC, spvR, ssel, srfH, sseK2, and sspH2 were found only in S. Typhimurium strains, while the entE gene was detected in only S. Mokola strains (Table 3). The result of the raw sequence data for the serovar prediction using SISTR and SeqSero indi- cated a pass QC status for 42 isolates. Only six isolates of serovar Mokola had a warning QC status (Table S1), as only 186 cgMLST330 loci matched the number of cgMLST330 loci found (n = 330). The reason for this warning might be the relatively low number of publicly available genome sequence of serovar Mokola which have beend used for training these tools. In fact, Enterobase, the largest collection of Salmonella genome sequences with 403.715 strains (accessed May 2023), contained only three Mokola strains. The multi-locus sequence typing (cMLST), allelic profiles, and sequence types (ST) of 48 Salmonella enterica isolates were assigned by comparing the sequences with those in the MLST profile database. The 48 Salmonella isolates yielded six unique serovars, and the 7-gene MLST identified one sequence type (ST) for each serovar. Sequence type ST617 is shared by all 17 Salmonella Cotham strains while ST516 is shared by all 16 Salmonella Give strains. Similar findings were made with the ST19 sequence type for the four Salmonella Typhimurium strains and the ST1483 sequence type for the four Salmonella Abony strains. The only strain of Salmonella enterica subsp. enterica serovar Senftenberg belongs to the ST14 sequence type. In the MLST profile database, no sequence type for Salmonella Mokola was found (Table S5). The sequence core-genome multi-locus typing (cMLST)clustering of 48 Salmonella isolates based on the source of the isolate, type of samples, and clinical diag- nosis revealed five distinct clusters of 46 Salmonella iso- lates. Two of the Salmonella isolates (S. Senftenberg and one S. Typhimurium) were not assigned to any cluster. Cluster 1 with ST617 consisted of 17 Salmonella strains distributed among human isolates (2 strains), animal iso- lates (7 strains from cattle and 3 strains from poultry), and five environmental isolates. Cluster 2 with ST516 included 16 Salmonella strains, i.e., 12 human isolates, 2 Page 7 of 17 animal isolates (one cattle and one poultry), and 2 envi- ronmental isolates. Cluster 3 with unknown ST consisted of 6 strains, of which 5 strains were from animals (three strains from poultry and two from cattle) and one strain from an environmental source. Cluster 4 with ST1483 comprised 4 strains, of which 3 were from environmen- tal samples and one was from a human patient. Cluster 5 with ST19 was made up of 3 strains, of which 2 were from human patients and one from animal sources (Table S6 and Fig. 3. The distribution of the isolates within the local government areas is shown in Fig. 4. Discussion The intensity with which non-typhoidal Salmonella (NTS), responsible for foodborne diseases, acquires anti- microbial resistance genes over the years is worrisome and of public health concern [50]. Using WGS and bio- informatics tools, this study examined clonal relation- ships among NTS isolates from humans, animals, and the environment, their virulence potential, and the presence of antimicrobial resistance genes that may pose a pub- lic health threat if spread to other bacterial agents. Two thousand five hundred twenty-two samples yielded 48 different Salmonella. The infection was present in 0.9% (19/2002) of human samples, 4% (17/420) of animal sam- ples, and 12% (12/100) of environmental sample (sewage and wastewater). The 4% prevalence rate from animal sources (poultry: 7.3% (11/150) and cattle: 2.2% (6/270)) reported in this study is lower than that reported by Jibril et al. [51] with 14.3% for animal faecal samples. A similar high preva- lence was documented in other African countries, such as Ethiopia with 14.9% [52] and Ghana with 44% [53]. This difference could be caused by sample size, as a much lower number of samples was collected in this study. In Denmark, an annual report on Salmonella enterica zoo- nosis revealed a prevalence of 0–1.8% [54]. The low prev- alence recorded in Denmark has been associated with effective surveillance and control programs, a situation that is not well-footed in Nigeria. The dominant serotype in this study was S. Cotham (35.4%), followed by S. Give (33.3%), S. Mokola (12.5%), S. Typhimurium (8.3%), S. Abony (8.3%), and S. Senftenberg (2.1%). Except for S. Typhimurium, the serovars found in this study had not been characterized in Nigeria before, either from animals (poultry and cattle), humans, or the environment. Besides, little is known about their poten- tial to cause human disease. Interestingly, serovars S. Give and S. Cotham were cultured from patients, animals, and wastewater from abattoirs and the hospital environ- ment. Thus, both serovars (S. Give and S. Cotham) may be emerging Salmonella serovars in Nigeria. Although some of the serovars isolated in this study are not com- monly associated with human salmonellosis, they may be Akinyemi et al. BMC Microbiology (2023) 23:164 Table 2 Summary of the intrinsic and acquired resistance genes, transporter genes and plasmid replicons in 48 Salmonella enterica strains from Nigeria Strain ID Acquired genes/Transporters Intrinsic genes Serotype Source 19CS0255 Senftenberg Animal (cattle) aac.6…Iaa/mdf(A) 19CS0257 19CS0290 19CS0294 19CS0295 19CS0250 19CS0263 19CS0267 19CS0269 19CS0271 19CS0272 19CS0274 19CS0275 19CS0277 19CS0278 19CS0279 19CS0283 19CS0284 19CS0285 19CS0288 19CS0291 19CS0292 19CS0245 19CS0246 19CS0247 19CS0248 19CS0249 19CS0251 19CS0259 19CS0260 19CS0261 19CS0262 19CS0264 19CS0266 19CS0268 19CS0280 19CS0289 19CS0293 19CS0270 19CS0273 19CS0276 19CS0281 19CS0282 19CS0287 19CS0253 19CS0256 19CS0258 19CS0286 Abony Abony Abony Abony Cotham Cotham Cotham Cotham Cotham Cotham Cotham Cotham Cotham Cotham Cotham Cotham Cotham Cotham Cotham Cotham Cotham Give Give Give Give Give Give Give Give Give Give Give Give Give Give Give Give Mokola Mokola Mokola Mokola Mokola Mokola Typhimurium Typhimurium Typhimurium Typhimurium aac.6…Iaa/mdf(A) Sewage/wastewater aac.6…Iaa/mdf(A) Sewage/wastewater aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Animal (poultry) aac.6…Iaa/mdf(A) Sewage/wastewater aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Animal (cattle) aac.6…Iaa/mdf(A) Sewage/wastewater aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Sewage/wastewater aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Animal (poultry) aac.6…Iaa/mdf(A) Sewage/wastewater aac.6…Iaa/mdf(A) Animal (poultry) aac.6…Iaa/mdf(A) Sewage/wastewater aac.6…Iaa/mdf(A) Human Sewage/wastewater aac.6…Iaa/mdf(A) anderes Resistogramm aac.6…Iaa/mdf(A) aac.6…Iaa/mdf(A) Animal (cattle) aac.6…Iaa/mdf(A) Animal (poultry) aac.6…Iaa/mdf(A) Sewage/wastewater aac.6…Iaa/mdf(A) Sewage/wastewater aac.6…Iaa/mdf(A) Animal (poultry) aac.6…Iaa/mdf(A) Animal (cattle) aac.6…Iaa/mdf(A) Animal (cattle) aac.6…Iaa/mdf(A) Animal (cattle) aac.6…Iaa/mdf(A) Animal (cattle) aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Animal (poultry) aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Animal (cattle) aac.6…Iaa/mdf(A) Animal (cattle) aac.6…Iaa/mdf(A) Sewage/wastewater aac.6…Iaa/mdf(A) Sewage/wastewater aac.6…Iaa/mdf(A) Animal (cattle) aac.6…Iaa/mdf(A) Human aac.6…Iaa/mdf(A) Animal (poultry) sinH, mdsB. mdsA, golT, golS, pcoE,pcoS, pocR, pcoD, pcoC, pcoB, pcoA,silP,silA,silB, silF,silC,silR,silS,silE sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS qnrB, sinH, mdsB, mdsA, golT, golS qnrB19, sinH, mdsB, mdsA, golT, golS qnrB19, sinH, mdsB, mdsA, golT, golS qnrB19, sinH, mdsB, mdsA, golT, golS qnrB19, sinH, mdsB, mdsA, golT, golS qnrB, sinH, mdsB, mdsA, golT, golS qnrB19, sinH, mdsB, mdsA, golT, golS qnrB19, sinH, mdsB, mdsA, golT, golS qnrB, sinH, mdsB, mdsA, golT, golS qnrB19, sinH, mdsB, mdsA, golT, golS qnrB19, sinH, mdsB, mdsA, golT, golS qnrB19, sinH, mdsB, mdsA, golT, golS qnrB19, sinH, mdsB, mdsA, golT, golS qnrB, sinH, mdsB, mdsA, golT, golS qnrB19, sinH, mdsB, mdsA, golT, golS qnrB19, sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS sinH, mdsB, mdsA, golT, golS Page 8 of 17 Plasmid replicon - - - - - - - - - - - - - - - - - - - - - - Col440I_1 Col440I_1 Col440I_1 Col440I_1 Col440I_1 Col440I_1 Col440I_1 Col440I_1 Col440I_1 Col440I_1 Col440I_1 Col440I_1 Col440I_1 Col440I_1 Col440I_1 Col440I_1 - - - - - - incFIB.B/incFII.S incFIB.B/incFII.S incFIB.B/incFII.S incFIB.B/incFII.S Akinyemi et al. BMC Microbiology (2023) 23:164 Table 3 Distribution of Salmonella virulence genes clustered within several Salmonella pathogenicity Islands (SPIs) and plasmid operons from different source from Nigeria Page 9 of 17 Virulence loci Virulence genes Strain ID 19CS0245 19CS0246 19CS0247 19CS0248 19CS0249 19CS0250 19CS0251 19CS0253 19CS0255 19CS0256 Serovar Num of virulence gene 100 Give 100 Give 100 Give 100 Give 100 Give 100 Cotham Give 100 Typhimurium 116 SPI-1 SPI-2, SPI-3 SPI-11SPI-5, SPI-24/CS54 fimbriae operon SPI-1 SPI-11 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-11 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-11 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-11 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-11 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-11 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 Long polar fimbriae cluster, Plasmid encoded fim- briae cluster, Plasmid virulence Operon SPI-2 SPI-3 SPI-5 SPI-12 SPI-24/CS54 fimbriae operon Senftenberg 101 SPI-1 Long polar fimbriae cluster SPI-3 SPI-5 Typhimurium 116 SPI-1 SPI-2 SPI-3 Long polar fimbriae cluster, Plasmid encoded fimbriae cluster, Plasmid virulence Operon SPI-5 SPI-12 SPI-24/CS54 fimbriae operon 19CS0257 Abony 106 SPI-1 SPI-2 SPI-3 Long polar fimbriae cluster SPI-5 SPI-12 SPI-24/CS54 fimbriae operon 19CS0258 Typhimurium 118 SPI-1 SPI-2 SPI-12 SPI-24/CS54 Long polar fimbriae cluster, Plasmid encoded fimbriae SPI-5iae cluster, Plasmid viru- lence Operon fimbriae operon 19CS0259 19CS0260 19CS0261 19CS0262 19CS0263 19CS0264 19CS0266 19CS0267 19CS0268 19CS0269 19CS0270 19CS0271 19CS0272 19CS0273 19CS0274 19CS0275 19CS0276 19CS0277 19CS0278 19CS0279 19CS0280 19CS0281 Give Give Give Give Cotham Give Give Cotham Give Cotham Mokola Cotham Cotham Mokola Cotham Cotham Mokola Cotham Cotham Cotham Give Mokola 100 100 100 100 100 100 100 100 100 100 102 100 100 102 100 100 102 100 100 100 100 100 SPI-1 SPI-11 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-11 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-11 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-11 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-11 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-11 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-11 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-11 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-11 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-11 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-3 SPI-5 SPI-12 SPI-24/CS54 fimbiae operon SPI-1 SPI-2 SPI-11 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-11 SPI-3 SP1-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-3 SPI-5 SPI-12 SPI-24/CS54 fimbriae operon SPI-1 SPI-2SPI-11 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2SPI-11 SPI-3 SPI-5 f SPI-24/CS54 imbriae operon SPI-1 SPI-2 SPI-3 SPI-5 SPI-12 SPI-24/CS54 fimbriae operon SPI-1 SPI-2SPI-11 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-11 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-11 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2SPI-11 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon ctdB, fae. faeD faeE sspH1 CtdB fae. faeD faeE sspH1* CtdB fae. faeD faeE sspH1* CtdB fae. faeD faeE sspH1* CtdB fae. faeD faeE sspH1* CtdB fae. faeD faeE pipB2* ctdB fae. faeD faeE sspH1* gogB grvA ipfA ipfB ipfC ipfD ipfE pefA pefB pefC pefD pipB2 rck sodC1 sopD2 spvB spvC spvR ssel. srfH sseK2 sspH2* ipfA ipfB ipfC ipfD ipfE pipB2 sopD2* gogB grvA ipfA ipfB ipfC ipfD ipfE pefA pefB pefC pefD pipB2 rck sodC1 sopD2 spvB spvC spvR ssel. srfH sseK2 sspH2* ipfA ipfB ipfC ipfD ipfE pipB2 shdA sodC1 sopD2 sseK2 sspH2* gogB grvA ipfA ipfB ipfC ipfD ipfE pefA pefB pefC pefD pipB2 rck shdA sodC1 sopD2 spvB spvC spvR ssel. srfH sseK2 sspH2 sspH1* ctdB fae. faeD faeE sspH1* ctdB fae. faeD faeE sspH1* ctdB fae. faeD faeE sspH1* CtdB fae. faeD faeE sspH1* ctdB fae. faeD faeE pipB2* CtdB fae. faeD faeE sspH1* ctdB fae. faeD faeE sspH1* ctdB fae. faeD faeE pipB2* ctdB fae. faeD faeE sspH1* ctdB fae. faeD faeE pipB2* entE pipB2 shdA sodC1 sopD2 sseK2 sspH2* ctdB fae. faeD faeE pipB2* ctdB fae. faeD faeE pipB2* entE pipB2 shdA sodC1 sopD2 sseK2 sspH2* ctdB fae. faeD faeE pipB2* ctdB fae. faeD faeE pipB2* entE pipB2 shdA sodC1 sopD2 sseK2 sspH2* ctdB fae. faeD faeE pipB2* ctdB fae. faeD faeE pipB2* ctdB fae. faeD faeE pipB2* ctdB fae. faeD faeE sspH1* entE pipB2 shdA sodC1 sopD2* Akinyemi et al. BMC Microbiology (2023) 23:164 entE pipB2 shdA sodC1 sopD2 sseK2 sspH2* ctdB fae. faeD faeE pipB2* ctdB fae. faeD faeE pipB2* ctdB fae. faeD faeE pipB2* gogB grvA ipfA ipfB ipfC ipfD ipfE pefA pefB pefC pefD pipB2 rck sodC1 sopD2 spvB spvC spvR ssel. srfH sseK2 sspH2* entE pipB2 shdA sodC1 sopD2 sspH2* ctdB fae. faeD faeE pipB2* ctdB fae. faeD faeE sodC1 sopD2 sspH1 sspH1* ipfA ipfB ipfC ipfD ipfE pipB2 shdA sseK2 sspH2* ctdB fae. faeD faeE pipB2* ctdB fae. faeD faeE pipB2* ctdB fae. faeD faeE sspH1* ipfA ipfB ipfC ipfD ipfE pipB2 shdA sodC1 sopD2 sseK2 sspH2* ipfA ipfB ipfC ipfD ipfE pipB2 shdA sodC1 sopD2 sseK2 sspH2* gogB grvA ipfA ipfB ipfC ipfD ipfE pefA pefB pefC pefD pipB2 rck sodC1 sopD2 SshdA spvB spvC spvR ssel.srfH sseK2 sspH2* Table 3 (continued) Strain ID Serovar Page 10 of 17 Virulence loci Virulence genes 19CS0282 19CS0283 19CS0284 19CS0285 19CS0286 19CS0287 19CS0288 19CS0289 19CS0290 19CS0291 19CS0292 19CS0293 19CS0294 Num of virulence gene 102 Mokola 100 Cotham 100 Cotham Cotham 100 Typhimurium 116 Mokola Cotham Give Abony Cotham Cotham Give Abony 101 100 100 106 100 100 100 106 SPI-1 SPI-2 SPI-3 SPI-5 SPI-12 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI- SPI-3 2 Long polar fimbriae cluster, Plasmid encoded fimbriae cluster, Plasmid virulence Operon SPI-5 SPI-12 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-3 SPI-5 SPI-12 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-3 SPI-24/CS54 Long polar fimbriae cluster SPI-5 fimbriae operon SPI-1 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-3 SPI-5 SPI-24/CS54 fimbriae operon SPI-1 SPI-2 SPI-3 SPI-12 SPI-24/CS54 Long polar fimbriae cluster SPI-5 fimbriae operon 19CS0295 Abony 106 SPI-1 SPI-2 SPI-3 SPI-12 SPI-24/CS54 Long polar fimbriae cluster SPI-5 fimbriae operon GCF0000069452ASM694v2 LT2 117 SPI-1 SPI-2 SPI-3 SPI-5 SPI-12 SPI-24/CS54 Long polar fimbriae cluster, Plasmid encoded fimbriae cluster, Plasmid virulence Operon fimbriae operon *Represents the following 95 virulence genes: avrA csgA csgB csgC csgD csgE csgF csgG entA entB fepC fepG fimC fimD fimF fimH fimI invA invB invC invE invF invG invH invI invJ mgtB mgtC mig.14 misL ompA orgA orgB orgC pipB prgH prgI prgJ prgK ratB sicA sicP sifA sifB sinH sipA.sspA sipB.sspB sipC.sspC sipD slrP sopA sopB. sigD sopD sopE2 spaO spaP spaQ spaR spaS spiC.ssaB sptP ssaC ssaD ssaE ssaG ssaH ssaI ssaJ ssaK ssaL ssaM ssaN ssaO ssaP ssaQ ssaR ssaS ssaT ssaU ssaV sscA sscB sseA sseB sseC sseD sseE sseF sseG sseJ sseK1 sseL steA steB steC 3° 24’ 23.2128’’ E. (Longitude) of special importance in the Nigerian setting. S. Give, for example, was reported in a multistate outbreak in Ger- many in 2004 that resulted in severe gastroenteritis and hospitalization of those infected [55].S. Give has become widespread recently in poultry in Nigeria [51] and Burkina Faso [56], and serovars S. Give and S. Cotham were reported repeatedly from animal sources in Nigeria in the past [11, 57]. This finding needs further research for a reliable risk assessment. In sub-Saharan Africa, S. Typhimurium and S. Enteritidis strains were the pre- vailing iNTS serovars associated with invasive systemic infections in children and adults [58, 59], with an esti- mated mortality rate among in-patients ranging from 4.4 to 27% for children [60] and 22 to 47% for adults [61]. In Burkina Faso, Salmonella Enteritidis-associated bactere- mia was also documented [56]. In this study, seven strains of S. Give, one each of S. Typhimurium, and S. Senften- berg strain, were iNTS. Although a previous study in Nigeria documented S. Typhimurium and S. Enteritidis- associated bacteremia [62], these are the first reported iNTS Give and Senftenberg-associated bacteremia cases in Nigeria. The emerging iNTS S. Typhimurium and S Enteritidis seem to be already prominent in Nigeria, and their potential to cause human disease and spread to other neighboring countries is not in doubt. All forty-eight Salmonella enterica isolates were 100% susceptible to piperacillin-tazobactam, cefotaxime, ceftazidime, cefepime, azithronam, ertapenem, imipe- nem, meropenem, tigecycline, fosfomycin, colistin, trim- ethoprim, and trimethoprim-sulfamethoxazole. The results also revealed that none of the Salmonella isolates produced extended-spectrum β-lactamases Akinyemi et al. BMC Microbiology (2023) 23:164 Page 11 of 17 Fig. 3 The minimum spanning tree (MST) of Salmonella strains (nodes) was used in this study. The node colour corresponds to the source of the strains (see legend). The allelic distances from cgMLST analysis are denoted at branches. Clusters of genetically similar strains were defined using a cut-off of 10 alleles and are visualized in grey gentamicin, which are known to bind the bacterial 50 S subunit of the ribosome and prevent protein synthesis. The presence of this gene has made the use of amino- glycoside ineffective in vivo especially for the treatment of invasive Salmonellosis and as such EUCAST expert rule 3.2 of 2019 reported all aminoglycoside should be reported as resistant irrespective of the result of the sus- ceptibility results. Acquired quinolone resistance genes were detected in all 16 (100%) strains of S. Give. Plasmid-mediated qui- nolone resistance (PMQR) qnrB gene was detected in 4, while the qnrB19 gene was found in 12 S. Give strains. All S. Give strains harbored plasmid Col440I. Fifteen S. Give strains (93.75%) were resistant to ciprofloxacin and 16 (100%) to moxifloxacin. These strains were also phe- notypically resistant to this antibiotic family but suscep- tible to all other tested antibiotics. This result agrees with the results of a recent report on phenotypic and geno- typic antimicrobial resistance and the presence of resis- tance genes in Salmonella isolates from poultry, where neither phenotypic expression of ESBL nor ESBL genes were present in the Salmonella strains studied [51]. This observation corresponds well with the fact that β-lactams are reported to be used rarely or not often in poultry production in Nigeria [63, 64], possibly due to their high price. However, fluoroquinolones have been reported to be used as a growth promoter and possibly contributed to the emergence of Salmonella and other bacteria resistant to ofloxacin and ciprofloxacin. Predictions by ResFinder indicated that these S. Give strains carried either qnrB19 or qnrB genes with or without the presence of point Fig. 4 Map of Nigeria showing the location of Lagos State and the distri- bution of Salmonella enterica serovars across different local government areas of Lagos State (ESβL), Metallo-β-lactamases, AmpC, or OXA-48 phenotypically. Chromosomally encoded aminoglycoside acetyltrans- ferase aac.(6)-Iaa resistance genes were present in all 48 Salmonella isolates (100%) and conferred resistance like tobramycin, amikacin, and to aminoglycosides Akinyemi et al. BMC Microbiology (2023) 23:164 Page 12 of 17 mutations in DNA gyrase and topoisomerase. It has been documented that the qnrB19 and qnrB genes encode transferable fluoroquinolone resistance mechanisms that are responsible for reduced susceptibility to quinolones [65]. Also, a point mutation in the quinolone resistance- determining region (QRDR) of the DNA gyrase-A (gyrA) and topoisomerase C (parC) genes is known to cause clinical resistance in members of Enterobacteriaceae [65]. Similar reports have been documented in Salmonella isolates from Nigeria [57, 66, 67]. The high-level detec- tion of qnr genes in S. Give may contribute not only to the stepwise development of high-level fluoroquinolone resistance but also to its spread between bacteria species [68]. The first report of a plasmid-mediated quinolone resistance (PMQR) mechanism, qnrA, was described in the late 1990s, and since then, several variants of the qnr gene have been discovered [5]. Among the 48 Salmonella isolates in this study, 20 isolates belonging to two serovars contained plasmid replicons. In total, three different plasmid replicas were detected, with Col440I being the most predominant rep- licon found in the “16” “S”. Give was predicted to carry qnrB, qnrB-19, sinH, mdsB, mdsA, golT, and golS, respec- tively. The Col440II-like plasmid detected in S. Schwar- zengrund isolates in Chile highlighted the fact that a small pPAB19-4-like plasmid plays an important role in the dissemination of qnrB19 [69]. Incompatible plasmids incFIB.B and incFII.S were found in all four strains of S. Typhimurium predicted to carry the sinH, mdsB, mdsA, golT, and golS genes. The plasmids incFIB.B and incFII.S from S. Typhimurium were aligned with the reference sequence GCF0000069452ASM694v2 (LT2). There was 100% identity with 100.0% query cover between the sequences in this study and the reference sequence. In this study, acquired golT genes coding for resistance to copper or gold and golS genes coding for resistance to gold were detected in all 48 isolates, with a percent- age of similarity ranging from 96.71 to 100% when com- pared to the GCF0000069452ASM694v2 (LT2) reference strain. Furthermore, the only iNTS S. Senftenberg strain detected in this study harbored several genes that confer resistance to heavy metals such as copper (pcoE, pcoS, pocR, pcoD, pcoC, pcoB, and pcoA), copper/silver (silA, silB, silF, silC, silR, and silS), and silver (silP and silE). The presence of these heavy metal resistance genes in this strain and its role as an invasive pathogen remain sub- jects of concern. Several virulence gene markers of Salmonella enterica distributed across several Salmonella pathogenicity islands (SPIs) have been found responsible for systemic transmission leading to severe infections [70, 71]. Viru- lence genes found in all 48 Salmonella isolates were pre- dicted by all-vfbd.xls. 100 to 118 virulence gene markers distributed across several Salmonella pathogenicity islands (SPIs), clusters, prophages, and plasmid oper- ons were found in each isolate. A total of 95 virulence genes were predicted to be common to all six Salmo- nella enterica serovars. The virulence genes ctdB, fae, faeD, and faeE were common to Salmonella Give, and S. Cotham. While ipfA, ipfB, ipfC, ipfD, ipfE, pipB2, and sopD2 were detected in S. Senftenberg, S. Abony, and S. Typhimurium. The virulence genes spvB, spvC, spvR, ssel.srfH, sseK2, and sspH2 were found only in S. Typhimurium, while the entE was unique to S. Mokola. However, rck genes that confer resistance to or protect against complement-mediated immune response were detected in all S. Typhimurium strains with 100% homol- ogy to the gene of strain GCF0000069452ASM694v2 (LT2). In Salmonella pathogenicity, the type 3 secretion sys- tem (T3SS), encoded by SPI-1 and SPI-2, contains major virulence determinants. The presence of the major viru- lence factors avrA, mgtC, sopB, ssaQ, and invA in all 48 isolates is an indication of their ability to colonize the liver of the host [50] and therefore may cause serious human disease. The ssp.H1 and steB genes coding for effectors are found in all 16 (100%) S. Give strains and one (25%) S. Typhimurium strain. These effector genes are mediators of cell invasion and modifications, which are major contributing factors to intracellular growth [72]. The cytolethal distending toxin islet gene (cdtB) was detected in all the S. Give and S. Cotham in this study. This gene has been reported to play a vital role in disease pathogenesis. This toxin islet has been known to cause DNA damage and cell cycle arrest in impaired cells [73]. The islet gene (cdtB) was detected in Salmonella Telelke- bir in a similar study conducted in Southwestern Nigeria [27]. The sopA and sopE2 pseudogenes were detected in all 48 isolates. SopD2 pseudogenes were found in all S. Abony, S. Typhimurium, S. Senftenberg, and S. Mokola isolates, while shdA pseudogenes were confined to S. Abony and S. Mokola isolates. Langridge et al. [74] defined a pseudogene as a “gene with a mutation” (i.e., a premature stop codon, frameshift, truncation, or syntenic deletion) compared to an intact version of that gene. It is easier for Salmonella to enter epithelial cells when the effector genes found in Salmonella pathogenicity island 1 (SPI-1) are pseudogenized. This enhances Salmonella adaptability to systemic infection in humans [75, 76]. As part of the limitations of the study, it was not pos- sible to determine if the number of pseudogenes pres- ent in each of the 48 Salmonella enterica strains was due to identical or non-identical mutations. The Salmonella plasmid virulence (spv) locus harbors five genes desig- nated spv RABCD. The Salmonella virulence plasmid (spv)-RBC was found in the four S. Typhimurium strains. The expression of the spv genes has been reported to Akinyemi et al. BMC Microbiology (2023) 23:164 Page 13 of 17 play a role in the intracellular multiplication of Salmo- nellae by distorting the cytoskeleton of the eukaryotic cells using its spvB ADP-ribosylates actin [77, 78]. The two operons of the chaperone–usher class pef and ipf (plasmid-encoded fimbriae) known to mediate adhesion of S. Typhimurium to the small intestine in mice were detected and confined to four S. Typhimurium strains (pefA, pefB, pefC, and pefD), while the long polar fimbriae operons (ipfA, ipfB, ipfC, and ipfD) were detected in four S. Abony and four S. Typhimurium. Other fimbriae oper- ons of the chaperon class detected contain fimC, fimD, fimF, fimH, fimI, steA, steB, and steC, which are well con- served in all 48 Salmonella isolates. The four Salmonella invasion proteins (SipsABCD), which have been shown to play essential roles in the secretion and translocation of SPI-1 effectors, are present in all isolates [79]. The in silico serotype prediction with Seqsero in com- parison to the serotyping scheme passed the Fast-QC threshold, with the QC status indicating “PASS” for 5 serotypes except for serotype S. Mokola (6 isolates) show- ing “WARN QC” status for the quality of the sequences. There was no ST number available for S. Mokola from the MLST profile database. This is an indication that the complete genomic sequence of serovar S. Mokola has not been deposited in the global Salmonella database yet. This result represents the first complete genomic sequence of S. Mokola serovar from Nigeria. Multilocus sequence typing (MLST), allelic profiles, and sequence types (ST) of the 48 Salmonella enterica isolates were assigned by comparing the sequences with those in the MLST profile database. Only one sequence type (ST) each was found for the 17 S. Cotham (ST617), 16 S. Give (ST516), 4 S. Typhimurium (ST19), and 4 S. Abony (ST1483), while S. Senftenberg (ST14) is the only ST generated from segments of seven housekeeping genes (aroC, dnaN, hemD, hisD, purE, sucA, and thrA). All seven MLST loci were successfully recovered for the 48 Salmonella enterica strains isolated from different sources. The presence of such clones in the samples of human, animal, and environmental origin indicates epi- demiological links between STs and reservoir isolates. In Sub-Saharan Africa, NTS and iNTS serovars have been a challenge. The MLST of S. Typhimurium is known to be prevalent in Burkina Faso in humans and poultry [80]. Also, S. Typhimurium ST313 strain D23580 isolated from a patient with an iNTS infection has been reported from Malawi [81]. S. Typhimurium ST19 detected in this study belongs to the globally circulating lineage [82]. It is the dominant ST of the MLST database, which, however, consists of sequences of strains isolated from Europe and Northern America. S. Typhimurium ST19 has already caused gastroenteritis in humans [83], which is in accordance with this study as S. Typhimurium ST19 was detected in stool samples of patients with diarrheal disease from two study centers 20 km apart. The isola- tion of S. Typhimurium ST19 in hospital wastewater in this study is an indication of its dissemination from clinical sources into the environment. In a related study of WGS analysis of Salmonella serovars from animals in North-Central Nigeria, eight diverse sequence types (STs) were detected and the most common STs were ST-321 and ST-19 (n = 4) exhibited by S. Muenster and S. Typhimurium, respectively [26]. The detection of inva- sive S. Typhimurium ST19 clones in the blood of febrile patients with systematic infection has also been reported in Iran [84]. S. Typhimurium ST19 has been documented to colonize the gut and cause inflammation by a Sal- monella pathogenicity island (SPI)-1-mediated process when ingested orally [83]. The MLST of the 48 Salmo- nella isolates based on the source of isolates, type of sam- ple, and clinical prognosis revealed five distinct clusters in the 46 Salmonella isolates. Two of the Salmonella iso- lates could not be assigned to any cluster. WGS revealed that strains in each MLST Salmonella cluster from this study were closely related and likely shared a common ancestor. The distribution of these clusters within the study areas showed that all MLST clusters, including S. Senftenberg, that were not assigned to any cluster were found in the Alimosho local government area (LGA). All of the LGAs chosen for this investigation had isolates in clusters 1 and 2. Blood from a feverish patient at the Lagos State University teaching hospital and stool from a 3-5-year-old with diarrhea at Messy Street Children Hospital both contained invasive iNTS Cotham ST617 in cluster 1. While a genetically identical strain was discov- ered in wastewater taken from Gbagada general hospital, a few kilometers from Badagry LGA and Alimosho LGA, Salmonella Cotham ST617 from the same cluster was also detected in stool samples from poultry birds in Bada- gry and cattle dung along the Governor Road. Similar to this, S. Give (ST516) strains in cluster 2 were found in the Alimosho LGA from human, animal, and environmental samples. A strain of this serovar was also identified from cattle at Odo-eran abattoir in the same LGA, around 1.6 km distant from Alimosho general hospital. It was noted that the Salmonella Give strains that were isolated from wastewater and human samples (stool and blood) were from Alimosho general hospital. Additionally, S. Give (ST516) strains were found in the Lagos Island LGA (Messy Street Children hospital), Badagry LGA (Oko- Afor poultry facility), and Ikeja LGA (place of Lagos Uni- versity teaching hospital). The discovery of this disease in these three LGAs illustrates the spread and circulation of ST516 within Lagos environments because they are geo- graphically apart. In Nigeria, S. Give and S. Cotham sero- types have recently been reported in water, fecal samples feed, dust, and boot swabs from different poultry farms within the same and different localities [85]. However, Akinyemi et al. BMC Microbiology (2023) 23:164 Page 14 of 17 pathogens in food chains, proper disposal of refuse, treat- ment of wastewater from hospitals and food production industries, and control of dump sites near hospitals are essential to preventing outbreaks. Abbreviations NTS INTS MLST CgMLST CgSNP MDR EsβL KPC MBL ST MT NAFDAC PMQR SPI LGA RFLP VNTR MLVA MALDI-TOF MS Non-typhoidal Salmonella Invasive non-typhoidal Salmonella Multi-locus sequence typing Core genome multi-locus sequence typing Core genome single nucleotide polymorphism Multiple-drug resistant Extended spectrum beta-lactamases Klebsiella pneumoniae producing carbapenemase Metallo-beta-lactamase sequence type Metric tons National Agency for Food and Drug Administration and Control. plasmid-mediated quinolone resistance Salmonella Pathogenicity Island Local Government Area Restriction fragment length polymorphism Variable number of tandem repeats Multiple Locus Variable-Number Tandem Repeat Analysis matrix-assisted laser desorption/ionization-time of flight mass spectrometry Supplementary Information The online version contains supplementary material available at https://doi. org/10.1186/s12866-023-02901-1. Supplementary Material 1 Acknowledgements We are grateful to the staff of the Department of Microbiology and the management of Lagos State University (LASU). We are equally grateful to the management and staff of the Ministry of Health and the staff of the various hospitals, abattoirs, and animal farms for their contributions to this research. We are grateful to the Alexander von Humboldt (AvH) Foundation, Germany, for sponsoring and funding this research work. We are sincerely grateful to the management of the Friedrich-Loeffler-Institute (FLI), Jena, Germany, for financial and technical support. Authors’ contributions K.O.A conceived the study. C.O.F., K.O.A., J.L., and U.M. were responsible for the methodology. C.O.F. J.L., and U.M, were responsible for the software, the validation of the study was done by K.O.A., C.O.F., J.L., U.M., G.W., H.T., and H.N. The formal analysis was done by K.O.A., C.O.F., J.L., and U.M., the investigation was done by K.O.A., C.O.F., J.L., U.M., G.W., and H.T. writing—original draft manuscript was done by C.O.F., critical review of original draft by K.O.A., review, and editing of the manuscript by K.O.A., J.L., G.W., H.T., and H.N., and supervision was done by K.O.A. and H.N. Funding This study was funded by the Alexander von Humboldt (AvH) Foundation, Germany, and the Friedrich-Loeffler Institute (FLI), Jena, Germany. AvH has no role in the design of the study, the collection, analysis, and interpretation of data, or in writing the manuscript. Data Availability Raw sequences from this study are available and were deposited in the European Nucleotide Archive (ENA) with bio project accession PRJEB56537 in the ENA bio project database: https://www.ebi.ac.uk/ena/browser/view/ PRJEB56537. local environmental conditions and a lack of biosecurity measures may have been responsible for the dissemina- tion of similar strains [85]. The presence of the two domi- nant clones (S. Give ST516 and S. Cotham ST617) in samples from human, animal, and environmental sources demonstrated the need for further epidemiologic stud- ies to identify the infection and to tailor countermea- sures for the local setting. This study points to the fact that transmission of S. Give ST516 and S. Cotham ST617 may occur not only from person to person but also from animal to human. Controlling environmental contami- nation and potential control methods that could serve as a guide for appropriate waste management require special consideration. Near Alimosho General Hospital are two large, overburdened garbage dump sites, one of which is only 300 m away and the other is 1.3 km distant. The area is occupied by low-income, lower, and upper- middle-class residents, but no functional waste disposal treatment unit is available. These dump sites have grossly polluted the environment, including the groundwater [86]. This may have contributed to the high prevalence of Salmonella infection within that area and continuous spread to other areas of the state. Conclusion The characterization of Salmonella isolates from hos- pital, environmental, and animal production sources using different molecular tools was conducted. The study revealed six serotypes, with S. Give and S. Cotham as the most predominant ones. Closely related Salmonella clones were detected in these samples, pointing to an epidemiological link between serotypes and sequence types. The study also showed that the isolates were resis- tant to multiple antibiotics, as reflected by the presence of intrinsic and acquired genes conferring resistance to antibiotics and heavy metals. A wide range of virulence genes that help the organism become pathogenic in the host were detected from the whole genomic sequences. It becomes obvious that it is time to act and develop a strat- egy for Nigeria against the spread of some emerging NTS Salmonella serovars, such as S. Give and S. Cotham. This strategy must take into consideration food-producing animals, i.e., occupational animal contacts and food con- tamination, spill-back risk of the already heavily polluted environment, which arises from indiscriminate disposal of refuse and untreated wastewater discharge (effluent) from the hospital environment, and/or other sources, and consumer behavior and food availability. For the One Health context, these results are alarming, and the risk of further spread of plasmid replicon Col440I_1 and viru- lence genes is imminent if no immediate action is taken. The first step must be the control and prevention of the evolution of new and more virulent NTS in hospitals and veterinary clinics. Prompt surveillance of emerging Akinyemi et al. BMC Microbiology (2023) 23:164 Declarations Ethics approval and consent to participate Ethical approval from the ethics committee of the following institutions was obtained before patients’ enrolment: The Human Research and Ethics Committee of the Lagos State University Teaching Hospital with reference number LREC/06/10/1012 and the Lagos State Health Service Commission with reference number LSHSC/2222/VOL.VC/352. All methods were carried out in accordance with relevant guidelines and regulations, and informed consent was obtained from all subjects and/or their legal guardian(s). Consent for publication Not applicable. 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10.1371_journal.pone.0244053.pdf	Data Availability Statement: The data are accessible via Dataverse (https://doi.org/10.7910/ DVN/8ZVOKW).	The data are accessible via Dataverse ( https://doi.org/10.7910/ DVN/8ZVOKW ).	RESEARCH ARTICLE Gender specific differences in COVID-19 knowledge, behavior and health effects among adolescents and young adults in Uttar Pradesh and Bihar, India Jessie PinchoffID D. Ngo1 1, KG Santhya2, Corinne White1, Shilpi Rampal2, Rajib Acharya2, Thoai 1 Population Council, One Dag Hammarskjold Plaza, New York, NY, United States of America, 2 Population Council, India Habitat Centre, New Delhi, Delhi, India jpinchoff@popcouncil.org Abstract On March 24, 2020 India implemented a national lockdown to prevent spread of the novel Coronavirus disease (COVID-19) among its 1.3 billion people. As the pandemic may dispro- portionately impact women and girls, this study examines gender differences in knowledge of COVID-19 symptoms and preventive behaviors, as well as the adverse effects of the lock- down among adolescents and young adults. A mobile phone-based survey was imple- mented from April 3–22, 2020 in Uttar Pradesh and Bihar among respondents randomly selected from an existing cohort study. Respondents answered questions related to demo- graphics, COVID-19 knowledge, attitudes, and preventive behaviors practiced, and impacts on social, economic and health outcomes. Descriptive analyses and linear probability regression models were performed for all participants and separately for men and women. A total of 1,666 adolescents and young adults (18–24 years old) were surveyed; 70% were women. While most participants had high awareness of disease symptoms and preventive behaviors, there was variation by gender. Compared to men, women were seven percent- age points (pp) less likely to know the main symptoms of COVID-19 (coeff = -0.071; 95% confidence interval: -0.122 - -0.021). Among women, there was variation in knowledge by education level, urban residence, and household wealth. Women were 22 pp less likely to practice key preventive behaviors compared to men (coeff = -0.222; 95% CIL -0.263, -0.181). Women were also more likely to report recent depressive symptoms than men (coeff = 0.057; 95% CI: 0.004, 0.109). Our findings underscore that COVID-19 is already disproportionately impacting adolescent girls and young women and that they may require additional targeted, gender-sensitive messaging to foster behavior change. Gender-sensi- tive information campaigns and provision of health services must be accessible and provide women and girls with needed resources and support during the pandemic to ensure gains in public health and gender equity are not lost. a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Pinchoff J, Santhya K, White C, Rampal S, Acharya R, Ngo TD (2020) Gender specific differences in COVID-19 knowledge, behavior and health effects among adolescents and young adults in Uttar Pradesh and Bihar, India. PLoS ONE 15(12): e0244053. https://doi.org/10.1371/journal. pone.0244053 Editor: Kannan Navaneetham, University of Botswana, BOTSWANA Received: August 24, 2020 Accepted: November 25, 2020 Published: December 17, 2020 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0244053 Copyright: © 2020 Pinchoff et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The data are accessible via Dataverse (https://doi.org/10.7910/ DVN/8ZVOKW). PLOS ONE \| https://doi.org/10.1371/journal.pone.0244053 December 17, 2020 1 / 13 PLOS ONEFunding: The initial UDAYA cohort was funded by the Bill and Melinda Gates Foundation and Packard Foundation. No additional funds were received for the COVID-19 survey. Competing interests: The authors have declared that no competing interests exist. Gender specific variation in COVID-19 knowledge, behavior and health effects among young adults Introduction To control the spread of the novel Coronavirus disease (COVID-19), the Indian government swiftly instituted a shutdown of international borders and a stay-at-home order on March 24, 2020 [1]. Such ‘lockdown’ policies to prevent the spread of COVID-19 originated in high- income countries and China; little primary research has explored the potential unintended consequences in countries including India characterized by densely populated urban slums, a highly mobile population, high proportions of informal sector workers, and stark variation in poverty levels [2]. Despite rising cases, the lockdown was lifted on June 8, 2020 to begin a phased reopening. As of August 2020, India surpassed 2.3 million cases of COVID-19, the third highest case load after the United States and Brazil [3]. Historically, epidemics and humanitarian crises have disproportionately impacted the most vulnerable, including women and girls [4]. Entrenched inequalities in access to education, job opportunities, and healthcare often leave women inadequately equipped to effectively protect themselves and their families against infection during an outbreak, and they are also more likely to bear secondary negative effects of prolonged crises, such as economic insecurity or challenges accessing essential health services [5]. Existing gender disparities in India may be exacerbated or reinforced by the pandemic and are likely to affect women’s ability to make informed decisions about adopting behaviors that mitigate risk of COVID-19. Prevention campaigns and behavior change communication interventions across various media, including a government-run mobile app (“Aarogya Setu”) that sends automated mes- sages, are informing the public about COVID-19 symptoms, risk factors, and promoting pre- ventive behaviors such as handwashing, social distancing, and wearing masks in India. To date, there is little to no research tracing how COVID-19 messages are reaching men and women or which sub-groups are adopting these behavioral recommendations. However, a rapid situational assessment in the South Asia region (not including India) suggests that women are less likely than men to have received COVID-19 information [6]. Moreover, liter- acy, internet usage and smartphone ownership is lower among women compared to men in India [7–9]. Accessing and understanding health promotion messages increases knowledge, which needs to be accompanied with structural facilitators and access to resources to adopt promoted preventive behaviors (e.g., making soap and water available for handwashing) [10– 12]. These gender gaps may result in lower adoption of promoted health behaviors and increased risk of infection for women and girls. The worsening COVID-19 pandemic in India is causing prolonged social and economic disruptions that are yielding unintended consequences including economic and food insecu- rity, and challenges in accessing healthcare. Challenges in accessing essential health services may lead to increases in other adverse health outcomes, from vaccine preventable diseases to poor birth outcomes and malnutrition [13,14]. This often disproportionately harms women who may require healthcare themselves and are also often responsible for taking care of their family’s health needs. Potential reasons for these challenges may include inability to pay clinic fees as COVID-19 related economic insecurity persists, mobility challenges, or fear of seeking care due to stigma or concerns about COVID-19 infection at the facility. Indeed, compared to March 2019, March 2020 data from the Indian National Health Mission showed marked reductions in indicators of regular health system usage [2]. In addition to physical health, lockdowns may exacerbate household stress, contributing to increases in sexual and gender-based violence (SGBV) and poor mental health symptoms [15,16]. While psychological distress increases generally during crises, experience of depressive symptoms is more common among women compared to men [17]. In addition to gender, a recent study also found that adolescents and younger adults (<25 years), those that had lost PLOS ONE \| https://doi.org/10.1371/journal.pone.0244053 December 17, 2020 2 / 13 PLOS ONEGender specific variation in COVID-19 knowledge, behavior and health effects among young adults employment, and/or lacked formal education were more likely to experience depressive symp- toms as a result of the pandemic’s effects [18]. Relatedly, stress and ongoing lockdowns have been linked with violence against women, as in past humanitarian crises [19]. Some countries reported increases in SGBV during COVID-19 lockdowns [15,20]. Concerns around these sec- ondary health and well-being effects are significant. As India is home to the largest population of adolescents and young adults of any country worldwide, understanding the impact of the pandemic on this important age cohort will also be critical. In the age- and gender-stratified settings of India, prevailing gender disparities and traditional gender norms affect health and well-being of adolescents and young people dispro- portionately. However, little is known regarding the experience during the COVID-19 pan- demic of Indian adolescent girls and young women compared to men. A cross-sectional mobile phone-based survey of households in Uttar Pradesh (UP) and Bihar was carried out four to six weeks after lockdown was imposed. This analysis highlights the gender specific vari- ation in COVID-19 knowledge and practice of preventive behaviors, and mental health effects among a cohort of adolescent and young adults. Findings from this study can inform the development of social service programs and education campaigns to ensure that adolescent and young women have access to tailored information and resources during this protracted crisis to ensure development and equity gains are not lost. Methods Sampling strategy A rapid telephone survey was conducted with a sample of participants drawn from an existing Population Council cohort study of adolescents and young adults. Understanding the Lives of Adolescents and Young Adults (UDAYA) is a state-level representative longitudinal study of adolescent girls and boys (aged 10–19) in rural and urban settings in Bihar (n = 10,433) and UP (n = 10,161), with baseline conducted in 2015–2016 and endline in 2018–19. The original UDAYA study objectives were to better understand adolescents’ acquisition of assets and their transition from adolescence to adulthood [21,22]. UDAYA researchers used the 2011 Indian Census to create a systematic, multi-stage sampling frame for the selection of 150 primary sampling units (PSU) in each state, with an equal breakdown between urban and rural areas. UDAYA was designed to provide estimates for five categories of adolescents, namely unmar- ried younger boys and girls aged 10–14, unmarried older boys and girls aged 15–19, and mar- ried older girls aged 15–19 that represent each state [21,22]. UDAYA households eligible for inclusion in the COVID-19 survey were those in which we interviewed a 15-19-year-old boy or girl in 2015–16. Phone numbers were available for 9,771 of such UDAYA participants– 2,437 boys and men and 7,334 girls and women. We randomly sampled households for the mobile phone survey from this list of telephone num- bers, stratified by gender. The enumerators contacted telephone numbers belonging to 5,520 UDAYA participants– 1,512 boys and men and 4,008 girls and women–attempting each number up to 3 times and completing about 10 interviews per day. Of those attempted, 51% of telephone numbers were no longer functional (of UDAYA participants, 44% of boys and men and 53% of girls and women). Of numbers we successfully reached, 5% of respon- dents refused to participate in the study. Overall, participants in the COVID-19 study had slightly higher educational attainment, were slightly more urban, and had slightly higher household wealth compared to the source cohort. The characteristics of the UDAYA base- line cohort compared to those who were enrolled in the COVID-19 mobile-phone survey is summarized in a S1 Table. PLOS ONE \| https://doi.org/10.1371/journal.pone.0244053 December 17, 2020 3 / 13 PLOS ONEGender specific variation in COVID-19 knowledge, behavior and health effects among young adults Mobile phone questionnaire Participants were contacted via mobile phone to remove the risk to field staff and participants of COVID-19 infection. After verbal consent for participation, a short questionnaire lasting no longer than 30 minutes was administered. The questionnaire included questions regarding basic demographics, awareness of COVID-19 or coronavirus, knowledge of symptoms, risk groups and transmission, perceived risk, COVID-19 prevention behaviors, and fears or con- cerns regarding the outbreak. Questions assessing household and individual needs under the government lockdown were also included. In the survey participants self-reported their sex as male or female; throughout this paper we will refer to respondents as men and women to illus- trate that our analysis reports how the pandemic impacts gender (the socially constructed characteristics of men and women) not biological sex. Ethical review We received expedited ethical approval from the Population Council’s Institutional Review Board (IRB) by meeting criteria for research conducted during COVID-19. The IRB permitted data collection with participants with previous consent from existing cohort studies, provided the research is aligned with national mitigation efforts. The UDAYA study protocol originally received IRB approval in 2015 for longitudinal data collection. Participants were told they could terminate the study at any time or skip any sections. No incentives were offered for tak- ing part in the study. Data management and analysis The survey responses were entered in mini laptops using instruments developed with CSPro 7.1 and exported to Stata v15 for analysis. Each household had a unique ID number, and all personally identifiable information was removed to ensure confidentiality. Two summary outcome variables were created. First, participants who correctly identified all three COVID-19 symptoms (fever, cough and difficulty in breathing) were considered to have correct knowledge (dichotomous variable). Participants who reported implementing all four preventive behaviors (staying home more, wearing a mask, washing hands/using sanitizer, and staying 2m apart) were categorized as implementing the four main preventive behaviors (dichotomous variable). Depressive symptoms, as measured by reporting feeling lonely, depressed or irritable during the lockdown, was collected as a dichotomous variable. To con- trol for household wealth, we created a proxy variable constructed from the presence of four basic amenities: safe drinking water, electricity, toilet facility and safe cooking fuel. Educational attainment was categorized into three levels, with grade 8 indicating completion of primary education and grade 10 indicating completion of secondary education. Religion was catego- rized as Hindu or Muslim (dichotomous variable), with 9 indicating ‘other’ and excluded from models. Lastly, caste was categorized as scheduled caste/tribe (SC/ST), other backward castes (OBC) and general (neither SC/ST nor OBC); these designations, as provisioned in the Indian constitution, are used to identify marginalized groups in the population. Only women were asked if they had experienced any violence in the home in the last 15 days under lockdown. All survey responses were tabulated by gender and tested for statistical significance (p<0.05) using chi-square tests. We implemented linear probability regression models based on three outcomes of interest. First, knowledge of all three key symptoms of COVID-19. Sec- ond, practicing all four of the key preventive behaviors. The third outcome was self-reported experience of loneliness, depression, or irritability (dichotomous variable) in the previous seven days used to define experience of depressive symptoms. Three separate linear probability PLOS ONE \| https://doi.org/10.1371/journal.pone.0244053 December 17, 2020 4 / 13 PLOS ONEGender specific variation in COVID-19 knowledge, behavior and health effects among young adults regression models were constructed for each of the three outcome variables, first for the full set of respondents and then stratified by gender. Results A total of 1,666 adolescents and young adults (18–24 years) previously enrolled in the UDAYA study were surveyed. Of these, 70% were women, over half had completed 10+ years of educa- tion (72%) and nearly half resided in urban areas (47%) (Table 1). Fewer women (40%) than Table 1. Demographics and COVID-19 related outcomes of interest tabulated by gender. Demographic/Household characteristics Age group 18–19 years 20–24 years Religion Hindu Muslim Other Completed years of education 0–7 years 8–9 years 10 and above years Caste General caste Other backward caste (OBC) Scheduled caste/tribe Current place of residence Urban (vs Rural) Have four key amenities1 Yes (vs No) COVID-19 Outcomes of Interest Mental Health: have you felt depressed, lonely or irritable under lockdown? Never Sometimes Most of the time Knowledge and behaviors Knows all 3 top symptoms2 Reports practicing all 4 main preventive measures3 Economic and health access effects Self or household member lost job/income source due to COVID-19 Among women who required each health service but could not access it: Antenatal care Family planning Child immunization Nutrition Men N = 506 Women N = 1,160 Total N = 1,666 88 (17%) 418 (83%) 432 (85%) 68 (13%) 6 (1%) 25 (5%) 59 (12%) 422 (83%) 108 (21%) 268 (53%) 130 (26%) 160 (14%) 1,000 (86%) 911 (79%) 246 (21%) 3 (0%) 195 (17%) 191 (16%) 774 (67%) 262 (23%) 615 (53%) 283 (24%) 248 (15%) 1,418 (85%) 1,343 (81%) 314 (18%) 9 (1%) 220 (13%) 250 (15%) 1,196 (72%) 370 (22%) 883 (53%) 413 (25%) 274 (54%) 502 (43%) 776 (47%) 180 (36%) 342 (29%) 522 (31%) 972 (58%) 578 (35%) 116 (7%) 266 (53%) 199 (39%) 321 (63%) 159 (31%) 26 (5%) 463 (40%) 158 (14%) 651 (56%) 419 (36%) 90 (8%) 729 (44%) 357 (21%) p-value 0.058 <0.001 <0.001 0.785 <0.001 0.014 0.011 <0.001 <0.001 274 (54%) 788 (68%) 1,062 (64%) <0.001 - - - - 138 (12%) 239 (21%) 433 (37%) 595 (51%) - - - - - - - - Notes 1 Includes source of light i.e. electricity, source of water i.e., improved water, source of clean fuel i.e. LPG/bio-gas and type of toilet facility i.e., own/public flush toilet 2 Three main symptoms are fever, cough, and difficulty breathing 3 Four main behaviors are stay home unless urgent, keep 2m apart from others, wear a mask, and wash hands/use sanitizer https://doi.org/10.1371/journal.pone.0244053.t001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0244053 December 17, 2020 5 / 13 PLOS ONEGender specific variation in COVID-19 knowledge, behavior and health effects among young adults Table 2. Linear probability model of factors associated with knowledge of all 3 main COVID-19 symptoms (fever, cough, difficulty breathing), stratified by gender. VARIABLES Model 1: All Model 2: Men Model 3: Women (1) (2) (3) Women (vs men) Muslim (vs Hindu) Educational attainment (0–7 years = REF) 8–9 years 10+ years Caste (OBC = REF) General Category Scheduled Caste/ Tribe -0.069�� (-0.122 - -0.021) -0.047 NA -0.067 NA -0.049 (-0.109–0.015) (-0.198–0.064) (-0.120–0.023) 0.059 -0.102 0.088 (-0.028–0.146) 0.242�� (-0.336–0.132) 0.136 (-0.006–0.182) 0.250�� (0.171–0.314) (-0.070–0.342) (0.173–0.328) 0.069� (0.010–0.128) 0.022 0.138� (0.026–0.251) 0.044 0.040 (-0.029–0.110) 0.014 (-0.035–0.080) (-0.061–0.149) (-0.056–0.083) Age 20–24 (vs 18–19 years) 0.008 0.020 0.001 (-0.056–0.073) -0.067� (-0.123 - -0.011) 0.111�� (-0.093–0.133) -0.045 (-0.146–0.055) 0.104 (-0.078–0.080) -0.077� (-0.146 - -0.009) 0.109�� (0.049–0.172) (-0.004–0.211) (0.033–0.185) -0.039 0.017 -0.061 (-0.091–0.013) (-0.082–0.116) (-0.123–0.001) 1,666 0.095 506 0.073 1,160 0.092 Rural (vs urban) Household has all 4 amenities Bihar (vs UP) Observations R-squared CI in parentheses �� p<0.01 � p<0.05 https://doi.org/10.1371/journal.pone.0244053.t002 men (53%) knew the main symptoms of COVID-19 and fewer women than men practiced key preventive behaviors such as staying home unless it is urgent and wearing a mask (Table 1). Fewer women reported doing all prevention behaviors (14% vs 39% of men). A greater propor- tion of women respondents reported experience of depressive symptoms. In the full model, women were less likely than men to know COVID-19 symptoms (coeff = -0.069; 95% CI: -0.122 - -0.021) (Table 2). The model was then stratified by gender (men- and women-only models). For the men-only model, there were no key characteristics associated with more or less knowledge of symptoms, except that those in the general caste category were 14 pp more likely to know the symptoms compared with those in the OBC category (coeff = 0.138; 95% CI: 0.026–0.251). In the women-only model, several characteristics were associated with having more knowledge of key symptoms. Women who had completed 10 + years of education were 25 pp more likely to know the symptoms compared with those only having zero to seven years of education (coeff = 0.250; 95% CI: 0.173–0.328); relatedly, women residing in households with key amenities were much more likely to know the symptoms PLOS ONE \| https://doi.org/10.1371/journal.pone.0244053 December 17, 2020 6 / 13 PLOS ONEGender specific variation in COVID-19 knowledge, behavior and health effects among young adults Fig 1. Proportion of respondents that practice all four the key preventive behaviors, by gender and educational attainment. https://doi.org/10.1371/journal.pone.0244053.g001 (coeff = 0.109; 95% CI: 0.033–0.185). Women living in rural areas had lower knowledge of the symptoms. Fig 1 highlights the education and gender differences in reportedly practicing all four main preventive behaviors; this proportion increases across categories of educational attainment for both men and women (Fig 1). Findings also show that women respondents with secondary education (10+ years) were less likely than men respondents with less than primary education (0–7 years) to report practicing all four prevention measures. In the full model exploring characteristics associated with doing all four prevention behav- iors, women were 22 pp less likely than men to report doing all behaviors (coeff = -0.221; 95% CI: -0.263 - -0.180) (Table 3). The full model was re-run stratified by gender. Among men, sev- eral characteristics contributed to reportedly practicing all four prevention behaviors. Men who knew the top three symptoms were more likely to practice the four key preventive behav- iors (coeff = 0.107; 95% CI: 0.020–0.194). Men in rural areas and in Bihar were much less likely to carry out the four behaviors. For the women-only model, the only characteristic that was associated with conducting the four behaviors was knowledge of the three main symptoms (coeff = 0.160; 95% CI: 0.119, 0.201) (Table 3). The last model explored characteristics associated with self-reported experience of depres- sive symptoms. In the full model, women were 5 pp more likely to report that they were experiencing depressive symptoms compared to men (coeff = 0.052; 95% CI: -0.001, 0.104) (Table 4). When stratified by gender, among men only, household loss of employment was the only factor associated with depressive symptoms (coeff = 0.169; 95% CI 0.083, 0.254). Among women only, household loss of employment, religion, and experience of violence were signifi- cantly associated with depressive symptoms. Women belonging to the Muslim religion com- pared to those who identified as Hindu, were more likely to report experience of depressive symptoms (coeff = 0.084; 95% CI:0.012, 0.156). Women who reported violence in the home in the last 15 days were 30 pp more likely to report experience of depressive symptoms (coeff = 0.304; 95% CI: 0.133; 0.475). Women reported whether they had required health services in the previous week, and if so, if they were able to access them (this question was not included for men). Most women had not required health services in the previous week. Of the types of services that were required, nutrition services and child immunization services were the most reported. Among women who sought nutrition services, 51% required but could not access them, 1% required and were able to access them. For child immunization services 37% were unable to access them, none who needed child immunization services could access them. For family planning, 76% stated PLOS ONE \| https://doi.org/10.1371/journal.pone.0244053 December 17, 2020 7 / 13 PLOS ONEGender specific variation in COVID-19 knowledge, behavior and health effects among young adults Table 3. Linear probability model of factors associated with reporting all four main preventive behaviors are being implemented, by gender. VARIABLES Women (vs men) Knowledge of 3 key COVID symptoms (1) Model 1 -0.221�� (-0.263 - -0.180) 0.134�� (2) (3) Model 2: Men Model 3: Women NA 0.107� NA 0.160�� Muslim (vs Hindu) -0.045 -0.090 -0.018 (0.09–0.173) (0.020–0.194) (0.119–0.201) Educational attainment (0–7 years REF) 8–9 years 10+ years Caste (OBC = REF) General Category (-0.096–0.005) (-0.219–0.039) (-0.069–0.033) REF 0.009 REF 0.126 REF -0.008 (-0.062–0.079) (-0.105–0.357) (-0.075–0.059) 0.037 0.187 0.022 (-0.022–0.096) (-0.015–0.390) (-0.033–0.077) REF -0.022 REF -0.106 REF 0.020 (-0.070–0.026) (-0.217–0.004) (-0.029–0.069) Scheduled Caste/Tribe -0.002 0.011 -0.007 (-0.049–0.045) (-0.092–0.115) (-0.056–0.042) -0.004 -0.053 (-0.056–0.049) -0.019�� (-0.074 - -0.017) -0.056�� (-0.165–0.059) -0.147�� (-0.234 - -0.060) -0.103� 0.018 (-0.039–0.074) -0.011 (-0.052–0.029) -0.036 (-0.099 - -0.014) (-0.201 - -0.006) (-0.081–0.008) 1,666 0.128 506 0.059 1,160 0.066 Age group Rural (vs Urban) Bihar (vs UP) Observations R-squared CI in parentheses �� p<0.01 � p<0.05 https://doi.org/10.1371/journal.pone.0244053.t003 they did not require this service in the previous week, of those that did, 21% could not access family planning services (84% of those with a family planning service need) (Fig 2). Discussion Conducted early in the pandemic, our study identifies gender disparities in COVID-19 related knowledge and uptake of promoted preventive behaviors among young people in two states in India. Overall, women were less likely to be able to identify all three of the main COVID-19 symptoms correctly, potentially due to challenges in accessing information or receiving less accurate information of COVID-19 symptoms. Women were also less likely to be practicing the most effective prevention behaviors and they were also more likely to report symptoms of depression. Access to health services is also reportedly affected by the pandemic, with most women in need of services unable to access them, including nutrition, child immunization, family planning and antenatal care services. As of Fall 2020, the pandemic is still not under control globally, and the threat of continued infections remains; therefore, understanding the PLOS ONE \| https://doi.org/10.1371/journal.pone.0244053 December 17, 2020 8 / 13 PLOS ONEGender specific variation in COVID-19 knowledge, behavior and health effects among young adults Table 4. Linear probability model of factors associated with self-reported experience of depressive symptoms dur- ing lockdown, by gender. VARIABLES Women (vs men) Household lost employment Educational attainment (0–7 years REF) 8–9 years 10+ years Age 20–24 (vs 18–19 years) Rural (vs urban) Bihar (vs UP) Muslim (vs Hindu) Under lockdown, experienced any violence in the home in the last 15 days (women only) Observations R-squared CI in parentheses �� p<0.01 � p<0.05 (1) Model 1 0.052� (0.000– 0.104) 0.133�� (0.083– 0.183) REF -0.006 (-0.095– 0.083) 0.018 (-0.055– 0.091) 0.021 (-0.046– 0.088) -0.013 (-0.061– 0.035) 0.038 (-0.015– 0.092) 0.073�� (0.011– 0.135) NA (2) Model 2: Men (3) Model 3: Women NA NA 0.169�� (0.083– 0.254) REF 0.019 (-0.211– 0.250) 0.026 (-0.174– 0.226) -0.001 (-0.113– 0.110) -0.036 (-0.121– 0.049) 0.059 (-0.038– 0.156) 0.041 (-0.085– 0.168) NA 0.117�� (0.055–0.179) REF -0.022 (-0.121–0.076) 0.013 (-0.067–0.092) 0.033 (-0.050–0.115) 0.002 (-0.057–0.060) 0.021 (-0.044–0.086) 0.084�� (0.012–0.156) 0.304�� (0.133–0.475) 1,658 0.027 501 0.038 1,157 0.028 https://doi.org/10.1371/journal.pone.0244053.t004 needs and experiences of adolescents and young adults is critical to offering resources and social support, with attention to gender. Gender differences in accurate knowledge of key COVID-19 symptoms likely reflect young women’s lower levels of educational attainment and lower media exposure, as well as lower access to mobile phones [21,22]. Among women, there was significant variation in the charac- teristics of who had COVID-19 information, such as higher educational attainment, urban res- idence, and higher economic status. These factors likely reflect higher literacy and access to information among some young women. Interestingly, no variation was observed within men, and overall, their knowledge was higher than for women. This finding is supported by available literature on past pandemics. During an outbreak of influenza A (H1N1) in India, a small study found that men had more knowledge of H1N1; this was attributed to men having more PLOS ONE \| https://doi.org/10.1371/journal.pone.0244053 December 17, 2020 9 / 13 PLOS ONEGender specific variation in COVID-19 knowledge, behavior and health effects among young adults Fig 2. Among women, number requiring health services and of those, number unable to obtain them by type of service. https://doi.org/10.1371/journal.pone.0244053.g002 social interactions through employment and having higher literacy rates than women [23]. Higher knowledge among men may be influenced by their greater exposure to risk outside the home for work and socializing shaped by gendered social norms. A recent study from India found differential COVID-19 risk and mortality by gender, reporting that most infections are among men [24]. Our study also suggests that men have higher potential exposure but also higher knowledge of COVID-19 symptoms and prevention; gender dynamics and social norms may increase both knowledge and infection risk among men. Among women, lower adoption of promoted behaviors may also reflect the gender roles and the fact that women spend more time indoors. If women are not going outside, they may not be wearing masks or keeping 2m distance from others because they are not interacting outside the household. Knowledge was the only factor associated adoption of promoted behaviors among women; potentially there are other unmeasured characteristics that are associated with observed varia- tion among women. To bridge this knowledge gender gap, additional research on whether and how the pandemic is reinforcing gender roles may help inform gender sensitive education campaigns via media that women can access and understand even with limited literacy. Mental health and healthcare-seeking behavior for young people are also affected. Our find- ings suggest that loss of employment among household members due to the lockdown was associated with depressive symptoms among both men and women. Approximately 400 mil- lion informal sector workers in India have lost their livelihood due to COVID-19 and related lockdowns [25]; interviews with informal sector workers describe impending poverty, evic- tions and hunger as incomes and work opportunities are sharply curtailed [26]. Previous research has also found a link between loss of employment and SGBV, both of which likely relate to depressive symptoms during lockdown [15,16]. A recent study conducted prior to COVID-19 of mental health in India found being a woman, younger age, loss of employment, and other characteristics were associated with symptoms of depression, anxiety and stress [18]. Many women reported that they had forgone necessary medical services, which may lead to adverse secondary health outcomes and outbreaks of other diseases. Among women surveyed, most of those who did require a health service could not access them. Public transit commonly used to visit clinics was closed during lockdown, which may have affected access [2]. Chal- lenges in accessing health services must be carefully monitored to avoid unintended secondary health crises, including outbreaks of vaccine preventable disease, stunting/undernutrition, and unintended pregnancy or poor birth outcomes [27]. While most women reported they did not require any health services, this study was conducted early in the pandemic. If lockdowns PLOS ONE \| https://doi.org/10.1371/journal.pone.0244053 December 17, 2020 10 / 13 PLOS ONEGender specific variation in COVID-19 knowledge, behavior and health effects among young adults resume or access continues to be disrupted, utilization of essential services should be moni- tored, and steps taken to ensure accessibility. This study has several limitations. First there are inherent challenges in conducting surveys that are not face-to-face; mobile phone-based data collection relies on self-reported informa- tion conveyed by participants who may have challenges understanding questions, and we can- not guarantee protections for participants who may be vulnerable in their households [28]. Secondly, the representativeness of the sample may be compromised as we could only inter- view those with working phone numbers from the 2015–16 UDAYA survey. Our survey respondents had slightly higher educational attainment and household wealth compared to the full UDAYA cohort, suggesting that the most vulnerable from the original sample were not reachable. Third, we asked questions regarding knowledge of COVID-19 prevention behav- iors, then later asked about behaviors respondents were doing. Potentially, question order nudged recall, which could explain why the proportion aware of certain behaviors was lower than those who reported implementing them. However, both the knowledge and behavior questions were based on spontaneous responses, not a list read by the interviewer, so this effect should be minimal. Lastly, our measure of mental health was very simple and self-reported, validated depression measures are necessary but challenging to collect via mobile phone interview. Our findings suggest that early in the pandemic lockdown, there were significant knowl- edge gaps and secondary health effects disproportionately impacting adolescent girls and young women. To increase knowledge of symptoms and preventive behaviors, gender-sensi- tive behavior change campaigns should be developed, and adapted for cultural context, liter- acy, and accessibility. Improved access to information may lead to adoption of promoted behaviors, reducing risk of infection. Relatedly, steps to address mental health and the unin- tended secondary health impacts of the pandemic are required. To date, the Government of India has introduced several initiatives to address these issues, for example activating a toll- free helpline for those requiring psychosocial counseling and issuing guidelines for the sus- tained provision of essential health services. Government agencies are also launching special social protection initiatives. It is critical that these measures reach the most vulnerable popula- tions, including messaging targeted to women. Longer term efforts may also be necessary to address the prolonged and potentially gendered effects of COVID-19 and ensure that health and development gains are not lost due to the pandemic, especially as India’s case load has grown to one of the highest worldwide. Supporting information S1 Table. Differences in key background characteristics between respondents aged 15–19 whose number was not available, who were interviewed in the COVID-19 survey and who were not interviewed in COVID-19 survey. (TIF) S1 File. COVID-19 study questionnaire. (PDF) Acknowledgments The authors would like to acknowledge the dedicated team at Population Council Inc. in India that collected all of these surveys and made this research happen. Author Contributions Conceptualization: Jessie Pinchoff, KG Santhya, Rajib Acharya, Thoai D. Ngo. PLOS ONE \| https://doi.org/10.1371/journal.pone.0244053 December 17, 2020 11 / 13 PLOS ONEGender specific variation in COVID-19 knowledge, behavior and health effects among young adults Data curation: Shilpi Rampal. Formal analysis: Jessie Pinchoff, Shilpi Rampal. Investigation: Rajib Acharya, Thoai D. Ngo. Methodology: KG Santhya, Corinne White, Shilpi Rampal, Rajib Acharya. Project administration: KG Santhya, Corinne White, Rajib Acharya. Resources: KG Santhya. Supervision: Jessie Pinchoff, KG Santhya, Rajib Acharya, Thoai D. Ngo. Writing – original draft: Jessie Pinchoff, Corinne White. Writing – review & editing: KG Santhya, Shilpi Rampal, Rajib Acharya, Thoai D. Ngo. References 1. Lancet The. India under COVID-19 lockdown. The Lancet. 2020; 395: 1315. https://doi.org/10.1016/ S0140-6736(20)30938-7 PMID: 32334687 2. Cash R, Patel V. Has COVID-19 subverted global health? The Lancet. 2020; 395: 1687–1688. https:// doi.org/10.1016/S0140-6736(20)31089-8 PMID: 32539939 3. 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10.1371_journal.pone.0250044.pdf	Data Availability Statement: Data are available here: Wall, Kristin, 2021, "Replication Data for: "Etiologies of genital inflammation and ulceration in symptomatic Rwandan men and women responding to radio promotions of free screening and treatment services"", https://doi.org/10.7910/ DVN/CFX6UU, Harvard Dataverse.	Data are available here: Wall, Kristin,	RESEARCH ARTICLE Etiologies of genital inflammation and ulceration in symptomatic Rwandan men and women responding to radio promotions of free screening and treatment services 1, Julien Nyombayire2, Rachel Parker1, Rosine Ingabire2, Kristin M. WallID Jean Bizimana2, Jeannine Mukamuyango2, Amelia Mazzei2, Matt A. Price3, Marie Aimee UnyuzimanaID 2, Amanda Tichacek1, Susan Allen1, Etienne Karita2 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Rwanda Zambia HIV Research Group, Department of Pathology & Laboratory Medicine, School of Medicine and Hubert Department of Global Health and Department of Epidemiology, Rollins School of Public Health, Laney Graduate School, Emory University, Atlanta, Georgia, United States of America, 2 Project San Francisco, Rwanda Zambia HIV Research Group, Kigali, Rwanda, 3 IAVI, NY, NY, University of California San Francisco, San Francisco, CA, United States of America OPEN ACCESS Citation: Wall KM, Nyombayire J, Parker R, Ingabire R, Bizimana J, Mukamuyango J, et al. (2021) Etiologies of genital inflammation and ulceration in symptomatic Rwandan men and women responding to radio promotions of free screening and treatment services. PLoS ONE 16(4): e0250044. https://doi.org/10.1371/journal. pone.0250044 Editor: Antonella Marangoni, Universita degli Studi di Bologna Scuola di Medicina e Chirurgia, ITALY Received: January 13, 2021 Accepted: March 30, 2021 Published: April 20, 2021 Copyright: © 2021 Wall et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Data are available here: Wall, Kristin, 2021, "Replication Data for: "Etiologies of genital inflammation and ulceration in symptomatic Rwandan men and women responding to radio promotions of free screening and treatment services"", https://doi.org/10.7910/ DVN/CFX6UU, Harvard Dataverse. Funding: This work was funded by the National Institutes of Health (NIH) (NIAID R01 AI51231), the kmwall@emory.edu Abstract Introduction The longstanding inadequacies of syndromic management for genital ulceration and inflam- mation are well-described. The Rwanda National Guidelines for sexually transmitted infec- tion (STI) syndromic management are not yet informed by the local prevalence and correlates of STI etiologies, a component World Health Organization guidelines stress as critical to optimize locally relevant algorithms. Methods Radio announcements and pharmacists recruited symptomatic patients to seek free STI services in Kigali. Clients who sought services were asked to refer sexual partners and symptomatic friends. Demographic, behavioral risk factor, medical history, and symptom data were collected. Genital exams were performed by trained research nurses and physi- cians. We conducted phlebotomy for rapid HIV and rapid plasma reagin (RPR) serologies and vaginal pool swab for microscopy of wet preparation to diagnose Trichomonas vaginalis (TV), bacterial vaginosis (BV), and vaginal Candida albicans (VCA). GeneXpert testing for Neisseria gonorrhoeae (NG) and Chlamydia trachomatis (CT) were conducted. Here we assess factors associated with diagnosis of NG and CT in men and women. We also explore factors associated with TV, BV and VCA in women. Finally, we describe genital ulcer and RPR results by HIV status, gender, and circumcision in men. Results Among 974 men (with 1013 visits), 20% were positive for CT and 74% were positive for NG. Among 569 women (with 579 visits), 17% were positive for CT and 27% were positive for NG. In multivariate analyses, factors associated with CT in men included younger age, PLOS ONE \| https://doi.org/10.1371/journal.pone.0250044 April 20, 2021 1 / 21 PLOS ONENIH AIDS International Training and Research Program Fogarty International Center (D43 TW001042); and the NIH-funded Emory Center for AIDS Research (P30 AI050409). This work was partially funded by IAVI with the generous support of USAID and other donors; a full list of IAVI donors is available at https://www.iavi.org. The contents of this manuscript are the responsibility of the authors and do not necessarily reflect the views of USAID or the US Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: No authors have competing interests. Etiologies of genital abnormalities in Rwandan men and women responding to radio advertisements, <17 days since suspected exposure, and not having dysuria. Factors associated with NG in men included not having higher education or full- time employment, <17 days since suspected exposure, not reporting a genital ulcer, and having urethral discharge on physical exam. Factors associated with CT in women included younger age and < = 10 days with symptoms. Factors associated with NG in women included younger age, lower education and lack of full-time employment, sometimes using condoms vs. never, using hormonal vs. non-hormonal contraception, not having genital ulcer or itching, having symptoms < = 10 days, HIV+ status, having BV, endocervical dis- charge noted on speculum exam, and negative vaginal wet mount for VCA. In multivariate analyses, only reporting >1 partner was associated with BV; being single and RPR+ was associated with TV; and having < = 1 partner in the last month, being pregnant, genital itch- ing, discharge, and being HIV and RPR negative were associated with VCA. Genital ulcers and positive RPR were associated with being HIV+ and lack of circumcision among men. HIV+ women were more likely to be RPR+. In HIV+ men and women, ulcers were more likely to be herpetic rather than syphilitic compared with their HIV- counterparts. Conclusions Syndromic management guidelines in Rwanda can be improved with consideration of the prevalence of confirmed infections from this study of symptomatic men and women repre- sentative of those who would seek care at government health centers. Inclusion of demo- graphic and risk factor measures shown to be predictive of STI and non-STI dysbioses may also increase diagnostic accuracy. Introduction Globally, over 1 million new sexually transmitted infections (STI) occur each day [1]. The prevalence of STI increased an estimated 59% in sub Saharan Africa between 1999 and 2005 and has continued to rise [2]. The World Health Organization (WHO) 2016–2021 Global Health Sector Strategy on Sexually Transmitted Infections aims to reduce STI 90% by 2030 using “[epidemiologic] information for focused action” [3]. The association between genital ulceration and inflammation (GUI) due to STI and non- STI etiologies and heterosexual HIV transmission and acquisition has been extensively studied in Africa [4–12]. Broadly, in observational studies GUI is associated with both transmitting and acquiring HIV in both men and women, and with transmission of more than one virion, an otherwise rare event, in cohabiting heterosexual discordant couples which comprise one of the largest HIV risk groups [6, 13–17]. Ulcerative STI that may facilitate HIV transmission include syphilis (Treponema pallidum, TP), Herpes simplex virus (HSV), and chancroid (Haemophilus ducreyi, HD) [18–20]. Inflam- matory STI that increase HIV transmission include gonorrhea (Neisseria gonorrhoeae, NG), chlamydia (Chlamydia trachomatis, CT), and Trichomonas vaginalis (TV) [21–24]. Common non-STI dysbioses associated with genital inflammation include bacterial vaginosis (BV) and vaginal Candida albicans (VCA) [25–29]. Untreated TP, HD, HSV, NG, CT and TV can cause severe morbidity and, along with BV and VCA (which are troublesome but non-invasive), can contribute to HIV transmission. In our studies in African HIV discordant heterosexual couples, GUI contribute a substantial pop- ulation attributable fraction of HIV transmission in both donor and recipient [15]. PLOS ONE \| https://doi.org/10.1371/journal.pone.0250044 April 20, 2021 2 / 21 PLOS ONEEtiologies of genital abnormalities in Rwandan men and women The longstanding inadequacies of syndromic management for GUI are well-described [30– 37] but this approach remains the default in many resource-limited settings in Africa due to the high cost of molecular and culture-based diagnostics. The Rwanda National Guidelines for HIV and STI syndromic management were last updated in 2019 but these guidelines are not yet informed by the local prevalence and correlates of STI etiologies, a component WHO guidelines stress as critical to optimize locally relevant algorithms. We have previously pub- lished results of a survey of GUI among Female Sex Workers (FSW) in Kigali, but that study lacked molecular diagnostics for NG and CT [38]. Here we contribute to the epidemiologic data needed to inform improved diagnostic and treatment algorithms in Rwanda by exploring demographic, behavioral, medical history, symptom, genital exam, and laboratory factors associated with molecular diagnosis of NG and CT in men and women. We also explore factors associated with vaginal pathogens TV, BV and VCA in women. Finally, we describe genital ulcer and rapid plasma reagin (RPR) results strati- fied by gender, HIV status, and among men, by male circumcision status. Methods Ethics This program was approved as non-research by the Rwandan National Ethics Committee. This program was determined to be non-research by the Emory Institutional Review Board criteria. Diagnostic and treatment were provided anonymously as free services. Setting Kigali, the capital of Rwanda, has a population of over 1 million people and an adult HIV prev- alence of 4.3% [39]. Between January 2016 and August 2019, The Center for Family Health Research (CFHR), a research site established in Kigali in 1986 and affiliated with Emory Uni- versity in Atlanta, GA, USA, implemented a program for diagnosis and treatment of symptom- atic GUI residents of Kigali. CFHR has worked closely with the Rwanda Ministry of Health (MoH) on research for improved HIV and reproductive health care in government-run health centers for many years [25, 40–43]. Patient recruitment Patients were residents of Kigali, Rwanda and were recruited in three ways: radio announce- ments, partner/friend referral, and pharmacist referral. Radio announcements were made in Kinyarwanda, Rwanda’s vernacular, encouraging men and women with symptoms suggestive of GUI (e.g., genital discharge, discomfort, ulcer) to seek free services at CFHR clinic and were broadcast throughout Kigali. Clients who sought services were then asked to refer sexual part- ners and symptomatic friends. Local pharmacists were alerted to the program and asked to refer individuals seeking treatments for suggestive symptoms. There were no inclusion/exclu- sion criteria applied to participant recruitment. Participants are representative of residents of Kigali with genital symptoms who self-selected to receive care. Data collection and diagnostic procedures Demographics, behavioral risk factors, medical histories, and symptoms were collected using a standard instrument (S1 Fig). This information was obtained during interviews conducted by nurses who recorded data on paper and entered it into MS Access. Similarly, findings from genital exams performed by trained physicians and nurses were recorded on paper and entered into MS Access. Samples for laboratory testing were taken from all patients and included PLOS ONE \| https://doi.org/10.1371/journal.pone.0250044 April 20, 2021 3 / 21 PLOS ONEEtiologies of genital abnormalities in Rwandan men and women phlebotomy for rapid HIV and RPR serologies and vaginal pool swab for microscopy of wet preparation to diagnose TV, BV and VCA. GeneXpert testing for NG and CT (Cepheid, Sun- nyvale USA) was conducted for all patients using endocervical swabs obtained from women and either urethral swabs (when discharge was reported or noted on physical exam) or urine samples from men. In collaboration with the MoH, CFHR developed a uniform alphanumeric identifier to allow anonymous data recording. Data analysis Analyses were conducted with Statistical Analysis Software (SAS, Cary, NC). Frequencies of single and multiple infections were stratified by gender and HIV status. Demographic, behavioral, medi- cal history, physical exam, microscopy and serology results were tabulated by gender and by NG and CT results. Bivariate and multivariate analyses of factors associated with NG or CT are pre- sented in tables. Multivariable logistic regression models included variables associated with each outcome at p<0.05 in bivariate analysis and then backward selection was applied. Prevalence odds ratios (crude and adjusted, cPOR and aPOR, respectively) and 95% confidence intervals (CIs) and 2-sided p-values are presented. Variable multi-collinearity was assessed. Repeated visits by STI clients with new complaints were accounted for using the GENMOD procedure. Bivariate and multivariate factors associated with vaginal pathogens TV, BV and VCA in women were analyzed in analogous fashion with results summarized in text. Demographic, behavioral, medical history, and HIV and RPR serology results were considered for model inclusion. Finally, genital ulcer and RPR results were described by gender, HIV status, and among men, by male circumcision status. Results Unless specified in text, p-values are <0.05 for comparisons with details presented in Tables. Summary of GUI diagnosed in men and women (Table 1) GeneXpert for NG and CT were provided to men during 1013 visits (974 unique men) between March 2017 and February 2019. Men tested HIV+ during 5% of these visits. Preva- lence of NG was 74% and prevalence of CT was 20%, with no differences by HIV status. In the 975 visits with RPR results, TP prevalence was significantly higher among HIV+ (13%) com- pared with HIV- (5%) men. Nineteen percent of visits were negative for all pathogens, and 17% of visits had more than one infection identified. GeneXpert for NG and CT were provided to women during 579 visits (569 unique women) between March 2017 and February 2019. Women tested HIV+ during 13% of these visits. Prevalence of NG was 26% and prevalence of CT was 17%, with higher prevalence of NG among HIV+ women. The prevalence of TV (overall 13%) was higher in HIV+ women, whereas the prevalence of VCA (overall 21%) was higher in HIV- women. In the 568 visits with RPR results, TP prevalence was significantly higher among HIV+ (22%) compared with HIV- (6%) women and having multiple pathogens identified was more prevalent among HIV + (36%) compared with HIV- (24%) women’s visits. Conversely, having no pathogen identi- fied was more prevalent in HIV- (31%) versus HIV+ (18%) women’s visits. Demographics and factors associated with CT and NG in men (Tables 2 and 3) Men averaged 30.8 years of age, 77% were single, 64% had at least a secondary education, 55% were employed full time, 22% reported more than one partner in the last 30 days and 57% reported never using condoms in the past three months. The most common symptoms PLOS ONE \| https://doi.org/10.1371/journal.pone.0250044 April 20, 2021 4 / 21 PLOS ONEEtiologies of genital abnormalities in Rwandan men and women Table 1. Distribution of pathogens identified in symptomatic men and women in Kigali, Rwanda. Total HIV+ (N = 54) HIV- (N = 958) p-value Among all men (N = 1013 visits)� None identified CT NG CT and NG Among men with RPR results (N = 975 visits) None identified TP Any multiple infection Among all women (N = 579 visits) None identified CT NG CT and NG BV TV VCA Among women with RPR results (N = 568 visits) None identified TP Any multiple infection N 196 204 751 138 184 52 164 N 176 98 152 45 113 72 118 169 46 146 Col % 19% 20% 74% 14% 19% 5% 17% N 14 7 40 7 14 7 11 Col % 26% 13% 74% 13% 26% 13% 21% N 182 196 711 131 170 45 153 Col % 19% 20% 74% 14% 18% 5% 17% Total HIV+ (N = 75) HIV- (N = 504) Col % 30% 17% 26% 8% 21% 13% 21% 30% 8% 26% N 13 8 34 5 20 15 6 13 16 26 Col % 17% 11% 45% 7% 28% 20% 8% 18% 22% 36% N 163 90 118 40 93 57 112 156 30 120 Col % 32% 18% 23% 8% 19% 12% 23% 31% 6% 24% 0.210 0.181 0.981 0.882 0.150 0.019 0.433 0.008 0.121 <0.0001 0.702 0.087 0.039 0.004 0.020 <0.0001 0.031 TP: Treponema pallidum, NG: Neisseria gonorrhoeae, CT: Chlamydia trachomatis, TV: Trichomonas vaginalis, BV: bacterial vaginosis, VCA: vaginal Candida albicans; RPR: rapid plasma regain �One man missing HIV status https://doi.org/10.1371/journal.pone.0250044.t001 reported were urethral discharge (89%) and dysuria (80%). Physical findings included urethral discharge in 91% and genital ulcer in 5% of men (Table 2). Multivariate analyses (Table 3) showed younger age, responding to radio advertisements, <17 days since suspected exposure, and not having dysuria as independent factors associated with CT. Multivariate analyses (Table 3) showed not having higher education or full-time employ- ment, <17 days since suspected exposure, not reporting a genital ulcer, and urethral discharge on physical exam as independent factors associated with NG. HIV, RPR serologic results, and circumcision status were not associated with either CT or NG. Demographics and factors associated with CT and NG in women (Tables 4 and 5) The mean age women was 28.7, they had 1.3 children and desired 1.4 more on average, 54% were single, 53% had a secondary education or more, 34% had full-time employment, 83% reported < = 1 partner in the last 30 days and 63% reported never using condoms in the past three months. Vaginal discharge was the most common presenting symptom (82%) and endo- cervical inflammation or discharge was noted on 49% of speculum exams. (Table 4) Multivariate analyses (Table 5) showed younger age and having symptoms < = 10 days as independent factors associated with CT. PLOS ONE \| https://doi.org/10.1371/journal.pone.0250044 April 20, 2021 5 / 21 PLOS ONEEtiologies of genital abnormalities in Rwandan men and women Table 2. Factors associated with CT or NG infection in men in Kigali, Rwanda (N = 1013). Demographics Age, continuous (years) Referrer Radio Advert Friends/Walk-in/Pharmacy/Contact Partner/ Internet Living and Marital Status Married and Cohabiting Single or Divorced/Separated/Widow Education Level None Primary Secondary Higher Employment Status Full-time employment Part-time/Student/Jobless Sexual behaviors Number of partners in last 30 days None or one partner More than one partner Condom use during vaginal sex in the last three months No partners or always used condoms Sometimes Never Number of days since sexual contact you suspect STI was acquired from < = 8 9–16 > = 17 Self-reported symptoms Urethral discharge Yes No Dysuria Yes No Genital itching Yes No Genital ulcer Yes No Total (N = 1013) CT-infected (N = 204) CT-uninfected (N = 809) p- value NG-infected (N = 751) NG-uninfected (N = 262) p-value n /mean 30.8 Col% /SD 7.1 n /mean 29.4 Row% /SD 5.6 n /mean 31.1 Row% /SD n /mean Row% /SD 7.4 0.001 30.5 7.0 n /mean 31.6 Row% /SD 7.3 0.029 688 325 232 781 25 339 454 193 552 459 704 203 27 363 517 331 288 323 895 114 810 199 67 854 41 878 68% 32% 23% 77% 2% 34% 45% 19% 55% 45% 78% 22% 3% 40% 57% 35% 31% 34% 89% 11% 80% 20% 7% 93% 4% 96% 151 53 33 171 1 66 89 47 122 82 138 45 4 69 110 83 58 50 188 16 153 51 14 171 6 183 22% 16% 14% 22% 4% 19% 20% 24% 22% 18% 20% 22% 15% 19% 21% 25% 20% 15% 21% 14% 19% 26% 21% 20% 15% 21% 537 272 199 610 24 273 365 146 430 377 566 158 23 294 407 248 230 273 707 98 657 148 53 683 35 695 0.037 0.011 0.095 0.095 0.422 0.555 0.010 0.081 0.034 0.864 0.336 78% 84% 86% 78% 96% 81% 80% 76% 78% 82% 80% 78% 85% 81% 79% 75% 80% 85% 79% 86% 81% 74% 79% 80% 85% 79% 488 263 156 595 16 267 343 123 392 357 518 163 14 279 387 292 235 177 717 32 599 150 39 649 13 681 71% 81% 67% 76% 64% 79% 76% 64% 71% 78% 74% 80% 52% 77% 75% 88% 82% 55% 80% 28% 74% 75% 58% 76% 32% 78% 200 62 76 186 9 72 111 70 160 102 186 40 13 84 130 39 53 146 178 82 211 49 28 205 28 197 0.001 0.006 0.001 0.015 0.051 0.015 <0.0001 <0.0001 0.680 0.001 <0.0001 29% 19% 33% 24% 36% 21% 24% 36% 29% 22% 26% 20% 48% 23% 25% 12% 18% 45% 20% 72% 26% 25% 42% 24% 68% 22% Number of days with symptoms 1–5 385 41% 100 26% 285 74% 0.004 332 86% 53 14% <0.0001 PLOS ONE \| https://doi.org/10.1371/journal.pone.0250044 April 20, 2021 (Continued ) 6 / 21 PLOS ONEEtiologies of genital abnormalities in Rwandan men and women Table 2. (Continued) Total (N = 1013) CT-infected (N = 204) CT-uninfected (N = 809) p- value NG-infected (N = 751) NG-uninfected (N = 262) p-value Demographics n /mean Col% /SD n /mean Row% /SD n /mean Row% /SD n /mean Row% /SD n /mean Row% /SD 6–10 11–21 >21 Laboratory and physical exam HIV Status Positive Negative RPR Result Positive Negative Urethral discharge Yes No Genital ulcer Yes No Circumcision status Circumcised Uncircumcised 254 192 105 54 958 52 923 858 87 46 898 524 259 27% 21% 11% 5% 95% 5% 95% 91% 9% 5% 95% 67% 33% 40 33 17 7 196 13 182 178 13 8 183 122 45 16% 17% 16% 13% 20% 25% 20% 21% 15% 17% 20% 23% 17% 214 159 88 47 762 39 741 680 74 38 715 402 214 84% 83% 84% 87% 80% 75% 80% 79% 85% 83% 80% 77% 83% 0.181 0.354 0.199 0.623 0.058 191 121 56 40 711 43 677 692 15 17 686 416 195 75% 63% 53% 74% 74% 83% 73% 81% 17% 37% 76% 79% 75% 63 71 49 14 247 9 246 166 72 29 212 108 64 25% 37% 47% 26% 26% 17% 27% 19% 83% 63% 24% 21% 25% 0.981 0.136 <0.0001 <0.0001 0.192 Not significant not shown include: Self-reported symptoms dyspareunia, unpleasant odor, abdominal pain, anal discharge, anal ulcer, anal warts, and sore throat; genital exam results white accumulation, condyloma/warts, inguinal adenopathy >1cm unilateral and bilateral, inflammation, and testicular mass/tenderness RPR: Rapid plasma reagin; STI: Sexually transmitted disease; NG: Neisseria gonorrhoeae, CT: Chlamydia trachomatis https://doi.org/10.1371/journal.pone.0250044.t002 Multivariate analyses (Table 5) showed younger age, lower education and lack of full-time employment, sometimes using condoms vs. never, using hormonal contraception vs. other or no contraception, not having a genital ulcer or itching, having symptoms for < = 10 days, HIV + status, endocervical discharge noted on speculum exam, BV, and negative VCA as indepen- dent factors associated with NG. Factors associated with of BV, TV and VCA in women (not tabled) Only reporting >1 partner remained independently associated with BV in multivariate analy- ses (POR 2.21, p = 0.003). Factors associated with TV in multivariate analyses were being sin- gle and RPR+ (aPOR 2.05, p = 0.009 and aPOR 2.37, p = 0.023, respectively). Factors associated with VCA were having < = 1 partner in the last month (aPOR 4.26, p = 0.005), being pregnant (aPOR 3.05, p = 0.002), always using condoms or not having sex in the last three months vs. never using condoms (aPOR 2.42, p = 0.023), genital itching (aPOR 1.69, p = 0.034), genital discharge (aPOR 2.56, p = 0.011), and being HIV and RPR negative (aPOR 2.93, p = 0.025 and aPOR 4.94, p = 0.031, respectively). Genital ulcers in men and women (not tabled) Reported and/or observed genital ulcers were more common among HIV+ (20%) compared with HIV- (5%) men (p<0.001). Genital ulcers were noted during physical examination in PLOS ONE \| https://doi.org/10.1371/journal.pone.0250044 April 20, 2021 7 / 21 PLOS ONEEtiologies of genital abnormalities in Rwandan men and women Table 3. Univariate and multivariate analysis of factors associated with CT or NG infection in men in Kigali, Rwanda (N = 1013). Demographics cPOR 95% CI CT infection aPOR 95% CI p- value p- value cPOR 95% CI p-value aPOR 95% CI p-value NG infection Age (per year increase) 0.96 0.94 0.99 0.001 0.96 0.94 0.98 0.001 0.98 0.96 1.00 0.029 Referrer Radio Advert 1.44 1.02 2.04 0.038 1.44 1.01 2.07 0.046 ref --- --- --- Friends/Walk-in/Pharmacy/Contact ref ref 1.76 1.28 2.43 0.001 Partner/Internet Living and Marital Status Married and Cohabiting ref Single or Divorced/Separated/Widow 1.69 1.13 2.54 0.011 Education Level ref --- --- --- 1.56 1.14 2.15 0.006 None/Primary/Secondary ref 1.84 1.32 2.58 0.000 2.39 1.57 3.63 <0.0001 Higher Employment Status Full-time employment Part-time/Student/Jobless Sexual behaviors Number of partners in last 30 days None or one partner More than one partner Condom use during vaginal sex in the last 3 months No partners or always used condoms Sometimes Never Number of days since sexual contact you suspect STI was acquired from 0–16 > = 17 Self-reported symptoms Urethral discharge Yes No Dysuria Yes No Genital itching Yes No Genital ulcer Yes No 1.37 0.95 1.99 0.092 ref --- --- --- ref --- --- --- 1.30 0.96 1.78 0.094 ref --- --- --- ref --- --- --- ref ref 1.17 0.80 1.71 0.424 0.22 1.90 0.426 0.62 1.21 0.411 0.64 0.87 ref 1.45 1.09 1.92 0.011 1.51 1.05 2.17 0.028 ref 1.5 --- 1.02 --- 2.2 --- 0.040 0.37 1.13 ref 0.17 0.83 --- 0.8 1.54 --- 0.012 0.450 --- 1.61 1.13 2.30 0.009 1.64 1.15 2.35 0.007 4.68 3.43 3.37 <0.0001 3.29 2.30 4.7 <0.0001 ref 1.63 0.94 2.83 0.084 ref ref ref ref ref --- --- --- ref --- --- --- 10.00 6.41 15.61 <0.0001 ref --- --- --- ref --- --- --- 1.48 1.03 2.13 0.034 1.51 1.03 2.22 0.035 1.05 0.74 1.49 0.792 1.05 0.57 1.93 0.872 ref ref ref --- --- --- 2.24 1.35 3.72 0.002 ref --- --- --- ref --- --- --- 1.53 0.63 3.70 0.345 7.50 3.79 14.85 <0.0001 4.50 2.22 9.13 <0.0001 Number of days with symptoms 1–10 > = 11 Laboratory and physical exam HIV Status Positive 1.39 0.97 1.98 0.075 ref 3.06 2.26 4.15 <0.0001 ref --- --- --- 0.58 0.26 1.31 0.189 0.99 0.53 1.86 0.980 PLOS ONE \| https://doi.org/10.1371/journal.pone.0250044 April 20, 2021 (Continued ) 8 / 21 PLOS ONEEtiologies of genital abnormalities in Rwandan men and women Table 3. (Continued) Demographics cPOR 95% CI CT infection aPOR 95% CI p- value p- value cPOR 95% CI p-value aPOR 95% CI p-value NG infection Negative RPR Result Positive Negative Urethral discharge Yes No Genital ulcer Yes No ref ref --- --- --- 1.30 0.68 2.52 0.429 ref 1.65 ref 0.82 --- 3.3 --- 0.158 --- 1.49 0.81 2.75 0.204 19.94 11.12 35.76 <0.0001 16.38 7.28 36.89 <0.0001 ref ref --- --- --- ref --- --- --- 0.82 0.38 1.80 0.626 ref ref --- --- --- 5.52 2.96 10.28 <0.0001 aPOR: Adjusted prevalence odds ratio; cPOR: Crude prevalence odds ratio; RPR: Rapid plasma reagin; CI: Confidence interval; STI: Sexually transmitted disease; NG: Neisseria gonorrhoeae, CT: Chlamydia trachomatis Not significant not shown include: Self-reported symptoms dyspareunia, unpleasant odor, abdominal pain, anal discharge, anal ulcer, anal warts, and sore throat; genital exam results white accumulation, condyloma/warts, inguinal adenopathy >1cm https://doi.org/10.1371/journal.pone.0250044.t003 19% of RPR+ and 4% of RPR- men and conversely 20% of men with ulcers were RPR+ com- pared to 4% of men without ulcers (p<0.001). Among HIV+ men, none of the seven who were RPR+ had reported and/or observed ulcers while 23% of 43 HIV+ RPR- men had ulcers (p = 0.319). In contrast, among HIV- RPR+ men 21% had reported or observed ulcers com- pared to only 4% of HIV-RPR- men (p<0.001). This suggests that ulcers among HIV+ men were more likely herpetic while among HIV- men at least one fifth were syphilitic. Although HIV- men were more likely to be circumcised than HIV+ men (67% vs. 58%) in our program, this difference was not significant (p = 0.196). Among circumcised men, those who were HIV+ were more likely to have ulcers (13% vs. 4%, p = 0.074) and to be RPR+ (20% vs. 4%, p = 0.003). Among uncircumcised men, those who were HIV+ were also more likely to have ulcers (27% vs. 7%, p = 0.001) while the difference in RPR+ results was not significant (12% vs. 6%, p = 0.324). Among women, the prevalence of reported or observed ulcers was not significantly differ- ent by HIV serostatus (20% in HIV+ vs.14% p = 0.196). Genital ulcers were noted during phys- ical examination for 28% of RPR+ women compared with 14% of RPR- women (p<0.001). As with men, the association between RPR results and reported and/or observed ulcers differed in HIV+ and HIV- women: 25% of HIV+RPR+ vs. 20% of HIV+RPR- had ulcers, p = 0.729, com- pared with 37% of HIV-RPR+ vs. 13% of HIV-RPR- women having ulcers (p = 0.001). Discussion We found a high prevalence of NG and CT among symptomatic men and women in Kigali. Among men, urethral discharge was strongly associated with a diagnosis of NG while dysuria was not associated with either infection. Specific symptoms were less helpful in identifying NG and CT among women. Physical exam findings, demographic variables and reported risk behaviors were independently predictive of NG and/or CT in both men and women, as were vaginal wet mount findings and HIV serologies among women. Among women, TV and BV were associated with sexual risk behaviors but not with symptoms while VCA was associated with vaginal itching and discharge and with low-risk profiles. There were complex inter- PLOS ONE \| https://doi.org/10.1371/journal.pone.0250044 April 20, 2021 9 / 21 PLOS ONEEtiologies of genital abnormalities in Rwandan men and women Table 4. Factors associated with CT or NG infection in women in Kigali, Rwanda (N = 579). Demographics n /mean Col %/SD n /mean Row %/SD n /mean Row% /SD n /mean Row %/SD n /mean Row %/SD Age, continuous (years) 28.7 7.2 25.6 6.1 29.3 7.2 <0.0001 26.8 6.3 29.4 7.4 <0.0001 Total (N = 579) CT-infected (N = 98) CT-uninfected (N = 481) p-value NG-infected (N = 152) NG-uninfected (N = 427) p-value Referrer Radio Advert Friends/Walk-in/Pharmacy/Contact Partner/ Internet Living and Marital Status Married and Cohabiting Single or Divorced/Separated/Widow Education Level None Primary Secondary Higher Employment Status Full-time employment Part-time/Student/Jobless Sexual behaviors Number of partners in last 30 days None or one partner More than one partner Condom use during vaginal sex in the last 3 months No partners or always used condoms Sometimes Never Number of days since sexual contact you suspect STI was acquired from < = 8 9–16 > = 17 Number of children under 18, continuous Number of additional children desired, continuous Pregnant Yes No Want more children in next two years Yes No Family planning method among women not pregnant and do not want more children in next two years 284 295 268 311 25 242 246 66 199 379 444 88 35 163 334 46 78 409 1.3 1.4 48 528 125 419 49% 51% 46% 54% 4% 42% 42% 11% 34% 66% 83% 17% 7% 31% 63% 9% 15% 77% 1.3 1.1 8% 92% 23% 77% 37 61 34 64 2 38 46 12 30 68 70 20 6 34 50 8 20 61 1.0 1.6 8 90 20 74 13% 21% 13% 21% 8% 16% 19% 18% 15% 18% 16% 23% 17% 21% 15% 17% 26% 15% 1.1 1.1 17% 17% 16% 18% 247 234 234 247 23 204 200 54 169 311 374 68 29 129 284 38 58 348 1.3 1.4 40 438 105 345 87% 79% 87% 79% 92% 84% 81% 82% 85% 82% 84% 77% 83% 79% 85% 83% 74% 85% 1.3 1.2 83% 83% 84% 82% Non-Hormonal Method (IUD/Condom/Tubal 268 66% 47 18% 221 82% 0.498 Ligation/Natural Method) or No Method Hormonal Implant Injectable 50 48 12% 12% 9 5 18% 10% 41 43 82% 90% 0.014 0.012 0.513 67 85 60 92 9 81 54 8 0.383 39 113 0.112 0.259 0.066 0.026 0.040 0.947 0.666 95 43 4 66 68 15 31 92 1.2 1.3 11 141 33 106 56 24 16 24% 29% 22% 30% 36% 33% 22% 12% 20% 30% 21% 49% 11% 40% 20% 33% 40% 22% 1.1 1.0 23% 27% 27% 26% 217 210 208 219 16 161 192 58 160 266 349 45 31 97 266 31 47 317 1.3 1.4 37 387 88 298 0.153 0.050 0.001 0.008 76% 71% 78% 70% 64% 67% 78% 88% 80% 70% 79% <0.0001 51% 89% <0.0001 60% 80% 67% 60% 78% 1.3 1.2 77% 73% 73% 74% 0.003 0.600 0.279 0.569 0.821 21% 212 79% 0.001 48% 33% 26 32 52% 67% (Continued ) PLOS ONE \| https://doi.org/10.1371/journal.pone.0250044 April 20, 2021 10 / 21 PLOS ONEEtiologies of genital abnormalities in Rwandan men and women Table 4. (Continued) Demographics Pills Family planning method and pregnancy composite Pregnant Hormonal method (implant, injectable, pills) Non-Hormonal (IUD/Condom/ Tubal Ligation/ Natural Method) or No Method Self-reported symptoms Vaginal discharge Yes No Genital itching Yes No Dysuria Yes No Genital ulcer Yes No Number of days with symptoms 1–5 6–10 11–21 >21 Laboratory and physical exam HIV Status Positive Negative RPR Result Positive Negative Trichomonas Positive Negative Candida Positive Negative Bacterial vaginosis Positive Negative Vaginal Inflammation or Discharge Yes No Endocervical Inflammation or Discharge Total (N = 579) CT-infected (N = 98) n /mean 40 Col %/SD 10% n /mean 9 Row %/SD 23% CT-uninfected (N = 481) n /mean 31 Row% /SD 78% p-value NG-infected (N = 152) NG-uninfected (N = 427) p-value n /mean 12 Row %/SD 30% n /mean 28 Row %/SD 70% 48 139 388 475 101 320 254 266 311 64 508 72 77 131 257 75 504 46 522 72 491 118 437 113 438 469 69 8% 24% 67% 82% 18% 56% 44% 46% 54% 11% 89% 13% 14% 24% 48% 13% 87% 8% 92% 13% 87% 21% 79% 21% 79% 87% 13% 8 23 67 78 20 52 44 44 54 9 89 16 17 18 37 8 90 10 88 18 75 12 80 25 65 75 15 17% 17% 17% 16% 20% 16% 17% 17% 17% 14% 18% 22% 22% 14% 14% 11% 18% 22% 17% 25% 15% 10% 18% 22% 15% 16% 22% 40 116 321 397 81 268 210 222 257 55 419 56 60 113 220 67 414 36 434 54 416 106 357 88 373 394 54 0.979 0.412 0.732 0.793 0.489 0.170 0.121 0.401 0.038 0.035 0.062 0.232 83% 83% 83% 84% 80% 84% 83% 83% 83% 86% 82% 78% 78% 86% 86% 89% 82% 78% 83% 75% 85% 90% 82% 78% 85% 84% 78% 11 52 89 123 28 57 92 75 76 9 140 24 27 40 48 34 118 21 128 18 129 13 132 47 96 116 24 23% 37% 23% 26% 28% 18% 36% 28% 24% 14% 28% 33% 35% 31% 19% 45% 23% 46% 25% 25% 26% 11% 30% 42% 22% 25% 35% 37 87 299 352 73 263 162 191 235 55 368 48 50 91 209 41 386 25 394 54 362 105 305 66 342 353 45 0.003 0.704 77% 63% 77% 74% 72% 82% <0.0001 0.306 0.020 0.003 64% 72% 76% 86% 72% 67% 65% 69% 81% 55% <0.0001 77% 54% 75% 75% 74% 0.002 0.818 89% <0.0001 70% 58% <0.0001 78% 75% 65% 0.076 (Continued ) PLOS ONE \| https://doi.org/10.1371/journal.pone.0250044 April 20, 2021 11 / 21 PLOS ONEEtiologies of genital abnormalities in Rwandan men and women Table 4. (Continued) Demographics Yes No Genital Ulcer Yes No Total (N = 579) CT-infected (N = 98) n /mean Col %/SD n /mean 262 275 48 481 49% 51% 9% 91% 55 35 9 78 Row %/SD 21% 13% 19% 16% CT-uninfected (N = 481) n /mean Row% /SD 207 240 39 403 79% 87% 81% 84% p-value NG-infected (N = 152) NG-uninfected (N = 427) p-value 0.010 0.652 n /mean 88 52 12 126 Row %/SD 34% 19% 25% 26% n /mean 174 223 36 355 Row %/SD 66% 81% 75% 74% <0.001 0.857 Not significant not shown include: Self-reported symptoms anal discharge, anal ulcer, anal warts, and sore throat; genital exam results non-menstrual bleeding (cervix and vagina), condyloma/warts (cervix and vagina), inguinal adenopathy >1cm unilateral and bilateral, adnexal tenderness and adnexal mass. IUD: intrauterine device; RPR: Rapid plasma reagin; CI: Confidence interval; STI: Sexually transmitted disease; NG: Neisseria gonorrhoeae, CT: Chlamydia trachomatis https://doi.org/10.1371/journal.pone.0250044.t004 relationships between HIV and RPR serologies and genital ulcers, and these were further influ- enced by circumcision status among men. These findings exemplify the locally relevant data that can inform approaches to diagnosis and treatment in Rwanda as called for by WHO. Our models had good discrimination and use of these data may offer improvement over the current algorithm recommended by the Rwandan National Guidelines. As in other studies, syndromic management may perform better among men compared to women due to the ease of detecting abnormalities on external genitalia and the high likelihood of NG among men reporting urethral discharge [44]. Surprisingly, dysuria was as common as discharge in men but contrary to conventional wisdom we did not find an association between dysuria and NG or CT [45]. The most common presenting symptom among women was vaginal discharge which was only associated with VCA and not with NG, CT, BV or TV. Genital itching was reported by over half of patients and was also predictive of VCA. Itching was also useful in pointing away from NG, as was reported ulcer. Gynecologic exam, specifically endocervical discharge, was helpful in the diagnosis of NG. Interestingly, wet mount results were predictive NG (BV+, VCA-), suggesting that these inexpensive and simple tests should be included in any workup of symptomatic women. Despite extensive laboratory testing, we failed to find an etiology for a substantial proportion of women seeking care. This may reflect poor sensitivity of microscopy as well as non-infectious causes of symptoms. As has been noted elsewhere, factors associated with NG were more useful in predicting infections than those for CT [46, 47]. For both men and women, younger age was predictive of both NG and CT and lower edu- cation level and jobless or part-time employment status were predictive of NG. Interestingly, number of partners was not independently associated with CT or NG. Most men and women reported never using condoms and very few reported always using condoms. Women who sometimes used condoms were at higher risk of NG than those who never used them. This may be due to increased condom use in women with higher risk partners. Genital ulcers were not a common presenting symptom and were not associated with RPR results among HIV+ patients. RPR provided a diagnosis for 20% of ulcers among HIV- men and 15% among HIV- women. As others in Africa have reported, HSV is the most likely diag- nosis for RPR- ulcers which was more common among HIV+ patients [48]. Non-circumcision among men is associated with HIV acquisition and with increased prevalence and incidence of ulcerative STI [49–52]. We have previously shown a relationship between ulcers, smegma and HIV acquisition in uncircumcised men [15]. Among HIV- men, those who were uncircum- cised were not more likely to report ulcers or to be RPR+ than their circumcised counterparts. PLOS ONE \| https://doi.org/10.1371/journal.pone.0250044 April 20, 2021 12 / 21 PLOS ONEEtiologies of genital abnormalities in Rwandan men and women Table 5. Univariate and multivariate analysis of factors associated with CT or NG infection in women in Kigali, Rwanda (N = 579). cPOR 95% CI p-value aPOR 95% CI p-value cPOR 95% CI p-value aPOR 95% CI p-value CT infection NG infection Demographics Age (per year increase) 0.91 0.88 0.95 <0.0001 0.90 0.86 0.94 <0.0001 0.95 0.92 0.97 < .001 0.93 0.89 0.97 <0.001 Referrer Radio Advert ref ref Friends/Walk-in/Pharmacy/Contact 1.74 1.11 2.72 0.015 1.31 0.91 1.90 0.150 Partner/Internet Living and Marital Status Married and Cohabiting Other Education Level None/Primary Secondary/Higher Employment Status Full-time employment Part-time/Student/Jobless Sexual behaviors Number of partners in last 30 days None or one partner More than one partner Condom use during vaginal sex in the last 3 months No partners or always used condoms Sometimes Never Number of days since sexual contact you suspect STI was acquired from 0–8 9–16 > = 17 Number of children under 18 (per child increase) Number of additional children desired (per child increase) Family planning method and pregnancy composite Pregnant Hormonal method (implant, injectable, pills) Non-Hormonal (IUD/Condom/Tubal Ligation/Natural Method) or No Method Self-reported symptoms Vaginal discharge Yes No Genital itching Yes No Dysuria ref ref 1.78 1.13 2.80 0.012 1.46 1.00 2.13 0.048 ref 1.30 0.83 2.01 0.248 ref 2.09 1.44 3.04 0.000 2.13 1.30 3.48 0.003 ref ref ref ref 1.23 0.77 1.96 0.383 1.76 1.16 2.66 0.008 1.95 1.12 3.39 0.019 ref ref 1.56 0.89 2.75 0.119 3.53 2.19 5.69 <0.0001 0.46 2.97 0.92 2.42 0.741 0.107 0.53 2.69 1.1 3.49 0.666 0.022 1.17 1.49 ref 1.20 1.96 ref 0.15 1.5 0.207 1.81 4.18 <0.0001 0.22 2.41 1.07 2.98 0.611 0.025 0.74 1.79 ref 0.87 3.16 1.36 3.87 0.126 0.002 0.48 2.75 ref 1.66 2.29 ref 0.82 0.69 0.99 0.037 0.96 0.84 1.10 0.594 1.22 1.02 1.45 0.027 0.92 0.79 1.07 0.274 0.96 0.95 0.43 2.14 0.56 1.59 0.915 0.837 1.00 2.01 0.49 2.03 1.32 3.05 0.999 0.001 1.30 1.73 0.57 2.99 1.02 2.94 0.532 0.040 ref ref ref ref 1.26 0.73 2.18 0.408 1.10 0.68 1.78 0.692 ref ref ref ref 1.08 0.69 1.68 0.738 2.62 1.79 3.84 <0.0001 2.54 1.55 4.17 0.0002 PLOS ONE \| https://doi.org/10.1371/journal.pone.0250044 April 20, 2021 (Continued ) 13 / 21 PLOS ONEEtiologies of genital abnormalities in Rwandan men and women Table 5. (Continued) Yes No Genital ulcer Yes No Number of days with symptoms 1–10 11 or more Laboratory and physical exam HIV Status Positive Negative RPR Result Positive Negative Trichomonas Positive Negative Candida Positive Negative Bacterial vaginosis Positive Negative Vaginal Inflammation OR Discharge Yes No Endocervical Inflammation or Discharge Yes No Genital Ulcer Yes No cPOR 95% CI p-value aPOR 95% CI p-value cPOR 95% CI p-value aPOR 95% CI p-value CT infection NG infection ref 1.06 0.68 1.64 0.796 ref 1.21 0.84 1.75 0.303 ref ref ref 1.30 0.62 2.73 0.489 2.33 1.12 4.84 0.024 2.52 1.09 5.80 0.030 1.72 1.06 2.78 0.027 1.76 1.07 2.88 0.026 1.76 1.16 2.68 0.008 1.78 1.05 3.00 0.032 ref ref ref ref ref 2.73 1.66 4.47 <0.0001 2.05 1.10 3.83 0.024 1.83 0.85 3.96 0.124 ref ref 1.37 0.66 2.88 0.401 2.58 1.41 4.7 0.002 ref 1.85 1.03 3.32 0.041 ref ref ref ref 1.06 0.60 1.88 0.838 ref ref 1.98 1.04 3.77 0.038 3.56 1.89 6.69 <0.0001 2.20 1.11 4.36 0.024 1.63 0.98 2.72 0.063 2.63 1.67 4.15 <0.0001 1.89 1.07 3.34 0.028 ref ref ref ref ref 1.47 0.79 2.75 0.2201 1.67 0.97 2.86 0.063 1.83 1.15 2.91 0.010 2.17 1.46 3.23 0.000 1.80 1.11 2.93 0.018 ref 1.19 0.56 2.56 0.649 ref ref ref ref 1.06 0.53 2.10 0.875 IUD: intrauterine device; aPOR: Adjusted prevalence odds ratio; cPOR: Crude prevalence odds ratio; RPR: Rapid plasma reagin; CI: Confidence interval; STI: Sexually transmitted disease; NG: Neisseria gonorrhoeae, CT: Chlamydia trachomatis https://doi.org/10.1371/journal.pone.0250044.t005 In contrast, among HIV+ men, those who were uncircumcised were more likely to have an ulcer and less likely to be RPR+ than circumcised men. Circumcision is widely promoted in Rwanda and available at no cost in most government health centers as part of HIV prevention services. Though the focus is on protecting HIV- men, our results here suggest that circumci- sion can benefit HIV+ men by reducing ulcer incidence [53]. It is likely that we missed other less common ulcer etiologies including HD, lymphogranu- loma venereum (LGV), and granuloma inguinale (Klebsiella granulomatis) [54]. Our clinicians did suspect chancroid in a few cases, but the service program did not record detailed descrip- tions or photographs of ulcers and we lacked laboratory diagnostics. The most recent PLOS ONE \| https://doi.org/10.1371/journal.pone.0250044 April 20, 2021 14 / 21 PLOS ONEEtiologies of genital abnormalities in Rwandan men and women publication presenting confirmed chancroid diagnoses in Rwanda was based on data collected in 1992, which found 27% of ulcers in men and 20% in women had culture-confirmed HD [55–59]. For many years the prevalence of HD had been decreasing in much of Africa [48, 54], but recent publications indicate HD may be staging a comeback [21]. More investigations are needed in Rwanda. Physical exam findings made important contributions in our program. Examination of male genitalia does not require specialized equipment, but speculum exam requires a skilled cli- nician, a gynecologic exam table and light which are in limited supply in low resource settings. While genital exams would not be feasible for all symptomatic patients, targeted genital exams in specific circumstances would be feasible and potentially very useful. Distinguishing between vaginal and endocervical discharges would greatly improve diagnostic accuracy and bi-manual exam would identify pelvic inflammatory disease. Similarly, in our setting where less than one in five ulcer patients are RPR+, assessing ulcer characteristics may be worthwhile. Visual exam has traditionally been viewed as unreliable as many ulcers do not have a paradigmatic presenta- tion (e.g. painless ‘clean’ TP ulcer, painful ‘dirty’ HD with inguinal adenopathy, multiple chronic or recurrent shallow vesicular HSV lesions). However, a recent study in Jamaica com- pared clinical diagnosis with M-PCR and found visual diagnoses of TP, HSV, and HD were 67.7%, 53.8%, and 75% sensitive and 91.2%, 83.6%, and 75.4% specific, respectively [60]. The advent of point-of-care diagnostics for NG and CT has transformed STI diagnosis, but given relatively expensive equipment and reagents, this remains out of reach in many low resource settings. We have used pooling to reduce the per-patient cost in Zambia and this could be explored in other settings [61]. GeneXpert kits are also available for TV and they are more sensitive than microscopy. The US CDC has in-house multiplex PCR (M-PCR) for ulcer etiologies including syphilis, HSV and chancroid. A focused study would provide prevalence information that could inform the next update of national guidelines. Our program has several limitations. Social desirability bias may have led to under-report- ing of risky sexual behaviors. We focused on symptomatic men and women and thus missed the many people who are asymptomatically infected [62, 63]. We did not screen for active viral hepatitis as recent unpublished surveys have shown a low prevalence of both hepatitis B and C (4% and 3%, respectively reported nationally, 4% and 5% among female sex workers tested in our laboratory). We did not have funding or resources to perform any direct method of detec- tion for TP using ulcer material, and thus may have misclassified some recently infected people who were negative by RPR test. While we did treat TV in male partners referred by TV + women, we did not systematically test for TV in men. Microscopy for TV detection in men is extremely insensitive, and we did not have resources to conduct GeneXpert testing for TV. TV could therefore be the reason for a portion of the symptomatic men with unknown etiol- ogy. We did not include HSV serologies because adult seroprevalence is high [64]. Assessment of cervical intraepithelial neoplasia requires more resources than would be achievable on a large scale in Rwandan health centers so we did not address this important problem. Fortu- nately, 93% of Rwandan girls now receive the human papillomavirus vaccine and future gener- ations will be protected [65]. Lastly, we and others have published an association between female genital schistosomiasis and HIV [66, 67], but this is most commonly seen with S.Hae- matobium while only S.Mansoni is endemic in Rwanda, thus we did not screen for genital schistosomiasis [68]. Conclusions Syndromic management guidelines in Rwanda can be improved with consideration of the prevalence of confirmed infections from this program offering services to symptomatic men PLOS ONE \| https://doi.org/10.1371/journal.pone.0250044 April 20, 2021 15 / 21 PLOS ONEEtiologies of genital abnormalities in Rwandan men and women and women representative of those who would seek care at government health centers. Our findings indicate that syndromic management performs better among men but is poor among women. Inclusion of demographic and risk factor measures shown to be predictive of STI and non-STI dysbioses may also increase diagnostic accuracy. In symptomatic women, wet mount results for BV and VCA may help diagnose NG and are inexpensive and could be offered for management of women. Targeted genital exams for women in specific circumstances (e.g., in women without genital itching) may also be useful to diagnose NG. More data is needed on how often local prevalence and epidemiology should be reassessed to maintain improved syn- dromic management. Supporting information S1 Fig. STI baseline clinical form. (DOCX) Author Contributions Conceptualization: Julien Nyombayire, Rosine Ingabire, Susan Allen, Etienne Karita. Data curation: Kristin M. Wall, Julien Nyombayire, Rachel Parker, Susan Allen. Formal analysis: Kristin M. Wall, Rachel Parker. Funding acquisition: Susan Allen. Investigation: Julien Nyombayire, Rosine Ingabire, Jean Bizimana, Jeannine Mukamuyango, Amelia Mazzei, Matt A. Price, Marie Aimee Unyuzimana, Susan Allen, Etienne Karita. Methodology: Kristin M. Wall, Jean Bizimana, Matt A. Price, Marie Aimee Unyuzimana, Amanda Tichacek, Susan Allen, Etienne Karita. Project administration: Julien Nyombayire, Rosine Ingabire, Jean Bizimana, Jeannine Muka- muyango, Amelia Mazzei, Amanda Tichacek, Susan Allen, Etienne Karita. Resources: Susan Allen, Etienne Karita. 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10.1126_sciadv.abn5709.pdf	null	null	Supplementary Materials for A cooperative network at the nuclear envelope counteracts LINC-mediated forces during oogenesis in C. elegans Chenshu Liu et al. Corresponding author: Chenshu Liu, chenshu.liu@berkeley.edu; Abby F. Dernburg, afdernburg@berkeley.edu Sci. Adv. 9, eabn5709 (2023) DOI: 10.1126/sciadv.abn5709 The PDF file includes: Figs. S1 to S18 Tables S1 to S7 Legends for movies S1 to S5 Legend for Data S1 References Other Supplementary Material for this manuscript includes the following: Movies S1 to S5 Data S1 Fig. S1. Fig. S1. (Related to Figs. 1 and 3) CRISPR tagging and auxin-inducible degradation of LMN-1 in C. elegans germline. (A) Multiple sequence alignments of nematode LMN-1 proteins were generated using Clustal Omega and visualized using Jalview, showing the position of the degron/V5 insertion in C. elegans LMN-1. (B) Morphology of eggs/embryos laid by hermaphrodite worms treated ± auxin for 48 hrs (from young adulthood). Images acquired using DIC. Scale bar, 20 µm. Fig. S2. Fig. S2. (Related to Fig. 1) Representative images showing co-staining of DAPI and nucleoporins NPP-7 (A) or NPP-10 (B). Composite images are maximum-intensity projections. Meiosis progresses from left to right. Scale bars, 10 µm. Fig. S3. Fig. S3. (Related to Fig. 1) Quantifying nuclear collapse. (A) Composite images (maximum- intensity projections) showing nuclei in late meiotic prophase stained for DAPI (cyan), NPP-7 (red) or SYP-1 (red). Meiosis progresses from top left to bottom right. Scale bars, 10 µm. (B) Segmentation of chromosome volume based on DAPI fluorescence. Meiosis progresses from top left to bottom right. Scale bar, 10 µm. (C) Quantification of nuclear radii based on NPP-7 or SYP-1 staining (with maximum Z projection), nuclear volume based on radius3, nuclear size based on DAPI volume and integrated DAPI fluorescence intensity per nucleus. Nuclear sizes in late pachytene were normalized to one. Unpaired two-sample two-sided t-test was used to calculate p-values. At least 58 nuclei from three animals were analyzed per stage. Fig. S4. Fig. S4. (Related to Figs. 1 and 3) LMN-1 depletion causes SPO-11 independent DNA damage. (A) LMN-1 depletion causes persistent DNA damage marked by RAD-51 foci. Scale bar, 10 µm. (B) RAD-51 staining in mid-pachytene nuclei from control and hermaphrodites depleted of SPO-11, LMN-1, LMN-1 and SPO-11 both, or LMN-1 and SPO-11 and SUN-1 simultaneously. Control hermaphrodite has TIR1 but no AID-tagged genes. Animals of indicated genotypes (all homozygous for Psun-1::TIR1) were exposed to auxin for 24 hours from the L4 stage to young adulthood prior to dissection. Scale bar, 5 µm. (C) Quantification of RAD-51 foci per nucleus as a function of meiotic progression. Gonads were divided into five zones of equal length spanning the premeiotic region to early diplotene (as in Fig. S6C). Control, N = 964 nuclei (4 animals); SPO-11 depletion, N = 1480 nuclei (4 animals); LMN-1 depletion, N = 1781 nuclei (6 animals); LMN-1, SPO-11 double depletion, N = 1329 nuclei (5 animals); LMN-1, SPO-11, SUN-1 triple depletion, N = 844 nuclei (5 animals); SUN-1 depletion, N = 1201 nuclei (4 animals). Pairwise comparisons for proportions were performed to compute the p-values (adjusted by the Benjamini-Hochberg method, see Data S1). Experimental conditions were the same as in (B). (D) Designated crossover sites marked by GFP::COSA-1 foci in late prophase nuclei. Following LMN-1 depletion, nuclei still showed six designated crossover sites, whereas GFP::COSA-1 foci were absent from apoptotic nuclei. Animals of indicated genotypes (all homozygous for Psun-1::TIR1) were exposed to auxin for 24 hours from the L4 stage to young adulthood prior to dissection. Dashed arrows indicate meiotic progression. Scale bar, 5 µm. Fig. S5. Fig. S5. ATG-7 depletion does not rescue nuclear collapse. (A) Raw (non-deconvolved) maximum-intensity Z-projection showing the cytoplasmic localization of ATG-7 in the germline and the effectiveness of depletion by 12hr auxin treatment. Gray-scale images were scaled identically. Scale bar, 10 µm. (B) Nuclear morphology at later stages of meiotic prophase. Colored dashed lines mark meiotic stages based on the anatomical positions of gonads from control animals. Scale bar, 10 μm. Fig. S6. Fig. S6. (Related to Fig. 2) Chromosome pairing and synapsis occur normally following LMN-1 depletion. (A) Dynamics of synapsis, based on immunostaining of SYP-1 (synaptonemal complex) and HTP-3 (chromosome axes). Yellow asterisks indicate the distal end of gonads. Scale bar, 10 µm. (B) Normal pairing of X chromosomes is revealed by immunofluorescence of HIM-8, which localizes to the X-chromosome pairing centers. Scale bar, 2 µm. (C) Quantification of homolog pairing and synapsis. Diagram of distal gonad divided into five zones of equal length. Three gonads were measured per condition. Mean ± SD are plotted. Two-sided two-proportions z-test was used for computing the p-values. *, p < 0.001; , p < 0.01; ns, p ≥ 0.05 (see Data S1). Fig. S7. Fig. S7. (Related to Fig. 2) Prolonged ZYG-12::GFP clustering following LMN-1 depletion. (A) Mean intensities of ZYG-12::GFP at the nuclear envelope per nucleus, normalized against intensity levels at the transition zone. Medians (black crossbars) and means (black boxes) are shown. Fluorescence intensity surrounding each nucleus was manually segmented and quantified from additive projection images after background subtraction. 30 nuclei per stage were measured per condition (without or with auxin). TZ, transition zone; MP, mid-pachytene; LP, late pachytene; Dip, diplotene. Two-way ANOVA was used to calculate the p-value. (B) Clustering of ZYG-12::GFP, defined as the relative standard deviation (ratio of the standard deviation to the mean value) of fluorescence intensity at the NE in each nucleus. Medians (black crossbars) and means (black boxes) are shown. Fluorescence intensity was measured in the same way as in (A). 30 nuclei per stage were measured per condition. TZ, transition zone; MP, mid-pachytene; LP, late pachytene; Dip, diplotene. p-value was calculated by two-way ANOVA. Fig. S8. Fig. S8. (Related to Figs. 2 and 3) Quantifying LINC complex distribution at the NE. (A) Quantifying SUN-1 distribution at the NE. Representative line-scan profiles of SUN-1::mRuby fluorescence intensity at the circumference of individual nuclei. Grayscale images are additive projections and are scaled using the same look up table (LUT). Scale bar, 5 µm. (B) Quantifying asymmetric ZYG-12 distribution at the NE. Top panel shows grayscale images of additive projections with background subtraction showing polarized ZYG-12::GFP distribution at the NE in a LMN-1 depleted diplotene nucleus. The right panel shows line scan profile along the circumference of the nucleus and approximate locations of angles mapped subsequently during data alignment. Scale bar, 5 µm. Bottom panels show data alignment and normalization. Raw intensity measurement from line-scan was aligned and mapped such that 180° corresponds to the coordinate along the NE’s circumference with the maximum-intensity, and the intensity at 0° was normalized to one (red); or in cases where one nucleus has multiple ZYG-12 intensity peaks on the NE, 180° corresponds to the coordinate along the NE’s circumference with the minimum- intensity, and the intensity at 180° was normalized to one (green). Fig. S9. Fig. S9. (Related to Fig. 3) Co-depletion of DHC-1 or DLI-1, but not DYLT-1, also rescues nuclear collapse despite marked nuclear mispositioning. Nuclear morphology upon depleting DHC-1, DLI-1 or DYLT-1 in lmn-1::AID::V5 worms ±auxin treatment. Mitotic defects are seen in the proliferative region, and meiotic nuclei are mispositioned throughout the gonad following depletion of DHC-1 or DLI-1. Scale bar, 10 µm. Fig. S10. Fig. S10. (Related to Figs. 3 and 4) Efficacy of auxin-induced degradation or RNAi. (A) Nuclear morphology in hermaphrodites depleted of ZYG-12, SUN-1 or LEM-2 with auxin- induced degradation, or depleted of LEM-2 or LMN-1 using RNAi. The duration of auxin treatment was at least 8 hours and the duration of feeding RNAi was 48hr. Scale bars, 10 µm. All worms were homozygous for Psun-1::TIR1 or Pgld-1::TIR1. ZYG-12 depletion results in mispositioning of meiotic nuclei. (B) DNC-1 at NE can be effectively depleted using RNAi. Live imaging stills of DIC and DNC-1::GFP in the late pachytene region of the germline in worms fed with Control RNAi or dnc-1(RNAi). Arrows indicate meiotic progression. Scale bar, 10 µm. Fig. S11. Fig. S11. (Related to Fig. 3) CRISPR tagging and auxin-inducible degradation of SUN-1 in C. elegans germline. (A) Prediction of transmembrane region in C. elegans SUN-1 using TMHMM (http://www.cbs.dtu.dk/services/TMHMM/). Probability of amino acids inside the INM (green), outside the ONM (blue) and in the transmembrane region (magenta) is plotted. (B) Multiple sequence alignments of nematode SUN-1 proteins were generated using Clustal Omega and visualized using Jalview, showing position of degron/V5 insertion in C. elegans SUN-1. (C) X-chromosome pairing (HIM-8) and SC assembly (SYP-1) in sun-1::AID::V5 worms without or with auxin treatment. All worms were homozygous for Psun-1::TIR1. Scale bar, 10 µm. (D) SUN- 1 is required for ZYG-12 localization at the NE of meiotic cells. Composite images showing live meiotic nuclei from mid/late pachytene in worms of indicated genotypes or treatments. All worms were homozygous for Psun-1::TIR1 or Pgld-1::TIR1. ZYG-12::GFP in green and mRuby::SYP-3 in magenta. All images are maximum-intensity projections and scaled identically. Scale bar, 5 µm. Fig. S12. Fig. S12. (Related to Fig. 3) LMN-1 depletion is equally efficient upon auxin-induced co- degradation. (A) Four strains carrying lmn-1::AID::V5 alone or in combination with other AID- tagged genes were immunostained for V5 after the same 12hr treatment from young adult (-/+ Auxin). All strains have Psun-1 or Pgld-1 driven TIR1::mRuby. All images are maximum-intensity projections and scaled identically between -/+ Auxin. Scale bar, 5 µm. (B) Quantification of mean intensity of V5 staining per nucleus. Fluorescence intensity was measured from additive projection images after background subtraction. 30 nuclei in late pachytene/early diplotene were measured per condition. Medians (black crossbars) and means (black boxes) are shown. p-values were calculated using one-way ANOVA and post hoc pairwise t-tests (two-sided; adjusted by the Benjamini-Hochberg method). p > 0.05 between all + Auxin groups. A.U., arbitrary unit. Fig. S13. Fig. S13. (Related to Fig. 3) SUN-1, but not ZYG-12, is required for damage-induced apoptosis during meiosis. (A) Germline apoptosis is increased upon depleting ZYG-12. Apoptosis was quantified using CED-1::GFP. syp-1(me17)/nT1 heterozygous and syp-1(me17) homozygous animals were used as controls. Mean ± SD are plotted. Pairwise Mann-Whitney test was used for computing the p-values. (B) Germline apoptosis does not change upon SUN-1 depletion. Red dashed arrows indicate the direction of meiotic progression. Scale bar, 10 µm. Unpaired two-sample two-sided Mann-Whitney test was used for computing the p-value. (C) X- chromosome pairing (HIM-8) and SC assembly (SYP-1) in HA::AID::zyg-12 worms without or with auxin treatment. Yellow arrow heads indicate nuclei with unpaired HIM-8 foci, white arrow heads indicate nuclei with incomplete synapsis (HTP-3 staining devoid of SYP-1). Scale bar, 10 μm. (D) RAD-51 staining in mid-pachytene nuclei in HA::AID::zyg-12 worms without or with auxin treatment. Scale bar, 10 µm. (E) Single Z-section of non-deconvolved immunofluorescence images showing CED-1::GFP positive meiotic nuclei stain positive for SUN-1 but negative for ZYG-12 (orange arrow heads). One worm per row is shown. Scale bar, 5 µm. Fig. S14. Fig. S14. (Related to Fig. 3) The absence of the meiotic NE protein MJL-1 does not rescue nuclear collapse. Nuclear morphology in wild-type (N2) or mjl-1 null (tm1651) worms following Control RNAi or lmn-1(RNAi). Scale bar, 10 µm. Fig. S15. Fig. S15. (Related to Fig. 4) Pairing and synapsis upon depleting SAMP-1, EMR-1 or LEM- 2. X-chromosome pairing (HIM-8) and SC assembly (SYP-1 and HTP-3) upon RNAi-mediated depletion of SAMP-1 or of LEM-2 in emr-1(gk119) mutants. Insets showing X-chromosome pairing in early pachytene and bivalents formation in diakinesis under each condition. Scale bars, 10 µm. Fig. S16. Fig. S16. (Related to Fig. 4) Immunostaining of nuclear pores in germlines depleted of EMR-1, LEM-2 and LMN-1. Composite images showing a representative gonad from an emr- 1(gk119), lmn-1::AID animal with lem-2(RNAi) and Auxin treatment, DAPI in cyan and NPP-7 (marking nuclear pore complex) in red. Insets show that nuclei undergoing exacerbated collapse can still be surrounded by nuclear pore complexes marked by NPP-7. All scale bars, 10 µm. Fig. S17. Fig. S17. (Related to Fig. 4) Asymmetric distribution of ZYG-12::GFP at early pachytene NE upon co-depleting LMN-1 and SAMP-1 or EMR-1/LEM-2. (A and C) Composite images showing mRuby::SYP-3 (magenta) and ZYG-12::GFP (green) at the NE of early pachytene nuclei of indicated genotypes or treatments. All images are maximum-intensity projections and scaled identically per experiment. Scale bar, 5 µm. (B and D) Quantification of ZYG-12::GFP fluorescence as a function of angle along the circumference of NE in early pachytene nuclei. Intensity measurement was performed as in Fig. S8B. Because of the multiple bright foci/patches of LINC complexes at the NE in each early pachytene nucleus, data had to be aligned and normalized differently than that in Fig. 3B: data were aligned and mapped so that 180° corresponds to the coordinate along the NE’s circumference with the minimum-intensity of ZYG-12::GFP, which was normalized as one. In (B), N = 32 early pachytene nuclei pooled from four animals were measured for each condition. p < 2.2e-16 between the control RNAi and samp-1(RNAi) with 2-way ANOVA. In (D), N = 51 (Control RNAi) and 35 (lem-2(RNAi)) early pachytene nuclei pooled from seven or four animals were measured. p < 2.2e-16 between the control RNAi and lem-2(RNAi) with 2-way ANOVA. See Data S1. All worms were homozygous for Pgld-1::TIR1. Fig. S18. Fig. S18. (Related to Fig. 5) Isolating meiotic nuclei for stiffness measurement using mechano-NPS. (A) Schematic of the workflow of microdissecting C. elegans gonads to isolate meiotic nuclei. (B) Nuclear size measured with mechano-NPS. The nuclear size is calculated using supplemental equation 1. Welch ANOVA with Games-Howell multiple comparisons test revealed no significant differences between nuclear diameter in each group, p>0.05. Sample sizes for the Control RNAi, -Auxin, the Control RNAi, +Auxin, and the samp-1(RNAi), +Auxin were n=79, n=94, n=113 nuclei respectively. The darker region of the histogram to the left of the dotted line contains nuclei which were excluded from wCDI analysis because they were too small to experience any strain, i.e. their size was smaller than or equal to the width of the contraction channel employed. All nuclei to the right of the dotted line were included in wCDI analysis. Auxin n Embryos laid (±SD) Embryonic viability (±SD) %* Male progeny (±SD) % Table S1. Strains Psun-1::TIR1IV Psun-1::TIR1 Psun-1::TIR1 IV - + (from L1) + (from L4) lmn-1::AID::V5 I; Psun-1::TIR1 IV - lmn-1::AID::V5 I; Psun-1::TIR1 IV + (from L1) lmn-1::AID::V5 I; Psun-1::TIR1 IV + (from L4) HA::AID::zyg-12 II; Psun-1::TIR1 IV - HA::AID::zyg-12 II; Psun-1::TIR1 IV + (from L1) HA::AID::zyg-12 II; Psun-1::TIR1 IV + (from L4) sun-1::AID::V5 V; Psun-1::TIR1 IV - sun-1::AID::V5 V; Psun-1::TIR1 IV + (from L1) sun-1::AID::V5 V; Psun-1::TIR1 IV + (from L4) emr-1(gk119) I; lem-2::HA::AID II; Psun- 1::TIR1 IV - 6 4 3 7 6 3 3 3 3 6 5 3 6 225.5 ± 18.9 106.1 ± 3.2 228.0 ± 26.5 106.8 ± 3.8 245.7 ± 43.8 108.0 ± 2.3 252.1 ± 30.4 106.4 ± 5.1 83.3 ± 45.8 39.7 ± 44.0 9.6 ± 23.5 0.0 ± 0.0 254.3 ± 76.4 104.1 ± 4.7 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 225.5 ± 53.5 81.3 ± 21.7 31.0 ± 26.5 33.3 ± 35.3 0.0 ± 0.0 0.0 ± 0.0 216.3 ± 22.5 106.4 ± 2.4 0.0 ± 0.0 0.0 ± 0.0 0.1 ± 0.2 0.1 ± 0.2 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.0 ± 0.0 0.7 ± 0.7 0.0 ± 0.0 0.0 ± 0.0 0.1 ± 0.2 * Embryonic viability of >100% reflects the fact that some embryos are overlooked when counting, but adult worms hatched from these embryos are easier to count accurately. Table S1. Quantification of brood size, embryonic viability, and male self-progeny of worm strains with AID alleles generated in this study. Sample size n indicates the number of broods examined. Note that high levels of uncertainty are usually associated with counting deformed eggs resulting from auxin treatment. Table S2. Allele Genotype ie137 lmn-1(ie137[lmn-1::AID::V5]) ie138 zyg-12(ie138[HA::AID::zyg-12]) ie139 sun-1(ie139[sun-1::AID::V5]) ie140 lem-2(ie140[lem-2::HA::AID]) ie203 atg-7(ie203[atg-7::AID::HA]) Table S2. Alleles generated in this study. Information about mutagenesis Internally tagged; generated using dpy-10 Co-CRISPR in ieSi38[sun-1p::TIR1::mRuby::sun-1 3'UTR, Cbr-unc-119(+)] IV generated using dpy-10 Co-CRISPR in ieSi38[sun- 1p::TIR1::mRuby::sun-1 3'UTR, Cbr-unc-119(+)] IV Internally tagged; generated using dpy-10 Co-CRISPR in ieSi38[sun-1p::TIR1::mRuby::sun-1 3'UTR, Cbr-unc-119(+)] IV generated using dpy-10 Co-CRISPR in ieSi38[sun- 1p::TIR1::mRuby::sun-1 3'UTR, Cbr-unc-119(+)] IV generated using dpy-10 Co-CRISPR in ieSi38[sun- 1p::TIR1::mRuby::sun-1 3'UTR, Cbr-unc-119(+)] IV Table S3. Strains C. elegans: N2 Bristol, wild isolate C.elegans: bcIs39(ced-1::GFP) V C. elegans: dnc-1::GFP Source Caenorhabditis Genetics Center Caenorhabditis Genetics Center Zhang, Skop and White (89) C.elegans: syp-1 (me17) bcIs39(ced-1::GFP) V/ nT1 [qIs51] (IV;V) Bhalla et al. (43) C.elegans: ieDf2/mIs11 IV C. elegans: ieSi38[sun-1p::TIR1::mRuby::sun-1 3’ UTR, Cbr-unc-119 (+)] IV C.elegans: ieSi64[gld-1p::TIR1::mRuby::gld-1 3'UTR, Cbr-unc-119(+)] II; unc- 119(ed3) III C.elegans: ieSi65[sun-1p::TIR1::sun-1 3'UTR, Cbr-unc-119(+)] II; unc-119(ed3) III C. elegans: meIs8[pie-1p::GFP::cosa-1, unc-119(+)] II; spo-11(ie59[spo- 11::AID::3xFLAG]), ieSi38[sun-1p::TIR1::mRuby::sun-1 3'UTR, Cbr-unc-119(+)] IV C. elegans: lmn-1(ie137[lmn-1::AID::V5]) I; unc-119 (ed3) III; ieSi38 [Psun- 1::TIR1::mRuby::sun-1 3’UTR, cb-unc-119(+)] IV C. elegans: lmn-1(ie137[lmn-1::AID::V5]) I; unc-119 (ed3) III; ieSi38 [Psun- 1::TIR1::mRuby::sun-1 3’UTR, cb-unc-119(+)] IV; bcIs39 (ced-1::GFP) V C. elegans: lmn-1(ie137[lmn-1::AID::V5]) I; ;ced-4(n1162) III; ieSi38 [Psun- 1::TIR1::mRuby::sun-1 3’UTR, cb-unc-119(+)] IV C. elegans: lmn-1(ie137[lmn-1::AID::V5]) I; ieSi65[sun-1p::TIR1::sun-1 3'UTR, Cbr-unc-119(+)] II; unc-119(ed3) III; ieSi21 (sun-1::mRuby) IV C. elegans: unc-119 (ed3) III; ieSi38 [Psun-1::TIR1::mRuby::sun-1 3’UTR, cb-unc- 119(+)] IV; sun-1(ie139[sun-1::AID::V5]) V C. elegans: lmn-1(ie137[lmn-1::AID::V5]) I; ieSi64[gld-1p::TIR1::mRuby::gld-1 3'UTR, Cbr-unc-119(+)] II; unc-119(ed3) III; sun-1(ie139[sun-1::AID::V5]) V C. elegans: zyg-12(ie138[HA::AID::zyg-12]) II; unc-119 (ed3) III; ieSi38 [Psun- 1::TIR1::mRuby::sun-1 3’UTR, cb-unc-119(+)] IV C. elegans: lmn-1(ie137[lmn-1::AID::V5]) I; zyg-12(ie138[HA::AID::zyg-12]) II; unc- 119 (ed3) III; ieSi38 [Psun-1::TIR1::mRuby::sun-1 3’UTR, cb-unc-119(+)] IV C. elegans: lmn-1(ie137[lmn-1::AID::V5]), emr-1(gk119) I; unc-119 (ed3) III; ieSi38 [Psun-1::TIR1::mRuby::sun-1 3’UTR, cb-unc-119(+)] IV C. elegans: lem-2(ie140[lem-2::HA::AID]) II; ieSi64[gld-1p::TIR1::mRuby::gld-1 3'UTR, Cbr-unc-119(+)] II; unc-119(ed3) III; C. elegans: lmn-1(ie137[lmn-1::AID::V5]) I; lem-2(ie140[lem-2::HA::AID]), ieSi64[gld-1p::TIR1::mRuby::gld-1 3'UTR, Cbr-unc-119(+)] II; unc-119(ed3) III; C. elegans: lmn-1(ie137[lmn-1::AID::V5]), emr-1(gk119) I; unc-119 (ed3) III; ieSi38 [Psun-1::TIR1::mRuby::sun-1 3’UTR, cb-unc-119(+)] IV; sun-1(ie139[sun- 1::AID::V5]) V C. elegans: syp-3 (ok857) I; ieSi64[gld-1p::TIR1::mRuby::gld-1 3'UTR, Cbr-unc- 119(+)] II; unc-119(ed3) III; ieSi19 (mRuby::SYP-3),ojIs9 [zyg-12(all)::GFP + unc- 119(+)] IV; sun-1(ie139[sun-1::AID::V5]) V C. elegans: lmn-1(ie137[lmn-1::AID::V5]) I; ieSi64[gld-1p::TIR1::mRuby::gld-1 3'UTR, Cbr-unc-119(+)] II; unc-119(ed3) III; ieSi19 (mRuby::SYP-3),ojIs9 [zyg- 12(all)::GFP + unc-119(+)] IV; sun-1(ie139[sun-1::AID::V5]) V C. elegans: lmn-1(ie137[lmn-1::AID::V5]) I; ieSi64[gld-1p::TIR1::mRuby::gld-1 3'UTR, Cbr-unc-119(+)] II; unc-119(ed3) III; ieSi19 (mRuby::SYP-3),ojIs9 [zyg- 12(all)::GFP + unc-119(+)] IV; C. elegans: lmn-1(ie137[lmn-1::AID::V5]), emr-1(gk119) I; ieSi64[gld- 1p::TIR1::mRuby::gld-1 3'UTR, Cbr-unc-119(+)] II; unc-119(ed3) III; ieSi19 (mRuby::SYP-3),ojIs9 [zyg-12(all)::GFP + unc-119(+)] IV; C. elegans: lmn-1(ie137[lmn-1::AID::V5]) I; meIs8[pie-1p::GFP::cosa-1, unc- 119(+)] II; unc-119 (ed3) III; ieSi38 [Psun-1::TIR1::mRuby::sun-1 3’UTR, cb-unc- 119(+)] IV C. elegans: lmn-1(ie137[lmn-1::AID::V5]) I; meIs8[pie-1p::GFP::cosa-1, unc- 119(+)] II; unc-119 (ed3) III; spo-11(ie59[spo-11::AID::3xFLAG]), ieSi38 [Psun- 1::TIR1::mRuby::sun-1 3’UTR, cb-unc-119(+)] IV Identifier N2 CA195 (MD701) CA846 CA885 CA998 CA1199 CA1352 CA1353 CA1423 CA1532 CA1561 CA1562 CA1563 CA1564 CA1565 CA1566 CA1567 CA1568 CA1569 CA1570 CA1571 Harper et al. (57); Caenorhabditis Genetics Center Zhang et al. (35); Caenorhabditis Genetics Center Zhang et al. (35); Caenorhabditis Genetics Center Zhang et al. (35); Caenorhabditis Genetics Center Zhang et al. (82); Caenorhabditis Genetics Center This paper This paper This paper This paper This paper This paper This paper This paper This paper This paper This paper This paper This paper CA1572 This paper CA1573 This paper CA1574 This paper CA1575 This paper CA1576 This paper CA1577 40 C. elegans: lmn-1(ie137[lmn-1::AID::V5]) I; unc-119 (ed3) III; spo-11(ie59[spo- 11::AID::3xFLAG]), ieSi38 [Psun-1::TIR1::mRuby::sun-1 3’UTR, cb-unc-119(+)] IV; sun-1(ie139[sun-1::AID::V5]) V C. elegans: zyg-12(ie138[HA::AID::zyg-12]) II; unc-119 (ed3) III; ieSi38 [Psun- 1::TIR1::mRuby::sun-1 3’UTR, cb-unc-119(+)] IV; bcIs39 (ced-1::GFP) V C. elegans: emr-1(gk119) I; lem-2(ie140[lem-2::HA::AID]) II; ieSi64[gld- 1p::TIR1::mRuby::gld-1 3'UTR, Cbr-unc-119(+)] II; unc-119(ed3) III; C. elegans: mjl-1(tm1651) I; hT2 [bli-4(e937) let-?(q782) qIs48] (I,III) C. elegans: unc-119 (ed3) III; atg-7(ie203[atg-7::AID::HA]) IV; ieSi38 [Psun- 1::TIR1::mRuby::sun-1 3’UTR, cb-unc-119(+)] IV This paper CA1578 This paper This paper CA1579 CA1677 National Bioresource Project CA1728 This paper CA1729 Table S3. Genotypes of worm strains generated and used in this study. Table S4. Transgenes crRNAs and repair templates (mostly gBlock) lmn-1::AID::V5 in ie137 HA::AID::zyg-12 in ie138 sun-1::AID::V5 in ie139 lem-2::HA::AID in ie140 atg-7::AID::HA in ie203 dpy-10 5’ – AGAAGTTCGTCACAAGAGAC – 3’; 5’ - TCTGGAAGAAGATCTCGCTTTTGCTCTTCAA CAGCACAAGGGAGAACTTGAAGAAGTTCGcC ACAAGAGgCAGGTCGACATGACAACCTACG GCGGCGGAGGATCCatgcctaaagatccagccaaac ctccggccaaggcacaagttgtgggatggccaccggtgagatc ataccggaagaacgtgatggtttcctgccaaaaatcaagcggtg gcccggaggcggcggcgttcgtgaagggaggatccggaGG AAAGCCAATTCCAAACCCACTTCTTGGACTC GACTCCACCGCCAAGCAGATTAATGATGAGT ATCAATCTAAGCTT -3’ 5’ – GAATCTGAGTCGTCAGACAA – 3’; 5’ – aaaatctatcaatttcttttttttcagaacaaaatcatgTACCCAT ACGATGTTCCAGATTACGCTggaggatccggaatg cctaaagatccagccaaacctccggccaaggcacaagttgtgg gatggccaccggtgagatcataccggaagaacgtgatggtttcct gccaaaaatcaagcggtggcccggaggcggcggcgttcgtga agGGCGGCGGAGGATCCGGAGGAGGAGGC AGTGGAGGCGGCGGTTCTGGCGGTGGCGG CTCAGGCGGAGGTGGATCGTTAGACCTGAC AAACAAAGAGTCCGAGTCTTCAGACAACGGA AATAGCAAGTACGAAGATTCCATAGACGGAC GA – 3’ 5’ – GCTGGAATATCGCATTCGCA – 3’; 5’ – TACAAGGAGCATTTTAGCTACAAAGAAATCA CTTCGATGAAGAAGGAAATGTGGTATGACTG GCTGGAATATCGCATcCGtGGCGGCGGAGGA TCCatgcctaaagatccagccaaacctccggccaaggcaca agttgtgggatggccaccggtgagatcataccggaagaacgtg atggtttcctgccaaaaatcaagcggtggcccggaggcggcgg cgttcgtgaagggaggatccggaGGAAAGCCAATTCC AAACCCACTTCTTGGACTCGACTCCACCATG GTTCGGCGTCGTTTTGTTCCAACGTGGGCC CAGTTTAAACGTACTCTT – 3’ 5’ – TGTGCCGTGTGGAAGTGGAT – 3’; 5’ – CTACCGATGTTCTTGTGCTTCCGTCTGGAAA TGAGTGcGCtGTcTGGAAaTGGATCGGAAATC AGTCTCAGAAGAGATGGTACCCATACGATGT TCCAGATTACGCTggaggatccggaatgcctaaagatc cagccaaacctccggccaaggcacaagttgtgggatggccacc ggtgagatcataccggaagaacgtgatggtttcctgccaaaaat caagcggtggcccggaggcggcggcgttcgtgaagTAGatc attgttttgctgtataatttttcgatttt– 3’ 5’ – GATGATGAAGATTTCTGAat – 3’; 5’ – CAGAACTCTGTTAATGCTATTGATATCGATTT TGAGGATGATGAAGATTTCGGCGGCGGAGG ATCCGGAGGAGGAGGCAGTGGAGGCGGCG GTTCTGGCGGTGGCGGCTCAatgcctaaagatcca gccaaacctccggccaaggcacaagttgtgggatggccaccg gtgagatcataccggaagaacgtgatggtttcctgccaaaaatc aagcggtggcccggaggcggcggcgttcgtgaagggaggatc cggaTACCCATACGATGTTCCAGATTACGCTT GAattggtcgcctcaaatttttaccttttctgtataattg – 3’ 5’ – GCTACCATAGGCACCACGAG – 3’; 5’ – ATACGGCAAGATGAGAATGACTGGAAACCGT ACCGCATGCGGTGCCTATGGTAGCGGAGCT TCACATGGCTTCAGA – 3’ (ssDNA repair template) Genotyping primer names oCL41 (F) Primer sequences 5' - AAA GCA GAA CAT CAC TCT TCG TGA CAC CGT AGA AG -3’; Fragment sizes oCL42 (R) 5' - TTT GAT GCA AAT TGT TCT TGA ACT GAG CAC GCA TCT C -3’ WT, 277bp; inserted, 481bp oCL95 (F) 5’ – TTGTAAACTCTACCAGCC T -3’; oCL96 (R) 5’ – TCAGAGGTAGTTTAGTGG C -3’; WT, 401bp; inserted, 650bp oCL101 (F) 5'- CTT CGA TGA AGA AGG AAA TGT GGT ATG ACT GGC -3'; oCL102 (R) 5'- CTC TTC GAT TGC CGA CTC TTT CCA TCC TTT -3'; WT, 456bp; inserted, 660bp oCL137 (F) 5’ – GAAGCTCTACGAGCTCAT C – 3’; oCL138 (R) 5’ – gtcattgtgataccttaggc – 3’; WT, 379bp; inserted, 553bp Atg-7-Fw (F) 5’ – GTCGTCTCGAAGAAGTCA C – 3’; WT, 186bp; inserted, 420bp Atg-7-Rw (R) 5’ – ggaggcaaaatagaatcac – 3’; N.A. N.A. N.A. Table S4. Sequences of crRNAs, repair templates, and DNA primers used to genotype edited progeny. Table S5. Target gene GenePairs Name Plate dnc-1 dlc-1 lis-1 dhc-1 dli-1 dylt-1 lem-2 samp-1 lmn-1 ZK593.5 T26A5.9 T03F6.5 T21E12.4 C39E9.14 F13G3.4 W01G7.5 T24F1.2 DY3.2 111 75 88 4 116 11 63 58 14 Table S5. RNAi clones used in this study. Well B12 H6 F4 H2 A3 E6 B2 A6 D12 Table S6. Channel A Channel B 5.00 x 10-5 ∆𝐼𝐼𝑠𝑠/𝐼𝐼 4.23 x 10-5 0.768 x 10-5 0.719 x 10-5 𝜎𝜎𝐼𝐼 De 10.2 11.1 n 25 21 Table S6. Measuring channel effective diameters. Polystyrene microspheres (Sigma-Aldrich) with a diameter of 2 µm ± 0.05 µm suspended in the 1x Wash Buffer were measured with the mechano-NPS platform. One platform consisted of two independent microfluidic channels for measurement (Channel A and B), which acted as two independent devices. Polystyrene beads were measured in both devices to calculate their effective diameters. corresponds to the average ratio of current pulse amplitude to baseline current produced as the polystyrene beads transited the sizing segment and diameter, as calculated from Equation S1 using beads. n corresponds to the number of beads measured in each device. corresponds to the standard deviation. De is the effective and the known diameter of the polystyrene ∆𝑰𝑰𝒔𝒔/𝑰𝑰 𝝈𝝈𝑰𝑰 ∆𝑰𝑰𝒔𝒔/𝑰𝑰 Table S7. ( m/msec) Channel A Channel B 10.84 flow 10.15 U µ m/msec) ( 0.80 𝜎𝜎𝑈𝑈 1.23 µ n 109 68 Table S7. Measuring fluid velocity. Uflow is the approximate fluid velocity ( m/msec), its standard deviation ( m/msec), and n is the number of nuclei measured in each device (Channel A and B). The Uflow in a device is the average nuclear velocity in the sizing segment of 𝜎𝜎𝑈𝑈 is all nuclei measured in that device (Equation S3). One platform consisted of two independent microfluidic channels for measurement, Channel A and B, which acted as independent devices, therefore Uflow was calculated for each device. Three replicas of both Channel A and B were used to calculate the Uflow values reported. Only nuclei whose wCDI was measured (nuclei that underwent > 0% strain) were included in the Uflow calculation for each device. µ µ Equations Particle size was calculated using, 3 𝑑𝑑 is the magnitude of the current drop produced by the particle as it transits the sizing 𝐷𝐷𝑒𝑒� where 1−0.8� segment (Fig. 5B), I is the baseline current, d is the diameter of the particle, L is the overall channel length, and De is the effective diameter (Table S6) (96). ∆𝐼𝐼𝑠𝑠 𝐿𝐿 � � (Eq. S1) ∆𝐼𝐼𝑠𝑠 𝐼𝐼 = 3 𝑑𝑑 2 𝐷𝐷𝑒𝑒 1 The whole-cell deformability index (wCDI) was calculated using, (Eq. S2) 𝑣𝑣𝑐𝑐 where dn is the nuclear diameter, h is the channel height, vc is the velocity of the nucleus in the 𝑈𝑈𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 � contraction segment, and Uflow is the fluid velocity (67). The contraction segment velocity is , where Lc is the contraction segment length and tc is the nuclear transit defined as time in the contraction segment. The fluid velocity, Uflow, can be approximated as the average nuclear velocity in the sizing segment, 𝑣𝑣𝑐𝑐 = 𝐿𝐿𝑐𝑐 𝑡𝑡𝑐𝑐⁄ 𝑤𝑤𝑤𝑤𝐷𝐷𝐼𝐼 = 𝑑𝑑𝑛𝑛 ℎ � (Eq. S3) ∑ 𝑣𝑣𝑠𝑠 𝑈𝑈𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓~𝑣𝑣𝑠𝑠_𝑎𝑎𝑣𝑣𝑎𝑎 = where vs is the nuclear velocity in the sizing segment and n is the number of nuclei measured. The sizing segment velocity is defined as , where Ls is the sizing segment length and ts is the nuclear transit time through the sizing segment. An average of all nuclei’s vs is used to 𝑣𝑣𝑠𝑠 = 𝐿𝐿𝑠𝑠 𝑡𝑡𝑠𝑠⁄ calculate Uflow to take into account variations in the fluid velocity due to off-axis hydrodynamic effects (97). For the experiments reported here, we calculated Uflow for each device (Channel A and B) utilized (Table S7). 𝑛𝑛 Movie S1. (separate file) Nuclear envelope dynamics marked by SUN-1::mRuby in late prophase after LMN-1 depletion. Two representative time-lapse recordings of SUN-1::mRuby at the NE of meiotic nuclei in late meiotic prophase after LMN-1 depletion are shown. Arrow points to a collapsing nucleus. Note many meiotic nuclei with asymmetrically distributed SUN-1::mRuby at the NE. Time stamp is hr:min:sec. Scale bars, 10 µm. Movie S2. (separate file) Tracking SUN-1::mRuby patches in early meiosis. An example of drift-corrected time-lapse recordings of SUN-1::mRuby patch movement on the NE of transition zone nuclei, using reference frame followed by 3D particle tracking in Imaris. Time stamp is hr:min:sec. Scale bar, 5 µm. Movie S3. (separate file) LMN-1 depletion changes the mobility of SUN-1::mRuby patches throughout prophase. Side-by-side comparison of time-lapse recordings of the movement of SUN-1::mRuby patches or foci during different stages of meiosis, with or without LMN-1 depletion. Time stamp is hr:min:sec. Scale bars, 5 µm. Movie S4. (separate file) Dynamics of diplotene nuclear collapse. Time-lapse recording of a dual-color labeled oocyte nucleus during diplotene collapse. Green, ZYG-12::GFP; Magenta, mRuby::SYP-3. Scale bar, 5 µm. Movie S5. (separate file) Contact between NE and SC happens during diplotene nuclear collapse. Time-lapse recording of another dual-color labeled diplotene nucleus during collapse. The frame showing initial contact between NE and SC is annotated. Green, ZYG-12::GFP; Magenta, mRuby::SYP- 3. Time stamp is hr:min:sec. Scale bar, 2 µm. Data S1. (separate file) Statistical source data. Additional output of statistical tests performed in this study (with related figures or supplementary figure numbers). REFERENCES AND NOTES 1. N. Bhalla, A. F. Dernburg, Prelude to a division. 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10.1126_sciadv.ade1817.pdf	null	null	Supplementary Materials for Starvation-induced changes in somatic insulin/IGF-1R signaling drive metabolic programming across generations Merly C. Vogt et al. Corresponding author: Merly C. Vogt, merly.vogt@helmholtz-munich.de Sci. Adv. 9, eade1817 (2023) DOI: 10.1126/sciadv.ade1817 The PDF file includes: Figs. S1 to S7 Legends for data S1 to S12 Other Supplementary Material for this manuscript includes the following: Data S1 to S12 Fig. S1. Inter- and transgenerational increase in exploration can be observed after one round of ancestral starvation, related to Fig. 1 (A) Schematic overview of exploratory behavior assay. (B) Exploratory behavior was assessed over a 16h period starting with animals at the young L4 stage. Data was obtained from 2-4 independent biological replicates (indicated by different colors of each data point) with n=15-20 animals/replicate. Data is presented as mean +/-SEM. Significance was determined by Mann Whitney U test. Fig. S2, Early life starvation results in differential expression of genes involved in lipid metabolism transgenerationally, but does not increase lifespan, related to Fig. 2 (A) Transcriptome analysis was performed across 3 independent biological replicates with ~10.000 L1 animals/replicate. Samples were collected under fed and starved conditions. Adjusted p-values were determined by Wald test with subsequent Benjamini and Hochberg correction using the DESeq2 package (97). (B) In our hands, early life starvation does not increase lifespan transgenerationally in either P0L1starved+3fed (n control= 139; n 1xAS=130; data combined from two independent biological replicates) or F4L1starved+3fed (n Control=381; n 5xAS=399; data combined from four independent biological replicates) as previously reported (42, 43). Fig. S3, L1 starvation affects fertility and transcriptome of individuals within a population to different extents, related to Fig. 4 (A) Transcriptome analysis was performed across 3 independent biological replicates with ~100 manually picked animals/group at day 1 of adulthood under fed conditions. Animals were separated into fertile and sterile F4L1starved. Differential gene expression between control, fertile and sterile F4L1starved animals and overlap between groups is displayed as Venn-Diagram (not drawn to scale). (B) Downstream DAF-16/FoxO target genes that showed an overall relative upregulation in fertile F4L1starved animals compared to control animals (Fig. 3B), showed an even further upregulation in sterile F4L1starved compared to fertile F4L1starved animals. Values in heatmap were z-scored normalized and plotted using heatmap3 in RStudio. Each row represents a single gene and each column represents a single RNA-seq replicate. Blue=relative downregulation, red=relative upregulation in sterile F4L1starved animals. Adjusted p-values were determined by Wald test with subsequent Benjamini and Hochberg correction using the DESeq2 package (97). Significant enrichment was determined by hypergeometric distribution. Fig. S4, DAF-16/FoxO does not act directly in the germline to mediate metabolic programming across generations, related to Fig. 5 (A) Acute starvation in control animals results in overall upregulation of class 1 IIS downstream target genes. Values of differentially expressed genes (adj.p<0.1) between fed and starved L1 control animals were z-score normalized and plotted using heatmap3 using RStudio. Each row represents a single gene and each column represents a single RNA-Seq replicate. Blue=relative downregulation, red=relative upregulation in starved L1 animals. Imaging of fed and acutely starved L1 animals with an endogenously-tagged mNeonGreen DAF-16/FoxO allele (ot853) confirms nuclear accumulation, and thus activation, of DAF-16/FoxO::mNeonGreen across the animal, including germline precursor cells, epidermis, neurons and intestines upon starvation. Scale bar =15µm. (B) DAF-16/FoxO::mNG also localizes (peri)nuclearly in the mitotic germline upon acute starvation in control fed adult animals. However, the severity and subcellular distribution of DAF-16/FoxO::mNG is different in acutely starved adult compared to L1 starved, currently fed adult animals. Scale bar = 15 µm. (C) Nearly 100% of adult fed sterile F4L1starved animals display nuclear accumulation of DAF-16/FoxO::mNG in mitotic germline. (D) Relative nuclear accumulation of DAF-16/FoxO::mNG in mitotic germline is still significantly increased in immediate progeny, but lost in subsequent descendants of F4L1starved. Data was collected from n=10 animals/group and 3 biological replicates and is presented as mean+/-SEM. Significance was determined by Chi-square test. Color indicates sub-cellular localization of DAF-16/FoxO in mitotic germline: White= cytoplasm; Green= Nucleus. Control and ancestrally starved animals were imaged in presence of acute auxin unlike animals displayed in Fig. 4A and S4B, which could explain slight increase in nuclear accumulation in mitotic germline even in control animals. (E) Impaired fertility of F4L1starved is not mediated across generations to F4L1starved +1fed animals. (F) One round of L1 starvation is sufficient to impair fertility later in life. Data is displayed as mean +/- SEM and was collected from n=16/group across two biological replicates. Significance was determined by Mann Whitney U test. (G) One round of L1 starvation is sufficient to induce DAF-16/FoxO nuclear accumulation in mitotic germline in adult animals. Data was collected from n=10 animals/group and 3 biological replicates and is presented as mean+/-SEM. Significance was determined by Chi-square test. (H-K) Results for exploratory behavior and oxidative stress resistance in F4L1starved +1fed upon auxin-induced DAF-16/FoxO depletion specifically in the germline and only during indicated generation and time-points (same outline as displayed in Fig. 5C-5F for F4L1starved +3fed). Data for exploratory behavior was collected from n=20/group across 3 biological replicates per condition and displayed as mean +/-SEM. Statistical significance was determined using One Way ANOVA with posthoc Tukey. For oxidative stress resistance, the combined data of 3 independent biological replicates per group and condition with n=70-103/replicate is displayed. Statistical significance was determined by Log-rank (Mantel-Cox test). Fig. S5, Starvation-induced increase of DAF-16/FoxO activity in somatic tissues causes metabolic programming across generation, related to Fig. 6 (A-D) AID/TIR1 system efficiently and specifically depletes DAF-16/FoxO::mNG::AID from tissues of interest. DAF-16/FoxO::mNG::AID is efficiently depleted from (A) germline precursor cells in animals expressing TIR1 under the control of a germline- specific promoter (ot853;ieSi38), (B) pan-somatically in animals expressing TIR1 under control of a pan-somatic promoter (ot853; ieSi57), (C) the intestine in animals expressing TIR1 under control of intestine-specific promoter (ot853, ieSi61), and (D) pan-neuronally in animals expressing TIR1 under the control of a panneuronal-specific promoter (ot853; reSi7) in the presence of auxin only. Scale bar =15 µm. Animals are imaged at L2 stage under fed conditions. (E) Auxin exposure does not efficiently deplete DAF- 16/FoxO::mNG::AID in embryos in our paradigm. Displayed are embryos expressing DAF-16/FoxO::mNG::AID and pan-somatic TIR1 (ot853; ieSi57) at various stages in the absence or presence of auxin. Scale bar =15 µm. F-H) Results for oxidative stress resistance upon pan-somatic depletion of DAF-16/FoxO during distinct time points are displayed as combined data from three independent biological replicates with n=80- 114/replicate are displayed. Statistical significance was determined by Log-rank (Mantel- Cox test). (F) Acute pan-somatic DAF-16/FoxO depletion decreased oxidative stress in control and F4L1starved +1fed animals. (G) Pan-somatic DAF-16/FoxO depletion during L1 starvation in the parental generation reverted decreased oxidative stress resistance in F4L1starved +1fed (ot853; ieSi57 control vs ot853; ieSi57 F4L1starved +1fed animals not significant). (H) Pan-somatic DAF-16/FoxO depletion during recovery phase in the parental generation reverted decreased oxidative stress resistance in F4L1starved +1fed (ot853; ieSi57 control vs ot853; ieSi57 F4L1starved +1fed animals not significant). (I) Acute pan-somatic depletion of DAF-16/FoxO was insufficient to increase exploration in either control or ancestrally starved animals. (J) Pan-somatic DAF-16/FoxO depletion during L1 starvation or (K) recovery phase in parental generation does not revert increased exploratory behavior inter- (F4L1starved +1fed) or transgenerationally (F4L1starved +3fed). Data was obtained from n=20/group and 3 independent biological replicates and is displayed as mean+/-SEM. Statistical significance was determined using One Way ANOVA with posthoc Tukey. Fig. S6, Intestinal TIR1- and DAF-16/FoxO::mNG::AID expressing strain displays slight increase in oxidative stress resistance even in absence of auxin, related to Fig. 7 (A) Oxidative stress resistance is significantly increased in animals expressing DAF- 16/FoxO::mNG::AID and intestine-specific TIR1 (ot853;ieSi61) compared to animals expressing DAF-16/FoxO::mNG::AID (ot853) animals even in the absence of auxin. Data is displayed as combined data from three independent biological replicates with n=80-100/replicate. Statistical significance was determined by Log-rank (Mantel-Cox test). Fig. S7, A large subset of genes encoding for critical factors involved in the biogenesis and function of small RNAs are “class 2” downstream targets of IIS, related to Fig. 8 (A) Transcriptome analysis between control and fertile F4L1starved revealed that genes encoding for critical factors involved in biogenesis and function of small RNAs displayed relative downregulation in fertile F4L1starved compared to control animals at day 1 of adulthood under fed conditions. Values of genes between fed and starved L1 control animals were z-score normalized and plotted using heatmap3 using RStudio. Each row represents a single gene and each column represents a single RNA-Seq replicate. Blue=relative downregulation, red=relative upregulation. Adjusted p-values were determined by Wald test with subsequent Benjamini and Hochberg correction using the DESeq2 package (97). Data S1. (Separate file) Data S1: Transcriptome Analysis Control vs F4+3 – All Genes and Conditions Related to Fig. 2 Data S2: Significantly Expressed Genes Control vs F4+3 under fed conditions Related to Fig. 2A, 2B Data S3: Significantly Expressed Genes Control vs F4+3 under starved conditions Related to Fig. 2A, 2B Data S4: Enrichment Differentially Expressed Genes Control vs F4+3 Fed for class1 IIS genes Related to Fig. 2E Data S5: Enrichment Differentially Expressed Genes Control vs F4+3 Fed for class2 IIS genes Related to Fig. 2E Data S6: Transcriptome Analysis Parental Control vs F4 – All Genes and Groups Related to Fig. 4 Data S7: Differentially expressed Genes Parental Control vs F4 fertile Related to Fig. 4C Data S8: Enrichment Analysis Parental Control vs F4 fertile– DAF16/FoxO class1 Related to Fig. 4D Data S9: Enrichment Analysis Parental Control vs F4 fertile– TGFb Related to Fig. 4D Data S10: Enrichment Analysis Parental Control vs F4 fertile– 5HT_tph1mut Related to Fig. 4D Data S11: Enrichment Analysis Parental Control vs F4 fertile– AMPK_aak2mutdown Related to Fig. 4D Data S12: Enrichment Analysis F4 fertile vs F4 sterile – DAF16/FoxO class1 Related to Fig. S3	Could not heal snippet
10.1073_pnas.2301985120.pdf	Data, Materials, and Software Availability. Cryo-EM density maps of the KCNQ1 channel with the voltage sensor in the up, intermediate, and down conformation have been deposited in the electron microscopy data bank under accession codes EMD-40508 (70), EMD-40509 (71), and EMD-40510 (72), respectively. Atomic coordinates of the KCNQ1 channel with the voltage sen- sor in the up, intermediate, and down conformation have been deposited in the protein data bank under accession codes 8SIK (73), 8SIM (74), and 8SIN (75), respectively.	null	RESEARCH ARTICLE \| BIOCHEMISTRY OPEN ACCESS The membrane electric field regulates the PIP2-binding site to gate the KCNQ1 channel Venkata Shiva Mandalaa,b and Roderick MacKinnona,b,1 Edited by David Clapham, HHMI, Ashburn, VA; received February 3, 2023; accepted April 13, 2023 Voltage-dependent ion channels underlie the propagation of action potentials and other forms of electrical activity in cells. In these proteins, voltage sensor domains (VSDs) regulate opening and closing of the pore through the displacement of their positive-charged S4 helix in response to the membrane voltage. The movement of S4 at hyperpolarizing membrane voltages in some channels is thought to directly clamp the pore shut through the S4–S5 linker helix. The KCNQ1 channel (also known as Kv7.1), which is important for heart rhythm, is regulated not only by membrane voltage but also by the signaling lipid phosphatidylinositol 4,5-bisphosphate (PIP2). KCNQ1 requires PIP2 to open and to couple the movement of S4 in the VSD to the pore. To understand the mechanism of this voltage regulation, we use cryogenic electron microscopy to visualize the movement of S4 in the human KCNQ1 channel in lipid membrane vesicles with a voltage difference across the membrane, i.e., an applied electric field in the membrane. Hyperpolarizing voltages displace S4 in such a manner as to sterically occlude the PIP2-binding site. Thus, in KCNQ1, the voltage sensor acts primarily as a regulator of PIP2 binding. The voltage sensors’ influence on the channel’s gate is indirect through the reaction sequence: voltage sensor movement → alter PIP2 ligand affinity → alter pore opening. KCNQ1 channel \| Kv7.1 channel \| voltage sensor \| cryo-EM \| membrane potential Voltage sensor domains (VSDs) are integral membrane proteins that undergo conforma- tional changes in response to voltage differences across the cell membrane. These domains regulate pore opening and closing in voltage-dependent ion channels (1) and enzymatic activity in voltage-dependent phosphatases (2). Voltage sensors have a conserved structure consisting of four transmembrane (TM) helices (S1 to S4) that form a helical bundle (3–6). The fourth helix, S4, contains a repeated sequence of positive-charged amino acids (typically arginines), every third residue that confers sensitivity to voltage. Inside the lipid bilayer, a gating charge transfer center, composed of aspartate, glutamate, and phenylala- nine residues, stabilizes the arginines one at a time as they traverse the hydrophobic core of the membrane (7, 8). The movement of S4 in response to the TM voltage difference is ultimately responsible for the regulation of protein activity. This mechanism underlies the action potential in neurons (1, 9) and the initiation of muscle contraction (4, 10), among other cellular processes. While the structure of a VSD is highly conserved across all voltage-dependent ion channels, there are two configurations for VSD attachment to the pore of the channel (formed by the S5 and S6 helices). In the so-called domain-swapped channels (Fig. 1A), which include voltage-dependent K+ (Kv) channels 1 to 9, Na+ (Nav) channels, Ca2+ (Cav) channels, and most transient receptor potential channels, the VSD of one subunit interacts with the pore domain of an adjacent subunit, connected through a long interfacial helix— the S4–S5 linker (7, 11–16). Meanwhile, in nondomain-swapped channels (Fig. 1A) such as Kv10-12, Slo1, and hyperpolarization-activated cyclic nucleotide-gated (HCN) chan- nels, the VSD contacts the pore domain of the same subunit through a short S4–S5 loop (17–19). This naturally raises the question: how do the conserved VSDs mediate voltage-dependent gating in these two sets of channels with different structures? We have shown recently that in a nondomain-swapped channel Eag (Kv10.1) (20), the S4 helix on the cytoplasmic side forms an interfacial helix in the hyperpolarized (i.e., negative voltage inside) conformation, which functions as a constrictive cuff around the pore, prevent- ing it from opening. Domain-swapped channels already have an interfacial S4–S5 linker helix that contacts the S6 helix in the depolarized (i.e., no applied or positive inside voltage) con- formation (Fig. 1A) (7, 16), suggesting a gating mechanism that is distinct from that in nondomain-swapped channels. In domain-swapped channels, it has been proposed that the displacement of S4 in response to a hyperpolarizing potential moves the S4–S5 linker helix into a position that clamps the pore shut by pushing down on the S6 helical bundle (7, 16). Structures of domain-swapped channels in detergent micelles at zero mV with chemical Significance Voltage-gated ion channels underlie electrical signaling in cells. The structures and functions of voltage-dependent K+, Na+, and Ca2+ and transient receptor potential ion channels have been studied extensively since their discovery. Despite these efforts, it is still not well understood how the voltage sensors in these different ion channels change their conformation in response to membrane voltage changes, and how these movements regulate the opening or closing of the channel’s gate. This study presents structures of the human KCNQ1 (Kv7.1) voltage– dependent and phosphatidylinositol 4,5-bisphosphate (PIP2)- dependent K+ channel in electrically polarized lipid vesicles using cryogenic electron microscopy, showing how the voltage sensors influence gating indirectly by regulating the ability of PIP2 to bind to the channel. Author affiliations: aLaboratory of Molecular Neuro- biology and Biophysics, The Rockefeller University, New York, NY 10065; and bHHMI, The Rockefeller University, New York, NY 10065 Author contributions: V.S.M. and R.M. designed research; V.S.M. performed research; V.S.M. and R.M. analyzed data; and V.S.M. and R.M. wrote the paper. The authors declare no competing interest. This article is a PNAS Direct Submission. Copyright © 2023 the Author(s). Published by PNAS. This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY). 1To whom correspondence may be addressed. Email: mackinn@rockefeller.edu. This article contains supporting information online at https://www.pnas.org/lookup/suppl/doi:10.1073/pnas. 2301985120/-/DCSupplemental. Published May 16, 2023. PNAS 2023 Vol. 120 No. 21 e2301985120 https://doi.org/10.1073/pnas.2301985120 1 of 12 cross-links, toxins, mutations, and metal affinity bridges thought to mimic the hyperpolarized condition are supportive of this mecha- nism (21–27). Here, we present a cryo-EM analysis of the domain-swapped human KCNQ1 (Kv7.1) channel in lipid mem- brane vesicles with a hyperpolarizing voltage generated across the membrane, illustrating—at least in some domain-swapped chan- nels—a different gating mechanism than previously thought. Results The Rationale for Polarizing KCNQ1. The KCNQ1 Kv channel, also known as Kv7.1, is the pore-forming subunit of the slow delayed rectifier potassium channel (IKS) (28, 29) that plays an important role in the repolarization phase of cardiac action potentials (30, 31). Mutations in the kcnq1 gene are associated with several congenital cardiac diseases, including long and short QT syndromes as well as familial atrial fibrillation (32). Importantly, KCNQ1 and other Kv7 members are regulated both by membrane voltage and the signaling lipid phosphatidylinositol 4,5-bisphosphate (PIP2) (33–36). The voltage sensors close the channel at hyperpolarizing membrane voltages, while PIP2 is required for the channel to open. When PIP2 is depleted in the membrane, such as when phospholipase C is activated through stimulation of Gq-coupled receptors (34, 37), the voltage sensors undergo voltage-dependent conformational changes, but the pore does not open at depolarizing voltages (38, 39). PIP2 is thus thought to be required for the coupling of voltage sensor movements to pore opening. In other words, KCNQ1 is thought to act as a ligand-regulated voltage-dependent channel, where the binding of PIP2 allows the channel to be gated by the membrane potential. In the absence of PIP2 at zero mV, we would expect a closed pore and depolarized voltage sensors, which is exactly the KCNQ1 struc- ture observed in detergent micelles (12, 40). If we now apply a hyperpolarizing voltage across the membrane, the pore should remain closed, but the voltage sensors should adopt the polarized conformation. Because in this circumstance the voltage sensors do not have to perform mechanical work to close the pore, it should be easier to move the voltage sensors when the pore is already closed (due to the absence of PIP2). We note that we exclude the KCNE beta subunits (41) in this study because they are known to modify the voltage sensitivity of KCNQ1 and thus could trap the voltage sensor in a specific conformation (for instance, KCNE3 appears to stabilize the depolarized conformation) (40, 42). KCNQ1 Reconstitution and Polarization. The human KCNQ1 channel was purified as a complex with the structurally obligate subunit calmodulin (40, 43) in the presence of Ca2+ and reconstituted into liposomes composed of 90: 5: 5 1-palmitoyl-2- Fig. 1. Structures of voltage-dependent ion channels and the preparation of unpolarized and polarized KCNQ1 (Kv7.1) proteoliposomes. (A) The two domain arrangements in voltage-dependent ion channels. Channels are shown with α-helix cylinders and one of the four subunits colored blue and the S4–S5 linker colored red. In domain-swapped channels (Left), the VSD of one subunit interacts with the pore domain of an adjacent subunit and is connected to the pore domain through a long interfacial helix––the S4–S5 linker (red). The structure of Kv1.2 paddle chimera (PDB ID: 2R9R) (7) is shown as an example. In nondomain-swapped channels (Right), the VSD interacts with the pore domain of the same subunit through a short S4–S5 loop. The Eag channel (PDB ID: 8EOW) (20) is shown here as an example. (B) Schematic of the protocol used to obtain polarized vesicles for cryo-EM analysis. Kv7.1 is reconstituted into liposomes with symmetrical KCl, and valinomycin (val.) is added to mediate K+-flux. The external KCl is exchanged for NaCl using a buffer-exchange column. Potassium efflux through valinomycin generates a potential difference across the membrane such that the inside of the vesicle is negative with respect to the outside. Unpolarized and polarized vesicles containing Kv7.1 were frozen on a holey carbon grid for structure determination. (C) Two-dimensional class-averages of membrane-embedded Kv7.1 in unpolarized (Left) and polarized (Right) vesicles from cryo-EM. (D) Liposome-based flux assay to test polarization of vesicles. Recordings (n = 5, mean ± SD) were made using empty vesicles (black) or vesicles with Kv7.1 (blue). Addition of the H+-ionophore CCCP allows entry of protons, which is detected by quenching of the fluorescent reporter ACMA. Protons enter when the K+ ionophore valinomycin is added. 2 of 12 https://doi.org/10.1073/pnas.2301985120 pnas.org oleoyl-sn-glycero-3-phosphocholine (POPC) to 1-palmitoyl-2- oleoyl- sn-glycero-3-phosphoglycerol (POPG) to cholesterol [wt: wt: wt] with 300 mM KCl (SI Appendix, Fig. S1 A and B). Following our work on Eag (20), valinomycin was added to the vesicles and the extravesicular solution was exchanged to 300 mM NaCl using a buffer-exchange column (Fig. 1B). The valinomycin-mediated K+-efflux generates a membrane voltage with an upper limit of about −145 mV, such that the inside is negative with respect to the outside. These polarized vesicles were immediately applied to a holey carbon grid and frozen for cryogenic electron microscopy (cryo-EM) analysis (Fig. 1C and SI Appendix, Fig. S1C). Grids for an unpolarized control lacking the buffer exchange step (i.e., with symmetric 300 mM KCl) were also prepared. The permeability of these KCNQ1-containing liposomes to small ions was tested using a liposome flux assay (Fig. 1D) (44). The vesicles prepared in 300 mM KCl (without valinomycin) were diluted into a buffer with a fluorescent dye, 9-amino-6-chloro-2- methoxyacridine (ACMA), and isotonic NaCl to generate a K+ gradient. The proton ionophore carbonyl cyanide m-chlorophenylhydrazone (CCCP) was added to allow H+ influx, which leads to quenched ACMA fluorescence. Without valinomycin, no flux was detected, consistent with the channel being tightly closed under these con- ditions. Subsequent addition of the K+-selective ionophore valino- mycin gave rise to rapid quenching of ACMA in both KCNQ1 proteoliposomes and in control liposomes without protein (Fig. 1D), indicating that the valinomycin-generated membrane potential is stable for at least a few minutes. Identification of Three Structural Classes in the Polarized Dataset. We collected large cryo-EM datasets on polarized and unpolarized vesicles using the same microscope, and the structures of KCNQ1 in both were determined using single- particle analysis (SI Appendix, Figs. S2–S4 and Table S1). As we found for the Kv channel Eag, channels were reconstituted exclusively in an inside-in orientation and thus, when polarized, experience hyperpolarizing (i.e., negative inside) potentials under the applied electric field (Fig. 1C). After two rounds of three- dimensional (3D) classification to select for the best subset of particles in each dataset, we carried out 3D classification without alignment with a mask on the TM domain while imposing C4 symmetry (SI Appendix, Fig. S2). The unpolarized dataset showed little heterogeneity: 88% of the particles were in a homogeneously “up” (detailed below) conformation that closely resembled the detergent structure of KCNQ1, while the remaining particles were in an indeterminate state. Meanwhile, the polarized dataset was noticeably more heterogeneous, with only 34% of particles in a homogeneously up conformation, 19% consistent with an “intermediate” conformation, 10% consistent with a “down” conformation, and the remaining indeterminate. Classification without symmetry on a symmetry-expanded particle set showed that these 34% of particles had all four voltage sensors in an up conformation—suggesting that these channels are in vesicles that have lost the ion gradient, and likely do not reflect the distribution of voltage sensor states under the applied potential. Similar classification on the remaining 66% of particles showed classes with voltage sensors in different states, indicating that the higher proportion of indeterminate particles in the polarized dataset is due to a mixture of conformations. In summary, the observation of distinct structural classes for the voltage sensor in the polarized but not the unpolarized dataset indicates that these conformational changes are likely caused by the application of an electric field (the alternative being due to Na+ in the external solution). From the unpolarized dataset, the best up structure (C4-symmetric; SI Appendix, Fig. S3) had an overall resolution of 2.9 Å (Fig. 2A and SI Appendix, Fig. S4). We solved three structures from the polarized dataset (SI Appendix, Figs. S3 and S4): C4-symmetric up and inter- mediate structures with overall resolutions of 3.4 Å and 6.2 Å, respec- tively, and a C1-symmetric down structure from a symmetry-expanded particle set with an overall resolution of 6.8 Å. The up structures from the unpolarized and polarized datasets are nearly identical (SI Appendix, Fig. S5 A and B), so we focus on the better-resolved former structure. We note that one interesting difference between the two up structures regards the occupancy of K+ ions in the selectivity filter (SI Appendix, Fig. S5 C and D). In the polarized sample, due to the low extravesic- ular concentration of K+, density is only visible at the first and third positions in the selectivity filter, while density is present at all four positions in the unpolarized sample. Similar differences were observed in our previous study on Eag (20) and are qualitatively consistent with crystal structures of KcsA solved under symmetrical high and low K+ concentrations (45). The Up Conformation of the Voltage Sensor. The up map is best defined in the TM domain, with local resolution estimates of ~2.4 to 2.8 Å for much of S1 through S6 (SI Appendix, Fig. S4D). Density for individual hydrogen-bonded water molecules is visible in the voltage sensor (SI Appendix, Fig. S6A). These water molecules do not represent a bulk water-filled crevice, but nevertheless undoubtedly contribute to the stabilization of positive-charged residues (20). Tightly bound phospholipid and sterol molecules (SI Appendix, Fig. S6B) are also visible at both the outer and inner leaflets of the membrane. These features are not discussed further in this paper but are highlighted to demonstrate the feasibility of obtaining high-quality cryo-EM reconstructions in lipid bilayers. A structural model was built by fitting the detergent structure of KCNQ1 (40) and making adjustments where needed (Fig. 2 B–D). The up structure in lipid bilayers is very similar to the depolarized structure in detergent micelles. The S4–S5 linker is an α-helix from I257 to G245 and a short loop (Q244 to D242) connects the S4–S5 linker to S4 (Fig. 2D). The S4 is a 310 helix from V241 to R237 and an α-helix from L236 to T224 (Fig. 2D). The S3–S4 loop is partially flexible—with the four residues (GQVF) in between K218 (top of S3) and A223 (top of S4) not well defined—a point we shall return to later. The six positive-charged residues in S4 are positioned as such (Fig. 2C): R6 (R243) lies below the gating charge transfer center. H5 (H240) occupies the gating charge transfer center consisting of F167 from S2 and the negative-charged E170 and D202 from S2 and S3, respectively. R4 (R237) is directly above the gating charge transfer center and interacts with E160 in S2. Q3 (Q234), R2 (R231), and R1 (R228) lie further toward the extracellular side of the membrane. Q3 lies within the voltage sensor helical bundle, R2 is at the periphery, and R1 is pointed toward the headgroups of the phospholipid bilayer (Figs. 2C and 3 A–C). The Down and Intermediate Conformations of the Voltage Sensor. The intermediate and down maps are less well defined due to heterogeneity, but clear differences in the main chain compared to the up map (modeled in Fig. 3A) were used to build partial models. We compare the down (Fig. 3 E and F) and intermediate (Fig. 3D) maps to an up map that is filtered to a comparable resolution (Fig. 3 B and C). Compared to the up map, the down map shows a dramatic change in the bottom half of S4, near the intracellular surface (Fig. 3 C and F and Movie S1). At the intracellular surface, the loop connecting PNAS 2023 Vol. 120 No. 21 e2301985120 https://doi.org/10.1073/pnas.2301985120 3 of 12 Fig. 2. Structure of KCNQ1 in lipid vesicles with the voltage sensor in the up conformation. (A) Cryo-EM density map of the up structure of the KCNQ1 channel from the unpolarized dataset. Each channel subunit is shown in a different color and calmodulin (CaM) is shown in magenta. Bound lipids or sterols are shown as gray density. (B) Structure of the KCNQ1–CaM complex (cartoon representation) showing domains within one monomer (blue) from the N- to C-terminus: voltage sensor, pore domain, and the cytosolic domain. The other monomers are colored gray for clarity and the bound calmodulin is colored magenta. (C) Stereoview of the KCNQ1 voltage sensor (Cα trace) in the up (depolarized) conformation. The six positive charges in S4 (α carbon marked by blue spheres), three negative charges in S2 and S3 (E160, E170, and D202), and the hydrophobic Phe in S2 (F167, green sticks) are shown in stick-and-ball representation. (D) Stereoview of the main chain in S4 and the S4–S5 linker (stick representation) in the up conformation. The α carbons of the six positive charges in S4 are marked by blue spheres. Regions with different secondary structures are indicated: α-helix (green), 310 helix (cyan), and loop (magenta). S4 to the S4–S5 linker helix becomes lengthened by eight or nine amino acids. The lengthening occurs while the S4–S5 linker helix on the C-terminal side of W248, whose side chain density is apparent even at the lower resolution of the down map, remains unchanged in its position. Given that the S4–S5 linker helix does not move, the lengthened loop must result from amino acids originating in the downward displacement of the S4 helix and the four residues in the S3–S4 loop. The density in the newly formed extended loop suggests that as S4 moves downward, it forms a broken helix ~30° relative to the bilayer normal, and an extended loop (Fig. 3F and Movie S1). On the extracellular side, the top of helical densities for both S3 and S4 appears embedded about one turn below the expected plane of the extracellular membrane surface, while a short loop connecting them reaches to the extracellular surface (Fig. 3 B and E). 4 of 12 https://doi.org/10.1073/pnas.2301985120 pnas.org Fig. 3. Characterization of electric field–induced movements in the KCNQ1 voltage sensor. (A) Stereoview (Cα trace) of the voltage sensor in the up model. For reference, the positions of the alpha carbons of the six positive charges in S4 are marked by blue spheres, that of K218 at the top of S3 is marked by a red sphere, and that of W248 at the end of the S4–S5 linker is marked by a green sphere. F167 in S2, which is part of the gating charge transfer center, is shown in magenta stick representation. (B and E) Stereoviews of the top part of S3 and S4 in the lowpass-filtered up model and map (unpolarized dataset, B) and in the down model and map (polarized dataset, E). (C, D and F) Stereoview of the bottom part of S4 and the S4–S5 linker in the lowpass-filtered up model and map (unpolarized dataset, C), intermediate (inter.) model and map (polarized dataset, D), and in the down model and map (polarized dataset, F). The up map was lowpass filtered to 6.5 Å to facilitate comparison to the down and intermediate maps. In the absence of side chain density, given the large conforma- tional change in the position of S4 required to form the large loop on the intracellular side, we could not build a model of this region with certainty in the polypeptide register. We built two tentative polyalanine models into the continuous main chain density, one invoking a three helical turn displacement of S4 and the other invoking a two helical turn displacement (SI Appendix, Fig. S7). One and four helical turn models are incompatible with the observed density. The three helical turn displacement, which would place Q3-R6 below, R2 in, and R1 above the gating charge transfer, more reliably accounts for density, but additional data will be needed to establish this conclusion. We note at this point that the mechanism presented in the current study (to be dis- cussed) does not rely on modeling side chains or the detailed register of the S4 helix, because the main chain movements we do observe clearly interfere with the PIP2-binding site and thus explain the basic mechanism of this channel’s gating. In contrast to the down structure, the intermediate structure largely preserves the secondary structure of the up conformation but displays a ~4 Å downward displacement of the loop connecting S4 to the S4–S5 linker (Fig. 3 C and D). As in the down structure, the S4–S5 linker helix does not move appreciably. Given that the motion is likely to be a rigid body movement of S4, we included S4 sidechains in the structural model of the intermediate conformation. The PNAS 2023 Vol. 120 No. 21 e2301985120 https://doi.org/10.1073/pnas.2301985120 5 of 12 intermediate structure places R6 and H5 below the gating charge transfer center; R4 in the gating charge transfer center; and Q3, R2, and R1 above the gating charge transfer center. In all three structures, the pore appears tightly closed, as expected in the absence of PIP2 (SI Appendix, Fig. S8C). The pore radius is ~1 Å at S349 in the up structure, which is notably smaller than the radius of a hydrated K+ ion (~4 Å). While the side chain of S349 is not visible in the intermediate and down maps, the position of S6 is the same as in the up structure with a closed pore and different from the PIP2-bound structure with an open pore (SI Appendix, Fig. S8C), thus being consistent with a closed pore. Discussion The Relationship between Voltage Sensor Movements and PIP2 Binding. The three structures presented in this study delineate the movement of the S4 helix in the KCNQ1 channel in response to polarization, while the pore of the channel is closed in all the three cases due to the absence of PIP2 in our preparation. The structure of PIP2-bound KCNQ1 with an open pore and the voltage sensor in the up conformation was already determined (40, 46, 47). By comparing these structures, we can deduce how the voltage sensor movements relate to PIP2 binding. The PIP2-binding site in KCNQ1 comprises positive-charged and polar residues in the S4–S5 linker, S4 helix, the S2–S3 foot, and the S0 helix (Fig. 4A, see also Fig. 2C). This structure of the pocket is maintained when the pore of the channel is open or closed as long as the voltage sensor is in the up conformation (Fig. 4B). In other words, when the voltage sensor is up, PIP2 can bind to this pocket and promote channel opening, as described previously (33–36, 40). We note that all the structures of KCNQ1 solved in the presence of PIP2 show an open pore, but only some also show a large conformational change in the cytoplasmic domain (SI Appendix, Fig. S8 C–E) (40, 46). The relationship between the two is not clear, but it is apparent that PIP2-binding causes the pore to open, which is what we focus on here. Overlays of the intermediate (Fig. 4C) and down (Fig. 4D) volt- age sensor conformations with the PIP2-bound, voltage sensor up conformation show that the PIP2-binding site is reshaped when S4 moves. The position of S4 in the down conformation sterically occludes the PIP2-binding site altogether (Fig. 4D). Thus, while the voltage sensor is in the down conformation, PIP2 cannot bind to the channel and open the pore. In the intermediate conformation, the residues that bind PIP2 are displaced relative to one another due to the movement of S4 (Fig. 4C). This intermediate conformational change would likely alter the affinity of the PIP2-binding site, but it might not definitively preclude the binding of PIP2. Voltage-Dependent Regulation of PIP2 Binding in KCNQ1. A mechanism for voltage-dependent regulation of KCNQ1 channel activity thus follows (Fig. 5E). We have made a movie to visualize the sequence of events (Movie S2). At hyperpolarized membrane voltages (i.e., at the resting potential of a cell, corresponding to our polarized vesicles), the voltage sensor is in the down conformation, which prevents PIP2 from binding because the site is occluded. Depolarization drives the S4 helix up, which is coupled to the formation of the PIP2 binding site. PIP2 can then bind, which causes the pore to open through an allosteric mechanism (40, 46, 47). In other words, KCNQ1 activity is modulated by a ligand (PIP2), the binding of which is regulated by the voltage sensor. This is different in detail from a mechanism in which the binding of PIP2 permits voltage sensor conformational changes to regulate the pore through direct mechanical coupling (Fig. 5E). This voltage-dependent regulation of PIP2-binding mechanism is compatible with electrophysiological studies of KCNQ channels, Fig. 4. The relationship between voltage sensor movements and PIP2 binding. (A) Stereoview (gray Cα trace) of the PIP2-bound structure of KCNQ1 (PDB ID: 6V01) (40) with the voltage sensor in the up conformation and an open pore. Positive-charged and polar residues that interact with PIP2 are labeled and shown as gray sticks (α carbon marked by gray spheres) and PIP2 is shown as yellow sticks. (B) Stereoview of the PIP2-free structure of KCNQ1 with the voltage sensor in the up conformation and a closed pore (blue Cα trace) and the PIP2-bound structure shown in panel A (gray Cα trace). (C) Stereoview of the PIP2-free structure of KCNQ1 with the voltage sensor in the intermediate conformation and a closed pore (magenta Cα trace) and the PIP2-bound structure shown in panel A (gray Cα trace). (D) Stereoview of the PIP2-free structure of KCNQ1 with the voltage sensor in the down conformation and a closed pore (green Cα trace) and the PIP2-bound structure shown in panel A (gray Cα trace). In panels (B–D), the α carbon positions of the PIP2-interacting residues are shown as spheres in the same color as the α carbon trace. which show that voltage sensor movements slightly precede pore opening (48, 49), that PIP2 is required for activity (33–36), and that in the absence of PIP2, the voltage sensors move but the pore does not open (38). Moreover, if the voltage sensors were to perform work directly on the pore to open it, and if PIP2 was required for this coupling, one would expect a shift in the voltage activation midpoint (i.e., as measured by the movement of S4) depending on whether PIP2 is bound or not. But, the movement of S4 happens at the same membrane voltage whether PIP2 is present in the mem- brane or not (38), suggesting that the voltage sensors do not perform 6 of 12 https://doi.org/10.1073/pnas.2301985120 pnas.org Fig. 5. The structure of S4 and the S4–S5 linker of Kv7.1 (KCNQ1) and Kv2.1 determined in lipid vesicles. (A) Sequence alignment of S4 and the S4–S5 linker for all domain-swapped Kv channel families. All members of the Kv7 family are included for comparison to Kv7.1. Residues conserved across all families are highlighted in blue. (B) Cryo-EM density map of the human Kv2.1 channel determined in lipid vesicles. Each channel subunit is shown in a different color and associated lipids or sterols are shown as gray density. (C and D) Stereoviews of the connection between S4 and the S4–S5 linker (stick representation) in the depolarized conformations of Kv7.1 (C) and Kv2.1 (D) overlaid with cryo-EM density (blue mesh). (E) Cartoons depicting the new gating model (Top) and the old gating model (Bottom) for KCNQ1. The pore domain is colored orange, the voltage sensor is gray, the S4 helix is blue, the S4–S5 linker is green, and PIP2 is depicted as a magenta hexagon. In the new model, the voltage sensor regulates the binding of PIP2 by occluding the binding site in the down conformation (Left). When membrane depolarization occurs, the voltage sensor moves to the up conformation (Middle), which then allows PIP2 to bind to the channel and open the pore (Right). In the old model that is inconsistent with our data, a PIP2-binding site is present in the down conformation, allowing PIP2 to bind to the channel. work on the pore at depolarized potentials to open it. Finally, voltage clamp fluorometry using a reporter on the S3–S4 linker shows two components: a larger fluorescence change that has a midpoint of ~−60 mV and a smaller change in fluorescence with a midpoint of ~30 mV (50, 51). The pore begins to open during the first (more negative voltage) fluorescence change, which has led to the proposal that there are two open states of the channel. These observations could be related to the intermediate and up voltage sensor confor- mations that we observe. Unique Features of the KCNQ1 Voltage Sensor. KCNQ1 is unique among domain-swapped Kv channels in its requirement of PIP2 to open. To this point, a comparison of the KCNQ1 structure to that of a different domain-swapped Kv channel is informative. A primary sequence alignment from S4 through the S4–S5 linker for the Shaker channel, one member from each of the domain-swapped Kv channel families (Kv1-9), and other members of the Kv7 (KCNQ) family, is given in Fig. 5A. Stereoviews of the S4 to S4–S5 helix linker connection along with cryo-EM density PNAS 2023 Vol. 120 No. 21 e2301985120 https://doi.org/10.1073/pnas.2301985120 7 of 12 are shown for the depolarized structures of Kv7.1 (Fig. 5C, see also Fig. 2D) and human Kv2.1 (52), both in lipid vesicles (Fig. 5D). The Kv2.1 structure was determined to an overall resolution of 3.0 Å (Fig. 5B and SI Appendix, Fig. S9). An important difference between Kv7.1 and Kv2.1 becomes apparent at the junction of S4 and the S4–S5 linker. In KCNQ1, these residues form a helix– loop–helix motif, with three flexible amino acids (G245, G246, and T247) in or adjacent to the loop. Meanwhile, in Kv2.1, the junction is a helix–turn–helix motif. In other words, Kv7.1 has a natural propensity to form a loop in this region, which is not shared by the domain-swapped channel Kv2.1. The S4 movement that we observe in the intermediate and down conformations is centered exactly at this flexible “GGT” motif. Moreover, this motif (and in fact, most of the S4–S5 linker) is conserved among Kv7 family members but is absent in other domain-swapped Kv channels (Fig. 5A), indicating that it is a hallmark of the PIP2- gated KCNQ channels. Whether Shaker-related channels under an applied electric field undergo similar or distinct voltage sensor movements compared to KCNQ1 remains to be seen. Implications for Other Voltage-Dependent Channels. In KCNQ1, membrane polarization causes the S4 helix to displace by one helical turn (~5 Å) in the intermediate structure and most likely three helical turns (~15 Å) in the down structure, but the S4–S5 linker helix does not move appreciably (SI Appendix, Fig. S8A). One might argue that this is because the pore is closed due to the lack of PIP2. But, structures of KCNQ1 with an open pore are known (40, 46), and the S4–S5 linker helix occupies a similar position in those as well (SI Appendix, Fig. S8B). This finding suggests that the position of the S4–S5 linker helix is not strictly coupled to pore opening and closing in the KCNQ1 channel. It is still possible that small movements in the S4–S5 linker bias the conformational state of the pore, but S4–S5 helix movements are minimal when the S4 helix is displaced. What does this static S4–S5 helix in KCNQ1, if anything, suggest for other domain-swapped channels like Shaker-related Kv channels, Nav, or Cav channels? When the first molecular structure of a eukar- yotic voltage-dependent ion channel—the Shaker-related Kv1.2— was determined (16), the S4–S5 linker was found to contact S6 directly. A simple mechanical model for voltage-dependent regula- tion of the pore was proposed: When S4 moves in response to an electric field, the amino terminal end of the S4–S5 linker is displaced, applying a force on S6 and causing pore closure through straighten- ing of the S6 helix at a conserved “PxP” motif (53). Many years later, structures of chemically cross-linked or trapped Nav channels (21, 24, 25) and metal bridge–linked Kv4.2 channels (26) showed that it is indeed possible to trap channels in conformations consistent with the simple mechanical model. We observe in the present study, however, that KCNQ1 does not function according to this model. While KCNQ1 is an outlier among domain-swapped voltage-dependent channels for the reasons discussed above, we remain open minded to the possibility that the simple mechanical model assumed for other domain-swapped voltage-dependent ion channels (16, 21, 24, 25), despite support from mutational and chemical crossbridge data, could be incorrect. Mutations and chem- ical crossbridges likely do not replicate the forces applied to a polar- ized voltage sensor in membranes because an electric force field acts on all charged atoms spread throughout the protein. Ultimately, to know whether the simple mechanical model is correct for other domain-swapped channels, we will need to determine their structures in lipid bilayers with an applied electrostatic force field. Comparison of Voltage Sensor Movements in EAG and KCNQ1. We now know how the voltage sensors in two potassium channels, KCNQ1 (domain-swapped) and Eag (nondomain-swapped) (20), undergo conformational changes in response to an applied voltage difference across the membrane. For comparison, side views of the voltage sensors in these channels are shown in Fig. 6. In both channels, as S4 displaces downward (i.e., toward the cytoplasm), an extended interfacial segment is formed through a break in S4, which is accompanied by a remodeling of the connection between S4 and the S4–S5 linker helix (KCNQ1; Fig. 6A) or S4 and S5 (Eag; Fig. 6B) (20). Apparently, because S4 is both charged and hydrophobic, an interfacial location is energetically more favorable than an aqueous location. The extra amino acids that account for the downward displacement of S4 originate from the S3–S4 linker and the top of S3 in both channels. While the S4 displacement and interfacial helix formation are similar in KCNQ1 and Eag, the helices bend in opposite direc- tions with respect to the pore. In Eag, the polarized S4 bends toward the pore axis, causing it to clamp down on the pore-lining S6 helix, which prevents pore opening (Fig. 6B) (20). In KCNQ1, the polarized S4 bends away from the pore axis so that it occludes the PIP2-binding site. These variations show how the same struc- tural element—a voltage sensor—confers conformational sensi- tivity to an electric field in two Kv channels that differ both in their voltage sensor configuration (i.e., domain-swapped versus nondomain-swapped) and in their modulation by other effectors. Future studies of other voltage-dependent ion channels might uncover other interesting mechanisms for coupling the movement of S4 to gating the pore. On the Magnitude of S4 Displacement in KCNQ1. As we state above, our inability to define the register of the S4 helix main chain (SI Appendix, Fig. S7) prevents us from distinguishing with certainty whether the down map, with its occluded PIP2-binding site, corresponds to a two or three helical turn displacement of S4. A two-turn displacement was anticipated because that is what we observed in a polarized Eag channel (20), and what has been seen in cross-linked Nav and HCN voltage sensors (21, 23–25). Moreover, if S4 can displace three helical turns, it must pass through a two-turn-displaced intermediate. Why then would we not observe this intermediate? A possible answer lies in the unique S4 sequence of KCNQ1, which contains a neutral glutamine at “charged” position 3 (Fig. 5A). A two helical turn displacement would place the neutral glutamine into the highly negative- charged gating charge transfer center (Fig. 2C). For this reason, conformations with one or three helical turn displacements (which both place an arginine in the gating charge transfer center) may be energetically more stable in KCNQ1 than a conformation with two. If this is the case, a two helical turn displacement would function as a transient energy barrier in the conformational change of the voltage sensor. The notion that different residues are stable to varying degrees when they occupy the gating charge transfer center is apparent from functional measurements in other voltage-dependent ion channels. For instance, in the Shaker channel, it has been shown that it is more favorable for a lysine than an arginine to occupy the gating charge transfer center (8). Depending on the position of the substitution within the S4 helix, either the open or the closed state of the channel can be stabilized (corresponding to an up or down conformation of the voltage sensor). Given that even two positive-charged residues, arginine or lysine, can differ in their relative stability, it seems quite possible that a neutral glutamine behaves differently than an arginine. It is also useful to look at another example: Consider the Shaker channel and the domain-swapped channel Kv2.1 (Fig. 5A). Both have lysine at the fifth position (K5) in the gating 8 of 12 https://doi.org/10.1073/pnas.2301985120 pnas.org Fig. 6. Comparison of voltage sensor movements in KCNQ1 and Eag. (A and B) Stereoviews of KCNQ1 (A) and Eag (B) showing the up conformation (Left, red S4) and the down conformation (Right, blue S4). The view looks through one voltage sensor in each channel toward the pore axis. The approximate position of the lipid membrane bilayer is marked by yellow lines and the protein is shown in Cα trace representation. charge transfer center. In Shaker, all four residues above the gating charge transfer center are arginines, while Kv2.1 has a glutamine at position one (Q1) followed by three arginines. The gating charge estimated by nonlinear membrane capacitance for Shaker is ~12 to 14 elementary charges per channel (~3 to 3.5 per voltage sensor) and that for Kv2.1 is only ~6 to 7 per channel (~1.5 to 2 per voltage sensor) (54, 55). The gating charge esti- mates for Shaker indicate that the first residue (R1) does not traverse the membrane potential. Yet the presence of a neutral glutamine at the first position in Kv2.1 reduces the apparent gating charge in half. We suppose that in the down conformation of Kv2.1, there is a tendency for R2 to neutralize the extracellular negative-charged residue while R3 occupies the gating charge transfer center, consistent with the net movement of ~2 gating charges. These observations are consistent with the idea that it is more favorable for an arginine (compared to a glutamine) to interact with negative-charged residues in the voltage sensor. In summary, this study provides the structural description of a domain-swapped Kv channel in a lipid bilayer under the influence of a polarizing electric field. The structures reveal a mechanism in which the voltage sensor regulates the affinity of PIP2, thus con- trolling its ability to gate the pore. Materials and Methods Cell Lines. Sf9 (Spodoptera frugiperda Sf21) cells were used for production of baculovirus and were cultured in Sf-900 II SFM medium (GIBCO) supplemented with 100 U/mL penicillin and 100 U/mL streptomycin at 27 °C under atmospheric CO2. HEK293S GnTl− cells were used for protein expression and were cultured in Freestyle 293 medium (GIBCO) supplemented with 2% fetal bovine serum, 100 U/mL penicillin, and 100 U/mL streptomycin at 37 °C in 8% CO2. Expression and Purification of the KCNQ1–Calmodulin Complex. The KCNQ1(Kv7.1)–calmodulin complex (hereby referred to as KCNQ1) was expressed and purified as described before (40), with slight modifications. We used a con- struct corresponding to human KCNQ1 with N-terminal and C-terminal trunca- tions, leaving residues 76 to 620. The construct was cloned into the BacMan expression vector with a C-terminal green fluorescent protein (GFP)-His6 tag linked by a preScission protease (PPX) site (56). A separate BacMan expression vector without a tag was used for vertebrate calmodulin (CaM). Bacmids were generated for KCNQ1 and CaM using DH10Bac Escherichia coli cells. Baculoviruses for KCNQ1 and CaM were produced in SF9 cells transfected with bacmid DNA using the Cellfectin II reagent (Invitrogen). Baculovirus was amplified three times in suspension cultures of SF9 cells grown at 27 °C. Four liters of suspension cultures of HEK293S GnTI- at ~3 × 106 cells/mL were infected with 12% (v/v) of 5:1 KCNQ1:CaM baculovirus at 37 °C for ~8 h. Protein expres- sion was induced by adding 10 µM sodium butyrate, and the incubation tem- perature was changed to 30 °C for the duration of expression. Cell pellets were harvested ~48 h after induction and flash frozen in liquid nitrogen for later use. Four liters of cell pellet were resuspended in ~100 mL of lysis buffer (25 mM Tris pH 8.0, 300 mM KCl, 1 mM MgCl2, 5 mM CaCl2, 2 mM dithiothreitol (DTT), 1 µg/mL leupeptin, 1 µg/mL pepstatin, 1 mM benzamidine, 1 µg/mL aprotinin, 1 mM phenylmethylsulfonyl fluoride, 1 mM 4-(2-aminoethyl) benzenesulfo- nyl fluoride, and 0.1 mg/mL DNase), stirred for 10 min at 4 °C, and Dounce homogenized with a loose pestle till homogenous. The resultant suspension was clarified by centrifugation at 39,800 × g for 15 min at 4 °C. The pellet was resuspended in ~100 mL of lysis buffer and Dounce homogenized with a tight PNAS 2023 Vol. 120 No. 21 e2301985120 https://doi.org/10.1073/pnas.2301985120 9 of 12 pestle. To extract the KCNQ1–CaM complex, we added 15 mL of a 10%:2% n-no- decyl-β-D-maltopyranoside (DDM):cholesteryl hemisuccinate (CHS) mixture and stirred for 1 h at 4 °C. The mixture was clarified by centrifugation at 39,800 × g for 30 min at 4 °C. The supernatant was bound to ~2.5 mL GFP nanobody-coupled Sepharose resin (prepared in-house) (57) in a 250-mL conical centrifuge tube (Corning) by gentle rotation for 1 h at 4 °C. The resin was transferred to a glass gravity flow column (Bio-Rad) and washed with ~30 column volumes of wash buffer (10 mM Tris pH 8.0, 300 mM KCl, 0.05%:0.01% DDM:CHS, 1 mM CaCl2, and 2 mM DTT). The resin was resuspended in five column volumes of wash buffer, PPX (prepared in-house) was added at a concentration of 0.05 mg/mL to remove the GFP tag, and the solution was rotated for 1 h at 4 °C. The cleaved protein was collected in the flow through and a subsequent wash step with five column volumes of wash buffer. The protein was concentrated to ~500 µL at 3,000 × g and 4 °C using a 15-mL Amicon spin concentrator with a 100-kDa molecular weight cutoff mem- brane. The concentrated protein was filtered through a Corning 0.2 µm spin filter and then purified by size-exclusion chromatography (SEC) using a Superose 6 Increase column (10/300 GL) preequilibrated with SEC buffer (10 mM Tris pH 8.0, 300 mM KCl, 0.025%:0.005% DDM:CHS, 1 mM CaCl2, and 5 mM DTT). Fractions containing hKCNQ1 and calmodulin (SI Appendix, Fig. S1 A and B) were pooled and concentrated to an A280 of 3.8 mg/mL at 3,000 × g and 4 °C using a 4-mL Amicon spin concentrator with a 100-kDa molecular weight cutoff membrane. Purified protein was immediately used for reconstitution into liposomes. Reconstitution of the KCNQ1–Calmodulin Complex and Cryo-EM Grid Preparation. The purified KCNQ1 complex was reconstituted into liposomes con- sisting of 90%: 5%: 5% POPC:POPG:cholesterol (wt: wt: wt, Avanti Polar Lipids) (20). The phospholipids and sterol were mixed together in chloroform at a concentration of 10 mg/mL. Ten milligrams of the lipid mixture were dried to a thin film in a screw-top glass tube under a gentle stream of argon. The lipid film was further dried for ~3 h in a room-temperature vacuum desiccator, and then resuspended at a concentra- tion of 10 mg/mL by gentle vortexing in reconstitution buffer (10 mM Tris pH 8.0, 300 mM KCl and 1 mM DTT). Small unilamellar vesicles (SUVs) were formed by bath sonication (Branson Ultrasonics M1800) at room temperature till the solution was mostly transparent (A400 ~ 0.2), which typically took ~40 min. To permeabilize but not solubilize the lipid vesicles, the detergent C12E10 was added to the 10 mg/mL lipid stock solution to a final concentration of 2 mg/mL (5:1 lipid:detergent, wt/wt) and incubated on ice for ~15 min. Two hundred microliters of this permeabilized vesicle solution was mixed with 27 µL of the purified KCNQ1 complex (3.8 mg/ mL) and 173 µL of reconstitution buffer, giving a total reaction volume of 400 µL (chosen to ensure proper mixing in a 1.5 mL Eppendorf tube), a protein:lipid ratio of 1:20 (wt/wt), and a final lipid concentration of 5 mg/mL. The lipid–protein–detergent mixture was incubated on ice for ~1.5 h. Detergent was removed using adsorbent Bio-Beads SM-2 resin (Bio-Rad) by adding 20 mg of a 50% (wt/vol) Bio-Beads slurry in reconstitution buffer and rotating at 4 °C for ~14 h. The biobeads procedure was repeated twice again for 3 h each at 4 °C to ensure complete removal of detergent. The suspension was bath sonicated briefly (twice for 10 s each) after the biobeads step to minimize vesicle clumping. Polarized and unpolarized vesicles were prepared from the same batch of proteoliposomes. From an 8 mM stock in dimethyl sulfoxide, 2 µM valinomycin was added to the proteoliposomes and incubated for 30 min on ice. Polarized vesicles were prepared as follows: 70 µL of the above solution was added to a 0.5-mL Zeba spin desalting column (40 kDa cutoff, Thermo Scientific), preequili- brated with sodium reconstitution buffer (10 mM Tris pH 8.0 and 300 mM NaCl), to exchange the external K+ for Na+. The sample was centrifuged for ~20 to 30 s at room temperature at 1,500 × g and ~20 µL of flow-through containing vesicles was collected. The residual external K+ concentration is about 1 mM (20). Onto a glow-discharged Quantifoil R1.2/1.3 400 mesh Au grid, 3.5 µL of the polarized vesicle solution was immediately applied. The vesicle solution was incubated on the grid for 3 min at 20 °C under a humidity of 100%. The grid was then manually blotted from the edge of the grid using a piece of filter paper. Another 3.5 µL of the polarized vesicle solution was applied to the same grid for 20 s (58), and then the grid was blotted for 3 s with a blotting force of 0 and flash frozen in liquid ethane using a FEI Vitrobot Mark IV (FEI). Each grid with polarized vesicles used a freshly buffer exchanged sample. Grids for the unpolarized vesicles were frozen by skipping the buffer exchange step, i.e., directly applying the proteoliposomes (with valinomycin) on the Quantifoil grids. Expression and Purification of Kv2.1. Full-length human Kv2.1 (NP_004966.1) with a C-terminal GFP-His6 tag linked by a PPX site and full-length 14-3-3 protein epsilon (empirically found to increase the yield of Kv2.1, XP_040497056.1) were both cloned into a pBig1a vector from the biGBac system (59). Bacmids and baculovirus were generated, and protein was expressed in HEK293S GnTI- cells as described above for KCNQ1. The channel (hKv2.1) was purified following essentially the same protocol as KCNQ1 except that 150 mM KCl (instead of 300 mM KCl) was used for the wash buffer and calcium chloride was not included after the lysis buffer step. The final purification step entailed SEC on a Superose 6 Increase column (10/300 GL) preequilibrated with SEC buffer (10 mM Tris pH 8.0, 150 mM KCl, 0.03%:0.006% DDM:CHS, and 5 mM DTT). Fractions containing hKv2.1 (SI Appendix, Fig. S9A) were pooled and concentrated to an A280 of 1.4 mg/mL at 3,000 × g and 4 °C. Reconstitution of Kv2.1 and Cryo-EM Grid Preparation. Purified protein was reconstituted into liposomes of 90%:5%:5% POPC:POPG:cholesterol prepared in 150 mM KCl using a protein:lipid ratio of 1:20 (wt/wt), following the same protocol as for KCNQ1. A fourfold-molar excess of hanatoxin (compared to hKv2.1 monomers) isolated from Chilean rose tarantula (Grammostola rosea) venom (60) was incubated with the proteoliposomes before freezing grids, but the toxin was not visible in the cryo-EM reconstructions. Grids were frozen exactly as described for unpolarized vesicles containing KCNQ1 (but without added valinomycin). Liposome Flux Assay. The flux assay was carried out as described before (44), with minor modifications. The proteoliposome vesicles or control vesicles without protein (subjected to a mock reconstitution) prepared in 300 mM KCl were diluted 10-fold in isotonic sodium buffer (10 mM Tris pH 8.0 and 300 mM NaCl) imme- diately prior to the assay. Six microliters of the diluted vesicle solution was mixed with 6 µL ACMA solution (10 mM Tris pH 8.0, 300 mM NaCl, and 5 mM ACMA) and 12 µL buffer (10 mM Tris pH 8.0 and 300 mM NaCl). ACMA fluorescence was recorded every 5 s (excitation wavelength = 410 nm, emission wavelength = 490 nm) using a 384-well plate (Grainger) on a fluorescence plate reader (Tecan Infinite M1000). After the ACMA fluorescence stabilized, 6 µL of CCCP solution (10 mM Tris pH 8.0, 300 mM NaCl, and 15 mM CCCP) was added. The resultant KCNQ1-dependent flux, or in this case, the lack thereof because of the absence of PIP2, was measured. At the end of the assay, 2 µL of a 1.2-µM valinomycin solution (in 10 mM Tris pH 8.0 and trace dimethyl sulfoxide) was added to initiate K+ efflux from all the vesicles and determine the minimum ACMA fluorescence. The fluorescence data for each run were normalized by the fluorescence value right before the addition of CCCP (i.e., at 90 s). The normalized data were averaged across five independent measurements, and the mean and SDs are reported. Cryo-EM Data Acquisition and Processing. Data for the polarized and unpo- larized KCNQ1 liposomes were collected on the same microscope—a 300-keV FEI Titan Krios2 microscope located at the HHMI Janelia Research Campus. The microscope was equipped with a Gatan Image Filter (GIF) BioQuantum energy filter and a Gatan K3 camera. A total of 33,057 movies (polarized sample) or 19,998 movies (unpolarized sample) were recorded on Quantifoil grids in super- resolution mode using SerialEM (61). The movies were recorded with a physical pixel size of 0.839 Å (superresolution pixel size of 0.4195 Å) and a target defocus range of −1.0 to −2.0 µm. The total exposure time was ~2 s (fractionated into 50 frames) with a cumulative dose of ~60 e−/Å2. The data-processing workflow is detailed in SI Appendix, Figs. S2 and S3, and followed the same strategy we previously reported for Eag (20). Data processing was carried out using cryoSPARC v3.3.1 (62) and RELION 4.0 (63). The super- resolution movies were gain-normalized, binned by a factor of 2 with Fourier cropping, and corrected for full-frame and sample motion using the Patch motion correction tool (grid = 15 × 10). Contrast transfer function parameters were esti- mated from the motion-corrected micrographs using the Patch CTF estimation tool, which uses micrographs without dose weighting. All subsequent processing was performed on motion-corrected micrographs with dose weighting. Particle picking was initially carried out using the Blob picker. 2D classes with clear protein density were used to train a TOPAZ picking model (64), which was used to pick additional particles. Particles with clear protein density after 2D classification were pooled and duplicate picks were removed. An ab initio model was generated from 2D classes with clear secondary structure features, and 3D classification and refinement was carried out either in cryoSPARC or RELION as detailed in SI Appendix, Figs. S2 and S3. 10 of 12 https://doi.org/10.1073/pnas.2301985120 pnas.org Data for the hKv2.1 liposomes were collected on a 300-keV FEI Titan Krios microscope located at the HHMI Janelia Research Campus. The microscope was equipped with a spherical aberration corrector (Cs corrector), a GIF BioQuantum energy filter, and a Gatan K3 camera. A total of 17,007 movies were recorded on a single Quantifoil grid in superresolution mode using SerialEM. The movies were recorded with a physical pixel size of 0.844 Å (superresolution pixel size of 0.422 Å) and a target defocus range of −1.0 to −2.0 µm. The total exposure time was ~2 s (fractionated into 50 frames) with a cumulative dose of ~60 e−/Å2. Data processing was carried out as described for KCNQ1 liposomes. Model Building and Refinement. A structural model for the up conformation was built by docking the structure of KCNQ1–CaM in detergent micelles (PDB ID: 6UZZ) (40) into the up map and making adjustments where needed. The model was edited and refined using the ISOLDE (65) plugin in ChimeraX v1.2.0 (66) or WinCoot v0.98.1 (67) followed by real-space refinement in Phenix (68). The down and intermediate models were built starting from the up model. The up model was initially fit in the intermediate or down maps as a rigid body using Phenix. The S4 helix and the surrounding regions were manually adjusted and then a final step of real-space refinement was carried out in Phenix. The quality of the final models was evaluated using the MolProbity plugin in Phenix (SI Appendix, Table S1). Graphical representations of models and cryo-EM density maps were prepared using PyMOL (69) and ChimeraX. Data, Materials, and Software Availability. Cryo-EM density maps of the KCNQ1 channel with the voltage sensor in the up, intermediate, and down conformation have been deposited in the electron microscopy data bank under accession codes EMD-40508 (70), EMD-40509 (71), and EMD-40510 (72), respectively. Atomic coordinates of the KCNQ1 channel with the voltage sen- sor in the up, intermediate, and down conformation have been deposited in the protein data bank under accession codes 8SIK (73), 8SIM (74), and 8SIN (75), respectively. ACKNOWLEDGMENTS. We thank Rui Yan, Zhiheng Yu, and the team at the Howard Hughes Medical Institute Janelia CryoEM Facility for their effort in cryo-EM microscope operation and data collection; Mark Ebrahim, Johanna Sotiris, and Honkit Ng at the Evelyn Gruss Lipper Cryo-EM Resource Center for assistance with cryo-EM grid screening; Yi Chun Hsiung for assistance with insect and mammalian cell cultures; Dr. Chen Zhao and other members of the MacKinnon Lab for helpful discussions; and Dr. Jue Chen and her group for their input. Dr. Chia-Hseuh Lee carried out the cloning and initial biochemical characterization of the Kv2.1 channel. V.S.M. is supported by the Jane Coffin Childs Memorial Fund Fellowship. R.M. is an investigator in the HHMI. 1. 2. B. Hille, Ionic Channels of Excitable Membranes (Sinauer Associates, ed. 3, 2001) (January 27, 2023). Y. Murata, H. Iwasaki, M. Sasaki, K. Inaba, Y. Okamura, Phosphoinositide phosphatase activity coupled to an intrinsic voltage sensor. 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10.1002_advs.202200181.pdf	Data Availability Statement The data that support the ﬁndings of this study are available from the cor- responding authors upon reasonable request.	Data Availability Statement The data that support the findings of this study are available from the corresponding authors upon reasonable request.	RESEARCH ARTICLE www.advancedscience.com Moiré-Driven Topological Transitions and Extreme Anisotropy in Elastic Metasurfaces Simon Yves, Matheus Inguaggiato Nora Rosa, Yuning Guo, Mohit Gupta, Massimo Ruzzene,* and Andrea Alù* The twist angle between a pair of stacked 2D materials has been recently shown to control remarkable phenomena, including the emergence of ﬂat-band superconductivity in twisted graphene bilayers, of higher-order topological phases in twisted moiré superlattices, and of topological polaritons in twisted hyperbolic metasurfaces. These discoveries, at the foundations of the emergent ﬁeld of twistronics, have so far been mostly limited to explorations in atomically thin condensed matter and photonic systems, with limitations on the degree of control over geometry and twist angle, and inherent challenges in the fabrication of carefully engineered stacked multilayers. Here, this work extends twistronics to widely reconﬁgurable macroscopic elastic metasurfaces consisting of LEGO pillar resonators. This work demonstrates highly tailored anisotropy over a single-layer metasurface driven by variations in the twist angle between a pair of interleaved spatially modulated pillar lattices. The resulting quasi-periodic moiré patterns support topological transitions in the isofrequency contours, leading to strong tunability of highly directional waves. The ﬁndings illustrate how the rich phenomena enabled by twistronics and moiré physics can be translated over a single-layer metasurface platform, introducing a practical route toward the observation of extreme phenomena in a variety of wave systems, potentially applicable to both quantum and classical settings without multilayered fabrication requirements. S. Yves, A. Alù Photonics Initiative Advanced Science Research Center City University of New York New York, NY 10031, USA E-mail: aalu@gc.cuny.edu M. I. N. Rosa, Y. Guo, M. Gupta, M. Ruzzene Department of Mechanical Engineering University of Colorado Boulder Boulder, CO 80309, USA E-mail: massimo.ruzzene@colorado.edu A. Alù Physics Program Graduate Center City University of New York New York, NY 10026, USA The ORCID identiﬁcation number(s) for the author(s) of this article can be found under https://doi.org/10.1002/advs.202200181 © 2022 The Authors. Advanced Science published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. DOI: 10.1002/advs.202200181 1. Introduction New discoveries in condensed matter physics have recently shown how a twist in pairs of 2D stacked layers can produce highly unexpected emergent phenomena. Notably, the ﬁne-tuning of such twist allows the emergence of a magic angle at which a plethora of new phenom- ena can be observed, including ﬂat-band superconductivity,[1] the quantum Hall eﬀect,[2] the creation of moiré excitons,[3–8] as well as interlayer magnetism.[9] Based on these concepts, atomic photonic crystals in twisted bilayer graphene have shown the ability to route solitons[10,11] and pro- duce quasi-crystalline phases,[12] higher- topology,[13] non-Abelian gauge order potential,[14] and helical topological state mosaics.[15,16] These phenomena, at the heart of the thriving ﬁeld of twistronics,[17] arise from the hybridization of the band structures associated with the two isolated monolayers, and the associated formation of moiré superlattices. Macroscopic-scale implementations of these concepts using phononic and photonic metamaterials[18,19] have demonstrated ﬂat bands in macroscopic analogues of bi- layer graphene,[20–23] ﬁeld localization within moiré lattices,[24–26] the destruction of valley topological protection,[27] artiﬁcial gauge ﬁelds,[28] and broadband tunable bianisotropy for biosensing applications.[29–31] These concepts have also been recently transposed to optical metamaterials, based on extreme anisotropic responses over hy- perbolic metasurfaces (HMTs).[32] Their iso-frequency contours (IFCs) support an open, hyperbolic topology,[33–37] featuring wave propagation with enhanced local density of states, and enabling subwavelength imaging, as well as negative refraction and canal- ization, inherently broadband in nature. By stacking two hyper- bolic metasurfaces and rotating one with respect to the other, it is possible to largely modify the IFCs, inducing transitions be- tween diﬀerent topologies, from hyperbolic to elliptical.[38] Such eﬀect is the wave analogue of a Lifshitz transition in electronic band structures,[39] which is known to play a crucial role in the physics of Weyl and Dirac semimetals.[40] These exciting phe- nomena have also been recently demonstrated in polaritonic systems.[41–43] The remarkable features of twisted bilayers exploit the in- terplay between two distinct layers with exotic wave responses, Adv. Sci. 2022, 9, 2200181 2200181 (1 of 8) © 2022 The Authors. Advanced Science published by Wiley-VCH GmbH www.advancedsciencenews.com www.advancedscience.com especially for ﬁeld localization eﬀects, and generally require a precise control over their coupling, alignment and twist angle. Hence, experimental setups, however reconﬁgurable, are quite challenging.[44] In an attempt to circumvent such diﬃculties, a few recent studies have theoretically explored the emergence of analogous responses in single-layer systems, with interac- tions or properties modulated by a second virtual layer. For in- stance, quasi-ﬂat bands, Dirac cones, and quantum anomalous phases have been predicted in modulated optical lattices,[45,46] while topological spectral gaps characterized by second Chern numbers akin to the 4D quantum Hall eﬀect were illustrated in phononic lattices.[47] Lifting the requirement of two stacked layers opens new prospects for the implementation of twistronics across several electronic, photonic, and phononic platforms. Toward this goal, in this Letter we explore the eﬀects of emergent moiré patterns in monolayer pillared metasurfaces formed by the relative twist of two 2D spatial features: the lattice deﬁned by the position of the pillars and the one deﬁned by the anisotropic modula- tion proﬁle of their height, which deﬁnes their resonant fea- tures. We ﬁrst demonstrate that aligning these two lattices, re- sulting in an untwisted metasurface, and controlling their fea- tures, can produce a wide range of elliptical and hyperbolic IFCs. Next, we show that introducing a relative rotation between these two 2D lattices generates quasi-periodic moiré patterns govern- ing topological transitions between open and closed IFCs for spe- ciﬁc twist angles. Such transitions inherently occur in a diﬀer- ent way from those emerging in twisted bilayers,[32,41,42,43] for which the interplay between two material hyperbolic surfaces de- ﬁnes the transition instead of the emerging moiré patterns.We demonstrate the extreme wave phenomena in such twisted inter- leaved lattices with a highly reconﬁgurable metasurface formed by LEGO pillar-cone resonators over an elastic plate, which is a 2D extension of previously employed implementations used to study the role of disorder[48] and quasi-periodicity.[49] Our re- sults show the great potential of this platform to study analogues of condensed matter phenomena at the macroscopic scale and in classical settings, and open the door to applications harness- ing both strong anisotropy and moiré physics for enhanced wave manipulation. 2. Results and Discussion Our metasurface consists of a thin elastic plate featuring an ar- ray of pillars in a square lattice of period a (Figure 1a). The pillars can be modeled as mechanical dipolar resonators cou- pled to the transverse motion of the plate, and they are char- acterized by two bending resonant modes (along x and y) of equal frequency due to their symmetric cross section. Pillar-type resonators have been employed in the design of metamateri- als and metasurfaces, notably in the context of bandgaps,[50–54] cloaking,[55] and for seismic mitigation.[56] Nonsymmetric em- bedded resonators have previously been used to implement elas- tic hyperbolic metamaterials,[57–61] whereby large asymmetric couplings within the plate can generate anisotropic eﬀective properties, which can be exploited in the context of waveguiding and subdiﬀraction imaging. Rather than breaking the resonator symmetry, here we induce strong anisotropy through lattice ef- fects, by spatially modulating the resonant features of the array with a wavelength 𝜆 = Na. This eﬀect introduces a spatial mis- match within the lattice, eﬀectively creating a resonant macrocell including N distinct resonators responsible for asymmetric cou- plings across the metasurface. More speciﬁcally, we modify the pillar heights according to the modulation proﬁle S (x, y, 𝜃) = cos [2𝜋/𝜆(cos 𝜃x + sin 𝜃y)], where 𝜃 is a twist angle measured with re- spect to the x axis (Figure 1a). The height hn of each pillar deﬁnes the resonant frequency of the dominant mode of interest, and it is assigned by sampling the modulation surface at the lattice sites xn, yn, i.e., hn = h0 [1 + 𝛼S(xn,yn,𝜃)], where h0 is the mean height and 𝛼 is the modulation amplitude. This scheme generates two interleaved spatial features, consisting of the underlying square lattice of period a and of the sampled height distribution at the lattice sites. We begin by highlighting the wave propagation features of the plate in the untwisted (𝜃 = 0◦ , 𝛼 = 0.1) conﬁguration, with pe- riod a along y and Na along x. We consider N = 2, resulting in a diatomic lattice of resonators (Figure 1b), for which the band structure is shown in Figure 1c (the complete band structure can be found in Figure S1, Supporting Information). All band struc- ture computations and response simulations here are calculated with COMSOL Multiphysics, with details provided in the Sec- tion S1 (Supporting Information). The interleaving of the two lattices corresponding to the position of the resonators on the plate and to the resonance modulation, introduces an asymme- try and therefore a mismatch in IFCs along x and y, which re- sults in hyperbolic IFCs around the resonance, two of which are highlighted by black lines in the ﬁgure, as well as elliptical ones. The existence of hyperbolic and elliptical IFCs is conﬁrmed by simulating the harmonic response due to a point source exci- tation applied at the center of a ﬁnite sample comprising 80 × 80 unit cells. The resulting out-of-plane displacement ﬁeld, and its Fourier transform (FT) displayed in Figure 1d, illustrate the emergence of hyperbolic and elliptic bands for the three frequen- cies marked in Figure 1c, namely 485 Hz, 625 Hz and 670 Hz. Modifying the height modulation, quantiﬁed by the parameter 𝛼, can dramatically change the coupling asymmetry within the sur- face, and correspondingly tailor the IFC shape. Two examples are displayed in Figure 1e at the frequencies of the hyperbolic con- tours in Figure 1c. In the top panel, at 485 Hz, an increase in 𝛼 results in an inversion of IFC curvature, which changes from hyperbolic, to ﬂat, to open-elliptical, to ﬁnally close into an ellip- tical shape, demonstrating a topological transition. In the bottom panel, at 625 Hz, another topological transition from hyperbolic to elliptical phases occurs, this time as 𝛼 decreases. In this case, the presence of elliptical IFCs at neighboring frequencies (Fig- ure 1c) facilitates the transition between the two regimes, requir- ing smaller variations of 𝛼 to drive the process. We conﬁrm these phenomena experimentally using our elastic metasurface platform, exploiting the fact that underlying sampling lattice is square. Our metasurface comprises 44 × 44 resonators whose heights are modulated with 𝜆 = 2a (Figure 1f), and we choose the maximum value of 𝛼 allowed by the LEGO pillar geometry, as shown in the inset. The resonances are tuned by sliding the cones along the pillars, following the modulation of hn deﬁned above (Figure 1f, inset). Our LEGO platform provides straightforward tunability and reconﬁgurability, which we harness to demonstrate extreme wave phenomena and topological transitions. The plate is excited at its center by an Adv. Sci. 2022, 9, 2200181 2200181 (2 of 8) © 2022 The Authors. Advanced Science published by Wiley-VCH GmbH www.advancedsciencenews.com www.advancedscience.com Figure 1. a) Moiré interleaved metasurface: A square lattice of pillars whose heights are modulated according to a rotating proﬁle, here for 𝛼 = 0.1. b) Schematic of the periodic system with 𝜃 = 0◦ , and corresponding unit cell (inset). c) Numerical band structure of (b) with three contours corresponding to hyperbolic (at 485 Hz) along y, hyperbolic along x (at 625 Hz) and elliptical (at 670 Hz) highlighted with black lines. d) Simulated displacement ﬁeld and corresponding spatial FT zoomed in at the center of the Brillouin zone for the three frequencies highlighted in (c). e) Modiﬁcation of the IFCs as a function of height modulation at 485 Hz (top) and 625 Hz (bottom). f) Corresponding sample made of LEGO elements with cones at alternating heights (inset). g) Experimentally measured out-of-plane displacement ﬁeld map and corresponding spatial FT at 345 Hz (left), 470 Hz (center), and 510 Hz (left). electrodynamic shaker, which applies a pseudo-random excita- tion in the 200 − 700 Hz range, and the resulting out-of-plane wave ﬁelds are recorded by a scanning Doppler vibrometer (see Figure S2, Supporting Information for the experimental setup). While some hybridization exists between symmetric and asym- metric Lamb waves in the close vicinity of the resonances due to out-of-plane breaking of mirror symmetry, the modes of interest are mainly polarized along the out-of-plane direction (see Figure S3, Supporting Information for the out-of-plane polarization). Figure 1g displays the real and reciprocal space maps of the mea- sured ﬁelds at three selected frequencies (345, 470, and 510 Hz): the measured hyperbolic and elliptical propagation are consis- tent with those predicted in simulations, with a small frequency shift attributed to minor diﬀerences between experimental and numerical models (see Figure S1, Supporting Information for the simulation of the LEGO lattice band structure). These results clearly show that a spatially anisotropic resonance frequency modulation can generate broadband hyperbolic mechanical Lamb waves, easily implemented over our platform. Moreover, the straightforward tuning of the height modulation amplitude enables a precise control and drastic variations of the supported IFCs. Next, we explore the eﬀect of rotating the interleaved lattices, by twisting the modulation proﬁle relative to the underlying square lattice of resonators (Figure 1a). The misalignment be- tween the lattice and modulation proﬁle produces moiré patterns associated with complex spatial arrangements of the couplings. As illustrated in Figure 2a, 2D modulation patterns with a strong angular dependence appear for 0◦ < 𝜃 < 45◦ (after 45◦, the be- havior is simply inverted because of symmetry). For a generic twist angle, the resulting pattern is quasiperiodic, and the peri- odicity is only restored for speciﬁc angles 𝜃 = cos −1(p2/q), where {p2,q} are integers belonging to a Pythagorean triple satisfying p2 1 + p2 2 = q2.[24] These periodic conﬁgurations are characterized by unit cells that are typically very large: for instance, the two smallest super-cells are obtained for 𝜃 = cos −1(4/5)≅36.87°, re- sulting in a 5 × 10 super-cell, and 𝜃 = cos −1(12/13) ≅22.62°, re- sulting in a 13 × 26 super-cell. The complexity of the periodic angles and the increasing size of the super-cells makes the analy- sis through Bloch procedures very challenging, if not prohibitive. Adv. Sci. 2022, 9, 2200181 2200181 (3 of 8) © 2022 The Authors. Advanced Science published by Wiley-VCH GmbH www.advancedsciencenews.com www.advancedscience.com Figure 2. a) Pillar height modulation proﬁle as a function of the rotation angle (zoomed detail in inset). b) Simulated out-of-plane displacement ﬁeld maps (top) and spatial FT (bottom) as a function of the rotation angle for hyperbolicity along the y axis at 485 Hz. c) Same as (b) for the hyperbolicity along the x axis at 625 Hz. Instead, we observe the proposed moiré phenomena by analyz- ing the out-of-plane displacement in real and reciprocal spaces for ﬁxed frequency as a function of the twist angle. Overall, we ﬁnd evidence of a very rich behavior of the result- ing metasurfaces, whereby diﬀerent IFC transitions occur at dif- ferent frequencies. We focus on the two hyperbolic regimes pre- sented in Figure 1c. The ﬁrst example at 485 Hz is illustrated in Figure 2b, at which the wave directionality rotates in the opposite direction compared to the twist angle, until 𝜃 = 30°. The eﬀec- tive wavelength of the guided waves drastically increases in the small angle regime (𝜃 < 10◦), as displayed on Figure 2b, evidence of the progressive emergence of a moiré pattern introducing a super-lattice with long spatial wavelengths (Figure 2a). We note that the metasurface response is strongly aﬀected by the twist an- gle, as long as the wavelength of the moiré pattern is larger than the wavelength of the propagating waves. In reciprocal space, we correspondingly observe the presence of spatial harmonics that move away from the center of the Brillouin zone toward larger wavenumbers as the twist angle increases, in line with a decrease in moiré periodicity. When 𝜃 gets closer to 45◦, an inverted phe- nomenon arises, albeit less noticeable in the 2D modulation pro- ﬁle, and some spatial harmonics move closer to the center, caus- ing a distortion of the IFCs. Similar to the case at 𝜃 = 4◦ , this eﬀect hampers surface wave propagation, which is linked to the emergence of partial bandgaps and band ﬂattening caused by the interaction of diﬀerent spatial harmonics. The rigorous analysis of this phenomenon is inherently complex due to the quasiperi- odic nature of the system and it goes beyond the scope of this work. A diﬀerent evolution of the supported band structure as a func- tion of the twist angle can be observed in Figure 2c, correspond- ing to excitation at 625 Hz. At 𝜃 = 0◦ the wave propagation is hyperbolic but with opposite orientation. As 𝜃 increases, the ﬁeld progressively loses directionality, and becomes completely delocalized above 10◦. In reciprocal space (bottom row), this ﬁeld evolution manifests itself as a topological transition of the asso- ciated IFCs, which evolve from open hyperbolic to closed ellip- tical for increasing twist angle. Similar to Figure 2b, this is ex- plained by the distortion of the original contours due to emerging quasi-periodic modulation and super-lattice patterns. Although the transition here is driven by the twist angle, its emergence is inherently diﬀerent from the ones observed in previous studies of twisted hyperbolic metasurface bilayers:[32,41,42,43] here the sys- tem consists of a single layer whose interleaved spatial modu- lation lattices and emerging moiré patterns directly control the coupling between resonators, governing the IFC features. Adv. Sci. 2022, 9, 2200181 2200181 (4 of 8) © 2022 The Authors. Advanced Science published by Wiley-VCH GmbH www.advancedsciencenews.com www.advancedscience.com Figure 3. a) Doubling the modulation period enables a better sampling of the resulting modulation proﬁle. b) The modulation proﬁle is better preserved during the twist. c) Pillar height modulation as a function of rotation angle, with a zoom-in inset for four angles. d) Simulated out-of-plane displacement ﬁeld maps (top) and spatial FT (bottom) as a function of the rotation angle for hyperbolicity along y at 470 Hz. e–g) Same as (d) in the case of topological transitions at 595, 620, and 645 Hz, respectively. Next, we explore the possibility of precisely tuning the dis- persion proﬁle across smoother topological transitions. As noted above, the spatial features emerging from twisting the two inter- leaved lattices are characterized by a large change in their peri- odicities as the twist angle is varied. Increasing the twist angle can quickly degrade the nature of the modulation, as a function of how coarse the height modulation is sampled by the period of the square lattice. The associated angular sensitivity of this phe- nomenon can be expectedly reduced by increasing the periodicity of the modulation proﬁle. For example, Figure 3a considers the untwisted scenario when the modulation period is doubled to 𝜆 = 4a. This change translates into a smoother correlation between twist angle and resulting anisotropic contours, with considerably smaller distortions (Figure 3b,c). As a consequence, the original spatially anisotropic distribution of couplings within the mono- layer, which is responsible for the hyperbolic features, can be bet- ter preserved as the twist angle changes. The resulting wave propagation features are summarized in Figure 3. Figure 3d considers the frequency for which the original untwisted structure supports directional hyperbolic waves ori- ented along the y axis, for excitation at 470 Hz. Although super- lattice phenomena occur at small angles, their impact on the IFCs Adv. Sci. 2022, 9, 2200181 2200181 (5 of 8) © 2022 The Authors. Advanced Science published by Wiley-VCH GmbH www.advancedsciencenews.com www.advancedscience.com Figure 4. a) LEGO metasurfaces as a function of rotation angle for 𝜆 = 4a. b) Experimentally measured ﬁeld maps (top) and spatial FT (bottom) as a function of the rotation angle in the case of hyperbolicity along the y axis at 311 Hz. c–e) Same as (b) in the case of topological transitions at 430, 452.5, and 462.5 Hz, respectively. is less pronounced now, compared to Figure 2a. Indeed, as the angle increases, the propagation of directional waves smoothly follows the modulation rotation. In reciprocal space, the corre- sponding hyperbolic contours, albeit progressively ﬂatter due to small changes in the couplings induced by moiré eﬀects, undergo a similar rotation, indicating an eﬀective twist of the metasurface properties occurring over a large angular range. Next, we focus on the frequency range associated with hyper- bolicity along the x direction, displayed on Figure 3e–g (for 595, 620, and at 645 Hz, respectively). In the untwisted case (𝜃 = 0◦ ), these frequencies are related to diﬀerent anisotropic phases: the contour in (e) is an ellipse that progressively opens as the fre- quency is increased to become ﬂat in (f). A further frequency in- crease results in a curvature inversion, leading to a hyperbolic IFC (Figure 3g). As we increase the twist angle, Figure 3e demon- strates an opening of the IFC, and correspondingly a topological transition from elliptical to hyperbolic. The resulting canalized waves follow the rotation of the modulation proﬁle until 𝜃 = 45◦ . In the case of Figure 3f, an overall rotation of the ﬂat contour, as well as its progressive curvature inversion, is observed as a func- tion of the twist angle. Finally, Figure 3g shows a complete topo- logical transition from hyperbolic to elliptical contours, driven by the twist. These ﬁndings clearly show that the moiré patterns in- duced by the twist between the interleaved lattices are responsi- ble for topological transitions and canalized waves. The increased modulation wavelength (𝜆 = 4a) results in a better preservation of the untwisted anisotropic coupling distribution. This smoothens the transitions compared to the results of Figure 2b and allows to observe these moiré phenomena over larger angular ranges. These results suggest a straightforward experimental imple- mentation and observation of these phenomena on our reconﬁg- urable LEGO platform comprising 44 × 44 resonators. We im- plemented several conﬁgurations for 𝜃 = 0◦ , 15◦, 30◦, 45◦, as shown in Figure 4a. The sample snapshots illustrate the rotation of the modulation proﬁle, as well as the distortion caused by the sampling as it is twisted relative to the interleaved metasurface lattice (see the pattern formed by the black and blue stripes in the insets). Figure 4b shows the experimentally measured ﬁeld Adv. Sci. 2022, 9, 2200181 2200181 (6 of 8) © 2022 The Authors. Advanced Science published by Wiley-VCH GmbH www.advancedsciencenews.com www.advancedscience.com proﬁle (top) and corresponding spatial FT (bottom) for hyperbol- icity along y (at 311 Hz) as a function of the twist angle. As 𝜃 in- creases, the wave directionality accordingly rotates, reﬂected into the corresponding IFCs, which also become ﬂatter and closer to the origin in Fourier space. This behavior, albeit less clear than in Figure 3d because of the smaller size of the sample, follows the trend seen in simulations. Figure 4c–e consider frequencies that support hyperbolic waves along x. Panel (c), for excitation at 430 Hz, starts from delocalized ﬁelds for 𝜃 = 0◦ , and the prop- agation becomes strongly canalized as the angle increases, with a directionality following the modulation twist, consistent with Figure 3e. Our measurements conﬁrm a moiré-driven topologi- cal phase transition between closed and open contours. Next, Fig- ure 4d, for excitation at 452.5 Hz, shows a rotation in wave direc- tionality from 𝜃 = 0◦ to 45◦. Although less evident due to the smaller size of the plate, this transition is consistent with the re- sults in Figure 3f. Finally, Figure 4e presents results at 462.5 Hz, at which the waves are canalized and twisted from 𝜃 = 0◦ to 30◦, and then become delocalized at 45◦, experimentally conﬁrming a reverse topological transition, from open to closed contours, as the twist angle increases, similar to Figure 3f. Overall, these ex- perimental results clearly illustrate that tailoring the modulation parameters of twisted interleaved lattices over a metasurface pro- duces topological transitions between delocalized and canaliza- tion regimes, as well as an eﬀective rotation of the guided wave directionality, with the frequency being a key parameter that de- ﬁnes the type of observed transition. Overall, these results showcase the rich behavior associated with moiré physics and hyperbolic dispersion within a single- layer metasurface. We note that not all moiré physics associated to bilayer systems can be easily transposed to single layers. No- tably, the interlayer coupling parameter is important for some applications, and it can be challenging to ﬁnd its equivalent in single-layer systems.[45,46] Moreover, while bilayers may be mod- eled based on the properties of the individual layers,[32,41] addi- tional moiré eﬀects and quasi-periodicity are inevitable in our sys- tem, which makes their modeling more complex. Finally, while inducing dynamical reconﬁgurability in our single layer moiré system requires a more complex implementation of active de- vices, it does not rely on any physical displacement between the layers, making it more robust and fully controllable. 3. Conclusion In this paper, we have investigated the eﬀect of twisting inter- leaved lattices over a single-layer pillared metasurface. We ﬁrst explored the case where the two governing spatial features, the position and height modulation proﬁle of the pillars, are aligned but feature a mismatch in their spatial period along one direc- tion. The resulting periodic metasurface supports hyperbolic fea- tures over a broad range of frequencies, whose emergence has been observed both numerically and experimentally over a LEGO platform. Next, we introduced a relative rotation between the in- terleaved lattices, which induces moiré patterns that generates emerging wave phenomena, resulting in drastic modiﬁcations of the IFCs that undergo a transition from open to closed contours as the twist angle varies. A coarser sampling of the modulation patterns causes these transitions to be abrupt and to occur over a limited range of twist angles. The eﬀect of sampling is expounded by increasing the modulation wavelength, which results in a bet- ter preservation of the spatial features as the twist angle changes, and produces smoother topological transitions. Such transitions are associated with extreme anisotropic features, inducing wave canalization along speciﬁc directions that are controlled by the twist angle within a range of angles and frequencies. In stark contrast to the case of twisted hyperbolic bilayers,[32,41,42,43] these transitions are driven by the moiré patterns emerging within the sample as the rotation angle changes. Moreover, albeit of topo- logical nature, they diﬀer from topological phases in chiral hy- perbolic metamaterials, which are related to pseudo-spin propa- gation at the edge of the system.[62] We have observed these phe- nomena over a simple, practical, and highly reconﬁgurable LEGO platform, which allowed us to observe with ﬂexibility the various regimes discovered in our study. As such and considering ad- ditional practical implementation challenges, they can be trans- lated over a broad range of physical domains, including quan- tum and nanophotonic systems or microphononics in the con- text of pillared media,[63,64] opening opportunities for twistronic- induced phenomena that do not require multilayered fabrication and careful control alignment, interlayer couplings, and twisting. The reconﬁgurability of our approach, in contrast with previously investigated twisted bilayer conﬁgurations, opens new opportu- nities for single-layer moiré metasurfaces featuring high tunabil- ity of anisotropic responses. Such tunability emerges as a func- tion of the twist angle, which is a single parameter deﬁning the considered modulation. This suggests new opportunities stem- ming from the rich physics of twistronics and moiré phenomena, which may also open the door to dynamically reconﬁgurable de- vices capable of real-time enhanced wave manipulation. Supporting Information Supporting Information is available from the Wiley Online Library or from the author. Acknowledgements S.Y. and M.I.N.R. contributed equally to this work. S.Y. and A.A. acknowl- edge funding from the Simons Foundation, the National Science Founda- tion EFRI program, and the Air Force Oﬃce of Scientiﬁc Research MURI program. M. I. N.R. and M.R. gratefully acknowledge the support from the National Science Foundation (NSF) through the EFRI 1741685 grant and from the Army Research Oﬃce through grant W911NF-18-1-0036. LEGO is a trademark of the LEGO Group, which does not sponsor, authorize, or endorse this paper. Conﬂict of Interest The authors declare no conﬂict of interest. Data Availability Statement The data that support the ﬁndings of this study are available from the cor- responding authors upon reasonable request. Keywords hyperbolic, metasurface, moiré materials, phononics, quasi-periodicity, topological transitions, wave steering Adv. 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10.1371_journal.pone.0257328.pdf	Data Availability Statement: LANYERO, HINDUM (2021), Validity of caregivers’ reports on prior use of antibacterials in children under five years presenting to health facilities in Gulu, northern Uganda, Dryad, Dataset, https://doi.org/10.5061/ dryad.sj3tx9642.	null	RESEARCH ARTICLE Validity of caregivers’ reports on prior use of antibacterials in children under five years presenting to health facilities in Gulu, northern Uganda Hindum LanyeroID Katureebe Agaba4, Joan N. Kalyango5,6, Jaran Eriksen3,7, Sarah Nanzigu1* 1, Moses Ocan1, Celestino Obua2, Cecilia Stålsby Lundborg3, 1 Department of Pharmacology and Therapeutics, Makerere University College of Health Sciences, Kampala, Uganda, 2 Mbarara University of Science and Technology, Mbarara, Uganda, 3 Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden, 4 Infectious Diseases Research Collaboration, Kampala, Uganda, 5 Department of Pharmacy, Makerere University College of Health Sciences, Kampala, Uganda, 6 Clinical Epidemiology Unit, Makerere University College of Health Sciences, Kampala, Uganda, 7 Department of Infectious Diseases, South General Hospital, Stockholm, Sweden * snanzigu@yahoo.com Abstract Introduction Given the frequent initiation of antibacterial treatment at home by caregivers of children under five years in low-income countries, there is a need to find out whether caregivers’ reports of prior antibacterial intake by their children before being brought to the healthcare facility are accurate. The aim of this study was to describe and validate caregivers’ reported use of antibacterials by their children prior to seeking care at the healthcare facility. Methods A cross sectional study was conducted among children under five years seeking care at healthcare facilities in Gulu district, northern Uganda. Using a researcher administered questionnaire, data were obtained from caregivers regarding reported prior antibacterial intake in their children. These reports were validated by comparing them to common anti- bacterial agents detected in blood and urine samples from the children using liquid chroma- tography with tandem mass spectrometry (LC-MS/MS) methods. Results A total of 355 study participants had a complete set of data on prior antibacterial use col- lected using both self-report and LC-MS/MS. Of the caregivers, 14.4% (51/355, CI: 10.9– 18.5%) reported giving children antibacterials prior to visiting the healthcare facility. How- ever, LC-MS/MS detected antibacterials in blood and urine samples in 63.7% (226/355, CI: 58.4–68.7%) of the children. The most common antibacterials detected from the laboratory analysis were cotrimoxazole (29%, 103/355), ciprofloxacin (13%, 46/355), and metronida- zole (9.9%, 35/355). The sensitivity, specificity, positive predictive value (PPV), negative predictive value and agreement of self-reported antibacterial intake prior to healthcare a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Lanyero H, Ocan M, Obua C, Stålsby Lundborg C, Agaba K, Kalyango JN, et al. (2021) Validity of caregivers’ reports on prior use of antibacterials in children under five years presenting to health facilities in Gulu, northern Uganda. PLoS ONE 16(9): e0257328. https://doi. org/10.1371/journal.pone.0257328 Editor: Orvalho Augusto, University of Washington, UNITED STATES Received: February 12, 2021 Accepted: August 28, 2021 Published: September 16, 2021 Copyright: © 2021 Lanyero et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: LANYERO, HINDUM (2021), Validity of caregivers’ reports on prior use of antibacterials in children under five years presenting to health facilities in Gulu, northern Uganda, Dryad, Dataset, https://doi.org/10.5061/ dryad.sj3tx9642. Funding: Makerere University -SIDA collaboration (Sida PI0010) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. PLOS ONE \| https://doi.org/10.1371/journal.pone.0257328 September 16, 2021 1 / 14 PLOS ONEValidity of caregivers’ reports on prior use of antibacterials in children under five years Competing interests: The authors have declared that no competing interests exist. facility visit were 17.3% (12.6–22.8), 90.7% (84.3–95.1), 76.5% (62.5–87.2), 38.5% (33.0– 44.2) and 43.9% (k 0.06) respectively. Conclusion There is low validity of caregivers’ reports on prior intake of antibacterials by these children. There is need for further research to understand the factors associated with under reporting of prior antibacterial use. Introduction Antibacterial agents are used to treat a wide range of bacterial infections and are essential life- saving medicines. They are the most commonly used medicines in Sub-Saharan Africa due to the high prevalence of infectious diseases [1]. Used correctly, they deliver enormous benefits to the health of the population worldwide [2]. Antibacterials are, according to the national drug policy of Uganda, prescription only medi- cines [3]. However, they are readily accessible and affordable to most patients within the com- munities in Uganda, not only as prescription medicines as they can often also be obtained over-the-counter especially in private medicine outlets [4]. The relative ease with which com- munities access these medicines poses several challenges for antibacterial stewardship [4]. The majority of caregivers in low-income countries initiate treatment of their children at home [5]. The use of antibacterials prior to hospital visits is common, especially in low-income countries, and may influence patient treatment outcomes. According to a study in Nigeria, 85% of patients reported to have self-medicated before coming to the health facility and antibacterials were among the most common medicines used [6]. A study in Uganda reported that 62.2% of patients had used antibacterial agents prior to coming to health facility [4]. Another study done in Haiti to assess self-medication among patients presenting at an out-patient depart- ment found that 45.5% practiced self-medication with antibacterials [7]. Caregivers’ ability to report antibacterial intake prior to coming to a health facility is crucial for appropriate prescription of medicines at the health facility. Self-reports have been shown to have low validity as they are prone to recall bias and social desirability bias. Respondents nor- mally provide information that conforms to their perceived expectations of the health workers or researchers [5, 8]. A study carried out in Uganda in 2009 reported a limited validity of care- givers’ reports of use of sulfamethoxazole, chloroquine and sulfadoxine in their children prior to arrival to the hospital [5]. Similarly, a study from Tanzania reported that 97% of the children without history of prior chloroquine treatment had detectable levels of chloroquine in blood [9]. Another study in Ghana reported a high prevalence (64%) of antibacterials detected in urine samples of patients compared to the self-reported use (13%) [10]. To our knowledge no study has validated caregivers’ reports of intake of antibacterials in children under five years in rural communities in low resource settings. In this study we describe and validate caregivers’ reported use of antibacterials by their children under five years for treatment prior to seeking care at the healthcare facility. Materials and methods Ethics statement The protocol was reviewed and approved by the Makerere University School of Biomedical Sciences Research and Ethics Committee (reference SBS-570) and the Uganda National PLOS ONE \| https://doi.org/10.1371/journal.pone.0257328 September 16, 2021 2 / 14 PLOS ONEValidity of caregivers’ reports on prior use of antibacterials in children under five years Council of Science and Technology (reference HS235ES) (S1 Appendix). Administrative clear- ance was obtained from the healthcare facilities where the study was conducted. Written informed consent was obtained from caregivers of children under five years prior to data col- lection (S2 Appendix). Study design and setting A cross-sectional study was conducted among children under five years and their caregivers in healthcare facilities in Gulu district, northern Uganda. Gulu is located about 360 km from the capital city Kampala. In Uganda, the lowest level of the district-based healthcare system con- sists of the village health teams/community medicine distributors, which constitute level 1 of health care. This is operated by members of the community who can read and write at least in the local language of the community. The next level is health centre II which is operated by a professionally trained nurse with a diploma and is intended to serve 5000 patients. This is fol- lowed by health centre level III which is operated by a professionally trained clinical officer with a diploma in clinical medicine and intended to serve 10,000 patients. Above health centre level III is health centre level IV and then district hospitals headed by medical officers with a basic degree in medicine and surgery and intended to serve about 100,000 patients. Regionally there are regional referral hospitals where patients are referred to from the district hospitals. The regional referral hospitals are expected to have specialist health professionals covering the major disciplines such as surgery, internal medicine, and paediatrics. At the top of the health care system are the national referral hospitals [11]. Gulu district has a total of 19 health centre level II, 10 health centre level III, one health centre level IV, 31 registered pharmacies and 135 licensed drug shops [12–14]. This study was carried out in three health centre level III and one health centre level IV. These healthcare centers were purposively selected because they serve the greatest number of patients in the out-patient departments in Gulu district. The most com- mon diseases in children under five years seeking care at healthcare facilities in this area include; malaria, diarrhea, pneumonia, acute childhood malnutrition and HIV/AIDS [15–17]. Study population Sick children under five years and their caregivers seeking care at the four healthcare facilities were included in the study after caregivers’ consent. Children who were brought to the health center by caregivers who did not take care of the children from the onset of the current illness were excluded from the study. Children who had come for review or continuation of treatment for current illness were also excluded from the study. Sample size The sample size was computed based on formula for estimation of sample size for a single pro- portion [18]. Assuming that the proportion of children getting antibacterial treatment prior to health facility visit was 50%, in order to have a 95% confidence interval and a 5% margin of error, the minimum sample size needed was set to 385. The number of children sampled from each facility was determined from the volume of patients at the health facility using propor- tionate sampling. Sampling procedures The patients were selected by systematic random sampling. On each of the data collection dates the first patient to be recruited into the study was randomly selected by having a blind- folded data collector walk around in the waiting area and point at a random patient among the PLOS ONE \| https://doi.org/10.1371/journal.pone.0257328 September 16, 2021 3 / 14 PLOS ONEValidity of caregivers’ reports on prior use of antibacterials in children under five years patients waiting in line to be seen by the healthcare worker in the outpatient department. Thereafter, every fourth patient in line towards the entrance of the healthcare workers room was selected for recruitment. In the event that the selected patient was above five years of age, they were skipped and the next patient recruited while maintaining the sampling interval. Approximately 10 days were spent collecting data in each healthcare facility. Data collection An interviewer administered questionnaire was used for data collection. The questionnaire was pre-tested on caregivers of 30 children in outpatient departments of Gulu regional referral hospital. This tool was adapted from a tool used to collect data on prevalence and predictors of prior antibacterial use among patients presenting to hospitals in northern Uganda in a previ- ous study [4], it was written in English and translated to Acholi (the most common local lan- guage spoken in the study area). The data collection team was divided into four groups each comprising of two people, one pharmacy technician (health professional with diploma in pharmacy) and a laboratory techni- cian. The pharmacy technician conducted interviews while the laboratory technician collected the blood and urine samples. Information on the following variables was collected; sub-county of residence, age of child, age of care-giver, sex of child, sex of caregiver, whether medication was given to child before coming to the healthcare facility since the onset of this current illness, the type and source of the medicine, and the person who recommended the medicine. In case the caregiver did not know the name of the medicine, the interviewer asked them to describe it or show the packing material if at all they had come with it to the health center. Each interview lasted about 20 min- utes per patient. Sample collection and transportation. Two hundred microlitres (200μL) of blood was collected from the fingertips of children under five years using a 200μL micro-pipette with ethylenediamine tetra-acetic acid (EDTA), and spotted on a filter paper and left to dry for 3 hours in room temperature. After the blood had dried on the filter paper, each filter paper was put in a separate plastic zip bag with a desiccant and transported to the laboratory for analysis. Urine samples were collected in sterile wide mouth containers. In the very young children who couldn’t void in the wide mouth containers, urine samples were collected by placing a thick layer of cotton wool inside the child’s nappy and squeezing the urine in the urine sample bottles. Two hundred microlitres (200μL) of urine was collected from the wide mouth contain- ers using a plastic pipette and spotted on a filter paper and left to dry for 3 hours at room tem- perature. After the urine had dried on the filter paper, each filter paper was put in a separate plastic zip bag with a desiccant and transported to the laboratory for analysis. The dried blood spot (DBS) and dried urine spot (DUS) samples obtained from patients were stored at -20˚C and -80˚C respectively until analysis. Extraction and analysis of antibacterials in dry blood spot and dry urine spot sam- ples. The whole diameter disk (containing 200μl of blood or urine) was cut out from each DBS and DUS. The cut disc was placed in an Eppendorf tube (1.5 mL capacity) and mixed with 1000 μL of methanol (20%) and acetonitrile (80%). The sample was vortex-mixed twice for 20 s at 10-min intervals and then centrifuged at 3500 revolutions per minute (RPM) for 5 minutes. After the extraction period, the filter paper was removed, and 500 μL of the extract was transferred into an auto-sampler vial to be injected onto the LC-MS/MS system for analysis. A simple, fast, sensitive and selective qualitative LC-MS/MS method for identification of fifteen (15) antibacterials in DBS and DUS was used for analysis (S3 Appendix). The limit PLOS ONE \| https://doi.org/10.1371/journal.pone.0257328 September 16, 2021 4 / 14 PLOS ONEValidity of caregivers’ reports on prior use of antibacterials in children under five years of detection for the different antibacterials were: amoxicillin (1.34 ng/mL), ampicillin (0.001 ng/mL), penicillin G (0.005 ng/mL), penicillin V (0.03 ng/mL), cloxacillin (0.2 ng/mL), cephalexin (0.22 ng/mL), sulfamethoxazole (0.95 ng/mL), trimethoprim (0.52 ng/mL), eryth- romycin (1.1 ng/mL), ciprofloxacin (0.1 ng/mL), tetracycline (0.14 ng/mL), clarithromycin (1.4 ng/mL), metronidazole (0.0004 ng/mL), chloramphenicol (0.0001ng/mL) and azithromy- cin (0.22 ng/mL). Data on key pharmacokinetics properties that may have affected the interpretation of our results, have been presented in the supporting information section (S1 Table), and these include: clearance, terminal half-life, percentage of medicine excreted in urine, time to peak plasma concentrations and volume of distribution. Data management Double data entry was done using Epi-Data 3.1 software for both the questionnaire and labora- tory data. The two datasets were reconciled by comparing them for each field in the question- naire and laboratory result, in case of any discrepancies, the corresponding questionnaire or patient laboratory record was checked to establish the correct entry. Data were then imported into Stata 14/IC (Stata Inc., Texas USA) for analysis. Statistical analysis Descriptive statistics were presented using median and interquartile range (IQR) for continu- ous variables or frequencies and proportions for categorical variables. The dependent vari- ables, treatment of child with antibacterials prior to healthcare facility visit as reported by their caregiver and detectable antibacterials in DBS or DUS samples, were summarized as propor- tions. In order to adjust for potential biases associated with point estimates from the sampling design, we used svy commands in stata to compute proportions and respective 95% confidence intervals. Pearson’s chi-square test was used to assess associations for the categorical variables. In order to validate caregivers’ reported use of antibacterials, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), prevalence, agreement and kappa coefficient were calculated. Laboratory results for detection of antibacterials in dry blood spot or dry urine spot samples were considered as the gold standard and caregivers’ reports of use of antibacterials prior to health facility visit were considered as the test results. Results Socio-demographic characteristics of the caregivers and children under five years Of the 385 sampled children, 355 (92.2%) had data on both caregiver’s report on antibacterial use prior to health facility visit and results from urine and blood analysis and were thus included in the analysis. The 30 (7.8%) observations were dropped because they were missing blood analysis data. Over half (53.2%, n = 189) of the children were female. The median age of the children was 29 (IQR: 16–46) months. The majority (96.1%, n = 341) of the caregivers were female. The median age of the caregivers was 25 (IQR: 21–31) years. About half (53.2%, n = 189) of the children attended a healthcare facility located in a rural area. (Table 1). Prevalence of antibacterial use prior to coming to the health facility as reported by caregivers of children under five years Out of the 355 children under five years who were included in the analysis, 51 (14.4%, CI: 10.9–18.5) were reported by the caregivers to have been treated with antibacterials prior to PLOS ONE \| https://doi.org/10.1371/journal.pone.0257328 September 16, 2021 5 / 14 PLOS ONEValidity of caregivers’ reports on prior use of antibacterials in children under five years Table 1. Socio-demographic characteristics and prevalence of antibacterial use in children under five years prior to health facility visit as reported by caregivers of children under five years in rural communities of Gulu district, northern Uganda (August, 2019). Characteristics Description Respondent’s Frequency (%) Proportion of reported antibacterial use, n (%) 95% CI P-value (Pearson’s chi-square test) Overall Sex of child Location of health facility Age of child (months) Age of child caregiver (years) Sex of child caregiver Source of antibacterials 355 (100) 166 (46.8) 189 (53.2) 166 (46.8) 189 (53.2) 64 (18.0) 176 (49.6) 115 (32.4) 121 (34.1) 168 (47.3) 51 (14.4) 7 (2.0) 8 (2.2) 14 (3.9) 341 (96.1) Male Female Urban Rural 1–12 13–36 37–59 13–22 23–32 33–42 43–52 � 53 Male Female Home cabinet Public health facility Private clinics Drug shops Retail shops Traditional healers Antibacterials recommended by Caregiver Other household member Friend/neighbor Doctor/nurse Drug seller/pharmacist Traditional healer Age of child (months), median (IQR) Age of child caregiver (years), median (IQR) 29 (16.46) 25 (21.31) n: Sample size; CI: Confidence Interval; %: Percentage; IQR: Interquartile range https://doi.org/10.1371/journal.pone.0257328.t001 51 (14.4) 22 (13.3) 29 (15.3) 36 (21.7) 15 (7.9) 6 (9.4) 32 (18.2) 13 (11.3) 15 (12.4) 27 (16.1) 7 (13.7) 1 (14.3) 1 (12.5) 1 (7.1) 50 (14.7) 11 (23.9) 9 (37.5) 8 (30.8) 18 (35.3) 4 (30.8) 1 (50) 11 (31.4) 3 (37.5) 1 (25.0) 17 (34.0) 18 (29.0) 1 (33.3) 0.575 <0.001 0.119 0.936 0.432 <0.001 10.9–18.5 8.9–19.4 10.9–21.3 16.0–28.6 4.8–12.8 4.2–19.5 13.1–24.6 6.7–18.6 7.6–19.6 11.2–22.5 6.6–26.3 1.7–62.3 1.5–57.5 0.9–39.0 11.3–18.9 12.5–38.8 18.8–59.4 14.3–51.8 22.4–49.9 9.1–61.4 1.3–98.7 16.9–49.3 0.253 8.5–75.5 0.6–80.6 21.2–48.8 18.2–41.9 0.8–90.6 coming to the healthcare facility. Of these 51 children, the prevalence of antibacterial use was higher in those from urban areas (21.7%, CI: 16.0–28.6) and in those who got antibacterials from public health facilities (37.5%, CI: 18.8–59.4) (Table 1). Prevalence of antibacterials detected in blood and urine samples of children under five years Of the 355 children under five years who were included in the analysis, 226 (63.7%, CI: 58.4– 68.7) had detectable levels of antibacterials in urine or blood in the samples taken upon arrival to the healthcare facility (Table 2). Most commonly used antibacterials. The most commonly used antibacterials as reported by the care givers were amoxicillin (6.2%, 22/355), cotrimoxazole (2.8%, 10/355), and metroni- dazole (2.3%, 8/355). The most common antibacterials detected from the laboratory analysis were cotrimoxazole (29%, 103/355), ciprofloxacin (13%, 46/355), and metronidazole (9.9%, 35/355) (Fig 1) PLOS ONE \| https://doi.org/10.1371/journal.pone.0257328 September 16, 2021 6 / 14 PLOS ONEValidity of caregivers’ reports on prior use of antibacterials in children under five years Table 2. Prevalence of antibacterials detected in blood or urine samples of children under five years in rural communities of Gulu district, northern Uganda (August, 2019). Characteristics Description Respondent’s Frequency (%) Proportion of antibacterial detected, n (%) Overall Sex of child Location of health facility Age of child (months) Age of child caregiver (years) Sex of child caregiver Male Female Urban Rural 1–12 13–36 37–59 13–22 23–32 33–42 43–52 � 53 Male 355 (100) 166 (46.8) 189 (53.2) 166 (46.8) 189 (53.2) 64 (18.0) 176 (49.6) 115 (32.4) 121 (34.1) 168 (47.3) 51 (14.4) 7 (2.0) 8 (2.2) 14 (3.9) 226 (63.7) 108 (65.1) 118 (62.4) 103 (62.0) 123 (65.1) 45 (70.3) 112 (63.6) 69 (60.0) 83 (68.6) 99 (58.9) 36 (70.6) 5 (71.4) 3 (37.5) 8 (57.1) Female 341 (96.1) 218 (63.9) n: Sample size; CI: Confidence Interval; %: Percentage https://doi.org/10.1371/journal.pone.0257328.t002 95% CI 58.4–68.7 57.5–71.9 55.3–69.1 54.4–69.1 57.9–71.6 57.9–80.3 56.2–70.4 50.7–68.6 59.7–76.3 51.3–66.2 56.6–81.5 29.7–93.7 11.4–73.6 30.7–80.1 58.7–68.9 P-value (Pearson’s chi-square test) 0.608 0.554 0.389 0.164 0.605 Validity of caregivers’ reports of antibacterial intake in children under five years The sensitivity, specificity, PPV, NPV, agreement and kappa coefficient of the caregivers’ reports of use of antibacterials for treatment of children prior to healthcare facilities visit were 17.3% (12.6–22.8), 90.7% (84.3–95.1), 76.5% (62.5–87.2), 38.5% (33.0–44.2), 43.9% (38.7– 49.3%) and 0.06 (0.01–0.12) respectively. The sensitivity, specificity, PPV,NPV, agreement and kappa coefficient varied between the different antibacterials (see Table 3). Discussion In this study we demonstrated that the prevalence of antibacterial use prior to health facility visit was high and that caregivers under reported the use of antibacterials in the children under five years prior to coming to the health facility. Antibacterial use prior to healthcare facility visit is a common practice in many resource limited settings globally. Caregivers’ ability to report antibacterial use before coming to the health facility is crucial for appropriate prescrip- tion of antibacterial upon reaching health facilities [5]. Appropriate prescription of antibacter- ials is important because it reduces the emergence of antibacterial resistance, poor clinical outcomes, increased mortality and wastage of financial resources [19]. In the current study, almost two thirds (63.7%) of the samples (blood and/or urine) tested positive for antibacterials. This implies that the prevalence of antibacterial use prior to health facility visit is much higher than what was self-reported (14.4%). This finding is similar to those from other low and middle income countries (LMIC) [4, 5, 10], a study carried out in Ghana reported a prevalence of self-reported antibacterial use prior to health facility visit of 13%, however, analysis of urine samples reported a much higher prevalence of 64% [10]. In Uganda, self-medication with antibacterials is a common practice [1, 4, 20] which is reflected in the high prevalence of antibacterials found in the samples (blood and/or urine) in the cur- rent study [1]. Another reason for the high prevalence of antibacterial use in our study is the PLOS ONE \| https://doi.org/10.1371/journal.pone.0257328 September 16, 2021 7 / 14 PLOS ONEValidity of caregivers’ reports on prior use of antibacterials in children under five years Fig 1. Commonly used antibacterials according to the laboratory analysis. https://doi.org/10.1371/journal.pone.0257328.g001 high prevalence of infectious diseases in these communities. In Uganda, 71% of children under five years attending healthcare facilities do so due to acute respiratory infections [21], however, in the community in this study, the most common diseases in children under five years seeking care at healthcare facilities include; malaria, diarrhea, pneumonia, acute child- hood malnutrition and HIV/AIDS [15–17]. High prevalence of antibacterials found in the samples in the current study could also be due to exposure to antibacterials through Table 3. Validity of caregivers’ reports of antibacterial intake in children under five years in rural communities of Gulu district, northern Uganda (August, 2019). Parameters Sensitivity (95% CI) Specificity (95% CI) PPV (95% CI) NPV (95% CI) Prevalence (95% CI) Agreement (95% CI) κ (95% CI) Overall 17.3 (12.6–22.8) 90.7 (84.3–95.1) 76.5 (62.5–87.2) 38.5 (33.0–44.2) 63.7 (58.4–68.7) 43.9 (38.7–49.3) 0.06 (0.01–0.12) Amoxicillin 5.6 (0.1–27.3) 93.8 (90.6–96.1) 4.5 (0.1–22.8) 94.9 (92.0–97.0) 5.1 (3.0–7.9) 89.3 (85.6–92.3) Cotrimoxazole 5.8 (2.3–12.2) 98.4 (96.0–99.6) 60.0 (26.3–87.8) 71.9 (66.8–76.6) 29.0 (24.3–34.0) 71.5 (66.5–76.2) -0.01 (-0.11–0.09) 0.06 (-0.01–0.12) Metronidazole 2.9 (0.1–14.9) 97.8 (95.5–99.1) 12.5 (0.3–52.7) 90.2 (86.6–93.1) 9.9 (7.0–13.4) 88.5 (84.7–91.6) 0.01 (-0.08–0.1) Ciprofloxacin 4.3 (0.5–14.8) 99.7 (98.2–100) 66.7 (9.4–99.2) 87.5 (83.6–90.8) 13.0 (9.6–16.9) 87.3 (83.4–90.6) 0.07 (-0.03–0.16) %: percentage; CI: Confidence interval; κ: Kappa coefficient https://doi.org/10.1371/journal.pone.0257328.t003 PLOS ONE \| https://doi.org/10.1371/journal.pone.0257328 September 16, 2021 8 / 14 PLOS ONEValidity of caregivers’ reports on prior use of antibacterials in children under five years consumption of water, vegetables and animal products [22, 23]. In the Hong Kong survey to determine the presence of veterinary antibiotics in food, drinking water, and the urine of pre- school children, it was found that 13 veterinary antibiotics were detectable in the urine of 77.4% of primary school children with norfloxacin and penicillin having the highest detection rates. Enrofloxacin, penicillin, and erythromycin were the most detected veterinary antibiotics in raw and cooked food [21]. Studies in Uganda, report a high prevalence of veterinary use of antibacterials. The most commonly used antibacterials in veterinary medicine in Uganda include; procaine penicillin, trimethoprim/sulfadiazine, erythromycin sulphate, tylosin tar- trate, oxytetracycline hydrochloride [24, 25]. This high prevalence of antibacterial use can lead to increased risk of resistance within the community [26]. A study was carried out in Uganda to determine the epidemiology and antibiotic susceptibility of Vibrio cholerae associated with the 2017 outbreak in Kasese district, and it reported that V. cholerae was highly resistant to the commonly used antibiotics [27]. Most caregivers reported to have given their children amoxicillin, cotrimoxazole and met- ronidazole. This is consistent with reports from a study in northern Uganda where metronida- zole, amoxicillin, ciprofloxacin, doxycycline or cotrimoxazole were reported as the most commonly used antibacterials by patients prior to hospital visit [4]. Metronidazole is com- monly used for bacterial gastroenteritis, amoxicillin is used for bacterial chest infections, and cotrimoxazole is used to treat pneumonia, bronchitis, infections of the urinary tract, ears intes- tines and as prophylaxis against opportunistic infections in HIV [28, 29]. In our study the most commonly detected antibacterials in the laboratory analysis results were cotrimoxazole, ciprofloxacin and metronidazole, similar to findings from a study carried out in Ghana which reported ciprofloxacin, trimethoprim or metronidazole as the most common antibacterials detected in urine samples [30]. Ciprofloxacin is commonly used to treat pneumonia, typhoid fever, infectious diarrhea, skin and bone infections [28, 29]. Amoxicillin was the most com- monly reported antibacterial used and yet it was not among the most commonly detected anti- bacterials from the laboratory analysis. This could be explained by the pharmacokinetics of amoxicillin, which has a very short half-life of about 1 hour and will usually be out of the sys- tem within 5 hours. Thus, meaning that for it to be detected in the blood or urine samples, it should have been taken within a few hours before healthcare facility visit [28, 29]. We also observed that the number of children who had cotrimoxazole in their biological samples was higher than those who reported the use. It is possible that some of these children may have tested positive for cotrimoxazole since they could have been receiving it as prophylaxis against opportunistic infections in HIV [31, 32]. The prevalence of HIV/AIDS in northern Uganda as of 2019 when data for this study was collected, was 7.2% in adults and 0.5% in children under five years [33]. Since we were interested in antibacterial use for current illness, for which the children were brought to the healthcare facility, caregivers might not have found it not neces- sary to report the use of cotrimoxazole as prophylaxis against opportunistic infections in HIV. The positive predicative value we found for reported use of antibacterials is not high enough to allow caregivers reports to guide treatment. The high specificity values indicate under reporting but the negative predictive value indicate that many children were given drugs that were not reported by caregivers. This study was carried out in rural communities of Gulu dis- trict in Uganda where the adult literacy levels are low [1, 20], and the inconsistencies in care- givers’ response to interview questions and laboratory findings, could have been because of caregivers inability to identify medicines taken as antibacterials. Another reason for the incon- sistencies in self-reported antibacterial use and laboratory findings could have been due to social desirability bias [34]. The caregivers could have been aware that self-medication is not a good practice, and therefore feared to tell the interviewers the truth. Another reason for the inconsistencies could have been due to consumption of these medicines from diffuse sources PLOS ONE \| https://doi.org/10.1371/journal.pone.0257328 September 16, 2021 9 / 14 PLOS ONEValidity of caregivers’ reports on prior use of antibacterials in children under five years such as milk, water or food, studies in Uganda have reported veterinary use of antibacterials [24, 25]. Another worrying explanation for the inconsistencies could be the quality of antibac- terial medicines, some of these antibacterials may not contain the actual quantity of the active medicine the manufacturers claim they contain. Although we did not set out to study the qual- ity of antibacterials in this study, high prevalence of substandard antibacterial medicines has been previously reported in developing countries [35]. Furthermore, inaccuracies in self- reports may lead to duplication of therapy, incorrect management of the ill child, failure to appreciate non-compliance leading to exacerbation of chronic medical conditions, or inaccu- rate research conclusions [36]. We observed a strong association between high self-reported prior antibacterial use and the source of antibacterials being from public health facilities. This could be attributed to the low financial status of the people in these communities [1] forcing them to seek free healthcare from public healthcare facilities. The district-based healthcare system in Uganda consists of level I, II, III, IV and district hospitals [11]. This therefore means that by the time these chil- dren were brought to health care level III and IV, they could have already sought care from the lower levels and were referred to these higher levels for further management. There is need for further research to understand the reasons for caregivers’ poor reports on their children’s prior intake of antibacterials before coming to the health facility. Improved validity could be promoted by encouraging health care workers to carefully explain to the care- givers the medicines they administer to these children when they fall sick. Proper documenta- tion of the medicines given to these children when they are sick could also improve the validity of self-reported medicine use. There is need for the healthcare workers to educate the caregiv- ers about the dangers of using antibacterials without consulting a healthcare worker, and also further research is required to better understand why caregivers initiate antibacterial use at home without consulting a healthcare service provider. This all will allow policy makers to be better informed when planning interventions to reduce the large amount of incorrect antibac- terial use in the community. The results of our study should be considered in light of some limitations. This study could have been affected by recall bias, where antibacterials given may have been forgotten. The study could have also been affected by social desirability bias since the study was carried out in a hospital setting and probably caregivers feared telling the truth because they thought it could affect patient care. Under reporting could have been affected by how the questions were understood by the caregivers. Failure to detect some of the antibacterials in the samples could have been due to the pharmacokinetics of the antibacterials. Factors such as education level of the caregivers could have contributed to the under reporting of antibacterial use prior to healthcare facility visit, however, we didn’t collect this information. This is because adult liter- acy levels in this community are low [1, 20] and to our knowledge previous studies have not reported associations between self-report and education level [10, 37]. However, there is need for further research to determine if there’s an association between caregivers’ education level and reporting of prior antibacterial use in this setting. The discrepancy between the reported use and the detected antibacterials in blood/urine samples could have also been because the antibacterial could have been given for the management of another condition, such as cotri- moxazole for prophylaxis against HIV related opportunistic infections, but we did not collect this information. Our tool was designed to capture only antibacterial use for the illness for which the children were brought to the healthcare facility. We were unable to report the levels of antibacterials in relation to how far back the antibacterials were taken, this is because we used a qualitative LC-MS/MS method which was developed to report the presence or absence of antibacterials and not to quantify them. PLOS ONE \| https://doi.org/10.1371/journal.pone.0257328 September 16, 2021 10 / 14 PLOS ONEValidity of caregivers’ reports on prior use of antibacterials in children under five years Conclusion A high proportion of children under five years take antibacterials prior to visiting a healthcare facility in northern Uganda. However, there is low validity of caregivers’ reports on prior intake of antibacterials by these children. There is need for further research to understand the factors associated with under reporting of prior antibacterial medicine use by caregivers of children under five years. In addition, we suggest that health care workers should endeavor to explain the role and names of medicines during dispensing, as well as the importance of reporting correctly on prior medication intake. There is also need to educate the caregivers about the dangers of using antibacterials without consulting a healthcare worker, and also fur- ther research is required to better understand why caregivers initiate antibacterial use at home without consulting a healthcare service provider. This all will allow policy makers to be better informed when planning interventions to reduce the large amount of incorrect antibacterial use in the community. Supporting information S1 Table. Summary of key pharmacokinetic properties of some of the antibacterials that are commonly used among children under five years in rural communities of Gulu district, northern Uganda (August, 2019). (DOCX) S1 Appendix. Ethical approval letters. (DOCX) S2 Appendix. Consent form. (DOCX) S3 Appendix. LC-MS/MS method. (DOCX) S4 Appendix. Questionnaire. (DOCX) Acknowledgments We appreciate the tireless effort of the data collection team; Apio Patricia, Ojok Albert, Kagood Francis, Hassan Chakaal and the village local chair persons for their guidance. Author Contributions Conceptualization: Hindum Lanyero, Moses Ocan, Celestino Obua, Cecilia Stålsby Lund- borg, Joan N. Kalyango, Jaran Eriksen. Data curation: Hindum Lanyero, Joan N. Kalyango, Jaran Eriksen. Formal analysis: Hindum Lanyero, Katureebe Agaba. Funding acquisition: Celestino Obua, Cecilia Stålsby Lundborg, Joan N. Kalyango, Jaran Eriksen. Investigation: Hindum Lanyero, Sarah Nanzigu. Methodology: Hindum Lanyero, Moses Ocan, Katureebe Agaba, Joan N. Kalyango, Sarah Nanzigu. PLOS ONE \| https://doi.org/10.1371/journal.pone.0257328 September 16, 2021 11 / 14 PLOS ONEValidity of caregivers’ reports on prior use of antibacterials in children under five years Project administration: Moses Ocan, Celestino Obua, Cecilia Stålsby Lundborg, Joan N. Kalyango, Jaran Eriksen. Resources: Hindum Lanyero, Jaran Eriksen. Software: Hindum Lanyero. Supervision: Moses Ocan, Celestino Obua, Cecilia Stålsby Lundborg, Joan N. Kalyango, Jaran Eriksen, Sarah Nanzigu. Validation: Hindum Lanyero, Jaran Eriksen, Sarah Nanzigu. Visualization: Hindum Lanyero. Writing – original draft: Hindum Lanyero. Writing – review & editing: Hindum Lanyero, Moses Ocan, Celestino Obua, Cecilia Stålsby Lundborg, Katureebe Agaba, Joan N. Kalyango, Jaran Eriksen, Sarah Nanzigu. References 1. Ocan M, Bwanga F, Bbosa GS, Bagenda D, Waako P, Ogwal-Ogeng J, et al. Patterns and predictors of self-medication in Northern Uganda. PLoS ONE. 2014; 9(3):e92323. https://doi.org/10.1371/journal. pone.0092323 PMID: 24658124 2. Wellcome Trust, UK Government. Safe, secure and controlled:managing the supply chain of antimicro- bials. Review on antimicrobial Resistance. 2015. 3. Ministry of Health Uganda. Uganda National Drug Policy. 2002. 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10.3389_fpsyt.2022.1086038.pdf	null	Data availability statement The datasets presented in this article are not readily available because ethics approval did not include public data sharing. Requests to access the datasets should be directed to the corresponding author. organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.	fpsyt-13-1086038 January 13, 2023 Time: 17:35 # 1 OPEN ACCESS EDITED BY Nuno Madeira, University of Coimbra, Portugal REVIEWED BY Sandra Vieira, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, United Kingdom Marco Simoes, University of Coimbra, Portugal João Valente Duarte, University of Coimbra, Portugal CORRESPONDENCE Diana Prata diana.prata@kcl.ac.uk SPECIALTY SECTION This article was submitted to Neuroimaging, a section of the journal Frontiers in Psychiatry RECEIVED 31 October 2022 ACCEPTED 29 December 2022 PUBLISHED 19 January 2023 CITATION Tavares V, Vassos E, Marquand A, Stone J, Valli I, Barker GJ, Ferreira H and Prata D (2023) Prediction of transition to psychosis from an at-risk mental state using structural neuroimaging, genetic, and environmental data. Front. Psychiatry 13:1086038. doi: 10.3389/fpsyt.2022.1086038 COPYRIGHT © 2023 Tavares, Vassos, Marquand, Stone, Valli, Barker, Ferreira and Prata. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Original Research PUBLISHED 19 January 2023 DOI 10.3389/fpsyt.2022.1086038 Prediction of transition to psychosis from an at-risk mental state using structural neuroimaging, genetic, and environmental data Vânia Tavares1,2, Evangelos Vassos3,4, Andre Marquand5,6, James Stone7, Isabel Valli8,9, Gareth J. Barker10, Hugo Ferreira1 and Diana Prata1,11 1Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal, 2Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal, 3Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom, 4National Institute for Health Research Maudsley Biomedical Research Centre, South London and Maudsley National Health System Trust, London, United Kingdom, 5Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, 6Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, Netherlands, 7Brighton and Sussex Medical School, University of Sussex, Brighton, United Kingdom, 8Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom, 9Institut d’Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain, 10Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom, 11Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom Introduction: Psychosis is usually preceded by a prodromal phase in which patients are clinically identiﬁed as being at in an “At Risk Mental State” (ARMS). A few studies have demonstrated the feasibility of predicting psychosis transition from an ARMS using structural magnetic resonance imaging (sMRI) data and machine learning (ML) methods. However, the reliability of these ﬁndings is unclear due to possible sampling bias. Moreover, the value of genetic and environmental data in predicting transition to psychosis from an ARMS is yet to be explored. Methods: In this study we aimed to predict transition to psychosis from an ARMS using a combination of ML, sMRI, genome-wide genotypes, and environmental risk factors as predictors, in a sample drawn from a pool of 246 ARMS subjects (60 of whom later transitioned to psychosis). First, the modality-speciﬁc values in predicting transition to psychosis were evaluated using several: (a) feature types; (b) feature manipulation strategies; (c) ML algorithms; (d) cross-validation strategies, as well as sample balancing and bootstrapping. Subsequently, the modalities whose at least 60% of the classiﬁcation models showed an balanced accuracy (BAC) statistically better than chance level were included in a multimodal classiﬁcation model. Results and discussion: Results showed that none of the modalities alone, i.e., neuroimaging, genetic or environmental data, could predict psychosis from an ARMS statistically better than chance and, as such, no multimodal classiﬁcation model was trained/tested. These results suggest that the value of structural MRI data and genome-wide genotypes in predicting psychosis from an ARMS, which has been fostered by previous evidence, should be reconsidered. KEYWORDS machine learning, biomarker, schizophrenia, ARMS, prognosis Frontiers in Psychiatry 01 frontiersin.org fpsyt-13-1086038 January 13, 2023 Time: 17:35 # 2 Tavares et al. 10.3389/fpsyt.2022.1086038 1. Introduction Psychosis is a severe condition usually within the context of a mental disorder such as a schizophrenia, some neurological disorders (e.g., Alzheimer’s disease) or other medical conditions (e.g., induced by drugs or illicit substances), characterized by a disconnection from reality (1). The onset of psychosis, when in the context of a mental disorder, is typically preceded by a prodromal phase that lasts months to years (2); and usually starts early during adolescence and precedes the onset of psychotic symptoms by 10 or more years (3). In this prodromal phase, subtle and subjectively experienced disturbances in mental processes emerge (basic symptoms). These are the ﬁrst manifestations of the neurobiological processes underlying psychosis and are mainly distinguished from other symptoms (i.e., positive or negative symptoms) by their self-experience nature (4). As the course of the psychotic illness evolves, increasingly disabling behavioral symptoms start to emerge, generally called negative symptoms, in particular a reduction of motivation and/or expressiveness (5). Additionally, cognitive deﬁcits in attention, memory, reasoning, lack of concentration and executive functioning appear (6). Lastly, positive symptoms emerge, such as hallucinations, delusions, disorganized speech, and behavior (1). A patient may be clinically identiﬁed as being at a late prodromal phase of psychosis or having an “At Risk Mental State” (hereinafter: ARMS) if they present a functional decline in association with one or more of the following commonly used criteria (2, 7): (1) attenuated psychotic symptoms (APS), such as delusions, hallucinations, or disorganized speech with a frequency of at least once per week in the past month; (2) a brief limited intermittent psychotic (BLIP) episode lasting less than 1 week which resolves without antipsychotic medication; or (3) a genetic liability to psychosis or schizotypal traits, i.e., having either a ﬁrst-degree relative with psychosis or a schizotypal personality disorder. Transition to psychosis from an ARMS may be evaluated based on the severity, frequency, and total duration of the psychotic symptoms, i.e., when the subject experiences a ﬁrst episode of psychosis (FEP). Subjects with an ARMS and seeking help have a transition rate to psychosis of about 9% in the ﬁrst 6 months and 25% in the ﬁrst 3 years (8) and, in particular, an increased risk of transition to schizophrenia of 15.7% within an average period of 2.35 years, as shown by a meta-analysis (9). Thus, most of the people with an ARMS who later develop a psychotic illness will be diagnosed with schizophrenia. Furthermore, since about 70% of subjects diagnosed with an ARMS never develop a full- blown psychotic illness (9), these people may beneﬁt from a less intensive treatment to ameliorate symptoms or need no treatment at all. Such increase in treatment cost-eﬀectiveness would represent a substantial decrease in healthcare costs, and treatment burden to patients, including pharmacological side eﬀects. However, there is no method for distinguishing between individuals with an ARMS who will subsequently develop a psychotic illness from those who will not (i.e., before a FEP onset). Given the above need, an eﬀective, precise, and quantitative tool for the prediction of transition to psychosis from an ARMS has been sought by several studies employing machine learning (ML) methods and structural magnetic resonance imaging (sMRI). Indeed, several studies have consistently showed prediction of transition to psychosis from as ARMS with accuracies ranging between 74 and 84% (10– 15). Transition to psychosis from an ARMS using only sMRI and ML was ﬁrst predicted using whole-brain gray matter volume metrics with an accuracy of 82% [(15 ARMS who transitioned to psychosis (ARMS-T) and 18 who did not (ARMS-NT)] (10). This ﬁnding was later replicated: (a) in an independent sample by the same group [balanced accuracy (BAC) = 84%, 16 ARMS-T and 21 ARMS-NT] (11); (b) combining both these samples (BAC = 80%, 33 ARMS-T and 33 AMRS-NT) (12); (c) using also one of the above samples for graph- extracted network metrics from cortical gyriﬁcation (BAC = 81%, 16 ARMS-T and 63 ARMS-NT) (15), and regional gray matter metrics (BAC = 74%, 16 ARMS-T and 19 ARMS-NT) (14); and (d) using regional gray matter metrics in an independent sample (BAC = 77%, 17 ARMS-T and 17 ARMS-NT; speciﬁcity of a replication sample of individuals with an ARMS who did not develop psychosis = 68%, 40 ARMS-NT) (13). To date, only two, relatively small, ARMS samples have been used for sMRI and ML analysis: FETZ (10, 12, 15) and FePsy (11, 12, 14). Thus, the robustness and generalizability of the above ﬁndings are still unclear due to possible speciﬁc sample characteristics, i.e., small sample sizes (from 33 individuals to at most 79 individuals with ARMS), with several studies stemming from a single site (10, 11, 13–15) or a combination of previously studied sites (12), which makes them not actual replications, with one exception (13). Interestingly, to the best of our knowledge, genetic data has been explored for the prediction of the transition to psychosis from an ARMS only once (16). In this study, a schizophrenia polygenic risk score (PRS) was able to predict transition to psychosis in individuals with an European [area under the curve (AUC) = 0.65; 32 ARMS- T and 92 ARMS-NT] and with a Non-European (AUC = 0.59; 48 ARMS-T and 156 ARMS-NT) ancestry, respectively. This is despite there being several classiﬁcation studies showing that genetic markers can predict schizophrenia (17–22), FEP (23) or ARMS (23), both of individual polymorphisms (18, 19, 21, 23) or, composite polygenic scores (20–22), and gene expression proﬁles (24). From an environmental exposure perspective, and to the best of our knowledge, environmental data have never been explored for predicting individual transition to psychosis from an ARMS. The combination of neuroimaging measures and genetics or environmental measures, using ML, has, to the best of our knowledge, been explored once to predict ARMS prognosis (i.e., transition to psychosis from an ARMS) in a study running in parallel to ours (25). Therein, a large sample from the PRONIA project (26 ARMS-T and 308 ARMS-NT from 7 sites) was used to build a sequential stacked multimodal model using clinical-neurocognitive (including environmental data), genetic (in the form of a PRS for schizophrenia) and neuroimaging (in the form of voxel-based gray matter volume maps) data and - unlike the present study–human prognostic ratings, showing a ﬁnal balanced accuracy in predicting transition to psychosis of 86%. In the present longitudinal prognostic biomarker study, we aimed to explore the use of ML models trained with sMRI, genetic, and environmental baseline data to predict the individual-level transition to psychosis from an ARMS within a 2-year follow up. While providing such preliminary (given the unprecedented data combinations/features and a limited sample size) evidence at the multimodal level, we took the opportunity to attempt to replicate previous promising sMRI-ML ﬁndings of studies using similar or smaller sample size (10–15). Methods-wise, we used naturalistically diverse samples but balanced them for demographic (age and sex) and imaging (scan acquisition sMRI protocol) variables. We set out to train and test modality-speciﬁc models ﬁrst and then, provided Frontiers in Psychiatry 02 frontiersin.org F r o n t i e r s TABLE 1 Socio-demographic and clinical information of the At Risk Mental State (ARMS) sample with structural MRI data. Protocol 1 Protocol 2 Protocol 3 Group comparison i n P s y c h a t r y i Age at baseline (years) Age at follow-up or transition (years) Age at scan (years) Interval between baseline and scan age (years) ARMS-T (n = 14) 23.2 ± 3.4 [15.6 26.9] 25.6 ± 4.2 [17.3 33.4] 23.0 ± 3.6 [17.5 27.8] –0.2 ± 1.4 [–2.3 1.9] ARMS-NT (n = 19) 24.5 ± 4.8 [19.2 34.5] 32.7 ± 5.2 [22.6 43.9] 23.9 ± 4.8 [18.5 34.8] –0.5 ± 1.1 [–2.3 2.1] ARMS-T (n = 3) 26.2 ± 7.0 [20.1 33.8] 29.2 ± 5.4 [20.2 38.8] 27.0 ± 8.2 [20.2 36.1] 0.9 ± 1.3 [0.1 2.4] ARMS-NT (n = 16) 24.5 ± 5.2 [17.8 35.3] 28.8 ± 5.6 [22.9 43.1] 25.1 ± 5.4 [18.6 37.4] 0.5 ± 0.5 [0.1 2.1] ARMS-T (n = 6) 23.4 ± 4.5 [17.5 29.2] 25.2 ± 4.8 [18.3 31.0] 24.1 ± 4.8 [18.3 30.8] 0.6 ± 0.5 [0.2 1.6] ARMS-NT (n = 41) 21.8 ± 4.3 [17.1 33.1] 25.6 ± 4.8 [20.3 41.2] 22.4 ± 4.6 [17.7 38.3] 0.6 ± 1.0 [0.0 5.1] Sex (male/female) 11/3 9/10 2/1 14/2 3/3 19/22 Handednessa (right/left/ambidextrous) 12/0/1 16/0/0 3/0/0 13/1/0 4/0/0 36/4/0 0 3 Self-reported ethnicity (White/Black/Asian/other) 7/5/1/1 11/5/1/2 2/1/0/0 13/1/1/1 4/1/1/0 19/19/1/2 Years of education IQ (z-standardized)b GAF at baseline GAF at follow-upc CAARMS at baselined CAARMS at follow-upe 13.4 ± 2.1 [10 18] –1.1 ± 1.1 [–2.1 1.0] 52.9 ± 16.0 [35 90] 49.3 ± 18.6 [10 69] 33.2 ± 13.0 [9 56] 19.6 ± 23.0 [0 63] 13.1 ± 1.9 [10 17] 0.0 ± 1.1 [–2.1 1.8] 57.8 ± 11.4 [35 75] 58.5 ± 17.9 [20 94] 28.4 ± 15.3 [8 58] 11.6 ± 10.9 [0 31] 11.7 ± 2.3 [9 13] 0.1 ± 0.1 [0.0 0.2] 58.7 ± 3.2 [55 61] 27.3 ± 6.8 [22 35] 29.3 ± 21.9 [12 54] 42.0 ± 43.3 [6 90] 14.1 ± 2.6 [11 20] 0.5 ± 0.9 [–1.3 1.6] 58.6 ± 9.9 [41 75] 62.3 ± 13.5 [46 93] 23.2 ± 14.3 [0 51] 14.7 ± 18.4 [0 54] 15.2 ± 2.5 [11 18] −0.1 ± 1.3 [–2.1 1.6] 50.3 ± 11.4 [35 65] 52.5 ± 20.0 [30 78] 39.7 ± 24.1 [0 69] 42.7 ± 42.1 [0 102] 13.0 ± 2.2 [9 20] 0.1 ± 1.1 [–2.1 3.5] 53.6 ± 14.8 [0 75] 66.2 ± 13.6 [33 87] 28.5 ± 16.7 [0 81] 15.5 ± 17.2 [0 60] Protocol: p = 0.271 Transition: p = 0.592 Protocol × Transition: p = 0.447 Protocol: p = 0.027* Transition: p = 0.099 Protocol × Transition: p = 0.025* Protocol: p = 0.261 Transition: p = 0.499 Protocol × Transition: p = 0.541 Protocol: p < 0.001*** Transition: p = 0.419 Protocol × Transition: p = 0.795 Protocol × Transition: Protocol 1: p = 0.070 Protocol 2: p = 0.422 Protocol 3: p = 1 Protocol × Transition: Protocol 1: p = 0.448 Protocol 2: p = 1 Protocol 3: p = 1 Protocol × Transition: Protocol 1: p = 0.933 Protocol 2: p = 0.530 Protocol 3: p = 0.212 Protocol: p = 0.298 Transition: p = 0.966 Protocol × Transition: p = 0.024* Protocol: p = 0.427 Transition: p = 0.252 Protocol × Transition: p = 0.923 Protocol: p = 0.402 Transition: p = 0.475 Protocol × Transition: p = 0.877 Protocol: p = 0.064 Transition: p < 0.001* Protocol × Transition: p = 0.095 Protocol: p = 0.505 Transition: p = 0.153 Protocol × Transition: p = 0.824 Protocol: p = 0.082 Transition: p = 0.001* Protocol × Transition: p = 0.262 f r o n t i e r s i n o r g . Data format: mean ± standard deviation [min max]. Information not available for a1 ARMS-T and 3 ARMS-NT (Protocol 1), 2 ARMS-NT (Protocol 2), 2 ARMS-T and 1 ARMS-NT (Protocol 3); b1 ARMS-T and 1 ARMS-NT (Protocol 2), 1 ARMS-NT (Protocol 3); c2 ARMS and 5 ARMS-NT (Protocol 1), 4 ARMS-NT (Protocol 2), 8 ARMS-NT (Protocol 3); d2 ARMS-T and 7 ARMS-NT (Protocol 1), 3 ARMS-NT (Protocol 2), 2 ARMS-NT (Protocol 3); e3 ARMS-T and 6 ARMS-NT (Protocol 1), 3 ARMS-NT (Protocol 2), 8 ARMS-NT (Protocol 3). ARMS, at-risk mental state; ARMS-T, individuals at ARMS that did not transition to psychosis; ARMS-NT, individuals at ARMS that did not transitioned to psychosis. p < 0.05; *p < 0.001. f p s y t - 1 3 - 1 0 8 6 0 3 8 J a n u a r y 1 3 , 2 0 2 3 T i m e : 1 7 : 3 5 # 3 T a v a r e s e t a l . . 1 0 3 3 8 9 / f p s y t . 2 0 2 2 . 1 0 8 6 0 3 8 fpsyt-13-1086038 January 13, 2023 Time: 17:35 # 4 Tavares et al. 10.3389/fpsyt.2022.1086038 these performed above chance-level, a multimodal one. For the sMRI data, we used state-of-the-art preprocessing and ML pipelines; and explored several unprecedented combinations of brain structural measures, feature manipulation and cross-validation (CV) strategies. For the genetic data, we explored several approaches: a schizophrenia individual GWA-implicated SNPs (27), and a brain- PRS (26), speciﬁc expression Quantitative Trait Loci (eQTL) score. For the environmental data, we employed a schizophrenia environmental risk score (ERS) (28), and individual risk factors. 2. Materials and methods 2.1. Sample description The total sample consisted of 246 individuals with an ARMS, recruited at ﬁrst presentation from consecutive referrals to the Outreach and Support in South London (OASIS) high-risk service, South London and Maudsley NHS Foundation Trust (29). The presence of ARMS was assessed using the CAARMS, a detailed clinical assessment (30). When the subjects were ﬁrst diagnosed as having an ARMS (i.e., baseline) a set of data were acquired: (a) a sMRI scan; (b) genome-wide genotypes; and (c) assessment of environmental risk exposures. Subjects were labeled as transitioned to psychosis (ARMS-T) if they later presented a FEP or as not- transitioned to psychosis (ARMS-NT) if they did not present a FEP within a period of at least 2 years. For a detailed description of the recruitment, inclusion and exclusion criteria please refer to the Supplementary material. Additional socio-demographic including: and clinical measures were also assessed at baseline, age; sex; handedness; self-reported ethnicity; full scale intelligence TABLE 2 Socio-demographic and clinical information of the At Risk Mental State (ARMS) sample with genetic data and an European ancestry. ARMS-T (n = 21) ARMS-NT (n = 54) Group comparison Age at baseline (years) Age at follow-up or transition (years) 23.8 ± 5.3 [15.6 33.8] 25.3 ± 5.9 [17.3 38.8] 22.5 ± 4.0 [14.6 34.5] 27.9 ± 5.1 [18.5 43.9] Sex (male/female) 14/7 30/24 Years of education IQ (z-standardized)a GAF at baseline GAF at follow-upb CAARMS at baselinec CAARMS at follow-upd 13.0 ± 2.2 [10.0 18.0] 0.1 ± 1.0 [–2.1 2.2] 54.0 ± 15.7 [0 80] 47.8 ± 24.3 [0 79] 37.6 ± 17.5 [6 69] 24.4 ± 27.9 [0 90] 12.0 ± 4.4 [0 18.0] 0.2 ± 1.0 [–2.1 1.8] 53.6 ± 16.0 [0 78] 59.2 ± 21.0 [0 94] 29.9 ± 16.2 [0 81] 12.4 ± 14.0 [0 60] p = 0.284 p = 0.069 p = 0.380 p = 0.292 p = 0.678 p = 0.923 p = 0.050 p = 0.097 p = 0.019 quotient measured by the National Adult Reading Test (31); years of education; and global assessment of function using the GAF instrument tool at baseline and at follow-up (32), and CAARMS (at baseline and follow-up) (30). Regarding the sMRI, genetic and environmental sub-samples: 99, 135 and all the 246 individuals with an ARMS had a baseline sMRI scan (Table 1), genome-wide genotyped data (Table 2), and environmental risk factors assessment data (Table 3), respectively (more details in the Supplementary material). Over the 2-years follow-up period, 23, 41, and 60 individuals at an ARMS from each of the previous sub-samples developed psychosis (AMRS-T) and the remaining 15, 94, and 186 did not (ARMS-NT), respectively. Moreover, part of the study’s data collection occurred under the Genetic and Psychosis (GAP) umbrella project (33). Ethics approval was obtained by the NHS South East London Research Ethics Committee (Project GAP; Ref. 047/04), consistent with the Helsinki Declaration of 1975 (as revised in 2008) and all subjects gave written informed consent. Socio-demographic and clinical variables were analyzed using a two-tailed independent t-test or a Univariate Analysis of Variance (ANOVA) for continuous data and a chi-square test or Fisher’s exact test (if there were less than 5 subjects in one group) for ordinal data (Tables 1–3). These statistical analyses were performed using the Statistical Package for the Social Sciences 26 (SPSS 26 for Windows, Chicago, IL, USA). 2.2. Structural neuroimaging data 2.2.1. Structural magnetic resonance imaging acquisition Structural magnetic resonance imaging (sMRI) scans were acquired with one of two scanners (one with a ﬁeld strength of 1.5T, three 3-Dimensional enhanced fast gradient echo protocols (detailed description in Supplementary material). the other 3T) using one of 2.2.2. Image processing High spatial resolution volumetric T1-weighted images were processed with the Computational Anatomy Toolbox for Statistical Parametric Mapping (SPM) –12 (CAT12; v10921), an SPM12 add- on (v69092) using default settings and MATLAB (9.3) as we have described elsewhere (34) (detailed description in Supplementary material). In summary, gray and white matter volumes for 64 regions-of-interest is in the Supplementary Table 1) were extracted using the Hammers atlas (35). Additionally, regional-based cortical thickness and surface measures (i.e., folding measures)–gyriﬁcation index, the depth of sulci and the measurement of local surface complexity were extracted for 68 ROIs (description of each ROI is in the Supplementary Table 2) deﬁned by the Desikan–Killiany atlas (36). (ROIs; description of each ROI 2.2.3. Image quality control The quality of each processed image was empirically assessed using the quality assurance framework of CAT12 (detailed description in the Supplementary material). We set the subject’s image inclusion threshold at D (suﬃcient), i.e., only subjects whose Data format: mean ± standard deviation [min max]. Information not available for a2 ARMS-T and 9 ARMS-NT; b4 ARMS-NT; c1 ARMS and 9 ARMS-NT; d1 ARMS-T and 3 ARMS-NT. ARMS, at-risk mental state; ARMS-T, individuals at ARMS that did not transition to psychosis; ARMS-NT, individuals at ARMS that did not transitioned to psychosis. p < 0.05. 1 http://www.neuro.uni-jena.de/cat/ 2 http://www.ﬁl.ion.ucl.ac.uk/spm/ Frontiers in Psychiatry 04 frontiersin.org fpsyt-13-1086038 January 13, 2023 Time: 17:35 # 5 Tavares et al. 10.3389/fpsyt.2022.1086038 images had an image quality rate of A (excellent) to D (suﬃcient) (in a scale that goes up to F–unacceptable/failed) were included in the ﬁnal sample, as it has been shown that typical scientiﬁc (clinical) data get good-to-satisfactory ratings (37). All this study’s images passed the above criteria and thus were included in all analyses (see Supplementary material for more details). migrant; (4) belonging to an ethnic minority; (5) the upbringing urbanicity level; (6) the parental age at birth; (7) the presence of childhood trauma; and (8) the season of birth (detailed description of how the risk for psychosis was assessed in each factor is in Supplementary material). 2.3. Genetic data Genotyping procedures have been previously described (26, 38). In summary, samples were genotyped at two diﬀerent sites with two distinct chips (Illumina HumanCore Exome BeadChip and Genome-wide Human SNP Array 6.0). A standard quality control screening (exclusion of SNPs with low minor allele frequency, high genotypic failure and not in Hardy Weinberg equilibrium) followed by imputation procedures were conducted. Then, samples from both sites were merged by keeping only the overlapped imputed SNPs followed by a second quality control screening. Finally, a population stratiﬁcation analysis was conducted with principal component analysis (PCA) to select only subjects with a European ancestry (the number of subjects per self-reported ethnicity is in the Supplementary Table 3). For a detailed description see the Supplementary material. 2.4. Environmental data Each subject was assessed on at least one of eight environmental risk factors: (1) tobacco and (2) cannabis consumption; (3) being TABLE 3 Socio-demographic and clinical information of the At Risk Mental State (ARMS) sample with environmental data (with less than 20% of the environmental risk factors missing). ARMS-T (n = 37) ARMS-NT (n = 97) Group comparison Age at baseline (years) Age at follow-up or transition (years)a 23.6 ± 4.8 [15.6 33.6] 25.6 ± 5.6 [17.3 39.2] 21.9 ± 3.7 [14.6 33.1] 27.1 ± 4.7 [18.5 41.2] Sex (male/female) 22/15 50/47 Years of educationb IQ (z-standardized)c GAF at baselined GAF at follow-upe CAARMS at baselinef CAARMS at follow-upg 13.2 ± 2.7 [8 18] −0.3 ± 1.0 [–2.1 2.2] 55 ± 12.5 [35 90] 50.4 ± 19.9 [10 88] 30.9 ± 19.4 [0 78] 29.7 ± 31.2 [0 102] 13.3 ± 2.0 [9 18] 0.1 ± 1.0 [–2.1 3.5] 56.7 ± 8.6 [40 80] 63.2 ± 14.2 [20 94] 28.3 ± 16.0 [0 81] 13.3 ± 14.2 [0 60] p = 0.027 p = 0.131 p = 0.411 p = 0.686 p = 0.049* p = 0.523 p =< 0.001* p = 0.478 p =<0.001* Data format: mean ± standard deviation [min max]. Information not available for a1 ARMS-T; b5 ARMS-T and 6 ARMS-NT; c7 ARMS-T and 13 ARMS-NT; d5 ARMS-T and 4 ARMS-NT; e5 ARMS-T and 8 ARMS-NT; f6 ARMS-T and 13 ARMS-NT; g4 ARMS-T and 8 ARMS-NT; subject. ARMS, at-risk mental state; ARMS-T, individuals at ARMS that did not transition to psychosis; ARMS-NT, individuals at ARMS that did not transition to psychosis. *p < 0.05. 2.5. Machine learning approach Several ML strategies to generate prediction models for transition to psychosis from sMRI data using our ARMS sample were investigated (Figures 1, 2). These include: (a) sample balancing and bootstrapping; and testing several: (b) feature types; (c) feature manipulation approaches; and (d) CV approaches. The analyses were conducted using the neuroimaging ML tool NeuroMiner v1.0 ELESSAR3 for sMRI data, chosen given that it was used in the previous above-mentioned ARMS prognosis studies and provided therein high accuracy results (12, 39, 40), and R software 4.0.5 (41) for genetic (16) and environmental data. As detailed below, we have used SVM on the neuroimaging data since that is the approach which not only is more often employed with sMRI data but also that which has shown higher accuracies in psychiatric diagnostic classiﬁcations using sMRI data including in the ARMS population (10–14) which we herein attempt to replicate. We have used elastic-net algorithm for the genetic data (SNPs and eQTL scores) and environmental risk factors as it a well-suited method for dealing with high-dimensional data and possibly correlated data; and it performs an embedded feature selection and model ﬁtting at once. The PRS and the environmental risk score were analyzed with logistic regression, given that only one feature was used. 2.5.1. Sample balancing and bootstrapping The ﬁnal sample used in the ML analyses was deﬁned by all the ARMS-T subjects available (23 subjects for the sMRI predictors, 19 for the PRS predictor, 21 for the SNP’s alleles predictors, 21 for eQTL scores predictors, 37 for the ERS predictor, and 17 for the individual environmental predictors), and the same number of ARMS-NT subjects randomly selected to match the ARMS-T for age and sex (for each data modality), and for scan acquisition protocol (for sMRI data). The matching criteria for age and sex were based on the non-rejection of the null hypothesis (i.e., p > 0.05) that the ARMS-T and ARMS-NT groups had the same median age (tested with a two-sided Mann–Whitney U-test) and sex proportions (tested with a two-sided chi-square statistic). The matching for the scan acquisition protocol was done in a one-to-one manner, i.e., the number of ARMS-NT subjects is the same as the number of ARMS-T. within each protocol Of note, we have considered the approach of applying a class- weighted support vector machine for our neuroimaging measures and have detected that diﬀerences in terms of accuracies between a model with weights vs. no-weights (considering the full unbalanced samples) were practically null (results not shown)–and therefore we did not pursue that approach. Then, each subsampling was repeated ﬁve times, i.e., 5 bootstrapped samples were created, and the subsequent ML analyses were conducted for each of the bootstrapped sample. 3 http://proniapredictors.eu/neurominer/index.html Frontiers in Psychiatry 05 frontiersin.org fpsyt-13-1086038 January 13, 2023 Time: 17:35 # 6 Tavares et al. 10.3389/fpsyt.2022.1086038 FIGURE 1 Overall machine learning approach taken for assessing the predictive value, i.e., the accuracy, of each type of extracted neuroimaging, genetic or environmental feature in predicting transition to psychosis from an At Risk Mental State (ARMS). ERS, environmental risk score; eQTL score, expression quantitative trait loci; PRS, polygenic risk score; ROIGM, regional-based gray matter volumes; ROISurface, surface-based regional cortical thickness, and gyriﬁcation, sulci, and complexity indexes; ROIWM, regional-based white matter volumes; SNP, single nucleotide polymorphism; VMGM, voxel-based gray matter volume maps; VMWM, voxel-based white matter volume maps. 2.5.2. Feature types 2.5.2.1. Structural magnetic resonance imaging data Individual ML models were trained and validated for each of the following brain measures: (a) voxel-based gray matter (VBGM) maps (297,811 initial features); (b) voxel-based white matter (VBWM) maps (204,706 initial features); (c) regional-based gray (ROIGM) and (d) white (ROIWM) matter volumes (each with 64 initial features) scaled to the total intracranial volume (TIV); and (e) surface-based regional cortical thickness, and gyriﬁcation, sulci, and complexity indexes (ROISurface; 272 initial features). Each feature is scaled between 0 and 1 before entering a support vector machine (SVM) classiﬁcation algorithm. 2.5.2.2. Genetic data We tested whether a PRS which we have previously found to predict (R2 = 0.94) a cross-sectional diagnosis of FEP (vs. healthy controls) would be a good longitudinal predictor for ARMS prognosis. Following the same methodology (26), this PRS was computed as the sum of SNPs alleles statistically associated with schizophrenia in a GWAS meta-analysis study (42) weighted by the eﬀect size of that association (more details in Supplementary material). In addition, two other novel prediction models using the present ARMS sample were trained and tested. One used SNPs’ alleles (79,247 SNPs) as predictors and the other used eQTL scores of genes expressed in brain tissue (141 genes across several brain tissues). Both SNPs and genes’ eQTL scores were chosen as the ones most associated with psychosis as ascertained in a recent meta-analysis (27). The eQTL score of each gene was extracted with the eGenScore which we developed and published previously (43) and it was computed as the sum of the alleles of SNPs showing a statistically signiﬁcant association with the brain gene expression in a standard genomic and transcriptomic sample weighted by the size of that eﬀect (further details available in Supplementary material). 2.5.2.3. Environmental data We tested whether an ERS for psychosis which we have previously developed (28) would be a good longitudinal predictor for ARMS prognosis. Only subjects with less than 20% of missing information (i.e., missing data for less than 2 environmental risk factors) were considered for the ERS-based ML analysis. Therefore, the ﬁnal sample included 37 ARMS-T subjects and 97 ARMs-NT subjects. Then, each environmental risk factor (see Section “2.4. Environmental data”) was used as an individual feature in the model. For this ML analysis only subjects with information for all the environmental risk factors (i.e., with no missing information) were considered (i.e., 17 ARMS-T and 49 ARMS-NT subjects). Further details available in Supplementary material. 2.5.3. Feature manipulation Feature manipulation was performed only in ML analyses using sMRI data. In particular, feature dimensionality reduction was performed for VBGM and VBWM features using robust PCA (44, Frontiers in Psychiatry 06 frontiersin.org fpsyt-13-1086038 January 13, 2023 Time: 17:35 # 7 Tavares et al. 10.3389/fpsyt.2022.1086038 FIGURE 2 Scheme of the cross-validation (CV) approach taken to train, test, and validate classiﬁcation models trained with (A) neuroimaging data and support vector machines (SVM); genetic (single nucleotide polymorphisms or expression quantitative trait loci) or environmental (environmental risk factors) data and elastic-net; or (B) genetic (polygenic risk score) or environmental (environmental risk score) data and logistic regression. 45). Here the robust PCA was applied during the inner CV cycle (see Section “2.5.5. Cross-validation”). The number of principal components that were retained explained up to 80% of the variance in the data and were limited by the inner CV cycle’s sample size, n, i.e., a maximum of only n/2 components could indeed be extracted. Supplementary Table 5 shows the maximum number of principal components that can be extracted for each inner CV cycle in each CV scheme that was used (see also Section “2.5.5. Cross-validation”) (for detailed description see the Supplementary material). Feature selection was performed on regional brain features (i.e., ROIGM, ROIWM, and ROISurface) using a greedy forward search feature selection algorithm. This is a stepwise algorithm that starts with an empty set of features and then tests the predictive value of every single feature, selecting the ones improving the overall accuracy across the inner CV cycle folds (see Section “2.5.5. Cross- validation”). The ﬁnal set of features is, then, composed by the 10% most predictive variables. Additionally, no feature selection, i.e., using the total number regional brain features, was also tested. 2.5.4. Machine learning algorithm Binary classiﬁcation of transition to psychosis from an ARMS (i.e., ARMS-T vs. ARMS-NT) was performed using linear SVM for sMRI data, and logistic regression and elastic net for both genetic and environmental data. 2.5.4.1. Support vector machine classiﬁcation Binary classiﬁcation of transition to psychosis from an ARMS (i.e., ARMS-T vs. ARMS-NT) using sMRI data was performed using linear SVM (46, 47). In this study we exclusively used a linear kernel SVM to reduce the risk of overﬁtting the data (given our ﬁnal sample size being relatively small). Furthermore, the linear SVM classiﬁer has a penalty parameter C that controls the trade- oﬀ between having zero training error and allowing misclassiﬁcation. Herein, a parameter search was carried out to identify the optimal C value (i.e., 2l, l [−5 : 1 : 4]) in the inner CV cycle (see Section “2.5.5. Cross-validation”). (ERS) data was performed using simple logistic regression. A threshold of 0.5 was applied to the probability of observing i.e., an ARMS-T (see Supplementary material the outcome, for more details). 2.5.4.3. Elastic net for classiﬁcation transition to psychosis Binary classiﬁcation of from an ARMS (i.e., ARMS-T vs. ARMS-NT) using genetic (psychosis- associated SNPs or eQTL scores of psychosis-associated genes) or environmental (environmental risk factors) data was performed using logistic regularized regression with elastic net (48) using l1 and λ values hyperparameters search to identify the optimal (regression weights shrinkage) (i.e., l1 0 : 0.1 : 1; λ 0.01 : 0.01 : 1) in the inner CV cycle (see Section “2.5.5. Cross-validation”) (for detailed description see the Supplementary material). The elastic net was implemented using the “glmnet” v4.0 R package. 2.5.5. Cross-validation Each model (trained with sMRI, psychosis-associated SNPs or eQTL scores of psychosis-associated genes and environmental risk factors) was trained in a nested-CV scheme for hyperparameter tuning (in the inner CV cycle) and to estimate the generalizability of the trained prediction model and its performance (in the outer CV cycle) (Figure 2A). For more details see the Supplementary material. For sMRI models, we tested three diﬀerent nested-CV schemes: (a) leave-one scan acquisition protocol-out (LSO); (b) leave-one per group from the same scan acquisition protocol-out (LPO); and (c) classic 5-fold CV. For the remaining sMRI, genetic (trained with psychosis-associated SNPs or eQTL scores of psychosis-associated genes data) and environmental (trained with environmental risk factors data) models, nested-CV was deﬁned with an inner 5- fold and an outer leave-one per group-out (LPO) CV schemes. Furthermore, the logistic regression (trained with genetic–PRS–and environmental–ERS–data) was trained and tested in a simple LPO CV scheme (Figure 2B). 2.5.6. Performance measures 2.5.4.2. Logistic regression for classiﬁcation Binary classiﬁcation of transition to psychosis from an ARMS (i.e., ARMS-T vs. ARMS-NT) using genetic (PRS) or environmental Each model’s performance was evaluated using measures derived from the confusion matrix: sensitivity; speciﬁcity; BAC; positive likelihood ratio; negative likelihood ratio; and diagnostic odds ratio Frontiers in Psychiatry 07 frontiersin.org fpsyt-13-1086038 January 13, 2023 Time: 17:35 # 8 Tavares et al. 10.3389/fpsyt.2022.1086038 (DOR). Moreover, permutation testing was used to test if the BAC was higher than chance–50%–with a statistical signiﬁcance of 5% (For a detailed description of each measure see the Supplementary material). The prediction ability of each tested combination of feature type, feature manipulation, and CV scheme was deﬁned as signiﬁcant if the BAC was higher than chance–50% in at least 3 out of 5 bootstrapped samples. evaluated by testing the statistical signiﬁcance of the median BAC across bootstrapped samples using a one-tailed Wilcoxon signed rank test (i.e., to test if the median BAC across bootstrapped samples is higher than chance– 50%, with a statistical signiﬁcance level of 5%). P-values were not adjusted for multiple comparisons due to non-independence of the samples used in each statistical test. 3. Results Overall, the BAC of the classiﬁcation models trained and validated using each combination of feature type (i.e., ROIGM, ROIWM, ROISurface, VBGM, or VBWM–for sMRI data; PRS, psychosis-associated SNPs or psychosis-associated brain eQTL score genes scores–for genetic data; or ERS or individual environmental risk factors–for environmental data), feature manipulation (i.e., feature dimensionality reduction through PCA; no feature selection; or forward feature selection), CV scheme (i.e., LSO CV; LPO CV; or 5-fold CV), and bootstrapped sample (i.e., one of the 5 samples) ranged from 37 to 67% for the classiﬁcation models trained with sMRI (Tables 4, 5 and Figures 3, 4), from 26 to 62% for the models trained with genetic data (Table 6 and Figure 5) and from 38 to 61% for models trained with environmental data (Table 6 and Figure 6). The prediction ability of each model was not signiﬁcant as less than 3 bootstrapped samples per each feature type showed a BAC statistically higher than chance–50%. 4. Discussion This study aimed to predict transition to psychosis from an ARMS using ML applied to quantitative data across modalities– i.e., neuroimaging (sMRI), genetics (genome-wide genotypes), and environment–collected when subjects ﬁrst sought clinical help (i.e., at baseline) and were identiﬁed with an ARMS. This is, to the best of our knowledge, the ﬁrst study: (1) of longitudinal design exploring sMRI, genetic and environmental data to predict the development of a psychotic disorder from a prodromal stage; and (2) when considering each modality individually, exploring a range of approaches (for genetics and environmental data) and/or feature combinations (for sMRI data). 4.1. Prediction of transition to psychosis using structural neuroimaging data In this study we applied ML to structural neuroimaging data using a relatively larger sample and an ML approach, improved to the best of our ability, to detect transition to psychosis from an ARMS and to replicate previous positive ﬁndings of accuracies 74 to 84% of six studies, which together used 3 independent samples (10–15). For this, we decided: to: (1) use only the most recent versions of the image processing tools (i.e., CAT12) and ML tools (i.e., NeuroMiner); (2) replicate as accurately as possible the methods that were described in the abovementioned MRI papers since it was not possible to access their processing and ML pipelines; (3) add a layer of ML generalizability by bootstrapping and ﬁtting a model to each subsample; and (4) overcome previous studies’ limitations (e.g., sample unbalancing for demographics). Furthermore, we explored, for the ﬁrst time, the use of whole brain white matter volume and regional white matter volume, cortical thickness, and surface-based brain gyriﬁcation, sulci depth, and complexity indexes with ML to predict transition to psychosis. Unexpectedly, we did not replicate previous ﬁndings. After balancing the samples for binary classiﬁcation of transition to psychosis accounting for age, sex, and the three diﬀerent scan acquisition protocols to avoid overoptimistic results, the performance of all tested combinations (i.e., of feature type–ROIGM, ROIWM, ROISurface, VBGM, or VBWM; feature manipulation–feature dimensionality reduction through PCA, no feature selection, or forward feature selection; and CV scheme–LSO CV, LPO CV, or 5-fold CV) were not signiﬁcantly better than chance level. Compared to the previous studies reporting high balanced accuracies (74 to 84%) in predicting transition to psychosis from sMRI maps (10–15), the current study has some advantages. First, this study’s sample is drawn from a more naturalistic ARMS population as it includes subjects whose sMRI images were acquired using three diﬀerent scan acquisition protocols. Training a classiﬁcation model with data from diﬀerent centers potentially increases its generalizability. Only one of the previous transition to psychosis prediction studies used a two-site group balanced sample (12), combining the samples reported in two previous studies by the same authors (10, 11). The main diﬀerences between this report and our study are the following: (a) Their sample was larger than our balanced bootstrapped samples (i.e., 36% larger than ours, measured as the absolute value of the change in sample size, divided by the average of the size of the two samples). However, we tested our ML models on ﬁve balanced subsamples (i.e., through bootstrapping), allowing us to obtain a measure of generalizability of these models’ performance. Moreover, they do not present a measure of the statistical signiﬁcance of the model’s BAC, which we do herein. (b) They controlled the eﬀect of site on the classiﬁcation using partial correlations during the training phase of the CV cycle, whereas we controlled it by keeping the same proportion of subjects at an ARMS that transitioned to psychosis and those who did not in each scan protocol during the training phase of the CV cycle (i.e., when using the LPO CV scheme as the previous study did). Additionally, we also guaranteed that the pair of subjects left out for testing/validation were from the same site. This potentially increases the generalizability of the classiﬁcation model by training it with a more heterogeneous sample (and, as explained above, more naturalistic) and diminishing the eﬀect of site on the testing/validation classiﬁcation accuracy, which is not taken into account in the previous report (12). Second, we trained our classiﬁcation models with samples balanced for group (subjects at an ARMS who later transitioned to psychosis and who did not), age at scan and sex. Balancing for group is important to avoid biasing the classiﬁcation model to the most represented group and it was not taken into account by three out of six previous reports (10, 11, 14). Moreover, the eﬀects of age (49) and sex (50) on brain structure, rate of transition to psychosis from ARMS (2), and prevalence of psychosis (3, 51), have been consistently reported and, therefore, should be taken into Frontiers in Psychiatry 08 frontiersin.org fpsyt-13-1086038 January 13, 2023 Time: 17:35 # 9 Tavares et al. 10.3389/fpsyt.2022.1086038 TABLE 4 Performance measures of each structural magnetic resonance imaging (sMRI) classiﬁcation model based on brain regional features across bootstrapped samples. ROIGM No-FS 55.7 ± 6.4 [47.8, 65.2] 55.7 ± 10.4 [43.5, 69.6] 55.7 ± 5.5 [47.8, 63.0] 1.3 ± 0.3 [0.9, 1.9] 0.8 ± 0.2 [0.6, 1.1] 1.7 ± 0.8 [0.8, 3.0] 1 47.8 ± 6.1 [43.5, 56.5] 54.8 ± 6.6 [47.8, 60.9] 51.3 ± 4.8 [45.7, 58.7] 1.1 ± 0.2 [0.8, 1.4] 1.0 ± 0.2 [0.7, 1.2] 1.2 ± 0.5 [0.7, 2] 0 42.6 ± 3.6 [39.1, 47.8] 54.8 ± 12.5 [34.8, 65.2] 48.7 ± 6.6 [39.1, 56.5] 1.0 ± 0.3 [0.7, 1.4] 1.1 ± 0.3 [0.8, 1.6] 1.0 ± 0.5 [0.4, 1.7] 0 LSO CV scheme SE (%) SP (%) BAC (%) PLR NLR DOR Signiﬁcant models LPO CV scheme SE (%) SP (%) BAC (%) PLR NLR DOR Signiﬁcant models 5-fold CV scheme SE (%) SP (%) BAC (%) PLR NLR DOR Signiﬁcant models FFS 59.1 ± 11.7 [47.8, 78.3] 40.9 ± 10.5 [26.1, 52.2] 50.0 ± 3.8 [43.5, 52.2] 1.0 ± 0.1 [0.8, 1.1] 1.0 ± 0.2 [0.8, 1.4] 1.0 ± 0.3 [0.6, 1.3] 0 67.0 ± 7.9 [56.5, 73.9] 44.3 ± 10.8 [34.8, 60.9] 55.7 ± 7.1 [45.7, 65.2] 1.3 ± 0.3 [0.9, 1.8] 0.8 ± 0.3 [0.5, 1.3] 1.9 ± 1.1 [0.7, 3.6] 0 40.9 ± 5.8 [34.8, 47.8] 40.9 ± 7.3 [30.4, 47.8] 40.9 ± 2.4 [37.0, 43.5] 0.7 ± 0.1 [0.6, 0.8] 1.5 ± 0.2 [1.3, 1.9] 0.5 ± 0.1 [0.3, 0.6] 0 ROIWM No-FS 57.4 ± 19.1 [30.4, 82.6] 46.1 ± 14 [21.7, 56.5] 51.7 ± 5.6 [43.5, 58.7] 1.1 ± 0.2 [0.7, 1.4] 0.9 ± 0.2 [0.7, 1.2] 1.3 ± 0.5 [0.6, 2.0] 0 49.6 ± 8.5 [39.1, 60.9] 52.2 ± 5.3 [43.5, 56.5] 50.9 ± 5.2 [43.5, 56.5] 1.0 ± 0.2 [0.8, 1.3] 1.0 ± 0.2 [0.8, 1.3] 1.1 ± 0.4 [0.6, 1.7] 0 59.1 ± 6.6 [52.2, 69.6] 40.9 ± 8.5 [30.4, 52.2] 50.0 ± 5.1 [45.7, 56.5] 1.0 ± 0.2 [0.9, 1.2] 1.0 ± 0.3 [0.7, 1.3] 1.1 ± 0.5 [0.7, 1.8] 0 ROISurface FFS No-FS FFS 62.6 ± 13.3 [52.2, 82.6] 27.8 ± 22.3 [0.0, 56.5] 45.2 ± 6.6 [37.0, 54.3] 0.9 ± 0.2 [0.7, 1.2] 1.5 ± 0.7 [0.8, 2.5] 0.6 ± 0.5 [0.0, 1.4] 0 39.1 ± 9.2 [26.1, 47.8] 50.4 ± 12.5 [34.8, 69.6] 44.8 ± 6.6 [37.0, 54.3] 0.8 ± 0.3 [0.5, 1.3] 1.3 ± 0.3 [0.9, 1.5] 0.7 ± 0.4 [0.3, 1.5] 0 45.2 ± 8.5 [34.8, 56.5] 53 ± 11.3 [34.8, 65.2] 49.1 ± 2.5 [45.7, 52.2] 1.0 ± 0.1 [0.9, 1.1] 1.1 ± 0.1 [0.9, 1.3] 0.9 ± 0.2 [0.7, 1.2] 0 41.7 ± 14.6 [26.1, 60.9] 61.7 ± 17.8 [34.8, 82.6] 51.7 ± 7.4 [43.5, 63.0] 1.2 ± 0.4 [0.8, 1.8] 1.0 ± 0.3 [0.6, 1.4] 1.4 ± 0.9 [0.6, 2.9] 1 53.9 ± 6.6 [43.5, 60.9] 54.8 ± 5.0 [47.8, 60.9] 54.3 ± 4.3 [50.0, 60.9] 1.2 ± 0.2 [1.0, 1.6] 0.8 ± 0.1 [0.6, 1.0] 1.5 ± 0.6 [1.0, 2.4] 1 53.0 ± 10.4 [39.1, 65.2] 54.8 ± 6.6 [47.8, 60.9] 53.9 ± 5.2 [47.8, 60.9] 1.2 ± 0.2 [0.9, 1.6] 0.9 ± 0.2 [0.6, 1.1] 1.5 ± 0.6 [0.8, 2.4] 0 39.1 ± 20.6 [17.4, 65.2] 63.5 ± 12.1 [43.5, 73.9] 51.3 ± 9.8 [43.5, 67.4] 1.1 ± 0.6 [0.6, 2.1] 1.0 ± 0.3 [0.5, 1.2] 1.5 ± 1.6 [0.5, 4.3] 1 52.2 ± 6.9 [43.5, 60.9] 54.8 ± 12.9 [39.1, 69.6] 53.5 ± 7.5 [43.5, 60.9] 1.2 ± 0.3 [0.8, 1.6] 0.9 ± 0.3 [0.6, 1.3] 1.5 ± 0.8 [0.6, 2.4] 0 57.4 ± 8.4 [47.8, 69.6] 53 ± 11.7 [39.1, 65.2] 55.2 ± 9.3 [47.8, 67.4] 1.3 ± 0.5 [0.9, 2.0] 0.9 ± 0.3 [0.5, 1.1] 2.0 ± 1.6 [0.8, 4.3] 1 Measures for each tested combination of brain regional feature type [i.e., regional-based gray (ROIGM) and white (ROIWM) matter volume; and surface-based regional cortical thickness, gyriﬁcation, sulci, and complexity indexes (ROISurface)], feature selection [i.e., no feature selection (No-FS); and forward feature selection (FFS)], and cross-validation (CV) scheme [i.e., leave-one scan acquisition protocol-out (LSO) CV; leave-one per group-out (LPO) CV; and 5-fold CV] are presented. Statistical signiﬁcance of the balanced accuracy (BAC) for each bootstrapped sample was tested using permutation testing with a signiﬁcance level of 5%. Data format: mean ± standard deviation [min max]. DOR, diagnostic odds ratio; NLR, negative likelihood ratio; PLR, positive likelihood ratio; SE, sensitivity; SP, speciﬁcity. Signiﬁcant models: number of models with statistically signiﬁcant BAC higher than 50%. account in these studies. All previous reports (and the current study) matched transition proportion for age and sex (10–14), except for one (15). Das and colleagues reported a statistically signiﬁcant and better than chance level BAC in predicting transition to psychosis using a sample unbalanced for both group and sex. Although they used a ML algorithm with class (i.e., group) weighing–which in summary increases the inﬂuence of the minority class when training the model by assigning higher weights to rare cases, the authors Frontiers in Psychiatry 09 frontiersin.org fpsyt-13-1086038 January 13, 2023 Time: 17:35 # 10 Tavares et al. 10.3389/fpsyt.2022.1086038 TABLE 5 Performance measures of each structural magnetic resonance imaging (SMRI) classiﬁcation model based on voxel-wise features across bootstrapped samples. LSO CV scheme LPO CV scheme 5-fold CV scheme SE (%) SP (%) BAC (%) PLR NLR DOR Signiﬁcant models VBGM 20.9 ± 34.8 [0, 82.6] 72.2 ± 35.7 [8.7, 91.3] 46.5 ± 3.6 [43.5, 52.2] 0.6 ± 0.6 [0.0, 1.5] 1.3 ± 0.4 [1.0, 2.0] 0.7 ± 0.9 [0.0, 2.2] 0 VBWM 46.1 ± 38.2 [4.3, 78.3] 53 ± 35.4 [21.7, 95.7] 49.6 ± 2.4 [45.7, 52.2] 0.9 ± 0.3 [0.3, 1.1] 1.0 ± 0.1 [0.9, 1.1] 0.8 ± 0.4 [0.1, 1.1] 1 VBGM 47.0 ± 10.4 [34.8, 60.9] 55.7 ± 8.4 [43.5, 65.2] 51.3 ± 7.5 [45.7, 63.0] 1.1 ± 0.4 [0.8, 1.8] 1.0 ± 0.3 [0.6, 1.2] 1.3 ± 1.0 [0.7, 3.1] 0 VBWM 50.4 ± 11.3 [34.8, 60.9] 53.0 ± 7.1 [47.8, 65.2] 51.7 ± 2.8 [47.8, 54.3] 1.1 ± 0.1 [0.9, 1.2] 0.9 ± 0.1 [0.8, 1.1] 1.1 ± 0.2 [0.8, 1.4] 0 VBGM 30.4 ± 10.2 [21.7, 43.5] 51.3 ± 7.8 [43.5, 60.9] 40.9 ± 2.4 [37.0, 43.5] 0.6 ± 0.1 [0.5, 0.8] 1.4 ± 0.1 [1.3, 1.5] 0.4 ± 0.1 [0.2, 0.6] 0 VBWM 41.7 ± 8.5 [34.8, 56.5] 52.2 ± 6.1 [43.5, 60.9] 47.0 ± 6.8 [41.3, 58.7] 0.9 ± 0.3 [0.7, 1.4] 1.1 ± 0.3 [0.7, 1.4] 0.9 ± 0.7 [0.5, 2.1] 0 Measures for each tested combination of voxel-wise feature type [i.e., voxel-based gray (VBGM) and white (VBWM) matter volume maps], feature dimensionality reduction through principal component analysis and cross-validation (CV) scheme [i.e., leave-one scan acquisition protocol-out (LSO) CV; leave-one per group-out (LPO) CV; and 5-fold CV] are presented. Statistical signiﬁcance of the balanced accuracy (BAC) for each bootstrapped sample was tested using permutation testing with a signiﬁcance level of 5%. Data format: mean ± standard deviation [min max]. DOR, diagnostic odds ratio; NLR, negative likelihood ratio; PLR, positive likelihood ratio; SE, sensitivity; SP, speciﬁcity. Signiﬁcant models: number of models with statistically signiﬁcant BAC higher than 50%. FIGURE 3 Balanced accuracy across bootstrapped samples for each tested combination of regional feature type [i.e., regional-based gray and white matter volume; and surface-based regional cortical thickness, gyriﬁcation, sulci, and complexity indexes (surface-based regional measures)], feature selection [i.e., no feature selection; and forward feature selection (FFS)], and cross-validation (CV) scheme [i.e., leave-one scan acquisition protocol-out (LSO) CV; leave-one per group-out (LPO) CV; and 5-fold CV]. Dots represent the balanced accuracy value in each of the ﬁve bootstrapped samples and are red colored if the balanced accuracy is statistically signiﬁcant (i.e., p < 0.05) or blue colored if it is not (i.e., p > 0.05). The statistical signiﬁcance of the balanced accuracy in each bootstrapped sample was evaluated through permutation testing. performed an unspeciﬁed correction for sex eﬀect (as well as for age and TIV eﬀects) to the data during the training CV cycle. This approach may not be the most appropriate given the known eﬀect of sex on brain structure (50) and the, abovementioned, empirically tested association between sex and group (i.e., transition to psychosis from an ARMS vs. no transition) (15), which makes sex a potential confounder in this analysis. Furthermore, in three of the six previous reports, the eﬀects of age and sex were corrected before entering the ML analysis (10), and during the training CV cycle (11, 15) using partial correlations (10, 11) or an unspeciﬁed method (15)– which we did not perform. Correction for age eﬀects in ML analysis has been previously shown to increase classiﬁcation accuracy in Alzheimer’s disease, when it is estimated from healthy subjects (52). Correction for eﬀects of no interest in ML analyses should be done with extreme caution as it can easily remove relevant subject-speciﬁc information (53). This is especially important when the correction Frontiers in Psychiatry 10 frontiersin.org fpsyt-13-1086038 January 13, 2023 Time: 17:35 # 11 Tavares et al. 10.3389/fpsyt.2022.1086038 FIGURE 4 Balanced accuracy across bootstrapped samples for each tested combination of voxel-wise feature type [i.e., voxel-based gray (VBGM) and white (VBWM) matter volume maps], feature dimensionality reduction through principal component analysis and cross-validation (CV) scheme [i.e., leave-one scan acquisition protocol-out (LSO) CV; leave-one per group-out (LPO) CV; and 5-fold CV. Dots represent the balanced accuracy value in each of the ﬁve bootstrapped samples and are red colored if the balanced accuracy is statistically signiﬁcant (i.e., p < 0.05) or blue colored if it is not (i.e., p > 0.05). The statistical signiﬁcance of the balanced accuracy in each bootstrapped sample was evaluated through permutation testing. TABLE 6 Performance measures of: (1) a genetic schizophrenia polygenic risk score (PRS), (2) a list of psychosis-associated single nucleotide polymorphisms (SNPs), (3) expression quantitative trait loci (eQTL) scores (43) of a list of psychosis-associated genes expressed in the brain; (4) an environmental schizophrenia risk score (ERS), and (5) a list of schizophrenia-associated environmental risk factors, classiﬁcation models across bootstrapped samples. PRS SNP eQTL score ERS Environmental risk factors SE (%) SP (%) BAC (%) PLR NLR DOR 42.1 ± 20.0 [21.1, 63.2] 46.3 ± 11.4 [31.6, 57.9] 44.2 ± 15.3 [26.3, 60.5] 0.9 ± 0.5 [0.3, 1.5] 1.4 ± 0.8 [63.6, 2.5] 1.0 ± 1.0 [0.1, 2.4] Signiﬁcant models 0 41.9 ± 13.6 [23.8, 61.9] 50.5 ± 16.4 [28.6, 66.7] 46.2 ± 10.7 [33.3, 61.9] 0.9 ± 0.4 [0.5, 1.6] 1.3 ± 0.6 [0.6, 2.2] 1.0 ± 1.0 [0.2, 2.6] 0 61.0 ± 17.0 [47.6, 85.7] 31.4 ± 23.2 [4.8, 57.1] 46.2 ± 4.9 [40.5, 52.4] 0.9 ± 0.1 [0.8, 1.1] 1.9 ± 1.1 [0.9, 3.0] 0.7 ± 0.4 [0.3, 1.2] 1 44.9 ± 5.1 [29.7, 56.8] 50.8 ± 8.2 [45.9, 64.9] 47.8 ± 8.8 [37.8, 60.8] 1.0 ± 0.4 [0.6, 1.6] 1.1 ± 0.3 [0.7, 1.5] 1.0 ± 0.8 [0.4, 2.4] 0 10.6 ± 4.9 [5.9, 17.6] 70.6 ± 7.2 [64.7, 82.4] 40.6 ± 2.5 [38.2, 44.1] 0.4 ± 0.1 [0.2, 0.5] 1.3 ± 0.1 [1.1, 1.4] 0.3 ± 0.1 [0.2, 0.4] 0 Statistical signiﬁcance of the balanced accuracy (BAC) for each bootstrapped sample was tested using permutation testing with a signiﬁcance level of 5%. Data format: mean ± standard deviation [min max]. DOR, diagnostic odds ratio; NLR, negative likelihood ratio; PLR, positive likelihood ratio; SE, sensitivity; SP, speciﬁcity. Signiﬁcant models: number of models with statistically signiﬁcant BAC higher than 50%. is being performed in a non-healthy (i.e., non-standard) population, because the eﬀect of external variables such as age and sex might be modulated by the presence of the disease (e.g., being at ARMS or having schizophrenia). Third, this study’s sample is composed of subjects whose clinical diagnosis of an ARMS was based on having a schizotypal personality disorder or on the subject’s familial-high risk coupled with functioning decline and on the CAARMS (54), which mainly evaluates positive symptoms. These were not the same criteria as those used in the previous studies predicting transition to psychosis from an ARMS. These previous studies all used samples of subjects clinically assessed with tools that evaluate not only positive symptoms, but also basic and negative symptoms (10–12, 14, 15), except one (13), which included only familial-high risk subjects in its sample. This potentially increases the inclusion of subjects in the early phase of the psychosis prodrome (characterized by the presence of basic and negative symptoms), whereas our sample includes mainly subjects in the late prodromal phase of psychosis (characterized mainly by the presence of positive symptoms) (2). Therefore, our results suggest that previously reported accuracies in predicting transition to psychosis may be population-speciﬁc, poorly generalizable to diﬀerently clinically characterized populations (as ours herein). 4.2. Prediction of transition to psychosis using genetic data In this study we applied ML to genetic data and used three types of genetic features to detect transition to psychosis from an ARMS: (a) a schizophrenia PRS that we have previously shown to distinguish Frontiers in Psychiatry 11 frontiersin.org fpsyt-13-1086038 January 13, 2023 Time: 17:35 # 12 Tavares et al. 10.3389/fpsyt.2022.1086038 FIGURE 5 Balanced accuracy across bootstrapped samples for each model trained with the polygenic risk score, the list of psychosis-associated single nucleotide polymorphism (SNPs) or with the list of psychosis-associated genes for which an expression quantitative trait loci (eQTL) score was extracted. Dots represent the balanced accuracy value in each of the 5 bootstrapped samples and are red colored if the balanced accuracy is statistically signiﬁcant (i.e., p < 0.05) or blue colored if it is not (i.e., p > 0.05). The statistical signiﬁcance of the balanced accuracy in each bootstrapped sample was evaluated through permutation testing. FIGURE 6 Balanced accuracy across bootstrapped samples for each model trained with the environmental risk score or with each environmental risk factors as features. Dots represent the balanced accuracy value in each of the 5 bootstrapped samples and are red colored if the balanced accuracy is statistically signiﬁcant (i.e., p < 0.05) or blue colored if it is not (i.e., p > 0.05). The statistical signiﬁcance of the balanced accuracy in each bootstrapped sample was evaluated through permutation testing. FEP patients from healthy controls (26) and ARMS-T from ARMS- NT (16), (b) a set of psychosis-associated SNPs previously associated with schizophrenia in a recent GWAS meta-analysis (27), and (c) a brain-speciﬁc expression Quantitative Trait Loci (eQTL) score including the latter genes. Genetic data showed a poor performance in predicting transition to psychosis from an ARMS. SNPs-based classiﬁcation models have been previously shown to classify schizophrenia (18, 19, 21), and FEP patients (23) (vs. healthy controls) better than chance level, but not subjects at an ARMS vs. healthy controls or FEP patients (23). Furthermore, one of these studies has selected a list of SNPs from the Psychiatric Genomics Consortium 2 (PGC2) (21, 42), which potentially overlaps with the ones selected in this study (27). Despite the (scarce) evidence of the potential of PRS for schizophrenia (20–22) to classify schizophrenia patients (vs. healthy controls) and the one report showing the schizophrenia PRS’s ability to predict transition to psychosis (16) we were not able to predict transition to psychosis from an ARMS using this type of genetic feature. Although the latter study (16) used a larger sample (i.e., 106% higher than ours, measured as the absolute value of the change in sample size, divided by the average of the size of the two samples) to train the PRS-based model, sample balancing in terms of group and age or sex were not taken into account or that was unclear, respectively. Furthermore, herein we applied a bootstrapped sample approach to estimate generalizability of the PRS-based model by assuring that each bootstrapped sample met the balancing conditions for group, age, and sex–which does not seem to be the case in that study (16). Furthermore, another possible explanation for the PRS negative results is that although the genetic architecture, conveyed through a PRS, has been shown to diﬀer between patients with schizophrenia and healthy controls, one cannot exclude the possibility that it is speciﬁc to schizophrenia (a fully developed psychotic disorder), and might even be present in all subjects at an ARMS, i.e., those who later transition to psychosis and those who do not. The constellation of genetic variations (i.e., SNPs) that might confer susceptibility to transition to psychosis already from a prodromal stage is not necessarily the same as the one for schizophrenia (when drawn in comparison to healthy controls). This Frontiers in Psychiatry 12 frontiersin.org fpsyt-13-1086038 January 13, 2023 Time: 17:35 # 13 Tavares et al. 10.3389/fpsyt.2022.1086038 may justify the advantage of using a less hypothesis-based approach for the selection of genetic features (as we did by pre-selecting a large list of SNPs and performing an embedded feature selection using elastic net regression). Lastly, using a PRS formula made speciﬁcally for transition to psychosis from an ARMS would require a larger and independent sample to estimate SNP eﬀect sizes, which might be better provided by multicenter projects, such as NAPLS 2 (55) and PRONIA4 over the next years. Expression Quantitative Trait Loci (eQTL) scores for psychosis associated genes expressed in the brain were also not able to predict transition to psychosis from an ARMS. Only one previous study has shown the predictive value of gene expression proﬁling in the frontal brain region in classifying schizophrenia patients (vs. healthy controls) (17). In the present study, instead of actual gene expression measures we used a proxy for a-genetically regulated component of the expression of genes, the eQTL scores. Although we have computed eQTL scores only for the genes having a validated eQTL score model (43), this does not guarantee that the estimated gene expression represents (or correlates perfectly with) the real levels of the expression. Furthermore, although we have selected the initial list of genes as the ones most associated with schizophrenia (vs. healthy controls), this selection did not take into account the expression proﬁle of these genes in the brain, and we have computed an eQTL score for several brain tissues. A future improvement of this step would be to test an eQTL scores-based model with a selection of genes that: (a) are highly expressed in the brain in healthy subjects, and (b) their expression is associated to a schizophrenia diagnosis, or even better with the transition to psychosis from an ARMS. 4.3. Prediction of transition to psychosis using environmental data In this study we applied, for the ﬁrst time, ML to environmental data using two types of features to detect transition to psychosis from an ARMS: (a) a schizophrenia ERS which we have previously reported (28), and (b) a set of environmental risk factors as predictors. Overall, neither environmental risk assessment, could predict transition to psychosis from an ARMS with an averaged accuracy, i.e., across bootstrapped samples, better than chance level. Although we know of no similar longitudinal ARMS transition study, the closest other report using ML and environmental data to diagnose schizophrenia (vs. healthy controls) (22) also found a BAC not statistically better than chance level, even having included features such as the presence of obstetric complications and of developmental anomalies, the parental socio-economic status; and –without feature selection– trained and tested the model in a 13 times larger, albeit age, sex, and group -unbalanced, sample (103 patients and 337 controls) than ours (22). However, due to the still poorly understood environmental risk mechanisms one cannot exclude the lack of statistical power as a potential explanation for these negative ﬁndings including ours. The ML model trained with the ERS for schizophrenia, which we have tested as an (admittedly limited) exploratory predictor of the transition to psychosis from an ARMS, showed a poor performance, i.e., a BAC similar to chance level. Indeed, ERS is a composite score of individual risk factors computed under the assumption that the risk factors are completely independent (28), which has been shown 4 http://pronia.eu not to be the case (56)–i.e., intercorrelated risk factors may inﬂate the ERS estimation. This crude approach may limit the ability of the ERS to capture the detailed environmental architecture underlying psychosis. Moreover, just as for a PRS, an ERS for schizophrenia may not be a good substitute of a potential ERS for transition to psychosis from an ARMS (57). Lastly, our criterion for training and testing a fully multimodal ML model with modalities that would show an ML model performance statistically better than chance (i.e., 50%) predicting transition to psychosis from an ARMS in at least 3 of 5 bootstrapped samples was not fulﬁlled given that none of the modality-based ML models survived that threshold. This conservative criterion was chosen given the already small sample size available for the training of the multimodal ML model, i.e., only 6 ARMS-T and 23 ARMS-NT (only this subset of subjects had data for the three data modalities, simultaneously). The decrease in sample size, remarkably impairs the prediction power of the model, i.e., its accuracy. Without previous evidence of the ability to predict transition to psychosis from an ARMS by modality supporting its integration in a multimodal ML model, negative results from this multimodal model would be highly diﬃcult to explain, as they could theoretically be explained by the increase of noise in the model due to the inclusion of features that did show previous predictive ability or by the lack of predictive power due to the very small sample size. Moreover, the parallel-to-ours, multi-site study, albeit very group-unbalanced (only 26 ARMS-T patients vs. 308 ARMS-NT), from the PRONIA project, showed that a stacked model combining similar data to our study’s plus human prognostic ratings could predict transition to psychosis with a balanced accuracy of 86% and a good geographical generalizability (25). This multimodal approach was showed to improve biological-based unimodal models by 15% (VBGM volume maps-based model) and 20% (PRS for schizophrenia-based model). As such, the replication of this promising ﬁnding, following the same multimodal approach as that study, using in our study’s sample and data features co-existing in both samples, would be interesting as an additional method to ascertain whether our negative ﬁndings are due to lack of power or to no discriminability with our feature sets. 4.4. Limitations This study was limited by several factors. First, and foremost, the small sample size may have limited the performance of classiﬁcation models, even though our sample size was informed by previous ML studies showing 74–84% accuracies in predicting transition to psychosis from an ARMS (10–15). Indeed, this is a critical limitation when dealing with high dimensional data, such as neuroimaging and genetics–which we have used herein. Although we have taken measures to avoid overﬁtting and an overestimation of the classiﬁcation models’ performance such as artiﬁcially increasing the sampling through bootstrapping and employing CV strategies, this might not be enough to overcome this limitation. Indeed, our complementary analysis comparing the models’ training and testing performance (results in the Supplementary material) is indicative that some of the tested classiﬁcation models (mainly trained with neuroimaging or with SNPs) might suﬀer from some degree of overﬁtting. Ultimately, we cannot determine whether our negative ﬁndings were due to lack of power to obtain a good performance or due to a true lack of association between the predictors and the transition to psychosis from an ARMS (and hence inﬂated ﬁndings Frontiers in Psychiatry 13 frontiersin.org fpsyt-13-1086038 January 13, 2023 Time: 17:35 # 14 Tavares et al. 10.3389/fpsyt.2022.1086038 from previous studies). This is one of the reasons why replication studies in independent datasets are essential in ML literature. As a ﬁnal note, a power analysis for this study design would have been the most informative way to deﬁne the sample size needed to achieve an accuracy in predicting transition to psychosis from an ARMS better than chance level. However, this is not a trivial task in ML analysis and there is no established method to perform this analysis as there is for univariate analysis [for examples of studies exploring innovative ways of computing sample size for classiﬁcation problems see Refs. (58, 59)] and, therefore, it was not performed. Second, in order to dilute possible confounding eﬀects in the developed classiﬁcation models we have restricted the samples used to train the models to: (a) be class-balanced, i.e., with the same number of ARMS-T and ARMS-NT subjects; (b) be matched for age, sex, scanning acquisition protocols for neuroimaging data; (c) include subjects with European ancestry only for genetic data; and (d) limit the proportion of missing data for the environment data. Although this has artiﬁcially homogenized the study sample thus avoiding the presence of statistical confounders, it has deemed the sample to be less representative of the ARMS population. Third, overall, the ﬁndings of this study are only valid to young help-seeking individuals, i.e., that are clinically screened for ARMS criteria, and whose ARMS diagnosis was based on having a schizotypal personality disorder or on the subject’s familial-high risk coupled with functioning decline and on the CAARMS (54), which mainly evaluates positive symptoms. 5. Conclusion and future directions In this study, we explored the value of using exclusively quantitative and multimodal data (i.e., as predictors) to predict transition to psychosis from an ARMS. Overall, we found that, contrary to what has been previously reported, sMRI could not predict transition to psychosis from an ARMS. We have employed several ML strategies aiming to replicate the highly promising previous positive sMRI ﬁndings (74–84%) (10–15). This is even though our sample was larger than four of the above 6 studies (10, 11, 13, 14), respectively (Conversely, our sample was smaller than two of the above studies [Das et al. (15); Koutsouleris et al. (12), respectively]. This points to the need for a cautious interpretation of small sample size studies. Also, we could not replicate the one previous evidence of the value of the schizophrenia PRS in predicting transition to psychosis. Moreover, and to the best of our knowledge, we explored for the ﬁrst time the value of environment in the prediction of psychosis already from a prodromal stage. Lastly, the genetic and the environmental data used could not predict transition to psychosis from an ARMS. In summary, the present study should serve as a call for caution and skepticism regarding the currently achievable prognostic and diagnostic biomarker development goals, with the existing modeling tools and data measurement tools. Additionally, our study’s methodological approaches tailored to each data modality, may serve as suggestive proofs-of-concept for the exploration of future multimodal datasets, either for novel discovery or replication of previous promising ﬁndings, across psychiatric disorders, not exclusive to ARMS. We further suggest larger samples (in the several hundreds) should be employed for both model training and testing, given the inherent high data dimensionality (specially of neuroimaging and genetics) and the little established relevance of individual features. Although still heterogeneity in phenotypic measurements is increased in larger they bring not only statistical power but ecological samples, generalizability, and thus carry a higher potential to be clinically useful. This is best achieved with consortia multi-center studies which are increasingly common albeit not without challenges (60). Alternatively, methods for synthetic generation of data such as the Generative Adversarial Networks (GAN)-based are also a promising avenue for sample size augmentation, now starting to be applied in the clinical research ﬁeld (61). Last, but not least, we recommend the use of objective and quantitative criteria-based tools for the assessment of a ML biomarker’s clinical applicability, once high eﬀect size and accuracy estimates are achieved, such as one we have previously proposed (62). Data availability statement The datasets presented in this article are not readily available include public data sharing. should be directed to the because ethics approval did not Requests the datasets corresponding author. to access Ethics statement The studies involving human participants were reviewed and approved by NHS South East London Research Ethics Committee. The patients/participants provided their written informed consent to participate in this study. Author contributions VT ran most data preprocessing and statistical analyses and drafted the manuscript. EV coordinated genotyping and advised the genetic and environmental data analysis. AM and HF provided advise on imaging data processing and machine learning analysis. JS and IV collected imaging data. GB provided advise on imaging data processing. DP collected genetic and environmental data, co- designed the study, ran preliminary data preprocessing and machine learning analyses, and supervised the study. All authors revised the manuscript and agreed with its ﬁnal version. Funding This study, VT received support from Fundação para a Ciência e a Tecnologia (FCT) Ph.D. fellowship PD/BD/114460/2016 and DSAIPA/DS/0065/2018 grants; DP received primary support from National Institute for Health Research (NIHR) PDF-2010-03-047 grant, and additionally from FCT FCT-IF/00787/2014, LISBOA- 01–0145-FEDER-030907, and DSAIPA/DS/0065/2018 grants, and a European Commission (EC) Marie Curie Career Integration Grant (FP7-PEOPLE-2013-CIG 631952). EV was part-funded by the NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. IV was supported by EC’s Horizon 2020 Marie Skłodowska-Curie grant (Ref. 754550, project BITRECS) and “La Caixa” Foundation (LCF/PR/GN18/50310006). Frontiers in Psychiatry 14 frontiersin.org fpsyt-13-1086038 January 13, 2023 Time: 17:35 # 15 Tavares et al. 10.3389/fpsyt.2022.1086038 Acknowledgments We thank Prof. Philip McGuire for his invaluable guidance during data design and collection, the OASIS team, and all volunteers with an ARMS who made this study possible. organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Conﬂict of interest Author disclaimer The authors declare that the research was conducted in the absence of any commercial or ﬁnancial relationships that could be construed as a potential conﬂict of interest. The reviewer SV declared a shared aﬃliation with the authors EV, IV, GB, and DP to the handling editor at the time of review. Publisher’s note The views expressed were those of the authors and not necessarily those of any of the above sponsors. 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10.3390/ijerph19127064	Data Availability Statement: The code and data underlying this article will be shared on reasonable request to the corresponding author.	Data Availability Statement: The code and data underlying this article will be shared on reasonable request to the corresponding author.	Article The Impact of Inﬂuencers on Cigar Promotions: A Content Analysis of Large Cigar and Swisher Sweets Videos on TikTok Jiaxi Wu 1 Traci Hong 1 and Jessica L. Fetterman 9,* , Alyssa F. Harlow 2, Derry Wijaya 3, Micah Berman 4, Emelia J. Benjamin 5,6,7 , Ziming Xuan 8, 1 College of Communication, Boston University, Boston, MA 02215, USA; jiaxiw@bu.edu (J.W.); tjhong@bu.edu (T.H.) 2 Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA; afharlow@usc.edu 3 Department of Computer Science, Boston University, Boston, MA 02215, USA; wijaya@bu.edu 4 College of Public Health & Moritz College of Law, The Ohio State University, Columbus, OH 43210, USA; berman.31@osu.edu 5 National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA 20892, USA; 6 emelia@bu.edu Section of Cardiovascular Medicine, Boston Medical Center, Department of Medicine, School of Medicine, Boston University, Boston, MA 02118, USA 7 Department of Epidemiology, School of Public Health, Boston University, Boston, MA 02118, USA 8 Department of Community Health Sciences, School of Public Health, Boston University, 9 Boston, MA 02118, USA; zxuan@bu.edu Evans Department of Medicine, Whitaker Cardiovascular Institute, School of Medicine, Boston University, Boston, MA 02118, USA * Correspondence: jefetter@bu.edu; Tel.: +1-617-358-7544 Citation: Wu, J.; Harlow, A.F.; Wijaya, D.; Berman, M.; Benjamin, E.J.; Xuan, Z.; Hong, T.; Fetterman, J.L. The Impact of Inﬂuencers on Cigar Promotions: A Content Analysis of Large Cigar and Swisher Sweets Videos on TikTok. Int. J. Environ. Res. Public Health 2022, 19, 7064. https://doi.org/10.3390/ ijerph19127064 Academic Editor: David Berrigan Received: 16 March 2022 Accepted: 7 June 2022 Published: 9 June 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional afﬁl- iations. Abstract: Little is known about the content, promotions, and individuals in cigar-related videos on TikTok. TikTok videos with large cigar and Swisher Sweets-related hashtags between July 2016 and September 2020 were analyzed. Follower count was used to identify inﬂuencers. We compared con- tent characteristics and demographics of featured individuals between cigar types, and by inﬂuencer status. We also examined the association between content characteristics and video engagement. Compared to large cigar videos, Swisher Sweets videos were more likely to feature arts and crafts with cigar packages, cannabis use, and ﬂavored products. In addition, Swisher Sweets videos were also more likely to feature females, Black individuals, and younger individuals. Both Swisher Sweets and large cigar inﬂuencers posted more videos of cigar purchasing behaviors than non-inﬂuencers, which was associated with more video views. None of the videos disclosed sponsorship with #ad or #sponsored. Videos containing the use of cigar packages for arts and crafts, and ﬂavored products highlight the importance of colorful packaging and ﬂavors in the appeal of Swisher Sweets cigars, lending support for plain packaging requirements and the prohibition of ﬂavors in cigar products to decrease the appeal of cigars. The presence and broad reach of cigar promotions on TikTok requires stricter enforcement of anti-tobacco promotion policies. Keywords: promotion; TikTok cigars; little cigars; ﬂavored cigars; Swisher Sweets; social media; inﬂuencer Copyright: © 2022 by the authors. 1. Introduction Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). In 2020, cigars were the most used combustible tobacco product with a higher preva- lence of use than cigarettes among US youth [1]. Between 2009 and 2020, the increase in US cigar sales was primarily driven by the sale of ﬂavored cigars [2]. Swisher Sweets, a leading ﬂavored cigar brand, accounted for over 22% of market shares in US Convenience Stores in 2020 [3]. Factors that contribute to the increased cigar use among youth and young adults include the availability of ﬂavors [4], small pack sizes [5], the industry’s targeted Int. J. Environ. Res. Public Health 2022, 19, 7064. https://doi.org/10.3390/ijerph19127064 https://www.mdpi.com/journal/ijerph International Journal ofEnvironmental Researchand Public HealthInt. J. Environ. Res. Public Health 2022, 19, 7064 2 of 14 marketing [6], features that facilitate cannabis use [7], psychosocial factors [8], and reduced health risk perceptions of cigar smoking compared to cigarette smoking [9]. Cigars are broadly categorized into three types: large cigars, cigarillos, and little cigars [10]. The large cigar category includes both premium hand-rolled and machine- made large cigars. Cigarillos are short and narrow cigars that usually do not contain a ﬁlter and are available in a wide array of ﬂavors. Little cigars are similar in size and shape to cigarettes and typically contain a ﬁlter. Historically, cigars were primarily premium cigars and were used mainly by older, predominantly white men who reported infrequent use and no inhalation [11]. As a result, cigars are not as heavily regulated and taxed as cigarettes [12]. Cigar companies promote product features prohibited in cigarettes, such as ﬂavors and small pack sizes [13]. Regulatory loopholes have resulted in the generation of cigar products, speciﬁcally ﬂavored little cigars and cigarillos (LCCs), designed to appeal to youth, young adults, and individuals of low-socioeconomic status [12]. As a result, the demographic characteristics of users, cigar product use patterns, purchasing behaviors, and reasons for use vary by cigar type [14]. Compared to users of premium large cigars, users of LCCs tend to be younger, non-Hispanic Black, have low educational attainment, and have low income. People who smoked LCCs are also more likely to report the use of a ﬂavored, commonly used brand cigar than individuals who used premium cigars. Thus, it is critical to distinguish between people who use premium large cigars from people who use ﬂavored LCCs, and to develop targeted public health and intervention efforts toward users of different cigar products [15]. Cigar smoking in the US also presents a critical health equity issue. Cigar companies use targeted strategies to promote products to communities of color [16]. As a result, the prevalence of cigar use is higher among non-Hispanic Black individuals than among other racial/ethnic groups [15]. In addition, Black youth are more likely to initiate tobacco use with cigars compared to White youth [17]. Recent surveillance data also indicate a higher prevalence of cigar use among high school students who are Black, compared to White and Hispanic youth [1]. Social media use is pervasive among youth, with 85% of youth using at least one social media site, and 45% say they are constantly online [18]. Social media has given rise to a class of non-traditional celebrities called inﬂuencers, who have large online followings and are valued as opinion leaders [19]. Inﬂuencers promoting tobacco products can potentially affect their followers’ attitudes and use of tobacco products. Followers of tobacco inﬂuencers are more likely to be an especially vulnerable group because they tend to be younger, have lower education, and are more likely to report past month tobacco use than those who do not follow tobacco inﬂuencers [20]. The positive association between exposure and engagement with tobacco-related social media content and tobacco use among youth warrants investigation into the prevalence of tobacco-related inﬂuencer promotions on social media [21]. Social learning theory provides a basis for explaining the effects of exposure to tobacco- related inﬂuencer posts on youth tobacco use [22]. Social learning theory posits that people acquire behaviors through observing, modeling, and imitating the behaviors of others [22]. Individuals who are observed are referred to as models. In real life, young people are surrounded by different types of inﬂuential models, from parents and teachers to peers. In contrast, on social media, models for behaviors are more limited, primarily to inﬂuencers who are often paid for sponsorships of products and services. Thus, by observing examples of behavior through social media, people, especially youth, are more likely to adopt the attitudes and behaviors exhibited by the inﬂuencer [23]. TikTok is the fastest-growing social media platform in the world and was the most downloaded mobile app in 2021 [24]. TikTok is especially popular among youth, with those 14 years and younger accounting for more than a third of TikTok’s 49 million daily users in the US [25]. On TikTok, users are continuously provided with new content, as videos start automatically one after another, unprompted [26]. On average, users spend Int. J. Environ. Res. Public Health 2022, 19, 7064 3 of 14 52 min on the platform and consume more than 200 videos per day, including carefully targeted ads [27]. TikTok’s user guidelines prohibit the posting of “content that depicts minors consuming, possessing, or suspected of consuming alcoholic beverages, drugs, or tobacco”, and the advertising or trade of tobacco products is also prohibited [28,29]; however, previous research found widespread tobacco promotions on Facebook, despite the platform’s policies prohibiting tobacco advertising [30]. It is unknown if TikTok’s anti-tobacco promotion policies are enforced, warranting further examination. The goal of the current study was to examine the portrayals and promotions of large cigars and LCCs on TikTok, which is largely unexplored. Speciﬁcally, we compared the content features and individuals in videos of the different cigar types, and between inﬂuencer and non-inﬂuencer cigar videos. Our analysis of cigar-related videos on TikTok adds to the literature describing the differences in the users and use patterns of different types of cigar products. We also examined the association between content features and video popularity on TikTok. 2. Materials and Methods 2.1. Data Collection We searched for different large cigar and LCC-related hashtags on TikTok to identify the most representative hashtags for cigar-related videos. Our initial observation on TikTok revealed that the hashtags “#cigar” and “#cigars” were commonly used in videos of traditional large cigars (see Supplemental Table S1 for details on the number of videos identified by hash- tag). We also searched hashtags #littlecigar, #littlecigars, #cigarillo, and #cigarillos to identify LCC-related videos; however, these LCC-related hashtags were rarely used by TikTok users. To identify content on TikTok related to LCCs, we extended our search to include Swisher Sweets-related hashtags (“#swisher”, “#swishers”, “#swishersweet”, “#swishersweets”), given that LCC users are more familiar with brand names rather than the terms “cigarillos” or “little cigars” [31] and are more likely to mention specific brands when posting about LCCs [32]. We found that Swisher Sweets-related hashtags were viewed over 16 million times on TikTok. Because Swisher Sweets is the leading cigar brand in the US for LCCs [33], is preferred among the US youth and young adults [34], and is commonly used as a keyword in previous social media research of LCCs [35–38], we only used Swisher Sweets-related hashtags to retrieve LCC-related TikTok videos for the current study. TikTok’s Terms of Service [39] prohibits the use of public videos for commercial purposes, which was not the intent of this study. On September 17, 2020, using an open- source TikTok scraping tool [40], we scraped all 4361 publicly available videos with cigar and Swisher Sweets-related hashtags ever posted on TikTok. Pre-determined large cigar and Swisher Sweets hashtags were used to identify all publicly available TikTok videos that contained those hashtags up to the scraping date. We scraped: (1) 3456 videos with cigar-related hashtags and (2) 905 videos with Swisher Sweets-related hashtags. We also collected the associated metadata, including numbers of video views, likes, shares, and follower counts. Data for this study were stored in a password-protected computer and were only accessible to the authors. Research procedures were deemed to not meet the deﬁnition of human subjects research by the Authors’ Institutional Review Board due to the use of publicly available data. 2.2. Sample Previous research has deﬁned inﬂuencers as individuals with a minimum of 1000 followers [37]. For our study, we deﬁned inﬂuencers as those with the top 75th percentile of total followers within each hashtag dataset because the follower counts varied between the large cigar and Swisher Sweets videos. Because people who smoke large cigars and Swisher Sweets differ in demographic features, cigar-smoking patterns, and purchasing behaviors [14], we sampled inﬂuencers for large cigar and Swisher Sweets videos separately; thus, instead of using a ﬁxed number, which may cause biased sampling, Int. J. Environ. Res. Public Health 2022, 19, 7064 4 of 14 we used the 75th percentile of followers within each hashtag to ensure we identiﬁed the top inﬂuential users for each of the two cigar types. The minimum number of followers for an inﬂuencer was 15,000 and 1759 for large cigar videos and Swisher Sweets videos, respectively. We included all videos from inﬂuencers and randomly selected the matched numbers of videos from non-inﬂuencers for each of the respective hashtag categories. We also excluded non-English and non-relevant videos, resulting in a ﬁnal sample of N = 1700 videos (1333 cigar videos and 367 Swisher Sweets videos, Figure 1). Figure 1. Data sampling procedure. Influencers’ videos were identified by defining influencers as those individuals with the top 75th quantile of number of followers within each hashtag category. The same number of non-influencers’ videos were randomly sampled for each hashtag category. The final sample of large cigar and Swisher Sweets videos was N = 1700 after excluding non-English videos. 2.3. Video Coding and Inter-Coder Reliability We created a coding scheme of ﬁfteen content features identiﬁed from previous studies and that emerged from the current dataset. Speciﬁcally, we identiﬁed nine themes from previous social media analyses of cigar and LCC-related posts including: (1) Product promotion [35]; (2) Smoking cigars [32,35]; (3) Cannabis use (e.g., removing some, or all the tobacco from the cigar and replacing it with cannabis, Figure 2A) [35]; (4) Smoke trick [35]; (5) Prevention [32]; (6) Flavor [38]; (7) Purchasing behaviors (Figure 2B) [38]; (8) Product review (Figure 2C) [41]; and (9) Cigar-related marketing events (Figure 2D) [36]. We further identiﬁed six themes observed from the current dataset including: (10) Arts and crafts with cigar packages (Figure 2E); (11) Individuals dancing with background music referring to Int. J. Environ. Res. Public Health 2022, 19, x 4 of 15 behaviors [14], we sampled influencers for large cigar and Swisher Sweets videos separately; thus, instead of using a fixed number, which may cause biased sampling, we used the 75th percentile of followers within each hashtag to ensure we identified the top influential users for each of the two cigar types. The minimum number of followers for an influencer was 15,000 and 1759 for large cigar videos and Swisher Sweets videos, respectively. We included all videos from influencers and randomly selected the matched numbers of videos from non-influencers for each of the respective hashtag categories. We also excluded non-English and non-relevant videos, resulting in a final sample of N = 1700 videos (1333 cigar videos and 367 Swisher Sweets videos, Figure 1). Figure 1. Data sampling procedure. Influencers’ videos were identified by defining influencers as those individuals with the top 75th quantile of number of followers within each hashtag category. The same number of non-influencers’ videos were randomly sampled for each hashtag category. The final sample of large cigar and Swisher Sweets videos was N = 1700 after excluding non-English videos. 2.3. Video Coding and Inter-Coder Reliability We created a coding scheme of fifteen content features identified from previous studies and that emerged from the current dataset. Specifically, we identified nine themes from previous social media analyses of cigar and LCC-related posts including: (1) Product promotion [35]; (2) Smoking cigars [32,35]; (3) Cannabis use (e.g., removing some, or all the tobacco from the cigar and replacing it with cannabis, Figure 2A) [35]; (4) Smoke trick [35]; (5) Prevention [32]; (6) Flavor [38]; (7) Purchasing behaviors (Figure 2B) [38]; (8) Int. J. Environ. Res. Public Health 2022, 19, 7064 5 of 14 cigar smoking (making smoking gestures); (12) Use of the Cardi B Swisher Sweets song as video background music; (13) Showing off multiple Swisher Sweets packages (Figure 2F). We additionally coded: (14) Written health warnings; and (15) Audio health warnings based upon previous research suggesting that health warnings in tobacco-related social media posts results in a more negative tobacco brand perception [42]. Table 1 displays descriptions of the coded video content features. Video content features were not mutually exclusive, meaning that a video could contain multiple content features simultaneously. Figure 2. Representative images of select video themes. (A) Cannabis use: a cigar that has been hollowed out and ﬁlled with cannabis; (B) Purchasing behavior: a video of a user purchasing cigar products in a convenience store; (C) Product review: a video review of the “double corona” large cigar; (D) Cigar-related marketing events: a video of a Swisher Sweets marketing event; (E) Arts and crafts: a video of paraphernalia emblazoned with the Swisher Sweets logo through arts and crafts; (F) Showing off multiple Swisher Sweets packages: a video of one individual showing off all of the Swisher Sweets packages the person has smoked. Int. J. Environ. Res. Public Health 2022, 19, x 5 of 15 Product review (Figure 2C) [41]; and (9) Cigar-related marketing events (Figure 2D) [36]. We further identified six themes observed from the current dataset including: (10) Arts and crafts with cigar packages (Figure 2E); (11) Individuals dancing with background music referring to cigar smoking (making smoking gestures); (12) Use of the Cardi B Swisher Sweets song as video background music; (13) Showing off multiple Swisher Sweets packages (Figure 2F). We additionally coded: (14) Written health warnings; and (15) Audio health warnings based upon previous research suggesting that health warnings in tobacco-related social media posts results in a more negative tobacco brand perception [42]. Table 1 displays descriptions of the coded video content features. Video content features were not mutually exclusive, meaning that a video could contain multiple content features simultaneously. Figure 2. Representative images of select video themes. (A) Cannabis use: a cigar that has been hollowed out and filled with cannabis; (B) Purchasing behavior: a video of a user purchasing cigar products in a convenience store; (C) Product review: a video review of the “double corona” large cigar; (D) Cigar-related marketing events: a video of a Swisher Sweets marketing event; (E) Arts and crafts: a video of paraphernalia emblazoned with the Swisher Sweets logo through arts and crafts; (F) Showing off multiple Swisher Sweets packages: a video of one individual showing off all of the Swisher Sweets packages the person has smoked. Int. J. Environ. Res. Public Health 2022, 19, 7064 6 of 14 Table 1. Descriptions of content features in English and relevant cigar/Swisher Sweets videos. Video Content Features Product Promotion Smoking Cigars Cannabis Use Smoke Trick Prevention Flavor Purchasing Behavior Product Review Cigar-related Marketing Events Arts and Crafts with Cigar Packages Individual Dancing Cardi B Swisher Sweets Music Showing off multiple Swisher Sweets packages Written Health Warnings Audio Health Warnings Sex Presence of Males Presence of Females Race Black White Spanish/Hispanic Asian Presence of Young Individuals Descriptions A video selling cigar products or promoting cigar stores, and professional ads A video showing individuals smoking featured cigar products A video of cannabis use (e.g., blunt: a cigar that has been hollowed out and ﬁlled with cannabis) A video of smoke tricks such as making smoke rings A video with a main theme of the negative effects and prevention of cigar and LCC products A video showing or referring to ﬂavored cigar products A video of accessing and purchasing cigar or LCC products A video commenting on or reviewing ﬂavors, tastes, or features of cigar or LCC products A video promoting cigar companies’ marketing events (e.g., musical events) A video of using cigar/LCC products’ packages to create arts and crafts such as a rolling tray A video of people dancing and making smoking gestures without smoking real cigar products A video using Cardi B’s “Swisher music” as background music A video showing more than ﬁve Swisher Sweets packages simultaneously A video containing written form of health warnings or disclaimers of cigar/LCC smoking superimposed on the video A video containing oral form of health warnings or disclaimers of cigar/LCC smoking A video featuring males A video featuring females A video featuring Black individuals A video featuring White individuals A video featuring Spanish/Hispanic individuals A video featuring Asian individuals A video featuring individuals who look like teens in middle/high school to people who are under the age of 21 For videos containing people, we also coded for the following demographic features: (1) perceived sex—the presence of males and females; (2) perceived race—the presence of Black, White, Asian, and Hispanic or Latino individuals; and (3) perceived age—the presence of younger or older individuals. Coders used all available visual and audial cues (e.g., skin color, background voices, appearances) to inform their coding choices. Consistent with a previous study that coded social media users’ age from their proﬁle pictures [32], we assigned “younger” to individuals who look like they were under the age of 21 (i.e., individuals who look like teens in middle or high school to young adults under the age of 21) and older individuals (≥21 years of age) based on visual and audio cues in the videos. In the event demographic features were difﬁcult to determine, coders could select “unknown” for the sex/race/age of featured individuals if none of the visual and audial cues were available to determine the demographics. To attain high coding reliability, three coders were ﬁrst trained on 20 videos together to ensure the visual/audio cues used to code the content features and demographic categories were consistent across all coders. Next, three coders independently coded 50 videos, after which discrepancies were discussed to resolve coding disagreements. Coders ﬁrst determined whether the video was in English and relevant to cigars and Swisher Sweets. A total of 280 videos (273 large cigar and 7 Swisher Sweets videos) were not in the English language and were excluded from the analyses. A video was considered relevant only when the video showed or referred to cigar smoking. An example of non-relevant videos included videos of Swisher brand lawn mowers. Next, following the coding scheme, coders Int. J. Environ. Res. Public Health 2022, 19, 7064 7 of 14 identiﬁed the content features and demographics of individuals in the videos. The inter- coder reliability was calculated using 10% (N = 221) of the sample. Coding agreements were assessed with Cohen’s Kappa values, which were above 0.7 across all content variables, indicating a high level of intercoder reliability [43]. Three coders independently coded the remaining videos in the sample. 2.4. Statistical Analyses We performed chi-square tests to compare the content features and individual char- acteristics for large cigar and Swisher Sweets videos, between inﬂuencer’s and non- inﬂuencer’s videos within each of the cigar types, and between large cigars and Swisher Sweets inﬂuencers’ videos. When comparing large cigars and Swisher Sweets videos, we excluded the content features (1) use of the Cardi B Swisher Sweets song as video back- ground music” and (2) showing off multiple Swisher Sweets packages from the analysis because these features were unique to Swisher Sweets videos. We additionally calculated odds ratios and 95% conﬁdence intervals. Chi-square analyses were conducted using SPSS (Version 26) with an alpha level of 0.05 (2-tailed) with Bonferroni correction to account for multiple testing. 2.5. Modeling Video Engagement with Video Content Features To identify the video content features associated with engagement (i.e., number of views, likes, and shares) of a large cigar or Swisher Sweets videos, we formulated negative binomial models for views and likes and negative binomial hurdle models for shares within each of the large cigar and Swisher Sweets datasets (see SI Section 2 for modeling details). For each of the main models predicting video popularity (i.e., views, likes, shares), we used video content features as predictors. Given that some content features were rare in the data sets, we only included content features that appeared more than ten times within each dataset. We also adjusted for follower counts, which can potentially affect the engagement of social media posts, including videos on TikTok. Negative binomial and hurdle models were ﬁtted using the glmmTMB package in R (version 4.1.0). A p-value less than 0.05 (two-tailed) was considered statistically signiﬁcant. The p-values of each negative binomial and hurdle model were adjusted with the Bonferroni correction method. 2.6. Hashtag Analysis of Video Descriptions To prohibit misleading or deceptive advertising, the Federal Trade Commission (FTC) requires that any type of online sponsored content must clearly disclose sponsorship [44]. For each of the video post descriptions, we analyzed whether the video description con- tained the FTC recommended sponsorship disclosure hashtags #ad and #sponsored [44]. The FTC requires disclosure of any ﬁnancial relationship to the brand (including the provi- sion of free products) and suggests the use of hashtags as one method of disclosure [45]. We used string matching techniques in R (Version 4.1.0) to determine if the description of a post for a video contained either of the two FTC recommended hashtags #ad and #sponsored. 3. Results At the time of the study, the 1700 videos with large cigar and Swisher Sweets-related hashtags had been viewed over 159 million times on TikTok. The median follower counts for large cigar and Swisher Sweets hashtag videos were 14,900 and 1740, respec- tively. With respect to video engagement, inﬂuencers’ cigar videos received an average of 88,749 views, 4455 likes, and 41 shares; non-inﬂuencers’ cigar videos attracted an average of 8718 views, 304 likes, and 7 shares. Inﬂuencers’ Swisher Sweets videos received an average of 45,625 views, 4962 likes, and 116 shares; non-inﬂuencers’ Swisher Sweets videos elicited an average of 2434 views, 150 likes, and 10 shares (See Supplemental Table S2 for details of video engagement for the two cigar types). For large cigar videos, the top two prevalent video themes were smoking cigars (59.2%) and product review (13.5%). For Swisher Sweets videos, the top two themes were ﬂavor (48.8%) and cannabis use (28.9%) Int. J. Environ. Res. Public Health 2022, 19, 7064 8 of 14 (See Supplemental Table S3). Coders also determined if Swisher Sweets LCCs or packaging were included in large cigar videos. Coders found that none of the 1333 sampled large cigar videos contained a Swisher Sweets LCC product or packaging, which is consistent with previous research that suggests users of LLC often mentioned speciﬁc brands when posting about LCCs, instead of using general tobacco product terms such as “cigar” and “cigars” [32]. 3.1. Comparisons of Video Content Features and Featured Individual Demographics Chi-square analyses comparing large cigar and Swisher Sweets videos indicated that large cigar videos contained more product promotions (p = 0.005), product reviews (p < 0.001), and individuals smoking cigars (p < 0.001) compared to Swisher Sweets videos. Swisher Sweets videos contained more videos of purchasing behaviors (p < 0.001), arts and crafts with cigar packages (p < 0.001), cannabis use (p < 0.001), dancing individuals making smoking gestures (p < 0.001), and ﬂavored products (p < 0.001) compared to large cigar videos (See Supplemental Table S3). Large cigar videos were more likely to feature males (p < 0.001), while Swisher Sweets videos were more likely to show females (p < 0.001). In addition, large cigar videos contained more White individuals than Swisher Sweets videos (p < 0.001). In contrast, Swisher Sweets videos were associated with Black (p = 0.017), Asian (p = 0.001), and younger individuals (p < 0.001) (See Supplemental Table S4). Compared to large cigar non-inﬂuencers, large cigar inﬂuencers were more likely to post about purchasing behaviors (p = 0.025) and product reviews (p < 0.001). For Swisher Sweets videos, inﬂuencers were also more likely to post purchasing behaviors content (p < 0.001), dancing individuals (p < 0.001), and ﬂavored products (p = 0.041) compared to Swisher Sweets non-inﬂuencers (See Supplemental Table S5). Interestingly, large cigar inﬂuencers were less likely to be younger compared to the non-inﬂuencers (p = 0.009), while the opposite association was observed for Swisher Sweets inﬂuencers, who were more likely to be younger than non-inﬂuencers (p = 0.002) (See Supplemental Table S6). Lastly, Chi-square analyses comparing large cigar inﬂuencer posts and Swisher Sweets inﬂuencer posts suggested that large cigar inﬂuencers were more likely to post product promotions (p = 0.020), product reviews (p < 0.001), and smoking individuals (p < 0.001); however, Swisher Sweets inﬂuencers were more likely to post videos of purchasing behav- iors (p < 0.001), arts and crafts with cigar packages (p < 0.001), cannabis use (p < 0.001), dancing individuals making smoking gestures (p < 0.001), and ﬂavored products (p < 0.001) (See Supplemental Table S7). Lastly, we found large cigar inﬂuencers were more likely to be males (p < 0.001) and White individuals than Swisher Sweets inﬂuencers (p < 0.001). On the contrary, when compared to large cigar inﬂuencers, Swisher Sweets inﬂuencers were more likely to be females (p < 0.001), Asian (p < 0.001), and young individuals (p < 0.001) (See Supplemental Table S8). 3.2. Predicting Video Popularity with Video Content Features When interpreting results of a predictor from negative binomial and Hurdle models, all other predictors are held constant. For large cigar videos, negative binomial models showed that after adjusting for the number of account followers and other content feature predictors, video content of purchasing behaviors (IRR = 29.03, p < 0.001, CI = 17.03, 49.47) and product reviews (IRR = 2.50, p < 0.001, CI = 1.98, 3.17) elicited more likes. Purchasing behaviors (IRR = 20.92, p < 0.001, CI = 11.98, 36.53) and product reviews (IRR = 2.03, p < 0.001, CI = 1.58, 2.61) also had a greater number of views compared to videos without these content features. We found that product promotions (IRR = 0.30, p < 0.001, CI = 0.21, 0.44) and content of an individual smoking cigars (IRR = 0.65, p < 0.001, CI = 0.54, 0.77) was associated with fewer likes. Product promotions (IRR = 0.32, p < 0.001, CI = 0.22, 0.47) and content of individuals smoking cigars (IRR = 0.56, p < 0.001, CI = 0.46, 0.67) were also associated with fewer views (See Supplemental Table S9). As for shares, the zero portion of Hurdle models revealed no signiﬁcant results for any of the content features. The positive portion of Hurdle model suggested that videos of purchasing behaviors (IRR = 44.70, p < 0.001, CI = 9.13, 218.86) Int. J. Environ. Res. Public Health 2022, 19, 7064 9 of 14 predicted more shares. Consistent with likes and views, product promotions (IRR = 0.29, p = 0.019, CI = 0.12, 0.74) and content of individuals smoking cigars (IRR = 0.47, p = 0.007, CI = 0.29, 0.77) were associated with fewer shares (See Supplemental Table S10). For Swisher Sweets videos, negative binomial analyses showed that, after adjusting for the number of account followers and other content feature predictors, videos that contained content on purchasing behavior received more video views (IRR = 2.96, p = 0.036, CI = 1.26, 7.00); however, video content of an individual smoking cigars was associated with fewer video likes (IRR = 0.37, p = 0.005, CI = 0.21, 0.68) and fewer video views (IRR = 0.32, p = 0.001, CI = 0.18, 0.59) (See Supplemental Table S11). When predicting video shares, none of the content features were associated with video shares in the zero portion of the model. The non-zero portion Hurdle model showed that smoking cigars also led to fewer shares of a Swisher Sweets post on TikTok (IRR = 0.01, p = 0.008, CI = 0.00, 0.17) (See Supplemental Table S12). 3.3. Disclosure of Sponsorship in Video Description Text analyses of all of the 1700 cigars and Swisher Sweets video descriptions showed that none of the video descriptions contained hashtags that disclosed sponsorship, including #ad and #sponsored. Only three large cigar videos contained oral health warnings or disclaimers. 4. Discussion The demographic characteristics, including age, sex, and race/ethnicity, differ be- tween users of large cigars and LCCs [14]. Research suggests that socially disadvantaged communities, especially non-Hispanic Black individuals, are more likely to smoke cigars and to develop established cigar-smoking behaviors compared with non-Hispanic White individuals [46]. Our ﬁndings conﬁrmed the literature on the demographic differences between users posting about large cigars or Swisher Sweets on TikTok. We found that Swisher Sweets TikTok videos were more likely to feature females, younger, Black, and Asian individuals compared to large cigar TikTok videos. When comparing between in- ﬂuencers and non-inﬂuencers’ videos, we found that compared to large cigar inﬂuencers, Swisher Sweets inﬂuencers tend to be younger and were more likely to be female. Future prevention campaigns for cigar products (i.e., large cigars and LCCs) should consider the different use patterns and demographics of users. Swisher Sweets is the leading LCC brand popular among youth and young adults in the US [3]. Our study sheds light on the Swisher Sweets-related content that youth and young adults may be exposed to on TikTok, the fastest growing social media platform in the world that appeals to a younger population [24,25]. Future studies are needed to evaluate the content of videos of additional cigar brands to gain a more comprehensive understanding of LCC inﬂuencer promotions on TikTok. Consistent with previous research across other social media platforms [35,37], our study noted that Swisher Sweets videos on TikTok were more likely to feature cannabis use and ﬂavored products compared to videos featuring large cigars [35,38]. In addition, our study found that both large cigar and Swisher Sweets inﬂuencers were more likely to post about purchasing behaviors than non-inﬂuencer users. Large cigar inﬂuencers also posted product review videos more frequently than non-inﬂuencer users—a common promotional strategy of large cigars observed in traditional media such as magazines [47]. Our ﬁndings suggest that content of purchasing behavior and product reviews lead to more views of large cigar and Swisher Sweets videos. Future research is needed to investigate how exposure to short-form videos of cigar-related content on TikTok affect youth’s attitudes towards cigars and their susceptibilities to initiate and use cigars. Exposure to celebrity-endorsed LCC promotions correlates with brand-specific cigar smoking among young adult smokers [48]. We found that one out of ten Swisher Sweets videos on TikTok used Cardi B’s “Swisher Sweets” song as the background music, suggesting that celebrity celebration of tobacco products can echo widely on social media. Moreover, our study also revealed that TikTok users, through arts and crafts, are re-purposing Swisher Int. J. Environ. Res. Public Health 2022, 19, 7064 10 of 14 Sweets packages to streetwear and paraphernalia emblazoned with the Swisher Sweets logo. Many LCC brands, including Swisher Sweets, use colorful and shiny packages that boldly communicate the flavor appeal to youth and young adults. Visual cues from the cigar packaging impact young adults’ affect and increase their susceptibility to cigar smoking [49]. Regulations on the packaging of flavored cigars, including the use of pictorial warning labels, plain packaging, and restrictions on celebrity endorsement and other youth appealing promotional content may decrease the appeal and use of cigar products among youth. To prohibit misleading or deceptive advertising, the FTC requires that any type of online sponsored content must clearly disclose sponsorship. For sponsored social media posts, the FTC recommends the use of clear hashtags such as “#ad” and “#sponsored”, or other similarly clear methods to disclose sponsorship [44]. None of the videos in our analyzed data set contained hashtags indicating sponsorship. We acknowledge that we do not have access to data regarding a ﬁnancial transaction between the manufacturers and the inﬂuencers; however, given that many of the inﬂuencers in our dataset were retailers and cigar manufacturers, the promotional regulations could also be applied to those users. Future research may examine the prevalence and potential effects of exposure to promotions from retailers and cigar manufacturers on purchasing intentions and behaviors. Research has indicated that labeling commercially sponsored tobacco content on social media with #ad and #sponsored attracts the attention of youth and young adults, making it a viable strategy to inform audiences of the promotional nature of the posts [50]. Sponsorship disclosure promotes critical evaluation of the promotional post and ultimately decreases advertising effectiveness [51]. A study found that clear sponsorship disclosure decreased young adult participants’ perceptions of inﬂuencer credibility and their intentions to engage with e-cigarette Instagram posts [52]. Examining the effects of inﬂuencer posts and regulatory interventions in inﬂuencer promotions on youth tobacco experimentation is an important direction for future research. Even though TikTok prohibits the promotion of tobacco products and the posting of minors consuming tobacco products [29], we still observed such content on TikTok. Exposure and engagement with tobacco-related social media content are associated with tobacco use among youth [21]. Because more than a third of TikTok’s daily users are under the age of 14 [25], it is crucial to restrict the promotion of youth-appealing tobacco content on TikTok to reduce the effects of such promotions on tobacco use among youth. Similar to a study that found that few, if any, inﬂuencers’ cigar-related posts on Twitter contained health warnings [37], we observed that only three of all large cigar and Swisher Sweets promotional videos contained audio health warnings or disclaimers, and no written health warning labels were observed. Prior research has reported that the inclusion of health warning statements in tobacco-related social media posts results in more negative brand perceptions [52]; therefore, from a public health perspective, it is important to enforce disclosures of sponsorship and health warnings on all content promoting tobacco products and to remove any content featuring the use of tobacco by underage users. Our study has several limitations. Our study was cross-sectional and observational and hence, we cannot rule out residual confounding or establish causation. We also cannot exclude misclassiﬁcation of the features we coded. For instance, identiﬁcation of an individual’s demographic features was by external appearance and auditory cues, which may not be as accurate as self-reported demographic data, especially for age; however, we attempted to limit misclassiﬁcation by including the option of coders to select unknown for the demographic identiﬁcation. In addition, coders were instructed to code the individuals as over 21-years old when they were in doubt about the age determination. It is possible that we may be under-estimating the number of videos featuring younger individuals. We also do not know the tobacco use status of the individuals who engaged with the content; thus, we cannot determine causation or whether engagement with inﬂuencers’ cigar videos leads to cigar experimentation. In violation of FTC guidance [44], many inﬂuencers do not use the recommended methods of disclosure, such as the use of the hashtags #ad or #sponsored to indicate that Int. J. Environ. Res. Public Health 2022, 19, 7064 11 of 14 they have a “material connection” with a brand. As a result, paid inﬂuencer posts can be difﬁcult to identify and study. We identiﬁed inﬂuencers as those in the top 75th percentile for the number of followers but acknowledge that we may be missing inﬂuencers who do not fall within the 75th percentile or include individuals with large numbers of followers who have no material connections to the industry. Previous research has identiﬁed LCC inﬂuencers with follower counts by categorizing users with 1000 and more followers as inﬂuencers [10]. Our study utilized a novel approach that considers the distribution of follower counts; however, the selection of the 75th percentile was arbitrary. To address this limitation, we conducted a sensitivity analysis identifying inﬂuencers as those individuals in the top 90th percentile for followers (Supplemental Table S13) and compared the results against those within the top 75th percentile. The sensitivity analysis resulted in similar ﬁndings when comparing the individual demographics and content features in videos of the two cigar types (see Supplemental Table S14, Supplemental Table S15, and Supplemental Table S16 for details on the sensitivity analysis), lending validation to our methodlogy. Our study focused on the TikTok platform and a single brand, Swisher Sweets; hence, our ﬁndings may not be generalizable to other social media platforms or other LCC brands. We studied the English language content on TikTok; the generalizability to other languages is unknown. Despite these limitations, the content and user characteristics identiﬁed in this study could inform the design of media campaigns and the development of tobacco control efforts. 5. Conclusions In summary, our study found that the demographics of the featured individuals and content features differ between videos of large cigar and Swisher Sweets on TikTok. Speciﬁcally, compared to large cigar videos, Swisher Sweets videos were more likely to contain content of arts and crafts with cigar packages, cannabis use, and ﬂavored products. Swisher Sweets videos were also more likely to feature individuals who are female, younger, Black, and Asian compared to large cigar TikTok videos. In addition, Swisher Sweets inﬂuencers’ videos tend to feature younger, and female individuals than large cigar inﬂuencers’ videos. We also identiﬁed speciﬁc content features that may facilitate the engagement of large cigar and Swisher Sweets videos on TikTok. Both Swisher Sweets and large cigar inﬂuencers posted more videos of cigar purchasing behaviors than non- inﬂuencers, which was associated with more video views. For large cigar videos, the content of product reviews was associated with more video views and likes on TikTok. Our ﬁndings may inform the design and implementation of cigar prevention cam- paigns with targeted demographic populations. Our ﬁndings lend support for enforcement of disclosures of sponsorship and health warnings on TikTok and related regulatory actions to restrict the promotions of youth-appealing tobacco content on social media. Our ﬁndings also suggest an urgent need for the prohibition of ﬂavors in cigars and extension of plain packaging to cigar products. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/ijerph19127064/s1, Supplemental Table S1. Views of videos with different cigar-related hashtags on TikTok. Supplemental Table S2. Average followers and engagement with large cigar and Swisher Sweets TikTok Videos. Supplemental Table S3. Multiple comparisons of video features between large cigar and Swisher Sweets videos. Supplemental Table S4. Multiple comparisons of featured individual demographics for videos with people between large cigar and Swisher Sweets videos. Supplemental Table S5. Multiple comparisons of video content features between inﬂuencer and non-inﬂuencer videos within each hashtag category. Supplemental Table S6. Multiple comparisons of featured individual demographics for videos with recognizable individuals between inﬂuencer and non-inﬂuencer videos within each hashtag category. Supplemental Table S7. Multiple comparisons of content features between large cigar and Swisher Sweets inﬂuencers’ videos. Supplemental Table S8. Multiple comparisons of featured individual demographics between large cigar and Swisher Sweets inﬂuencers’ videos. Supplemental Table S9. Predicting likes and views of large cigar videos with video content features. Supplemental Table S10. Predicting shares of large cigar Int. J. Environ. Res. Public Health 2022, 19, 7064 12 of 14 videos with video content features. Supplemental Table S11. Predicting likes and views of Swisher Sweets videos with video content features. Supplemental Table S12. Predicting shares of Swisher Sweets videos with video content features. Supplemental Table S13. Different cutoffs of follower count in large cigar and Swisher Sweets videos. Supplemental Table S14. Multiple comparisons of video features between large cigar and Swisher Sweets videos using the 90th percentile of followers to deﬁne inﬂuencers. Supplemental Table S15. Multiple comparisons of featured individual demographics for videos with people between large cigar and Swisher Sweets videos using the top 90th percentile of followers to deﬁne inﬂuencers. Supplemental Table S16. Multiple comparisons of featured individual demographics between large cigar and Swisher Sweets inﬂuencers’ videos using the top 90th percentile of followers to deﬁne inﬂuencers. Author Contributions: Conceptualization, J.W., T.H., J.L.F., M.B. and E.J.B.; data curation, J.W., T.H. and J.L.F.; methodology, J.W., T.H. and J.L.F.; writing—Original draft, J.W., T.H. and J.L.F.; writing— Review and editing, J.W., A.F.H., D.W., M.B., E.J.B., Z.X., T.H. and J.L.F. All authors have read and agreed to the published version of the manuscript. Funding: Research reported in this publication was supported, in part, by the National Heart, Lung, And Blood Institute of the National Institutes of Health under Award Number U54HL120163. The content is solely the responsibility of the authors and does not necessarily represent the ofﬁcial views of the National Institutes of Health. J. Wu reports grants from American Heart Association. J.L. Fetterman reports grants from American Heart Association and a National Heart, Lung, and Blood Institute K01 HL143142. A.F. Harlow reports grants from American Heart Association. E.J. Benjamin reports grants from NIH R01HL092577 and American Heart Association AHA_18SFRN34110082. Institutional Review Board Statement: Research procedures were deemed to not meet the definition of human subjects by the Authors’ Institutional Review Board due to the use of publicly available data. Informed Consent Statement: Not applicable. Data Availability Statement: The code and data underlying this article will be shared on reasonable request to the corresponding author. Conﬂicts of Interest: The authors declare no conﬂict of interest. References 1. 2. Gentzke, A.S.; Wang, T.W.; Jamal, A.; Park-Lee, E.; Ren, C.; Cullen, K.A.; Neff, L. Tobacco Product Use Among Middle and High School Students—United States, 2020. MMWR Morb. Mortal. Wkly. Rep. 2020, 69, 1881–1888. [CrossRef] [PubMed] Delnevo, C.D.; Miller Lo, E.; Giovenco, D.P.; Cornacchione Ross, J.; Hrywna, M.; Strasser, A.A. Cigar Sales in Convenience Stores in the US, 2009–2020. JAMA 2021, 326, 2429–2432. [CrossRef] [PubMed] 3. 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10.1103_physrevb.107.094206.pdf	null	null	PHYSICAL REVIEW B 107, 094206 (2023) Probability transport on the Fock space of a disordered quantum spin chain Isabel Creed ,1,* David E. Logan ,1,2,† and Sthitadhi Roy 3,‡ 1Physical and Theoretical Chemistry, Oxford University, South Parks Road, Oxford OX1 3QZ, United Kingdom 2Department of Physics, Indian Institute of Science, Bengaluru 560012, India 3International Centre for Theoretical Sciences, Tata Institute of Fundamental Research, Bengaluru 560089, India (Received 12 January 2023; revised 9 February 2023; accepted 8 March 2023; published 29 March 2023) Within the broad theme of understanding the dynamics of disordered quantum many-body systems, one of the simplest questions one can ask is, given an initial state, how does it evolve in time on the associated Fock-space graph, in terms of the distribution of probabilities thereon? A detailed quantitative description of the temporal evolution of out-of-equilibrium disordered quantum states and probability transport on the Fock space is our central aim here. We investigate it in the context of a disordered quantum spin chain, which hosts a disorder-driven many-body localization transition. Real-time dynamics/probability transport is shown to exhibit a rich phenomenology, which is markedly different between the ergodic and many-body localized phases. The dynamics is, for example, found to be strongly inhomogeneous at intermediate times in both phases, but while it gives way to homogeneity at long times in the ergodic phase, the dynamics remain inhomogeneous and multifractal in nature for arbitrarily long times in the localized phase. Similarly, we show that an appropriately deﬁned dynamical lengthscale on the Fock-space graph is directly related to the local spin autocorrelation, and as such sheds light on the (anomalous) decay of the autocorrelation in the ergodic phase, and lack of it in the localized phase. DOI: 10.1103/PhysRevB.107.094206 I. INTRODUCTION The out-of-equilibrium dynamics of isolated quantum many-body systems can show a rich range of behavior in the presence of disorder. One of the most striking examples is the driving of such a system from the default ergodic phase into a many-body localized (MBL) phase at sufﬁciently strong disorder [1–8], via a dynamical phase transition [9,10]. In contrast to the ergodic phase, the system in the MBL phase fails to thermalize under its own dynamics, and memory of the initial state survives locally for arbitrarily long times. Standard signatures of these include the absence of transport of conserved quantities, and autocorrelations of local observ- ables saturating to ﬁnite values at long times [4,11,12], rather than vanishing. As such behavior falls outside the paradigm of conventional statistical mechanics, the dynamics in the MBL phase is naturally of fundamental interest. At the same time, even within the ergodic phase but at disorder strengths preceding the MBL transition, the dynamics is anomalously slow. This is commonly manifest in subdiffusive transport of conserved quantities, and autocorrelations of local observ- ables decaying in time with anomalous power-law exponents *isabel.creed@chem.ox.ac.uk †david.logan@chem.ox.ac.uk ‡sthitadhi.roy@icts.res.in Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. [13–18]. This behavior attests to the fact that the out-of- equilibrium dynamics of disordered quantum systems across a range of disorder strengths straddling the MBL transition is an interesting question. From a phenomenological point of view, there has been substantial progress in understanding the dynamics, both in the MBL phase as well as in the anomalous ergodic regime. The absence of transport in the MBL phase can be ex- plained via the presence of an extensive number of emergent local integrals of motion (or equivalently, local conserved charges), such that an effective model for the MBL phase involves interactions only between these entities [19–21]. More recently, resonances between conﬁgurations of these charges have been shown to further explain several features of the MBL phase [22–27]. In the anomalous ergodic regime, progress in understanding the slow dynamics has centered on phenomenological theories based on rare Grifﬁths regions, as well as anomalous spectral properties of local observables [13,16,18]. It is nevertheless desirable to have a theoretical framework, rooted in microscopics, for understanding both the slow dynamics preceding the MBL phase and the arrested dynamics in the MBL phase. Mapping the dynamics of the many-body system to that of probabilities on its Fock-space graph provides such a framework. Indeed, understanding the physics of many-body localiza- tion from the perspective of the associated Fock space (FS) has emerged as a fruitful approach over the last few years [1,28–52]. This approach involves recasting the Hamiltonian of a disordered, interacting quantum system as a tight-binding Hamiltonian on the complex, correlated FS graph of the system [53]. The problem then becomes one of Anderson localization (AL) of a ﬁctitious particle on the FS graph, albeit 2469-9950/2023/107(9)/094206(19) 094206-1 Published by the American Physical Society CREED, LOGAN, AND ROY PHYSICAL REVIEW B 107, 094206 (2023) a distinctly unconventional AL problem due to the strong correlations in effective disorder on the FS graph [43]. This mapping opens a new window into the connections between the anatomy of the eigenstates on the FS graph, and their manifestations in terms of real-space properties. For instance, the spread of the eigenstates on the FS graph, and an asso- ciated emergent correlation length, has been shown to carry information about eigenstate expectation values of local ob- servables [49] as well as that of the l-bit localization length [46]. Higher-point correlations of eigenstate amplitudes en- code their entanglement structure [51]. However, most of these studies have focused on eigenstate properties, and much less so in the context of dynamical, time-dependent properties. Motivated by this, we investigate here the dynamics of an out-of-equilibrium quantum state on the FS graph. Ar- guably, the most fundamental question one can ask in this regard is, given an initial out-of-equilibrium state, how do the probability densities of the state on the FS graph evolve in time and spread out on the graph? As will be shown, a detailed characterization of this probability transport carries a plethora of information providing insights into the dynamics of disordered quantum many-body systems. This is the central goal of the work. We begin with a brief overview of the paper. Overview As a concrete setting for our analysis, we consider a quantum Ising chain with disordered longitudinal ﬁelds and interactions, together with a constant transverse ﬁeld (of strength (cid:2)). A description of the model and its associated FS graph is given in Sec. II. Classical spin conﬁgurations form a convenient set of basis states; they also form the nodes (or “sites”) of the FS graph, with the transverse ﬁeld generating links between them. The Hamming distance [54] between two classical spin conﬁgurations endows the FS graph with a natural measure of distance. Initialising the state in a classical spin conﬁguration corresponds to initializing it on a site on the FS graph. Consequently, the FS graph can be organized such that the given initial state sits at the apex and all sites at a ﬁxed Hamming distance from the initial site are arranged row-wise (see Fig. 1). Although the FS graph for a chain of length L is an L- dimensional hypercube, the above organization of the graph gives rise to two natural “axes” along which the probability transport can be deﬁned; we refer to them as longitudinal and lateral probability transport. The former quantiﬁes how the probability ﬂows down sites which are at increasing dis- tances from the initial FS site. Lateral probability transport on the other hand measures how the probability spreads across sites at the same Hamming distance from the initial site, i.e., on a given row. Section III formalizes these two no- tions of FS probability transport. We show in particular that a time-dependent lengthscale r(t ), which characterizes the longitudinal spread of the wavefunction, is directly related to the real-space spin autocorrelation function. We also quantify the extent to which the time-evolving state is (de)localized on the graph, via t-dependent inverse participation ratios (IPR) and their corresponding fractal exponents. These IPRs can be deﬁned over the entire FS graph, or can be deﬁned row-wise (which corresponds to the lateral transport). FIG. 1. Fock-space (FS) graph of the disordered TFI model Eq. (1) in the basis of σ z-product states, illustrated for L = 8. An arbitrary FS site I is placed at the apex. The graph has L + 1 rows, and the number of FS sites on row r is Nr = . Any FS site J on row r is a Hamming distance rIJ = r from I. Links/hoppings can connect only FS sites on adjacent rows; with each FS site connected to precisely L others. (cid:2) L r (cid:3) In Sec. IV we analyze the short-time dynamics, which is independent of whether the ultimate late-time behavior of the system is ergodic or MBL in character. For (cid:2)t (cid:2) 1, the probability of ﬁnding the system in a given FS site/state at distance r is shown to scale as ∼((cid:2)t )2r. An essential outcome of this is an emergent multifractality of the wavefunction over the full FS, with a fractal exponent growing ∝ t 2, independent of disorder strength. By contrast, the row-resolved IPRs on these timescales do not show fractal statistics, indicating that the short-time wavefunction is spread homogeneously across any given row of the FS graph. A further, rather striking consequence of the analysis, is that r(t ) becomes extensive in system size L at any ﬁnite O(1) time. This is mandated by the spin autocorrelation being strictly <1 at any ﬁnite O(1) time, and can be understood via the extensive connectivity of the FS graph. Section V is devoted to consideration of longitudinal prob- ability transport, notably for long times. A central result here is that, in the ergodic regime, the lengthscale r(t ) grows sub- diffusively, ∼t α with α < 1/2, until it reaches its maximal value of L/2 (modulo the role of mobility edges and ﬁnite-size effects, as explained later). This is shown to imply that the spin autocorrelation also decays as a power law with the same exponent. In the MBL regime by contrast, r(t ) saturates to an extensive but submaximal value, which in turn implies that the spin autocorrelation remains nonzero at arbitrarily long times. A further implication of these results is that the emergent fractality present at short to intermediate times gives way to fully delocalized states at long times in the ergodic regime, whereas the fractality persists for arbitrarily long times in the MBL regime. In Sec. VI we turn to the analysis of lateral probability transport, via row-resolved IPRs. The picture that emerges is that, following the short-time homogeneity, at intermediate times—and for any disorder strength—the time-dependent 094206-2 PROBABILITY TRANSPORT ON THE FOCK SPACE OF A … PHYSICAL REVIEW B 107, 094206 (2023) probabilities on any row develop strong inhomogeneities, re- ﬂected in (multi)fractal scalings of the row-resolved IPRs. For sufﬁciently long times, however, this fractality gives way to complete homogeneity in the ergodic regime, while it persists in the MBL regime. Since the lateral transport in essence cap- tures inhomogeneity in the evolution of probabilities on the FS graph, it is also natural to study t-dependent distributions of probabilities over sites on a given row. Consistent with the above picture, we ﬁnd that the inhomogeneities are accom- panied by heavy-tailed Lévy distributions, whereas temporal regimes in which probability spreads homogeneously are characterized by narrow distributions. We summarize our results in Sec. VII (see Fig. 16 for a visual summary), and close with concluding remarks and a future outlook. II. MODEL AND FOCK-SPACE GRAPH We consider a disordered transverse-ﬁeld Ising (TFI) spin- 1/2 chain, speciﬁed by the Hamiltonian H = L−1(cid:4) (cid:5)=1 J(cid:5) ˆσ z (cid:5) ˆσ z (cid:5)+1 (cid:5) h(cid:5) ˆσ z (cid:5) + (cid:2) ˆσ x (cid:5) (cid:6) , (1) + L(cid:4) (cid:5)=1 where h(cid:5) and J(cid:5) are i.i.d. random variables, uniformly dis- tributed with h(cid:5) ∈ [−W, W ] and J(cid:5) ∈ [J − δJ , J + δJ ]. For numerical studies, we consider J = 1, δJ = 0.2, and (cid:2) = 1. With these parameters, and the range of system sizes accessible in practice to exact diagonalization (ED), the crit- ical disorder strength above which all eigenstates are MBL is estimated to be Wc (cid:6) 3.8 [55]. Some recent papers on standard disordered models [23,27,56–62] have, however, suggested that a genuine MBL phase, stable in the thermody- namic limit L → ∞, can arise only for much larger values of W , and that the apparent localization found for ﬁnite systems at W > Wc is indicative of a prethermal regime. Here we take the view that the MBL phenomenology clearly observed at W > Wc ∼ 4 for ED-accessible system sizes persists in the thermodynamic limit, albeit for larger W values. Fock space (FS) provides a natural framework for studying many-body localization [28–52], in part because a generic many-body Hamiltonian maps exactly onto a tight-binding model on the associated FS graph (or “lattice”), of form H = (cid:4) J E J \|J(cid:9)(cid:10)J\| + (cid:4) (cid:11) J,K TJK \|J(cid:9)(cid:10)K\| (2) (where (cid:11) means K (cid:12)= J). The FS graph of the TFI model in the basis of σ z-product states is an L-dimensional hypercube with NH = 2L vertices, or FS sites, as illustrated in Fig. 1. A FS site J represents a many-body quantum state \|J(cid:9) of L spins, which is an eigenstate of each ˆσ z (cid:5) \|J(cid:9) = S(cid:5),J \|J(cid:9) (cid:5) operator, ˆσ z where S(cid:5),J = ±1. It is thus an eigenstate of H0 = (cid:5) + (cid:5)[h(cid:5) ˆσ z J(cid:5) ˆσ z (cid:5)+1], i.e., H0\|J(cid:9) = EJ \|J(cid:9), with EJ the corresponding site energy for the FS site (with the {EJ } maximally correlated [43], and not i.i.d.). Links, or hoppings, on the FS graph are generated by the term H1 = H − H0 = (cid:2) (cid:5) . Each FS site is thus connected to precisely L others, lying solely on adjacent rows of the graph, and each of which corresponds to ﬂipping a spin on a particular real-space site. This generates (cid:5) ˆσ x (cid:5) ˆσ z (cid:7) (cid:7) the hopping contribution to Eq. (2), in which all nonvanishing hopping matrix elements are simply TJK = (cid:2). As illustrated in Fig. 1 the graph consists of L + 1 rows, r = 0 − L. A single FS site, denoted by I in Fig. 1 (and with arbitrary spin orientations for the real-space sites) lies at the apex of the graph, r = 0. The number of FS sites on (cid:3) (cid:2) L ; with the ﬁnal site, r = L, row r of the graph is Nr := r corresponding to the state \|I(cid:9) in which all real-space spins on \|I(cid:9) have been ﬂipped. As a measure of distance between two sites on the FS graph we use the Hamming distance, as mentioned in Sec. I. For any pair of FS sites J, I separated by a Hamming distance rIJ = r, then by deﬁnition r real-space sites (cid:5) have S(cid:5),J = −S(cid:5),I while L − r sites have S(cid:5),J = +S(cid:5),I . Hence L−1 (cid:4) (cid:5) S(cid:5),I S(cid:5),J = 1 − 2 . rIJ L (3) This connection between Hamming distance on the FS graph and the spin orientations will prove important in Sec. III A in relating the real-space spin autocorrelation function (or imbalance) to the ﬁrst moment of the FS probabilities. III. DIAGNOSING PROBABILITY TRANSPORT The basic underlying quantities considered are the proba- bilities PIJ (t ) = \|GIJ (t )\|2 (cid:2) 0, given by PIJ (t ) = \|(cid:10)J\|(cid:7)(t )(cid:9)\|2 = \|(cid:10)J\|e−iHt \|I(cid:9)\|2 (4) with \|(cid:7)(t )(cid:9) the t-dependent wavefunction. We add here that, unless stated otherwise, time is shown in units of (cid:2)−1 in all ﬁgures (i.e., (cid:2) ≡ 1). Physically, PIJ (t ) gives the proba- bility that the system will be found on FS site J at time t, given its initiation on site I (and with PII (t ) the commonly studied return probability). As reﬂected in PIJ (t = 0) = δIJ , the initial state \|I(cid:9) is site-localized on the FS graph, and as such wholly Anderson-localized thereon. On increasing t, the distribution of probabilities spreads in some fashion through the FS graph/lattice. Understanding at least some aspects of this many-sided process, both temporally and as a function of disorder strength, is the aim of this paper. In the following H is presumed real symmetric, as relevant to the TFI model considered explicitly, such that PIJ (t ) = PJI (t ) = PIJ (−t ). Expressed in terms of eigenstate amplitudes AnI = (cid:10)I\|n(cid:9), with eigenstates \|n(cid:9) and corresponding eigenval- ues En, note for later use that e−i(En PIJ (t ) = −Em )t AnI AnJ AmI AmJ (cid:4) (5) . n,m (cid:7) Probability is of course conserved, viz., J PIJ (t ) = 1 for all t and any initial FS site I. For any given I, PIJ (t ) can thus be regarded as the time-dependent distribution, over all FS sites J, of the conserved “mass” MI = J PIJ (t ) = 1. A natural way to quantify such a distribution is via its moments. To this end, ﬁrst deﬁne (cid:7) PI (r; t ) = (cid:4) PIJ (t ), (cid:4) J:rIJ =r I P(r; t ) = N −1 H PI (r; t ), (6) 094206-3 CREED, LOGAN, AND ROY PHYSICAL REVIEW B 107, 094206 (2023) (cid:3) (cid:2) L r with the J sum over all Nr = FS sites on a given row r of the graph, for which the Hamming distance rIJ = r (I lying at the apex of the graph, see Fig. 1). PI (r; t ) gives the r=0 PI (r; t ) = 1 ∀t, I]; its total probability on row r [with sample average over initial FS sites I is denoted P(r; t ). An average over disorder realizations will be denoted, according to convenience, either by an overbar [e.g., PI (r; t )], or by angle brackets ((cid:10)· · · (cid:9)d). The r and t dependence of P(r; t ) in particular will be considered explicitly in Sec. V. (cid:7) L Moments of the {PIJ (t )} follow directly, e.g., the ﬁrst mo- ment rI (t ) = (cid:4) J L(cid:4) rIJ PIJ (t ) = rPI (r; t ) , (7) r=0 (cid:7) and its sample average r(t ) = N −1 H consider the disorder-averaged moments I rI (t ). In Sec. V we will r(t ) = δr2(t ) = L(cid:4) r=0 L(cid:4) r=0 rP(r; t ), (8a) r2P(r; t ) − (cid:9)2 rP(r; t ) , (8b) (cid:8) L(cid:4) r=0 in particular the former. As now shown, for any disorder realization, rI (t ) and r(t ) are in fact directly related to the real- space spin autocorrelation function; providing thereby a direct connection between real-space and Fock-space perspectives. A. Longitudinal transport and spin autocorrelator Consider C(t ) deﬁned by L(cid:4) C(t ) = 1 L (cid:5)=1 C(cid:5)(cid:5)(t ), C(cid:5)(cid:5)(t ) = 1 NH (cid:2) (cid:5) (t ) ˆσ z ˆσ z (cid:5) (cid:3) , Tr (9) (cid:7) with C(cid:5)(cid:5)(t ) the local real-space spin autocorrelator. The trace Tr can equivalently be either over FS sites, C(cid:5)(cid:5)(t ) = N −1 (cid:5)(cid:5)(t ) with C [I] (cid:5) \|I(cid:9), or over eigen- (cid:5)(cid:5)(t ) = (cid:10)I\| ˆσ z C [I] (cid:7) H states, C(cid:5)(cid:5)(t ) = N −1 C [n] (cid:5)(cid:5)(t ). A simple calculation then H relates C [I] n (cid:5)(cid:5)(t ) to the probabilities {PIJ (t )}, (cid:5) (t ) ˆσ z I C[I] (cid:5)(cid:5) (t ) = S(cid:5),I S(cid:5),J PIJ (t ), (10) J (cid:5) \|I(cid:9) (= ±1). Using Eq. (3), together with where S(cid:5),I = (cid:10)I\| ˆσ z conservation of probability, it follows directly that C[I](t ) := L−1 (cid:4) C[I] (cid:5)(cid:5) (t ) (cid:4) is given by (cid:4) C[I](t ) = 1 − 2 L J (cid:4) ⇒ C(t ) = N −1 H (cid:5) rIJ PIJ (t ) = 1 − 2 L rI (t ) C[I](t ) = 1 − 2 L I r(t ). (11) Equation (11) relates directly the real-space spin autocor- relation function to the ﬁrst moment of the FS probabilities {PIJ (t )} (and is not conﬁned to the TFI model, holding equally for XXZ or spinless fermion models). It is also interesting to note that experiments where MBL has been observed [63,64] essentially measure C[I] (cid:5)(cid:5) (t ) by employing a similar protocol– initializing the system in a speciﬁc σ z conﬁguration \|I(cid:9), and measuring the expectation value (cid:10) ˆσ z (cid:5) (t )\|I(cid:9) such that C[I] (cid:5) (t )(cid:9) ≡ (cid:10)I\| ˆσ z (cid:5)(cid:5) (t ) = (cid:10) ˆσ z (cid:5) (t )(cid:9) S(cid:5),I . A striking feature of the dynamics is that, on timescales for which C[I](t ) departs by merely a nonvanishing amount from its t = 0 value of 1, the ﬁrst moment rI (t ) ∝ L is extensive in system size. Intuitively, one expects such timescales to be determined by the hopping energy scale (cid:2), which acts to de- phase the initially synchronized spins, and as such to be on the order (cid:2)t ∼ O(1). The resultant extensivity of rI (t ) means that an excitation, initially Anderson-localized on the single FS site I, spreads signiﬁcantly throughout the Fock space on the shortest timescales of order (cid:2)t ∼ O(1)—and would appear to do so regardless of whether the system is ultimately ergodic or MBL. Understanding how this behavior arises, the essential characteristics of the Fock-space graph which it reﬂects, and the physical picture it gives rise to, is conceptually signiﬁcant and considered in Sec. IV (see also Sec. V). \|(cid:10)n\| ˆσ z One can also bound r(t ). Since rIJ (cid:3) L, it follows triv- J PIJ (t ) = 1 ∀t] that r(t )/L (cid:3) 1 ially from Eq. (11) [using for all t. More useful is a bound in the t → ∞ limit. Re- solving C(cid:5)(cid:5)(t ) as an eigenstate trace, its inﬁnite-time limit C(cid:5)(cid:5)(∞) = N −1 (cid:5) \|n(cid:9)\|2, so C(cid:5)(cid:5)(∞) and thus C(∞) can- H not be negative; whence [Eq. (11)] r(∞) (cid:3) L/2 necessarily. Sufﬁciently deep in an ergodic phase, with essentially all many-body eigenstates delocalized and no remnant memory of initial conditions, one expects C(cid:5)(cid:5)(∞) to vanish. Hence r(∞) = L/2—the midpoint of the FS graph—is characteristic of such “complete” ergodicity. In an MBL phase by contrast, persistent memory of initial conditions means C(cid:5)(cid:5)(∞) > 0. In that case, the long-time limit of r(t ) is perforce less than L/2. (cid:7) (cid:7) n B. Lateral transport For any disorder realization, PI (r; t ) gives [Eq. (6)] the total probability on row r of the graph/lattice. Study of its (r, t )-dependence thus reveals how probability ﬂows in time “down” the FS graph, row by row. It does not, however, give information on the important issue of how the distribution of probabilities spreads out laterally, and in general inhomoge- neously, across the rows of the graph. One such measure of the latter, studied numerically in Sec. VI, is provided by RI (r; t ) (cid:2) 1 deﬁned by RI (r; t ) = (cid:2) 1 Nr 1 Nr J:rIJ =r P2 IJ (t ) (cid:3) 2 J:rIJ =r PIJ (t ) (cid:7) (cid:7) . (12) For any given disorder realization, this is simply the ratio of the mean squared probability per FS site on row r, to the square of the corresponding mean probability, [N −1 r PI (r; t )]2. So it provides an obvious measure of ﬂuctuations in the dis- tribution of PIJ ’s along a given row. In particular, RI (r; t ) = 1 in a limit of extreme homogeneity where all PIJ (t )’s on the row are the same. The latter behavior will in fact be shown in Sec. IV to arise at sufﬁciently short times, independently of disorder strength W ; before evolving in t to a distribution which is W dependent, and strongly inhomogeneous in the MBL regime (Sec. VI). 094206-4 PROBABILITY TRANSPORT ON THE FOCK SPACE OF A … PHYSICAL REVIEW B 107, 094206 (2023) The average of RI (r; t ) over disorder realizations and FS sites I will be denoted for brevity by (cid:10)R(cid:9) ≡ (cid:10)R(cid:9)(r; t ), (cid:4) (cid:10)R(cid:9) = N −1 H (cid:10)RI (r; t )(cid:9) d (13) I with (cid:10)· · · (cid:9)d the disorder average. More generally, we also study in Sec. VI A the full probability distribution of RI (r; t ), given by (cid:4) PR (x) = N −1 H (cid:10)δ(x − RI (r; t ))(cid:9) d (14) I (cid:10) dx xPR(x) = (cid:10)R(cid:9)). In the MBL (of which the ﬁrst moment is regime in particular, PR(x) at sufﬁciently long times will be shown to be characterized by a heavy-tailed Lévy alpha-stable distribution. (cid:7) The quantity RI (r; t ) is directly related to another nat- ural measure of ﬂuctuations in the distribution of PIJ (t )’s along a given FS row: the row-resolved, t-dependent in- verse participation ratio (IPR). To motivate this, consider the t-dependent wavefunction following the initial quench, \|(cid:7)(t )(cid:9) = e−iHt \|I(cid:9), expanded as \|(cid:7)(t )(cid:9) = A(I ) J (t )\|J(cid:9); such that, from Eq. (4), the squared amplitudes \|A(I ) J (t )\|2 = PIJ (t ) are just the probabilities of interest. Time-dependent wave- function densities, normalized on any given row r, are then given by \|B(I ) J (t )\|2 = \|A(I ) J (t )\|2/ (cid:7) J:r J:r J:rIJ =r); for which the associated generalized shorthand for (cid:7) IPR is II,q = J (t )\|2q. Hence, for the standard case of J:r q = 2 on which we focus explicitly, the IPR is related simply (cid:2) L to RI (r; t ) [Eq. (12)] via Nr = r J (t )\|2 (with \|A(I ) \|B(I ) (cid:3) , (cid:7) (cid:7) J (cid:7) I I,2(r; t ) = (cid:2) (cid:7) ⇒ (cid:10)I (cid:9) = N −1 H 2 J:rIJ =r P2 IJ (t ) (cid:3) 2 J:rIJ =r PIJ (t ) (cid:4) (cid:10)I I,2(r; t )(cid:9) d = N −1 r RI (r; t ) = N −1 r (cid:10)R(cid:9) (15) I [with the corresponding probability distribution of II,2 follow- ing trivially from that for RI (r; t ), Eq. (14)]. r (cid:7) \|2 ∼ N −1 . Hence (cid:10)I2(cid:9) ∼ N −1 We can then reason physically as follows, consider- ing some particular time t. If the amplitudes \|B(I ) J (t )\|2 = PIJ (t )/ J:r PIJ (t )—and hence the probabilities PIJ (t )—are essentially uniformly distributed over the Nr FS sites on row r, and in turn (cid:10)R(cid:9) ∼ then each \|B(I ) J O(1) should be of order unity (and as such L independent). If by contrast the wavefunction is strongly inhomogeneously distributed on the row, one might anticipate (cid:10)I2(cid:9) ∼ N −ν r with a fractal exponent ν ≡ ν(t ) < 1; and hence from Eq. (15) that (cid:10)R(cid:9) ∼ N 1−ν r —which thus grows with increasing system size L. These two behaviors will indeed be shown to arise in Sec. VI, the former characteristic at long time of the ergodic regime, and the latter characteristic of the MBL regime at larger disorder strengths. r Finally, as a complement to PR(x) [Eq. (14)], we also study in Sec. VI A the probability distribution Prel(x) = 1 NH (cid:4) I 1 Nr (cid:4) J:rIJ =r (cid:11) (cid:8) δ x − (cid:7) PIJ (t ) J:rIJ =r PIJ (t ) 1 Nr (cid:9)(cid:12) . d (16) For any given row r on the graph, this gives the distri- bution of PIJ (t ) relative to its mean value on the row, x = PIJ (t )/[N −1 r PI (r; t )]; its second moment being precisely (cid:10) dx x2Prel(x) = (cid:10)R(cid:9), see Eqs. (13) and (12) (and its ﬁrst mo- ment is 1 by construction). IV. SHORT-TIME BEHAVIOR We turn now to the short-time behavior of probability transport for the disordered TFI model. While the underlying calculations are simple, the physical picture arising is rather rich; including the emergence at short times of multifractality in the t-dependent wavefunction \|(cid:7)(t )(cid:9) = e−iHt \|I(cid:9)—for any disorder strength W , and as such independent of whether the ultimate long-time behavior of the system is ergodic or MBL in nature. Consider PIJ (t ) = \|GIJ (t )\|2, where [Eq. (4)] GIJ (t ) = (cid:10)J\|e−iHt \|I(cid:9) = ∞(cid:4) n=0 (−i)n n! t n(cid:10)J\|Hn\|I(cid:9), (17) K [Eq. and (cid:7) separate H ≡ H0 + H1 (2)], with H0 = EK \|K(cid:9)(cid:10)K\| and H1 the hopping term. With Km denoting any FS site on row m, H\|Km(cid:9) connects solely to FS sites in rows m ± 1 (and m), since nonzero hopping matrix elements ((cid:2)) connect only FS sites on adjacent rows of the graph. Now let J in Eq. (18) be some given FS site on row r, call it Jr. Obviously, (cid:10)Jr\|Hn\|I(cid:9) vanishes identically for all n < r. Hence GIJr (t ) = (−i)r r! t r(cid:10)Jr \|(H1)r\|I(cid:9) + O(t r+1). (18) The leading term here will clearly dominate GIJr (t ) for suf- ﬁciently small t. Importantly, it involves solely FS hoppings, consisting of “forward paths” from I to Jr, each containing precisely r hops (i.e., r factors of (cid:2)). For any given FS site Jr there are however r! identical contributions to (cid:10)Jr\|(H1)n\|I(cid:9), because there are r! distinct forward paths from I to Jr on the FS graph; and each such contribution has a value of (cid:2)r. This cancels the 1/r! factor in Eq. (18), from which the leading (t ) ∼ (−i)r ((cid:2)t )r, and that of PIJr (t ) small-t behavior is GIJr thus PIJr (t ) ∼ ((cid:2)t )2r. (19) Note the following points about behavior: this leading short-time (i) It holds for any r, and for all FS sites on row r. By virtue of the latter, the distribution of probabilities along any given row is fully homogeneous in the time window over which Eq. (19) holds. In consequence, RI (r; t ) = 1 [Eq. (12)], the distribution PR(x) = δ(x − 1) [Eq. (14)] is δ distributed, and the row-resolved IPR I2(r; t ) = N −1 [Eq. (15)]. By it- self the above calculation does not of course prescribe the timescale over which such behavior occurs, but we ascertain it below. (ii) Relatedly, since solely the disorder-independent hoppings (cid:2) generate Eq. (19), the result is independent of disorder strength W . (iii) Although PIJr (t ) ∼ ((cid:2)t )2r decreases exponentially rapidly with r, the number Nr = of FS sites on row r grows exponentially with r. Hence, even for short times, one cannot neglect the contribution of sites on any row r to, e.g., the ﬁrst moment of the probability distribution, (cid:2) L r (cid:3) r 094206-5 CREED, LOGAN, AND ROY PHYSICAL REVIEW B 107, 094206 (2023) are thus embodied in δr2(t )/L2, direct evaluation of which using Eq. (20) gives δr2(t )/L2 = ((cid:2)t )2[1 − ((cid:2)t )2]/L. Since this is ∝1/L, such ﬂuctuations vanish in the thermodynamic limit, with r(t )/L distributed as a Dirac-delta function at its mean. Finally, although by itself a somewhat limited diagnostic of probability transport on Fock space, we comment parentheti- cally on the commonly studied [14,65,66] return probability, PII (t ). This corresponds to r = 0 in Eq. (20), which for (cid:2)t (cid:2) 1 recovers the known behavior [66] PII (t ) ∼ exp(−L((cid:2)t )2), whereby for any nonzero (cid:2)t, even if small, the return proba- bility is exponentially suppressed in system size L. FIG. 2. ED results with L = 13 for ¯r(t )/L vs t ((cid:2) ≡ 1), shown over the indicated range of disorder strengths W . For times t (cid:2) 0.1, the W -independent behavior of Eq. (21) (dashed line) is seen to arise. Inset: Same results, on smaller t scale. (cid:7) (cid:7) L r=0 J rIJ PIJ (t ) ≡ (cid:3) (cid:2) L rI (t ) = rPIJr (t ), as considered be- r low. (iv) The calculation above naturally reﬂects the intrinsic structure of the FS graph (Fig. 1) for the disordered TFI model. We simply remark that the result arising would be quite different if one considered a tree graph (Cayley tree/Bethe lattice); for while in that case Eq. (18) holds for any given site Jr on generation r of the tree, there is just a single path connecting the root site I to the given Jr. As it stands, direct use of Eq. (19) for each r fails to conserve total probability. This, however, is readily taken into account by writing PIJr (t ) = g(r; t )((cid:2)t )2r where, for all r, g(r; t ) must satisfy (a) g(r; t = 0) = 1, such that the leading low-t behavior of PIJr (t ) is Eq. (19); (b) g(r; t ) > 0 for all times for which the calculation is valid; and (c) J PIJ (t ) = 1, i.e., overall probability must be conserved, (cid:3) (cid:7) g(r; t )((cid:2)t )2r = 1 ∀t. This has the solution g(r; t ) = [1 − ((cid:2)t )2](L−r). And g(r; t ) > 0 ∀r is satisﬁed provided (cid:2)t < 1, which upper bounds the time-window over which the cal- culation holds. (cid:2) L r L r=0 (cid:7) The essential result for the short-t behavior of PIJr (t ) is then (t ) = ((cid:2)t )2r[1 − ((cid:2)t )2](L−r). (20) P IJr As this is independent of both the initial FS site I and disorder strength W , the resultant ﬁrst moments [Eqs. (7) and (8a)] rI (t ) ≡ r(t ) ≡ r(t ) coincide, and follow from Eq. (20) as (cid:13) (cid:14) rI (t ) ≡ r(t ) = r P IJr (t ) = L((cid:2)t )2. (21) L(cid:4) r=0 L r That the short-time behavior is indeed W independent is cor- roborated in Fig. 2, which shows ED results for r(t )/L vs (cid:2)t, over a range of disorder strengths W . In all cases, the asymptotic behavior Eq. (21) indeed arises at short times—in practice for (cid:2)t (cid:2) 0.1 or so, consistent with the bound above. The fact that r(t ) ∝ L is extensive for ﬁnite (cid:2)t means of course that it is r(t )/L, which remains ﬁnite in the thermody- namic limit L → ∞. The relevant ﬂuctuations in this quantity Emergent multifractality As shown above, for (cid:2)t small compared to unity but ﬁ- nite, the probability density has spread through Fock space to macroscopically large Hamming distances on the order of L. The probabilities PIJr (t ) are uniform on any given row of the FS graph [Eq. (20)], symptomatic of which the row-resolved IPR [Eq. (15)] is II,2(r; t ) = N −1 r One can however also ask for the behavior of the con- ventional IPR over the full Fock-space. For a wavefunction \|(cid:7)(t )(cid:9) = J (t )\|2 (= PIJ (t )) normalized to unity over all FS sites J, the generalized (q-dependent) IPR is deﬁned by J (t )\|J(cid:9), with squared amplitudes \|A(I ) A(I ) (cid:7) . J L I,q(t ) = (cid:4) (cid:15) (cid:15)A(I ) J (t ) (cid:15) (cid:15)2q = (cid:4) Pq IJ (t ) , (22) J J where only q > 1 is considered henceforth (trivially, for all t, LI,0(t ) = NH and LI,1(t ) = 1). The L dependence of L I,q(t ) is embodied in the exponent τq ≡ τq(t ) deﬁned by −τq LI,q(t ) ∼ N H . If τq = 0 for any speciﬁed t, then the wave- function \|(cid:7)(t )(cid:9) is Anderson localized on O(1) FS sites of the graph/lattice, while if τq = q − 1 it is essentially uniformly spread over all FS sites on the graph, and as such ergodic. But if by contrast 0 < τq < q − 1, then the wavefunction is fractal; more speciﬁcally, if τq is a nonlinear function of q, then it is multifractal. To consider this in the present context, it is convenient to rewrite Eq. (20) in the binomial form (t ) = [z(t )]r[1 − z(t )](L−r) P IJr (23) with z(t ) = ((cid:2)t )2 for short times (cid:2)t (cid:2) 1. This in turn can be expressed as (t ) = P IJr (cid:5) 1 + e−1/ξF (t ) (cid:6)−L e−r/ξF (t ), (24) in terms of a correlation length ξF (t ) deﬁned by ξ −1 F (t ) = − 1). Since the short-time PIJr (t )’s are the same for ln( 1 (cid:3) (cid:3) (cid:2) z(t ) Pq L sites on row r of the graph, LI,q(t ) ≡ (t ). all IJr r Hence from Eq. (24) (cid:2) L r L r=0 (cid:7) τ q (t ) = log2 (cid:16) (1 + e−1/ξF (t ))q (1 + e−q/ξF (t )) (cid:17) , (25) where e−1/ξF (t ) ∼ ((cid:2)t )2 for (cid:2)t (cid:2) 1. For t = 0 precisely, τq = 0. This is just as expected, reﬂecting the fact that \|(cid:7)(t = 0)(cid:9) = \|I(cid:9) is Anderson localized on the FS graph. 094206-6 PROBABILITY TRANSPORT ON THE FOCK SPACE OF A … PHYSICAL REVIEW B 107, 094206 (2023) FIG. 3. t dependence of the IPR exponent τ2(t ) obtained from ED calculations, with W = 1, 1.5, 2 exemplifying the ergodic phase and W = 6, 7 the MBL regime. The data is obtained by ﬁtting the −τ2 (t ) instantaneous IPR, L2(t ) to c(t )N over the range L = 8 − 13 H for each t. Dashed line shows the low-t asymptotic behavior τ2(t ) ∼ 2((cid:2)t )2/ ln 2 [from Eq. (25)]. Full discussion in text. However, for any nonzero (cid:2)t (cid:2) 1, Eq. (25) is readily seen to be nonlinear in q and to satisfy 0 < τq(t ) (cid:2) q − 1. The wavefunction is thus multifractal. Moreover, this behavior arises for any disorder strength W . Emergent multifractality at short times is therefore common both to W ’s for which the system is ergodic in the long-time limit, as well as for W ’s for which it is MBL at long times. In the latter case, one anticipates continued persistence of multifractality beyond the short-time window. In the ergodic case by contrast, one expects multifractality to dissipate with further increasing t, as the distribution of probabilities homogenises over the entire graph and the long-time limit of τq(t = ∞) = q − 1 arises [67]. That the above behavior indeed arises is illustrated in Fig. 3, which, for the standard case q = 2, shows ED results for the t-dependent exponent τ2(t ). We deﬁne the latter in general via the averaged IPR, (cid:4) L2(t ) = N −1 H LI,2(t ) , written as I L2(t ) = c(t )N −τ2 (t ) H . (26) (27) Note that for short times (cid:2)t (cid:2) 1, this deﬁnition is the same as that arising from Eq. (25), since PIJr (t ) in Eq. (24) is independent of both disorder and the FS site I. For any chosen t, a plot of ln L2(t ) vs ln NH ∝ L then gives −τ2(t ) from the slope (c(t ) is assumed to be L independent); and very good linear ﬁts are indeed found for the data shown. times (cid:2)t (cid:2) 0.1, As seen in Fig. 3, for short the W - independent result from Eq. (25) is indeed recovered, viz., τ2(t ) ∼ 2((cid:2)t )2/ ln 2, and the wavefunction is multifractal for all W . For W = 6, 7 illustrative of the MBL regime, τ2(t ) remains <1 on increasing t beyond the short-time regime and multifractality persists at all times. But for W = 1, 1.5, 2 illus- trating the ergodic regime, τ2(t ) grows with increasing t and ultimately plateaus to a long-time value of τ2 = 1 (≡ q − 1), indicating ergodic behavior. FIG. 4. t-dependent Fock-space distribution P(r; t ), for W=1.5 (ergodic phase) in the left column, and for W = 7 (MBL) in the right column. Top panels show P(r; t ) as a color map in the (r, t ) plane, with white denoting 0 and black denoting 1. Bottom panels show P(r; t ) as a function of r/L for different time slices as indicated in the legend. Data for L = 14, averaged over 2 − 3 × 103 disorder realizations. V. LONGITUDINAL PROBABILITY TRANSPORT In this section we consider how, following a t = 0 quench into some FS site, probability ﬂows in time down the FS graph, row by row. To give an initial broad overview, Fig. 4 shows the r and t dependence of the disorder-averaged total probability on row r, P(r; t ) [Eq. (6)]; for W = 1.5 (left panels) as representative of the ergodic phase, and for W = 7 (right panels) as typical of the MBL regime. The top panels show P(r; t ) as a color map in the (r, t )-plane, while the bottom panels show it as function of r/L, for the (logarithmic) sequence of time slices indicated. The qualitative features arising are clear. At short times, (cid:2)t (cid:2) 0.1, P(r; t ) is the same for both W ’s, as expected from the considerations of Sec. IV. The distributions begin to spread out in an obvious sense for times (cid:2)t (cid:3) 0.5, and in prac- tice reach their long-time steady state by (cid:2)t ∼ 101 − 102. For the W = 1.5 example, the mode of the long-time P(r; t ) lies at r/L = 1/2, the midpoint of the FS graph; and its r-proﬁle is Gaussian (with a width that decreases with increasing system size, a point to which we return later). Similar behavior is found for W = 7, but with the notable difference that in this case the mode of the long-time P(r; t ) occurs at an r/L that is markedly less than 1/2. (cid:7) Quite a bit of information is contained in plots such as Fig. 4. To interrogate it, we turn now to the ﬁrst moment of L the probability distribution, r(t ) = r=0 rP(r; t ) [Eq. (8a)]. More speciﬁcally, we consider r(t )/L, since it is this quan- tity which necessarily remains ﬁnite in the thermodynamic limit L → ∞ (Secs. III and IV). We add here that in all ﬁgures shown in the paper, disorder averages are taken over a minimum of 103 realizations. 094206-7 CREED, LOGAN, AND ROY PHYSICAL REVIEW B 107, 094206 (2023) (cid:7) n with D(ω) = N −1 δ(ω − En) the (self-averaging) many- H body density of states. The behavior of ξF (ω) with disorder strength W is known from a detailed scaling analysis [49]. For W ’s greater than the critical Wc(ω) for which states at the chosen energy ω become MBL, ξF (ω) remains ﬁnite (includ- ing W = Wc(ω)+). For W < Wc(ω) by contrast, ξF (ω) ∝ L and thus diverges in the thermodynamic limit, as expected for delocalized states. The disorder strength denoted throughout as Wc is that above which all states in the band are MBL [i.e., Wc ≡ Wc(ω = 0), as band center states are the last to localize]. For W < Wc, some states in the band will be delocalized, and others MBL—the spectrum hosts mobility edges. For any such W then, from the above, delocalized states contribute a factor of 1/2 to the summand in Eq. (30) as L → ∞, while MBL states contribute a factor strictly <1/2. It is therefore only if all states in the band are delocalized—or in practice all but a tiny fraction—that the long-time limit r(∞)/L will be 1/2. From Fig. 5, this indeed appears to be the case for W = 1. On further increasing W , however, a non-negligible fraction of MBL states must arise, resulting in r(∞)/L < 1/2. The W = 2 case in Fig. 5 appears to provide an example of this (at least up to the largest L considered here). And the trend certainly becomes more pronounced with increasing W , e.g., for W = 3, r(∞)/L is (cid:2) 0.4 for the largest L studied. Equation (30) shows that r(t = ∞)/L can be resolved as a sum over contributions from all eigenstates in the band. This in fact is true for any t. As elaborated in Appendix A, it arises because PIJ (t ) can be eigenstate resolved in the form PIJ (t ) = N −1 IJ (t ), with P(n) IJ (t ) pertaining to a particular state n of H energy En, and given by n P(n) (cid:7) N −1 H P(n) IJ (t ) = cos[(En − Em)t]AnI AnJ AmI AmJ ; (31) (cid:4) m such that for times (cid:2)t (cid:16) 1, P(n) IJ (t ) is controlled by states m lying in a progressively narrowing window \|En − Em\| (cid:2) (cid:2) in the vicinity of the chosen energy En. We remark in passing that N −1 IJ (t ) can equally be expressed as an eigenstate ex- pectation value of an operator, see Eq. (A2). (cid:7) Since r(t ) is linear in the {PIJ (t )}, it too can be eigenstate H P(n) resolved, r(t ) = N −1 H n r (n)(t ), with (cid:4) r (n)(t ) = N −1 H rIJ P (n) IJ (t ) . (32) I,J In particular, from Eq. (30), r (n)(∞)/L = 1/[1 + e1/ξF,n ]. The lower panels in Fig. 5 show the t dependence of r (n)(t )/L for states n in the immediate vicinity of the band center, with W = 1, 2. Since W < Wc here, one expects the long- time limit of r (n)(t )/L for band center states to be 1/2, which appears consistent with the data. For the W = 2 example, it is also seen from Fig. 5 that the regime of slower dynamics mentioned above, setting in above (cid:2)t ∼ 1, is evident in both r(t )/L and r (n)(t )/L; suggesting that this behavior is associated with delocalized states in the spectrum. To examine further these slow dynamics at intermediate times, we consider equivalently the t dependence of the spin autocorrelation functions, C(t ) = 1 − 2r(t )/L [Eq. (11)] and its eigenstate-resolved counterpart C [n] (t ) = 1 − 2r (n)(t )/L. The former is shown on a log-log scale in the left panel of FIG. 5. For W = 1 and 2, upper panels show ED results for ¯r(t )/L vs t, for L = 8 − 14. Lower panels show corresponding r (n)(t )/L for band center eigenstates n. Full discussion in text. A. r(t ): Ergodic regime For disorder strengths W = 1, 2, the upper panels in Fig. 5 show the t dependence of r(t )/L, over a time window com- parable to or in excess of the associated Heisenberg times tH [the inverse of the mean level spacing, tH is discussed brieﬂy in Appendix B and given for the model by Eq. (B1)]. For both W ’s, r(t )/L for (cid:2)t (cid:2) 0.1 is given by the W- and L-independent short-time result Eq. (21) (as shown in Fig. 2). For the W = 1 case, on further increasing t, r(t )/L rapidly increases towards a value, which, for practical purposes, is ∼1/2 for (cid:2)t (cid:3) 10 or so. For W = 2, the situation is rather different. In that case, while r(t )/L again grows rapidly up to around (cid:2)t ∼ 1, the “elbow” seen in Fig. 5 around this time is succeeded at longer, intermediate times by a regime of slower dynamics, and the long-time limit is discernibly <1/2 for the largest system size studied. To obtain some understanding here, it is ﬁrst helpful to con- nI A2 nJ , I,J rIJ PIJ (∞) can be expressed as sider the inﬁnite-t limit. From Eq. (5), PIJ (∞) = from which r(∞) = N −1 H n A2 (cid:7) (cid:7) (cid:4) L(cid:4) r(∞) = N −1 H rF n(r) (28a) with F n(r) = (cid:4) n r=0 nI A2 A2 nJ . I,J:rIJ =r (28b) F n(r) itself was studied in detail in [49], where it was shown to be of form F n(r) = (cid:14) (cid:13) L r (1 + e−1/ξ F,n )−Le−r/ξ F,n , (29) with ξF,n a FS correlation length for eigenstates n at the partic- ular energy ω considered (while band center states ω = 0 were considered explicitly in [49], there is nothing special about this energy). Equations (28a) and (29) give r(∞) L = N −1 H (cid:4) n 1 1 + e1/ξ F,n (cid:18) = dω D(ω) 1 + e1/ξ F (ω) (30) 094206-8 PROBABILITY TRANSPORT ON THE FOCK SPACE OF A … PHYSICAL REVIEW B 107, 094206 (2023) FIG. 6. For W = 1.5, 2, and 2.5, with L = 14, ED results for spin autocorrelation functions vs t, on a log-log scale. Left panel: C(t ) = 1 − 2r(t )/L. Right panel: C [n] (t ) = 1 − 2r (n)(t )/L for band center eigenstates n. In either panel, for each W , dashed lines show power-law ﬁts to the intermediate-time behavior, with the power-law exponents found to decrease with increasing W . Fig. 6 for W = 1.5, 2, and 2.5, with L = 14. As seen from the ﬁgure, C(t ) and hence r(t )/L exhibits an intermediate-time power-law decay, C(t ) ∝ t −α with α < 1. With increasing W , the exponent α is found to decrease steadily and, subject to the usual caveat of modest system sizes, appears to vanish in the vicinity of W (cid:6) Wc ∼ 4. For eigenstates n in the vicinity of the band center, which are themselves ergodic for W < Wc, the corresponding behavior of C [n] (t ) is shown in the right panel of Fig. 6. It too shows an intermediate-time power-law with an exponent α(cid:11), which, while larger decay, C [n] than the corresponding α at the same W , likewise decreases steadily with increasing W and vanishes around Wc. The L- dependence of C [n] (t ) vs t for band center states is shown in Fig. 7, from which the data is seen to scale progressively further onto the power-law decay with increasing L. (t ) ∝ t −α(cid:11) Subdiffusive dynamics of the spin autocorrelation function, in the ergodic phase for a wide range of disorder strength preceding the MBL regime, has been extensively studied in models with conserved total magnetization, such as the disordered XXZ chain [13–18]. In the Ising spin chain (1) considered here, total magnetization is not by contrast con- served, so the appearance of subdiffusive dynamics warrants explanation. Indeed, for a Floquet version of the Ising chain (1), a previous numerical study raised the possibility that the spin autocorrelation decays as a stretched exponential in FIG. 7. For W = 1.5, 2, (t ) = 1 − 2r (n)(t )/L vs t, for band center states n. Dashed lines show power-law ﬁts to the intermediate-time behavior, onto which the data scales progressively with increasing L. showing 2.5, and C [n] FIG. 8. For W = 7, ED results for the spin autocorrelation func- tion C(t ) = 1 − 2r(t )/L (left panel) and r(t )/L itself (right panel), vs t ((cid:2) ≡ 1) and for the system sizes L indicated. Solid black line shows for comparison the corresponding exact result for MBL0 Eqs. (36) and (34); red line in the left panel gives the asymptotic behavior Eq. (39). (cid:5) + 1 time [68]. However, the key point here is that although total magnetization is not conserved in our model, total energy is (trivially, the Hamiltonian being time independent). As a result, the autocorrelator of the local energy density shows subdiffusive dynamics; (cid:10) ˆH(cid:5)(t ) ˆH(cid:5)(cid:9) ∼ t −α(cid:11)(cid:11) (cid:5) + (cid:2) ˆσ x (cid:5)−1 ˆσ z (cid:5) is not, however, orthogonal to the local energy density operator, Tr[ ˆσ z (cid:5) ˆH(cid:5)] (cid:12)= 0. Therefore at intermediate to late times, the spin autocorrelation picks up the (sub)diffusive tails emerging from the autocorrrelator of the local energy density; explain- ing physically the origin of the power-law decay of the spin autocorrelator. where ˆH(cid:5) ≡ h(cid:5) ˆσ z (cid:5) ]. The spin operator ˆσ z + J(cid:5)−1 ˆσ z 2 [J(cid:5) ˆσ z (cid:5) ˆσ z (cid:5)+1 B. r(t ): MBL regime To illustrate results in the MBL regime, Fig. 8 shows the spin autocorrelation function C(t ), and r(t )/L itself, for dis- order strength W = 7. The behavior seen is representative of the MBL regime for W (cid:3) 4.5 or so, and qualitatively different from that characteristic of the ergodic regime. C(t ) = 1 − 2r(t )/L in Fig. 8 shows clear damped oscilla- tory behavior. It plateaus to a nonzero long-time value (∼0.7, well above zero), indicative of persistent memory of initial conditions; and is barely L dependent over the range studied. Equivalently, the long-time limit of r(t )/L is (cid:2) 1/2 (as seen also in Fig. 4 for the mode of P(r; t ) at long times). This in turn is consistent with Eq. (30) above, where, with all states n MBL for W > Wc, all correlation lengths ξF,n are ﬁnite and hence r(∞)/L < 1/2. Two further points about Fig. 8 should be made at this stage, each of which merits some understanding (Sec. V B 1 below). First, while the long-time behavior is seen to be reached in practice by (cid:2)t ∼ 102, damped oscillations about that limit set in at shorter times (cid:2)t ∼ O(1), above which the envelope of the oscillation is in fact rather well ﬁt by a power-law decay ∝ t −β with β ≈ 1/2. Second, in paral- lel to Sec. V A for the ergodic phase, in the MBL regime one can equally consider the eigenstate-resolved C [n] (t ) = 1 − 2r (n)(t )/L, e.g., for states n in the vicinity of the band center. On doing that, one ﬁnds essentially no discernible difference from the results for C(t ) shown in Fig. 8. 094206-9 CREED, LOGAN, AND ROY PHYSICAL REVIEW B 107, 094206 (2023) 1. MBL0 To obtain an understanding of the above results, we con- sider now what we refer to as MBL0 [49]. Sufﬁciently deep in the MBL phase, the model (1) is perturbatively connected to the noninteracting limit J(cid:5) = 0 (MBL0). Here, although the system is “trivially” MBL—because H [Eq. (1)] is site- separable in real-space and the system a set of noninteracting spins—the behavior on the Fock space is known [49] to be nontrivial, and the Fock-space H Eq. (2) remains fully con- nected on the graph. As outlined in Appendix C, for MBL0 the exact disorder- averaged PIJ (t ) can be obtained, starting from the basic deﬁnition PIJ (t ) = \|GIJ (t )\|2, Eq. (4). With J, I any pair of FS sites separated by a Hamming distance rIJ = r, the result is PIJ (t ) = [z0(t )]r[1 − z0(t )](L−r) (33) with z0(t ) given by (cid:19) z0(t ) = = (cid:2)2 h2 + (cid:2)2 (cid:18) W dh W 0 (cid:20) sin2( h2 + (cid:2)2 t ) (cid:21) d (cid:2)2 h2 + (cid:2)2 sin2( (cid:20) h2 + (cid:2)2 t ). (34) Note that PIJ (t ) again has the binomial form Eq. (23), de- duced on general grounds in Sec. IV for short times; and Eq. (34) obviously recovers, as it ought, the known asymptotic behavior z0(t ) = ((cid:2)t )2 as (cid:2)t → 0. Equation (33) in fact depends solely on the Hamming distance r and is otherwise independent of the particular FS sites J, I (see Appendix C). Hence, from Eq. (6), PI (r; t ) is independent of I, and P(r; t ) ≡ PI (r; t ) is thus (cid:13) L r (cid:14) [z0(t )]r[1 − z0(t )](L−r). P(r; t ) = (35) From this follows the ﬁrst moment r(t ) [Eq. (8a)] and hence C(t ), r(t ) L = z0(t ), C(t ) = 1 − 2z0(t ), (36) each of which is L independent for all t. Figure 8 compares this result for C(t ) and r(t )/L, to ED results for the interacting case. The strong qualitative parallels between the two are self evident. MBL0 is fully determined by z0(t ) [Eq. (34)], which, via the double-angle formula for sin2θ (and setting (cid:2) ≡ 1) is z0(t ) = p − 1 2 K (t ), p = tan−1(W ) 2W (37) √ with K (t ) = (cid:10)(h2 + 1)−1cos(2 h2 + 1 t )(cid:9)d. K (t ) vanishes as t → ∞ [see Eq. (38)]. The long-time limits are then r(∞)/L = p and C(∞) = 1 − 2p; with p < 1/2 necessarily such that C(∞) > 0 and r(∞)/L < 1/2, as characteristic of an MBL phase. For W = 7, as in Fig. 8, the MBL0 C(∞) (cid:6) 0.8, only slightly larger than its interacting counterpart of C(∞) (cid:6) 0.7. We add that in Appendix C we also point out the connec- tion P(r; ∞) ≡ F n(r) between the long-time limit of P(r; t ) and the eigenstate correlation function F n(r) [Eq. (28b), which for MBL0 is the same for all eigenstates n]. The t dependence of K (t ) is readily determined. Its asymp- totic behavior, formally for t (cid:16) 1, is given by (cid:22) K (t ) ∼ 1 2W π 2 [cos(2t ) − sin(2t )] √ t , (38) vanishing as a power law ∝ 1/ t superimposed on the os- cillating envelope of period π . The maxima of the oscillatory part occur at the discrete set of points t = 7 π + π n (n ∈ N0), 8 at which C(t ) ∼ (cid:13) 1 − tan−1(W ) W (cid:14) + √ π 2W . 1√ t (39) √ This is superimposed on the MBL0 result for C(t ) shown in Fig. 8, and in practice is seen to account very well for the behavior down to times t on the order of unity. For MBL0 one can also determine the eigenstate-resolved C [n] (t ) = 1 − 2r (n)(t )/L for an arbitrary eigenstate \|n(cid:9). In this case, reﬂecting the real-space site-separability of H, it can be shown (although we do not prove it here) that the disorder- averaged C [n] (cid:5) \|n(cid:9) is independent of both the site (cid:5) and the particular eigenstate \|n(cid:9). In consequence, C [n] (t ) ≡ C(t ); providing a rationale for the fact, mentioned in Sec. V B above, that our ED calculations of C [n] (t ) in the interacting case are barely discernible from those for C(t ). (cid:5)(cid:5)(t ) = (cid:10)n\| ˆσ z (cid:5) (t ) ˆσ z 2. Fluctuations While our primary focus has been the ﬁrst moment of P(r; t ), higher central moments are also of course calculable. As previously mentioned, the fact that it is r(t )/L, which generically remains ﬁnite in the thermodynamic limit means that it is ﬂuctuations in this quantity one should consider, as reﬂected in σ 2(t ) := δr2(t )/L2 [with δr2(t ) from Eq. (8b)]. As shown in Sec. IV for the short-t domain, which holds for all disorder/interaction strengths, σ 2(t ) ∝ 1/L. Fluctuations are thus entirely suppressed in the thermodynamic limit. Just the same situation arises for MBL0, which, from Eq. (35) for P(r; t ), gives σ 2(t ) = z0(t )[1 − z0(t )]/L. Indeed, employing steepest descents on Eq. (35) shows P(r; t ) as a function of y ≡ r/L to be Gaussian, with a mean of z0(t ) (= r(t )/L) and variance σ 2(t ) ∝ 1/L; such that it becomes δ distributed in the thermodynamic limit. More generally, across essentially the full range of disorder strengths, our ED calculations are also qualitatively consistent with the above conclusions: aside from a small W interval around Wc ∼ 4, with increasing L we ﬁnd δr2(t )/L2 to pro- gressively decrease, and the P(r; t ) proﬁle to narrow. VI. LATERAL PROBABILITY TRANSPORT We turn now to the substantive question of how the t- dependent wavefunction spreads out laterally, as reﬂected in the time-dependent distribution of probabilities across the rows of the FS graph. As explained in Sec. III B, the quantity RI (r; t ) [Eq. (12)] provides a natural measure of ﬂuctuations in the distribution of PIJ ’s along any given row r, and is related directly to the row-resolved, t-dependent IPR by RI (r; t ) = NrII,2(r; t ) [Eq. (15)], with Nr = the number of FS sites on row r. We (cid:3) (cid:2) L r 094206-10 PROBABILITY TRANSPORT ON THE FOCK SPACE OF A … PHYSICAL REVIEW B 107, 094206 (2023) FIG. 9. t dependence of the average (cid:10)R(cid:9) (= Nr(cid:10)I2(cid:9)), see Eqs. (12), (13), and (15). Shown for r = L/2 with W = 1, 2 (top row) and W = 5, 7 (bottom row), for system sizes indicated. consider ﬁrst the averages, (cid:10)R(cid:9) ≡ (cid:10)R(cid:9)(r; t ) or (cid:10)I2(cid:9) [Eqs. (13) and (15)], over disorder realizations and FS sites I, before turning to the full probability distribution PR(x) [Eq. (14)] of RI (r; t ). There is no a priori requirement here to average over all FS sites I, so in this section we choose (for numerical convenience) to average over FS sites I whose site energies EI lie close to their mean value of zero. Since our interest lies in dynamics, we also focus on a particular, representa- tive r throughout the section. We choose the midpoint of the FS graph, r = L/2 [or r = (L − 1)/2 for odd L], and have checked that the key results arising are not dependent on this choice. Figure 9 shows the t dependence of (cid:10)R(cid:9) for the system sizes indicated, with W = 1, 2 representative of the ergodic regime and W = 5, 7 of the MBL regime. r r Three notable points are evident in Fig. 9. First, in the short-time domain t (cid:2) 0.1, (cid:10)R(cid:9) = 1 independently of L, for all interaction strengths W . As pointed out in Sec. III B [under Eq. (12)], this is the limit of complete homogeneity, where all PIJ (t )’s on any given row of the graph are the same (itself shown in Sec. IV). In consequence, RI (r; t ) = 1 [Eq. (12)] and hence (cid:10)R(cid:9) = 1, as seen; equivalently the row-resolved IPR (cid:10)I2(cid:9) = N −1 (cid:10)R(cid:9) = N −1 . Second, consider now the opposite limit in Fig. 9, viz. the long-time behavior. For W = 1, 2, (cid:10)R(cid:9) here is O(1) and L independent, just as it is in the short-time domain; while for W = 5, 7 by contrast, (cid:10)R(cid:9) clearly grows with increasing L. As explained in Sec. III B [under Eq. (15)], the former behavior again reﬂects the essentially uniform distribution of probabilities PIJ (t ) over FS sites on the row, with (cid:10)I2(cid:9) = N −1 , as one expects for an ergodic regime at late r times. In the MBL regime by contrast, the growth of (cid:10)R(cid:9) with L reﬂects that probabilities, and hence the wavefunction, are strongly inhomogeneously distributed on the row. (cid:10)R(cid:9) ∝ N −1 r FIG. 10. Results here refer to long-time behavior (taken at t = 104). Left panel: (cid:10)R(cid:9) vs W for system sizes indicated. Right panel: ln(cid:10)R(cid:9) vs ln Nr, shown for W = 5, 6, 7 and W = 1; showing the scal- ≡ (cid:10)I2(cid:9) ∼ N −ν ing behavior (cid:10)R(cid:9) ∼ N 1−ν , with exponent ν < 1 in the MBL regime and ν = 1 in the ergodic regime. r r to decrease with increasing W (for W = 5, 7, ν (cid:6) 0.4, 0.3 respectively). This ﬁgure also shows the same plot for W = 1, conﬁrming the L independence of (cid:10)R(cid:9) (corresponding to ν = 1). The left panel of Fig. 10 gives the late-time (cid:10)R(cid:9) as a function of W , conﬁrming both the strong L dependence inside the MBL regime, and its corresponding absence in the ergodic phase. Equally, it shows a typical “crossover W win- dow”, whose presence is inevitable given accessible system sizes; and which, without further detailed scaling analysis, precludes substantive consideration of W ’s in the vicinity of Wc ∼ 3.8 [55] (which is not our aim here). Third, consider again Fig. 9 for the ergodic phase W ’s. Al- though as above (cid:10)R(cid:9) ≡ (cid:10)R(cid:9)(t ) is L independent at both short- and long-times, for times t on the order of unity (cid:10)R(cid:9)(t (cid:6) 1) shows a strong L dependence. This too is found to have the form (cid:10)R(cid:9)(t (cid:6) 1) ∼ N 1−ν , directly analogous to Fig. 10 (right panel), and with an exponent ν ≡ ν(t (cid:6) 1) that likewise de- creases with increasing W [for W = 1, 2, ν(t = 1) (cid:6) 0.8 and 0.7 respectively]. r The overall physical picture arising from the above is then as follows. Following the W -independent, short- time complete homogeneity of the squared wavefunction amplitudes/probabilities along the row of the graph, the L dependence arising by t ∼ 1—again for all W —indicates the dynamical emergence of multifractal behavior of the wave- function. The latter persists with increasing t in the MBL regime, until by t ∼ 10 or so the long-time multifractality is well established. For the ergodic W ’s by contrast, that evo- lution is arrested; and the system instead crosses over from incipient multifractality to the ergodic behavior reﬂected in (cid:10)R(cid:9) ∼ N 0 ), indicating an essentially r uniform distribution of probabilities along the row. This pic- ture, pertaining to the row-resolved IPR, provides a rather natural and consistent complement to that shown in Sec. IV to arise for the behavior of the conventional IPR over the full Fock space (see Fig. 3). ∼ O(1) (i.e., (cid:10)I2(cid:9) ∼ N −1 r As was conjectured on physical grounds in Sec. III B, the L dependence of (cid:10)R(cid:9) in the MBL regime is indeed found to be (cid:10)R(cid:9) ∼ N 1−ν r —or equivalently (cid:10)I2(cid:9) ∼ N −ν for the row- resolved IPR—with a long-time (multi)fractal exponent ν < 1. That this is so is demonstrated in Fig. 10, right panel, where in the MBL regime the long-time exponent ν is also seen r Probability distributions The discussion above has centered on the average value (cid:10)R(cid:9) of RI (r; t ) = NrII,2(r; t ) [Eq. (12)]. Now we consider the full probability distribution of RI (r; t ), given by [Eq. (14)] PR(x) = (cid:10)δ(x − RI (r; t ))(cid:9)d,I (with the I averaging over sites 094206-11 CREED, LOGAN, AND ROY PHYSICAL REVIEW B 107, 094206 (2023) FIG. 11. Probability distribution PR(x) of x ( ≡ RI (r; t )) for W = 1 (top row) and W = 7 (bottom), at the sequence of t’s speciﬁed, and for different system sizes L as indicated. Full discussion in text. whose FS-site energies are close to their mean, as mentioned above); and the ﬁrst moment of which distribution is precisely (cid:10)R(cid:9) considered above. To illustrate the key points here, Fig. 11 shows PR(x) vs x for W = 1 (top row) and W = 7 (bottom), at the sequence of t’s indicated, and over the range of system sizes studied. Note that, for either W , PR(x) for t = 0.1 is Dirac-delta distributed at x = 1. This reﬂects the short-time regime (t (cid:2) 0.1) for which, as discussed above in relation to Fig. 9, RI (r; t ) = 1 (for any r, I and all W ), and hence PR(x) = δ(x − 1). First consider W = 1 in Fig. 11, illustrating the ergodic regime. For any given L, the PR(x) distribution evolves most signiﬁcantly with t over the interval 0.1 (cid:2) t (cid:2) 1. On further increasing t, the mean (= (cid:10)R(cid:9)) of PR(x) decreases, as also evident from Fig. 9. By t = 100 the mean of the evidently symmetrical PR(x) appears rather well converged over the accessible L range; and the distribution is both narrow and sharpening with increasing L (indeed that behavior is evident by t ∼ 10). A simple ﬁt to PR(x) for t = 100, shows it clearly to be normally distributed, with a variance decreasing with L. The situation is quite different in the MBL regime, illus- trated by W = 7 in Fig. 11. Here again, PR(x) evolves most signiﬁcantly with t over the interval 0.1 (cid:2) t (cid:2) 1. For ﬁxed L, the distributions are in fact practically converged to their long-time limit by t ∼ 1, above which little further tempo- ral evolution occurs. Clearly, however, the late-time PR(x) is much broader than its counterpart in the ergodic regime (note the the greatly increased x scale compared to W = 1), reﬂecting the substantial inhomogeneity arising in the MBL regime, as discussed above. With increasing L the mean and mode of PR(x) continue to increase, as discussed in regard to Fig. 9. And the distribution is not only visibly broad but appears to be heavy tailed. To obtain some understanding of the form of PR(x) in the MBL regime, note ﬁrst that, in contrast to the ergodic phase, the mean (cid:10)R(cid:9) of PR(x) is itself increasing with L (as per Fig. 9). To distill this out from the large-x tail of PR(x), we thus consider the distribution P˜R (x) = (cid:10)δ(x − ˜R)(cid:9) d,I : ˜R = RI (r; t ) (cid:10)R(cid:9) (40) of ˜R = RI (r; t )/(cid:10)R(cid:9), which has a mean of unity for all t. This is shown in Fig. 12 (left panel) from which, given the modest accessible L range, reasonable scaling behavior is seen; and showing a power-law tail P˜R(x) ∼ x−α with α (cid:6) 2.5, such that the variance of the distribution, and all higher moments, are unbounded. It appears in fact that PR(x) itself is described by a general- ized Lévy distribution, PR,L´evy(x) = Aα−1 (cid:2)(α − 1) 1 xα exp(−A/x) x(cid:16)A∝ x−α (41) [with A an L-dependent constant and (cid:2)(z) the gamma func- tion]; and which heavy-tailed distribution is stable provided α < 3. The mode of PR,L´evy(x) is xmode = A/α and, provided FIG. 12. Left panel: For W = 7 at t = 100, showing P˜R(x) [Eq. (40)] vs x for L values indicated. Dashed line shows comparison to the corresponding Lévy distribution P˜R,L´evy(x) [Eq. (42)]. Inset: PR(x) for L = 13, compared to a two-parameter ﬁt to PR,L´evy(x) [Eq. (41)]. Right panel: Now for W = 2 at t = 1, showing P˜R(x) vs x. Dashed line again compares to corresponding P˜R,L´evy(x). 094206-12 PROBABILITY TRANSPORT ON THE FOCK SPACE OF A … PHYSICAL REVIEW B 107, 094206 (2023) FIG. 13. For W = 1. Top panels: Distribution Prel(x) of x [Eq. (43)] vs x, at the t’s speciﬁed, and for system sizes L indicated. Bottom panels: Cumulative distribution Prel,c(x). Red dashed line in ﬁnal panel shows half-Gaussian ﬁt to data (see text). α > 2, its mean is ﬁnite and given by x = A(cid:2)(α − 2)/(cid:2)(α − 1) := A/ f (α). The corresponding Lévy distribution for ˜R is then P˜R,L´evy (x) = [ f (α)]α−1 (cid:2)(α − 1) x−α exp(− f (α)/x), (42) and depends solely on α and not on A. The inset to the left panel of Fig. 12 shows PR(x) itself (for L = 13 at t = 100), compared to a two-parameter ﬁt (viz., α, A) to PR,L´evy(x) (leading to α (cid:6) 2.5); the agreement is rather good. The left panel of Fig. 12 shows P˜R(x) for increasing system sizes, com- pared to the corresponding P˜R,L´evy(x) Eq. (42) (dashed line). We add that the L dependence of the ﬁt PR,L´evy(x) itself arises largely from the L dependence of A; by contrast, over the accessible L window, α varies relatively little (and for which reason P˜R,L´evy(x) in Fig. 12 shows reasonable convergence with increasing L). While we have latterly focused on the MBL regime, it was pointed out above that in the ergodic phase—e.g., W = 1, 2 in Fig. 9—the average (cid:10)R(cid:9)(t ) shows strong L depen- dence at times t (cid:6) 1; reﬂecting incipient multifractality in the wavefunction, which is arrested at later times as the system crosses over to characteristic ergodic behavior. Naturally, such behavior around t (cid:6) 1 is equally apparent in the full PR(x) dis- tributions for the ergodic phase shown in Fig. 11. Accordingly, the right panel in Fig. 12 shows (for W = 2) the distributions P˜R(x) for t = 1, in direct analogy to Fig. 12 left panel. Once again the Lévy form appears to describe the data rather well; now with a larger tail exponent (α (cid:6) 7) than for the MBL regime [such that the variance of P˜R(x) is ﬁnite]. Prel(x) distribution Complementary insight into the spread of probabilities across a row of the FS graph comes from the distribu- tion Prel(x) [Eq. (16)]. For a given row r, this gives the distribution—over disorder realizations, FS sites J on the row, and initial FS sites I—of PIJ (t ) relative to its mean value on the row, x ≡ (cid:7) PIJ (t ) J:rIJ =r PIJ (t ) 1 Nr = PIJ (t ) 1 Nr PI (r; t ) . (43) The ﬁrst moment of Prel(x) is 1 by construction, while its second moment is the average (cid:10)R(cid:9) studied above. First, consider W = 1 (again choosing r = L/2). The top row of Fig. 13 show Prel(x) vs x at the sequence of t’s speciﬁed, and for different system sizes L. Corresponding cumulative distributions, (cid:18) Prel,c(x) = 0 x dy Prel(y) , (44) are shown in the bottom panels. While Prel(x) is not converged in L for t = 1—as expected from the preceding discussion— the distributions appear converged in L for the other t’s shown. The long-time Prel(x) is reached by t = 102 (indeed essentially so by t ∼ 10); and consistent with the convergence of Prel(x) with L, the long-time value of (cid:10)R(cid:9) = dx x2Prel(x) is seen from Fig. 9 to be O(1) and L independent. This long-time Prel(x) is in fact rather well captured by a half-Gaussian distri- bution, of form PG(x) = (2/π ) exp(−x2/π ) (for x (cid:2) 0), with a mean of unity and a corresponding cumulative distribution π ). The latter is compared to the ED data in Fig. 13 Erf (x/ (ﬁnal panel, dashed line), and seen to agree well with it. √ (cid:10) The important physical point here is that the long-time Prel(x) (or PG(x)) has a mean of unity, and ﬂuctuations that are also O(1). This means the probabilities PIJ are essentially uniformly distributed across the row; as evident, e.g., from Eq. (43) where, if all PIJ ’s on the row are comparable, then x ∼ O(1). This is symptomatic of the ergodic behavior one expects for weak disorder. But now consider the case of W = 7, representative of the MBL regime, for which corresponding results are shown in Fig. 14. The situation here is very different since—particularly 094206-13 CREED, LOGAN, AND ROY PHYSICAL REVIEW B 107, 094206 (2023) FIG. 14. For W = 7. Top panels: Distribution Prel(x) of x [Eq. (43)] vs x, at the sequence of t’s speciﬁed, and for system sizes L indicated. Bottom panels: Corresponding cumulative distribution Prel,c(x). in the wings of Prel(x)—the distribution is clearly not con- verging with L for any t (save for t (cid:2) 0.1, as known on general grounds, Sec. IV). This is to be expected because, as shown above, e.g., the long-time value of (cid:10)R(cid:9) = dx x2Prel(x) grows with increasing L, as (cid:10)R(cid:9) ∼ N 1−ν r with exponent ν < 1. The question then is, what features of the long-time Prel(x) distribution determine that behavior? It must surely arise from the large-x tails of Prel(x), which, from Fig. 14, are “ﬁlling out” in an obvious sense with increasing L. (cid:10) To examine this, consider the effective cumulative distribu- tion (cid:18) x dy y2Prel(y), (45) Fc (x) = 0 giving the contribution to (cid:10)R(cid:9) arising from different parts of the Prel distribution. This is shown in Fig. 15 for x > 1 (dashed lines and right axis), together with Prel(x) itself (solid lines, FIG. 15. For W = 7, with t = 100. Fc(x) [Eq. (45)] vs x (dashed lines, right-hand scale), shown for x > 1 and different L as indicated; and Prel(x) (solid lines, left-hand scale). Black arrowheads show Nr (= for odd L). Dashed black line shows ﬁt to Prel(x) data (see text). for even L, and L (L±1)/2 L L/2 (cid:2) (cid:2) (cid:3) (cid:3) left axis). We add in passing that while the large-x behavior of Prel(x) does not appear to be a pure power law, it is quite well captured by Prel(x) ∼ ax−n ln x (with n (cid:6) 2.3), shown as the black dashed line in Fig. 15. As seen from the ﬁgure, Fc(x) for a given L tends to its saturation value at the x = xm(L) for which Prel(x) “crashes” in a self-evident sense. xm(L) grows strongly with L, and the Fc(x)’s for different L progressively collapse onto an essentially common curve. (cid:3) (cid:2) L r As discussed below, the maximum possible value of x [Eq. (43)] in Prel(x) is in fact Nr = (and thus exponentially large in L for any ﬁnite r/L). That this is indeed the xm(L) for which Prel(x) crashes and Fc(x) consequently plateaus is seen in Fig. 15, where black arrows show Nr. The fact that max(x) = Nr is evident from Eq. (43). Since all PIJ (cid:2) 0 then, over the set of probabilities PIJ for the Nr FS sites J on row r, it arises in the case where only a single PIJ —call it PIJ ∗— J:rIJ =r PIJ ≡ completely dominates the others (such that PIJ ∗). More generally, if the set of PIJ are correspondingly non-negligible for an O(1) number of FS sites J on the row, then the associated x is again O(Nr ). (cid:7) As shown, it is then the large-x behavior of Prel(x), which governs the second moment (cid:10)R(cid:9), and consequently all higher moments, of the distribution. Physically, this arises from FS sites J for which PIJ greatly exceeds the mean probabil- ity N −1 J:rIJ =r PIJ on the row. And that of course reﬂects the strong inhomogeneity in the distribution of PIJ ≡ PIJ (t ) across a row, which is symptomatic of the MBL regime for sufﬁciently long times. (cid:7) r VII. SUMMARY AND DISCUSSION The central question we posed at the outset was, given an initial spin conﬁguration, how do the probability densities of the time-evolving quantum state spread out on the FS graph of a disordered quantum spin chain? In the course of investigat- ing this question, a rather rich phenomenology was uncovered for the anatomy of probability transport on FS. This can be 094206-14 PROBABILITY TRANSPORT ON THE FOCK SPACE OF A … PHYSICAL REVIEW B 107, 094206 (2023) quantum spin chain, it also motivates several further ques- tions of immanent interest. For conserved quantities, or local observables which have a ﬁnite overlap with the former it is worth asking if there exists a connection between potentially anomalous transport of the conserved quantity in real space, and FS probability transport. This naturally involves space- time correlations in real space, and not just autocorrelations. Going beyond systems with conserved quantities, one can also ask about the fate of probability transport in the absence of any conserved quantities, such that r(t ) is not restricted to be subdiffusive nor C(t ) to decay as a power-law in time. In this paper we focused on FS probability transport, which is clearly a two-point correlation function on FS. One can generalize the question to that of the dynamics of four-point correlations on FS, with the aim of understanding entangle- ment growth in disordered quantum systems [69–71], both in the ergodic as well as the MBL phase. The persistence of dynamical inhomogeneities in the MBL phase can also provide us with a starting point for under- standing and theorising about the role of resonances in the MBL phase from a FS point of view. Speculating that these resonances are caused by rare disorder ﬂuctuations in real space, it is also interesting to ask similar questions for MBL phases with quasiperiodic potentials, which are devoid of such rare regions [72–74]. Finally, we add that understanding probability transport on Fock space is not solely of theoretical interest, but is also of direct experimental relevance; as evident, e.g., in a recent preprint [52] in which aspects of longitudinal FS probability transport were studied in an experimental realization of a two-dimensional disordered hard-core Bose-Hubbard model on a superconducting quantum processor. ACKNOWLEDGMENTS This work was supported in part by EPSRC, under Grant No. EP/L015722/1 for the TMCS Centre for Doctoral Train- ing, and Grant No. EP/S020527/1. S.R. also acknowledges support from an ICTS-Simons Early Career Faculty Fellow- ship, via a grant from the Simons Foundation (677895, R.G.). We are grateful to Qiujiang Guo for drawing our attention to the recent preprint [52]. APPENDIX A: PIJ (t ) EIGENSTATE RESOLUTION As mentioned in Sec. V, the probabilities PIJ (t ) = \|(cid:10)J\|e−iHt \|I(cid:9)\|2 can be eigenstate resolved in the form PIJ (t ) = N −1 H (cid:4) n P(n) IJ (t ) (A1) (cid:7) (cid:7) n r (n) I (t ) = with the sum over eigenstates n. Any quantity linear in the {PIJ (t )}’s can thus likewise be eigenstate resolved. For J rIJ PIJ (t ) is rI (t ) = example, the ﬁrst moment rI (t ) = (cid:7) N −1 I (t ) with r (n) IJ (t ), and it is the dis- H order averaged r (n)(t )/L = N −1 I (t )/L shown in Fig. 5; (cid:7) H similarly [see Eqs. (9) and (11)], C(t ) = N −1 C [n](t ) with H C [n](t ) = 1 − 2 (cid:7) Since PIJ (t ) ≡ RePIJ (t ) is pure real, Eq. (5) gives PIJ (t ) = n,m cos[(En − Em)t]AnI AnJ AmI AmJ . Hence on comparison to J rIJ P(n) (cid:7) I r (n) L r (n)(t ). n FIG. 16. Schematic summary of the three main time windows in the dynamics, and their characteristic features in the ergodic and MBL phases. conveniently summarized by considering three time windows, as follows (see Fig. 16 for a visual summary). (i) Short times, t (cid:2) 1: In this regime, the system is agnos- tic to which phase it is in, ergodic or MBL. The dynamics on these scales is characterized by an emergent (multi)fractality of the time-evolving state, but homogeneous lateral proba- bility transport across rows of the FS graph. Another crucial feature of this regime is that the emergent lengthscale r(t ) ∼ O(L) for any ﬁnite t, however small; this ensures that the spin-autocorrelation C(t ) is necessarily less than unity. The fractal exponent τ2, as well as the lengthscale r(t ), grows as ∝ t 2 in this regime. (ii) Intermediate times, 1 (cid:2) t (cid:2) tH : This is arguably the most interesting dynamical regime. While the emergent (multi)fractality of the entire wavefunction persists, albeit with an increasing τ2, strong inhomogeneities in the lateral probability transport also set in, reﬂected in (multi)fractal scalings of the row-resolved IPRs. This is also manifest in the distributions PR and Prel not being converged with L. On these timescales, at intermediate disorder strengths preced- ing the MBL regime, r(t ) (or r (n)(t )) grows subdiffusively, ∼t β with β < 1/2, implying an anomalous t −β power-law decay of the real-space spin autocorrelation. In the MBL phase as well, there is a power-law envelope to the decay of the spin autocorrelation, but with clear signatures of the incipient saturation to a ﬁnite value, characteristic of that phase. (iii) Long times, t (cid:3) tH: This is the regime where the dynamics is essentially saturated and one sees the eigenstate properties. In the ergodic phase, the (multi)fractality gives way to a fully extended homogeneous state, both in terms of the IPR of the entire state as well as the row-resolved IPRs; and as also reﬂected in (cid:10)R(cid:9) saturating to an L-independent value, and similarly for the distributions PR and Prel. This is qualitatively different from the MBL regime, in which the (multi)fractality, for both the full state and at the row-resolved level, persists for arbitrarily long times. This is symptomatic of strongly inhomogeneous probability transport on the FS graph in the MBL phase, and is also manifest in PR exhibiting a heavy-tailed Lévy alpha-stable distribution. While the paper has presented quite a comprehensive pic- ture of probability transport on the FS graph of a disordered 094206-15 CREED, LOGAN, AND ROY PHYSICAL REVIEW B 107, 094206 (2023) Eq. (A1), Eq. (31) follows directly, expressed in terms of (real) eigenstate amplitudes (AmJ = (cid:10)J\|m(cid:9)) and eigenvalues. Considering PIJ (t ) = Re[(cid:10)I\|eiHt \|J(cid:9)(cid:10)J\|e−iHt \|I(cid:9)], and in- (cid:7) serting the identity operator ˆ1 = \|n(cid:9)(cid:10)n\|, gives n N −1 H P(n) (A2) = \|J(cid:9)(cid:10)J\| \|n(cid:9) + ˆOI ˆOJ : ˆOJ (t ))\|n(cid:9) IJ (t ) = Re(cid:10)n\| ˆOJ (t ) ˆOI = 1 (cid:10)n\|( ˆOJ (t ) ˆOI 2 ˆOJ = \|J(cid:9)(cid:10)J\| thus deﬁned (a so-called be- with the operator hemoth operator [75]); showing that P(n) IJ (t ) can equally be expressed as an eigenstate expectation value of the self-adjoint operator, ˆO(t ) = 1 2 ( ˆOJ (t ) ˆOI + ˆOI ˆOJ (t )). While any PIJ (t ) itself is non-negative for all t, we add that the same is not guaranteed for P(n) IJ (t ); although it is obvious from Eq. (31) that this does hold in the short- and long-time limits, for which N −1 nI and N −1 IJ (t = ∞) = A2 nI A2 nJ . In practice, this is, however, of little import, with quantities such as r(n)(t )/L shown in Fig. 5 found to be non-negative for all t, as expected physically. IJ (t = 0) = δIJ A2 H P(n) H P(n) APPENDIX B: HEISENBERG TIMES The mean level spacing at energy ω is [NHD(ω)]−1, with D(ω) the density of states/eigenvalues normalized to unity over ω. Reﬂecting the central limit theorem, D(ω) is known to be a Gaussian [34,53] with vanishing mean for the disor- ∝ L dered TFI model under consideration, and a variance μ2 E given exactly by [43] μ2 3W 2 + (cid:2)2]. E The Heisenberg time tH is the inverse of the mean level spacing. We consider it at the band center, ω = 0 (where it is largest), so tH = NHD(0) = NH/ 3 (δJ )2 + 1 = L[J 2 + 1 2π μ2 E and hence (cid:23) tH = (cid:23) 2π L (cid:5) J 2 + 1 2L 3 (δJ )2 + 1 3W 2 + (cid:2)2 (cid:6) . (B1) For all ED calculations, J = 1, δJ = 0.2 and (cid:2) ≡ 1 are ﬁxed. tH obviously increases with L and decreases with disor- der strength W . For W = 1, 2, and L ∈ [8, 14], tH ranges from ∼20 to ∼103, while for W = 6, 7 it correspondingly ranges from ∼10 to ∼400. APPENDIX C: MBL0 We outline basic steps underlying the results given in Sec. V B 1 for MBL0, which corresponds to the noninteracting limit J(cid:5) = 0 of H, Eq. (1). The Hamiltonian in this case is site-separable, H = (cid:5) . The latter is diagonalized as H(cid:5), with H(cid:5) = h(cid:5) ˆσ z (cid:5) + (cid:2) ˆσ x L (cid:5)=1 (cid:7) (cid:23) H(cid:5) = φ(cid:5) ˆ˜σ z (cid:5) : φ(cid:5) = (cid:5) + (cid:2)2 h2 (C1) in terms of the spin-1/2 operator h(cid:5) ˆσ z (cid:23) (cid:5) = ˆ˜σ z (cid:5) + (cid:2) ˆσ x (cid:5) (cid:5) + (cid:2)2 h2 (C2) (such that [ ˆ˜σ z product state of the set of ˜σ spins, \|n(cid:9) = \|{ ˜σ z either +1 or −1. (cid:5) ]2 = 1). An eigenstate \|n(cid:9) of H is simply a (cid:5) }(cid:9) with each ˜σ z (cid:5) Now consider the probability amplitude GIJ (t ) = (cid:10)J\|e−iHt \|I(cid:9) (with a general FS site \|K(cid:9) ≡ \|{S(cid:5),K }(cid:9) in the notation speciﬁed in Sec. II). Since H is site-separable, GIJ (t ) is a separable product, GIJ (t ) = (cid:10)J\|e−iHt \|I(cid:9) = L(cid:24) (cid:5)=1 (cid:10)S(cid:5),J \|e−iH(cid:5)t \|S(cid:5),I (cid:9), (C3) and e−iH(cid:5)t = cos(φ(cid:5)t ) − i ˆ˜σ z the product are readily evaluated, (cid:5) sin(φ(cid:5)t ). The matrix elements in (cid:10)S(cid:5),J ⎧ ⎨ (cid:9) = \|e−iH(cid:5)t \|S(cid:5),I cos(φ(cid:5)t ) − ih(cid:5)√ (cid:5) +(cid:2)2 h2 − i(cid:2)√ sin(φ(cid:5)t ) (cid:5) +(cid:2)2 h2 ⎩ S(cid:5),I sin(φ(cid:5)t ) : S(cid:5),J = S(cid:5),I (C4) : S(cid:5),J = −S(cid:5),I according to whether the local spin S(cid:5),J = ±S(cid:5),I . Let the FS sites J, I be separated by a Hamming distance rIJ = r. Then by deﬁnition r real-space sites have S(cid:5),J = −S(cid:5),I , while (L − r) sites have S(cid:5),J = +S(cid:5),I . Equations (C3) and (C4) then give ⎡ ⎤ GIJ (t ) = (cid:24) ⎢ ⎣ (cid:5)∈r (cid:23) −i(cid:2) (cid:5) + (cid:2)2 h2 ⎡ sin(φ(cid:5)t ) ⎥ ⎦ (cid:24) × (cid:5)∈(L−r) ⎢ ⎣cos(φ(cid:5)t ) − ih(cid:5)(cid:23) (cid:5) + (cid:2)2 h2 S(cid:5),I sin(φ(cid:5)t ) ⎤ ⎥ ⎦ in an obvious notation. From this (recalling [S(cid:5),I ]2 = 1) PIJ (t ) = \|GIJ (t )\|2 follows, (cid:16) (cid:24) PIJ (t ) = (cid:17) sin2(φ(cid:5)t ) (cid:2)2 (cid:5) + (cid:2)2 h2 (cid:16) 1 − (cid:24) (cid:5)∈r × (cid:5)∈(L−r) (cid:17) (C5) sin2(φ(cid:5)t ) . (cid:2)2 (cid:5) + (cid:2)2 h2 This can now be averaged over disorder realizations, and since the random ﬁelds {h(cid:5)} are i.i.d., (cid:19) PIJ (t ) = sin2(φ(cid:5)t ) (cid:21)r d (cid:2)2 (cid:5) + (cid:2)2 h2 (cid:19) × 1 − (cid:21)(L−r) (cid:2)2 (cid:5) + (cid:2)2 h2 sin2(φ(cid:5)t ) , (C6) d which is Eqs. (33) and (34) as required. Equation (C6) is indeed seen to depend solely on the Hamming distance rIJ = r between FS sites J, I; such that, from Eq. (6), P(r; t ) ≡ PI (r; t ) ≡ (cid:3) PIJ (t ), as given explicitly in Eq. (35). Equation (35) can obviously be cast in the form (cid:14) (cid:2) L r (cid:13) P(r; t ) = [1 + e−1/ξ 0 F (t )]−Le−r/ξ 0 F (t ) (C7) L r in terms of a correlation length ξ 0 ln( 1 z0 (t ) F (t ) = − 1). This is the MBL0 counterpart of the short-time F (t ) deﬁned by 1/ξ 0 094206-16 PROBABILITY TRANSPORT ON THE FOCK SPACE OF A … PHYSICAL REVIEW B 107, 094206 (2023) (cid:3) result Eq. (24) [in the latter case, P(r; t ) ≡ PIJr (t )]. Equa- tion (24) itself is of course general—in the sense that it holds for all interaction and disorder strengths—and Eq. (C7) cor- rectly reduces to it for (cid:2)t (cid:2) 1. (cid:2) L r We also point out the connection between the long-time limit P(r; t = ∞) of Eq. (C7), and the eigenstate corre- lation function F n(r) deﬁned generally by Eq. (28b) and given in terms of FS correlation lengths ξF,n for eigenstates n by Eq. (29). 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10.1371_journal.ppat.1010103.pdf	Data Availability Statement: The RNA-seq datasets generated during this study are available at Bioproject accession number PRJNA742496 in the NCBI Bioproject database (http://www.ncbi. nlm.nih.gov/bioproject/742496).	The RNA-seq datasets generated during this study are available at Bioproject accession number PRJNA742496 in the NCBI Bioproject database ( http://www.ncbi. nlm.nih.gov/bioproject/742496 ).	RESEARCH ARTICLE γδ T cell IFNγ production is directly subverted by Yersinia pseudotuberculosis outer protein YopJ in mice and humans Timothy H. ChuID Yue ZhangID Vincent W. Yang3, James B. Bliska6, Brian S. SheridanID 1,2, Camille KhairallahID 1,2, Onur Eskiocak4, David G. Thanassi1,2, Mark H. KaplanID 1,2* 1,2, Jason Shieh3, Rhea Cho1,2, Zhijuan QiuID 1,2, 5, Semir Beyaz4, a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 1 Department of Microbiology and Immunology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, United States of America, 2 Center for Infectious Diseases, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, United States of America, 3 Department of Medicine, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, United States of America, 4 Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America, 5 Department of Microbiology and Immunology, School of Medicine, Indiana University, Indianapolis, Indiana, United States of America, 6 Department of Microbiology and Immunology, Geisel School of Medicine, Dartmouth College, Dartmouth, New Hampshire, United States of America OPEN ACCESS Citation: Chu TH, Khairallah C, Shieh J, Cho R, Qiu Z, Zhang Y, et al. (2021) γδ T cell IFNγ production is directly subverted by Yersinia pseudotuberculosis outer protein YopJ in mice and humans. PLoS Pathog 17(12): e1010103. https:// doi.org/10.1371/journal.ppat.1010103 Editor: Denise M. Monack, Stanford University School of Medicine, UNITED STATES Received: July 29, 2021 Accepted: November 9, 2021 Published: December 6, 2021 Copyright: © 2021 Chu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The RNA-seq datasets generated during this study are available at Bioproject accession number PRJNA742496 in the NCBI Bioproject database (http://www.ncbi. nlm.nih.gov/bioproject/742496). Funding: This work was supported by The G. Harold and Leila Y. Mathers Foundation grant MF- 1901-00210 (B.S.S.), the NIH grants T32 AI007539 (T.H.C.), R01 AI099222 (J.B.B.), K12 GM102778 (Z.Q.), and R01 AI141633 (D.G.T.), and funds provided by The Research Foundation for the State * brian.sheridan@stonybrook.edu Abstract Yersinia pseudotuberculosis is a foodborne pathogen that subverts immune function by translocation of Yersinia outer protein (Yop) effectors into host cells. As adaptive γδ T cells protect the intestinal mucosa from pathogen invasion, we assessed whether Y. pseudotu- berculosis subverts these cells in mice and humans. Tracking Yop translocation revealed that the preferential delivery of Yop effectors directly into murine Vγ4 and human Vδ2+ T cells inhibited anti-microbial IFNγ production. Subversion was mediated by the adhesin YadA, injectisome component YopB, and translocated YopJ effector. A broad anti-pathogen gene signature and STAT4 phosphorylation levels were inhibited by translocated YopJ. Thus, Y. pseudotuberculosis attachment and translocation of YopJ directly into adaptive γδ T cells is a major mechanism of immune subversion in mice and humans. This study uncov- ered a conserved Y. pseudotuberculosis pathway that subverts adaptive γδ T cell function to promote pathogenicity. Author summary Unconventional γδ T cells are a dynamic immune population important for mucosal pro- tection of the intestine against invading pathogens. We determined that the foodborne pathogen Y. pseudotuberculosis preferentially targets an adaptive subset of these cells to subvert immune function. We found that direct injection of Yersinia outer proteins (Yop) into adaptive γδ T cells inhibited their anti-pathogen functions. We screened all Yop effec- tors and identified YopJ as the sole effector to inhibit adaptive γδ T cell production of IFNγ. We determined that adaptive γδ T cell subversion occurred by limiting activation of the transcription factor STAT4. When we infected mice with Y. pseudotuberculosis PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 1 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function University of New York (B.S.S.) and Stony Brook University (B.S.S.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. expressing an inactive YopJ, this enhanced the adaptive γδ T cell response and led to greater cytokine production from this subset of cells to aid mouse recovery. This mecha- nism of immune evasion appears conserved in humans as direct injection of Y. pseudotu- berculosis YopJ into human γδ T cells inhibited cytokine production. This suggested to us that Y. pseudotuberculosis actively inhibits the adaptive γδ T cell response through YopJ as a mechanism to evade immune surveillance at the site of pathogen invasion. Introduction Pathogens in the genus Yersinia include three species (Y. pestis, Y. pseudotuberculosis, and Y. enterocolitica) that can cause human disease. Y. pseudotuberculosis and Y. enterocolitica cause enteric infections [1,2] while Y. pestis is the causative agent of bubonic, septicemic, and pneu- monic plague that has claimed over 200 million human lives [3,4]. Bubonic and septicemic plague is transmitted by blood sucking fleas while aerosols spread the pneumonic plague. Despite vaccine availability [5], and sensitivity to antibiotic treatment, pneumonic plague com- monly results in fatality in part due to the rapid course of the infection [6]. Pathogenic Yersinia spp. harbor a virulence plasmid that encodes numerous virulence fac- tors to subvert host immune responses, including IFNγ production [7–9]. Immune cell subver- sion requires Yersinia adherence to host cells through bacterial adhesins and translocation of Yersinia outer proteins (Yop) effectors into the host cell cytoplasm by a type III secretion sys- tem (T3SS). Yersinia spp. predominately target host phagocytes like macrophages, dendritic cells (DC), neutrophils, and B cells to subvert immune function during infection, but injection into other immune populations like conventional T cells has been reported, albeit to a lesser degree than their phagocytic counterparts [10–12]. Yersinia virulence factors include compo- nents of the T3SS (e.g., YopB) and translocated effectors (e.g., YopJ and YopH). YopB forms a pore in the host cell membrane necessary for translocation of Yop effectors [13,14]. Numerous Yop effectors translocate into host cells to inhibit immune responses and promote Yersinia spp. pathogenesis. One notable example is YopJ, an acetyl transferase and a possible cysteine protease that inhibits the mitogen-activated protein kinase (MAPK) pathway and tumor necrosis factor receptor-associated factor (TRAF) ubiquitination [15–19]. YopJ is the major Yop effector responsible for the induction of pyroptosis in macrophages during infection [20] and limits toll-like receptor 4 (TLR4) dependent signaling pathways [21]. While YopJ has no known direct effects on conventional T cell activation, YopP (a YopJ homolog in Y. enterocoli- tica) indirectly inhibits T cell priming via DC subversion [22]. YopH has been reported to have direct effects on conventional T cells in vitro. Transfection of a YopH expression plasmid into Jurkat or human T cells inhibited T cell receptor (TCR) signaling and promoted T cell apoptosis [23,24]. Additionally, stimulation of Jurkat cells with a YopH deficient Y. pseudotu- berculosis restored T cell signaling and IL-2 production [25,26]. Even in this context, it is nota- ble that many of the downstream targets in the αβ TCR signaling pathway were inhibited at an excessively high (>50) multiplicity of infection (MOI) and in vivo relevance is unclear [23,26]. Thus, the role of direct subversion of T cell function, especially among unconventional T cells, by Yersinia spp. remains largely unexplored. γδ T cells make up a large proportion of lymphocytes at barrier surfaces and mucosal tissues including the intestines of mice and humans [27,28]. This is particularly pertinent to infections caused by Y. pseudotuberculosis, which has evolved to invade the intestinal barrier. The activity of γδ T cells can be modulated by numerous cell-intrinsic and environmental factors like the γδ TCR, cytokines, and co-stimulatory or inhibitory receptors [29]. For example, IL-12 and PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 2 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function IL-18 may promote IFNγ production from some γδ T cell subsets whereas IL-1β and IL-23 predominantly drive IL-17A production from other γδ T cell subsets [30–35]. Vγ4Vδ1 (Gar- man nomenclature [36]) T cells have traditionally been considered an innate-like cell. How- ever, our group recently characterized a long-lived CD27- CD44hi Vγ4Vδ1 T cell memory population in the context of foodborne Listeria monocytogenes infection [37,38]. While Vγ4 T cells are typically programmed for IL-17A production, this subset has the multifunctional capacity to produce both IL-17A and IFNγ [37]. Similar observations of IFNγ production were made in clonally expanded Vγ4 T cells in response to Staphylococcus aureus in the skin [39]. IFNγ activates macrophages to kill intracellular pathogens or phagocytosed bacteria and induces chemokines that attract immune cells to the site of infection. IFNγ is a critical cytokine in protection from Y. enterocolitica infection [2,40], Y. pestis intranasal challenge [41], and associated with protection from Y. pseudotuberculosis [42]. Interestingly, IFNγ but not IL-17A production from type-3 innate lymphoid cells is critical for the control of foodborne Y. entero- colitica infection [43]. As such, unconventional T cells like Vγ4 T cells that are ideally placed to provide protection against pathogen invasion at mucosal sites may be particularly relevant to Yersinia infections that invade mucosal barriers of the lungs (pneumonic Y. pestis) and gut (Y. pseudotuberculosis and Y. enterocolitica). Despite a foundational understanding of Yersinia pathogenesis, physiologically robust evi- dence linking Yersinia pathogenesis to direct subversion of T cell function is lacking. Here, we uncovered a novel YopJ-dependent immunomodulatory pathway used by Y. pseudotuberculo- sis to directly subvert a murine Vγ4Vδ1 anti-microbial response to aid Y. pseudotuberculosis pathogenesis. Y. pseudotuberculosis also directly subverted a human Vδ2+ T cell IFNγ response, suggesting that this pathway may function similarly in human infection to aid Y. pseudotuberculosis pathogenesis. Results Viable Y. pseudotuberculosis inhibits IFNγ production by adaptive γδ T cells in a YopB- and YadA-dependent manner Initial experiments were carried out to determine if Y. pseudotuberculosis inhibits adaptive γδ T cell function ex vivo. To overcome the extremely low number of Vγ4 T cells in gut-associated lymphoid tissues of naïve specific pathogen free (SPF) mice, a previously established in vivo methodology was utilized to generate a sizable population of adaptive Vγ4 T cells for in vitro manipulation. As such, naïve Balb/c mice were exposed to foodborne L. monocytogenes and MLN enriched in adaptive γδ T cells were isolated 9 days after infection [37], several days after mice typically clear L. monocytogenes [44]. MLN single cell suspensions were infected directly ex vivo with heat-killed or live wild-type (WT) Y. pseudotuberculosis (Yptb) 32777 (Table 1) at a multiplicity of infection (MOI) of 10 for 2 hours. Antibiotics were then added to prevent overgrowth of the live bacteria, and the cultures were incubated an additional 22 hours. Flow cytometry in conjunction with intracellular cytokine staining was used to assess IFNγ produc- tion from Vγ1.1/2- CD44hi CD27- γδ T cells (identifying the adaptive Vγ4 T cell subset [37,38]). Heat-killed Y. pseudotuberculosis elicited a significantly higher IFNγ response from adaptive Vγ4 T cells than was detectable after stimulation with live Y. pseudotuberculosis (Fig 1A). This observation suggests that live Y. pseudotuberculosis subverts adaptive Vγ4 T cell function. The virulence activity of Y. pseudotuberculosis relies substantially on its T3SS and translocation of Yop effectors into host cells. To determine if the T3SS is required for live Y. pseudotuberculosis inhibition of γδ T cell function, MLN single cell suspensions were infected with WT Y. pseudotuberculosis or Y. pseudotuberculosis that were unable to translocate Yop effectors (ΔYopB) or lacked the virulence plasmid that encodes the T3SS (32777c) (Table 1) PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 3 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function Table 1. Y. pseudotuberculosis strains and mutants used in this study. Y. pseudotuberculosis Notation Relevant Characteristics 32777 32777c 32777 YopJC172A 32777 YopHR409A 32777 ΔYopB 32777 YopER144A 32777 YopTC139A 32777 ΔYopM 32777 ΔYpkA 32777 ΔYopK 32777 YopE/β-lac 32777 ΔYopB YopE/β-lac IP2666 IP40 IP2666 ΔInv IP2666 ΔYadA IP2666 ΔInv ΔYadA IP40 ΔInv ΔYadA WT WT2777c YopJC172A YopHR409A ΔYopB YopER144A YopTC139A ΔYopM ΔYpkA ΔYopK WT Yptb-βla ΔYopB Yptb-βla WT ΔYopB ΔInv ΔYadA ΔInv ΔYadA Yptb wild-type serogroup O:1 strain Virulence pYV-cured derivative of 32777 that lacks the T3SS Catalytically inactive YopJ Catalytically inactive YopH Deletion of YopB Catalytically inactive YopE Catalytically inactive YopT Deletion of YopM Deletion of YpkA Frameshift mutation in YopK YopE TME-1 β-lactamase fusion protein Deletion of YopB in the YopE/β-lac Yptb wild-type serogroup O:3 strain IP2666 yopB40 (a stop codon at codon 8 of YopB followed by a frameshift) Deletion of adhesin and invasin Deletion of adhesin and YadA Deletion of adhesin, invasion, and YadA ΔYopB ΔInv ΔYadA Deletion of invasion and YadA in IP40 and pMMB207 mCherry https://doi.org/10.1371/journal.ppat.1010103.t001 References [47] [47] [45] [45,48,49] [45,46] [45] [45] [50] [51] [52] [53–55] [53–55] [47] [56] [12,57] [12,57] [12,57] [12,57] [45–47]. γδ T cell function was assessed 24 hours later as described above. Stimulation with live Y. pseudotuberculosis strains ΔYopB or 32777c restored the IFNγ response of Vγ1.1/2- CD44hi CD27- γδ T cells (Fig 1B), similar to levels seen after stimulation with heat-killed WT Y. pseudotuberculosis (Fig 1A). These data indicate that Y. pseudotuberculosis inhibits IFNγ production by Vγ4 T cells in a manner that requires the T3SS and translocation of Yop effectors. Y. pseudotuberculosis adheres to host cells with the bacterial adhesins invasin (Inv) and YadA to translocate effectors through the T3SS [58–60]. For Y. enterocolitica, both Inv and YadA bind β1-integrin either directly or indirectly through the extracellular matrix, respec- tively [61]. Additionally, β1-integrin expressed on host cells is a known adhesion target for Y. pseudotuberculosis [60,62]. To evaluate the role of these adhesins in the inhibition of γδ T cell function, live Y. pseudotuberculosis with a deletion of Inv (ΔInv), YadA (ΔYadA), or both (ΔInv ΔYadA) (Table 1) were utilized to infect MLN cell suspensions. Vγ1.1/2- CD44hi CD27- γδ T cells stimulated with ΔInv bacteria produced only minimal IFNγ, comparable to unstimu- lated cells or cells stimulated with WT (Fig 1C). In contrast, ΔYadA or ΔInv ΔYadA stimula- tion led to partial restoration of IFNγ production, and stimulation with ΔYopB or ΔYopB ΔInv ΔYadA bacteria led to full restoration of IFNγ production (Fig 1C). Thus, YadA but not Inv contributes to translocation dependent inhibition of IFNγ production by Vγ4 T cells. Translocation of Yop effectors into adaptive γδ T cells by Y. pseudotuberculosis is associated with IFNγ inhibition To determine if Y. pseudotuberculosis can translocate Yop effectors into adaptive Vγ4 T cells, a WT strain expressing a YopE-β-lactamase fusion protein (Yptb-βla) in conjunction with a FRET-based β-lactamase reporter assay was used [63]. YopE translocation into target cells can be readily assessed by a change in fluorescence using flow cytometry. Thus, translocation of the YopE-β-lactamase fusion protein reports Yop effector translocation by emission in the blue range (Yop+) or lack thereof by emission in the green range (Yop-) [10,64,65]. A YopB PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 4 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function Fig 1. Y. pseudotuberculosis inhibition of Vγ1.1/2- CD44hi CD27- γδ T cell function is YopB- and YadA- dependent. MLN cell suspensions from L. monocytogenes infected Balb/c mice were left unstimulated or stimulated with 10 MOI of the indicated Y. pseudotuberculosis for 24 hours. Antibiotics were given 2 hours post-stimulation and brefeldin A was added for the last 5 hours of stimulation. Vγ1.1/2- CD44hi CD27- γδ T cells were analyzed for IFNγ production. Representative histograms are displayed. (A) Cells were stimulated with live or heat-killed (HK) wild-type (WT) Y. pseudotuberculosis. The graph depicts the mean ± SEM and represents at least two independent experiments with 4 mice/group/experiment. (B) Cells were stimulated with live WT, ΔYopB, or 32777c Y. pseudotuberculosis. The graph depicts the mean ± SEM and represents at least two independent experiments with 4 mice/group/experiment. (C) Cells were stimulated with WT, ΔYopB, ΔYadA, ΔInv, ΔInv ΔYadA, or ΔYopB ΔInv ΔYadA Y. pseudotuberculosis. The graph depicts the mean ± SEM pooled from two independent experiments with 3 mice/group/experiment. ��p < 0.0001, ��p < 0.01, and �p < 0.05. An unpaired t-test was used for (A) and a repeated measures one-way ANOVA was used for (B) and (C). Experimental groups were compared to live WT Y. pseudotuberculosis in (A) and WT Y. pseudotuberculosis in (B) and (C). https://doi.org/10.1371/journal.ppat.1010103.g001 deficient β-lactamase Y. pseudotuberculosis reporter (ΔYopB Yptb-βla) was used as a transloca- tion deficient control. Stimulation of MLN cell suspensions with WT or ΔYopB Yptb-βla con- firmed reporter activity at various MOI (S1A and S1B Fig). Two hours post stimulation with WT Yptb-βla, the majority of Vγ1.1/2- CD44hi CD27- γδ T cells were positive for Yop effector PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 5 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function translocation (Fig 2A). Yop translocation into Vγ1.1/2- CD44hi CD27- γδ T cells was compara- ble to known DC and macrophage targets (Fig 2A). Additionally, Yop translocation was more efficient into Vγ1.1/2- CD44hi CD27- γδ T cells than CD4 or CD8 T cells (Fig 2A). Y. pseudotu- berculosis also preferentially targeted Vγ1.1/2- CD44hi CD27- γδ T cells over CD44- γδ T cells and activated phenotype CD4 or CD8 T cells for Yop translocation (S1C Fig). YadA and Inv promote Yersinia adherence by direct or indirect interactions with the β1-integrin [66–69]. Analysis of β1-integrin expression on Vγ1.1/2- CD44hi CD27- γδ T cells, CD4 T cells, and CD8 T cell revealed that most Vγ1.1/2- CD44hi CD27- γδ T cells expressed the β1-integrin (S2A Fig). In contrast, most conventional CD4 and CD8 T cells did not express the β1-integrin. In addition, use of the WT Yptb-βla reporter for Yop translocation demonstrated that Yop trans- location was associated with higher β1-integrin expression among γδ T cells (S2B Fig). Thus, Y. pseudotuberculosis selectively targets adaptive γδ T cells for Yop translocation among a diverse group of immune populations assessed in an ex vivo culture system. As adaptive Vγ4 T cells were directly targeted with Yop effector translocation, WT Yptb-βla was utilized to determine whether Vγ1.1/2- CD44hi CD27- γδ T cells that contained Yop effec- tors were functionally impaired. An MOI of 1 was used as it provided similarly sized popula- tions of Vγ1.1/2- CD44hi CD27- γδ T cells that did or did not contain translocated effectors from the same culture conditions (S1B Fig). Among WT Yptb-βla stimulated cells, Yop+ Vγ1.1/2- CD44hi CD27- γδ T cells had reduced IFNγ production as compared to their Yop- counterparts (Fig 2B). To extend these results, the ability of Y. pseudotuberculosis to translocate Yop effectors into human γδ T cells and inhibit IFNγ production was assessed in peripheral blood mononuclear cells (PBMC) cultures stimulated with the WT Yptb-βla reporter. Approx- imately 8% of human Vδ2+ T cells were Yop+ and these cells had significantly reduced IFNγ production as compared to the Yop- counterparts (Fig 2C and 2D). These data indicate that Y. pseudotuberculosis is capable of translocating Yop effectors into γδ T cell subsets and inhibiting IFNγ production in mice and humans. YopJ is necessary for Y. pseudotuberculosis to inhibit IFNγ production in adaptive γδ T cells As multiple effectors are translocated into target cells, a panel of yop mutant Y. pseudotubercu- losis (Table 1) [45] was screened to determine if individual Yop effectors inhibit IFNγ produc- tion. Similar to the ΔYopB mutant, stimulation with a catalytically inactive YopJ (YopJC172A) mutant that lacks acetyl transferase activity, but not other mutant Y. pseudotuberculosis, restored IFNγ production in Vγ1.1/2- CD44hi CD27- γδ T cells (Fig 3A). The C172A mutation in YopJ prevents YopJ mediated inhibition of MAPK and NF-κB signaling pathways by abol- ishing its serine and threonine acetylation activity [70]. A similar restoration of IFNγ produc- tion was observed in human Vδ2+ T cells from PBMC of healthy donors upon YopJC172A Y. pseudotuberculosis stimulation (Fig 3B). Thus, the YopJ effector is responsible for inhibition of IFNγ production from murine Vγ4 and human Vδ2+ T cells. YopJ inhibits expression of multiple genes, including ifng, in adaptive γδ T cells To uncover mechanisms by which YopJ inhibits IFNγ production, the transcriptome of cell sorter-purified Vγ1.1/2- CD44hi CD27- γδ T cells after WT or YopJC172A Y. pseudotuberculosis stimulation of MLN cells was assessed by RNA-Seq. Principal component analysis revealed unique gene expression clustering, and approximately 900 genes were expressed at higher lev- els in the YopJC172A stimulation as compared to WT Y. pseudotuberculosis (Fig 4A and 4B). These differentially expressed genes may include genes that are directly inhibited by YopJ PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 6 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function Fig 2. Direct translocation of Yop effectors inhibits the function of murine Vγ4 and human Vδ2+ T cells. MLN suspensions from L. monocytogenes infected mice (A and B) or human PBMC (C and D) were left unstimulated or stimulated with WT or ΔYopB Yptb-βla as indicated. Cells were loaded with CCF4-AM dye prior to stimulation to measure β-lactamase activity. FITC indicates CCF4-AM loaded cells without translocation (Yop-) and BV421 indicates CCF4-AM loaded cells with Yop translocation (Yop+). (A) Adaptive γδ T cells (Vγ1.1/2- CD44hi CD27- γδ T cells), DC (CD11chi MHCIIhi), Macrophages (F4/80+ CD11b+), and CD4 and CD8 T cells were analyzed for Yop translocation 2 hours post stimulation at an MOI of 10. Representative contour plots are displayed. Yop translocation among the indicated populations is depicted as mean ± SEM and is pooled from 2 experiments with a total of 4–8 mice per group. (B) Antibiotics were given 2 hours post-stimulation and brefeldin A was added for the last 5–6 hours of stimulation. Vγ1.1/2- CD44hi CD27- γδ T cells were analyzed for Yop translocation and IFNγ production 24 hours after stimulation. Representative contour plots and histograms are shown. IFNγ production among the indicated populations is depicted as mean ± SEM and is pooled from 3 experiments with a total of 8 mice per group. (C and D) Antibiotics were given 2 hours post-stimulation and brefeldin A was added for the last 5–6 hours of stimulation. Vδ2+ T cells were analyzed for Yop translocation and IFNγ production post stimulation. Representative contour plots are displayed and IFNγ production is quantified among Yop+ or Yop- Vδ2+ T cells. The graph depicts mean ± SEM and is pooled from 3 experiments with 5 healthy donors per group. ��p < 0.0001, ��p < 0.001, ��p < 0.01, and �p < 0.05. An ordinary one-way ANOVA was used for (A), a repeated measures one-way ANOVA was used for (B), and a paired t-test was used for (D). Comparisons were performed to adaptive γδ T cells in (A), to unstimulated or as depicted in figure in (B), and to Yop+ in (C). https://doi.org/10.1371/journal.ppat.1010103.g002 activity in Vγ1.1/2- CD44hi CD27- γδ T cells or indirectly inhibited by YopJ activity in other cells such as DC or macrophages. To resolve this, the WT Yptb-βla reporter provided an opportunity to evaluate the molecular changes elicited by the activity of translocated Yop in adaptive γδ T cells. The transcriptome of sort purified Yop- and Yop+ Vγ1.1/2- CD44hi CD27- γδ T cells after WT Yptb-βla stimulation was assessed by RNA-Seq. Principal component anal- ysis revealed unique gene expression clustering, and approximately 900 genes were more highly expressed in Yop- vs Yop+ Vγ1.1/2- CD44hi CD27- γδ T cells after WT Yptb-βla stimula- tion (Fig 4C and 4D). Overlapping gene expression profiles from the two datasets were assessed to narrow the analysis to direct YopJ effects on Vγ1.1/2- CD44hi CD27- γδ T cells. This comparison revealed 130 genes that were differentially expressed in both datasets, sug- gesting they are regulated directly by translocated YopJ in adaptive Vγ4 T cells (Fig 4E). These genes were categorized into different groups depending on their known functions. Some dif- ferentially expressed genes play a particular role in anti-infection functions (3.9%), stress sens- ing (1.6%), and lymphocyte activation/regulation (7.9%), genes that may be important for protective T cell responses (Fig 4E and 4F). Among these genes, IFNγ was the single most sig- nificant differentially expressed gene suggesting it is a major target of direct YopJ-mediated inhibition of adaptive γδ T cell function (Fig 4F). Differentially expressed genes among those that promote antimicrobial function included several that are important in augmenting type-1 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 7 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function Fig 3. YopJ is necessary for inhibition of IFNγ production in murine Vγ4 and human Vδ2+ T cells. (A) MLN from L. monocytogenes infected mice were left unstimulated or stimulated with 10 MOI of the indicated Y. pseudotuberculosis for 24 hours. Antibiotics were given 2 hours post- stimulation and brefeldin A was added for the last 5–6 hours. Vγ1/2- CD44hi CD27- γδ T cells were analyzed for IFNγ post stimulation. Representative histograms are displayed. The graph depicts mean ± SEM and represents at least two independent experiments with 2–4 mice per group. (B) Human PBMC were stimulated with 1 MOI of WT or YopJC172A Y. pseudotuberculosis for 24 hours. Antibiotics were given 2 hours post- stimulation. Brefeldin A was added for the last 5–6 hours of stimulation. Vδ2+ γδ T cells were analyzed for IFNγ production post stimulation. Representative flow plots gated on Vδ2+ T cells are displayed. The graph depicts mean ± SEM and is pooled from 2 experiments with 4 healthy donors. ��p < 0.01 and �p < 0.05. A repeated measures one-way ANOVA was used for (A) and a paired t-test was used for (B). Experimental groups were compared to WT Y. pseudotuberculosis. https://doi.org/10.1371/journal.ppat.1010103.g003 and -3 inflammation in T cells. For example, Ptgs2 (encodes cyclooxygenase-2, COX2), Nkg7 (natural killer cell granule protein 7), Prf1 (perforin-1), and Il17a (IL-17A) appear to be regu- lated directly by translocated YopJ in adaptive Vγ4 T cells (Fig 4F) [71–73]. However, analysis of IL-17A protein after stimulation of MLN cell suspensions with YopJC172A, WT, and ΔYopB Y. pseudotuberculosis demonstrated that YopJ did not regulate IL-17A production from Vγ1.1/ 2- CD44hi CD27- γδ T cells (S4 Fig). Some of the observed differentially expressed genes are important in the activation status of T cells (e.g., Il2ra, Ctla4, and Cd69) and suggest that trans- located YopJ may limit the activation of adaptive Vγ4 T cells. There was also a notable impact (48.0%) on genes associated with cell proliferation, metabolism and energy, mitosis and cell cycle, RNA/DNA processing, and ER/Golgi processing (Figs S3A and S3B and 4E), suggesting that YopJ influences the adaptive γδ T cell transcriptional profile more broadly than just tar- geting the IFNγ pathway. Genes that were differentially expressed upon WT Y. pseudotubercu- losis stimulation or among Yop+ cells also suggest that many of the processes associated with immune responses and cellular activity were regulated by YopJ (S3C–S3E Fig). Collectively, YopJ appears to regulate the expression of many genes associated with T cell function in Vγ1.1/2- CD44hi CD27- γδ T cells, suggesting that adaptive Vγ4 T cells are broadly constrained in their immune functions by Y. pseudotuberculosis. YopJ inhibits the IL-12p40-mediated STAT4 pathway in adaptive γδ T cells To gain potential mechanistic insights into YopJ inhibition of IFNγ production and other Vγ1.1/2- CD44hi CD27- γδ T cell functions, a motif discovery algorithm designed for regula- tory element analysis was utilized to assess our RNA sequencing results [74]. Several transcrip- tion factor binding motifs related to IFNγ signaling were differentially expressed after YopJC172A Y. pseudotuberculosis but not WT Y. pseudotuberculosis stimulation including mem- bers of the E twenty-six (ETS)-domain family, Kru¨ppel-like factor and specificity protein (KLF/SP) transcription factor gene family, and the interferon regulatory factors (IRF) family PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 8 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function Fig 4. YopJ translocation leads to the inhibition of a broad anti-microbial gene response from Vγ4 T cells. (A and B) MLN suspensions from L. monocytogenes infected mice were stimulated with 10 MOI of WT or YopJC172A Y. pseudotuberculosis for 24 hours. Antibiotics were given 2 hours post- stimulation. Five hundred Vγ1.1/2- CD44hi CD27- γδ T cells from each stimulation were flow sorted and processed for RNA sequencing. (A) PCA plots are depicted for similarity of groups YopJC172A and WT Y. pseudotuberculosis stimulated Vγ1.1/2- CD44hi CD27- γδ T cells. (B) Heat maps are depicted for differentially expressed genes of YopJC172A or WT Y. pseudotuberculosis stimulated Vγ1.1/2- CD44hi CD27- γδ T cells. (C and D) MLN suspension from L. monocytogenes infected mice were stimulated with 1 MOI of WT Yptb-βla. Five hundred Yop+ or Yop- Vγ1.1/2- CD44hi CD27- γδ T cells were flow sorted and processed for RNA sequencing. (C) PCA plots are depicted for similarity of Yop+ or Yop- Vγ1.1/2- CD44hi CD27- γδ T cells. (D) Heat maps are depicted for differentially expressed genes of Yop- or Yop+ stimulated Vγ1.1/2- CD44hi CD27- γδ T cells. (E-G) A Venn diagram of differentially expressed genes (higher) that overlapped between RNA sequencing analyses favoring YopJC172A Y. pseudotuberculosis stimulation or Yop- cells is displayed. Shared genes were categorized by gene function. (F) The heat map highlights differentially expressed genes among Vγ1.1/2- CD44hi CD27- γδ T cells from the indicated stimulations and categories. (G) Homer motif analysis was performed on the RNA sequencing dataset. Motifs and associated genes to YopJC172A stimulated Vγ1.1/2- CD44hi CD27- γδ T cells are highlighted. Each experiment was performed with 3 biologic samples per group. Cutoffs for significant genes are p < 0.05 and FDR < 0.10. https://doi.org/10.1371/journal.ppat.1010103.g004 of transcription factors (S5A Fig). IRF8 protein was validated after WT, ΔYopB, and YopJC172A Y. pseudotuberculosis stimulation. Indeed, a higher percentage of Vγ4 T cells expressed IRF8 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 9 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function protein after stimulation with ΔYopB compared to WT Y. pseudotuberculosis stimulation (S5B Fig). Stimulation with YopJC172A Y. pseudotuberculosis was also able to partially restore IRF8 levels to those seen after ΔYopB Y. pseudotuberculosis stimulation (S5B Fig). IRF8 was also impacted by IL-12p40 blockade, which signals through signal transducer and activator of tran- scription 4 (STAT4) (S5B Fig). Interestingly, a number of transcription factor binding motifs downstream of STAT4 signaling were enriched including Etv5, Runx3, and Tead1 (Fig 4G) [75–78]. The RNAseq and homer motif analyses were also compared to an existing STAT4 ChIP-on-chip [79]. 7 genes identified from our main analyses (Figs 4F and 4G and S5B) were STAT4 target genes (S5C Fig). In summary, transcriptional profiling revealed a global subver- sion of anti-pathogen immune functions that may be associated with YopJ subversion of STAT4 activity. IL-12 signaling leads to STAT4 phosphorylation and formation of STAT4-STAT4 homodi- mers that re-localize to the nucleus where they directly bind to the Ifng promoter to induce IFNγ expression [79–81]. To determine whether YopJ inhibits IFNγ production by interfering with the STAT4 pathway, STAT4 protein and phosphorylation were assessed by flow cytome- try of MLN cells stimulated with Y. pseudotuberculosis. STAT4 phosphorylation was analyzed 6 hours after stimulation with WT, ΔYopB, or YopJC172A Y. pseudotuberculosis. Consistent with suppression of IFNγ production and the RNA-Seq analysis, WT Y. pseudotuberculosis sig- nificantly reduced the percentage of pSTAT4+ CD44hi CD27- γδ T cells as compared to ΔYopB and YopJC172A Y. pseudotuberculosis (Fig 5A). However, STAT4 protein levels were the same in all three infection conditions (WT, ΔYopB, and YopJC172A Y. pseudotuberculosis) (Fig 5B). Flow cytometry antibodies for STAT4 protein were validated by comparing STAT4 from WT and STAT4 KO splenocytes (S5D Fig). These data suggest that STAT4 phosphorylation but not protein is decreased upon YopJ translocation. STAT4 phosphorylation was also evaluated using the Yptb-βla reporter system described above. Among CD44hi CD27- γδ T cells, Yop- cells had a higher percentage of pSTAT4+ cells compared to Yop+ cells suggesting intrinsic Yop mediated inhibition of STAT4 phosphorylation levels (Fig 5C). Additionally, as STAT4 phosphorylation is downstream of IL-12 signaling, an anti-IL-12/23p40 subunit antibody (anti-p40) was used to determine whether IL-12 signals in the environment regulated STAT4 phosphorylation after Y. pseudotuberculosis stimulation. Indeed, IL-12/23p40 neutralization abrogated STAT4 phosphorylation levels regardless of Yop translocation (Fig 5C). As IL-12/ 23p40 was required to elicit IFNγ production from adaptive Vγ4 T cells in the culture condi- tions, we assessed whether YopJC172A Y. pseudotuberculosis stimulation modulated IL-12p70. The concentration of IL-12p70 was comparable between WT and YopJC172A Y. pseudotubercu- losis stimulated cultures (Fig 5D). Thus, changes in IL-12 were unlikely to contribute to adap- tive Vγ4 T cell subversion in vitro. To understand the role of YopJ and IL-12 on Vγ1.1/2- CD44hi CD27- γδ T cells in a more simplified system, purified γδ T cells were stimulated with YopJC172A Y. pseudotuberculosis in the presence of excessive IL-12p70. Adaptive Vγ4 T cells were unable to produce IFNγ in response to YopJC172A Y. pseudotuberculosis and IL-12p70 (S6A Fig). Finally, we assessed whether the addition of IL-12p70 could overcome the YopJ mediated inhibition of IFNγ production after WT Y. pseudotuberculosis stimulation. While a supraphysiologic level of IL-12p70 (50 ng/ml) was able to partially overcome YopJ mediated inhibition, lower levels of IL-12p70 addition (2 and 10 ng/ml) were unable to overcome YopJ mediated inhibition (S6B Fig). Importantly, these latter concentrations were orders of magni- tude higher than those detected in our culture conditions. Thus, IL-12 is not sufficient to induce IFNγ production from adaptive Vγ4 T cells. Collectively, these results suggest that Y. pseudotuberculosis stimulation elicits IL-12 production to promote adaptive Vγ4 T cell IFNγ responses, and that YopJ translocation into adaptive Vγ4 T cells inhibits IL-12 mediated STAT4 phosphorylation. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 10 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function Fig 5. YopJ inhibits IL-12p40 mediated STAT4 phosphorylation. (A) MLN cell suspensions from L. monocytogenes infected mice were stimulated with 10 MOI of WT, YopJC172A, or ΔYopB Y. pseudotuberculosis for 6 hours. Antibiotics were given 2 hours after stimulation. Vγ1.1/2- CD44hi CD27- γδ T cells were analyzed for pSTAT4 after stimulation. Representative contour plots are displayed. The graph depicts mean ± SEM and represents at least two independent experiments with 2–4 mice per group. (B) The same experimental setup was used as in (A), but Vγ1.1/2- CD44hi CD27- γδ T cells were analyzed for STAT4 protein after stimulation. Representative plots for mean fluorescent intensity (MFI) are displayed. The graph depicts mean ± SEM and represents two independent experiments with 2 mice per group. (C) MLN suspensions from L. monocytogenes infected mice were either treated or untreated with IL-12/23p40 neutralizing antibody prior to stimulation with 1 MOI of WT Yptb-βla for 6 hours. Antibiotics were given 2 hours post-stimulation. Vγ1.1/2- CD44hi CD27- γδ T cells were analyzed for pSTAT4 after stimulation. Representative contour plots are displayed. The graph depicts mean ± SEM and represents at least two independent experiments with 2–4 mice per group. (D) MLN cell suspensions from L. monocytogenes infected mice were stimulated with 10 MOI of WT or YopJC172A Y. pseudotuberculosis for 24 hours. Antibiotics were given 2 hours after stimulation. Supernatants were collected 24 hours post stimulation and IL-12p70 concentration was determined via ELISA. ��p < 0.0001, ��p < 0.001, ��p < 0.01, and �p < 0.05. A repeated measures one-way ANOVA was used for (A-C). Comparisons were performed to WT Y. pseudotuberculosis in (A) and as depicted in (B-D). https://doi.org/10.1371/journal.ppat.1010103.g005 Foodborne infection with YopJC172A Y. pseudotuberculosis induces IFNγ production in adaptive Vγ4 T cells To determine whether YopJ subverts adaptive γδ T cell function in vivo, foodborne infection with WT and YopJC172A Y. pseudotuberculosis was performed on naïve Balb/c mice. As YopJC172A Y. pseudotuberculosis is attenuated in vivo [82], a one log higher (2-4x108 CFU) infection dose of YopJC172A Y. pseudotuberculosis was administered to normalize the internal bacteria burdens in the MLN between infection groups (S7A Fig). While mice infected with WT and YopJC172A Y. pseudotuberculosis lost a similar amount of weight, mice infected with YopJC172A Y. pseudotuberculosis recovered slightly faster (Fig 6A). MLN were isolated 9 days after infection to evaluate adaptive γδ T cell function. Consistent with the ex vivo stimulation of L. monocytogenes-elicited Vγ4 T cells with Y. pseudotuberculosis, Vγ1.1/2- CD44hi CD27- γδ T cells from the MLN of YopJC172A Y. pseudotuberculosis infected mice displayed enhanced IFNγ production when stimulated ex vivo compared to their WT Y. pseudotuberculosis infected counterparts (Fig 6B and 6C). When the same infectious doses were used for both WT and YopJC172A Y. pseudotuberculosis (5x107 CFU), Vγ1.1/2- CD44hi CD27- γδ T cells from the MLN of YopJC172A Y. pseudotuberculosis infected mice also displayed enhanced IFNγ PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 11 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function Fig 6. Foodborne YopJC172A Y. pseudotuberculosis infection elicits an IFNγ response from Vγ4 T cells. (A-E) Balb/c mice were foodborne infected with WT (2-4x107 CFU) or YopJC172A Y. pseudotuberculosis (2-4x108 CFU). (A) Mouse weight was assessed daily after infection. (B and C) Nine days post infection, MLN were processed into single cell suspensions and stimulated with PMA/ionomycin in the presence of brefeldin A for 4 hours. Vγ1.1/2- CD44hi CD27- γδ T cells were analyzed for IFNγ production. Representative histograms are displayed. Graphs represent mean ± SEM and are pooled from 3 experiments with a total of 5–8 mice per group. (D) Mouse survival was assessed daily after infection. anti-IL-12p40 antibody was administered at 0.2 mg/mouse on 0, 2, 4, and 6 days post infection. A Kaplan-Meier survival plot is shown. Study endpoint was 9 days post infection. The data represent 2 independent experiments with a total of 9 mice per group. (E and F) Balb/c mice were foodborne infected with 2x109 CFU L. monocytogenes to elicit a Vγ1.1/2- CD44hi CD27- γδ T cell population in vivo. 30 days post infection, immunized mice were foodborne infected with WT Yptb-βla (2-4x109 CFU) or left uninfected. (E) CFU of WT Yptb-βla were enumerated in MLN 3 days post WT Yptb-βla infection. (F) Three days post foodborne WT Yptb-βla infection, Vγ1.1/2- CD44hi CD27- γδ T cells from the MLN were analyzed for Yop translocation using the CCF4-AM assay. Representative contour plots are shown. Yop translocation (Yop+) among the indicated populations is depicted as mean ± SEM and is pooled from 2 experiments with a total of 4 mice per group. ��p < 0.001, ��p < 0.01, and �p < 0.05. A repeated measures one-way ANOVA was used for (A and F) and an unpaired t- test was used for (B, C, and E). Experimental groups were compared to WT Y. pseudotuberculosis in (A-C), uninfected controls in (E), and Vγ1.1/2- CD44hi CD27- γδ T cells in (F). A logrank test was used for survival curves in (D). https://doi.org/10.1371/journal.ppat.1010103.g006 production compared to their WT Y. pseudotuberculosis infected counterparts (S7B Fig). These experiments demonstrate that the increased IFNγ observed was not a result of a higher infectious dose nor of a higher bacteria burden at the time of analysis. As IL-12/23p40 was required for adaptive Vγ4 T cell IFNγ production in vitro (Fig 5), the impact of IL-12/23p40 was assessed in vivo. Naïve Balb/c mice were infected with WT or YopJC172A Y. pseudotubercu- losis and treated with an IL-12/23p40 neutralizing antibody or isotype control. All YopJC172A Y. pseudotuberculosis infected mice treated with anti-IL-12/23p40 succumbed by day 8 post PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 12 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function infection (Fig 6D). This data suggests that IL-12 promotes the protective capacity of catalyti- cally inactive YopJ. Similarly, IL-12 contributed to the protection of mice infected with WT Y. pseudotuberculosis. Serum was also collected on days 2, 4 and 6 after infection to assess circu- lating IL-12p70 levels. IL-12p70 was detectable 6 days after YopJC172A Y. pseudotuberculosis infection but was mostly below the limit of detection after WT Y. pseudotuberculosis infection (Fig 6E). Collectively, these data suggest that IL-12 is important in the protection of mice infected with WT or YopJC172A Y. pseudotuberculosis. Foodborne infection with WT Yptb-βla was performed to determine whether Y. pseudotu- berculosis could target adaptive Vγ4 T cells with Yop translocation in vivo. Balb/c mice were foodborne infected with L. monocytogenes to elicit a population of Vγ1.1/2- CD44hi CD27- γδ T cells as described previously [37,83]. After a return to homeostasis at 30 days post L. monocy- togenes infection, mice were foodborne infected with WT Yptb-βla. Y. pseudotuberculosis bur- den was assessed in the MLN 3 days post foodborne infection to determine whether WT Yptb- βla could establish a productive infection where Vγ4 T cells reside. Indeed, infected mice con- tained detectable Y. pseudotuberculosis in the MLN 3 days after foodborne infection (Fig 6F). Analysis of the translocation of Yop into Vγ1.1/2- CD44hi CD27- γδ T cells, myeloid cells (CD11b+), and CD4 and CD8 T cells was performed. Consistent with our in vitro observations, Vγ1.1/2- CD44hi CD27- γδ T cells and myeloid cells contained translocated Yop in vivo (Fig 6G). Y. pseudotuberculosis was relatively inefficient at Yop translocation into CD4 and CD8 T cells (Figs 6G and S7C). Collectively, these data show that foodborne YopJC172A Y. pseudotu- berculosis infection of naïve mice elicits IFNγ production in adaptive γδ T cells and that Yop can be translocated into adaptive Vγ4 T cells in vivo. Discussion In this study, we assessed the subversion of an adaptive subset of γδ T cells specialized in the promotion of pathogen resistance at the intestinal mucosa through the production of anti- infective cytokines like IFNγ and IL-17A [37]. While limited evidence suggests that Yop effec- tors directly target T cells to subvert their function, we identified that the Y. pseudotuberculosis effector molecule YopJ directly inhibits IFNγ production from adaptive CD44hi CD27- γδ T cells to subvert host immunity in mice. Additionally, we demonstrate that circulating human Vδ2+ T cells are similarly inhibited by direct translocation of YopJ, demonstrating that the direct targeting of γδ T cells by Y. pseudotuberculosis to inhibit IFNγ production is a conserved pathway of immune evasion in humans. Thus, Y. pseudotuberculosis Yop effectors translocated into murine Vγ4 T cells and human Vδ2+ T cells directly subvert their anti-microbial functions and host immunity by limiting IFNγ production. While Yersinia mediated inhibition of conventional T cells has been previously reported, studies have largely focused on the indirect subversion of T cells that is mediated by transloca- tion of Yop effectors into myeloid cells [55,82]. For example, YopJ/P appears to primarily sub- vert conventional T cell function through indirect mechanisms associated with inhibiting DC [22,26]. On the contrary, the study of γδ T cells in the context of Y. pseudotuberculosis infection has been primarily limited to the potential antigens that drive γδ T cell recognition of infection [84–89]. After phagocytosis of pathogens, activated DC migrate to lymph nodes and present antigen to T cells. APC-derived IL-12 further shapes T cell responses by providing a critical signal dur- ing T cell activation [90]. Interestingly, Y. pestis can limit both the migratory capacity of DC and the production of IL-12 [91], and Y. enterocolitica can induce programmed death of DC and inhibit antigen presentation [22]. Y. pseudotuberculosis YopJ can also indirectly limit NK cell function by interfering with DC TLR4 signaling pathways and YopP in Y. enterocolitica PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 13 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function can limit NK cell function through STAT4 inhibition [21,92]. Given that IL-12 signaling pro- motes STAT4 phosphorylation and IFNγ production in γδ T cells [29,93] and Yersinia spp. can inhibit DC, a potential extrinsic mechanism emerges for Y. pseudotuberculosis to inhibit γδ T cell responses by suppressing DC functions. In line with these observations, IL-12p40 was criti- cal for the induction of phospho-STAT4 in Vγ4 T cells after stimulation with Y. pseudotuber- culosis in in vitro cultures. In contrast, Y. pseudotuberculosis and IL-12 were unable to directly elicit IFNγ production from a highly enriched population of in vitro expanded γδ T cells sug- gesting that IL-12 is required but not sufficient for Vγ4 T cell IFNγ production. However, stimulation of MLN cell suspensions with ΔYopB or YopJC127A Y. pseudotuberculosis elicited STAT4 phosphorylation among Vγ4 T cells suggesting that translocation of Yop effectors and YopJ in particular subverts Vγ4 T cell function. Tracking Yop translocation in vitro revealed that Vγ4 T cells that contain Yop had reduced pSTAT4 levels and inhibited IFNγ production. Importantly, Vγ4 T cells that did not contain Yop effectors from the same cultures expressed higher pSTAT4 levels and comparable IFNγ production as Vγ4 T cells stimulated with ΔYopB-βla Yptb. Additionally, IL-12 levels were comparable between cultures stimulated with WT and YopJC172A Y. pseudotuberculosis. Collectively, these data suggest that functional impairment of Vγ4 T cells was mediated by direct translocation of YopJ into Vγ4 T cells and cell intrinsic mechanisms in vitro. The YopJ effector family has been increasingly described by their acetyltransferase function on serine, threonine, and lysine amino acid residues [19,94]. Serine and threonine are com- mon targets of phosphorylation to propagate signaling cascades or elicit functional conse- quences. For example, phosphorylation of STAT4 leads to dimerization and transport to the nucleus to promote transcription of STAT4 target genes. Acetylation of these target residues may inhibit phosphorylation and downstream signaling events [70]. Thus, a potential mecha- nism of YopJ subversion of Vγ4 T cells is through acetylation of STAT4 to inhibit the phos- phorylation or dimerization of STAT4. Other potential targets of YopJ acetyltransferase activity are the IL-12R and Janus kinases upstream of STAT4 activation. YopJ has also been reported to have cysteine protease, lysine acetyltransferase, ubiquitin-like protein protease, and deubiquitinase activity that may provide other potential avenues for YopJ to modulate the function of Vγ4 T cells through STAT4 [94–96]. An important aspect of Y. pseudotuberculosis pathogenesis unveiled by this work is the pref- erential targeting of a specialized subset of γδ T cells for delivery of inhibitory Yop effector molecules. Y. pseudotuberculosis injected Yop effectors into adaptive γδ T cells in a similar pro- portion as macrophages and DC and to a much greater extent than conventional CD4 or CD8 T cells. Of note, Y. pseudotuberculosis has been reported to translocate Yop effectors more effi- ciently into Treg cells than conventional CD4 T cells at high MOI to modulate their function [65]. The preferential targeting of Vγ4 T cells in this system is associated with expression of the β1-integrin by adaptive Vγ4 T cells. Additionally, the majority of adaptive Vγ4 T cells are anatomically segregated from conventional T cells in the paracortex by their localization in the interfollicular and medullary areas of the gut draining lymph nodes [38]. The distinct localiza- tion of adaptive Vγ4 T cells may facilitate interactions with Y. pseudotuberculosis in vivo. Loss of the adhesin YadA but not Inv abrogated YopJ mediated γδ T cell inhibition, suggesting that Y. pseudotuberculosis utilizes YadA to target adaptive γδ T cells for Yop translocation, consis- tent with previous studies suggesting that the adhesin Inv is largely dispensable for Y. enteroco- litica virulence [61,97]. Interestingly, this appears distinct from the targeting of conventional CD4 T cells that relies on Inv and is enhanced in the absence of YadA [11,65]. While our data demonstrates that STAT4 phosphorylation is inhibited by YopJ, our RNA- Seq analysis suggests that other targets may also be affected. One of YopJ’s known targets is the MAPK family of proteins that can have broad effects on cell proliferation, differentiation, PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 14 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function survival, and apoptosis [18]. The MAPK pathway in CD4 T cells and NK cells may also pro- mote STAT4 activity and downstream IFNγ mRNA stabilization, respectively [98,99]. Our data demonstrate a broad impact of YopJ on adaptive γδ T cell proliferation, metabolism, cell cycle, RNA/DNA processing, and ER/Golgi processing gene expression networks. These path- ways may be regulated by MAPK family member activity [100–105]. Homer motif analysis identified other potential means by which YopJ may regulate IFNγ production. For example, Ets-1 is a T-bet cofactor and necessary for Th1 IFNγ responses [106]. Increases in ETS-domain family of transcription factor motifs were associated with type 3 innate lymphoid cells (ILC3) but not Th17 cells [107], which may suggest that one of the potential mechanisms of YopJ mediated inhibition may target conserved pathways in unconventional lymphocyte popula- tions. Many NK cell receptors are also expressed on γδ T cells and may facilitate TCR indepen- dent effector functions [108–110]. In line with this, our profiling demonstrates that YopJ limits gene expression of Nkg7, which has recently been reported to promote cytotoxic granule release and inflammation during infection and cancer [72], and Prf1, which encodes the pore forming molecule perforin necessary to deliver lytic machinery into target cells [111]. Finally, interactions with Y. pseudotuberculosis YopJ led to the upregulation of Ulbp1, which encodes a stress-induced NKG2D ligand, and Idi1, which encodes an enzyme in the mevalonate pathway. As human γδ T cells respond to phospho-antigens derived from the non-mevalonate pathway in bacteria and mammalian mevalonate pathway in humans [112], this may limit the removal of translocated cells through NK or γδ T cell sensing mechanisms and suggests a broad mecha- nism to subvert human immunity. Thus, our findings suggest that adaptive Vγ4 T cells provide dynamic anti-infectious immunity that is subverted by direct translocation of YopJ. A number of studies have highlighted the importance of IFNγ production from conven- tional CD4 and CD8 T cells, NK cells, and ILC3 for Yersinia resistance [40,43,113,114], although the in vivo relevance of Yop inhibition of conventional T cells has not been addressed. Foodborne infection with YopJC172A Y. pseudotuberculosis led to an enhanced response from Vγ4 T cells, including increased IFNγ production, that was associated with a more rapid recovery of weight. As IL-12 was critical for Vγ4 T cell derived IFNγ production in vitro, the role of IL-12 after foodborne infection of Balb/c mice was assessed. Consistent with previous studies [22,40,115,116], IL-12 appeared critical for protection against foodborne WT and YopJC172A Y. pseudotuberculosis infection. As serum IL-12 was only readily detectable after YopJC172A Y. pseudotuberculosis infection, increased IL-12 may also contribute to the enhanced IFNγ response from Vγ4 T cells in vivo. Finally, assessment of cell populations tar- geted for Yop translocation in vivo was comparable to the results from the ex vivo MLN cul- tures. The highest percentage of Yop+ cells were among CD11b+ cells and Vγ4 T cells. Given the low MOI used in our ex vivo studies with the WT Yptb-βla reporter and the lack of inhibi- tion observed in Vγ4 T cells that lack Yop effectors from the same culture conditions, it is likely that intrinsic Yop effects on Vγ4 T cells is a mechanism of inhibiting Vγ4 T cell function in vivo. Thus, while Y. pseudotuberculosis can use γδ T cell intrinsic mechanisms to subvert the γδ T cell IFNγ response, multiple mechanisms may be available for Yersinia spp. to subvert γδ T cell functions to aid pathogenesis in vivo. As we previously demonstrated that foodborne but not i.v. infection led to adaptive Vγ4 T cell responses [37], our physiologic foodborne Y. pseudotuberculosis infection model revealed novel aspects of Yersinia pathogenesis and adap- tive Vγ4 T cell biology. In summary, the Y. pseudotuberculosis effector YopJ directly inhibits essential anti-effective functions of murine Vγ4 T cells and human Vδ2+ T cells. Y. pseudotuberculosis targeted Vγ4 T cells in a T3SS- and YadA-dependent process to deliver Yop effectors directly into Vγ4 T cells. Ex vivo whole tissue cultures revealed that direct inhibition of Vγ4 T cell function was the major mechanism of Vγ4 T cell subversion. YopJ translocation led to a dramatic reduction in PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 15 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function STAT4 phosphorylation levels and IFNγ production, which is important for protection from Yersinia. YopJ also inhibited a broad anti-infective gene signature. Thus, these findings add substantial insight into YopJ effector functions on murine and human γδ T cells and the patho- genesis of foodborne Y. pseudotuberculosis infection. Materials and methods Ethics statement All animal experiments were conducted in accordance with the Stony Brook University Insti- tutional Animal Care and Use Committee and National Institutes of Health guidelines. Blood collection from healthy human donors was approved by the Institutional Review Board at Stony Brook University. Mice Female 8–12 week old BALB/cJ mice were purchased from the Jackson Laboratory. Mice were euthanized by CO2 inhalation. Human studies Blood was sampled from a total of 6 adult healthy human donors of either gender between the ages of 20 and 40. Studies were designed so no randomization to experimental groups was nec- essary. Donors provided written informed consent. Bacteria Bacteria strains used in this study include: Y. pseudotuberculosis on the 32777 background WT strain, WT32777c, YopJC172A, YopHR409A, ΔYopB, YopER144A, YopTC139A, ΔYopM, ΔYpkA, ΔYopK, WT Yptb-βla, and ΔYopB Yptb-βla. Y. pseudotuberculosis on the IP2666 background WT strain, ΔYopB, ΔInv, ΔYadA, ΔInv ΔYadA, and ΔYopB ΔInv ΔYadA. See Table 1 for details. All strains were stored in 25% glycerol stocks at -80˚C. For stimulations, Y. pseudotu- berculosis strains were cultured overnight at 28˚C and 220 RPM in LB media. The following morning, Y. pseudotuberculosis was sub-cultured 1:10 in LB and 50 mM CaCl2 at 37˚C and 220 RPM for approximately 2 hours. Stimulation doses were based on the OD600. Foodborne L. monocytogenes immunization L. monocytogenes (EGDe strain) expressing a mutation in the internalin A gene (InlAM) was used for foodborne infection to facilitate epithelial cell invasion [117]. InlAM L. monocytogenes was cultured overnight at 37˚C and 220 RPM in BHI media. The following morning, L. mono- cytogenes was sub-cultured 1:10 in BHI at 37˚C and 220 RPM for approximately 2 hours. Infec- tion doses were based on the OD600. Mice were food and water deprived for 4 hours. Approximately 0.5 cm3 bread pieces were inoculated with 2x109 CFU L. monocytogenes in 50 μL. Mice were monitored to ensure the inoculated bread was consumed within 1 hour. Mice that did not fully consume bread were removed from the study. Bacterial infection doses were confirmed by plating inoculum on BHI. Foodborne Y. pseudotuberculosis infection Y. pseudotuberculosis strains (Table 1) were cultured overnight at 28˚C and 220 RPM in LB media. Infection doses were based on the OD600. Mice were food and water deprived for 4 hours. Approximately 0.5 cm3 bread pieces were inoculated with 2-4x107 CFU for WT32777 PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 16 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function Y. pseudotuberculosis, 2-4x107–2-4x108 CFU for YopJC172A Y. pseudotuberculosis, or 2x109 CFU for WT Yptb-βla infection in 50 μL. Mice were monitored to ensure the inoculated bread was consumed within 1 hour. Mice that did not fully consume bread were removed from the study. Bacterial infection doses were confirmed by plating inoculum on LB. Single cell preparations, Y. pseudotuberculosis stimulations, and flow cytometry MLN from L. monocytogenes immunized mice were harvested 9 days after immunization and mechanically dissociated using a syringe plunger through a 70 μm cell strainer into a single cell suspension. Cells were resuspended in IMDM (Gibco) supplemented with 10% fetal bovine serum, 0.01 M HEPES, 100 μM non-essential amino acids (Gibco), 2 mM L-alanyl-L- glutamine dipeptide in 0.85% NaCl or 1x Glutamax (Gibco), and 1 mM sodium pyruvate. Cells were counted using a Vi-CELL Viability Analyzer (Beckman Coulter). Cells were stimu- lated in 96 well round-bottom tissue culture treated plates with various strains of Y. pseudotu- berculosis at 1 or 10 MOI (1 MOI for WT or ΔYopB Yptb-βla and 10 MOI for all other Y. pseudotuberculosis stimulations, unless otherwise indicated) at 37˚C/5% CO2. 100 U/mL of penicillin and 100 μg/mL of streptomycin were added to cells 2 hours post-stimulation. Cells were stimulated for a total of 24 hours or as indicated. BD GolgiPlug (BD Biosciences) was added 5 hours prior to the end of stimulation. If translocation was assessed, β-lactamase Load- ing Solutions kit (Invitrogen) was used to load CCF4-AM by incubating CCF4-AM at RT with cells for 1 hour in the dark. Cells were then processed for surface staining via incubation with live/dead stain, antibody, and Fc block (BioXcell) for 20 min in the dark at 4˚C. Antibodies used included antibodies specific to CD45, CD3, TCRδ, CD8, CD4, Vγ1.1, Vγ2, CD44, CD27, F4/80, CD11b, MHCII, CD11c, and CD29 (BioLegend). Cells were fixed, permeabilized, and stained with anti-IFNγ, anti-IRF8, or anti-STAT4 using BD Cytofix/Cytoperm kit (BD Biosci- ences) for intracellular cytokine staining. Functional γδ T cell analysis was done by stimulation with BD leukocyte activation cocktail (containing PMA, ionomycin, and brefeldin A; BD Pharmingen) for 4 hours prior to staining. Flow cytometry data were acquired using a BD LSRFortessa and analyzed by FlowJo software (BD Biosciences). Cell culture supernatant was analyzed for IL-12p70 using the BioLegend ELISA MAX Deluxe Set Mouse IL-12 (p70) kit per manufacturer instructions. Human γδ T cell response Blood was drawn and collected from healthy human donors in BD Vacutainer sodium heparin tubes (BD Biosciences). Blood was diluted 1:1 with 1x PBS at room temperature. Peripheral blood mononuclear cells (PBMC) were isolated from the buffy coat of Ficoll-paque PLUS gra- dient centrifugation (GE Healthcare) for 20 min at 1,400 × g without a brake. PBMC were washed with 1x PBS at room temperature and resuspended in IMDM supplemented with 10% fetal bovine serum, 0.01 M HEPES, 100 μM Non-essential amino acids (Gibco), 2 mM L-ala- nyl-L-glutamine dipeptide in 0.85% NaCl or 1x Glutamax (Gibco), and 1 mM sodium pyru- vate. Y. pseudotuberculosis strains were cultured overnight at 28˚C and 220 RPM in LB media the night prior. The following morning, Y. pseudotuberculosis was sub-cultured 1:10 in LB and 50 mM CaCl2 at 37˚C and 220 RPM for approximately 2 hours. Stimulation doses were based on the OD600. Cells were counted using a Vi-CELL Viability Analyzer (Beckman Coulter). Cells were stimulated in 96 round-bottom tissue culture treated plates with various strains of Y. pseudotuberculosis at 1 MOI at 37˚C/5% CO2. 1x penicillin and streptomycin (100 U/mL penicillin and 100 μg/mL streptomycin) were added to cells 2 hours post-stimulation. Cells were stimulated for a total of 24 hours or as indicated. BD GolgiPlug (BD Biosciences) was PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 17 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function added 5 hours prior to the end of stimulation. If translocation was assessed, β-lactamase Load- ing Solutions kit (Invitrogen) was used to load CCF4-AM by incubating CCF4-AM at RT with cells for 1 hour in the dark. Cells were then processed for surface staining via incubation with live/dead stain, antibody, and Fc block (BioXcell) for 20 min in the dark at 4˚C. Antibodies used included antibodies specific to Vδ2, CD3, TCRδ, (BioLegend). Cells were fixed, permea- bilized, and stained with anti-IFNγ using BD Cytofix/Cytoperm kit (BD Biosciences) for intra- cellular cytokine staining. Flow cytometry data were acquired using a BD LSRFortessa and analyzed by FlowJo software (BD Biosciences). Phospho-flow cytometry After surface staining for flow cytometry, cells were washed and stained for pSTAT4 using a methanol-based approach. Cells were fixed in 4% PFA/1.5% methanol for 30 minutes in the dark at 4˚C. Cells were then washed and incubated in methanol in the dark at 20˚C for 45 min- utes. After washing, cells were stained with anti-pSTAT4 (Y693)-PE (BD Biosciences) and washed once more. Flow cytometry data were acquired using a BD LSRFortessa and analyzed by FlowJo software (BD Biosciences). Enumeration of Y. pseudotuberculosis burden MLN were crushed and diluted in media prior to plating on LB agar. Total Y. pseudotuberculo- sis burden per organ was calculated. Sequencing and analysis Samples were prepared after Y. pseudotuberculosis stimulation as described above. Cell prepa- rations were stimulated with 10 MOI of WT or YopJC172A Y. pseudotuberculosis or 1 MOI of WT Yptb-βla for 24 hours. 500 Vγ1.1/2- CD44hi CD27- γδ T cells were flow sorted directly into a tube with NEBNext Cell Lysis Buffer and Murine RNase Inhibitor and processed for RNA sequencing using NEBNext Single Cell/Low Input RNA Library Prep Kit (Illumina). Sequenc- ing was performed at the Cold Spring Harbor Laboratory sequencing core on a NextSeq500. Fastq files were produced as an output of the sequencing files. Fastq were run through FastQC to perform quality control of transcripts prior to alignment. Fastq files were pair-ended aligned to GRCm38/mm10 by way of HISAT2 and output as .BAM files [118]. Raw counts of aligned transcripts were quantified with FeatureCounts [119]. Dimensionality reduction was per- formed with PCA analysis with the axes PC1 and PC2 in R-studio [120]. To determine differ- ential expression between samples, FeatureCounts raw count matrix was analyzed through DESeq2 with a parametric fitting normalized to the geometric mean of each individual gene across samples [121]. Cutoff values for significance and quality control were a p-value of <0.05 and FDR-value of <0.10, respectively. Significantly differentially expressed genes were visual- ized on a heatmap with a dendrogram that was clustered through average linkage. The distance measurement on the dendrogram used was through the Euclidean method. Overlapping expressions between gene differential expression sets were filtered with R-studio. Upregulated and downregulated genes from the differential expression analysis were separated with R-stu- dio and these Gene IDs were used for HOMER motif analysis [74]. Parameters of analysis of each gene used were 400bp preceding the initiation site and 100bp after the initiation site. The length of the motifs analyzed was set between 8 and 10. PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 18 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function γδ T cell purification γδ T cells were expanded in vitro according to published protocols [122]. The MLN and spleen were isolated and processed into a single cell suspension 9 days post foodborne L. monocyto- genes infection of Balb/c mice. Red blood cells were lysed with red blood cell lysis buffer or ammonium chloride for 1 minute and cells from the MLN and spleen were combined. γδ T cells were enriched by negative selection using the following rat IgG primary antibodies from BioLegend: anti-CD4 (clone GK1.5), anti-CD8α (clone 53–6.7), anti-B220 (clone RA3-6B2), anti-MHC-II (clone M5/114.15.2), and anti-CD11b (clone M1/70). The MACS goat anti-rat IgG kit (Miltenyi Biotec) was used per manufacturer instructions with MACS LD columns and a QuadroMACS magnet. Enriched cells were cultured in 48-well plates coated overnight with 5 ug/ml anti-TCRδ (clone GL3). Cells were cultured in RPMI 1640 supplemented with 25 mM HEPES (Gibco), 1x glucose (Gibco), 10 g/ml folate (Sigma Aldrich), 1x sodium pyruvate (Gibco), 5x105 M 2 beta-mercaptoethanol (Sigma Aldrich), 1x Glutamax (Gibco), 1x penicil- lin-streptomycin (Gibco), and 10% FBS with 100 U/ml recombinant human IL-2. After 2 days of culture, cells were transferred into new wells with the same culture media to rest for 5 days. Cells were then stimulated with 10 MOI YopJC172A Y. pseudotuberculosis with 0.1, 1, or 10 ng/ ml of recombinant murine IL-12p70 (Peprotech). In vivo anti-IL-12p40 antibody treatment and serum collection On the day of foodborne WT or YopJC172A Y. pseudotuberculosis infection and on day 2, 4, and 6 after infection, mice were treated with 0.2 mg of anti-IL-12p40 (clone C17.8; BioLegend) by i.p. injection. Blood was collected via tail vein on day 2, 4, and 6 after infection. Blood was incubated at ambient temperature for 30 minutes before being spun down at 1500G for 10 minutes at 4˚C. Serum was isolated and analyzed for IL-12p70 with the BioLegend ELISA MAX Deluxe Set Mouse IL-12 (p70) kit. Ex vivo anti-IL-12p40 and recombinant IL-12p70 treatments At the start of Y. pseudotuberculosis stimulation of MLN cell suspensions, cultures were treated with 10 μg/ml anti-IL-12p40 (clone C17.8; BioLegend) for neutralization. In other conditions, recombinant murine IL-12p70 (Peprotech) was added at the start of Y. pseudotuberculosis stimulation of MLN cell suspensions at 2, 10, or 50 ng/ml. Statistical analysis GraphPad Prism 6 software (GraphPad Software Inc.) was used for statistical analysis. The dif- ferences between the means were compared using the statistical analysis described in the asso- ciated figure legends. All the data are presented as mean ± SEM and p < 0.05 was considered significant. �p < 0.05, ��p < 0.01, ��p < 0.001, ��p < 0.0001. Supporting information S1 Data. Excel spreadsheet containing the underlying numerical data for Figs 1A–1C, 2A and 2B, 3A, 5A–5D and 6A–6F. (XLSX) S1 Fig. Use of the Yptb-βla reporter to track Yop translocation. (A) MLN suspensions from L. monocytogenes infected mice were stimulated with WT or ΔYopB Y. pseudotuberculosis con- taining a β-lactamase translocation reporter (Yptb-βla) for 2 hours and given antibiotics. Cells were loaded with CCF4-AM dye to measure β-lactamase activity. FITC indicates CCF4-AM PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 19 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function loaded cells without translocation (Yop-) and BV421 indicates CCF4-AM loaded cells with Yop translocation (Yop+). Vγ1.1/2- CD44hi CD27- γδ T cells were analyzed for Yop transloca- tion 2 hours post stimulation at an MOI of 10. Representative contour plots are displayed. (B) MLN from L. monocytogenes infected mice were stimulated with Yptb-βla for 2 hours and given antibiotics. Yop translocation was detected as described above. The indicated cell popu- lations were analyzed for Yop translocation 2 hours after stimulation. Representative contour plots are displayed. (C) MLN from L. monocytogenes infected mice were stimulated with Yptb- βla for 2 hours and given antibiotics. Vγ1.1/2- CD44hi CD27- γδ T cells were analyzed for Yop translocation 2 hours post stimulation at the indicated MOI and quantified for Yop transloca- tion. Data consists of one experiment with 2–10 mice/group and the graphs depict the mean ± SEM in (A-C). ��p < 0.0001, ��p < 0.001, and ��p < 0.01. A t-test was used for (A), and a repeated measures one-way ANOVA was used for (C). Comparisons were performed to ΔYopB Y. pseudotuberculosis in (A) and as depicted in (C). (TIF) S2 Fig. The majority of Vγ1.1/2- CD44hi CD27- γδ T cells and γδ T cells containing Yop express β1-integrin. (A) MLN from L. monocytogenes infected mice were isolated and pro- cessed into single cell suspensions. Vγ1.1/2- CD44hi CD27- γδ T cells, CD4 T cells, and CD8 T cells were analyzed for β1-integrin expression. (B) MLN suspensions from L. monocytogenes infected mice were loaded with CCF4-AM dye and stimulated with 10 MOI WT Y. pseudotu- berculosis containing a β-lactamase translocation reporter. CCF4-AM dye reports the occur- rence of β-lactamase activity and Yop translocation. γδ T cells that contain Yop (Yop+) or do not contain Yop (Yop-) were analyzed for β1-integrin expression 2 hours after stimulation. Representative histogram plots are displayed. Data is pooled from two experiments with a total of 7 mice/group and the graphs depict the mean ± SEM in (A-C). ��p < 0.0001 and ��p < 0.001. A repeated measures one-way ANOVA was used for (A) and a t-test was used for (B), and. Comparisons were done to adaptive γδ T cells in (A) and as depicted in (B). (TIF) S3 Fig. YopJ regulates the transcriptional profile of Vγ1.1/2- CD44hi CD27- γδ T cells. (A and B) MLN from L. monocytogenes infected mice were stimulated with 10 MOI of WT Y. pseudotuberculosis (WT) or mutant YopJ Y. pseudotuberculosis (YopJC172A) for 24 hours. Anti- biotics were given 2 hours post-stimulation. Five hundred Vγ1.1/2- CD44hi CD27- γδ T cells from each stimulation were flow sorted and processed for RNA sequencing. The heat map depicts upregulated genes in Vγ1.1/2- CD44hi CD27- γδ T cells after YopJC172A Y. pseudotuber- culosis stimulation and individual genes are listed. (C-E) Genes differentially expressed (down- regulated) that overlapped between RNA sequencing analyses as displayed in the Venn diagram in (C) to select for direct effects of YopJ on Vγ1.1/2- CD44hi CD27- γδ T cells. The heat map depicts downregulated genes in Vγ1.1/2- CD44hi CD27- γδ T cells from the analysis in (C). Individual genes are listed in (E). Each experiment was performed once with biologic replicates. The cutoff for gene significance was p < 0.05 and FDR < 0.10. (TIF) S4 Fig. YopJ does not inhibit IL-17A production in Vγ1.1/2- CD44hi CD27- γδ T cells. MLN cell suspensions from L. monocytogenes infected mice were stimulated with 10 MOI of WT, YopJC172A, or ΔYopB Y. pseudotuberculosis for 24 hours. Antibiotics were given 2 hours after stimulation and brefeldin A was added for the last 5–6 hours. Vγ1.1/2- CD44hi CD27- γδ T cells were analyzed for IL-17A production after stimulation. Representative histograms are displayed. The graph depicts mean ± SEM and represents two independent experiments with PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 20 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function 4 mice per group. A repeated measures one-way ANOVA was used. � p < 0.05. (TIF) S5 Fig. YopJ impacts IFNγ related transcription factor motifs but not STAT4 protein. (A) Homer motif analysis was performed on the RNA sequencing results for the YopJC172A and WT Y. pseudotuberculosis comparison from Fig 5. The panel highlights the top transcription factor motifs of the ETS, SP/KLF, and IRF family of proteins identified in YopJC172A stimulated Vγ1.1/2- CD44hi CD27- γδ T cells. (B) MLN from L. monocytogenes infected mice were stimu- lated with 10 MOI of WT, YopJC172A, or ΔYopB Y. pseudotuberculosis for 6 hours. Antibiotics were given 2 hours post-stimulation. Vγ1.1/2- CD44hi CD27- γδ T cells were analyzed for IRF8 levels with or without anti-IL12p40 neutralizing antibody. The graph depicts the percentage of IRF8 protein expression among Vγ1.1/2- CD44hi CD27- γδ T cells after WT, YopJC172A, or ΔYopB Y. pseudotuberculosis stimulation. Data depict two pooled experiments with a total of 8 mice/group and represents the mean ± SEM. (C) The genes from the RNAseq and Homer motif analysis in Figs 4F and 4G and S3B and S5A were compared to an existing STAT4 ChIP- on-chip dataset to identify common genes. Genes from our dataset that were represented in the top 1000 genes of the Chip-on-chip dataset are displayed. (D) STAT4 KO spleens are shown in maroon and WT spleens are shown in black in representative histograms. The graph depicts the MFI of STAT4 protein expression in bulk γδ T cells. Data depicts one experiment with 4 mice/group and represents the mean ± SEM. ��p < 0.0001, ��p < 0.001, ��p < 0.01, �p < 0.05. A repeated measures one-way ANOVA was used for (B), and a t-test was used for (D). Comparisons were performed as depicted in (B) and to Naïve WT in (D). (TIF) S6 Fig. IL-12 is insufficient to induce IFNγ and does not readily overcome the inhibition of YopJ. (A) γδ T cells enriched from the MLN and spleen of L. monocytogenes infected mice were expanded with plate bound γδTCR antibody for 2 days and rested for 5 days. After expansion, ~ 50% of cells were γδ T cells, and the majority of those were Vγ4 T cells. The enrichment summary reflects the mean enrichment from 4 samples. Afterwards, γδ T cells were isolated from cultures and stimulated with YopJC172A Y. pseudotuberculosis with 0.1, 1, or 10 ng/ml IL-12p70 for 24 hours. Antibiotics were added 2 hours after stimulation and brefel- din A was added for the last 5–6 hours. Histograms display IFNγ production from Vγ1.1/2- CD44hi CD27- γδ T cells under different culture conditions. Data depicts one experiment with 4 mice pooled and split into the indicated stimulation conditions. (B) MLN cell suspensions from L. monocytogenes infected mice were stimulated with 10 MOI of WT Y. pseudotuberculo- sis in the presence of 2, 10, or 50 ng/ml IL-12p70 or 10 MOI of YopJC172A Y. pseudotuberculosis for 24 hours. Antibiotics were given 2 hours after stimulation and brefeldin A was added for the last 5–6 hours. Vγ1.1/2- CD44hi CD27- γδ T cells were analyzed for IFNγ production. Rep- resentative histograms of IFNγ production from Vγ1.1/2- CD44hi CD27- γδ T cells are dis- played. The graph depicts mean ± SEM from one experiment with 4 mice per group �p < 0.05. A repeated measures one-way ANOVA was used for comparisons to YopJC172A Y. pseudotu- berculosis in (B). (TIF) S7 Fig. The impact of foodborne infection of mice with Y. pseudotuberculosis. (A) Balb/c mice were foodborne infected with the indicated doses of WT or mutant YopJC172A Y. pseudo- tuberculosis and tissues were analyzed 9 days post-infection. Bacteria burden was quantified from the MLN. Data reflect 3–5 mice per group pooled from 3 independent experiments and the graphs depict the mean ± SEM. (B) Balb/c mice were foodborne infected with the indicated doses of WT or mutant YopJC172A Y. pseudotuberculosis. Nine days post infection, MLN PLOS Pathogens \| https://doi.org/10.1371/journal.ppat.1010103 December 6, 2021 21 / 29 PLOS PATHOGENSYersinia pseudotuberculosis subversion of γδ T cell function suspensions from WT or YopJC172A Y. pseudotuberculosis infected mice were stimulated with PMA/ionomycin and brefeldin A for 4 hours. Vγ1.1/2- CD44hi CD27- γδ T cells were analyzed for IFNγ production. Representative histograms are displayed and quantified. Data depicts one experiment with 3 mice per group. (C) Balb/c mice were foodborne infected with WT (2- 4x107 CFU) or YopJC172A Y. pseudotuberculosis (2-4x108 CFU) and treated with 0.2 mg/mouse of anti-IL12p40 on days 0, 2, 4, and 6 post infection. IL-12p70 concentrations were determined from serum at days 2, 4, and 6 post infection. Data represent 2 independent experiments with a total of 9 mice per group. Serum samples were pooled into groups of 3 per experimental con- dition. (D) Balb/c mice were foodborne infected with 2x109 CFU L. monocytogenes to elicit a Vγ1.1/2- CD44hi CD27- γδ T cell population in vivo. 30 days post infection, adaptive Vγ1.1/2- CD44hi CD27- γδ T cells from the MLN of immune mice were analyzed for Yop translocation using the CCF4-AM assay. Representative contour plots are shown. Yop translocation (Yop+) among the indicated populations represents background staining as a negative control for Fig 6F. The graph depicts the mean ± SEM and is pooled from 2 experiments with a total of 4 mice per group. ��p < 0.0001, �p < 0.05. A one-way ANOVA was used for (A), and an unpaired t-test was used for (B). Comparisons were performed to uninfected in (A) and to 5x107 WT Y. pseudotuberculosis in (B). (TIF) Author Contributions Conceptualization: Timothy H. Chu, Camille Khairallah, James B. Bliska, Brian S. Sheridan. Data curation: Timothy H. Chu, Camille Khairallah, Jason Shieh. Formal analysis: Timothy H. Chu, Camille Khairallah, Jason Shieh. Funding acquisition: Brian S. Sheridan. Investigation: Timothy H. Chu, Camille Khairallah, Rhea Cho, Zhijuan Qiu. Methodology: Timothy H. Chu, Camille Khairallah, Yue Zhang, Onur Eskiocak, Semir Beyaz, Brian S. Sheridan. Project administration: Brian S. Sheridan. Resources: David G. Thanassi, Mark H. Kaplan, Semir Beyaz, Vincent W. Yang, James B. Bliska. Supervision: Brian S. Sheridan. Validation: Timothy H. Chu. Visualization: Timothy H. Chu, Brian S. Sheridan. Writing – original draft: Timothy H. Chu. Writing – review & editing: Timothy H. Chu, Camille Khairallah, David G. Thanassi, James B. Bliska, Brian S. Sheridan. References 1. Barnes PD, Bergman MA, Mecsas J, Isberg RR. 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10.1186_s12874-023-01902-y.pdf	Data Availability We provide R modules as the basis for the dashboard development on GitHub (https://github.com/CTU-Basel/viewTrial). Qualitative data that supported the development of the risk assessment and study dashboard is provided in the supplementary material.	Data Availability We provide R modules as the basis for the dashboard development on GitHub ( https://github.com/CTU-Basel/viewTrial ). Qualitative data that supported the development of the risk assessment and study dashboard is provided in the supplementary material.	Klatte et al. BMC Medical Research Methodology (2023) 23:84 https://doi.org/10.1186/s12874-023-01902-y BMC Medical Research Methodology Development of a risk-tailored approach and dashboard for efficient management and monitoring of investigator-initiated trials Katharina Klatte1, Suvitha Subramaniam1, Pascal Benkert1, Alexandra Schulz1, Klaus Ehrlich1, Astrid Rösler1, Mieke Deschodt2,3, Thomas Fabbro1, Christiane Pauli-Magnus1† and Matthias Briel1,4† Abstract Background Most randomized controlled trials (RCTs) in the academic setting have limited resources for clinical trial management and monitoring. Inefficient conduct of trials was identified as an important source of waste even in well-designed studies. Thoroughly identifying trial-specific risks to enable focussing of monitoring and management efforts on these critical areas during trial conduct may allow for the timely initiation of corrective action and to improve the efficiency of trial conduct. We developed a risk-tailored approach with an initial risk assessment of an individual trial that informs the compilation of monitoring and management procedures in a trial dashboard. Methods We performed a literature review to identify risk indicators and trial monitoring approaches followed by a contextual analysis involving local, national and international stakeholders. Based on this work we developed a risk-tailored management approach with integrated monitoring for RCTs and including a visualizing trial dashboard. We piloted the approach and refined it in an iterative process based on feedback from stakeholders and performed formal user testing with investigators and staff of two clinical trials. Results The developed risk assessment comprises four domains (patient safety and rights, overall trial management, intervention management, trial data). An accompanying manual provides rationales and detailed instructions for the risk assessment. We programmed two trial dashboards tailored to one medical and one surgical RCT to manage identified trial risks based on daily exports of accumulating trial data. We made the code for a generic dashboard available on GitHub that can be adapted to individual trials. Conclusions The presented trial management approach with integrated monitoring enables user-friendly, continuous checking of critical elements of trial conduct to support trial teams in the academic setting. Further work is needed in order to show effectiveness of the dashboard in terms of safe trial conduct and successful completion of clinical trials. Keywords Clinical trial, Trial management, Risk-tailored monitoring, Trial dashboard †Shared senior authorship. Correspondence: Katharina Klatte Katiklatte@icloud.com 1Department of Clinical Research, University Hospital Basel and University of Basel, Spitalstrasse 12, Basel CH- 4031, Switzerland 2Department of Public Health & Primary Care, KU Leuven, Leuven, Belgium 3Competence Centre of Nursing, University Hospitals Leuven, Leuven, Belgium 4Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. RESEARCHOpen AccessPage 2 of 11 Introduction Randomized controlled trials (RCTs) are the gold stan- dard for assessing the effects of medical interventions. However, they are typically resource intense and pose various organisational challenges [1–3]. Inefficient man- agement and monitoring of RCTs have been identified as an important source of waste [1–5]. Monitoring efforts are traditionally quite generic and extensive, [6–8] but problems such as slow participant recruitment, con- siderable losses to follow-up, or poor data quality are often recognized too late during trial conduct delaying necessary adjustments of processes or the protocol. In addition, resources for clinical trial monitoring and man- agement are usually scarce in the academic setting and sophisticated commercial solutions can be costly [9, 10]. Organisational challenges and critical factors jeopar- dizing trial integrity and quality may vary considerably across trials; therefore, a risk assessment conducted prior to trial initiation or at certain intervals during trial con- duct may yield different risk profiles for individual trials. Trial monitoring protects the safety and rights of partici- pants, ensures data are accurate, complete and verifiable, and that the trial follows the principles of good clinical practice [11, 12]. Currently recommended risk-based trial monitoring allows for an adaptation of the monitor- ing intensity according to an initial risk assessment of a trial and has been developed to reduce resource intense onsite visits with source data verification for non-high- risk trials [1–3, 13–15, 16, 17. However, this approach typically does not consider individual risk profiles of RCTs, but rather classifies trials by generic risk catego- ries [16]. To accommodate individual trial risks, a moni- toring strategy may include several components such as centralized monitoring (evaluation of accumulated trial data performed in a timely manner at a central location), onsite monitoring (performed at investigator sites with source data verification and review of protocol-specified processes), or remote monitoring (same tasks as onsite monitoring but performed away from investigator sites) [17, 18, 19]. Trial management should provide for smooth and reli- able trial procedures including participant recruitment, randomisation, intervention application, data collection, and data cleaning [20, 21]. Data cleaning and checking of recruitment and retention rates, for instance, need to be performed in a timely fashion, so that corrective mea- sures can be taken early on and detrimental effects on the trial can be avoided [22]. Trial monitoring is most effec- tive when performed on cleaned data, because incorrect processes may be missed due to poor data quality and monitoring efforts are wasted on individual data errors. Therefore, trial management and monitoring ideally are integrated tasks that make use of accumulating data dur- ing trial conduct, i.e. continuously keeping oversight of complex study processes and performing centralized data monitoring [23–25]. The objective of this project was to develop a risk- tailored approach that integrated trial management and monitoring in investigator-initiated RCTs. We closely collaborated with relevant stakeholders (trial coordina- tors, principal investigators, data managers, trial moni- tors, statisticians) to create a user-friendly dashboard that efficiently visualizes data on critical processes of individual trials. Methods Overview of research process In the first phase of this user-centred project, [26] we developed a concept of a risk-tailored trial monitor- ing and management approach with corresponding trial dashboard (Fig. 1). We anticipated users to be primarily trial managers, principal investigators, and trial moni- tors. The development involved relevant stakeholder groups and was based on the results of systematic litera- ture reviews on existing monitoring strategies, [17] and a contextual analysis to identify current practices and needs of anticipated users. The concept and dashboard were piloted and refined in an iterative process involving different end users and other stakeholder groups. In the second phase, we performed formal user testing of the developed risk assessment and dashboard. Experiences of investigators and trial staff of one medical and one surgi- cal investigator-initiated RCT were gathered using semi- structured interviews to further refine the concept and dashboard. Setting Before the introduction of the new concept, a risk assess- ment was routinely performed by the monitoring team to assess the extent of the monitoring needed for the trial according to the ADAMON criteria. This approach allowed the rough classification of trials into the catego- ries low, medium, or high risk [27]. The new risk assess- ment incorporates many more factors related to the study specific conduct including challenges in the study management. It is not meant to categorize trials and adjust the extent of monitoring based on the category. The trial teams included in our project were not involved in other pre-trial risk assessments. Both trial teams assessing the benefits of the risk assessment and dash- board tool had started participant recruitment and data collection before the implementation of the new tool and, thus, compared it to the situation without structured risk assessment and tool support.” Systematic literature review To identify and structure components for the initial risk assessment of individual trials, we systematically Klatte et al. BMC Medical Research Methodology (2023) 23:84 Page 3 of 11 Fig. 1 Overview of the two phases of the development and user-testing of the risk-tailored approach and trial dashboard searched for published risk assessment approaches and risk indicators used to support trial oversight and to identify centres in need for support. We considered dif- ferent components and qualitative evidence from process evaluations of tested monitoring strategies summarized in a previously conducted systematic review [17]. We further considered the guideline of the European Clini- cal Research Infrastructure Network (Ecrin) [16] and the risk assessment guideline developed by the Swiss Clinical Trial Organization [28], TransCelerate metrics [29, 30], Whitham metrics [31], and the trial specific metrics used by the Medical Research Council (MRC) Clinical Trials Unit (CTU) at University College London (UCL) Trial specific metrics [32]. Results from this literature review are summarized in Supplementary Table 1. groups provided an additional opportunity for feedback and exchange of information on the risk assessment and dashboard development as well as on the application strategy. In order to get input from a national group of stakeholders in Switzerland, we contacted the national platform of the Swiss Clinical Trial Organisation for trial monitoring. Finally, we gathered experiences from inter- national methodological research groups and UK-based CTUs using risk-based approaches or study dashboards to support trial conduct. The different activities with stakeholders at all levels are summarized in Supplemen- tary Table 2. We extracted information from protocols of meetings and interviews and summarized the output in Supplementary Table 3. Contextual analysis Stakeholder involvement We set up a local, multidisciplinary working group including end users and representatives of different stakeholder groups within the Department of Clinical Research (DKF) and associated research groups at the University Hospital Basel. At this local level, we involved members from the Data Science and Data Management Teams of the DKF experienced in central monitoring, R shiny applications, dashboard development, database structures and exports; we involved trial monitors with experience in on-site and remote monitoring, knowl- edge of study site structures and processes; study coor- dinators and investigators experienced in managing RCTs. Stakeholder meetings with all members of these Gathering contextual input from various end users and the above-mentioned stakeholders guided the devel- opment of the risk-tailored approach and helped to determine relevant domains and applications to be con- sidered in the initial risk assessment. We structured the identified stakeholder needs into content related fac- tors such as the inclusion of the follow-up visits into the risk assessment, and design related factors such as the suggested separation of severity and likelihood in the assessment or the colour code for the status of queries visualized in the dashboard (Supplementary Table 3). In terms of content of the risk assessment, it became clear, for instance, that the assessment covers a wide spectrum of risks applicable to a large variety of RCTs. The design Klatte et al. BMC Medical Research Methodology (2023) 23:84 of the risk assessment guide should support the intuitive assessment by different end user groups (monitors, study managers, principal investigators). The study dashboard should reflect the outcome of the risk assessment and the design of the dashboard should enable an efficient navi- gation within the routine study procedure by end-users. The findings of the contextual analysis are summarized in Supplementary Table 3. Development and piloting of the concept and dashboard Based on the systematically reviewed literature, our contextual analysis and stakeholder input, we drafted a generic risk-assessment template. We then created trial- specific dashboards for a medical and a surgical mul- ticentre trial that differed in their risk profile, but both comprised complex study procedures and data collec- tion. The risk-tailored approach continued to evolve as we gathered contextual information, detected gaps in the assessment procedure, and identified critical components of study management. We developed R code to extract data values from exported data tables of the trial database secuTrial and summarized, compared, and calculated rel- evant information to create pathways for the identified risks. The output of these operations was then visual- ized in the trial dashboard. The piloting and refinement was an iterative process incorporating repeated feedback from the end-users and the stakeholder representatives in the project group on dashboard content, structure, user-friendly interface, and visualization of critical study data. User testing The aim of the user testing was to identify challenges in the routine use of the dashboard experienced by different user groups. Each of the six users (i.e. 2 trial managers, 2 monitors, 2 principal investigators) received a detailed manual of the features and operation mode of the study dashboard. Table 1 Domains and their attributed risk elements Domain Participant Safety and Rights Overall Study Management Device/ Medication Management Study Data Risk Elements Informed consent AE/SAE reporting and documentation Inclusion/exclusion Recruitment Retention Study procedures and endpoint assessment (e.g. bio sampling, imaging quality) Participant schedule (e.g. timeframe of visits) AE/SAE management Administration Accountability/ storage Data quality – completeness, consistency, timeliness Documentation/ storage Abbreviations: AE, adverse event; SAE, serious adverse event Page 4 of 11 We interviewed users 6–12 weeks after using the study dashboard in daily trial routine. We followed a semi- structured interview guide, which allowed for expan- sion on topics that emerged during the interview. All interviews took approximately 30 min. The interviewer (KK) transcribed the recorded interviews and extracted suggestions for improvement. We then updated the trial dashboard based on the feedback of the users and pro- vided the adapted version for further use and evaluation. Results The final concept consisted of the following three steps: trial-specific risk assessment prior to study start, selec- tion and development of data-based pathways to address identified risks, and visualization of pathways output in a trial dashboard. Trial-specific risk assessment The four trial-specific risk assessment comprised domains (participant safety and rights, overall study management, device/medication management, study data), and each domain contained several risk elements (Table 1). To better assess if these elements are critical for a specific trial and which trial components are at par- ticular risk, we determined trial assets and corresponding risk scenarios. Trial assets are conditions essential for the successful and proper conduct of a trial, e.g. visits must be scheduled and take place in the required timeframe, Serious Adverse Events (SAEs) have to be reported on time and need to be closely followed over the whole study conduct. If a trial includes many follow-up visits over a long follow-up time and assessments have to take place in a very narrow time window, this asset would be con- sidered at risk (example shown in Table 2, Part A). Other assets, for example SAE reporting and oversight, are essential for all clinical trials and, thus, are considered as a risk that applies to all trials (marked in red, Example shown in Table 2, Part B). The identified risks are then analysed in terms of severity and likelihood. For exam- ple, if many follow-up visits need to be coordinated but the time window of the endpoint assessment is wide the severity is rated as less critical. The likelihood is highly influenced by the experience of the trial team and partici- pating centres with similar trials, training and experience of all involved staff members, and the resources available for the study. The complete list of assets, as well as the corresponding risk scenarios, is provided in the full risk assessment in Supplementary Table 4. We suggest that the risk assess- ment is done by an experienced trial manager (e.g. from a trials support unit) supported by a trial monitor, a clini- cal expert, and the principal investigator. The first risk assessment should be performed before the start of the trial based on the study protocol, Case Report Forms Klatte et al. BMC Medical Research Methodology (2023) 23:84 Table 2 Example of assets and risk scenarios for risk elements in the domain Overall Study Management (Part A) and Participant Safety and Rights (Part B). Assets that apply to all trials are marked in red A) Domain Overall Study Management Risk element Participant Schedule Asset Visits/Phone calls must be within the given Timeframe Risk scenario (A) Time point of visit is critical for the endpoint assess- ment of the study (B) Large number of visits are difficult to organize and coordinate between centres and patients B) Domain Participant Safety and Rights Risk element SAE/AE Asset SAE have to be re- ported and documented correctly in the required timeframe Risk scenario Complexity of CRF or missing SOPs for SAE Reporting leads to (A) Incorrect docu- mentation and (B) Delayed report- ing of SAEs Abbreviations: CRF, case report form; SOPs, standard operating procedures; SAE, serious adverse events (CRFs), the planned and actual budget of the study, expected recruitment rates for all participating centres, information on the trial intervention, and information about planned study staff (see Appendix for detailed Manual). Pathways to manage identified risks In order to continuously manage identified risks, we cre- ated pathways that eventually allowed for tailored visu- alization of accumulating trial data and implemented action at suitable time intervals (e.g., email reminders, staff overviews) in a study dashboard. The operations applied to the exported data tables via R code are depen- dent on the specific information needed to provide a clear oversight on identified risk elements. The code is structured into modules that contain the operations of all pathways visualized in one dashboard tab (e.g. SAE management). For example, the module SAE contains operations that count the number of SAEs, determine the number of patients with SAE and calculate the ratio SAEs per patient randomized. In addition, information like severity, causality and outcome are extracted from the SAE form data table and percentages of value options (e.g. SAE outcome: Continuing, Resolved without sequel, Resolved with sequel, others) are calculated and graphi- cally displayed (Fig. 2, Panel A and B). The developed study dashboards contain tabs that visualize the output of created pathways reflecting identified study-specific risks. These tabs are based on the R modules contain- ing the pathways as well as the code required for a clear Page 5 of 11 visual presentation (value boxes, graphs, lists). When pilot testing our risk assessment guide, it became appar- ent that some risks apply to almost all trials (marked in red in the full risk assessment Supplementary Table 4). The management of these risks is, thus, based on tabs classified as “generic” in the study dashboard, while other, more seldom and study-specific risks are considered in “optional” tabs (Table 3). The content of generic tabs can also be adapted depending on, for instance, the complex- ity or time point of outcome assessment in a trial. The generic dashboard template is freely available on GitHub (https://github.com/CTU-Basel/viewTrial). Visualization of data based pathways The output of the pathways is visualized in the corre- sponding tabs in the study dashboard. The arrangement of the tabs within the study dashboard can be determined by study teams; a division into study management related tabs and oversight/study progress tabs may provide a better overview for the different user groups (principal investigator, study manager, and trial monitor). The main tabs can also contain sub-tabs. For example, the num- ber of due visits is displayed under the visits tab in the sub-category “due visits”. In this context, the definitions of due, overdue, and missed visits are dependent on the specific timeframes of the study protocol. Total num- bers are provided as well as a list of the patient ID and a direct link to the corresponding eCRF in the database (Fig. 2, Panel A). Each tab or sub-tab can represent sev- eral pathway outputs displayed in form of value boxes, graphical presentations, or lists of relevant patients. For example, the SAE management tab provides an overview on SAE prevalence in boxes, and in additional panels the user can switch between the graphical representation of SAE severity, causality, and outcome. Additionally, a list of patients with SAE is provided below, displaying infor- mation on SAE status (e.g. ongoing/closed) and a short description of the event (Fig. 2, Panel B). The informa- tion is provided for the overall study, including all ran- domized patients as numbers and percentages in boxes, while graphs differentiating between centres are provided to better assess which centres are in need for support in a certain aspect of the study conduct. In addition, the dashboard allows filtering for specific centres and time ranges of interest or choosing particular study visits from drop down menus to provide users with more detailed information (see Supplementary Fig. 1 for an example). The output of the pathways visualized in the dash- board is based on a daily export of trial data and, thus, includes up-to-date information on randomised patients and entered data. The generic and some of the optional tabs are listed in Table 3. Examples of the tabs from the two study dashboards are provided in Supplementary Figs. 2–5. The generic dashboard is accessible via GitHub Klatte et al. BMC Medical Research Methodology (2023) 23:84 Page 6 of 11 Fig. 2 Dashboard screenshots of the Visits tab, sub-tab “Due visits” (Panel A), and the Safety management tab, sub-tab “Serious adverse events” (Panel B) and generic data is provided to test the different code modules behind each tab (examples provided in Supple- mentary Figs. 6 and 7). suggestions for further elements to be included in the dashboard. A detailed summary of the results from the user testing is provided in Supplementary Table 5. User testing The user testing of our study dashboards provided posi- tive feedback in terms of improved study oversight and facilitated conduct. Trial monitors and study staff agreed that the initial risk assessment was beneficial, because it increased the awareness of critical processes in the col- lection of outcome data, enabling corrective measures at an early time point, e.g. adaptation of database struc- tures. A clear benefit perceived by all user groups was the more frequent and improved communication with trial sites; sites were better prepared for remote or on-site monitoring visits, because many issues were recognized and solved in advance. In addition, users made several Discussion Using a systematic approach involving relevant stake- holder groups, we developed a concept of risk-tailored trial monitoring and management that focuses on the identification and control of trial specific risks during trial conduct. The continuous evaluation of most impor- tant risks provides important information about the study progress, e.g. in terms of recruitment, endpoint assessment, as well as in terms of data management and data quality, e.g. CRF completion, timeliness of follow- up visits. Completeness of essential data points as the basis for analysable patient data is continuously evalu- ated and trial monitors and study managers maintain an Klatte et al. BMC Medical Research Methodology (2023) 23:84 Table 3 Structure and content of dashboard tabs Domain Participant Safety and Rights Risk Elements Informed consent Example Tabs Informed consent AE/SAE reporting and documentation AE/SAE Inclusion/exclusion Safety Overall Study Management Recruitment Recruitment Patient Characteristics Retention Retention Study procedures and endpoint assessment Bio sampling (e.g. blood samples) Imaging quality Content of Tab In case of a re-consent this tab can provide an overview of patients patients who have previously not been able to give consent themselves Provides an overview of timeliness and completeness of AE/SAE entries In case of safety-relevant inclusion or exclusion criteria, a verification of relevant information available in the database can provide ad- ditional security (e.g. blood pressure has to be within a certain range – check for the entry of blood pressure in the database) Recruitment trajectories for expected and actual recruit- ment in total and per centre (Supplementary Fig. 2) Relevant patient character- istics are summarized and presented (e.g. gender, age, background of treatment) Patients who have ended the study resulting in missing outcome data, reasons for leaving the study, kind of data collected before study end (Primary outcome data avail- able) (Supplementary Fig. 3) Overview of samples taken and availability of sample results Automated and visual verifica- tion of imaging data quality, e.g., for MRI or CT Participant schedule: Follow-up visits Overview of follow-up visits AE/SAE management Safety manage- ment (SAEs, AEs) with a particular focus on visits where primary outcome data is collected. (Fig. 2, Panel A) The Safety tab provides an overview of SAEs and AEs that have been reported in the study and information on severity and outcome of SAEs/AEs (Fig. 2, Panel B) Page 7 of 11 Generic/Optional Optional Generic Optional Functionality/Purpose To ensure patient rights and support of re-consent process through site-specific reminders, list of patients that still need a re-consent. To ensure that all AE/SAE forms are complete and that the date of first entry is within the required reporting timeframe To provide the option for addi- tional checks for inclusion/ exclu- sion criteria besides the marked list of criteria in the eCRF To monitor the progress of partici- pant recruitment enabling early action in case of slow recruitment. Generic Generic Generic Optional Optional Optional Generic To inform the study team on the accuracy of inclusion/exclusion criteria and provide an overview of the sample population in terms of relevant characteristics To monitor the progress of partici- pant retention, consider reasons for ending study in recruitment. Time point of ending the study important for amount of data analysable. To support sample management in terms of localization and status of bio sample. Important for biomarker determination. To enable early adjustments in case of low quality imaging data and ensure that the imaging data is analysable. To assist in integrating follow-up visits on time into the daily clinical routine might be difficult for trial sites. Support through remind- ers for due visits can be initiated through the dashboard. To estimate potential safety issues (e.g. SAEs occurring more often in one study arm, number of SAEs in total, number of patients with SAE) Klatte et al. BMC Medical Research Methodology (2023) 23:84 Table 3 (continued) Domain Device/ Medication Management Risk Elements Administration Accountability/ storage Example Tabs Medication Study Data Data Quality Data quality – com- pleteness, consis- tency, timeliness Documentation/ storage Content of Tab Overview of medication con- sumption based on number of patients and their current position in the medication plan per protocol and com- parison with IMP stock at sites Completeness of forms (Primary end point, secondary endpoint, SAE/AE forms) Timeliness of data entry, Number of queries, status of queries (open, resolved) (Supplementary Figs. 4,5) Page 8 of 11 Functionality/Purpose To assist in the managing of IMP stock overview and enable reminders for restocking Generic/Optional Optional Generic To increase awareness of items missing in the database Trial sites may have different chal- lenges when integrating a trial in their daily clinical routine and therefore need support in differ- ent aspects of the study conduct. Completeness and timeliness of data entry as well as query man- agement constitute indicators for need of support. Query status helps the study monitor to decide which centre needs more assistance/ on-site visit. Abbreviations: AEs, adverse events; CT, computerized tomography ;IMP, investigational medicinal product; MRI, magnetic resonance imaging; SAEs, serious adverse events overview of visit timeframes, SAE reporting, and query management. Strengths and limitations Strengths of our study are the systematic and structured process of development of the risk assessment and the trial dashboard, which included the involvement of all local stakeholder groups and the performance of a com- prehensive contextual analysis. In addition, the devel- opment was based on prior evidence gathered through systematic literature searches and exchange with interna- tional stakeholder groups. Directly involving end users in developing and evaluating the usability of our tool may facilitate the implementation process, promote wider adoption, maintain involvement, and increase user satis- faction with the concept as well as the tool [33]. Providing an R code repository for other study teams that can be adapted and applied to differently structured databases, constitutes a software-independent, affordable approach for the limited budget of investigator-initiated trials. Our study has the following limitations: First, we per- formed user testing in two ongoing RCTs only, and, thus, the spectrum of feedback may have been limited and may compromise the extrapolation of mentioned ben- efits and disadvantages to other trials. Both RCTs had already started participant recruitment when the dash- board was implemented. This allowed for a qualitative comparison of management and monitoring processes without and with the dashboard tool in place. However, it will be crucial to subsequently evaluate the impact and value of the study dashboard during the entire course of a clinical trial. Since both RCTs are still ongoing, we could not evaluate the impact of the tool on participant safety and overall trial success, including the percentage of analysable data, at the end of a trial. Lastly, we have not yet evaluated any cost-effectiveness of our developed approach, e.g. assessing whether the dashboard has the potential to reduce monitoring and management hours needed to ensure a safe and successful trial conduct. While some users felt that our dashboard would only be worthwhile for multicentre trials, others found that the costs of providing a study dashboard will always depend on the needs and preferences of the study team and the complexity of the study. Comparison with similar studies and frameworks Following the recommendations of the Clinical Trials Transformation Initiative (CTTI), effective and efficient monitoring and management needs to first determine what matters for a specific trial and focus on areas of highest risk for generating errors that matter [34, 35]. With our risk assessment guide and the study dashboard we address the need for this focus and provide a tool that supports the continuous oversight of the quality of the trial conduct. Dashboards that visualize time-dependent parameters have recently met a growing acceptance in medical and administrative health care settings [36–43]. Dashboards have been introduced to support various aspects of clinical trials, including web applications for eligibility screening and overview of the enrolment progress [41], web-based support of recruitment management and Klatte et al. BMC Medical Research Methodology (2023) 23:84 Page 9 of 11 communication; [42] graphical summaries and diagrams of the progress of patient accrual and form completion [43], feedback on data completeness by using a traffic light system [44], and automated reports of data compli- ance, protocol adherence and safety [45]. These available dashboards typically focus on specific elements of trial conduct and communication with trial sites; however, our dashboard provides a comprehensive overview of all elements of a trial identified as critical. In addition, tables and graphical representations are often limited to certain time intervals [41]. The daily export of trial data providing up-to-date trial information is part of the core idea of our approach as it enables immediate actions and improves communication with site staff. Various methods for assessing the risk of non-conform trial conduct at trial sites including central statistical monitoring have been introduced in the academic set- ting with increasing prevalence [46]. Most methods use statistical testing of all or a subset of trial data items to compare sites and identify atypical trial centres. While many methods focus on the detection of data errors and fraud, [47] triggered monitoring is frequently used to direct on-site monitoring to atypical trial sites [46]. In our approach components of central data evaluations are used to assess whether actions are required constituting some sort of triggered intervention. However, the data evaluation is not based on statistical testing, it is rather an assessment of trial progress (recruitment, retention), management challenges, and conform data collection progress. It is also not intended to categorize trials and predetermine the extent of on-site monitoring [48]. Our concept focuses on directing attention to the most criti- cal areas of a trial and should help to minimize and tailor on-site monitoring. Several commercial solutions supporting the over- all trial conduct in various aspects are readily available [9, 49–53], but for investigator-initiated trials with tight budgets such software packages typically remain unaf- fordable. We wanted to provide a comprehensive and affordable option for investigator-initiated trials that can be adapted to individual needs and preferences and fur- ther developed by the research community. Therefore, we transparently present all details of the structured risk assessment and manual as well as the generic code for our dashboard in publicly accessible repositories via GitHub. We invite users to report difficulties or sugges- tions for improvement for consideration in future modifi- cations of the generic dashboard via GitHub. Implications Besides the emphasis on the feasibility and design of clin- ical trials, measures to increase the efficiency of clinical trial conduct are needed [54]. Current challenges include premature discontinuation of a significant proportion of clinical trials, and inflated costs mainly due to delayed recruitment and organisational issues [54]. We propose a comprehensive approach integrating management and monitoring of a clinical trial into one risk management tool supporting the conduct of investigator-initiated trials. Overseeing the progress of a trial in each centre based on up-to-date information, provides the opportunity for trial monitors to prioritize centres for on-site visits or remote interactions, tailor their action to the specific issues of a centre, and guide decisions on where resources and training is needed the most. In addition, providing automated reminders for upcoming visits or sampling, overview of investigational medicinal product supply, overview of patients who need a re-consent, overview of ongoing SAEs, etc. could increase the efficiency of the trial management processes. The tool further provides the opportunity to improve the overall communication between the study team and trial sites and may increase motivation through the involvement of sites in the trial progress and the option to compliment active partici- pation in the trial. The dashboard tool is intended to address site-level monitoring, trial-wide monitoring, and finding per-patient issues. Feedback from the user testing also revealed a positive perception of study managers and investigators to improved data quality visible in the dash- board: “If incomplete is empty, I am at ease. The impact of this tool is largely dependent on the suc- cessful implementation into clinical trial practice. The perception of benefits and opportunities by stakeholders and end-users have been collected while the effectiveness of the tool in terms of analysable data collected, timeline of recruitment, conformity of SAE/Adverse Event (AE) reporting and documentation, support of the overall study management still have to be evaluated. The next step is now to implement the risk assessment as a routine step in the joint planning of clinical trials with the respective study teams. The timely generation of a dashboard on the basis of the generic template and further study-specific risks has to be organized. Strate- gies to further evaluate this implementation process as well as the effectiveness of this new approach in studies of different design and structure have to be developed. As an implementation outcome, the amount of studies tak- ing advantage of the study dashboard in relation to the studies for which a dashboard was recommended could be assessed along with the frequency of risk assessments performed per trial. The effectiveness of the concept of risk assessment and dashboard tool will be evaluated based on structured feedback from study teams on their experience and quantitative measures of the trial, e.g. proportion of analysable patients/data at the end of the trial. These evaluations will provide more information on the feasibility of study-specific dashboards supporting Klatte et al. BMC Medical Research Methodology (2023) 23:84 trial monitoring and management in the heterogeneous field of clinical trials. Conclusion In summary, the presented risk-assessment guide and dashboard tool provide a systematically developed and user-tested instrument for the risk-tailored support of trial monitoring and trial management. Feedback from the user testing of the instrument revealed many benefits for the involved stakeholder groups. However, the effec- tiveness of the dashboard in terms of a safe trial conduct and overall support for a successful completion of clinical trials needs to be further evaluated. Supplementary Information The online version contains supplementary material available at https://doi. org/10.1186/s12874-023-01902-y. Supplementary Material 1 Supplementary Material 2 Acknowledgements We would like to thank all stakeholders that provided feedback in the development process of our risk assessment and the study dashboard. Author contributions K.K., M.B. and C.P.M. wrote the main manuscript text and K.K. prepared the figures. P.B., A.S., K.E., A.R. and T.F. were involved in the development of the concept, the risk assessment and dashboard content. S.S. and K.K. developed the study-specific dashboards and the generic dashboard. K.K. conducted the interviews for the user testing. All authors reviewed the manuscript. Funding Open access funding provided by University of Basel Data Availability We provide R modules as the basis for the dashboard development on GitHub (https://github.com/CTU-Basel/viewTrial). Qualitative data that supported the development of the risk assessment and study dashboard is provided in the supplementary material. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing Interest The authors declare no competing interests. Received: 10 September 2022 / Accepted: 23 March 2023 References 1. Yusuf S. Randomized clinical trials: slow death by a thousand unnecessary policies? CMAJ 2004;171(8):889 – 92; discussion 92 – 3. doi: https://doi. org/10.1503/cmaj.1040884 [published Online First: 2004/10/13] Page 10 of 11 3. 4. 2. Eisenstein EL, Lemons PW 2nd, Tardiff BE, et al. Reducing the costs of phase III cardiovascular clinical trials. 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10.1093_nar_gkad331.pdf	DA T A A V AILABILITY The online r esour ce is available without restriction at https: //www.flyrnai.org/tools/pangea/	null	Published online 1 May 2023 Nucleic Acids Research, 2023, Vol. 51, Web Server issue W419–W426 https://doi.org/10.1093/nar/gkad331 PANGEA: a new gene set enrichment tool for Drosophila and common research organisms Yanhui Hu 1 , 2 ,* , Aram Comjean 1 , 2 , Helen Attrill 3 , Giulia Antonazzo 3 , Jim Thurmond 4 , Weihang Chen 1 , 2 , Fangge Li 2 , Tiffany Chao 2 , Stephanie E. Mohr 1 , 2 , Nicholas H. Brown 3 and Norbert Perrimon 1 , 2 , 5 ,* 1 Department of Genetics, Blavatnik Institute, Harvard Medical School, Harvard University, Boston, MA 02115, USA, 2 Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA, 3 Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge CB2 3DY, UK, 4 Department of Biology, Indiana University, Bloomington, IN 47405, USA and 5 Ho w ard Hughes Medical Institute, Boston, MA 02138, USA Received February 21, 2023; Revised March 28, 2023; Editorial Decision April 17, 2023; Accepted April 29, 2023 ABSTRACT GRAPHICAL ABSTRACT Gene set enrichment analysis (GSEA) plays an im- portant role in large-scale data analysis, helping sci- entists discover the underlying biological patterns o ver -represented in a gene list resulting fr om, f or e xample, an ‘omics’ stud y. Gene Ontology (GO) an- notation is the most frequently used classiﬁcation mechanism for gene set deﬁnition. Here we present a new GSEA tool, PANGEA (PAthwa y, Netw ork and Gene-set Enrichment Analysis; https://www.ﬂyrnai. org/ tools/ pangea/ ), developed to allow a more ﬂexi- ble and conﬁgurable approach to data analysis us- ing a variety of classiﬁcation sets. PANGEA allows GO analysis to be performed on different sets of GO annotations, for e xample e xcluding high-throughput studies. Beyond GO, gene sets for pathway anno- tation and protein complex data fr om v arious re- sources as well as expression and disease anno- tation from the Alliance of Genome Resources (Al- liance). In addition, visualizations of results are en- hanced by providing an option to view network of gene set to gene relationships. The tool also al- lows comparison of multiple input gene lists and ac- companying visualisation tools for quick and easy comparison. This new tool will facilitate GSEA for Drosophila and other major model organisms based on high-quality annotated inf ormation av ailable f or these species. INTRODUCTION Modern genetics and genomics owe much to work done us- ing common model organisms. These models continue to make a significant contribution to the understanding of de- velopment, metabolism, neuroscience, behaviour and dis- ease. With the onset of the ‘big data’ era has come a need for analysis platforms that deconvolute complex data from multispecies studies. Model organism databases (MODs) are knowledgebases dedicated to the cur ation, stor age and integration of species-specific data for their r esear ch com- munity. The past decade has seen a number of efforts aimed at pulling together model organism and human data to facilitate a more inter disciplinary approach; e xamples in- clude MARRVEL (Model organism Aggregated Resources for Rare Variant ExpLoration) ( 1 ), Gene2Function ( 2 ) and * To whom correspondence should be addressed. Tel: +1 6174327672; Fax: +1 6174327688; Email: perrimon@receptor.med.harvard.edu Correspondence may also be addressed to Yanhui Hu. Tel: +1 6174327672; Fax: +1 6174327688; Email: claire hu@genetics.med.harvard.edu C (cid:2) The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creati v e Commons Attribution License (http: // creati v ecommons.org / licenses / by / 4.0 / ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. W420 Nucleic Acids Research, 2023, Vol. 51, Web Server issue the Monarch Initiati v e ( 3 ). Furthermore, the Alliance of Genome Resources (Alliance) ( 4 ), a consortium of se v en model organism databases and the Gene Ontology (GO) Consortium (GOC), formed recently with the objecti v e of building an umbrella resource from which users can navi- gate combined data within a single integrated knowledge- base. To help support such a r esour ce, the MODs ar e work- ing to reduce the di v ergence in the way the primary data is curated, stor ed and pr esented to facilita te compara ti v e and translational r esear ch. Although these integrated r esour ces allow sear ch and comparison across certain data classes, large-scale data analysis remains in the domain of stand-alone bioinfor- matic tools such as DAVID ( 5 ), TermMapper ( 6 ), GOrilla ( 7 ), PANTHER Gene List Analysis ( 8 ), WebGestalt ( 9 ) and g:Profiler ( 10 ), with a focus on processing gene list to extract a statistical measure of shared biological features, usually termed gene set enrichment analysis (GSEA). The most frequently used gene set classification in GSEA is GO annotation, which is based on the most widely-used on- tology (a hierarchical controlled vocabulary) in biological r esear ch for the wild-type molecular function(s), biologi- cal process(es) and cellular component(s) associated with a gi v en gene product ( 11 , 12 ). Many GSEA tools also incor- pora te classifica tions from other sources such as Reactome ( 13 ) and KEGG ( 14 ) pathways (e.g. DAVID, WebGestalt and g:Profiler) and, for human studies, there may be addi- tional data sources such as the Human Phenotype Ontol- ogy (HPO) ( 15 ) and Online Mendelian Inheritance in Man (OMIM) ( 16 ) (e.g. WebGestalt). The addition of gene sets beyond the GO allows users to extract more classification information, as well as seek trends and overlap in the en- riched sets. DAVID and g:Profiler are two of the few re- sources that make it possible for users to compare differ- ent sets on the same display; howe v er, the ability to interact with the results post-processing at these r esour ces is rather limited. Despite the abundance of tools, we found that they do not fully meet community needs, primarily because they were overly focused on human gene data and were not us- ing the most up to date data on other species. For exam- ple, Reactome pa thway annota tion is based on computa- tional predictions deri v ed from manually curated human pathways. Ther e ar e a few or ganism-tar geted analysis tools; the prokaryote-centred GSEA FUNAGE-Pro ( 17 ) is one example in which the underlying knowledgebase was as- sembled to cater to the needs of a specific r esear ch commu- nity. A number of useful gene classification r esour ces (e.g. pathways , complexes , gene groups) have been developed by the Drosophila RNAi Screening Center (DRSC) and Fly- Base, the Drosophila knowledgebase ( 18–22 ). Furthermore, in FlyBase and indeed across the MODs, ther e ar e se v eral types of curated data in common, including disease mod- els, phenotypes and gene expression, which could be used for GSEA. In contrast to the annotation of gene function data with GO, which is done in a consistent manner across multiple organisms, other data types ar e r epr esented within the MODs in di v erse ways, reflecting some of the technical differences in the genetics of these organisms. The Alliance was founded to integra te da ta across many MODs ( 4 ) and now provides a source of harmonised data that can also be used for GSEA. To take full advantage of the r esear ch in di v erse model organisms, we describe our creation of a new tool that we name PANGEA. Although our primary focus was on Drosophila genes, we de v eloped PANGEA to also include r at, mouse, zebr afish, nematode worm data, as well as harmonised human data to facilitate translational r esear ch. PANGEA not only incorporates additional gene set classifications from Alliance and MODs, but also have implemented the features that enhance the presentation of enrichment results by allowing the user to select sets and compare them visually to facilitate interpretation as well as making it easy to do parallel GSEA for multiple gene lists. This fle xibility enab les users to adapt the tool to their needs and allow ‘fortuitous’ discovery by widening the pool of knowledge for the purpose of analysis. MATERIALS AND METHODS Building the knowledgebase for PANGEA The gene set classification is a way to group genes based on commonality such as the same biological pathway. We have collected > 300 000 gene sets from various public r esour ces (Tables 1 , 2 ) for fruit fly D. melanogaster , the nematode worm C. elegans , the zebrafish D. rerio , the mouse M. mus- culus , the rat R. norvegicus and human H. sapiens . For an- notations based on a controlled vocabulary arranged in hi- erar chical structur e, such as gene group and phenotype an- notations from FlyBase, gene-to-gene set relationships were assembled after the hierar chical structur e was flattened. An exception was made for GO annotations, which were as- sembled in two ways, with and without being flattened, al- lowing users to choose which output is used in the anal- ysis. GO annotations include evidence codes that indicate the type of evidence supporting the annotation. For exam- ple, ‘IDA’ means that an annotation was supported by a dir ect assay, wher eas ‘ISS’ means that the annotation was inferred from sequence or structural similarity. Using such evidence codes, GO gene sets were built with additional configurations: (i) subsets based on experimental evidence codes, i.e. excluding annotations only based on phyloge- netic, sequence or structural similarity and other computa- tional analyses (IEA, IBA, IBD, IKR, IRD, ISS, ISO, ISA, ISM, IGC, RCA); (ii) subsets excluding annotations only supported by high-throughput (HTP) evidence codes (HTP, HDA, HMP, HGI and HEP); (iii) subsets of GO generic terms (GO slim) provided by the GOC ( http://geneontology. or g/docs/do wnload-ontology/#subsets ); (iv) subsets of very high-le v el GO term classifications used by FlyBase and the Alliance originally generated to support GO summary rib- bon displays. For Drosophila phenotype annotations from FlyBase, we assembled the gene-to-phenotype association using the ‘genotype phenotype data’ file available in the FlyBase Downloads page, in which phenotypes are associ- ated with individual genotypes and controlled vocabulary identifiers are indicated. This allowed us to extract only those genotypes where we could be certain that the phe- notype was associated with the perturbation of a single Drosophila melanogaster gene (i.e. single classical or in- sertional alleles). Because different resources use different gene or protein identifiers, we used an in-house mapping Nucleic Acids Research, 2023, Vol. 51, Web Server issue W421 pr ogram to synchr onise IDs to NCBI Entrez gene IDs, offi- cial gene symbols and gene identifiers of species-specific re- sources, such as MGI and FlyBase (Table 1 ). The PANGEA knowledgebase stores the information of gene set classifica- tion, gene annotation obtained from NCBI as well as the inf ormation f or ID mapping among various r esour ces. Building gene sets of pr eferr ed tissue expression To study the di v ersity and dynamics of the Drosophila transcriptome, the modENCODE consortium sequenced the transcriptome in twenty-nine dissected tissues ( 23 ) and the processed datasets are available at FlyBase ( http: //ftp.flybase.net/r eleases/curr ent/pr ecomputed files/genes/ ). A program was implemented in the Python programming language to identify genes expressed at a substantially higher le v els in one tissue versus any other tissue. This pr ogram first gr oups the RNA-seq datasets based on tissue. For example, all data related to the nervous system are grouped together. It then calculates the average reads per kilobase per million mapped r eads (RPKM) expr ession values for each gene in each tissue group. Genes were identified as pr efer entially expr essed in a gi v en tissue group if their average expression in the tissue group is 3-fold or higher than the av erage e xpression in any other tissue group. Genes with average RPKM value lower than 10 were excluded. Genes defined in this way as ‘tissue-specific’ then get annotated with the relevant tissue to generate the tissues expression classification set. Datasets used for testing Drosophila cell RNAi screen phenotype data was obtained from the DRSC ( https://www.flyrnai.org/ ) via download of a file of all available public screen ‘hits’ (results) ( https: //www.flyrnai.org/RN Ai all hits.txt ). RNAi reagents of op- timal design were selected. The criteria for optimal design were no CAN or CAR repeat, fewer than six predicted OTEs (off-target alignment sites of 19 bp) and a single gene target. CAN and CAR r epeats ar e thr ee base tandem r e- peats such as CAA CAGCA CCAT (CAN repeat, the third position can be A, G, C or T) and CAACA GCA GCAA (CAR repeat, the 3rd position can be A or G). RNAi r eagents wer e mapped to curr ent FlyBase gene identifiers using a DRSC internal mapping tool. Screens focused on major signalling pathways were selected for PANGEA anal- ysis ( 24–29 ). Proteomics data was obtained from Tang et al. ( 30 ) and high-confident prey proteins identified by mass-spec (Supplementary Table S2) were used for analy- sis. Gene set enrichment statistics used at PANGEA Hypergeometric testing is performed to calculate P val- ues for GSEA using the PypeR function in R. Bonfer- roni correction for multiple statistical tests, Benjamini- Hochberg procedure for false discovery rate adjustment, and Benjamini-Yekutieli procedure for false discovery rate adjustment were performed using the p.adjust function in R. Web tool implementation PANGEA is a SaaS (Software as a Service) w e b tool ( https: //www.flyrnai.org/tools/pangea/ ) and is built following a three-tier model, with a w e b-based user-interface at the front end, the knowledgebase at the backend, and the busi- ness logic in the middle tier communicating between the front and back ends by matching input genes with gene sets, doing statistical analysis and building visualization graphs. The front page is written in PHP using the Sym- f on y frame wor k and front-end HTML pages using the Twig template engine. The JQuery JavaScript library is used to facilitate Ajax calls to the back end, with the DataTables plugin f or displa ying tab le vie ws and Cytoscape and Veg- aLite packages for the da ta visualisa tions. The Bootstrap frame wor k and some custom CSS are used on the user in- terface. A mySQL database is used to store the knowledge- base. Both the w e bsite and databases are hosted on the O2 high-performance computing cluster, which is made avail- able by the Research Computing group at Harvard Medical School. RESULTS Pr epar ation of the classified gene sets for GSEA: the PANGEA knowledgebase GSEA relies on high-quality annotation of genes / gene products with information related to their biological func- tions. For PANGEA, we used multiple sources of annota- tion to generate > 300 000 different classes of gene func- tion for fiv e major model organisms ( D. melanogaster, C. elegans, D. rerio, M. musculus, R. norvegicus ) and human. For example, pathway annotations allow users to identify metabolic or signalling pathways that are over-represented in a gene list and help understand causal mechanisms un- derlying the observed phenotype from a scr een. P athway annotations from KEGG, PantherDB, and Reactome, as well as manually curated Drosophila gene sets, such as Fly- Base Signalling Pathways and the DRSC PathON annota- tion ( 18 , 21 ), are included in the PANGEA knowledgebase. The GO annotation set provides the comprehensi v e knowledge on gene functions and we store the gene-to-gene set relationships from GO in two ways. One is the direct gene-to-GO term associations as obtained from the gene association file while the other stores the gene-to-GO term associa tions with considera tion gi v en to child-par ent r ela- tionships. The latter is recommended for use in GSEA as it reflects the intended use of the ontology in curation prac- tice. The direct, gene-to-term set may be useful to under- stand the depth of annotation for each gene. In addition, we also generated two gene annotation subsets using evi- dence codes. The ‘experimental data only’ subset includes only those gene associations that are supported by experi- mental evidence codes. The ‘excluding high-throughput ex- periments’ subset excludes annotations only supported by HTP evidence codes. Excluding HTP data may be impor- tant to avoid bias when analysing similar studies ( 31 ). GO slim subsets are the cut-down versions of GO that give a broad ov ervie w of the ontology content without the detail of the specific, fine-grained terms. The PANGEA knowl- W422 Nucleic Acids Research, 2023, Vol. 51, Web Server issue Table 1. Species coverage by PANGEA and corresponding species-specific databases Species Abbreviation Species specific database URL Example Drosophila melanogaster Homo sapiens Mus musculus Caenorhabditis elegans Danio rerio Rattus norvegicus dm hs mm ce dr rn FlyBase https://flybase.org wg, FBgn0284084 HGNC MGI WormBase ZFIN RDG https://www.genenames.org http://www.informatics.jax.org/ https://www.wormbase.org https://zfin.org https://rgd.mcw.edu/ WNT1, HGNC:12774 Wnt1, MGI:98953 cwn-1, WBGene00000857 wnt1,ZDB-GENE-980526–526 Wnt1, RGD:1597195 Table 2. Knowledgebase of PANGEA built from various gene annotation r esour ces Type Source URL Species covered at PANGEA Gene Ontology GO http://geneontology.org/ hs,mm,rn,dr,dm,ce pathway pathway pathway pathway pathway group group group protein protein KEGG REACTOME PantherDB FlyBase pathway PathON HGNC FlyBase gene group GLAD COMPLEAT EBI protein complex phenotype AGR disease phenotype expression FlyBase phenotype AGR expression https://www.genome.jp/kegg/ https://reactome.org/ http://www.pantherdb.org/ https://flybase.org/ https: //www.flyrnai.org/tools/pathon/ https://www.genenames.org/ https://flybase.org/ https: //www.flyrnai.org/tools/glad/ https: //www.flyrnai.org/compleat/ https: //www.ebi.ac.uk/complexportal https: //www.alliancegenome.org/ https://flybase.org/ https: //www.alliancegenome.org/ hs,mm,rn,dr,dm,ce hs,mm,rn,dr,dm,ce dm dm dm hs dm dm dm hs,mm,rn,dr,dm,ce hs,mm,rn,dr,dm,ce dm mm,rn,dr,dm,ce Source update frequency irregular, usually 1–2 months unknown unknown irregular 2 months irregular unknown 2 months irregular irregular 2 months 3–4 months 2 months 3–4 months edgebase includes two sets of GO slim annotations from differ ent r esour ces. In addition to GO and pa thway annota tions, MODs provide organism-specific curation of important aspects of gene information, such as gene expression and mutant phe- notype, that are not captured in GO. The Alliance is focused on the harmonisation and centralisation of major MODs data ( 4 , 32 ). To take advantage of this effort, we integrated gene-to-tissue expression and gene-to-disease (model) asso- cia tion annota tions from the Alliance into the PANGEA kno wledgebase. As all or ganisms in the Alliance use the Disease Ontology (DO) for annotation, this set is easily comparable across species. The Alliance DO annotation set also includes disease association to model organism genes via an electronic pipeline using orthology with human dis- ease genes which expands the set provide by the MODs. Moreover, for Drosophila genes we assembled an additional gene set from phenotype annota tion a t FlyBase by extract- ing phenotype data associated with a ‘single allele’ genotype (i.e. single classical or insertional alleles), allowing users to perform meaningful enrichment analyses on this data class for the first time. Also included in PANGEA are gene group classifica- tion (eg. kinases and transcription factors) from organism- specific r esour ces (human and fly), protein complex anno- tations for multiple organisms from the EMBL-EBI Com- plex Portal ( 33 ) and COMPLEAT ( 22 ) and bespoke gene sets using Drosophila modENcode RNAseq data to iden- tify genes particularly highly expressed in one tissue (see Materials and Methods). In summary, we have assembled > 300 000 different gene sets that can be used in PANGEA to assess the enrichment of particular biological features in an input gene list. Features of the PANGEA user interface GSEA can be computationally intensi v e because of the number of gene sets being tested and potentially large num- ber of genes entered by users. Ther efor e, the step of pre- processing user’s input by mapping the input gene identi- fiers to the gene identifiers used for gene set annotation, is set up as a standalone ID mapping page (accessed by click- ing ‘Gene Id Mapping’ on the top toolbar) instead of com- bining it with the analysis step. Gene identifiers supported by PANGEA include Entrez Gene IDs, official gene sym- bols and primary gene identifiers from MODs. Users might need to analyse lists of other identifiers such as UniPro- tKB IDs and Ensembl gene IDs. Users can use ‘Gene Id Mapping’ tool and select an organism of choice to map IDs. As gene annotation is an on-going process, the gene identifiers as well as gene symbols might change over time. Even with the same type of gene identifiers such as FlyBase gene ID, the IDs used by users might be from a different FlyBase r elease. Ther efor e, ID-mapping step is an optional Nucleic Acids Research, 2023, Vol. 51, Web Server issue W423 Figure 1. Example of analysing a single gene list using PANGEA. A proteomic interaction dataset was selected from a study of the m6A methyltr ansfer ase complex MTC ( 30 ). The 75 high-confidence interactors of the four subunits of the MTC (METTL3, METTL14, Fl(2)d and Nito) identified by affinity- purified mass spectrometry from Drosophila S2R+ cells were submitted via the ‘Search Single’ option at PANGEA and enrichment analysis was performed over phenotype, GO SLIM2 BP and protein complex annotation from COMPLEAT (literature based). The result was filtered using P value 1 × 10 −5 cut-off and was illustrated as ( A ) a bar graph and ( B ) a network graph of selected gene sets from phenotype annotation and GO SLIM2 BP. Triangle nodes r epr esent gene sets and circle nodes represent genes while the edges r epr esent gene to gene set associations. ( C ) A network graph for selected gene sets from phenotype annotation and COMPLEAT protein complex annotation (literature based). but recommended first step to ensure that the entered IDs are synchronized with the IDs used by PANGEA gene set annotation. Users of FlyBase may also directly export a ‘HitList’ of genes generated in FlyBase to the tool by se- lecting the ‘PANGEA Enrichment Tool (DRSC)’ from the dropdown ‘Export’ menu (Supplementary Figure S1). An option for users to upload a background gene list for the analysis is provided; this may be useful when analysing hits from a focused screen using a kinase sub-library instead of a genome-scale library, for example. PANGEA identi- fies all relevant gene sets and provides enrichment statis- tics such as P -values, adjusted P -values, and fold enrich- ment, as well as the genes shared by the input gene list and gene set members. Users have the option to set differ- ent P -value cut-offs and visualise the results using a bar graph, the height and colour intensity of which can be customised (Figure 1 A). In addition, users can select gene sets of interest to examine the overlap of genes in differ- ent gene sets using the ‘Gene Set Node Graph’ visualisa- tion option. Nodes of different shapes in the network indi- cate genes or gene sets while edges reflect the gene-to-gene set relationship. This type of visualisation can help users identify the most relevant genes in each gene set as well as commonly shared or distinct gene members of the selected gene sets (Figure 1 B, C). An under-appreciated use of GSEA tools is that re- searchers often use them as simple gene classification tools, for example, asking ‘which genes in my list are kinases?’ to help inform further computational or experimental analy- sis. Having di v erse classification sets is important because depending on the type of data / experiment being analysed, different gene sets may be more useful than others. It is often useful to be able to compare similar gene sets from different sources to help evaluate the evidence for support. In addi- tion, PANGEA not only reports genes in an enriched gene set but also reports genes not covered by the gene set cate- gory selected, which may be interesting because of their lack of characterisation. This feature can help user answer ques- tion like ‘which genes in my list are not covered by KEGG annotation?’. W424 Nucleic Acids Research, 2023, Vol. 51, Web Server issue Figure 2. Examples of analysing multiple gene lists using PANGEA. ( A ) The prey proteins of multiple baits from AP mass-spec dataset ( 30 ) were submitted via the ‘Search Multiple’ option at PANGEA. The gene sets of protein complex annotation from COMPLEAT were selected. The comparison of enrichment over annotated protein complexes from the interacting proteins of four different baits was illustrated using a heatma p. ( B ) RN Ai screen data of signalling pathway studies were obtained from DRSC RNAi data repository ( 24 ) and the hits were submitted via the ‘Search Multiple’ option at PANGEA. The gene sets of FlyBase pathway annotation were used. The comparison of the enrichment of signalling pathway components from the screen hits of fiv e studies was illustrated using a heatmap. Often users need to anal yse m ultiple gene lists and com- par e the r esults; howe v er, majority of current w e b-based tools only allow the analysis of a single input list (plus back- ground). Thus, users have to perform comparisons manu- ally or using different tools. To address this need, PANGEA allows users to input multiple gene lists and compare results directly via a heatmap or a dot plot visualisation. For exam- ple, users might input gene hits from different phenotypic scr eens and compar e wha t pa thways, gene groups or biolog- ical processes that are common or different among results (Figure 2 ). Testing the utility of PANGEA To test the utility of PANGEA, we first analysed a pro- teomic interaction dataset from a study of the m6A methyl- tr ansfer ase complex MTC ( 30 ). In this study, using individ- ual pull-downs of the four subunits (METTL3, METTL14, Fl(2)d and Nito) of the MTC complex, high-confidence interactors were identified by mass-spectrometry from Drosophila S2R + cells. Submitting the combined list of 75 interacting proteins from the four baits via the ‘Search Single’ option at PANGEA (accessed by click- ing ‘Search Single’ on the top toolbar) for Fly and per- forming GSEA over phenotype, SLIM2 GO BP as well as protein complex annotation from COMPLEAT (liter- ature based) enrichment, we identified mRNA metabolic pr ocess (GO:0016071), pr otein folding (GO:0006457), ab- nor mal sex-deter mination (FBcv:0000436), abnor mal neu- roanatomy (FBcv:0000435), CCT complex and Spliceo- some complex among the top enriched gene sets with the −5 ) (Figure 1 A). most significant p-values (all < 1 × 10 Next, we visualised SLIM2 GO BP and phenotype annota- tions using a network gra ph. GO mRN A metabolic process hits overlap with both abnormal sex-determination and ab- normal neuroanatomy phenotypes, but GO protein folding hits onl y overla p with the abnormal neuroanatomy pheno- type (Figure 1 B). We also visualised protein complex and phenotype annotations using a different network graph, showing Spliceosome hits overlap with the phenotypes of abnor mal sex-deter mination and abnor mal neuroanatomy while the CCT complex hits only overlap with the abnor- Nucleic Acids Research, 2023, Vol. 51, Web Server issue W425 mal neuroanatomy phenotype (Figure 1 C). Enrichment of se x / reproducti v e phenotypes align with known function of MT C in r egulating the splicing of female-specific Sex lethal (Sxl) and its roles in alternati v e splicing and se xual dimor- phism, as well as the germ stem cell dif ferentia tion in the ovary ( 34 ). These GSEA results are also concordant with the fact that MTC is also known to have a significant role in neuronal mRNA regulation. The benefit of the network visualization is apparent when viewing how the gene set as- signment overlap (Figure 1 B, C), which re v eals that some of the MTC interacting proteins are associated with abnor- mal neuroanatomy phenotype and that the mechanism of the association is through the CCT complex in the pro- cess of protein folding. In contr act, the inter acting pro- teins from Spliceosome have more broad impacts related to both abnormal neuroanatomy phenotype and abnormal sex-determination phenotypes through mRNA metabolic process. We further analysed the protein complexes associated with each individual subunit using the ‘Search multiple’ option at PANGEA (accessed by clicking ‘Search Multi- ple’ on the top toolbar) and inputting the interacting pro- tein lists for each bait, then compared the enrichment re- sult using a heatmap visualisation (Figure 2 A). The re- sults indicated that some complexes, such as spliceosome subunits, are common to all MTC subunits, whereas some ar e mor e specific, such as protein complex es CCT com- plex for METTL14 and METTL13. In addition, we fur- ther analysed phenotype enrichment for proteins associated with each individual subunit using the ‘Search multiple’ op- tion, and comparison of the enrichment results shows many over lapping phenotypes, particular ly with regard to sterility (Supplementary Figure S2). At another use case, we looked at phenotypic cell screen data. Large-scale RN A interference (RN Ai) screening is a powerful method for functional studies in Drosophila . At the DRSC, datasets generated from more than one hundred scr eens ar e pub licly availab le ( 24 ). We selected fiv e screens designed to identify the genes for major signalling pathways and performed a GSEA analysis of the hits using the multi- ple gene list enrichment function of PANGEA. FlyBase sig- nalling pathway gene sets were selected and the results of the fiv e scr eens wer e compar ed side-by-side using a heatmap, w hich clearl y illustrated enrichment of the core components of the corresponding pathways, as well as potential cross- talks between pathways (Figure 2 B). These use cases of PANGEA for phenotype screening data as well as proteomics data demonstrate the value of the tool in validating screen results as well as generating new hypotheses for further study. DISCUSSION GSEA is a computational method used to identify sig- nificantly over-r epr esented gene classes within an input gene list(s) by testing against gene sets assembled based on prior knowledge. Input gene lists are typically from high- throughput screens or analyses. Here we present PANGEA, a newly developed GSEA tool with major model organ- isms as its focus, that includes gene sets that are usually not utilized by other GSEA tools, such as expression and disease annotations from the Alliance, phenotype annota- tions from FlyBase, and GO subsets with different configu- rations. PANGEA is easy to use and has new features such as allowing enrichment analyses for multiple input gene lists and gener ating gr aphical outputs that make comparisons straightf orward f or users. In addition to the use cases pre- sented here, i.e. analysing phenotypic screening and pro- teomic data, we anticipate that the tool will also facilitate analysis of gene lists from other types of data. For exam- ple, analysis of single-cell RNA-seq datasets at PANGEA might help users identify pathways and biological processes that are characteristic of various cell types. Users will also be able to answer questions on classification such as, ‘which genes in this list are kinases?’. PANGEA is designed to ac- commodate a wide range of biological data types and ques- tions, providing users with a w e b-based analysis tool that is easily accessible and user-friendly. We also note that gene classifications are not static, and the generic design of the tool means that it will be easy to update or expand PANGEA for more gene set classification and / or more species. In de v eloping PANGEA, we sought to improv e the effecti v eness of GSEA by (i) providing multiple collections of genes classified by their function in different ways (classified gene sets); (ii) ensuring the data underly- ing the classification of gene function was up to date and (iii) improving the visualization so that results from multi- ple gene sets or multiple gene lists could be compared easily. DA T A A V AILABILITY The online r esour ce is available without restriction at https: //www.flyrnai.org/tools/pangea/ . SUPPLEMENT ARY DA T A Supplementary Data are available at NAR Online. ACKNOWLEDGEMENTS We would like to thank the members of the Perrimon lab- oratory, the FlyBase consortium, the Drosophila RNAi Screening Center (DRSC), and the Transgenic RNAi Project (TRiP) for the discussion and suggestions during the design and implementation of the tool as well as the feed- back during the tool testing. Additional thanks to Gil dos Santos (Harvard, US) and Gillian Millburn (Cambridge, UK) at FlyBase for their genotype-to-phenotype work. [P41 GM132087]; FlyBase FUNDING NIH / NIGMS grant NIH / NHGRI [U41HG000739]; UK Medical Research Council [MR / W024233 / 1]; N.P. is an investigator of Howard Hughes Medical Institute. Funding for open access charge: NIH / NIGMS grant [P41 GM132087]. Conflict of interest statement. None declared. REFRENCES 1. 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10.1021_acschembio.3c00413.pdf	null	null	pubs.acs.org/acschemicalbiology This article is licensed under CC-BY 4.0 Articles Direct Modulators of K‑Ras−Membrane Interactions Johannes Morstein,* Rebika Shrestha, Que N. Van, César A. López, Neha Arora, Marco Tonelli, Hong Liang, De Chen, Yong Zhou, John F. Hancock, Andrew G. Stephen, Thomas J. Turbyville, and Kevan M. Shokat* Cite This: ACS Chem. Biol. 2023, 18, 2082−2093 Read Online ACCESS Metrics & More Article Recommendations sı Supporting Information ABSTRACT: Protein−membrane interactions (PMIs) are ubiq- uitous in cellular signaling. Initial steps of signal transduction cascades often rely on transient and dynamic interactions with the inner plasma membrane leaflet to populate and regulate signaling hotspots. Methods to target and modulate these interactions could yield attractive tool compounds and drug candidates. Here, we demonstrate that the conjugation of a medium-chain lipid tail to the covalent K-Ras(G12C) binder MRTX849 at a solvent-exposed site enables such direct modulation of PMIs. The conjugated lipid tail interacts with the tethered membrane and changes the relative membrane orientation and conformation of K-Ras(G12C), as shown by molecular dynamics (MD) simulation-supported NMR studies. In cells, this PMI modulation restricts the lateral mobility of K-Ras(G12C) and disrupts nanoclusters. The described strategy could be broadly applicable to selectively modulate transient PMIs. ■ INTRODUCTION Bifunctional molecules targeting biological interfaces are emerging therapeutic modalities that are undergoing a rapid expansion (e.g., PROTACS).1−4 To date, the majority of these strategies are focused on the modulation of protein−protein to target protein−membrane interactions, and methods interactions (PMIs) have remained relatively unexplored,5,6 despite their central importance in cellular signaling.7,8 Many targets in cancer signaling (e.g., Ras, PI3K, PKC, AKT) undergo transient and dynamic recruitment to the inner leaflet of the plasma membrane (PM), which could be susceptible to a relatively subtle pharmacological intervention. These targets include K-Ras4b (hereafter simply referred to as K-Ras), which is one of the most widely mutated cancer oncogenes.9−11 The lysine hypervariable region of K-Ras exhibits a patch of residues that aid in transiently associating K-Ras with the PM upon post-translational farnesylation. Inhibition of farnesyla- tion was extensively explored as a therapeutic strategy to inhibit K-Ras function but ultimately failed due to alternative rescued membrane attachment.12 More prenylation that recently, switch II pocket engagement has emerged as a direct strategy to covalently target the mutant allele K-Ras(G12C) giving rise to two clinically approved inhibitors sotorasib and adagrasib (Figure 1A).13−18 Moreover, this strategy has been translated to other mutant alleles K-Ras(G12S),19 K-Ras- (G12R),20 and K-Ras(G12D),21,22 this approach could be quite general. suggesting that by the C-terminal membrane anchor that consists of a farnesylated hexa-lysine polybasic domain. This anchor selectively associates with defined species of phosphatidylser- ine to form nanoclusters, comprising 4−6 K-Ras proteins.23−27 In addition, K-Ras diffusion is distinctive when compared to other paralogs, indicating that the lipid−protein environment that K-Ras explores is unique.11,28,29 Importantly, the specific lipid environment within K-Ras nanoclusters facilitates effector recruitment and activation.30−32 However, the precise mechanism underlying this PMI dependence in effector recruitment is currently unknown. New chemical tools that enable a precise modulation of these PMIs could therefore meet a critical need. Additionally, PMIs may present a therapeutic vulnerability that could be utilized in drug design. A number of monofunctional approaches to target PMIs have previously been reported for lipid clamp domains,8,33,34 and a screening hit for K-Ras with unique membrane-dependent behavior was found to modulate its PMIs in vitro.35 Herein, we attempted the rational design of bifunctional K- Ras(G12C) inhibitors with the capacity to directly modulate K-Ras−membrane interactions (Figure 1B). We envisioned Received: Accepted: Published: August 14, 2023 July 14, 2023 July 31, 2023 K-Ras’ association and interaction with plasma membrane lipids are essential for its function. K-Ras PMIs are mediated © 2023 The Authors. Published by American Chemical Society 2082 https://doi.org/10.1021/acschembio.3c00413 ACS Chem. Biol. 2023, 18, 2082−2093 ACS Chemical Biology pubs.acs.org/acschemicalbiology Articles Figure 1. Design and synthesis of direct modulators for K-Ras−membrane interactions. (A) Scheme of direct Ras inhibition. (B) Scheme of a direct Ras inhibitor that simultaneously modulates its membrane interaction. (C) Crystal structure of K-Ras(G12C) in complex with MRTX849 (PDB 6UT0), highlighting the solvent exposed site of MRTX849.17 (D) Chemical structures of lipidated analogues of MRTX849, C5-MRTX, C11-MRTX, C18-MRTX, and the noncovalent control compound C11′-MRTX. (E) Synthesis of lipidated MRTX849 conjugates. that the installation of a second lipid tail on the surface of K- Ras would allow for modulation of PMIs. To this end, we the solvent-exposed site of proposed the modification of known covalent binders of K-Ras(G12C) with lipophilic groups. Effects on PMIs were characterized extensively in vitro and in cellulo. ■ RESULTS AND DISCUSSION Design and Synthesis of Lipid-Conjugated K-Ras- (G12C) the crystal structure of MRTX849 bound to K-Ras(G12C)17 (PDB 6UT0) revealed partial solvent exposure of the pyrrolidine fragment of the Inhibitors. Analysis of covalently bound ligand (Figure 1C).36 We envisioned that this site could be utilized to append lipophilic groups on the surface of K-Ras with the capacity to directly interact with the membrane. To this end, a series of lipid-conjugated MRTX849 analogues with varying lipid chain lengths were designed (Figure 1D). A small-chain lipid (SCL) conjugate with a 5- carbon containing tail (C5-MRTX), a medium-chain lipid (MCL) conjugate with a 11-carbon tail (C11-MRTX), and a long-chain lipid (LCL) conjugate with an 18-carbon containing tail (C18-MRTX) were synthesized. The control compound C11′-MRTX, which replaced the cysteine-reactive acrylamide warhead with a nonreactive saturated analogue was 2083 https://doi.org/10.1021/acschembio.3c00413 ACS Chem. Biol. 2023, 18, 2082−2093 ACS Chemical Biology pubs.acs.org/acschemicalbiology Articles Figure 2. Biochemical and cell biological characterization of K-Ras(G12C) inhibitors. (A) LC/MS detection of covalent adducts of respective MRTX849-lipid conjugates to K-Ras(G12C) in vitro after 60 min. (B) Cellular target engagement using a TAMRA-Click assay.40 After 4 h incubation in H358 cells (G12C/WT), cells were harvested and incubated with TAMRA-azide to label the terminal end of lipids with a fluorophore. Pellets were blotted for RAS, and the shift of the upper band is indicative of cellular covalent engagement of K-Ras(G12C). (C) Dynamic light scattering measurement to determine critical aggregation concentration (CAC). Scattering intensity was plotted against logarithmic concentration. The origins of slope were used to identify the CAC as a starting point of aggregation. (D) Thermal stability shift assay using SYPRO Orange and covalently modified K-Ras(G12C) in vitro. (E) Cellular viability assay (CellTiter-Glo) of H358 cells with MRTX849, C5-MRTX, C11- MRTX, and C11′-MRTX (CTRL) after 72 h incubation at varying concentrations. also produced. All compounds were synthesized from a previously described MRTX849 intermediate37 and an N- functionalized prolinol derivative (Figure 1E). C11-MRTX is a Nonaggregating Potent Cellular Inhibitor of K-Ras(G12C). In vitro labeling of recombinant K-Ras(G12C) showed that C5-MRTX and C11-MRTX undergo rapid covalent modification of K-Ras(G12C), while the control compound C11′-MRTX and C18-MRTX do not label K-Ras(G12C) covalently (Figure 2A). To test if these results translate into cellular labeling of K-Ras(G12C), the alkyne moiety at the lipid terminus was utilized for copper- catalyzed azide-alkyne click chemistry38,39 leading to a shift in sodium dodecyl sulfate−polyacrylamide gel electrophoresis. Incubation of H358 (WT/G12C) cells with respective analogues of MRTX for 4 h, subsequent click labeling, SDS electrophoresis, and western blotting revealed effective cellular engagement of K-Ras(G12C) in cells by C5-MRTX and C11- MRTX as observed through a shift in SDS gel electrophoresis (Figure 2B) of the K-Ras band (note: H358 is a heterozygous cell line; partial labeling is observed due to the presence of a wildtype allele). Similar to our intact mass spectrometry experiments, covalent target engagement was not detected for C18-MRTX. We hypothesized that this could be due to an increased propensity of longer lipid tails to form aggregates, which was confirmed by a dynamic light scattering experiment (Figure 2C). Interestingly, the critical aggregation concen- tration of C5-MRTX was lower than that of MRTX849 and C11-MRTX was comparable to MRTX849. By contrast, C18- MRTX exhibited a much lower critical aggregation concen- tration (∼80 nM), which could be limiting its labeling efficiency and bioactivity. MRTX849 engages the switch II pocket of K-Ras(G12C) leading to a marked stabilization of its fold. To assess if our lipid conjugates behave similarly, we used a thermal shift assay with SYPRO Orange (Figure 2D). Notably, MRTX849-, C5- MRTX-, and C11-MRTX-labeled K-Ras(G12C) variants all showed a large thermal shift compared to nonlabeled K- Ras(G12C). At the same time, the shift between the three labeled variants exhibits no detectable differences, suggesting that the lipid tail does not strongly bind to K-Ras(G12C), which is desirable for it to potentially interact with the inner leaflet of the PM. To confirm that C11-MRTX exhibits specific cellular toxicity in K-Ras(G12C)-driven cancer cell lines, we performed a cell viability assay with C11-MRTX and the noncovalent control compound C11′-MRTX (CellTiter- Glo).13,15 C11-MRTX was found to be significantly more potent than the negative control compound (Figure 2E), which verifies that this inhibitor exhibits potent cellular activity despite the MCL conjugation. C11-MRTX Alters the Relative Conformation of K- Ras(G12C) on the PM. To study the capacity of C11-MRTX to modulate K-Ras−membrane interactions, we decided to employ coarse-grained MD simulations with Martini 3 force fields in conjunction with NMR paramagnetic relaxation enhancement (NMR-PRE) for K-Ras(G12C)·MRTX849 and K-Ras(G12C)·C11-MRTX that were chemically tethered to 2084 https://doi.org/10.1021/acschembio.3c00413 ACS Chem. Biol. 2023, 18, 2082−2093 ACS Chemical Biology pubs.acs.org/acschemicalbiology Articles Figure 3. MD simulations and NMR-PRE experiments with membrane-tethered K-Ras(G12C). (A) Model of C11-MRTX modified K-Ras(G12C) tethered to a model membrane for MD simulations. (B) MD simulations revealed transient membrane engagement through the MCL of C11- MRTX leading to a novel bianchored conformation of K-Ras(G12C) on the membrane. (C) Ranking of membrane contacts of ligands for simulation with K-Ras(G12C)·MRTX849 (top) and K-Ras(G12C)·C11-MRTX (bottom). (D) Selected peaks from K-Ras(G12C/C118S)· MRTX849 and K-Ras(G12C/C118S)·C11-MRTX on nanodisks with and without the PRE tag Tempo. (E) Structure of K-Ras(G12C) highlighting areas that are moved close to the membrane when bound to C11-MRTX relative to MRTX849 in blue and moved further away in red. (F) NMR-PRE ratios for K-Ras·MRTX849 and K-Ras·C11-MRTX tethered to nanodisks. lipid nanodisks. MD simulations of K-Ras(G12C)·C11-MRTX (Figure 3A) revealed transient binding of the C11-MRTX MCL to the membrane leading to unusual bianchored conformations of K-Ras(G12C) (Figure 3B). To visualize the membrane contacts induced by C11-MRTX relative to ligand−membrane contacts were MRTX849, counted (Figure 3C), showing frequent membrane contacts for for C11-MRTX but near zero membrane contacts the direct 2085 https://doi.org/10.1021/acschembio.3c00413 ACS Chem. Biol. 2023, 18, 2082−2093 ACS Chemical Biology pubs.acs.org/acschemicalbiology Articles Figure 4. Single-molecule tracking of K-Ras(G12C)·C11-MRTX in HeLa cells. (A) Schematic of HaloTag-tagged K-Ras used for TIRF single- particle tracking experiment. (B) TIRF image of JF549-chloroalkane labeled HaloTag K-Ras(G12C) in HeLa cells. (C) Representative trajectories of diffusion for labeled K-Ras(G12C) on the inner leaflet of the PM. Colors represent different single-molecule tracks over time. (D−F) Mean- square displacement plots calculated from the trajectories obtained for HaloTag K-Ras(G12C) labeled with 50 pM JF549 treated with no drug (black), 10 μM C11-MRTX (blue), 10 μM C11′-MRTX (green), and 10 μM MRTX849 (orange) for 30 min (D), 1 h (E), and 2 h (F) of compound incubation. In panels (E, F), the orange and green lines partially cover each other. MRTX849. To test these predictions experimentally, we tethered K-Ras(G12C) to nanodisks and conducted protein NMR studies. We observed marked chemical shift perturba- tions comparing K-Ras(G12C) bound to MRTX849 versus C11-MRTX. These shifts occurred on residues of SI, SII, and α3 regions (Figure 3D). Residue 63 from the switch II region had a particularly strong chemical shift response. This was consistent with the MD prediction of regions in K-Ras(G12C) moving into closer proximity of the membrane (marked in blue, Figure 3E). We further conducted NMR-PRE experi- ments which confirmed greater membrane proximity of residues 62 to 66 in the switch II region of K-Ras and an overall decrease in NMR-PRE ratios for β1, α3, and α4 residues (Figure 3F). K-Ras(G12C)·C11-MRTX had a longer rotational correlation time of 22.8 vs 18.4 ns for K-Ras(G12C)· MRTX849, which provided additional support for its closer membrane proximity (Figure S4). C11-MRTX Modulates the Diffusion of PM-Localized K-Ras(G12C) in Live Cells. To study if the PMI modulations observed in MD simulations and NMR experiments translate to live cells, we decided to study the lateral diffusion of labeled live cells.11 K- K-Ras(G12C) on the inner PM leaflet of internal Ras(G12C) diffusion was measured using total reflection microscopy (TIRF) employing a charge-couple 2086 https://doi.org/10.1021/acschembio.3c00413 ACS Chem. Biol. 2023, 18, 2082−2093 ACS Chemical Biology pubs.acs.org/acschemicalbiology Articles Figure 5. Nanoclustering of K-Ras(G12C)·C11-MRTX in MDCK cells. (A) TEM image of 4.5 nm gold nanoparticles immunolabeling the GFP- tagged K-Ras(G12C) at a magnification of 100,000X. (B-D) Color-coded TEM images of the gold-labeled GFP-tagged K-Ras(G12C) treated with DMSO (B), C11-MRTX (C), and MRTX849 (D). (E, F) Analysis of PM localization and nanoclustering for GFP-K-Ras(G12C) (E) and GFP-K- Ras(G12D) (F). Error bars indicate mean ± SEM of the at least 15 PM sheet images for each condition. Bootstrap tests evaluated the statistical significance of the Lmax data, while one-way ANOVA calculated the statistical significance of the gold labeling data, with indicating p < 0.05. that device (CCD) camera for fast frame rate acquisition and a bright organic dye covalently linked to HaloTag K-Ras(G12C) overexpressed in HeLa cells (Figure 4A,B). The result demonstrates labeling of K-Ras(G12C) with C11- MRTX leads to marked changes in its dynamic diffusion along the PM. While no clear trends could be observed within 30 min (Figure 4C), C11-MRTX showed a marked reduction in diffusion rates compared to MRTX849 and C11′-MRTX after 1 h (Figure 4D) and further pronounced after 2 h (Figure 4E). We reasoned that the lateral restriction in K-Ras(G12C) mobility along the plasma membrane is a likely effect of the additional membrane contacts established by the C11-lipid tail. We further the subcellular distribution of K-Ras, for example by shifting its localization from the plasma membrane to endomembranes.41 Confocal imaging of GFP-fused K-Ras did not reveal alterations in the subcellular localization of K-Ras (Figure S5). tested if our molecules alter C11-MRTX Disrupts K-Ras(G12C) Nanoclusters. The spatial organization of K-Ras on the inner PM leaflet is critical for its physiological function. Transient nanoclusters were found to be the sites where effectors preferentially interact with K-Ras and are therefore especially critical for its physiological function.9,42 To test the lateral if C11-MRTX affects organization of K-Ras into nanoclusters, we conducted electron microscopy (EM) combined with spatial analysis43 in MDCK cells stably expressing GFP-K-Ras(G12C) or GFP- K-Ras(G12D) as control. Intact 2D PM sheets from cells treated with DMSO vehicle control, 10 μM C11-MRTX, or 10 μM MRTX849 for 2 h were fixed and labeled with 4.5 nm gold nanoparticles conjugated directly to anti-GFP antibody (Figure 5A). The gold particle spatial distributions were quantified using univariate K-functions expressed as L(r) − r. The maximum value of this function, Lmax, can be used as a summary statistic for the extent of nanoclustering. The extent of nanoclustering, L(r) − r, was plotted as a function of the length scale, r. The L(r) − r value of 1 is the 99% confidence the values above which indicate the statistically interval, meaningful clustering. Based on this K-function analysis, the EM images were color-coded to indicate the population distribution of the gold-labeled GFP-K-Ras(G12C). Larger L(r) − r values indicate more clustering (Figure 5B−D). We found that MRTX849 and C11-MRTX treatment both decreased the gold labeling density when compared with control, indicating that MRTX849 and C11-MRTX both reduced the localization of K-Ras(G12C) to the PM. C11- MRTX significantly reduced the Lmax value for GFP-K- Ras(G12C), indicating that C11-MRTX also disrupted the nanoclustering of K-Ras(G12C) (Figure 5E). Both MRTX849 and C11-MRTX had no effect on localization or nano- indicating selectivity for K- clustering of K-Ras(G12D), Ras(G12C) (Figure 5F). Combined, these data demonstrate the ability of the lipidated drug to selectively disrupt the lateral spatial organization of K-Ras(G12C) on the PM, which is function of GTP-bound K- critical Ras.30,31 the physiological for 2087 https://doi.org/10.1021/acschembio.3c00413 ACS Chem. Biol. 2023, 18, 2082−2093 ACS Chemical Biology pubs.acs.org/acschemicalbiology Articles ■ CONCLUDING REMARKS leaflet of Herein, we report a bifunctional chemical approach to directly modulate the interactions between K-Ras(G12C) and the inner the PM. This is achieved through the installation of a C11 medium-chain lipophilic group to the solvent-exposed site of the covalent K-Ras(G12C) inhibitor MRTX849. Medium-chain lipids are common in natural products, occur in drugs (e.g., fingolimod or orlistat), and may present a sweet spot for bioactive amphiphiles due to their capacity to partition, while exhibiting a lower propensity to aggregate compared to longer membrane lipids.44 In our lead molecule C11-MRTX, the conjugated lipid tail establishes new interactions with the inner leaflet of the plasma membrane, resulting in novel bi-anchored conformations of membrane- tethered K-Ras(G12C). Thereby, the nucleotide binding site and switch I/II regions are brought in closer proximity to the PM, as demonstrated through a combination of MD simulations and NMR experiments. In cells, C11-MRTX restricts the lateral mobility of K-Ras which was observed through a marked reduction in diffusion rates. Finally, C11- MRTX was found to disrupt K-Ras(G12C) nanoclusters, which are the sites of Ras effector recruitment and activation and thus essential for signal transmission of noninhibited K- Ras. Combined, these results demonstrate a targeted modulation of protein−membrane interactions. These types of interactions are ubiquitous in early steps of cellular signaling, and our strategy could be translatable to target other signaling or lipid binding factors. PMI modulators could provide useful tools to dissect the function of these interactions and hold promise for the design of novel therapeutic agents. ■ MATERIALS AND METHODS General Methods. Anhydrous solvents were purchased from Acros Organics. Unless specified below, all chemical reagents were purchased from Sigma-Aldrich, Oakwood, Ambeed, or Chemscene. Analytical thin-layer chromatography (TLC) was performed using aluminum plates precoated with silica gel (0.25 mm, 60 Å pore size, 230−400 mesh, Merck KGA) impregnated with a fluorescent indicator (254 nm). TLC plates were visualized by exposure to ultraviolet light (UV). Flash column chromatography was performed with Teledyne ISCO CombiFlash EZ Prep chromatography system, employing prepacked silica gel cartridges (Teledyne ISCO RediSep). Proton nuclear magnetic resonance (1H NMR) spectra were recorded on a Bruker Avance III HD instrument (400/100/376 MHz) at 23 °C operating with the Bruker Topspin 3.1. NMR spectra were processed using Mestrenova (version 14.1.2). Proton chemical shifts are expressed in parts per million (ppm, δ scale) and are referenced to residual protium in the NMR solvent (CHCl3: δ 7.26, MeOD: δ 3.31). Data are represented as follows: chemical shift, multiplicity (s = singlet, d = doublet, t = triplet, q = quartet, dd = doublet of doublets, dt = doublet of triplets, m = multiplet, br = broad, app = apparent), integration, and coupling constant (J) in hertz (Hz). High-resolution mass spectra were obtained using a Waters Xevo G2-XS time-of-flight mass spectrometer operating with Waters MassLynx software (version 4.2). When liquid chromatography−mass spectrometry (LC−MS) analysis of the reaction mixture is indicated in the procedure, it was performed as follows. An aliquot (1 μL) of the reaction mixture (or the organic phase of a mini-workup mixture) was diluted with 100 μL 1:1 acetonitrile/water. 1 μL of the diluted solution was injected onto a Waters Acquity UPLC BEH C18 1.7 μm column and eluted with a linear gradient of 5−95% acetonitrile/water (+0.1% formic acid) over 3.0 min. Chromatograms were recorded with a UV detector set at 254 nm and a time-of-flight mass spectrometer (Waters Xevo G2-XS). Intact Protein Mass Spectrometry. Purified K-Ras variants (4 μM final) were incubated with compounds at 50 or 100 μM (1% v/v DMSO final) in 20 mM HEPES pH 7.5, 150 mM NaCl, 1 mM MgCl2 in a total volume of 150 μL. After the noted time, the samples were analyzed by intact protein LC/MS using a Waters Xevo G2-XS system equipped with an Acquity UPLC BEH C4 1.7 μm column. The mobile phase was a linear gradient of 5−95% acetonitrile/water + 0.05% formic acid. The spectra were processed using QuantLynx, giving the ion counts observed for the most abundant species. TAMRA-Click Assay. This assay was performed as previously described.40 Briefly, cells (500,000 to 1,000,000 cells per well) were seeded into six-well ultralow attachment plates (Corning Costar #3471) and allowed to incubate at 37 °C overnight. Cells were treated with the indicated concentrations of compound combinations and then incubated at 37 °C for the indicated lengths of time. In preparation for sodium dodecyl sulfate−polyacrylamide gel electro- phoresis (SDS−PAGE) and immunoblotting, cells were pelleted at 4 °C at 500 g and washed twice with ice-cold phosphate-buffered saline (PBS). Lysis was conducted, and copper-catalyzed click chemistry was performed by addition of the following to each lysate at the following final concentrations: 1% SDS (20% SDS in water stock), 50 μM TAMRA-N3 (5 mM in DMSO stock), 1 mM TCEP (50 mM in water stock), 100 μM TBTA (2 mM in 1:4 DMSO/t-butyl alcohol stock), and 1 mM CuSO4 (50 mM in water stock). After 1 h at room temperature, the reaction was quenched with 6× Laemmli sample buffer before SDS−PAGE. Dynamic Light Scattering. Measurements were performed using a DynaPro MS/X (Wyatt Technology) with a 55 mW laser at 826.6 nm, using a detector angle of 90°. Histograms represent the average of three data sets. Differential Scanning Fluorimetry. The protein of interest was diluted with HEPES buffer [20 mM HEPES 7.5, 150 mM NaCl, 1 mM MgCl2] to 2 μM. 1 μL of SYPRO Orange (500×) was mixed with 99 μL of protein solution. This solution was dispensed into wells of a white 96-well PCR plate in triplicate (25 μL/well). Fluorescence was measured at 0.5 °C temperature intervals every 30 s from 25 to 95 °C on a Bio-Rad CFX96 qPCR system using the FRET setting. Each data set was normalized to the highest fluorescence, and the normalized fluorescence reading was plotted against temperature in GraphPad Prism 8.0. Tm values were determined as the temper- ature(s) corresponding to the maximum (ma) of the first derivative of the curve. Cell Viability Assay. Cells were seeded into 96-well white flat bottom plates (1000 cells/well) (Corning) and incubated overnight. Cells were treated with the indicated compounds in a seven-point threefold dilution series (100 μL final volume) and incubated for 72 h. Cell viability was assessed using a commercial CellTiter-Glo (CTG) luminescence-based assay (Promega). Briefly, the 96-well plates were equilibrated to room temperature before the addition of diluted CTG reagent (100 μL) (1:4 CTG reagent/PBS). Plates were placed on an orbital shaker for 30 min before recording luminescence using a Spark 20M (Tecan) plate reader. Molecular Simulations. Coordinates of K-Ras bound to MRTX849 were downloaded from the pdb database (6UTO). Missing residues in the HVR were modeled using Modeller,45 as a disordered region.46 The protein was represented using the Martini 347 coarse-grained force field in combination with the structure- based48 approach in order to maintain its secondary structure. The farnesyl group was represented using parameters published before49 and updated in order to keep consistency with Martini 3. MRTX849, C11-MRTX, and GDP molecules were modeled using the method- ology published before,50 and bead types were updated accordingly to match the Martini 3 force field interaction matrix. Harmonic bonds were used to maintain the stability of the ligands in their respective binding regions, a methodology used successfully in the past.2 A membrane lipid bilayer composed of 70:30 POPC/POPS was constructed using the “insane” tool.51 Before insertion of K-Ras, the membrane was pre-equilibrated at 310 K for 100 ns. Protein and ligands were inserted, embedding the farnesyl group into the lipid bilayer and removing overlapping Martini water beads. Systems were charge-neutralized, and ions (Na+, Cl−) were added to mimic a 150 mM ionic strength environment. Before production, system boxes were energy-minimized and trajectories were saved every 2 ns for 2088 https://doi.org/10.1021/acschembio.3c00413 ACS Chem. Biol. 2023, 18, 2082−2093 ACS Chemical Biology pubs.acs.org/acschemicalbiology Articles analysis. Each trajectory (2 total) was run for 30 μs. Simulations were carried out with GROMACS 2018.6,52 using a 20 fs time step for updating forces as recommended in the original publication. Reaction- field electrostatics was used with a Coulomb cutoff of 1.1 nm and dielectric constants of 15 or 0 within or beyond this cutoff, respectively. A cutoff of 1.1 nm was also used for calculating Lennard-Jones interactions, using a scheme that shifts the van der Waals potential to zero at this cutoff. Membranes were thermally coupled to 310 K using the velocity rescaling53 thermostat. Semi- isotropic pressure coupling was set for all systems at 1 bar using a Berendsen54 barostat with a relaxation time of 12.0 ps. DNA for Protein Production of K-Ras4b(1−185) G12C/ C118S. The gene for protein expression of Hs.K-Ras4b(1−185) initially G12C/C118S was generated from a DNA construct synthesized as a Gateway Entry clone (ATUM, Newark, CA). The construct consisted of an Escherichia coli gene-optimized fragment containing an upstream tobacco etch virus (TEV) protease site (ENLYFQ/G), followed by the coding sequence of human K- Ras4b(1−185). An entry clone was transferred to an E. coli destination vector containing an amino terminal His6-MBP (pDest- 566, Addgene #11517) tag by gateway LR recombination (Thermo Scientific, Waltham, MA). The construct generated was R949-x95- 566: His6-MBP-tev-Hs.K-Ras4b(1−185) G12C/C118S. The mem- brane scaffolding protein expression clone (pMSP delH5) was obtained from the group of Gerhard Wagner at Harvard University.55 Protein Expression and Purification. K-Ras4b(1−185) G12C/ C118S was expressed following the protocols described in Travers et al. for 15N/13C incorporation with modifications.49 Specifically, ZnCl2 was omitted and induction after IPTG addition was at 16 °C. Highly deuterated and 15N-labeled K-Ras protein was expressed using the protocols described in Chao et al.56 and purified essentially as outlined in Kopra et al.57 for K-Ras(1−169). pMSP delH5 was expressed and purified as described in Travers et al. NMR-PRE Sample Preparation and NMR Data Collection and Processing. Uniformly 15N/2H-labeled K-Ras(G12C/C118S) was first labeled with 2.5× excess of MRTX849 and C11-MRTX in 20 mM Hepes, pH 7.48, and 150 mM NaCl overnight at room temperature (∼11 h), and excess compounds were removed using a PD10 column equilibrated with 20 mM Hepes, pH 7.0, and 150 mM NaCl. Then, the MRTX849 and C11-MRTX bound K-Ras, concentration between 182 and 195 μM, were tethered to 2× excess of delH5 nanodisks composed of 63.75/30/6.25 POPC/POPS/PE MCC and 57.5/30/6.25/6.25 POPC/POPS/PE MCC/Tempo PC at room temperature overnight, followed by purification on an AKTA FPLC with a Superdex 200 Increase 10/300 column to remove nontethered K-Ras. All lipids were purchased from Avanti Polar Lipids. Empty delH5 nanodisks were made as described in Van et al. with pH 7.0 buffer.58 The final NMR buffer was 20 mM Hepes, pH 7.0, 150 mM NaCl, 0.07% NaN3, and 7.0% D2O. 280 μL of each sample was enclosed in 5 mm susceptibility-matched Shigemi tubes (Shigemi, Allison Park, PA) for NMR data collection. All NMR experiments were acquired on a Bruker AVANCE III HD spectrometer operating at 900 MHz (1H), equipped with a cryogenic triple-resonance probe. The temperature of the sample was regulated at 298 K throughout the experiments. Two-dimensional (2D) 1H,15N- TROSY-HSQC spectra were recorded with 1024 × 128 complex points for the 1H and 15N dimension, respectively, 128 scans, and a recovery delay of 1.5 s for a total collection time of 15 h. All 2D spectra were processed using NMRPipe59 and analyzed using NMRFAM-SPARKY.60 The NMR-PRE ratios were calculated from peak intensities and normalized to 1 (Figure 3F). Chemical shift perturbations (CSP) were calculated using CSP (ppm) = Sqrt((ΔN2/ 25 + ΔH2)/2) (Figure S1). The TROSY spectra for K-Ras·MRTX849 and K-Ras·C11-MRTX tethered to nanodisks without the Tempo PRE tag are shown in Figures S2 and S3, respectively. Expansion of the spectral region for residues 61 to 67 is shown in Figure 3D. To estimate the tumbling time of the K-Ras proteins in solution, 1H/15N-TRACT61 experiments were recorded as a series of one- dimensional (1D) spectra for the α and β states. For the 15N-α state, the relaxation delays were set to 0, 5, 10, 16, 22, 30, 40, 50, 64, 80, 100, 130, 170, and 240 ms. The relaxation delays for the faster- relaxing 15N-β state were set to 0, 1, 2, 4, 7, 11, 15, 20, 26, 32, 39, 47, 56, and 70 ms. Spectra for both the α and β states were recorded in a in an interleaved fashion. Each FID was single experiment accumulated for 1536 scans with a repetition delay between scans of 1.5 s for a total recording time of 18.5 h for both the α and β states. The interleaved spectra were separated in topspin using inhouse written scripts and analyzed using Mestrelab Research Mnova software. Plots showing the fits to calculate the rotational correlation time are shown in Figure S4. K-Ras·MRTX849 Backbone Chemical Shift Assignments. A sample of uniformly 13C,15N-labeled K-Ras bound to MRTX849 (6.4 mM in 20 mM Hepes, pH 7.0, with 150 mM NaCl, 1 mM MgCl2, 1 mM TCEP, 0.07% NaN3, and 7.0% D2O) was used to collect sequence-specific assignments of backbone resonances: two-dimen- sional (2D) 1H,15N-HSQC and three-dimensional (3D) HNCACB, 3D CBCA(CO)NH, 3D HNCA, 3D HN(CA)CO, 3D HNCO spectra, as well as a 3D NOESY 1H,15N-HSQC spectrum with a 100 ms mixing time. The 1H/15N assignments are shown in Figure S6. To increase the resolution of the C α cross-peaks in the 13C dimension of the 3D HNCA spectrum, band-selective shaped pulses (BADCOP) developed by optimal control theory were utilized to decoupled C α from C β nuclei.62 All NMR experiments were acquired on a Bruker AVANCE III HD spectrometer operating at 750 MHz (1H), equipped with a cryogenic triple-resonance probe. The temperature of the sample was regulated at 298 K throughout the experiments. All 3D spectra were recorded using nonuniform sampling (NUS) with sampling rates ranging between 30.5 and 33.3%. All spectra were processed using NMRPipe and analyzed in NMRFAM-SPARKY. The 3D spectra recorded with NUS were reconstructed and processed using the SMILE package available with NMRPipe. Single-Particle Tracking Experiments. HeLa cells were grown in Dulbecco’s modified Eagle medium (DMEM) (Thermo Fisher Scientific) supplemented with 1% 200 mM L-Glutamine and 10% FBS in a 6-well plate. The HaloTag fusion construct of K-Ras4b(G12C) was transiently transfected into each well using Fugene 6 transfection reagent (Promega) and 1.1 μg DNA per well. The protocol for plasmid design is described in Goswami et al.11 On the following day, cells were transferred on to plasma-cleaned coverslips (#1.5, 25 mm). On the day of imaging, the cells were first labeled with the fluorescent JF549 HaloTag ligand (Tocris) and then treated with the compounds. For labeling, the cells were first washed with 3 mL of PBS 3 times, incubated with 50 pM of JF54963 in complete media for 40 min, washed with 3 mL of PBS, and then allowed to recover in complete media for 30 min. For drug treatment, cells were first washed with 3 mL of PBS and then incubated with 10 μM of compound in complete media for the indicated time course. Single-particle tracking experiments were performed on the Nikon NStorm Ti-81 inverted microscope equipped with thermo-electric-cooled Andor iX EMCCD camera (Andor Technologies). During imaging experiments, the cells were maintained at 37 °C and 5% CO2 using a Tokai hit stage incubator (Tokai Hit Co., Ltd., Japan). The JF549 fluorescent molecules were illuminated under TIRF mode with the continuous 561 nm laser line from the Agilent laser module at 15% and imaged with an APO x100 TIRF objective with 1.49 NA (Nikon Japan). A 100 by 100-pixel region (16 × 16 μm2) of interest (ROI) was created in the cytoplasmic region of the PM in a cell and imaged at a frame rate of 10ms/frame for a total of 5000 frames. For each experiment, a minimum of 17 cells were imaged. Single-particle tracking movies were analyzed using the Localizer plugin embedded in Igor Pro software.64 Single particles in each frame were localized as spots based on the eight-way adjacency particle detection algorithm with a generalized likelihood ratio test (GLRT) sensitivity of 30 and a point spread function (PSF) of 1.3 pixels. The position of the PSF was estimated based on a symmetric 2D Gaussian fit function. If the particles persisted for more than 6 frames, they were then linked between consecutive frames into tracks. The particles were allowed a maximum jump distance of 5 pixels and blinking for one frame. For each experiment, tracks from all of the movies were combined into a single Matlab file and used to calculate mean-square displacement 2089 https://doi.org/10.1021/acschembio.3c00413 ACS Chem. Biol. 2023, 18, 2082−2093 ACS Chemical Biology pubs.acs.org/acschemicalbiology Articles plots using a home-written script in Matlab. The plots were created using GraphPad Prism software. FLIM Imaging. In this study, we conducted fluorescence lifetime imaging (FLIM) experiments on doxycycline (Dox)-inducible eGFP- tagged K-Ras4b G12C HeLa cells. To generate the Dox-inducible cell line, HeLa cells (ATCC #CCL-2) were transduced with lentivirus containing the plasmid construct R733-M42-663 (TRE3Gp > eGFP- Hs.K-Ras4b G12C) at an MOI of 1.0. The cells were cultured in DMEM media supplemented with 10× L-Glutamine, 10% fetal bovine serum (complete media), 4 μg/mL of blastocydin, and 1 μg/mL of puromycin. Prior to imaging, the cell media was replaced with complete media containing doxycycline at a concentration of 500 ng/ mL, and drug treatment was administered at 10 μM for at least 2 h. FLIM imaging was performed using an Olympus Fluoview FV1000 inverted confocal microscope equipped with the Picoquant LSM upgrade kit and Picoharp 300 TCSPC module. A picosecond pulsed diode laser for the green channel (LDH-D-C-485) was used to illuminate the samples at a repetition rate of 40 MHz, allowing us to obtain the fluorescence lifetime decay curve. PicoQuant Symphotime 64 software was utilized for fluorescence lifetime fitting and image analysis. The fluorescence decay curve was fitted to a single- component n-Exponential tailfit to calculate the fluorescence lifetime for each pixel. The color scale on the right represents the fluorescence the mean lifetime of each pixel fluorescence lifetime of eGFP-K-Ras G12C was calculated to be approximately 2.6 ns, as depicted in green within the FLIM images.65,66 in the FLIM image. Notably, EM Spatial Analysis. MDCK cells stably expressing GFP-K- Ras(G12C) or GFP-K-Ras(G12D) were maintained in Dulbecco’s modified Eagle medium (DMEM) containing 10% fetal bovine serum (FBS). Cells were treated with DMSO, C11-MRTX, or MRTX849 at a concentration of 10 μM for 2 h, followed by preparation of the cell PM for electron microscopy (EM) analysis. An EM spatial distribution method is used to quantify the extent of K-Ras protein lateral spatial segregation in the inner leaflet of the PM.26,67 Gold grids with basal PM were prepared as described previously.30,68 Briefly, MDCK cells expressing GFP-tagged K-Ras mutants were grown on pioloform and poly-L-lysine-coated gold EM grids. After treatment, intact basal PM sheets attached to the gold grids were fixed with 4% paraformaldehyde and 0.1% glutaraldehyde, labeled with 4.5 nm gold nanoparticles coupled to anti-GFP antibody, and embedded in methyl cellulose containing 0.3% uranyl acetate. Distribution of gold particles on the basal PM sheets was imaged using a JEOL JEM- 1400 transmission electron microscope at 100,000× magnification. The EM images were analyzed using ImageJ software to assign x and y coordinates to gold particles in a 1 μm2 area of interest on the PM sheets. We use Ripley’s K-function to quantify the gold particle distribution and the extent of nanoclustering eqs A and B. (A) (B) where K(r) indicates the univariate K-function for the number of gold particles (n) within a selected area (A), r is the radius or length scale, \|\|·\|\| is the Euclidean distance, the indicator function 1(·) is assigned a −1 is the value of 1 if \|\|xi − xj\|\| ≤ r and a value of 0 otherwise, and wij proportion of the circumference of a circle with center at xi and a radius \|\|xi − xj\|\|. K(r) is linearly transformed to yield a parameter of L(r) − r, which is normalized on the 99% confidence interval (99% C.I.) using Monte Carlo simulations. The maximum value of the L(r) − r function Lmax provides a statistical summary for the extent of nanoclustering. For each treatment condition (DMSO, C11-MRTX, or MRTX849), at least 15 PM sheets were imaged, analyzed, and data pooled. Bootstrap tests were used to calculate the statistical significance of the nanoclustering data, while one-way ANOVA was used to estimate the statistical significance of the gold labeling density as previously described. ■ ASSOCIATED CONTENT *sı Supporting Information The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acschembio.3c00413. Chemical shift perturbation plot; plots of the alpha and beta state signal decay; experimental procedures; and compound characterization by high-resolution mass spectrometry (HRMS) and NMR (PDF) Final video (MP4) ■ AUTHOR INFORMATION Corresponding Authors Johannes Morstein − Department of Cellular and Molecular Pharmacology and Howard Hughes Medical Institute, University of California, San Francisco, California 94158, United States; Email: johannes.morstein@ucsf.edu orcid.org/0000-0002-6940-288X; Kevan M. Shokat − Department of Cellular and Molecular Pharmacology and Howard Hughes Medical Institute, University of California, San Francisco, California 94158, United States; Email: kevan.shokat@ucsf.edu orcid.org/0000-0001-8590-7741; Authors Rebika Shrestha − NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States Que N. Van − NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States César A. López − Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States; orcid.org/0000-0003-4684-3364 Neha Arora − Department of Integrative Biology and Pharmacology, McGovern Medical School, University of Texas Health Science Center, Houston, Texas 77030, United States Marco Tonelli − National Magnetic Resonance Facility at Madison, Biochemistry Department, University of Wisconsin- Madison, Madison, Wisconsin 53706, United States Hong Liang − Department of Integrative Biology and Pharmacology, McGovern Medical School, University of Texas Health Science Center, Houston, Texas 77030, United States De Chen − NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States Yong Zhou − Department of Integrative Biology and Pharmacology, McGovern Medical School, University of Texas Health Science Center, Houston, Texas 77030, United States John F. Hancock − Department of Integrative Biology and Pharmacology, McGovern Medical School, University of Texas Health Science Center, Houston, Texas 77030, United States Andrew G. Stephen − NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States Thomas J. Turbyville − NCI RAS Initiative, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States Complete contact information is available at: 2090 https://doi.org/10.1021/acschembio.3c00413 ACS Chem. Biol. 2023, 18, 2082−2093 ACS Chemical Biology pubs.acs.org/acschemicalbiology Articles https://pubs.acs.org/10.1021/acschembio.3c00413 Notes The authors declare the following competing financial interest(s): K.M.S. and J.M. are inventors on patents owned by UCSF covering K-Ras targeting small molecules. K.M.S. has consulting agreements for the following companies, which involve monetary and/or stock compensation: Revolution Medicines, Black Diamond Therapeutics, BridGene Bioscien- ces, Denali Therapeutics, Dice Molecules, eFFECTOR Therapeutics, Erasca, Genentech/Roche, Janssen Pharmaceut- icals, Kumquat Biosciences, Kura Oncology, Mitokinin, Nested, Type6 Therapeutics, Venthera, Wellspring Biosciences (Araxes Pharma), Turning Point, Ikena, Initial Therapeutics, Vevo and BioTheryX. ■ ACKNOWLEDGMENTS J.M. thanks the NCI for a K99/R00 award (K99CA277358). K.M.S. thanks NIH grant 5R01CA244550 and the Samuel Waxman Cancer Research Foundation. The authors thank J. from B. Shoichet’s lab for assistance with the O’Connell dynamic light scattering (DLS) measurement. The authors wish to acknowledge C. J. DeHart, J.-P. Denson, P. H. Frank, M. Hong, S. Messing, A. Mitchell, N. Ramakrishnan, W. for cloning, protein Burgan, K. Powell, and T. Taylor expression, protein purification, cell line production, and electrospray ionization mass spectroscopy. The authors thank J. B. Combs, P. Pfaff, and D. M. Peacock for the critical review of the manuscript. The authors also thank Q. Zheng for providing optimized conditions for the Cbz-deprotection step. This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. 75N91019D00024. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services nor does the mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. This study made use of the National Magnetic Resonance Facility at Madison, which is supported by NIH grants P41GM136463 and R24GM141526. ■ REFERENCES (1) Alabi, S. B.; Crews, C. M. Major Advances in Targeted Protein Degradation: PROTACs, LYTACs, and MADTACs. J. Biol. 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